Rapport d’Enquête Création d’une revue d’articles sur des jeux de données Data Journal SHS

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Auteur.ices/Authors : Laurence Bizien, Véronique Cohoner, Fiona Edmond, Arnaud Natal, Pierre Peraldi-Mittelette

La présente enquête a été menée dans le cadre du projet de création d’une revue de données interdisciplinaire en Sciences Humaines et Sociales à l’horizon 2025. Le groupe de travail (GT) œuvrant à ce projet a vu le jour suite à la journée d’études organisée par la Maison des Sciences de l’Homme Lorraine le 10 mars 2023; intitulée : « Un data journal interdisciplinaire pour les sciences humaines et sociales. Enjeux scientifiques et mise en œuvre pratique »

URL : Rapport d’Enquête Création d’une revue d’articles sur des jeux de données Data Journal SHS

HAL : https://hal.univ-lorraine.fr/hal-04541094

The role of non-scientific factors vis-a-vis the quality of publications in determining their scholarly impact

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Authors : Giovanni Abramo, Ciriaco Andrea D’Angelo, Leonardo Grilli

In the evaluation of scientific publications’ impact, the interplay between intrinsic quality and non-scientific factors remains a subject of debate. While peer review traditionally assesses quality, bibliometric techniques gauge scholarly impact. This study investigates the role of non-scientific attributes alongside quality scores from peer review in determining scholarly impact.

Leveraging data from the first Italian Research Assessment Exercise (VTR 2001-2003) and Web of Science citations, we analyse the relationship between quality scores, non-scientific factors, and publication short- and long-term impact.

Our findings shed light on the significance of non-scientific elements overlooked in peer review, offering policymakers and research management insights in choosing evaluation methodologies. Sections delve into the debate, identify non-scientific influences, detail methodologies, present results, and discuss implications.

Arxiv : https://arxiv.org/abs/2404.05345

Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest

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Author : Walid Hariri

Scientific articles play a crucial role in advancing knowledge and informing research directions. One key aspect of evaluating scientific articles is the analysis of citations, which provides insights into the impact and reception of the cited works. This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles.

By leveraging advanced natural language processing (NLP) techniques, ChatGPT can discern the nuanced positivity or negativity of citations, offering insights into the reception and impact of cited works. Furthermore, ChatGPT’s capabilities extend to detecting potential biases and conflicts of interest in citations, enhancing the objectivity and reliability of scientific literature evaluation.

This study showcases the transformative potential of artificial intelligence (AI)-powered tools in enhancing citation analysis and promoting integrity in scholarly research.

Arxiv : https://arxiv.org/abs/2404.01800

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge

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Authors  : Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai, Robert Leaman, Shubo Tian, Ling Luo, Qiao Jin, Zhizheng Wang, Qingyu Chen, Zhiyong Lu

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly.

PubTator 3.0’s online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results.

We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

URL : PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge

DOI : https://doi.org/10.1093/nar/gkae235

Text mining arXiv: a look through quantitative finance papers

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Author : Michele Leonardo Bianchi

This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022.

We extract and analyze crucial information from the entire documents, including the references, to understand the topics trends over time and to find out the most cited researchers and journals on this domain. Additionally, we compare numerous algorithms to perform topic modeling, including state-of-the-art approaches.

Arxiv : https://arxiv.org/abs/2401.01751

Authorship conflicts in academia: an international cross‑discipline survey

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Authors : Elizaveta Savchenko, Ariel Rosenfeld

Collaboration among scholars has emerged as a significant characteristic of contemporary science. As a result, the number of authors listed in publications continues to rise steadily. Unfortunately, determining the authors to be included in the byline and their respective order entails multiple difficulties which often lead to conflicts. Despite the large volume of literature about conflicts in academia, it remains unclear how exactly these are distributed over the main socio-demographic properties, as well as the different types of interactions academics experience.

To address this gap, we conducted an international and cross-disciplinary survey answered by 752 academics from 41 fields of research and 93 countries that statistically well-represent the overall academic workforce. Our findings are concerning and suggest that conflicts over authorship credit arise very early in one’s academic career, even at the level of Master and Ph.D., and become increasingly common over time.

URL : Authorship conflicts in academia: an international cross‑discipline survey

DOI : https://doi.org/10.1007/s11192-024-04972-x