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184 results for “Deliverable”
Deliverable 2.2: Biodiversity indicator database for focus regions
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km<sup>2 </sup>resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism<sup> </sup>index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a valuable first step in describing human-induced changes in vegetation and biodiversity patterns across both South America and Africa.</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Local Earthquake Tomography of the Alpine Region from 24 Years of Data - DELIVERABLES
<h1><strong>Local Earthquake Tomography of the Alpine Region from 24 Years of Data</strong></h1> <p>M. Bagagli(1), I. Molinari(2), T. Diehl(3), E. Kissling(4)</p> <p><em>(1) Dipartimento Scienze della Terra, Università di Pisa, 56126 Pisa, Italy</em><br><em>(2) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, 40127 Bologna, Italy</em><br><em>(3) Swiss Seismological Service, ETH Zurich, 8006 Zürich, Switzerland</em><br><em>(4) Institute of Geophysics, Department of Earth Sciences, ETH Zürich, 8006 Zürich, Switzerland</em></p> <p>mail-to: matteo.bagagli@dst.unipi.it<br>date: 08.11.2024<br>version: 1.0</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>This repository contains the all the deliverables of the aforementioned manuscript.<br>The folder is organized into subfolders for the relative tasks.</p> <p>- Min1D_StatDelays<br>- 3Dtomo<br>- EMSC_Catalog_May2007_Dec2015<br>- tomo2plt_scripts<br>- inventories</p> <p>For additional details, we refer the reader to the main manuscript and its supplementary materials.</p>
LiftWEC deliverable D4.3: Dataset from 2D experimental test campaign
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. "LiftWEC Deliverable D4.3 Report on 2D experimental testing dataset" describes this dataset and for a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Codes to replicate statistical analysis in UPLIFT project Deliverable 2.4 Synthesis report, Chapter 6
<p>Policies attempting to mitigate the effects of urban inequality, often disregard affected citizens’ experiences, and thus fail to achieve maximum impact. By incorporating these perspectives into the policy design process, the project "Urban PoLicy Innovation to address inequality with and for Future generaTions" (UPLIFT), funded under the EU Horizon 2020 program aims to find innovative interventions in a bottom-up approach. The aims of UPLIFT project are to understand patterns and trends of inequality across Europe and to understand how individuals experience and adapt to inequality through participatory research. Moreover the project will together with the communities in four locations, co-design a policy tool aimed at addressing and reducing inequality and socio-economic divisions. The activity and results of the project can be followed at <a href="https://www.uplift-youth.eu/">https://www.uplift-youth.eu/</a>.</p> <p>Deliverable 2.4 (Synthesis report: socioeconomic inequalities in different urban contexts) is the final deliverable of work package 2 of the UPLIFT project, which aims to synthetize the main outcomes of the urban reports that described the policy environment around vulnerable individuals in the fields of education, employment and housing in 16 functional urban areas of the EU. In addition section 6.2 "Statistical analysis of linkages between economic development of cities, their public policy performance and inequality outcomes" of the report provides a statistical analysis of how local economic competitiveness and the local policy context affect urban deprivation and inequality among the young in European cities. The analysis is based on data from 2006, 2009, 2012, 2015 and 2019 of the Quality of Life in European Cities survey.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
MUSTEC Deliverable 9.1 Inputs and results. MUSTEC project.
<p>This dataset contains the main inputs needed to perform sustainability assessment of concentrated solar power (CSP) plants through an input-output analysis. Results are also included. Additional information is available in the document <em>Deliverable 9.1. Sustainability assessment of future CSP cooperation projects in Europe.</em></p> <p>For information on the project see: https://www.mustec.eu/</p>
WorldCOM Deliverable 1: Prevalence of ESBL subtypes in bacterial pathogens and a sequence database of selected alleles
<p><strong>OHEJP Project: WorldCOM, Deliverable 1, Work Package 1.</strong></p> <p>This dataset is connected to Work Package 1, Task1 of the WorldCOM consortium grant within the One Health EJP group. The aim was to analyse publicly available sequences for antimicrobial resistance genes associated with <em>Salmonella</em>, <em>Campylobacter</em> and <em>E. coli</em>. For the initial phase of this work package, we have focused on ESBL-related AMR genes. As these genes are absent from <em>Campylobacter</em>, we have not included this bacterium in these analyses, and have used the important pathogens <em>Klebsiella</em> and <em>Acinetobacter</em>. All types and subtypes of Extended Spectrum β-Lactamases (ESBLs) and plasmid-mediated colistin resistance genes have been analysed for frequency among reported and extracted sequences. High frequency resistant genes subtypes have been highlighted for further sequence analysis to illustrate geographic distribution and geographic-specific single nucleotide polymorphisms (SNPs). The data shown are work in progress. </p>
Planmap's Deliverable 6.2- 3D geo-models based on multiple datasets of the Moon (implicit or explicit modelling)
<p>Outputs of the 3D geomodelling of the shallow-surface layered deposits on the Chang'e 3 landing site. This is based on the Yutu rover GPR channel 2B data gathered along its traverse in Sinus Iridum on the Moon.</p> <p> </p> <p>Notebooks at https://doi.org/10.5281/zenodo.4055213</p>
MAGIC Deliverable 5.5 - Datasets - Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP)
<p>This repository contains datasets used in the production of figures contained in MAGIC Deliverable 5.5</p> <p>Matthews K.B., Blackstock K.L., Waylen K.A., Juarez-Bourke A., Miller D.G., Wardell-Johnson D.H., Rivington M. (2018) Report on the Quality Check of the Robustness of the Narrative behind the Common Agricultural Policy (CAP).</p> <p>MAGIC (H2020-GA 689669) Project Deliverable 5.5 - 29th November 2018</p> <p><a href="https://magic-nexus.eu/documents/d55-report-narratives-behind-cap">MAGIC Deliverable 5.5</a></p>
DICE H2020 Deliverable D3.9 — Final version
<p>The data hereby published supports the results of the European research<br> project DICE H2020 deliverable D3.9, which can be downloaded at http://www.dice-h2020.eu/deliverables/.<br> When referring to the data, please cite the above mentioned deliverable.</p>
FLAME Deliverable 8 - Extreme Wildfires Dataset (D3.1)
<p>This dataset comprises extreme wildfire growth events that occurred in Greece during the 2002 - 2020 period. The data are provided in GeoPackage (.gpkg) format. The accompanying report documents information concerning the methods used for deriving the dataset. </p>
Deliverable 2.3: Biodiversity impact estimates and documentation for indicators on multi-dimensional biodiversity aspects ready for use in WP3
<p>To quantify human-driven impacts on biodiversity, we used advanced modeling techniques paired with a few key variables such as habitat quality, vegetation structure, climate, and topography to develop an innovative predictive model that estimates both 1) current biodiversity patterns and distributions and 2) a baseline model of biodiversity. By comparing both of these models, we are able to identify regions with significant human-driven reductions in species richness, endemism, and species composition across both of our focus regions, South America and Africa.</p> <p>The files provided in this repository (1 km2 resolution) include:</p> <p>1- Species richness (number of species).</p> <p>2- Endemism (species rarity). For this metric, we used the corrected weight endemism index, which is the inverse of a species’ range size and effectively assigns higher scores to species with more restricted distributions. For this index, we used species ranges found within our study area (of South America and Africa).</p> <p>3- Species composition of vertebrates, invertebrates, and plants (which species occur in a given location). This metric, also known as beta diversity, summarizes which sets of species occur at each location. For this, we used the Sorensen index metric, as it does not depend on species absence data.</p> <p>These results provide a improve in previous deliverable 2.2.</p>
CURE Deliverable D3.3 Data
<p>Sample Products from the CURE project Copernicus cross-cutting applications focusing on climate change adaptation and mitigation; healthy cities and social environments; and energy and economy. Products for cities: Berlin, Germany; Copenhagen, Denmark; Heraklion, Greece; Sofia, Bulgaria; Bristol, United Kingdom, Ostrava, Czech Republic; Basel, Switzerland; Munich, Germany; San Sebastian, Spain; Vitoria-Gasteiz, Spain.</p>
Database containing harmonized datasets - FAIRWAY Project Deliverable 3.3
<p>A database has been developed and delivered during the <a href="https://www.fairway-project.eu/">FAIRWAY project</a>. This database was developed as a response to the need to harmonize datasets and assessment methods related to pressure and state indicators for water quality in the EU member states, in order to compare and assess indicators using a harmonized approach.</p> <p>The dataset that is made available here provides two files:</p> <ul> <li>a <em>public version*</em> of the <strong>Excel database</strong>, which contains <strong>all "tabular" (non-GIS)</strong> data related to the 13 case studies that was gathered for the purposes of FAIRWAY's Monitoring & Indicators research theme. It is structured as one "data sheet" and one "summary sheet" per case study. The data sheets contain various parameters (ideally time-dependent data series i.e. time series) that were used, wherever possible, to compute relevant Agri-drinking water quality indicators (ADWIs) such as "nitrogen budget" (a compound Pressure indicator) or "lag time" (a statistically-inferred Link indicator).</li> <li>a <strong>ZIP folder</strong> containing <strong>all GIS data</strong> gathered for the FAIRWAY's Monitoring & Indicators research theme. The GIS files are grouped in subfolders, by case study, and then by keywords describing the nature of the spatial data.</li> </ul> <p>The Excel database contains near 385,000 rows of data from the 13 case study sites, with more than 65 parameters and more than 500 sub-parameters. The dataset also contains spatial information in a GIS-data zipped folder. The spatial mapping information can be made visible using basic <a href="https://www.qgis.org/en/site/">QGIS</a> project files (.qgz), so that GIS data from each case study can be explored.</p> <p>The indicators database can be used in several ways. It may be used to explore data or to calculate additional indicators. Depending of the case studies’ interests, the most commonly available State indicators are about nitrate and pesticides concentrations in water.</p> <p>From a practical point of view based on its actual content, the database may notably be used to explore statistical relations (or Links) between related Pressure and State indicators. This database can also be used as a spatial mapping portal for other usages.</p> <p>For more information on the database, follow <a href="https://fairway-is.eu/index.php/farm-management/workpackages/harmonised-indicator-database">this link</a>.</p> <p>* Note that this is a <em>public version</em> of the database, which means that all confidential data was removed from the data sheets.</p>
LiftWEC deliverable D4.4: Dataset from 2D experimental test campaign, with calculated hydrodynamic forces
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. </em> <em>This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. </em><em>This new version contains all data from version 1 of the first set, which consists of measurement from the experimental testing, plus results from the data analysis calculating the hydrodynamic forces. These forces are calculated by removing the static force and the centrifugal force. For a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
In-network data collection and data processing - Supplementary materials for deliverable D5.1 - EU-H2020 FET project 'Watchplant'
<p>Supplementary material for D5.1 - Watchplant. Contains collected dataset from plant experiments with blue and red light stimuli, classification results based on statistical methods, and plots of the recorded electropotentials.</p>
SECURE Deliverable 2.1: Annex 3 of First Draft of SECURE Research Career Framework
<p>This data is Annex 3 of the First Draft of SECURE Research Career Framework [<a title=" First Draft of SECURE Research Career Framework" href="../records/10958921" target="_blank" rel="noopener">https://zenodo.org/records/10958921</a>].</p> <p>The data is available as a Microsoft Excel file (xlsx) or alternatively as separate files for each tab of the Excel file as CSV (comma-separated values) files.</p>
ScienceDex guides
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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.