Frontiers in research metrics and analytics最新文献

Indigenous Peoples' right to sovereignty forms the foundation for advocacy and actions toward greater Indigenous self-determination and control across a range of domains that impact Indigenous Peoples' communities and cultures. Declarations for sovereignty are rising throughout Indigenous communities and across diverse fields, including Network Sovereignty, Food Sovereignty, Energy Sovereignty, and Data Sovereignty. Indigenous Research Sovereignty draws in the sovereignty discourse of these initiatives to consider their applications to the broader research ecosystem. Our exploration of Indigenous Research Sovereignty, or Indigenous self-determination in the context of research activities, has been focused on the relationship between Indigenous Data Sovereignty and efforts to describe Indigenous Peoples' Rights in data.

This article discusses the challenges of Indigenous research in relation to open science, more particularly in relation to Sámi research in Sweden. Based on interviews with active scholars in the multidisciplinary field of Sámi studies, and on policy documents by Sámi organizations, this article points at the challenges that can be identified, and the practices and strategies adopted or suggested by researchers. Topics addressed include ownership, control, sensitivity and accessibility of data, the consequences of experienced limitations, the role of the historical context, and community-groundedness. This article has the ambition to contribute with a discussion about the tensions between standards of data management/open science and data sovereignty in Indigenous contexts. This is done by bringing in perspectives from Indigenous methodologies (the 4 R) and by contextualizing research practices and forms of data colonialism in relation to our contemporary context of surveillance culture. Research-in relation to ethics and social sustainability-is an arena where tensions between various agendas becomes obvious. This is illustrated in this article by researchers' dilemmas when working with open science and the advancement of Indigenous research. Efforts toward ethically valid and cultural-sensitive modes of data use are taking shape in Indigenous research, calling for an increased awareness about the topic. In the context of Sámi research, the role of academia in such a transformation is also essential.

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

Mental disorders and suicide are considered global health problems faced by many countries worldwide. Even though advancements have been made to improve mental wellbeing through research, there is room for improvement. Using Artificial Intelligence to early detect individuals susceptible to mental illness and suicide ideation based on their social media postings is one way to start. This research investigates the effectiveness of using a shared representation to automatically extract features between the two different yet related tasks of mental illness and suicide ideation detection using data in parallel from social media platforms with different distributions. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidity on suicide ideation and use two datasets during inference to test the generalizability of the trained models and provide satisfactory evidence to validate the increased predictive accurateness of suicide risk when using data from users diagnosed with multiple mental disorders compared to a single mental disorder for the mental illness detection task. Our results also demonstrate different mental disorders' impact on suicidal risk and discover a noticeable impact when using data from users diagnosed with Post-Traumatic Stress Disorder. We use multi-task learning (MTL) with soft and hard parameter sharing to produce state-of-the-art results for detecting users with suicide ideation who require urgent attention. We further improve the predictability of the proposed model by demonstrating the effectiveness of cross-platform knowledge sharing and predefined auxiliary inputs.

The academic research assessment system, the academic reward system, and the academic publishing system are interrelated mechanisms that facilitate the scholarly production of knowledge. This article considers these systems using a Foucauldian lens to examine the power/knowledge relationships found within and through these systems. A brief description of the various systems is introduced followed by examples of instances where Foucault's power, knowledge, discourse, and power/knowledge concepts are useful to provide a broader understanding of the norms and rules associated with each system, how these systems form a network of power relationships that reinforce and shape one another.

Background: This study outlines a comprehensive analysis of the primary characteristics of managing research integrity (RI) in domestic colleges and universities in China. RI education in China consists primarily of soft advocacy, with no hard requirements or continuous and systematic support. Together with other stakeholders, such as funders and publishers, higher education institutions (e.g., colleges and universities) are one of the vital actors that have a lot of influence on RI promotion and implementation among researchers. However, the literature on the regulation of RI policies in China's universities is limited.

Methods: We investigate the top 50 colleges and universities in the 2021 Best Chinese Universities Ranking. Their guidance and policy documents on RI were collected via their official websites. By integrating the use of scientometrics analysis, including descriptive statistical analysis, inductive content analysis, and quantitative analysis, we examine whether and how these higher education institutions respond to national policies in a timely manner, especially in terms of their frequency of updates, topic clustering analysis, terms clustering analysis, content aggregation. To further understand the composition mechanism and the main working systems of university RI management organizations, we conducted in-depth research on the organizational functions, meeting system, staff composition mechanism, and scientific research misconduct acceptance and investigation mechanisms.

Results: The regulations on the treatment of RI in China's universities have, in response to the government's call to establish their own management policies and working mechanisms, maintained a zero-tolerance stance on research misconduct. The sampled universities listed the definition and principles of misconduct practices, investigation procedures, and sanctions of research misconduct in their own policy documents. Some of them listed inappropriate research practices All 50 sampled universities have formed relevant organizations responsible for RI management, they all provide the detailed regulations of the committees. Yet, there is still a need to further define Questionable Research Practice, foster higher standards for integrity in research and, establish and improve an efficient, authoritative, well-restrained and supervision working mechanism for organizations responsible for RI treatment.

We have applied the sensitize-train-hack-community model to build awareness of and capacity in bioinformatics in Kenya. Open science is the practice of science openly and collaboratively, with tools, techniques, and data freely shared to facilitate reuse and collaboration. Open science is not a mandatory curriculum course in schools, whereas bioinformatics is relatively new in some African regions. Open science tools can significantly enhance bioinformatics, leading to increased reproducibility. However, open science and bioinformatics skills, especially blended, are still lacking among students and researchers in resource-constrained regions. We note the need to be aware of the power of open science among the bioinformatics community and a clear strategy to learn bioinformatics and open science skills for use in research. Using the OpenScienceKE framework-Sensitize, Train, Hack, Collaborate/Community-the BOSS (Bioinformatics and Open Science Skills) virtual events built awareness and empowered researchers with the skills and tools in open science and bioinformatics. Sensitization was achieved through a symposium, training through a workshop and train-the-trainer program, hack through mini-projects, community through conferences, and continuous meet-ups. In this paper, we discuss how we applied the framework during the BOSS events and highlight lessons learnt in planning and executing the events and their impact on the outcome of each phase. We evaluate the impact of the events through anonymous surveys. We show that sensitizing and empowering researchers with the skills works best when the participants apply the skills to real-world problems: project-based learning. Furthermore, we have demonstrated how to implement virtual events in resource-constrained settings by providing Internet and equipment support to participants, thus improving accessibility and diversity.

Introduction: This team science case study explores one cross-disciplinary science institute's change process for redesigning a weekly research coordination meeting. The narrative arc follows four stages of the adaptive process in complex adaptive systems: disequilibrium, amplification, emergence, and new order.

Methods: This case study takes an interpretative, participatory approach, where the objective is to understand the phenomena within the social context and deepen understanding of how the process unfolds over time and in context. Multiple data sources were collected and analyzed.

Results: A new adaptive order for the weekly research coordination meeting was established. The mechanism for the success of the change initiative was best explained by complexity leadership theory.

Discussion: Implications for team science practice include generating momentum for change, re-examining power dynamics, defining critical teaming professional roles, building multiple pathways towards team capacity development, and holding adaptive spaces. Promising areas for further exploration are also presented.