Where is your field going? A machine learning approach to study the relative motion of the domains of physics

Palmucci, Andrea; Liao, Hao; Napoletano, Andrea; Zaccaria, Andrea

doi:10.1371/journal.pone.0233997

Cited by 12 publications

(8 citation statements)

References 28 publications

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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 Those who stay in familiar topics do not publish faster than those who venture out and establish themselves in a new field.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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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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“…In an even more recent work, Palmucci et al . [ 25 ] propose a framework for describing the time evolution of scientific fields, allowing the authors to make predictions about their relative dynamics. The authors use records from the American Physics Society data on articles published during a given period to learn embeddings for PACS codes, which are numerical identifiers used as tags to categorize papers according to physics subfields.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study

Azevedo

Murai

2021

PLoS ONE

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Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network. Understanding how research areas are connected is of paramount importance for adequately allocating funding and human resources (e.g., assembling teams to tackle multidisciplinary problems). The relationship between disciplines can be drawn from data on the trajectory of individual scientists, as researchers often make contributions in a small set of interrelated areas. Two recent works propose methods for creating research maps from scientists’ publication records: by using a frequentist approach to create a transition probability matrix; and by learning embeddings (vector representations). Surprisingly, these models were evaluated on different datasets and have never been compared in the literature. In this work, we compare both models in a systematic way, using a large dataset of publication records from Brazilian researchers. We evaluate these models’ ability to predict whether a given entity (scientist, institution or region) will enter a new field w.r.t. the area under the ROC curve. Moreover, we analyze how sensitive each method is to the number of publications and the number of fields associated to one entity. Last, we conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.

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“…It has been mostly used for the purpose of information retrieval and documents classification (which is the reason why it appears in the same group as LDA in Fig. 1), but can also be a useful tool to analyze science's evolution, mostly at the micro-level of terms-to-terms similarities (Palmucci et al 2019;Tshitoyan et al 2019;Tacchella et al 2020).…”

Section: Mapping Science and Knowledgementioning

confidence: 99%

Draw me Science

Chavalarias

Lobbé²,

Delanoë³

2021

Scientometrics

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Where is your field going? A machine learning approach to study the relative motion of the domains of physics

Cited by 12 publications

References 28 publications

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

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

Evaluating the state-of-the-art in mapping research spaces: A Brazilian case study

Draw me Science

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