103 research outputs found
Editorial for the special issue on carbon based electronic devices
For more than 50 years, silicon has dominated the electronics industry [...]
Fostering research and innovation in materials manufacturing for Industry 5.0: The key role of domain intertwining between materials characterization, modelling and data science
Recent advances in materials modelling, characterization and materials informatics suggest that deep integration of such methods can be a crucial aspect of the Industry 5.0 revolution, where the fourth industrial revolution paradigms are combined with the concepts of transition to a sustainable, human-centric and resilient industry. We pose a specific deep integration challenge beyond the ordinary multi-disciplinary modelling/characterization research approach in this short communication with research and innovation as drivers for scientific excellence. Full integration can be achieved by developing com-mon ontologies across different domains, enabling meaningful computational and experimental data integration and interoperability. On this basis, fine-tuning of adaptive materials modelling/characteriza-tion protocols can be achieved and facilitate computational and experimental efforts. Such interoperable and meaningful data combined with advanced data science tools (including machine learning and artifi-cial intelligence) become a powerful asset for materials scientists to extract complex information from the large amount of data generated by last generation characterization techniques. To achieve this ambi-tious goal, significant collaborative actions are needed to develop common, usable, and sharable digital tools that allow for effective and efficient twinning of data and workflows across the different materials modelling and characterization domains.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)
Metadata stewardship in nanosafety research: learning from the past, preparing for an "on-the-fly" FAIR future
Introduction: Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this "silo" approach is hampering their general adoption by the broader research community and individual labs.Methods: State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results: The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable "on-the-fly" (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion: The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability
Metadata stewardship in nanosafety research: learning from the past, preparing for an "on-the-fly" FAIR future
Introduction: Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this βsiloβ approach is hampering their general adoption by the broader research community and individual labs.
Methods: State-of-the-art data/knowledge collection, curation management FAIRification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.
Results: The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable βon-the-flyβ (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, non-standard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.
Discussion: The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.
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WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393
Education and training policies for research integrity: insights from a focus group study
Education is important for fostering research integrity (RI). Although RI training is increasingly provided, there is little knowledge on how research stakeholders view institutional RI education and training policies. Following a constructivist approach, we present insights about research stakeholdersβ views and experiences regarding how research institutions can develop and implement RI education and training policies. We conducted thirty focus groups, engaging 147 participants in eight European countries. Using a mixed deductive-inductive thematic analysis, we identified five themes: (1) RI education should be available to all; (2) education and training approaches and goals should be tailored; (3) motivating trainees is essential; (4) both formal and informal educational formats are necessary; and (5) institutions should take into account various individual, institutional, and system-of-science factors when implementing RI education. Our findings suggest that institutions should make RI education attractive for all and tailor training to disciplinary-specific contexts.Horizon 2020(H2020)824481Merit, Expertise and Measuremen
Metadata stewardship in nanosafety research: learning from the past, preparing for an βon-the-flyβ FAIR future
Introduction: Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this βsiloβ approach is hampering their general adoption by the broader research community and individual labs.Methods: State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results: The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable βon-the-flyβ (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion: The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability
Comparison of the nanomechanical and nanoscratch performance of antiscratch layers on organic lenses
Nanomaterials in Joining
Joining techniques in engineering are of major importance. Innovations in the field of composites now allows design of nanomaterials with tailored properties. This book adresses techniques for similar and dissimilar joining, characterization of joint structures and damage prediction by simulation. A special focus is laid on welding of lightweight structures, which are of special economic interest for aeronautical and automotive applications.The book presents state-of-the-art discussion on materials joining: from simulation and damage prediction to synthetic processes and application.ΠΠ΅ΡΠΎΠ΄Ρ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ Π² ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΠ½ΠΎΠΌ Π΄Π΅Π»Π΅ ΠΈΠΌΠ΅ΡΡ Π±ΠΎΠ»ΡΡΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅. ΠΠ½Π½ΠΎΠ²Π°ΡΠΈΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΠ² ΡΠ΅ΠΏΠ΅ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ Π½Π°Π½ΠΎΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ Ρ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ²ΠΎΠΉΡΡΠ²Π°ΠΌΠΈ. Π ΡΡΠΎΠΉ ΠΊΠ½ΠΈΠ³Π΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈ Π½Π΅ΠΏΠΎΡ
ΠΎΠΆΠ΅Π³ΠΎ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° ΡΡΡΡΠΊΡΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΉ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»ΡΠ΅ΡΡΡ ΡΠ²Π°ΡΠΊΠ΅ Π»Π΅Π³ΠΊΠΈΡ
ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡ ΠΎΡΠΎΠ±ΡΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ Π΄Π»Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² Π°Π²ΠΈΠ°ΡΠΈΠΈ ΠΈ Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»Π΅ΡΡΡΠΎΠ΅Π½ΠΈΠΈ. Π ΠΊΠ½ΠΈΠ³Π΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅ Π²ΠΎΠΏΡΠΎΡΠΎΠ² ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ²: ΠΎΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ²ΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΠΉ Π΄ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΡΠΈΠ½ΡΠ΅Π·Π° ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ.ΠΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Adobe Acroba
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