Find research datasets worth reusing
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Dataset results
54 results for “demo data”
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
NEANIAS UW-BAT Demo Data Shipwreck Image
Digital terrain model (DTM) of shipwreck recorded with Teledyne T50-R echosounder. Format: JPG
NEANIAS UW-BAT Demo Data Shipwreck DTM
Digital terrain model (DTM) of shipwreck recorded with Teledyne T50-R echosounder. Format: GMT NetCDF
Demo data to evaluate zenodo-govdata integration
<p>This data will be used to investigate the feasibility of using Zenodo as a data repository for publishing BMWK data. In particular, the linking of Zenonod with GovData will be examined.</p>
Pixelflow Seed Phenotyping Use Case Demo Data
<p>Example 2D and 3D seed phenotyping data for pixelflow use case demo notebooks on the Scivision gallery. Contains original images (.tif) and associated segmented labels of each seed (.npy)</p>
Data for the Demo of the Image2Reg pipeline
<p>This entry contains the two data directories required by the demo of our Image2Reg pipeline, which is described in our GitHub repository https://github.com/uhlerlab/image2reg . The data is automatically downloaded from Zenodo during the execution of the demo application. The image data contained in the repository is taken from the data from Mohammad Hossein Rohban Shantanu Singh Xiaoyun Wu Julia B Berthet Mark-Anthony Bray Yashaswi Shrestha Xaralabos Varelas Jesse S Boehm Anne E Carpenter(2017) Systematic morphological profiling of human gene and allele function via Cell Painting, eLife 6:e24060. Please cite this resource if you use our application or this data. All rights for the imaging data remain with the authors of the before mentioned publication.</p>
Demo Data for CoastalLens
<p>Data required to run DEMO version of <a href="https://github.com/AthinaLange/CoastalLens" target="_blank" rel="noopener">CoastalLens</a>. <br>Video taken at Torrey Pines State Park on Dec 15th 2021 with a DJI Phantom 4 RTK by the Scripps Institution of Oceanography Coastal Processes Group.</p>
SCIMAP Demo Data
<p>Sample data for following along with the scimap tutorial. </p>
DeepCAD-RT demo data 2
<p>Calcium imaging data (using Naomi simulation) </p>
Elastic Data Analytics for the Cloud-to-Things Continuum - Demo Video
<p>The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need of sharing them, and the fact that many consumers are interested in the same data call for an efficient management of the available resources. The cloud-to-thing continuum can be used to execute different analytics closer to the data source so infrastructure consumption and data circulation can be optimized. In this paper, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior, is proposed.</p> <p>This artifact corresponds with a descriptive video of the framework presented in the paper. </p>
DeepCAD-RT demo data
<p>DeepCAD-RT demo data. It can be downloaded automatically using pipeline file.</p>
edtools_demo_data
<p>Batch 3D electron diffraction (3D ED) datasets for phase analysis and structure determination using <em>edtools</em>. </p> <p><em><strong>edtools</strong></em> is a python package for automated processing of a large number of 3D ED datasets. It can be downloaded from <a href="https://doi.org/10.5281/zenodo.5727189">https://doi.org/10.5281/zenodo.5727189</a>.</p> <p>The datasets were collected on a zeolite mixture sample using the serial rotation electron diffraction (SerialRED) data collection technique implemented in the program <strong>Instamatic</strong> (available at <a href="https://doi.org/10.5281/zenodo.5175957">https://doi.org/10.5281/zenodo.5175957</a>), which runs on a JEOL JEM-2100-LaB6 at 200 kV equipped with a 512 x 512 Timepix hybrid pixel detector (55 x 55 µm pixel size, QTPX-262k, Amsterdam Scientific Instruments).</p>
Demo data and models for: Automated speech detection in eco-acoustic data enables privacy protection and human disturbance quantification
<p>Folder containing a <strong>demo dataset</strong> and the <strong>model weights</strong> resulting from the ecoVAD pipeline. The data contained in this folder allows for full reproducibility of the pipeline described on the <a href="https://github.com/NINAnor/ecoVAD">ecoVAD GitHub repository</a>.</p> <p>If you have any questions or issues with the dataset, please open an issue on the ecoVAD GitHub repository.</p>
Wiki3DRank demo data for IBERSID 2022
<p>Wiki3DRank application demo data for IBERSID 2022. Includes results, SPARQL queries used, and data obtained from Wikidata Query Service and XTools on literary works, music albums, movies, and Tolkien's Legendarium.</p>
Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"
<p>Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Extra data to run the demo for marmo3Dpose
<p>Extra data to run the demo code for marmo3Dpose, <a href="https://github.com/PrimatoModelling/marmo3Dpose">https://github.com/PrimatoModelling/marmo3Dpose</a></p> <p> </p>
AMP PD Target Explorer Demo for MJFF Data Community
<p>This demo, provided by Victoria Dardov, Ha Duong, Barry Landin, and Dave Vismer of Technome, was hosted by the Michael J. Fox Foundation's Data Community of Practice (DCOP). For more information on the DCOP, please contact: researchcommunity@michaeljfox.org.</p> <p>During this recording, colleagues from Technome provided an overview from the recently released <a href="https://target-explorer.amp-pd.org/" target="_blank" rel="noopener">AMP® PD Target Explorer.</a> </p> <p>AMP® PD (the Accelerating Medicines Partnership Parkinson’s Disease) program is a partnership between FNIH, NINDS, NIA, FDA, Abbvie, GSK, Pfizer, Sanofi, BMS, Verily, ASAP and MJFF) generates and consolidates data from eight unified cohorts (BioFIND, HBS, LBD, LCC, PDBP, PPMI, STEADY-PD3 and SURE-PD3). Data was generated using standardized technology and centrally harmonized and quality controlled. All data was generated from samples collected under similar protocols. This data harmonization process and single data use policy facilitates and simplifies cross-cohort analysis. </p> <p>The AMP PD <a href="https://target-explorer.amp-pd.org/" target="_blank" rel="noopener">Target Explorer</a> is a public resource with open access for exploring nominated genes and proteins from AMP PD data that might be implicated in Parkinson's Disease. The Target Explorer integrates many AMP PD work streams focused on identifying new biomarkers and novel drug targets, with the goal of accelerating treatment discovery for patients with Parkinson's Disease.</p> <p>With the Target Explorer, researchers can explore targets from genomics, transcriptomics, and proteomics data, contributed by the AMP PD community. Use the Target Explorer to compare your findings with the findings of other researchers by entering your list of potential targets into the search engine to find out where they appear in other researchers’ analyses.</p> <p>Researchers can also <a href="https://target-explorer.amp-pd.org/become-a-submitter" target="_blank" rel="noopener">request to contribute</a> to the target lists and be featured in <a href="https://target-explorer.amp-pd.org/teams" target="_blank" rel="noopener">the teams and lists section.</a>. If you are interested in contributing findings from a recent publication or preprint, you can fill out the form in the <a href="https://target-explorer.amp-pd.org/become-a-submitter" target="_blank" rel="noopener">Become a Contributor</a> section.</p> <p>To access a streaming copy of this recording with a variable resolution, please <a href="https://share.vidyard.com/watch/2cGHZiPH1fieQWg9APUKaN" target="_blank" rel="noopener">visit this link</a>.</p>
Integration-based Extraction and Visualization of Jet Stream Cores - Demo Data
<p>Demo data for the publication "Integration-based Extraction and Visualization of Jet Stream Cores", containing the meteorological attirbutes for September 01, 2016 at 00:00. The data is derived from ERA5.</p> <p>The ERA5 data is courtesy of the European Centre for Medium-Range Weather Forecasts (ECMWF) and is documented here: <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation</a> The data is available under the Copernicus License Agreement: <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p>
Data for Change Detection Demo
<p>Data accompanying Github notebook demonstrating how to conduct change detection for protein localization changes in high-content image screens: <a href="https://github.com/alexxijielu/change_detection_book_chapter/blob/master/change_detection.ipynb">change_detection_book_chapter/change_detection.ipynb at master · alexxijielu/change_detection_book_chapter (github.com)</a></p> <ul> <li>data.zip contains all of the data required to run the Python notebook.</li> <li>single_cell_yeast.zip contains all of the single-cell image crops required to reproduce the analysis.</li> </ul>
Air quality data in Florence demo pilot (annex to D7.2)
<p>Research data acquired by ARPAT mobile lab. Procedures and information can be found in D7.2-Florence pilot project report of NEMO project</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.