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The Europe 2020 strategy for a smart, sustainable, and inclusive economy underlines the central role of knowledge and innovation in generating growth. For this reason, the European Union strives to improve access to scientific information and to boost the benefits of public investment in the research funded under the EU Framework Programme for Research and Innovation Horizon 2020 (2014-2020). According to this strategy, in Horizon 2020 a limited pilot action on open access to research data has been implemented so that participating projects will be required to develop an Open Data Management Plan (ODMP), in which they will specify what data will be open. This deliverable of the Q-SORT project is prepared as part of WP1, Open Data Management Plan (1st version). In this document we initiate the discussion of management, life cycle, processes of data generated by Q-SORT in order to make such data findable, accessible, interoperable, and reusable (FAIR). The aim of this document is to provide an analysis of the main elements of the data management policy that will be used with regard to all the data sets that will be generated by the project.

'Dieser Beitrag schildert Erfahrungen mit der Sekundäranalyse qualitativer Interviewdaten über soziale Beziehungen in virtuellen Gemeinschaften, wobei die Primärdaten in drei Phasen sowohl induktiven als auch deduktiven Auswertungsmethoden unterzogen und schließlich in einen ganz neuen Forschungskontext transferiert wurden. Die hierdurch ermöglichte vergleichende Betrachtung und Diskussion verschiedener Vorgehensweisen und Rahmenbedingungen bei Sekundäranalysen zeigt, dass sowohl induktive als auch deduktive Auswertungen gewinnbringend angewendet werden können und auch der (zumindest teilweise) Kontextverlust bei der Wiederverwendung nicht zwangsläufig zu einer Verarmung der Daten führen muss. Zudem erwies sich in den hier geschilderten Untersuchungen die Triangulation von Sekundäranalysen und eigener ergänzender Datenerhebung als besonders geeignete Forschungsstrategie.' (Autorenreferat)

Die Verfasser setzen sich zunächst mit der Wahl des Stichprobenplans auseinander. Vier unterschiedliche Verfahren der Anlage eines Stichprobenplans werden diskutiert: Quota-Verfahren, Random-Route-Verfahren, Adressenstichprobe über Einwohnermeldeämter sowie Telefonbuchstichprobe. Die Verfasser machen deutlich, daß die unterschiedlichen Vor- und Nachteile der einzelnen Verfahren jeweils mit Blick auf die Grundgesamtheit einer Erhebung gegeneinander abzuwägen sind. Im folgenden werden Probleme der praktischen Stichprobenziehung und der Beurteilung der Resultate diskutiert. Die Verfasser empfehlen hier, den Übergang von der Bruttostichprobe zur Nettostichprobe möglichst gut zu kontrollieren. (ICE)

Data sets quantifying phenomena of social-scientific interest often use multiple experts to code latent concepts. While it remains standard practice to report the average score across experts, experts likely vary in both their expertise and their interpretation of question scales. As a result, the mean may be an inaccurate statistic. Item-response theory (IRT) models provide an intuitive method for taking these forms of expert disagreement into account when aggregating ordinal ratings produced by experts, but they have rarely been applied to cross-national expert-coded panel data. We investigate the utility of IRT models for aggregating expert-coded data by comparing the performance of various IRT models to the standard practice of reporting average expert codes, using both data from the V-Dem data set and ecologically motivated simulated data. We find that IRT approaches outperform simple averages when experts vary in reliability and exhibit differential item functioning (DIF). IRT models are also generally robust even in the absence of simulated DIF or varying expert reliability. Our findings suggest that producers of cross-national data sets should adopt IRT techniques to aggregate expert-coded data measuring latent concepts.

Data from political elections provide a snapshot of the political landscape of a country or region. This snapshot is filtered, and maybe also distorted, through the lens of the voting method in place. The standard Plurality Voting method, by virtue of asking every voter to report only the maximum of his or her preferences, might provide too little data for an accurate snapshot. We analyze data from two large-scale field experiments in Germany, where voters employed Approval Voting for both parties and candidates. The analysis reveals that the underlying political landscapes, as perceived by the voters, are inherently multidimensional and cannot be reduced to a single left-right dimension, or even to a two-dimensional space. We compare the obtained representations with those derived from party positions as revealed by the 'Wahl-o-Mat' voting advice application, and further compare the results with those of the W-Nominate procedure.

We review the literature that identifies selection bias in media-based data & propose a theoretical model of the sources of these biases. Given the implications for validity & reliability, we conclude that newspaper data often do not reach acceptable standards for event analysis & that using them can distort findings & misguide theorizing. Furthermore, media selection biases are resistant to correction procedures largely because they are unstable across media sources, time, & location. We end with a plea for more circumspect approaches to media data that fully & openly consider the implications of their inherent limitations. Tables, Figures, References. Adapted from the source document.

The open standard for particle-mesh data (openPMD) is a FAIR community standard. openPMD meta-data adds scientific self-description in a machine-actionable format to data sets, which allows sharing data processing frameworks, chaining data through simulations and designing long analysis pipelines. Developed in the open as a FAIR data standard, openPMD is based on portable and scalable file formats such as HDF5 and ADIOS2, among others. In this talk, we will present the community principles, existing implementations, recent research results for file and file-less analysis, and possible integration directions. ; This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 654220. Supported by the Consortium for Advanced Modeling of Particles Accelerators (CAMPA), funded by the U.S. DOE Office of Science under Contract No. DE-AC02-05CH11231. This work was partially funded by the Center of Advanced Systems Understanding (CASUS), which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725 and of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.

AbstractData mining (DM) has been applied in many advanced science and technology fields, but it has still not been used for domino effect risk management to explore minimum risk scenarios. This work investigates the feasibility of DM in minimizing the risk of fire‐induced domino effects in chemical processing facilities. Based on DM, an evidential failure mode and effects analysis (E‐FMEA), which could bridge chemical facilities' operational reliability and domino effect risk, is combined with fault tree analysis (FTA) for the occurrence risk modeling of loss of containment (LOC) event of chemical facilities, which is often the triggering point of fire‐induced domino effects. Industry specific data such as reliability data, inspection records, and maintenance records are of great value to model the potential occurrence criticality of LOC. The data are used to characterize the LOC risk priority number (RPN) of chemical facilities through FTA and E‐FMEA, search and statistics rules are proposed to mine inspection records to assess LOC risk factors. According to the RPN scores of facilities, inherent safety strategies to minimize risk via inventory control are proposed, and their effectiveness is tested using a well‐known probit model. In this way, the approach proposes a unit‐specific evidence‐based risk minimization strategy for fire‐induced domino effects. A case study demonstrates the capability of DM in the risk minimization of fire‐induced domino effects.

Die Einrichtung von Archiven zur Archivierung und Nachnutzung qualitativer Forschungsdaten wurde vonseiten qualitativer Sozialforscher*innen mit intensiven Debatten um konzeptuelle und methodologische Fragen begleitet. Die konkrete Forschungspraxis blieb dabei jedoch meist ausgeklammert, obwohl sich Erfolg und Mehrwert der Datenarchivierung letztlich an der Praxis des Data Sharings und der Sekundärnutzung bemessen. Wir nehmen deshalb eigene praktische Erfahrungen mit der Sekundärnutzung archivierter Interviewdaten in einem methodologischen Forschungsprojekt zur qualitativen Interviewforschung zum Anlass, organisatorische, method(olog)ische und epistemologische Reflexionen zur Datenarchivierung anzustellen. Anhand verschiedener Prozesse der Beschaffung und Prüfung geeigneter Interviewtranskripte zeigen wir, wie sich die Herstellung des Data Fit in Sekundärforschungsprojekten innerhalb einer bedeutungsstiftenden kommunikativen Triade von Primärforschenden, Archivar*innen und Sekundärforschenden vollzogen hat.

AbstractQualitative data are commonly used in the development of system dynamics models, but methods for systematically identifying causal structures in qualitative data have not been widely established. This article presents a modified process for identifying causal structures (e.g., feedback loops) that are communicated implicitly or explicitly and utilizes software to make coding, tracking, and model rendering more efficient. This approach draws from existing methods, system dynamics best practice, and qualitative data analysis techniques. Steps of this method are presented along with a description of causal structures for an audience new to system dynamics. The method is applied to a set of interviews describing mental models of clinical practice transformation from an implementation study of screening and treatment for unhealthy alcohol use in primary care. This approach has the potential to increase rigour and transparency in the use of qualitative data for model building and to broaden the user base for causal‐loop diagramming.

We have surveyed a sample of massive star-forming regions located over a range of distances from the Galactic centre for methyl formate, HCOOCH3, and its isotopologues H13COOCH3 and HCOO13CH3. The observations were carried out with the APEX telescope in the frequency range 283.4-287.4 GHz. Based on the APEX observations, we report tentative detections of the 13C-methyl formate isotopologue HCOO13CH3 towards the following four massive star-forming regions: Sgr B2(N-LMH), NGC 6334 IRS 1, W51 e2 and G19.61-0.23. In addition, we have used the 1 mm ALMA science verification observations of Orion-KL and confirm the detection of the 13C-methyl formate species in Orion-KL and image its spatial distribution. Our analysis shows that the 12C/13C isotope ratio in methyl formate toward Orion-KL Compact Ridge and Hot Core-SW components (68.4±10.1 and 71.4±7.8, respectively) are, for both the 13C-methyl formate isotopologues, commensurate with the average 12C/13C ratio of CO derived toward Orion-KL. Likewise, regarding the other sources, our results are consistent with the 12C/13C in CO. We also report the spectroscopic characterization, which includes a complete partition function, of the complex H13COOCH3 and HCOO13CH3 species. New spectroscopic data for both isotopomers H13COOCH3 and HCOO13CH3, presented in this study, has made it possible to measure this fundamentally important isotope ratio in a large organic molecule for the first time. ; This work was supported by the National Science Foundation under grant 1008800. We are grateful to the Ministerio de Economia y Competitividad of Spain for the financial support through grant No. FIS2011-28738-C02-02 and to the French Government through grant No. ANR-08-BLAN-0054 and the French PCMI (Programme National de Physique Chimie du Milieu Interstellaire). This paper makes use of the following ALMA data: ADS/JAO. ALMA#2011.0.00009.SV.ALMAis a partnership of ESO (representing its member states), NSF (USA), and NINS (Japan), together with NRC (Canada) and NSC and ASIAA (Taiwan), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. C.F. thanks Dahbia Talbi, Eric Herbst, and Anthony Remijan for enlightening discussions. Finally, we thank the anonymous referee for helpful comments.

"This book is an introduction to regression analysis focusing on the practicalities of doing regression analysis on real life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complimented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. The first two parts of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. The third part of the book deals with how to practically handle violations of the classical linear regression assumptions, regression modelling for categorical y-variables and instrumental variable (IV) regression. The fourth part of the book puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques. This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book's accessible approach is recommended for students from across the social sciences"--