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556 results for “infrastructure”
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project.</p>
Dataset with the results of the e-infrastructures Austria National Survey about Research Data
<p>This is the dataset accompanying the report with the results of our national survey regarding the management of research data</p>
Example Instantiations of the e-Infrastructure Catalogue of Services
<p>This document contains example instantiations of the e-Infrastructure Catalogue of Services. This supports a published framework for creating a Catalogue of Services (CoS), primarily intended for e-Infrastructure services, which describes services at a high level and makes them discoverable.</p>
Datasets on global patterns of settlements and infrastructures
<p>Supplementary datasets used for calculating spatial pattern indicators as presented in research discussed in a paper provisionally entitled “Settlement and infrastructure patterns influence energy use and CO2 emissions almost as much as economic activity”. This repository contains spatially explicit data on (1) built-up patches and urban agglomerations (BL), (2) main infrastructure features (road and railway, R and RW) and (3) a reference inhabited land area (IH).</p>
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Research Infrastructure Contact Zones
<p>The landscape of biodiversity data infrastructures and organisations is complex and fragmented. Many occupy specialised niches representing narrow segments of the multidimensional biodiversity informatics space, while others operate across a broad front but differ from others by data type(s) handled, their geographic scope and the life cycle phase(s) of the data they support. To characterise the various dimensions of the biodiversity informatics landscape, we developed a framework to survey these dimensions for ten organisations (<a href="https://www.dissco.eu/">DiSSCo</a>, <a href="https://www.gbif.org/">GBIF</a>, <a href="https://ibol.org/">iBOL</a>, <a href="https://www.catalogueoflife.org/">Catalogue of Life</a>, <a href="https://www.inaturalist.org/">iNaturalist</a>, <a href="https://www.biodiversitylibrary.org/">Biodiversity Heritage Library</a>, <a href="https://geocase.eu/">GeoCASe</a>, <a href="https://www.lifewatch.eu/">LifeWatch</a>, <a href="https://www.lter-europe.net/elter-esfri">eLTER</a>, <a href="https://elixir-europe.org/">ELIXIR</a>), relative to both their current activities and long-term strategic ambitions.</p> <p>The results of the survey are presented in this dataset. Details of the assessment methodology, data model, scope and high-level results are described in an accompanying paper, which is currently under review and will be linked to this dataset on publication.</p>
Data and ancillary data for publication: Natural infrastructure and water erosion mitigation in the Andes
<p>The data contain information on the effectiveness of natural infrastructure to mitigate soil erosion. Data were compiled from 118 case studies from the Andean region, whereby information on natural infrastructure interventions, soil erosion and soil quality were tabulated and analysed.</p> <p>The data contains the following documents:<br> -Database with data on soil erosion, soil quality for different types of natural infrastructure (118 case studies)<br> -Metadata<br> -Summary of terms used in the systematic review of the literature (in Spanish and English)<br> -List of bibliographic data sources that were searched with the search terms<br> -Full bibliographic references of all 118 case studies</p> <p><strong>Full reference </strong></p> <p><em>Vanacker V, Molina A, Rosas-Barturen M, Bonnesoeur V, Román-Dañobeytia F, Ochoa-Tocachi B, Buytaert W (2022). The effect of natural infrastructure on water erosion mitigation in the Andes. </em></p> <p> </p> <p> </p>
D1.2 Requirements and needs of scientific communities from ICT-based Research Infrastructures (Dataset)
<p>A user survey was conducted between December 2020 and January 2021 gathering inputs from potential SLICES users from the research community. The survey was distributed among the research community to identify the technological domains, the use cases, the requirements and other expectations from the future users of the SLICES research infrastructure. This dataset contains the results of the survey; 226 people participated.</p>
Three-dimensional building and mobility infrastructure of the CONUS
<p>Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.</p><p>This dataset features intermediate mapping results for estimating material stocks in the CONUS (see related identifiers) on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), Microsoft building footprints, NLCD Impervious data, and crowd-sourced geodata (OSM). These data may also be useful on their own.</p><p><strong>Provided layers @10m resolution</strong><br>- Building height<br>- Building type<br>- Building area<br>- Impervious fraction<br>- street, and rail area<br>- Building and street climate zones<br>- County zones<br>- State masks<br>- EQUI7 correction factors</p><p><strong>Spatial extent</strong><br>This dataset covers the whole CONUS. </p><p><strong>Temporal extent</strong><br>The maps are representative for ca. 2018.</p><p><strong>Data format</strong><br>The data are organized in 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p><p><strong>Further information</strong><br>For further information, please see the main publication.<br>A web-visualization of the resulting dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Publication</strong><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nature Communications</i> <strong>14</strong>, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a></p><p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p><p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
Data for: The State of Open Infrastructure Grant Funding, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to better understand the amount, distribution, impact, and limitations of grant funding to open infrastructures that support research and scholarship.</p> <p>The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “The state of open infrastructure grant funding.” The full report is available at https://doi.org/10.5281/zenodo.10934089.</p> <p>A readme, data dictionary, and additional metadata definition file are provided with the dataset with additional detail.</p>
Data for: Open Infrastructure Governance: Current structures, nomenclature, composition, and service trends, 2024 State of Open Infrastructure Report
<p>The purpose of the analysis based on these data was to<span> record information about community governance groups for open infrastructures, focused primarily on the individuals and institutions that serve in these groups. The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Open infrastructure governance: Current structures, nomenclature, composition, and trends.” The full report is available at <a href="The%20data%20were%20summarized%20and%20reported%20in%20the%20&ldquo;2024%20State%20of%20Open%20Infrastructure%20Report&rdquo;%20section%20&ldquo;Open%20infrastructure%20governance:%20Current%20structures,%20nomenclature,%20composition,%20and%20trends,&rdquo;%20available%20at%20https:/doi.org/10.5281/zenodo.10934089.">https://doi.org/10.5281/zenodo.10934089</a>.</span></p> <p><span>A readme is provided with the dataset with additional detail.</span></p>
Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure
<p>This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom.</p> <p>This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies.</p> <p>For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.</p>
Recordings Q&A and matchmaking sessions for call for proposals 'Open Science Infrastructure'
<p>On Thursday, July 11, and Tuesday, July 16, 2024, Open Science NL organised two online Q&A sessions combined with a matchmaking opportunity for the Open Science NL call 'Open Science Infrastructure'.</p> <p>These are the two recordings of the two Q&A sessions. The Open Science NL team has drafted a Frequently Asked Questions document addressing all the questions that came up during the meetings. This is added as a seperate text-file (PDF). The slides presented during both meetings are shared as well as a PDF.</p> <p>For more information about the call and how to apply, please go to: <a href="https://www.openscience.nl/en/calls/open-science-infrastructure" target="_blank" rel="noopener">https://www.openscience.nl/en/calls/open-science-infrastructure</a></p>
An Automatic Neuroimaging Infrastructure For Synthesis and Analysis of Structural MRI Data
<p>We have designed, implemented and distributed a fully automatic neuroimaging infrastructure for the synthesis and analysis of structural magnetic resonance imaging (MRI) data. The framework provides a concrete environment for the quantitative validation of various methods for the analysis of brain asymmetries, for comparisons of methods and measures of brain shape asymmetry, and possibly for clarifying contradicting neuroimaging findings of brain lateralizations.</p> <p>See <a href="https://sites.google.com/site/brainmorphorg/home">https://sites.google.com/site/brainmorphorg/home </a></p> <p>and </p> <p>A. Pepe, I. Dinov, and J. Tohka . An Automatic Framework for Quantitative Validation of Voxel Based Morphometry Measures of Anatomical Brain Asymmetry. <a href="http://dx.doi.org/10.1016/j.neuroimage.2014.06.029">NeuroImage , 100: 444 - 459, 2014</a><a href="https://doi.org/10.1016/j.neuroimage.2014.06.029"> </a></p> <p>for more information. </p>
Topic Map for KPI analysis of e-Infrastructure projects
<p>This is a Topic Map from the e-IRG Knowledge Base used by the e-IRGSP5 project to analyse Key Performance Indicators (KPIs) for e-Infrastructure projects funded by Horizon 2020.</p>
Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"
<p>This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>"</p> <p>The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>.</p> <p>The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.</p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
Dataset: Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments
<p>The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:</p> <ul> <li><em>Table_D1_Summary_CI_Vulnerability_Data:</em> summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.</li> <li><em>Table_D2_Hazard_Fragility_and_Vulnerability Curves:</em> collection of fragility and vulnerability curves</li> <li><em>Table_D3_Costs:</em> cost values that can be used in combination with the curves for the estimation of asset damages</li> </ul> <p>Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.</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)
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.
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.