Welcome to the Open Scholarship Knowledge Base! When you find that there is a gap in this knowledge base or have a good idea for a new resource to add, you can add your own by following this guide. This resource includes a how-to video showing the steps needed. It also includes written instructions on adding the resource, describing it, aligning it with the OSKB metadata, and making it discoverable.
Welcome to the Open Scholarship Knowledge Base! When you find that there is a gap in this knowledge base or have a good idea for a new resource to create, you can author your own by following this guide. This resource includes a how-to video showing the steps needed. It also includes written instructions on editing the resource, adding formatting and sections, aligning it with the OSKB metadata, and making it discoverable.
Purpose: To introduce methods and tools in organization, documentation, automation, and dissemination of research that nudge it further along the reproducibility spectrum.OutcomeParticipants feel more confident applying reproducibility methods and tools to their own research projects.ProcessParticipants practice new methods and tools with code and data during the workshop to explore what they do and how they might work in a research workflow. Participants can compare benefits of new practices and ask questions to help clarify which would provide them the most value to adopt.
Understanding reproducible research
Setting up a reproducible project
Preregistering your study
Keeping track of things
Sharing your work
An ecosystem of free open source tools for improving the rigor and reproducibility of research is thriving. Information professionals at research institutions must stay informed about what tools are available and how they compare. Ideally, information professionals can also onboard researchers to kickstart adoption of these tools. However, developing quality curriculum to train researchers on new tools requires expertise in the tool itself, which leaves many researchers without training on tools that may benefit their research.
This course will train participants to run hands-on, quality modules designed to onboard researchers to four free open source tools. Participants will experience each module, practice the exercises, and explore the training material needed to run the module themselves. An instructor guide that includes the module outline, objectives, description, frequently asked questions, pre- and post-participant surveys, target audience, and instructions for running a successful module is provided for each tool taught.
This course will train participants to run modules on unique aspects of four free open source tools for researchers:
Binder: Share your computational environment, code, and research notebooks.
Renku: Document and share your analysis pipelines.
Open Science Framework: Create a centralized, structured workspace for your research materials.
KnitR: Knit your R code with your analysis narrative in one executable research notebook and capture your dependencies.
Many participants already run short-duration training events at their institutions. This course is ideal for those participants who wish to improve the quality and variety of the training they already offer to researchers. Participants who do not currently run short-duration training events at their institutions will benefit from the course by learning an accessible and efficient way of getting started with these four modules.
Computational analyses are playing an increasingly central role in research. Journals, funders, and researchers are calling for published research to include associated data and code. However, many involved in research have not received training in best practices and tools for sharing code and data. This course aims to address this gap in training while also providing those who support researchers with curated best practices guidance and tools.This course is unique compared to other reproducibility courses due to its practical, step-by-step design. It is comprised of hands-on exercises to prepare research code and data for computationally reproducible publication. Although the course starts with some brief introductory information about computational reproducibility, the bulk of the course is guided work with data and code. Participants move through preparing research for reuse, organization, documentation, automation, and submitting their code and data to share. Tools that support reproducibility will be introduced (Code Ocean), but all lessons will be platform agnostic.Level: IntermediateIntended audience: The course is targeted at researchers and research support staff who are involved in the preparation and publication of research materials. Anyone with an interest in reproducible publication is welcome. The course is especially useful for those looking to learn practical steps for improving the computational reproducibility of their own research.
Reproducibility for Everyone produces resources to help grow researchers' awareness of and ability to do reproducible research.