2022, Janev, Valentina, Vidal, Maria-Esther, Pujić, Dea, Popadić, Dušan, Iglesias, Enrique, Sakor, Ahmad, Čampa, Andrej

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces.

2021, Tzoraki, Ourania, Dimitrova, Svetlana, Barzakov, Marin, Yaseen, Saad, Gavalas, Vasilis, Harb, Hani, Haidari, Abas, Cahill, Brian P., Ćulibrk, Alexandra, Nikolarea, Ekaterini, Andrianopulu, Eleni, Trajanovic, Miroslav

The ongoing ‘refugee crisis’ of the past years has led to the migration of refugee researchers (RRs) to European countries. Due to the COVID-19 pandemic, RRs often had to work from home and/or to continue their social, cultural and economic integration process under new conditions. An online survey carried out to explore the impact of the pandemic on the refugee researchers showed that RRs found it difficult to adapt their everyday working life to the ‘home’ setting. The majority have had neither a suitable work environment at home nor the appropriate technology. Although they stated that they are rather pleased with the measures taken by the public authorities, they expressed concern about their vulnerability due to their precarious contracts and the bureaucratic asylum procedures, as the pandemic has had a negative impact on these major issues. The majority of RRs working in academia seem not to have been affected at all as far as their income is concerned, while the majority of those employed in other sectors became unemployed during the pandemic (58%). Recommendations are provided to the public authorities and policy makers to assist RRs to mitigate the consequences of the pandemic on their life.

2016, Kraft, Angelina, Razum, Matthias, Potthoff, Jan, Porzel, Andrea, Engel, Thomas, Lange, Frank, van den Broek, Karina, Furtado, Filipe

The aim of the RADAR (Research Data Repository) project is to set up and establish an infrastructure that facilitates research data management: the infrastructure will allow researchers to store, manage, annotate, cite, curate, search and find scientific data in a digital platform available at any time that can be used by multiple (specialized) disciplines. While appropriate and innovative preservation strategies and systems are in place for the big data communities (e.g., environmental sciences, space, and climate), the stewardship for many other disciplines, often called the “long tail research domains”, is uncertain. Funded by the German Research Foundation (DFG), the RADAR collaboration project develops a service oriented infrastructure for the preservation, publication and traceability of (independent) research data. The key aspect of RADAR is the implementation of a two-stage business model for data preservation and publication: clients may preserve research results for up to 15 years and assign well-graded access rights, or to publish data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms that desire an adaptable digital infrastructure to archive and publish data according to their institutional requirements and workflows.

2021, Vyas, Akhilesh, Aisopos, Fotis, Vidal, Maria-Esther, Garrard, Peter, Paliouras, George

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.