Dataset Information

Exploring the relationship between the engineering and physical sciences and the health and life sciences by advanced bibliometric methods.

ABSTRACT: We investigate the extent to which advances in the health and life sciences (HLS) are dependent on research in the engineering and physical sciences (EPS), particularly physics, chemistry, mathematics, and engineering. The analysis combines two different bibliometric approaches. The first approach to analyze the 'EPS-HLS interface' is based on term map visualizations of HLS research fields. We consider 16 clinical fields and five life science fields. On the basis of expert judgment, EPS research in these fields is studied by identifying EPS-related terms in the term maps. In the second approach, a large-scale citation-based network analysis is applied to publications from all fields of science. We work with about 22,000 clusters of publications, each representing a topic in the scientific literature. Citation relations are used to identify topics at the EPS-HLS interface. The two approaches complement each other. The advantages of working with textual data compensate for the limitations of working with citation relations and the other way around. An important advantage of working with textual data is in the in-depth qualitative insights it provides. Working with citation relations, on the other hand, yields many relevant quantitative statistics. We find that EPS research contributes to HLS developments mainly in the following five ways: new materials and their properties; chemical methods for analysis and molecular synthesis; imaging of parts of the body as well as of biomaterial surfaces; medical engineering mainly related to imaging, radiation therapy, signal processing technology, and other medical instrumentation; mathematical and statistical methods for data analysis. In our analysis, about 10% of all EPS and HLS publications are classified as being at the EPS-HLS interface. This percentage has remained more or less constant during the past decade.

SUBMITTER: Waltman L

PROVIDER: S-EPMC4216103 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring the relationship between the engineering and physical sciences and the health and life sciences by advanced bibliometric methods.

Waltman Ludo L van Raan Anthony F J AF Smart Sue S

PloS one 20141031 10

We investigate the extent to which advances in the health and life sciences (HLS) are dependent on research in the engineering and physical sciences (EPS), particularly physics, chemistry, mathematics, and engineering. The analysis combines two different bibliometric approaches. The first approach to analyze the 'EPS-HLS interface' is based on term map visualizations of HLS research fields. We consider 16 clinical fields and five life science fields. On the basis of expert judgment, EPS research ...[more]

PMID: 25360616

Similar Datasets

Project description:BackgroundMany scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available. However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown. This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy.MethodsFrom each of the 178 categories of the Web of Science's 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis. Four of the authors independently reviewed the results of the journal website searches, categorized the journals' data sharing policies, and extracted the characteristics of individual journals. Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy. Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy.ResultsOf the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies. Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17-0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05-7.14]). Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98-15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85-4.81]).ConclusionsThese findings may account for the increase in commercial publishers' engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals. Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.

Dataset Information

Exploring the relationship between the engineering and physical sciences and the health and life sciences by advanced bibliometric methods.

Publications

Exploring the relationship between the engineering and physical sciences and the health and life sciences by advanced bibliometric methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets