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The South Carolina Joint Citizens & Legislative Committee on Children annually publish as data reference book as a supplement to the annual report with statistical indicators of the well-being of children in the state.

The South Carolina Joint Citizens & Legislative Committee on Children annually publish as data reference book as a supplement to the annual report with statistical indicators of the well-being of children in the state.

The Research Data Working Group of the Digital Scientific Library (BSN10) launched in 2015 an inventory of French research data management services, which will be referenced in the future online catalog Cat OPIDoR. This initiative echoes the national and international political context, in which governments and funding agencies gradually implement open science policy frameworks, whereas research activities are also altered by the ubiquity of data and the computing capacities to generate, mine and distribute them. It aims to help research teams to identify services most able to provide them appropriate data management support and to inform political stakeholders about where resources investment is needed.

Purpose: A recent three-arm randomized controlled trial (RCT) with depressed Hong Kong adults demonstrated the comparable effectiveness of integrative Body-Mind-Spirt (IBMS) and Qigong interventions in relieving sleep disturbance and depression, but not which is best for whom? Guided by concept and theory-based hypotheses, clinical data-mining (CDM), the RCT data answers the more clinically relevant question: who responds best to which intervention? Method: Paired-sample t-tests and Wilcoxon signed-ranked tests were adopted to compare the within-subgroup differences; linear mixed models for normally distributed outcomes and generalized linear mixed models for non-normally distributed outcomes were used to compare the between-subgroup differences. Results: Results indicate that IBMS is more efficacious for older, more educated females, suffering from physical pain and illness; whereas younger, less educated males, not in full-time employment benefit more from Qigong. Discussion: This productive joining together of RCT and CDM recommends itself to both past and future RCTs, further informing evidence-based practice decision making.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

This report presents a summary of existing experimental data holdings together with current access policies in European experiments. It also presents an assessment of their compliance with FAIR Principles and makes suggestions for how to improve the present practices/policies towards a more FAIR and more Open Data management. All European tokamak and stellarator experiments grant access to their measured and processed data on an individual basis, to collaborators who are formally identified as members of the experiment's team. Once a researcher is authorized for a given experiment, he has access to all measured data and processed data (Plasma Reconstruction Chain, PRC) of that experiment. Data has some degree of FAIRness at the level of a given experiment, but EU experiments are presently not interoperable, which prevents from exploiting results of the EU fusion experiments at their full potential. In particular, Data Mining / Machine Learning activities cannot be conducted across multiple experiments, or would require the ad-hoc creation of specific databases. A few international multi-machine databases have been created in the last decades of fusion research but their perimeter is limited to specific physics topics (e.g. confinement, disruptions, …) and they are not fed on an automated/systematic basis. In addition to improving the EU fusion science community Open Science and FAIR practices, making metadata and data interoperable across EU experiments is a key target of our recommendations since it would bring unique benefits to the EU fusion research, increasing the potential for new discoveries. The IMAS Data Dictionary is recommended as the standard ontology for achieving interoperability.

"Administrative data of the unemployment insurance system in Switzerland have been made available by the Swiss State Secretariat for Economic Affairs (seco) for specific research purposes. They contain rich Information about unemployed and job seekers. The records are linked to social security data. The combination of the two sources leads to a very rich database. This paper describes the data as well as some administrative procedures generating it." (author's abstract)

IntroductionTo enhance the value of the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) we recently linked these data with administrative datasets including, Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS). Understanding the validity of administrative data is important in establishing the reliability of these data for informing clinical practice and policy.
Objectives & ApproachThe study objective was to determine the validity of MBS data for capturing the occurrence of a joint replacement procedure. Using the AOANJRR procedures as the gold standard, we determined the sensitivity of the MBS data in correctly identifying hip joint replacement procedures.
ResultsOf the 178,047 patients with a single primary total hip replacement occurring in a private hospital setting and recorded in the AOANJRR, 76% had a same-day MBS service claim indicative of that procedure, 2% had MBS procedures within +/- 7 days of the procedure while 18% had no MBS procedure codes indicative of a total hip joint replacement procedure. Of the procedures with no total hip MBS codes, 2% had MBS procedures codes indicating a total knee procedure, 1.7% had MBS procedure codes indicating a revision hip on that day and 13% of procedures had an in-hospital MBS hip anaesthetic administration code claimed on that day.
Conclusion / ImplicationsGiven the increasing application of MBS data to describe health service use it is important to understand the validity of these data for identifying procedures undertaken in the private hospital setting. Using validated, gold standard data captured by the AOANJRR we identified that MBS data likely underestimate the occurrence of total hip replacement procedures. In addition, some MBS procedure codes are misattributed to other procedure types such as knee procedures and revision procedures.

Introduction: New legal challenges of big data /Joe Cannataci, Valeria Falce and Oreste Pollicino --Big data and big databases between privacy and competition /Sofia Oliveira Pais --Competition challenges of big data : algorithmic collusion, personalised pricing and privacy /Antonio Capobianco and Pedro Gonzaga --Antitrust enforcement and privacy standards /Renato Nazzini --Mergers, data markets and competition /Damiano Canapa --Platform role and intermediary responsibility /Vicente Bagnoli --Global perspectives on big data and consumer law /Mateja Durovic and Franciszek Lech --Data as an input in competition law cases : standards, difficulties and biases in EU merger control /Rupprecht Podszun and Sarah Langenstein --Breaking down information silos with big data : a legal analysis of data sharing /Giovanni De Gregorio and Sofia Ranchordás --The relationship between freedom of expression and big data /Oleg Soldatov --Big data and children's rights : new legal challenges alongside new opportunities /Shulamit Almog and Liat Franco --Artificial intelligence in the big data era : risks and opportunities /Francesca Lagioia and Giovanni Sartor.

Based on statistics from data.id, in the first quarter of 2016, there are 1,137 datasets distributed at 32 institutions and 18 groups in Indonesia. DKI Jakarta Province contributes to these data at the most, i.e. 714 datasets. A lot of accessible open datasets have an impact on the availability of valuable information that can be extracted to good use, for businesses, governments, and personal lives. To get the desired information, an exploratory data analysis is needed to make data more alive. The goal of this research is to provide a proper visualization of the given data. Data visualization is a way (perhaps a solution) to communicate abstract data, to aid in data understanding by leveraging human visual system. The result of this visualization is effective and engaging charts appropriates to the given data and can be run on mobile platforms.

The purpose of this article is to explain what a digital form of journalism, called data journalism, is and how it is applied in practice related to politics. Definitions and various aspects of data journalism (i.a. exemplary variables, most common in data journalism in general, ways of presenting data, factors that data journalists focus on in their work etc.) appear in the first part of article. Politics, as the title of this work indicates, is one of the areas in which this type of journalism is used. In this article, three projects related to data journalism are described. The first example of a tool, based on American politics, used to visualize and obtain data on congressmen and elections in general, is Election DataBot. This paper provides a description of this tool, as well as information about organizations that launched it. The next two initiatives related to data journalism are: European Data Journalism Network (as the name suggests, it refers to European politics) and Media 3.0 Foundation (related to Polish politics). They offer many practical options to observe, analyze and show political data. The research method used in the study is the analysis of thematic online sources. The hypothesis is that data journalism is still a growing branch of journalism that has its adhibition in politics, thus supporting journalists, researchers and others interested in obtaining and visualizing data.

The Data Management Plan clarifies the handling of research data during and after the project. It includes data that will be collected, processed or generated during the project, methodology and standards that will be applied, whether data will be shared and how data will be curated and preserved, taking into account all data-related aspects of the project. The document describes: The guiding principles for data management in the project The legal framework constituted by the General Data Protection Directive (GDPR) An overview of what data will be gathered and processed in the project How data will be sotred and processed according to the H2020 FAIR data management principles Resource allocation for making data FAIR Data security aspects ePLANET project is a Coordination and Support Action cofounded by the European Commission through Horizon 2020 program. ePLANET aims to deploy a new clustering governance for energy transition based on a digital framework to share harmonized information, facilitating the adoption of coordinated energy transition actions by the European public sector.