| name | TCIA Dataset Proposal Skill |
| description | Guide users through proposing a new dataset or analysis results for TCIA, collecting all necessary metadata, and generating a standardized proposal package. |
TCIA Dataset Proposal Skill
Overview
This Skill assists users in preparing a formal proposal for publishing a new dataset or analysis results on The Cancer Imaging Archive (TCIA). It mirrors the logic and requirements of the tcia-dataset-proposal.py application.
Workflow
1. Proposal Type Selection
Determine the type of submission:
- New Collection Proposal: For contributing original imaging data not previously on TCIA.
- Analysis Results Proposal: For contributing derived data (segmentations, annotations, radiomics) based on existing TCIA collections.
2. Contact Information
Collect Name and Email for three mandatory Points of Contact (POC):
- Scientific POC: Primary contact for proposal and data collection.
- Technical POC: Person responsible for data transfer.
- Legal POC: Authorized signatory for the Data Submission Agreement (should not be the PI).
3. Dataset Publication Details
- Title: Descriptive title for the dataset.
- Nickname: Short identifier (< 30 characters, alphanumeric and dashes only).
- Authors: List of authors. Action: Encourage providing ORCIDs. Use
orcid_helper.py logic to validate and fetch profile details.
- Abstract: Brief overview of the dataset (Max 1,000 characters). Steering: Refer to
tcia-cicadas-skill for high-quality abstract generation.
4. Data Collection Details
- Published Elsewhere: Has the data been published? If so, why TCIA?
- Adaptive Fields (New Collection):
- Primary disease site/location (from
permissible_values.json).
- Histologic diagnosis (from
permissible_values.json).
- Image types (MR, CT, PET, etc.).
- Supporting data (Clinical, Genomics, Radiation Therapy Plans/Structures, etc.).
- Software/Related Resources:Standalone question about source code, Jupyter notebooks, web sites or other software.
- File formats.
- Modifications prior to submission.
- Presence of patient faces.
- Adaptive Fields (Analysis Results):
- TCIA collection(s) analyzed.
- Types of derived data.
- Specificity of image records.
- File formats.
5. Additional Metadata
- Disk Space: Approximate size.
- Time Constraints: Any deadlines for sharing.
- Publications: Related dataset descriptors or derived publications.
- Acknowledgements: Funding or support statements.
- Why TCIA: Motivation for using TCIA.
Output Generation
The goal is to generate a ZIP package containing:
- Proposal Summary TSV:
{nickname}_proposal_summary_{date}.tsv
- A single-row TSV containing all responses mapped to the labels in
tcia-dataset-proposal.py.
- Investigators TSV:
{nickname}_investigators_{date}.tsv
- A TSV containing investigator metadata (first_name, last_name, person_orcid, organization_name, email).
- Proposal Summary DOCX:
{nickname}_proposal_summary_{date}.docx
- A formatted document using full-text question labels as headers.
- Updated PDF Agreement:
{nickname}_agreement_updated_{date}.pdf
- A copy of the TCIA Data Submission Agreement with "Exhibit A" (Page 6) replaced by the dataset abstract.
Logic Reference
For specific implementation details on file generation and validation, refer to:
tcia-dataset-proposal.py: Main application logic and file generation.tcia-remapping-skill/orcid_helper.py: ORCID validation and lookup.tcia-remapping-skill/resources/agreement_template.pdf: PDF template.tcia-remapping-skill/resources/permissible_values.json: Controlled vocabularies for sites and diagnoses.
