Scholarly Communication at Texas A&M

At the recent Annual External Funding Conference of the American Association of State Colleges & Universities, an NSF representative shared some details about the soon-to-be-implemented Data Management Plan requirement. Jean Feldman, Head of the NSF Policy Office, Division of Institution & Award Support, presented the following information (Original source available here)

• The DMP is a change in implementation of NSF’s existing data sharing policy, which requires awardees to share their data within a reasonable length of time, as long as the cost is modest
• The DMP requirement is the first step in what will be a more comprehensive approach to data
• The changes are designed to address trends and needs in the modern era of data-driven science
• NSF wants to avoid a one-size-fits-all apporach to data sharing
• The DMP is a supplement to the proposal document, and should describe how the proposal will conform to NSF Policy on the dissemination and sharing of research results
• The DMP will be reviewed as an intergral part of the proposal, falling under Intellectual Merit or Broader Impacts or both, as appropriate for the community of relevance
• NSF FastLane will automatically check for compliance with the DMP requirement, in the same way it currently handles mentoring plans
• (and, perhaps most interestingly, an apparent opt out provision for the DMP): A valid DMP may include only the statement that no detailed plan is needed, as long as the statement is accompanied by a clear justification

On a related note, Texas A&M’s VPR Office is establishing, through the Council of Principal Investigators, an NSF Data Management Plan Committee to “review and address NSF requirements for data management plans; and implement and establish services and guidelines to the faculty when developing plans”.

Some additional information about the National Science Foundation’s requirements for Data Management Plans (DMP) is available on the website for the agency’s Directorate for Engineering. An excerpt from this blurb follows:

Beginning in January 2011 (actual implementation date to be announced), a Data Management Plan (DMP) will be required for all new NSF proposals. FastLane will be updated to enable its upload as a separate Supplementary Document. Proposals that do not include the requisite DMP will be stopped from submission. Specific guidance will be included in an upcoming revision to the NSF Proposal & Award Policies & Procedures Guide. Please note, the Engineering Directorate (ENG) will have additional guidance for proposals submitted to ENG programs. Detailed instructions, including responses to Frequently Asked Questions will be provided at the time of implementation.

An accompanying document, Data Management for NSF Engineering Directorate Proposals and Awards, provides very helpful details about their expectations for the DMP. According to these guidelines, we can expect the following components to be required:

Expected data

- Describe the types of data, samples, physical collections, software, curriculum materials, and other materials to be proced in the course of the project.

- Describe the expected types of data to be retained

Period of data retention

- Minimum retention of research data is 3 years after the conclusion of the award or three years after public release, whichever is later.

- Additional guidelines on data retention provisions with respect to publication, patents, student research, etc. are provided in the document.

Data formats and dissemination

- Describe specific data formas, media, and dissemination approaches used to share the data and metadata with others

- Describe policies for public access, including provisions for protecting privacy, confidentiality, security, intellectual property, other rights or managing other restrictions.

- Describe how data are to be shared and managed with partners, if applicable, or other major stakeholders or user communities.

- Clearly indicate publication delay policies, if applicable

Data storage and preservation of access

- Describe physical and cyber resources and facilities used for preservation and storage of research data.

For Principal Investigators in the Texas A&M community, please feel free to contact the Libraries’ Digital Services & Scholarly Communications staff at digital@library.tamu.edu for assistance in preparing your DMP. We have a range of services and facilities you may wish to take advantage of, including metadata consultation, hosting of digital resources via the Texas A&M Digital repository, and a robust preservation facilitiy managed by the Texas Digital Library and hosted at the Texas Advanced Computing Center (TACC).

As reported in earlier postings, the National Science Foundation (NSF) has announced that it will begin requiring all proposal submissions to include a data management plan in the form of a two-page supplementary document. The new requirement is expected to take effect in October 2010.

In the wake of this announcement (and the absence of any follow-up guidelines from NSF), university research administrators around the country are taking action. At a minimum, they are adding the NSF announcement (Press release 10-077) to their websites, newsletters or blogs. Most blurbs conclude with a pledge to keep the research communities informed of instructions from NSF as soon as they are made available.

In a few noteworthy cases, research offices are more proactively preparing for the new NSF requirement.

Woods Hole’s Director of Research is “planning activities through the summer of 2010 to look at shifting data management requirements from funders ” and assess “the larger data needs of WHOI scientists.” They are organizing meetings with PI’s to discussing the changing NSF policy and identify PI needs. Ultimately they aim to create a set of template data management plans that PI’s can adapt and include in their future NSF proposals. The summer plan is described on their website.

The Office of Research Integrity at University of Alaska, Fairbanks has produced a helpful Data Management page on their website that details not only principles and guidelines for data management but also University policy regarding the oversight of data collection, the transfer of data to other institutions and University requirements for the retention of data. These “local” considerations must be woven into any data management plan prepared for an outside funding agency, such as NSF.

And in an interesting example from the University of Utah, the Associate VP for Research has chosen to link the announcement about the new NSF requirement to a pitch for the university’s Institutional Repository, ‘Uspace’. Her letter to Faculty and Deans aims to raise awareness of the IR as

“a possible repository for your data at the university. Uspace (Uspace.utah.edu) is an online repository for data/software/theses/dissertations/publications/reports/etc. It is made possible by the Institutional Repository Initiative, which is a collaborative project between the libraries at the University of Utah and the University community. Their goal is to collect and archive the intellectual capital of the institution and make these scholarly materials freely available on the Internet.”/strong>

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The letter concludes with the contact information at the Library which will provide assistance and further information.

Each of these examples demonstrates ways in which Universities can take steps now to prepare for the new NSF requirement. We’d love to hear from other Universities about your plans to prepare for October! 

Data-curation-minded librarians are now watching NSF for details about their forthcoming data management plan requirement. This librarian, in particular, speculates that we can learn much by examining data management requirements already in place for specific NSF programs. For example, the 2010 Project from the Directorate of Biological Sciences, which aims to “determine the functions of all genes in the model plant Arabidopsis thaliana by the year 2010″, describes their required data management plan as follows:

A-1) Data Management Plan (maximum 1 page): Development and adherence to community-wide standards for collection and presentation of data, such as microarray or interactome data, are highly encouraged. Large-scale datasets must be made available in a format that enables rapid comparison and effective utilization of reproducible information. All proposals must include a detailed data management plan if the project is expected to generate significant digital data for preservation (maximum 1 page). The contents of the data management plan should include:

The types of data to be produced
The standards that would be applied for format, metadata content, etc.
Provisions for archiving and preservation
Access policies and provisions
Plans for eventual transition or termination of the data collection after the NSF funding period

There are no surprises here. These parameters look just like those proposed by the National Science Board, as described in our posting of May 19 2010. This librarian, who is eager to get busy with the real-life work of data curation, is going to work up some sample data management plans based on these parameters as a starting point for educaton and outreach. They will be posted here when ready.

As reported in an earlier post, researchers applying for grants from the U.S. National Science Foundation will soon be required to include a data management plan as a two-page supplement to their proposal submissions. The data management plan will be evaluated by peer reviewers along with the body of the proposal.

Requiring a data management plan as part of NSF’s proposal evaluation and funding process was originally suggested by the National Science Board in their 2005 report, Long-Lived Digital Data Collections Enabling Research and Education in the 21st Century . The authors’ Recommendation #4 stated:

So what might the NSF Data Management Plan look like?

The data management plan is expected to describe “the data that will be authored as well as how the data will be managed and made accessible throughout its lifetime.” The National Science Board recommended that such a plan cover several general elements:

1. the types of data to be authored;
2. the standards that would be applied for format, metadata content, etc.;
3. provisions for archiving and preservation;
4. access policies and provisions; and
5. plans for eventual transition or termination of the data collection in the longterm future.

This set of elements, while fairly general, provides a basic view of what NSF is likely to expect in a data management plan. It is also helpful to see that the elements recommended by the National Science Board resemble those specified in data management plan requirements found elsewhere. In the UK, for example, a number of funding agencies have already established requirements for data management plans. By analyzing these plans, the UK-based Data Curation Centre has develop guidelines to help researchers address funder requirements. Their
Checklist for a Data Management Plan includes specific questions for investigators to consider as they prepare their plans.