Data Carpentry

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain.

Data Carpentry https://tess.elixir-europe.org/content_providers/data-carpentry Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. /system/content_providers/images/000/000/010/original/DC_logo_vision.png?1469458743
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By default, Data Carpentry does not have people pull the whole repository with all the scripts and addenda. Therefore, you, as the instructor, get to decide how you’d like to provide this script to learners, if at all. To use this, students can navigate into includes/scripts terminal, and execute...

Python for ecologists: Instructor NotesChallenge solutions https://tess.elixir-europe.org/materials/python-for-ecologists-instructor-noteschallenge-solutions By default, Data Carpentry does not have people pull the whole repository with all the scripts and addenda. Therefore, you, as the instructor, get to decide how you’d like to provide this script to learners, if at all. To use this, students can navigate into includes/scripts terminal, and execute the following: If learners receive an AssertionError, it will inform you how to help them correct this installation. Otherwise, it will tell you that the system is good to go and ready for Data Carpentry! What happens when you type a_tuple[2] = 5 vs a_list[1] = 5? As a tuple is immutable, it does not support item assignment. Elements in a list can be altered individually. Type type(a_tuple) into the Python interpreter - what is the object type? 2017-10-09

Note the file types OpenRefine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with OpenRefine extensions. In this first step, we’ll browse our computer to the sample data file for this lesson (If you haven’t...

Open Refine for Ecology: Instructor Notes https://tess.elixir-europe.org/materials/open-refine-for-ecology-instructor-noteslesson Note the file types OpenRefine handles: TSV, CSF, *SV, Excel (.xls .xlsx), JSON, XML, RDF as XML, Google Data documents. Support for other formats can be added with OpenRefine extensions. In this first step, we’ll browse our computer to the sample data file for this lesson (If you haven’t already, download the data from: https://ndownloader.figshare.com/files/7823341). In this case, I’ve modified the Portal_rodents.csv file. I added several columns: scientificName, locality, county, state, country and I generated several more columns in the lesson itself (JSON, decimalLatitude, decimalLongitude). Data in locality, county, country, JSON, decimalLatitude and decimalLongitude are contrived and are in no way related to the original dataset. Once OpenRefine is open, you’ll be asked if you want to Create, Open, or Import a Project. Exploring data by applying multiple filters OpenRefine supports faceted browsing as a mechanism for 2017-10-09

Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for...

Data Carpentry: R for data analysis and visualization of Ecological Data https://tess.elixir-europe.org/materials/data-carpentry-r-for-data-analysis-for-ecology Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools, but working through this lesson requires working copies of the software described below. To most effectively use these materials, please make sure to download the data and install everything before working through this lesson. Data files for the lesson are available and can be downloaded manually here: http://dx.doi.org/10.6084/m9.figshare.1314459 2017-10-09

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecological data in Python. Data for this lesson is from the...

Data Carpentry Python for Ecologists https://tess.elixir-europe.org/materials/data-carpentry-python-for-ecologists Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecological data in Python. Data for this lesson is from the Portal Project Teaching Database - available on FigShare. The data files used in this lesson are surveys.csv download link - https://ndownloader.figshare.com/files/2292172 and species.csv download link - https://ndownloader.figshare.com/files/3299483. Requirements: Data Carpentry's teaching is hands-on, so participants are encouraged to bring in and use their own laptops to insure the proper setup of tools for an efficient workflow once you leave the workshop. (We will provide instructions on setting up the required software several days in advance, and the classroom will have computers with the software installed). There are no pre-requisites, and we will assume no prior knowledge about the tools. Participants are required to abide by Software Carpentry's Code of Conduct. Twitter: #datacarpentry 2016-03-07

This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there...

Instructor notes https://tess.elixir-europe.org/materials/instructor-notes This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there is a decent internet connection in the room where the workshop is being taught. To facilitate the download process, the chunk of code that includes the URL where the csv file lives, and where the file should go and be named is included in the code handout (see next paragraph). Using this approach ensures that the file will be where the lesson expects it to be, and teaches good/reproducible practice of automating the download. If the learners haven’t created the data/ directory and/or are not in the correct working directory, the download.file command will produce an error. Therefore, it is important to use the stickies at this point. The code handout (a link to download it is also available on the top bar of the lesson website) is useful for Data Carpentry workshops. It includes an outline of the lesson content, the text for the challenges, the links for the files that need to be downloaded for the lesson, and pieces of code that may be difficult to type for learners with no programming experience/who are unfamiliar with R’s syntax. We encourage you to distribute it to the learners at the beginning of the lesson. As an instructor, we encourage you to do the live coding directly in this file, so the participants can follow along. Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library; We are currently using R 3.4.x. If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the 3.4.x directory:

This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of...

SQL for Ecology https://tess.elixir-europe.org/materials/data-carpentry-sql-for-ecology This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. This lesson requires a working copy of SQLite Manager for SQL. To most effectively use these materials, please make sure to install everything before working through this lesson. If you are teaching this lesson in a workshop, please see the Instructor notes. 2017-10-09

This lesson is optional The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality...

Data Organization in Spreadsheets: Instructor Notes https://tess.elixir-europe.org/materials/data-organization-in-spreadsheets-instructor-notes This lesson is optional The challenge with this lesson is that the instructor’s version of the spreadsheet software is going to look different than about half the room’s. It makes it challenging to show where you can find menu options and navigate through. Instead discuss the concepts of quality control, and how things like sorting can help you find outliers in your data. Provide information on setting up your environment for learners to view your live coding (increasing text size, changing text color, etc), as well as general recommendations for working with coding tools to best suit the learning environment. The main challenge with this lesson is that Excel looks very different and how you do things is even different between Mac and PC, and between different versions of Excel. So, the presenter’s environment will only be the same as some of the learners. 2017-10-09

This lesson assumes no prior experience with the tools covered in the workshop. However, learners are expected to have some familiarity with biological concepts, including nucleotide abbreviations and the concept of genomic variation within a population. Participants should bring their laptops...

Genomics WorkshopWorkshop OverviewTeaching Platform https://tess.elixir-europe.org/materials/genomics-workshopworkshop-overviewteaching-platform This lesson assumes no prior experience with the tools covered in the workshop. However, learners are expected to have some familiarity with biological concepts, including nucleotide abbreviations and the concept of genomic variation within a population. Participants should bring their laptops and plan to participate actively. To get started, follow the directions in the Setup tab to get access to the required software and data for this workshop. This workshop uses data from a long term evolution experiment published in 2012: Genomic analysis of a key innovation in an experimental Escherichia coli population by Blount ZD, Barrick JE, DAvidson CJ, and Lenski RE. (doi: 10.1038/nature11514) More information about these data will be presented in the first lesson of the workshop. This workshop is designed to be run on pre-imaged Amazon Web Services (AWS) instances. All the software and data used in the workshop are hosted on an Amazon Machine Image (AMI). If you want to run your own instance of the server used for this workshop, follow the directions in the Setup tab.

Note that the figshare download is an archive (.zip) file that rudely explodes all of the files into your current directory. By default SQLite Manager opens in a separate window and it is not possible to zoom in to enlarge the font so that it is more readable, especially for students in the back...

SQL for Ecology: Instructor Notes https://tess.elixir-europe.org/materials/sql-for-ecology-instructor-notes Note that the figshare download is an archive (.zip) file that rudely explodes all of the files into your current directory. By default SQLite Manager opens in a separate window and it is not possible to zoom in to enlarge the font so that it is more readable, especially for students in the back rows. The way to fix this is to: You can then use Ctrl - + to zoom just like any other web page. See this slide deck as a sample intro for the lesson: SQL Intro Deck 2017-10-09

We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data...

Data Organization in Spreadsheets https://tess.elixir-europe.org/materials/data-carpentry-spreadsheets-for-ecology We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. 2017-10-09

There are no pre-requisites, and the materials assume no prior knowledge about the tools. The data for this workshop are is the Portal Project Teaching Database available on FigShare, with a CC-BY license available for reuse. The Portal Project Teaching Database is a simplified version of the...

Ecology Workshop Overview https://tess.elixir-europe.org/materials/ecology-workshop-overview There are no pre-requisites, and the materials assume no prior knowledge about the tools. The data for this workshop are is the Portal Project Teaching Database available on FigShare, with a CC-BY license available for reuse. The Portal Project Teaching Database is a simplified version of the Portal Project Database designed for teaching. It is a tabular dataset of observations of small mammals in a desert ecosystem in Arizona, USA, collected over more than 40 years. It provides a real world example of life-history, population, and ecological data, with sufficient complexity to teach many aspects of data analysis and management, but with many complexities removed to allow students to focus on the core ideas and skills being taught. More information on this dataset The workshop can be taught using R or Python as the base language.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some...

Python for ecologists https://tess.elixir-europe.org/materials/python-for-ecologists Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. This lesson requires a working copy of Python. To most effectively use these materials, please make sure to install everything before working through this lesson. 2017-10-09

OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you...

Open Refine for Ecology https://tess.elixir-europe.org/materials/data-carpentry-openrefine-for-ecology OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand. Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to insure the proper setup of tools for an efficient workflow. These lessons assume no prior knowledge of the skills or tools. To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions. This lesson requires a working copy of OpenRefine (also called GoogleRefine). To most effectively use these materials, please make sure to install everything before working through this lesson. 2017-10-09
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