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Data Management Plans

General Data Management Plan Template

This general template for a data management plan is based on general NSF guidelines. BE SURE TO CHECK YOUR CALL FOR PROPOSALS AND WITH YOUR DIRECTORATE FOR MORE SPECIFIC DMP CONTENT REQUIREMENTS. In cases where the expected content of a DMP is not specified by your funding body, using this template should enable you to cover all of the appropriate material. This material is largely based on the DMPTool template and the Digital Curation Centre guidance; we strongly encourage you to use DMPtool because it keeps all of the funder requirements up to date. The DMPTool allows you to create, maintain and archive your DMPs in one location.


1. Types of data & materials produced

Give a short description of the data and materials collected and created, and amounts (if known).

  • Description of data to be produced (experimental, observational, raw or derived, physical collections, models, images, etc.)
  • How data will be acquired (When? Where? Methods?)
  • How data will be processed (software used, algorithms, workflows, protocols)
  • File types and formats (CSV, tab-delimited, proprietary formats, naming conventions)
  • How much data will be generated? (range is OK)
  • Existing data: if existing data are used, what are their origins? Will your data be combined with existing data? What is the relationship between your and existing data?

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2. Documentation and Metadata

Metadata includes all of the contextual information that would be necessary for someone to understand, use or recreate your dataset (even many years from now). It may include methodological information, definition of variables, units, assumptions made, etc... Metadata can take many forms, from readme files manually created, to automatically generated metadata by sensors, to metadata recorded in a metadata standard. Recording part of the metadata in normalized schemas and metadata standards will enhance discoverability and make your metadata computer friendly. 

  • What metadata are needed
    • Any details that make data understandable and useable
  • How metadata will be created and/or captured
    • Lab notebooks? GPS units? Auto-saved on instrument? Manually entered?
  • What format will be used for metadata?
    • Standards for community (EML, ISO 19115, Dublin Core, etc... . 
    • Justification for format chosen

TIPS

  • Information about data documentation and metadata in the Research Data Services website
  • When possible use data standards and/or metadata standards. 
  • A list of research metadata standards compiled by the Research Data Alliance can be found here.
  • If you chose not to use a metadata standard, consider structuring metadata in the research project with your own template. 


3. Ethics and Intellectual Property

If you are doing Human Subjects research or working with other types of sensitive data explain  how you are going to manage the data to protect the identity of the participants if required.

  • How will you store the data?
  • Who will get access to the data?
  • How will you gain consent for preserving and sharing the data?
  • Explain how the research project will get an ethical review.

Are there any intellectual property issues that need to be addressed? If you are going to use a third party dataset, are there any restrictions that should be considered? 

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4. Storage and backup

Describe how the data will be stored.

Describe how the data will be backed up.

  •  Which locations
  • Automatic or manual
  • Who will be responsible
  • How will the data be recovered in the event of an accident

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5. Policies for Access and Sharing

This section is very important. Your funding agency needs to see that you have thought carefully about how you will prepare (manage) your data for sharing and how you will share your data with the public in reasonable time after the conclusion of your project. If the data are of a sensitive nature - human subject concerns, potential patentability, species/ecological endangerment concerns – such that public access is inappropriate, address here the means by which granular control and access will be achieved (e.g. formal consent agreements; anonymization of data; restricted access, only available within a secure network). 

 

  • How will potential users find out about your data?
  • When will you make the data available? 
  • How will you make the data available? (include resources needed to make the data available: equipment, systems, expertise, etc.)
  • What is the process for gaining access to the data? (by request, open-access repository, etc.)
  • Will access be chargeable?
  • Will you be using persistent identifiers (e.g. DOI) for your datasets?
  • Will a data sharing agreement or similar be required to share the dataset?


Explain how the policies & procedures you outlined above relate to the reuse and redistribution of your data. Identify who will be allowed to use your data, how they will be allowed to use it, and if they will be allowed to disseminate your data.

  • Will any permission restrictions need to be placed on the data? If you will restrict access to certain users explain why.
  • Are you going to attach any licenses to the dataset?

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  • Information about data sharing in the Research Data Services website
  • You are welcome to share your research data in OSU's institutional repository, ScholarsArchive@OSU
  • ScholarsArchive@OSU is a digital repository that promises preservation, discoverability, and persistency of digital objects and is supported by the OSU Libraries & Press. All datasets deposited to ScholarsArchive@OSU will be assigned DOIs, and metadata for discoverability, usability, and administrative needs of the data.

  • A list of repositories can be found in re3data.org. Make sure that you evaluate the reliability of a repository first, if you choose it from this website without knowing much about it. 


6. Plans for archiving data, samples, and other research products, and for preservation of access to them
This section should cover your long-term strategy for preserving the data produced during your project. See section on data archiving.

  • What data will be preserved for the long-term?
  • What is the long-term strategy for maintaining, curating and archiving the data?
  • Where will it be preserved?
    • Which archive/repository/database have you identified as a place to deposit data?
    • What procedures does your intended long-term data storage facility have in place for preservation and backup?
    • How long will/should data be kept beyond the life of the project?
  • Data transformations/formats needed
    • What transformations will be necessary to prepare data for preservation / data sharing?
    • What metadata/ documentation will be submitted alongside the data or created on deposit/ transformation in order to make the data reusable?
    • What related information will be deposited?

TIPS:

  • Information about data archival and preservation in the Research Data Services website
  • You are welcome to preserve your research data in OSU's institutional repository, ScholarsArchive@OSU
  • ScholarsArchive@OSU is a digital repository that promises preservation, discoverability, and persistency of digital objects and is supported by the OSU Libraries & Press. All datasets deposited to ScholarsArchive@OSU will be assigned DOIs, and metadata for discoverability, usability, and administrative needs of the data.

  • Sometimes the sharing and preservation section of a Data Management Plan overlap if you are using a repository as both a sharing and a preservation strategy. 

7. Roles and responsibilities

Who will be responsible for making sure that the Data Management Plan will be implemented? Who will perform each of the data management tasks (data collection, data assurance and quality control, data documentation, data archiving, etc.). What are the procedures in place that ensure that if one member of the team leaves the project data management tasks will still be performed?


8. Budget
You are encouraged to ask for funding support for the management and preservation of your data. Consider not only hardware costs, but also the personnel time needed to maintain them. 

  • Time for data preparation and documentation