Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

Qian, Yue; Liu, Yu; Sheng, Quan Z.

doi:10.1016/j.joi.2020.101047

Cited by 33 publications

(18 citation statements)

References 42 publications

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“…In the 2019 edition of the AI index 5 , chapter 3 ('Technical performance') collects the results of several benchmarks and their SOTA fronts over time. This complements more classical bibliometric analysis [20][21][22][23][24][25][26] . However, in this and other reports and repositories, benchmark results are not considered themselves as elements over which scientometric analysis can be done.…”

Section: Discussionmentioning

confidence: 69%

Research community dynamics behind popular AI benchmarks

et al. 2021

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Section: Discussionmentioning

confidence: 69%

Research community dynamics behind popular AI benchmarks

et al. 2021

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“…The classification scheme provided by the PACS code has certain limitations as well. Given the availability of large scale data sets, such as Microsoft Academic Graph (Wang et al, 2020) and advances in machine learning tools to identify and classify topics from papers (Qian et al, 2020;Chinazzi et al, 2019;Palmucci et al, 2020;Shen et al, 2019), it would be important to check if similar patterns can be observed in other data set. Finally, we adopt the whole counting in this study that gives equal credit to all co-authors.…”

Section: Discussionmentioning

confidence: 99%

Become a better you: correlation between the change of research direction and the change of scientific performance

Yu,

Szymanski,

Jia

2021

Preprint

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It is important to explore how scientists decide their research agenda and the corresponding consequences, as their decisions collectively shape contemporary science. There are studies focusing on the overall performance of individuals with different problem choosing strategies. Here we ask a slightly different but relatively unexplored question: how is a scientist's change of research agenda associated with her change of scientific performance. Using publication records of over 14,000 authors in physics, we quantitatively measure the extent of research direction change and the performance change of individuals. We identify a strong positive correlation between the direction change and impact change. Scientists with a larger direction change not only are more likely to produce works with increased scientific impact compared to their past ones, but also have a higher growth rate of scientific impact. On the other hand, the direction change is not associated with productivity change. Those who stay in familiar topics do not publish faster than those who venture out and establish themselves in a new field. The gauge of research direction in this work is uncorrelated with the diversity of research agenda and the switching probability among topics, capturing the evolution of individual careers from a new point of view. Though the finding is inevitably affected by the survival bias, it sheds light on a range of problems

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“…Research topic was de ned as a subcategory of discipline, with categories outlined in Table S2 in the supplementary information document 33 . The sector was de ned as public and government, academia, civic society, private sector, or intersectoral according to the sector of their primary place of employment.…”

Section: Methodsmentioning

confidence: 99%

Assembling the puzzle for conducting research through the lens of diversity and cohesion: The SALURBAL experience

Baquero¹,

Montes²,

Stankov

et al. 2023

Preprint

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The SALURBAL (Urban Health in Latin America) Project is an interdisciplinary multinational network aimed at generating and disseminating actionable evidence on the drivers of health in cities of Latin America. We conducted a temporal multilayer network analysis where we measured cohesion over time using the structural properties of the network and assessed diversity within and between different project activities according to participant attributes. Between 2017 and 2020 the SALURBAL network comprised 395 participants across 26 countries, 23 disciplines, and 181 institutions. We found that the cohesion of the SALURBAL network increased over time (clustering coefficient increased [0.83–0.91] and shortest path decreased [1.70–1.68]). SALURBAL also exhibited balanced overall diversity within project activities (0.5–0.6) by designing activities for different purposes such as capacity building, team-building, research, and dissemination. The network’s growth was facilitated by the creation of new diverse collaborations across a range of activities over time, while maintaining the diversity of existing collaborations (0.69–0.75 between activity diversity depending on the attribute). The SALURBAL experience can serve as an example for multinational research projects aiming to build cohesive networks while leveraging heterogeneity in countries, disciplines, career stage, and across sectors.

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Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

Cited by 33 publications

References 42 publications

Research community dynamics behind popular AI benchmarks

Research community dynamics behind popular AI benchmarks

Become a better you: correlation between the change of research direction and the change of scientific performance

Assembling the puzzle for conducting research through the lens of diversity and cohesion: The SALURBAL experience

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