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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Videos: Research Transparency and Reproducibility Training (RT2) – Washington, D.C. Data Management and De-identification
NRIN Collection of Resources on Research Integrity Data Management and De-identification
Course materials: PhD Toolkit on Transparent, Open, and Reproducible Research Economics and Finance
Catalyst Ada Gonzalez-Torres developed and delivered a PhD course on Transparent, Open, and Reproducible Research for PhD students at the European University Institute (EUI), in Florence, Italy. Find all course materials here.
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.
Transparency Training Module for Undergraduate Experimental Economics Dynamic Documents and Coding Practices
These materials were used in the final weeks of an undergraduate course experimental economics at Wesleyan University taught by Professor Jeffrey Naecker.
These materials were developed as part of a BITSS Catalyst Training Project “Incorporating Reproducibility and Transparency in an Undergraduate Economics Course” led by Catalyst Jeffrey Naecker.
PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. PRISMA focuses on the reporting of reviews evaluating randomized trials, but can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of interventions.
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.
JASP Dynamic Documents and Coding Practices
JASP is a cross-platform software program with a state-of-the-art graphical user interface. The JASP interface allows you to conduct statistical analyses in seconds, and without having to learn programming or risking a programming mistake. JASP is statistically inclusive as it offers both frequentist and Bayesian analysis methods. Open source and free of charge.
The p-uniform package provides meta-analysis methods that correct for publication bias. Three methods are currently included in the package. The p-uniform method can be used for estimating effect size, testing the null hypothesis of no effect, and testing for publication bias. The second method in the package is the hybrid method. The hybrid method is a meta-analysis method for combining an original study and replication and while taking into account statistical significance of the original study. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The third method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method. This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null hypothesis significance testing.
DMAS Economics and Finance
The Distributed Meta-Analysis System is an online tool to help scientists analyze, explore, combine, and communicate results from existing empirical studies. It’s primary purpose it to support meta-analyses, by providing a database for empirically estimated models and methods to integrate their results. The current version supports a range of tools that are useful for analyzing empirical climate impact results, but it’s creators intend to expand its applicability to other fields including social sciences, medicine, ecology, and geophysics.
Metalab Data Visualization
MetaLab is a research tool for aggregating across studies in the language acquisition literature. Currently, MetaLab contains 887 effect sizes across meta-analyses in 13 domains of language acquisition, based on data from 252 papers collecting 11363 subjects. These studies can be used to obtain better estimates of effect sizes across different domains, methods, and ages. Using our power calculator, researchers can use these estimates to plan appropriate sample sizes for prospective studies. More generally, MetaLab can be used as a theoretical tool for exploring patterns in development across language acquisition domains.
Transparent and Open Social Science Research Dynamic Documents and Coding Practices
Demand is growing for evidence-based policymaking, but there is also growing recognition in the social science community that limited transparency and openness in research have contributed to widespread problems. With this course created by BITSS, you can explore the causes of limited transparency in social science research, as well as tools to make your own work more open and reproducible.
You can access the course videos for self-paced learning on the BITSS YouTube channel here, (also available with subtitles in French here). You can also enroll for free during curated course runs on the FutureLearn platform.
Curate Science Issues with transparency and reproducibility
Curate Science is a crowd-sourced platform to track, organize, and interpret replications of published findings in the social sciences. Curated replication study characteristics include links to PDFs, open/public data, open/public materials, pre-registered protocols, independent variables (IVs), outcome variables (DVs), replication type, replication design differences, and links to associated evidence collections that feature meta-analytic forest plots.
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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