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2,672 results for “services”
Sentinel-1 InSAR Browse Service Image of the October 2016 Central Italian Earthquakes
<p>The surface deformation caused by the central Italian earthquakes which occured in October 2016 is captured in this terrain corrected interferogram produced by the Sentinel-1 InSAR Browse Service for the Geohazards Exploitation Platform.</p> <p>Two earthquakes occured on 26<sup>th</sup> October and one on 30<sup>th</sup> October. The Sentinel-1 datasets were acquired on 26-10-2016 for the master and 01-11-2016 for the slave from a descending pass so that the line of sight deformation is viewed from the east.</p> <p>Contains modified Copernicus Sentinel data (2016), processed by DLR/ESA/Terradue.</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
Great Britain's primary substation service areas and annual domestic energy statistics
<p>This geospatial data is a combination of Great Britain's 4436 primary substation service areas which have been parsed into a single shapefile for energy systems analysis. The original component datasets were provided by the six distribution network operator (DNO) companies in Great Britain (National Grid Electricity Distribution, Electricity North West Ltd, Scottish and Southern Electricity Networks, UK Power Networks, Scottish Power Energy Networks and Northern Power Grid). Attribution is given to the original data owners at each of these six DNOs and the resulting dataset from this work has been created and published under an open licence with each DNO's permission. </p><p>The data is available to download as two geojson files in the WGS84 coordinate system. One is a streamlined version which just contains the polygons along with a unique primary identifier (UPID), primary substation name, DNO licence area and local authority. The other contains the polygons along with richer energy data which was aggregated to the primary substation level from publicly available Department for Energy Security and Net Zero, Office for National Statistics and National Grid ESO datasets. This data is also available to download in tabular form as a csv file. The meter numbers and consumption values are the means of those reported from 2015-2020. The substation polygons were those as received or publicly available as of the time period of this study (2021-22).</p><p>The pre-print manuscript of the methodology used to create this dataset can be found on arXiv at:</p><p>https://doi.org/10.48550/arXiv.2311.03324</p><p>Funding to support this work was received from the Engineering and Physical Sciences Research Council (EP/W008726/1) under the Gas Net New project and the Alan Turing Institute's Science of Cities and Regions Programme. Thanks are also given to the contributors of QGIS and the Geopandas Python library, both of which were used in this analysis. </p>
Synthetic mobile service traffic time series
<p>This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.</p>
Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder
<p>This database corresponds to the results of the paper:</p><p>Lacruz-Pérez, I., Pastor-Cerezuela, G., Tárraga-Mínguez, R. & Lüke, T. (2023): Implicit and explicit measurement of pre-service teachers' attitudes toward autism spectrum disorder, <i>European Journal of Special Needs Education</i>. <a href="https://doi.org/10.1080/08856257.2023.2185858">https://doi.org/10.1080/08856257.2023.2185858</a> </p><p> </p>
Jacdac: Service-based Prototyping of Embedded Systems (PLDI 2024 Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
EJPSOIL ARTEMIS on-farm monitoring of soil health and ecosystems services (meta)data
<p>This database includes the data and metadata from the initial on-farm monitoring od soil health and soil related ecosystem services of the EJPSOIL ARTEMIS project. </p>
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ACTIVAGE User needs_requirements and services
<ul> <li>AUC: List of Activage Use cases</li> <li>RUC: List of Reference Ucs</li> <li>Needs: list of DS needs</li> <li>DSReq_list: List of All DS Requirements</li> <li>SLEawRq_list: List of Smart Living Environment for Ageing Well Requirements</li> <li>SLEaw-DS req map: Mapping DSs requirements to ACTIVAGE SLEaw requirements</li> <li>Initial DSs Service list: list provided by DS in Jun 2018 with services, partial description</li> <li>DSs SUBservice list: Decomposition of DS Services in atomic components</li> <li>DSReq_Cl_descr: Desciption of attributes (columns) of DS requirments</li> <li>SLEawReq_Cl_descr: Desciption of attributes (columns) of SLEaw requirments</li> <li>Legenda: This sheet. Include description of sheets and change proposals</li> </ul>
Dataset for: Multi-scale approach to biodiversity proxies of biological control service in European farmlands
<p>Dataset for the BiodivERsA COFUND Woodned project. Information on which spatio-temporal factors are simultaneously affecting crop pests and their natural enemies is required to improve conservation biological control practices. The study was conducted in 80 winter wheat crop fields distributed in three regions of North-western Europe (Brittany, Hauts-de-France and Wallonia), along intra-regional gradients of landscape complexity. Five taxa : aphids, slugs, spiders, carabids, and parasitoids were sampled for two consecutive years. We analysed the influence of regional, landscape and local factors on the abundance and species richness of crop-dwelling organisms, as proxies of the service/disservice they provide. Firstly, there was higher biocontrol potential in areas with mild winter climatic conditions. Secondly, natural enemy communities were less diverse and had lower abundances in landscapes with high crop and wooded continuities, contrary to slugs and aphids. Finally, field boundaries with grass strips were more favourable to spiders and carabids than boundaries formed by hedges, while the opposite was found for crop pests, with the latter being less abundant towards the centre of the fields. These results are quite unexpected because they show that hedgerows and woodlots should not be the unique cornerstones of agro-ecological landscape design strategies. We point out that combining woody and grassy habitats to take full advantage of the features and ecosystem services they both provide may promote sustainable agricultural ecosystems. It may be possible to both reduce pest pressure and promote natural enemies by accounting for taxa-specific antagonistic responses to multi-scale environmental characteristics.</p>
Dataset for CoordiNet D6.2 - Chapter 4 - Evaluation of Combinations of Coordination Schemes and Products for Grid Services
<p>This is a supporting material for chapter 4 of CoordiNet D6.2. The deliverable is available at:</p> <p><a href="https://coordinet-project.eu/publications/deliverables">CoordiNet deliverables (coordinet-project.eu)</a></p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>In addition, table check_system contains the forecasted flows over lines before flexibility activation. For the transmission system, the SFTN is used and only active power is calculated. For the distribution systems, a linearized model of the power flow is used, thus active and reactive power are calculated:</p> <ul> <li>System: the system (TN, DN_18, DN_69, DN_141) where the line is located;</li> <li>From/to: bus numbers of the connection;</li> <li>Flow: forecasted active flow over transmission system lines;</li> <li>Flow P/Flow Q: forecasted active/reactive flow over distribution system lines (also for interface flows TN-DN);</li> <li>Congestion: if congestion is forecasted over the line.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Railway services operated with diesel multiple units in Spain and Portugal
<p>Identifier: DOI</p> <p>Creator: German Aerospace Center, Institute of Vehicle Concepts</p> <p>nameType: Organizantional</p> <p>Title: Railway services operated with diesel multiple units in Spain and Portugal.</p> <p>Publisher: Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Fahrzeugkonzepte.</p> <p>Publication Year: 2022</p> <p>ResourceType: Simulated Trajectories</p> <p>Subject: This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes.</p> <p>Date: 2022-02-10</p> <p>Description:<br> This data set comprises railway services operated with diesel multiple units in Spain and Portugal with a set of infrastructural and operational attributes. Methodology is described in relatedItem.</p> <p>FundingReference: FCH2Rail; Fuel Cell Hybrid Power Pack for Rail Applications; Grant Agreement Number: 101006633</p> <p>RelatedItem: "D1.1 - Report on line and use case based requirements" of the FCH2Rail project.</p> <p>Related Item can be found on the project website (https://www.fch2rail.eu/en/projects/fch2rail) and/or in Cordis (https://cordis.europa.eu/project/id/101006633/results)</p> <p><br> This dataset comprises following attributes:</p> <p>service:<br> First and last station of the railway service.</p> <p>length:<br> Length of the railway service in km.</p> <p>electrified_length:<br> Length of electrified sections in km.</p> <p>not_electrified_length:<br> Length of not electrified sections in km.</p> <p>electrification_degree:<br> Electrfiicatioin degree in %.</p> <p>longest_autonomy:<br> Longest not electrified section in km.</p> <p>first_station_electrified:<br> Binary of first station is electrified with catenary.</p> <p>last_station_electrified:<br> Binary of last statin is electrified with catenary.</p> <p>elevation_first_station:<br> Elevation of first station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>elevation_last_station:<br> Elevation of last station in meter above sea level [m.a.s.l.]. Reference global sea level of Jaxa Alos 0.1*0.1.</p> <p>daily_trips:<br> Count of daily trips on the service.</p> <p>vehicle:<br> Vehicle type used on this service.</p> <p>stop_number:<br> Numver of stops at stations throughout a trip.</p> <p>trip_time:<br> Trip duration in hours.</p> <p>gauge:<br> Railway gauge in mm.</p> <p>type:<br> Railway vehicle type. Mainline Loc = Mainline Locomotive, MU Iber. gauge = Multiple unit on iberian gauge, MU Feve Gauge = multiple unit on feve gauge.</p> <p>avg_stop_distance:<br> Average stop distance in km.</p> <p>avg_speed:<br> Average velocity in km/h.</p> <p>daily_autonomy:<br> Cumulated distance under not electrified sections in km.</p> <p>annual_train_km:<br> Train kilometers per year.</p> <p>annual_train_km_wo_catenary:<br> Train kilometers per year not under catenary. </p> <p> </p>
Mataws annotated Web service collection
<p><strong>Description. </strong>The Mataws annotated Web service collection is a set of WS descriptions under the WSDL and OWL-S formats. It contains 816 descriptions, which were originally only syntactically described, and were annotated using our tool Mataws. Consequently, each description appears twice (once in a syntactical version, and once in a semantic version). The descriptions are also classified thematically.</p> <p>Our collection is based primarily on the FullDataset collection of the Assam project (<a href="http://www.andreas-hess.info/projects/annotator/">http://www.andreas-hess.info/projects/annotator/</a>), which we extended using WS descriptions found on the web. These individual files were classified thematically with the rest of the WSDL files, and used to assess the quality of annotation of Mataws.</p> <p><strong>Source code. </strong>The source code of our tool Mataws is available online: <a href="https://github.com/CompNet/mataws">https://github.com/CompNet/mataws</a></p> <p><strong>License. </strong>The annotated descriptions are shared under a Creative Commons 0 license. The original descriptions belong to their authors.</p> <p><strong>Citation. </strong>If you use our dataset, please cite the following article:</p> <ul> <li>Aksoy, C., Labatut, V., Cherifi, C. & Santucci, J.-F (2011). MATAWS: A Multimodal Approach for Automatic WS Semantic Annotation. In International Conference on Networked Digital Technologies. Macau, CN : Springer. ⟨<a href="https://hal.archives-ouvertes.fr/hal-00620566">hal-00620566</a>⟩ - DOI: <a href="https://doi.org/10.1007/978-3-642-22185-9_27">10.1007/978-3-642-22185-9_27</a></li> </ul> <p><br><code>@InProceedings{Aksoy2011,</code><br><code> author = {Aksoy, Cihan and Labatut, Vincent and Cherifi, Chantal and Santucci, Jean-François},</code><br><code> title = {{MATAWS}: A Multimodal Approach for Automatic WS Semantic Annotation},</code><br><code> booktitle = {3\textsuperscript{rd} International Conference on Networked Digital Technologies},</code><br><code> year = {2011},</code><br><code> volume = {136},</code><br><code> series = {Communications in Computer and Information Science},</code><br><code> pages = {319-333},</code><br><code> address = {Macau, CN},</code><br><code> publisher = {Springer},</code><br><code> doi = {10.1007/978-3-642-22185-9_27},</code><br><code>}</code></p>
NDVI from Copernicus Global Land Service over Mumbai (India)
<p>This data collection contains two datasets over the region of Mumbai (India):</p> <p>- Long-term statistics of NDVI computed over 1999-2019</p> <p>- NDVI values for 2021 (10-days)</p> <p> </p> <p>The original NDVI datasets can be downloaded from the <a href="https://land.copernicus.vgt.vito.be/PDF/portal/Application.html">Copernicus Global Land Service portal</a> (registration is mandatory but free of charge). <br> Data can be downloaded as well directyl from <a href="https://scihub.copernicus.eu/">Copernicus Open Access Hub</a> but data are not based on atmospherically and BRDF corrected data.</p>
Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia
<p>Sentinel-3 NDVI Analysis Ready Data (ARD) (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250) - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values).</p> <p>The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed.</p> <p>The file called Italy.geojson has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.</p> <p> </p> <p>Further info about drought indexes can be found in the Integrated Drought Management Programme [5]</p> <p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a></p>
HPC-JEEP: Energy Usage on ARCHER2 and the DiRAC COSMA HPC services dataset
<p>This package contains the data and tools used to analyse the energy use on the ARCHER2 and DiRAC COSMA UK HPC facilities. This analysis was performed as part of the HPC-JEEP project. HPC-JEEP is funded by the UKRI DRI Net Zero Scoping project.</p>
Data layers for FORCOAST service module A3 Limfjorden 2009-2017
<p>The environmental bottom data layers behind the FORCOAST service module A3 is generated by the 3D FlexSem model consisting of a hydrodynamic model coupled to the biogeochemical model ERGOM. The original data is on an unstructured grid (varying size of polygons), but for this purpose the data was interpolated to a structured grid and converted to netcdf files. The data layers are:</p> <p>1) bottom temperature</p> <p>2) bottom salinity</p> <p>3) bottom Chl a</p> <p>4) resuspension of detritus</p> <p>5) bottom oxygen</p> <p>6) bottom detritus</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>
Farmers' fields network of oilseed rape intercropped with service plant in Western Switzerland.
<p>These data are associated with the publication of Bousselin et al. (2024) in which the experiment and the protocols are explained into details.</p> <p><span>Bousselin, X., Lorin, M., Valantin-Morison, M. </span><em>et al.</em><span> Determinants of oilseed rape-service plant intercropping performance variability across a farmers’ fields network in Western Switzerland. </span><em>Agron. Sustain. Dev.</em><span> </span><strong>44</strong><span>, 40 (2024). https://doi.org/10.1007/s13593-024-00972-6</span></p>
Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services
<p>This dataset includes enhanced analysis of the machine translation data. The original dataset has been reported in [1], where white spaces were used to separate words in different languages. This is however not the best method of analyzing some Asian languages such as the Chinese language. In the present analysis, we used a character-based approach to separating the Chinese and Japanese results, hence obtaining a different set of BLEU and Cosine Similarity scores. These new scores are given in the present dataset.</p> <p>[1] Daniel Pesu, Zhi Quan Zhou, Jingfeng Zhen, & Dave Towey. (2018). Accompanying dataset for: A Monte Carlo Method for Metamorphic Testing of Machine Translation Services (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1194560</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.