hands-on data management planning for life sciences

Hands-‐On Data Management Planning for Life Sciences

Andrew Sallans Head of Strategic Data Ini5a5ves University of Virginia Library

[email protected]

Andrea Denton Research and Data Services Manager Claude Moore Health Sciences Library

[email protected]

Crea5ve Commons License ”Hands-‐On Data Management Planning for Life Sciences", 3/19/13 by Andrew L. Sallans is licensed under a Crea5ve Commons APribu5on-‐ShareAlike 3.0 Unported License.

Goals for the workshop

•  Learn about data management planning •  Learn about available resources •  Develop rough draU of a data management plan for a grant

•  Gain peer and expert feedback


Why should you care about data management planning?

•  It’s good science: reproducible results and con5nuity •  Transparency and accountability •  Gain a compe55ve edge in grant compe55on •  Get credit by making your data citable, more impact •  Be efficient and avoid data loss •  It’s complex and requires aPen5on to many parts •  You may be required to by your government, funder, ins5tu5on, publishers, etc.


Why not?

h"p://memegenerator.net/Fist-‐Pump-‐Baby


Recent news

•  White House, Office of Science and Technology Policy from February 22, 2013

•  Federal research agencies funding more than $100M/year must develop plan to make the results (papers and data) of federally funded research available to the public within one year of publica5on

•  Also requires researchers to bePer account for and manage data

h"p://www.whitehouse.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf


Example: Na5onal Science Founda5on

– Data Sharing Policy: Awards & Administra5on Guide Chapter IV.D.4

– Data Management Plan requirement: Grant Proposal Guide Chapter II.C.2.j

– Addi5onal requirements from individual Directorates and Divisions (e.g., BIO, CISE, EHR, GEO, MPS, SBE): Dissemina5on and Sharing of Results


Caveat: it’s not just the NSF

Read calls for proposals carefully and ask program director about specific data management requirements. Build 5me into your proposal development to formulate a data management plan!

CDC NEH

DOE NIH

EPA USDA

IMLS Private and public founda5ons

NASA

Many research funding agencies in the U.K., Australia, and other countries

NOAA Etc…

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What is a Data Management Plan?

•  A comprehensive plan of how you will manage your research data throughout the lifecycle of your research project

AND

•  Brief descrip5on of how you will comply with funder’s data sharing policy

•  Reviewed as part of a grant applica5on Crea5ve Commons License ”Hands-‐On Data Management Planning for Life Sciences", 3/19/13 by Andrew L.

Sallans is licensed under a Crea5ve Commons APribu5on-‐ShareAlike 3.0 Unported License.

Dissemina=on & Sharing of Research Results

“Inves5gators are expected to share with other researchers, at no more than incremental cost and within a reasonable 5me, the primary data, samples, physical collec5ons and other suppor5ng materials created or gathered in the course of work under NSF grants. Grantees are expected to encourage and facilitate such sharing.”

Na=onal Science Founda=on: Award & AdministraGon Guide (AAG) Chapter VI.D.4


Plan for Data Management & Sharing of the Products of Research

As of January 18, 2011:

“Proposals must include a supplementary document of no more than two pages labeled “Data Management Plan”. This supplement should describe how the proposal will conform to NSF policy on the dissemina5on and sharing of research results, and may include…...”

NSF: Grant Proposal Guide (GPG) Chapter II.C.2.j


Which NSF requirement to use?

•  Which Guideline Should I follow? §  First: follow the requirements laid out in the specific solicita5on, if any.

§  Second: follow the guidelines published by the appropriate NSF directorate and/or division. If there is a conflict, the laPer takes precedence.

§  Third: follow the more general guidelines. •  Interdisciplinary Proposals

§  Use guidelines appropriate to the lead program (if there are specific guidelines)


Parts of a (Generic) NSF Data Management Plan

I.   Products of the Research: The types of data, samples, physical collec5ons, soUware, curriculum materials, and other materials to be produced in the course of the project.

II.   Data Formats: The standards to be used for data and metadata format and content (where exis5ng standards are absent or deemed inadequate, this should be documented along with any proposed solu5ons or remedies).

III.   Access to Data and Data Sharing Prac=ces and Policies: Policies for access and sharing including provisions for appropriate protec5on of privacy, confiden5ality, security, intellectual property, or other rights or requirements.

IV.  Policies for Re-‐Use, Re-‐Distribu=on, and Produc=on of Deriva=ves.

V.   Archiving of Data: Plans for archiving data, samples, and other research products, and for preserva5on of access to them.

Grant Proposal Guide (GPG) Chapter II.C.2.j


•  Ques=ons to answer: §  What data will be generated in the research? §  What data types will you be crea5ng or capturing? §  How/when/where will you capture or create the data? §  How will the data be processed? §  If you will be using exis5ng data, state that fact and include where you got it. What is the rela5onship between the data you are collec5ng and the exis5ng data? 13

I. Types of Data


•  Ques=ons to answer: §  Which file formats will you use for your data, and why?

§  What form will metadata describing/ documen5ng your data take?

§  How will you create or capture these details? §  Which metadata standards will you use and why have you chosen them?

§  What contextual details (metadata) are needed to make the data you capture or collect meaningful? 14

II. Data and Metadata Standards


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III. Policies for Access and Sharing & Provisions for Appropriate Protec=on/Privacy

•  Ques=ons to answer:

§  How/when will you make the data available? §  What is the process for gaining access to the data? §  Does the original data collector/creator/principal inves5gator retain the right to use the data before opening it up to wider use?

§  Are there any embargo periods for poli5cal/ commercial/ patent reasons? If so, give details.


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III. Policies for Access and Sharing & Provisions for Appropriate Protec=on/Privacy (Cont.)

•  More Ques=ons to answer: §  Are there ethical and privacy issues? If so, how will these be resolved?

§  What have you done to comply with your obliga5ons in your IRB Protocol?

§  Who will hold the intellectual property rights to the data and how might this affect data access?


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IV. Policies and Provisions for Re-‐Use, Re-‐Distribu=on

•  Ques=ons to answer: §  Will any permission restric5ons need to be placed on the data?

§  Which bodies/groups are likely to be interested in the data?

§  What could be the intended uses of the data?


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V. Plans for Archiving and Preserva=on of Access

•  Ques=ons to answer: §  What data will be preserved for the long-‐term? §  What is the long-‐term strategy for maintaining, cura5ng and archiving the data?

§  Which archive/repository/database have you iden5fied as a place to deposit data?

§  What procedures does your intended long-‐term data storage facility have in place for preserva5on and backup?

§  How long will/should data be kept beyond the life of the project?


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V. Plans for Archiving and Preserva=on of Access (Cont.)

•  More Ques=ons to answer: §  What transforma5ons will be necessary to prepare data for preserva5on / data sharing?

§  What metadata/ documenta5on will be submiPed alongside the data or created on deposit/ transforma5on in order to make the data reusable?

§  What related informa5on will be deposited?


What needs to be in a data management plan for a grant?

Example: NSF •  Two pages long •  Reviewed for merit and/or impact with proposal •  Your plan should minimally address five points:

–  Data being produced –  Format and descrip5on –  Access and sharing –  Reuse –  Archiving

Be sure to address addi5onal Directorate or Division guidelines and specific program requirements!


Three Data Management Planning Resources

•  DMPTool, hPp://dmptool.org – Helps you create a data management plan to meet grant requirements and iden5fy UVA support resources and policies

•  Databib, hPp://databib.org – Helps you find an appropriate place to deposit your data

•  Libra, hPp://libra.virginia.edu -‐ Helps UVA faculty, graduate students, and staff by providing a place to deposit and share datasets


Goals of the DMPTool

I.  To provide researchers a simple way to create a DMP for their funding agency

•  Ques5ons asked by the agency •  Addi5onal explana5on/context provided by the agency •  Links to the agency website for policies, help, guidance


Goals of the DMPTool

II.  To provide researchers with DMP informa=on from their home ins=tu=on

•  Resources and services to help them manage data •  Help text for specific ques5ons •  Suggested answers to ques5ons; easy to cut-‐N-‐paste •  News & events related to data management on campus


Last point: Grant requirements versus ideal

•  Grant Driven – Requirements – Sharing and public access to research

•  Opera5onal – Research con5nuity – Avoiding data loss – Efficiency


Team Exercise 30 minutes

1.  Iden5fy a grant that you have or might apply for. 2.  Locate the requirements for that grant in the DMPTool. 3.  Go through plan sec5ons in DMPTool workflow to

produce draU plan. –  Be sure to address metadata, access policies, repositories .

4.  Iden5fy solu5ons and available support through DMPTool sec5ons or ask for guidance.

5.  Record issues and ques5ons for discussion.

Crea5ve Commons License ”Hands-‐On Data Management Planning for Life Sciences", 3/19/13 by Andrew L.

Sallans is licensed under a Crea5ve Commons APribu5on-‐ShareAlike 3.0 Unported License.

Presenta5on of DraU DMPs 15 minutes

•  Iden5fy grant •  Describe project briefly •  Explain requirements •  Describe planned solu5ons – Must address metadata, access policies, and repositories.


Ques5ons and Discussion?


Follow-‐up

•  Contact the Scien5fic Data Consul5ng Group for help with DMP prepara5on – Grant driven: hPp://www2.lib.virginia.edu/brown/data/DMP_Support.html

– Opera5onal •  Email: [email protected]


hands-on data management planning for life sciences

Technology

research data

data management sharing

data hp

primary data

data loss

data citable

unported license

data management countries