Archives des mots-clés : Casey S. Greene

Examining linguistic shifts between preprints and publications

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Authors : David N. Nicholson, Vincent Rubinetti, Dongbo Hu, Marvin Thielk, Lawrence E. Hunter, Casey S. Greene

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online.

A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents.

The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model.

We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint–peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint.

We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish.

Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

URL : Examining linguistic shifts between preprints and publications

DOI : https://doi.org/10.1371/journal.pbio.3001470

Linguistic Analysis of the bioRxiv Preprint Landscape

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Authors : David N. Nicholson, Vincent Rubinetti, Dongbo Hu, Marvin Thielk, Lawrence E. Hunter, Casey S. Greene

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online.

A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents.

The most prevalent features that changed appear to be associated with typesetting and mentions of supplementary sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model.

We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint.

We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish.

Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

DOI : https://doi.org/10.1101/2021.03.04.433874

Responsible, practical genomic data sharing that accelerates research

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Authors : James Brian Byrd, Anna C. Greene, Deepashree Venkatesh Prasad, Xiaoqian Jiang, Casey S. Greene

Data sharing anchors reproducible science, but expectations and best practices are often nebulous. Communities of funders, researchers and publishers continue to grapple with what should be required or encouraged.

To illuminate the rationales for sharing data, the technical challenges and the social and cultural challenges, we consider the stakeholders in the scientific enterprise. In biomedical research, participants are key among those stakeholders.

Ethical sharing requires considering both the value of research efforts and the privacy costs for participants. We discuss current best practices for various types of genomic data, as well as opportunities to promote ethical data sharing that accelerates science by aligning incentives.

URL : Responsible, practical genomic data sharing that accelerates research

DOI : https://doi.org/10.1038/s41576-020-0257-5

Open collaborative writing with Manubot

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Authors : Daniel S. Himmelstein, Vincent Rubinetti, David R. Slochower, Dongbo Hu, Venkat S. Malladi, Casey S. Greene, Anthony Gitter

Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale.

We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts.

With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes.

A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript’s source changes, the rendered outputs are rebuilt and republished to a web page.

Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style.

Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.

URL : Open collaborative writing with Manubot

DOI : https://doi.org/10.1371/journal.pcbi.1007128

Sci-Hub provides access to nearly all scholarly literature

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Authors : Daniel S. Himmelstein, Ariel R. Romero, Bastian Greshake Tzovaras, Casey S. Greene

The website Sci-Hub provides access to scholarly literature via full text PDF downloads. The site enables users to access articles that would otherwise be paywalled. Since its creation in 2011, Sci-Hub has grown rapidly in popularity.

However, until now, the extent of Sci-Hub’s coverage was unclear. As of March 2017, we find that Sci-Hub’s database contains 68.9% of all 81.6 million scholarly articles, which rises to 85.2% for those published in closed access journals.

Furthermore, Sci-Hub contains 77.0% of the 5.2 million articles published by inactive journals. Coverage varies by discipline, with 92.8% coverage of articles in chemistry journals compared to 76.3% for computer science.

Coverage also varies by publisher, with the coverage of the largest publisher, Elsevier, at 97.3%. Our interactive browser at greenelab.github.io/scihub allows users to explore these findings in more detail.

Finally, we estimate that over a six month period in 2015–2016, Sci-Hub provided access for 99.3% of valid incoming requests. Hence, the scope of this resource suggests the subscription publishing model is becoming unsustainable. For the first time, the overwhelming majority of scholarly literature is available gratis to anyone.

URL : https://greenelab.github.io/scihub-manuscript/