This repository holds code and data for the preprint Peer review and preprint policies are unclear at most major journals on bioRxiv.
The whole analytic pipeline is reproducible through the drake package.
Executing r_make("drake.R") will rebuild the analysis.
The pipeline is specified in the file R/plan.R. You can visualise the
dependencies with the following code:
library(drake)
source("R/plan.R")
vis_drake_graph(plan)A rendered version of the dependency graph is available under
documentation/analysis_pipeline.png.
All packages that are used during the analysis are specified in R/packages.R.
For the analysis files to render you will need to install the font "Hind" (for
example from Google Fonts) and
register it with
extrafont.
See the file load_fonts.R for specific instructions.
The basis for the paper is 02-analysis-writeup.Rmd (which is transformed into
02-analysis-writeup.html with the knitr package). Since writing the paper
was a collaborative effort, we used a GoogleSheet to work on the manuscript. For
this reason, the text in 02-analysis-writeup.html is not identical to the
paper, but rather a first draft version. The code and figures are identical to
the paper. Data for all figures has been exported to data/figures/.
Initial explorations can be found in 01-overview.html, as well as
executing R/01-explore.R and R/02-explore-missings.R.
Core files for reproducing the analysis
| Filename | Purpose |
|---|---|
| drake.R | Core file for building the analysis. Run with drake::r_make("drake.R"). |
| R/plan.R | Describes all inputs and outputs along with the functions processing them. |
| R/packages.R | Lists and loads all packages relevant to the analysis. |
| R/plotting_functions.R | Functions for plotting that are re-used in multiple places. |
| R/drake_functions.R | Functions that wrap preprocessing tasks as outlined in plan.R. |
| R/correspondence_analysis_funs.R | Functions for conducting and plotting the multiple correspondence analysis (Fig 2). |
| R/helpers.R | General helper functions. |
Additional files for scraping and exploring data
| Filename | Purpose |
|---|---|
| analysis-scripts/01-explore.R | General exploratory graphs from early in the analysis. |
| analysis-scripts/02-explore-missings.R | Exploration of missing values. |
| analysis-scripts/03-get-oa-status.R | Matching the journals surveyed against the DOAJ to determine whether they are open access or not. |
| analysis-scripts/04-scrape-gs-fields.R | Scraping h5-index and h5-median for all journals from GSM to fill in gaps in the initial data collection. |
| analysis-scripts/05-do-correspondence-analysis.R | Script which records the steps taken while developing the multiple correspondence analysis for figure 2. |
| analysis-scripts/06-clean-coreview.R | Script computing string-distances between co-review-policies to identify duplicates. |
| analysis-scripts/07-print-coreview-policies.Rmd | Notebook that displays all co-review-policies so they can be printed out on paper for further inspection (finding duplicates). |
| analysis-scripts/08-upload-analysis.R | Script used to upload the rendered analysis to GoogleDrive. |
The original data file for the data we collected lives in a Google Sheet (A), which is available here: https://docs.google.com/spreadsheets/d/1WcvxxmDhaV3BwBiIfwC_nEAr6-EKCDD11R6eJ5vZElA/edit?usp=sharing
The original data from Google Scholar Metrics (GSM) on which journals are included, alongside data on their respective h5-index (B), is available here: https://docs.google.com/spreadsheets/d/1Dluo5DNWU4UrmwIZLzqcToioRdyZ1qc1EofAk4ypiW8/edit?usp=sharing
The files were downloaded as .csv and .xlsx for archival. The analysis
builds on the .csv files, while the .xlsx files are supposed to preserve as
much of the original formatting as possible, should the GoogleSheets become
unavailable one day. These data, along with data which we scraped from
GSM and the DOAJ, as well as a plain language description of the fields from (A)
can be found in the data/raw directory.
Some data cleaning was necessary before conducting the analysis. The output of
these operations are the files data/refined.csv and data/refined_w_areas.csv.
The latter file contains more rows, since some journals belong to more than
one area in GSM. Those journals have duplicate entries
except for the column area which specifies the GSM area.
All subsequent analysis is based on either of the two files (depending on
whether area was considered or not). The files - along with other intermediate
data-files - can be found in the directory data/transformed.
Raw data files
| Filename | Purpose |
|---|---|
| data/raw/TRANSPOSE landscape study - round 3 - Raw.csv | Raw data of the landscape scan on 171 journals. Conducted with the data collection instrument. |
| data/raw/Compiled_Landscape study journals list - G_ALL_dedup.csv | Data from GSM on journals and their respective ranking within the overall top 100 and top 20 per subject area. Retrieved on 2018-10-13. |
| data/raw/data_collection_instrument.csv | This file preserves the instrument used when collecting data on journals. |
| data/raw/gs_scraped_total.csv | All journals listed in GSM as of 2019-06-18, including h5-index and h5-median. This file was generated via the script analysis-scripts/04-scrape-gs-fields.R |
| data/raw/oa_data.csv | Data on open access status of all journals surveyed. Generated via analysis-scripts/04-get-oa-status.R |
Transformed data files
| Filename | Purpose |
|---|---|
| data/transformed/refined.csv | Cleaned data from our landscape scan, joined with data from GSM. This file is the basis for the analysis presented in our paper. |
| data/transformed/refined_w_areas.csv | This file is identical to refined.csv, except for the additional variable "area" which holds data on the subject category from GSM. Since some journals appear in multiple categories, this file has more rows (193) than refined.csv (which has 171 rows on 171 journals). |
| data/transformed/var_overview.csv | Plain language descriptions of the variables in data/raw/TRANSPOSE landscape study - round 3 - Raw.csv. |
| data/transformed/coreview-policies.csv | String distances between different coreview policies for detecting duplicates. Produced via analysis-scripts/06-clean-coreview.R |
| data/transformed/journals_with_missing_categories.csv | List of journals for which no subject category from GSM is available in data/raw/Compiled_Landscape study journals list - G_ALL_dedup.csv. The missing data for these journals is taken from data/raw/gs_scraped_total.csv. |
| data/transformed/policies.txt | A plain language file containing the raw text of the journals' policies on coreviewing. |
You can find more information on the drake package here:
https://books.ropensci.org/drake/
The analysis was last rendered with the following package versions:
─ Session info ─────────────────────────────────────────
setting value
version R version 4.0.2 (2020-06-22)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate German_Austria.1252
ctype German_Austria.1252
tz Europe/Berlin
date 2020-07-04
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