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The top 1 percent most highly cited articles are watched closely as the vanguards of the sciences. Using Web of Science data, one can find that China had overtaken the USA in the relative participation in the top 1 percent in 2019, after outcompeting the EU on this indicator in 2015. However, this finding contrasts with repeated reports of Western agencies that the quality of Chinese output in science is lagging other advanced nations, even as it has caught up in numbers of articles. The difference between the results presented here and the previous results depends mainly upon field normalizations, which classify source journals by discipline. Average citation rates of these subsets are commonly used as a baseline so that one can compare among disciplines. However, the expected value of the top 1 percent of a sample of N papers is N 100, ceteris paribus. Using the average citation rates as expected values, errors are introduced by using the mean of highly skewed distributions and a specious precision in the delineations of the subsets. Classifications can be used for the decomposition, but not for the normalization. When the data is thus decomposed, the USA ranks ahead of China in biomedical fields such as virology. Although the number of papers is smaller, China outperforms the US in the field of Business and Finance in the Social Sciences Citation Index when p is less than .05. Using percentile ranks, subsets other than indexing based classifications can be tested for the statistical significance of differences among them., Comment: 26 pages, 8 figures accepted for publication in Scientometrics

Purpose: We address the question of whether differences are statistically significant in the rankings of universities. We propose methods measuring the statistical significance among different universities and illustrate the results by empirical data. Design/methodology/approach: Based on z-testing and overlapping confidence intervals, and using data about 205 Chinese universities included in the Leiden Rankings 2020, we argue that three main groups of Chinese research universities can be distinguished. Findings: When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020, the results similarly indicate three main groups: high, middle, low. Using this data (Leiden Rankings and Web-of-Science), the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap. Research limitations: We show empirically that differences in ranking may be due to changes in the data, the models, or the modeling effects on the data. The scientometric groupings are not always stable when we use different methods. R&D policy implications: Differences among universities can be tested for their statistical significance. The statistics relativize the values of decimals in the rankings. One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings. Originality/value: In the discussion about the rankings of universities, the question of whether differences are statistically significant, is, in our opinion, insufficiently addressed.

Recently, Hirsch (2019a) proposed a new variant of the h index called the $h_\alpha$ index. He formulated as follows: "we define the $h_\alpha$ index of a scientist as the number of papers in the h-core of the scientist (i.e. the set of papers that contribute to the h-index of the scientist) where this scientist is the $\alpha$-author" (p. 673). The $h_\alpha$ index was criticized by Leydesdorff, Bornmann, and Opthof (2019). One of their most important points is that the index reinforces the Matthew effect in science. We address this point in the current study using a recently developed Stata command (h_index) and R package (hindex), which can be used to simulate h index and $h_\alpha$index applications in research evaluation. The user can investigate under which conditions $h_\alpha$ reinforces the Matthew effect. The results of our study confirm what Leydesdorff et al. (2019) expected: the $h_\alpha$ index reinforces the Matthew effect. This effect can be intensified if strategic behavior of the publishing scientists and cumulative advantage effects are additionally considered in the simulation.

The Journal Impact Factor (JIF) is linearly sensitive to self-citations because each self-citation adds to the numerator, whereas the denominator is not affected. Pinski & Narin (1976) derived the Influence Weight (IW) as an alternative to Garfield's JIF. Whereas the JIF is based on raw citation counts normalized by the number of publications, IWs are based on the eigenvectors in the matrix of aggregated journal-journal citations without a reference to size: the cited and citing sides are combined by a matrix approach. IWs emerge as a vector after recursive iteration of the normalized matrix. Before recursion, IW is a (vector-based) non-network indicator of impact, but after recursion (i.e. repeated improvement by iteration), IWs can be considered a network measure of prestige among the journals in the (sub)graph as a representation of a field of science. As a consequence (not intended by Pinski & Narin in 1976), the self-citations are integrated at the field level and no longer disturb the analysis as outliers. In our opinion, this is a very desirable property of a measure of quality or impact. As illustrations, we use data of journal citation matrices already studied in the literature, and also the complete set of data in the Journal Citation Reports 2017 (n = 11,579 journals). The values of IWs are sometimes counter-intuitive and difficult to interpret. Furthermore, iterations do not always converge. Routines for the computation of IWs are made available at http://www.leydesdorff.net/iw.

What are the landmark papers in scientific disciplines? On whose shoulders does research in these fields stand? Which papers are indispensable for scientific progress? These are typical questions which are not only of interest for researchers (who frequently know the answers - or guess to know them), but also for the interested general public. Citation counts can be used to identify very useful papers, since they reflect the wisdom of the crowd; in this case, the scientists using the published results for their own research. In this study, we identified with recently developed methods for the program CRExplorer landmark publications in nearly all Web of Science subject categories (WoSSCs). These are publications which belong more frequently than other publications across the citing years to the top-per mill in their subject category. The results for three subject categories "Information Science and Library Science", "Computer Science, Information Systems", and "Computer Science, Software Engineering" are exemplarily discussed in more detail. The results for the other WoSSCs can be found online at http://crexplorer.net.

Recently, the integrated impact indicator (I3) indicator was introduced where citations are weighted in accordance with the percentile rank class of each publication in a set of publications. I3 can also be used as a field-normalized indicator. Field-normalization is common practice in bibliometrics, especially when institutions and countries are compared. Publication and citation practices are so different among fields that citation impact is normalized for cross-field comparisons. In this study, we test the ability of the indicator to discriminate between quality levels of papers as defined by Faculty members at F1000Prime. F1000Prime is a post-publication peer review system for assessing papers in the biomedical area. Thus, we test the convergent validity of I3 (in this study, we test I3/N - the size-independent variant of I3 where I3 is divided by the number of papers) using assessments by peers as baseline and compare its validity with several other (field-normalized) indicators: the mean-normalized citation score (MNCS), relative-citation ratio (RCR), citation score normalized by cited references (CSNCR), characteristic scores and scales (CSS), source-normalized citation score (SNCS), citation percentile, and proportion of papers which belong to the x% most frequently cited papers (PPtop x%). The results show that the PPtop 1% indicator discriminates best among different quality levels. I3 performs similar as (slightly better than) most of the other field-normalized indicators. Thus, the results point out that the indicator could be a valuable alternative to other indicators in bibliometrics.

We propose the I3* indicator as a non-parametric alternative to the Journal Impact Factor (JIF) and h-index. We apply I3* to more than 10,000 journals. The results can be compared with other journal metrics. I3* is a promising variant within the general scheme of non-parametric indicators I3 introduced previously: it provides a single metric which correlates with both impact in terms of citations (c) and output in terms of publications (p). We argue for weighting using four percentile classes: the top-1% and top-10% as excellence indicators; the top-50% and bottom-50% as output indicators. Like the h-index, which also incorporates both c and p, I3*-values are size-dependent; however, division of I3* by the number of publications (I3*/N) provides a size-independent indicator which correlates strongly with the two- and five-year Journal Impact Factors (JIF2 and JIF5). Unlike the h-index, I3* correlates significantly with both the total number of citations and publications. The values of I3* and I3*/N can be statistically tested against the expectation or against one another using chi-square tests or effect sizes. A template (in Excel) is provided online for relevant tests.

Twitter accounts have already been used in many scientometric studies, but the meaningfulness of the data for societal impact measurements in research evaluation has been questioned. Earlier research focused on social media counts and neglected the interactive nature of the data. We explore a new network approach based on Twitter data in which we compare author keywords to hashtags as indicators of topics. We analyze the topics of tweeted publications and compare them with the topics of all publications (tweeted and not tweeted). Our exploratory study is based on a comprehensive publication set of climate change research. We are interested in whether Twitter data are able to reveal topics of public discussions which can be separated from research-focused topics. We find that the most tweeted topics regarding climate change research focus on the consequences of climate change for humans. Twitter users are interested in climate change publications which forecast effects of a changing climate on the environment and to adaptation, mitigation and management issues rather than in the methodology of climate-change research and causes of climate change. Our results indicate that publications using scientific jargon are less likely to be tweeted than publications using more general keywords. Twitter networks seem to be able to visualize public discussions about specific topics., Comment: 31 pages, 1 table, and 7 figures

In a recent paper, Hirsch (2018) proposes to attribute the credit for a co-authored paper to the {\alpha}-author--the author with the highest h-index--regardless of his or her actual contribution, effectively reducing the role of the other co-authors to zero. The indicator h{\alpha} inherits most of the disadvantages of the h-index from which it is derived, but adds the normative element of reinforcing the Matthew effect in science. Using an example, we show that h{\alpha} can be extremely unstable. The empirical attribution of credit among co-authors is not captured by abstract models such as h, h_bar , or h{\alpha}.

Following discussions in 2010 and 2011, scientometric evaluators have increasingly abandoned relative indicators in favor of comparing observed with expected citation ratios. The latter method provides parameters with error values allowing for the statistical testing of differences in citation scores. A further step would be to proceed to non-parametric statistics (e.g., the top-10%) given the extreme skewness (non-normality) of the citation distributions. In response to a plea for returning to relative indicators in the previous issue of this newsletter, we argue in favor of further progress in the development of citation impact indicators., Comment: ISSI Newsletter #55, September 2018

Questions of definition and measurement continue to constrain a consensus on the measurement of interdisciplinarity. Using Rao-Stirling (RS) Diversity produces sometimes anomalous results. We argue that these unexpected outcomes can be related to the use of "dual-concept diversity" which combines "variety" and "balance" in the definitions (ex ante). We propose to modify RS Diversity into a new indicator (DIV) which operationalizes variety, balance, and disparity independently and then combines them ex post. "Balance" can be measured using the Gini coefficient. We apply DIV to the aggregated citation patterns of 11,487 journals covered by the Journal Citation Reports 2016 of the Science Citation Index and the Social Sciences Citation Index as an empirical domain and, in more detail, to the citation patterns of 85 journals assigned to the Web-of-Science category "information science & library science" in both the cited and citing directions. We compare the results of the indicators and show that DIV provides improved results in terms of distinguishing between interdisciplinary knowledge integration (citing) versus knowledge diffusion (cited). The new diversity indicator and RS diversity measure different features. A routine for the measurement of the various operationalizations of diversity (in any data matrix) is made available online.

Using the 6,638 case descriptions of societal impact submitted for evaluation in the Research Excellence Framework (REF 2014), we replicate the topic model (Latent Dirichlet Allocation or LDA) made in this context and compare the results with factor-analytic results using a traditional word-document matrix (Principal Component Analysis or PCA). Removing a small fraction of documents from the sample, for example, has on average a much larger impact on LDA than on PCA-based models to the extent that the largest distortion in the case of PCA has less effect than the smallest distortion of LDA-based models. In terms of semantic coherence, however, LDA models outperform PCA-based models. The topic models inform us about the statistical properties of the document sets under study, but the results are statistical and should not be used for a semantic interpretation - for example, in grant selections and micro-decision making, or scholarly work-without follow-up using domain-specific semantic maps.

Using firm-level data collected by Statistics Italy for 2008, 2011, and 2015, we examine the Triple-Helix synergy among geographical and size distributions of firms, and the NACE codes attributed to these firms, at the different levels of regional and national government. At which levels is innovation-systemness indicated? The contributions of regions to the Italian innovation system have increased, but synergy generation between regions and supra-regionally has remained at almost 45%. As against the statistical classification of Italy into twenty regions or into Northern, Central, and Southern Italy, the greatest synergy is retrieved by considering the country in terms of Northern and Southern Italy as two sub-systems, with Tuscany included as part of Northern Italy. We suggest that separate innovation strategies should be developed for these two parts of the country. The current focus on regions for innovation policies may to some extent be an artifact of the statistics and EU policies. In terms of sectors, both medium- and high-tech manufacturing (MHTM) and knowledge-intensive services (KIS) are proportionally integrated in the various regions.

The dilemma which remained unsolved using Rao-Stirling diversity, namely of how variety and balance can be combined into "dual concept diversity" (Stirling, 1998, pp. 48f.) can be clarified by using Nijssen et al.'s (1998) argument that the Gini coefficient is a perfect indicator of balance. However, the Gini coefficient is not an indicator of variety; this latter term can be operationalized independently as relative variety. The three components of diversity--variety, balance, and disparity--can thus be clearly distinguished and independently operationalized as measures varying between zero and one. The new diversity indicator ranges with more resolving power in the empirical case., Comment: Scientometrics, accepted June 7, 2018

Using three-year moving averages of the complete Journal Citation Reports 1994-2016 of the Science Citation Index and the Social Sciences Citation Index (combined), we analyze links between citing and cited journals in terms of (1) whether discontinuities among the networks of consecutive years have occurred; (2) are these discontinuities relatively isolated or networked? (3) Can these discontinuities be used as indicators of novelty, change, and innovation in the sciences? We examine each of the N2 links among the N journals across the years. We find power-laws for the top 10,000 instances of change, which we suggest interpreting in terms of "self-organized criticality": co-evolutions of avalanches in aggregated citation relations and meta-stable states in the knowledge base can be expected to drive the sciences towards the edges of chaos. The flux of journal-journal citations in new manuscripts may generate an avalanche in the meta-stable networks, but one can expect the effects to remain local (for example, within a specialty). The avalanches can be of any size; they reorient the relevant citation environments by inducing a rewrite of history in the affected partitions.

Nations can be distinguished in terms of whether domestic or international research is cited. We analyzed the research output in natural sciences of three leading European research economies (Germany, the Netherlands, and the UK) and ask where their researchers look for the knowledge that underpins their most highly-cited papers. Is one internationally oriented or is citation limited to national resources? Do the citation patterns reflect a growing differentiation between the domestic and international research enterprise? To evaluate change over time, we include natural-sciences papers published in the countries from three publication years: 2004, 2009, and 2014. The results show that articles co-authored by researchers from Germany or the Netherlands are less likely to be among the globally most highly-cited articles if they also cite "domestic" research (i.e. research authored by authors from the same country). To put this another way, less well-cited research is more likely to stand on domestic shoulders and research that becomes more highly-cited is more likely to stand on international shoulders. A possible reason for the results is that researchers "over-cite" the papers from their own country - lacking the focus on quality in citing. However, these differences between domestic and international shoulders are not visible for the UK.

We apply Patent Citation Spectroscopy (PCS)--originally developed as Reference Publication Year Spectroscopy for studying landmarks and milestones in scientific literature--to patent literature classified into the nine Y-subclasses of the Cooperative Patent Classification (CPC) that describe material photovoltaic technologies. For this study we extended the routine with the option to use the advanced search queries at PatentsView. On the basis of two normalizations of the longitudinal distribution of the publication years of the patents cited by the retrieved patents, the routine (at http://www.leydesdorff.net/comins/pcs/index.html) provides a best guess of the foundational patent for the subject specified in the string. In five of the nine cases, we found corroborating evidence for the foundational character of the patent indicated by the routine.

In a recent publication in Nature, Wagner & Jonkers (2017) report that public R&D funding is only weakly correlated with the citation impact of a nation's papers as measured by the field-weighted citation index (FWCI; defined by Scopus). On the basis of the supplementary data, we upscaled the design using Web-of-Science data for the decade 2003-2013 and OECD funding data for the corresponding decade assuming a two-year delay (2001-2011). Using negative binomial regression analysis, we find very small coefficients, but the effects of international collaboration are positive and statistically significant, whereas the effects of government funding are negative, an order of magnitude smaller, and statistically non-significant (in two of three analyses). In other words, international collaboration improves the impact of average research papers, whereas more government funding tends to have a small adverse effect when comparing OECD countries.

Social-media data provides increasing opportunities for automated analysis of large sets of textual documents. So far, automated tools have been developed to account for either the social networks between the participants of the debates, or to analyze the content of those debates. Less attention has been paid to mapping co-occurring actors (participants) and topics (content) in online debates that form socio-semantic networks. We propose a new, automated approach that uses a whole matrix approach of co-addressed topics and the actors. We show the advantages of the new approach with the analysis of a large set of English-language Twitter messages at the Rio+20 meeting, in June 2012 (72,077 tweets), and a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological and substantive implications of our approach, also for the analysis of other social-media data.

The Leiden Rankings can be used for grouping research universities by considering universities which are not statistically significantly different as homogeneous sets. The groups and intergroup relations can be analyzed and visualized using tools from network analysis. Using the so-called "excellence indicator" PPtop-10%--the proportion of the top-10% most-highly-cited papers assigned to a university--we pursue a classification using (i) overlapping stability intervals, (ii) statistical-significance tests, and (iii) effect sizes of differences among 902 universities in 54 countries; we focus on the UK, Germany, Brazil, and the USA as national examples. Although the groupings remain largely the same using different statistical significance levels or overlapping stability intervals, these classifications are uncorrelated with those based on effect sizes. Effect sizes for the differences between universities are small (w <.2). The more detailed analysis of universities at the country level suggests that distinctions beyond three or perhaps four groups of universities (high, middle, low) may not be meaningful. Given similar institutional incentives, isomorphism within each eco-system of universities should not be underestimated. Our results suggest that networks based on overlapping stability intervals can provide a first impression of the relevant groupings among universities. However, the clusters are not well-defined divisions between groups of universities., Comment: Journal of the Association for Information Science and Technology (JASIST), forthcoming 2018

Using information theory, we measure innovation systemness as synergy among size-classes, zip-codes, and technological classes (NACE-codes) for 8.5 million American companies. The synergy at the national level is decomposed at the level of states, Core-Based Statistical Areas (CBSA), and Combined Statistical Areas (CSA). We zoom in to the state of California and in more detail to Silicon Valley. Our results do not support the assumption of a national system of innovations in the U.S.A. Innovation systems appear to operate at the level of the states; the CBSA are too small, so that systemness spills across their borders. Decomposition of the sample in terms of high-tech manufacturing (HTM), medium-high-tech manufacturing (MHTM), knowledge-intensive services (KIS), and high-tech services (HTKIS) does not change this pattern, but refines it. The East Coast -- New Jersey, Boston, and New York -- and California are the major players, with Texas a third one in the case of HTKIS. Chicago and industrial centers in the Midwest also contribute synergy. Within California, Los Angeles contributes synergy in the sectors of manufacturing, the San Francisco area in KIS. Knowledge-intensive services in Silicon Valley and the Bay area -- a CSA composed of seven CBSA -- spill over to other regions and even globally., Comment: Journal of the Association of Information Science and Technology (JASIST, forthcoming)

One essential component in the construction of patent landscapes in biomedical research and development (R&D) is identifying the most seminal patents. Hitherto, the identification of seminal patents required subject matter experts within biomedical areas. In this brief communication, we report an analytical method and tool, Patent Citation Spectroscopy (PCS), for rapidly identifying landmark patents in user-specified areas of biomedical innovation. PCS mines the cited references within large sets of patents and provides an estimate of the most historically impactful prior work. The efficacy of PCS is shown in two case studies of biomedical innovation with clinical relevance: (1) RNA interference and (2) cholesterol. PCS mined and analyzed 4,065 cited references related to patents on RNA interference and correctly identified the foundational patent of this technology, as independently reported by subject matter experts on RNAi intellectual property. Secondly, PCS was applied to a broad set of patents dealing with cholesterol - a case study chosen to reflect a more general, as opposed to expert, patent search query. PCS mined through 11,326 cited references and identified the seminal patent as that for Lipitor, the groundbreaking medication for treating high cholesterol as well as the pair of patents underlying Repatha. These cases suggest that PCS provides a useful method for identifying seminal patents in areas of biomedical innovation and therapeutics. The interactive tool is free-to-use at: www.leydesdorff.net/pcs/.

This study is intended to find an answer for the question on which national "shoulders" the worldwide top-level research stands. Traditionally, national scientific standings are evaluated in terms of the number of citations to their papers. We raise a different question: instead of analyzing the citations to the countries' articles (the forward view), we examine referenced publications from specific countries cited in the most elite publications (the backward-citing-view). "Elite publications" are operationalized as the top-1% most-highly cited articles. Using the articles published during the years 2004 to 2013, we examine the research referenced in these works. Our results confirm the well-known fact that China has emerged to become a major player in science. However, China still belongs to the low contributors when countries are ranked as contributors to the cited references in top-1% articles. Using this perspective, the results do not point to a decreasing trend for the USA; in fact, the USA exceeds expectations (compared to its publication share) in terms of contributions to cited references in the top-1% articles. Switzerland, Sweden, and the Netherlands also are shown at the top of the list. However, the results for Germany are lower than statistically expected.

Which studies, theories, and ideas have influenced Eugene Garfield's scientific work? Recently, the method reference publication year spectroscopy (RPYS) has been introduced, which can be used to answer this and related questions. Since then, several studies have been published dealing with the historical roots of research fields and scientists. The program CRExplorer (http://www.crexplorer.net) was specifically developed for RPYS. In this study, we use this program to investigate the historical roots of Eugene Garfield's oeuvre., Comment: 16 pages, 3 tables, and 3 figures

We combine the Integrated Impact Indicator (I3) and the h-index into the I3-type framework and introduce the publication vector X = (X1, X2, X3) and the citation vector Y = (Y1, Y2, Y3) , the publication score I3X=X1+X2+X3 and the citation score I3Y=Y1+Y2+Y3, and alternative indicators based on percentile classes generated by the h-index. These multivariate indicators can be used for academic evaluation. The empirical studies show that the h-core distribution is suitable to evaluate scholars, the X1 and Y1 are applied to measure core impact power of universities, and I3X and I3Y are alternatives of journal impact factor (JIF). The multivariate indicators provide a multidimensional view of academic evaluation with using the advantages of both the h-index and I3., Comment: Journal of Library Science in China (in press). This is the English version of a Chinese article. DOI:10.13530/j.cnki.jlis.170018

Journals were central to Eugene Garfield's research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science (WoS) and Scopus. In addition to disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines, the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary--that is, non-disciplined--variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed, differences among (sub)sets can be tested for statistical significance. In an appendix, a general-purpose routine for measuring diversity in networks is provided.

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016., Comment: Scientometrics 112(3) (2017) 1573-1591; http://link.springer.com/article/10.1007/s11192-017-2449-0

This paper considers the relationships among meaning generation, selection, and the dynamics of discourse from a variety of perspectives ranging from information theory and biology to sociology. Following Husserl's idea of a horizon of meaning in intersubjective communication, we propose a way in which, using Shannon's equations, the generation and selection of meanings from a horizon of possibilities can be considered probabilistically. The information-theoretical dynamics we articulate considers a process of meaning generation within cultural evolution: information is imbued with meaning, and through this process, the number of options for the selection of meaning in discourse proliferates. The redundancy of possible meanings contributes to a codification of expectations within the discourse. Unlike hard-wired DNA, the codes of non-biological systems can co-evolve with the variations. Spanning horizons of meaning, the codes structure the communications as selection environments that shape discourses. Discursive knowledge can be considered as meta-coded communication which enables us to translate among differently coded communications. The dynamics of discursive knowledge production can thus infuse the historical dynamics with a cultural evolution by adding options, that is, by increasing redundancy. A calculus of redundancy is presented as an indicator whereby these dynamics of discourse and meaning may be explored empirically., Comment: accepted for publication by the Journal of the Association for Information Science and Technology, 24 March 2018

International collaboration in science continues to grow at a remarkable rate, but little agreement exists about dynamics of growth and organization at the discipline level. Some suggest that disciplines differ in their collaborative tendencies, reflecting their epistemic culture. This study examines collaborative patterns in six previously studied specialties to add new data and conduct analyses over time. Our findings show that the global network of collaboration continues to add new nations and new participants; each specialty has added many new nations to its lists of collaborating partners since 1990. We also find that the scope of international collaboration is positively related to impact. Network characteristics for the six specialties are notable in that instead of reflecting underlying culture, they tend towards convergence. This observation suggests that the global level may represent next-order dynamics that feed back to the national and local levels (as subsystems) in a complex, networked hierarchy., Comment: data on figsharae

Cities are engines of the knowledge-based economy, because they are the primary sites of knowledge production activities that subsequently shape the rate and direction of technological change and economic growth. Patents provide a wealth of information to analyse the knowledge specialization at specific places, such as technological details and information on inventors and entities involved, including address information. The technology codes on each patent document indicate the specialization and scope of the underlying technological knowledge of a given invention. In this paper we introduce tools for portfolio analysis in terms of patents that provide insights into the technological specialization of cities. The mapping and analysis of patent portfolios of cities using data of the Unites States Patent and Trademark Office (USPTO) website (at http://www.uspto.gov) and dedicated tools (at http://www.leydesdorff.net/portfolio) can be used to analyse the specialisation patterns of inventive activities among cities. The results allow policy makers and other stakeholders to identify promising areas of further knowledge development and 'smart specialisation' strategies.