Skip to main content

Linked Document Analysis - A provenance-driven project management system

Project description

Linked Document Analysis (LDA)

A project management and provenance system where every analytical process, input, and output is mapped directly to a section of the working document (e.g., manuscript, protocol, regulatory report).

Each analysis folder, manifest, and result is named and organized to mirror the document outline, creating a one-to-one link between text, code, data, and results.

This architecture ensures that every figure, table, or claim in the document is transparently and immutably traceable back to its generating code and data—enabling instant audit, replication, and regulatory review.

Installation

Requires Python 3.10 or later with the Rich library for enhanced terminal output.

pip install rich

Usage

Create a new LDA project scaffold:

python lda_scaffold.py

The scaffold will:

  1. Create section folders matching your document structure
  2. Generate file manifests for inputs and outputs
  3. Set up provenance tracking with unique IDs
  4. Initialize logging and documentation

Project Structure

Each LDA project contains:

  • Section folders: One-to-one mapping with document sections
  • File manifests: Explicit lists of expected inputs and outputs
  • Provenance tracking: Hashes, timestamps, and analyst attribution
  • Audit logs: Complete history of all changes

Documentation

See CLAUDE.md for detailed architecture and usage instructions.

License

MIT License - see LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ldanalysis-0.1.6.tar.gz (1.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ldanalysis-0.1.6-py3-none-any.whl (32.8 kB view details)

Uploaded Python 3

File details

Details for the file ldanalysis-0.1.6.tar.gz.

File metadata

  • Download URL: ldanalysis-0.1.6.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.0

File hashes

Hashes for ldanalysis-0.1.6.tar.gz
Algorithm Hash digest
SHA256 cf53685f338ddb28447ea7acd3a0391aaf915ffc0ea32ce9e07dc901b75dba83
MD5 c93ced5b83e4503c46e86bca340c6758
BLAKE2b-256 3fca53d4e9546714bc335291b1031e97f802869b178f8de3192040d2cdabee78

See more details on using hashes here.

File details

Details for the file ldanalysis-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: ldanalysis-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 32.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.0

File hashes

Hashes for ldanalysis-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 1248e5f872db062e6b2bc92ded0dd1782cc97785111f3520b458c3f7dbbc56cb
MD5 7acf42ac50ab6758ada72d9c2d91adc4
BLAKE2b-256 6c9287993607219c7bd30a4e61e2f2d6f0623793308305fcae95fd75d9c8f7e8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page