Since January 25, 2023, the new National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS) has been in effect for all grant applications. Under this policy, any funded research that generates scientific data must have an approved data management and sharing plan in place. We encourage investigators to review the following helpful resources:
- NIH DMS Policy Overview
- NIH DMS Policy FAQs
- NIH DMS plan template (MSWord) and example from Bonnie LaFleur (MSWord)
- NIH website on scientific data sharing
- DMPTool to help build your Data Management Plan
Below is a list of useful websites for grants.
- Sponsored Projects Services - a plethora of information regarding grants, specific to U of A as well as offering links to external resources.
- Grants dot Gov - the predominant grants website; used to find government funding sources, apply for grants and to track submittals.
- U of A Foundation - the center supports University of Arizona personnel with non-governmental grantseeking activities.
- Grantspace - an online short course in proposal writing provided by the Foundation Center.
- University Information Technology Services (UITS) Statistics help
- Data Science Institute: many services, including but not limited to the following:
- Data Science Drop-In Hours (data management, sharing, visualization, and analysis platforms and techniques; Python; R; RStudio; Jupyter Notebooks; and more)
- Data & Viz Drop-In (programming in R & python, data management, data visualization)
Many researchers contact Stat Lab for advice on how to conduct a systematic review or meta-analysis. We recommend the following resources provided by Cochrane, because Cochrane Reviews are recognized internationally as representing a gold standard for high-quality, trusted systematic reviews and meta-analyses. The Cochrane Handbook walks through all the steps for writing a systematic review, from starting the review, to determining the scope and questions, to deciding the inclusion and exclusion criteria and many more steps, to finally interpreting results.
The following calculators (a small selection of many available online) may be useful for obtaining sample size estimates in ideal "textbook" situations. Many project scenarios require more thorough consideration of design and assumptions, however. Researchers are encouraged to work with a statistician to determine most appropriate sample sizes for achieving project aims.
- UCSF Clinical & Translational Science Institute sample size calculators for designing clinical research
- Sealed Envelope sample size calculator for binary outcome superiority trial (find calculators for other outcomes and trial types in tab at upper right titled Power Calculators)