Data Management and Sharing

Lane Classes and Workshops

Lane Library leads a variety of data-related workshops, discussion groups, and demos throughout the year. See below for information about what data-related events we have coming up soon or head over to our classes and events page to see our full schedule.

Data Resources at Stanford Medicine

In addition to Lane Library, there are a number of experts and services at your disposal as a member of the Stanford Medicine community. The list below includes services that are available to help you address questions related to data collection, analysis, and storage. For help with any of Stanford's cluster services (Sherlock, Nero, Farmshare, etc), contact SRCC support.

Please note that some of the services below operate on a cost-recovery model or through special arrangements with specific departments or units.

Statistics Consulting by the Department of Statistics

The Department of Statistics offers a free online consulting service to members of the broader research community during each Stanford academic quarter. Under the supervision of a senior faculty member, Statistics graduate students arrange meetings with clients to help with statistical research questions in areas such as:

  • Experimental design and data acquisition
  • Data exploration, analysis, and interpretation
  • Modeling data and model fitting
  • Statistical inference for estimation, testing, and prediction
Social Science Data and Software

Social Science Data and Software (SSDS) is a group within the Stanford Libraries that provides services and support to Stanford faculty, staff, and students in the acquisition, curation, and preservation of social science data and the selection and use of quantitative (statistical) and qualitative analysis software. SSDS staff members provide these services in a variety of ways that include consulting, workshops, and help documentation. 

Quantitative Science Unit

The Quantitative Sciences Unit (QSU) is a unit of statistical scientists in the Department of Medicine who engage in interdisciplinary research.  Members of the QSU are available to collaborate on study design and analysis for medical studies.  The QSU offers professional data analysis using the most modern statistical techniques and secure HIPAA- and IRB-compliant management and coordination of data. 

The QSU facilitates medical research for faculty on the medical campus in the following areas:

  • Study design
  • Data analysis
  • Methods and software development
  • Data management and coordination
  • Education and training
Data Studio

The Data Studio is a collaboration between Spectrum (The Stanford Center for Clinical and Translational Research and Education) and the Department of Biomedical Data Science. The Data Studio is open to the Stanford community, and we expect it to have educational value for students and postdocs interested in biomedical data science. Most sessions are an extensive and in-depth consultation for a Medical School researcher based on research questions, data, statistical models, and other material prepared by the researcher with the aid of a facilitator. The last session of each month is devoted to drop-in consulting. BDS faculty are available to provide assistance with your research questions. Bring any data, prior analyses, or other materials that you have. No advance notification is required. 

Research IT

Research IT has built and operated STRIDE since 2008 and Stanford REDCap since 2010. These resources are paid for by Dean's Office and support 1000s of researchers at Stanford. Their platforms meet Stanford regulatory requirements, are petascale, cloud-enabled, and use a variety of sophisticated technologies. Research IT also uses their expertise to support smaller projects via our consultation services. They have expertise in a number of areas including:

  • Data management strategies: Retrospective, prospective, real-time studies
  • Databases: Oracle, MySQL, PostgreSQL, Google Cloud Platform BigQuery
  • Software development: Multi-tier software, APIs, UI/UX, Java/PHP
  • Common Data Models: i2b2, OHDSI OMOP, PEDSNet
  • Data types and sources: EHR, imaging, omics, environmental, population health, IoT, patient- and device-reported
  • Data collection and storage: Electronic data capture, survey platforms, mobile apps
  • Analytical environments: Servers, High Performance Computing, Cloud
  • Regulatory: HIPAA, 21CFR11, FISMA, NIH dbGaP
  • Security and Privacy: De-identification, Stanford Minimum Security standards
  • Stanford processes: Data Risk Assessments, Human Subject Research, IRB
  • Institutional, multi-party requirements: Data Use Agreements, Business Associate Agreements, Master Service Agreements
  • Hospital ecosystem: Clarity, HL7, SMART on FHIR

Further Reading

We recommend the following journal articles and books for additional information on data management and sharing-related practices. If you would like to suggest articles and books for this list, contact the guide administrator.

Data Organization

Broman, K. W., & Woo, K. H. (2018). Data organization in spreadsheets. The American Statistician, 72(1), 2-10.

Shannon E. Ellis & Jeffrey T. Leek (2018) How to share data for collaboration, The American Statistician, 72(1), 53-57

Wickham H. (2014) Tidy data. Journal of Statistical Software, 59(1), 1-23. https://

Data Management

Borghi, J.A. et al. (2018). Support Your Data: A research data management guide for researchers. Research Ideas and Outcomes, 4, e26439.

Briney, K. (2015). Data management for researchers: Organize, maintain, and share your data for research success. Pelagic Press: UK. [Get it from Stanford Libraries]

Goodman A, Pepe A, Blocker AW, Borgman CL, Cranmer K, et al. (2014) Ten simple rules for the care and feeding of scientific data. PLOS Computational Biology 10(4): e1003542.

Wilkinson, M. D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.

Wilson, G. et al. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510.

Data Sharing

Borgman, C.L. (2012). The conundrum of sharing research data. Journal of the Association for Information Science and Technology, 63(6), 1059–1078.

Carroll, M.W. (2015). Sharing research data and intellectual property law: A primer. PLOS Biology, 13(8),

Meyer, M. N. (2018). Practical tips for ethical data sharing. Advances in Methods and Practices in Psychological Science, 1(1) 131–144.

Morin A, Urban J, Sliz P (2012). A quick guide to software licensing for the scientist-programmer. PLOS Computational Biology 8(7), e1002598.