Diversity of Expertise is Key to Scientific Impact: a Large-Scale
Analysis in the Field of Computer Science
- URL: http://arxiv.org/abs/2306.15344v2
- Date: Fri, 30 Jun 2023 08:42:12 GMT
- Title: Diversity of Expertise is Key to Scientific Impact: a Large-Scale
Analysis in the Field of Computer Science
- Authors: Angelo Salatino, Simone Angioni, Francesco Osborne, Diego Reforgiato
Recupero, Enrico Motta
- Abstract summary: We analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years.
This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.
- Score: 1.8794304012790348
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding the relationship between the composition of a research team and
the potential impact of their research papers is crucial as it can steer the
development of new science policies for improving the research enterprise.
Numerous studies assess how the characteristics and diversity of research teams
can influence their performance across several dimensions: ethnicity,
internationality, size, and others. In this paper, we explore the impact of
diversity in terms of the authors' expertise. To this purpose, we retrieved
114K papers in the field of Computer Science and analysed how the diversity of
research fields within a research team relates to the number of citations their
papers received in the upcoming 5 years. The results show that two different
metrics we defined, reflecting the diversity of expertise, are significantly
associated with the number of citations. This suggests that, at least in
Computer Science, diversity of expertise is key to scientific impact.
Related papers
- A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
Large language models (LLMs) have revolutionized the way text and other modalities of data are handled.
We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
arXiv Detail & Related papers (2024-06-16T08:03:24Z) - MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects.
MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years.
We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z) - Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
We investigate the role of social media influencers in enhancing the visibility of machine learning research.
We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023.
Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
arXiv Detail & Related papers (2024-01-24T20:05:49Z) - A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and
Why? [84.46288849132634]
We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
We define three variables to encompass diverse facets of the evolution of research topics within NLP.
We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
arXiv Detail & Related papers (2023-05-22T11:08:00Z) - Expanding the Role of Affective Phenomena in Multimodal Interaction
Research [57.069159905961214]
We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing.
We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers.
We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
arXiv Detail & Related papers (2023-05-18T09:08:39Z) - A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a
case study on four scientific domains [5.330844352905488]
The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection.
Record types 'article' and'review' from four different scientific domains, namely social sciences, economics, oceanography and computer science were considered.
A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete.
arXiv Detail & Related papers (2022-10-18T09:50:47Z) - Research Topic Flows in Co-Authorship Networks [0.0]
We propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields.
Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information)
We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics.
arXiv Detail & Related papers (2022-06-16T07:45:53Z) - SciTweets -- A Dataset and Annotation Framework for Detecting Scientific
Online Discourse [2.3371548697609303]
Scientific topics, claims and resources are increasingly debated as part of online discourse.
This has led to both significant societal impact and increased interest in scientific online discourse from various disciplines.
Research across disciplines currently suffers from a lack of robust definitions of the various forms of science-relatedness.
arXiv Detail & Related papers (2022-06-15T08:14:55Z) - Women, artificial intelligence, and key positions in collaboration
networks: Towards a more equal scientific ecosystem [0.0]
This study investigates the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens.
It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial in possessing the "social researcher" in the network.
arXiv Detail & Related papers (2022-05-19T15:15:04Z) - Research on Domain Information Mining and Theme Evolution of Scientific
Papers [5.747583451398117]
Cross-disciplinary research results have gradually become an emerging frontier research direction.
How to effectively use the huge number of scientific papers to help researchers becomes a challenge.
arXiv Detail & Related papers (2022-04-18T14:36:17Z) - Extracting a Knowledge Base of Mechanisms from COVID-19 Papers [50.17242035034729]
We pursue the construction of a knowledge base (KB) of mechanisms.
We develop a broad, unified schema that strikes a balance between relevance and breadth.
Experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature.
arXiv Detail & Related papers (2020-10-08T07:54:14Z)
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.