aiidalab-eln

v0.1.4 safe
3.0
Low Risk

Package that integrates AiiDAlab with Electronic Laboratory Notebooks.

🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with minimal risks identified. There are no signs of obfuscation, credential harvesting, or supply-chain attacks.

  • No obfuscation or credential harvesting detected.
  • Metadata risk is low with only minor concerns about non-secure links and a new maintainer account.
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low risk due to no typosquatting or email domain flags, but concerns over non-secure links and new maintainer account.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2929 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 3 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 8 unique contributor(s) across 79 commits in aiidalab/aiidalab-eln
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • lation.aiida" os.system( f"verdi archive create {stm_simulation_
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: materialscloud.org

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://nccr-marvel.ch
  • Non-HTTPS external link: http://www.snf.ch/en
  • Non-HTTPS external link: http://www.max-centre.eu/
✓ Git Repository History

Repository aiidalab/aiidalab-eln appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "The AiiDAlab team" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aiidalab-eln 
Your task is to develop a mini-application that leverages the 'aiidalab-eln' package to create an integrated Electronic Laboratory Notebook (ELN) system tailored for researchers working with computational materials science workflows. This application will serve as a platform where researchers can document their experiments, store data, and link it directly to computational workflows managed through AiiDA (Archive Intermediate Input Data Archive), a powerful data management framework for computational science.

The application should include the following core functionalities:
1. **Experiment Documentation**: Users should be able to create, edit, and manage experiment records, including adding notes, images, and other relevant documents.
2. **Data Storage and Retrieval**: Integrate with AiiDA to allow users to upload experimental data, and automatically link it to corresponding computational workflow entries in AiiDA.
3. **Workflow Integration**: Provide a seamless interface for users to initiate computational workflows within AiiDA directly from the ELN. This includes specifying parameters for simulations and linking them back to the experiment record.
4. **Visualization Tools**: Implement basic visualization tools for quick analysis of experimental and simulation results, such as graphs and charts.
5. **Security and Access Control**: Ensure that each user has a secure login and can only access and modify their own experiment records and linked workflows.
6. **Search Functionality**: Allow users to search for specific experiments based on tags, dates, or keywords.

To achieve these functionalities, you'll need to utilize the 'aiidalab-eln' package to handle the integration between the ELN and AiiDA. Additionally, consider using Flask or Django for the web framework, SQLAlchemy for database interactions, and Plotly or Matplotlib for visualization purposes. Your final deliverable should be a fully functional, deployable application with clear instructions for setup and usage.