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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Data Carpentry Lessons Data Management and De-identification
Developed by Data Carpentry, these lessons can be used across the social sciences to teach data cleaning, management, analysis, and visualization. R is the base language for instruction, and there are no pre-requisites in terms of prior knowledge about this topic.
Conda Data Visualization
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.
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.
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