Resource Library

The BITSS Resource Library contains resources for learning, teaching, and practicing research transparency and reproducibility, including curricula, slide decks, books, guidelines, templates, software, and other tools. All resources are categorized by i) topic, ii) type, and iii) discipline. Filter results by applying criteria along these parameters or use the search bar to find what you’re looking for.

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176 Results

Open Research Calendar Data Management and De-identification+

Open Research Calendar is an open-source community tool that collates information on worldwide events related to open science and research.

Development Research in Practice : The DIME Analytics Data Handbook Data Management and De-identification+

“Development Research in Practice” leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data on how to handle data effectively, efficiently, and ethically. See an accompanying online course here.

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An Introduction to Open Science Interdisciplinary+

This presentation by Felix Schönbrodt gives an overview of the motivation for open science and an introduction to the research tools and practices commonly associated with open science. The slides are can be re-used and distributed under the CC BY license.

Reproducible Data Science with Python Data Visualization+

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.

 

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Framework for Open and Reproducible Research Training (FORRT) Data Management and De-identification+

FORRT is a pedagogical infrastructure designed to recognize and support the teaching and mentoring of open and reproducible science tenets in tandem with prototypical subject matters in higher education. FORRT also advocates for the opening of teaching and mentoring materials as a means to facilitate access, discovery, and learning to those who otherwise would be educationally disenfranchised.

 

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Data Sharing and Replication ReproducibilityTransparency

Find slides from a presentation by Garret Christensen titled “Data Sharing and Replication: Enabling Reproducible Research”.

Analysis Plans in Economics EconomicsPre-Analysis Plans

Find slides from a presentation by Benjamin Olken titled “Analysis Plans in Economics”.

Data Adaptive Pre-Specification Statistics and Data Science

Find slides from a presentation by Maya Petersen titled “Data Adaptive Pre-Specification for Experimental and Observational Data”.

Perspectives from Biomedical Research Health Sciences+

Find slides from a presentation by Maya Petersen titled “Pre-Registration, Pre-analysis, and Transparent Reporting: Perspectives from biomedical research”.

Protocols That Work MethodologySocial Science

Find slides from a presentation by Nick Adams titled “Protocols That Work”.

Pre-Analysis Plans (French) Pre-Analysis Plans

Find slides from a presentation by Zachary Tsala Dimbuene titled “Pre-Analysis Plans (French)”.

Implementing an RTR Strategy Issues with transparency and reproducibility

Find slides from a presentation by Arnaud Vaganay titled “Implementing an RTR Strategy”.

Drafting RTR Guidelines Issues with transparency and reproducibility

Find slides from a presentation by Arnaud Vaganay titled “Drafting RTR Guidelines”.

Gates Open Research Interdisciplinary

Find slides from a presentation by the Center for Effective Global Action (CEGA) titled “Gates Open Research”.

Data Citations module Data Management and De-identification+

Created by the Federal Reserve Bank of St. Louis, this module introduces students to the key elements of data citations. See also related modules for Data Literacy.

Handbook on Using Administrative Data for Research and Evidence-Based Policy Data Management and De-identification+

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.

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Survey of Registered Reports Editors Interdisciplinary+

Between December 15, 2017 and January 31, 2018, BITSS surveyed the editors of 76 academic journals which at the time, accepted submissions in the Registered Report (RR) format. Find summary statistics of the results in this document.

CRediT (Contributor Roles Taxonomy) InterdisciplinaryTransparent Reporting

CRediT (Contributor Roles Taxonomy) is high-level taxonomy, including 14 roles, that can be used to represent the roles typically played by contributors to scientific scholarly output. The roles describe each contributor’s specific contribution to the scholarly output.

Comparison of multiple hypothesis testing commands in Stata Economics+

In this post on the Development Impact blog, David McKenzie (World Bank) compares various Stata packages used for multiple hypothesis testing adjustments and discusses settings where each package is best applied.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Educational Expansion Epidemiology+

Created by Catalyst Melissa Sharp, this is an open-source repository for epidemiological research methods and reporting skills for observational studies, structured based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. Use it to discover new methods and reporting guidelines and contribute through the GitHub repository (https://github.com/sharpmel/STROBECourse/).

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Data for Development Impact (Resource Guide) Data Management and De-identification+

“Data for Development Impact: The DIME Analytics Resource Guide” is intended to serve as an introduction to the primary tasks required in development research, from experimental design to data collection to data analysis to publication. It serves as a companion to the DIME Wiki and is produced by DIME Analytics.

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Open Science Module for Behavioral Science graduate course Economics+

Instructors Kelly Zhang (MIT GOV/LAB) and Chaning Jang (Busara) integrated a module on research transparency and the use of pre-analysis plans as part of the Behavioral Science in the Field course designed for graduate students who use behavioral science games as part of their research.

J-PAL Guide to De-Identifying Data Data Management and De-identification+

Developed by J-PAL’s Sarah Kooper, Anja Sautmann, and James Turrito, this guide includes:

  • An overview of personally identifiable information (PII) and the responsibility of data users not to use data to try to identify human subjects
  • Recommendations for handling direct identifiers (such as full name, social security number, or phone number), as well as indirect identifiers (such as month/year of birth, nationality, or gender)
  • Guidance on de-identification steps to take throughout the research process, such as encrypting all data containing identifying information as soon as possible
  • A list of common identifiers, including those labeled by the United States’ Health Insurance Portability and Accountability Act (HIPAA) guidelines as direct identifiers
  • And more.

See also the accompanying Guide to Publishing Research Data.

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J-PAL Guide to Publishing Research Data Data Management and De-identification+

Developed by J-PAL’s Sarah Kooper, Anja Sautmann, and James Turrito, this guide includes:

  • A list of considerations to make before publishing data, such as what information was provided to study participants and the IRB, the sensitivity of the data collected, and legal requirements
  • Sample consent form language that will allow future publication of de-identified data
  • A checklist for preparing data for publication
  • And more.

See also the accompanying Guide to De-identifying Data.

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Data Sharing Checklist for NGOs and Practitioners Data Management and De-identification+

This checklist developed by Teamscope can help NGOs and Practitioners understand the common pitfalls in open data, and how open data impacts every step of a project’s pipeline, from proposal writing to dissemination.

Videos: Research Transparency and Reproducibility Training (RT2) – Washington, D.C. Data Management and De-identification+

BITSS hosted a Research Transparency and Reproducibility Training (RT2) in Washington DC, September 11-13, 2019. This was the eighth training event of this kind organized by BITSS since 2014.

RT2 provides participants with an overview of tools and best practices for transparent and reproducible social science research. Click here to videos of presentations given during the training. Find slide decks and other useful materials on this OSF project page (https://osf.io/3mxrw/).

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Replicability Seminar Issues with transparency and reproducibility+

Course syllabus for “Replicability Seminar”, an advanced undergraduate and graduate-level course led by Simine Vazire.

Open Data Metrics: Lighting the Fire Data Management and De-identification+

In this book, Daniella Lowenberg and colleagues describe the journey towards open data metrics, prompting community discussion and providing implementation examples along the way. Data metrics are a pre-condition to realize the benefits of open data sharing practices.