DUST 2026 · SUPERFUND RESEARCH PROGRAM TRAINING · LESSON 2
MODERN DATA
MANAGEMENT
decide before you collect
Tyson L. Swetnam, PhD · Center for Advanced Research Computing · University of New Mexico
THE PLAN FOR TODAY
Segment
Minutes
What happens
Why it matters
5
The number one problem, and data that disappear
Part 1 · The data life cycle
6
Eight stages, eight decisions, file names and metadata
Part 2 · FAIR and CARE
8
Why FAIR is not open, and who governs tribal data
Part 3 · Plans, repositories, licenses
9
The 2026 NIH and NSF formats, where data go, which license
Wrap-up
2
Five takeaways
Discussion
20
Draft the NIH 2026 plan for a two-site study
DUST 2026 · LESSON 2 · DATA MANAGEMENT
REFLECT · 1 MINUTE
1
If you gave your data to a colleague unfamiliar with your project, could they make sense of it?
2
If you returned to your own data in five years, would you understand it?
3
Which federal dataset does your project depend on, and where is your copy?
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE NUMBER ONE PROBLEM
MAKING IT AN
AFTERTHOUGHT
Poor data management has no upfront cost. By the time you notice, fixing it is exponentially harder. Decide it first.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
WHEN THE DATA DISAPPEAR
5 FEB 2025
EJScreen removed
EPA takes its environmental-justice screening tool off its website.
SAME WEEKS
203 CDC datasets
Go offline until a court orders them restored.
COMMUNITY
Mirrors fill the gap
Public Environmental Data Partners, Harvard LIL data.gov archive, Data Rescue Project.
SEPT 2026
Still absent
EJScreen is still not back on epa.gov.
Download, DOI and document what you depend on. Cite the copy you actually analyzed.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
SRP EXAMPLE: WHAT A COLLABORATOR NEEDS
ARIZONA
Tailings, tissue, biomass
Arsenic in 50 tailings samples, lung tissue images, phytoremediation biomass. Would they know the units, the detection limits, and which sample came from which site?
NEW MEXICO
Water, wells, a survey
Uranium, arsenic and vanadium water chemistry on Navajo Nation, well screening, an NNHRRB-approved survey. Would they know which records may never leave the community that owns them?
DUST 2026 · LESSON 2 · DATA MANAGEMENT
PART 1
THE DATA
LIFE CYCLE
eight stages, eight decisions
DUST 2026 · LESSON 2 · DATA MANAGEMENT
EIGHT STAGES, ONE DECISION EACH
01
Plan
Data, formats, storage, access. Write the plan.
02
Collect
Naming and folders set before the first sample.
03
Assure
Flag missing, estimated and questionable values.
04
Describe
Metadata to a standard, people with ORCID.
05
Preserve
Repository with a persistent ID. Backup is not preservation.
06
Discover
Findable via re3data, Dataset Search, DataCite.
07
Integrate
Never assume two columns mean the same thing.
08
Analyze
Notebooks, version control, recorded versions.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
NAME IT AND DESCRIBE IT BEFORE THE FIRST SAMPLE
YYYY-MM-DD_site_sample-ID_analysis-type_details.ext
Site codes, not coordinates
Site codes in filenames, never GPS coordinates or household IDs
Metadata to a standard
DataCite for datasets, ISO 19115-1 for spatial, MIxS for environmental samples
What metadata holds
Dataset, people with ORCID, context, variables and units, quality, access terms
DUST 2026 · LESSON 2 · DATA MANAGEMENT
PART 2
FAIR AND
CARE
can it be reused, and should it be?
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE FAIR PRINCIPLES (2016)
F
Findable
A persistent identifier (DOI), rich metadata, a searchable registry.
A
Accessible
Retrievable by a standard protocol; metadata stay available even when data are restricted.
I
Interoperable
Standard formats (CSV, NetCDF, GeoTIFF; GeoParquet, COG, Zarr) and shared vocabularies.
R
Reusable
A clear license, documented provenance, community standards.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
FAIR DOES NOT MEAN OPEN
Human subjects data
Can be FAIR and access-controlled
Endangered species locations
Findable in metadata, not accessible
Data collected on Navajo Nation
FAIR to the Nation and its chapters; everyone else is governed by NNHRRB conditions, with a metadata-only public record
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE CARE PRINCIPLES FOR INDIGENOUS DATA GOVERNANCE
C
Collective benefit
Data serve the community's development, governance and equitable outcomes.
A
Authority to control
Indigenous peoples govern their data.
R
Responsibility
Researchers build relationships and capacity.
E
Ethics
Minimize harm, maximize benefit, consider future use.
CARE asks what FAIR does not: not can the data be reused, but should they be, by whom, and on whose terms.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
RESEARCH ON NAVAJO NATION
BEFORE COLLECTION
Approve and resolve
NNHRRB approves the protocol; the affected chapter passes a supporting resolution.
DURING
The Nation owns the data
Stored and governed under the research agreement.
BEFORE ANY RELEASE
Final Report and Dissemination Plan
NNHRRB approves both before sharing in any form: preprints, talks, deposits.
Also: Pueblo of Laguna partners have their own review. A university IRB approval is never a substitute. Write these conditions into the plan, the metadata and the data governance agreement.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
SRP EXAMPLE: FAIR AND CARE TOGETHER
ARIZONA
Public, with care for plots
Arsenic and phytoremediation data from state land go to Zenodo or EDI with a DOI and CC BY; coordinates of plots that could be looted are generalized.
NEW MEXICO
Findable, under community control
Well-water and biomonitoring data stay in tribally controlled storage or the Native BioData Consortium, with a metadata-only record and a Local Contexts Notice.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
PART 3
PLANS, REPOSITORIES,
LICENSES
what changed in 2026
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE 2026 PLAN FORMATS
Funder
What the plan is now
NIH
Due on or after 25 May 2026: Yes/No sharing commitments, a 300-word justification for any limitation, a 100-word table of data types and repositories. Compliance reported in RPPR C.5.c from 1 October 2026.
NSF
A two-page Data Management and Sharing Plan via a Research.gov webform since 27 April 2026; data underlying publications shared at publication; PIDs and minimum metadata.
Superfund Research Program
A Data Management and Analysis Core (DMAC) is mandatory for every P42 center. Budget its time and repository fees.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE NIH FORMAT IN THREE NUMBERS
COMMITMENTS
Yes / No
For each data type: will it be shared? Every No must be explained.
JUSTIFICATION
300 words
The only free text. Use it for consent scope, tribal data governance, site-location sensitivity.
TABLE
100 words
Data types paired with repositories.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
WHERE THE DATA GO
Data
Repository
Toxicology and biomarkers
NIEHS CEBS, SRP Tox Data Commons; dbGaP for human subjects (controlled access)
Environmental chemistry and geospatial
EDI, DataONE, EPA Science Inventory; Zenodo for large spectroscopy files
Anything, from your institution
UA ReDATA; UNM Digital Repository; Dryad (UNM member, no fee)
Tribal community data
Tribally controlled storage or the Native BioData Consortium; a metadata-only record elsewhere
DUST 2026 · LESSON 2 · DATA MANAGEMENT
CHOOSING A LICENSE
RESEARCH DATA
CC0
No restrictions. Maximizes reuse and integration.
RESEARCH DATA
CC BY
Attribution required.
AVOID
NC licenses
Non-commercial terms block integration and reuse.
NOT YOURS TO CHOOSE
Tribal data
The research agreement and review board set the terms. Publish metadata with a Local Contexts Notice instead.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
FIVE TAKEAWAYS
1 · Plan early
Data management starts before data collection
2 · FAIR is not open
A framework that needs interpretation, not a mandate to publish everything
3 · CARE
Authority sits with the community: on Navajo Nation, the NNHRRB and the chapter decide
4 · Short formats
The 2026 plans reward honest, specific answers; budget the DMAC
5 · Preserve
Federal datasets can vanish: keep and cite your own copy
DUST 2026 · LESSON 2 · DATA MANAGEMENT
DISCUSSION · 20 MINUTES
DRAFT THE
PLAN
two sites, one page
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THE SCENARIO · FOUR YEARS, DUE AFTER 25 MAY 2026
SITE A · STATE LAND
Arizona mine tailings
ICP-MS for arsenic and metals; plant tissue from phytoremediation plots; quarterly hyperspectral drone imagery; a weather station; GPS site data. Public by publication; some plot locations may need restriction against looting.
SITE B · NAVAJO NATION
Abandoned uranium mine
Well and livestock water ICP-MS for U, As, V; dust wipes and gamma spectrometry; urine biomonitoring; a household survey. The Nation owns the data; chapter resolution first; NNHRRB approves any release; household locations never public.
Team: two SRP centers, an external analytical lab, a tribal community partner, a DMAC data manager.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
ON ONE PAGE, DRAFT
1 · YES / NO
Commitments
For each data type, will it be shared? Which answers are No, and why?
2 · 300 WORDS
Justification
In bullets: what goes in it for Site B, and for the Site A plot locations?
3 · 100 WORDS
The table
Data type to repository: Zenodo? EDI? Tox Data Commons? Tribally controlled storage with a metadata-only record?
REPORT-OUT: each group reads its table and its hardest No.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
THREE QUICK QUESTIONS
Question
Answer
True or false: FAIR data must be openly available to everyone
False. Access can be controlled; the metadata must still be findable and persistent.
Your NIH application is due in October 2026. What does the plan look like?
The NOT-OD-26-046 format: Yes/No commitments, up to 300 words of justification, a 100-word table.
Who decides whether Navajo Nation well-water data go to a public repository?
The Navajo Nation, through the NNHRRB and the chapter. Not the PI, the IRB or the repository.
DUST 2026 · LESSON 2 · DATA MANAGEMENT
BEFORE LESSON 3
Complete Lesson 2 homework: Data Management, self-paced, about 90 to 120 minutes.
The life cycle in full with naming and metadata examples, repository lists, FAIR and CARE in depth, the 2026 plan formats element by element, licenses, three self-assessments and the complete quiz.
unm-carc.github.io/dust-2026 · Next: Lesson 3, Ethics and Artificial Intelligence
DUST 2026 · LESSON 2 · DATA MANAGEMENT