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  • The Carpentries
Image Processing with Python
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CC BY
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This lesson shows how to use Python and skimage to do basic image processing. With support from an NSF iUSE grant, Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA have developed a curriculum for teaching image processing in Python. This lesson is currently being piloted at different institutions. This pilot phase will be followed by a clean-up phase to incorporate suggestions and feedback from the pilots into the lessons and to make the lessons teachable by the broader community. Development for these lessons has been supported by a grant from the Sloan Foundation.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Mark Meysenberg
Date Added:
08/07/2020
Introduction to Cloud Computing for Genomics
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CC BY
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Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Abigail Cabunoc Mayes
Adina Howe
Amanda Charbonneau
Bob Freeman
Brittany N. Lasseigne, PhD
Bérénice Batut
Caryn Johansen
Chris Fields
Darya Vanichkina
David Mawdsley
Erin Becker
François Michonneau
Greg Wilson
Jason Williams
Joseph Stachelek
Kari L. Jordan, PhD
Katrin Leinweber
Maxim Belkin
Michael R. Crusoe
Piotr Banaszkiewicz
Raniere Silva
Renato Alves
Rémi Emonet
Stephen Turner
Taylor Reiter
Thomas Morrell
Tracy Teal
William L. Close
ammatsun
vuw-ecs-kevin
Date Added:
03/28/2017
Introduction to Geospatial Concepts
Unrestricted Use
CC BY
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Data Carpentry lesson to understand data structures and common storage and transfer formats for spatial data. The goal of this lesson is to provide an introduction to core geospatial data concepts. It is intended for learners who have no prior experience working with geospatial data, and as a pre-requisite for the R for Raster and Vector Data lesson . This lesson can be taught in approximately 75 minutes and covers the following topics: Introduction to raster and vector data format and attributes Examples of data types commonly stored in raster vs vector format Introduction to categorical vs continuous raster data and multi-layer rasters Introduction to the file types and R packages used in the remainder of this workshop Introduction to coordinate reference systems and the PROJ4 format Overview of commonly used programs and applications for working with geospatial data The Introduction to R for Geospatial Data lesson provides an introduction to the R programming language while the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data. The R for Raster and Vector Data lesson assumes that learners are already familiar with both geospatial data concepts and the core concepts of the R language.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Anne Fouilloux
Chris Prener
Dev Paudel
Ethan P White
Joseph Stachelek
Katrin Leinweber
Lauren O'Brien
Michael Koontz
Paul Miller
Tracy Teal
Whalen
Date Added:
08/07/2020
Introduction to Geospatial Raster and Vector Data with R
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CC BY
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Data Carpentry lesson to open, work with, and plot vector and raster-format spatial data in R. The episodes in this lesson cover how to open, work with, and plot vector and raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference systems), reprojecting spatial data, and working with raster time series data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ana Costa Conrado
Angela Li
Anne Fouilloux
Brett Lord-Castillo
Ethan P White
Joseph Stachelek
Juan F Fung
Katrin Leinweber
Klaus Schliep
Kristina Riemer
Lachlan Deer
Lauren O'Brien
Marchand
Punam Amratia
Sergio Marconi
Stéphane Guillou
Tracy Teal
zenobieg
Date Added:
08/07/2020
Introduction to R for Geospatial Data
Unrestricted Use
CC BY
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The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Anne Fouilloux
Chris Prener
Claudia Engel
David Mawdsley
Erin Becker
François Michonneau
Ido Bar
Jeffrey Oliver
Juan Fung
Katrin Leinweber
Kevin Weitemier
Kok Ben Toh
Lachlan Deer
Marieke Frassl
Matt Clark
Miles McBain
Naupaka Zimmerman
Paula Andrea Martinez
Preethy Nair
Raniere Silva
Rayna Harris
Richard McCosh
Vicken Hillis
butterflyskip
Date Added:
08/07/2020
Introduction to the Command Line for Economics
Unrestricted Use
CC BY
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Command line interface (OS shell) and graphic user interface (GUI) are different ways of interacting with a computer’s operating system. The shell is a program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard combination. There are quite a few reasons to start learning about the shell: The shell gives you power. The command line gives you the power to do your work more efficiently and more quickly. When you need to do things tens to hundreds of times, knowing how to use the shell is transformative. To use remote computers or cloud computing, you need to use the shell.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Andras Vereckei
Arieda Muço
Miklós Koren
Date Added:
08/07/2020
Introduction to the Command Line for Genomics
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CC BY
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Data Carpentry lesson to learn to navigate your file system, create, copy, move, and remove files and directories, and automate repetitive tasks using scripts and wildcards with genomics data. Command line interface (OS shell) and graphic user interface (GUI) are different ways of interacting with a computer’s operating system. The shell is a program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard combination. There are quite a few reasons to start learning about the shell: For most bioinformatics tools, you have to use the shell. There is no graphical interface. If you want to work in metagenomics or genomics you’re going to need to use the shell. The shell gives you power. The command line gives you the power to do your work more efficiently and more quickly. When you need to do things tens to hundreds of times, knowing how to use the shell is transformative. To use remote computers or cloud computing, you need to use the shell.

Subject:
Applied Science
Computer Science
Genetics
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amanda Charbonneau
Amy E. Hodge
Anita Schürch
Bastian Greshake Tzovaras
Bérénice Batut
Colin Davenport
Diya Das
Erin Alison Becker
François Michonneau
Giulio Valentino Dalla Riva
Jessica Elizabeth Mizzi
Karen Cranston
Kari L Jordan
Mattias de Hollander
Mike Lee
Niclas Jareborg
Omar Julio Sosa
Rayna Michelle Harris
Ross Cunning
Russell Neches
Sarah Stevens
Shannon EK Joslin
Sheldon John McKay
Siva Chudalayandi
Taylor Reiter
Tobi
Tracy Teal
Tristan De Buysscher
Date Added:
08/07/2020
Introduction to web scraping
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CC BY
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Web scraping is the process of extracting data from websites. Some data that is available on the web is presented in a format that makes it easier to collect and use it, for example in the form of downloadable comma-separated values (CSV) datasets that can then be imported in a spreadsheet or loaded into a data analysis script. Often however, even though it is publicly available, data is not readily available for reuse. For example it can be contained in a PDF, or a table on a website, or spread across multiple web pages. There are a variety of ways to scrape a website to extract information for reuse. In its simplest form, this can be achieved by copying and pasting snippets from a web page, but this can be unpractical if there is a large amount of data to be extracted, or if it spread over a large number of pages. Instead, specialized tools and techniques can be used to automate this process, by defining what sites to visit, what information to look for, and whether data extraction should stop once the end of a page has been reached, or whether to follow hyperlinks and repeat the process recursively. Automating web scraping also allows to define whether the process should be run at regular intervals and capture changes in the data.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Belinda Weaver
Joshua Dull
Thomas Guignard
Date Added:
08/07/2020
Intro to R and RStudio for Genomics
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CC BY
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Welcome to R! Working with a programming language (especially if it’s your first time) often feels intimidating, but the rewards outweigh any frustrations. An important secret of coding is that even experienced programmers find it difficult and frustrating at times – so if even the best feel that way, why let intimidation stop you? Given time and practice* you will soon find it easier and easier to accomplish what you want. Why learn to code? Bioinformatics – like biology – is messy. Different organisms, different systems, different conditions, all behave differently. Experiments at the bench require a variety of approaches – from tested protocols to trial-and-error. Bioinformatics is also an experimental science, otherwise we could use the same software and same parameters for every genome assembly. Learning to code opens up the full possibilities of computing, especially given that most bioinformatics tools exist only at the command line. Think of it this way: if you could only do molecular biology using a kit, you could probably accomplish a fair amount. However, if you don’t understand the biochemistry of the kit, how would you troubleshoot? How would you do experiments for which there are no kits? R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures. Rather than get into an R vs. Python debate (both are useful), keep in mind that many of the concepts you will learn apply to Python and other programming languages. Finally, we won’t lie; R is not the easiest-to-learn programming language ever created. So, don’t get discouraged! The truth is that even with the modest amount of R we will cover today, you can start using some sophisticated R software packages, and have a general sense of how to interpret an R script. Get through these lessons, and you are on your way to being an accomplished R user! * We very intentionally used the word practice. One of the other “secrets” of programming is that you can only learn so much by reading about it. Do the exercises in class, re-do them on your own, and then work on your own problems.

Subject:
Applied Science
Biology
Computer Science
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ahmed Moustafa
Alexia Cardona
Andrea Ortiz
Jason Williams
Krzysztof Poterlowicz
Naupaka Zimmerman
Yuka Takemon
Date Added:
08/07/2020
La Terminal de Unix
Unrestricted Use
CC BY
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Software Carpentry lección para la terminal de Unix La terminal de Unix ha existido por más tiempo que la mayoría de sus usuarios. Ha sobrevivido tanto tiempo porque es una herramienta poderosa que permite a las personas hacer cosas complejas con sólo unas pocas teclas. Lo más importante es que ayuda a combinar programas existentes de nuevas maneras y automatizar tareas repetitivas, en vez de estar escribiendo las mismas cosas una y otra vez. El uso del terminal o shell es fundamental para usar muchas otras herramientas poderosas y recursos informáticos (incluidos los supercomputadores o “computación de alto rendimiento”). Esta lección te guiará en el camino hacia el uso eficaz de estos recursos.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Huffman
Alejandra Gonzalez-Beltran
AnaBVA
Andrew Sanchez
Anja Le Blanc
Ashwin Srinath
Brian Ballsun-Stanton
Colin Morris
Dani Ledezma
Dave Bridges
Erin Becker
Francisco Palm
François Michonneau
Gabriel A. Devenyi
Gerard Capes
Giuseppe Profiti
Gordon Rhea
Jake Cowper Szamosi
Jared Flater
Jeff Oliver
Jonah Duckles
Juan M. Barrios
Katrin Leinweber
Kelly L. Rowland
Kevin Alquicira
Kunal Marwaha
LauCIFASIS
Marisa Lim
Martha Robinson
Matias Andina
Michael Zingale
Nicolas Barral
Nohemi Huanca Nunez
Olemis Lang
Otoniel Maya
Paula Andrea Martinez
Raniere Silva
Rayna M Harris
Shirley Alquicira
Silvana Pereyra
Steve Leak
Stéphane Guillou
Thomas Mellan
Veronica Jimenez-Jacinto
William L. Close
Yee Mey
csqrs
sjnair
Date Added:
08/07/2020
Library Carpentry: Introduction to Git
Unrestricted Use
CC BY
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Library Carpentry lesson: An introduction to Git. What We Will Try to Do Begin to understand and use Git/GitHub. You will not be an expert by the end of the class. You will probably not even feel very comfortable using Git. This is okay. We want to make a start but, as with any skill, using Git takes practice. Be Excellent to Each Other If you spot someone in the class who is struggling with something and you think you know how to help, please give them a hand. Try not to do the task for them: instead explain the steps they need to take and what these steps will achieve. Be Patient With The Instructor and Yourself This is a big group, with different levels of knowledge, different computer systems. This isn’t your instructor’s full-time job (though if someone wants to pay them to play with computers all day they’d probably accept). They will do their best to make this session useful. This is your session. If you feel we are going too fast, then please put up a pink sticky. We can decide as a group what to cover.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
222064h
Alex Mendes
Alexander Gary Zimmerman
Alexander Mendes
Amiya Maji
Amy Olex
Andrew Lonsdale
Annika Rockenberger
Begüm D. Topçuoğlu
Belinda Weaver
Benjamin Bolker
Bill McMillin
Brian Moore
Casey Youngflesh
Christoph Junghans
Christopher Erdmann
DSTraining
Dan Michael O. Heggø
David Jennings
Erin Alison Becker
Evan Williamson
Garrett Bachant
Grant Sayer
Ian Lee
Jake Lever
Jamene Brooks-Kieffer
James Baker
James E McClure
James O'Donnell
James Tocknell
Janoš Vidali
Jeffrey Oliver
Jeremy Teitelbaum
Jeyashree Krishnan
Joe Atzberger
Jonah Duckles
Jonathan Cooper
João Rodrigues
Katherine Koziar
Katrin Leinweber
Kunal Marwaha
Kurt Glaesemann
L.C. Karssen
Lauren Ko
Lex Nederbragt
Madicken Munk
Maneesha Sane
Marie-Helene Burle
Mark Woodbridge
Martino Sorbaro
Matt Critchlow
Matteo Ceschia
Matthew Bourque
Matthew Hartley
Maxim Belkin
Megan Potterbusch
Michael Torpey
Michael Zingale
Mingsheng Zhang
Nicola Soranzo
Nima Hejazi
Nora McGregor
Oscar Arbeláez
Peace Ossom Williamson
Raniere Silva
Rayna Harris
Rene Gassmoeller
Rich McCue
Richard Barnes
Ruud Steltenpool
Ryan Wick
Rémi Emonet
Samniqueka Halsey
Samuel Lelièvre
Sarah Stevens
Saskia Hiltemann
Schlauch, Tobias
Scott Bailey
Shari Laster
Simon Waldman
Stefan Siegert
Thea Atwood
Thomas Morrell
Tim Dennis
Tommy Keswick
Tracy Teal
Trevor Keller
TrevorLeeCline
Tyler Crawford Kelly
Tyler Reddy
Umihiko Hoshijima
Veronica Ikeshoji-Orlati
Wes Harrell
Will Usher
William Sacks
Wolmar Nyberg Åkerström
Yuri
abracarambar
ajtag
butterflyskip
cmjt
hdinkel
jonestoddcm
pllim
Date Added:
08/07/2020
Library Carpentry: Introduction to Working with Data (Regular Expressions)
Unrestricted Use
CC BY
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This Library Carpentry lesson introduces librarians and others to working with data. This Library Carpentry lesson introduces people with library- and information-related roles to working with data using regular expressions. The lesson provides background on the regular expression language and how it can be used to match and extract text and to clean data.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Alex Volkov
Alexander Mendes
Angus Taggart
Belinda Weaver
BertrandCaron
Bianca Peterson
Christopher Edsall
Christopher Erdmann
Chuck McAndrew
Dan Michael Heggø
Dan Michael O. Heggø
Elizabeth Lisa McAulay
Felix Hemme
François Michonneau
James Baker
Janice Chan
Jeffrey Oliver
Jeremy Guillette
Jodi Schneider
Jonah Duckles
Katherine Koziar
Katrin Leinweber
Kunal Marwaha
PH03N1X007
Paul R. Pival
Saskia Scheltjens
Shari Laster
Tim Dennis
fdsayre
lsult
remerjohnson
yvonnemery
Date Added:
08/07/2020
Library Carpentry: OpenRefine
Unrestricted Use
CC BY
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Library Carpentry lesson: an introduction to OpenRefine for Librarians This Library Carpentry lesson introduces people working in library- and information-related roles to working with data in OpenRefine. At the conclusion of the lesson you will understand what the OpenRefine software does and how to use the OpenRefine software to work with data files.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Alexander Mendes
Anna Neatrour
Antonin Delpeuch
Betty Rozum
Christina Koch
Christopher Erdmann
Daniel Bangert
Elizabeth Lisa McAulay
Evan Williamson
Jamene Brooks-Kieffer
James Baker
Jamie Jamison
Jeffrey Oliver
Katherine Koziar
Naupaka Zimmerman
Paul R. Pival
Rémi Emonet
Tim Dennis
Tom Honeyman
Tracy Teal
andreamcastillo
dnesdill
hauschke
mhidas
Date Added:
08/07/2020
Library Carpentry: SQL
Unrestricted Use
CC BY
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Library Carpentry, an introduction to SQL for Librarians This Library Carpentry lesson introduces librarians to relational database management system using SQLite. At the conclusion of the lesson you will: understand what SQLite does; use SQLite to summarise and link data.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
222064h
Anna-Maria Sichani
Belinda Weaver
Christopher Erdmann
Dan Michael Heggø
David Kane
Elaine Wong
Emanuele Lanzani
Fernando Rios
Jamene Brooks-Kieffer
James Baker
Janice Chan
Jeffrey Oliver
Katrin Leinweber
Kunal Marwaha
Reid Otsuji
Ruud Steltenpool
Tim Dennis
mdschleu
orobecca
thegsi
Date Added:
08/07/2020
Library Carpentry: The UNIX Shell
Unrestricted Use
CC BY
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Library Carpentry lesson to learn how to use the Shell. This Library Carpentry lesson introduces librarians to the Unix Shell. At the conclusion of the lesson you will: understand the basics of the Unix shell; understand why and how to use the command line; use shell commands to work with directories and files; use shell commands to find and manipulate data.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Huffman
Alex Kassil
Alex Mendes
Alexander Konovalov
Alexander Morley
Ana Costa Conrado
Andrew Reid
Andrew T. T. McRae
Ariel Rokem
Ashwin Srinath
Bagus Tris Atmaja
Belinda Weaver
Benjamin Bolker
Benjamin Gabriel
BertrandCaron
Brian Ballsun-Stanton
Christopher Erdmann
Christopher Mentzel
Colin Sauze
Dan Michael Heggø
Dave Bridges
David McKain
Dmytro Lituiev
Elena Denisenko
Eric Jankowski
Erin Alison Becker
Evan Williamson
Farah Shamma
Gabriel Devenyi
Gerard Capes
Giuseppe Profiti
Halle Burns
Hannah Burkhardt
Ian Lessing
Ian van der Linde
Jake Cowper Szamosi
James Baker
James Guelfi
Jarno Rantaharju
Jarosław Bryk
Jason Macklin
Jeffrey Oliver
John Pellman
Jonah Duckles
Jonny Williams
Katrin Leinweber
Kevin M. Buckley
Kunal Marwaha
Laurence
Marc Gouw
Marie-Helene Burle
Marisa Lim
Martha Robinson
Martin Feller
Megan Fritz
Michael Lascarides
Michael Zingale
Michele Hayslett
Mike Henry
Morgan Oneka
Murray Hoggett
Nicola Soranzo
Nicolas Barral
Noah D Brenowitz
Owen Kaluza
Patrick McCann
Peter Hoyt
Rafi Ullah
Raniere Silva
Ruud Steltenpool
Rémi Emonet
Stephan Schmeing
Stephen Jones
Stephen Leak
Stéphane Guillou
Susan J Miller
Thomas Mellan
Tim Dennis
Tom Dowrick
Travis Lilleberg
Victor Koppejan
Vikram Chhatre
Yee Mey
colinmorris
csqrs
earkpr
ekaterinailin
hugolio
jenniferleeucalgary
reshama shaikh
sjnair
Date Added:
08/07/2020
OpenRefine for Social Science Data
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CC BY
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Lesson on OpenRefine for social scientists. 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. 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.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
Erin Becker
François Michonneau
Geoff LaFlair
Karen Word
Lachlan Deer
Peter Smyth
Tracy Teal
Date Added:
08/07/2020
Plotting and Programming in Python
Unrestricted Use
CC BY
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This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Steer
Allen Lee
Andreas Hilboll
Ashley Champagne
Benjamin
Benjamin Roberts
CanWood
Carlos Henrique Brandt
Carlos M Ortiz Marrero
Cephalopd
Cian Wilson
Dan Mønster
Daniel W Kerchner
Daria Orlowska
Dave Lampert
David Matten
Erin Alison Becker
Florian Goth
Francisco J. Martínez
Greg Wilson
Jacob Deppen
Jarno Rantaharju
Jeremy Zucker
Jonah Duckles
Kees den Heijer
Keith Gilbertson
Kyle E Niemeyer
Lex Nederbragt
Logan Cox
Louis Vernon
Lucy Dorothy Whalley
Madeleine Bonsma-Fisher
Mark Phillips
Mark Slater
Maxim Belkin
Michael Beyeler
Mike Henry
Narayanan Raghupathy
Nigel Bosch
Olav Vahtras
Pablo Hernandez-Cerdan
Paul Anzel
Phil Tooley
Raniere Silva
Robert Woodward
Ryan Avery
Ryan Gregory James
SBolo
Sarah M Brown
Shyam Dwaraknath
Sourav Singh
Steven Koenig
Stéphane Guillou
Taylor Smith
Thor Wikfeldt
Timothy Warren
Tyler Martin
Vasu Venkateshwaran
Vikas Pejaver
ian
mzc9
Date Added:
08/07/2020
Programming with MATLAB
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CC BY
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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. We use MATLAB in our lessons because: we have to use something for examples; it’s well-documented; it has a large (and growing) user base among scientists in academia and industry; and it has a large library of packages available for performing diverse tasks. But the two most important things are to use whatever language your colleagues are using, so that you can share your work with them easily, and to use that language well.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Gerard Capes
Date Added:
03/20/2017
Programming with Python
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CC BY
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The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis. Arthritis Inflammation We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, columns represent successive days. The first three rows of our first file look like this: 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1 Each number represents the number of inflammation bouts that a particular patient experienced on a given day. For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study. So, we want to: Calculate the average inflammation per day across all patients. Plot the result to discuss and share with colleagues. To do all that, we’ll have to learn a little bit about programming.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Anne Fouilloux
Lauren Ko
Maxim Belkin
Trevor Bekolay
Valentina Staneva
Date Added:
08/07/2020
Programming with R
Unrestricted Use
CC BY
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The best way to learn how to program is to do something useful, so this introduction to R is built around a common scientific task: data analysis. Our real goal isn’t to teach you R, but to teach you the basic concepts that all programming depends on. We use R in our lessons because: we have to use something for examples; it’s free, well-documented, and runs almost everywhere; it has a large (and growing) user base among scientists; and it has a large library of external packages available for performing diverse tasks. But the two most important things are to use whatever language your colleagues are using, so you can share your work with them easily, and to use that language well. We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in CSV format (comma-separated values): each row holds information for a single patient, and the columns represent successive days. The first few rows of our first file look like this: 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1 0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1 0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1 We want to: load that data into memory, calculate the average inflammation per day across all patients, and plot the result. To do all that, we’ll have to learn a little bit about programming.

Subject:
Applied Science
Computer Science
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Diya Das
Katrin Leinweber
Rohit Goswami
Date Added:
03/20/2017