Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 16:17 UTC

Dana-Farber-AIOS / pathml

Tools for computational pathology

PythonGPL-2.0★ 462 stars⑂ 87 forkssince Jul 2019View on GitHub ↗

Dana-Farber-AIOS/pathml holds a health index of 73 out of 100, placing it in the Good band. It scores highest on Engineering Quality (91/100) and lowest on Security (21/100). It was last updated 22 days ago. 2 contributors account for most of its recent work.

73
overall / 100
Good

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.

73
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 64 is calibrated to 73 on the published index scale (record calibration 2026-08-02).

Ownership

Dana-Farber-AIOSOrganization
35 followers4 public repossince Jan 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpathml3.0.86292322 days ago

Metrics by category

Vitality

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

68Good · 21% of overall
How it's scored
28.8/36Push recencylast push 22 days ago
4.8/36Commit cadence7/52 weeks with commits
17.3/18Commit volume84 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year84
human_commit_share1
days_since_last_push22
active_weeks_last_year7
How it's scored
27/27Ships releases25 releases published
36/36Release recencylatest release 22 days ago
12.6/27Release cadencea release every ~131.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count25
latest_release_tagv3.0.8
releases_from_tagsno
days_since_latest_release22
mean_days_between_releases131.5

Community & Adoption

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

62Moderate · 17% of overall
How it's scored
43.2/60Stars462 stars
16.1/25Forks87 forks
5.6/15Watchers11 watchers
Inputs used
forks87
stars462
watchers11
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (GPL-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_servicescodecov.io, github.com, readthedocs.org, shields.io
has_pull_request_templateno
How it's scored
37.3/80Monthly downloads629 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagespathml
dependents
ecosystemspypi
total_downloads
monthly_downloads629
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?

72Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.8/22.5Commit distributiontop contributor authored 39% of commits
13.5/13.5Contributor breadth15 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor2
contributors_sampled15
top_contributor_share0.387
How it's scored
33.2/42Issue resolution79% of issues closed
24.4/30PR acceptance215/264 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs215
open_issues43
closed_issues163
prs_merged_7d0
prs_decided_7d0
prs_merged_30d4
prs_decided_30d4
issue_closed_ratio0.791
closed_unmerged_prs49
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 backingorganization-owned
0/20Verified domain
11.2/25Owner reach35 followers of Dana-Farber-AIOS
16.4/25Track record4 public repos, account ~5 yr old
Inputs used
followers35
owner_typeOrganization
is_verifiedno
owner_loginDana-Farber-AIOS
public_repos4
account_age_days2,060

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 22 days ago
20/20Version history23 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagespathml
ecosystemspypi
any_deprecatedno
min_days_since_publish22

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter config.flake8, pyproject.toml ([tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://pathml.org
10/10Repository description
10/10Topics15 topics
0/10Wiki
Inputs used
topicsmachine-learning, digital-pathology, computational-pathology, biomedical-image-processing, pathology, histopathology, spatial-transcriptomics, image-analysis, microscopy, fluorescence-microscopy-imaging, deep-learning, python, pytorch, research, pathml
has_wikino
homepagehttps://pathml.org
docs_sitehttps://pathml.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfilespublished library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements/requirements_torch.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories1
affected_packages1
assessed_packages20
unassessed_packages13
affected_by_severitymoderate 1
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 20 resolved dependencies against OSV. 13 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. 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 instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
11.7/40Legible commit history22 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.22
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format config.flake8, pyproject.toml ([tool.isort])
0/11Static type checking
10/10Reproducible environmentDockerfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54.3/55Manageable file sizes1/78 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes62,110
source_files_sampled78
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

462GitHub stars
15contributors
84commits, last 12 months
22days since last push
25releases
2bus factor
43open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/05 16:17:12 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 87 ⇿
0Stars
87Forks
21Releases

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.

02040608010085102021-082023-112026-02
Major 2Minor 1Patch 13

Each point covers 5 days.

All dependencies 33

Full resolved dependency set from the GitHub dependency graph: 0 direct and 33 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIanndataindirect
PyPIdaskindirect
PyPIh5py3.10.0indirect
PyPIipython8.10.0indirect
PyPIjpype1indirect
PyPIloguru0.7.2indirect
PyPImatplotlibindirect
PyPInbsphinx0.9.3indirect
PyPInbsphinx-link1.3.0indirect
PyPInetworkxindirect
PyPInumpyindirect
PyPIonnx1.22.0indirect
PyPIonnxruntimeindirect
PyPIonnxscript0.7.1indirect
PyPIopencv-contrib-python4.8.1.78indirect
PyPIopenslide-python1.3.1indirect
PyPIpandasindirect
PyPIpydicom3.0.2indirect
PyPIpython-bioformats4.1.0indirect
PyPIpython-javabridge4.0.4indirect
PyPIscanpy1.9.6indirect
PyPIscikit-imageindirect
PyPIscikit-learnindirect
PyPIscipyindirect
PyPIsetuptoolsindirect
PyPIsphinx7.1.2indirect
PyPIsphinx-autoapi3.0.0indirect
PyPIsphinx-copybutton0.5.2indirect
PyPIsphinx-rtd-theme1.3.0indirect
PyPIstatsmodelsindirect
PyPItorch2.12.0indirect
PyPItorch-geometric2.8.0indirect
PyPItqdm4.66.3indirect
Dependency advisories 1

This repository publishes no package the index resolves, so its own dependency graph was assessed — 20 packages, which also include development and test pins that never ship: 1 carry known advisories, of which 0 are direct. 13 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
torch2.12.0indirectmoderate12.13.0

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 statisticsPyPI.