Is Anemoi Datasets Safe?
Anemoi Datasets — Nerq Trust Score 65.8/100 (B- grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-05-09.
Use Anemoi Datasets with some caution. Anemoi Datasets is a Python package with a Nerq Trust Score of 65.8/100 (B-), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Anemoi Datasets safe?
CAUTION — Anemoi Datasets has a Nerq Trust Score of 65.8/100 (B-). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Anemoi Datasets's trust score?
Anemoi Datasets has a Nerq Trust Score of 65.8/100, earning a B- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Anemoi Datasets?
Anemoi Datasets's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Anemoi Datasets and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
Similar Pypi by Trust Score
Safety Guide: Anemoi Datasets
What is Anemoi Datasets?
Anemoi Datasets is a Python package — A package to hold various functions to support training of ML models on ECMWF data..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=anemoi-datasets
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Anemoi Datasets has a Nerq Trust Score of 66/100 (B-) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Anemoi Datasets has a Trust Score of 66/100 (B-).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 100/100 |
| Popularity | 0/100 |
| Quality | 65/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Anemoi Datasets collect?
Privacy assessment for Anemoi Datasets is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Anemoi Datasets secure?
Security score: 90/100. Anemoi Datasets has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
Licensed under Apache License Version 2.0, January 2004 , allowing code inspection. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Anemoi Datasets Security Report
How we calculated this score
Anemoi Datasets's trust score of 65.8/100 (B-) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (0/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on May 09, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.