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Dr. Robert Hanisch

Director, Office of Data and Informatics (ODI)

Material Measurement Laboratory

US National Institute of Standards and Technology

NIST Research Data Framework (RDaF)
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What is a Research Data Framework?

• A map of the research data space: who, what, where, why, 

when?

• A dynamic guide for the various stakeholders in research data 

to understand best practices for research data management 

and dissemination

• A resource for understanding costs, benefits, and risks 

associated with research data management

• A consensus document based on inputs and conversations 

amongst the stakeholders in research data

Why a Research Data Framework?

• Research data ecosystem is very complex!

• Lots of players, various funding models and sustainability plans

• How long should data be kept?

• How should data quality be assessed?

• How do we measure the value of research data?
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Stakeholders

• Government agencies

• National laboratories

• Universities and research libraries

• Data service/infrastructure providers, e.g., data repositories

• Scholarly publishers

• Professional societies and trade organizations

• National and international collaboration organizations (e.g., CENDI, BRDI, 

CODATA, RDA, WDS, GO-FAIR)

• Non-profit organizations and NGOs

• Standards bodies

• Funders (public and private)

• Industry and the private sector

• Researchers

• General public
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RDaF Benefits

• Increase research integrity with quality data and improved 

transparency of the research process 

• Reduce costs and maximize efficiency by establishing best 

practices for data management

• Guide risk management and reduction through assessment of 

risk positions and roadmaps for improvement

• Increase scientific discovery and innovation with the FAIR 

principles (Findable, Accessible, Interoperable, Reusable) for 

better utilization of data 
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National and International Need

• Data is proliferating at an exponential rate 

• Data management is complex and confusing

• Mismanaged data has dire social and economic consequences, 

including loss of global leadership in critical technical fields

• The U.S. needs a coordinated effort to establish a research data 

infrastructure, but research data are global in nature so 

international collaboration / coordination is necessary

• NIST is well-positioned to lead the project; our business is 

consensus building through being a neutral convener of diverse 

communities
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Process

• Pilot program to provide an overall guide to the actors and 

stakeholders in the research data space

• NIST Cybersecurity Framework is the model

• Community consensus, not NIST imposition

• If I am a ____ , then I need to know ____ . 

• Initial scoping workshop held in December 2019 at NIST

• 50 invited participants representing stakeholders, both US and 

international
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Robert Hanisch

Director, Office of Data 

and Informatics

Material Measurement 

Laboratory

National Institute of 

Standards and 

Technology

Bonnie Carroll

Founder & CDO,

Information 

International 

Associates

Secretary General, 

CODATA

Research Data Framework
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RDaF Steering Committee (SC)

Laura Biven

NIH

Barend Mons

CODATA, GO-FAIR

Hilary Hanahoe

RDA

Bonnie Carroll

CODATA, SC Chair

Martin Halbert

NSF

Heather Joseph

SPARC 

Beth Plale

Indiana Univ

Anita de Waard

Elsevier

Carly Strasser

Zuckerberg Inst.

Mark Leggott

Research Data Canada

Sarah Nusser

Iowa State U, UVA

Cate Brinson

Duke Univ
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Workshop Summary

• Status: Confirmed support by government agencies, academic 

organizations, private sector companies, not-for-profit 

organizations, and international stakeholders.

• Next Steps: Management commitment to complete the 

scoping, pilot testing, and community building for the 

Framework.

• Proposed pilots

• Materials science

• Universities and research libraries (AAU, APLU, ARL)

______________________________

Will need cooperation across government to move fully forward 

with the Framework. 
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RDaF Publication

https://doi.org/10.6028/NIST.SP.1500-18

Framework Core

Functions (Six Research 

Data Lifecycle Stages)

● Categories (topics)

● Subcategories (sub-topics)

● Informative References
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RDaF Structure Based on “Functions”

Envision

Plan

Generate / Acquire

Process / Analyze

Use / Reuse

Preserve / Discard
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RDaF Lifecycle Stages

4) Process/Analyze- actions 

performed on generated or 

acquired research data to 

yield processed research data

5) Share/Use/Reuse- outlines 

how raw and processed 

research data are 

disseminated, used, and 

reused within an organization 

and any constraints or 

encouragements to use/reuse

6) Preserve/Discard- end-of-

use and end-of-life provisions 

for research data in an 

organization and includes 

records management, 

archiving, and safe disposal

1) Envision- chart a high-

level course of action to 

achieve desired 

organizational goals

2) Plan- preparation in an 

organization for effective 

research data management 

3) Generate/Acquire -

generation of raw research 

data, both experimentally 

and computationally, within 

an organization, and the 

collection or acquisition of 

research data produced 

outside of an organization
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RDaF Structure

Function Category Subcategory

1) Envision Data governance― Data vision, data policy

strategic/qualitative Data management organization

Data quality, data stewardship

Data governance― Data privacy and ethics

legal and regulatory Data inventory

compliance Risk assessment and management

Community Communication, interactions

engagement Cross-domain

Data culture and FAIR principles

reward structure Value of data and data professionals

Roles and responsibilities

Incentives for sharing and re-use
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RDaF Structure

Function Category Subcategory

2) Plan Financial aspects Cost-benefit analysis

of planning Costs by data lifecycle stage

Funding models, funding sources

Data objects Data (quantitative and qualitative)

Software, models

Observations, surveys

Data management DMPs (intent, update)

planning Metadata, analysis tools, workflows

Data architecture LIMS, configuration management

Cloud storage and computing
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RDaF Structure

Function Category Subcategory

3) Generate / Types of raw data Measurements, text files

Acquire Images, audio recordings, photos/videos

Sources of data In-house and remote generation

Generation at a user facility

Acquired data Collaborators, repositories

Extraction from the literature

FAIR principles Data born FAIR; data made FAIR

Data not FAIR, e.g., legacy

Community-based Data format and file structure

standards General vs. domain-specific
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RDaF Structure

Function Category Subcategory

4) Process / Provenance Origin, version, time-stamp

Analyze Data copied or derived from other data

Scientific workflow Electronic or paper laboratory notebooks

processes Containerization

Software tools Commercial or custom software

Versioning, documentation

Resilience, adaptability, maintenance

Metadata Responsible parties

Linked data structure

Persistent identification
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RDaF Structure

Function Category Subcategory

5) Use / Reuse Legal and licenses Ownership, IP, rights and restrictions

Agreements, permissions

Citation expectations

Data access Internal, external

APIs

Downloads vs. visiting

New applications AI/ML

and analyses Repurposing

Data attribution Citation metrics and impact

Provenance
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RDaF Structure

Function Category Subcategory

6) Preserve / Sustainability Longevity requirements

Discard Who pays?

Orphan data sets

Preservation Media and media migration

Back-up

Repositories (domain, institutional, general)

File integrity, data recovery

Retention and disposition Technical and administrative/policy decisions

End-of-life (dark archives, deaccession,

tombstones)
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Status

• Briefed OSTP Subcommittee on Open Science (01/27/22) and the Materials 

Research Data Alliance (MaRDA, 06/03/21)

• Held two Opening Plenary Workshops as pilots: materials science (12/10/21) 

and research universities/libraries/scholarly publishers (10/31/21) 

• Strong interest and engaged participation from stakeholder groups, e.g.,

• Government departments/agencies (DOD, DOE, NASA, NIH, NSF)

• Professional societies and trade organizations (AGU, ACerS, APS, ASM 

International, MRS, AAU, APLU, ARL)

• Scholarly publishing community (AAAS/Science, AIP Publishing, Elsevier, 

Springer Nature)

• Generated RDaF version 1.1 with input from the workshop participants

https://www.nist.gov/programs-projects/research-data-framework-rdaf
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Next Steps

• Hold about 15 Stakeholder Workshops, each focused on a specific stakeholder role, e.g., 

Senior Executives, Researchers, Scholarly Publishers

• Two-hour long workshops with 10-12 participants, in the June to mid-July timeframe

• NIST team will develop preliminary “Profiles” for each stakeholder role—essentially a 

checklist of those categories and subcategories across the research data lifecycle that are 

most relevant for that role. 

• Participants will be asked to provide input on the following:

• The preliminary Profile for their specific job role

• The entire RDaF Core version 1.1 (all categories and subcategories)

• What elements of the Framework do you influence? What elements influence you? 

Where in the Framework are your primary responsibilities represented?

• After the Stakeholder Workshops, the NIST team will develop RDaF version 1.2 and finalize a 

set of Profiles for representative job roles

https://www.nist.gov/programs-projects/research-data-framework-rdaf
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Partial Preliminary Profile for a Senior Executive 

*scale: 3 denotes the greatest relevance and 0 is not relevant

FUNCTION (Data 

Lifecycle Stage) CATEGORY SUBCATEGORY Relevancy (0 to 3)*

ENVISION 

Review of the 

overall strategies 

and drivers of an 

organization’s 

research data 

program.

Data Governance—

Strategic/Qualitative

Identification of Goals and Roles 2

Data vision and/or data policy 3

Data management organization 2

Data quality 1

Data stewardship 1

Data Governance—

Legal and Regulatory 

Compliance 

Data privacy 2

Safety and security assurance 3

Data inventory 0

Risk mitigation and management 1

Data Culture and 

Reward Structure

Roles and responsibilities 2

Value of data to organization and leadership 3

Disincentives for data sharing 0

Resources—Allocation 

and Sustainability

Sources of funding 3

Long-term funding 3
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Partial Preliminary Profile for a Data Publisher

FUNCTION (Data 

Lifecycle Stage) CATEGORY SUBCATEGORY RELEVANCY 

(0 to 3)*

SHARE/USE/

REUSE

How research 

data are 

disseminated, 

used, and reused 

within and 

outside an 

organization. 

Data 

Publishing

Repositories 2

Referencing data/digital objects from journal articles 3

Internal and 

External Data 

Access

Internal access e.g., data generator 0

External access 0

Programmatic access aka through Smart API 2

Data access vs. data visiting 1

Economic constraints 0

Legal and 

Licenses

Ownership of data 3

Constraints and encouragement for data use 2

Intellectual property rights/restrictions 3

Usage agreements/terms/licenses and required permissions 3

Terms of Service 0

Data sharing agreements and licensing 0

Data citation 3

*scale: 3 denotes the greatest relevance and 0 is not relevant
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Example of Profile Development

CDO: Chief Data Officer
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RDaF Summary

• Successful in building community interest and engagement

• Diverse stakeholders

• National and international

• Challenges

• Resources

• Timeliness: the research data ecosystem is changing rapidly. How to keep pace and 

assure ongoing updates?

• Controlling scope and scale

• Strategy for moving forward

• Hold a series of Stakeholder Workshops focused on specific job roles to obtain 

feedback on RDaF version 1.1 and to develop role-based profiles, i.e., checklists of key 

topics for a specific job role

• Collaborate with other federal agencies, professional societies, scholarly publishing 

community, etc., to garner the necessary resources and take advantage of work in 

progress
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Contacts

Robert Hanisch

robert.hanisch@nist.gov

https://nist.gov/people/robert-hanisch

Debra Kaiser, debra.kaiser@nist.gov

Bonnie Carroll, bcarrolltn@gmail.com
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NIST Frameworks

Framework for Improving Critical Infrastructure Cybersecurity:

https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.04162018.pdf

NIST Privacy Framework: A Tool for Improving Privacy Through Enterprise Risk 

Management, September 6, 2019 (Preliminary Draft)

https://www.nist.gov/system/files/documents/2019/09/09/nist_privacy_fram

ework_preliminary_draft.pdf

NIST Big Data Interoperability Framework: Volume 1, Definitions October 

2019 Version 3

https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1500-1r2.pdf