Welcome! This guide is designed to centralize information about working with research data. It contains guidance and information about tools and resources to help OHSU faculty, students and staff manage data effectively. Below you'll find an overview of the guide's contents and common definitions of what research data is and includes.

Navigating OHSU Resources and Data Management Plans:

Data Practices and Resources

In general, research data, which may also be referred to as scientific data, underlying data, or the data that is integral to a publication, is the factual information and materials needed to substantiate research findings. This can include raw, processed, and analyzed data as well as code, software, and tangible research materials.

Here are some examples of funders' definitions of research data:

Scientific Data: The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications. Scientific data do not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects, such as laboratory specimens.
Final NIH Policy for Data Management and Sharing

In general, data, software, and tangible research materials are integral to a publication if they are necessary to support the major claims of the publication or to reproduce and verify the published results.
Sharing Published Materials/Responsibilities of HHMI Authors

Underlying data encompasses all primary data, associated metadata, and any additional relevant data necessary to understand, assess, and replicate the reported study findings in totality. Underlying data can be compiled into any file type, including any necessary access instructions, code, or supporting information files, to ensure the file(s) can be accessed and used by others.
Note: We do not require sharing of data that is ethically unsound or legally encumbered.
Bill & Melinda Gates Foundation Open Access Policy Data Sharing Requirements

The types of data that may be subject to a data management and sharing policy may include:

• Primary data
• Datasets that have been cleaned and processed for final analysis
• Data that supports findings and figures in publications
• Data required to reproduce or replicate published findings or figures
• Computational artefacts such as models, algorithms, scripts, code, or software
• Metadata that describes all the above

Implementing good data management practices can make data sharing easier.

Data management and sharing policies generally do not require you to share:

• Raw data
• Secondary data
• Protocols
• Laboratory notebooks
• Measurements from laboratory or field equipment
• Survey responses, transcripts, and codebooks
• Completed case report forms
• Physical objects, such as slides, artifacts, specimens, or samples
• Preliminary analyses
• Drafts of scientific papers
• Plans for future research
• Peer reviews
• Communications with colleagues
• Trade secrets
• Commercial information
• Materials that must be kept confidential until publication
• Data or information that would unnecessarily invade personal privacy
• Data that could be used to identify individual participants in research studies
• Data that is legally encumbered or protected by law

While sharing these types of data is not required by data management and sharing policies, open and reproducible science practices increasingly encourage sharing protocols and samples, and all types of data may all benefit from good data management practices.