A systematic examination of preprint platforms for use in the medical and biomedical sciences setting
Bibliographic record
Abstract
ABSTRACT Objectives The objective of this review is to identify all preprint platforms with biomedical and medical scope and to compare and contrast the key characteristics and policies of these platforms. We also aim to provide a searchable database to enable relevant stakeholders to compare between platforms. Study Design and Setting Preprint platforms that were launched up to 25 th June 2019 and have a biomedical and medical scope according to MEDLINE’s journal selection criteria were identified using existing lists, web-based searches and the expertise of both academic and non-academic publication scientists. A data extraction form was developed, pilot-tested and used to collect data from each preprint platform’s webpage(s). Data collected were in relation to scope and ownership; content-specific characteristics and information relating to submission, journal transfer options, and external discoverability; screening, moderation, and permanence of content; usage metrics and metadata. Where possible, all online data were verified by the platform owner or representative by correspondence. Results A total of 44 preprint platforms were identified as having biomedical and medical scope, 17 (39%) were hosted by the Open Science Framework preprint infrastructure, six (14%) were provided by F1000 Research Ltd (the Open Research Central infrastructure) and 21 (48%) were other independent preprint platforms. Preprint platforms were either owned by non-profit academic groups, scientific societies or funding organisations (n=28; 64%), owned/partly owned by for-profit publishers or companies (n=14; 32%) or owned by individuals/small communities (n=2; 5%). Twenty-four (55%) preprint platforms accepted content from all scientific fields although some of these had restrictions relating to funding source, geographical region or an affiliated journal’s remit. Thirty-three (75%) preprint platforms provided details about article screening (basic checks) and 14 (32%) of these actively involved researchers with context expertise in the screening process. The three most common screening checks related to the scope of the article, plagiarism and legal/ethical/societal issues and compliance. Almost all preprint platforms allow submission to any peer-reviewed journal following publication, have a preservation plan for read-access, and most have a policy regarding reasons for retraction and the sustainability of the service. Forty-one (93%) platforms currently have usage metrics, with the most common metric being the number of downloads presented on the abstract page. Conclusion A large number of preprint platforms exist for use in biomedical and medical sciences, all of which offer researchers an opportunity to rapidly disseminate their research findings onto an open-access public server, subject to scope and eligibility. However, the process by which content is screened before online posting and withdrawn or removed after posting varies between platforms, which may be associated with platform operation, ownership, governance and financing. What is already known on this topic In concurrence with an increase in the number of preprint servers and platforms supporting biomedical and medical sciences research since 2013, there has been substantial growth in the number of preprints posted in this research area. The significant benefits of accelerated dissemination of research that preprints offer has attracted the support of many major funders. The raised profile of preprints has led to their wider acceptance in institutional and individual level assessment. What this study adds This is the first full examination of the characteristics and policies of 44 preprint platforms with biomedical and medical scope. We use a robust methodological approach to extract relevant information from web-based material with input from preprint platform owners. Despite concerns regarding the permanence and quality of preprints, most preprint platforms have long-term preservation strategies and many have screening checks (for example, a basic check for the relevance of content) in place. For some platforms, these checks are performed by researchers with content expertise. We provide a searchable database as a valuable resource for researchers, funders and policymakers in the biomedical and medical science field to determine which preprint platforms are relevant to their research scope and which have the functionality and policies that they value most.
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 | 0.050 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".