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Carpentries Instructor Training
Unrestricted Use
CC BY
Rating
0.0 stars

A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

Subject:
Applied Science
Computer Science
Education
Higher Education
Information Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Aleksandra Nenadic
Alexander Konovalov
Alistair John Walsh
Allison Weber
Amy E. Hodge
Andrew B. Collier
Anita Schürch
AnnaWilliford
Ariel Rokem
Brian Ballsun-Stanton
Callin Switzer
Christian Brueffer
Christina Koch
Christopher Erdmann
Colin Morris
Dan Allan
DanielBrett
Danielle Quinn
Darya Vanichkina
David Jennings
Eric Jankowski
Erin Alison Becker
Evan Peter Williamson
François Michonneau
Gerard Capes
Greg Wilson
Ian Lee
Jason M Gates
Jason Williams
Jeffrey Oliver
Joe Atzberger
John Bradley
John Pellman
Jonah Duckles
Jonathan Bradley
Karen Cranston
Karen Word
Kari L Jordan
Katherine Koziar
Katrin Leinweber
Kees den Heijer
Laurence
Lex Nederbragt
Maneesha Sane
Marie-Helene Burle
Mik Black
Mike Henry
Murray Cadzow
Neal Davis
Neil Kindlon
Nicholas Tierney
Nicolás Palopoli
Noah Spies
Paula Andrea Martinez
Petraea
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Sarah Brown
Sarah M Brown
Sarah Stevens
Sean
Serah Anne Njambi Kiburu
Stefan Helfrich
Steve Moss
Stéphane Guillou
Ted Laderas
Tiago M. D. Pereira
Toby Hodges
Tracy Teal
Yo Yehudi
amoskane
davidbenncsiro
naught101
satya-vinay
Date Added:
08/07/2020
Data Analysis and Visualization in R for Ecologists
Unrestricted Use
CC BY
Rating
0.0 stars

Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. 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.

Subject:
Applied Science
Computer Science
Ecology
Information Science
Life Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ankenbrand, Markus
Arindam Basu
Ashander, Jaime
Bahlai, Christie
Bailey, Alistair
Becker, Erin Alison
Bledsoe, Ellen
Boehm, Fred
Bolker, Ben
Bouquin, Daina
Burge, Olivia Rata
Burle, Marie-Helene
Carchedi, Nick
Chatzidimitriou, Kyriakos
Chiapello, Marco
Conrado, Ana Costa
Cortijo, Sandra
Cranston, Karen
Cuesta, Sergio Martínez
Culshaw-Maurer, Michael
Czapanskiy, Max
Daijiang Li
Dashnow, Harriet
Daskalova, Gergana
Deer, Lachlan
Direk, Kenan
Dunic, Jillian
Elahi, Robin
Fishman, Dmytro
Fouilloux, Anne
Fournier, Auriel
Gan, Emilia
Goswami, Shubhang
Guillou, Stéphane
Hancock, Stacey
Hardenberg, Achaz Von
Harrison, Paul
Hart, Ted
Herr, Joshua R.
Hertweck, Kate
Hodges, Toby
Hulshof, Catherine
Humburg, Peter
Jean, Martin
Johnson, Carolina
Johnson, Kayla
Johnston, Myfanwy
Jordan, Kari L
K. A. S. Mislan
Kaupp, Jake
Keane, Jonathan
Kerchner, Dan
Klinges, David
Koontz, Michael
Leinweber, Katrin
Lepore, Mauro Luciano
Li, Ye
Lijnzaad, Philip
Lotterhos, Katie
Mannheimer, Sara
Marwick, Ben
Michonneau, François
Millar, Justin
Moreno, Melissa
Najko Jahn
Obeng, Adam
Odom, Gabriel J.
Pauloo, Richard
Pawlik, Aleksandra Natalia
Pearse, Will
Peck, Kayla
Pederson, Steve
Peek, Ryan
Pletzer, Alex
Quinn, Danielle
Rajeg, Gede Primahadi Wijaya
Reiter, Taylor
Rodriguez-Sanchez, Francisco
Sandmann, Thomas
Seok, Brian
Sfn_brt
Shiklomanov, Alexey
Shivshankar Umashankar
Stachelek, Joseph
Strauss, Eli
Sumedh
Switzer, Callin
Tarkowski, Leszek
Tavares, Hugo
Teal, Tracy
Theobold, Allison
Tirok, Katrin
Tylén, Kristian
Vanichkina, Darya
Voter, Carolyn
Webster, Tara
Weisner, Michael
White, Ethan P
Wilson, Earle
Woo, Kara
Wright, April
Yanco, Scott
Ye, Hao
Date Added:
03/20/2017
R for Social Scientists
Unrestricted Use
CC BY
Rating
0.0 stars

Data Carpentry lesson part of the Social Sciences curriculum. This lesson teaches how to analyse and visualise data used by social scientists. 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 social sciences 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.

Subject:
Applied Science
Information Science
Mathematics
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
Angela Li
Ben Marwick
Christina Maimone
Danielle Quinn
Erin Alison Becker
Francois Michonneau
Geoffrey LaFlair
Hao Ye
Jake Kaupp
Juan Fung
Katrin Leinweber
Martin Olmos
Murray Cadzow
Date Added:
08/07/2020