Some rip-RORing news for affiliation metadata – Crossref

“We’ve just added to our input schema the ability to include affiliation information using ROR identifiers. Members who register content using XML can now include ROR IDs, and we’ll add the capability to our manual content registration tools, participation dashboards, and metadata retrieval APIs in the near future. And we are inviting members to a Crossref/ROR webinar on 29th September at 3pm UTC.”

Day-to-day discovery of preprint–publication links | SpringerLink

Abstract:  Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint–publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint–publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative (covid-nma.com). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint–publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers’ workflows and enhance a daily preprint–publication chase that is useful to all readers, including systematic reviewers. This preprint–publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

 

Day-to-day discovery of preprint–publication links | SpringerLink

Abstract:  Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint–publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint–publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative (covid-nma.com). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint–publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers’ workflows and enhance a daily preprint–publication chase that is useful to all readers, including systematic reviewers. This preprint–publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

 

Broader reach in searching for adverse events articles – a case study with DOAJ and Crossref

“An efficient strategy for searching for adverse events in scientific literature should find as many relevant events as possible and maintain screening effort within reasonable levels.

 

Naturally, finding more adverse events is directly related to the question of where to search. Past studies suggest results do improve when searching multiple established proprietary global literature databases. We decided to investigate databases that favor open models of scholarly publications, now gaining traction in the academic world. Can they be a cost-effective way to more adverse events results from the literature?

 

In this post, we investigate the use of alternative scientific literature sources to complement searching for adverse events on a mainstream index (PubMed). In particular we explored:

The Directory of Open Access Journals (DOAJ) indexes academic literature with an open access license from publishers worldwide. It currently hosts over 5 million records.

Crossref: a community organization dedicated to supporting scholarly communication by generating metadata and providing services for content discoverability. The Crossref metadata spans over 120 million records, with a growing proportion being published as open abstracts….”

Crossref expects rapid growth in use of unique grant identifiers – Research Professional News

“A representative of Crossref has said that the not-for-profit scholarly communications organisation is expecting a rapid expansion in the number of research grants that are allocated unique identifiers to allow anyone to easily search for resulting papers or data.

Speaking at the annual conference of the European Association of Research Managers and Administrators on 15 April, Rachael Lammey, head of special programmes at Crossref, said the organisation had already labelled just under 17,000 grants with unique codes known as digital object identifiers….”

Accessing early scientific findings | Early Evidence Base

“Early Evidence Base (EEB) is an experimental platform that combines artificial intelligence with human curation and expert peer-review to highlight results posted in preprints. EEB is a technology experiment developed by EMBO Press and SourceData.

Preprints provide the scientific community with early access to scientific evidence. For experts, this communication channel is an efficient way to accesss research without delay and thus to accelerate scientific progress. But for non-experts, navigating preprints can be challenging: in absence of peer-review and journal certification, interpreting the data and evaluating the strength of the conclusions is often impossible; finding specific and relevant information in the rapidly accumulating corpus of preprints is becoming increasingly difficult.

The current COVID-19 pandemic has made this tradeoff even more visible. The urgency in understanding and combatting SARS-CoV-2 viral infection has stimulated an unprecedented rate of preprint posting. It has however also revealed the risk resulting from misinterpretation of preliminary results shared in preprint and with amplification or perpetuating prelimature claims by non-experts or the media.

To experiment with ways in which technology and human expertise can be combined to address these issues, EMBO has built the EEB. The platform prioritizes preprints in complementary ways:

Refereed Preprints are preprints that are associated with reviews. EEB prioritizes such preprints and integrates the content of the reviews as well as the authors’ response, when available, to provide rich context and in-depth analyses of the reported research.
To highlight the importance of experimental evidence, EEB automatically highlights and organizes preprints around scientific topics and emergent areas of research.
Finally, EEB provides an automated selection of preprints that are enriched in studies that were peer reviewed, may bridge several areas of research and use a diversity of experimental approaches….”