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.
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.
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:
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.
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:
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 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:
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.
Broman, K. W., & Woo, K. H. (2018). Data organization in spreadsheets. The American Statistician, 72(1), 2-10. https://doi.org/10.1080/00031305.2017.1375989
Shannon E. Ellis & Jeffrey T. Leek (2018) How to share data for collaboration, The American Statistician, 72(1), 53-57 https://doi.org/10.1080/00031305.2017.1375987
Wickham H. (2014) Tidy data. Journal of Statistical Software, 59(1), 1-23. https:// doi.org/10.18637/jss.v059.i10
Borghi, J.A. et al. (2018). Support Your Data: A research data management guide for researchers. Research Ideas and Outcomes, 4, e26439. https://doi.org/10.3897/rio.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. https://doi.org/10.1371/journal.pcbi.1003542
Wilkinson, M. D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
Wilson, G. et al. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), e1005510. https://doi.org/10.1371/journal.pcbi.1005510
Borgman, C.L. (2012). The conundrum of sharing research data. Journal of the Association for Information Science and Technology, 63(6), 1059–1078. https://doi.org/10.1002/asi.22634
Carroll, M.W. (2015). Sharing research data and intellectual property law: A primer. PLOS Biology, 13(8), https://doi.org/10.1371/journal.pbio.1002235
Meyer, M. N. (2018). Practical tips for ethical data sharing. Advances in Methods and Practices in Psychological Science, 1(1) 131–144. https://doi.org/10.1177/2515245917747656
Morin A, Urban J, Sliz P (2012). A quick guide to software licensing for the scientist-programmer. PLOS Computational Biology 8(7), e1002598. https://doi.org/10.1371/journal.pcbi.1002598