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Dataset results
29 results for “toy dataset”
Slice-by-Slice X-ray Tomography dataset of Dog Toy
<p>This submission contains a dataset used in the paper</p> <p>"Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." <em>arXiv preprint <a href="https://arxiv.org/abs/2311.05269">arXiv:2311.05269</a></em> (2023)."</p> <p>The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 <strong>μ</strong>m each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the <a href="https://arxiv.org/abs/2311.05269">above paper</a> and in </p> <p>Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6"><em>Sci Data</em> <strong>10</strong>, 576 (2023)</a> or <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a></p> <p>The upload consists of two files, namely:</p> <ol> <li>GrayBone90kV4Filter.zip: contains the raw measurement data.</li> <li>GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. </li> </ol> <p>In the Github repository <a href="https://github.com/ajinkyakadu/DynamicXRayCT">https://github.com/ajinkyakadu/DynamicXRayCT</a>, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in <a href="https://www.nature.com/articles/s41597-023-02484-6">Kiss et al 2023</a> paper referenced above. </p>
Datasets of fitted toy Monte Carlo samples for "Gaussian Process-based calculation of look-elsewhere trials factor"
<p>For the paper "Gaussian Process-based calculation of look-elsewhere trials factor", we generated and fitted a number of toy Monte Carlo samples which can be used to reproduce figures from the paper or do tests of hypotheses inspired by the method suggested in the article.</p> <p>Together with the hdf5 files containing the samples, we also provide a Jupyter notebook which was used to produce the figures.</p>
Antihypertensive drug metabolite screening toy dataset for MS/MS application
<p><strong>Objectives :</strong></p> <p>Detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments</p> <p><strong>Cohort :</strong></p> <p>6/26 patients on antihypertensive therapy</p> <p><strong>Mass spectrometer :</strong></p> <p>Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments</p>
Sacurine toy dataset - 6 samples / 3 QC / 3 Blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
Dataset: Dow Jones U.S. Toys Total Stock (RILYM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Sacurine toy dataset - 2 samples / 2 blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
AdaptSearch - toy dataset - Trinity 4 samples
<p><a href="https://github.com/abims-sbr/adaptsearch"><strong>AdaptSearch</strong></a> is dedicated to the search of selection within branches and sites of phylogenetic trees built from groups of orthologous genes. The first tools proceeds to data filtering and building of genes groups, while the second part proceeds to phylogeny building (using RAxML) and selection detection (codeML).</p> <p>This dataset can be used as toy example to test the pipeline</p>
Coralshift Master of Research Project Toy Datasets
<p><a href="https://pro.arcgis.com/en/pro-app/latest/help/data/multidimensional/what-is-netcdf-data.htm#:~:text=NetCDF%20(network%20Common%20Data%20Form,view%20from%20the%20netCDF%20file.">.netcdf</a> files for demonstrating methods and visualising data reported in the "Coralshift" Master of Research project. Datasets simplified to reduce computational demand and/or limited to <a href="http://pid.geoscience.gov.au/dataset/ga/115066">Great Barrier Reef A</a>.</p> <p><strong>GBR_A_Couce_comparison.nc </strong>– time-parameterised statistics for comparison to Couce et al. (<a href="https://doi.org/10.1111/j.1365-2699.2012.02706.x">2013</a>, <a href="https://doi.org/10.1111/gcb.16690">2023</a>).</p> <p><strong>GBR_A_all_variables_timeseries.nc </strong>– annual means of all variables used in the accompanying report.</p>
Toy datasets for compression by Picopore
<p>A toy dataset including 1D and 2D runs on Oxford Nanopore Technologies MinION with R9 and R9.4 chemistries.</p> <p>The files for the toy datasets were chosen randomly from the <em>pass</em> folder of four MinION datasets generated at the Australian Genome Research Facility. For the R9 1D dataset, DNA was extracted from the lung of a juvenile 129/Sv mouse using the DNeasy Blood and Tissue kit (Qiagen). For the R9 2D, R9.4 1D and R9.4 2D datasets, DNA was extracted from a culture of <em>escherichia coli </em>K12 MG1655 using the Blood & Cell Culture DNA Kit (Qiagen). Quality control performed by visualisation on the TapeStation (Agilent).</p>
Toy dataset with pairs of molecules differing in total charge
<pre>On the inclusion of charge and spin states in Cartesian tensor neural network potentials. (2024)<br>Guillem Simeon, Antonio Mirarchi, Raul P. Pelaez, Raimondas Galvelis and Gianni De Fabritiis. Two toy datasets, Dataset A and Dataset B, each one comprising five members of five pairs of<br>unique molecular systems, each of the elements in the pair differing in total charge. <br><br>These pairs are indistinguishable to a neural network that does not account for total charge,<br>as they share identical atomic numbers and geometric configurations.<br><br>The datasets include total charges, Gasteiger partial charges computed with RDKit,<br>and calculated energies and atomic forces for 2000 conformers per molecule using GFN2-xTB.<br><br>Therefore, each dataset contains a total of 10k data points.<br><br>Conformers were generated by minimizing each molecule, displacing atomic positions with Gaussian<br>noise with a standard deviation of 0.2Å, and filtering them such that maximum atomic forces are <br><100 eV/Å. <br>Data Format and Availability:<br> Both datasets are available in HDF5 (Hierarchical Data Format version 5) format,<br>compatible with Ace dataset (layout version 2.0) in TorchMD-Net <br>(https://github.com/torchmd/torchmd-net/blob/main/torchmdnet/datasets/ace.py). Data Structure: Each molecule is assigned a unique integer identifier, consistent across both sub-datasets,<br>facilitating pairing between conformations. The HDF5 file is structured as follows:<br> Each top-level group corresponds to a unique molecule. Within each group, considering N is the number of atoms in the molecule and M is the number of<br>conformations, the following datasets are provided: - atomic_numbers: Atomic numbers of atoms in the molecule, with a shape of (N,); - formal_charges: Formal charges of atoms in the molecule, with a shape of (N,); - formation_energies: Formation energy of each conformation, with a shape of (M,),<br> expressed in units of eV;<br>- forces: Forces on atoms in each conformation, with a shape of (M, N, 3), expressed in eV/Å. - partial_charges: Gasteiger partial charges of atoms in each conformation,<br> with a shape of (M, N), expressed in e; - positions: Cartesian coordinates of atoms in each conformation, with a shape of (M, N, 3),<br> expressed in Å. </pre>
Toy dataset for GLOSSA Shiny app
<p>This toy dataset is designed to help users get started with the GLOSSA Shiny app. It includes occurrences for two species: one globally distributed (<code>sp1.txt</code>) and another endemic to the continental shelf of Australia (<code>sp2.csv</code>). The dataset also contains four predictor variables: two continuous and two categorical (<code>fit_layers.zip</code>). Additionally, it includes files representing two projected climate scenarios (<code>project_layers_1.zip</code> and <code>project_layers_2.zip</code>). We also include two polygons to define the extent of the study area: a global polygon object (<code>world.gpkg</code>) and a polygon delimiting the Australian region (<code>oceania.gpkg</code>).</p>
EBAII - Linux training toy dataset - bed file of hg38 exons
<p>This is a toy dataset for the Linux training session at EBAII (Initiation au traitement des données de génomique obtenues par séquençage à haut débit)</p>
Toy dataset for metaGEM documentation (Gut v1)
<p>This dataset was generated to test and benchmark the <a href="https://github.com/franciscozorrilla/metaBAGpipes">metaGEM</a> workflow.</p> <p>The 100 bp illumina WGS reads consist of ~10% subsets of 3 paired end sets of reads from the following <a href="https://www.nature.com/articles/nature12198">publication</a>:</p> <blockquote> <p>Karlsson, Fredrik H., et al. “Gut Metagenome in European Women with Normal, Impaired and Diabetic Glucose Control.”<em> Nature</em>, vol.498,no.7452,2013,pp.99–103., doi:10.1038/nature12198.</p> </blockquote> <p>SRA Accession code <a href="https://www.ncbi.nlm.nih.gov/sra?term=ERP002469">ERP002469</a>.</p> <p>The subsets were generated using the command line tool <a href="https://github.com/lh3/seqtk">seqtk</a>:</p> <pre><code class="language-bash">seqtk sample -s100 sample_X.fastq.gz 3000000 > subset_X.fastq.gz</code></pre> <p>The sample names in the original publication used to generate the toy dataset are ERR260162 (sample1), ERR260173 (sample2), ERR260184 (sample3).</p>
Toy datasets for GTN materials about Metabolomics data processing
<p>These are the datasets needed for the GTN tutorial about Data processing of Metabolomics MS-based data.</p> <p>The dataMatrix and corresponding sampleMetadata files are simulated data, specially created from scratch for the GTN tutorial.</p> <p>The variableMetadata file's columns of m/z and rt characteristics have been filled with values picked from a table obtained by an extraction process of some untargetted MS raw files. MS raw files used are from https://zenodo.org/record/3757956; extraction was performed using XCMS with parameters that might not be optimal.</p>
Toy glacier elevation change dataset
<p>Dataset for conference paper: 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' at the ICML Workshop tructured Probabilistic Inference and Generative Modelling, 2023 (Honolulu, Hawaii, USA).</p> <p><a href="https://arxiv.org/abs/2307.03093">https://arxiv.org/abs/2307.03093</a></p>
Small Toy Adipose TPM Expression Dataset
<p>Small TPM expression of genes in the adipose (subcutaneous). Original data is from GTEX V8 on TPM counts of genes in the adipose.</p>
RIC-seq toy dataset
<p>RIC-seq test data for RIC-contacts pipeline</p>
Xdas Toy DAS Datasets
<h3>Overview</h3> <p>This data repository contains some toy DAS (Distributed Acoustic Sensing) datasets to play with the Xdas library.</p> <h3>Source</h3> <p>The datasets were obtained during the POST and the ABYSS experiments by interrogating several telecom cables from the GTD company in central Chile. Several OptoDAS interogator from the ASN (Alcatel Submaring Network) company were used.<br>This project is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101041092 – ABYSS)</p> <h3>Details</h3> <p>This data repository contains three datasets located in three folders:<br>- <code>ntp_sync</code>: an ~2 min example of dataset that was laking GPS syncronization and that present some timing issues.<br>- <code>gps_multiacq</code>: a ~6 min example where the acquisition parameters where changed two times.<br>- <code>gps_multicable</code>: a ~10 min example of simultanous recordings on three cables. An regional earthquake is clearly visible in that dataset.</p>
clusterCleaver toy dataset
<p>A processed anndata of MDA-MB-231 cells and a list of surface marker genes. </p>
Toy Dataset for DeepCFD PINN for 2D incompressible NS flow
<p>Toy Dataset for DeepCFD PINN for 2D incompressible Navier Stokes flow</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.