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167 results for “Demonstration Data”
Demonstration of Ecosystem Management Options (DEMO) Study, western Oregon and Washington (post-treatment data, 1998-2016)
The Demonstration of Ecosystem Management Options (DEMO) Study is a regional-scale experiment in variable-retention harvest, established at six sites in western Oregon and Washington. Initiated in 1994, DEMO was designed to assess newly established standards and guidelines for regeneration harvests in mature, coniferous forests of the Pacific Northwest. The experiment is a randomized complete block design. It includes six treatments that represent strong contrasts in the level of retention (15-100% of original basal area) and the spatial pattern in which trees are retained (uniformly dispersed vs. aggregated in 1-ha patches). The factorial nature of the design (15 and 40% retention in both an aggregated and dispersed pattern) is unique among variable-retention experiments, regionally and globally. Long-term measurements of vegetation response lie at the core the DEMO Study. Key response variables include overstory tree growth and mortality, the dynamics of snags, regeneration of conifers (including planted seedlings and natural recruitment), and the composition, structure and diversity of the understory (including herbaceous, woody, and bryophyte species). Pre-treatment measurements were made between 1994 and 1996 (data are archived under Study Code TP104). Post-treatments measurements have occurred at ~5- to 7-year intervals between 1998 and 2016 (data are archived under Study Code TP108).
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
H2020 Platone German Demonstrator Use Case 1 Asset and Topolgy Data
<p>This dataset asset and topology data of the field test setup. The dataset gives details about:</p> <p>- Low Voltage (LV) network</p> <p>- Tranformer located in the secondary substation</p> <p>- Community Battery Energy Storage System (CBES) connected to the LV-busbar in the 2nd. Substation</p> <p>- PV and number of households located in the energy community</p> <p>- PV installed generation power of the community</p> <p>- Domestic Storages and Inverter</p> <p> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
Example of datasets processed to demonstrate a multisource data integration methodology
<p>This dataset contains the data processed to demonstrate the multi-source spatial data integration methodology proposed in the paper "Multisource spatial data integration for use cases applications".</p> <p>It contains:</p> <p>- the building footprint extracted from the IFC model of a newly designed building in WKT format, by using the GeoBIM_Tool (<a href="https://github.com/twut/GEOBIM_Tool">https://github.com/twut/GEOBIM_Tool</a>);</p> <p>- the extrusion of the footprint until the measured height measured with the same GeoBIM_Tool;</p> <p>- a portion of the Rotterdam 3D city model generated with 3dfier and available at https://3d.bk.tudelft.nl/opendata/3dfier/, converted in CityJSON with the citygml-tools (https://www.cityjson.org/tutorials/conversion/), developed to convert data between CityGML and CityJSON.</p>
Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"
<p>Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below).</p> <p>R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.</p>
Testing and Demonstration Data for DataRig Software
<p>This repository holds the testing and demonstration data for <a href="https://github.com/mscaudill/datarig">DataRig</a>, an opensource software program for downloading datasets from data repositories utilizing RESTful APIs. This repository contains 5 sample datasets.</p> <p> </p> <p><strong>annotations_001.txt</strong></p> <p>This data set is a tab-separated text file containing 6 columns that start on line number 7. The column headers are; </p> <p> 'Number' 'Start Time' 'End Time' 'Time From Start' 'Channel' 'Annotation'</p> <p>There are 13 rows of data under each of these column headers representing the start and end times of annotated events from an eeg recording file in this repository called recording_001.edf. The events describe the behavior of a mouse in 5 sec increments with each behavior being one of 'exploring', 'grooming' or 'rest'.</p> <p> </p> <p><strong>recording_001.edf</strong></p> <p>A European Data Format file consisting of 4 channels of EEG data lasting approximately 1 hour. The times in the annotations_001.txt file are referenced against this file.</p> <p> </p> <p><strong>sample_arr.npy</strong></p> <p>A numpy array of shape (4, 250) with values sequentially running from 0 to 1000.</p> <p> </p> <p><strong>sample_excel.xls</strong></p> <p>An excel file with a single column of 10 numbers from 0-9 sequentially.</p> <p> </p> <p><strong>sample_text.txt</strong></p> <p>A text file with 4 rows containing 250 values per row. The values in the file run from 0 to 1000 sequentially.</p>
OneNet Portuguese demonstration - Open Data sets
<p>File containing the open data sets from the Portuguese demonstration of the OneNet project. The file includes the flexibility assets data used for the demonstration, as well as: 1) the data series for the estimation of the accumulated flexibility potential of MV customers (supermarkets) connected at the two substations considered; 2) the consumption and generation forecasts, with generation disaggregated by source; 3) short-circuit current values calculated at the EHV/HV interface level, including the TSO contribution, the DSO contribution and the joint TSO-DSO contribution. </p><p>Scope/objective of the demonstration: Test an optimized procedure for data exchange between the Portuguese DSO and TSO for flexibility and operational planning purposes.</p>
The LEXIS Distributed Data Infrastructure: Demonstrator System
<p><strong>The LEXIS Distributed Data Infrastructure: Demonstrator System</strong><br> by: LEXIS Project and Work Package 3 Team</p> <p>Project Lead: IT4Innovations National Supercomputing Centre (Czech Republic)<br> Work Package 3 Lead: Leibniz Supercomputing Centre (LRZ, Garching b. M., Germany)</p> <p>The enormous amounts of data generated in modern industry, business and science pose a significant challenge to those extracting actionable intelligence from data, using various filtering and analysis techniques. In this "Big Data" setting, the LEXIS project (Large-scale EXecution for Industry & Society) provides a user-friendly portal and platform for optimised execution of mixed Cloud-HPC (HPC: High-Performance Computing) workflows. The system will rely on advanced, distributed orchestration solutions (Bull Ystia Orchestrator, based on TOSCA and Alien4Cloud technologies), the High-End Application Execution Middleware HEAppE, and new hardware capabilities for maximizing efficiency in data processing, analysis and transfer (e.g. Burst Buffers with GPU- and FPGA-based data reprocessing).</p> <p>LEXIS handles computation tasks and data from three Pilots, based on representative and demanding HPC/Cloud-Computing use cases in Industry and Science: i) compute-/data-intensive and time-consuming simulations of turbo-machinery and gearbox systems in Aeronautics, ii) Earthquake and Tsunami simulations which are accelerated to enable accurate real-time analysis, and iii) Weather and Climate HPC simulations where massive amounts of in situ data are assimilated to improve forecasts.</p> <p>Here, we introduce and show a demonstrator of the LEXIS Distributed Data Infrastructure (DDI), the core data back-end of the LEXIS project. The DDI provides a unified "File Space" for LEXIS, across the participating sites and computing centres. Based on iRODS (irods.org) and EUDAT-B2SAFE (eudat.eu), it will ensure reliable and efficient access to large datasets in the Terabyte range and beyond. We have prepared virtual machine templates for the LEXIS Cloud resources which implement a Demonstrator of the LEXIS DDI. The system, once instantiated, consists of two iRODS-iCAT (provider) servers, representing the LRZ and IT4I iRODS zones, and of two client machines for access to the distributed data management system. Thus, interested colleagues can explore the possibilities offered by this system on an "own" demonstrator instance. Please contact us at info[at]lexis-project.eu if you are interested.</p>
H2020 Platone German Demonstrator Use Case 1 Measurement Data
<p>This dataset contains measurement datas collected from mesurements devices in the field (substation, battery storage, etc) during the application of Use Case 1 (Islanding/Maximization of local self-consumption).</p> <p> </p>
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
Supplementary data for the time-bidirectional states tomography demonstration
<p>Supplementary data for the time-bidirectional tomography demonstration realized on ibm_oslo seven-qubit superconducting quantum processor.</p> <ul> <li>ibm_oslo_calibrations_data.csv contains calibration data at the time of the demonstration realized.</li> <li>input.json contains three input quantum circuits in qiskit format.</li> <li>output.json contains corresponding measurement counts in qiskit format.</li> </ul> <p> </p>
Demonstration record for discoverable IPCC WGIII data
<p>This is a record used to demonstrate the concept of discoverable data for IPCC AR7 WGIII.</p>
TraceVis: Visualization for DSMC: tool, demonstration video, data
<p>Tool demonstration video and source code of <em>TraceVis</em>, the visualization tool for Deep Statistical Model Checking, presented in the paper <em>TraceVis: Towards Visualization for Deep Statistical Model Checking</em>, published at ISoLA 2020 (9th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation).</p>
Demonstration data for the python package applefy.
<p>Demonstration data for the python package applefy. </p> <p>30_data: Contains the NACO L' dataset of Beta Pic (planet removed) as used in the user documentation</p> <p>70_results: Contains the results of the user documentation tutorials</p> <p>laplace_lookup_tables.csv: Contains the lookup table for the LaplaceBootstrapTest</p>
Majorana Demonstrator Data Release for AI/ML Applications
<p>The enclosed data release consists of a subset of the 228Th calibration data from the Majorana Demonstrator<br> experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimina-<br> tion cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release<br> is specifically designed to support the training and testing of Artificial Intelligence and Machine Learning (AI/ML)<br> algorithms upon our data. Please read the following ArXiV posting before using this dataset: https://arxiv.org/abs/2308.10856. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).</p>
H2020 Platone German Demonstrator - Use Case Setting Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). The dataset contain information that have been set for Use Cases (UCs) parameterization during the project phase. UC have been parameterized along a grafical user interface (GUI) named "Use Case Selector". Each parameterized UC triggered has been logged in the dataset.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single point of common coupling (PCC). i.e., a secondary substation that includes a transformer with senors on the LV busbar, to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Definition of data:</strong></p> <p><strong>RequID</strong> - Request ID - Identifier for each triggered UC</p> <p><strong>Alert</strong> - Indicates, whether UC has been executed successfull ( " "and " true" indacates successfull implementation by Energy Management System (EMS); "false" indicates that UC has not been implemented by EMS)</p> <p><strong>Submission -</strong> timestamp of UC submission<strong> </strong></p> <p><strong>Note -</strong> Annotations entered by UseCase operator</p> <p><strong>Priority -</strong> Defines UC priority set by operator (priority: 1 - high , 2 - medium, 3 - low, 4 - very low) (only relevant for UC 2)</p> <p><strong>Status - </strong>Indicates the status of the UC (closed - UC has been executed, cancelled, UC has been has been canceled before or during application)</p> <p><strong>Start</strong> - Point of time set for the beginning of UC</p> <p><strong>End</strong> - Point of time set for the end of UC</p> <p><strong>Type</strong>- Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p><strong>Subtype</strong> - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p><strong>bulkStart</strong> - Point of time of start of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulkEnd</strong> - Point of time of end of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulk Energy</strong> - Amount of energy triggered to be imported or exported as bulk (only relevant for UC 3 and 4)</p> <p><strong>Final_SOF </strong>- State of Charge (SOC) of CBES that should be achieved at end of UC (End)</p> <p><strong>FlexDemand </strong>- Requested power exchange that should be achieved at MV/LV PCC (Only relevant for UC 2)</p> <p><strong>Ptcb - </strong>Measured CBES charging power at poin of time of UC submission </p> <p><strong>Ptei </strong>- Measured power exchange at PCC at point of time of UC submission </p> <p><strong>SoC </strong>- State of Charge of CBES at point of time of UC submission </p> <p><strong>SoE</strong> - State of Energyof CBES at point of time of UC submission </p> <p><strong>ActiveSet </strong>- State of Energyof CBES at point of time of UC submission </p> <p><strong>maxSoC </strong>- Maximum SoC set for CBES at point of time of UC submission </p> <p><strong>minSoc - </strong>MinimumSoC set for CBES at point of time of UC submission </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
Data from: Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats
Vaccines that autonomously transfer among individuals have been proposed as a strategy to control infectious diseases within wildlife populations. However, understanding rates of spread and epidemiological efficacy in real world systems remain elusive. Here, we investigated whether topical vaccines that transfer among bats through social contacts can control vampire bat rabies, a medically and economically important zoonosis in Latin America. Field experiments in 3 Peruvian bat colonies which used fluorescent biomarkers as a proxy for the bat-to-bat transfer and ingestion of an oral vaccine revealed that vaccine transfer would increase population-level immunity up to 2.6 times beyond the same effort using conventional, non-spreadable vaccines. Mathematical models demonstrated that observed levels of vaccine transfer would reduce the probability, size, and duration of rabies outbreaks, even at low, but realistically achievable levels of vaccine application. Models further predicted that existing vaccines provide substantial advantages over culling bats, the policy currently implemented in North, Central, and South America. Linking field studies with biomarkers to mathematical models can inform how spreadable vaccines may combat pathogens of health and conservation concern prior to costly investments in vaccine design and testing.
Data from "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias"
<p>Supporting material for Baumgartner et al. (2017): "Asymmetries in behavioral and neural responses to spectral cues demonstrate the generality of auditory looming bias" in Proc Natl Acad Sci USA; www.pnas.org/cgi/doi/10.1073/pnas.1703247114</p>
Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests
<p>Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests </p> <p>https://ifishienci.eu/wp-content/uploads/2024/01/iFishIENCi_D3.6.pdf</p> <p>Corresponding Author</p> <p>Name: Anneli Rost<br>ttz Bremerhaven, Germany<br>Address: Knurrhahnstraß 22-24 /Packhalle X 27572 Bremerhaven<br>Email: arost@ttz-bremerhaven.de</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.