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Assessing data availability and research reproducibility in hydrology and water resources
Unrestricted Use
CC BY
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There is broad interest to improve the reproducibility of published research. We developed a survey tool to assess the availability of digital research artifacts published alongside peer-reviewed journal articles (e.g. data, models, code, directions for use) and reproducibility of article results. We used the tool to assess 360 of the 1,989 articles published by six hydrology and water resources journals in 2017. Like studies from other fields, we reproduced results for only a small fraction of articles (1.6% of tested articles) using their available artifacts. We estimated, with 95% confidence, that results might be reproduced for only 0.6% to 6.8% of all 1,989 articles. Unlike prior studies, the survey tool identified key bottlenecks to making work more reproducible. Bottlenecks include: only some digital artifacts available (44% of articles), no directions (89%), or all artifacts available but results not reproducible (5%). The tool (or extensions) can help authors, journals, funders, and institutions to self-assess manuscripts, provide feedback to improve reproducibility, and recognize and reward reproducible articles as examples for others.

Subject:
Applied Science
Hydrology
Information Science
Physical Science
Material Type:
Reading
Provider:
Scientific Data
Author:
Adel M. Abdallah
David E. Rosenberg
Hadia Akbar
James H. Stagge
Nour A. Attallah
Ryan James
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
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