How can energy data spaces decrease privacy risks in respect to smart energy services?5 answersEnergy data spaces can decrease privacy risks in smart energy services by implementing privacy-preserving technologies and fostering collaboration among stakeholders. Solutions like blockchain-based authentication schemes with zero-knowledge protocols and federated learning with differential privacy can mitigate privacy concerns related to sharing sensitive energy data. By utilizing lightweight encryption, blockchain, and smart contracts, along with off-chain storage techniques, the scalability and security of energy data sharing can be enhanced. Additionally, trusted data spaces, such as those within the GAIA-X initiative, facilitate collaboration among energy companies and partners to accelerate the deployment of low carbon solutions, enhance energy efficiency, and support sector digitalization while ensuring data privacy and security. These collaborative efforts and technological advancements contribute to reducing privacy risks associated with smart energy services.
What are the common challenges faced during the implementation of research repository systems?5 answersThe common challenges faced during the implementation of research repository systems include:
1. Modelling and implementing processes for preparing, depositing, sharing, maintaining, and curating resources, especially multimedia content.
2. Overcoming fears and misconceptions regarding open access (OA) and institutional repositories (IR), and difficulties in convincing university managers about OA and promoting IR development.
3. Ensuring the construction and operation of repositories for spent nuclear fuel with long-term safety assurance, requiring structured research and development, data qualification, version control, and peer review.
4. Building institutional repositories that address the needs of modern research data management, including storing all research assets, consistent data models, and efficient workflows.
5. Improving the usability of repository web interfaces to enhance user interactions, involving usability assessment techniques such as user studies, paper prototypes, competitive analysis, journey mapping, and heuristic evaluation.
What are the key challenges in research data management?5 answersResearch data management (RDM) faces several key challenges. These challenges include the alignment of research management and data management, resourcing, researcher openness, and research data governance. Another challenge is the integration, comparison, and visualization of large, multi-dimensional datasets from different sources and scientific approaches. Many organizations lack proper information systems for managing data, resulting in disparate and fragmented data that make it difficult to uphold the FAIR principles of data management. Additionally, there is a need for detailed plans and roadmaps for managing and sharing research data, especially in large and complex projects. Inadequate policies, knowledge and skills, technological infrastructure, and collaborative partnerships also pose challenges to RDM. These challenges highlight the need for further investigation, practical tools, decision aids, and training to support researchers in implementing good RDM practices.
What are the consequences of research waste?3 answersResearch waste has several consequences. It reduces the usefulness of research results and hinders the ability of practitioners to benefit from them. Poor quality research, especially from medical students, is a common outcome of research waste. Inadequate reporting of research leads to a waste of time and resources invested in the research process. This problem is prevalent in various types of studies, including animal studies, diagnostic studies, and clinical prediction research. Research waste also affects the production and reporting of health and medical research, leading to the misuse of resources and inefficiency. The deluge of poor quality research resulting from research waste undermines the effectiveness of evidence-based responses. Overall, research waste has significant implications for the quality and impact of research in various fields.
What are the potential barriers to data sharing exoerienced by researchers?2 answersThe potential barriers to data sharing experienced by researchers include challenges of ethical, cultural, legal, financial, and technical nature. Organizing the data in a useful way is a major issue in data sharing. In the field of cluster-randomized trials, sharing data can be difficult due to the involvement of a large number of patients and the need to protect the privacy of health systems and providers. Some clinical investigators oppose the proposal of making clinical trial data freely available to other researchers. Senior researchers often perceive little benefit from public data archiving and may be less willing to share their data compared to early career researchers.
What is the carbon footprint of data transfer devices?4 answersData transfer devices have a carbon footprint that contributes to greenhouse gas emissions. The electronic sector, which includes data transfer devices, is responsible for a significant amount of global gas emissions. In particular, the information and communication technology sector and the media sector contribute to these emissions. The emissions from the operation of these sectors, including the use of electricity, are higher than the emissions from their manufacture. Media electronics, such as data transfer devices, have been found to have a greater impact on emissions compared to other electronic products like personal computers. Understanding the role of electronics in climate change, including the disposal of electronic waste, is important for the industry to take actions to reduce its impact on the climate.