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Dataset results
123 results for “Research Infrastructure”
Supplementary material 12 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 12
Supplementary material 8 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 8
Supplementary material 2 from: Lymer G, Leliaert F, Mergen P, Pijls S (2023) Pre-Commercial Procurement framework and European funding sources for European Research Infrastructure Consortiums: Insights from the DiSSCo ERIC development. Research Ideas and Outcomes 9: e113294. https://doi.org/10.3897/rio.9.e113294
ANNEXE 1: Additional and non-EU funding sources
Supplementary material 14 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Online participants of the BiCIKL hackathon at Meise Botanic Garden
Supplementary material 2 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 2
Supplementary material 7 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 7
Supplementary material 10 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 10
Supplementary material 6 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 6
Supplementary material 5 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 5
Supplementary material 4 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 4
Supplementary material 9 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 9
Supplementary material 11 from: Meeus S, Addink W, Agosti D, Arvanitidis C, Balech B, Dillen M, Dimitrova M, González-Aranda JM, Holetschek J, Islam S, Jeppesen TS, Mietchen D, Nicolson N, Penev L, Robertson T, Ruch P, Trekels M, Groom Q (2022) Recommendations for interoperability among infrastructures. Research Ideas and Outcomes 8: e96180. https://doi.org/10.3897/rio.8.e96180
Hackathon Topic 11
Figure 1 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 1 Conceptual model of a Knowledge Object (KO) containing a payload, machine-actionable service and deployment specifications, metadata and a unique persistent identifier. We are exploring aligning our conceptual model with emerging best practices for FAIR Digital Objects. Derived from Wittenburg et al's Digital Objects as Drivers towards Convergence in Data Infrastructures (Wittenburg et al. 2019).
Figure 3 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 3 This figure illustrates the dual nature of Knowledge Objects: knowledge-as-resource and knowledge-as-service. A KO can be curated and maintained in a repository, pass metadata to a knowledge graph or deployed into applications. Different to other digital objects, the methods to deploy the KO to applications via custom or generic runtimes called by microservices are built into the KO.
Figure 2 from: Conte M, Flynn AJ, Barrison P, Boisvert P, Landis-Lewis Z, Friedman C (2023) Digital objects to make computable biomedical knowledge FAIR: an infrastructural approach to knowledge representation, dissemination and implementation. Research Ideas and Outcomes 9: e109307. https://doi.org/10.3897/rio.9.e109307
Figure 2 (L) Sample KO as viewed from the KGrid Library, from which the KO can be implemented in a hosted runtime environment or downloaded. (R) Sample output results from deploying the KO.
Figure 5 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 5 Option C: GDP/cap and GERD. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 3 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 3 Option A: GDP and GERD testing. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to table (left).
Figure 4 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 4 Option B with GDP and GERD/cap. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 10 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 10 Visualisation of annual membership fees distribution according to the two proposals selected.
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.