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.
Know of a great resource that we haven’t included or have questions about the existing resources? Email us!
J-PAL Guide to De-Identifying Data Data Management and De-identification
- 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.
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.
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/).
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.
ResonsibleData.io Data Management and De-identification
Using data for social change work offers many opportunities, but it brings challenges, too. The RD community develops practical ways to deal with the unintended consequences of using data in social change work, establishes best practices, and shares approaches between leading thinkers and doers from different sectors. We discuss thorny topics in-person, facilitate online group discussions on the RD mailing list, and share resources on this site.
NRIN Collection of Resources on Research Integrity Data Management and De-identification
PhD Course Materials: Transparent, Open, and Reproducible Policy Research Data Management and De-identification
BITSS Catalyst Sean Grant developed and delivered a PhD course on Transparent, Open, and Reproducible Policy Research at the Pardee RAND Graduate School in Policy Analysis. Find all course materials at the project’s OSF page.
Improving the Credibility of Social Science Research: A Practical Guide for Researchers Data Management and De-identification
SPARC (Scholarly Publishing and Academic Resources Coalition) Data Management and De-identification
This community resource for tracking, comparing, and understanding both current and future U.S. federal funder research data sharing policies is a joint project of SPARC & Johns Hopkins University Libraries.
Impact Evaluation in Practice Data Management and De-identification
The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policymakers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluation and the best ways to use impact evaluations to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation.
The New Statistics (+OSF Learning Page) Data Management and De-identification
This OSF project helps organize resources for teaching the “New Statistics”–an approach that emphasizes asking quantitative questions, focusing on effect sizes, using confidence intervals to express uncertainty about effect sizes, using modern data visualizations, seeking replication, and using meta-analysis as a matter of course (Cumming, 2011).
Handbook of the Modern Development Specialist Data Management and De-identification
Created by the Responsible Data Forum, this handbook is offered as a first attempt to understand what responsible data means in the context of international development programming. The authors have taken a broad view of development, opting not to be prescriptive about who the perfect “target audience” for this effort is within the space. This book builds on a number of resources and strategies developed in academia, human rights and advocacy, but aims to focus on international development practitioners. The handbook includes chapters on project design, data management, collection, analysis, sharing, and more.
OSF Data Management and De-identification
Open Science Framework (OSF) is part version control system, part data repository, part collaboration software that allows researchers to move study materials to the cloud, share and find materials, detail individual contributions, make research design more visible, and register materials to certify research design was not modified to alter outcomes. To increase workflow flexibility OSF offers a system where researchers can register a description of their study and its goals. The OSF emphasizes versatility with a very wide range of tools and features including add-ons from other related sites such as Dataverse and Github. Uploaded materials can also be archived and receive a Digital Object Identifier (DOI) or Archival Resource Key (ARK).
Dryad Data Management and De-identification
Dryad is a curated repository of data underlying peer-reviewed scientific and medical literature, particularly data for which no specialized repository exists. All material in Dryad is associated with a scholarly publication. Its notable features include easy integration into the manuscript submission workflow of its partner journals, the flexibility to make data privately available during peer review, and allowing submitters to set limited-term embargoes post-publication.
Qualitative Data Repository Data Management and De-identification
QDR selects, ingests, curates, archives, manages, durably preserves, and provides access to digital data used in qualitative and multi-method social inquiry. The repository develops and publicizes common standards and methodologically informed practices for these activities, as well as for the reusing and citing of qualitative data. Four beliefs underpin the repository’s mission: data that can be shared and reused should be; evidence-based claims should be made transparently; teaching is enriched by the use of well-documented data; and rigorous social science requires common understandings of its research methods.
Scan.R Data Management and De-identificationInterdisciplinary
Scan.R searches all Stata (.dta), SAS (.sas7bdat), and comma-separated values (.csv) files found in the specified directory for variables that may contain personally identifiable information (PII) using strings that commonly appear as part of variable names or labels that contain PII. (Note: Scan.R does not search labels in .csv files.) Results are displayed to the screen and saved to a comma-separated values file in the current working directory containing the variables and data flagged as potential PII.
Open Science Training Initiative Data Management and De-identification
Open Science Training Initiative (OSTI), provides a series of lectures in open science, data management, licensing and reproducibility, for use with graduate students and postdoctoral researchers. The lectures can be used individually as one-off information lectures in aspects of open science, or can be integrated into existing course curriculum. Content, slides and advice sheets for the lectures and other training materials are being gradually released on the GitHub repository as the official release versions become available.
Reproducible Research Data Management and De-identification
Reproducible Research taught by Roger D. Peng, Jeff Leek, and Brian Caffoof of Johns Hopkins University is a course on Coursera that teaches methods to organize data analysis so that it is reproducible and accessible to others. In this course students will learn to write a document using R markdown, integrate live R code into a literate statistical program and compile R markdown documents using knitr and related tools.
The Workflow of Data Analysis Using Stata Data Management and De-identification
Stata by J. Scott Long, explains how to manage aspects of data analysis including cleaning data; creating, renaming, and verifying variables; performing and presenting statistical analyses and producing replicable results.
Experimental Lab Standard Operating Procedures Data Management and De-identification
This standard operating procedure (SOP) document describes the default practices of the experimental research group led by Donald P. Green at Columbia University. These defaults apply to analytic decisions that have not been made explicit in pre-analysis plans (PAPs). They are not meant to override decisions that are laid out in PAPs. The contents of our lab’s SOP available for public use. We welcome others to copy or adapt it to suit their research purposes.
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