Publications by US Federal Govt Authors 2019-2025

Repository Overview

This repository is for the cleanup, organization, and archiving of data collected as part of the FedPubs Project. These procedures are carried out by executing two R scripts (see Workflow, below).

An overview of the data and associated metadata will be archived in the Dryad Digital Repository upon acceptance of the resulting publications.

This repository includes the following:

1. Data used in analyses/publications:

   • Data will be archived at Dryad upon manuscript acceptance (files are too large to store on Github). However, the list of Scopus Affiliation ID codes used for searches of university and US federal agency productivity (Supplementary Tables 2 & 3) are available for download as .csv files.

2. Scopus Affiliation ID codes used in API Queries:

   Federal Agency Scopus ID Codes: (fed_scopus_codes.csv)
   Focal University Scopus ID Codes: (uni_scopus_codes.csv)

3. R Code used to:

   • Search for and download download bibliographic records using the Scopus API. The API code queries each affiliation ID for each year of the search window provided and saves three .csv files: one with the list of the affiliation ID's articles in a given year, one with names the authors of these articles and a code for each author's affiliation, and one with information about each of these affiliations. Files are saved in three folders by category -- papers, affiliations, authors - with each year's results nested within category (e.g,. under the affiliations folder are folders for 2019, 2020, 2021, 2022, 2023, 2024, and 2025, and inside each of these folders are the affiliation csv files, with one for each ID queried)

     (01a_scopus_API_fed.R)
     (01b_scopus_API_uni.R)

   • Organize the csv file of USGS publications downloaded from the USGS Publications Warehouse

     (02_process_usgs.R)

   • Combine the .csv files returned by Scopus for the affiliation IDs searched each year into a single .csv (one each for authors, affiliations, and papers):

     (03_csv_binder_within_year.R)

   • Bind the 'papers', 'affiliation', and 'authors' csv files for each year into a single one with the data for all years. Also saves as an RDS file:

     (04_csv_binder_across_years.R)

   • Clean up and correct author, affiliation, and publication records (uses several functions in the code,code\figs, code\figs_uni folders):

     (05a_clean_fed.R)
     (05b_clean_uni.R)

   • Generate and save data summaries, make figures (uses several functions in the code,code\figs, code\figs_uni folders):

     (06a_make_figs_fed.R) (06b_make_figs_fed.R)

   • Bootstrap analysis

     Bootstrap analyses - federal productivity in 2025 vs 2024 (code/bootstrap_npubs.R)
     Bootstrap analyses - university productivity in 2025 vs 2024 (code/bootstrap_npubs_uni.R)

4. Rmd files used to prepare the manuscript:

   • The (docs/) folder includes Rmd files for manuscript and supplementary materials

5. A log of updates and corrections.

Workflow

Coming shortly - I am in the process of streamlining the workflow to allow working from a single MakeFile.

Improvements, Suggestions, & Questions

We welcome any suggestions for package improvement or ideas for features to include in future versions. If you have Issues, Feature Requests and Pull Requests, here is how to contribute. We expect everyone contributing to the package to abide by our Code of Conduct.

Contributors

• Emilio M. Bruna, University of Florida

Citation

Until a manuscript is accepted and data are archived at Dryad, please cite this preprint when using these data for research, publications, teaching, etc.

If you wish to cite this repository, please cite as follows:

@misc{BrunaSurveys2023,
author = {Bruna, E.M.},
title = {FedPub Project},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
note = {data v0.9.0.},
url={https://github.com/BrunaLab/fed_pubs}
}