Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-14 07:30 UTC

equinor / fmu-datamodels

FMU data standard, including models and schemas

PythonApache-2.0★ 1 star⑂ 9 forkssince Jun 2025View on GitHub ↗

equinor/fmu-datamodels holds a health index of 92 out of 100, placing it in the Excellent band. It scores highest on Vitality (97/100) and lowest on Community & Adoption (46/100). It was last updated today. 3 contributors account for most of its recent work.

92
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

92
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 78 is calibrated to 92 on the published index scale (record calibration 2026-08-02).

Ownership

EquinorOrganization · verified domain
1,837 followers760 public repossince Dec 2010

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIfmu-datamodels0.26.47,585450 days agofmusumo

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

97Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
31.2/36Commit cadence — 45/52 weeks with commits
18/18Commit volume — 132 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year132
human_commit_share0.62
days_since_last_push0
active_weeks_last_year45

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 43 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~7.2 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count43
latest_release_tag0.26.4
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases7.2
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

46Weak · 17% of overall
How it's scored
0/60Stars — 1 stars
7.5/25Forks — 9 forks
0/15Watchers — 0 watchers
Inputs used
forks9
stars1
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateyes
How it's scored
51.7/80Monthly downloads — 7,585 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesfmu-datamodels
dependents—
ecosystemspypi
total_downloads—
monthly_downloads7,585
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

88Excellent · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
17.9/22.5Commit distribution — top contributor authored 20% of commits
13.5/13.5Contributor breadth — 12 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor3
contributors_sampled12
top_contributor_share0.205
How it's scored
36.3/42Issue resolution — 86% of issues closed
26.6/30PR acceptance — 177/200 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs177
open_issues17
closed_issues109
prs_merged_7d4
prs_decided_7d4
prs_merged_30d9
prs_decided_30d9
issue_closed_ratio0.865
closed_unmerged_prs23
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
23.5/25Owner reach — 1,837 followers of equinor
25/25Track record — 760 public repos, account ~15 yr old
Inputs used
followers1,837
owner_typeOrganization
is_verifiedyes
owner_loginequinor
public_repos760
account_age_days5,780

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 45 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesfmu-datamodels
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://fmu-dataio.readthedocs.io
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_sitehttps://fmu-dataio.readthedocs.io
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

83Excellent · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
2.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate7.9
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages70
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 70 resolved dependencies against OSV. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

54Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
24.1/40Legible commit history — 28 of 62 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.452
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/fmu/datamodels/py.typed
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 38 of the last 100 commits are automated dependency updates
5/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configssrc/fmu/datamodels/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.38
How it's scored
27/45Type-checkable code — Python with type-check config (src/fmu/datamodels/py.typed)
55/55Manageable file sizes — 0/60 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes26,557
source_files_sampled60
oversized_source_files0

Key facts

1GitHub stars
12contributors
132commits, last 12 months
0days since last push
43releases
3bus factor
17open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:fmu-datamodels@0.26.4; advisories assessed against the repository dependency graph instead

More detail

Star and fork history 0 ★ / 9 ⇿
0Stars
9Forks
29Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0246810912025-082025-122026-05
Major 0Minor 9Patch 20
OpenSSF Scorecard 7.9 / 10
7.9aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-14 07:30 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIpydantic—pyproject.toml
All dependencies 70

Full resolved dependency set from the GitHub dependency graph: 1 direct and 69 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIpydantic2.13.5direct
PyPIannotated-types0.8.0indirect
PyPIast-serialize0.12.1indirect
PyPIcloudpickle3.1.2indirect
PyPIcolorama0.4.6indirect
PyPIcontourpy1.3.3indirect
PyPIcontourpy1.4.0indirect
PyPIcoverage7.16.2indirect
PyPIcycler0.12.1indirect
PyPIdeprecation2.1.0indirect
PyPIemcee3.1.6indirect
PyPIexecnet2.1.2indirect
PyPIfmu-config1.7.3indirect
PyPIfmu-settings1.5.0indirect
PyPIfonttools4.66.1indirect
PyPIgstools1.7.0indirect
PyPIgstools-cython1.2.0indirect
PyPIh5py3.16.0indirect
PyPIhankel1.2.2indirect
PyPIhdf5plugin7.1.0indirect
PyPIhypothesis6.168.5indirect
PyPIiniconfig2.3.0indirect
PyPIjoblib1.6.0indirect
PyPIkiwisolver1.5.1indirect
PyPIlibrt0.16.0indirect
PyPImarkdown-it-py4.2.0indirect
PyPImatplotlib3.11.2indirect
PyPImdurl0.1.2indirect
PyPImeshio5.3.5indirect
PyPImpmath1.4.1indirect
PyPImypy2.4.0indirect
PyPImypy-extensions1.1.0indirect
PyPInumpy2.4.6indirect
PyPInumpy2.5.3indirect
PyPIpackaging26.3indirect
PyPIpandas3.0.6indirect
PyPIpathspec1.1.1indirect
PyPIpillow12.3.0indirect
PyPIpluggy1.6.0indirect
PyPIpyarrow25.0.1indirect
PyPIpyarrow-stubs20.0.0.20260819indirect
PyPIpydantic-core2.46.5indirect
PyPIpydocstyle6.3.0indirect
PyPIpyevtk1.7.0indirect
PyPIpygments2.21.0indirect
PyPIpyparsing3.3.3indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-mock3.16.0indirect
PyPIpytest-runner6.0.1indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpyyaml6.0.3indirect
PyPIresfo5.0.1indirect
PyPIrich15.0.0indirect
PyPIroffio1.1.1indirect
PyPIruff0.16.10indirect
PyPIscipy1.17.1indirect
PyPIscipy1.18.1indirect
PyPIsegyio1.9.14indirect
PyPIshapely2.1.2indirect
PyPIsix1.17.0indirect
PyPIsnowballstemmer3.1.1indirect
PyPIsortedcontainers2.4.0indirect
PyPItomli2.4.1indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPItzdata2026.5indirect
PyPIxtgeo4.26.0indirect
PyPIxtgeoviz0.3.2indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 70 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.