Öffentliches RegisterExzellent
Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-30 22:15 UTC
mne-tools / mne-python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
mne-tools/mne-python erreicht einen Gesundheitsindex von 89 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Engineering Quality (96/100) ab, am schwächsten bei AI Readiness (64/100). Zuletzt heute aktualisiert. 3 Mitwirkende tragen den Großteil der jüngsten Arbeit.
89
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
89
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Eigentümerschaft
MNE tools for MEG and EEG data analysisOrganisation
312 Follower49 öffentliche Reposseit Jan. 2011
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | mne | 1.12.1 | 424.271 | 86 | vor 101 Tagen | ecogeegmegbrainfnirsneuroimagingneuroscience |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
93
Entwicklungsaktivität
100Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 36/36 | Commit-Rhythmus — 52/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 452 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 452 |
| human_commit_share | 0,87 |
| days_since_last_push | 0 |
| active_weeks_last_year | 52 |
Release-Disziplin
82Gut
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 64 Releases veröffentlicht |
| 27/36 | Release-Aktualität — letztes Release vor 101 Tagen |
| 19.8/27 | Release-Rhythmus — ein Release etwa alle 81,2 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 64 |
| latest_release_tag | v1.12.1 |
| releases_from_tags | nein |
| days_since_latest_release | 101 |
| mean_days_between_releases | 81,2 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
93
Popularität & Verbreitung
93Exzellent
Wie die Bewertung erfolgt
| 57.4/60 | Stars — 3.475 Stars |
| 25/25 | Forks — 1.576 Forks |
| 10.6/15 | Watcher — 83 Watcher |
Verwendete Eingangsdaten
| forks | 1.576 |
| stars | 3.475 |
| watchers | 83 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
92Exzellent
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (BSD-3-Clause) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | ja |
Ökosystem-Verbreitung (Downloads)
94Exzellent
Wie die Bewertung erfolgt
| 75/80 | Downloads pro Monat — 424.271 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | mne |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 424.271 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
83
Wie die Bewertung erfolgt
| 36/54 | Bus-Faktor — 3 Beitragende decken die Hälfte aller Commits ab |
| 16.3/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 28 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 97 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 61 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 3 |
| contributors_sampled | 97 |
| top_contributor_share | 0,276 |
Reaktionsfähigkeit bei Issues & PRs
87Exzellent
Wie die Bewertung erfolgt
| 42.1/46.8 | Issue-Lösungsquote — 90 % der Issues geschlossen |
| 33.2/38.3 | PR-Annahme — 7.630/8.788 entschiedene PRs gemergt |
| 12/15 | OpenSSF Scorecard: Code-Review — Found 26/29 approved changesets -- score normalized to 8 |
Verwendete Eingangsdaten
| merged_prs | 7.630 |
| open_issues | 520 |
| closed_issues | 4.656 |
| issue_closed_ratio | 0,9 |
| closed_unmerged_prs | 1.158 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 17.9/25 | Reichweite des Inhabers — 312 Follower von mne-tools |
| 24.4/25 | Kontohistorie — 49 öffentliche Repos, Kontoalter ca. 15 Jahre |
Verwendete Eingangsdaten
| followers | 312 |
| owner_type | Organization |
| is_verified | — |
| owner_login | mne-tools |
| public_repos | 49 |
| account_age_days | 5.664 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 101 Tagen |
| 20/20 | Versionshistorie — 86 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | mne |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 101 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
96
Engineering-Praktiken
94Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 9 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://mne.tools |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 12 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | python, neuroscience, electroencephalography, magnetoencephalography, electrocorticography, machine-learning, statistics, visualization, eeg, meg, ecog, neuroimaging |
| has_wiki | ja |
| homepage | https://mne.tools |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
82
Sicherheitslage
78Gut
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.5/2.5 | CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 1.8/2.5 | CII-Best-Practices — badge detected: Silver |
| 6/7.5 | Code-Review — Found 26/29 approved changesets -- score normalized to 8 |
| 2.5/2.5 | Contributors — project has 61 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 4.5/5 | SAST — SAST tool detected but not run on all commits |
| 5/5 | Security-Policy — security policy file detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 7,8 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Exzellent
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 24 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:mne@1.12.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 24 Paketen. Erreichbarkeit wird nicht analysiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
64
AI-Agentenkontext & Leitlinien
85Exzellent
Wie die Bewertung erfolgt
| 45/45 | Agentenanweisungen — AGENTS.md, CLAUDE.md |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 40/40 | Lesbare Commit-Historie — 78 von 87 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,897 |
| agent_instruction_files | AGENTS.md, CLAUDE.md |
| agent_instruction_max_bytes | 7.654 |
Wie die Bewertung erfolgt
| 18/18 | Bootstrap mit einem Befehl — Makefile, doc/Makefile, tools/dev/Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 2/10 | Belegte Agentenpraxis — 1 der letzten 100 Commits von Agenten verfasst oder ihnen zugeschrieben |
| 8/8 | Automatisierte Wartung — 5 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | Makefile, doc/Makefile, tools/dev/Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | 0,01 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,05 |
AI-Code-Lesbarkeit für Modelle
52Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 52/55 | Handhabbare Dateigrößen — 50/908 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 219.130 |
| source_files_sampled | 908 |
| oversized_source_files | 50 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — examples, sample |
Verwendete Eingangsdaten
| example_dirs | examples, sample |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
3.475GitHub-Sterne
97Mitwirkende
452Commits, letzte 12 Monate
0Tage seit letztem Push
64Releases
3Bus-Faktor
520offene Issues
PyPIPaket-Ökosysteme
Warnungen zur Datenerhebung
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Weitere Details
Stern- und Fork-Verlauf 0 ★ / 1.576 ⇿
0Sterne
1.576Forks
51Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Es wird nur die jüngste Historie angezeigt — dieses Repository überschreitet das Erfassungsfenster, sodass die früheste Historie nicht erfasst wird.
Major 1Minor 18Patch 32
Jeder Punkt umfasst 7 Tage.
OpenSSF Scorecard 7.8 / 10
7.8Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal 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 |
| 10 | CI-Tests | 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 7 | CII-Best-Practices | badge detected: Silver |
| 8 | Code-Review | Found 26/29 approved changesets -- score normalized to 8 |
| 10 | Contributors | project has 61 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 9 | SAST | SAST tool detected but not run on all commits |
| 10 | Security-Policy | security policy file detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direkte Abhängigkeiten 10
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | decorator | >= 5.1 | pyproject.toml |
| PyPI | jinja2 | >= 3.1 | pyproject.toml |
| PyPI | lazy_loader | >= 0.3 | pyproject.toml |
| PyPI | matplotlib | >= 3.9 | pyproject.toml |
| PyPI | numpy | >= 2.0, < 3 | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
| PyPI | pooch | >= 1.5 | pyproject.toml |
| PyPI | scipy | >= 1.14 | pyproject.toml |
| PyPI | tqdm | >= 4.66 | pyproject.toml |
| PyPI | typing-extensions | >= 4.15 | pyproject.toml |
Alle Abhängigkeiten 64
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 8 direkte und 56 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | decorator | — | direkt |
| PyPI | jinja2 | — | direkt |
| PyPI | lazy-loader | — | direkt |
| PyPI | matplotlib | — | direkt |
| PyPI | numpy | — | direkt |
| PyPI | pooch | — | direkt |
| PyPI | scipy | — | direkt |
| PyPI | tqdm | — | direkt |
| PyPI | antio | — | indirekt |
| PyPI | curryreader | — | indirekt |
| PyPI | dipy | — | indirekt |
| PyPI | docutils | — | indirekt |
| PyPI | edfio | — | indirekt |
| PyPI | filelock | — | indirekt |
| PyPI | h5io | — | indirekt |
| PyPI | h5py | — | indirekt |
| PyPI | hatchling | — | indirekt |
| PyPI | imageio | — | indirekt |
| PyPI | imageio-ffmpeg | — | indirekt |
| PyPI | intersphinx-registry | — | indirekt |
| PyPI | ipython | — | indirekt |
| PyPI | joblib | — | indirekt |
| PyPI | memory-profiler | — | indirekt |
| PyPI | mffpy | — | indirekt |
| PyPI | mne-qt-browser | — | indirekt |
| PyPI | nibabel | — | indirekt |
| PyPI | nilearn | — | indirekt |
| PyPI | nitime | — | indirekt |
| PyPI | numba | — | indirekt |
| PyPI | numpydoc | — | indirekt |
| PyPI | openmeeg | — | indirekt |
| PyPI | openneuro-py | — | indirekt |
| PyPI | pandas | — | indirekt |
| PyPI | pillow | — | indirekt |
| PyPI | pip | — | indirekt |
| PyPI | pydata-sphinx-theme | — | indirekt |
| PyPI | pygments | — | indirekt |
| PyPI | pyobjc-framework-cocoa | — | indirekt |
| PyPI | pyqt6 | — | indirekt |
| PyPI | pyqt6-qt6 | — | indirekt |
| PyPI | pyside6 | — | indirekt |
| PyPI | pytest | — | indirekt |
| PyPI | pytest-cov | — | indirekt |
| PyPI | pytest-qt | — | indirekt |
| PyPI | pytest-timeout | — | indirekt |
| PyPI | pytest-xdist | — | indirekt |
| PyPI | python-picard | — | indirekt |
| PyPI | pyvista | — | indirekt |
| PyPI | pyvistaqt | — | indirekt |
| PyPI | pyzmq | — | indirekt |
| PyPI | qdarkstyle | — | indirekt |
| PyPI | rcssmin | — | indirekt |
| PyPI | refleak | — | indirekt |
| PyPI | ruff | — | indirekt |
| PyPI | scikit-learn | — | indirekt |
| PyPI | seaborn | — | indirekt |
| PyPI | selenium | — | indirekt |
| PyPI | sphinx | — | indirekt |
| PyPI | sphinx-gallery | — | indirekt |
| PyPI | sphinxcontrib-bibtex | — | indirekt |
| PyPI | sphinxcontrib-towncrier | — | indirekt |
| PyPI | statsmodels | — | indirekt |
| PyPI | trame-vuetify | — | indirekt |
| PyPI | vtk | — | indirekt |
Abhängigkeits-Advisories 0
Die Installation von pypi:mne@1.12.1 zieht 24 Pakete nach sich, direkt und transitiv: 0 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.
Keine bekannten Advisories betreffen die bewerteten Abhängigkeiten.
Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.
JSON-Rohbericht maschinenlesbar
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"data": {
"repo": {
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"python",
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"electroencephalography",
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"pushed_at": "2026-07-30T21:01:09Z",
"created_at": "2011-01-28T03:31:13Z",
"owner_type": "Organization",
"updated_at": "2026-07-30T21:01:41Z",
"description": "MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python",
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"name": "MNE tools for MEG and EEG data analysis",
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"location": null,
"followers": 312,
"avatar_url": "https://avatars.githubusercontent.com/u/583875?v=4",
"created_at": "2011-01-26T02:24:38Z",
"is_verified": null,
"public_repos": 49,
"account_age_days": 5664
},
"license": {
"state": "standard",
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"activity": {
"releases": [
{
"tag": "v1.12.1",
"kind": "patch",
"published_at": "2026-04-20T17:07:30Z"
},
{
"tag": "v1.12.0",
"kind": "minor",
"published_at": "2026-04-07T19:35:14Z"
},
{
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"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
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"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 19 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
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"details": [],
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},
{
"key": "packaging",
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"detail": "packaging workflow detected",
"points": 5,
"status": "met",
"details": [],
"max_points": 5
},
{
"key": "pinned_dependencies",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "sast",
"name": "SAST",
"detail": "SAST tool detected but not run on all commits",
"points": 4.5,
"status": "partial",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
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"detail": "security policy file detected",
"points": 5,
"status": "met",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
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"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
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"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"key": "token_permissions",
"name": "Token-Permissions",
"detail": "detected GitHub workflow tokens with excessive permissions",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
"detail": "0 existing vulnerabilities detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "dependency_advisories",
"band": "excellent",
"name": "Dependency advisories",
"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mne@1.12.1 runtime dependency closure — what installing the published package pulls in — 24 packages. Reachability is not analyzed.",
"notes": [
{
"code": "excluded_no_data",
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},
{
"code": "weights_renormalized",
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{
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"assessed": 24
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},
{
"code": "advisories_reachability",
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}
],
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"inputs": {
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"advisories": 0,
"affected_packages": 0,
"assessed_packages": 24,
"unassessed_packages": 0,
"affected_by_severity": "none",
"direct_affected_packages": 0
},
"components": [
{
"key": "direct_dependencies_free_of_known_advisories",
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"detail": "no direct dependency carries a known advisory",
"points": 35,
"status": "met",
"details": [
{
"code": "no_direct_advisories",
"params": {}
}
],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
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"detail": "no indirect dependency carries a known advisory",
"points": 25,
"status": "met",
"details": [
{
"code": "no_indirect_advisories",
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],
"max_points": 25
},
{
"key": "no_advisories_left_outstanding",
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"points": 0,
"status": "excluded",
"details": [
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"code": "advisories_no_publication_date",
"params": {}
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],
"max_points": 40
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]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
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"meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
"packages": [],
"red_flag": false,
"assessed_packages": 24,
"malicious_packages": 0,
"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
},
"components": [
{
"key": "no_dependency_reported_as_a_malicious_package",
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"points": 100,
"status": "met",
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"params": {}
}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
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"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
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"red_flag": false,
"exposures": [],
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"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 14
},
"components": [
{
"key": "policy_exposure_multiplier",
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"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "moderate",
"name": "AI Readiness",
"value": 64,
"weight": 0,
"metrics": [
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"key": "ai_agent_context",
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"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 85,
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"legible_history_share": 0.897,
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},
"components": [
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"key": "agent_instructions",
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"detail": "AGENTS.md, CLAUDE.md",
"points": 45,
"status": "met",
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"code": "file_list",
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}
],
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{
"key": "machine_readable_docs_llms_txt",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
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"points": 40,
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{
"code": "legible_history",
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}
],
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]
},
{
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"note": null,
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"value": 61,
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"lockfiles": [],
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"typecheck_configs": [],
"agent_commit_share": 0.01,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0.05
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"components": [
{
"key": "one_command_bootstrap",
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"detail": "Makefile, doc/Makefile, tools/dev/Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
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{
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"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
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"detail": null,
"points": 11,
"status": "met",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
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"points": 2,
"status": "partial",
"details": [
{
"code": "agent_authored_commits",
"params": {
"count": 1,
"sampled": 100
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}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "5 of the last 100 commits are automated dependency updates",
"points": 8,
"status": "met",
"details": [
{
"code": "dependency_bot_commits",
"params": {
"count": 5,
"sampled": 100
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}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "moderate",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 52,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 219130,
"source_files_sampled": 908,
"oversized_source_files": 50
},
"components": [
{
"key": "type_checkable_code",
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"detail": "Python without a type-check config",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
"params": {
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "50/908 source files over 60KB",
"points": 52,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 908,
"oversized": 50
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
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"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
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"has_mcp_signal": false,
"api_schema_files": []
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"components": [
{
"key": "api_schema_openapi_graphql_proto",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
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"detail": "examples, sample",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples, sample"
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}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
],
"report_type": "repository",
"generated_at": "2026-07-30T22:15:03.022222Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/mne-tools/mne-python.svg",
"full_name": "mne-tools/mne-python",
"license_state": "standard",
"license_spdx": "BSD-3-Clause"
}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.