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344 results for “demo”
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).
MesoLF demo data and auxiliary files
<p>Demo data and auxiliary files accompanying the article:</p> <p>Nöbauer, T., Zhang, Y., Kim, H. & Vaziri, A.<br> Mesoscale volumetric light-field (MesoLF) imaging of neuroactivity across cortical areas at 18 Hz.<br> <em>Nature Methods</em> 1–10 (2023). doi:<a href="https://doi.org/10.1038/s41592-023-01789-z">10.1038/s41592-023-01789-z</a><br> <br> The files provided here are required for running a demo of the MesoLF pipeline. These files will be downloaded automatically by the Matlab live notebook "mesolf_demo.mlx" that was published as part of "Supplementary Software 1" with the associated article. For installation instructions, see file "README.md" in "Supplementary Software 1". For future software updates, check <a href="https://github.com/vazirilab">https://github.com/vazirilab</a></p>
BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report
<p><em>A Metabolomics unknown feature identification report industry standards from <a href="http://www.bionovogene.com/">BioNovoGene</a> corporation.</em></p> <p>2019.08.16# at Suzhou, China</p> <p>There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.</p> <p>This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.</p> <p>Some terms in this article that address to:</p> <ul> <li>feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.</li> <li>annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.</li> <li>alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.</li> <li>score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.</li> </ul> <p>Our metabolite identification report consist with two parts of data which present to our user:</p> <ol> <li>Report excel table that contains the raw sample information and the meta annotation information of the metabolite.</li> <li>Data visual plot for the mass spectrum alignment details.</li> </ol>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
THÖR-Magni (Demo Subset): a new multi-modal context-rich dataset of human-robot motion
<p>The Magni Human Motion Dataset provides high-quality tracking information from motion capture, eye-gaze trackers, and on-board robot sensors in a semantically rich environment. To induce natural behavior of recorded participants, we utilized loosely scripted task assignment, which induced participants to navigate through a dynamic laboratory environment in a natural and purposeful way. The dataset sets a high-quality standard as realistic and accurate data is enhanced with semantic information, enabling development of new algorithms that rely not only on tracking information but also on contextual cues of moving agents, static and dynamic environments.</p> <p> </p> <p>Link to dashboard that uses the data: https://magni-dash.streamlit.app/</p> <p><br> Here we publish a subset of the final dataset, to accompany the presentation at the 2023 IEEE International Conference on Robotics and Automation (ICRA)</p>
Copernicus Global Land Service: Land Cover 100m: epoch 2018: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518037">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2018, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the near-real time (nrt) epoch 2018, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2017) and three months pastor (Jan-March 2019) data. The nrt map can then be supplied in the fourth month after the most recent completed calendar year, and is updated (consolidated) afterwards by using a full year of paster data (when epoch 2019-nrt is produced, epoch 2018 is consolidated).</p> <p>The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for this near-real time epoch</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2018">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p>
Copernicus Global Land Service: Land Cover 100m: epoch 2017: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518035">this global dataset</a> instead.</em></strong></p> <p>emonstration land cover maps over Africa at 100m resolution for epoch year 2017, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the consolidated epoch 2017, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2016) and pastor (2018) data. The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) year (epoch 2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2017">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
Copernicus Global Land Service: Land Cover 100m: epoch 2015: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3243508">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2015, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>As a base year, the classification and regression models for 2015 are saved for re-use in subsequent consolidated (with full year prior and pastor observations) and near-real time years (with full year prior and 3 months pastor data). The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for this base epoch. The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) epoch (2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2015">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
UnityMol demo movie showing custom user-added menus
<p>This video provides more detailed supportive information about using Unitymol.</p> <p> </p> <p>1) start up UnityMol</p> <p>2) activate the functionality to remote control Unitymol</p> <p>3) edit the provided example script menu-spike1.py to include the right filepath</p> <p>4) execute menu-spike1.py with python</p> <p>5) first there is only one button, allowing you to load the scene</p> <p>6) once loaded, several customized views are available through dedicated buttons</p>
Demo bids for to demonstration areas in OneNet project of the Hungarian demonstration
<p>Bid auction data of a DSO flexibility market simulation data in two demo areas based on past real measurement, power gas exchange data.</p> <p>The two .csv files contain the bid data for all FSP assets in two demonstration areas, both are a given snippet of DSO networks used for congestion simulations, Demo Area 1 and Demo Area 2, respectively. The bids are simulated and based on post hoc day-ahead market data and measurements. All FSP assets are photovoltaic generators. For a given day every asset submits stepwise hourly bids for every hour of the day.</p> <p>The two .CSV files consist of the following columns:</p> <p>Date: the date that the bid is submitted to (YYYY-MM-DD)</p> <p>Time: the hour that the bid is submitted to (HH)</p> <p>AssetId: ID of the bidding asset (photovoltaic generator)</p> <p>quantity: quantity of a bid step for an hour of a day in MW</p> <p>price: the price of the bid step for an hour of a day in EUR</p> <p> </p> <p>More information on the demo areas can be found in the <a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">D10.4 Report on demonstration</a> deliverable of the OneNet project.</p> <p>public_demo_area_1.csv file represents the bids in the E.On demo area and the public_demo_area_2.csv file in the MVM demo area, respectively. </p> <p> </p>
ABRomics genomic paired-end FASTQ data demo files
<p>This dataset contains the demo files for the <em>Genomic paired-end FASTQ</em> template of the ABRomics platform:</p> <ul> <li>Raw data: Paired-end Illumina sequencing files of sample ARDIG49.</li> <li>Metadata: Filled out <em>Genomic paired-end FASTQ</em> template for sample ARDIG49.</li> </ul>
Demo showing what RELIANCE project has achieved on Open Science, FAIR and EOSC
<p>This demo shows what we have achieved on Open Science and FAIR. </p> <p> </p> <p>- Starting from <a href="https://beta.explore.openaire.eu/">OpenAIRE EXPLORE</a>, we search for "Copernicus air quality" and find lots of resources, mostly publications and only 2 software. The reason is that to be "classified" as "Software", we have to add specific metadata when publishing.</p> <p>- The "Software" we found is a "EOSC Jupyter notebook" created by <a href="https://orcid.org/0000-0003-3979-3645">Simone Mantovani</a> with a DOI and additional metadata so that OpenAIRE explore can "associate" it to a specific EOSC service, namely <a href="https://www.egi.eu/services/notebooks/">EGI Notebook</a>. </p> <p>- When we click on "<a href="https://marketplace.eosc-portal.eu/services/egi-notebooks?q=EGI+Notebook">EOSC Service: EGI Notebook</a>", we are re-directed directly to the service that has been used to generate the original scientific results we found in OpenAIRE explore.</p> <p>- Any EOSC service needs to be requested and you have to plave an "order" to get access to it, where you may have to explain why you would like to access this EOSC service. To authenticate to any EOSC service, you can use for instance your <a href="https://orcid.org/">ORCID </a>identifier. if you do not have one, we suggest to register: this is very handy for EOSC services and you keep your ORCID identifier when you move from one institution to another (in addition, your institutional login may not work).</p> <p>- You will get notified by email (check your SPAM folder!) when you got access to an EOSC service.</p> <p>- We login to EGI notebook using ORCID identifier and upload (manually) the jupyter notebook we found in OpenAIRE (following the link e.g. from zenodo (<a href="https://doi.org/10.5281/zenodo.5554786">https://doi.org/10.5281/zenodo.5554786</a>)</p> <p>- The Jupyter notebook uses CAMS European air quality analysis from Copernicus Atmosphere Monitoring Service. The input data is accessible through an external service called the <a href="https://reliance.adamplatform.eu/">ADAM platform</a> (Advanced geospatial Data Management platform). It hosts datacubes (easy and fast access to large amount of data).</p> <p>- We can re-execute the Jupyter notebook but more importatnly we can create derivative work. However, make sure you check the license of the original result you find in OpenAIRE explore: it needs to have a license that allows you to create derivative work. Also make sure the Jupyter notebook is well documented.</p> <p>- We duplicate the Jupyter notebook and customize it. To bring the Open Science aspect from the beginning and not only when publishing the Jupyter Notebook, we need to use storage that can be shared. We use another service called "<a href="https://www.egi.eu/services/datahub/">EGI datahub</a>".</p> <p>- As when collaboratively writing scientific papers, we agree on how to organize the data: we create an "input folder" (containing all the input datasets used in the Jupyter notebook), an "output" folder with all the outputs we generate and a tool folder with the Jupyter notebook.</p> <p>- The new analysis is very similar to the previous one but over a different geographical area (France). </p> <p>- Finally, we create a Research Object that aggrgate all the resources. We use another external service called <a href="https://reliance.rohub.org/">RoHub </a> (Research Object Hub) and create and "executable Research Object" which we hope will be found, accessed and reused!</p> <p> </p>
Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO
<p>The measured data, processed data and metadata related to the publication "Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO" (doi: 10.1109/TASC.2022.3141699) are uploaded. The raw data correspond to susceptibility measurement as a function of temperature. Out of this measurement, strand distribution in the superconducting cable is determined by analysis.</p> <p>This work was supported by the Swiss National Science Foundation (SNF) under contract number 200021_179134.</p>
Minirhizotron images for RootPainter demo
<p>This dataset consists of 100 minirhizotron images (2340 x 2400 pixels; resolution: 148 px/mm) taken with the manual UHD minirhizotron camera (<a href="https://www.vienna-scientific.com/products/minirhizotron-systems/manual/">VSI-BARTZ MS-190</a>) from Vienna Scientific Instruments.</p> <p>The images were acquired in a grassland field experiment (<a href="https://gepris.dfg.de/gepris/projekt/420444099?language=en">POEM experiment</a>) in which the order of arrival of three plant functional groups (forbs, grasses and legumes) was manipulated. In each plot, two root observation tubes were installed at a 45° angle six months before the start of the experiment (i.e. the first sowing event on April 13, 2021). Along each tube, images are taken at 18 different depths (from 1.4 to 49.5 cm) twice a month from April to September and once a month from October to March.</p> <p>The POEM experiment is funded by the <a href="https://www.dfg.de/">German Research Foundation</a>.</p>
Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system (dataset)
<p>Set of design maps for the EU DEMO VVPSS, for each:</p> <ul> <li>VVPSS size</li> <li>Number of VVPSS connections</li> <li>Initial VVPSS temperature</li> </ul> <p>reporting peak and equilibrium pressure in the VV following an in-vessel LOCA initiated by a double-ended guillotine break of the largest feeding pipe. Model details reported in publication A. Froio and I. Moscato, Effect of different system parameters on the design of the EU DEMO vacuum vessel pressure suppression system, submitted to Fusion Engineering and Design.</p> <p>The README.txt file has been written according to the Dubline Core Standard for Metadata (https://www.dublincore.org/).</p>
Demo code for "ESPRESSO: Spatiotemporal omics based on organelle phenotyping": https://doi.org/10.1101/2024.06.13.598932
<p>The files and code provided are sufficient to run end-to-end the ESPRESSO analysis, from the microscope data to the feature analysis.<br>In the following, we provide guidance for the installation and running of the program, the output for each of the scripts are also stored in the <br>"ESPRESSO - Demo\Output of scripts" folder and can be consulted directly.</p>
Dataset _ Influence of liquid-to-biogas ratio and alkalinity on the biogas upgrading performance in a demo scale algal-bacterial photobioreactor
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of liquid-to-biogas ratio and alkalinity on the biogas upgrading performance in a demo scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Demo of the alpha version of MEMORISE Document Manager
<p>Video demonstration showing the basic workflow of the MEMORISE Document Manager</p>
Dataset for SPRINTER demos
<p>This dataset contains the two files required for the complete demos of the SPRINTER algorithm, corresponding to two datasets obtained from subsets of 500 cells from the diploid and tetraploid ground truth datasets generated in the related SPRINTER study.</p>
Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions and in Steady-State (dataset)
<p>Database for reproducing the calculations presented in the publication "Parametric Study of the Radiative Load Distribution on the EU-DEMO First Wall Due to SPI-Mitigated Disruptions", submitted to <em>Fusion Engineering and Design</em>.</p> <p>Work carried out within the framework of the EUROfusion Consortium.</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.