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Introduction to R for Geospatial Data
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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
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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
Intro to Calculating Confidence Intervals
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CC BY
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This video will introduce how to calculate confidence intervals around effect sizes using the MBESS package in R. All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/

Subject:
Applied Science
Computer Science
Information Science
Material Type:
Lecture
Provider:
Center for Open Science
Author:
Center for Open Science
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
Jupyter Notebooks with R & Git
Conditional Remix & Share Permitted
CC BY-SA
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Today we are going to learn the basics of literate programming using Jupyter Notebooks, a popular tool in data science, with the R kernel, so we can run R code in our notebooks. We’ll then take a look at how we use Git and GitHub to keep track of all the versions of our work, collaborate with others, and be open!

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Activity/Lab
Provider:
New York University
Author:
Vicky Steeves
Date Added:
12/01/2018
La Terminal de Unix
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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
Learning Statistics with R
Conditional Remix & Share Permitted
CC BY-SA
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The book is associated with the lsr package on CRAN and GitHub. The package is probably okay for many introductory teaching purposes, but some care is required. The package does have some limitations (e.g., the etaSquared function does strange things for unbalanced ANOVA designs), and it has not been updated in a while.

Subject:
Mathematics
Statistics and Probability
Material Type:
Textbook
Author:
Danielle Navarro
Date Added:
06/23/2020
Level up the reproducibility of your data and code! A 2-hour, hands-on workshop
Unrestricted Use
CC BY
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Purpose: To introduce methods and tools in organization, documentation, automation, and dissemination of research that nudge it further along the reproducibility spectrum.OutcomeParticipants feel more confident applying reproducibility methods and tools to their own research projects.ProcessParticipants practice new methods and tools with code and data during the workshop to explore what they do and how they might work in a research workflow. Participants can compare benefits of new practices and ask questions to help clarify which would provide them the most value to adopt.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Activity/Lab
Author:
April Clyburne-Sherin
Date Added:
10/29/2019
Leveraging Open Ecosystems to Enhance Reproducible Workflows
Unrestricted Use
CC BY
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Open source infrastructure has paved the way for mission-aligned research stakeholders to create a united vision of interoperable tools and services that accelerate scholarly communication, fill technology gaps, converge solutions, and enable access and discoverability.

Hear from a panel of research groups that have taken advantage of interoperable infrastructure to leverage more robust workflows to support rigorous, reproducible research. We also discuss the steps stakeholders and institutions can take to integrate OSF’s open API with existing services to establish streamlined researcher workflows.

View the slides from this presentation by visiting osf.io/ux7ed.

Subject:
Education
Material Type:
Lesson
Provider:
Center for Open Science
Date Added:
03/31/2021
Library Carpentry: Introduction to Git
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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:
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)
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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:
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
Licensing your research
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CC BY
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Join us for a 30 minute guest webinar by Brandon Butler, Director of Information Policy at the University of Virginia. This webinar will introduce questions to think about when picking a license for your research. You can signal which license you pick using the License Picker on the Open Science Framework (OSF; https://osf.io). The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github, Mendeley, and now is integrated with JASP, to streamline workflows and increase efficiency.

Subject:
Applied Science
Computer Science
Information Science
Material Type:
Lecture
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Likelihood of Null Effects of Large NHLBI Clinical Trials Has Increased over Time
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CC BY
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Background We explore whether the number of null results in large National Heart Lung, and Blood Institute (NHLBI) funded trials has increased over time. Methods We identified all large NHLBI supported RCTs between 1970 and 2012 evaluating drugs or dietary supplements for the treatment or prevention of cardiovascular disease. Trials were included if direct costs >$500,000/year, participants were adult humans, and the primary outcome was cardiovascular risk, disease or death. The 55 trials meeting these criteria were coded for whether they were published prior to or after the year 2000, whether they registered in clinicaltrials.gov prior to publication, used active or placebo comparator, and whether or not the trial had industry co-sponsorship. We tabulated whether the study reported a positive, negative, or null result on the primary outcome variable and for total mortality. Results 17 of 30 studies (57%) published prior to 2000 showed a significant benefit of intervention on the primary outcome in comparison to only 2 among the 25 (8%) trials published after 2000 (χ2=12.2,df= 1, p=0.0005). There has been no change in the proportion of trials that compared treatment to placebo versus active comparator. Industry co-sponsorship was unrelated to the probability of reporting a significant benefit. Pre-registration in clinical trials.gov was strongly associated with the trend toward null findings. Conclusions The number NHLBI trials reporting positive results declined after the year 2000. Prospective declaration of outcomes in RCTs, and the adoption of transparent reporting standards, as required by clinicaltrials.gov, may have contributed to the trend toward null findings.

Subject:
Applied Science
Health, Medicine and Nursing
Material Type:
Reading
Provider:
PLOS ONE
Author:
Robert M. Kaplan
Veronica L. Irvin
Date Added:
08/07/2020
Linking to Data - Effect on Citation Rates in Astronomy
Read the Fine Print
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Is there a difference in citation rates between articles that were published with links to data and articles that were not? Besides being interesting from a purely academic point of view, this question is also highly relevant for the process of furthering science. Data sharing not only helps the process of verification of claims, but also the discovery of new findings in archival data. However, linking to data still is a far cry away from being a "practice", especially where it comes to authors providing these links during the writing and submission process. You need to have both a willingness and a publication mechanism in order to create such a practice. Showing that articles with links to data get higher citation rates might increase the willingness of scientists to take the extra steps of linking data sources to their publications. In this presentation we will show this is indeed the case: articles with links to data result in higher citation rates than articles without such links. The ADS is funded by NASA Grant NNX09AB39G.

Subject:
Physical Science
Material Type:
Reading
Author:
Alberto Accomazzi
Edwin A. Henneken
Date Added:
08/07/2020
Local Grassroots Networks Engaging Open Science in Their Communities
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CC BY
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This recorded webinar features insights from international panelists currently nurturing culture change in research among their local communities.Representat...

Subject:
Education
Material Type:
Lesson
Provider:
Center for Open Science
Author:
Brian Nosek
Date Added:
03/31/2021