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The main objective of this deliverable is to provide resources that can assist life sciences researchers and data stewards in creating reference Data Management Plans (DMPs) for their research projects. The resources provided in this deliverable are intended to promote good research data management practices across the EU research landscape. To achieve this objective, a range of activities were undertaken, with a particular focus on the needs of the different domains covered by the demonstrator use-cases. In order to create these resources, the RDMkit pages were extended to include domain-specific information, which can be used as a reference when developing DMPs for different research projects. These pages fall under the "Your Domain" category and provide specific information on the data management needs and considerations for each domain. They also highlight challenges that are specific to each domain, such as data types, species, or areas, and offer solutions and considerations to overcome these challenges. In this deliverable, the RDMkit page for the Toxicology data demonstrator use-case was completed and added to the existing RDMkit pages for the other demonstrator use-cases. Additionally, a new Tool Assembly was added to the RDMkit corresponding to the Plant Sciences demonstrator use-case, covering the entire life cycle of experimental plant phenotyping data. In addition, the general Knowledge Models (KMs) of the Data Stewardship Wizard (DSW) were adapted to address the specific DMP questions needed for each demonstrator use-case. The DSW is a collaborative tool that enables data stewards and researchers to efficiently create DMPs for their research projects and it is designed with a hierarchical KM that guides users through the creation of DMPs. Since the relevant information for DMPs can vary across different domains, these KMs can be modified to contain the information relevant for each demonstrator use-case. For this deliverable, special focus was put on two of the demonstrator use-cases, namely Toxicology and Epitranscriptomics data. Additionally, related to the Human Data use-case, separate efforts are underway to enhance the sensitive data section of the KM system to ensure the proper management of such data. The improvements and new question suggestions that were found during these sessions were incorporated into the DSW KM by the DSW team. Furthermore, DMP templates were created in DSW for the demonstrator use-case using two standard approaches: creating a KM or a project template (PT). When creating a PT, a set of answers is saved and can be used to generate a partially pre-filled questionnaire for a new project. In the ideal case scenario, the two methods can be used together to provide domain-specific recommendations by answering questions that better reflect a scientific domain, such as metadata standards. In conclusion, this deliverable provides several valuable resources for life sciences researchers and data stewards, including extended RDMkit pages, customised DSW KMs, domain-specific DMP templates, and a new KM for creating DPIAs. These resources are designed to encourage good research data management practices across the EU research landscape, ensuring that valuable research data is effectively managed before, during, and after a project.

The overall goal of ELIXIR-CONVERGE’s WP5 was, based on the analysis of a set of very diverse use cases, to contribute to the development of a set of resources supporting the development and implementation of Data Management Plans (DMPs) in national and transnational projects. Six use cases were considered to co-develop and test a method to address domain specific data management planning associated to a set of resources in collaboration with WP1, WP2 and WP3. In this context, another objective of WP5 was to develop, implement and refine key performance indicators (KPIs) in order to monitor the demonstrator projects’ implementation of data management plans and possibly assess their adoption by the relevant community. WP5 developed two sets KPIs in collaboration with WP4, in charge of developing KPIs across the entire ELIXIR-CONVERGE project and for assessing the impact of the project : a first set aiming at monitoring the development of guidance, resources for the development of DMP and for training in the context of the use cases a second set aiming at addressing the adoption of these resources by relevant communities of users as a way to assess the impact of the work achieved In parallel, in order to ensure long term maintenance/update of the developed resources and to increase their impact, WP5 started to engage with ELIXIR communities that could be natural owners of these resources. The KPI developed and collected by WP5 during the ELIXIR-CONVERGE project were useful to follow the partner’s progress in the development and test of a sort of starter kit for domain specific data management support. Success stories could be collected showing adoption by communities and new projects and were mapped on ELIXIR’s categories of impacts showing already “hits” on several of these.

Monitoring and controlling the power grid is extremely important to prevent power outages and blackouts. Traditionally, the most common methods for inspecting high-voltage towers and power lines include visual inspections by qualified personnel, inspections by helicopters, and, in some cases, the use of specialized robots, among others. These inspections aim to detect anomalies near the infrastructure, anomalies due to hotspots in the insulation or visual defects in the various structural elements. One of the main problems of these techniques is the high economic cost and the lack of accuracy. As an alternative, unmanned aerial vehicles (UAVs) are becoming more popular in the market, and the trend is gradually moving towards this technology, as it offers significant cost reduction, better mobility and flexibility, and great potential for obtaining high-quality images. This thesis (TFG) studies the feasibility of developing a system capable of autonomously detecting high-voltage towers using an unmanned aerial vehicle and conducting power line inspections. This system consists of a desktop application that allows the user to program legs of a flight plan, and a drone that executes them and detects the high-voltage towers using an artificial intelligence model. The system developed in this study is part of a contribution to the Drone Engineering Ecosystem (DEE), a platform for controlling and monitoring unmanned aerial vehicles using desktop and/or web applications. The main goal of this platform is the improvement and continuity of its development by future students. Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura

National audience; Virtual reality (VR) relies heavily on the sense of presence - the feeling of being physically present in a virtual environment (VE). Currently, presence is mainly assessed through subjective questionnaires, which cannot be completed while users experience VR as it would disrupt their immersion. To address this limitation, we propose to explore the feasibility of using electroencephalography (EEG) to monitor users' level of presence and classify it based on the reaction to oddball stimuli.

En aquest treball, s'estudiarà el comportament de dispositius piezoelèctrics davant esdeveniments d'impacte, amb la finalitat d'utilitzar-los com a sensors per preveure el dany ocasionat en un substrat determinat per única causa de l'impacte. Per fer-ho, es dissenyarà el pertinent circuit elèctric capaç de generar un senyal a partir de la resposta piezoelèctrica, i s'establirà un procediment de lectura d'aquest mitjançant un dispositiu, en aquest cas, un microcontrolador. A més es dissenyarà una configuració d'assaig i una matriu d'assajos, on sotmetent els sensors a impactes en un substrat determinat, es buscarà esclarir el comportament dels sensors envers diverses condicions d'impacte tenint en compte diferents variables com l'energia d'impacte o la velocitat d'impacte entre d'altres. Amb la informació provinent dels resultats dels assajos, i mitjançant raonaments científics es faran afirmacions i hipòtesis sobre el comportament dels sensors i es raonarà la viabilitat d'aplicar-los com a sensors per a la previsió del dany en impacte. Finalment i si escau, es proposaran noves vies per continuar la investigació amb la finalitat de confirmar o desmentir les afirmacions i hipòtesis mencionades en aquest treball. This project will study the behavior of piezoelectric devices against impact events, with the purpose of using them as sensors to foresee the damage caused in a certain substrate by only cause of the impact on itself. To do that, the corresponding electric circuit will be designed to generate an electric signal based on the piezoelectric response. After that, a procedure for reading this measurement through an external device will be established, concretely for the case of this project we will use a microcontroller. Besides that, the corresponding test method and test matrix will be designed, in which the sensors will be submitted to impact events placed in a specific substrate. We will try to clarify the behavior of piezoelectric sensors by exposing the substrate to different impact conditions, using diverse variables such as impact energy or impact speed among others. Using the information provided by the testing results, and through scientific reasoning, statements and hypothesis will be made related to sensor's behavior, and we will discuss the viability of applying them as impact damage sensors. Finally, and if applicable, new investigation lines will be purposed to continue the investigation with the intention of confirming or denying the already mentioned statements and hypothesis.

Aims Longitudinal data on the mental health impact of the coronavirus disease 2019 (Covid-19) pandemic in healthcare workers is limited. We estimated prevalence, incidence and persistence of probable mental disorders in a cohort of Spanish healthcare workers (Covid-19 waves 1 and 2) -and identified associated risk factors. Methods 8996 healthcare workers evaluated on 5 May–7 September 2020 (baseline) were invited to a second web-based survey (October–December 2020). Major depressive disorder (PHQ-8 ≥ 10), generalised anxiety disorder (GAD-7 ≥ 10), panic attacks, post-traumatic stress disorder (PCL-5 ≥ 7), and alcohol use disorder (CAGE-AID ≥ 2) were assessed. Distal (pre-pandemic) and proximal (pandemic) risk factors were included. We estimated the incidence of probable mental disorders (among those without disorders at baseline) and persistence (among those with disorders at baseline). Logistic regression of individual-level [odds ratios (OR)] and population-level (population attributable risk proportions) associations were estimated, adjusting by all distal risk factors, health care centre and time of baseline interview. Results 4809 healthcare workers participated at four months follow-up (cooperation rate = 65.7%; mean = 120 days s.d. = 22 days from baseline assessment). Follow-up prevalence of any disorder was 41.5%, (v. 45.4% at baseline, p < 0.001); incidence, 19.7% (s.e. = 1.6) and persistence, 67.7% (s.e. = 2.3). Proximal factors showing significant bivariate-adjusted associations with incidence included: work-related factors [prioritising Covid-19 patients (OR = 1.62)], stress factors [personal health-related stress (OR = 1.61)], interpersonal stress (OR = 1.53) and financial factors [significant income loss (OR = 1.37)]. Risk factors associated with persistence were largely similar. Conclusions Our study indicates that the prevalence of probable mental disorders among Spanish healthcare workers during the second wave of the Covid-19 pandemic was similarly high to that after the first wave. This was in good part due to the persistence of mental disorders detected at the baseline, but with a relevant incidence of about 1 in 5 of HCWs without mental disorders during the first wave of the Covid-19 pandemic. Health-related factors, work-related factors and interpersonal stress are important risks of persistence of mental disorders and of incidence of mental disorders. Adequately addressing these factors might have prevented a considerable amount of mental health impact of the pandemic among this vulnerable population. Addressing health-related stress, work-related factors and interpersonal stress might reduce the prevalence of these disorders substantially. Study registration number: NCT04556565