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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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About NIST and ODI

• The National Institute of Standards and Technology is a federal agency under the US Department of 

Commerce

• Known as the National Bureau of Standards until 1988, originally founded in 1901

• Non-regulatory

• State of the art in measurement science and technology

• US National Metrology Institute, amongst network of 103 NMIs globally organized under the Bureau 

International des Poids et Mesures (BIPM, or International Bureau of Weights and Measures), Paris

• ~5,000 staff at NIST (Gaithersburg, 

Maryland headquarters; Boulder, 

Colorado; Charleston, South 

Carolina; Brookhaven National 

Laboratory)

• 6 major research laboratories

• Material Measurement Laboratory

• Office of Data and Informatics (15 

people)
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Primary ODI Activities

• Data management

• Public Data Repository (PDR) and Science Data Portal (SDP), 

data.gov compliance

• Laboratory Information Management Systems (LIMS)

• Configurable Data Curation System (CDCS), Python-based metadata 

extractors (HyperSpy)

• Data Management Plans (DMPs)

• Standard Reference Data (SRD)

• Informatics and analytics

• External engagements
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Science Data Portal and Public Data Repository

• Modern website for search 

and discovery of NIST public 

data sets

• https://data.nist.gov

• Developed and operated by 

ODI for NIST

• Front end to the NIST Public 

Data Repository

• Implements the NIST 

taxonomy for research 

domains

• Open source code base –

hosted on github/USNISTGOV
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Laboratory Information Management Systems
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NIST Standard Reference Data

• Most highly vetted data products of NIST

• SRD Act of 1968

• 65 databases, free and subscription based

• 6,000 units sold/year as downloads and 

agreements including royalties on instrument 

sales

• Online SRD Metrics

• 2M views a month webbook.nist.gov

• 300K views a month XPS - NIST X-Ray 

Photoelectron Spectroscopy Database

Stephen E. Stein (2014), NIST/EPA/NIH Mass Spectral Library with 

Search Program - SRD 1a, National Institute of Standards and 

Technology, https://doi.org/10.18434/T4H594 (Accessed 2020-09-

08)
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Informatics and Analytics Support
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External Engagement

• Commerce Data Governance Board, Data Inventory Working Group

• OSTP/NSTC subcommittees (Subcommittee on Open Science, Subcommittee on Rigor and 

Integrity of Research)

• Research Data Alliance, CODATA (Digital Representation of Units of Measure task group), 

GO-FAIR (FAIR Digital Object Framework), World Data Service (Technical Advisory Board)

• Digital SI (BIPM/CIPM), Digital Calibration Certificates

• Commerce, Energy, NASA, Defense Information (CENDI) network

• National Academies Roundtable, Incentives for Open Data

• Association of American Universities (AAU) / Association of Public and Land-Grant 

Universities (APLU) / Association of Research Libraries (ARL) workshops on improving public 

access to research data

• Materials Research Data Council (MaRDaC) / Materials Research Data Network (MaRDaN)
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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 repositories

• Scholarly publishers

• Professional societies

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

CODATA, RDA, WDS, GO-FAIR)

• Standards bodies

• Funders (public and private)

• Industry and the private sector

• Researchers

• General public
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Why a Research Data Framework?

• Leverage research data to address global challenges

United Nations Sustainable Development Goals (SDGs)
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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 Group

Laura Biven, DOE Mercé Crosas, Harvard Josh Greenberg, Sloan Hilary Hanahoe, RDA

Heather Joseph, SPARC Barend Mons, CODATA Beth Plale, NSF Anita de Waard, Elsevier

and GO-FAIR
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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 Structure Based on “Functions”

Envision

Plan

Generate / Acquire

Process / Analyze

Use / Reuse

Preserve / Discard
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RDaF Structure

Function Category Subcategory

1) Envision Data governance Data vision, data policy

Data management organization

Data quality, privacy, ethics

Community Communication, interactions

engagement Cross-domain

Data culture FAIR principles

Value of data

Roles and responsibilities

Reward structure Value of data professionals

Incentives for sharing and re-use
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RDaF Structure

Function Category Subcategory

2) Plan Costs Cost-benefit analysis

Costs by data lifecycle stage

Funding Direct, overhead, mixed, other

Data objects Data (experimental, simulation)

Software, instruments

Publications, presentations

Data management DMPs (intent, update)

planning Formats, standards
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RDaF Structure

Function Category Subcategory

3) Generate / Sources In-house, experiment or simulation

Acquire Collected from external sources

Experiment Instruments and their metadata

Measurement protocols

Data capture and recording

Simulation Commercial or custom software

Metadata capture and recording

External sources Identification, provenance

Metadata harvesting

Data formats Standards development and/or adoption
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RDaF Structure

Function Category Subcategory

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

Analyze Data copied or derived from other data

Data architecture Design, security, configuration management

Hosting and storage

On-premise or Cloud

Software Commercial or custom software

Versions

Stability, resilience, adaptability, maintenance

Workflows, ELNs, LIMs

Publishing, curation Processes, tools, stewardship

Metadata
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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

Analysis tools AI/ML

Performance

Impact Usage tracking, citation
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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)

Migration between organizations

Retention and disposition Decision processes

End-of-life (dark archives, deaccession,

gravestones)
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Status

• Briefed OSTP Subcommittee on Open Science and OSTP Director Kelvin 

Droegemeier (03/26/2020)

• Developed roadmap and structure, vetted with Steering Group

• Seeking ~$500k to fund two pilots: materials science and research 

universities/libraries/scholarly publishers

• NIST plus other agencies/laboratories, either $$ or in-kind support

• Professional societies

• Scholarly publishing community

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

• Start with pilot projects in order to validate approach and re-tune as necessary

• 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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Contact

Robert Hanisch

robert.hanisch@nist.gov

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