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Library Carpentry: The UNIX Shell
Unrestricted Use
CC BY
Rating
0.0 stars

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
Secondary Data Preregistration
Unrestricted Use
Public Domain
Rating
0.0 stars

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Subject:
Life Science
Social Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K.D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Stuart J Ritchie
Thuy-vy Nguyen
William J. Chopik
Sara J. Weston
Date Added:
08/03/2021
Secondary Data Preregistration
Unrestricted Use
Public Domain
Rating
0.0 stars

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Subject:
Applied Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K. D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Stuart J. Ritchie
Thuy-vy Ngugen
William J. Chopik
Sara J. Weston
Date Added:
08/12/2021
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:
Applied Science
Computer Science
Mathematics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Huffman
Adam James Orr
Adam Richie-Halford
AidaMirsalehi
Alex Kassil
Alex Mac
Alexander Konovalov
Alexander Morley
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
Damien Irving
Dan Jones
Dana Brunson
Daniel Baird
Daniel McCloy
Daniel Standage
Danielle M. Nielsen
Dave Bridges
David Eyers
David McKain
David Vollmer
Dean Attali
Devinsuit
Dmytro Lituiev
Donny Winston
Doug Latornell
Dustin Lang
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
M Carlise
Mahdi Sadjadi
Marc Rajeev Gouw
Marcel Stimberg
Maria Doyle
Marie-Helene Burle
Marisa Lim
Mark Mandel
Martha Robinson
Martin Feller
Matthew Gidden
Matthew Peterson
Megan Fritz
Michael Zingale
Mike Henry
Mike Jackson
Morgan Oneka
Murray Hoggett
Nicola Soranzo
Nicolas Barral
Noah D Brenowitz
Noam Ross
Norman Gray
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
Robert A Beagrie
Ruud Steltenpool
Ry4an Brase
Rémi Emonet
Sarah Mount
Sarah Simpkin
Scott Ritchie
Stephan Schmeing
Stephen Jones
Stephen Turner
Steve Leak
Stéphane Guillou
Susan Miller
Thomas Mellan
Tim Keighley
Tobin Magle
Tom Dowrick
Trevor Bekolay
Varda F. Hagh
Victor Koppejan
Vikram Chhatre
Yee Mey
csqrs
earkpr
ekaterinailin
nther
reshama shaikh
s-boardman
sjnair
Date Added:
03/20/2017