Beginning January 18, 2011, NSF grant proposals must include a supplementary document of no more than two pages titled, "Data Management Plan." Details
for this NSF policy are available here.
Other funders, including NIH, have also begun to require data management plans for grant proposals.
For more information on the context of this new NSF requirement, see the very comprehensive "Unpacking the NSF Requirement" from the Association for Research Libraries (ARL).
Raynor Memorial Libraries have put together a set of resources to help you understand, plan for, and implement data management plans for your research.
Data management plans should describe how research results and data created in the course of grant-funded research will be managed, disseminated, and shared. The data management plan is required (your proposal will not be reviewed if a plan is not included, or you do not make a clear case for why a plan is not necessary) and, like the rest of your proposal, is subject to peer-review.
Since the NSF's announcement, many libraries and data centers have drafted guides to help researchers write and implement their data management plan. The Libraries have put together an annotated "guide to the guides" in an effort to help you locate the most relevant advice for your own research needs. This list will be continually updated.
While ICPSR is a social science organization, the data management framework they have developed is valuable across disciplines. The framework describes what ICPSR has determined to be the key elements of a good data management plan, the relative importance of each element, and the rationale for including this information in your plan, along with examples.
The Data Conservancy, an initiative based at Johns Hopkins University and aimed at developing "data curation infrastructure for cross disciplinary discovery of observational data" has developed a Questionnaire(Word doc) for NSF data management plans that they describe as including, "common elements across NSF directorates." The Data Conservancy adds this caveat: "It is not intended to address the elements or requirements of a particular directorate which may identify additional conditions."
Several researchers at UC San Diego have agreed to make their actual proposed data management plans openly available as examples. As of March 2011, examples include proposals in Engineering, Geoscience, Cyberinfrastructure, Integrative Activities, and two proposals that cross multiple directorates and offices.
Documenting your data
An important step toward making your data useful both to you and other researchers is to develop a framework for documenting and describing your data and the context in which it was created. The Pennsylvania State University Libraries suggest that data documentation might include the following:
Describing your data with Metadata
Metadata is the data used to describe your data. This makes it easier to store and locate your data, and makes it much easier for future researchers to use your data. A number of metadata schemas exist to help you organize and structure your data description. Metadata schemas can be viewed at the JISC Digital Media website.
The Libraries at MIT have put together a guide to the most basic elements to document, regardless of discipline. These include Title, Creator, Identifier, Subject, Funders, Rights, Access information, Language, Dates, Location, Methodology, Data processing, Sources, List of file names, File Formats, File structure, Variable list, Code lists, Versions, and Checksums. For more detail, see the MIT Libraries' guide to metadata for data management.
For a more comprehensive overview of metadata in general: NISO distinguishes between three types of metadata: descriptive, structural and administrative. Descriptive metadata is the information used to search and locate an object such as title, author, subjects, keywords, publisher; structural metadata gives a description of how the components of the object are organized; and administrative metadata refers to the technical information including file type. Two sub-types of administrative metadata are rights management metadata and preservation metadata.
The methodology you choose for managing your data will vary depending on the collection method, nature of the data, and the types of analyses to be applied. Some more common methods for managing data are databases, spreadsheets, data management tools, and standard file systems. The lists below summarize the benefits of each approach and provide links to further resources on the Web.
For more information on the basic organization of your data files, see the MIT Libraries Guide to "Organizing Files"
Storing and backing up your data
Where data is stored and backed up may depend on funding considerations, collection processes, the need for encryption or increased security, and available resources. Data storage locations may include one or all of the following options: an internal or external hard drive on a personal computer, a departmental or university server, an institutional repository such as e-Publications@Marquette, or cloud storage such as Amazon S3. Subject archives and data repositories, such as Genbank, may also be an option, depending on your discipline, the nature of your data, funding guidelines, and other issues. See the "Sharing Data" tab for more information on external data repositories.
Securing your data
Know the implications of working with confidential, sensitive, or proprietary data. Restrictions upon the ownership or sharing of student, patient, or other personal data may be governed by federal HIPPA, or FERPA guidelines. Marquette's Office of Research Compliance can help researchers working with sensitive data.
NSF guidelines require grantees to detail how they will disseminate and share their research results: "Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants." NSF Award and Administration Guide, Chapter VI.D.4
Why is sharing data important?
Some data may not be shared, based on policies from funding agencies or other relevant bodies. One example is the HIPPA (Health Insurance Portability and Accountability Act) Privacy Rule, which protects all "individually identifiable health information" derived from health care records and requires specification of data handling responsibilities. Marquette's Office of Research Compliance can help researchers working with sensitive data.
Some issues you may want to consider:
Consider the following options:
The Distributed Data Curation Center (D2C2) at Purdue University Libraries has put together Databib a listing of data repositories. These are repositories where researchers may be able to deposit and share their research data. The list is both browse-able and searchable.