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This paper presents an approach how to create FAIR data for prehistoric mining archaeology, based on the CIDOC CRM ontology and semantic web standards. The interdisciplinary Research Centre HiMAT (History of mining activities in the Tyrol and adjacent areas, University of Innsbruck) investigates mining history from prehistoric to modern times with an interdisciplinary approach. One of the projects carried out at the research centre is the multinational DACH project "Prehistoric copper production in the eastern and central Alps". For a specific geographical region of the project, the data transformation to open and re-usable data is investigated in a separate Open Research Data pilot project. The methodological approach will use the FAIR principles to make data Findable, Accessible, Interoperable and Re-usable. Every archaeological investigation in Austria has to be documented according to the requirements of the Austrian Federal Monuments Office. This documentation is deposited in the CERN-based EU supported research data repository ZENODO. For each deposited file, metadata are created through the application of the conceptual metadata schema CIDOC CRM, an ISO standard for Cultural Heritage Information, which was adopted by ARIADNE, the European Union Research Infrastructure for archaeological resources. Concepts specific to mining archaeology research are organized with the DARIAH Back Bone Thesaurus, a model for sustainable interoperable thesauri maintenance, developed in the European Union Digital Research Infrastructure for the Arts and Humanities. Metadata are created through the extraction of information from the documentation and the transformation to a knowledge graph using semantic web standards. To facilitate usage, graph data are exported to hierarchical and tabular formats representing sites and objects with their geographic locations, temporal and typological assignments and links to the research activities and documents. Metadata are deposited together with the documentation into the repository. [ABSTRACT FROM AUTHOR]

The huge amount of data made available by the massive usage of social media has opened up the unprecedented possibility to carry out a data-driven study of political processes. While particular attention has been paid to phenomena like elite and mass polarization during online debates and echo-chambers formation, the interplay between online partisanship and framing practices, jointly sustaining adversarial dynamics, still remains overlooked. With the present paper, we carry out a socio-semantic analysis of the debate about migration policies observed on the Italian Twittersphere, across the period May-November 2019. As regards the social analysis, our methodology allows us to extract relevant information about the political orientation of the communities of users-hereby called partisan communities-without resorting upon any external information. Remarkably, our community detection technique is sensitive enough to clearly highlight the dynamics characterizing the relationship among different political forces. As regards the semantic analysis, our networks of hashtags display a mesoscale structure organized in a core-periphery fashion, across the entire observation period. Taken altogether, our results point at different, yet overlapping, trajectories of conflict played out using migration issues as a backdrop. A first line opposes communities discussing substantively of migration to communities approaching this issue just to fuel hostility against political opponents; within the second line, a mechanism of distancing between partisan communities reflects shifting political alliances within the governmental coalition. Ultimately, our results contribute to shed light on the complexity of the Italian political context characterized by multiple poles of partisan alignment., Competing Interests: The authors have declared that no competing interests exist.

Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data., (© The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data., Objective: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG., Methods: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models., Results: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains., Interpretation: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov ., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com)