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1,163 results for “demonstrators”
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>
Simulation of an imaging calorimeter to demonstrate GarNet on FPGA
<p>This data set is an output of a simulation of electrons and pions shot at a chunk of an imaging calorimeter. It is used in the case study for GarNet-on-FPGA, documented in <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a>.</p> <p>Each HDF5 file contains the following arrays:</p> <p> Name | Shape | Description</p> <ul> <li>cluster | (10000, 128, 4) | Samples for training and inference. Outermost dimension is the event (cluster). Each cluster has maximum 128 hits, each of which has four features: x, y, z, and energy. The coordinates of the hits are in cm. The energy is in GeV. The x and y coordinates are relative to the seed hit, while the z coordinate is with respect to the calorimeter front face.</li> <li>size | (10000) | Number of hits in each cluster. The cluster array is zero-padded when the cluster size is below 128.</li> <li>truth_pid | (10000) | Identity of the primary particle (0: electron, 1: pion).</li> <li>truth_energy | (10000) | True energy of the primary particle.</li> <li>raw | (10000, 4375, 2) | Raw data (actual output of the simulation). For each event (outermost dimension), hit energy and primary fraction (innermost dimension indices 0 and 1) are given for each of the 4375 sensors. Energy is in MeV.</li> <li>coordinates | (4375, 3) | The x, y, and z coordinates of the 4375 sensors, to be used to interpret the raw data.</li> </ul> <p>See the paper for the details of the simulation.</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>
H2020 Platone Italian Demonstrator Use Case 1-2 Measurements
<p>Description of the database "areti_profile_flexibility_customer_2021":</p> <p>Data about the flexibility measurement of the users involved in the trial.</p> <p>In the database you will find: </p> <p>Date: Measurement date (Mmm dd, yyyy);<br> Timestamp: Measurement time (hh:mm:ss.sss @UTC);<br> pod: Point of Delivery of the users' place (PoD) identification code;<br> measures.energy.absorbedActiveEnergy.value: Quarter-hour sample of active energy absorbed (kWh) [as per ID 6 in Tab. A.5 - CEI 13-82];<br> measures.energy.injectedActiveEnergy.value: Quarter-hour sample of active energy injected (kWh) [as per ID 7 in Tab. A.5 - CEI 13-82];<br> measures.energy.absorbedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy absorbed (kVARh) [as per ID 9 in Tab. A.5 - CEI 13-82]; <br> measures.energy.absorbedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy absorbed (kVARh) [as per ID 10 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedInductiveReactiveEnergy.value: Quarter-hour sample of inductive reactive energy when active energy injected (kVARh) [as per ID 11 in Tab. A.5 - CEI 13-82]; <br> measures.energy.injectedCapacitiveReactiveEnergy.value: Quarter-hour sample of capacitive reactive energy when active energy injected (kVARh) [as per ID 12 in Tab. A.5 - CEI 13-82];<br> measures.power.activePower.value: Quarter-hour average of active power exchange (kW) [as per ID 16 in Tab. A.5 - CEI 13-82];</p> <p> </p> <p>(Useful link to consult Italian UC:</p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a></p> <p><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a></p> <p><a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</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>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Plant image identification application demonstrates high accuracy in Northern Europe dataset
<p><strong>Images and data for the study "Plant image identification application demonstrates high accuracy in Northern Europe"</strong></p> <p><strong>Details: Jaak Pärtel, Meelis Pärtel, Jana Wäldchen, Plant image identification application demonstrates high accuracy in Northern Europe, <em>AoB PLANTS</em>, Volume 13, Issue 4, August 2021, plab050, <a href="https://doi.org/10.1093/aobpla/plab050">https://doi.org/10.1093/aobpla/plab050</a></strong></p> <p>The data table displays Flora Incognita's identification results together with species and observations characteristics. All (3199) used images are included.</p> <p>The study was conducted in two parts: database and field study.</p> <p>Database study images have been taken from eBiodiversity database (https://elurikkus.ee/en) under Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). Please cite the original source for the images as well when using the dataset.</p> <p>Field study images were taken by Jaak Pärtel in 2020 in field conditions from different habitats across Estonia.</p>
BrainIAK IEM notebook demonstration
<p>These datasets are used to demonstrate how to use the BrainIAK Inverted Encoding Model (IEM) class. The original data are associated with pre-existing publications & Open Science Framework repositories, but have been sorted and cleaned for easier use.</p> <p> </p> <p><strong>Dataset 1</strong></p> <p>Rademaker, R., Chunharas, C., Serences, J.T. 2019. Coexisting representations of sensory and mnemonic information in human visual cortex. Nature Neuroscience 22:8.</p> <p>Publicly available data at <a href="https://osf.io/dkx6y/">https://osf.io/dkx6y/</a></p> <p> </p> <p><strong>Dataset 2 </strong></p> <p>Itthipuripat, S., Sprague, T.,C., Serences, J.T. 2019. Functional MRI and EEG Index Complementary Attentional Modulations. J. Neurosci. 31:6162-6179.</p> <p>Publicly available data at <a href="https://osf.io/savfp/">https://osf.io/savfp/</a></p>
Atom probe tomography nomad-FAIR demonstrator dataset R76-23219-v01.epos.apth5
<p>This is the dataset of an atom probe tomography experiment which is provided open source for testing the possibility of implementing an open source encyclopedia for experimental materials science datasets, including techniques to begin with such as Scanning Transmission Electron Microscopy (STEM), Multidimensional Photo Emission Spectroscopy (MPES), and Atom Probe Tomography (APT) / Field Ion Microscopy (FIM).</p> <p><strong>This repository serves three aims:</strong></p> <p>1. The dataset is of scientific interest. Specifically, it captures the result of a cutting-edge APT experiment detailed exemplarily in DOI: 10.1038/s41467-018-03115-0 (Fig. 6a "Se+Na2Se treatment") by Torsten Schwarz and coworkers.</p> <p>2. The dataset contributes to tests of an extension to "The NOMAD Laboratory" (https://nomad-coe.eu/): nomad-FAIR. Specifically, to test various aspects of an automatized metadata parsing and processing pipeline to enable the extraction of domain-specific JSON metadata files into a NOMAD-conformant JSON file, ultimately aiming for searchable and repurposable dataset documentation. This serves two purposes: on the one hand to contextualize each dataset within NOMAD. On the other hand to serve as a starting point to parse potential interesting content from the heavy data HDF5 file to reduce unnecessary file access.<br> The implementation of nomad-FAIR is coordinated by Markus Scheidgen.<br> The APT domain-specific parser is developed by Markus Kühbach.</p> <p>3. The dataset constitutes further a test of an open format specification for storing atom probe tomography data using the Hierarchical Data Format (HDF5). This is a recent initiative of the International Field Emission Society's (IFES) atom probe tomography technical committee. In this repository it is detailed an exemplar proposal of how to store acquisition-side relevant results and context of an APT experiment into a HDF5 file and complementary metadata files such as JSON. Implementation of this HDF5-based storage solution for APT data is lead by Markus Kühbach.</p> <p><br> <strong>The organization of this repository with respect to above aims is as follows:</strong></p> <p>-The original EPOS file of the measured is contained in the compressed *.epos.tar.gz archive.</p> <p>-The *.apth5 file is a transcoded version of the EPOS file. Therein, x,y,z data columns are stripped.</p> <p>-The correspondingly named *.json file is the file which nomad-FAIR parses metadata from.</p> <p>-Other files constitute logs of the transcoding process.</p> <p><br> <strong>Funding:</strong><br> The work was partially supported by BiGmax, the Max Planck Society's Research Network on Big-Data-Driven Materials-Science.</p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</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>
Example of Force Digital Calibration Certificate used in ComTraForce 18SIB08 project to demonstrate Digital Twin concept
<p>Force Digital Calibration Certificate (DCC) was developed in the frameworks of 18SIB08 ComTraForce project. It was used to demonstrate the way of data connection between the physical object (force transducer) and virtual object (Finite Element model) within the developed Digital Twin concept. The developed at PTB v3.1.2 xsd schema was used to convert analog calibration certificate to machine readable XML format. The DCC covers static and continuous calibration processes. Note that the current Force DCC is not a Good Practice example. Please follow further developments of force DCC Good Practice example at https://gitlab.com/ptb/dcc.</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>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
Demonstration of 100 Gbit/s active measurements in dynamically provisioned optical paths
<p>New techniques, based on Software-Defined Networks, are used to deploy optical paths dynamically. This demonstration shows how active measurements at 100 Gbit/s are performed to check before operation that the performance requirements are met in terms of capacity, delay or packet loss.</p>
Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"
<p>Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", <em>npj Quantum Information</em><strong> 3</strong>, Article number: 16 (2017).</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.