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Deriving meaning and knowledge from data. Software, code, licensing, maintenance, statistics, methods, code sharing, documentation, and more.
 

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Accessing Your Account – OSF Guides
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topic of accessing your OSF account: Create an OSF Account Sign in to OSF Claim an Unregistered Account Reset Your Password

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Add-ons – OSF Guides
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topics using add-on storage services in the OSF, including: Connect Amazon S3 to a Project Connect Bitbucket to a Project Connect Box to a Project Connect Dataverse to a Project Connect Dropbox to a Project Connect figshare to a Project Connect GitHub to a Project Connect GitLab to a Project Connect Google Drive to a Project Connect OneDrive to a Project Connect ownCloud to a Project

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology
Read the Fine Print
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Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results, without contacting the original authors, for Registered Reports published in the psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria, data was available for 40 articles, and analysis scripts for 37 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, Python, MATLAB, or JASP, we could run the scripts for 31 articles, and reproduce the main results for 20 articles. Although the articles that shared both data and code (35 out of 62, or 56%) and articles that could be computationally reproduced (20 out of 35, or 57%) was relatively high compared to other studies, there is clear room for improvement. We provide practical recommendations based on our observations, and link to examples of good research practices in the papers we reproduced.

Subject:
Psychology
Material Type:
Reading
Author:
Daniel Lakens
Jaroslav Gottfried
Nicholas Alvaro Coles
Pepijn Obels
Seth Ariel Green
Date Added:
08/07/2020
Análisis y visualización de datos usando Python
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Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Alejandra Gonzalez-Beltran
April Wright
chekos
Christopher Erdmann
Enric Escorsa O'Callaghan
Erin Becker
Fernando Garcia
Hely Salgado
Juan Martín Barrios
Juan M. Barrios
Katrin Leinweber
Laura Angelone
Leonardo Ulises Spairani
LUS24
Maxim Belkin
Miguel González
monialo2000
Nicolás Palopoli
Nohemi Huanca Nunez
Paula Andrea Martinez
Raniere Silva
Rayna Harris
rzayas
Sarah Brown
Silvana Pereyra
Spencer Harris
Stephan Druskat
Trevor Keller
Wilson Lozano
Date Added:
08/07/2020
Automation and Make
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A Software Carpentry lesson to learn how to use Make Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries, but Make can also be used to: run analysis scripts on raw data files to get data files that summarize the raw data; run visualization scripts on data files to produce plots; and to parse and combine text files and plots to create papers. Make is called a build tool - it builds data files, plots, papers, programs or libraries. It can also update existing files if desired. Make tracks the dependencies between the files it creates and the files used to create these. If one of the original files (e.g. a data file) is changed, then Make knows to recreate, or update, the files that depend upon this file (e.g. a plot). There are now many build tools available, all of which are based on the same concepts as Make.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Richie-Halford
Ana Costa Conrado
Andrew Boughton
Andrew Fraser
Andy Kleinhesselink
Andy Teucher
Anna Krystalli
Bill Mills
Brandon Curtis
David E. Bernholdt
Deborah Gertrude Digges
François Michonneau
Gerard Capes
Greg Wilson
Jake Lever
Jason Sherman
John Blischak
Jonah Duckles
Juan F Fung
Kate Hertweck
Lex Nederbragt
Luiz Irber
Matthew Thomas
Michael Culshaw-Maurer
Mike Jackson
Pete Bachant
Piotr Banaszkiewicz
Radovan Bast
Raniere Silva
Rémi Emonet
Samuel Lelièvre
Satya Mishra
Trevor Bekolay
Date Added:
03/20/2017
Bayesian inference for psychology. Part II: Example applications with JASP
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Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP (http://www.jasp-stats.org), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder’s BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Subject:
Psychology
Material Type:
Reading
Provider:
Psychonomic Bulletin & Review
Author:
Akash Raj
Alexander Etz
Alexander Ly
Alexandra Sarafoglou
Bruno Boutin
Damian Dropmann
Don van den Bergh
Dora Matzke
Eric-Jan Wagenmakers
Erik-Jan van Kesteren
Frans Meerhoff
Helen Steingroever
Jeffrey N. Rouder
Johnny van Doorn
Jonathon Love
Josine Verhagen
Koen Derks
Maarten Marsman
Martin Šmíra
Patrick Knight
Quentin F. Gronau
Ravi Selker
Richard D. Morey
Sacha Epskamp
Tahira Jamil
Tim de Jong
Date Added:
08/07/2020
Being a Reviewer or Editor for Registered Reports
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Experienced Registered Reports editors and reviewers come together to discuss the format and best practices for handling submissions. The panelists also share insights into what editors are looking for from reviewers as well as practical guidelines for writing a Registered Report. ABOUT THE PANELISTS: Chris Chambers | Chris is a professor of cognitive neuroscience at Cardiff University, Chair of the Registered Reports Committee supported by the Center for Open Science, and one of the founders of Registered Reports. He has helped establish the Registered Reports format for over a dozen journals. Anastasia Kiyonaga | Anastasia is a cognitive neuroscientist who uses converging behavioral, brain stimulation, and neuroimaging methods to probe memory and attention processes. She is currently a postdoctoral researcher with Mark D'Esposito in the Helen Wills Neuroscience Institute at the University of California, Berkeley. Before coming to Berkeley, she received her Ph.D. with Tobias Egner in the Duke Center for Cognitive Neuroscience. She will be an Assistant Professor in the Department of Cognitive Science at UC San Diego starting January, 2020. Jason Scimeca | Jason is a cognitive neuroscientist at UC Berkeley. His research investigates the neural systems that support high-level cognitive processes such as executive function, working memory, and the flexible control of behavior. He completed his Ph.D. at Brown University with David Badre and is currently a postdoctoral researcher in Mark D'Esposito's Cognitive Neuroscience Lab. Moderated by David Mellor, Director of Policy Initiatives for the Center for Open Science.

Subject:
Computer Science
Information Science
Material Type:
Lecture
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Best Practices – OSF Guides
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topic of best practices in open science, including: File Management and Licensing File naming Organizing files Licensing Version Control Research Design Preregistration Creating a data management plan (DMP) document Handling Data How to Make a Data Dictionary Sharing Research Outputs Sharing data Publishing Research Outputs Preprints

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
06/18/2020
Carpentries Instructor Training
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CC BY
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A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

Subject:
Computer Science
Information Science
Education
Higher Education
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Aleksandra Nenadic
Alexander Konovalov
Alistair John Walsh
Allison Weber
amoskane
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
davidbenncsiro
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
naught101
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
satya-vinay
Sean
Serah Anne Njambi Kiburu
Stefan Helfrich
Stéphane Guillou
Steve Moss
Ted Laderas
Tiago M. D. Pereira
Toby Hodges
Tracy Teal
Yo Yehudi
Date Added:
08/07/2020
Collaborating – OSF Guides
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topics of collaborating on the OSF, including: Requesting access Request Access to a Private Project Request Access to a Public Project Grant Access to a Project Commenting Comment on a Project Wiki Enable Wiki Contributions Edit the Wiki Add and Delete Wiki Pages Rename Wiki Pages View Versions of the Wiki Disable the Wiki

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Connecting Research Tools to the Open Science Framework (OSF)
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This webinar (recorded Sept. 27, 2017) introduces how to connect other services as add-ons to projects on the Open Science Framework (OSF; https://osf.io). Connecting services to your OSF projects via add-ons enables you to pull together the different parts of your research efforts without having to switch away from tools and workflows you wish to continue using. 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 and Mendeley, to streamline workflows and increase efficiency.

Subject:
Computer Science
Information Science
Material Type:
Lecture
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Consequences of Low Statistical Power
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This video will go over three issues that can arise when scientific studies have low statistical power. All materials shown in the video, as well as the content from our other videos, can be found here: https://osf.io/7gqsi/

Subject:
Computer Science
Information Science
Material Type:
Lecture
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Creating and Managing Projects – OSF Guides
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CC BY
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topics of creating and managing OSF projects, including: Projects and Components Create a Project Create Components Create a Project from a Template Delete a Project Delete a Project with Components Delete a Component See all 7 articles Contributors and Permissions Understand Contributor Permissions Add Contributors to Projects and Components Edit Contributor Permissions Remove Contributors from a Project Import Contributors from a Parent Project into a Component Add Admins from the Parent Project to a Component Management Control Your Privacy Settings View Recent Activity Rename a Project License Your Project Configure Notifications View Project Analytics

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
CyVerse Learning Institute
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CC BY
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The CyVerse Learning center is a release of our learning materials in the popular “Read the Docs” formatting. We are transitioning our leaning materials from our wiki into this format to make them easier to search, use, and update. We will be making regular contributions to these materials, and you can suggest new materials or create and share your own. If you have ideas or suggestions please email Tutorials@CyVerse.org. You can also view, edit, and submit contributions on GitHub.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Activity/Lab
Provider:
CyVerse
Author:
Jason Williams
Date Added:
12/16/2019
Data Analysis and Visualization in Python for Ecologists
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CC BY
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Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in one and a half days (~ 10 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Maxim Belkin
Tania Allard
Date Added:
03/20/2017
Data Analysis and Visualization in R for Ecologists
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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
Data Analysis and Visualization with Python for Social Scientists
Unrestricted Use
CC BY
Rating

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Geoffrey Boushey
Stephen Childs
Date Added:
08/07/2020
Data Carpentry for Biologists
Unrestricted Use
CC BY
Rating

The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R.

Subject:
Computer Science
Information Science
Biology
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Ethan White
Zachary Brym
Date Added:
08/07/2020
Data Cleaning with OpenRefine for Ecologists
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CC BY
Rating

A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identified and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis. OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Cam Macdonell
Deborah Paul
Phillip Doehle
Rachel Lombardi
Date Added:
03/20/2017
Data Intro for Archivists
Unrestricted Use
CC BY
Rating

This Library Carpentry lesson introduces archivists to working with data. At the conclusion of the lesson you will: be able to explain terms, phrases, and concepts in code or software development; identify and use best practice in data structures; use regular expressions in searches.

Subject:
Applied Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
James Baker
Jeanine Finn
Jenny Bunn
Katherine Koziar
Noah Geraci
Scott Peterson
Date Added:
08/07/2020
Data Management with SQL for Ecologists
Unrestricted Use
CC BY
Rating

Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Christina Koch
Donal Heidenblad
Katy Felkner
Rémi Rampin
Timothée Poisot
Date Added:
03/20/2017
Data Management with SQL for Social Scientists
Unrestricted Use
CC BY
Rating

This is an alpha lesson to teach Data Management with SQL for Social Scientists, We welcome and criticism, or error; and will take your feedback into account to improve both the presentation and the content. Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Subject:
Computer Science
Information Science
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
Peter Smyth
Date Added:
08/07/2020
Data Organization in Spreadsheets for Ecologists
Unrestricted Use
CC BY
Rating

Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Christie Bahlai
Peter R. Hoyt
Tracy Teal
Date Added:
03/20/2017
Data Organization in Spreadsheets for Social Scientists
Unrestricted Use
CC BY
Rating

Lesson on spreadsheets for social scientists. Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. Typically we organize data in spreadsheets in ways that we as humans want to work with the data. However computers require data to be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Subject:
Information Science
Measurement and Data
Social Science
Material Type:
Module
Provider:
The Carpentries
Author:
David Mawdsley
Erin Becker
François Michonneau
Karen Word
Lachlan Deer
Peter Smyth
Date Added:
08/07/2020
Data Wrangling and Processing for Genomics
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CC BY
Rating

Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.

Subject:
Computer Science
Information Science
Genetics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Thomas
Ahmed R. Hasan
Aniello Infante
Anita Schürch
dbmarchant
Dev Paudel
Erin Alison Becker
Fotis Psomopoulos
François Michonneau
Gaius Augustus
Gregg TeHennepe
Jason Williams
Jessica Elizabeth Mizzi
Karen Cranston
Kari L Jordan
Kate Crosby
Kevin Weitemier
Lex Nederbragt
Luis Avila
Peter R. Hoyt
Rayna Michelle Harris
Ryan Peek
Sheldon John McKay
Sheldon McKay
Taylor Reiter
Tessa Pierce
Toby Hodges
Tracy Teal
Vasilis Lenis
Winni Kretzschmar
Date Added:
08/07/2020
Databases and SQL
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Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amy Brown
Andrew Boughton
Andrew Kubiak
Avishek Kumar
Ben Waugh
Bill Mills
Brian Ballsun-Stanton
Chris Tomlinson
Colleen Fallaw
Daniel Suess
Dan Michael Heggø
Dave Welch
David W Wright
Deborah Gertrude Digges
Donny Winston
Doug Latornell
Erin Alison Becker
Ethan Nelson
Ethan P White
François Michonneau
George Graham
Gerard Capes
Gideon Juve
Greg Wilson
Ioan Vancea
Jake Lever
James Mickley
John Blischak
JohnRMoreau@gmail.com
Jonah Duckles
Jonathan Guyer
Joshua Nahum
Kate Hertweck
Kevin Dyke
lorra
Louis Vernon
Luc Small
Luke William Johnston
Maneesha Sane
Mark Stacy
Matthew Collins
Matty Jones
Mike Jackson
Morgan Taschuk
Patrick McCann
Paula Andrea Martinez
Pauline Barmby
Piotr Banaszkiewicz
Raniere Silva
Ray Bell
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Seda Arat
Sheldon John McKay
Sheldon McKay
slimlime
Stephen Davison
Thomas Guignard
Trevor Bekolay
Date Added:
03/20/2017
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
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The designing, collecting, analyzing, and reporting of psychological studies entail many choices that are often arbitrary. The opportunistic use of these so-called researcher degrees of freedom aimed at obtaining statistically significant results is problematic because it enhances the chances of false positive results and may inflate effect size estimates. In this review article, we present an extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research. The list can be used in research methods education, and as a checklist to assess the quality of preregistrations and to determine the potential for bias due to (arbitrary) choices in unregistered studies.

Subject:
Psychology
Material Type:
Reading
Provider:
Frontiers in Psychology
Author:
Coosje L. S. Veldkamp
Hilde E. M. Augusteijn
Jelte M. Wicherts
Marcel A. L. M. van Assen
Marjan Bakker
Robbie C. M. van Aert
Date Added:
08/07/2020
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
Unrestricted Use
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The designing, collecting, analyzing, and reporting of psychological studies entail many choices that are often arbitrary. The opportunistic use of these so-called researcher degrees of freedom aimed at obtaining statistically significant results is problematic because it enhances the chances of false positive results and may inflate effect size estimates. In this review article, we present an extensive list of 34 degrees of freedom that researchers have in formulating hypotheses, and in designing, running, analyzing, and reporting of psychological research. The list can be used in research methods education, and as a checklist to assess the quality of preregistrations and to determine the potential for bias due to (arbitrary) choices in unregistered studies.

Subject:
Psychology
Material Type:
Reading
Author:
Coosje L. S. Veldkamp
Hilde E. M. Augusteijn
Jelte M. Wicherts
Marcel A. L. M. van Assen
Marjan Bakker
Robbie C. M. van Aert
Date Added:
11/25/2016
Did awarding badges increase data sharing in BMJ Open? A randomized controlled trial
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Sharing data and code are important components of reproducible research. Data sharing in research is widely discussed in the literature; however, there are no well-established evidence-based incentives that reward data sharing, nor randomized studies that demonstrate the effectiveness of data sharing policies at increasing data sharing. A simple incentive, such as an Open Data Badge, might provide the change needed to increase data sharing in health and medical research. This study was a parallel group randomized controlled trial (protocol registration: doi:10.17605/OSF.IO/PXWZQ) with two groups, control and intervention, with 80 research articles published in BMJ Open per group, with a total of 160 research articles. The intervention group received an email offer for an Open Data Badge if they shared their data along with their final publication and the control group received an email with no offer of a badge if they shared their data with their final publication. The primary outcome was the data sharing rate. Badges did not noticeably motivate researchers who published in BMJ Open to share their data; the odds of awarding badges were nearly equal in the intervention and control groups (odds ratio = 0.9, 95% CI [0.1, 9.0]). Data sharing rates were low in both groups, with just two datasets shared in each of the intervention and control groups. The global movement towards open science has made significant gains with the development of numerous data sharing policies and tools. What remains to be established is an effective incentive that motivates researchers to take up such tools to share their data.

Subject:
Information Science
Material Type:
Reading
Provider:
Royal Society Open Science
Author:
Adrian Aldcroft
Adrian G. Barnett
Anisa Rowhani-Farid
Date Added:
08/07/2020
Economics Lesson with Stata
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A Data Carpentry curriculum for Economics is being developed by Dr. Miklos Koren at Central European University. These materials are being piloted locally. Development for these lessons has been supported by a grant from the Sloan Foundation.

Subject:
Computer Science
Information Science
Measurement and Data
Economics
Material Type:
Module
Provider:
The Carpentries
Author:
Andras Vereckei
Arieda Muço
Miklós Koren
Date Added:
08/07/2020
El Control de Versiones con Git
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Software Carpentry lección para control de versiones con Git Para ilustrar el poder de Git y GitHub, usaremos la siguiente historia como un ejemplo motivador a través de esta lección. El Hombre Lobo y Drácula han sido contratados por Universal Missions para investigar si es posible enviar su próximo explorador planetario a Marte. Ellos quieren poder trabajar al mismo tiempo en los planes, pero ya han experimentado ciertos problemas anteriormente al hacer algo similar. Si se rotan por turnos entonces cada uno gastará mucho tiempo esperando a que el otro termine, pero si trabajan en sus propias copias e intercambian los cambios por email, las cosas se perderán, se sobreescribirán o se duplicarán. Un colega sugiere utilizar control de versiones para lidiar con el trabajo. El control de versiones es mejor que el intercambio de ficheros por email: Nada se pierde una vez que se incluye bajo control de versiones, a no ser que se haga un esfuerzo sustancial. Como se van guardando todas las versiones precedentes de los ficheros, siempre es posible volver atrás en el tiempo y ver exactamente quién escribió qué en un día en particular, o qué versión de un programa fue utilizada para generar un conjunto de resultados en particular. Como se tienen estos registros de quién hizo qué y en qué momento, es posible saber a quién preguntar si se tiene una pregunta en un momento posterior y, si es necesario, revertir el contenido a una versión anterior, de forma similar a como funciona el comando “deshacer” de los editores de texto. Cuando varias personas colaboran en el mismo proyecto, es posible pasar por alto o sobreescribir de manera accidental los cambios hechos por otra persona. El sistema de control de versiones notifica automáticamente a los usuarios cada vez que hay un conflicto entre el trabajo de una persona y la otra. Los equipos no son los únicos que se benefician del control de versiones: los investigadores independientes se pueden beneficiar en gran medida. Mantener un registro de qué ha cambiado, cuándo y por qué es extremadamente útil para todos los investigadores si alguna vez necesitan retomar el proyecto en un momento posterior (e.g. un año después, cuando se ha desvanecido el recuerdo de los detalles).

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Alejandra Gonzalez-Beltran
Amy Olex
Belinda Weaver
Bradford Condon
butterflyskip
Casey Youngflesh
Daisie Huang
Dani Ledezma
dounia
Francisco Palm
Garrett Bachant
Heather Nunn
Hely Salgado
Ian Lee
Ivan Gonzalez
James E McClure
Javier Forment
Jimmy O'Donnell
Jonah Duckles
Katherine Koziar
Katrin Leinweber
K.E. Koziar
Kevin Alquicira
Kevin MF
Kurt Glaesemann
LauCIFASIS
Leticia Vega
Lex Nederbragt
Mark Woodbridge
Matias Andina
Matt Critchlow
Mingsheng Zhang
Nelly Sélem
Nima Hejazi
Nohemi Huanca Nunez
Olemis Lang
Paula Andrea Martinez
Peace Ossom Williamson
P. L. Lim
Rayna M Harris
Romualdo Zayas-Lagunas
Sarah Stevens
Saskia Hiltemann
Shirley Alquicira
Silvana Pereyra
Tom Morrell
Valentina Bonetti
Veronica Ikeshoji-Orlati
Veronica Jimenez
Date Added:
08/07/2020
Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature
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We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

Subject:
Psychology
Material Type:
Reading
Provider:
PLOS Biology
Author:
Denes Szucs
John P. A. Ioannidis
Date Added:
08/07/2020
Equivalence Tests: A Practical Primer for Tests, Correlations, and Meta-Analyses
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Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effect size of interest. The TOST procedure can be used to statistically reject the presence of effects large enough to be considered worthwhile. This practical primer with accompanying spreadsheet and R package enables psychologists to easily perform equivalence tests (and power analyses) by setting equivalence bounds based on standardized effect sizes and provides recommendations to prespecify equivalence bounds. Extending your statistical tool kit with equivalence tests is an easy way to improve your statistical and theoretical inferences.

Subject:
Psychology
Material Type:
Reading
Provider:
Social Psychological and Personality Science
Author:
Daniël Lakens
Date Added:
08/07/2020
Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses
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Rating

Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effect size of interest. The TOST procedure can be used to statistically reject the presence of effects large enough to be considered worthwhile. This practical primer with accompanying spreadsheet and R package enables psychologists to easily perform equivalence tests (and power analyses) by setting equivalence bounds based on standardized effect sizes and provides recommendations to prespecify equivalence bounds. Extending your statistical tool kit with equivalence tests is an easy way to improve your statistical and theoretical inferences.

Subject:
Psychology
Material Type:
Reading
Provider:
Social Psychological and Personality Science
Author:
Daniël Lakens
Date Added:
08/07/2020
FAQs - OSF Guides
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OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. How can it be free? How will OSF be useful to my research? What is a registration? Get your questions about OSF answered here.

Subject:
Computer Science
Information Science
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
File Management and Licensing – OSF Guides - Best Practices
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Best Practices Guides are a part of OSF Guides by the Center for Open Science. This Best Practices Guide covers File Management and Licensing, including: File naming Organizing files Licensing Version Control

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Student Guide
Provider:
Center for Open Science
Author:
Center for Open Science
Date Added:
08/07/2020
Foster Open Science
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The FOSTER portal is an e-learning platform that brings together the best training resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily workflows. Here you will find a growing collection of training materials. Many different users - from early-career researchers, to data managers, librarians, research administrators, and graduate schools - can benefit from the portal. In order to meet their needs, the existing materials will be extended from basic to more advanced-level resources. In addition, discipline-specific resources will be created.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Full Course
Provider:
FOSTER Open Science
Author:
FOSTER Open Science
Date Added:
08/07/2020
Genomics Workshop Overview
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Workshop overview for the Data Carpentry genomics curriculum. 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. This workshop teaches data management and analysis for genomics research including: best practices for organization of bioinformatics projects and data, use of command-line utilities, use of command-line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing. This workshop is designed to be taught over two full days of instruction. Please note that workshop materials for working with Genomics data in R are in “alpha” development. These lessons are available for review and for informal teaching experiences, but are not yet part of The Carpentries’ official lesson offerings. Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Genomics workshops.

Subject:
Computer Science
Information Science
Genetics
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amanda Charbonneau
Erin Alison Becker
François Michonneau
Jason Williams
Maneesha Sane
Matthew Kweskin
Muhammad Zohaib Anwar
Murray Cadzow
Paula Andrea Martinez
Taylor Reiter
Tracy Teal
Date Added:
08/07/2020