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Automation and Make
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Make is a tool which can run commands to read files, process ...

Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Gerard Capes
Data Carpentry: R for data analysis and visualization of Ecological Data
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This is an introduction to R designed for participants with no programming ...

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day. 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, and finish with how to calculate summary statistics for each level and a very brief introduction to plotting.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Ecology Materials
Author:
Auriel Fournier
François Michonneau
Data Carpentry R for Genomics
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Data Carpentry's aim is to teach researchers basic concepts, skills, and tools ...

Data Carpentry's aim is to teach researchers basic concepts, skills, and tools for working more effectively with data. The lessons below were designed for those interested in working with Genomics data in R.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Genomics Materials
Author:
Kate Hertweck
Ryan Williams
Susan McClatchey
Tracy Teal
Data Organization in Spreadsheets
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We organize data in spreadsheets how we as humans want to work ...

We organize data in spreadsheets how we as humans want to work with the data, but computers require that data be organized in a particular way. 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!

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Ecology Materials
Author:
Christie Bahlai
Tracy Teal
Instructor Training
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Over the last hundred years, researchers have discovered an enormous amount about ...

Over the last hundred years, researchers have discovered an enormous amount about how people learn and how best to teach them. Unfortunately, much of that knowledge has not yet been translated into common classroom practice, while many myths about education have proven remarkably persistent.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Christina Koch
Erin Becker
Introduction to cloud computing for genomics
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There are a number of reasons why accessing a remote machine is ...

There are a number of reasons why accessing a remote machine is invaluable to any scientists working with large datasets. In the early history of computing, working on a remote machine was standard practice - computers were bulky and expensive. Today we work on laptops that are more powerful than the sum of the world’s computing capacity 20 years ago, but many analyses (especially in genomics) won’t work on these laptops and must be run on remote machines.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Genomics Materials
Author:
Bob Freeman
Introduction to the workshop and dataset
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Data Carpentry's aim is to teach researchers basic concepts, skills, and tools ...

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. This is an introduction to the Genomics lessons.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Genomics Materials
Author:
Tracy Teal
Introduction to Working With Raster Data in R
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The tutorials in this series cover how to open, work with and ...

The tutorials in this series cover how to open, work with and plot raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference system), reprojecting spatial data and working with raster time series data.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Geospatial Materials
Author:
Joseph Stachelek
Leah Wasser
Introduction to Working With Vector Data in R
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The data tutorials in this series cover how to open, work with ...

The data tutorials in this series cover how to open, work with and plot vector-format spatial data (points, lines and polygons) in R. Additional topics include working with spatial metadata (extent and coordinate reference system), working with spatial attributes and plotting data by attribute.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Geospatial Materials
Author:
Joseph Stachelek
Leah Wasser
Open Refine for Ecology
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A part of the data workflow is preparing the data for analysis. ...

A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identifed and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Ecology Materials
Author:
Cam Macdonell
Deborah Paul
Programming with MATLAB
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The best way to learn how to program is to do something ...

The best way to learn how to program is to do something useful, so this introduction to MATLAB is built around a common scientific task: data analysis. Our real goal isn’t to teach you MATLAB, but to teach you the basic concepts that all programming depends on. 

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Ashwin Srinath
Isabell Kiral-Kornek
Python for Ecologists
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Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools ...

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.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Ecology Materials
Author:
April Wright
John Gosset
Mateusz Kuzak
R for Reproducible Scientific Analysis
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The goal of this lesson is to teach novice programmers to write ...

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Naupaka Zimmerman
Thomas Wright
SQL for Ecology
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This lesson will teach you what relational databases are, how you can ...

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.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Data Carpentry Ecology Materials
Author:
Paula Andrea Martinez
Timothée Poisot
The Unix Shell
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The Unix shell has been around longer than most of its users ...

The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a power tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including “high-performance computing” supercomputers). These lessons will start you on a path towards using these resources effectively.
This lesson guides you through the basics of file systems and the shell. If you have stored files on a computer at all and recognize the word “file” and either “directory” or “folder” (two common words for the same thing), you’re ready for this lesson.

If you’re already comfortable manipulating files and directories, searching for files with grep and find, and writing simple loops and scripts, you probably won’t learn much from this lesson.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Ashwin Srinath
Gabriel Devenyi
Using Databases and SQL
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In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and ...

In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Subject:
Computer Science
Material Type:
Lesson
Provider:
NumFocus
Provider Set:
Software Carpentry
Author:
Abigail Cabunoc Mayes
Sheldon McKay