VizCV: AI-assisted visualization of researchers' publications tracks

• URL: http://arxiv.org/abs/2505.08691v1
• Date: Tue, 13 May 2025 15:47:59 GMT
• Title: VizCV: AI-assisted visualization of researchers' publications tracks
• Authors: Vladimír Lazárik, Marco Agus, Barbora Kozlíková, Pere-Pau Vázquez,
• Abstract summary: VizCV is a novel web-based end-to-end visual analytics framework.<n>It incorporates AI-assisted analysis and supports automated reporting of career evolution.
• Score: 7.233541652625401
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Analyzing how the publication records of scientists and research groups have evolved over the years is crucial for assessing their expertise since it can support the management of academic environments by assisting with career planning and evaluation. We introduce VizCV, a novel web-based end-to-end visual analytics framework that enables the interactive exploration of researchers' scientific trajectories. It incorporates AI-assisted analysis and supports automated reporting of career evolution. Our system aims to model career progression through three key dimensions: a) research topic evolution to detect and visualize shifts in scholarly focus over time, b) publication record and the corresponding impact, c) collaboration dynamics depicting the growth and transformation of a researcher's co-authorship network. AI-driven insights provide automated explanations of career transitions, detecting significant shifts in research direction, impact surges, or collaboration expansions. The system also supports comparative analysis between researchers, allowing users to compare topic trajectories and impact growth. Our interactive, multi-tab and multiview system allows for the exploratory analysis of career milestones under different perspectives, such as the most impactful articles, emerging research themes, or obtaining a detailed analysis of the contribution of the researcher in a subfield. The key contributions include AI/ML techniques for: a) topic analysis, b) dimensionality reduction for visualizing patterns and trends, c) the interactive creation of textual descriptions of facets of data through configurable prompt generation and large language models, that include key indicators, to help understanding the career development of individuals or groups.

Related papers

• Vision Generalist Model: A Survey [87.49797517847132]
  We provide a comprehensive overview of the vision generalist models, delving into their characteristics and capabilities within the field.<n>We take a brief excursion into related domains, shedding light on their interconnections and potential synergies.
  arXiv  Detail & Related papers  (2025-06-11T17:23:41Z)
• SciSciGPT: Advancing Human-AI Collaboration in the Science of Science [7.592219145267612]
  Recent advances in large language models (LLMs) and AI agents have opened new possibilities for human-AI collaboration.<n>We introduce SciSciGPT, an open-source, prototype AI collaborator that uses the science of science as a testbed to explore the potential of LLM-powered research tools.
  arXiv  Detail & Related papers  (2025-04-07T23:19:39Z)
• Personalized Generation In Large Model Era: A Survey [90.7579254803302]
  In the era of large models, content generation is gradually shifting to Personalized Generation (PGen)<n>This paper presents the first comprehensive survey on PGen, investigating existing research in this rapidly growing field.<n>By bridging PGen research across multiple modalities, this survey serves as a valuable resource for fostering knowledge sharing and interdisciplinary collaboration.
  arXiv  Detail & Related papers  (2025-03-04T13:34:19Z)
• CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers [3.929864777332447]
  CS-PaperSum is a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences.<n>Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2025-02-27T22:48:35Z)
• Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
  Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science. Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords. We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
  arXiv  Detail & Related papers  (2024-10-08T05:13:27Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making.<n>Large foundation models, such as large language models, have revolutionized various natural language processing tasks.<n>This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations [0.0]
  Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models. We present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization.
  arXiv  Detail & Related papers  (2022-12-22T14:29:43Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.