Archives des auteurs : Hans Dillaerts

Gaps between Open Science activities and actual recognition systems: Insights from an international survey

Posté le par

Authors : Florencia Grattarola, Hanna Shmagun, Christopher Erdmann, Anne Cambon-Thomsen, Mogens Thomsen, Jaesoo Kim, Laurence Mabile

There are global movements aiming to promote reform of the traditional research evaluation and reward systems. However, a comprehensive picture of the existing best practices and efforts across various institutions to integrate Open Science into these frameworks remains underdeveloped and not fully known. The aim of this study was to identify perceptions and expectations of various research communities worldwide regarding how Open Science activities are (or should be) formally recognised and rewarded.

To achieve this, a global survey was conducted in the framework of the Research Data Alliance, recruiting 230 participants from five continents and 37 countries. Despite most participants reporting that their organisation had one form or another of formal Open Science policies, the majority indicated that their organisation lacks any initiative or tool that provides specific credits or rewards for Open Science activities. However, researchers from France, the United States, the Netherlands and Finland affirmed having such mechanisms in place. T

he study found that, among various Open Science activities, Open or FAIR data management and sharing stood out as especially deserving of explicit recognition and credit. Open Science indicators in research evaluation and/or career progression processes emerged as the most preferred type of reward.

URL : Gaps between Open Science activities and actual recognition systems: Insights from an international survey

DOI : https://doi.org/10.1371/journal.pone.0315632

Can scholarly publishers change the world? The role of the SDGs within the publishing industry

Posté le par

Authors : Stephanie Dawson, Agata Morka, Charlie Rapple, Nikesh Gosalia, Ritu Dhand

The United Nation’s Sustainable Development Goals (SDGs) aim to eradicate poverty and inequality, protect the planet, and ensure health, justice, and prosperity for all, emphasizing inclusivity. Within the realm of scholarly publishing, the panel discussion Can scholarly publishers change the world? The role of the SDGs within the publishing industry held at Academic Publishing in Europe 2024, highlighted the business advantages of aligning with SDGs and made a plea to reshape the narrative beyond mere moral obligation as well as to galvanize stakeholders to take action and promote engagement, offering a clear direction.

This paper expands on the panel discussion, which was moderated by Stephanie Dawson, CEO, ScienceOpen. Panellists were Agata Morka, Regional Director, Publishing Development, PLOS, Charlie Rapple, Chief Customer Officer and Co-founder, Kudos, Nikesh Gosalia, President Global Academic and Publisher Relations, Cactus Communications, and Ritu Dhand, Chief Scientific Officer, Springer Nature.

URL : Can scholarly publishers change the world? The role of the SDGs within the publishing industry

DOI : https://doi.org/10.3233/ISU-240017

Improving the reporting of research impact assessments: a systematic review of biomedical funder research impact assessments

Posté le par

Authors : Rachel Abudu, Kathryn Oliver, Annette Boaz

The field of research impact assessment (RIA) has seen remarkable growth over the past three decades. Increasing numbers of RIA frameworks have been developed and applied by research funders and new technologies can capture some research impacts automatically. However, RIAs are too different to draw comparable conclusions about what type of methods, data or processes are best suited to assess research impacts of different kinds, or how funders should most efficiently implement RIAs.

To usher in the next era of RIA and mature the field, future RIA methodologies should become more transparent, standardized and easily implementable. Key to these efforts is an improved understanding of how to practically implement and report on RIA at the funder-level. Our aim is to address this gap through two major contributions.

First, we identify common items across existing best practice guidelines for RIA, creating a preliminary reporting checklist for standardized RIA reporting. Next, we systematically reviewed studies examining funders’ assessment of biomedical grant portfolios to examine how funders reported the results of their RIAs across the checklist, as well as the operational steps funders took to perform their RIA and the variation in how funders implemented the same RIA frameworks.

We compare evidence on current RIA practices with the reporting checklist to identify good practice for RIA reporting, gaps in the evidence base for future research, and recommendations for future effective RIA.

URL : Improving the reporting of research impact assessments: a systematic review of biomedical funder research impact assessments

DOI : https://doi.org/10.1093/reseval/rvae060

Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database

Posté le par

Authors : Florian Wätzold, Bartosz Popiela, Jonas Mayer

The rapid development of artificial intelligence (AI) has significantly enhanced productivity, particularly in repetitive tasks. In the scientific domain, literature review stands out as a key area where AI-based tools can be effectively applied. This study presents a methodology for developing a search strategy for systematic reviews using AI tools. The Semantic Scholar database served as the foundation for the search process. The methodology was tested by searching for scientific papers related to batteries and hydrogen vehicles with the aim of enabling an evaluation for their potential applications. An extensive list of vehicles and their operational environments based on international standards and literature reviews was defined and used as the main input for the exemplary search.

The AI-supported search yielded approximately 60,000 results, which were subjected to an initial relevance assessment. For the relevant papers, a neighbourhood analysis based on citation and reference networks was conducted. The final selection of papers, covering the period from 2013 to 2023, included 713 papers assessed after the initial review. An extensive discussion of the results is provided, including their categorisation based on search terms, publication years, and cluster analysis of powertrains, as well as operational environments of the vehicles involved.

This case study illustrates the effectiveness of the proposed methodology and serves as a starting point for future research. The results demonstrate the potential of AI-based tools to enhance productivity when searching for scientific papers.

URL : Methodology for AI-Based Search Strategy of Scientific Papers: Exemplary Search for Hybrid and Battery Electric Vehicles in the Semantic Scholar Database

DOI : https://doi.org/10.3390/publications12040049

Evolution of the “long tail” concept for scientific data

Posté le par

Authors : Gretchen R. Stahlman, Inna Kouper

This review paper explores the evolution of discussions about “long-tail” scientific data in the scholarly literature. The “long-tail” concept, originally used to explain trends in digital consumer goods, was first applied to scientific data in 2007 to refer to a vast array of smaller, heterogeneous data collections that cumulatively represent a substantial portion of scientific knowledge. However, these datasets, often referred to as “long-tail data,” are frequently mismanaged or overlooked due to inadequate data management practices and institutional support.

This paper examines the changing landscape of discussions about long-tail data over time, situated within broader ecosystems of research data management and the natural interplay between “big” and “small” data.

The review also bridges discussions on data curation in Library & Information Science (LIS) and domain-specific contexts, contributing to a more comprehensive understanding of the long-tail concept’s utility for effective data management outcomes. The review aims to provide a more comprehensive understanding of this concept, its terminological diversity in the literature, and its utility for guiding data management, overall informing current and future information science research and practice.

Arxiv : https://arxiv.org/abs/2412.13307

Trust in science, trust in ChatGPT? How Germans think about generative AI as a source in science communication

Posté le par

Authors : Mike S. Schäfer, Bastian Kremer, Niels G. Mede, Liliann Fischer

Generative AI like ChatGPT has been diagnosed to fundamentally impact different realms of life. This includes science communication, where GenAI tools are becoming important sources of science-related content for many people. This raises the question of whether people trust GenAI as a source in this field, a question that has not been answered sufficiently yet.

Adapting a model developed by Roberts et al. [2013] and utilizing survey data from the German Science Barometer 2023, we find that Germans are rather sceptical about and do not strongly trust GenAI in science communication. Structural equation modelling shows that respondents’ trust in GenAI as a source in science communication is driven strongly by their general trust in science, which is largely driven by their knowledge about science and the perception that science improves quality of life.

URL : Trust in science, trust in ChatGPT? How Germans think about generative AI as a source in science communication

DOI : https://doi.org/10.22323/2.23090204