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Research Data Services @SDSU

Tools and services to help you manage your research data

Planning to Manage and Share your Data. What's in it for You?

You may be writing a data management plan (DMP) because your funder requires it, but there are other reasons to consider having a DMP.

  • You will be able to find and understand the data you have collected when you need it.

  • You can avoid unnecessary duplication.

  • You can easily share data with collaborators.

  • You can back up your results if required.

  • Your grant proposal will be more competitive if you have a good DMP.

Managing your data well also makes it easier to share your data. Some funders and journals require you to provide the data to support your articles, and sharing data has been shown to have many positive impacts. Sharing your data:

  • Allows wider dissemination of your work and can lead to more citations

  • Promotes scientific integrity and debate

  • Supports reproducibility.(1)

  • Leads to new collaborations between data users and data creators

  • Increases citations.(2)

  1. Collins, FS and LA Tabak (2014) Policy: NIH plans to enhance reproducibility. Nature, 505:612-613. January 30. doi:10.1038/505612a

  2. A study by Piwowar, Day and Fridsma showed a 69% increase in citation, http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0000308 
    based on: http://www.dcc.ac.uk/sites/default/files/documents/events/RDM-for-librarians/RDM-for-librarians-booklet.pdf

What can happen without a data management plan?

Making a Plan

Your plan needs to take into account the data type and format you will be collecting, how and where you will save the data, data standards for the subject area, data security, data sharing, and long-term access plans.

DMPTool is an easy to use template tool for creating dat management plans.  It will take you through the questions below and provide you with a ready to use plan for your grant.

Data Type and Format:

  • Data collection methods. Data formats.
  • Data size, per experiment and the whole data set.
  • Is the data reproducible?  i.e. climate data or data from lab experiment that can be run again.
  • Data organization plans and file structures.

Data Storage

  • Where will data be stored as it is collected and analyzed?
  • Who is backing up and where are backups kept?
  • Where will data be stored for retention (institutional policy)?
  • What format should be used to store data?

Data Standards

  • Are there standards for data collection and documentation in your subject area?
  • Is there a community standard for adding key words to your data for sharing and future use?

Data Security

  • Are there any special security considerations, e.g. HIPAA ?
  • Who controls the data? Who is responsible for the data?
  • Will any of the data require extra security or privacy?
  • Will there be an embargo on the data?

Data Sharing

  • Does your funder require sharing?
  • Who might want to use the data and how?
  • If there is a suitable subject repository, do they have any requirements for depositing data?
  • Is data saved in a way that will allow others to understand and use it?

Long-term Access

  • How long must the data be retained according to funder, institution, etc.?
  • Does the format need to be changed for long-term preservation?
  • Where can the data be stored?
  • Who will maintain it?

This is not an exhaustive list of considerations when developing a data management plan, but it will help you think about some of the things funders will be looking for when they review your plan.