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reproducibility-confidential

Reproducibility when data are confidential

https://github.com/labordynamicsinstitute/reproducibility-confidential

Science Score: 52.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ✓
    Institutional organization owner
    Organization labordynamicsinstitute has institutional domain (www.ilr.cornell.edu)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (4.9%) to scientific vocabulary
Last synced: 9 months ago · JSON representation ·

Repository

Reproducibility when data are confidential

Basic Info
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 8
  • Open Issues: 0
  • Releases: 3
Created over 4 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

Reproducbility when data are confidential

Reproducibility when data are confidential

Author

  • Lars Vilhuber
  • Laurel Krovetz

All versions (archived)

zenodo link

Building

  • The presentation is built using Quarto.
  • The text book website is built using Jupyter Book.

Building the website

  • Create Python environment: python -m venv book-env
  • Activate the environment:
    • On Windows: book-env\Scripts\activate
    • On macOS/Linux: source book-env/bin/activate
  • Install Python requirements: pip install -r requirements.txt
  • Build the website: jupyter-book build .

This is all in build-book.sh.

License

creative commoms license link

Owner

  • Name: Labor Dynamics Institute
  • Login: labordynamicsinstitute
  • Kind: organization
  • Email: ldi@cornell.edu
  • Location: Ithaca, NY, USA

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Creating reproducible packages when data are confidential
message: >-
  Presented at the 2024 FSRDC Conference. The opinions 
  expressed in this talk are solely the authors, and do not 
  represent the views of the U.S. Census Bureau, the American 
  Economic Association, or any of the funding agencies.
type: presentation
authors:
  - given-names: Lars
    family-names: Vilhuber
    email: lars.vilhuber@cornell.edu
    affiliation: Cornell University
    orcid: 'https://orcid.org/0000-0001-5733-8932'
repository-code: >-
  https://github.com/labordynamicsinstitute/reproducibility-confidential/
abstract: >-
  In this presentation, I focus on
  techniques to improve reproducibility for scholars using confidential data, in 
  particular in the US FSRDC.
license: CC-BY-NC-4.0
date-released: '2024-09-13'

GitHub Events

Total
  • Issues event: 3
  • Member event: 1
  • Push event: 16
  • Pull request event: 2
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 3
  • Member event: 1
  • Push event: 16
  • Pull request event: 2
  • Fork event: 1
  • Create event: 2