https://github.com/barbagroup/fda-collaboration

Will contain general project information

https://github.com/barbagroup/fda-collaboration

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Will contain general project information

Basic Info
  • Host: GitHub
  • Owner: barbagroup
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 38.7 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created about 5 years ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

FDA-collaboration

This project is motivated by the goal of elevating computational modeling and simulation from a scientific research tool to a high-quality regulatory tool, in response to priorities set by the U.S. Food and Drug Administration. It adopts and expands the framework of computational reproducibility, proposing that regulatory grade computational evidence hinges on unimpeachable provenance. This means capturing, documenting and managing the full iterative workflow, decision making, computational artifacts (data and code), and resulting evidence with maximum transparency. Through a case study, the project develops best practices for constructing computational evidence in support of regulatory submissions. The case consits of an electronic drug delivery system that derives from e-cigarette devices, and the study develops a computational model of the fluid flow in the device. In the process, credibility building activities are documented, including code and solution verification, following: ASME Standard, V&V 40–2018, Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices. The model was developed using the open source software OpenFOAM, and was based on preliminary FDA studies that established the risk, context of use, and question of interest to motivate device geometry and operaating conditions. Using sensitivity analysis, better understanding was gained of the relationship between inputs and outputs. A risk-informed credibility analysis was conducted based on the US FDA Guidance, ASME V&V 20 Standard, the ASME V&V 40 Standard, and the Credibility Goals established by the preliminary study. A PhD student was funded for one year to conduct the project, and the work was presented via posters at two research events.

Owner

  • Name: Barba group
  • Login: barbagroup
  • Kind: organization
  • Location: Washington, DC

GitHub Events

Total
  • Push event: 1
  • Public event: 1
Last Year
  • Push event: 1
  • Public event: 1

Committers

Last synced: 12 months ago

All Time
  • Total Commits: 33
  • Total Committers: 2
  • Avg Commits per committer: 16.5
  • Development Distribution Score (DDS): 0.03
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
asarmakeeva a****a@g****m 32
Lorena A. Barba l****a@g****u 1
Committer Domains (Top 20 + Academic)
gwu.edu: 1

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • labarba (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels