Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 15:02 UTC

datajoint / datajoint-python

Relational Workflows: where database schemas define executable data pipelines.

PythonApache-2.0★ 197 stars⑂ 98 forkssince Sep 2012View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

datajoint/datajoint-python holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Vitality (90/100) and lowest on AI Readiness (51/100). It was last updated 5 days ago. A single contributor accounts for most of its recent work.

89
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.

89
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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

DataJointOrganization · verified domain
74 followers92 public repossince Sep 2012

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

Package ecosystems

Metrics by category

Vitality

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

90Excellent · 21% of overall
How it's scored
36/36Push recency — last push 5 days ago
25.6/36Commit cadence — 37/52 weeks with commits
18/18Commit volume — 842 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year842
human_commit_share1
days_since_last_push5
active_weeks_last_year37
How it's scored
27/27Ships releases — 58 releases published
36/36Release recency — latest release 5 days ago
27/27Release cadence — a release every ~22.7 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count58
latest_release_tagv2.3.3
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases22.7

Community & Adoption

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

64Moderate · 17% of overall
How it's scored
37.2/60Stars — 197 stars
16.6/25Forks — 98 forks
5.6/15Watchers — 11 watchers
Inputs used
forks98
stars197
watchers11
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicescoveralls.io, github.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

79Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
11/22.5Commit distribution — top contributor authored 51% of commits
13.5/13.5Contributor breadth — 40 contributors
10/10OpenSSF Scorecard: Contributors — project has 66 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled40
top_contributor_share0.509
How it's scored
41.4/42Issue resolution — 98% of issues closed
26.7/30PR acceptance — 718/807 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_prs718
open_issues11
closed_issues711
prs_merged_7d3
prs_decided_7d3
prs_merged_30d6
prs_decided_30d6
issue_closed_ratio0.985
closed_unmerged_prs89
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
13.5/25Owner reach — 74 followers of datajoint
25/25Track record — 92 public repos, account ~13 yr old
Inputs used
followers74
owner_typeOrganization
is_verifiedyes
owner_logindatajoint
public_repos92
account_age_days5,109

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 5 days ago
20/20Version history — 85 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesdatajoint
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://docs.datajoint.com
10/10Repository description
10/10Topics — 17 topics
0/10Wiki
Inputs used
topicsdatajoint, scientific-computing, python, relational-model, mysql, data-pipelines, workflow-management, data-engineering, data-integrity, data-lineage, declarative, metadata-management, object-storage, postgresql, reproducibility, research-software, data-provenance
has_wikino
homepagehttps://docs.datajoint.com
docs_sitehttps://docs.datajoint.com
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

62Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 66 contributing companies or organizations
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 29 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — no data
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated14
scorecard_versionv5.5.0
checks_inconclusive4
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Branch-Protection, CI-Tests, Packaging, SAST. Remaining weights renormalized.

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.

51Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.datajoint.com/llms.txt)
40/40Legible commit history — 90 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.datajoint.com/llms.txt
legible_history_share0.9
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])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53.3/55Manageable file sizes — 4/133 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes91,166
source_files_sampled133
oversized_source_files4

Key facts

197GitHub stars
40contributors
842commits, last 12 months
5days since last push
58releases
1bus factor
11open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'datajoint' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • OSV advisory lookup failed: [Errno 101] Network is unreachable

More detail

Star and fork history 0 ★ / 98 ⇿
0Stars
98Forks
57Releases

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.

0204060801008742015-042020-122026-08
Major 1Minor 8Patch 39

Each point covers 11 days.

OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-09-15 15:02 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
n/aCI-Testsinternal error: internal error: Client.Repositories.ListCheckRunsForRef: error during graphqlHandler.setupCheckRuns: Something went wrong while executing your query on 2026-09-15T15:02:11Z. Please include `DB22:268D3D:73DC3D:706A2C:6AA95DEF` when reporting this issue.
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 66 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
n/aSASTinternal error: internal error: Client.Checks.ListCheckRunsForRef: error during graphqlHandler.setupCheckRuns: Something went wrong while executing your query on 2026-09-15T15:02:11Z. Please include `DB22:268D3D:73DC3D:706A2C:6AA95DEF` when reporting this issue.
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPInumpy—pyproject.toml
PyPIpymysql>=0.7.2pyproject.toml
PyPIdeepdiff—pyproject.toml
PyPIpyparsing—pyproject.toml
PyPIpandas—pyproject.toml
PyPItqdm—pyproject.toml
PyPInetworkx—pyproject.toml
PyPIpydot—pyproject.toml
PyPIfsspec>=2023.1.0pyproject.toml
PyPIpydantic-settings>=2.0.0pyproject.toml
PyPIpackaging—pyproject.toml
All dependencies 10

Full resolved dependency set from the GitHub dependency graph: 3 direct and 7 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIfsspec—direct
PyPIpydantic-settings—direct
PyPIpymysql—direct
PyPIadlfs—indirect
PyPIgcsfs—indirect
PyPIpolars—indirect
PyPIpsycopg2-binary—indirect
PyPIpyarrow—indirect
PyPIs3fs—indirect
PyPItestcontainers—indirect
Dependency advisories not assessed

Advisory matching could not run for this report: OSV advisory lookup failed: [Errno 101] Network is unreachable

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.