AI Analysis
Final verdict: SAFE
The package is deemed safe based on the analysis notes, with low risks across all categories except for network calls, which are likely legitimate given the package's purpose.
- Low network risk due to expected external data fetching
- No evidence of shell execution, obfuscation, or credential harvesting
Per-check LLM notes
- Network: The package makes external network calls which could be legitimate if it's designed to fetch data from remote sources like ORCID and ontologies.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activities targeting secrets or credentials.
- Metadata: No red flags detected in the provided metadata.
Heuristic Checks
Outbound Network Calls
score 4.5
Found 3 network call pattern(s)
try: response = requests.get( mds_ontology_url, headers={() response = requests.get(f"https://pub.orcid.org/v3.0/{clean_orcid}",ology data response = requests.get(url, headers={'Accept': 'text/turtle'}) response.rai
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: case.edu
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository cwru-sdle/FAIRLinked appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Van D. Tran, Brandon Lee, Henry Dirks, Ritika Lamba, Balashanmuga Priyan Rajamohan, Gabriel Ponon, Quynh D. Tran, Ozan Dernek, Yinghui Wu, Erika I. Barcelos, Roger H. French, Laura S. Bruckman" 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 FAIRLinked
Create a mini-application named 'DataFairifier' that leverages the FAIRLinked Python package to transform research datasets from materials science into FAIR-compliant RDF data, enhancing their interoperability and reusability. The application should perform the following steps: 1. **Dataset Input**: Allow users to upload their materials research dataset in CSV format. The dataset must contain columns such as 'Material', 'Property', 'Value', and 'Unit'. 2. **Validation**: Implement a feature to validate the uploaded dataset according to predefined rules (e.g., ensuring all required columns are present, checking data types). 3. **Transformation**: Use FAIRLinked to convert the validated dataset into RDF data aligned with the Materials Data Science Ontology (MDS-Onto). Ensure that the transformation process includes mapping of the original dataset's column names to appropriate ontology terms. 4. **Visualization**: Provide a visual representation of the RDF data using a graph visualization library like PyGraphviz. This will help users understand how their data is structured in the context of the ontology. 5. **Export Options**: Offer options to export the transformed RDF data in different formats such as Turtle, N-Triples, or JSON-LD. 6. **Metadata Management**: Include functionality to add metadata to the RDF data, such as authorship, publication date, and license information, which can be specified by the user during the export process. 7. **User Interface**: Develop a simple and intuitive web interface using Flask or Django to facilitate interaction with the application. The UI should guide users through each step of the process and display results clearly. 8. **Documentation**: Write comprehensive documentation detailing how to use the application, including setup instructions, usage examples, and troubleshooting tips. Suggested Features: - Integration with existing RDF stores like Blazegraph or Virtuoso for persistent storage of transformed data. - Support for multiple input file formats beyond CSV. - Automated quality checks and recommendations for improving the FAIRness of the data. - Option to compare RDF data before and after transformation visually or through metrics. By utilizing FAIRLinked, the 'DataFairifier' app aims to make it easier for researchers to align their materials science datasets with FAIR principles, thereby enhancing the discoverability and reusability of their work.