bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.01.19.633820

AuthormetriX: Automated Calculation of Individual Authors' Non-Inflationary Credit-Allocation Schemas' and Collaboration Metrics from a Scopus Corpus

Abstract

BackgroundPublication count is the currency in academia, but the most widely used whole count method is considered unfair and inflationary. Several non-inflationary author credit-allocation schemas (NIACAS) have been proposed, but none is widely adopted in practice (e.g., in evaluating faculty scholarly productivity) or in bibliometric research. This low adoption and implementation rate may be due to the complexity in operationalizing the schemas. AimTo develop an application that automates the calculation of individual authors scholarly output metrics based on multiple NIACAS. MethodPublished formulas of NIACAS were written as Python functions wrapped in a Streamlit user interface that takes .csv files of the relevant corpus, and a list of authors Scopus IDs as inputs. The functions calculate the authors output metrics based on NIACAS including first- and last-author straight counts, arithmetic, golden share, and multiple variations of fractional, geometric, and harmonic schemas. Collaboration metrics are also calculated. Secondary features include modelers for author counts and schemas. In a use-case, absolute rank displacement (ARD) and actual contribution proportions (ACP) were compared between highly cited clinical medicine researchers and high h-index pharmacy practice faculty populations. ResultsAuthormetriX accurately calculates individual authors aggregate scholarly output based on 14 NIACAS, from the file inputs. There were schema and population differences in ACP, but only schema differences in ARD within the populations studied. ConclusionsAuthormetriX simplifies the implementation of non-inflationary author credit-allocation schemas and will facilitate their broader adoption in practice and bibliometric research. AuthormetriX is freely available at https://sadeosun-a-uthormetrix-v1.streamlit.app/.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Adeosun, S. O.. 2025-01-20. AuthormetriX: Automated Calculation of Individual Authors' Non-Inflationary Credit-Allocation Schemas' and Collaboration Metrics from a Scopus Corpus. https://doi.org/10.1101/2025.01.19.633820

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Internal Grant Review: A Pre-Submission Program for Early-Career Clinical and Translational Researchers

The iTHRIV Scholars Mentored Career Development Program initiated an Internal Grant Review (IGR) program in 2020 for current and recently graduated Scholars seeking funding through K and R awards from the NIH. The IGR program is designed to replicate the NIH review process and provide Scholars the opportunity to receive valuable feedback on their applications prior to NIH submission. A key characteristic of the program is the integration of REDCap, enabling automation and year-round offering while improving tracking and reporting efforts and capabilities. Results of the program have been overall positive, both in proposal development and participant feedback. IGR is a sustainable, valuable resource for early-career faculty competing for limited resources in the pursuit to become independently funded clinical translational scientists.

scientific communication and education↗

Multi-Lab Testing of Early Preclinical Discoveries Identifies Promising Treatments

A fundamental challenge in drug development is the frequent failure of early laboratory research to translate into clinical benefit. One promising solution is to confirm findings from exploratory single-laboratory studies across multiple laboratories before clinical testing. We investigated this approach following the conduct of preclinical multi-laboratory studies across different fields of medicine. For this, we evaluated effect sizes, experimental rigor, and a set of criteria to identify determinants of confirmation success. When tested under increased rigor, only a fraction of multi-laboratory studies confirmed the initial results. The underlying effect size reduction was associated with outcome-relevant experimental differences between exploratory and confirmatory stages. In summary, multi-laboratory studies proved highly informative and served as an effective filter for promising treatments.

scientific communication and education↗

Technology-enhanced learning in undergraduate neuroscience education: tractography-based virtual dissection in psychology

Background: Neuroanatomy poses a significant challenge for Psychology students due to its spatial and conceptual complexity. Educational approaches that enhance the relevance and visualization of neuroanatomical content may improve students learning experiences. This study implemented a tractography-based activity focused on the virtual dissection of the arcuate fasciculus, a major white matter pathway, in undergraduate Psychology students and examined the relationships between perceived learning and students perceptions of utility, difficulty and handling, and organizational aspects of the activity. Methods: First-year undergraduate Psychology students participated in a two-session tractography-based activity combining instruction on white matter anatomy and diffusion tractography with a hands-on virtual dissection of the arcuate fasciculus using research-grade software routinely employed in neuroscience research. Following the activity, students completed an anonymous questionnaire assessing perceived learning, utility, difficulty and handling, and organizational aspects of the activity. Pearson correlations, multiple regression analyses, and relative importance analyses were performed. Results: Sixty-eight students completed the questionnaire. Students reported generally positive perceptions of the activity across the evaluated dimensions, with perceived learning receiving the highest mean score (M = 3.44, SD = .85). Perceived utility showed the strongest association with perceived learning (r = .64, p < .001). The regression model explained 41% of the variance in perceived learning (R2 = .41, adjusted R2 = .38, p < .001). Perceived utility was the only significant predictor in the model ({beta} = .59, p = .001), accounting for 67.9% of the explained variance. Conclusions: The findings support the feasibility of integrating authentic neuroimaging tools into undergraduate neuroanatomy teaching. Students who perceived the activity as more useful also reported higher perceived learning outcomes, with perceived utility emerging as the strongest predictor of perceived learning. In contrast, perceived difficulty and handling, and organizational aspects did not make significant independent contributions. These results suggest that students perceptions of educational relevance may play an important role in technology-enhanced STEM learning experiences.

scientific communication and education↗