Updating search results...

Search Resources

6 Results

View
Selected filters:
  • Ben Martin
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:
Computer Science
Information Science
Ecology
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
Lijnzaad, Philip
Li, Ye
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
Introduction to Philosophy: Logic
Unrestricted Use
CC BY
Rating
0.0 stars

Introduction to Philosophy: Logic provides students with the concepts and skills necessary to identify and evaluate arguments effectively. The chapters, all written by experts in the field, provide an overview of what arguments are, the different types of arguments one can expect to encounter in both philosophy and everyday life, and how to recognise common argumentative mistakes.

Subject:
Philosophy
Material Type:
Textbook
Provider:
Rebus Community
Author:
Ben Martin
Christina Hendricks
Date Added:
11/18/2020
Missing Data and Multiple Imputation Decision Tree
Rating
0.0 stars

This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

Subject:
Education
Measurement and Data
Statistics and Probability
Social Science
Economics
Psychology
Social Work
Sociology
Women's Studies
Material Type:
Diagram/Illustration
Reading
Author:
Adrienne D. Woods
Alex Uzdavines
Ben Van Dusen
Daria Gerasimova
David Moreau
Denver Brown
James M. Clay
Jayson Nissen
Jessica A. R. Logan
Kathleen Schmidt
Keven Joyal-Desmarais
Kevin M. King
Mahmoud M. Elsherif
Martin Vasilev
Max A. Halvorson
Menglin Xu
Pamela E. Davis-Kean
Rick A. Cruz
Sierra Bainter
Date Added:
04/25/2022
R for Reproducible Scientific Analysis
Unrestricted Use
CC BY
Rating
0.0 stars

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data 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. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam H. Sparks
Ahsan Ali Khoja
Amy Lee
Ana Costa Conrado
Andrew Boughton
Andrew Lonsdale
Andrew MacDonald
Andris Jankevics
Andy Teucher
Antonio Berlanga-Taylor
Ashwin Srinath
Ben Bolker
Bill Mills
bippuspm
Bret Beheim
butterflyskip
Clare Sloggett
Daniel
Dave Bridges
David J. Harris
David Mawdsley
Dean Attali
Diego Rabatone Oliveira
Drew Tyre
Elise Morrison
Erin Alison Becker
Fernando Mayer
François Michonneau
Giulio Valentino Dalla Riva
Gordon McDonald
Greg Wilson
Harriet Dashnow
Ido Bar
Jaime Ashander
James Balamuta
James Mickley
Jamie McDevitt-Irwin
Jeffrey Arnold
Jeffrey Oliver
John Blischak
Jonah Duckles
Josh Quan
Julia Piaskowski
Kara Woo
Kate Hertweck
Katherine Koziar
Katrin Leinweber
Kellie Ottoboni
Kevin Weitemier
Kiana Ashley West
Kieran Samuk
Kunal Marwaha
Kyriakos Chatzidimitriou
Lachlan Deer
Lex Nederbragt
Liz Ing-Simmons
Lucy Chang
Luke W Johnston
Luke Zappia
Marc Sze
Marie-Helene Burle
Marieke Frassl
Mark Dunning
Martin John Hadley
Mary Donovan
Matt Clark
Melissa Kardish
Mike Jackson
Murray Cadzow
Narayanan Raghupathy
Naupaka Zimmerman
Nelly Sélem
Nicholas Lesniak
Nicholas Potter
Nima Hejazi
Nora Mitchell
Olivia Rata Burge
Paula Andrea Martinez
Pete Bachant
Phil Bouchet
Philipp Boersch-Supan
Piotr Banaszkiewicz
Raniere Silva
Rayna Michelle Harris
Remi Daigle
Rémi Emonet
Research Bazaar
Richard Barnes
Robert Bagchi
Sam Penrose
Sandra Brosda
Sarah Munro
Sasha Lavrentovich
Scott Allen Funkhouser
Scott Ritchie
Sebastien Renaut
Thea Van Rossum
Timothy Eoin Moore
Timothy Rice
Tobin Magle
Trevor Bekolay
Tyler Crawford Kelly
Vicken Hillis
waiteb5
Yuka Takemon
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:
Information Science
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
The Unix Shell
Unrestricted Use
CC BY
Rating
0.0 stars

Software Carpentry lesson on how to use the shell to navigate the filesystem and write simple loops and scripts. 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.

Subject:
Computer Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Huffman
Adam James Orr
Adam Richie-Halford
AidaMirsalehi
Alexander Konovalov
Alexander Morley
Alex Kassil
Alex Mac
Alix Keener
Amy Brown
Andrea Bedini
Andrew Boughton
Andrew Reid
Andrew T. T. McRae
Andrew Walker
Ariel Rokem
Armin Sobhani
Ashwin Srinath
Bagus Tris Atmaja
Bartosz Telenczuk
Ben Bolker
Benjamin Gabriel
Bertie Seyffert
Bill Mills
Brian Ballsun-Stanton
BrianBill
Camille Marini
Chris Mentzel
Christina Koch
Colin Morris
Colin Sauze
csqrs
Damien Irving
Dana Brunson
Daniel Baird
Danielle M. Nielsen
Daniel McCloy
Daniel Standage
Dan Jones
Dave Bridges
David Eyers
David McKain
David Vollmer
Dean Attali
Devinsuit
Dmytro Lituiev
Donny Winston
Doug Latornell
Dustin Lang
earkpr
ekaterinailin
Elena Denisenko
Emily Dolson
Emily Jane McTavish
Eric Jankowski
Erin Alison Becker
Ethan P White
Evgenij Belikov
Farah Shamma
Fatma Deniz
Filipe Fernandes
Francis Gacenga
François Michonneau
Gabriel A. Devenyi
Gerard Capes
Giuseppe Profiti
Greg Wilson
Halle Burns
Hannah Burkhardt
Harriet Alexander
Hugues Fontenelle
Ian van der Linde
Inigo Aldazabal Mensa
Jackie Milhans
Jake Cowper Szamosi
James Guelfi
Jan T. Kim
Jarek Bryk
Jarno Rantaharju
Jason Macklin
Jay van Schyndel
Jens vdL
John Blischak
John Pellman
John Simpson
Jonah Duckles
Jonny Williams
Joshua Madin
Kai Blin
Kathy Chung
Katrin Leinweber
Kevin M. Buckley
Kirill Palamartchouk
Klemens Noga
Kristopher Keipert
Kunal Marwaha
Laurence
Lee Zamparo
Lex Nederbragt
Mahdi Sadjadi
Marcel Stimberg
Marc Rajeev Gouw
Maria Doyle
Marie-Helene Burle
Marisa Lim
Mark Mandel
Martha Robinson
Martin Feller
Matthew Gidden
Matthew Peterson
M Carlise
Megan Fritz
Michael Zingale
Mike Henry
Mike Jackson
Morgan Oneka
Murray Hoggett
Nicolas Barral
Nicola Soranzo
Noah D Brenowitz
Noam Ross
Norman Gray
nther
Orion Buske
Owen Kaluza
Patrick McCann
Paul Gardner
Pauline Barmby
Peter R. Hoyt
Peter Steinbach
Philip Lijnzaad
Phillip Doehle
Piotr Banaszkiewicz
Rafi Ullah
Raniere Silva
Rémi Emonet
reshama shaikh
Robert A Beagrie
Ruud Steltenpool
Ry4an Brase
Sarah Mount
Sarah Simpkin
s-boardman
Scott Ritchie
sjnair
Stéphane Guillou
Stephan Schmeing
Stephen Jones
Stephen Turner
Steve Leak
Susan Miller
Thomas Mellan
Tim Keighley
Tobin Magle
Tom Dowrick
Trevor Bekolay
Varda F. Hagh
Victor Koppejan
Vikram Chhatre
Yee Mey
Date Added:
03/20/2017