Use the UC Berkeley Data Services Guide to find out what campus entity can help with your data questions.
Campus entities include:
Question categories include:
Beginning January 2023, research funded by NIH will require a data management plan (DMP).
Here is an article from Nature: NIH issues a seismic mandate: share data publicly. Nature 602, 558-559 (2022).
The UC Berkeley Library Data Services has created a DMP template you can use for creating an NIH-compliant data management plan. This template was created using the DMPtool which has much guidance on creating data management plans.
Once your project is complete, share and archive your data in a data repository, such as Dryad.
UC Berkeley's Research Data Management Program is available to consult before, during and after on your research on writing data management plans, encryption and security, metadata enrichment, data publishing and sharing, analysis and workflows, and more. Consultants provide individual and group consultations and training for researchers in all disciplines.
A data management plan is a formal document that outlines:
Creating a data management plan will save you time by creating a clear structure for organizing your data throughout the research life cycle, and ensures that you and others will be able to use and understand your data in the future.
Set up and document workflows to ensure that data and other research outputs are secure. This includes properly backing up, protecting, and archiving data.
Start by following the 3-2-1 rule:
Resource for getting started: check out the active research data guidance grid to learn more about data types and storage options at UC Berkeley.
Upon completion of a project, select an archival data repository to publish your research data outputs. Repositories ensure that your data will be stored and can be accessed for future use, either by you or other researchers. Publishers and funding institutions have guidelines to address data access and archiving through using trusted data repositories that ensure long term archiving and discoverability.
By properly archiving data and other outputs, research is more likely to be cited, reused, and discovered in search engines.