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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Collect & Analyze Data
Videos: Research Transparency and Reproducibility Training (RT2) – Washington, D.C. Data Management and De-identification
Nextjournal Dynamic Documents and Coding Practices
Nextjournal is a container tool with features like polyglot notebooks, automatic versioning and real-time collaboration.
Software Carpentry Data Management and De-identification
Software Carpentry offers online tutorials for data analysis including Version Control with Git, Using Databases and SQL, Programming with Python, Programming with R and Programming with MATLAB.
Whole Tale Data Management and De-identification
Whole Tale is an infrastructure that allows users to share data, methods and analysis protocols, and final research outputs in a single, executable object (“living publication” or “tale”) alongside any research publication. Learn more 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.
Course Syllabi for Open and Reproducible Methods Anthropology, Archaeology, and Ethnography
A collection of course syllabi from any discipline featuring content to examine or improve open and reproducible research practices. Housed on the OSF.
rOpenSci Packages Data Management and De-identification
These packages are carefully vetted, staff- and community-contributed R software tools that lower barriers to working with scientific data sources and data that support research applications on the web.
Jupyter Notebooks Data Visualization
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.
Docker Data Visualization
Docker is the world’s leading software container platform. Developers use Docker to eliminate “works on my machine” problems when collaborating on code with co-workers. Operators use Docker to run and manage apps side-by-side in isolated containers to get better compute density. Enterprises use Docker to build agile software delivery pipelines to ship new features faster, more securely and with confidence for both Linux and Windows Server apps.
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).
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.
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).
Github Training InterdisciplinaryVersion Control
GitHub training offers free and premium educational material from beginner to advance on GitHub.
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.
Implementing Reproducible Research Dynamic Documents and Coding Practices
Implementing Reproducible Research by Victoria Stodden, Friedrich Leisch, and Roger D. Peng covers many of the elements necessary for conducting and distributing reproducible research. The book focuses on the tools, practices, and dissemination platforms for ensuring reproducibility in computational science.
Git InterdisciplinaryVersion Control
Git is a free and widely-used version control system. It allows researchers to preserve, track, and revert to different versions of their project files in what are called Git Repositories. Software Carpentry offers useful tutorials for version control with Git. Github is a well-designed and popular host for Git repositories, and also offers a graphical application for managing repositories. It is used for sharing project files and collaborating. Github Guides are excellent tutorials for learning how to use Github.
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