oncodatasets-py
importeddata/oncodatasets-py
The oncodatasets package provides a curated collection of oncological, clinical trial, and cancer survival datasets for data analysis, statistical modeling, and machine learning research.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Data & Standards
- Subcategory
- unknown
- License
- GPL-3.0(osi)
- Status
- active
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Documentation
- unknown
- Tags
- cancer · datasets · datasets-csv · oncology · oncology-data · opensource · pypi-package · python
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- dt4cooncology · oncology-data
Digital Twins for Computational Oncology
- meddatasets-pycancer · datasets
The meddatasets library contains clinical research datasets, cancer diagnostic records, chronic disease statistics, smoking and cancer risk data, worldwide COVID-19 case records, water pollution and…
- pharmOncoXoncology-data
Targeted and non-targeted anticancer drugs and drug regimens
- rdkit-pypipypi-package
⚛️ RDKit Python Wheels on PyPI. 💻 pip install rdkit
- Scopypypi-package
An integrated negative design python library for desirable HTS/VS database design
- cancer_researchcancer · oncology
Open-source cancer research using AI. Analyzing thousands of papers and running biochemical simulations to propose new approaches for targeting drug-resistant tumor cells. Free forever.…
- api.github.com/repos/lightbluetitan/oncodatasets-pyretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-07-04, 4 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/37.json→ .entries["oncodatasets-py"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.