APC Spend Analysis: Data and Code

This open source repository holds the data and code underlying:

Lisa Matthias, Schares, E., Alperin, J. P., Butler, L.-A., Kuang, S., Schönfelder, N., & Haustein, S. 2026. Estimating global article processing charges paid to 14 publishers for open access between 2019 and 2025. [preprint link, once available]

Computational analysis was done with R.

The underlying article processing charge (APC) price list data is available to download and described in a companion data paper:

Matthias, L., Chavarro, D., Schares, E., Alperin, J. P., Rose, M., Frost, M., Camargo, F., Höfting, J., Butler, L.-A., Schönfelder, N., & Haustein, S. (2026). A dataset of article processing charges from 14 scholarly publishers, 2019–2025 [Data set]. Harvard Dataverse. doi: 10.7910/DVN/AZ985C

Matthias, L., Chavarro, D., Schares, E., Alperin, J. P., Rose, M., Frost, M., Camargo, F., Höfting, J., Butler, L.-A., Schönfelder, N., & Haustein, S. (2026). A dataset of article processing charges from 14 scholarly publishers, 2019–2025. [DOI]

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Pipeline overview

This analysis combines two inputs — annual APC list prices (dataset above) and article publication counts from OpenAlex — to estimate global APC spend by publisher, year, and OA type. The repository is organized to reflect this pipeline, in order:

1. sql/ — SQL query used to extract article counts per journal-year from the OpenAlex snapshot (via Google BigQuery).
2. raw_data/ — the raw export returned by the query above.
3. processing/ — R code used to process the raw export into a usable format.
4. data/ — processed datasets combining article counts with APC list prices, used as direct input to the analysis.
5. results/ — R Markdown files (and their rendered .md output) that compute all figures, tables, and in-text statistics reported in the paper.
6. figures/ — figure files (.png/.svg) generated by the R Markdown files in results/.

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Contents

sql/

• extract_article_counts_openalex_gbq.sql — query used to extract article counts by journal-year from the OpenAlex Walden snapshot in Google BigQuery.

raw_data/

• BQ_export_Walden_fullAPCdataset_v5_297167rows.csv - raw BigQuery export file(s), prior to processing.
• apc_list_2025_5_analysis_v5_with_flatfees.csv — the analysis copy of the APC dataset (Matthias et al., 2026), which differs from the publicly available list-price dataset in the assumptions applied to non-flat-rate fees, as documented in the paper.

processing/

• clean_process_BQ_exports_FullDataset.qmd - R code used to process the raw BigQuery export into the merged dataset used in analysis.

data/

• 12348_Gold&Hybrid_Walden_with_delta_filteredtoAPCdataset_withflatfees_and_APCs.csv — the processed BigQuery export, merged on ISSN-L and year to match the APC dataset.
• 12348_journals_bypublisher_withflatfees_money_summary.csv — APC spend summary aggregated by publisher, OA type, and year.

results/

• roara_apc_paper_analysis.Rmd / .md — main analysis: figures, tables, and in-text statistics reported in the paper, adjusted for inflation.
• roara_apc_paper_without_inflation_adjustment.Rmd / .md — nominal (unadjusted) equivalents of the main analysis, reported in the supplement.
• roara_apc_paper_publication_counts.Rmd / .md — publication count figures and tables reported in the supplement.

figures/

• All figure files generated by the R Markdown files above.