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Macro-level domains of the science system, usually referred to as STM and SSH disciplines, have often been contrasted from various perspectives, regarding the characteristic composition of their publication channels, referencing or communication practices, and the related consequences in research evaluation. It is also long been conjectured that social science fields (along with the humanities) are more multidisciplinary than natural science fields, regarding their patterns of scholarly communication (“multidisciplinarity thesis”). The main goal of the study reported in this paper is twofold: (1) to revisit the differences in multidisciplinarity between the SSH versus STM domain, via a long-term longitudinal survey including the most recent trends, and (2) to utilize, for this task, state-of-the-art metrics and models of Interdisciplinary Research, taking into account their limitations, that is, the data sources that most naturally feed these models (typically the Web of Science). Our conclusions provides further confirmation, from the perspective of multidisciplinarity, that the concepts of SSH and STM are mainly tools for communication, rather than empirically valid constructs.

This paper evaluates the European Paradox according to which Europe plays a leading world role in terms of scientific excellence, measured in terms of the number of publications, but lacks the entrepreneurial capacity of the US to transform this excellent performance into innovation, growth, and jobs. Citation distributions for the US, the European Union (EU), and the Rest of the World are evaluated using a pair of high and low impact indicators, as well as the mean citation rate (MCR). The dataset consists of 3.6 million articles published in 1998 2002 with a common 5 year citation window. The analysis is carried at a low aggregation level, namely, the 219 sub fields identified with the Web of Science categories distinguished by Thomson Scientific. The problems posed by international co authorship and the multiple assignments of articles to sub fields are solved following a multiplicative strategy. We find that, although the EU has more publications than the US in 113 out of 219 sub fields, the US is ahead of the EU in 189 sub fields in terms of the high impact indicator, and in 163 sub fields in terms of the low impact indicator. Finally, we verify that using the high impact indicator the US/EU gap is usually greater than when using the MCR. Ruiz Castillo also acknowledges financial help from the Spanish MEC through grant SEJ2007 67436 Publicado

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004870 (IntelComp project), and has also been partially supported by FEDER/ Spanish Ministry of Science, Innovation and Universities, State Agency of Research, project TEC2017-83838-R.

This paper aims to analyze patterns of participation of higher education institutions (HEIs) to European Framework Programs (EU-FP) and their association with HEI characteristics, country and geographical effects. We have analyzed a sample of 2235 HEIs in 30 countries in Europe, derived from the European Tertiary Education Register (ETER), which has been matched with data on participations in EU-FPs in 2011 using the EUPRO database. Our findings identified (1) a high concentration of EU-FP participation in a small group of HEIs with high reputation; (2) the participation of non-doctorate awarding HEIs in EU-FPs is very limited despite the fact that they account for a significant share of tertiary student enrolments; (3) the number of participations tends to increase proportionally to organizational size, and is strongly influenced by international reputation; (5) there is limited evidence of significant country effects in EU-FP participations, as well as of the impact of distance from Brussels. We interpret these results as an outcome of the close association between HEI reputation and the network structure of EU-FP participants.

Bibliometrics is a common research instrument for systemic analysis of scientific progress in many disciplines, including veterinary sciences. However, bibliometric analyses are generally biased by limited information included in scientific productions databases. The aim of this study is to propose a method for implementing databases on livestock health and welfare in a bibliometric perspective. By using the detailed classification of the scientific disciplines and the list of keywords of the CAB Abstracts®, queries were first built to combine 120 descriptors related to animal species and 1680 descriptors related to the given thematics. Then, the extraction was done from the Web of Science®. The overall process focused on the research institutes localised in Europe and on publications written in English. To assess the database accuracy, supplementary filters were validated and applied to discard non-specific terms and neighbouring topics. Additional fields for species groups, infectious and non-infectious diseases, welfare components and geographical regions were incorporated according to thematic terminologies specifically established. The final database contains 57,523 articles published over a 12 years’ period (2003–2014). The developed method, based on the complementary use of both the CAB Abstracts® and Web of Science®, can be a reference to allow an adequate semi-automatic retrieval of relevant publications on livestock health and welfare, which will be of use to implement bibliometric study to produce a realistic assessment of tendency for driving science policy. Interestingly, it can be also a model process to be easily applied to implement bibliometric studies for any other scientific thematic domain.

Citation and coauthor networks offer an insight into the dynamics of scientific progress. We can also view them as representations of a causal structure, a logical process captured in a graph. From a causal perspective, we can ask questions such as whether authors form groups primarily due to their prior shared interest, or if their favourite topics are 'contagious' and spread through co-authorship. Such networks have been widely studied by the artificial intelligence community, and recently a connection has been made to nonlocal correlations produced by entangled particles in quantum physics -- the impact of latent hidden variables can be analyzed by the same algebraic geometric methodology that relies on a sequence of semidefinite programming (SDP) relaxations. Following this trail, we treat our sample coauthor network as a causal graph and, using SDP relaxations, rule out latent homophily as a manifestation of prior shared interest leading to the observed patternedness. By introducing algebraic geometry to citation studies, we add a new tool to existing methods for the analysis of content-related social influences. Comment: 14 pages, 1 figure, computational details are available at https://github.com/peterwittek/ipython-notebooks/blob/master/Citation_Network_SDP.ipynb. in Scientometrics, 2016

We take up the issue of performance differences between male and female researchers, and investigate the change of performance differences during the early career. In a previous paper it was shown that among starting researchers gendered performance differences seem small to non-existent (Van Arensbergen et al. 2012). If the differences do not occur in the early career anymore, they may emerge in a later period, or may remain absent. In this paper we use the same sample of male and female researchers, but now compare performance levels about 10 years later. We use various performance indicators: full/fractional counted productivity, citation impact, and relative citation impact in terms of the share of papers in the top 10 % highly cited papers. After the 10 years period, productivity of male researchers has grown faster than of female researcher, but the field normalized (relative) citation impact indicators of male and female researchers remain about equal. Furthermore, performance data do explain to a certain extent why male careers in our sample develop much faster than female researchers’ careers; but controlling for performance differences, we find that gender is an important determinant too. Consequently, the process of hiring academic staff still remains biased.

AbstractThis work aims to identify classes of DOI mistakes by analysing the open bibliographic metadata available in Crossref, highlighting which publishers were responsible for such mistakes and how many of these incorrect DOIs could be corrected through automatic processes. By using a list of invalid cited DOIs gathered by OpenCitations while processing the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) in the past two years, we retrieved the citations in the January 2021 Crossref dump to such invalid DOIs. We processed these citations by keeping track of their validity and the publishers responsible for uploading the related citation data in Crossref. Finally, we identified patterns of factual errors in the invalid DOIs and the regular expressions needed to catch and correct them. The outcomes of this research show that only a few publishers were responsible for and/or affected by the majority of invalid citations. We extended the taxonomy of DOI name errors proposed in past studies and defined more elaborated regular expressions that can clean a higher number of mistakes in invalid DOIs than prior approaches. The data gathered in our study can enable investigating possible reasons for DOI mistakes from a qualitative point of view, helping publishers identify the problems underlying their production of invalid citation data. Also, the DOI cleaning mechanism we present could be integrated into the existing process (e.g. in COCI) to add citations by automatically correcting a wrong DOI. This study was run strictly following Open Science principles, and, as such, our research outcomes are fully reproducible.