Research Data Management

1. Funding - Many sources of funding internal and external require your plan to be submitted during your proposal. AND they expect it to be followed as part of fulfilling your agreement. Different funding agencies have different requirements so be sure to check them and follow them precisely.


2. Transparency:
   • Increase the impact of your research - show the funders that their investment in your project was worthwhile and you’ll be more likely to attract funds in the future. Besides, what is the point of your research? Is it just so you can publish a paper or is it to improve something?
   • Increase the usefulness of your research - embed yourself in the academic conversation, let your research be a building block for the future of your field, encourage others to build on what you have started.
   • Increase the trustworthiness of your research - We are always telling our students, “Don’t believe everything you read” So give your fellow researchers a way to check your results.
   • Increase your own professional prestige and that of the college by putting your name on high quality, VERIFIABLE research. More people using your data and the research that is based on it, means more times you get cited.
      
3. Plan ahead - Save yourself time, and frustration, ensure that the method of collection and recording will be compatible with your future needs.

A Data Management Plan (DMP) is a formal document that describes how you will handle your data during the course of your research and at the conclusion of your study or project. 

A Data Sharing Plan (DSP) is a document that describes how you plan to disseminate your data at the conclusion of a research project. 
These two documents may be separate or combined, and DMP and DSP are often used interchangeably. 

https://guides.lib.umich.edu/datamanagement/planning

Writing a Data Management Plan - Informational Bulletin from the University of Michigan Library.

DMP Tool: Creation Wizard

DMP Tool (DMP Creation Wizard)

Guidance from ICPSR on creating your Data Management Plan (DMP):

Guidelines for Effective Data Management Plans

Framework for Creating a Data Management Plan

Elements of a Data Management Plan

Guidance from the Digital Curation Centre:

How to Develop a Data Management and Sharing Plan

Example Plans: 

UC San Diego Sample NSF Data Management Plans 

This Guide from UM includes templates for creating Data Management Plans for many of the important funding agencies in engineering research. Including NSF, DoE, and DOD, NASA, and DOT.

Check Lists:

Project Close-Out Checklist for Research Data

Sharing data is one way that researchers contribute knowledge and build up the scientific record. When data is shared, research can be replicated and new research to be conducted with the data. Additionally, if the data is collected in a standardized way, it can be combined with other data sets to help inform larger-scale research. It is useful to consider if and how you will share your data prior to beginning your project since it can affect the methods you use to collect your data. Here are some things to consider when sharing your data.

What data can/will be shared?
Will de-identified individual participant data be made available?
What other documents need to be shared to make this data usable?
When will the data be available?
Will access to the data be restricted in any way?
How will the data be made available?
Additionally, many funding agencies and respected journals require data to be shared in order to receive funding or have your paper published. Learn more about these requirements under the "Data Management/Sharing Plan" page of this guide.
 

Summary from Ann Arbor

NSF Data Management Plan Requirements

NEH Data Management Plan Requirements

NIH Data Management Plan Requirements

Although DMP requirements vary by funding agency, your plan will typically need to address the following topics:

Data Description:

• What type of data is it - numeric, text, images? What format is it in?
• How much data will there be?
• How will the data be collected or generated, and for how long? What tools and methodologies will be used?
• Will you be using secondary data? What is the source of the data?
• Who is responsible for managing the data and implementing the data management plan?

Data and Metadata Standards:

• What data and metadata standards will be used? If there are no existing standards, how will this be addressed?
• What file formats and naming conventions will be used? How will the data be organized?
• How will the metadata be managed and stored?

Data Access, Sharing, and Re-Use:

• Does the project involve human subject data? If so, what are your plans to protect and anonymize the data?
• Are there any intellectual property considerations that need to be addressed?
• Are there any patent or licensing restrictions to be considered?
• How should the data be attributed?

Archiving and Preservation:

• Where will the data be archived, and for how long?
• Is there a discipline-specific repository available?
• What software or tools should be archived with the data to facilitate re-use?

Summary copied from https://guides.lib.umich.edu/datamanagement/planning

Roles & Responsibilities: Often a DMP will require an accounting of who will accept responsibility for tasks associated with data management.  DataONE offers best practices in assigning roles and responsibilities.

Expected Data: Be able to describe (roughly) how much data your project will generate, at what rate, and if it will be generated as multiple data sets. Note also how much of that data will be retained and why. 

Metadata and Standards: Think about how you will describe the data in such a way that you and others will find meaningful.  Often there are industry or community standards that should be used to ensure that your data can work with other data and are accessible by other researchers.

Formats: Describe the file formats of your data.  Whenever possible, data should be stored in stable, non-proprietary formats, preferably those based on open and published standards. 

Retention: Think about how long you want your data to be available.  Often there is a minimum requirement by the funding agency of 3, 5, or more years.  

Dissemination: Explain if, how, and when your data will be available. You may be able to offer the data from a repository like Deep Blue Data, or you may need to require a request to the project PI.  

Preservation: A key part of any DMP is describing how you will preserve your data.  It is good to plan on several preservation and back-up options.  When putting your data in a repository, note their commitment to long-term preservation and curation of the data.

Summary copied from https://guides.lib.umich.edu/c.php?g=492693&p=3370800