Practical operation and theoretical basis of difference-in-difference regression in science of science:The comparative trial on the scientific performance of Nobel laureates versus their coauthors

Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.

Regression discontinuity design and its applications to Science of Science:A survey

Purpose:With the availability of large-scale scholarly datasets,scientists from various domains hope to understand the underlying mechanisms behind science,forming a vibrant area of inquiry in the emerging“science of science”field.As the results from the science of science often has strong policy implications,understanding the causal relationships between variables becomes prominent.However,the most credible quasi-experimental method among all causal inference methods,and a highly valuable tool in the empirical toolkit,Regression Discontinuity Design(RDD)has not been fully exploited in the field of science of science.In this paper,we provide a systematic survey of the RDD method,and its practical applications in the science of science.Design/methodology/approach:First,we introduce the basic assumptions,mathematical notations,and two types of RDD,i.e.,sharp and fuzzy RDD.Second,we use the Web of Science and the Microsoft Academic Graph datasets to study the evolution and citation patterns of RDD papers.Moreover,we provide a systematic survey of the applications of RDD methodologies in various scientific domains,as well as in the science of science.Finally,we demonstrate a case study to estimate the effect of Head Start Funding Proposals on child mortality.Findings:RDD was almost neglected for 30 years after it was first introduced in 1960.Afterward,scientists used mathematical and economic tools to develop the RDD methodology.After 2010,RDD methods showed strong applications in various domains,including medicine,psychology,political science and environmental science.However,we also notice that the RDD method has not been well developed in science of science research.Research Limitations:This work uses a keyword search to obtain RDD papers,which may neglect some related work.Additionally,our work does not aim to develop rigorous mathematical and technical details of RDD but rather focuses on its intuitions and applications.Practical implications:This work proposes how to use the RDD method in science of science research.Originality/value:This work systematically introduces the RDD,and calls for the awareness of using such a method in the field of science of science.

The scientific applications of big data in science of science

The value of big data in science of science for knowledge discovery is that it can reveal deeper information and knowledge, promote knowledge integration in the whole process of scientific research, guide interdisciplinary integration, and provide new ideas and new methods for knowledge discovery research. This paper discusses the value and role of big data in science of science in knowledge discovery from five aspects, including exploring the laws of scientific research, revealing scientific structure, analyzing scientific research activities, supporting technical recognition and prediction, and serving science and technology evaluation.

HISTORY OF SCIENCE AND MODERN SCIENCES

Using examples in biology and mathematics, the mutual reinforcement correlation between the history of science and modern sciences in respect to the method of thinking is expounded in this article. The great value of historical materials to modem sciences is explained by citing instances in astronomy, earth sciences and engineering. The importance of the study of the external history of science (i.e. the sociological history of science) to personnel training and policy formulation is stressed. This article also seeks to make clear that the history of science itself is a modern discipline, which has just taken shape in the world during the 20th century, and that the discipline demands prompt development in China in view of its even later start.

The notion of dominant terminology in bibliometric research

In this opinion paper, we introduce the expressions of dominant terminology and dominant term in the quantitative studies of science in analogy to the notion of dominant design in product development and innovation.

Confidence Intervals for Relative Intensity of Collaboration(RIC)Indicators

Purpose:We aim to extend our investigations related to the Relative Intensity of Collaboration(RIC)indicator,by constructing a confidence interval for the obtained values.Design/methodology/approach:We use Mantel-Haenszel statistics as applied recently by Smolinsky,Klingenberg,and Marx.Findings:We obtain confidence intervals for the RIC indicatorResearch limitations:It is not obvious that data obtained from the Web of Science(or any other database)can be considered a random sample.Practical implications:We explain how to calculate confidence intervals.Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement.Our approach presents a suggestion to solve this problem.Originality/value:Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.

Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020

Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.

New directions of digitally driven S&T evaluation

Science and Technology(S&T)evaluation plays a baton role in developing science and technology innovation.However,traditional S&T evaluation indicators and methods are difficult to apply effectively in S&T evaluation practice.This paper analyzes the transformation of the S&T evaluation paradigm in the digital environment.Theories,methods,and tools of S&T evaluation research are continuously innovated and optimized;big data becomes the driving force of S&T evaluation development;the role played by S&T evaluation is shifting from a provider of statistical data and information to a participant in S&T decision-making activities.S&T evaluation research should focus on improving data retrieval and organization,knowledge mining and knowledge discovery,and intelligent evaluation models.Moreover,we suggest that scientists carry out S&T evaluation in agreement with the needs of S&T development:1)monitoring and sensing the development of science and technology in real-time with the help of emerging digital technologies;2)exploring solutions to major concerns such as technical project management mechanisms,utilizing advanced data science and digital technologies to identify important scientific frontiers,and leveraging big data in science of science to reveal patterns and characteristics of scientific structures and activities;3)carrying out problem-oriented evaluation research practice focused on four aspects,including intelligent project evaluation,evaluation of the critical technology competitiveness,talent assessment,and diagnostic evaluation of the research entity competitiveness.

Directionality of paper reviewing and publishing of a scientist: A Granger causality inference

It has been evidenced that peer review activities are positively correlated to scientists’bibliometric performance(e.g.,Ortega,2017,2019).However,how the number of paper’reviewing’interacts with a scientist’s’publishing’has not been addressed in previous studies.This paper attempts to employ the Granger causality inference to explore the directionality between a scientist’s publication performance and his/her review activities.Our dataset comprises scientists’reviewed articles derived from Publons in the Web of Knowledge database,and their publications retrieved from Pub Med.We find that scientists who reviewed less or published less tend to have Granger causality between reviewing and publishing activities.In addition,compared with early-career researchers,reviewing advances publishing for senior scientists.

Why do we need causal inference in scientometric research?

This paper reviewed the fruitful achievements in the science of science,sociology of science and economics of science,and their benefits to scientometric research.Then,the causal inference was introduced,which has the potential to shape scientometric research by determining the cause and effect among variables.In the end,we proposed two detailed reasons why we need causal inference in scientometric research:(1)correlation-based scientometric research is not sufficient to support science&technology policy;(2)Scientometrics needs to go beyond metrics by explaining the mechanisms in science.