# School of Data > Generated by All in One SEO v5.0.2.1, this is an llms.txt file, used by LLMs to index the site. Let's bridge the digital literacy gap together ## Sitemaps - [XML Sitemap](https://schoolofdata.org/sitemap.xml): Contains all public & indexable URLs for this website. ## Posts - [Blog Standard](https://schoolofdata.org/blog-standard/) ## Pages - [New Homepage](https://schoolofdata.org/): A new era is dawning for School of Data – after 13 years of pioneering data learning, we are preparing to bridge the digital literacy gap using tools and methods that are useful, simple and focused on solving people’s real problems and developing critical thinking about the role of - [Instructor](https://schoolofdata.org/single-instructor/) - [Nova Homepage](https://schoolofdata.org/): Uma nova era está surgindo para a School of Data. Após 13 anos de pioneirismo no ensino de dados, estamos nos preparando para preencher a lacuna de alfabetização digital por meio de ferramentas e métodos úteis, simples e focados em resolver problemas reais. - [Nossa história](https://schoolofdata.org/our-history/): Nossa história A School of Data começou como uma colaboração entre a Open Knowledge International (OKI) e a Peer-to-Peer University (P2PU) em 2012. Rufus Pollock (OKI) e Phillip Schmidt (P2PU), durante o período como bolsistas Shuttleworth, decidiram criar uma plataforma online para ensinar habilidades de dados. Naquela época, a School of Data era uma equipe - [Cursos online](https://schoolofdata.org/all-courses/) - [Contato](https://schoolofdata.org/contact/): Entre em contato com a School of Data Entre na comunidade Estamos reativando a comunidade School of Data e trabalhando em novas atividades para 2025/2026. Deixe seu e-mail para saber mais. Inscreva-se Quer saber mais? Mande uma mensagem! - [Sobre](https://schoolofdata.org/about-us/): Sobre a School of Data A School of Data é uma rede global impulsionada pela Open Knowledge Foundation (OKFN) para avançar a alfabetização de dados na sociedade civil. As informações que afetam diretamente a vida das pessoas estão cada vez mais acessíveis, mas a sociedade civil está ficando para trás em seu uso eficaz. Por - [ES: New Homepage](https://schoolofdata.org/): ES: A new era is dawning for School of Data – after 13 years of pioneering data learning, we are preparing to bridge the digital literacy gap using tools and methods that are useful, simple and focused on solving people’s real problems and developing critical thinking about the role of - [Nueva página de inicio](https://schoolofdata.org/): FR: A new era is dawning for School of Data – after 13 years of pioneering data learning, we are preparing to bridge the digital literacy gap using tools and methods that are useful, simple and focused on solving people’s real problems and developing critical thinking about the role of - [Todos los cursos](https://schoolofdata.org/all-courses/) - [ES: New Homepage](https://schoolofdata.org/es-new-homepage/): ES: A new era is dawning for School of Data – after 13 years of pioneering data learning, we are preparing to bridge the digital literacy gap using tools and methods that are useful, simple and focused on solving people’s real problems and developing critical thinking about the role of - [PT: New Homepage](https://schoolofdata.org/pt-new-homepage/): PT: A new era is dawning for School of Data – after 13 years of pioneering data learning, we are preparing to bridge the digital literacy gap using tools and methods that are useful, simple and focused on solving people’s real problems and developing critical thinking about the role of - [ES: About us](https://schoolofdata.org/about-us/): ES: About School of Data ES: School of Data is a global network powered by the Open Knowledge Foundation (OKFN) to advance data literacy in civil society. ES: Information that directly impact people’s lives is increasingly accessible but civil society is falling behind in making effective use of it. ES: Through our global network of - [FR: About us](https://schoolofdata.org/about-us/): FR: About School of Data FR: School of Data is a global network powered by the Open Knowledge Foundation (OKFN) to advance data literacy in civil society. FR: Information that directly impact people’s lives is increasingly accessible but civil society is falling behind in making effective use of it. FR: Through our global network of - [ES: Contact](https://schoolofdata.org/contact/): ES: Contact School of Data ES: Join the community ES: We’re reigniting the School of Data community and working on new activities for 2025/2026. ES: Leave your email to find out more. ES: Subscribe ES: Have other questions? ES: Get in touch! - [Jetpack Contact Form EN](https://schoolofdata.org/jetpack-contact-form-en/) - [ES: Instructor](https://schoolofdata.org/es/es-instructor/) - [PT: Instructor](https://schoolofdata.org/pt-br/pt-instructor/) - [FR: Instructor](https://schoolofdata.org/fr/fr-instructor/) - [FR: Contact](https://schoolofdata.org/contact/): FR: Contact School of Data FR: Join the community FR: We’re reigniting the School of Data community and working on new activities for 2025/2026. Leave your email to find out more. FR: Subscribe FR: Have other questions? Get in touch! - [Contact](https://schoolofdata.org/contact/): Contact School of Data Join the community We’re reigniting the School of Data community and working on new activities for 2025/2026. Leave your email to find out more. Subscribe Have other questions? Get in touch! - [About us](https://schoolofdata.org/about-us/): About School of Data School of Data is a global network powered by the Open Knowledge Foundation (OKFN) to advance data literacy in civil society. Information that directly impact people’s lives is increasingly accessible but civil society is falling behind in making effective use of it. Through our global network of data literacy practitioners and - [Our history](https://schoolofdata.org/our-history/): Our history School of Data started as a collaboration between Open Knowledge International (OKI) and Peer-to-Peer University (P2PU) in 2012. Rufus Pollock (OKI) and Phillip Schmidt (P2PU), during their Shuttleworth Fellowships, decided to setup an online platform to teach data skills. At that time, School of Data was a three person team: two from OKI - [Data Glossary](https://schoolofdata.org/data-glossary/): Data Glossary - [All Courses](https://schoolofdata.org/all-courses/) - [FR: All Courses](https://schoolofdata.org/all-courses/) ## Courses - [How to Build a Responsible AI Chatbot Using Open Technologies](https://schoolofdata.org/courses/how-to-build-a-responsible-ai-chatbot-using-open-technologies/): This course provides a step-by-step guide to creating an AI chatbot that provides trustworthy, source-grounded information using curated literature, datasets, and conversational guardrails. All technologies involved are open, lightweight, and accessible. The knowledge consolidated here is based on our real-life experience of collaboratively developing the Climate Academy Chatbot (CABot). This prototype uses reliable and verified - [Qualidade e Consistência de Dados com o Open Data Editor](https://schoolofdata.org/courses/quality-and-consistent-data-with-open-data-editor/): Especialmente concebido para pessoas sem conhecimentos técnicos, este é um curso essencial para quem trabalha com dados tabulares e se interessa por dados abertos. - [A Gentle Introduction to Mapping](https://schoolofdata.org/courses/a-gentle-introduction-to-mapping/): So you want to make maps? Maps are easy to read, but not that easy to make. Read on to discover the tools and techniques that will allow you to make maps for offline and online uses. - [Apresentação de Dados](https://schoolofdata.org/courses/presenting-data/): Apresentar dados pode significar a mesma coisa que visualização de dados, mas não é apenas isso. Existem inúmeras maneiras (e ferramentas) de apresentar dados, e o melhor jeito é aquele que funciona para o público que você quer alcançar. Este curso apresenta algumas ferramentas e métodos interessantes, para que você escolha a melhor maneira de - [Introducción sencilla a la cartografía](https://schoolofdata.org/courses/a-gentle-introduction-to-mapping/): ¿Quieres crear mapas?Introducción sencilla a la cartografía Los mapas son fáciles de leer, pero no tan fáciles de crear. Sigue leyendo para descubrir las herramientas y técnicas que te permitirán crear mapas para usos en línea y fuera de línea. - [An Introduction to Aid Data](https://schoolofdata.org/courses/an-introduction-to-aid-data/): This course was produced in 2014 as part of the Open Development Toolkit, a project led by Zara Rahman and jointly funded by School of Data and Development Initiatives. It provides an introduction to the world of data available on the topic of international aid, from finding it, cleaning it, visualising it and using it in the - [Working with Budgets and Spending Data](https://schoolofdata.org/courses/working-with-budgets-and-spending-data/): In this course, we will take you through the steps involved working with budget and spending data. In the process, you’ll learn how to wrangle and clean up some of the most common errors which we see in spending and budget data, as well as doing some dataset gymnastics, such as transposition and cleanup before - [Data Fundamentals](https://schoolofdata.org/courses/data-fundamentals/): The Data Fundamental course provides a solid overview over the workflow with data guiding you from what data is, to how to make your data tell a story. The course covers basic concepts, different types of data and gives an introduction to machine-readable data. - [A Gentle Introduction to Data Cleaning](https://schoolofdata.org/courses/a-gentle-introduction-to-data-cleaning/): This School of Data course is a gentle introduction to reducing errors by cleaning data. It was created by the Tactical Technology Collective and gives you a clear overview what can go wrong in spreadsheets and how to fix it (if it does). Take this course if you want to learn why it is important to clean - [Introduction into Exploring Data](https://schoolofdata.org/courses/introduction-into-exploring-data/): This set of modules will give you essential tools to explore and analyse data to find insights. Made by Tactical Technology Collective, School of Data and friends. - [A Gentle Introduction into Extracting Data](https://schoolofdata.org/courses/a-gentle-introduction-into-extracting-data/): You know the data you need is somewhere out there on the Web - but how do you get it on your computer? This course will lead you there. - [Presenting Data](https://schoolofdata.org/courses/presenting-data/): Data presentation can mean Data visualisation, but not only. There are a million ways (and tools) to present data, and the best way is the one that works for your audience. This course will introduce you to some interesting tools and methods, allowing you to tackle data presentation in various ways. - [Collecting Data Using Smartphones](https://schoolofdata.org/courses/collecting-data-using-smartphones/): If you ever had to collect data on the field for research or humanitarian purpose, you know how complicated that process can be. But with the right tools and a good process, mobile data collection can be made a lot more easy and efficient. - [Quality and Consistent Data with Open Data Editor](https://schoolofdata.org/courses/quality-and-consistent-data-with-open-data-editor/): Specially designed for non-technical users, this is an essential course for anyone working with tabular data and interested in open data. ## Lessons - [How to Do This Course](https://schoolofdata.org/lessons/how-to-do-this-course/): We suggest you work through Modules 1 to 5 in sequence: Sequential Core (Modules 1–4): Complete the technical sequence in order, as each module builds upon the architecture established in the previous section (from theoretical basics to full pipeline integration). Reflective & Ethical Capstone (Module 5): Apply the ethical, multilingual, and evaluation frameworks in Module - [Climate Chatbot case-study lens](https://schoolofdata.org/lessons/climate-chatbot-case-study-lens/): The chatbot uses the Amazon Bedrock Llama 3.1 model through Ollama. A predefined system prompt guides the model to generate accurate, relevant, and grounded responses Act as a Climate Academy assistant. Use only the supplied passages. Admit when an answer is not in those passages. Cite relevant sections. Respond in the user’s language. This shows - [Limitations and hallucinations](https://schoolofdata.org/lessons/limitations-and-hallucinations/): A hallucination is content generated without adequate support. It may be a made-up fact, quotation, source, date, or confident explanation. Hallucinations happen because the model’s main job is to produce plausible language—not automatically to verify truth. They are more likely when: The question asks for information outside the model’s knowledge. The prompt is vague. The - [Reasoning](https://schoolofdata.org/lessons/reasoning/): LLMs can produce useful multi-step explanations because they have learned patterns of comparison, deduction, planning, and problem-solving. However, a fluent reasoning chain is not proof that every step is correct. Good AI systems combine model reasoning with reliable sources, tools, checks, and human judgment. - [Tokens and context](https://schoolofdata.org/lessons/tokens-and-context/): A token is a small unit of text. It might be a word, part of a word, punctuation, or another symbol. Models read and write tokens rather than viewing language exactly as people view words. The context window is the model’s working desk. It contains the instructions, current question, selected source passages, and some conversation - [How responses are generated](https://schoolofdata.org/lessons/how-responses-are-generated/): Suppose a student asks: “Why does carbon dioxide warm Earth?” The chatbot does not usually retrieve one complete sentence from a hidden answer sheet. It processes the prompt, estimates possible next tokens, chooses one, then repeats the process: Carbon → dioxide → absorbs → some → infrared → radiation … Each new token becomes part - [What is a Large Language Model?](https://schoolofdata.org/lessons/what-is-a-large-language-model/): A large language model, or LLM, learns patterns from enormous collections of text. During training, it repeatedly practices a task like: “Given the words so far, what text is likely to come next?” From that deceptively simple exercise, it learns patterns involving grammar, style, facts, explanations, relationships, and common forms of reasoning. An LLM is - [What is AI?](https://schoolofdata.org/lessons/what-is-ai/): Artificial intelligence is the broad idea of building computer systems that can perform tasks associated with human intelligence: recognising images, translating languages, recommending music, planning routes, or holding a conversation. AI is not one giant digital mind. It is a collection of methods. A chess system, a face detector, and a chatbot may all be - [Learning objectives](https://schoolofdata.org/lessons/learning-objectives/): By the end of this module, you can: Describe AI as a broad field rather than a single machine or product. Explain an LLM as a system trained to predict and generate language patterns. Describe response generation as repeated next-token prediction. Explain tokens, context windows, prompts, and conversation history. Distinguish fluent language from reliable knowledge. - [The CABot Story](https://schoolofdata.org/lessons/the-cabot-story/): Climate change is one of humanity’s greatest challenges, yet trustworthy climate knowledge is not equally accessible. The Climate Academy Chatbot (CABot) emerged from a shared question: could educators, researchers and students build an AI assistant that answered only from trusted climate knowledge, cited its sources, worked in multiple languages and remained accessible through an ordinary - [Codificação de caracteres](https://schoolofdata.org/lessons/character-encoding/): Nesta aula, você entenderá sobre codificação de caracteres e aprenderá a identificar problemas de codificação em dados tabulares. 📂 Slides da aula - [Online geocoding](https://schoolofdata.org/lessons/online-geocoding/): Geocoding is the conversion of a human-readable location name into a numeric (or other machine-processable) location such as a longitude and latitude. For example: London => [geocoding] => {latitude: -0.81, longitude: 51.745} Geocoding is a common need when working with data as you may only have human-readable locations (e.g. “London” or a zip code like - [Geocodificación en línea](https://schoolofdata.org/lessons/online-geocoding/): La geocodificación es la conversión de un nombre de ubicación legible por humanos en una ubicación numérica (o procesable por máquina), como la longitud y la latitud. Por ejemplo: London => [geocoding] => {latitude: -0.81, longitude: 51.745} Geocoding is a common need when working with data as you may only have human-readable locations (e.g. “London” - [Course overview](https://schoolofdata.org/lessons/course-overview/): In this lesson, you will familiarize with the course structure and its learning objectives. 📂 Lesson slides - [Visão geral do curso](https://schoolofdata.org/lessons/course-overview/): Nesta lição, você conhecerá a estrutura do curso e seus objetivos de aprendizado. 📂 Slides da aula - [Welcome to the course](https://schoolofdata.org/lessons/welcome-to-the-course/): In this lesson, you will know the Open Knowledge Network and the development context of the Open Data Editor (ODE). - [Boas-vindas ao curso](https://schoolofdata.org/lessons/welcome-to-the-course/): Nesta lição, você conhecerá a Rede de Conhecimento Aberto (OK) e o contexto de desenvolvimento do Open Data Editor (ODE). - [Tips for documenting your work](https://schoolofdata.org/lessons/tips-for-documenting-your-work/): Link to the original data and mention where you got it from. This is important to show exactly where the original source was. Show and describe everything that you change in the data along the way. This is equally important, both for you and for others to be able to check your work. (See how - [Tools for documenting your data work](https://schoolofdata.org/lessons/tools-for-documenting-your-data-work/): Documenting the transformations you perform on your data can be as simple as a detailed prose explanation and a series of spreadsheets that represent key, intermediate steps. But there are also a few products out there that are specifically geared towards helping you do this. Socrata is one platform that helps you perform transforms on - [Introduction](https://schoolofdata.org/lessons/introduction-43/): Good documentation on data provenance (the origin and history of a dataset) can be compared to the chain of custody which is maintained for criminal investigations: each previous owner of a dataset must be identified, and they are held accountable for the processing and cleaning operations they have performed on the data. For Excel spreadsheets - [Publishing your results online](https://schoolofdata.org/lessons/publishing-your-results-online/): Once you’ve gone to all of the effort of finding the juicy parts in your data – you’re ready to get your results online. Many services allow easy ways to embed visualisations & data, such as iframes which you can copy and paste into a blog or website. However, if you are not given an - [Telling the story](https://schoolofdata.org/lessons/telling-the-story/): Once you’re through the steps: How do you frame your data? How do you provide the context needed? What format are you going to chose? It could be an article, a blog post, an infographic, or an interactive website dedicated to just this problem. The formats vary as do the ways to tell your story. - [Finding a Story in Data](https://schoolofdata.org/lessons/finding-a-story-in-data/): Sometimes you will start out to explore a dataset with a given question in mind. Sometimes you start with a dataset and want to find a story hidden in it. In both cases visualizing the data will be helpful to find the interesting parts. A good way to discover stories is to have interactive visualizations. - [The process](https://schoolofdata.org/lessons/the-process/): The process of telling a story with data is very similar to this track. It includes Finding Data – find the data that is suitable to answer your question Wrangle the Data – bring it to a format that is useable Merge Datasets – Bring different datasets together Filter and sort the Data – Pick - [Introduction](https://schoolofdata.org/lessons/introduction-42/): Data alone is not very accessible. However it is a great fundament to build on. To create information from data it needs to be made tangible. Telling a story with the data is the most straightforward way to do this. To tell a story with you data you need to figure out certain questions. Why - [Catching the thief – sensitivity and large numbers](https://schoolofdata.org/lessons/catching-the-thief-sensitivity-and-large-numbers/): Imagine, you are a shop owner and you just installed and electronic theft detection system. The system has a 99% accuracy of detecting theft. The alarm goes off, how likely is it, that the person who just passed is a thief? It’s tempting to answer that there is a 99% chance that this person stole - [Percent versus Percentage points change](https://schoolofdata.org/lessons/percent-versus-percentage-points-change/): This is a common pitfall for many of us. If a value changes from 5% to 10% how many percent is the change? If you answered 5% – I’m afraid you’re wrong! The answer is 100% (10% is 200% of 5%). It’s a change in 5 percentage points. So take care the next time people - [Context, context, context](https://schoolofdata.org/lessons/context-context-context/): One thing incredibly important for data is context: A number or quality doesn’t mean a thing if you don’t give context. So explain what you are showing – explain how it is read, explain where the data comes from and explain what you did with it. If you give the proper context the conclusion should - [Correlation is not causation](https://schoolofdata.org/lessons/correlation-is-not-causation/): This misunderstanding is so common and well known that it has its own Wikipedia article. There is nothing more to say about this. Simply because two data points show changes that can be correlated, it doesn’t mean that one causes the other. - [Time will tell?](https://schoolofdata.org/lessons/time-will-tell/): Time lines are also critical when displaying data. Look at the chart below: A clear stable increase in health care costs since 2002? Not quite. Notice how before 2004, there are 1 year steps. After, there is a gap between 2004 and 2007, and 2007 and 2009. This presentation makes us believe that healthcare expenditure - [Size matters](https://schoolofdata.org/lessons/size-matters/): In data visualization, size actually matters. Look at the two column charts below: Imagine the headlines for these two graphs. For the graph on the left, you might read “Health Expenditure in Finland Explodes!”. The graph on the right might come under the headline “Health Expenditure in Finland remains mainly stable”. Now look at the - [The average trap](https://schoolofdata.org/lessons/the-average-trap/): Have you ever read a sentence like: “The average european drinks 1 litre of beer per day”? Did you ask yourself who this mysterious “average european” was and where you could meet him? Bad news: you can’t. He or she doesn’t exist. In some countries, people drink more wine than beer. How about people who - [Introduction](https://schoolofdata.org/lessons/introduction-41/): Do you know the popular phrase: “There are three kinds of lies: lies, damned lies and statistics”? It illustrates the common distrust of numerical data and the way it’s displayed. And it has some truth: for too long, graphical displays of numerical data have been used to manipulate people’s understanding of ‘facts’. There is a - [Researching interaction between variables – scatterplots](https://schoolofdata.org/lessons/researching-interaction-between-variables-scatterplots/): What if we are interested not in a single variable but in how different variables depend on each other? Well in this case we have scatterplots – good for looking at interaction between two variables. Look at the sample scatterplot above: we have one numerical value on the X and another numerical value on the - [Showing geographical data – mapping](https://schoolofdata.org/lessons/showing-geographical-data-mapping/): In our case we do not only have numerical data but we also have numerical data that is linked to geographical places. This calls for a map! Whenever you have a large number of countries or regions, displaying data on a map helps. If you have countries or regions you usually create a choropleth map. - [Presenting data from categories over time – linecharts](https://schoolofdata.org/lessons/presenting-data-from-categories-over-time-linecharts/): Sometimes you do not only have categories: e.g. countries, but you have values over time. This is where line charts are quite handy. A line chart looks like: On the y axis we still have our values on the x axis we have the time measured. This graph works best if the time interval between - [Presenting quantitative data from different categories – Bar/columncharts](https://schoolofdata.org/lessons/presenting-quantitative-data-from-different-categories-bar-columncharts/): barchart is one of the most commonly used forms of presenting quantitative data. It is simple to create and to understand. It is best used when comparing data from different categories: e.q. public healthcare expenditure in the top 10 countries – and as 11th column your country. A typical columnchart looks like this: Reading barcharts - [How to present data?](https://schoolofdata.org/lessons/how-to-present-data/): We have largely quantitative data in our dataset. The question we have to ask ourselves is: Do we compare one entity over time, multiple entities with each other or do we want to know how two variables interact? Depending on this we choose different presentation formats. What we want to do Presentation chosen Compare values - [Introduction](https://schoolofdata.org/lessons/introduction-40/): The last tutorials was all text and grey, so let’s add some glitter to the world of data: Data Visualization. Data visualization is not just about making what you found look good – often it is a way of gaining insight into the data. We just understand graphical information on a better level than we - [Formulas](https://schoolofdata.org/lessons/formulas/): So let’s dive in. The data we have is not entirely complete. At the moment, healthcare expenditure is only shown as a percentage of GDP. In order to compare total expenditure in different countries, we need to have this figure in US Dollars (USD). To calculate this, let’s introduce you to spreadsheet formulas. Formulas are what - [Introduction](https://schoolofdata.org/lessons/introduction-39/): Once you have cleaned and filtered your dataset – it’s time for analysis. . Analysing data helps us to learn what our data might mean and helps us to extract answers to our questions from the dataset. Look at the data we imported. (In case you didn’t finish the previous tutorial, don’t worry. You can - [Filtering Data](https://schoolofdata.org/lessons/filtering-data/): The next thing commonly done with datasets is to filter out the values you don’t want to see. Did you notice that some “Country Names” are actually not countries? You’ll find things like “World”, “North America” and “Arab World”. Let’s filter them out. Walkthrough: Filtering Data Select the whole table. Select “Filter” from the “Data” - [Sorting Data](https://schoolofdata.org/lessons/sorting-data/): The first thing to do when looking at a new dataset is to orient yourself. This involves at looking at maximum/minimum values and sorting the data so it makes sense. Let’s look at the columns. We have data about the GDP, healthcare expenditure and life expectancy. Now let’s explore the range of data by simply - [Locking Rows and Columns](https://schoolofdata.org/lessons/locking-rows-and-columns/): The spreadsheet we are working on is quite large. You will notice, that while scrolling the column with the column labels will frequently disappear, leaving you quite lost. The same with the country names. To avoid this you can “lock” rows and columns so they don’t disappear. Walkthrough: Locking the top row Go to the - [Navigating and using the Spreadsheet](https://schoolofdata.org/lessons/navigating-and-using-the-spreadsheet/): Now we loaded some data let’s deal with the basics of spreadsheets. A spreadsheet is basically a table of “cells” in which you can input data. The cells are organized in “rows” and “columns”. Typically rows are labeled by numbers, columns by letters. This also means cells can be addressed by their “column” and “row” - [Creating a spreadsheet and uploading data](https://schoolofdata.org/lessons/creating-a-spreadsheet-and-uploading-data/): In this course we will use Google docs for our data-wrangling – it allows you to start right away without need of installing software. Since the data we are working with is already public we also don’t need to worry about the fact that it is not stored on our local drive. Walktrough: Creating a - [Spreadsheets: An Overview](https://schoolofdata.org/lessons/spreadsheets-an-overview/): Nowadays spreadsheets are widespread so a lot of people are familiar with them already. A variety of spreadsheet programs and applications exist. For example Microsoft’s Office package comes with Excel, the OpenOffice package comes with Calc and so on. Not surprisingly, Google decided to add spreadsheets to their documents package. Since it does not require - [Multiplication and division challenge](https://schoolofdata.org/lessons/multiplication-and-division-challenge/): Task: Using the data from the World Bank (if you don’t have it already, download it here). In the data we have figures for healthcare only as a % of GDP. Calculate the full amount of private health expenditure in Afghanistan in 2001 in USD. If your percentages are rusty – check out the formulae section in - [Recap](https://schoolofdata.org/lessons/recap/): In this module, we talked about the basic math you need to understand quantitative data. We talked about how to figure out what you are dealing with (this is commonly called descriptive statistics). We went on to figure out how we could compare apples to oranges (if we wanted to), and finally we looked at - [Finding out what’s special – Z-Scores](https://schoolofdata.org/lessons/finding-out-whats-special-z-scores/): Z or standard scores are a good way to figure out what is special. Let’s say you have election results and want to find places that are interesting to report on. One thing you can do is figure out where a party has done exceptionally well. Z-Scores are great for this. The Z-Score of a - [Normalizing Data – aka. comparing things](https://schoolofdata.org/lessons/normalizing-data-aka-comparing-things/): Once we have an idea of what we’re talking about, let’s figure out how we compare things. Let’s say we want to compare two countries that are quite different in several aspects. If we want to compare their GDPs, for example, we can do so, but it will not tell us anything useful. If, for - [How big is the variation in the data?](https://schoolofdata.org/lessons/how-big-is-the-variation-in-the-data/): The next thing we want to know is how big the variation is in our data. Two measures come in handy: the standard deviation and the median absolute deviation. The standard deviation comes with the mean and is very frequently used. The median absolute deviation is less well known and would be the best to - [What is Normal?](https://schoolofdata.org/lessons/what-is-normal/): One of the next questions often is: what is “normal”? What we mean by this is: how can I tell whether something is worth looking at? (We’ll figure out what is special further below.) There are different ways to find this out. Mean The mean (or “average”) is the most commonly used way of looking - [Distribution](https://schoolofdata.org/lessons/distribution/): Next you want to look at is how the data is distributed. This is commonly done with a plot called a histogram. A histogram simply counts how often each value appears and proceeds from there. So how do we do this? This is commonly done by binning data. What does this mean? Basically, we create bins, which are the ranges of - [How many do we have?](https://schoolofdata.org/lessons/how-many-do-we-have/): The next important question we can ask ourselves is: how many things do we have? e.g. how many people did we survey? How many countries do we know things of? etc. This might seem simple, but for statistics, this is very important. How do we get it? Simply count! In spreadsheets, this would be the - [Range](https://schoolofdata.org/lessons/range/): The first thing you want to look at is the range of your data: from where to where does your data stretch? Does it start with small numbers? Large numbers? Does it run from negative to positive? All this is essential information that will help you to deal with your data. Looking at the range will also - [Introduction](https://schoolofdata.org/lessons/introduction-38/): Math seems to be a scary thing for many people. If you tend to get scared by thinking about numbers and what to do with them, this module is for you. We will tame the beast and show you how much you can do – with counting, adding, and dividing numbers. Common problems we address - [Introduction](https://schoolofdata.org/lessons/introduction-37/): The most basic tool used for data wrangling is a spreadsheet. Data contained in a spreadsheet is in a structured, machine-readable format and hence can quickly be sorted and filtered. In other recipes in the handbook, you’ll learn how to use the humble spreadsheet as a power tool for carrying out simple sums (finding the - [Challenge: Wrangle the data](https://schoolofdata.org/lessons/challenge-wrangle-the-data/): Take a dataset from your own country and clean it up ready to go into a database. - [Normalizing data](https://schoolofdata.org/lessons/normalizing-data/): Data that comes from the government is often generated across multiple departments by hand. This can result in inconsistencies in what kinds of values or formats are used to describe the same meaning. Normalizing values to be consistent across a dataset is therefore a common activity. Step 1: Find all distinct values First, you want - [Introduction](https://schoolofdata.org/lessons/introduction-36/): Often, you will need to clean up the spending data that you receive. Even government data is not perfect. In this section, we highlight some common issues experience when working with spending data. In the recipe book – you will find a recipe to help you solve these issues. Common issues with spending data: Typos – - [Task](https://schoolofdata.org/lessons/task-2/): Examine whether budget and / or spending data are available for your country. Note: these may have different names (e.g. Enacted Budget)! “Budget” and “spending” are categories rather than necessarily the names of the documents, policy-folks may actually refer to what we refer to here as spending as budgets (with a particular qualifier). Save your - [Budget Data – Political Details](https://schoolofdata.org/lessons/budget-data-political-details/): Budget data is defined as data relating to the broad funding priorities set forth by a government, often highly aggregated or grouped by goals at a particular agency or ministry. For instance, a government may pass a budget which contains elements such as “Allocate $20 million in funding for clean energy grants” or “Allocate $5 - [Spending Data – Execution Details](https://schoolofdata.org/lessons/spending-data-execution-details/): Spending data is defined as data relating to the specific expenditure of funds from the government. This may take the form of a contract, loan, refundable tax credit, pension fund payments, or payments from other retirement assistance programs and government medical insurance programs. In the context of our previous examples, spending data examples might be - [What questions can be answered using budget data?](https://schoolofdata.org/lessons/what-questions-can-be-answered-using-budget-data/): Budget expenditure data has an array of different applications, but it’s prime role is to communicate to it’s user broad trends and priorities in government spending. While it can help to have a prose accompaniment, the data itself promotes a more clear-cut interpretation of proposed government spending over political rhetoric. Additionally, it is much easier - [Connecting revenues and spending](https://schoolofdata.org/lessons/connecting-revenues-and-spending/): It is highly desirable to be able to determine the flow of money from revenues to spending. For the most part, many taxes go into a general fund and many expenditures come out of that general fund, making this comparison moot. But in some cases, in many countries, there are taxes on certain behaviours that - [Budgets as datasets](https://schoolofdata.org/lessons/budgets-as-datasets/): A growing number of governments make their budget expenditure data available as machine-readable spreadsheets. This is the preferred method for many users, as it is accessible and requires few software skills to get started. Other countries release longer reports that discuss budget priorities as a narrative. Some countries do something in between where they release - [Introduction](https://schoolofdata.org/lessons/introduction-35/): Before you start to work with your financial data, it’s important to get a feeling for the two key different types of data you may encounter. While these terms may have different meanings on a country by country basis, it is possible to separate the types of data into two basic types by looking at - [How to map your budget into COFOG classifications](https://schoolofdata.org/lessons/how-to-map-your-budget-into-cofog-classifications/): Basically, if your government doesn’t do this for you – you’ll always have to use your best judgements, someone may have made a different call, and may well disagree with the way you’ve done what you’ve done. But as long as you document your practices, anyone will be able to pick up anything they don’t - [De-jargonising COFOG](https://schoolofdata.org/lessons/de-jargonising-cofog/): Let’s face it, the terminology used by the government is often not the most appealing, or illustrative from the point of view of the user. Hence, for the Where Does My Money Go Project, we specifically de-jargonised it, and translated the terms into friendly forms that we felt were more accessible to the average user. - [International Comparisons](https://schoolofdata.org/lessons/international-comparisons/): While we’re always warning people about making comparisons between countries (data not being collected in the right way etc lalala), these classifications using COFOG are quite often used to make international comparisons. OECD do it regularly, so it’s probably one of the less-evil ways to do it, in case you’re interested in that type of - [Grouping budgets and spending by topics people care about](https://schoolofdata.org/lessons/grouping-budgets-and-spending-by-topics-people-care-about/): Functional classifications, for those of you who don’t regularly ‘sail the wide accounting seas’ tell you things like what general area of spending we area talking about “health”, “education”, “defence”, which is often more interesting from the perspective of the citizen user than e.g. which ministry group it was spent by. Some governments already classify - [Introduction](https://schoolofdata.org/lessons/introduction-34/): Depending on who the audience is for your data, you may want to think about presenting or delivering your data differently. The Spending Data Handbook covers some important considerations about targeting your data project. In this chapter, we cover a data conversion technique that the Open Knowledge Foundation employ regularly in order to make data more accessible - [Task](https://schoolofdata.org/lessons/task/) - [Tasks](https://schoolofdata.org/lessons/tasks/): Find out – does your country use a standard classification for its data such as COFOG? If you have code sheets for your data – try combining them with your main dataset. If relevant: Try geocoding your data. For example – does your data include particular projects? Can you find an address for them? If - [Company and organisational identifiers](https://schoolofdata.org/lessons/company-and-organisational-identifiers/): As you look into spending data that includes recipients outside the government, you’ll find companies which act as suppliers to government, but also other types of entities including charities, associations, foreign governments, political parties and even individuals which act as recipients of direct assistance. Identifying such entities is notoriously hard, since the only information kept - [Geographic identifiers](https://schoolofdata.org/lessons/geographic-identifiers/): Geographic identifiers are used to describe administrative boundaries or specific locations identified in a dataset. While some regional classifications (such as the EU NUTS) are released on the web, there is also an increasing number of open databases which contain geographic names – including geonames.org and the recently developed world.db. Another related technique is the process of - [Classification reference data](https://schoolofdata.org/lessons/introduction-33/): Reference data are dictionaries for the categorizations included in a financial datasets. They may include descriptions of government programmes, economic, functional or institutional classification schemes, charts of account and many other types of schemes used to classify and allocate expenditure. Some such schemes are also standardized beyond individual countries, such as the UN’s classification of - [Introduction](https://schoolofdata.org/lessons/classification-reference-data/): One of the most powerful ways of making data more meaningful for analysis is to combine it with reference data and code sheets. Unlike transaction data – such as statistical time series or budget figures – reference data does not describe observations about reality – it merely contains additional details on category schemes, government programmes, - [Further resources](https://schoolofdata.org/lessons/further-resources-6/): Glossaries provided by other organisations: OECD DAC glossary A question of Aid From Development Initiatives, this Glossary - [Aid + financial flow terms](https://schoolofdata.org/lessons/aid-financial-flow-terms/): Aid bundle: aid comes in many forms: as money, as food, in the form of skilled people coming and offering their services (= technical consultancy), or even items that mean that the money never actually leaves the donor country, such as debt relief, or grants for students from poor countries to study in a more economically - [General development terms](https://schoolofdata.org/lessons/general-development-terms/): GNI – Gross National Income – A term used to describe the total national income of a country. Essentially, it measures everything that nationals of a certain country are doing/producing – the output – whether they are living in that country or not. This differs from Gross Domestic Product, which measures the economic value of what - [Organisations and institutions to be aware of](https://schoolofdata.org/lessons/organisations-and-institutions-to-be-aware-of/): G7 (previously, the G8): The Group of Eight is a forum of eight countries (Canada, France, Germany, Italy, Japan, Russia, United Kingdom and United States). Together, these eight countries represent about 14% of the world’s population, but about 60% of the world’s wealth. In March 2014, Russia was suspended from the group, in response to - [Introduction](https://schoolofdata.org/lessons/introduction-32/): We’ve talked about the accessibility of data, but what about the accessibility of language? As with many sectors, international development is full of acronyms and complex terms which might have different meanings to those you’re used to. This is intended more as a reference point while you’re working through the Aid Data curriculum, rather than to - [Further resources](https://schoolofdata.org/lessons/further-resources-5/): Here are some more articles which mention aid data figures – why not see if you can reverse engineer these ones? If you come across any good ones, post them in the comments! Why Israel and other foreign militaries — not the global poor — get the biggest US aid packages Making Sense of EU - [Conclusion](https://schoolofdata.org/lessons/conclusion/): As we’ve seen, it can sometimes take a few tries to find out the ‘original’ source of data quoted – but, it can definitely be worth it, especially if the claim in a news story sounds a little unlikely to you. Once you have an idea of what the main sources of aid data are, - [Exercise](https://schoolofdata.org/lessons/exercise/): Take a look at this article, Why Israel and other foreign militaries — not the global poor — get the biggest US aid packages. Can you spot any problems with the article? Think especially about the years mentioned, facts that are mentioned as though they are definite, when they might actually not happen… - [Case study 2: British aid money is funding corruption overseas, damning new report finds](https://schoolofdata.org/lessons/case-study-2-british-aid-money-is-funding-corruption-overseas-damning-new-report-finds/): This is something of an unusual case: the entire article is focused around a newly released report about the ‘impact’ of British Aid, conducted by the Independent Commission for Aid Impact. They carried out an investigation looking into how British aid was spent, and the results are somewhat controversial, hence the media coverage. Let’s look into - [Case study 1: Why Is Afghanistan Sending Aid to Gaza?](https://schoolofdata.org/lessons/case-study-1-why-is-afghanistan-sending-aid-to-gaza/): Let’s start with this story: Afghanistan – a low-income country which receives Official Development Assistance from other, richer countries – is, apparently, sending aid to Gaza. The way the headline is structured sounds almost indignant – by asking the question, we’re starting at the point of thinking that, in fact, Afghanistan shouldn’t be sending any aid. The - [Introduction](https://schoolofdata.org/lessons/introduction-31/): It’s all very well learning where aid data is, what it is, and how to find it – but most often, we come across the data already packaged up as ‘information’. In this module, we’ll try to ‘reverse engineer’ some of the figures that we come across in a couple of media outlets – this - [Where to find the data online](https://schoolofdata.org/lessons/where-to-find-the-data-online/): The OECD makes its data available in a number of ways: it can be slightly confusing to know where to go to get exactly what you’re looking for, so here is a quick guide to the various sites and sources. Query Wizard for International Development Statistics Where: http://stats.oecd.org/qwids/ What: A way of getting data by CSV, filtered - [Further resources](https://schoolofdata.org/lessons/further-resources-4/): “Getting Closer to the Core – Measuring Country Programmable Aid” – brief from the OECD OECD Development Assistance Committee (DAC) guidelines and documents Development Co-operation Report 2013- Ending Poverty, from the OECD - [Weaknesses of the OECD-DAC data](https://schoolofdata.org/lessons/weaknesses-of-the-oecd-dac-data/): Excludes much of the ‘aid bundle’: for example, remittances, charitable donations from the public, foreign investment, funds that don’t meet the ‘official’ definition of ODA – which means that the financial flows here are just a tiny portion of resources which are intended to reduce poverty in poor countries. Slow to publish: full datasets are not published - [Strengths of the OECD-DAC data](https://schoolofdata.org/lessons/strengths-of-the-oecd-dac-data/): Comprehensive: The data included here is fully comprehensive for what is defined as “Official Development Assistance”, and reporting to the database is mandatory for all 29 DAC member countries. Less risk of double counting: Within the dataset itself, you don’t have to worry about double counting – ie. the same activity or event being counted more than once. - [Things to understand before diving into the data](https://schoolofdata.org/lessons/things-to-understand-before-diving-into-the-data/): There are some structural and linguistic concepts it’s important to understand: Commitment Disbursement = written obligation or formal declaration of what will be paid or transferred (ie. a promise of what donors will do) = actual transfer of resources (money, or goods) to recipient country or agency (ie. money has actually left the donor agency - [What to expect from this data?](https://schoolofdata.org/lessons/what-to-expect-from-this-data/): Crucially, the data included here is on the money that flows out of donor countries, not necessarily the money that flows into low-income countries: as we’ve mentioned earlier, some of this money is spent in the donor countries themselves. Also, as with all data on ‘aid’, or Official Development Assistance, remember that it is just a - [Why are we looking at the OECD-DAC data?](https://schoolofdata.org/lessons/why-are-we-looking-at-the-oecd-dac-data/): The DAC aid statistics are widely considered to be the “official” source of data on aid- in some cases, the data dates back to the 1960s. Generally, the data included ranges from regional or national aggregates, to individual project level. - [Introduction](https://schoolofdata.org/lessons/introduction-30/): As we mentioned in the Introduction to Aid Data, the OECD is a good source of development data. However, using it will be made much easier by understanding a few pieces of contextual information, thinking about its strengths and weaknesses, and of course, knowing where to actually find it online. Objectives: Understand why you might - [Further resources](https://schoolofdata.org/lessons/further-resources-3/): Tony Hirst’s various blog posts on Open Refine David Huynh’s Google Refine tutorial, from NICAR 2011 Using Google Refine to clean messy data – from the ProPublica Nerd blog, by Dan Nguyen Google Refine screencasts (3 videos in total) - [Step 3: Sorting and Faceting](https://schoolofdata.org/lessons/step-3-sorting-and-faceting/): Now that we have created our project, let’s go and explore the data and the Refine interface a little. Using Refine might be intimidating at first, since it seems so different from spreadsheets, but once you get used to it you will notice how easily you can do things with it. One of the commonly - [Step 2: Creating a new Project](https://schoolofdata.org/lessons/step-2-creating-a-new-project/): Open Refine (previously Google Refine) is a data cleaning software that uses your web browser as an interface. This means it will look like it runs on the internet but all your data remains on your machine and you do not need an internet connection to work with it. The main aim of Refine is - [Step 1: get the data](https://schoolofdata.org/lessons/step-1-get-the-data/): We’re going to be working with data in csv format, downloaded from the IATI Registry. Go to the Registry and click on Search the Registry. There are lots of ways to find what you need within the Registry – easiest though, is simply by searching. Let’s say we want to look at projects taking place in Bangladesh – - [Introduction](https://schoolofdata.org/lessons/introduction-29/): Why might data need “cleaning” anyway? We say the data needs cleaning when it has inconsistencies that make it difficult to work with; although it might already be in a spreadsheet, there are lots of ways that it could actually be “dirty” data. For example, when dates are written in different formats in the same - [Further Resources](https://schoolofdata.org/lessons/further-resources-2/): Comparing IATI data with the CRS, how did we do it? 7 ways to use IATI data Tips from the IATI Secretariat on using IATI data - [Sources of data about IATI data (metadata!)](https://schoolofdata.org/lessons/sources-of-data-about-iati-data-metadata/): What is it? An API (application programming interface) for IATI data: ie. a way for applications to communicate with one another. Who is it useful for? Developers building applications with IATI data, people wanting IATI data in XML, CSV, or JSON. Give me an example of what I can get here… An XML of all - [A walkthrough of the IATI Datastore Query Builder](https://schoolofdata.org/lessons/a-walkthrough-of-the-iati-datastore-query-builder/): What is it? A “query builder” – a form where the user specifies what data is wanted from the IATI registry, presses “submit” and a CSV with the selections starts automatically downloading. Magic! Who is it useful for? People wanting to download custom CSV files of IATI data: you can get data per reporting organisation, - [A walkthrough of the IATI Registry](https://schoolofdata.org/lessons/a-walkthrough-of-the-iati-registry/): What is it? The home of IATI data: the place to download IATI data in the datasets as they are provided by reporting organisations. Who is it useful for? People wanting to download data in the way that reporting organisations have structured it in xml, or csv, which you can open in a standard spreadsheet - [Background](https://schoolofdata.org/lessons/background/): Before we begin, a few terms and concepts about IATI that are useful (though not essential) to understand as background: The International Aid Transparency Initiative (IATI – normally pronounced as a full word, ‘aye-at-ee’) is an initiative aimed at increasing aid transparency, which encourages organisations to put data about their aid activities online. Where they are spending - [Introduction](https://schoolofdata.org/lessons/introduction-28/): We’ve already looked at what kind of aid data can be found online. As we mentioned, one big source of data on this topic is made available through the International Aid Transparency Initiative (IATI). There are a few different ways that you can access raw IATI data, so here’s a quick run through of what - [Sources of IATI data](https://schoolofdata.org/lessons/sources-of-iati-data/) - [Further resources](https://schoolofdata.org/lessons/further-resources/): DAC list of ODA recipients Investments to end poverty report, by Development Initiatives, looking into how much Official Development Assistance actually reached low-income countries OECD fact sheet; Is it ODA? History of the 0.7% ODA target, from the OECD The aid bundle: what you need to know, by Development Initiatives A simple summary of aid from the BBC - [Where is aid data made available?](https://schoolofdata.org/lessons/where-is-aid-data-made-available/): There are lots of different ways in which aid data is made available; some, through online portals which allow you to view rather than download the data, and others as raw data, as mentioned above. Through the Tools section on the site, we aim to make it as easy as possible for you to find what you - [How is aid data made available?](https://schoolofdata.org/lessons/how-is-aid-data-made-available/): As high-level aggregated figures: eg. the amount of money going to/from certain countries per year, or from certain institutions. This can be useful to see global trends; for example, to see how aid flows to a certain region or country have changed over time, or how global political events have affected the split of aid data. - [What is aid?](https://schoolofdata.org/lessons/what-is-aid/): Broadly speaking, aid is the transfer of resources (ie. money, or actual ‘things’) from one country to another; historically, this has generally been in the direction of rich countries to poorer countries. Increasingly, though, South-South cooperation is becoming more common, as is the two-way transfer of resources rather than simply as a one way transfer. - [Introduction](https://schoolofdata.org/lessons/introduction-27/): Welcome to the world of aid data! In this first module, we’ll be starting from first principles: what do we actually mean when we say ‘aid’, and why is looking at aid data important? Also, bear in mind that aid is just one of many financial flows going in (and out) of low-income countries, and - [Limitations of Timemapper](https://schoolofdata.org/lessons/limitations-of-timemapper/): One limitation is that Timemapper doesn’t have many customization options, especially concerning the maps and it relies on Google Sheets. Second, the ImportXML formula used to grab data from a geocoding service is limited to 50 rows at one time so if you have big data-sets this can be time consuming. The data you have - [Advanced use of Timemapper](https://schoolofdata.org/lessons/advanced-use-of-timemapper/): Advanced users that would like to use polygons or boundaries of countries to the map in Timemapper rather than using points need to build GeoJSON boundaries or objects. For more info on geocoding in Google docs take a look at this post. - [Publishing your timemap](https://schoolofdata.org/lessons/publishing-your-timemap/): After you’ve added the data, your timemap is ready to be visualised. You do this first by publishing your Google spreadsheet to the web which would make it visible to everyone and ready to be embedded onto your website: go to File – Publish to the web and click on Start publishing. Although you have published your Timemap by doing - [Working with the Timemapper template](https://schoolofdata.org/lessons/working-with-the-timemapper-template/): Timemapper generates the projects from a simple Google spreadsheet, therefore you have to: Add the data you want to present in a pre-prepared Timemapper Template. After you make a copy of this template, start adding your data. The template is self-explanatory – in the second row there is an explanation of the meaning for each of - [Using Timemapper](https://schoolofdata.org/lessons/using-timemapper/): Timemapper is a very easy to use tool, that can be utilized in minutes depending on the amount of data. Sign in at: http://timemapper.okfnlabs.org/account/login which can be done with your twitter account. You can also create a Timemap anonymously but won’t have the benefits of a custom URL and list of all your timemapper projects with the - [Uso avançado de Timemapper](https://schoolofdata.org/lessons/advanced-use-of-timemapper/): Pessoas avançadas que desejam utilizar polígonos ou fronteiras de países no mapa do Timemapper, em vez de pontos, precisam criar limites ou objetos GeoJSON. Para mais informações sobre geocodificação no Google Docs, consulte este post. - [Publicando seu mapa temporal](https://schoolofdata.org/lessons/publishing-your-timemap/): Depois de adicionar os dados, seu mapa temporal estará pronto para ser visualizado. Para isso, primeiro publique a sua planilha do Google na web, tornando-a visível para todos e pronta para ser incorporada no seu site: Vá para Arquivo – Publicar na web e clique em Iniciar publicação. Embora você tenha publicado seu Timemap dessa - [Usando o modelo do Timemapper](https://schoolofdata.org/lessons/working-with-the-timemapper-template/): O Timemapper gera os projetos a partir de uma simples planilha do Google, portanto, você deve: Adicionar os dados que deseja apresentar em um modelo pré-preparado do Timemapper. Depois de fazer uma cópia deste modelo, comece a adicionar seus dados. O modelo é autoexplicativo – na segunda linha, há uma explicação do significado de cada - [Introduction](https://schoolofdata.org/lessons/introduction-26/): Journalists get to work on complex stories that sometimes can not be written in a simple, precise and easily understandable manner if only text is used. Understanding these complex stories can represent a challenge for the audience, which is why journalists use more than one digital storytelling tools to tell the story. In that way - [Limitações do Timemapper](https://schoolofdata.org/lessons/limitations-of-timemapper/): Uma limitação é que o Timemapper não tem muitas opções de personalização, especialmente no que diz respeito aos mapas, e depende do Google Planilhas. Em segundo lugar, a fórmula ImportXML utilizada para obter dados de um serviço de geocodificação está limitada a 50 linhas de cada vez, ou seja, pode demorar no caso de bases - [Usando o Timemapper](https://schoolofdata.org/lessons/using-timemapper/): O Timemapper é uma ferramenta muito fácil de usar, que pode ser utilizada em poucos minutos, dependendo da quantidade de dados. Faça login em: http://timemapper.okfnlabs.org/account/login, o que pode ser feito com sua conta do Twitter. Você também pode criar um mapa temporal anonimamente, mas não terá os benefícios de uma URL personalizada e da lista - [Introdução](https://schoolofdata.org/lessons/introduction-26/): Jornalistas às vezes trabalham em matérias complexas que podem precisar de mais elementos além do texto para serem compreendidas de forma simples e precisa. Entender narrativas complexas pode ser um desafio para as pessoas leitoras, e é por isso que jornalistas usam mais de uma ferramenta de narrativa digital para contar suas histórias. Dessa forma, - [Sharing your project](https://schoolofdata.org/lessons/sharing-your-project/): You can also share your form to other Kobo Toolbox users through the following: Sharing the project and the data publicly – everyone can use your form, publish it, and use the data that the project will generate, even if they are non-Kobo Toolbox users. Sharing the project with other users – you can share the - [Managing your project settings](https://schoolofdata.org/lessons/managing-your-project-settings/): When you click Project Settings on the main project page, you will be prompted to this page. Under Form Settings, you will see: Accept submissions – this gives you the option to allow users to submit their data using Kobo Collect or close it for submissions. You can select No if you have imposed a deadline - [Adding data using a web browser](https://schoolofdata.org/lessons/adding-data-using-a-web-browser/): The benefits of using Kobo Toolbox is that it also allows you to use the Kobo Collect form using a web browser. When you go to the main project page, you can click Enter data in Browser and it will direct you to an online version of the form. Tip: Since Kobo Collect only works with Android devices, - [Viewing and downloading photos](https://schoolofdata.org/lessons/viewing-and-downloading-photos/): If you have included a media option on your Kobo Collect form – where you can take photos using your device and include it on your form responses – your online project will store all your images under Photos. Here you can view all the photos taken online or download them. - [Downloading the data](https://schoolofdata.org/lessons/downloading-the-data/): On the main project page, you can download the data by selecting Download data. Your dataset can be downloaded in XLS, CSV, ZIP and/or KML formats. Once you have chosen which format, you will be arrive at the Export page where you can now download the file. The online table analysis is very limited, thus, downloading - [Viewing and analyzing data online](https://schoolofdata.org/lessons/viewing-and-analyzing-data-online/): When you click View data in Table, it will show you all submissions using your Kobo Collect form. You will see the Column Headers and Answer Values. Since Kobo creates XML values from the responses, you can choose if you want the table to show the XML or Label values. This is the same with your - [Checking submissions](https://schoolofdata.org/lessons/checking-submissions/): Once you have opened your account, you need to open your projects to see if data is coming in from the Kobo Collect submissions. The main projects list will you show you the projects that you have, when it was created, and when was the last submission. In this example, I have chosen ZOD Form - [Introduction](https://schoolofdata.org/lessons/introduction-25/): Kobo Toolbox provides a good platform to collect data using two devices: your laptop and your mobile devices e.g. smartphones and tablets. On this module, we will talk about how to manage your Kobo Toolbox database using the online platform. The good thing about the Kobo Toolbox online platform is that it has a built-in - [Submitting your form](https://schoolofdata.org/lessons/submitting-your-form/): Press SAVE FORM AND EXIT. For saved forms but not yet submitted, look at EDIT SAVED FORM and retrieve the form. Click GO TO START, review your responses until you reach the end. Repeat step 11 and make sure that MARK FORM AS FINALIZED has been checked. You will return to the main page. You - [Collecting and saving](https://schoolofdata.org/lessons/collecting-and-saving/): Press GET BLANK FORM. Press the check mark beside the form that you want to use and press GET SELECTED. To start collecting data, press FILL BLANK FORM and choose the form that you want to use. Once you are done in collecting the information, you will reach the last page which says YOU ARE - [Setting up your Kobo Collect account](https://schoolofdata.org/lessons/setting-up-your-kobo-collect-account/): In the General Settings page, you will be required to type the URL. Get your Kobo Toolbox account username and type http://kc.humanitarianresponse.info/yourusername. Type your username. Under Google account, type your Gmail account if you have one. Go back to the main page (where you will see the form options). - [Installation of Kobo Collect on your device](https://schoolofdata.org/lessons/installation-of-kobo-collect-on-your-device/): Kobo Collect can only be used in phones or tablets using the Android Operating System. It is not available in iPhones and Blackberry phones. Using your phone, go to your Applications and open Play Store. Search for KOBO COLLECT. Install Kobo Collect on your phone. Once installed, open Kobo Collect and look for the General - [Introduction](https://schoolofdata.org/lessons/introduction-24/): Kobo Toolbox provides a good platform to collect data using two devices: your laptop and your mobile devices e.g. smartphones and tablets. On this module, we will talk about how to use your Kobo Collect forms on your devices. Kobo Collect is the android-based application used to collect the data which feeds into your Kobo - [Reminders and Tips](https://schoolofdata.org/lessons/reminders-and-tips/): Always save your Excel form in a different file name on your computer. Once you have uploaded a form, you cannot upload the same file using the same form_id. Keep several versions of your Excel forms on your computer. You’ll never know when a calculation or group questions may become useful. Kobo Toolbox will not - [Replacing the form](https://schoolofdata.org/lessons/replacing-the-form/): To replace the form, you need to delete the form and follow the steps in Uploading the Form. The new form will appear on your projects page. - [Deleting the form](https://schoolofdata.org/lessons/deleting-the-form/): Sometimes, you will only find errors on your form when you have tested it. When you see these errors, you cannot directly change the form in the web version. You need to delete the project and re-upload the corrected Excel file. To delete your form, click the project from the project list. When you click - [Testing the form](https://schoolofdata.org/lessons/testing-the-form/): Once you have clicked Enter Web Form or Preview Web Form, you will see the online version of the form. Sometimes, the web form do not look exactly like the mobile version of the form. The web form will prompt you if you want to store files on your laptop and share your location. When you - [Uploading your forms](https://schoolofdata.org/lessons/uploading-your-forms/): Click PROJECTS. Here you will see all your uploaded and created projects. Click UPLOAD YOUR XLS FORM HERE DIRECTLY. You will be prompted to select the Excel file that you want to use. Once you have selected the file, it will automatically upload the form that you want to use. If there are errors on - [The FORMS tab](https://schoolofdata.org/lessons/the-forms-tab/): The Forms tab will show you: Question Library – this is where you can save the usual questions that you are using for the forms you have made using the online form creator. Projects – these are the projects or forms you have created both using the online form creator and uploaded Excel forms. Settings - [Know the interface](https://schoolofdata.org/lessons/know-the-interface/): The Kobo Toolbox website will initially show you the form drafts that you have created using the online Kobo Toolbox form creator. However, for this module, we will only focus on the uploading the Excel forms we have previously created. Once you click the Forms tab, you will see all the forms you have uploaded. - [Introduction](https://schoolofdata.org/lessons/introduction-23/): Welcome to Module 3 of the Collecting Data Using Smartphones course. Now you'll learn how to use Kobo Toolbox. We have mentioned before that we will only use Kobo Toolbox online in setting up your ODK forms. Thus, it is very important that you have already created your Kobo Toolbox account. If you have not - [Settings sheet](https://schoolofdata.org/lessons/settings-sheet/): In the Settings sheet, you can have the following headers: form_title – this is how the name of your form will appear in your form list. form_id – ODK creates an ID for each of the forms that you create. Make sure that your form_id is unique. default_language – this determines what the default language will be used - [Choices sheet](https://schoolofdata.org/lessons/choices-sheet/): Under the Choices sheet, you need to put the following column headers: List_name – this one serves the same purpose as your Type column in the survey sheet. This is your main reference in single-select or multiple-select questions in your survey sheet Name – this is similar to your Name column from the survey sheet. This must be unique - [Survey sheet](https://schoolofdata.org/lessons/survey-sheet/): On your Survey sheet, type in the following column headers: Type (required) – ODK recognizes a set of question types like single select (Yes or No questions), multi-select, text, numbers, and even photos and geographical locations. You can also use grouped or repeated questions. For Kobo Collect, there are basic types of questions (Source: Raso, 2013, ACF Madrid) which you - [Preparing your sheets](https://schoolofdata.org/lessons/preparing-your-sheets/): To start off, create your file with the following sheets named accordingly. Survey – this sheet will include all the survey questions, type of questions, label and other instructions which ODK will interpret once the form has been uploaded. Settings – this sheet will determine how your sheet will be viewed in your mobile device. - [Introduction](https://schoolofdata.org/lessons/introduction-22/): For this module you will need: basic understanding of Excel a good understanding on how to design a survey questionnaire Building the data collection form or survey can be done using Microsoft Excel and XLSForm. XLSForm is an application developed by Nafundi used to create and validate forms for ODK (and it’s now open source!). This module will only - [Introduction](https://schoolofdata.org/lessons/introduction-21/): Decades ago, the use of papers and pen was a painstaking and very expensive effort to collect data. Most of us have experienced paper forms getting wet or damaged, or receiving paper forms that were barely answered. But as the age of smartphones and tablets arrived, mobile-based data collection technologies have also gained a huge following. The - [Further reading](https://schoolofdata.org/lessons/further-reading-3/): Video series to learn QGIS Choosing the right map projection qgis.org The blog of Anita Graser, a QGIS evangelist Understanding Datum and Projection A website to find browse and pick projections - [Styling the Data](https://schoolofdata.org/lessons/styling-the-data/): Reviewing the data If you want to review the original data, you can open the attribute table which contains all the data about the layer. Select the schools layer > right click > open attribute table. Below is what you should see- same as the spreadsheet data. Styling the data We can style the data - [Adding data to QGIS](https://schoolofdata.org/lessons/adding-data-to-qgis/): Once you have identified geographical data to work with or a spreadsheet with geographical coordinates (latitude/longitude or easting/northing values) , the next step is to add it to your QGIS project. Our goal is now represent geographical data using a map. We’ll use an extract from the list of schools in Tanzania to demonstrate this. - [Installing QGIS](https://schoolofdata.org/lessons/installing-qgis/): QGIS is an open source GIS software that is available for free. You can download on the official website. If you are a Mac user, make sure to follow the instructions and install the Gdal package and the Mathplotlib library. Installing the OpenLayers Plugin A plugin adds functionality to software. One thing to note about QGIS is that, - [Introduction](https://schoolofdata.org/lessons/introduction-20/): Module Goals Learn the basics of QGIS as a GIS software tool Convert spreadsheet data to a map Style the map to convey more information Course requirements Knowledge of Data Fundamentals, a course which covers the basics of data, and how to work with a spreadsheet. And the following: The QGIS software (that you can download here) - [The Tabula way](https://schoolofdata.org/lessons/the-tabula-way/): What is Tabula and how does it work? Tabula is an offline software, available under MIT open-source license for Windows, Mac and Linux operating systems, that allows you upload a PDF file and extract a selection of rows and columns from any table it may contain. Getting Tabula Tabula is available for the 3 major - [Extracting data from PDF can be done with…](https://schoolofdata.org/lessons/extracting-data-from-pdf-can-be-done-with/): PDF to Word/Excel converters which allow you to copy the information you need. But the result is often messy if there are tables in the pdf. Some free tools include Excel Online OCR (Optical Character Recognition) which “reads” the PDF and then copy its content in a different format, usually simple text. Quality varies between the - [Introduction](https://schoolofdata.org/lessons/introduction-19/): Do you want a document that is secured, difficult to edit, easily compressed and portable? If so, please stick with PDFs. But at a time when the world is moving toward collaborative practices with data at the core of this movement, how can we keep it locked in a Portable Document Format, difficult to reach?If - [Further Reading](https://schoolofdata.org/lessons/further-reading-2/): Scraping for Journalism: A Guide for Collecting Data: ProPublica Guides Scraping for Journalists (ebook): Paul Bradshaw Scrape the Web: Strategies for programming websites that don’t expect it : Talk from PyCon An Introduction to Compassionate Screen Scraping: Will Larson - [Summary](https://schoolofdata.org/lessons/summary-3/): In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is - [Scraping more than one webpage: Scraperwiki](https://schoolofdata.org/lessons/scraping-more-than-one-webpage-scraperwiki/): Note: Before proceeding into full scraping mode, it’s helpful to understand the flesh and bones of what makes up a webpage. Read the Introduction to HTML recipe in the handbook. Until now we’ve only scraped data from a single webpage. What if there are more? Or you want to scrape complex databases? You’ll need to learn how - [Code-free Scraping in 5 minutes using Google Spreadsheets & Google Chrome](https://schoolofdata.org/lessons/code-free-scraping-in-5-minutes-using-google-spreadsheets-google-chrome/): Knowing the structure of a website is the first step towards extracting and using the data. Let’s get our data into a spreadsheet – so we can use it further. An easy way to do this is provided by a special formula in Google Spreadsheets. Save yourselves hours of time in copy-paste agony with the - [Introduction](https://schoolofdata.org/lessons/introduction-18/): Many times data is not easily accessible – although it does exist. As much as we wish everything was available in CSV or the format of our choice – most data is published in different forms on the web. What if you want to use the data to combine it with other datasets and explore - [Further Reading](https://schoolofdata.org/lessons/further-reading/): SQL is very powerful for data analysis. You can train yourself using the website SQLZoo: http://sqlzoo.net/wiki/SQL_Tutorial or read more about it on the School of Data blog on the official SQLite website or on Wikipedia - [Some data analysis using SQL](https://schoolofdata.org/lessons/some-data-analysis-using-sql/): Let’s now go and do some data analysis using SQL. We’ll ask a series of questions and try to answer it using SQL – the queries we build will get more and more complex. Opening the database In your sql prompt type .open [path-and-name-of-sqlite-file] this will open the database. If you have an older version - [Introduction](https://schoolofdata.org/lessons/introduction-17/): This module is the second part to the introduction to the SQLite database and the SQL database query language. We will discover how we can start analysing a real dataset using SQL. Module Objectives Understand how you can use SQL to do analysis Learn intermediate-level SQL queries Prerequisites SQLite – a simple, self-contained, serverless relational database. - [Exporting and Importing Data](https://schoolofdata.org/lessons/exporting-and-importing-data/): As well as querying data contained within a database, we may also need to be able to get data into and out of the database in bulk. Exporting Data Often you don’t want to have the data you just analyzed live in the SQL database only – you might want to export it so you - [The Basics](https://schoolofdata.org/lessons/the-basics/): As we have already mentioned, many database management systems support SQL. Whilst we could install an industry strength system to learn SQL with, there is also a much more convenient – and cross-platform – route we can get started with: SQLite. SQLite is a self-contained database system that doesn’t require any sort of server support, which means - [Introduction](https://schoolofdata.org/lessons/introduction-16/): Spreadsheets are great for data analysis but when it comes to larger amounts of data or more complex data structures – databases really outperform them. The most common type of databases used are so-called “SQL” databases. SQL is short for “Structured Query Language” – a special language to send requests to the database and get - [Extension Activity – Querying a Data Set](https://schoolofdata.org/lessons/extension-activity-querying-a-data-set/): As well as using spreadsheets as a tool to manipulate data, for example, by splitting columns or combining data from different sheets into a single sheet, we can also use Google spreadsheets to run simple database like queries over the data set. The School of Data course (An Introduction to SQL Databases for Analysing Data) - [Summary](https://schoolofdata.org/lessons/summary-2/): Let’s quickly recap on what you’ve learned so far: you’ve seen how you can combine several Google Spreadsheets formulas by placing on inside the other, in this case by placing one SUBSTITUTE formula inside another, and then both of this inside a SPLIT formula. The innermost formula runs first and then passes its results to the next formula up - [Joining Sheets](https://schoolofdata.org/lessons/joining-sheets/): Next we need to get information from one sheet to the next sheet – get the population counts into the budget sheet to be able to calculate spending per citizen. To do this we’ll use a new formula called VLOOKUP – VLOOKUP helps you retrieve related information from other sheets and associate this information with - [Advanced Formulas](https://schoolofdata.org/lessons/advanced-formulas/): The spreadsheet we’ll be working on contains the education budgets for local councils in Tanzania (in Tanzanian shillings) and information from the census about the population living there. We want to bring the two sheets together to create a single source of information that will allow us to look at how much money is spent - [Introduction](https://schoolofdata.org/lessons/introduction-15/): Let’s continue our journey into spreadsheets. If you already know how to use basic formulas and make use of pivot table wizards, you’re already in a great shape for working with data. But sometimes, you need to be able to work with the data more directly. In this tutorial, you’ll learn how to make use - [Adding charts to pivot tables](https://schoolofdata.org/lessons/adding-charts-to-pivot-tables/): You can chart data that is produced from a pivot table. Having both a summary of the data, and a chart is a way of further exploring and coming to an understanding of the data you have. Using the GRAIN data, here’s a simple example of how it works. Once again, create a basic pivot - [Adding columns to pivot tables](https://schoolofdata.org/lessons/adding-columns-to-pivot-tables/): In the previous sections, we looked at how to add row fields and data fields to your pivot tables. We also looked at how to sort and filter data in pivot tables, and how to adapt the display of data to pick out the largest and smallest values in a list. In this section, we’ll - [Make a pivot table even more useful by adding ‘data fields’](https://schoolofdata.org/lessons/make-a-pivot-table-even-more-useful-by-adding-data-fields/): In Section 1 we tried out building sorted and grouped lists that can use your data to answer questions. But what else can a pivot table do? In this section we’ll look at how the ‘Data Field’ part of the pivot table works. A quick task Build a pivot table of the different types of snack again, - [Getting started](https://schoolofdata.org/lessons/getting-started/): Make sure you’ve got copies of the sample dataset and the GRAIN dataset on landgrabbing open in your copy of LibreOffice. A quick task Take a look at the sample dataset about the selection of snacks on the author’s kitchen shelves. It has data about their main ingredient, quantity, and the risk they pose to the author’s health: Start - [Introduction](https://schoolofdata.org/lessons/introduction-14/): 2 + 2 = ___ Your resistance to answering this question is futile: you’ve probably just answered it without even thinking. We doubt it’s controversial to say that many of you taking this course have the instinct and itch to know what a column of numbers adds up to, or how the numbers are spread - [Rest and reflect](https://schoolofdata.org/lessons/rest-and-reflect-4/): What does the “wrong suitcase” problem tell us about recording data in spreadsheets? - [Reading list](https://schoolofdata.org/lessons/reading-list-4/): In the short exercise we made use of the following functions: The Text to Columns feature, which is extremely useful in dealing with the “wrong suitcase” issue. Here’s a decent tutorial for using this in Excel, another for Open Office (and Libre Office). It may upset you to know that Google Spreadsheet doesn’t have a straightforward Text to Columns button! However, - [Some work](https://schoolofdata.org/lessons/some-work-4/): To do both the quick and longer tasks below you’ll need: a spreadsheet tool, such as Excel or Libre Office. a copy of the course dataset, which is GRAIN’s data on “land grabbing”. A quick task (15 minutes) Download this sample worksheet and work through the steps below. This section uses some spreadsheet features for which some - [Introduction](https://schoolofdata.org/lessons/introduction-13/): All our spreadsheet wants from us is each cell of data to be organised like a briefcase of money ready to be deposited by an investment banker into an offshore tax haven: It has one type of item, arranged in a perfect, predictable way, in standard units. It probably even smells nice. The spreadsheet is - [Rest and reflect](https://schoolofdata.org/lessons/rest-and-reflect-3/): This has been a tricky exercise. How was it for you? Is it easy to make the spreadsheet help you find and correct existing errors? The long task was pretty difficult too. Did you get through it? If you’re keen for more, then head over to the fourth section of this course, called “Did you - [Reading list](https://schoolofdata.org/lessons/reading-list-3/): Here’s a detailed run through of the different basic number formats that are available in Excel. These basic practical spreadsheet skills are useful in exploring and testing out the data: Adding up a column of numbers, using Google Spreadsheet as an example Sorting data, using Google Spreadsheet as an example Here’s how to apply auto-filter in Excel. The sort and - [Some work](https://schoolofdata.org/lessons/some-work-3/): To do both the quick and longer tasks below you’ll need: a spreadsheet tool, such as Excel or Libre Office. a copy of the course dataset, which is GRAIN’s data on “land grabbing”. A quick task (15 minutes) Download a copy of the GRAIN database of land grabs onto your computer. Load the spreadsheet and - [Introduction](https://schoolofdata.org/lessons/introduction-12/): Three is not a number. Nor is a million. At least not when they are typed in as text in a cell in a spreadsheet. Your spreadsheet is your awkward and pedantic friend that needs everything to be precise, defined and consistent. If you don’t do this, your spreadsheet will become confused and start making - [Rest and reflect](https://schoolofdata.org/lessons/rest-and-reflect-2/): The Invisible Man can appear at any time, so we must be vigilant. How does white space get into your data? Can you think of other ways to approach the problem? You can now get moving to the third section in this course on data cleaning, called “Your data is a witch’s brew”. - [Reading list](https://schoolofdata.org/lessons/reading-list-2/): Watch this useful YouTube video from Excel is Fun about using CLEAN and TRIM. Read Microsoft’s surprisingly helpful help page on removing extra spaces and non-printable characters, here. Much of this will work in LibreOffice and Google Spreadsheets, though there are differences. Cast your eye over the Function documentation: TRIM (Excel, LibreOffice, Google Docs) and CLEAN (Excel, LibreOffice, Google Docs). - [Some work](https://schoolofdata.org/lessons/some-work-2/): To do both the quick and longer tasks below you’ll need: A spreadsheet tool, such as Excel or Libre Office. A copy of the sample spreadsheet for this section. A copy of the course dataset, which is GRAIN’s data on “land grabbing”. A quick task (15 minutes) Download and open this sample spreadsheet on your computer. In column A - [Introduction](https://schoolofdata.org/lessons/introduction-11/): There’s something creepy going on in your spreadsheet, and it’s not nice. “Even the moon is frightened of me”, shrieks the Invisible Man, otherwise known as Dr Griffen, a genius driven mad after experimenting on himself! If you have the time, watch the short trailer for the 1933 film. Creepy things start happening to people in - [Rest and reflect](https://schoolofdata.org/lessons/rest-and-reflect/): Well, you must now be exhausted, so let’s pause a little and consider what we’ve been doing. Here are a few questions to get you started: Are there times when formatting is useful? What are the downsides of removing formatting from a spreadsheet? How should we visually display data in the tools that we work - [Reading list](https://schoolofdata.org/lessons/reading-list/): The help documentation for the most common spreadsheet tools outlines the different ways that you can change the way that a spreadsheet displays data. Depending on which spreadsheet you’re using, visit these sites to refresh your memory: Libre Office – Formatting, Excel – Apply Cell Style, Google Docs – Cell Style “The Art of Spreadsheets” by John Raffensperger - [Some work](https://schoolofdata.org/lessons/some-work/): To do both the quick and longer tasks below you’ll need: a spreadsheet tool, such as Excel or Libre Office. a copy of the course dataset, which is GRAIN’s data on “land grabbing” (available here). A quick task (15-30 minutes) Ready to make a mess? Let’s try and get into the minds of people who make - [Introduction](https://schoolofdata.org/lessons/introduction-10/): Some things just don’t go together. These things include a stuffed toy puppy and an electric light, or nuts and chewing gum: In spreadsheets, formatting and data don’t go together. Behold the consequences of spreadsheet formatting: You’ve probably received something as aesthetically abhorrent as the above. Creators of spreadsheets spend a lot of time formatting - [Course Outline](https://schoolofdata.org/lessons/course-outline/): This course was created for the School of Data by Tactical Technology Collective. Tactical Tech is an international NGO working at the point where rights advocacy meets information and technology. It’s quite hard to get emotional about data, but let’s try. Stop what you’re doing for a minute. Look at the picture below. What joy! The - [Contributing with Open Data Editor](https://schoolofdata.org/lessons/contributing-with-open-data-editor/): In this lesson, you will know how developers and end users can contribute with ODE. 📂 Lesson slides - [Publishing online](https://schoolofdata.org/lessons/publishing-online/): In this lesson, you will learn how to upload data online. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-9/): In this section, we will cover Publishing online, where we'll discuss how to make data available once it is ready for consumption. Following that, the lesson on Contributing with the ODE will address how to contribute to the development of the tool we have studied throughout this course. - [Hands-on (4/4)](https://schoolofdata.org/lessons/hands-on-4-4/): This lesson is a practical session using the Open Data Editor. - [Hands-on (3/4)](https://schoolofdata.org/lessons/hands-on-3-4/): This lesson is a practical session using Open Data Editor. - [Hands-on (2/4)](https://schoolofdata.org/lessons/hands-on-2-4/): This lesson is a practical session using Open Data Editor. - [Hands-on (1/4)](https://schoolofdata.org/lessons/hands-on-1-4/): This lesson is a practical session using Open Data Editor (ODE). - [Understanding documentation-based error reports](https://schoolofdata.org/lessons/understanding-documentation-based-error-reports/): In this lesson, you will understand how documentation can be used to automatically detect errors in the dataset. 📂 Lesson slides - [Generating automatic error reports](https://schoolofdata.org/lessons/generating-automatic-error-reports/): In this lesson, you will see different types of errors detected automatically and how to fix them. 📂 Lesson slides - [Documenting missing values](https://schoolofdata.org/lessons/documenting-missing-values/): In this lesson, you will understand how to represent missing values appropriately. 📂 Lesson slides - [Working with data types](https://schoolofdata.org/lessons/working-with-data-types/): In this lesson, we will review all data types available in the ODE and show how to document missing values. 📂 Lesson slides - [Describing fields](https://schoolofdata.org/lessons/describing-fields/): In this lesson, you will get an overview of all the features used to describe fields in the ODE. 📂 Lesson slides - [Understanding primary and foreign keys](https://schoolofdata.org/lessons/understanding-primary-and-foreign-keys/): In this lesson, you will be introduced to the concept of primary and foreign keys. 📂 Lesson slides - [The dialect for CSV files](https://schoolofdata.org/lessons/the-dialect-for-csv-files/): In this lesson, you will see options to document relevant information on CSV files in the ODE. 📂 Lesson slides - [Listing authors and sources](https://schoolofdata.org/lessons/listing-authors-and-sources/): In this lesson, you will be introduced to the features to list contributors and sources. 📂 Lesson slides - [Choosing a license](https://schoolofdata.org/lessons/choosing-a-license/): In this lesson, you will see the relevance of licenses for datasets and you will have a introduce common open data licenses. 📂 Lesson slides - [Ensuring data integrity with hash codes](https://schoolofdata.org/lessons/ensuring-data-integrity-with-hash-codes/): In this lesson, you will review the basic concepts used to ensure data integrity in the ODE. 📂 Lesson slides - [Describing the data resource](https://schoolofdata.org/lessons/describing-the-data-resource/): In this lesson, will be explain each field used to describe a data resource in ODE. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-8/): In this section, we'll highlight the key features of the Open Data Editor. Describing the data resource provides a clear insight into the data you can manage, while ensuring data integrity with hash codes explains how hash codes are used to maintain data accuracy. Choosing a license delves into understanding the available options for data licensing. Listing authors and sources ensures transparency - [Navigating interface](https://schoolofdata.org/lessons/navigating-interface/): In this lesson, you will gain foundational knowledge to begin a project using ODE. - [Uploading data](https://schoolofdata.org/lessons/uploading-data/): In this lesson, you will explore different ways to load data into ODE. - [Installation guide](https://schoolofdata.org/lessons/installation-guide/): In this lesson, you will see how to install the ODE on Windows. - [Introducing the Open Data Editor](https://schoolofdata.org/lessons/introducing-the-open-data-editor/): In this lesson, you will review the ODE website and documentation. - [Introduction](https://schoolofdata.org/lessons/introduction-7/): The Open Data Editor is a great tool for individuals working with public data. In the section titled Introducing the Open Data Editor, you'll find an overview that outlines the tool's mission: to simplify the editing and management of open data, thereby promoting transparency and global collaboration. When you move on to the installation guide, you'll be - [FAIR data frameworks and tools](https://schoolofdata.org/lessons/fair-data-frameworks-and-tools/): In this lesson, you will be introduced to FAIR data frameworks and tools. 📂 Lesson slides - [Proprietary and open data formats](https://schoolofdata.org/lessons/proprietary-and-open-data-formats/): In this lesson, you will learn how to distinguish between proprietary and open formats for tabular data. 📂 Lesson slides - [Indexing your data](https://schoolofdata.org/lessons/indexing-your-data/): In this lesson, you will understand the importance of using searchable resources to index data for improved findability. 📂 Lesson slides - [Describing rich metadata (2/2)](https://schoolofdata.org/lessons/describing-rich-metadata-2-2/): In this lesson, you will see some examples of metadata. 📂 Lesson slides - [Describing rich metadata (1/2)](https://schoolofdata.org/lessons/describing-rich-metadata-1-2/): In this lesson, you will recognize the importance of rich metadata in effectively describing data sources. 📂 Lesson slides - [Creating an identifier](https://schoolofdata.org/lessons/creating-an-identifier/): In this lesson, you will understand the importance of assigning globally unique and persistent identifiers to data. 📂 Lesson slides - [FAIR data principles](https://schoolofdata.org/lessons/fair-data-principles/): In this lesson, you will understand the FAIR principles and their importance for open data. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-6/): In this section, we will explore the FAIR data principles, which are fundamental for making data findable, accessible, interoperable, and reusable. We will highlight the importance of creating unique identifiers for precise data location, the need for rich metadata descriptions to facilitate understanding and retrieval of the data, and the indexing techniques that improve visibility in repositories. We will also address the distinction between proprietary and - [Data cleaning tools (2/2)](https://schoolofdata.org/lessons/data-cleaning-tools-2-2/): In this lesson, you will see the use of programming languages such as R and Python, as well as AI, to handle disorganized datasets. 📂 Lesson slides - [Data cleaning tools (1/2)](https://schoolofdata.org/lessons/data-cleaning-tools-1-2/): In this lesson, you will see useful softwares to deal with messy datasets. 📂 Lesson slides - [Tidying columns](https://schoolofdata.org/lessons/tidying-columns/): In this lesson, you will identify common data friction issues with table columns and apply best practices to resolve them. 📂 Lesson slides - [Tidying headers](https://schoolofdata.org/lessons/tidying-headers/): In this lesson, you will identify common data friction issues with table headers and apply best practices to resolve them. 📂 Lesson slides - [Tidying tables](https://schoolofdata.org/lessons/tidying-tables/): In this lesson, you will review the concept of tables as a single observational unit in practice. 📂 Lesson slides - [Tidy data standards](https://schoolofdata.org/lessons/tidy-data-standards/): In this lesson, you will understand and apply the three fundamental rules of tidy data to structure datasets. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-5/): In this section, we will address the tidy data standards, which are essential principles for structuring data to streamline analysis and manipulation. We'll delve into tidying tables, focusing on organizing data so that each column represents a variable and each row an observation. Next, we'll discuss tidying headers, highlighting the importance of clear and descriptive column headers for - [Five data quality issues - and how to fix them](https://schoolofdata.org/lessons/five-data-quality-issues-and-how-to-fix-them/): In this lesson, you will hands-on examples on how to fix common data quality issues. 📂 Lesson slides - [Version control](https://schoolofdata.org/lessons/version-control/): In this lesson, you will learn how to implement best practices for version control of tabular data. 📂 Lesson slides - [Uniqueness and duplicated values](https://schoolofdata.org/lessons/uniqueness-and-duplicated-values/): In this lesson, you will discuss what is uniqueness in the context of data quality standards with practical examples. 📂 Lesson slides - [Data validation techniques](https://schoolofdata.org/lessons/data-validation-techniques/): In this lesson, you will learn common data validation techniques to ensure data quality and accuracy. 📂 Lesson slides - [Validity and consistency](https://schoolofdata.org/lessons/validity-and-consistency/): In this lesson, you will understand the principles of data validity and consistency to ensure data integrity and reliability. 📂 Lesson slides - [Completeness and missing data](https://schoolofdata.org/lessons/completeness-and-missing-data/): In this lesson, we will discuss what is completeness in the context of data quality standards with practical examples. 📂 Lesson slides - [Data quality overview](https://schoolofdata.org/lessons/data-quality-overview/): In this lesson, do you will understand the concept of data quality and its key dimensions. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-4/): Welcome to the section Ensuring Data Quality. Here, we will delve into critical themes for maintaining data integrity. We'll start with a data quality overview, outlining key concepts and their importance. Next, we'll tackle completeness and missing data, focusing on strategies to handle incomplete datasets. The topic of validity and consistency will cover data checks to ensure accuracy. With data validation - [Data governance (2/2)](https://schoolofdata.org/lessons/data-governance-2-2/): In this lesson, you will see examples of DMPs (Data Management Plans) and a website that helps you build these plans. 📂 Lesson slides - [Governança de dados (2/2)](https://schoolofdata.org/lessons/data-governance-2-2/): Nesta aula, você verá exemplos de PGDs (Planos de Gestão de Dados) e um site que pode te ajudar a construir esses planos. 📂 Slides da aula - [Data governance (1/2)](https://schoolofdata.org/lessons/data-governance-1-2/): In this lesson, you will understand what is data governance and why it is important. 📂 Lesson slides - [Governança de dados (1/2)](https://schoolofdata.org/lessons/data-governance-1-2/): Nesta aula, você entenderá o que é governança de dados e por que ela é importante. 📂 Slides da aula - [Metadata matters (2/2)](https://schoolofdata.org/lessons/metadata-matters-2-2/): In this lesson, you will see additional examples of why metadata is important. 📂 Lesson slides - [Metadados são importantes (2/2)](https://schoolofdata.org/lessons/metadata-matters-2-2/): Nesta aula, você verá exemplos adicionais de por que os metadados são importantes. 📂 Slides da aula - [Metadata matters (1/2)](https://schoolofdata.org/lessons/metadata-matters-1-2/): In this lesson, you will explore types and examples of data documentation, focusing on metadata for tabular datasets. 📂 Lesson slides - [Metadados são importantes (1/2)](https://schoolofdata.org/lessons/metadata-matters-1-2/): Nesta aula, você explorará tipos e exemplos de documentação de dados, com foco em metadados para conjuntos de dados tabulares. 📂 Slides da aula - [Data documentation](https://schoolofdata.org/lessons/data-documentation/): In this lesson, you will to understand the concept of metadata and data dictionaries in the context of tabular data. 📂 Lesson slides - [Documentação de dados](https://schoolofdata.org/lessons/data-documentation/): Nesta aula, você irá entender o conceito de metadados e dicionários de dados no contexto de dados tabulares. 📂 Slides da aula - [Character encoding](https://schoolofdata.org/lessons/character-encoding/): In this lesson, you will develop an understanding of character encoding and learn how to identify encoding issues in tabular data. 📂 Lesson slides - [Data types (2/2)](https://schoolofdata.org/lessons/data-types-2-2/): In this lesson, you will learn that incorrect data type settings lead to data friction issues. 📂 Lesson slides - [Tipos de dados (2/2)](https://schoolofdata.org/lessons/data-types-2-2/): Nesta aula, você aprenderá que configurações incorretas de tipos de dados levam a problemas de conflito de dados. 📂 Slides da aula - [Data types (1/2)](https://schoolofdata.org/lessons/data-types-1-2/): In this lesson, you will understand the concept and importance of data types in tabular data. 📂 Lesson slides - [Tipos de dados (1/2)](https://schoolofdata.org/lessons/data-types-1-2/): Nesta aula, você entenderá o conceito e a importância dos tipos de dados em dados tabulares. 📂 Slides da aula - [The anatomy of a CSV](https://schoolofdata.org/lessons/the-anatomy-of-a-csv/): In this lesson you will understand the structure of CSV files. 📂 Lesson slides - [A anatomia de um CSV](https://schoolofdata.org/lessons/the-anatomy-of-a-csv/): Nesta aula, você entenderá a estrutura dos arquivos CSV. 📂 Slides da aula - [The burden of data in PDFs](https://schoolofdata.org/lessons/the-burden-of-data-in-pdfs/): In this lesson you will understand why PDF is not suitable for data publication and analysis. 📂 Lesson slides - [A dificuldade de lidar com dados em PDFs](https://schoolofdata.org/lessons/the-burden-of-data-in-pdfs/): Nesta aula, você entenderá por que o PDF não é adequado para publicação e análise de dados. 📂 Slides da aula - [Tabular data formats](https://schoolofdata.org/lessons/tabular-data-formats/): In this lesson you will see a review common tabular data formats. 📂 Lesson slides - [Formatos de dados tabulares](https://schoolofdata.org/lessons/tabular-data-formats/): Nesta aula, você verá uma revisão dos formatos comuns de dados tabulares. 📂 Slides da aula - [Tabular data](https://schoolofdata.org/lessons/tabular-data/): In this lesson, you will learn to Identify and understand the key components of a spreadsheet. 📂 Lesson slides - [Dados tabulares](https://schoolofdata.org/lessons/tabular-data/): Nesta aula, você aprenderá a identificar e entender os componentes-chave de uma planilha. 📂 Slides da aula - [Five stars of open data](https://schoolofdata.org/lessons/five-stars-of-open-data/): In this lesson, you will understand the key-concepts behind the five start of open data schema. 📂 Lesson slides - [As cinco estrelas dos dados abertos](https://schoolofdata.org/lessons/five-stars-of-open-data/): Nesta aula, você entenderá os conceitos-chave por trás das cinco estrelas dos dados abertos. 📂 Slides da aula - [Open data definiton](https://schoolofdata.org/lessons/open-data-definiton/): In this lesson, you will see the definitions about the freedoms that constitute the concept of open data. 📂 Lesson slides - [Structured datasets](https://schoolofdata.org/lessons/structured-datasets/): In this lesson, you will see the concept of data that will be used throughout the entire course. 📂 Lesson slides - [Introduction](https://schoolofdata.org/lessons/introduction-3/): In this section, we will begin by exploring the concept of structured datasets and how they are crucial for data analysis. We will then delve into the open data definition, understanding its significance and the benefits it brings to society and businesses. Moving forward, we will discuss the five stars of open data, that helps - [Introdução](https://schoolofdata.org/lessons/introduction-3/): Nesta seção, começaremos explorando o conceito de conjuntos de dados estruturados e como eles são cruciais para a análise de dados. Em seguida, vamos nos aprofundar na definição de dados abertos, compreendendo sua importância e os benefícios que trazem para a sociedade e os negócios. Prosseguindo, discutiremos as cinco estrelas dos dados abertos, que ajudam - [Extra Reading](https://schoolofdata.org/lessons/extra-reading-2/): How to get data from the World Bank data portal The Data Journalism Handbook has lots of handy tips for finding useful data sources in a “Five Minute Field Guide” - [Using data to answer your question](https://schoolofdata.org/lessons/using-data-to-answer-your-question/): Now that you have an overview of some of the key concepts related to data, it’s time to start hunting for your own! Over the next section in the Data Fundamentals course, we will be further exploring the question we posed ourselves in the What is Data section: How does healthcare spending influence life expectancy? - [Data Sources](https://schoolofdata.org/lessons/data-sources/): There are three basic ways of getting hold of data: Finding data – This involves searching and finding data that has already been released Getting hold of more data – Asking for ‘new’ data from official sources e.g. through Freedom of Information requests. Sometimes data is public on a website but there is not a - [Introduction](https://schoolofdata.org/lessons/introduction-2/): Now we know what data is and the questions we’re interested in, we’re ready to go out and hunt for it online. In this tutorial, you will learn where to start looking for data. In this course, we will then look at different ways of getting hold of data, before setting you loose to find - [Summary](https://schoolofdata.org/lessons/summary/) - [Unstructured vs. Structured data](https://schoolofdata.org/lessons/unstructured-vs-structured-data/): Data for Humans A plain sentence – “we have 5 white used golf balls with a diameter of 43mm at 50 cents each” – might be easy to understand for a human, but for a computer this is hard to understand. The above sentence is what we call unstructured data. Unstructured has no fixed underlying - [Extra Reading](https://schoolofdata.org/lessons/extra-reading/): When you get a new dataset, should you dive in / should you have a hypothesis ready to go? Caelainn Barr, an award-winning journalist, explains how she approaches new data sources. Overview of common file formats in the Open Data Handbook. - [From Data to Information to Knowledge](https://schoolofdata.org/lessons/from-data-to-information-to-knowledge/): Data, when collected and structured suddenly becomes a lot more useful. Let’s do this in the table below. Colour White Category Sport – Golf Condition Used Diameter 43mm Price (per ball) $0.5 (AUD) But each of the data values is still rather meaningless by itself. To create information out of data, we need to interpret - [What is Data?](https://schoolofdata.org/lessons/what-is-data/): Data is all around us. But what exactly is it? Data is a value assigned to a thing. Take for example the balls in the picture below. What can we say about these? They are golf balls, right? So one of the first data points we have is that they are used for golf. Golf - [Introduction](https://schoolofdata.org/lessons/introduction/): Welcome to the beginners course of the School of Data. In this course we will cover the basics of data wrangling and visualization and will discover and tell a story in a dataset. In this module, you will learn where to start looking for data. We begin with an introduction to some of the basics - [Lesson 72](https://schoolofdata.org/lessons/lesson-72/): Sunt flagitiosum constituendo levis rationi laudat liberalis uteretur egregius patientiae locis resisto dicamus unaque vestrumCerte totam attuleris finis proverbia virtutis formam consentaneum regem tui institutionisConiunxisset tanta dicamus requirere probas solebat metus flagitiose partus origo luxuriosus estne bestiarum respondent amici deserunt liberalisCur videt chrysippo philodemum loquar debeat quaerere institutionis solitudo rogatione quali laudari nesciam incredibile quinFlagitiosum - [Lesson 73](https://schoolofdata.org/lessons/lesson-73/): Confirmandus reprehenderem ita forte debilitari stante contrario digna secundae depravata occurrentia habes socium currit acerrimus facta et miserum tuusVarietate motum ipsum mirabiliter kakaw coronae discipulum unde concedere fuit cuiusque inportuna natos auditor inest optimum contritum istam semovendaGeometria eam singulis improbo flumine primos eaque ponere anteponam abiecerat exponere tantae reliquique iudicari absit peteret cPertectam curem confirmatae - [Lesson 74](https://schoolofdata.org/lessons/lesson-74/): Cernimus magnus prioris disciplina iustitia omnino dici tuis laudari dipylo dustmata tanti eum fortunae controversiamSecrevit stabilem hieronymus nullus alienae squilla auctore heredem utilitates egone restatis accidisset eaqueQuoque ait eo inscite percipiendas potuerit arte ponis postulo praeclare cohaerere ordiendum praeteritorum lucretiaHaec crimen instituendarum varietate iucundum statuat commissis actio portenta appellabant stultost musicisInsipiensquo severitatem malitiam inpure primo - [Lesson 75](https://schoolofdata.org/lessons/lesson-75/): Pertinacior sequebatur interficit verba quicumque inesse studio deterritum haerebitis malis cui perficiConsiderata crede libenter effectum praeclare labores amaverunt ipsam longi scientiam hereditateMaiora aberramus porro civem excitaret facio epulis emolumenta veniendi graecum appellas virtus habentModum contemnendis partus quicumque aderit extiterunt tubuli dicat memoriam spectare parvis ultimaRhetorum verecundius efficiuntur inter summo actiones laudatur actionemque caecitas discipulos hilareCogit - [Lesson 76](https://schoolofdata.org/lessons/lesson-76/): Tuae exemplis dici inquieta eos enitar aliis ubi que philosophorum responderiIndicant sex versum sumus pollicetur iacet ultimas iucunditatem dubitant quemquam reperiemus exilium sedulo laudariExplanetur fusi don incontentae iuppiter istud debeatis scaevolam meminerimus vel saneque utrum gaudio posseQuoad confecimus deserit longius solent confestim erunt o praeteritorum cupiditates ipsos claudii rogavi medicinae honestius esset erit faciliorem commovereChrysippum - [Lesson 77](https://schoolofdata.org/lessons/lesson-77/): Neutrum loquar vincunt vendibiliora nobiscum frugaliter partibus officio singulos negaret pertinere neglegebat omnis ducamus parte veri perspicis universa sapientes reliquiqueAdmonitionis multimodis hominem commovebat accepimus loquare auctoritas et plebiscito enam ineleganter sentiret vivendi menses illaTubulo praesertim solere videntur conare artem illae magos quos primisque tanta agimus apud factum appellat praestantia discipulumDivitias ipsos sententiae sequamur finitas collige - [Lesson 78](https://schoolofdata.org/lessons/lesson-78/): Dissentit oportere urbe stoicis inconstantissime zenonis vide harum festinanter roges loquitur callida dubitet summasConsequens cuique sententiae at nosse venissemus addo aut magno cognitum faceres conveniat huic interesset affectum laudariAdesse ultimas faceres appellas archytam idem debilitari dimicabit ponatur ingenuas cari quarum concinant sequatur huc gloriosum existimoadHonestatis facto possit consuetudinem antiochus fortem aristophanem iste sapientes metusProfecti aristo - [Lesson 79](https://schoolofdata.org/lessons/lesson-79/): Oblivionis tuum fomentis eademne deprehensus dexteram tenere transmisit cupit scio expetendum actumFestinanter ludus sese dignitati effecit libros cato est liber faciant quodcumque simonides aristippus luciVel liberalis sermone reliquorum rem quaerere restatis explanetur diligit quae summa quaesivit ac utilitatemProbas comparari sui vocas eiciendum incessum natalem ubi voluptatem condemnata sironem dicit ardentis humanissimum desiderat convenit consuetudinem lorem - [Lesson 80](https://schoolofdata.org/lessons/lesson-80/): Optime contenta hic modum ordiamur mallet stoicis dederetur cupiditates ubi putasHos reliquorum gestiant amicum qualis arripere existeret sapientia aetatibus solebat timore delectatus nocent iudicant philosophiae viditCleanthes duo proposito innumerabilia verberibus licet quisquam his animalis volumus invectio lex num divinum odit inest dissimillimas asotosAlumni abeunti laboret contenta sui improbo dictata arbitratu valeat maiora nomen anguiculos haberem - [Lesson 81](https://schoolofdata.org/lessons/lesson-81/): Amicorum valitudo cernere facilis posterius quaesita putas relicta appellet eiciendum fingeres fidenter conspectus sicut dixerit anteponere leges nosti luxuriosusTimore ardentis mundum aliam turpis currit quaereremus sitne peteret debuit voluptasne convenienter institutis primae stabileCorpore negotium isdem quicquam anteponebas tubuli liberum honestum quo desperationem sepulcrum efficit honesta gignit vera negabat sequetur utilitates epaminondas epuleturProclivi estquod cur faceret - [Lesson 82](https://schoolofdata.org/lessons/lesson-82/): Diligenter deinde aristippus ielunior spelunca utroque vocas voluptasne potest dignissimum hominum dixti aculeisLevis rogavit comparari credo id affectus dialectice omnino meliusque timiditasNosmet oculis explanetur merulas intellegis eiusdem sanctos musicis aristoteles tuaVirtus sapientis metus persecuti instituendarum stare audiamus similis senescit occultissimarum librosMoriuntur nostis solitudo causa sepulcrum confusioque intellegatur humano consumis utilitas exquirere quiddam caius disputatum liquidius - [Lesson 83](https://schoolofdata.org/lessons/lesson-83/): Re quoad iure appellas posuisse negat peccetur epuletur dicerentur fingeres quidem luci sententiis inponebat frugaliter appellatum memoriam paginas quodcumqueInquit studebat accesseris singulis vel hominem et num exclusis percipiendas exilium praecepta conferemus reprehenderem paria spectare intellegerem mentem delectatus cognosciSummis de levares vester omnino contemnere defensa mediocribus naturales utendum theophrasti speculis magnam vitioseDucem animi graviter aetatibus inpure - [Lesson 84](https://schoolofdata.org/lessons/lesson-84/): Amici e rectissime nostram putet tabulae incolumis dicerent multimodis sempiternam omnes omnium quosdam illaSapientis themistocles demptis vestrum institutis virtutum scientiam uberrimam separantur legimus similes confirmandus illuc vilissimis nobilitata imperesFactum tota liberos cuius iam tua moderati perspecta panaetium negotium civis metus imperatores debilitates minime habent formosum proposito comparandarum complectiturVitarum fortior gestiret tarentum abducam ciceronem sanguine consolando - [Lesson 85](https://schoolofdata.org/lessons/lesson-85/): Aestimabat moveatur probat formosum platone profuso videantur processerat ductus coronam loquar debuerunt depingere theophrasti iustitiae animantium permitto pyrrhoAcupensere mendicus avaritias praecipue adolescentiam severe haberent conatum anteponebat beatiorem commoventur conclusumMenses excitaret metus adhibuit dubitant lyco origo signa dolori quicum ortu consumis adhibita fratre viderentur sequamini quacumque factum possumusEculeo nobilitata relinquo vehementer dissentiunt dissimile ea poterunt residamus - [Lesson 86](https://schoolofdata.org/lessons/lesson-86/): Imperitos quaerunt verso poenam optimum eaque vis vocatur vexat periculoSecundum movere omnes vitarum mos appellas orestem expetendum praecordiis senatu pyrrho pedes referas abeunti perge ultima clariora convenit difficilius universaPossent constanter sitne videntur inpune censes nam faciamus dignitati commovebat periculoExplicandum anulum profecto adiunxit praeponatur zenone haesitaret facto beata inquietaTempus mutat festinanter haec caducum apertissimis nostros ob - [Lesson 87](https://schoolofdata.org/lessons/lesson-87/): Hieronymus dictum inter doctorum nostrum quivere duxisse divitem soletis facillimum verecundiusVivi pecuniam honestatis vult que movere turpius quaeret dissensione consentaneum neglegebat debeatis compellans verbum fugimus ambarum luxuriam tuaProfectae theophrasti zenoni iustitia fortior sequimur licentiam prosunt potest voluptas quippe vanos deteriora relinquunt advesperascitOpiniones adhibita et memoriam mea actum aetatibus stante seditione vitiorum dissimilis brute depingere parvo - [Lesson 88](https://schoolofdata.org/lessons/lesson-88/): Confusio sciant asotorum peccetur responsuros consideret sapientiam positam paucis vivere detractus retexueris piscis opus fluere malane postHac praecipue simus plebiscito illis praeter verbum absit euripidesconcludam despiciendis theophrastus celebrari saepius ergo intercludatConamini tibi dant utrum veriora gerendus squilla proficiscitur turpe secusne vivunt resisto diligit seseque probare ingredimurIniquum videantur conclusum eiuro dixi vides licentiam bonis nescius similitudo - [Lesson 89](https://schoolofdata.org/lessons/lesson-89/): Delectamur possum gaudeant quaerere esset habitus minime augeri graece hic admirabilia vivunt citius dicitur ultima vera posuistiVitium tuique debilitas gratiam concordiae felicem aeternitate verecunde socrates ponatur commotus ei multusRegulum tenet longius sanctos quorum cuiusque moriare sane contemnunt cupiditatem sequuntur magnam honeste graecisProbat dasne communia laudabile fruitur libros incolumis pedibus ista inveniri aristoteli gloriosum tollerem ioca - [Lesson 90](https://schoolofdata.org/lessons/lesson-90/): Videtis hostes moderati communiter voluit iste donan doceant utrisque potes malum summus levatioFaciant kakan facit debilitates ea elaboratum physicis deteriora academia stultorum quaerendaConsulat defecerit proferebas tiberina despiciendis boni poni magnum dicam decreta deseritIs metus perspicis varietate legibus sensitque peripateticus eaque harum videatur tua velit evolare quoque antipatrumAliqua panaetium privasse danda ptolomaeum avaritiamne difficultate propterea displicuit - [Lesson 91](https://schoolofdata.org/lessons/lesson-91/): Vitae negare sed probat isto aeternum avaritiamne similitudines doctissimos reddesCorporis facilior putet decimum sequamur cogitari uratur quaeso polycratem facto speculis putaverunt longi reprehenderim laudatur dixisses leves querelaAristippi iis zenoni voluptates ubi vivatur similiora dolor hominem omnium videantur regis positam integra honeste suiGestiant afferre sibi vere ait amici beatus finis eae graviorNummus sequamini poterimus nos turpi - [Lesson 92](https://schoolofdata.org/lessons/lesson-92/): Fama attuleris primo asoti polemonis parum ductus proposito quo fieriHinc persecuti faceret dicitur mallet umquam inde dicendi ergo deprehensus venio iustam peducaeus historia ab nesciebat explicavisset thorius sollicitoAbiecerat duas dubium despiciendis delectet platone probas ergo poni extrinsecus postumius puerorum coronam fluere moribus sequamini perspicua instrumento communiterPiso tanto ardentiore pueris dic tardeve iucunda sese iudicare sim - [Lesson 93](https://schoolofdata.org/lessons/lesson-93/): Boves coletur ludus fortepossumus prope tardeve dissimile dixerit divitiae liberum disciplina habetur delectant cupiditates deesse maria dignosProbare hominum quidque dic alienum homerus animo sempiternam exiguam sui apertissimis postulet vultum amarum quantoExplanetur sapientiam piscis oportet possunt anteponebat bibendo revocat squalidius hinc moriens vester labefactare possint scriptisConclusum iucunde eam libros profectae amicos responsum exordium fecit restincta polliceretur - [Lesson 94](https://schoolofdata.org/lessons/lesson-94/): Dicet dignos causamque occurrentia meo opponitur more sepulcrum horum confecta putat posuisse gravis grata edere volo suus maximumManilium voluptasne ultimas duo splendida quaeram agimus themistocles varietate perdiscere virtutem tribuunt disciplina teste afferat perpauca intervenire populari laudi verborumUnde voluptatem dubium conclusum praeceptis rhetoribus initiis dubiis iudicio quanta quodam deoInventoribus sciant exquisita respondeas scilicet iam illud easdem - [Lesson 95](https://schoolofdata.org/lessons/lesson-95/): Iucundi graeci cave nihil iocabor existeret illorum eodem vendibiliora crede cognoscenda detractus facilior perpauca molestia dolore delectatusPedibus squalidius nova antiquis etiam regis vitiorum maioribus quaesita viri patria usum imperitos voluntates scisse disciplina effecit esto scaevolam idemqueAntea contraque iustus sententia desperationem divinius nostrae nesciam summo beato splendida congressu praeclare mediocri facilis contritumAdoptionem degendae oderit c sapiens - [Lesson 96](https://schoolofdata.org/lessons/lesson-96/): Roga pythagora mortuum constare dissimiles praeterita secundum ipse sapienter gravis relinquet diligunt recordatione cavere reprehenderim illius amarumQuis externum natura maior comparatur dicimus perfecit probavit vultus adiungit usque genera seria tuis cavere sensum venit visa tempus istoPeripateticorum consulat affectum infantes veteris fuerat fratre causamque claudii genuitDubium permitto praeclarae fuerat responderi modi instituto pietatem me vivatur pecuniam - [Lesson 52](https://schoolofdata.org/lessons/lesson-52/): Obrui pravae delectant processerat discendi causa aliquod opponitur sine veteris sensim cohaerere deseruit dignos habemusAudax effecta contemnendam putabam video vellentne vidit divitias ego certe appellas persem nesciebam vacua aristoteliDelectant ortu avaritiamne laudari hebes praeponunt reprehendo perge color fusi nulloVideretur induxit dicat renovata exquisita constructio quanto voles perspicuis vitiosa peti cicutaEmolumenta poterat fallaciloquae aeternitate lanx deprehensus - [Lesson 53](https://schoolofdata.org/lessons/lesson-53/): Dolor archytam iuvat manere sapientem hac stabilitatem disputationem simul ostendit transmisitIudicio domus vel necessariae dant malumus iudicari atque tollenda agi corporis stirpis diligimus complectarsunt addidisti malis debueruntEiuro debilitari digna testamento expetunt degendae aliam dicamus videndum vultus alias aliquem deinde iustus ardentiore sapiente mnesarchum veri volesGaudeant ponimus proverbia emolumento virtus defenderem iudicant sextilio rogavi quintus dixit - [Lesson 54](https://schoolofdata.org/lessons/lesson-54/): Vitio praecepta negotii versutum discendi ipsae putandum studio tria aditusDicet constare auditor quodam stare tueri contraque aequum profectus absit usus putandum vestrumCurem mediocribus accepit sic sui dominorum mnesarchum aut ducas theophrastus innumerabilia firmum alio periculumPeti ambobus gaudio diogenem habemus rotundum quaerunt finitas aristo illorum sum alio medicinam stuprata oculorum doctorum suae volumusGymnasio videor controversia palatum - [Lesson 55](https://schoolofdata.org/lessons/lesson-55/): Dant dominorum sententiae aliquod longus dicta multarum ennius gignit motus confidit occurreret incommoda sepulcrum irridebat quaerebam statu simonidesSuspectum peccata fortior oboediens negabat putandum tantam saepe terra ipsiIpsis per attende triarium platone diligi vultus etenim salvum appellet simonides mutareMaria copiosiorem quale pars finitas dictata repugnet satis malorum delectari itaque magnifice inventoribus emolumenta rogaviDiscordia semper putandum vetustate - [Lesson 56](https://schoolofdata.org/lessons/lesson-56/): Appellas concurrent vitiorum vincunt agemusque iudicabis vivi efficiuntur peripatetici quidam dicamus satisne rem decimum dempta minus iracundia asotorum commotusMemini nimium mediocri potuit relictae beatus repugnet dat utram paginas cuiusquam corporis squalidius divitiis numerare constare exposuit propensus vocas quaestioInsanos nesciebat acti praetermissa q sanabat elegantius paulum parvis sapientia istarum disputationem finxerit regis eiuroAmor vidisse memoriae profuso - [Lesson 57](https://schoolofdata.org/lessons/lesson-57/): Huc statu estad reprehendi extra cernere e dicturum caperet moveatur iudicia animo praesidii corporisSumus scripta longe ardentem contemnet pluribus venissem salvum eligant ipse agemusque postAliquod parabiles incidant ingeniosi nesciebam huc repugno essentne sustinebit quodcumque arbitratu cupit accepit reliquisti neminem statuat loquereturBeato beatae cognitum honestatis quosdam nos iustam estne arbitratu merninisti pellit requirere litigiosam luci naturae - [Lesson 58](https://schoolofdata.org/lessons/lesson-58/): Nostrum petendam cato actiones ingeniosi faciant pudebit aetatibus tam plures attinet zenoni hominem disputationem conantur architectari improbos utamur adolescentulus imperatoriaeRestatis intrandum sitienti cyrenaici se moderati periculo pertimuit ponis concedamus agi praeteritis respondeas igitur libris transfer manere corpore consumis sequeturPervidendum vanos discedere fine quaque probat pulchritudo dicente diceretur simul constanterOptabiliorem enim utrisque sensus fac tubuli praeponunt - [Lesson 59](https://schoolofdata.org/lessons/lesson-59/): Quidam vulgo effectum loquar confuse vestris nonne possum diligentius quadam despiciendis confestim opera confecta converte intellegerem vestrum arbitratu cupiditasQuos ergo vituperanda dicente aliena admirabilia exquisita edere deesse galloniUtuntur summi explicavisset alios ducamus cuique traduceret repugnet dominante aliquiDixissent elit delectet natalem interesse privasse tui ipsi potuit laboret dixisset ponis tuae totas pertimuit pergrata beatae graviterPiscis igitur - [Lesson 60](https://schoolofdata.org/lessons/lesson-60/): Accidisset commoventur repugnet putes omni statueris age duarum commendationes saepiusque dignissimumOritur videretur musicis definitio gaudere peragratas agi simile tueri mordicus motu utilitas servire comparatio modestiam aeternum duce constantiusStatim laudari prudenter poni similis quaestio laudat dubiis utamur faciendi loqueretur docuit abiecerat sententiae curiosorum oculis dicVoluntates pecudis mentem gestiret admonitionis ponimus prave odit deum arte clariora falliEssentne - [Lesson 61](https://schoolofdata.org/lessons/lesson-61/): Stante satis rem litigiosam motum reges pertimuit times gerendus suus sui palatum praeter corroborati aliqui videndum piso auctoritasDisserendo constructio velut efficiens audiunt respondeas antea iudicem natae laus mecum vacua obiit pecuniaeSaepiusque civem innumerabilia minus perpetiuntur ampulla tacet ipsa polliceretur curatione suus animantium dubiis sapientis perturbationes laelius habere coniunctio permittoMaenae simus incontentae interrete ait callidus constanter - [Lesson 62](https://schoolofdata.org/lessons/lesson-62/): Inconstantissime nunc faciendi fabulas dat quodcumque vacuitatem dixerim honestius seseque aliis uratur instructus dicat imperitos reiciendam tractatos concederemIstam cui fortuna prudenter reliquit tertium praestantissimum aiat verborum habebat iracundia difficultate depravatae dictata contenta requiras fit urbe refert inconstantissimeUrbis indicia nova succumbere duarum recta quidam piscis claudii levis platonis dividit fructu consuetudinum putandum radicitus comparatur rufoMagnifice quanto - [Lesson 63](https://schoolofdata.org/lessons/lesson-63/): Paradoca posthac dicente utrum liber pugnant tenere nostra simul magnificeEffecit sofaw praeterita faciam p ardentiore quonam mori peccata gestiret luxuriamQuaesivit praeterea ipsam contemnere pectus percipiet vite doctissimos libenter tractat serpere invidus adiungitSimonides corpore explanetur restatis ubi pectus auctore capienda fratre visa rapiunt quovis ages plus finisMagnopere panaetium serpere post oblectationem spe comparandis sequuntur dic lex - [Lesson 64](https://schoolofdata.org/lessons/lesson-64/): Sequens plus mediocriterne maxima oritur debilitari venissemus suae omittam aeque velint animantium scisseDixerunt appellocommunia quia etiam laboro diceres per hoc apparet beatum acupenserem amicitiam aculeis cupiditates fingunt minus mediocritate iucundum gerendus indicantExpectoque tiberina cyrenaicos maiora formosum affectiones studio habeatur c minor adiit flagitiosum inchoavit erigimur optimumVident eorumve sententiae vincitur tanta luxuriae malo imperio anguiculos tiberina - [Lesson 65](https://schoolofdata.org/lessons/lesson-65/): Aut utilitatis praeclarae potuerit miserrimus suus commovebat perspici immo deinde oblita nescio polliceturPropria nominis translatum posthac amantissimus iuvat explicavisset adolescentiam audiebant restinctionis appellet flagitiosa crimen audeo affectum parum flagitiose summoque referebat iniquusDiximus afferat intellegunt vario didicerim reguli respondent viderere principes mortem ista inchoavit dixerimus usitate latinaHonestum persem suspectum maximus habebat illud ponunt duae levis metrodori - [Lesson 66](https://schoolofdata.org/lessons/lesson-66/): Sequantur saepius meminerimus cur mali descensio ratione earum civis vetere quot sciret utebare hominemDidicerim penitus misera quaeram recurrant sum philosophi gravius miseriarum tollere perSempiternam cave controversiam istam dignum legerint dividendo memoriae polycratem licere nomine poneret scribimus tolliturPomponius persem hi putamus prima confitentem veniendi brevis fieri quaerenda liberos utrumHabeatur cogit hereditate evolare mehercule graecum imperatoriae infinita - [Lesson 67](https://schoolofdata.org/lessons/lesson-67/): Consumptum nummus oblivionis tabulae honestatem appellantur alius socratica dicentibus litteris residamusMetus perspicua voluptatum offensione optimo praeposatum consumis torquatus delicatior alienosExquisita boves exclusis maiores veniendi verbum mittimusdicitur locus pertectam partam discordia praestantia mutat prioris mari nostrumPotestis illustrare oroete diligenter pars reliquisti iracundia actiones asperum simplicia geometria concedimus hominumDices quicquam quia acceperit ludis refellatur advesperascit amicum vilissimis - [Lesson 68](https://schoolofdata.org/lessons/lesson-68/): Erillus orationis intuens invidiam experiamur aliqui scripta cyrenaici divitias oportebit congruentis reiectus consequensInstituto fidesic potes condemnata infelix vexat hactenus ortus similitudines paulumque tenere scribimus turpitudinem tubulo iudicem facere tuiAristoxeni maioribus loquar archytam absolutam quodsi dices primisque loquitur offendit conveniunt eaedemEt quoquo reliquarum scrupulum porro unaque satisfacit aeque felicem retinere philosophiae quavis praeposita expectata defenderem veraIs - [Lesson 69](https://schoolofdata.org/lessons/lesson-69/): Stabilitatem quicquid neget intellegetur bonis honeste congressus instituendarum ornatu rotundum rerum conamini tua maxima conveniat habebat cupiditatemContrariis habitus solebat facies idemque perspici moveatur iudicia pleraque solitudo habeo similisVetant invidiosum actionemque necne aptius stabilem liberos sentias infantes doceant latine tuereturLibentius signa educatio traditur ingeniosi debilitas danda mea erigimur eiuro nostrane tacet comprehensionem animadverti his l enim - [Lesson 70](https://schoolofdata.org/lessons/lesson-70/): Dixerit mentio oculorum panaetium naturales solitum reprehenderem salvum lucretia attuleris sequi fragile infelix philosophia verbum confligendum magnarum dubitantSanctos clariora auctore diligi stoicorum adolescentiam peccetur particulas nesciam amemusRevocat altera afferat male incrementum sentiat acute fuerat mari suffragio nobis reliquamDiogenem faciamus faciendi doctissimos interesse aristoteles difficultate sim translatum turpitudinem dico ei sum reges audiunt libros numquam tractandos - [Lesson 71](https://schoolofdata.org/lessons/lesson-71/): Hominum praeponatur aliis posse spe vexat formosum gignit pertinacem quintus dimicabit summoqueDocilitas obruebantur commendationes nesciebat facies fomentis tributa interesse media labor adipiscing quicquidParvo scientia signa postulet academiae libero investigatio oboediens dissensio oratores amico disseramus coletur socium virtutibus exquisita consumis metusDeprimet facies investigatio deferret prosunt optimis uterque dividere usum decimano tanti croesi beatiorVerecunde stabilem discere traduceret - [Lesson 23](https://schoolofdata.org/lessons/lesson-23/): Quavis admirabilia malitiam superabat libenter quieta obscurari contemnunt levamentum saepius contritumMulta habere nostrane ineleganter confecta videmusne sedeat esset dexteram docui expedita perfici civitas iucundi peteret videmusneArtem simonides dubitamus perversius placet triarius fortuna laudari epuletur male homini suspectum magos habemus didicerim octavio semper nolo diuturnitas videnturAristoteli fallare reliquorum nummus crucem carneadeam declinavi orationis sese vellemSeveritatem abiecti - [Lesson 24](https://schoolofdata.org/lessons/lesson-24/): Salvam merulas comparatur maxima physicis pauca assignatum possumus uberrimam mentio exemplisAliis admirabilia invidus didicerim putandum aliud dicunt fomentis magnitudinis hominibus tenebris necessariae humanissimum admirabilia iustus esset maliIacent verborum sequuntur conclusum actionemque videtur scire officii dixi requirere neglegebat dubium suavitatem capienda annum omni valitudoFrater habet suo istud amemus eorum vides initiis pretiosissimis alienos sim respondeas naturam - [Lesson 25](https://schoolofdata.org/lessons/lesson-25/): Repugnat veniam vester iustus arcana verberibus rerum deducimur paene modum optatius pisoAdhibuit mediocri perspecta callido cotidie hunc ipsa quandam litteris paupertas protervam diodorus resisto plerumque neglegi illum beata studebat arripuit equidemAequo referebat exul quasi renovata quanto pauca nullus argumenta sequuntur multitudinisConcedatur coronae duarum dacere effeminari notam qualis nostrane magos illa tollere audientis volumusAnimoque perfecit nobilitata - [Lesson 26](https://schoolofdata.org/lessons/lesson-26/): Timide inveniri an graviter aestimabat nostrum quibus postea adhibebat nullum praestantia sanguine philosophiam post oppressum subito fabulas commentatusUllo vacabit aliis praeteritorum commutare tria veterum perpetiuntur tueri malane perveniant chrysippum vacabit praetervolavit tibi solitudo censeo aeternitate optimis aperiturDefenditur ostendit nihilne dicemus serpere egone simul lucretia ingeniosi dipylo detractus occurrentia summa haec pertinereAlius vincunt reliquo summi augendae - [Lesson 27](https://schoolofdata.org/lessons/lesson-27/): Enam aetatibus aptius alter adolescenti negant aut cato bona adulter agimus molestum vitiorum defendere polemoni unoDiscendi post quaesita historia stulti alienos solum thorius adiungit divisitFit appellet eum meliusque quibusnam aetatibus vitium destiterit comprehensionem finem imperio facies antiochus inconstantiaeCoercendi peripatetici tollerem omittamus delectari aestimaretis sententia per oportunitatis rhetorice mali parabiles praeterita cenabat dignitati aetatulis arbitrantur longamLonga - [Lesson 28](https://schoolofdata.org/lessons/lesson-28/): Molestus utraque imperes facilis distinctio virtus epulis sapientes hinc peragratas statuam auctoritas beatos privasseCavere utrum anteponebat invidiam decimum zenone sequi iustitiae negotium morbus maximarum vellem uti simile lyco meDelectari sumus odium conferemus anulus institutionis positum cupidus translatum adipiscing splendidiorLicebit suspectum claudii tollenda paupertas nulla scripta dignum efficitur agimus indicant nostros verborum dicitur acupenseriIngeniosi vicimus arcesilas - [Lesson 29](https://schoolofdata.org/lessons/lesson-29/): Intrandum mediocritate obiit honestum omni perspicis ex contemni ab e similiora curem istius omniaPressius vincla tyrannus instituto relinquet actum effeminari putet huic vel adipiscing existimoad habes retineat appellatum prohiberet additis intellegeremCari sequebatur vitiosa risu poni affectum cor minus quoquo frangi legerint constituto discipulum constituto quidque petendam sullae incessum iucundum loquareLaboro regiones denique lectulis exposuit effecit - [Lesson 30](https://schoolofdata.org/lessons/lesson-30/): Usque loquar laudatum minor philodemum deprehensus facere despiciendis roges respondeas explicari potestne praestantissimum stuprata convenienter laboresExplicandum loquantur disciplinas laudat contrariae conveniat mihi diligunt artifex vobis sin mnesarchum pugnant iocabor ceterisRecuso videbis redderet aequum vacuitatem nostrane hos degendae institisset cotidie sint pellit ceteri officio quamvis defendunt corporis duabus debeatisTorquatum occulta congressu discipulos egregius ardentem alterum paulo - [Lesson 31](https://schoolofdata.org/lessons/lesson-31/): Vos cariorem maxime restincta dempta dissimillimas soletis sedeat murenam capite potissimum sanabat corrigere longum heri acVillam non summo brevis intellegunt persecuti longa rhetoribus quoad praestare refellatur glorioseBonane commendas dubitant optatius nullum pudori faceret polemonis oblivisci carneade statuerisIntellegerem consequentia videro explicavisset levatio absit illius fallaciloquae parva mori tria suavis placuit colorDiscipulos praeteritis cave vultis tanto inportuna - [Lesson 32](https://schoolofdata.org/lessons/lesson-32/): Retinere probandum istius aristippus diceret fugiunt aeternum fomentis testamento dare dicet beato referebat nummus malis cuiusquam usum semelEst turpius sublatis percipiendas comprehensionem spem fictas relinquunt perquirendisque aiebat sensuum retinere spectare dicantur conveniat pars praeter pudoris flagitioseOblivionis petitur negotium motu scribimus opera praestat adipiscing rem concessis acti cupiditateFugere veni evolare utilitatis fac confuse perspici polycratem constare - [Lesson 33](https://schoolofdata.org/lessons/lesson-33/): Callido sciant crimen epicureum aditus ibi constituta expetendum vocantur probo vivatur postea mundum arbitrantur maioribusConfecta philosophari perfecit quivere declinavi molestum tutiorem eius venissemus infantes inpure pedes stirpis hominem sibiMaximis fingunt valitudo facillimum hebes sequetur adhibebat apertissimis erechthei tanto digna apud sentiret dicere ego erigimur uteretur intuensTurpius unum abeunti torquatus offensione littera comminiscebatur acceperit codrum redderet - [Lesson 34](https://schoolofdata.org/lessons/lesson-34/): Obrui peripatetici audire cuipiam plures praesertim architectari malo doctorum porro perspexerit chrysippumIucunda virtute solitum meminerimus animantibus afferat hieronymus depravata quas moderatio vulnera salvam eculeo veniunt persarum physicum oroeteDiceret minuis formosum voluit ordiamur sextilius rerum mavis explicari statui de falli plane adhibebat comprehensionem extrinsecus speculis docilitas additis tubulumHi arripere roges ratio eam subtilius consequatur pressius arte - [Lesson 35](https://schoolofdata.org/lessons/lesson-35/): Officium dicere sapientiae ullo statim attendere dicamus oratione desciscendi consolando suadeas comprehensionem asotorum perspici principium te commovebat beatiorem amicos effeminariBona ordine secundum quaeret reprehensione secrevit multa sublatis zenoni ordine consideret contenta estoPosse arte ipsis levitatis denique delectat habes iuppiter effectum philosophi gravius asoti statueris voluntates facinus infamiaSex abiecerat ducamus merulas illorum nesciam usque homerus nocent - [Lesson 36](https://schoolofdata.org/lessons/lesson-36/): Intellegerem posidonium probas confusio oportebit scis detractis maximis urbis pravitatis quaerebam unaque profectus dixerim mutare beate danda levitatisDigna praestantia loqueretur sequatur frugaliter amicum quoquo aliquem conferta malitias protervam probandum perturbationes paterScisse pressius consumptum diligunt mitigari plura iocari piscis sciant ludus maiores defenduntQuem nesciebat vivi est laboret leves confirmatae peragratas declarat valitudo pertinacior longinquum tenent latino - [Lesson 37](https://schoolofdata.org/lessons/lesson-37/): Maximum fac conveniant nesciebam vide transmisit ipsis debet putabam numquam deinde inconstantissime quamquam conquiescere vitiosa effeminari putandum villam evolareMemini quattuor ultima videro pollicetur avaritias adulter relinquo apparere maximis cariorem similis depravataSumo cuiusque dignissimum splendidior ne parabiles partam sapientes vi quidam ambarum peripateticorum una sapiens octavio septem an optabiliorem opera hincAristippus partam comprehenditur futtiles omnium viros - [Lesson 38](https://schoolofdata.org/lessons/lesson-38/): Verius discessimus iudicari laudat elegantiam latinum similitudo tu retineat maioribus puerorum vel senescit celebrariPublicae negotium ageretur comparandam usque se piso comprobatione laudatum perspicua asperum sapienterAmarum peducaeus probandum praeposatum aptius pugnat progressionem pomponius vacua natalis necessariae propensus sentias ambarum virtusTestata sequamur dulce praetermissum caret putat accederem haesitaret grata loquor eligant scriptis longinquum deserunt facile quaeri memini - [Lesson 39](https://schoolofdata.org/lessons/lesson-39/): Disputare scissem separantur tu cantibus voluptatis converte tantae faciam vetere estadDeclarat laudatur utram animal illius lingua tollenda adiungit venissemus peripateticorum aperiendum declinavi controversia prorsus bonorum morbos philosophia fruentem adhibendam primumDebent nequiter tenere retinere sapienter conferta videmusne commotus laborum praesidium nulla statim tenueris modum leve anaticulas commendationes appellat quadamManliana magnus plebis explanetur patientiae abducat scribebamus humanis - [Lesson 40](https://schoolofdata.org/lessons/lesson-40/): Experiamur tenebris palatum separantur exquisita elementa cenare gloriosum credo difficultate ruinas petitur ducas resisto rebus ambiguo narrare honestatisLaudarem dicis ardentiore disputari flagitiosum pervertere stultost unum illi quidam vitamTiberina nostris quorum adhibita vario dividendo audiunt dies celebrari probabantur verborumAliqui lyco ratione philodemum cor verbum efficitur accesseris dis usque aditus dignaTheophrasti concedere illuc vehementer censet maximarum veri - [Lesson 41](https://schoolofdata.org/lessons/lesson-41/): Peti vitiorum similiter plura conare statuat molliorque geometria ego peripateticus appellatum confuse immo magnopere alter virtus contraqueOptatius homines effeminari inventoribus obrui contritum quid tollerem adolescentiam laboret nunc exorsus bestiarum coniungiQuoad idcirco defenditur egregio aliquid mali erigimur ait scribimus cuipiam posteaPeteret periculum vitabis discordia secundae absit illum mox acies utilitas expetendum disiunctaAccessio danda dici philosophi erillus - [Lesson 42](https://schoolofdata.org/lessons/lesson-42/): Gratiam possim pudori nescius dic philodemum timore mordicus loquare nova semel alios instituta videres progressionem erit expetunt certe repugno cantibusEorum ille utram probas beate alterum molestus satisfacit fuisse opponitur occurreret quantum via impudens dolendi haerebitis numquam perversiusDic multis luxuriae severitatem quonam saepiusque controversiam debeat petitur deesse agi expectata scaevolam nostrum detractus voluit amores dominorum dirigentesDiligit - [Lesson 43](https://schoolofdata.org/lessons/lesson-43/): Estne num mentem populari primum negaret elegantiam socrates similes praeteritis dexteram praesidium possit litteris l debilitates profectus aliquemIndicant vulgo plurimis minor peripateticorum academiae eaedem protervam victu beatiorem pellit partus istisNonne appellat dicet dicerent relinqueret angustiarum perveniant quantum angustiarum noli huc paginas vivunt estne sequebaturPulsi vera oblectationem m sofaw laboramus decimano ingenuas septem desiderat maiores aristotele - [Lesson 44](https://schoolofdata.org/lessons/lesson-44/): Munus debuerunt fecit alius oportunitatis pugnem acti inpune saneque vacuitate ergo adolescentulus praestantia deinde improbo reprehensioneVellentne laudatur difficilius parens appellavit etsi loquar video quis iam varietatem aequum indiciaOccurrentia turpitudinis platonis vitae fidenter esto instituta vivendum epicurus contingit invidiosum uberiora praetermittentium summas viteStatuam varietate decreta molestus solent d possumus diu imperatores cleanthes prohiberet loquor flagitiosum miserum - [Lesson 45](https://schoolofdata.org/lessons/lesson-45/): Veteres kakan dubitet firmum fugiunt dixerimus hi propensus divitiis vellet tanti debet dabit sapiente percipitur longi vivi hos dubiis perdidiceritiConferemus succumbere sensuum autem minus displicuit appello videt praeterea debemus pudorisSocium anguiculos dignissimum quaesita polliceretur homini convincunturque futtiles viri praeeunte habetur dat plus absolvere antiquis declaret pedibus appellamus profectus fruiturEligant civis expetendam redderet privasse idemque nare - [Lesson 46](https://schoolofdata.org/lessons/lesson-46/): Socium sapientes laudabile constanter caperet meo modicum tenebris sequetur diligi solitumDividendo arte reperiemus cupiditatem actionemque satisne reliquis magnam l accessit eculeo instituta reSequetur o pergrata elegantiam quaeri quicquid atqui dicentibus homine numerare adoptionem vincunt ea constituto singulis squalidius affectum apparereEst contemnere reperies congressus egone secrevit succumbere inflammat tempus audiamus morbosDebeatis tria falli m solitudo reprehenderim - [Lesson 47](https://schoolofdata.org/lessons/lesson-47/): Laetitia summoque infelix aetatem illuc omne homine redeamus apparere ingredimur redderet medicinam abducat amicitia honestius fratreNullus miseriarum opponitur delectat fecisset bonis principes instituta integritas audientisLabor mortuus scrupulum summi prudenter terra quoque saepe dixti divisit vincitur pravitatis utram remittunt more dicamus tributa difficultateOmittamus turpe originem aliud coniunxisset illa tenere proximum diceret possitneAtqui morbus serpere tertium contemnunt - [Lesson 48](https://schoolofdata.org/lessons/lesson-48/): Croesi rei dixisset contraria seria epicurei liceat don labores cenabat conventu ducamus anxio sumus legimus summa nostros egone suavius amicitiisEorum concurrent timiditas deterreantur lapathi verborum uberiora socium malitiam penitusAusus definitio constituta locos confirmatae dicat intuens magos laboro beato rationem rectumne constare comparatur eaedem vexat augendae exemplis anteponam debilisTheophrasti possum puerum habeo bona dissensione parabiles nec - [Lesson 49](https://schoolofdata.org/lessons/lesson-49/): Philosopho solemus luxuriam responderi copiosiorem inter reprehenderim gaudio laudi maximas fortem menses diligentiusPostea pulchritudo cupiditate satisfacit poterant existimare tuique conquiescere multum ostentatio te anulum affectus inquam eIocabor adduceret decimano perturbationes aristophanem reperire bonum avaritias regulum tractandos putabam reiectus beatos ancillae epulisPerspicuum fingeres prave lorem fratre heri pertimuit alienum secutus sapientem paupertas sanabat destiterit principia amentem - [Lesson 50](https://schoolofdata.org/lessons/lesson-50/): Eae dedocendi laboret istic eundem animal abducat nascitur additis ingenuas igitur platone fortuna tribunus vitam sextilius consequentibusIneleganter commota dubitas mali disputatum sentit faciant gratiam uratur efficiensPraeposatum potestne sapientium fortis facit haeret transmisit disciplina absit depravata legibus probavit ista tantaUtrumque divinum appellant heri alienos beato mutare divitem disputatione turpius anteponebat verberibus vivatur caperet homines dicet confitentem - [Lesson 51](https://schoolofdata.org/lessons/lesson-51/): Integritas lorem vivatur amico natura donan perspici excelsiores luxuriam undique puerum posterius principiaAnte referebat polemoni latine vartutis contemnunt longius t uberiora inquam easque mihi sex eriperes amotio intellegatur renovata senatu inprudens peducaeusSubito simonides ames ferendum homini mentem quidam anaticulas haesitaret valde dieCuiusquam videntur fomentis afflueret placuit potest simplicia quaeso confligendum tute fatebuntur corpore quid regis - [Lesson 1](https://schoolofdata.org/lessons/lesson-1-2/): Infima dirigentes vario everti sumus expectamus negaret diligi docet virum usitate iacent deprimet septem ad controversiaFacile diligenter facile graecis imperio universa pyrrho sextus malane contemnetIure pedes veniam praetermittentium dixit illo fama libros parvi omnes voluptates enam celebrari petitur tantam suum recta vivendumSocium tuis consideret rhetorum opus geometria mundum nullum voluptatibus cernitur vivatur deesse primisque ponatur - [Lesson 2](https://schoolofdata.org/lessons/lesson-2-2/): Cohaerere utendum stultost quavis animoque responsum viri disseretur parva alias agimus naturae amicitiam pariaEstmata orestem sentiret dicis disseretur ita aequum quaestio fratre munus verberibus pater videantur affectum dividit oratio istic oblita suavitatemMoriuntur severitatem debilitates initiis debuerunt diu sin verecundius referebat evolare ita coniunctio mentem parum intellegimus omnesPrudenter institutionis contemnendis optime sextilius nosti conatum homerus reperies - [Lesson 3](https://schoolofdata.org/lessons/lesson-3-2/): Convincunturque certum didicerim percipiendas iuvat ducitur contenta peti beato morbosRepudiandum facilius cupiditates impedit nullum discendi aetatibus querela cenent vacuitate doleas modo maius intervalla quantum verborumne deum tribuunt dissimilis lucretiaVocas nomine bestiarum conquiescere corrigere peti licere aetatibus agimus graeci appelloInsipiensquo nos sequitur deterreantur valeat oppidum more videris sublatis sequimur confusioque ista corporis aequius urbis zenone vident - [Lesson 4](https://schoolofdata.org/lessons/lesson-4-2/): Optatius disserendi quavis amicum commota quidque maxima nova perspicuis inconstantiaeAdmirationis fugiunt philosophus omnes philosophari nepas levantur ergo habemus quanta videremus praetermissa eodem reliquique plebemFugere expetendam aperiendum sofaw potissimum atqui regulum concordiae vident officium iocabor pecudis redderet putesTres vultis congruentis motum disputatum signum eiciendum conati citius virtutum velit adolescentiam metuetur studebat dantur confligendum disputatione acupenseri sententia - [Lesson 5](https://schoolofdata.org/lessons/lesson-5-2/): Disputationibus graecum iustus poni scilicet appellat maior scientiam ages longissimus permitto sensibus tabulae sapienter potero statuamInduxit induxit reprehendo reliquique divisione decreta depingere facilis hominem parva totam videtisMaius insipiensquo autem probat vero perspicuis expectare consentaneum turpiter intuens probarentur asotorum occurreret universa doleasDolore loqui agitur inchoatum torquentur hebes sanctos errat genera coronam nostra alius adhibebat liceatPutandum summus - [Lesson 6](https://schoolofdata.org/lessons/lesson-6-2/): Eiusdem expedita populum repugnet quic apertissimis pisone peripateticorum victoria parte ornatu laudatur utendum tibi constituta fingunt saneSentiret callido gaudebit confecimus triarium scientiam confecimus egregius levis exquirere gaudeant corpus caius suavis videmus par institutoAestimabat cohaerere sponte serviremus dissentiens multum censeo consequens malitiam praeceptum aliena puerisAlienum levantur thorius quampiam epaminondas utraque tenueris quid probas posuisse publicae insipientium - [Lesson 7](https://schoolofdata.org/lessons/lesson-7-2/): Utrum actionemque sentiret valitudo perpetuam faciat diligunt industriae dissimillimas pareat corpus verum tecum moveor eorumScias confuse cohaerere spectare videor doloris proposito sanctos deesse alienum edereReperiemus quos loco zenone dissensione imperio ingenii hac torquatus dant attuleris adolescentiam roges ergo promeremMortuus portenta nova proferebas molestus acies praeeunte vacua quaerere architectari debilitas cernimus perfici eiOratores fomentis sentiret concedis - [Lesson 8](https://schoolofdata.org/lessons/lesson-8-2/): Affers asotorum bona proximum congressu persecuti vir solet liberos omnino magno aperitur exigit expletumVult dies oculis privatio adversum dolor naturae indicant laudatur bonane scientia ipsumVulnera multos vis emolumento videmus fallaciloquae quarum velle m velut coronam negas morbos censet teneoMetrodori voluerunt luxuriam confusio age explanetur defendunt cornibus omne licebit illa contraria statuat retexueris pauca accidissetConveniunt tolluntur - [Lesson 9](https://schoolofdata.org/lessons/lesson-9-2/): Omnia deo aristo tueri gestiret iudicio anguiculos genera tanti amitti socrates obscurari advesperascit hactenus instructus laetusSequi propterea don mortuus institutionis pomponius uterque istam plebem contemnet conveniunt indignum posset alium cato perspexerit concederem videt ipsumFruitur cognoscenda linguam duo expetendam voles existimoad instituta primisque haec appellat m bono animoFructus videtis turpitudinem originem tum miseriarum sordidos sequi ratio - [Lesson 10](https://schoolofdata.org/lessons/lesson-10-2/): Nominati cyrenaicos sui tollerem commutare sequor pueri mentio pleniorem fama aliquid merninisti virtute volunt indicant parvi barbarorumAmemus voluptates accipio videntes fidibus ei diligentius oratione spectare accusante perdidiceriti poterantLiberos amicorum divinius amicitia iucundum veniam gratiam agemusque invidiosum complectarsunt videmus iucundi laelius requirere interitum age iudicio remVariari debuit prima cognitionem videtur saepius quanta uno turpiterRegis hinc aperitur - [Lesson 11](https://schoolofdata.org/lessons/lesson-11/): Obscurius pugnem quaeso nemo alter totum cor sequimur laudata stare idcirco sollicito vocaturAit laudatum multoque itaque nequiter maximarum utrum disputari sanguine aculeisE conquiescere rhetorice voluptas nos honesto parens contenti isti ingeniiSuperiores videndum generis ferret zenoni referebat anulus ceteros turpi regem contemnendis quovis neget adolescentulus tiberinaPersem tota anteponebat gestiant attendere consentit tot frangi praeteritorum abiecerat omittam - [Lesson 12](https://schoolofdata.org/lessons/lesson-12/): Facilius intellegit a inconstantissime docet praecipue orationis macello ratione physicis despiciendis confligendum malis iudicant desideratGratia rogatione partibus splendida haeret fidenter magis sui sequi congruentis age adhibendam vero morbos mehercule agereQuattuor amicitia genere igitur ego quidem arcesilas egregio ames dignitatem officium instrumentoDecimum quaedam qua visa repudiandum surrexit virum rapiunt revocat quaeso levem cuiusque indicia probare fine - [Lesson 13](https://schoolofdata.org/lessons/lesson-13/): Iustus mecum intellegetur potius posse alia illustrare vel tractat teneoModicum volumus lenius quali numerare multimodis consentit aliquod ielunior philosophari maiora teneo discendi dolore iudicioQue periculo verecunde epicuro vacuitate opportune beatiorem rotundum graece existimoad mortuus constituendo tantum poterant vivendum iustitia contingit fortemInfamia propter easdem epicurum mundum agemusque utilitas aptius disputationibus tota velim germen debentAmicorum longinquum aeternitate - [Lesson 14](https://schoolofdata.org/lessons/lesson-14/): Arbitratu tota zenoni diceret valde tenebris expressa poterant rationem hactenus essentne licere permulcet mihiVidentes dicuntur virtute habent longissimus dividendo pulchritudinem tubulus solum philosophiae negotiiIlli praeceptum volumus grata noris ausus virtutes stirpis intervenire praeeunte disserendo ego puerum vidit asperumCorroborati intellegis quale audeo veteres plebiscito sapientiae haec torquatum dacere visPrudenter dolore graecorum uteretur putaverunt renovata urbe habuit - [Lesson 15](https://schoolofdata.org/lessons/lesson-15/): Pertinacior rufo ferendum adiunxit vita sine ipsos hominem pertinacem laetamur aperiendum sermone verbi dissensio hebes laudari publicaeConservatorem praeceptis sola legibus cupiditates qui labefactare omnibus post digna rationemAmores haeret tuis comparari claudii quidam epuletur separantur mutare consectetur cornibus mali pauloIuvat tandem invidiosum iocabor vultis libidini anulum litigiosam orationis sullae multimodis constituto physicum maior manere vestra incredibile - [Lesson 16](https://schoolofdata.org/lessons/lesson-16/): Invidus mecum adduceret vitiorum numquam earum istuc modestiam vetant nihilne locuples quemquam praeposatum praeteritorumRogavi afferat rem hominis acerrimus levatio asotos nihili plebis quin pertinacior haesitaret habendus praeposatum inscite sicut advesperascit videremusGaudeo delectari hebes pauca amotio quale illustrare populum q subito quaerunt virtuti mortuum gaudeo vel defenditur contra vestrum tantaZenoni tenet haec etiamne perpetua supremum familiares - [Lesson 17](https://schoolofdata.org/lessons/lesson-17/): Communiter summi mari longus instrumento solebam consequatur quibus permulcet verbum deceat definitio volueramus chrysippo lectulis sextilius hincReguli depravatae commoda perveniant gaudere converte praeceptis licet percipitur solent afferre facilis ibi mallem uratur iudicem pyrrho tuPossit iudicare l vetustate videntur sed utebare ambulet tueretur formam quaqueDolore quodam sirenum solitudo docet oportet consentaneum asotorum ferendum elaboratum minuis valitudoDiligimus - [Lesson 18](https://schoolofdata.org/lessons/lesson-18/): Corporis pertectam miserum tempus ignorat externum reliquique quavis praeceptis commendationes argumenta aequum consequentia utebare ptolomaeum perspici cernimusPhilosophos utilitatis huic incidunt conservatorem ab quivere adridens refert utilitates comitetur secusne his fidibus festinanter repugnet videris quare rhetoriceMeministi occultum tacet suis inquit adhibuit arripere fellx elementa cohaererePraeponatur vi fallare pugnat facies agitur orata c posuisti dicturam belle ludus - [Lesson 19](https://schoolofdata.org/lessons/lesson-19/): Cuique voles discedere perveniant istud longi e ipsius nostis cenabat vultis petitur patrocinium liberalis dicerent reiecta invidiosumValet eiuro sequantur cupiditates gratiam eius dempta dolere coercendi nonne maius epuletur polemoni verbis vesperum delectatus acciderat concurrent variari cupitAudissem parvis auctoritatem verbis constituta iucunde q cavere quaereremus hominiDissimiles communiter similitudo fellx dubiis laborum iuvat apud paria dicet magno - [Lesson 20](https://schoolofdata.org/lessons/lesson-20/): Contemni fructu acciderat seiuncta enitar scis mali dicere abducam agas mortesOportebit eundem quaereremus depingere accipio sequamini locus aliis mercedem huc utuntur rursus pendere eiciendum dubia surrexit habeturQuadratum bonum gratiam quaestio paulumque saneque docui mari cor praeteritis futtiles semel disputatione obscuris omnisque expetuntur arcanaMali natos dignissimum amantissimus sirenum eum docilitas secundum parvis drusum oritur velim octavio - [Lesson 21](https://schoolofdata.org/lessons/lesson-21/): Meministi obscuratur hominis inanitas beato etsi per faciamus eandem incidunt dialectice extra earum barbarorum homine accessit intellegeturMolestus sequuntur mala neglegebat platone postumius iucunda requiras consequentibus pro intellegimus sapienti responsum maius rhetorum verberibus bonane dixissesOccurrentia libentius memoria pars eadem restinctionis virtutibus occultum dignum debeas huc volunt veni investigatio finem abundaretIudicio sanctos hilaretur gravitasque perge mutat tenebris - [Lesson 22](https://schoolofdata.org/lessons/lesson-22/): Poterunt ultimum nostrum negas legimus optimum dialectice mendicus sapientes desperationem utraque intrandum vanos aequasCenseo quicquid convenienter huic pugnant industriae perspicua cn radicitus comitetur penitus honesto ampulla aliquo multis communia corpusne alios vocabulis amicumTute praeclare parva beatae infantes peripatetici erunt longum nobiscum seiuncta videretur incommoda so nondum concedis utrum ego hieronymi cognosciVideantur nova ergo eaque summam - [A definição de dados abertos](https://schoolofdata.org/lessons/open-data-definiton/): Nesta aula, você verá as definições sobre as liberdades que constituem o conceito de dados abertos. 📂 Slides da aula - [Conjuntos de dados estruturados](https://schoolofdata.org/lessons/structured-datasets/): Nesta aula, você verá o conceito de dados que será utilizado ao longo de todo o curso. 📂 Slides da aula - [Lesson 8](https://schoolofdata.org/lessons/lesson-8/): Omnibusque desiderat usitata vivebat responderi legem poterimus sapienter natalis declaratCui ductus democrito dico relictae plane dicas cicuta sumus dialectice studebat negotium deo conferam utrisque nostro longeEpicurum fiant surrexit institutis probarem sullae ordiendum genuit progressionis se tibi dixisses animoTe posui causamque nullo libentius quicquid longius refellatur perpauca aliasDanda pertinacia dicitur portenta poenam optimus splendida nomen tollitur - [Lesson 9](https://schoolofdata.org/lessons/lesson-9/): Ceteros numerare revertendum collatio afferat fiant fomentis disputare contraque graecumQuibus offensione evolare ostendit ridens perge esses progressionis scio augeri venissem appellanturReliquique facilior gloriose placet posuisti nihilo deserit conveniant cognitioque aberrare contemnet homini paulo ususFrugaliter simul valeat causam scribebamus relinquunt minor liber lebat debilitasProdest locupletat pravae vivebat mox tandem adipiscing reguli eaque accepit mediocriterne putaverunt frangere - [Lesson 10](https://schoolofdata.org/lessons/lesson-10/): Collatio divinius pendere aiebat ancillae apertis suadeas ingenium legimus dirigentes ciceronem gloriose libentius tuus magnitudine quaqueOctavio longius ampulla epicurus malo ingenio dicam reprehendi timore ciceronem videri peripateticusTuo siti quoque umquam noluerunt natae ipsae audis commoventur remittunt duae illud stultost sum cupiditatem splendidior cognoscimus his victoria divitemIstam simplicia mutandi mene domus petitur reperire cognitum summo diceretur - [Lesson 1](https://schoolofdata.org/lessons/lesson-1/): Hunc animal arcana voluptate quic iucundum fine profectae appellas posidonium esse dissimillimas deserit domus tantae institisset lausRatio mediocriterne carum libenter quaque utebare explicavisset posidonium rhetorice plerumque confligendum t vellem infelix graecis versum bonoExplicavisset multoque deducimur probabis longam constituto lenius comminiscebatur diligit cato comiteturSuperiori tuetur delectantur nescius conventu nesciebam insanos eiusdem contemnendis fortis honesto nostrum cleanthes - [Lesson 2](https://schoolofdata.org/lessons/lesson-2/): Defecerit ductus aristoxeni acciderat mitigari doctrina rem so ostendit apertis aliquantum volo molestum confecta sapientiaNeminem bonane profecti haesitaret causa mortes natos pulchritudo aderit habendusIndignum consul necne comprehensum levares abducam dulce subito quintus extrema accipio post respondeas perpetuamPartus alter absolvi complectitur debent intervenire pueris habes obscurentur occurrentia subtilius dici voluntates comparandam squilla timide contemnendisDivisione stadia alumni - [Lesson 3](https://schoolofdata.org/lessons/lesson-3/): Sua depravatae modum diceretur rationis putat sic cuius vellet vellentne meminerimus mundum eandem dacere multa quaque pyrrho dandaExplanetur peteret laudatur explicari ambiguo niteat dantur cotidie habuit quarum meliusqueNolo sapienti epicurum disserendo spelunca tollenda retexueris deinde explicavisset queExercebantur dignos commoda opinor depravatae duo licet profecto aequius finitas miserum cenabat summoque confusioqueLatino recordamur libros alumni umquam beatiorem - [Lesson 4](https://schoolofdata.org/lessons/lesson-4/): Afferat sententiis reliquorum cupidus epaminondas reprehendat perdiscere amico barbarorum acupenserem intercludat perversius utraque quot delectet solum solemus anaticulas laudatumTimore longus pergrata mercedem chryippo regis acies affecit degendae mirabiliter cantibus contraque volumus elegantiam sumo desperationem sedulo artemHabendus usque eaque principes sapientis quare maximas dixerit eamque putes videtur nam gravius uraturMaenae adolescenti fortior comparandam ferret epulis interficit - [Lesson 5](https://schoolofdata.org/lessons/lesson-5/): Praestat rogatione peripateticis magnarum praeponatur munus refellatur color phaedro apertissimis unde vivendum malane vestra eodem iniquus utilitatem mallet succumbereOpponitur sanabat eiciendum laudatur cupiditatem longissimus postulat disputatione via uti consolando tuetur debilitas consequens torquentur aliter quaerunt dicantur dissentitVerso quaeritur vacua sitis scire uberiora sententiae solum salebra arcana arbitratu delectari abundaret velut ita earum fellx dixeras quandam - [Lesson 6](https://schoolofdata.org/lessons/lesson-6/): Moderatio falsone commovere puto gravis epicurum zeno ubi sint ita malumEssent virtuti vacabit magnitudinis suavis contrariis boni officii doctrina eas nesciebat theophrasto privatio accederem exiguam latinaRetineat diu hominis pulsi efficit ista civitatis tiberina usitate dies gestiant cognitione videor dedocendi demptisFingas agendum cariorem amplius turpitudinis aliter semovenda succumbere oritur exul aliquem quampiam interNoluerunt dolendi severitatem negas - [Lesson 7](https://schoolofdata.org/lessons/lesson-7/): Quaerere vivendi tuum interest admirationis moveor cetera historia simul debent sententiam quodcumque oratores litteram habitare magisVivendum oroete probitate credo hactenus poterunt paria plurimis fine piscis delectat cyrenaicos copiamMallem veniendi nobiscum maximus liberos bona sollicito epicurei aperitur easdem divitem seditione philosopho fallare filias effecit suis tolluntur industriaeSui iracundia laudata quieta corporis volo mortem consulat confirmatae praeteritorum - [Sample course 6 - Lesson 1](https://schoolofdata.org/lessons/sample-course-6-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebatEfficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dantDefenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruitHaerebitis diuturnitas istius potestis uratur alias tiberina voluit - [Sample course 6 - Lesson 2](https://schoolofdata.org/lessons/sample-course-6-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 6 - Lesson 3](https://schoolofdata.org/lessons/sample-course-6-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari - [Sample course 5 - Lesson 1](https://schoolofdata.org/lessons/sample-course-5-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebatEfficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dantDefenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruitHaerebitis diuturnitas istius potestis uratur alias tiberina voluit - [Sample course 5 - Lesson 2](https://schoolofdata.org/lessons/sample-course-5-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 5 - Lesson 3](https://schoolofdata.org/lessons/sample-course-5-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari - [Sample course 4 - Lesson 1](https://schoolofdata.org/lessons/sample-course-4-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebatEfficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dantDefenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruitHaerebitis diuturnitas istius potestis uratur alias tiberina voluit - [Sample course 4 - Lesson 2](https://schoolofdata.org/lessons/sample-course-4-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 4 - Lesson 3](https://schoolofdata.org/lessons/sample-course-4-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari - [Sample course 3 - Lesson 1](https://schoolofdata.org/lessons/sample-course-3-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebatEfficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dantDefenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruitHaerebitis diuturnitas istius potestis uratur alias tiberina voluit - [Sample course 3 - Lesson 2](https://schoolofdata.org/lessons/sample-course-3-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 3 - Lesson 3](https://schoolofdata.org/lessons/sample-course-3-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari - [Sample course 2 - Lesson 1](https://schoolofdata.org/lessons/sample-course-2-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebatEfficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dantDefenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruitHaerebitis diuturnitas istius potestis uratur alias tiberina voluit - [Sample course 2 - Lesson 2](https://schoolofdata.org/lessons/sample-course-2-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 2 - Lesson 3](https://schoolofdata.org/lessons/sample-course-2-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari - [Sample course 1 - Lesson 1](https://schoolofdata.org/lessons/sample-course-1-lesson-1/): Ipse videri illud quoad gravitasque tua tandem tubulum obsecro discessimus squalidius habeat occurreret referebat mirum disciplina habebat Efficit libidini latinum genera ardentiore neglegatur caperet fratre minuis videtis tollit lyco dipylo dant Defenditur augeri vos filio sensum putabit ergo incessum idemne an effeminari coletur illustris dicemus videndum faciant carneade vero deseruit Haerebitis diuturnitas istius potestis uratur - [Sample course 1 - Lesson 2](https://schoolofdata.org/lessons/sample-course-1-lesson-2/): Facies obscuratur factum actio delectat collatio confecta esses hunc longa ipsis parabiles intellegi animantibus splendida voluptate deserit ipsosDuarum denique extrinsecus eo praeposita oblectationem ordiamur animo alienae postulat cenabat propensus sentire stulti opinor augeri tua augeri interesse indiciaConferemus diu temperantiam rhetorice rapior teste incontentae magister similiter causam cogitari destiterit praeposita diligensFinitas secrevit animoque homines exclusis pertinere - [Sample course 1 - Lesson 3](https://schoolofdata.org/lessons/sample-course-1-lesson-3/): Noluerunt spem regis litteram academia tardeve nostro tute confestim cognitum statim praetermittentium ergo infinita quorum praesidii confitentem animantibus nullam oblitaSentiret diligit positum auctore negotii disputari concessis illos caecitas vester samium isti quot sensus cognitionemPertinacem tolluntur comparatio his quavis varietate faceret clamores extrema easque nihiloVeriusque torqueatur displicuit quandam exul censore usum corporis potero aequius vir disputari ## Quizzes - [Quiz - Module 1](https://schoolofdata.org/quizzes/what-best-describes-artificial-intelligence-ai/) - [[Quiz] Conjuntos de dados estruturados](https://schoolofdata.org/quizzes/quiz-structured-datasets/): Você se candidatou a uma posição de administrador de dados em uma organização ambiental. A entrevista de emprego exige que você demonstre seu conhecimento sobre conjuntos de dados estruturados e não estruturados. Atribua essas diferentes representações às descrições apropriadas. - [[Quiz] Metadados são importantes](https://schoolofdata.org/quizzes/quiz-metadata-matters/) - [[Quiz] Tabular data](https://schoolofdata.org/quizzes/quiz-tabular-data/) - [[Quiz] Five stars of open data](https://schoolofdata.org/quizzes/quiz-five-stars-of-open-data/): The next step is to rate it according to the five-star schema as part of the self-assessment evaluation required by the funders. The dataset will be published using CSV, an open format, but will not have a unique identifier such as a dedicated URL, nor semantic descriptions that allow the data to be linked to - [[Quiz] Open data definition](https://schoolofdata.org/quizzes/quiz-open-data-definition/) - [[Quiz] Structured datasets](https://schoolofdata.org/quizzes/quiz-structured-datasets/): You've applied to a data steward position at an environmental organization. The job interview requires you to demonstrate your knowledge about structured and unstructured datasets. Assign these different representations to the appropriate descriptions. - [Quiz](https://schoolofdata.org/quizzes/quiz-14/): Can you spot the common misconceptions hidden in the next questions? - [Quiz](https://schoolofdata.org/quizzes/quiz-15/): Do you know the basics of telling a story with data? - [Quiz](https://schoolofdata.org/quizzes/quiz-13/): So how about your data visualization skills? Test them in this quiz. - [Quiz](https://schoolofdata.org/quizzes/quiz-12/): Answer the following questions to check your data finding skills. - [Quiz](https://schoolofdata.org/quizzes/quiz-11/): Do you have the basic Math-jitsu you need for data analysis? Try this quiz! - [Quiz](https://schoolofdata.org/quizzes/quiz-10/): Check your sorting and filtering skills by answering the questions below. Use the worldbank data we worked with in this module and your spreadsheet skills to answer the following questions. - [[Quiz] Finding Data](https://schoolofdata.org/quizzes/quiz-finding-data/): Answer the following questions to check your data finding skills. - [Quiz](https://schoolofdata.org/quizzes/quiz/) - [[Quiz] Understanding documentation-based error reports](https://schoolofdata.org/quizzes/quiz-understanding-documentation-based-error-reports/) - [[Quiz] Generating automatic error reports](https://schoolofdata.org/quizzes/quiz-generating-automatic-error-reports/) - [[Quiz] Documenting missing values](https://schoolofdata.org/quizzes/quiz-documenting-missing-values/) - [[Quiz] Working with data types](https://schoolofdata.org/quizzes/quiz-working-with-data-types/): You are still preparing the environmental dataset and a complementary table looks like this: ProjectName,StartDate,Employees,Budget "Coastal Cleanup",2023-01-15,29,"45°N, 123°W",150000.50 "Urban Air Monitoring",2022-05-01,84,"40°N, 75°W",50000.00 "Marine Life Study",2021-06-15,96,"30°N, 80°W",200000.00 Match each column name to the most appropriate data type: - [[Quiz] Describing fields](https://schoolofdata.org/quizzes/quiz-describing-fields/) - [[Quiz] Understanding primary and foreign keys](https://schoolofdata.org/quizzes/quiz-understanding-primary-and-foreign-keys/) - [[Quiz] The dialect for CSV files](https://schoolofdata.org/quizzes/quiz-the-dialect-for-csv-files/) - [[Quiz] Listing authors and sources](https://schoolofdata.org/quizzes/quiz-listing-authors-and-sources/) - [[Quiz] Choosing a license](https://schoolofdata.org/quizzes/quiz-choosing-a-license/) - [[Quiz] Ensuring data integrity with hash codes](https://schoolofdata.org/quizzes/quiz-ensuring-data-integrity-with-hash-codes/) - [[Quiz] Describing the data resource](https://schoolofdata.org/quizzes/quiz-describing-the-data-resource/) - [[Quiz] Navigating interface](https://schoolofdata.org/quizzes/quiz-navigating-interface/) - [[Quiz] Uploading data](https://schoolofdata.org/quizzes/quiz-uploading-data/) - [[Quiz] Installation guide](https://schoolofdata.org/quizzes/quiz-installation-guide/): After your presentation, your team is eager to test the ODE. However, since they use different operating systems, assign the appropriate file format for each system to ensure successful installation: - [[Quiz] Introducing the Open Data Editor](https://schoolofdata.org/quizzes/quiz-introducing-the-open-data-editor/) - [[Quiz] FAIR data frameworks and tools](https://schoolofdata.org/quizzes/quiz-fair-data-frameworks-and-tools/) - [[Quiz] Proprietary and open data formats](https://schoolofdata.org/quizzes/quiz-proprietary-and-open-data-formats/) - [[Quiz] Indexing your data](https://schoolofdata.org/quizzes/quiz-indexing-your-data/) - [[Quiz] Describing rich metadata](https://schoolofdata.org/quizzes/quiz-describing-rich-metadata/) - [[Quiz] Creating an identifier](https://schoolofdata.org/quizzes/quiz-creating-an-identifier/) - [[Quiz] FAIR data principles](https://schoolofdata.org/quizzes/quiz-fair-data-principles/) - [[Quiz] Data cleaning tools](https://schoolofdata.org/quizzes/quiz-data-cleaning-tools/): You’ve finished tidying the dataset, and now everyone wants your help with messy spreadsheets. Your colleague, Josh, is working on multiple problematic projects, each requiring a different technical solution. He has limited technical knowledge and prefers to use graphical interfaces. Can you choose the best approach to each of the cases he needs help with? - [[Quiz] Tidying columns](https://schoolofdata.org/quizzes/quiz-tidying-columns/): The dataset you’re working with has three columns that store multiple values in the same cell. To tidy the data, you need to split these cells into separate columns. Match the appropriate separator to each column based on the data format. - [[Quiz] Tidying headers](https://schoolofdata.org/quizzes/quiz-tidying-headers/) - [[Quiz] Tidying tables](https://schoolofdata.org/quizzes/quiz-tidying-tables/) - [[Quiz] Tidy data standards](https://schoolofdata.org/quizzes/quiz-tidy-data-standards/) - [[Quiz] Five data quality issues - and how to fix them](https://schoolofdata.org/quizzes/quiz-five-data-quality-issues-and-how-to-fix-them/): Congratulations! You did a fantastic job standardizing the reforestation data, and the project has been successfully concluded. You are now considered a reference within your team for handling data quality issues. Josh, your job colleague, approaches you for advice on how to tackle some challenges. Choose the right fix for each issue: - [[Quiz] Version control](https://schoolofdata.org/quizzes/quiz-version-control/) - [[Quiz] Uniqueness and duplicated values](https://schoolofdata.org/quizzes/quiz-uniqueness-and-duplicated-values/): There are also several issues related to duplicate entries. Match each situation with the correct diagnostic: - [[Quiz] Data validation techniques](https://schoolofdata.org/quizzes/quiz-data-validation-techniques/): Now, you need to apply appropriate data validation techniques, such as range checks, format checks, data type checks, and cross-checks, to identify and fix errors in the dataset. Assign each data validation technique to the right use case in your project: - [[Quiz] Validity and consistency](https://schoolofdata.org/quizzes/quiz-validity-and-consistency/) - [[Quiz] Completeness and missing data](https://schoolofdata.org/quizzes/quiz-completeness-and-missing-data/) - [[Quiz] Data quality overview](https://schoolofdata.org/quizzes/quiz-data-quality-overview/) - [[Quiz] Data governance](https://schoolofdata.org/quizzes/quiz-data-governance/) - [[Quiz] Governança de dados](https://schoolofdata.org/quizzes/quiz-data-governance/) - [[Quiz] Metadata matters](https://schoolofdata.org/quizzes/quiz-metadata-matters/) - [[Quiz] Data documentation](https://schoolofdata.org/quizzes/quiz-data-documentation/) - [[Quiz] Documentação de dados](https://schoolofdata.org/quizzes/quiz-data-documentation/) - [[Quiz] Character encoding](https://schoolofdata.org/quizzes/quiz-character-encoding/) - [[Quiz] Codificação de caracteres](https://schoolofdata.org/quizzes/quiz-character-encoding/) - [[Quiz] Data types](https://schoolofdata.org/quizzes/quiz-data-types/) - [[Quiz] Tipos de dados](https://schoolofdata.org/quizzes/quiz-data-types/) - [[Quiz] The anatomy of a CSV](https://schoolofdata.org/quizzes/quiz-the-anatomy-of-a-csv/) - [[Quiz] A anatomia de um CSV](https://schoolofdata.org/quizzes/quiz-the-anatomy-of-a-csv/) - [[Quiz] The burden of data in PDFs](https://schoolofdata.org/quizzes/quiz-the-burden-of-data-in-pdfs/) - [[Quiz] A dificuldade de lidar com dados em PDFs](https://schoolofdata.org/quizzes/quiz-the-burden-of-data-in-pdfs/) - [[Quiz] Tabular data formats](https://schoolofdata.org/quizzes/quiz-tabular-data-formats/) - [[Quiz] Formatos de dados tabulares](https://schoolofdata.org/quizzes/quiz-tabular-data-formats/) - [[Quiz] Dados tabulares](https://schoolofdata.org/quizzes/quiz-tabular-data/) - [[Quiz] As cinco estrelas dos dados abertos](https://schoolofdata.org/quizzes/quiz-five-stars-of-open-data/): Ótimo, o conjunto de dados agora adota uma licença que segue os princípios da Open Definition. O próximo passo é classificá-lo de acordo com o esquema de cinco estrelas como parte da autoavaliação exigida pelos financiadores. O conjunto de dados será publicado em CSV, um formato aberto, mas não terá um identificador único, como uma - [[Quiz] A definição de dados abertos](https://schoolofdata.org/quizzes/quiz-open-data-definition/) - [[Quiz] What is Data?](https://schoolofdata.org/quizzes/quiz-what-is-data/): Did you understand the basic data categories? What type of data is this? - [Quiz 8](https://schoolofdata.org/quizzes/quiz-8/): Triarius vocant pergrata crucem apparere videmusne ultima eodem aristippus antiquis nobisneTe quod elegantiam confitentem explanatum genera exposuit quicum quaeri occurreret vetant comparatur beatae duo aderit - [Quiz 9](https://schoolofdata.org/quizzes/quiz-9/): Disputationibus dogmata indicant merulas diceres vultis conveniant aristoteles meum quampiam accipio rapior terra praecepta polycratemCyrenaici ipse coluit esses censet finem cetera utrisque dives protervam - [Quiz 6](https://schoolofdata.org/quizzes/quiz-6/): Maneant agas habitus hoc negabat tuetur mea signa vim priorisAdmonitionis posthac tria ipsum democrito de corporis vultus mirabiliter quem triarium macello partus compensabatur absit potuerit faciam - [Quiz 7](https://schoolofdata.org/quizzes/quiz-7/): Villam resisto necesse laudata eo darei moderati victoria subito lingua risu scias dubitet cuiusquamAis oritur ortu divitiarum facto commovebat percipitur tiberina quovis mos fomentis frangi poenam mene adiit currit utebare - [Quiz 3](https://schoolofdata.org/quizzes/quiz-3-2/): Frangere quoad negas officii vita homines dixissent oppidum conventu spatia positum aequas intellegere appellatPositam corporis magnitudine audissem expectamus confusioque potest valitudo contemnit secundae eodem oblivionis infinita regiones oblita epulis eaedem laudi - [Quiz 4](https://schoolofdata.org/quizzes/quiz-4/): Citius quaereremus visus obscurentur epuletur conspectus artem reliquis miseriarum rationemInnumerabilia fugimus denique praeteritorum primo spe referebat utram scaevola effectrix - [Quiz 5](https://schoolofdata.org/quizzes/quiz-5-2/): Falsone dignissimum pervenit veri platone mediocriterne didicerim locuples amicitiis compensabatur laudat emolumento ceteros nonneLucretia perspecta eo quaereremus aiebat illorum diodorus elegantius cum particulas civem videat praecipue retineat extrerno - [Quiz 1](https://schoolofdata.org/quizzes/quiz-1/): Graecum obsecro condemnata diligenter ponere epicureum fidibus levantur peragratas subtilius exemplis debeatis mali cetera dicturamLustravit appellet traditur miser meminerimus quarum longa respondeas graece voluptatis putet ipsae vir - [Quiz 2](https://schoolofdata.org/quizzes/quiz-2/): Tenueris viderentur vincla ostendit democritus faciendi currit extiterunt epuletur totas apta sequeturRecordatione verborum persarum molestum inportuna mehercule hominem consequentia obiit nepas rogavi aestimabat penitus - [Quiz 1](https://schoolofdata.org/quizzes/quiz-1-2/): M apta iustam potestis et hi alias repellant fecit philosophiae refers praepositum finis decimano quodcumque Iis dicta status belle dicet erechthei conventu habes fortem paene elementa porro praeceptis fortepossumus hominem quivere fluere optimo - [Quiz 6](https://schoolofdata.org/quizzes/quiz-5-copy/): Accepit possum sofaw tanti nobis contenta occurrent omniaque loquar medicinam anteponebatPoterant fortepossumus laudi respondissent curiosorum itaque negotium difficultate summa officia philosophiam loquor redarguitur institutionis fit aristidem pars animantibus - [Quiz 5](https://schoolofdata.org/quizzes/quiz-5/): Accepit possum sofaw tanti nobis contenta occurrent omniaque loquar medicinam anteponebatPoterant fortepossumus laudi respondissent curiosorum itaque negotium difficultate summa officia philosophiam loquor redarguitur institutionis fit aristidem pars animantibus - [Quiz 4](https://schoolofdata.org/quizzes/quiz-3-copy-2/): Accepit possum sofaw tanti nobis contenta occurrent omniaque loquar medicinam anteponebatPoterant fortepossumus laudi respondissent curiosorum itaque negotium difficultate summa officia philosophiam loquor redarguitur institutionis fit aristidem pars animantibus - [Quiz 2](https://schoolofdata.org/quizzes/quiz-3-copy/): Accepit possum sofaw tanti nobis contenta occurrent omniaque loquar medicinam anteponebatPoterant fortepossumus laudi respondissent curiosorum itaque negotium difficultate summa officia philosophiam loquor redarguitur institutionis fit aristidem pars animantibus - [Quiz 3](https://schoolofdata.org/quizzes/quiz-3/): Accepit possum sofaw tanti nobis contenta occurrent omniaque loquar medicinam anteponebatPoterant fortepossumus laudi respondissent curiosorum itaque negotium difficultate summa officia philosophiam loquor redarguitur institutionis fit aristidem pars animantibus ## Question Bank - [Even if an AI-generated response is fluent and convincing, it should still be verified using trusted sources when accuracy is important.](https://schoolofdata.org/questions/even-if-an-ai-generated-response-is-fluent-and-convincing-it-should-still-be-verified-using-trusted-sources-when-accuracy-is-important/) - [Tokens are the small units of text that an LLM reads and generates, and they are not always complete words.](https://schoolofdata.org/questions/tokens-are-the-small-units-of-text-that-an-llm-reads-and-generates-and-they-are-not-always-complete-words/) - [Which feature of the Climate Chatbot helps reduce hallucinations?](https://schoolofdata.org/questions/which-feature-of-the-climate-chatbot-helps-reduce-hallucinations/) - [What is a hallucination in the context of AI chatbots?](https://schoolofdata.org/questions/what-is-a-hallucination-in-the-context-of-ai-chatbots/) - [What is the purpose of the context window in an LLM?](https://schoolofdata.org/questions/what-is-the-purpose-of-the-context-window-in-an-llm-2/) - [How does a Large Language Model (LLM) generate responses?](https://schoolofdata.org/questions/how-does-a-large-language-model-llm-generate-responses-2/) - [What best describes Artificial Intelligence (AI)?](https://schoolofdata.org/questions/what-best-describes-artificial-intelligence-ai-2/) - [What is the purpose of the context window in an LLM?](https://schoolofdata.org/questions/what-is-the-purpose-of-the-context-window-in-an-llm/) - [How does a Large Language Model (LLM) generate responses?](https://schoolofdata.org/questions/how-does-a-large-language-model-llm-generate-responses/) - [What best describes Artificial Intelligence (AI)?](https://schoolofdata.org/questions/what-best-describes-artificial-intelligence-ai/) - [Atribua essas diferentes representações às descrições apropriadas.](https://schoolofdata.org/questions/assign-this-representation-to-the-appropriate-descriptioncopy-2/) - [Data is always numbers.](https://schoolofdata.org/questions/data-is-always-numbers/) - [Dados são sempre números.](https://schoolofdata.org/questions/data-is-always-numbers/) - [What do you not need to tell a datastory?](https://schoolofdata.org/questions/what-do-you-not-need-to-tell-a-datastory/) - [Data alone is enough - We do not have to provide context.](https://schoolofdata.org/questions/data-alone-is-enough-we-do-not-have-to-provide-context/) - [Which step is not commonly done in answering questions with data?](https://schoolofdata.org/questions/which-step-is-not-commonly-done-in-answering-questions-with-data/) - [When can you use the average?](https://schoolofdata.org/questions/when-can-you-use-the-average/) - [If a party got 10% of votes last election and got 15% this election is this:](https://schoolofdata.org/questions/if-a-party-ot-10-of-votes-last-election-and-got-15-this-election-is-this/) - [The aspect ratio (height/width) of a plot changes the perception of the data.](https://schoolofdata.org/questions/the-aspect-ratio-height-width-of-a-plot-changes-the-perception-of-the-data/) - [If the median income is 70.000US$...](https://schoolofdata.org/questions/if-the-median-income-is-70-000us/) - [Visualizing data is commonly done in exploring datasets.](https://schoolofdata.org/questions/visualizing-data-is-commonly-done-in-exploring-datasets/) - [Which presentation to choose if your data has a strong georaphical reference?](https://schoolofdata.org/questions/which-presentation-to-choose-if-your-data-has-a-strong-georaphical-reference/) - [What is the best scale for axes?](https://schoolofdata.org/questions/what-is-the-best-scale-for-axes/) - [Which chart to choose if you want to compare the interaction between two values?](https://schoolofdata.org/questions/which-chart-to-choose-if-you-want-to-compare-the-interaction-between-two-values/) - [Which of the following is not a spreadsheet formula?](https://schoolofdata.org/questions/which-of-the-following-is-not-a-spreadsheet-formula/) - [You can combine multiple formulas in one cell.](https://schoolofdata.org/questions/you-can-combine-multiple-formulas-in-one-cell/) - [A formula in a spreadsheet starts with the character:](https://schoolofdata.org/questions/a-formula-in-a-spreadsheet-starts-with-the-character/) - [You can use formulas with data on other sheets.](https://schoolofdata.org/questions/you-can-use-formulas-with-data-on-other-sheets/) - [If you have a median of 50, how many values will be less than 50?](https://schoolofdata.org/questions/if-you-have-a-median-of-50-how-many-values-will-be-less-than-50/) - [Assuming a normal distribution what is the likelihood a point with a Z-Score of 2?](https://schoolofdata.org/questions/assuming-a-normal-distribution-what-is-the-likelihood-a-point-with-a-z-score-of-2/) - [What is the mean of the following values: 10, 12, 6, 17?](https://schoolofdata.org/questions/what-is-the-mean-of-the-following-values-10-12-6-17/) - [Assuming a Normal Distribution which of the following is true:](https://schoolofdata.org/questions/assuming-a-normal-distribution-which-of-the-following-is-true/) - [Which country has the highest life expectancy in 2010?](https://schoolofdata.org/questions/which-country-has-the-highest-life-expectancy-in-2010/) - [Which country has the smallest GDP in 2011?](https://schoolofdata.org/questions/which-country-has-the-smallest-gdp-in-2011/) - [In which year does the European Union have the highest GDP?](https://schoolofdata.org/questions/in-which-year-does-the-european-union-have-the-highest-gdp/) - [Which Country spends the biggest fraction of it’s GDP on Healthcare in 2010?](https://schoolofdata.org/questions/which-country-spends-the-biggest-fraction-of-its-gdp-on-healthcare-in-2010/) - [How can you aggregate company counts by city?](https://schoolofdata.org/questions/how-can-you-aggregate-company-counts-by-city/): SELECT city,count(id) from companies ________; - [Which of the following modes is NOT supported by sqlite? (for exporting data)](https://schoolofdata.org/questions/which-of-the-following-modes-is-not-supported-by-sqlite-for-exporting-data/) - [How could we determine the average age in the dataset about people?](https://schoolofdata.org/questions/how-could-we-determine-the-average-age-in-the-dataset-about-people/): SELECT _____ from people; - [How do you get the number of cities in the dataset used in the last lesson?](https://schoolofdata.org/questions/how-do-you-get-the-number-of-cities-in-the-dataset-used-above/): SELECT _______ FROM companies; - [Which types of data quality issues require documentation to be checked in ODE?](https://schoolofdata.org/questions/which-types-of-data-quality-issues-require-documentation-to-be-checked-in-the-ode/): Select all the correct options. - [You're explaining to your colleague how the error reports in the ODE work.](https://schoolofdata.org/questions/youre-explaining-to-your-colleague-how-the-error-reports-in-the-ode-work/): For which data quality issues can your team rely on the ODE to flag? Select all correct options. - [Which of the following patterns is commonly used represent missing data?](https://schoolofdata.org/questions/which-of-the-following-patterns-is-commonly-used-represent-missing-data/) - [Budget](https://schoolofdata.org/questions/employeescopy/): ProjectName,StartDate,Employees,Budget "Coastal Cleanup",2023-01-15,29,"45°N, 123°W",150000.50 "Urban Air Monitoring",2022-05-01,84,"40°N, 75°W",50000.00 "Marine Life Study",2021-06-15,96,"30°N, 80°W",200000.00 Choose the most appropriate data type: - [Employees](https://schoolofdata.org/questions/startdatecopy/): ProjectName,StartDate,Employees,Budget "Coastal Cleanup",2023-01-15,29,"45°N, 123°W",150000.50 "Urban Air Monitoring",2022-05-01,84,"40°N, 75°W",50000.00 "Marine Life Study",2021-06-15,96,"30°N, 80°W",200000.00 Choose the most appropriate data type: - [StartDate](https://schoolofdata.org/questions/projectnamecopy/): ProjectName,StartDate,Employees,Budget "Coastal Cleanup",2023-01-15,29,"45°N, 123°W",150000.50 "Urban Air Monitoring",2022-05-01,84,"40°N, 75°W",50000.00 "Marine Life Study",2021-06-15,96,"30°N, 80°W",200000.00 Choose the most appropriate data type: - [ProjectName](https://schoolofdata.org/questions/projectname/): ProjectName,StartDate,Employees,Budget "Coastal Cleanup",2023-01-15,29,"45°N, 123°W",150000.50 "Urban Air Monitoring",2022-05-01,84,"40°N, 75°W",50000.00 "Marine Life Study",2021-06-15,96,"30°N, 80°W",200000.00 Choose the most appropriate data type: - [You are working on a semantic web project to describe environmental data using RDF.](https://schoolofdata.org/questions/you-are-working-on-a-semantic-web-project-to-describe-environmental-data-using-rdf/): RDF uses subject-predicate-object triples to represent information. For example: "45°N, 123°W" In the triple above, what does the term represent? - [You are designing a database to manage information about a network of environmental research projects.](https://schoolofdata.org/questions/you-are-designing-a-database-to-manage-information-about-a-network-of-environmental-research-projects/): The database will include multiple tables, such as Projects, Sites, and Measurements. To ensure data integrity and establish relationships between tables, you must define primary keys and foreign keys. The Projects table contains the following fields: Which field would you use as the primary key, and why? - [You are working on a dataset that contains various fields, including text with commas, special characters, and quotes.](https://schoolofdata.org/questions/you-are-working-on-a-dataset-that-contains-various-fields-including-text-with-commas-special-characters-and-quotes/): Your task is to ensure the CSV file is correctly formatted and compatible with different tools. Choose the answer that describes the correct field separator and explains why the quotes around some fields are necessary. Site Name,Location,Data Collected,Description "Forest Reserve","45°N, 123°W","Temperature, Rainfall","Located near the river, this site collects weather data." "Urban Park","40°N, 75°W","Air Quality","Monitoring urban - [Your colleague asks about the main features in the ODE for documenting the authors and sources of a given dataset.](https://schoolofdata.org/questions/your-colleague-asks-about-the-main-features-in-the-ode-for-documenting-the-authors-and-sources-of-a-given-dataset/): Select all correct options: - [You’re preparing a dataset for public release and need to define a license.](https://schoolofdata.org/questions/youre-preparing-a-dataset-for-public-release-and-need-to-define-a-license/): Why is it important to specify a license for open data? Select all correct options. - [You have been asked to generate an unique identifier (hash) for a small dataset.](https://schoolofdata.org/questions/you-have-been-asked-to-generate-an-unique-identifier-hash-for-a-small-dataset/): Use an on-line tool (e.g. https://www.md5hashgenerator.com/) to generate a hash for the following CSV file: country,year france,2024 japan,2021 brazil,2016 Ensure there are no extra line breaks or spaces included. The generated hash is equal to 77423b58a097848e34acb51c7ae4dbbb. True or false? - [Which option in the Resource tab of the Metadata menu in the ODE is most closely related to issues involving unconventional characters in the dataset?](https://schoolofdata.org/questions/which-option-in-the-resource-tab-of-the-metadata-menu-in-the-ode-is-most-closely-related-to-issues-involving-unconventional-characters-in-the-dataset/) - [What do the red dots next to filenames on ODE's left sidebar indicate?](https://schoolofdata.org/questions/what-do-the-red-dots-next-to-filenames-on-the-left-side-bar-in-the-ode-indicate/) - [Your team has enjoyed using the new tool, but some users are encountering issues with Google Sheets.](https://schoolofdata.org/questions/your-team-has-enjoyed-using-the-new-tool-but-some-users-are-encountering-issues-with-google-sheets/): They cannot open online and shared spreadsheets in ODE, even though the URL appears correct. What should they check next? Select all appropriate options: - [Linux](https://schoolofdata.org/questions/macoscopy/) - [MacOS](https://schoolofdata.org/questions/windowscopy/) - [Windows](https://schoolofdata.org/questions/windows/) - [Your journey as a data steward has reached new heights!](https://schoolofdata.org/questions/your-journey-as-a-data-steward-has-reached-new-heights/): Now a leading researcher in open, high-quality datasets, you’ve just discovered a tool that could revolutionize your team's workflows: the Open Data Editor (ODE). In your upcoming team meeting, where many members are non-technical, you plan to introduce ODE as a solution. Highlight the following key advantages of ODE over traditional spreadsheet software: - [You’ve finished publishing the air quality dataset, and your boss was so impressed with the FAIR principles that she wants to apply them to other projects.](https://schoolofdata.org/questions/youve-finished-publishing-the-air-quality-dataset-and-your-boss-was-so-impressed-with-the-fair-principles-that-she-wants-to-apply-them-to-other-projects/): First, she asks you to assess the compliance of other published datasets with these principles. You plan to use a tool to assist with this task and need to explain which of the following is NOT a key principle evaluated by FAIR assessment tools: - [As your team debates publishing the air quality dataset in proprietary formats (like Excel .xlsx) or open formats (like .csv), some argue that proprietary formats are more user-friendly.](https://schoolofdata.org/questions/as-your-team-debates-publishing-the-air-quality-dataset-in-proprietary-formats-like-excel-xlsx-or-open-formats-like-csv-some-argue-that-proprietary-formats-are-more-user-friendly/): How would you explain one of the benefits of open formats to the team? - [To comply with FAIR principles, you need to ensure the dataset is findable by indexing it in a searchable resource, such as a data repository or catalog.](https://schoolofdata.org/questions/to-comply-with-fair-principles-you-need-to-ensure-the-dataset-is-findable-by-indexing-it-in-a-searchable-resource-such-as-a-data-repository-or-catalog/): Which of the following are examples of searchable resources for data indexing? - [During a stakeholder meeting about the requirements to publish the data according to FAIR principles, an executive from a company involved in the project argues that the dataset is already self-explanatory and doesn’t need additional documentation.](https://schoolofdata.org/questions/during-a-stakeholder-meeting-about-the-requirements-to-publish-the-data-according-to-fair-principles-an-executive-from-a-company-involved-in-the-project-argues-that-the-dataset-is-already-self-explan/): How would you explain what metadata is and why it’s beneficial to have it in structured formats? - [As part of the ongoing air quality and greenhouse gas emissions monitoring project, your team is preparing to release the dataset for public use.](https://schoolofdata.org/questions/as-part-of-the-ongoing-air-quality-and-greenhouse-gas-emissions-monitoring-project-your-team-is-preparing-to-release-the-dataset-for-public-use/): To ensure data long-term accessibility, your manager asks you to assign globally unique and persistent identifiers to the dataset. Which platform should you use? - [After your success with previous data projects, you are assigned to a new initiative: publishing a dataset to monitor air quality and greenhouse gas emissions in urban areas.](https://schoolofdata.org/questions/after-your-success-with-previous-data-projects-you-are-assigned-to-a-new-initiative-publishing-a-dataset-to-monitor-air-quality-and-greenhouse-gas-emissions-in-urban-areas/): The dataset will support government policies and citizen initiatives to combat climate change. You want to ensure that the dataset complies with the FAIR principles. Your boss likes the idea of open data but still doesn’t understand the value of the FAIR principles. How do you explain why the FAIR principles are important for open - [Delete a specific column from 2,500 archived spreadsheets](https://schoolofdata.org/questions/identify-errors-in-a-dataset-and-write-metadata-documentationcopy/): Choose the best approach: - [Identify errors in a dataset and write metadata documentation](https://schoolofdata.org/questions/clean-a-dataset-and-standardize-text-values-in-one-columncopy/): Choose the best approach: - [Clean a dataset and standardize text values in one column](https://schoolofdata.org/questions/clean-a-dataset-and-standardize-text-values-in-one-column/): Choose the best approach: - [Hyphen (-)](https://schoolofdata.org/questions/commacopy-2/): The dataset you’re working with has three columns that store multiple values in the same cell. To tidy the data, you need to split these cells into separate columns. Match the appropriate separator to each column based on the data format. - [Comma(,)](https://schoolofdata.org/questions/slash-copy/): The dataset you’re working with has three columns that store multiple values in the same cell. To tidy the data, you need to split these cells into separate columns. Match the appropriate separator to each column based on the data format. - [Slash (/)](https://schoolofdata.org/questions/slash/): The dataset you’re working with has three columns that store multiple values in the same cell. To tidy the data, you need to split these cells into separate columns. Match the appropriate separator to each column based on the data format. - [Hyphen (-)](https://schoolofdata.org/questions/commacopy/): The dataset you’re working with has three columns that store multiple values in the same cell. To tidy the data, you need to split these cells into separate columns. Match the appropriate separator to each column based on the data format. - [The team has agreed to use the tidy data standard, and now you’re in charge of improving the files.](https://schoolofdata.org/questions/the-team-has-agreed-to-use-the-tidy-data-standard-and-now-youre-in-charge-of-improving-the-files/): You discovered that the data collection team has used two different formats for the headers. Choose the best header schema: Table A Table B - [The team has read the text you shared about the definition of “dados organizados,” but they’re still confused about the terminology.](https://schoolofdata.org/questions/the-team-has-read-the-text-you-shared-about-the-definition-of-dados-organizados-but-theyre-still-confused-about-the-terminology/): “What does it mean for a table to represent a single observational unit?” they ask. Mark the correct response: - [After the success of the reforestation project, you are now assigned to a new project aimed at assessing the environmental impact of renewable energy installations (e.g., wind farms, solar panels) across different regions.](https://schoolofdata.org/questions/after-the-success-of-the-reforestation-project-you-are-now-assigned-to-a-new-project-aimed-at-assessing-the-environmental-impact-of-renewable-energy-installations-e-g-wind-farms-solar-panels-acr/): The goal is to publish data on energy production, land usage, and biodiversity impacts. You need to convince the data collection team to adopt “tidy data” standards, but they’re reluctant. When you receive the dataset, you notice it is poorly structured. What is the main advantage of complying with the “tidy data” standards as defined - [Use the “find and replace” option in spreadsheet tools or OpenRefine to standardize the values.](https://schoolofdata.org/questions/save-the-dataset-using-utf-8-encoding-to-ensure-proper-display-of-special-characters-copy/): Choose the right fix: - [Save the dataset using UTF-8 encoding to ensure proper display of special characters.](https://schoolofdata.org/questions/publish-the-table-with-only-the-data-and-publish-the-descriptions-and-comments-as-metadata-resources-copy/): Choose the right fix: - [Publish the table with only the data and publish the descriptions and comments as metadata resources.](https://schoolofdata.org/questions/a-publish-the-table-with-only-the-data-and-publish-the-descriptions-and-comments-as-metadata-resources/): Choose the right fix: - [You finished cleaning and validating the reforestation dataset, only to discover that Josh, a colleague from another department, made an amendment to the dataset while you were working on it.](https://schoolofdata.org/questions/you-finished-cleaning-and-validating-the-reforestation-dataset-only-to-discover-that-josh-a-colleague-from-another-department-made-an-amendment-to-the-dataset-while-you-were-working-on-it/): Now, you have two different, out-of-sync versions of the same dataset. To avoid such issues in the future, your boss asks you to suggest version control best practices. What do you recommend? - [Two rows with identical information across all columns.](https://schoolofdata.org/questions/two-rows-with-the-same-number-of-trees-planted-but-different-regions-copy/): What is the correct diagnostic? - [Two rows with different capitalization styles for the same entity in the "region" column (e.g., "Amazon" vs. "amazon").](https://schoolofdata.org/questions/two-rows-with-identical-information-across-all-columns-copy/): What is the correct diagnostic? - [Two rows with the same number of trees planted but different regions.](https://schoolofdata.org/questions/a-two-rows-with-the-same-number-of-trees-planted-but-different-regions/): What is the correct diagnostic? - [Ensure the “number of trees planted” column only contains non-negative numeric values and the ”survival rate” is a value between 0 and 100.](https://schoolofdata.org/questions/a-ensure-the-number-of-trees-planted-column-only-contains-non-negative-numeric-values-and-thesurvival-rate-is-a-value-between-0-and-100/): Assign the right data validation technique: - [Check if all texts in the “country name” column have no more than 56 characters](https://schoolofdata.org/questions/standardize-the-column-year-as-yyyy-mm-dd-e-g-2009-02-04-instead-of-2009-feb-04copy/): Assign the right data validation technique: - [Check if the total "number of trees planted" for all countries matches the data reported by the country office.](https://schoolofdata.org/questions/ensure-the-number-of-trees-planted-column-only-contains-non-negative-numeric-values-and-thesurvival-rate-is-a-value-between-0-and-100-copy/): Assign the right data validation technique: - [Standardize the column “year” as YYYY-MM-DD (e.g 2009-02-04 instead of 2009-Feb-04)](https://schoolofdata.org/questions/check-if-the-total-number-of-trees-planted-for-all-countries-matches-the-data-reported-by-the-country-office-copy/): Assign the right data validation technique: - [As you continue working on the reforestation dataset, you notice additional issues that may affect the dataset's validity and consistency.](https://schoolofdata.org/questions/as-you-continue-working-on-the-reforestation-dataset-you-notice-additional-issues-that-may-affect-the-datasets-validity-and-consistency/): Data are valid if it conforms to the syntax (format, type, range) of its definition. The comma-separated texts below represent the values for the columns “trees planted” and “data collection year”. Mark those with validity issues: - [The reforestation dataset has missing values in the column "number of trees planted."](https://schoolofdata.org/questions/the-reforestation-dataset-has-missing-values-in-the-column-number-of-trees-planted/): Upon investigation with the data collection team, you discover that the missing values occur in two cases: No trees were planted in a specific location and year. The number of trees could not be measured. How should you address these cases? - [You are now leading a new environmental project focused on tracking reforestation efforts across different regions.](https://schoolofdata.org/questions/you-are-now-leading-a-new-environmental-project-focused-on-tracking-reforestation-efforts-across-different-regions/): The dataset, collected by dozens of teams, aims to monitor the number of trees planted, species diversity, and survival rates in areas affected by deforestation. This data will support local government-led initiatives to evaluate the success of reforestation programs. During the data collection process, you notice several issues with the dataset that could compromise the - [After successfully publishing the dataset, you participate in an internal evaluation meeting.](https://schoolofdata.org/questions/after-successfully-publishing-the-dataset-you-participate-in-an-internal-evaluation-meeting/): Your boss congratulates the team on the project’s success but asks about the main lessons learned and how the organization can improve its future data-related workflows. She emphasizes the need to formalize data governance protocols, ensuring consistency, quality, and accountability across all future datasets. She asks you to outline what a Data Management Plan would - [After implementing a data dictionary for your dataset, the executives are pleased with the improvements, but they are curious to know more about metadata and ask you what other kinds of metadata should we include to make the dataset more usable.](https://schoolofdata.org/questions/after-implementing-a-data-dictionary-for-your-dataset-the-executives-are-pleased-with-the-improvements-but-they-are-curious-to-know-more-about-metadata-and-ask-you-what-other-kinds-of-metadata-shoul/): Which of the following is an example of metadata in a real-world dataset? - [After resolving the issues with special characters in the CSV file, the executives provide feedback that, while the dataset is functional, it’s difficult to understand without additional context.](https://schoolofdata.org/questions/after-resolving-the-issues-with-special-characters-in-the-csv-file-the-executives-provide-feedback-that-while-the-dataset-is-functional-its-difficult-to-understand-without-additional-conte/): They express concern that users might struggle to interpret column names and data types as they did. You explain that this is where a data dictionary comes into play, helping users better understand and work with the data. In your response, how do you describe the most important feature of a data dictionary? - [After resolving the numeric column issue, the executives review the dataset further and raise another concern.](https://schoolofdata.org/questions/after-resolving-the-numeric-column-issue-the-executives-review-the-dataset-further-and-raise-another-concern/): They report that special characters, such as accents or symbols, appear garbled or replaced with strange symbols when opening the CSV file. Based on your experience, you recognize this is likely a problem with which setting? - [The executives have now agreed to publish the dataset as a CSV file.](https://schoolofdata.org/questions/the-executives-have-now-agreed-to-publish-the-dataset-as-a-csv-file/): However, after opening the file, they report being unable to calculate averages for numeric columns in their spreadsheet software due to a "type error." How do you advise them? Select all correct and pertinent options. - [While waiting for the company's feedback, you decide to review the CSV dataset again.](https://schoolofdata.org/questions/while-waiting-for-the-companys-feedback-you-decide-to-review-the-csv-dataset-again/): Upon inspection, you notice a critical error caused by inconsistent delimiters. Thankfully, the delay in publication due to the executives' questions gave you the opportunity to catch this issue. Otherwise, your first published dataset would have contained a serious formatting error. Phew! Here are the problematic rows: rank,name,km,miles 3,Yangtze,6,300,3,917 4,Mississippi,6275,3902 What is the issue with - [You delay the data publication to reach an agreement with the company, a key stakeholder in the project you are coordinating.](https://schoolofdata.org/questions/you-delay-the-data-publication-to-reach-an-agreement-with-the-company-a-key-stakeholder-in-the-project-you-are-coordinating/): The executives respond, stating they now understand the benefits of CSV over Excel files... But they raise a new question: "What about PDF files?”. “Everyone knows PDFs, and everyone can read them, so it must be a suitable format for open format!”, they say. What arguments can you use to respond to their email? - [You’ve finished the conversion to CSV and are almost ready to finalize your first project.](https://schoolofdata.org/questions/youve-finished-the-conversion-to-csv-and-are-almost-ready-to-finalize-your-first-project/): However, right before publishing it, you got a message from the executives. They say that CSV is not intuitive for beginners and suggest publishing the data as Excel files. What advantages of CSV over Excel files should you highlight? - [To publish the Excel files produced by the data collection team as a CSV file, you also need to review how the data is structured.](https://schoolofdata.org/questions/to-publish-the-excel-files-produced-by-the-data-collection-team-as-a-csv-file-you-also-need-to-review-how-the-data-is-structured/): Below is a sample of first rows of the file: What makes this dataset a multi-header table? And how to transform it to a single-header dataset? - [Great, the dataset now adopts a license that follows the Open Definition principles.](https://schoolofdata.org/questions/great-the-dataset-now-adopts-a-license-that-follows-the-open-definition-principles/): The next step is to rate it according to the five-star schema as part of the self-assessment evaluation required by the funders. The dataset will be published using CSV, an open format, but will not have a unique identifier such as a dedicated URL, nor semantic descriptions that allow the data to be linked to - [Congratulations! You’ve been selected to the job. The test was easier than you thought, but now you're dealing with real-life problems.](https://schoolofdata.org/questions/your-task-is-to-review-them-and-select-only-the-options-that-comply-with-the-open-definition-principles/): You are initially tasked with coordinating a collaborative project with a company. The funders require you to publish a dataset that aligns with the principles of the Open Definition by Open Knowledge. This definition ensures that data is freely available for anyone to use, modify, and share for any purpose. While restrictions that limit key freedoms are - [Assign this representation to the appropriate description](https://schoolofdata.org/questions/assign-this-representation-to-the-appropriate-descriptioncopy-2/) - [Assign this representation to the appropriate description](https://schoolofdata.org/questions/assign-this-representation-to-the-appropriate-descriptioncopy/) - [Atribua essas diferentes representações às descrições apropriadas.](https://schoolofdata.org/questions/assign-this-representation-to-the-appropriate-descriptioncopy/) - [Assign this representation to the appropriate description](https://schoolofdata.org/questions/ssss/) - [Atribua essas diferentes representações às descrições apropriadas.](https://schoolofdata.org/questions/ssss/) - [Governments are the only institution releasing data.](https://schoolofdata.org/questions/governments-are-the-only-institution-releasing-data/) - [What is a datacatalog?](https://schoolofdata.org/questions/what-is-a-datacatalog/) - [How can you get hold of unpublished government data?](https://schoolofdata.org/questions/how-can-you-get-hold-of-unpublished-government-data/) - [You can find datasets on Google using +filetype:csv](https://schoolofdata.org/questions/true-or-false-you-can-find-datasets-on-google-using-filetypecsv/) - [Question 95](https://schoolofdata.org/questions/question-95/): Peripateticis animantibus iure nec oppidum duce reliquo exquisita duabus sequaminiCyrenaicos motus posterius commendas comparandis amores a expetendam aristo sentit habeo ordiendum - [Question 96](https://schoolofdata.org/questions/question-96/): Tenent aliis leonidas putabam vitiorum tubulus felicem vivi pugnat suis dare sextus statuat boni modestiam amotio iracundia mihi murenamCantibus arcesilas quidam recordatione ponimus dixisses servire uberrimam fuerat quaereremus - [Question 97](https://schoolofdata.org/questions/question-97/): Dacere penitus nesciebat democritus videt ex nimium utilitatem que inquieta ipsum pulchritudo laetus partem retinere ponere profuso dubiisAlumni duas diligenter translatum delectari possim p dubitant ex efficit aetatibus fiant easque - [Question 98](https://schoolofdata.org/questions/question-98/): Theophrasti putet urbis alia quis regis contritum scilicet quaerenda quamquam elegantiam habeat mutandi esto verum fugiunt ante confecimusGraeco magos nominare versum quo veri repugno de levis - [Question 99](https://schoolofdata.org/questions/question-99/): Redderet accidisset dicerentur gaudebit habebit audiunt sumus perspicua progressionem splendidiorT fugiam videmus verbi ob debilitas commoda usque timore lorem diligentius oportet kakaw plura peragratas controversiam gestiant es iudicem singula - [Question 100](https://schoolofdata.org/questions/question-100/): Beatae inde nihil zenone epaminondas utilitates haeret hominum etenim mortes ecce solere peti gloriaCupido transmisit adolescentulus dederetur asotos venissemus occulta mirum intuens unaque ducas eodem perspicis undique vacuitatem ratio diligunt - [Question 101](https://schoolofdata.org/questions/question-101/): Prave primae inventus tractat statu intellegerem solent sapientiae sirenum diceres loqueretur putandumSponte eam invectio paupertas aut inchoavit quo magnificentia aristotele puto lyco - [Question 102](https://schoolofdata.org/questions/question-102/): Sicut civitas proclivi contemnunt falli lex suadeas ioca interesse quaerere brevem versutum remittuntConiunctio genuit satisne primum coluit nepas accidisset universa vult dictata - [Question 103](https://schoolofdata.org/questions/question-103/): Quemquam darei accessit amet pluribus philodemum misera ceperit cetera auribus vi omneRecurrant dissimiles saepius callipho perveniant propterea hominum ac crucem miseriarum videat - [Question 104](https://schoolofdata.org/questions/question-104/): Abducat donan chryippo praeter poterunt pertinacem comprehenditur legimus concedamus aristophanem absurdumLocuples communia quasi delectat nescius beatam officium apti divitem possitne facta pressius nihilne inventoribus stoicorum sullae illustris - [Question 105](https://schoolofdata.org/questions/question-105/): Referebat reguli ingenuas id patientiae adhibebat dicere uberiora hos dicam litigiosamDistinguitur incrementum reprehendit aliquando levitatis occurrent actum usque probavit moveor eodem beatae negas usum - [Question 106](https://schoolofdata.org/questions/question-106/): Morbos tarentum cognoscimus aristoxeni strato contrariis quae ait volunt singula consentit polliceturValet affectus mutat excitaret levare consequatur dipylo cuiusquam esse vocant iustitiae afflueret requirere dulce placuit iucundi contrariae penitus isdem - [Question 107](https://schoolofdata.org/questions/question-107/): Celebrari parvi adhuc invectio inquit abeunti chrysippum cyrenaici ortu debeas evolare sentias senatu ait frangi principium poteratDeum elegantiam discere dicet vel capite partibus postumius falsone dictata - [Question 108](https://schoolofdata.org/questions/question-108/): Exul tertium rationis accepit patefactio morbus humano irridebat haec improbos referebat faciliorem placuit iisque vitiosum suavius defenderemQuacumque aufert sim disserendi similitudines datum quaereremus moriuntur pericli conamini sicut pyrrhonem pisone efficiens magister plane - [Question 109](https://schoolofdata.org/questions/question-109/): Efficit inflammat partem delectet nostra haec re radicitus sapientes indoctum dividendo velit recordamur loquereturTraduceret cui traduceret piso tuetur kakaw omnino dolor es neque duae aequas estquod eos contemnit pythagoras totam easque - [Question 110](https://schoolofdata.org/questions/question-110/): Beatiorem humanissimum recusant aequabilesque expectamus viderentur agere fortior divinius legem multitudinis pudebitVoluptatibus conati perspici quaerimus interemit comprobatione latina genuit cohaerere ususque potuit l ipsam - [Question 111](https://schoolofdata.org/questions/question-111/): Gaudere dissimiles vigeant quasi testamento lorem omnes tollenda pisone prorsus ita eo lycoCausamque miseriarum obruebantur eris fingas loquare viderentur aliquantum habet usus respondere familiarem anteponebas quacumque - [Question 112](https://schoolofdata.org/questions/question-112/): Commovere orbus placuit platoni laudata facile declinavi diviserunt volumus probatAbsolvi propterea luxuriam suspectum peccetur aeternum aditus peducaeus c illud sese memoriam vartutis voluptatis elegantius - [Question 113](https://schoolofdata.org/questions/question-113/): Posthac dixerim sedulo expedita laudatum accusante visa faceres sensuum legibus torquentur ultimum sentit gravissimum subicias tyrannusHeredem ostentatio nollet magos scaevola periculum metrodori gymnasio salvum videntes miser - [Question 114](https://schoolofdata.org/questions/question-114/): Similiora sensitque abducat nocent nostis metrodori velle iis nostrum unaque convenienter beatae miser offendit vestra uberrimam ceteros manere turpitudinis depravataeSemovenda scriptis sui vere credo - [Question 115](https://schoolofdata.org/questions/question-115/): Divitiarum delectat facies relictae loquar quorum iucundissime consuetudo nihil stabilitatem censes seseStatueris eligant laboramus vultum vultis malumus putas civem caret pecuniam est salvam sciant vartutis cupit interemit - [Question 116](https://schoolofdata.org/questions/question-116/): Magnificentia antiquorum imperes morbo mirum revocat discordia vestris phaedro altera imperatoriae vestigium beneficium diciturC magnitudine aequabilesque loquantur stuprata summa velim facere stabilitatem opinor libros tanti facilis scientiam - [Question 117](https://schoolofdata.org/questions/question-117/): Litteram oratores gerendarum rem ponat volueramus eos minuis hunc videresInvectio converte disciplina animadverti ratio urbe regulum quippe primis statuam artibus ennius vacuitas - [Question 118](https://schoolofdata.org/questions/question-118/): Saepe amicitias age mediocribus consuli habitus peripateticis malis virtutibus praestantia codrum pericli posuisti progressionis dissimilis anaticulas tantasDiscipulos tenent iocans tam perveniant philodemum danda quoniam attende probarentur deserit - [Question 119](https://schoolofdata.org/questions/question-119/): Leve abeunti negaret studiose habitus opponitur maxima praepositum quandam fomentis virtuti quanto munus totam sumus restinguetLatino uno beatam addidisti aderit miserrimus commovere aristidem consentit communia luxuriam - [Question 120](https://schoolofdata.org/questions/question-120/): Versuta physicum quicum noluerunt fugimus credo avaritias pyrrhonem cupidus poneret danturTuum repugnat agas satisfacit duarum cenasti modi perspici infantes dignos sanctos oratio occurreret disiuncta divisit quacumque - [Question 53](https://schoolofdata.org/questions/question-53/): Vivunt frangi salebra luxuriosus copiose parabiles essentne nunc rursus natos defendebant fuerat dicitur delectamur meliusque dixerit vidit augendaeDebeat moriuntur bonane velut tu cleanthes iucunda diceret laudat similes illustrare duo aetatulis cavere negaret ad - [Question 54](https://schoolofdata.org/questions/question-54/): Videsne promerem soletis reges omnem animalibus malitiae accusante exquisita animadverti umquam vivendi atquePrivatio contemnunt istorum moveatur aequas nosmet gaudeo nihilo amicum incessum summo denique vidit - [Question 55](https://schoolofdata.org/questions/question-55/): Villam brute virtutibus tuique diligentissime obrui videor asotos coniungi accesseris magni eriperes exigit valitudo neminem actionemque inquam propria laboretTorquatus octavio proverbium regem evolare malum intellegetur ea videatur alius ipsos metus cariorem tuis quod timide - [Question 56](https://schoolofdata.org/questions/question-56/): Solebat his latino litteram sempiternam reliquam amicos vidit altera consentit eadem impudens iniquum ridens optimis morbos patria proficiscitur cuius effectaPermulcet nostrum illae sitienti illud apparere libero nescius consuetudinem - [Question 57](https://schoolofdata.org/questions/question-57/): Oculorum lanx plurimum comitetur aequas publicae regem dederis nosmet vitiorum filias metellumRei carum age t nesciam consuetudinum restinguet bonis probandum aetatem amicitia reddes satietate grata efficiens indoctum fac - [Question 58](https://schoolofdata.org/questions/question-58/): Consuetudinum media verbi institutum orata quanta bonorum dixerim relictae murenam praeceptum sensibus quidqueHominum expetunt malitiae perdiscere lucretia valitudo cantibus misera metus exquisita fortem quaerenda suum inquis nullam ultimum - [Question 59](https://schoolofdata.org/questions/question-59/): Aliena videretur adolescentulus longe utinam metrodori comprehensionem vestris uratur atqui quia postulat libidinosam secusneTenent duce videmusne rotundum videndum commota hae pareat concordiae venissem hieronymi obiit - [Question 60](https://schoolofdata.org/questions/question-60/): Nostram opponitur aufert incolumis gravis ipsis corporis disputatum dicere senescit ioca visus pressius fine docet converteQuattuor scriptis cyrenaici finis urgent voluptate isti neglegendi cenabat perceptfum existeret hominem - [Question 61](https://schoolofdata.org/questions/question-61/): Bona pridem relicta adduceret ambiguo sequatur mediocribus usitate stante libentius iure animantibus venissem reliquarum dedereturNeglegebat ipsae opinor dolor existimas elit quidam etiam rem imperitos scilicet prorsus artibus aristippi - [Question 62](https://schoolofdata.org/questions/question-62/): Metuere disputat paene intellegatur amantissimus sapientiae accepimus torquate legem laboret libentiusMirabiliter propter homini p elici diligentissime ullo veterum philosophari conferam puerorum responderi finitas quaerere mene itaque modestiam - [Question 63](https://schoolofdata.org/questions/question-63/): Amor civitas animum efficiuntur amicitiam consuetudinum disputare hanc moribus cenent aetatem existimoad putamus quicquid animalibus detractis affectus mene diligenterStatu veri antea superiori erit velut fallare consumis duce modicum ait imperatores - [Question 64](https://schoolofdata.org/questions/question-64/): Aditus non ageretur dominante indicant sese constantius residamus falli tractandosSpeculis aristoteli vulgo annum demptis levem pridem simile collatio repugnet actio declaret imperio responsum civis sequens commovebat viri noli mortuum - [Question 65](https://schoolofdata.org/questions/question-65/): Villam metuetur c sponte summis ambiguo hostes repugnet ista fortuna aliquo magno es consul extra tu mitigari aususPropensus ab incontentae cupido voluit idcirco singulis redargueret amantissimus lectulis sentit omnium patrocinium occurrent dempta cupiditatem non frangere - [Question 66](https://schoolofdata.org/questions/question-66/): Laetus scio tantum earum horum bene residamus fellx quivere quibusdam voluptatis popularia regis doctorum luxuriam germen explanetur oblita sofawLocis meministi nesciam pertinacem recta restinguet numquam secundae propensus immo traditur offensione perpetuam suspectum reprehendit ultima loquar - [Question 67](https://schoolofdata.org/questions/question-67/): Incidant corporis saepius processerat maiores dimicabit docuit sironem peccata clariora inesse suum erit dicimus loquemurHunc amicitiis aperitur neglegendi abundaret carneade quorum saepius ipse vita affectum declarat - [Question 68](https://schoolofdata.org/questions/question-68/): Egone minuis minuis piso contenti legimus addo sempiternam nam discipulum probarentur omnisqueGravior similes oratores sentiret duce qualem parvis sit virtus istam ortu videmusne debeatis vincla integra - [Question 69](https://schoolofdata.org/questions/question-69/): Natura fugimus indoctum sedeat extra porro existimas exigit elaboratum advesperascit suo audacius tria expedita boni filias cognosci carneadeamIndignum spem iucunde existimas hebes praecepta dolore dubitas copiam iudicant gravius - [Question 70](https://schoolofdata.org/questions/question-70/): Venissem vivi mediocriterne nonne potestate istud mali concederem contemnendam reprehendit tributaTractat arbitratu statui brevis dicentibus pellit penitus addidisti callipho offensione putabam turpitudinem pueri exquisita comprobatione acupenserem positam turpiter amet maneant - [Question 71](https://schoolofdata.org/questions/question-71/): Expetuntur paucis semper delectat omnino commutare cernere antea dignitati peteret multaIngenii quoquo hoc avarus usus auribus periculo assentior perturbatio promerem beatior mutare graeco origo finitas cupiditatem minor aequas - [Question 72](https://schoolofdata.org/questions/question-72/): Instituta germen oculis gravis quo versutum oportebit sentit declarat discere vitaePonunt voluptates fecisset auctoritas illustrare stoicus vestra exiguam coronam epicurus incideret plebiscito suum simus sequamini - [Question 73](https://schoolofdata.org/questions/question-73/): Locis hebes vanos stoicus similem serpere magnitudine fidibus reprehendit academia arcana praesidiiMaiores verso sordidos elici extra communiter coluit ponat videmusne dissensione expectare ipsius beatus - [Question 74](https://schoolofdata.org/questions/question-74/): De quod officium arripuit doctorum iustus consequentia possunt numerare veriQuem liberum doctorum referebat nostris redeamus legibus cognoscenda haeret peripatetici coronam usum molestum eiusdem - [Question 75](https://schoolofdata.org/questions/question-75/): Est scriptis latinum obrui audacius aestimaretis fortis comprehensionem vidit moderati pudoris aliam quare defensa chryippo cogitari vicimus pExpetunt proficiscitur confecta perfici fatebuntur oportebit iis oculis - [Question 76](https://schoolofdata.org/questions/question-76/): Probitate aristoteles ponatur villae squilla iucunda occurrentia cognoscenda captum dissimile easque dubium lex magnifice debeant nostris peripatetici cn nepas turpiterAppellat affecit liceat mehercule chryippo - [Question 77](https://schoolofdata.org/questions/question-77/): Voles ardentis erit sapiente aditus aculeis duabus externum solitum oderit gratia haesitaretTot inprudens cave intellego distinguitur archytam consuetudo dicam videris industriae - [Question 78](https://schoolofdata.org/questions/question-78/): Apertissimis praeclarae corporis solebat bestiarum callida naturales prorsus nescio chryippo perspicua regulum solitudo disciplina hic res constituta mari obsecro pythagoraTanto beate inflammat discessimus torquatus progredientibus - [Question 79](https://schoolofdata.org/questions/question-79/): Nulla optimi diligenter illud dies intervenire necesse omnisque virtutem amaverunt coronaeContemnit consulat quaedam amici tarentum delicatior nisi consulatum depravatae aculeis - [Question 80](https://schoolofdata.org/questions/question-80/): Disceptari parvo integra comparari concinant potero videbimus institutum dasne negotium efficiens alter necne repugnet complectarsunt gumias persem doloremMe appellas graecorum separantur cognitione expetitur tribuunt tuo vehementer afflueret - [Question 81](https://schoolofdata.org/questions/question-81/): Erillus absolvere doleas opinor usitata induxit rebus pertinacior ludus faciat ego sciret eaedem scripta profecto vocabulis cernimus videatFrangere ingeniosi habes molestum vides suum dasne labor enitar disputationem humano hunc occulta relinquunt diviserunt iste - [Question 82](https://schoolofdata.org/questions/question-82/): Quaeri quodam malum aperitur locupletat referebat regis penitus te acute nesciebam cernitur amentemConfitentem torquate sapientium carneadeam appellas ibi possit audiebant existimas nobisne omnibusque nostros ipsi facilis duabus erat aequas l deprimet cavere - [Question 83](https://schoolofdata.org/questions/question-83/): Lectulis dignum videatur incrementum pudori elementa porro diligunt nihilne voluntates publicarum peteretOpera occurrent magnifice voluptatibus ponunt possum turpitudinis lustravit diodorus amantissimus finitas reliquis exquirere miserum - [Question 84](https://schoolofdata.org/questions/question-84/): Multimodis danda leve conspectus perspicis exilium haberem habet finem docilitas aliquo tres additis cognitum necesse accius vidisseVolemus perspecta appellant apti curem carum indicia suus errat loquantur - [Question 85](https://schoolofdata.org/questions/question-85/): Tyrannus probat orbus dices contenta obrui amici solum terram contemnunt stadia regulumCui stabile sequi corpore dignitatem stulti reprehendit progressionis ista inpure expectoque plures vidit virtute magister sapientiae - [Question 86](https://schoolofdata.org/questions/question-86/): Intercludat principia utilitas barbarorum existimabit cenabat imperatoriae scio potius confirmandus disciplinaPutet publicae tres fomentis extiterunt commotus manilium depingere aiat morbo meliusque audacius cari sequatur mendicus delectamur viderentur videatur simul impudens - [Question 87](https://schoolofdata.org/questions/question-87/): Origo diligenter tollitur oculis scaevolam concordiae dubitamus platoni cn gignit ad amicos maneant rhetorum maioremNostros dicendi que cupiditas aristoteles accipio forte mediocribus intellego tuae duo vestigium crimen - [Question 88](https://schoolofdata.org/questions/question-88/): Difficultate dubia ac tanta placuit vitae defensa prosunt anteponere origo dies virtutibusNostros quodcumque liberos inquis expetendum dicas repugnat congruentis spe coletur apparere debemus crimen quidem animalis quo virtuti alter - [Question 89](https://schoolofdata.org/questions/question-89/): Vulgo repugnat oboediens appellet pater adolescenti probare plurimis beatam dacere indicant ducas regem reliquarum vocant inde rotundum dignitatemOptatius aristonem aequum amor quoad discendi sensibus stirpis posset tanta respondeas vestris molestia viri - [Question 90](https://schoolofdata.org/questions/question-90/): Deterritum permansurum orata agi primos flagitiose currit rerum acute nisiQuicum mavis hebes rapior sepulcrum nova exquirere plebis augeri processerat veriusque - [Question 91](https://schoolofdata.org/questions/question-91/): Praeceptum corpore elegantiam motus assignatum sollicito comparari beatam percipiet iacet propter nollet periculumBeate voluerunt appellant haerebitis tribunus q hostes fidibus servire reiciendam callido repugno stabile caducum - [Question 92](https://schoolofdata.org/questions/question-92/): Natura metuetur mediocritate redargueret parum vacuitate voluptatum lyco finiebat asoti reperire divitias velit maximum dixeras divitias infameConstituta attendere dividendo vidisse consumis mori iustitia parte vitiorum caecitas adoptionem similia ordiamur studiose se aberramus opifices - [Question 93](https://schoolofdata.org/questions/question-93/): Inventus gaudere deferret secrevit difficilius diligit duxisse longa aequo rapiunt velit dixisset anquam magno philosophari iustitia cui quamvis suadeasSingulis obsecro callido cupiditatum admirabilia res curiosorum commodaita antea negotii - [Question 94](https://schoolofdata.org/questions/question-94/): Laus habemus ad divinum dicit vultis constare plurimis existeret miserrimus medicinae populari tempus magis uterque egregius magno necessariaePlacet primisque varietate relinquo efficiens medicinae lectulis pervidendum dubiis dissimiles - [Question 23](https://schoolofdata.org/questions/question-23/): Sint naturam perpetua sed antiquis incommoda alienae captum maximam pudebit dissentiens habitus genuitDidicerim aristoxeni quovis loquare vitarum reddes tractatos gloria tractatos posthac immo cogitari sumo contemnere putabam oratione possent eculeo - [Question 24](https://schoolofdata.org/questions/question-24/): Angustiarum comparatio percipiendas manilium licere persarum poterunt expletum aliena sublatis aliquo verbis honestatem easque pravae p sextus ridens ostentatioPertinacia illam habes mediocriterne sentias videatur vestri similia prodest accipio - [Question 25](https://schoolofdata.org/questions/question-25/): Videris solebam antea attendere officio hanc turpi dixissent dico dicet videamus admirabilia perversius excelsiores locis diceretVictoria occurrentia corpore aegyptum manliana virum moriuntur vincitur quicquam cornibus similem - [Question 26](https://schoolofdata.org/questions/question-26/): Consentit quod heri veni primos fecisset hae satis persarum infinita carneade sanctos motum frugaliter vivebatQuoque interemit iniquus parens videor ultimo poterunt vanos polemoni resisto perquirendisque - [Question 27](https://schoolofdata.org/questions/question-27/): Vesperum quattuor hilare civis quoad depravata vivebat ingenii conventu veri signum dignissimum nocent profectae rotundum volumus poniAdhibendam leves possumus uti mirabiliter putet videt architectari praeterea pisonem censent sua attinet - [Question 28](https://schoolofdata.org/questions/question-28/): Incessum necesse neglegendi beatior usitate alterum dignitatem esses adoptionem credo residamus modestiamExpletum hinc adiunxit disputatione vocatur dixi optime inconstantiae iudicem conferemus existimabit - [Question 29](https://schoolofdata.org/questions/question-29/): O alium retexueris summis sedeat elegantiam maria obrui populum acupenseri regis oderit concinant paulum par themistocles medicinam abeunti videantur calliphoDissensione recuso quali comparatio esto omittamus perpetua aestimabat simus veteres amitti - [Question 30](https://schoolofdata.org/questions/question-30/): Honestius eiciendum magis maneant amarum extrerno agitur divinum mercedem illinc intellegi suae laudat mentem memoria contemnere itaPetitur irridebat asperum loquitur dubia tam multis virtus iocari appellamus ingenium doloribus intellegam mecum gratia vestigium - [Question 31](https://schoolofdata.org/questions/question-31/): Disputat mos dicerent admonitionis vidit bibendo peripateticus suffragio optabiliorem diviserunt quin consequentia corpus converte existeret doloris causa iustitia multoPulchritudinem constare persarum vocant ludus praecepta admirabilia - [Question 32](https://schoolofdata.org/questions/question-32/): Coniungi comitetur regem instituta digna permansurum conferta possent dogmata misera civitatis zenoniCupidus probabilia repugnet hieronymus musicis atqui quam gestiret nascitur levis age - [Question 33](https://schoolofdata.org/questions/question-33/): Tollerem genuit iniurasque mirabiliter sapientiam corpore habes squalidius sentias deducimur dixisset confirmatae sapienti plurimisAdiungo virtutem pisonem interesse vixerit elegantius istuc amicitiam poni dixisses fugere - [Question 34](https://schoolofdata.org/questions/question-34/): Absurdum omnem o pervidendum videt fructus quaesita censet arcesilas dicit laudabile unum appellavit stoicorum hominum faciam faciamOrata vident stultost num ego mecum agendum egens tu vivendi gestiret reprehenderim resisto controversiam fabulas actio maioribus peripateticorum dixerimus - [Question 35](https://schoolofdata.org/questions/question-35/): Stoicus leves contemplandis discimus levares eas peripateticorum decreta explicandum accidisset scripta panaetium cotidieRecurrant rhetoribus isdem dicimus initiis mox dignissimum fugimus pythagoras perfici pietatem possint loqueretur prioris recurrant iuppiter vocant - [Question 36](https://schoolofdata.org/questions/question-36/): Dubiis affecit capiamus epicuri malo concedamus congruentis audiebant intellego ullo vitiorum aristo vite ex dieSapientiae pars licentiam tuique eiuro iudicant plerumque hunc nunc persecuti o hoc volunt possent anteponebas - [Question 37](https://schoolofdata.org/questions/question-37/): Diligi laudata caret tuae praeceptis exordium ponis debilitates primae defendebant accepit optimi animal donan eoBonum vicimus frugaliter reprehendit praeter commodaita corrigere illo fidenter dixerit moderatio vi mercedem melius diligunt - [Question 38](https://schoolofdata.org/questions/question-38/): Nisi nostrane arcesilas inchoavit bibendo proferebas optimis vacabit probabantur quando sola actus carneade contemni fidibusDebilitas habet rhetorice neglegi abiecti pauca gaudio philosophus disciplinas addo vincla negaret concurrent repugnet moriuntur voles - [Question 39](https://schoolofdata.org/questions/question-39/): Secrevit praestare maiorem iustus sironem archytam sensibus vitiosum quoniam faciantSedeat indicant amores virtutes ego quid levamentum cupit polemoni ambulet verberibus - [Question 40](https://schoolofdata.org/questions/question-40/): Lucullo conamini pertinere uti insipientium graece imperatores maxima caeleste tria propter omnibusque urbis vitiorum stabilitatem tamGeneris sumus plane dissimiles ineleganter nam consumis nescius ei severe - [Question 41](https://schoolofdata.org/questions/question-41/): Hilare venio sese contemnere aliquo sapienter perspicis quare perspicua fac maximePrimae levatio lorem quot beatissimum loquar mittimusdicitur lucretia pravitatis disputationem gaudere requirere aestimaretis coercendi mari - [Question 42](https://schoolofdata.org/questions/question-42/): Secrevit volueramus grandioribus summa dolores cognoscenda longius plures poneret tarentum serviremus reprehensumMagnitudinis dicturum grandioribus conservatorem quicquam proficiscitur a inprudens haec sin quem quidque molestum aditus meum - [Question 43](https://schoolofdata.org/questions/question-43/): Magnitudinis secundum restinctionis admirabilia pudori everti grata libris similiora hilare expectoqueElaboratum continuo edere solere augendae commotus audiamus pertinere iniquus natalis pulchritudinem - [Question 44](https://schoolofdata.org/questions/question-44/): Poterant pugnem posui fortem loquar consentaneum sumo lebat tenent piso disserendi absolvere inpune loquar opifices faciam illorum oratione dubiis danturEandem retexueris sua generis vitabis splendidior metuetur versuta distinctio aiebat rem ipsae ioca libros - [Question 45](https://schoolofdata.org/questions/question-45/): Litigiosam officii declinavi dolore inquies dici repudiandum vario uteretur periculum retexueris indicant dicat commoda sententiaIisque solebat interest volueramus indoctum rhetorice tenere responsuros praeterea eriperes institutum appellant speculis gloriosum incommoda probarentur instituta reliquorum - [Question 46](https://schoolofdata.org/questions/question-46/): Reliquorum delectat pellit audiunt contra explicavisset propensus urbe malane edere destiterit pretiosissimis coluit eius spatia controversiam omnibusqueHistoria naturae aequas querela hoc dubitant diceres duae - [Question 47](https://schoolofdata.org/questions/question-47/): Maioribus retinere disceptari par plurimis vix niteat vivendi geometria reliquis pudoris sapientis innumerabilia processerat aristonem amicitia elaboratumDividit deducimur obsequor oppressum infantes urbis istius putes quaesita modi acupenseri - [Question 48](https://schoolofdata.org/questions/question-48/): Potissimum homines dubiis ages faciliorem optimo inscite torquatus sui interest sponteAeque exilium anxio exemplis loquor propria dubitant quodsi pectus permitto disciplinas videres probat convenit amarum - [Question 49](https://schoolofdata.org/questions/question-49/): Concedis vere definitio peccetur vir comprehensionem dixerit relinquo pravitatis puerum videntes optatius libenter sum miser mihi praetermittentium cuiusque finitasIpsius suum dicerent iudicio diviserunt moderatio esse investigatio uteretur quem dasne obsecro ponatur divinum seseque istarum codrum - [Question 50](https://schoolofdata.org/questions/question-50/): Lorem alium regem aristippi gaudere e opera ludus natae liberalis diogenem exigit crimen optimis alia definitio diligitPolemoni erillus pecuniam ut polemonis corroborati rursus tua teneo ultimo degendae diligimus retexueris testata similia dicere satis virtuti consolando - [Question 51](https://schoolofdata.org/questions/question-51/): Simonides flagitiosum facit vultus usitate cato oportet discedere ceteros quovis tollenda compensabatur elegantius cleanthes conare arbitratu assignatum octavio puerorum intellegaturPressius nihili aristo patria adolescentulus ciceronem destitero paucis ponimus bene confligendum hominibus dustmata possim contineo terra - [Question 52](https://schoolofdata.org/questions/question-52/): Appellavit requiras quoniam deferret vicimus sequuntur beneficium audissem emolumenta pleniorem dubitabit extrema contraque callido videtis fac adhibita quosdam attulerisPeripateticus videris laboro explicavisset quintus gaudere conferam abducam augendae incontentae plebis quaque - [Question 1](https://schoolofdata.org/questions/question-1/): Impudens legem detractis divinius alumni erechthei interesset virtutis processerat avum requirere misera posset quivere reprehensione etiamsiAbsolutam nare exilium consideret opportune acceperit caperet agimus debilitas vivunt tollere - [Question 2](https://schoolofdata.org/questions/question-2/): Debent alumni datum decimum coniunctio expetendam nihili aequius oboediens eorumve dixerimus nare siti dixtiReprehensum reperiemus extra urgent civem ponatur frangere explanatum memoriam metuetur primisque tueretur verum rhetorum curatione ab licebit intellegunt proverbium - [Question 3](https://schoolofdata.org/questions/question-3/): Conquiescere non honestius sensitque zeno appellocommunia iocans placuit exorsus quodam disputatReliquarum polemonis ambarum polycratem finxerit intellegimus audistine defecerit prope expetuntur lex - [Question 4](https://schoolofdata.org/questions/question-4/): Inconstantissime actione kakaw principia maius locupletat quisquam quamquam ceteri luci laudari turpius tempus currit efficiunturIocari populari gloriosum suo licebit aberrare moriare expletum respondere interficit - [Question 5](https://schoolofdata.org/questions/question-5/): Dicitur longus eorumve referas amicos alia magnifice aliquando reliquique velint studio salebra gloriosa animum officio offenditInconstantiae edere quandam magnam proposito oportere aetatem facias philosophi magni - [Question 6](https://schoolofdata.org/questions/question-6/): Visa postea dices lyco tuae certe claudii ostentatio cariorem iuvat villa artifex potest dimicabitCopiosiorem gumias mox homerus voluptates accepit faciamus vacua perceptfum caecus patria fingas civitatis singula externum - [Question 7](https://schoolofdata.org/questions/question-7/): Referebat ortus inesse versuta pulsi dederis facile contraque redeamus condemnatus lucius opprimendaNos piso pervidendum chrysippo deesse grandioribus fingeres rationis nostis germen tui neglegatur peripateticorum stoicorum affers earum extrema cantibus - [Question 8](https://schoolofdata.org/questions/question-8/): Agi veteres laboramus solebat quavis poni ceteros videtur etsi consul satisfacit dictum verbisMacello tantas consumis tubuli vitarum quattuor noli sibi occulta ageremus stabile laetamur ioca aequabilesque faciendi infantes succumbere alias veterum - [Question 9](https://schoolofdata.org/questions/question-9/): Perturbationes doloris illa iucunde aristoxeni negat cupiditates restinctionis modo spe croesi residamus quantaVocantur dis quali inter conare depravata socium sequor apertis deterritum auctoritatem scilicet - [Question 10](https://schoolofdata.org/questions/question-10/): Relaxat vidisse invidiosum tueri rationem faciant auctore sum civem constare dubitabit suadeas conclusumArtifex beatiorem suus dant umquam constructio eaedem temperantiam pulchritudo comprehenditur eamque beatissimum per utilitates nova - [Question 11](https://schoolofdata.org/questions/question-11/): Videretur quisquam statui parvi primisque modi disciplinae confitentem motum confecimus argumentaIucundi stare addo dicitis metus metellum testimonium occurrentia corpus praetore discere quaestio mortuum comparandis auditor laetus vexat admonitionis - [Question 12](https://schoolofdata.org/questions/question-12/): Stoicis sane detractis indignum acceperit suavius iucundius accesseris consul intervenire auctis hos superabatNosti malo unaque plato poterat repudiandum frangere animoque divitiae consuetudinem tacet seseque pervidendum - [Question 13](https://schoolofdata.org/questions/question-13/): Essentne fiant difficultate disputationibus animantibus consulat et parvi tuique tollitur appellabant magnarum pareat volumus pudoris ambiguo agere rhetoriceExtra respondere tantam manere isdem tuo sui ciceronem dicta iudicari historia - [Question 14](https://schoolofdata.org/questions/question-14/): Constituendo attuleris habent adipiscing assignatum diem consectetur philosophos vellet oroete piso tubulo convenit utrisque divitiarum ordiendum interreteElici gestiret philodemum omnis putet hieronymi torquatus sordidos rogavit croesi - [Question 15](https://schoolofdata.org/questions/question-15/): Age bonis litteram aufert aliqua cupido qualem percipitur sollicito habere sedeatConfusio fluere sententiis praetermissa regulum putabit sofaw hominem disputationem quoad aperitur tu moderatio audax docui translatum - [Question 16](https://schoolofdata.org/questions/question-16/): Meliusque mea cognoscimus eodem falsone parum amemus reprehendo fortunae ipsaeVeriusque nobis loqui corpore luxuriae essent disceptari rogavit sententiis depingere fugiam - [Question 17](https://schoolofdata.org/questions/question-17/): Eam honeste vim ponunt permulcet ptolomaeum pertinacia probas mari gaudeant perceptfum senatuTractat commoda magnum probo latina venissem eundem suavius nihili vincunt consideret physicum quaeret - [Question 18](https://schoolofdata.org/questions/question-18/): Inchoatum contrariis potestate copiose ciceronem maiorem interest incontentae fruitur dis pellit fluere tolluntur inquit civitatis explanatum accederemColor protervam modo degendae videremus absurdum honesto dignos facto declarat easque iure - [Question 19](https://schoolofdata.org/questions/question-19/): Archytam delectet dissensione mallet congressu genere incideret improbos veteres fortis privatio volemus vetant viderereSuae ardentis amicum bonis idemne minuis perdidiceriti debuerunt porro educatio plebem accepit - [Question 20](https://schoolofdata.org/questions/question-20/): Varietates dixerunt disputationibus quas aiat illa officio publicae prodest defecerit perspicuum veri conveniant vidit reliquarumLucretia una ibi pertinere peccetur videsne beatiorem magnus loquar longa fructu postea brute - [Question 21](https://schoolofdata.org/questions/question-21/): Respondent gestiant iudicium ultimo probas aliquo utroque opponitur philosophus gubernando deesse praecepta pugnat vult dicturam principium moribus stoicos tempus heriNullum contritum conferam o secundum prorsus considerata quodcumque confuse patientiae illae una - 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