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Any type of scientific study or evaluation of research quality and impact enters into two types of problems if there is more than one topic area involved in the study: (1) How to account for differences in (paper) production? (2) How to account for differences in citation impact, i.e. influence over subsequent literature? This paper aims to show that these questions can be answered with the help of two methods; the Field Adjusted Production (FAP) indicator and a percentile indicator which is designed to include the FAP. Consequently, they are used in combination in order to express a score that includes both paper production an impact into one figure. Thereby is constructed a score that can be used for ranking of universities, departments, individuals. The paper first explains the background of the method, and then how to calculate the indicators belonging to the P-Model. Then the paper indicates some examples and will discuss methods for validation of the proposed indicator.

This paper investigates what factors affect the performance of research teams. We combine survey data about the team with bibliometric data about the performance of the team. The analysis shows that teams with a few PIs perform better than single PI teams – of course controlling for team size. On the other hand, gender diversity does not have an effect on performance. The good news is that gender objectives can be realized, without any performance problem.

As the various disciplines have different forms of social and intellectual organization (Whitley 2000), scholars in various fields may depend less on their peers, and more on other audiences for recognition and funding. Following Merton (1973) we distinguish between performance and reputation for building up recognition. We show that there are indeed differences between the disciplines: in life sciences and social sciences, the reputation related indicators are dominant in predicting the score that grant applicants get from the panel, whereas in the natural sciences, the performance related indicators dominate the panel scores. Furthermore, when comparing within the life sciences the grantees with the best performing non-grantees, we show that the former score higher on the reputation indicators and the second score better on the performance variables, supporting the findings that in life sciences one probably gains recognition over reputation more than over individual performance. We suggest that this may not be optimal for the growth of knowledge.

Classifying social science concepts by using machine learning and text-mining is often very challenging, particularly due to the fact that social concepts are often defined in a vague manner. In this paper, we put forward a first conceptual step to overcome this challenge. By using the case of social innovation, which has 252 distinct definitions, we qualitatively demonstrated that these definitions group around four different themes where various definitions utilise one or multiple of these criteria in different combinations to define social innovations. We designed an experiment where a database of social innovation projects annotated i) based on an overall understanding and ii) based on a decomposed definition of four criteria. As a next step, we will test the performance of various model specification on these two approaches.

International research performance indicators attain increased attention in science policy. More studies point to ongoing global bias in production, composition and assessment of research performance metrics (Rafols et al., 2012; van Leeuwen et al., 2001). This research examines international collaboration. It is an indicator routinely operationalised as co-authorship of articles between organisations by taking organisational address as a proxy of the collaborating country (Katz and Martin, 1997). We use geographical approximation of author heritage rooted in the morphology of the surname and find that in a significant minority of internationally collaborated papers, co-authors are likely to have the same origin. In other words, we observe an overestimation in the international collaboration indicator. The findings indicate that if a significant share of international collaborations of a national research system occurs with researchers previously affiliated with this system, internationally coauthored publications are therefore only 'inter-national' on a formal inter-organisational level. This contributes to the evidence that stresses more complex nature of scientific collaboration (Bozeman and Corley, 2004) and may have fundamental implications on the use of international collaboration indicator.

Understanding knowledge co-creation in key emerging areas of European research is a critical issue for policy makers in order to analyse impact and make strategic decisions. However, current methods for characterising and visualising the field have limitations concerning the changing nature of research, the differences in language and topic structure between policies and scientific topics, and the coverage of a broad range of scientific and political issues that have different characteristics. In this work, we discuss the novel use of ontologies and semantic technologies as a way to bridge the linguistic and conceptual gap between policy questions and data sources. Our experience suggests that a proper interlinking between intellectual tasks and the use of advanced techniques for language processing is key for the success of this endeavour.

The purpose of this paper is to discuss the conceptualisation of bibliometric analyses in terms of the levels on which they are performed, adding contextual factors to the dimension where the size of the unit being analysed is considered. Based on empirical investigations of resource allocation systems and research evaluation practices, as well as the previous literature conceptualising bibliometric analyses, a framework based on Whitley’s (2000) notion of research fields as ‘reputational work organisations’, is discussed. The results suggest adding a contextual ‘reputational dimension’ to the size-based dimension distinguishing between micro-, meso- andmacro-level analyses. Furthermore, we propose that ‘evaluative landscapes’ (Brandter, 2017) might be a fruitful approach for further analysing how complex and multifaceted landscapes of research assessment affects the individual researcher. Riksbankens Jubileumsfond: The Swedish Foundation for the Social Sciences and Humanities (SGO14-1153:1).

In this paper, we develop an analysis of the spatial distribution of knowledge production related to Key Emerging Technologies (KETs) and Societal Grand Challenges (SGCs) in Europe building on an extensive dataset developed in the H2020 KNOWMAK project. We first provide a broad characterization of European regions in terms of their knowledge volume and knowledge intensity, which leads to a distinction between the large metropolitan regions and smaller knowledge intensive regions. Second, by using principal component analysis, we identify two components of knowledge production that we broadly characterize as academic production and technology production. This distinction allows further categorizing regions in terms of the balance between the two components, which we suggest is also related to the ecology of actors in a region and, notably, of the importance of public-sector research and of knowledge producing firms. In a further step, we will adopt more advanced statistical techniques, i.e. latent class analysis, in order to provide a robust identification of classes of regions., Presented at ISSI/STI 2019

Whether as periodical conference spinoffs or extraordinary journal digressions, Special Issues, are a common yet unexplored area of scholarly communication. In this research, content and citation analysis of Special Issues in Library & Information Science Journals indexed in the Web of Science shows that special issues are distinct from regular issues “contentwise”, yet indiscernible “citationwise”, thus leaving their existence and persistence unexplained from a publisher’s economic perspective.

The aim of this phase II multicenter trial was to evaluate the activity of a novel combination of gemcitabine (GEM) and epirubicin (EPI) in advanced pancreatic cancer patients. Clinical benefit and response rate were the main efficacy end-points. From December 1997 to October 1999, 30 consecutive patients with measurable advanced pancreatic cancer were enrolled. Gemcitabine was administered intravenously in 30 min at a dose of 800 mg/m2 on days 1, 8, 15 followed by i.v. injection of epirubicin 25 mg/m(2); treatment was repeated every 28 days. With regard to clinical benefit response, 8/21 patients (38%) experienced significant palliation of tumor-related symptoms; the median symptom control time was 25 weeks. No complete responses were recorded while 6 patients achieved a partial remission, for an overall response rate of 20%; 10 patients (30%) had a stable disease and 14 (46%) had progressive disease. The median time to progression was 14 weeks. Median survival was 26 weeks, with 6 patients (20%) having long-term survival at 46 weeks. In general, chemotherapy was well tolerated; 9 patients (30%) suffered from WHO grade 3-4 haematological toxicity and 5 patients (16.6%) suffered from grade 3 non-haematological toxicity. In conclusion, the GEM plus EPI regimen represent a feasible approach for improvement of clinical benefit in advanced pancreatic cancer patients, but confirmatory investigations are required.

Social media metrics are often praised as an alternative or complement to traditional bibliometric metrics, especially in the social sciences. However, empirical investigations of the social sciences are scarce. This research in progress paper explores the extent economic research is communicated on social media platforms with an emphasis on mentions in policy documents. It examines how Altmetrics indicators relate to the Academic Journal Guide (AJG), SNIP and JCR journal rankings for economics articles. The investigated journals are selected based on Charted Association of Business School (CABS) list and extracted from Altmetric.com data. Then a statistical analysis is performed on the extracted data to get some insights into the data. The results show that the average policy count of the articles of economics journals is higher than the other subject areas mentioned in CABS and top-ranking economic journals are more likely to have articles with higher average policy count.

This work-in-progress proposes that the semantic similarity of articles published in a journal can be used to quantify the scope of the journal. Preliminary results of an analysis of a set of Library and Information Science (LIS) journals show that journals with a known broader scope have flatter, left-skewed similarity distributions, with lower averages, that can be used to identify the papers that best fit the journals in which they are published. Finally, we find different patterns regarding the evolution over time of the journals’ scope.