Open Peer Review for a Transparent and Engaging Scientific Environment: Towards a FAIR Journal

Curating research data to ensure it is findable, accessible, interoperable and reusable (FAIR) is gaining increasing importance in engineering sciences. ing.grid – FAIR Data Management in Engineering Sciences is a Diamond Open Access journal that provides a publishing platform for this field. As this field is being established, scientific discussion is of vital importance. Today, peer review hides much of the scientific discussion from the community and the public. In contrast, ing.grid uses an open peer review process that shows reviews, author responses and editor comments alongside the submitted preprints. By bringing the peer review process into the open, ing.grid ensures transparency and accountability and engages the relevant community. The hybrid open peer review concept is put into practice using the Janeway preprint server and journal platforms: After submission via the preprint repository, reviewers, editors, authors and community members post comments. The review comments are specially flagged. Once manuscripts are accepted, they are sent to the journal through a custom-built interface and typesetting. Finally, they are published as peer reviewed articles. ing.grid relies on the support of the publishing services provided by the University and State Library at Technical University of Darmstadt (Germany). The library facilitates the TUjournals press site for scholar-led Diamond Open Access journals. This cooperation allows ing.grid to draw on the library’s cataloguing and long term archiving experience. Moreover, common effort is made to develop a knowledge graph representing the journal. This will lead to improved discovery of published research and the transparency of interconnections between different contributions

Project DEMETER: Concept Note for an Emerging Risks Knowledge Exchange Platform (ERKEP) Framework

Researchers, governments, agencies, food producers and the civil society are increasingly concerned about ‘emerging food risks’. It is recognised that the successful identification of emerging risks is at the heart of protecting public health and the environment, and that this requires worldwide cooperation between all parties involved in the food supply chain. The objectives and research proposed in the DEMETER project are designed to support current (and future) EFSA procedures for emerging issue and risks identification, providing a community resource that will allow EFSA and EU Member State authorities to share data, data mining knowledge and methods in a rapid and effective manner. A prototype technical Platform called the Emerging Risks Knowledge Exchange Platform (ERKEP) will be developed by DEMETER.The ‘Concept Note’ is a vison document on a framework of Emerging Risk Knowledge Exchange in which ERKEP is embedded in – but goes beyond the ERKE platform. Outlined in this document are: definitions of relevant concepts in the context of emerging issues and risks identification; who are the contributors, users and stakeholders of ERKEP; what are the identified end‐user needs, how could the ERKEP Framework contribute to meeting these needs; what types of knowledge, data, and methods do they share; and how can a technical solution be implemented to support these activities

Determination and Metrics for Emerging Risks Identification DEMETER: Final Report

Identification of emerging risks in the food chain is essential if EFSA is to anticipate future needs in risk assessment, in relation to both data and methodology. The objectives and research proposed in the DEMETER project were specifically designed to support current (and future) EFSA procedures for emerging issue and risks identification by providing community resourcesto allow EFSA and EU Member State authorities to share data, knowledge and methods on emerging risks identification in a rapid and effective manner through a digital platform. To this end, an “Emerging Risk Knowledge Exchange Platform (ERKEP)” was developed as a prototype technical solution. Its design is based on a consultation on end‐users needs and the analysis of existing knowledge sharing solutions. ERKEP consists of three main components: 1) A content management system (CMS) providing the end‐user's “entry point” and Graphical User Interface (GUI) to ERKEP; 2) A web‐based data analytics platform (DAP) for sharing and executing data analytics workflows (DAWs), based on the KNIME Server infrastructure; 3) External web‐based services hosted by 3rd party service providers. Different DAWs were developed and added to the platform, these are: 1) Emerging risk identification system for the milk supply chain based on automated data retrieval; 2) NewsRadar; 3)Trending topics in news based on text mining and network analysis, and;4) Patent network analysis. Methodologies were identified to integrate social science information and data, into the emerging risk identification framework. Systematic reviews of the literature wereconducted in the areas of expert elicitation, citizen science, and behavioural science and a framework to incorporate data from Citizen Science into the EKREP platform was proposed. Finally, sustainability and maintenance of the project's outputs were conceptualized to enable use thereof beyond project DEMETER.<br/

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios