Étiquette : Mercè Crosas

Catégories
EN

When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge

Authors : Andrés F. Castro Torres, Joan Giner-Miguelez, Mercè Crosas

The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technologies have revolutionized data collection and analysis in specific scientific fields such as Chemistry, their overall impact depends on the scope of adoption and the ways scholars use them.

In this study, we document substantial differences in the timing and extent of AI adoption across countries and scientific domains from 1960 to 2015. After 2015, we find generalized exponential growth in AI adoption, with the number of AI-supported works multiplying by at least four across all domains. The transformative nature of this rapid growth is less apparent and points to multiple challenges should adoption trends persist.

According to our analyses, AI-supported research is confined to very few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited transformational potential in epistemological terms. AI-supported works are also associated with an unwarranted citation premium and exhibit substantially higher retraction rates than non-AI-supported works across most fields.

Geographically, AI adoption displays pronounced heterogeneity at the country level, along with an acceleration in the relevance of middle-income countries in Asia, from China and beyond.

Thus, the transformative capacity of AI in science remains largely untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics from a global perspective. We discuss how best research practices could boost the benefits of AI adoption and highlight fields and geographies where these trends warrant closer scrutiny.

DOI : https://doi.org/10.48550/arXiv.2605.06033

Catégories
EN

Repository Approaches to Improving the Quality of Shared Data and Code

Authors : Ana Trisovic, Katherine Mika, Ceilyn Boyd, Sebastian Feger, Mercè Crosas

Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible.

Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets.

This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code.

The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

URL : Repository Approaches to Improving the Quality of Shared Data and Code

DOI : https://doi.org/10.3390/data6020015

Catégories
EN

A Data Citation Roadmap for Scholarly Data Repositories

Authors : Martin Fenner, Mercè Crosas, Jeffrey S. Grethe, David Kennedy, Henning Hermjakob, Phillippe Rocca-Serra, Gustavo Durand, Robin Berjon, Sebastian Karcher, Maryann Martone, Tim Clark

This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies.

The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH BioCADDIE (https://biocaddie.org) program.

The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories.

URL : A Data Citation Roadmap for Scholarly Data Repositories

DOI : https://doi.org/10.1101/097196

 

Catégories
EN

Evaluating and Promoting Open Data Practices in Open Access Journals

Authors : Eleni Castro, Mercè Crosas, Alex Garnett, Kasey Sheridan, Micah Altman

In the last decade there has been a dramatic increase in attention from the scholarly communications and research community to open access (OA) and open data practices.

These are potentially related, because journal publication policies and practices both signal disciplinary norms, and provide direct incentives for data sharing and citation. However, there is little research evaluating the data policies of OA journals.

In this study, we analyze the state of data policies in open access journals, by employing random sampling of the Directory of Open Access Journals (DOAJ) and Open Journal Systems (OJS) journal directories, and applying a coding framework that integrates both previous studies and emerging taxonomies of data sharing and citation.

This study, for the first time, reveals both the low prevalence of data sharing policies and practices in OA journals, which differs from the previous studies of commercial journals’ in specific disciplines.

URL : Evaluating and Promoting Open Data Practices in Open Access Journals