Frontiers in research metrics and analytics最新文献

Debates on intellectual property rights and open source frequently stem from the business sector and higher education, where goals are typically oriented toward profit, academic status, credit, and/or reputation. What happens if we reconsider the ethics of intellectual property rights and open source when our driving motivation is advancing women's health and rights? How does this prioritization complicate our assumptions of copyright and open access? How can we embark on a journey that validates the complex realities of multiple stakeholders who have good intent, but do not always consider the unintended impacts and the broader power dynamics at play? This paper explores the tensions and nuances of sharing methodologies that aim to transform harmful gender norms in an ecosystem that does not always consider the complex challenges behind intellectual property and open-source material. As a thought-collective dedicated to using a feminist approach to unpack and promote the principles of ethical, effective, and sustainable scale, we hope to underscore how the current research and debates on intellectual property rights and open-source material have good aims but may also fall short in encompassing the realities of gendered social norms change in and with communities around the world. We conclude with key recommendations for donors, researchers, International Development Corporations, International Non-Governmental Organizations, and those interested in using or adapting dynamic, gender transformative materials created by others.

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

An increasingly popular form of collaboration involves forming partnerships among researchers, educators, and community members to improve or transform education systems through research inquiry. However, not all partnerships are successful. The field needs valid, reliable, and useful measures to help with assessing progress toward partnership goals. In this community case study, we present a participatory, mixed-methods approach for creating measures to assess the progress of education research-practice partnerships (RPPs). The case illustrates a novel approach to measurement design, driven by perspectives and feedback of over 300 members of 80 partnerships. As a result, the measures align with the values and practices of the very collaborations the measures were intended to assess.

Patents are essential for transferring scientific discoveries to meaningful products that benefit societies. While the academic community focuses on the number of citations to rank scholarly works according to their "scientific merit," the number of citations is unrelated to the relevance for patentable innovation. To explore associations between patents and scholarly works in publicly available patent data, we propose to utilize statistical methods that are commonly used in biology to determine gene-disease associations. We illustrate their usage on patents related to biotechnological trends of high relevance for food safety and ecology, namely the CRISPR-based gene editing technology (>60,000 patents) and cyanobacterial biotechnology (>33,000 patents). Innovation trends are found through their unexpected large changes of patent numbers in a time-series analysis. From the total set of scholarly works referenced by all investigated patents (~254,000 publications), we identified ~1,000 scholarly works that are statistical significantly over-represented in the references of patents from changing innovation trends that concern immunology, agricultural plant genomics, and biotechnological engineering methods. The detected associations are consistent with the technical requirements of the respective innovations. In summary, the presented data-driven analysis workflow can identify scholarly works that were required for changes in innovation trends, and, therefore, is of interest for researches that would like to evaluate the relevance of publications beyond the number of citations.

Researcher fraud is often easy and enticing in academic research, with little risk of detection. Cases of extensive fraud continue to occur. The amount of fraud that goes undetected is unknown and may be substantial. Three strategies for addressing researcher fraud are (a) retrospective investigations after allegations of fraud have been made, (b) sting operations that provide conclusive evidence of fraud as it occurs, and (c) data management practices that prevent the occurrence of fraud. Institutional and regulatory efforts to address researcher fraud have focused almost exclusively on the retrospective strategy. The retrospective approach is subject to controversy due to the limitations of post-hoc evidence in science, the difficulty in establishing who actually committed the fraud in some cases, the application of a legal standard of evidence that is much lower than the usual standards of evidence in science, and the lack of legal expertise by scientists investigating fraud. The retrospective strategy may be reliably effective primarily in cases of extensive, careless fraud. Sting operations can overcome these limitations and controversies, but are not feasible in many situations. Data management practices that are effective at preventing researcher fraud and unintentional errors are well-established in clinical trials regulated by government agencies, but appear to be largely unknown or unimplemented in most academic research. Established data management practices include: archiving secure copies of the raw data, audit trails, restricted access to the data and data collection processes, software validation, quality control checks, blinding, preregistration of data processing and analysis programs, and research audits that directly address fraud. Current discussions about data management in academic research focus on sharing data with little attention to practices that prevent intentional and unintentional errors. A designation or badge such as error-controlled data management could be established to indicate research that was conducted with data management practices that effectively address intentional and unintentional errors.