Open Science at the University of Toronto. Exploration of Researcher, Staff and Librarian Perspectives

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Authors : Madelin Burt-D’Agnillo, Mindy Thuna

Objective: The impetus for this project is to begin to understand open science practices and obstacles at the University of Toronto. This project uses open-ended questions to understand the ways in which university-affiliated individuals learn about, think about, and interact with open science. The goal of this study is to showcase the complexity and diversity of activity and challenges in this domain to help determine how best to move open science forward.

Methods: From March to October 2022, 45 semi-structured interviews were conducted with faculty, graduate students, librarians and administrative staff. Interviews were conducted and recorded using Zoom and the audio was transcribed using Otter.ai. As part of a commitment to open science practices, a data management plan was created and with participant consent, 26 transcripts were uploaded to Dataverse. Data analysis used structured coding and thematic development to investigate responses.

Results: The core finding of this study is that there is no singular status of open science at University of Toronto. The qualitative findings reflect a diversity of opinions, practices and relationships to open science.

Conclusion: For open science practices and scholarship to have longevity, there must be systemic changes to adopt more open activities. The University of Toronto is well positioned to guide the transition and harness open principles to move into the future.

URL : Open Science at the University of Toronto. Exploration of Researcher, Staff and Librarian Perspectives

DOI : https://doi.org/10.21083/partnership.v19i2.7847

Sustaining the “frozen footprints” of scholarly communication through open citations

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Author : Zehra Taşkın

This review examines the role of open citations in fostering transparency, reproducibility, and accessibility in scholarly communication. Through a critical synthesis of diverse sources—articles, proceedings, presentations, datasets, and blog posts—it explores the motivations behind citing, the evolving meanings of citations, and key milestones in the open citation movement. Particular attention is given to initiatives like OpenCitations and the Initiative for Open Citations (I4OC), highlighting their contributions to advancing open scholarship.

Key findings indicate that open citations democratize research by providing free access to citation data, improving discoverability, and facilitating the creation of public citation graphs. Technological advancements, such as advanced data models and reference mining tools, have significantly contributed to the management and utilization of citation data. Despite these benefits, challenges such as ensuring data quality and standardization, addressing structural inequalities in citation networks, and achieving universal publisher adoption persist.

The study concludes with recommendations for future efforts, emphasizing policy advocacy, technological innovation, global collaboration, and educational initiatives to promote the widespread adoption and effective use of open citations. These strategies aim to make the “frozen footprints” of scholarly communication accessible to all, fostering a more equitable and transparent scientific landscape.

URL : Sustaining the “frozen footprints” of scholarly communication through open citations

DOI : https://doi.org/10.1002/asi.24982

‘As of my last knowledge update’: How is content generated by ChatGPT infiltrating scientific papers published in premier journals?

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Author : Artur Strzelecki

The aim of this paper is to highlight the situation whereby content generated by the large language model ChatGPT is appearing in peer-reviewed papers in journals by recognized publishers. The paper demonstrates how to identify sections that indicate that a text fragment was generated, that is, entirely created, by ChatGPT. To prepare an illustrative compilation of papers that appear in journals indexed in the Web of Science and Scopus databases and possessing Impact Factor and CiteScore indicators, the SPAR4SLR method was used, which is mainly applied in systematic literature reviews.

Three main findings are presented: in highly regarded premier journals, articles appear that bear the hallmarks of the content generated by AI large language models, whose use was not declared by the authors (1); many of these identified papers are already receiving citations from other scientific works, also placed in journals found in scientific databases (2); and, most of the identified papers belong to the disciplines of medicine and computer science, but there are also articles that belong to disciplines such as environmental science, engineering, sociology, education, economics and management (3).

This paper aims to continue and add to the recently initiated discussion on the use of large language models like ChatGPT in the creation of scholarly works.

URL : ‘As of my last knowledge update’: How is content generated by ChatGPT infiltrating scientific papers published in premier journals?

DOI : https://doi.org/10.1002/leap.1650

Patent research in academic literature. Landscape and trends with a focus on patent analytics

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Authors : Cristian Mejia, Yuya Kajikawa

Patent analytics is crucial for understanding innovation dynamics and technological trends. However, a comprehensive overview of this rapidly evolving field is lacking. This study presents a data-driven analysis of patent research, employing citation network analysis to categorize and examine research clusters. Here, we show that patent research is characterized by interconnected themes spanning fundamental patent systems, indicator development, methodological advancements, intellectual property management practices, and diverse applications.

We reveal central research areas in patent strategies, technological impact, and patent citation research while identifying emerging focuses on environmental sustainability and corporate innovation. The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.

URL : Patent research in academic literature. Landscape and trends with a focus on patent analytics

DOI : https://doi.org/10.3389/frma.2024.1484685

Comparison of Feature Learning Methods for Metadata Extraction from PDF Scholarly Documents

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Authors : Zeyd Boukhers, Cong Yang

The availability of metadata for scientific documents is pivotal in propelling scientific knowledge forward and for adhering to the FAIR principles (i.e. Findability, Accessibility, Interoperability, and Reusability) of research findings. However, the lack of sufficient metadata in published documents, particularly those from smaller and mid-sized publishers, hinders their accessibility. This issue is widespread in some disciplines, such as the German Social Sciences, where publications often employ diverse templates. To address this challenge, our study evaluates various feature learning and prediction methods, including natural language processing (NLP), computer vision (CV), and multimodal approaches, for extracting metadata from documents with high template variance.

We aim to improve the accessibility of scientific documents and facilitate their wider use. To support our comparison of these methods, we provide comprehensive experimental results, analyzing their accuracy and efficiency in extracting metadata. Additionally, we provide valuable insights into the strengths and weaknesses of various feature learning and prediction methods, which can guide future research in this field.

URL : Comparison of Feature Learning Methods for Metadata Extraction from PDF Scholarly Documents

Arxiv : https://arxiv.org/abs/2501.05082

A role for qualitative methods in researching Twitter data on a popular science article’s communication

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Authors : Travis Noakes, Corrie Susanna Uys, Patricia Ann Harpur, Izak van Zyl

Big Data communication researchers have highlighted the need for qualitative analysis of online science conversations to better understand their meaning. However, a scholarly gap exists in exploring how qualitative methods can be applied to small data regarding micro-bloggers’ communications about science articles. While social media attention assists with article dissemination, qualitative research into the associated microblogging practices remains limited. To address these gaps, this study explores how qualitative analysis can enhance science communication studies on microblogging articles.

Calls for such qualitative approaches are supported by a practical example: an interdisciplinary team applied mixed methods to better understand the promotion of an unorthodox but popular science article on Twitter over a 2-year period. While Big Data studies typically identify patterns in microbloggers’ activities from large data sets, this study demonstrates the value of integrating qualitative analysis to deepen understanding of these interactions. In this study, a small data set was analyzed using NVivo™ by a pragmatist and MAXQDA™ by a statistician.

The pragmatist’s multimodal content analysis found that health professionals shared links to the article, with its popularity tied to its role as a communication event within a longstanding debate in the health sciences. Dissident professionals used this article to support an emergent paradigm. The analysis also uncovered practices, such as language localization, where a title was translated from English to Spanish to reach broader audiences.

A semantic network analysis confirmed that terms used by the article’s tweeters strongly aligned with its content, and the discussion was notably pro-social. Meta-inferences were then drawn by integrating the findings from the two methods. These flagged the significance of contextualizing the sharing of a health science article in relation to tweeters’ professional identities and their stances on health-related issues. In addition, meta-critiques highlighted challenges in preparing accurate tweet data and analyzing them using qualitative data analysis software. These findings highlight the valuable contributions that qualitative research can make to research involving microblogging data in science communication. Future research could critique this approach or further explore the microblogging of key articles within important scientific debates.

URL : A role for qualitative methods in researching Twitter data on a popular science article’s communication

DOI : https://doi.org/10.3389/frma.2024.1431298

How to build an Open Science Monitor based on publications? A French perspective

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Authors : Laetitia Bracco, Eric Jeangirard, Anne L’Hôte, Laurent Romary

Many countries and institutions are striving to develop tools to monitor their open science policies. Since 2018, with the launch of its National Plan for Open Science, France has been progressively implementing a monitoring framework for its public policy, relying exclusively on reliable, open, and controlled data. Currently, this monitoring focuses on research outputs, particularly publications, as well as theses and clinical trials.

Publications serve as a basis for analyzing other dimensions, including research data, code, and software. The metadata associated with publications is therefore particularly valuable, but the methodology for leveraging it raises several challenges. Here, we briefly outline how we have used this metadata to construct the French Open Science Monitor.

URL : How to build an Open Science Monitor based on publications? A French perspective

Arxiv : https://arxiv.org/abs/2501.02856