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123 results for “Research Infrastructure”

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zenodo28/100

Figure 4 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 4 - Time table for the eEcoLiDAR project (assuming a start in March 2017). The work plan covers tasks for the NLeSC engineers, the proposed PhD student, and two associated Postdoc projects.

opencc-by-4.0Jul 2017View details →
zenodo28/100

Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.

opencc-by-4.0Jul 2017View details →
zenodo28/100

Figure 2 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 2 - Generic workflow for object-based image analysis (OBIA) of LiDAR point clouds and proposed ecological applications. A workbench (blue) will be developed to handle the data storage, data exploration, and interactive OBIA of the massive LiDAR point clouds. Combined with datasets of bird distributions, climate, and other remote sensing layers (orange), the LiDAR data will be applied to several ecological case studies, e.g. by using species distribution modelling of birds and insect pollinators (green).

opencc-by-4.0Jul 2017View details →
zenodo28/100

Figure 1 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939

Figure 1 - The vertical and horizontal distribution of plants influences habitat structure and 3D characteristics of vegetation for animals. Illustrated are examples for (a) forests, (b) agricultural and open landscapes, and (c) reedbeds and marshlands. The height, openness and density of vegetation as well as specific habitat features (e.g. tree species, hedges etc.) are key aspects of animal habitat and space use.

opencc-by-4.0Jul 2017View details →
zenodo28/100

Figure 5 from: Wetzel F, Despot Belmonte K, Bingham H, Underwood E, Hoffmann A, Häuser C, Mikolajczyk P, Vohland K (2017) 4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure. Research Ideas and Outcomes 3: e11875. https://doi.org/10.3897/rio.3.e11875

Figure 5 - Group 2 discussion: Strategies, Business Plan and EU BON sustainability (Credits: F. Wetzel)

opencc-by-4.0Jan 2017View details →
zenodo28/100

Figure 4 from: Wetzel F, Despot Belmonte K, Bingham H, Underwood E, Hoffmann A, Häuser C, Mikolajczyk P, Vohland K (2017) 4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure. Research Ideas and Outcomes 3: e11875. https://doi.org/10.3897/rio.3.e11875

Figure 4 - Visualisation of Lepidoptera density occurences: World Map (darker colors indicate a higher number of occurrences) and Lepidoptera occurences chart bar per year, exemplified with the country data of Germany (EU BON European Biodiversity Portal, beta version 11/2016).

opencc-by-4.0Jan 2017View details →
zenodo28/100

Figure 2 from: Wetzel F, Despot Belmonte K, Bingham H, Underwood E, Hoffmann A, Häuser C, Mikolajczyk P, Vohland K (2017) 4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure. Research Ideas and Outcomes 3: e11875. https://doi.org/10.3897/rio.3.e11875

Figure 2 - Potential organizational structure of a future EU BON, e.g. with a 'core EU BON' (blue rectangle). Presentation Dirk Schmeller (UFZ) in collaboration with Katherine Despot-Belmonte (UNEP-WCMC). (Credits: Pan X., 2015, CC BY 4.0)

opencc-by-4.0Jan 2017View details →
zenodo28/100

Figure 1 from: Wetzel F, Despot Belmonte K, Bingham H, Underwood E, Hoffmann A, Häuser C, Mikolajczyk P, Vohland K (2017) 4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure. Research Ideas and Outcomes 3: e11875. https://doi.org/10.3897/rio.3.e11875

Figure 1 - How do we get from data to decisions? Extract from a presentation by Lauren Weatherdon (UNEP-WCMC). Credit: Scriberia, CC BY 4.0

opencc-by-4.0Jan 2017View details →
zenodo28/100

Figure 3 from: Wetzel F, Despot Belmonte K, Bingham H, Underwood E, Hoffmann A, Häuser C, Mikolajczyk P, Vohland K (2017) 4th European Biodiversity Observation Network (EU BON) Stakeholder Roundtable: Pathways to sustainability for EU BONs network of collaborators and technical infrastructure. Research Ideas and Outcomes 3: e11875. https://doi.org/10.3897/rio.3.e11875

Figure 3 - Screenshot of the beta version of the EU BON European Biodiversity Portal (biodiversity.eubon.eu).

opencc-by-4.0Jan 2017View details →
zenodo28/100

Supplementary material 1 from: Penev L, Groom Q, Casino A, Barov B (2024) Uniting FAIR data through interlinked, machine-actionable infrastructures. Research Ideas and Outcomes 10: e126588. https://doi.org/10.3897/rio.10.e126588

Uniting FAIR data through interlinked, machine-actionable infrastructures

opencc-zeroApr 2024View details →
zenodo28/100

Figure 1 from: Ćwiek-Kupczyńska H, Krajewski P (2021) Polish network of research infrastructure for plant phenotyping. Research Ideas and Outcomes 7: e73858. https://doi.org/10.3897/rio.7.e73858

Figure 1 The results of a survey conducted among Polish units involved in plant phenotyping in the part concerning national and international cooperation (n = 32).

opencc-by-4.0Sep 2021View details →
zenodo28/100

Figure 5 from: Duin D, van den Besselaar P (2011) Studying the effects of virtual biodiversity research infrastructures. ZooKeys 150: 193-210. https://doi.org/10.3897/zookeys.150.2164

Figure 5 - Graph of co-author ties† between the members of the Scratchpadlivingcreatures.info‡. (2001-2010). † Data sources: Web of Science and Publish or Perish. ‡ For privacy reasons we use a fictional name.

opencc-by-4.0Nov 2011View details →
zenodo28/100

Figure 4 from: Duin D, van den Besselaar P (2011) Studying the effects of virtual biodiversity research infrastructures. ZooKeys 150: 193-210. https://doi.org/10.3897/zookeys.150.2164

Figure 4 - Livingcreatures.info and ties with two other Scratchpads. This graph shows a fictional situation.

opencc-by-4.0Nov 2011View details →
zenodo28/100

Figure 2 from: Duin D, van den Besselaar P (2011) Studying the effects of virtual biodiversity research infrastructures. ZooKeys 150: 193-210. https://doi.org/10.3897/zookeys.150.2164

Figure 2 - Graph of co-author ties† between the members of the Scratchpad Livingcreatures.info‡. (2001-2010).† Data sources: Web of Science and Publish or Perish. ‡ For privacy reasons we use a fictional name.

opencc-by-4.0Nov 2011View details →
zenodo28/100

Supplementary material 1 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 1

opencc-zeroOct 2022View details →
ClinicalTrials.gov28/100

Development of a Research Infrastructure for Understanding and Addressing Multiple Myeloma Disparities

ClinicalTrials.gov study NCT04314752. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
zenodo24/100

Figure 9 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280

Figure 9 Funding sources as they correspond to the different development phases of the DiSSCo RI.

opencc-by-4.0May 2020View details →
zenodo24/100

Figure 8 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280

Figure 8 DiSSCo Programme of linked projects.

opencc-by-4.0May 2020View details →
zenodo24/100

Figure 6 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280

Figure 6 Some uses of natural science collections in formal and informal education.

opencc-by-4.0May 2020View details →
zenodo24/100

Figure 7 from: Hardisty A, Saarenmaa H, Casino A, Dillen M, Gödderz K, Groom Q, Hardy H, Koureas D, Nieva de la Hidalga A, Paul DL, Runnel V, Vermeersch X, van Walsum M, Willemse L (2020) Conceptual design blueprint for the DiSSCo digitization infrastructure - DELIVERABLE D8.1. Research Ideas and Outcomes 6: e54280. https://doi.org/10.3897/rio.6.e54280

Figure 7 Governance and management models during the different programme phases.

opencc-by-4.0May 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record