Scientific Publication Data Research Articles

A method for the identification of collaboration in large scientific databases

The analysis of scientific collaboration networks has contributed significantly to improve the understanding of the collaboration process between researchers. Additionally, it has helped to understand how scientific productions by researchers and research groups evolve. However, the identification of collaborations in large scientific databases is not a trivial task, given the high computational cost of the prevalent methods. This paper proposes a method for identifying collaborations in large scientific databases, namely, ISColl – Identification of Scientific Collaboration. Unlike methods that use techniques such as exhaustive comparisons of publication pairs, the proposed method produces satisfactory results with a low computational cost, thus providing an interesting alternative for the modelling and characterization of large scientific collaboration networks. To demonstrate the potential of the proposed technique, tests were conducted using scientific publications data registered in the Lattes Platform of CNPq, with the obtained results yielding excellent accuracy during the identification of scientific collaborations.

Cannabinoids for paediatric epilepsy: Weeding out the issues.

Cannabinoids for paediatric epilepsy: Weeding out the issues.

THE USE OF OBJECTIVE STATISTICAL PROCESSING FOR PRESENTING THE DATA IN SCIENTIFIC PUBLICATIONS

The most important components of the quality of scientific publications are correct applications of statistical methods for the analysis of the phenomena and its completeness. Underestimation of this fact causes incomplete or incorrect descriptions and proves the research as superficial or insufficiently reliable. The minimum of necessary statistical information should be present in the publication. It allows the reader to make some comparisons. The use of certain rules of presentation of scientific and clinical information, regular open discussion and expertise of scientific publications serve as tools of self-initiation in the methodology of biomedical research. Visual elements help authors present detailed results and complex relationships, patterns and trends clearly and concisely, reducing the length of the manuscript and enhancing readers’ understanding of the study results. The purpose of this paper is to highlight practical recommendations and to summarize relevant points of study analysis and reporting by using objective statistical processing for presenting data in scientific publications.

European Commission Policy on Open-Access to Scientific Publications and Research Data in Horizon 2020

A B S T R A C T This paper briefly presents the main lines of the European Commission's policy on Open Access to Scientific Publications and Research Data in the Horizon 2020 programme. The respective pilot on open research data is presented in detail. The pilot covers approximately 20 percent of the H2020 funding in 2014 and 2015 in the areas of future and emerging technologies; e-Infrastructures; leadership in enabling and industrial technologies – ICT; societal challenges related to smart cities and communities, climate action, environment, and resource efficiency, inclusive, innovative and reflective societies; and science with and for society, thus extending the move from open access to research results towards OA to research data. A next step will be to consider a more global open science approach encompassing a change of culture towards more openness, accessibility, re-usability, multidisciplinary, participatory collaboration, and an approach driven by what is of benefit for society.

Social and natural sciences differ in their research strategies, adapted to work for different knowledge landscapes.

Do different fields of knowledge require different research strategies? A numerical model exploring different virtual knowledge landscapes, revealed two diverging optimal search strategies. Trend following is maximized when the popularity of new discoveries determine the number of individuals researching it. This strategy works best when many researchers explore few large areas of knowledge. In contrast, individuals or small groups of researchers are better in discovering small bits of information in dispersed knowledge landscapes. Bibliometric data of scientific publications showed a continuous bipolar distribution of these strategies, ranging from natural sciences, with highly cited publications in journals containing a large number of articles, to the social sciences, with rarely cited publications in many journals containing a small number of articles. The natural sciences seem to adapt their research strategies to landscapes with large concentrated knowledge clusters, whereas social sciences seem to have adapted to search in landscapes with many small isolated knowledge clusters. Similar bipolar distributions were obtained when comparing levels of insularity estimated by indicators of international collaboration and levels of country-self citations: researchers in academic areas with many journals such as social sciences, arts and humanities, were the most isolated, and that was true in different regions of the world. The work shows that quantitative measures estimating differences between academic disciplines improve our understanding of different research strategies, eventually helping interdisciplinary research and may be also help improve science policies worldwide.

Sustainable long term scientific data publication: Lessons learned from a prototype Observatory Information System for the Illinois River Basin

In 2005 a prototype Observatory Information System (OIS) was developed for the Illinois River Basin Observatory (IRBO), connected to a federated scientific data network, populated with a representative collection of legacy datasets, and linked to external data streams. The perspective of seven years' time and the disestablishment of the system provide an opportunity to study the system life cycle. We detail best practices for multi-level OIS design for long-term performance, based on a publication-mandatory metadata implementation standard using ISO-19115. These principles balance general users' requirements against the requirements of specific scientific applications, and maximize the system's capacity to deal with legacy and heterogeneous data sources, enhancing long-term sustainability and flexibility for diverse multi-level user groups. These findings are relevant to ongoing developments of networked Scientific Information Systems that are increasingly critical to support and sustain the long-term benefits of modeling and observatory science.

Current trends in predictive analytics of big data

Predictive analytics is a driving force motivating considerable interest in big data. Although there is clear interest in big data, the adoption rate of analytical techniques fuelled by big data that can extract knowledge and value from these data is less well understood. In this paper, we present a quantitative analysis of trends in publications related to predictive analytics, predictive modelling, big data and data intensive computing. Our evaluation shows an increasing popularity of big data in scientific publications, with ten-fold increase in the last three years. Concomitantly, we find that predictive analytics are connected with this trend, with two-fold increase in the last three years, but also a seven-fold increase in the same period when used in context with big data. We also classify the main application domains for big data and predictive analytics. Contrary to popular belief that big data is focused primarily on social media and business intelligence, our analysis found that almost half of scientific publications using predictive analytics were in healthcare, smart services, the internet of things, and weather and environment. Our results indicate the early adoption of big data-based analytics in these domains.

Connectivity Analysis of Computer Science Centers based on Scientific Publications Datafor Major Russian Cities

In this paper we studyconnectivity between leading universities and academic institutions in the area of computer science for Moscow and Saint Petersburg. The research is based on scientific publications data available from http://eLibrary.ru. The focus is restricted to scientific papers in IT related areas, namely Informatics and Cybernetics, published during 2011-2013.We assume that two organizations are connected via co-authorship, a-linked, if there exists at least one paper, which has employees from these organizations among co-authors. In order to detect closely related yet not collaborating organizations formal context organizations-publishers is studied. We say that two institutions are connected via publishers, p-linked, if the pair forms extent of a formal concept.

"Corporate Science, Innovation and Firm Value"

Many firms actively disclose research findings in scientific peer-reviewed journals. The literature highlights several potential benefits of such scientific boundary-spanning activities, including privileged access to academic information networks. However, scientific disclosure may lead to unintended knowledge spillovers. It remains unclear whether active engagement in science leads to higher returns. This paper investigates the impact of scientific activities on the firm’s market value, using accounting data for US firms and matched patent and scientific publication data. We find evidence for the positive impact of scientific publications on a firm’s market value beyond the effects of RD, patent stocks and patent quality and also document heterogeneity with respect to this impact between different industrial sectors.

Response to Letter to the Editor regarding the “European Analytical Column No. 41”

Dear Sir, It is possible that the philosophy of consensus science may question, to some extent, the basis for both analytical chemistry and metrology [1]. Change of practices and procedures may be needed; genuine scientific results may be lost and theories that rely on data out of statistical control may incorrectly be proven true. There is an obligation of science to make fundamental changes, and consensus science may be a useful example of scientific methodology. There is no evidence in modern analytical chemistry for the idea that experts are able to distinguish between reliable and unreliable analytical results. The experts have no access to raw data of inter-laboratory comparisons (ILCs) and they have no access to raw data of scientific publications. Therefore, they are left with comparing highly processed data, which is unfortunate for comparisons in analytical sciences. Scientists are subjective when they propose a model and should remain objective when they compare theory to experiments. To the best of my knowledge, the staff never imposes contributions to the uncertainty. Laboratory staff prepare everything carefully and correctly in the laboratory, and even in cases where errors are committed, these errors are small and insignificant in the general picture of uncertainty. The main contribution to uncertainty is the apparatus long-time precision, which has been overlooked in uncertainty budgets [2]. If this contribution were included, would it increase the likelihood of correspondence between results in ILCs? Presumably, a university or a company would never employ incompetent staff to operate advanced apparatus. Accordingly, it is reasonable to assume that all results of ILCs belong to the same distribution. The analytical capability of laboratories cannot be checked unless the universal level of uncertainty of the involved method is known. It may safely be assumed that everyone who has invested in advanced scientific apparatus also has the competence to perform measurements. I agree that a two-step procedure for selection of qualified laboratories in advance of an ILC is not an option because that would be in conflict with consensus science. The idea of all laboratories showing equal degrees of competence is unattainable. Staff need acceptable working conditions and the results should be provided with a reasonable consumption of resources, labor, time etc. Nature does not care about ILCs and legislation. It cannot be decided by politics or debate whose fundamental constants in metrology or whose analytical results are right or wrong. We can only approach the true mechanisms of nature by performing measurements that possess a certain level of uncertainty. Hence, the method of consensus should be prevalent in science. Nothing can be decided by a single expert; results of many independent experts are required in order to maintain independence and credibility. It was interesting to me that the author of the ‘Letter to the Editor’ stated that outliers had been removed from the data set that constitutes the basis for certified reference materials (CRMs). Novel types of outliers, identified as ‘analytical outliers’, were invented for the occasion that supposedly should be distinguished from ‘statistical outliers’. It was not possible to find the technical reasons, as otherwise claimed possible. Hendrik Emons militates against better judgment: outliers were, in fact, rejected in BCR CRMs on the basis of statistical reasons and not due to technical reasons. Just a few examples: “The set of Lab 14 was rejected because its standard error exceeded s” [Bureau communautaire de reference, BCR Wholemeal flour CRM no. 189/Brown bread CRM no. 191] or “Laboratory 5 (TXRF) had an outlying variance for total Cr (Annex 1, Table 3a) and did not satisfy the criterion for This article is a response to the ‘Letter to the Editor’ to be found at DOI 10.1007/s00216‐013‐7141-5.

The Launch of PRS GO

The Launch of PRS GO

Triple Helix of university-industry-government relationships in West Africa

The West African countries’ scientific publications data are downloaded from Web of Science. Analyses focus on university‑industry‑government collaboration. We compute data on each sector’s production and collaboration among them. Particularly, the industrial production is extracted; the network, it induces is built and its structure analyzed with social network analysis methods and techniques, using Pajek. Results show that the university is the biggest information producer, followed by government; the number of industrial publications is meaningless; even some countries have no industrial output. Consequently, collaboration between the university and government is more visible. Collaboration between the three Triple Helix spheres occurred only in Nigeria. The mutual information between university, industry, and government is weak, illustrating the low level of knowledge flow between innovation actors in the region. These results could explain the negligible share of the developing countries in general and African and West African countries particularly to the World economy, and also the low level of development the region.

Corporate Science, Innovation and Firm Value

Many firms actively disclose research findings in scientific peer-reviewed journals. The literature highlights several potential benefits of such scientific boundary-spanning activities, including privileged access to academic information networks. However, scientific disclosure may lead to unintended knowledge spillovers. It remains unclear whether active engagement in science leads to higher returns. This paper investigates the impact of scientific activities on the firm’s market value, using accounting data for US firms and matched patent and scientific publication data. We find evidence for the positive impact of scientific publications on a firm’s market value beyond the effects of R&D, patent stocks and patent quality and also document heterogeneity with respect to this impact between different industrial sectors.

Why linked data is not enough for scientists

Scientific data represents a significant portion of the linked open data cloud and scientists stand to benefit from the data fusion capability this will afford. Publishing linked data into the cloud, however, does not ensure the required reusability. Publishing has requirements of provenance, quality, credit, attribution and methods to provide the reproducibility that enables validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of Research Objects as first class citizens for sharing and publishing.

Linked Open Science-Communicating, Sharing and Evaluating Data, Methods and Results for Executable Papers

Linked Open Science is an approach to solve challenges of an executable paper. It is a combination of four “silver bullets”: 1) publication of scientific data, metadata, results, and provenance information using Linked Data principles, 2) open source and web-based environments for executing, validating and exploring research, 3) Cloud Computing for effcient and distributed computing, and 4) Creative Commons for the legal infrastructure. We will use a realistic scientific research setting related to research on deforestation of the Brazilian Amazon rainforest to provide scenarios to illustrate the application of Linked Open Science.

Paper Productivity of Ground-based Large Optical Telescopes from 2000 to 2009

AbstractWe present an analysis of the scientific (refereed) paper productivity of the current largest (diameter > 8m) ground-based optical (and infrared) telescopes during the ten-year period from 2000 to 2009. The telescopes for which we have gathered and analysed the scientific publication data are the two 10-m Keck telescopes, the four 8.2-m Very Large Telescopes (VLT), the two 8.1-m Gemini telescopes, the 8.2-m Subaru telescope, and the 9.2-m Hobby–Eberly Telescope (HET). We have analysed the numbers of papers published in various astronomical journals produced by using these telescopes. While the total numbers of papers from these observatories are largest for the VLT, followed by Keck, Gemini, Subaru, and HET, the number of papers produced by each component of the telescopes is largest for Keck, followed by VLT, Subaru, Gemini, and HET. In 2009, each telescope of the Keck, VLT, Gemini, Subaru, and HET observatories produced 135, 109, 93, 107, and 5 refereed papers, respectively. We have shown that each telescope of the Keck, VLT, Gemini, and Subaru observatories is producing 2.1 ± 0.9 Nature and Science papers annually and these papers make up 1.7 ± 0.8% of all refereed papers produced by using each of those telescopes. Extending this relation, we propose that this ratio of the number of Nature and Science papers to the total number of refereed papers that will be produced by future extremely large telescopes (ELTs) will remain similar. From a comparison of the publication trends of the above telescopes, we suggest that (i) having more than one telescope of the same kind at the same location and (ii) increasing the number of instruments available at the telescope are good ways to maximize the paper productivity.

The Evolution of the Intellectual Property Management Strategy of an Emerging Multinational: Learning the Purpose of Patenting and Scientific Publications

The transition from developing country technology firm to true multinational requires both an upgrading of the underlying capability base and an understanding of the purpose of IP management. Through a detailed case study of Sasol, a leading R&D firm from South Africa, this paper tracks the coevolution of IP management with technological advancement amid the constraints of the developing country context. Using interviews, annual reports, patent, and scientific publication data, this study suggests that the value of formal IP extends beyond appropriation of own capabilities. Importantly, patents and publications also serve to signal capabilities in an attempt to gain legitimacy among peers. Fundamentally, IP management involves a social process, where firms learn to manage the sharing of key capabilities-with the attendant competition/cooperation tension-to peers.

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.

Skin care products and subtle data manipulation

The dermatologists of today need to master the skills to help their patients in choosing skin care products. As physicians and scientists, we are naturally inclined to trust sophisticated and objectively derived data that are published in scientific, peer-reviewed journals and are presented to us in the form of numbers, tables, and graphs. The question that should be asked is whether a product that scored well in sophisticated tests is really superior and more suitable to the needs of our patients? Or, how far can we trust the data in scientific publications? There is no generally accepted methodology for unbiased evaluation of the efficacy and performance of skin care products. There is also no consensus on which test method best reflects the real-life performance of these products. And, most importantly, even the most objective methods and measuring devices can be and often are manipulated to support the claims of superiority of given products. We will show several out of many, many ways of how the study design, protocols, techniques, and end points can legitimately be adapted to the special characteristics of the specific product, emphasizing its advantages.

The publication of scientific data by World Data Centers and the National Library of Science and Technology in Germany

In its 2004 report and information, the International Council for Science (ICSU) strongly recommended a new strategic framework for scientific data and information. On an initiative from a working group from the Committee on for Science and Technology (CODATA), the German Research Foundation (DFG) has started the project Publication and Citation of Scientific Primary Data as part of the program Information-infrastructure of network -based scientific-cooperation and digital publication in 2004. Starting with the field of earth science, the German National Library of Science and Technology (TIB) is now established as a registration agency for scientific primary data as a member of the International DOI Foundation (IDF).