IEEE DataPort is a universally accessible data platform that enables users to store, share, access, analyze and manage datasets from across all research disciplines. IEEE DataPort supports Open Data and provides a hosting platform for Data Competitions.
Catalyst Thomas Brailey developed a set of training materials to help transition J-PAL’s Payments and Governance Research Program (PGRP) towards a version-controlled research pipeline by onboarding all research team members to GitHub, GitHub desktop, and R. These teaching materials can be applied to onboard other research/lab teams across a variety of contexts in social science research.
Developed by Sean Higgins (Northwestern University) and colleagues, this guide provides instructions for using R on research projects. Its purpose is to use with collaborators and research assistants to make code consistent, easier to read, transparent, and reproducible. See also the Python Guide and Stata Guide.
The Social Science Reproduction Platform crowdsources and catalogs attempts to assess and improve the computational reproducibility of social science research. Instructors can use the SSRP in applied social science courses at the graduate or undergraduate levels to teach fundamental concepts, methods, and reproducible research practices. Get started by creating a free account and browsing some of the completed reproductions! Instructors can start by reviewing the guide for instructors, which contains tips and resources for teaching and grading reproductions using the platform.
The template README follows best practices as defined by a number of data editors at social science journals. A full list of endorsers is listed in Endorsers. The most recent version is available at https://social-science-data-editors.github.io/template_README/. Specific releases can be found at https://github.com/social-science-data-editors/template_README/releases. The template README is available in a variety of formats, including HTML (best for reading), LaTeX, Word, PDF, and Markdown.
The TIER Protocol specifies the contents and organization of reproduction documentation for a project involving computations with statistical data.
Open Research Calendar is an open-source community tool that collates information on worldwide events related to open science and research.
Written by Valentin Danchev, “Reproducible Data Science with Python” is a textbook that uses real-world social data sets related to the COVID-19 pandemic to provide an accessible introduction to open, reproducible, and ethical data analysis using hands-on Python coding, modern open-source computational tools, and data science techniques. Topics include open reproducible research workflows, data wrangling, exploratory data analysis, data visualization, pattern discovery (e.g., clustering), prediction & machine learning, causal inference, and network analysis.
Find slides from a presentation by Johanna Cohoon and Caner Uguz titled “Using the OSF (Open Science Framework)”.
Find slides from a presentation by Garret Christensen titled “Data Sharing and Replication: Enabling Reproducible Research”.
Find slides from a presentation by Mercè Crosas titled “Dataverse: Research Transparency through Data Sharing”.
Find slides from a presentation by Victoria Stodden titled “Framing Transparency in Research: Issues and Opportunities”.
Find slides from a presentation by Garret Christensen titled “ Software and Workflow for Reproducible Research”.
Co-edited by Shawn Cole, Iqbal Dhaliwal, Anja Sautmann, and Lars Vilhuber and published by J-PAL’s Innovations in Data and Experiments for Action Initiative (IDEA), this handbook includes case studies of large-scale randomized evaluations using private and national government administrative data, and technical guidance to support partnerships with governments, nonprofits, or firms to access data and pursue cutting-edge, policy-relevant projects.
