Overall, R code was only available in 49 of the 1001 papers examined (4.9%) (Figure 1). When included, code was most often in the Supplemental Information (41%), followed by Github (20%), Figshare (6%), or other repositories (33%). Open-access publications were 70% more likely to include code than closed access publications (7.21% vs. 4.22%, X2 = 4.442, p < 0.05). Code-sharing was estimated to increase at 0.5% / year, but this trend was not significant (p=0.11). The year of 2021 and 2022 showed a shift towards more frequent sharing, but the percentage of code-sharing has been consistently below 15% over the past decade (Figure 1).
We found papers including code disproportionately impact the literature (Figure 2), and accumulate citations faster (i.e., a marginally significant year-by-code-inclusion interaction; p = 0.0863). Further, we found a significant interaction between Open Access and code inclusion (p = 0.0265), with publications meeting both Open Science criteria (i.e., open code and open access) having highest overall predicted citation rates (Figure 2). For example, Open Science papers are expected to receive more than doubled citations (96.25 vs. 36.89) in year 13 post-publication compared with fully closed papers (Figure 2).
https://doi.org/10.21203/rs.3.rs-3222221/v1
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |