pkdb-data 1.0.9

pkdb_data: python utilities for PK-DB data

This repository stores the curated study data for https://pk-db.com and provides helpers for uploading studies to the database. The available choices are defined as part of this repository.

• ./studies/: curated study data with subfolders based on substances

• ./pkdb_data/info_nodes/: definition of data base choices

• ./CURATION.md: curation guidelines and choices

See the ./CURATION.md for detailed instructions on how to curate studies.

Installation

Installation instructions are available here.

Functionality in a nutshell

The following provides a short overview over the main functionality. Detailed information is provided in the ./CURATION.md.

(pkdb_data) upload_study -s <study_dir>

(pkdb_data) upload_studies

(pkdb_data) upload_studies -s caffeine acetaminophen

(pkdb_data) delete_study -s <study_sid>

(pkdb_data) create_nodes

(pkdb_data) upload_nodes

License

• Source Code: LGPLv3

• Documentation: CC BY-SA 4.0

The pkdb_data source is released under both the GPL and LGPL licenses version 2 or later. You may choose which license you choose to use the software under.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License or the GNU Lesser General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Funding

Matthias König is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054) and by the German Research Foundation (DFG) within the Research Unit Programme FOR 5151 “QuaLiPerF (Quantifying Liver Perfusion-Function Relationship in Complex Resection - A Systems Medicine Approach)” by grant number 436883643 and by grant number 465194077 (Priority Programme SPP 2311, Subproject SimLivA).

© 2017-2024 Jan Grzegorzewski & Matthias König