Raynor Memorial Libraries offers more than 1.8 million volumes, hundreds of research databases, computer access, laptops on loan, a multimedia
collection, group study spaces, 24-hour access and library staff members who help researchers from around the world.
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
Writing the Plan
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".
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
names, labels and descriptions for variables, records and their values
explanation of codes and classification schemes used
codes of, and reasons for, missing values
derived data created after collection, with code, algorithm or command file used to create them
weighting and grossing variables created and how they should be use
data listing with descriptions for cases, individuals or items studied, for example for logging qualitative interviews
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.
Databases and Spreadsheets: These common tools are relatively simple to set up, with spreadsheets being the simplest in most cases. Both
offer advantages in managing your data - databases are especially useful for setting up complex relationships and generating queries and
reports based on your data and the relationships between your data; spreadsheets are especially helpful for storing numeric and text data. The
University of Wisconsin Libraries provide helpful information for both: Databases
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?
To fulfill grant funding (e.g., NIH, NSF) and/or journal requirements (see above).
To raise visibility and interest in research and publications.
To add value to research.
To accelerate discovery rates.
To frame research as a public good, promoting community and collaboration.
Data sharing is essential for expedited translation of research results into knowledge, products and procedures to
improve human health
Panton Principles: Drafted by a group of faculty at Cambridge and refined by the Open Knowledge Foundation
Working Group on Open Data in Science, the Panton Principles lay out rationale and criteria for making science data openly available, particularly
policies on reuse.
Open Data.gov aims to bring together discussion on policy, recommendations for best practices,
and other issues relevant to making government-created-and/or-funded data open and accessible.
Considerations about sharing data:
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:
Do your data contain confidential or private information? If so, then they may require redaction or anonymization before they can be
made public. If the data is anonymized, can individuals be reidentified?
Are your datasets understandable to those who wish to use them? Supplementary materials for describing, deciphering, and contextualizing
data should be made available. Consider including metadata, methodology descriptions, codebooks, data dictionaries, and other descriptive
material to facilitate their use. (See the "Documenting Data" tab.)
Do your datasets comply with the standards in your field regarding description, format, metadata, and sharing?
What reuse policies do you wish for your data? Consider the Panton Principles carefully before you attach reuse restrictions.
How to Share your Data
Consider the following options:
Publish your data as supplementary material or a "data publication" in a journal. Check with individual journals about their data policies.
Deposit your data in e-Publications@Marquette, Marquette's institutional repository. Contact Heather James, Digital Programs Coordinator, to discuss what data sets might be suitable for e-Pubs
Deposit it in a disciplinary data repository if one exists for your research area. See list below for examples:
External Data Repositories
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