The document discusses requirements for data management plans from the National Science Foundation. It notes that as of January 2011, NSF will require a data management plan for all new grant proposals as well as existing grants. The plan must address what data will be collected and how it will be organized, preserved, shared, and accessed. It emphasizes the importance of effective data management for facilitating research by both the principal investigators and other researchers. The document provides guidance on developing a data management plan that meets NSF's criteria and effectively manages research data.

1. Wisconsin Cyberinfrastructure Days
November 5, 2010
Dorothea Salo & Brad Houston

2.  Document describing data (and/or digital
materials) that have been or will be gathered in
a study or project.
 Often includes details on how data will be
organized, preserved, and accessed
 Facilitates re-use of data sets by either PI or
other researchers
 Required component of grants for MANY
agencies (NSF and NIH)

3.  Starting January 2011 for NEW, non-
collaborative proposals
 Not voluntary – “integral part” of proposal
 Data Management Plans for all data resulting
from any level of NSF funding
 Supplementary 2-page document (max)
 Optional: Also part of 15-page (max) Project
Description

4.  Must address both physical and digital data
 “Efficiency and effectiveness” of the DMP will
be considered by NSF and disciplinary division
or directorate
 Must include sufficient information that peer
reviewers and project monitors can assess
present proposal and past performance

5. Such dissemination of data is necessary for the
community to stimulate new advancesstimulate new advances as quickly as
possible and to allow prompt evaluationallow prompt evaluation of the results
by the scientific community. “ – NSF (italics mine)
Part of Openness trend in federal government
(data.gov - Open Government Initiative)
NIH Public Access Policy (2008)
Public access to federally funded research hearings
- Information Policy, Census and National Archives
Subcommittee of U.S. Congress (July, 2010)

6.  It makes your research easier!
 Data available in case you need it later
 Helps avoid accusations of fraud or bad science
 To share it for others to use and learn from
 To get credit for producing it
 To keep from drowning in irrelevant stuff
 ... especially at grant/project end

7. Gene expression microarray data: “Publicly
available data was significantly (p=0.006)
associated with a 69% increase in citations,
independently of journal impact factor, date of
publication, and author country of origin.”
 Piwowar, Heather et al. “Sharing detailed research
data is associated with increased citation rate.” PLoS
One 2010. DOI: 10.1371/journal.pone.0000308
 Maybe there’s an advantage here!

8.  Discuss specific requirements for NSF
Data Management plans
 Suggest ways to manage, share, and
archive data more effectively
 Provide resources for more information

9. Requirements, retention, and planning

10.  What data are you collecting or making?
 Can it be recreated? How much would that cost?
 How much of it? How fast is it growing? Does it
change?
 What file format(s)?
 What’s your infrastructure for data collection and
 storage like?
 How do you find it, or find what you’re looking for
in it?
 How easy is it to get new people up to speed? Or
share data with others?

11.  Who are the audiences for your data?
 You (including Future You), your lab colleagues
(including future ones), your PIs
 Disciplinary colleagues, at your institution or at others
 Colleagues in allied disciplines
 The world!
 What are your obligations to others?
 Funder requirements
 Confidentiality issues
 IP questions
 Security

12.  How do you and your lab get from where you
are to where you need to be?
 Document, document, document all decisions and
all processes!
 Secret sauce: the more you strategize upfront,
the less angst and panic later.
 “Make it up as you go along” is very bad practice!
 But the best-laid plans go agley... so be flexible.
 And watch your field! Best practices are still in flux.

13.  All submitted plans must include, at
minimum:
1. Expected Data: types, physical/electronic collections,
materials to be produced
2. Standards for data and metadata format and content
3. Policies for access and sharing, including provisions for
appropriate protection of privacy, confidentiality,
security, intellectual property, etc.
4. Policies and provisions for re-use, re-distribution, and
the production of derivatives
5. Plans for archiving data, samples, and other research
products, and for preservation of access to them

14.  Four kinds of data defined by OMB:
 Observational
 Examples: Sensor data, telemetry, survey data, sample
data, neuroimages.
 Experimental
 Examples: gene sequences, chromatograms, toroid
magnetic field data.
 Simulation
 Examples: climate models, economic models.
 Derived or compiled
 Examples: text and data mining, compiled database, 3D
models, data gathered from public documents.

15.  Preliminary analyses
 Raw data is included in this definition
 Drafts of scientific papers
 Plans for future research
 Peer reviews or communications with
colleagues
 Physical objects, such as gel samples

16.  As early as possible, but no later than
guidelines laid down by relevant Directorate
 Engineering Section: “no later than the acceptance
for publication of the main findings of the final data”
 Earth Sciences: “No later than two (2) years after the
data were collected.”
 Social and Economic Sciences: “within one year after
the expiration of an award”
 Be aware of concerns that may require earlier
or later disclosure
 FERPA? Human Subjects data? HIPAA?

17.  Again, specific retention periods will depend
on the type of data and the Directorate
 Example: Engineering Section suggests retention
period of “three years after either completion of the
grant project or public release of research data,
whichever is later”
 Certain types of data will need to be retained
longer
 Patent data, longitudinal data sets, etc.
 Ask: is your data of permanent value?

18.  Analyzed data (incl. images, tables and tables of
numbers used for making graphs)
 Metadata that defines how data was generated,
such as experiment descriptions, computer code,
and computer-calculation input

19.  Investigators are expected to preserve/share
primary data, samples, physical collections, &
supporting materials
 Provide easily accessible information about data
holdings, including quality assessments and
guidance/finding aids
 Data may be made available through submission to
national data center, publication in journal, book, or
accessible website of institutional archives

20.  Data Management Plans are required even if a
project is not expected to generate data that
requires sharing
 DMP should clearly explain non-sharing in
light of COI standards (peer review)
 Between the lines: Not sharing will require
justification and close scrutiny by NSF
 Sharing is preferred

21. Preparing, sharing, and archiving your data sets

22.  Think about where you will put your data
 Local? Network drive? Online data management
system?
 Think about how you (or others) will find your
data
 Think about how others may use your data, when
found
 Think about how to store your data in the long
term (or if to store it long-term at all)

23.  Will anybody be able to read these files at the
end of your time horizon?
 Where possible, prefer file formats that are:
 Open, standardized
 Documented
 In wide use
 Easy to data-mine, transform, recast
 If you need to transform data for durability,
do it now, not later.

24.  Fundamental question: What would someone
unfamiliar with your data need in order to
find, evaluate, understand, and reuse them?
 Consider the differences between someone
inside your lab, someone outside your lab but
in your field, and someone outside your field.
 Two parts: metadata and methods

25.  About the project
 Title, people, key dates, funders and grants
 About the data
 Title, key dates, creator(s), subjects, rights, included
files, format(s),versions, checksums
 Interpretive aids: codebooks, data dictionaries,
algorithms, code
 Keep this with the data

26.  Reason #1 for not reusing someone else’s data: “I
don’t know enough about how it was gathered to
trust it.”
 Document what you did. (A published article may
or may not be enough.)
 Document any limitations of what you did.
 If you ran code on the data, document the code and
keep it with the data.
 Need a codebook? Or a data dictionary?
 If I can’t identify at sight what each bit of your dataset
means, yes, you do need a codebook or data dictionary.
 DO NOT FORGET UNITS!

27.  Your own drive (PC, server, flash drive, etc.)
 And if you lose it? Or it breaks?
 Somebody else’s drive
 Departmental or campus drive
 “Cloud” drive
 Do they care as much about your data as you do?
 What about versioning?
 Library motto: Lots Of Copies Keeps Stuff Safe.
 Two onsite copies, one offsite copy.
 Keep confidentiality and security requirements in
mind, of course

28.  If data need to persist beyond project end, you have to
deal with a new kind of risk: organizational risk.
 Servers come and go. So do labs. So do entire departments.
 This is especially important if you share data! Don’t let it 404!
 You need to find a trustworthy partner.
 On campus: try the library or your campus research office. (No,
campus IT is usually not good enough.)
 Off campus: look for a disciplinary data repository, or a journal
that accepts data. (It’s a good idea to do this as part of your
planning process.)
 Let somebody else worry! You have new projects to get
on with.