ScholarImpact: A Python tool to analyse, visualise, and share individual research impact, output and scholarly influence using bibliometric data

ScholarImpact: A Python tool to analyse, visualise, and share individual research impact, output and scholarly influence using bibliometric data Abhishek Tiwari1* 1Independent Researcher * [email protected] Abstract In this software paper we introduce ScholarImpact, an open-source Python tool empowering individual researchers to create personalized, deployable web dashboards showcasing their scholarly impact. The software generates shareable visualizations for grant applications and career development, democratizing sophisticated bibliometric analysis previously limited to institutional platforms. DOI 10.5281/zenodo.17645632 Published 2025-11-11 Software •Code Repository •Documentation •PyPI Package 1 Summary 1 ScholarImpact is a Python-based bibliometric analysis tool designed to help researchers 2 analyse, visualise, and share their research impact, output and scholarly influence. The 3 software extracts initial data from a given Google Scholar profile and performs 4 enrichment using OpenAlex [1] and Altmetric [2] to provide multidimensional insights 5 into citation patterns, geographic distribution, institutional reach, patent citations, and 6 interdisciplinary influence. Unlike traditional citation metrics that provide only 7 aggregate counts, ScholarImpact enables researchers to understand who is citing their 8 work, where citations originate geographically and institutionally, and how their 9 research impacts different domains and disciplines. 10 The tool features an interactive Streamlit-based dashboard that visualizes key 11 metrics including total citations, unique citing authors, institutional diversity, country 12 distribution, temporal citation trends, research domain analysis, and alternative metrics 13 such as patent citations and Wikipedia mentions. ScholarImpact is distributed as a 14 pip-installable package with a command-line interface that automates data extraction, 15 enrichment, and visualization, making sophisticated bibliometric analysis accessible to 16 researchers without specialized technical expertise. 17 2 Statement of Need 18 While numerous bibliometric tools exist, most focus exclusively on citation counts and 19 h-indices, providing limited insight into the breadth and diversity of research impact [3]. 20 Individual researchers increasingly need to demonstrate and communicate their impact 21 beyond traditional metrics, including geographic reach, interdisciplinary influence, and 22 societal engagement [4]. Existing commercial platforms like Web of Science, Dimension 23 AI, and Scopus offer some geographic and institutional analysis but require expensive 24 1/4 institutional subscriptions, lack personalization, and do not provide researchers with 25 shareable visualizations of their own impact [5].26 ScholarImpact addresses these gaps by providing an open-source, accessible solution 27 that combines data from multiple sources to deliver comprehensive impact analysis. The 28 tool is designed for individual researchers seeking to understand and communicate their 29 research impact for grant applications, tenure reviews, and personal career development. 30 3 Brief Overview 31 For data extraction, ScholarImpact uses publicly available APIs and web scraping 32 techniques that respect rate limits and terms of service. The modular design separates 33 data collection (extract−author and crawl−citations commands), enrichment 34 (OpenAlex and Altmetric integration), and visualization (Streamlit dashboard), 35 allowing researchers to customize workflows. The generate−dashboard command 36 creates standalone deployable projects suitable for sharing via Streamlit Cloud, enabling 37 researchers to publicly showcase their impact. 38 ScholarImpact leverages the scholarly-python library [6] for Google Scholar data 39 extraction and integrates with OpenAlex’s comprehensive open bibliographic database 40 to enrich citation data with institutional affiliations, country codes, and research 41 domain classifications [1]. Altmetric integration provides alternative impact indicators 42 including social media mentions, policy document citations, and patent references [2].43 The visualization framework uses Plotly [7] for interactive charts and maps, allowing 44 dynamic exploration of citation patterns across time, geography, and research domains. 45 The tool fills a critical niche by democratizing access to sophisticated bibliometric 46 analysis. By combining open data sources with an intuitive interface and 47 deployment-ready architecture, ScholarImpact enables researchers across disciplines and 48 career stages to gain actionable insights into their scholarly influence. The software has 49 been used to analyze citation patterns across computer science, social sciences, and 50 interdisciplinary research, demonstrating its flexibility and broad applicability. 51 Future development roadmap includes enhanced citation network analysis, analysis 52 for research software, co-authorship visualization, comparative benchmarking against 53 field averages, and integration with additional data sources such as ORCID [8] and 54 CrossRef [9]. Community contributions are welcome via the project’s GitHub repository. 55 4 Availability 56 ScholarImpact is distributed as a Python package on PyPI, with the source code, 57 testing modules, and a standalone script available under an MIT license through the 58 GitHub repository. A working demo of the deployed Streamlit-based dashboard can be 59 found here.60 5 Acknowledgements 61 The author acknowledges the open-source communities behind scholarly-python, 62 OpenAlex, Streamlit, and the broader Python scientific computing ecosystem. This 63 work builds upon the foundations established by these projects to advance open science 64 and reproducible research. 65 2/4 Figure 1. Example dashboard showing research domains analysis, interdisciplinary impact metrics including patents and wikipedia mentions, and alternative metrics. 3/4 References 66 [1] J. Priem, H. Piwowar, and R. Orr, “OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts,” arXiv preprint arXiv:2205.01833, 2022, doi: 10.48550/arXiv.2205.01833. 67 [2] E. Adie and W. Roe, “Altmetric: Enriching scholarly content with article-level discussion and metrics,” Learned Publishing, vol. 26, no. 1, pp. 11–17, 2013, doi: 10.1087/20130103. 68 [3] J. E. Hirsch, “An index to quantify an individual’s scientific research output,” Proceedings of the National Academy of Sciences, vol. 102, no. 46, pp. 16569–16572, 2005, doi: 10.1073/pnas.0507655102. 69 [4] L. Bornmann, “Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics,” Journal of Informetrics, vol. 8, no. 4, pp. 895–903, 2014, doi: 10.1016/j.joi.2014.09.005. 70 [5] ´ E. Archambault, D. Campbell, Y. Gingras, and V. Larivi`ere, “Comparing bibliometric statistics obtained from the web of science and scopus,” Journal of the American Society for Information Science and Technology, vol. 60, no. 7, pp. 1320–1326, 2009, doi: 10.1002/asi.21062. 71 [6] S. A. Cholewiak, P. Ipeirotis, V. Silva, and A. Kannawadi, “Scholarly: Simple access to google scholar authors and citations.” 2021. Available: https://github. com/scholarly-python-package/scholarly 72 [7] Plotly Technologies Inc., “Plotly python open source graphing library.” 2015. Available: https://plot.ly/python/ 73 [8] L. L. Haak, M. Fenner, L. Paglione, E. Pentz, and H. Ratner, “ORCID: A system to uniquely identify researchers,” Learned Publishing, vol. 25, no. 4, pp. 259–264, 2012, doi: 10.1087/20120404. 74 [9] G. Hendricks, D. Tkaczyk, J. Lin, and P. Feeney, “Crossref: The sustainable source of community-owned scholarly metadata,” Quantitative Science Studies, vol. 1, no. 1, pp. 414–427, 2020, doi: 10.1162/qss a 00022. 75 4/4