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Dataset results
54 results for “demo 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).
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>
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>
Testing data from SUPER PV demo site in Vilnius (Lithuania)
<p>Sets of the data, collected with SuperPV MLPE boxes from the Lithuanian demo site during the period 2021/08/15 - 2021/08/30. Contain information of the PV modules parameters, as follows:</p> <p>"1122334455667788" - Nr.1 / 90-degree angle (vertical);<br> "FFFFFFFFFFFFFFFF" - Nr.2 / 90-degree angle (vertical);</p> <p>"3333333333333333" - Nr.3 / 45-degree angle;<br> "4444444444444444" - Nr.4 / 45-degree angle;</p> <p>Data files column names explanation:</p> <p>Uoc - open circuit voltage<br> Isc - short circuit current<br> Uin - voltage at maximum power point<br> Iin - current at maximum power point</p> <p>Temp - temperature<br> P - power</p>
Demo data for global-canopy-height-model
<p>Demo data for the example scripts provided in <a href="https://github.com/langnico/global-canopy-height-model">https://github.com/langnico/global-canopy-height-model</a>.</p><p>Please see the README in the github repository for further information and see Lang, et al. (2023) for more information.</p><p><strong>Reference:</strong></p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p><p> </p>
OneNet project - T9.3 - Spanish demo open data
<p>-Anonymized technical data from flexible resources participating in OneNet Spanish demonstration </p> <p>-Market results assessed by the local market platforms considering market bids and DSOs requirement for the OneNet Spanish demonstration</p>
GNRS demo data and code
<p>Data and code for recreating Fig. 3 (maps) and all summary statistics in main text and Supporting Information of Boyle et al, "Geographic Name Resolution Service: A tool for the standardization and indexing of world political division names, with applications to species distribution modeling". </p>
[Demo Input Data] for SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells
<p>This archive (input.tar.gz) contains the demo data for SCAFE v1.0.0 (on <a href="https://doi.org/10.5281/zenodo.7023163">Zenodo</a> or <a href="https://github.com/chung-lab/SCAFE/releases/tag/v1.0.0">Github</a>)</p> <p><em>SCAFE</em> (Single Cell Analysis of Five-prime Ends) provides an end-to-end solution for processing of single cell 5’end RNA-seq data. It takes a read alignment file (*.bam) from single-cell RNA-5’end-sequencing (e.g. 10xGenomics Chromimum®), precisely maps the cDNA 5'ends (i.e. transcription start sites, TSS), filters for the artefacts and identifies genuine TSS clusters using logistic regression. Based on the TSS clusters, it defines transcribed cis-regulatory elements (tCRE) and annotated them to gene models. It then counts the UMI in tCRE in single cells and returns a tCRE UMI/cellbarcode matrix ready for downstream analyses, e.g. cell-type clustering, linking promoters to enhancers by co-activity <em>etc</em>.</p> <p>For details on installation, usage and test run on demo data, visit <a href="https://github.com/chung-lab/SCAFE">https://github.com/chung-lab/SCAFE</a></p>
Sample 3D image data from RIMS method for image analysis code demo
<p>Sample 3D image data from RIMS method applied to mechanical test on hydrogel sphere packings, to be used in image analysis code demo as demonstrated in the ALERT Geomechanics doctoral school 2022. The data is a small subset from a larger set of data as found on Dryad via 10.5061/dryad.6djh9w0x8 and is separated here on Zenodo to make the subset more machine-readable.</p>
The demo data set for the meta16S-Seq workflow using Qiime2
<p>The demo data used in the introduction of meta16S-Seq workflow using Qiime2. The original manuscript of the workflow introduction is written by Yuh Shiwa. The workflow is translated in Common Workflow Language by Tazro Ohta.</p>
Dataset - Hydrolisis and codigestion data from lab and demo scale in INCOVER project at Chiclana de la Frontera (SP).
<p>This dataset contains data obtained from pilot at lab and demo scales, for control and optimization of the hydrolysis and codigestion processes of algae, with sewage sludge and molasses.</p> <p>The dataset can help future projects to increase TRL of the pilot plants from TRL 5-6 to closest market applications (TRL 7-8).</p>
Demo Data for The Last Metric
<p>Demonstration dataset for "The Last Metric", information theory based metric for quantifying the performance of Rubin-LSST survey strategies for redshift inference. Corresponding code is found at https://github.com/aimalz/TheLastMetric and a description of The Last Metric approach in https://arxiv.org/abs/2104.08229 </p>
RSW gun fault prediction benchmark data set (demo)
<p>The resistance spot welding (RSW) welding gun fault prediction benchmark data set has 72 multivariate time series in the training set and 8 in the testing set. Each time series length 604800 sampled at 1 Hz with missing values and has 20 dimensions (c1-c19 and the error code). We retain the missing value and the outliers of the welding gun time series for the potential of imputation research in the future.<br> This data set supports an academic paper named 'benchmark study for welding gun fault prediction'.</p> <p><strong>Feature name and explanation:</strong></p> <p>c1 : Electrode cap offset;</p> <p>c2 : Electrode force;</p> <p>c3 : Electrode position;</p> <p>c4 : Force build-up;</p> <p>c5 : Balance pressure;</p> <p>c6 : Friction;</p> <p>c7 : Maximum aperture;</p> <p>c8 : Maximum electrode force;</p> <p>c9 : Mtart friction;</p> <p>c10 : US2;</p> <p>c11 : Welding point count;</p> <p>c12 : Position count;</p> <p>c13: Setpoints of counterbalance pressure;</p> <p>c14: Setpoints of electrode force;</p> <p>c15 : Setpoints of electrode position;</p> <p>c16: Setpoints of sheet thickness;</p> <p>c17 : Setpoints of velocity;<br> c18: Setpoints of force build-up;<br> c19 : Offset value in robot.</p> <p><strong>Machine Learning Task:</strong><br> This dataset is suitable for a time series forecasting task, where machine learning models can be trained to predict future welding parameters based on the provided welding parameters time series in history. </p> <p><strong>Code for quick start:</strong></p> <p><a href="https://zenodo.org/record/7655025">https://zenodo.org/record/7655025</a></p> <p>If you want to have an overview of the data before downloading all of it, you can download only the files with the word "Damo" in the file name.</p> <p>For any question, please contact 1910633@stu.neu.edu.cn</p>
PySerialEM demo data
<p>Demo data associated with <a href="https://github.com/stefsmeets/pyserialem">https://github.com/stefsmeets/pyserialem</a></p>
Pyxem 4D STEM Demo Data
<p>These are some example data files for doing 4D STEM using pyxem. </p>
Prometheus stress testing data from the microservices-demo "sockshop" application
<p>Stress testing done with Locust, stressing the various microservice API endpoints available from the sockshop microservices demo found in https://microservices-demo.github.io/ </p><p>Part of a master's thesis project</p>
Circusol Cloverleaf demo data
<p>Data on the second-life battery demonstrator Cloverleaf that was implemented in the context of Circusol:</p> <ul> <li>PV production</li> <li>Consumption</li> <li>Self-consumption</li> <li>Self-sufficiency</li> <li>Self-consuption vs self-suffiency</li> </ul>
Demo Data clin.iobio
<p>Clin.iobio - Workflow and reporting for iobio variant analysis pipeline, funded by NIH R01 HG009712.</p> <p>Code: https://github.com/iobio/clin.iobio</p> <p>Ward A, Velinder M, Di Sera T, Ekawade A, Malone Jenkins S, Moore B, Mao R, Bayrak-Toydemir P, Marth G. <em>Clin.iobio</em>: A Collaborative Diagnostic Workflow to Enable Team-Based Precision Genomics. <em>Journal of Personalized Medicine</em>. 2022; 12(1):73. https://doi.org/10.3390/jpm12010073</p>
Demo of Impatto: A Static Analyzer for Quantitative Input Data Usage
<p><strong>Impatto</strong> is a sound fully-automatic and always-terminating static analysis tool based on the quantitative framework for input data usage properties proposed by Mazzucato (https://hal.science/hal-04339001).<strong>Impatto</strong> leverages an underlying backward analyzer to compute the set of input-output relations of the program under analysis. This backward analyzer is a parameter of the tool, allowing different kind of analyses such as program or neural network analysis. Furthermore, the choice of the impact definition is also a parameter of the tool to better suit several factors, such as the program structure, the environment, and the intuition of the researcher.</p> <p>GitHub repository at https://github.com/denismazzucato/impatto</p>
Demo MRI data for recenter tool
<p>This is a small exported MRI dataset from animal imaging that can be used to demonstrate the tool for recentering DICOMs for ergonomic tests for image-guided interventions applications. The tool is available in https://github.com/ProteusMRIgHIFU/RecenterDICOM </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.