Indexing status of journals using Open Journal Systems and related properties
Bibliographic record
Abstract
This dataset is a comprehensive, journal-level collection of metadata for 47,625 active journals that publish using the Open Journal Systems (OJS) platform. It covers the period from 2020 to 2023 and aggregates information from multiple sources, including the PKP Beacon, ISSN.org, DOI resolution services, and bibliographic indices such as OpenAlex, DOAJ, and Scopus. The dataset not only captures the basic journal identifiers and descriptive metadata but also a rich set of indicators that support multifaceted analysis of scholarly publishing practices. Key characteristics of the dataset include: Identifiers and Journal Metadata: – Primary and secondary ISSNs validated against the official registry. – Journal titles as registered within OJS, with standardization measures applied (e.g., transliteration and phonetic comparisons). – A consolidated country of publication determined through multiple sources such as ISSN records, DOAJ listings, and IP-address based geolocation. Publication Activity: – Annual record counts for the years 2020 through 2023 along with cumulative document counts. – Detailed measures of scholarly output per journal that allow evaluation of publication volume, which serves as a proxy for journal activity and editorial engagement. Indexing and DOI Usage: – Indicators showing whether a journal is indexed in key bibliographic databases like OpenAlex, Scopus, and DOAJ. – Variables indicating whether the journal assigns Digital Object Identifiers (DOIs) through registration agencies (with specific fields for Crossref, DataCite, Medra, JALC, Airiti, etc.). – Matched counts of DOIs verified against external resolvers, highlighting the reliability and completeness of a journal's metadata. Economic and Regional Context: – Data on the country’s income group and GDP per capita, which serve as proxies for the resource environment and infrastructural capacity available to each journal. – The total number of JUOJS identified per country, providing a measure of the national landscape of scholarly publishing. Digital Presence and Repository Characteristics: – Web visibility metrics provided by Open PageRank scores for both the individual journal’s webpage and its hosting repository’s endpoint. – The size of the OJS repository (i.e., the number of journals hosted on the same installation), offering insight into shared infrastructure and editorial scale. Linguistic and Disciplinary Classification: – Automated language detection results and aggregated language proportions, highlighting the degree to which journals publish in English versus non‑English languages. – A machine-learning derived subject classification assigning each journal to a main scholarly discipline, which enables discipline-specific analysis. Designed for bibliometric and scientometric research, the dataset enables users to explore the relationships between a journal’s editorial practices, its digital identifier usage, national and economic contexts, and its likelihood of being indexed in inclusive scholarly databases. The extensive metadata and derived metrics support complex analyses, such as classification modeling to identify determinants of indexing in OpenAlex and factors associated with the adoption of Crossref DOIs. The dataset is contributes to exploring trends in global scholarly communication and assess structural disparities in the digital dissemination of knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.071 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".