Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,302
datasets available to search
ShareScore release 0.9.0
Dataset results
9,302 results for “DETECT”
Real-bogus scores for active anomaly detection
<p>Data description for <a href="https://arxiv.org/abs/2409.10256">Semenikhin et al., 2024</a></p> <p>The dataset consists of the following files:</p> <p><strong>feature_snad4_r_100.dat</strong> contains light curve feature data for objects, where each object is represented by 54 feature values. These values are encoded as little-endian single-precision IEEE-754 floating-point numbers (32-bit floats). Feature names are listed in the plain text file <strong>feature_snad4_r_100.name</strong>, with one name per line.<br><strong>sid_snad4_r_100.dat</strong> contains ZTF DR object identifiers, encoded as little-endian 64-bit unsigned integers.</p> <p><strong>exp_feature_snad4_r_100.dat</strong> contains the same features as <strong>feature_snad4_r_100.dat</strong>, but with an additional column representing the real-bogus classifier prediction. Each object in this file corresponds to 55 features: the original 54 features plus 1 additional feature. Feature names for this file are provided in <strong>exp_feature_snad4_r_100.name</strong>.</p> <p>The files <strong>sid_snad4_r_100.dat</strong>, <strong>feature_snad4_r_100.dat</strong>, and <strong>exp_feature_snad4_r_100.dat</strong> share the same object order.</p> <p><br>Below is a sample Python script for accessing the data using NumPy:</p> <p><code>import numpy as np</code></p> <p><code># Load object IDs</code><br><code>oid = np.memmap('sid_snad4_r_100.dat', mode='c', dtype=np.uint64)</code></p> <p><code># Load features and reshape</code><br><code>feature = np.memmap('feature_snad4_r_100.dat', mode='c', dtype=np.float32).reshape(oid.shape[0], -1)</code></p> <p><code># Print dataset information</code><br><code>print(f'Number of objects: {len(oid)}')</code><br><code>print(f'Features shape: {feature.shape}')</code></p>
Signal-to-Noise Ratios, DYNAMITE Posteriors, and Combined Detectability Plots for 173 TESS Multi-Planet Systems
<p>Collection of figures for 173 TESS Multi-Planet Systems. Each figure (titled with the system's TOI number) has 2 rows of planets, corresponding to results from the clustered period model (upper) and period ratio model (lower) predictions for an additional planet in each system. The left-most column of the plots show the signal-to-noise ratios (SNR) of simulated transits for planets with a given radius and period in the corresponding TESS light curve. The middle column shows the dot product of the radius and period posteriors generated by DYNAMITE using TESS light curves up to and including Sector 76. The right-most column shows the detectability (SNR) modulated by the DYNAMITE posteriors, showing the location in (P, Rp) space where an additional planet in this system is most likely to be detected under each model.</p>
On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection
<p>Additional resources for the paper:</p> <h3><strong><a href="https://ieeexplore.ieee.org/abstract/document/10826088">On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection.</a></strong></h3> <p>Lewis, N., Cavalcante, C. C., Boukouvalas, Z., & Corizzo, R.</p> <p><em>2024 IEEE International Conference on Big Data (BigData)</em> (pp. 3277-3281). IEEE.</p> <pre> </pre> <p> </p> <p>MMHS150K [1] is a manually labeled multimodal dataset that contains $150000$ tweets with two modalities: text, and corresponding image. Tweets are collected from September 2018 until February 2019 and are labeled according to different types of hate speech: no attacks to any community, racist, sexist, homophobic, religion-based attacks, or attacks to other communities. </p> <p>We extract vector embeddings leveraging different text (BERT, OpenAI) and image (ResNet, PVT, ViT) modele backbones and assess their effectiveness in the hate speech detection task.</p> <p> </p> <h2>Citation:</h2> <pre>@inproceedings{lewis2024effectiveness, title={On the Effectiveness of Text and Image Embeddings in Multimodal Hate Speech Detection}, author={Lewis, Nora and Cavalcante, Charles C and Boukouvalas, Zois and Corizzo, Roberto}, booktitle={2024 IEEE International Conference on Big Data (BigData)}, pages={3277--3281}, year={2024}, organization={IEEE} }</pre>
Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected by spacecraft 1 of the Cluster mission in the foreshock of Earth
<p>Database of Short Large-Amplitude Magnetic Structures (SLAMS) detected in the foreshock of Earth by spacecraft 1 of the Cluster mission between the years 2002-2012.</p> <p>An automated algorithm has been used for SLAMS identification followed by a manual verification process to remove bow shock oscillations and other false detections. SLAMS have been defined to have an amplitude of at least two times the background magnetic field. </p> <p>More details on the creation of the database are given in the following publication:</p> <p><span><span lang="EN-US">Bergman, S.</span></span><span lang="EN-US">, Karlsson, T., Wong Chan, T. K., & Trollvik, H. (2025). Statistical properties of Short Large</span><span lang="EN-US">‐</span><span lang="EN-US">Amplitude Magnetic Structures (SLAMS) in the foreshock of Earth from Cluster measurements. <em>Journal of Geophysical Research: Space Physics</em>, 130. </span><a href="https://doi.org/10.1029/2024JA033568"><span lang="EN-US">https://doi.org/10.1029/2024JA033568</span></a></p> <p>Contact: S. Bergman, sofiabergmanphd@gmail.com </p>
Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset
<p>Extension of the FFHQ dataset (https://github.com/NVlabs/ffhq-dataset) with precise bounding box annotations for eyeglasses detection.</p> <p>If you find this dataset useful in your research, please consider citing the original dataset and the following papers:</p> <p>@article{matuzevicius2024diverse,<br> title={Diverse Dataset for Eyeglasses Detection: Extending the Flickr-Faces-HQ (FFHQ) Dataset},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Sensors},<br> volume={24},<br> number={23},<br> pages={7697},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/s24237697}<br>}</p> <p>@article{matuzevicius2024retrospective,<br> title={A Retrospective Analysis of Automated Image Labeling for Eyewear Detection Using Zero-Shot Object Detectors},<br> author={Matuzevi{\v{c}}ius, Dalius},<br> journal={Electronics},<br> volume={13},<br> number={23},<br> pages={4763},<br> year={2024},<br> publisher={MDPI},<br> url = {https://doi.org/10.3390/electronics13234763}<br>}</p> <p> </p>
Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins
<p>The data set to paper: </p> <p>Name: Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins</p> <p>Authors: Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2*</p> <p>Affiliations: 1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic<br> 2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic<br> 3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic<br> 4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic<br> 5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic<br> *corresponding author: tibor.izsak@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of collection: 1. 5. 2023 - 31. 7. 2024</p> <p>Description: Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).<br> Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.<br> Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.<br> Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.<br> Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.<br> Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).<br> Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.<br> Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.<br> Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.</p>
Detection of detached ice-fragments at polar scarps of Mars
<p>This dataset includes the detections of the detached ice-fragments from 19 polar steep scarps of Mars. The results were detected by a deep learning model, and were saved in shapefile form. </p>
Dataset supporting the tool 'delfies: a Python package for the detection of DNA breakpoints with neo-telomere addition'
<h2>Purpose</h2> <p><br>These data can be used to test my tool <a href="https://github.com/bricoletc/delfies">delfies </a>on real data, to get a concrete sense of its inputs/outputs and test that it is <br>properly installed.</p> <h2>Description</h2> <h3>Genome</h3> <p>I downloaded the genome of <em>Oscheius onirici</em>, accession: <a href="https://www.ebi.ac.uk/ena/browser/view/GCA_932521025.1">GCA_932521025</a>.</p> <p>I subsampled the genome to the last 2kbp of chromosome I, which contains an elimination breakpoint, <br>using `seqkit` v2.8.2, giving the FASTA file in this release.</p> <h3>Sequencing data</h3> <p>I then downloaded the following sequencing data for *O. onirici*, from the European Nucleotide Archive:</p> <ul> <li>ERR5967937: Illumina NovaSeq 6000 paired end short reads. Reads are 2x150bp with average per-base quality of Q27.</li> <li>ERR10796202: Oxford Nanopore PromethION long reads. Reads have average length 11.9kbp and average per-base quality Q11.4.</li> <li>ERR7979900: Pacific Biosciences (PacBio) Sequel II long reads. Reads have average length 11.1kbp and average per-base quality Q28.<br><br></li> </ul> <p>And aligned them to the above genome with `minimap2` version 2.26-r1175, using the following presets: <br>"map-ont" for the Nanopore data, "map-hifi" for the PacBio data, "sr" for the Illumina data.</p> <p>After sorting with `samtools`, this gives the BAM files in this release.</p> <h3>Running delfies</h3> <p>I then ran `delfies` version 0.6.0 on each BAM and genome, as:</p> <p>```sh<br>delfies --threads 16 \<br> --telo_forward_seq TTAGGC \<br> --breakpoint_type all \<br> --min_mapq 20 \<br> --min_supporting_reads 6 \<br> \${genome} \${bam} \${odirname}<br>```</p> <p>The three resulting output directories are in this release, prefixed with `delfies_`.</p> <p><strong>A single, identical breakpoint is found using all three BAMs</strong> (see files '*breakpoint_locations.bed').</p> <h3>Data source</h3> <p>The above raw data were produced and released by the Wellcome Sanger Institute as part of projects <br><a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB51305">PRJEB51305</a> and <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB59023">PRJEB59023.</a></p>
Comprehensive Dataset for Fault Detection and Diagnosis in Inverter-Driven PMSM Systems
<p><span>#</span><span> New in Version 3.0</span> The dataset has been reorganized for improved accessibility and clarity:</p> <ul> <li>/code: Contains all source code (C++ and Python) for data acquisition and processing</li> <li>/metadata: Contains sensor specifications and fault definitions</li> <li>/processed_data: Contains the processed and derived features</li> <li>/raw_data: Contains: <ul> <li>Raw sensor measurements</li> <li>Fault scenario data</li> <li>Normal operation data</li> <li>Thermistor calibration data</li> </ul> </li> <li>/visualizations: Contains data visualization outputs</li> </ul> <p><br>This dataset contains multi-sensor measurements from an inverter-driven PMSM system under various fault conditions. It includes: </p> <ul> <li>10,892 samples across 9 operational conditions</li> <li>8 raw sensor measurements</li> <li>15 derived features</li> <li>Data collected at 10 Hz sampling rate</li> <li> Fault scenarios including open-circuit, short-circuit, and overheating conditions</li> </ul> <p> Keywords:</p> <ul> <li>PMSM</li> <li>Fault Detection</li> <li>Inverter Faults</li> <li>Motor Drive Systems</li> <li>Experimental Data</li> <li>Machine Learning</li> </ul>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection
Open the record for dataset details and reuse information.
Person Detection on Construction Sites
<p>All information can be found here:</p> <p><a href="https://github.com/aidresden/person_detection_construction_sites" target="_blank" rel="noopener">https://github.com/aidresden/person_detection_construction_sites</a></p> <p>The development of this data set was funded by the Federal Ministry of Labour and Social Affairs (BMAS) and the Federal Institute for Occupational Safety and Health (BAuA) under the administrative agreement ‘Artificial Intelligence in a Safe and Healthy Working Environment’.</p>
Polar Cap Patches Detected with RISR-N
<h3>Database of Polar Cap Patches detected with the North face of the Resolute Bay Incoherent Scatter Radar (RISR-N)</h3> <p>This dataset was generated by applying two polar cap patch detection algorithm described to the full RISR-N database.</p> <p>The Ren2018 algorithm was first described in <a href="http://https//doi.org/10.1029/2018JA025621">Ren et al., 2018 (doi:10.1029/2018JA025621)</a>. It includes the time, peak, and prominence of all detected patches, asd well as the average electron and ion temperature and electron density within each patch.</p> <p>The Perry2018 algorithm was first described in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018RS006600">Perry & St. Maurice, 2018 (doi:10.1029/2018RS006600)</a>. Instead of a list of patches, this algorithm generates a patch index for each time in the database which corresponds to the percentage of the F-region FoV filled with patches.</p> <p>The script that generates this database in available on GitHub: <a href="https://github.com/Polar-Cap-Scintillation/polar_cap_patch_detection">Polar-Cap-Scintillation/polar_cap_patch_detection</a></p> <p>Version 4 of this dataset includes RISR-N data through 2024 and has an error corrected that may cause some patches to be counted multiple times in the earlier versions.</p>
Time-lapse of AARTFAAC detections of radio meteors in Perseids 2020
<p>LOFAR AARTFAAC images (integrated over all observing bands) during the Perseids meteor shower in the night 2020 August 12 --13. The large-scale diffuse emission is the Galactic plane. The brightest radio sources Cassiopeia~A and Cygnus~A have been subtracted, sometimes leaving some artefacts. In red, trajectories as computed from the CAMS BeNeLux optical observations are overlaid.</p> <p>Observing frequencies were in the range 30 to 60 MHz.</p>
DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model
<p>Training and test images of <em>E. coli</em> cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example images show predictions of drug-treated <em>E. coli</em> cells.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (confocal fluorescence) and manual annotations</p> <p><strong>Microscopy data type</strong>: Confocal fluorescence images of fixed <em>E. coli</em> cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format</p> <p><strong>Microscope</strong>: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA)</p> <p><strong>Cell type</strong>: Chemically fixed <em>E. coli</em> NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated);</p> <p><strong>File format</strong>: .png (RGB)</p> <p><strong>Image size</strong>: 400 x 400 px² (Pixel size: 84 nm)</p> <p> </p> <p><strong>YOLOv2 model</strong></p> <p>The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping.</p> <p>The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model
<p>Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli</em> cells and the respective annotation for specific growth stages.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (bright field) and annotations in PASCAL VOC format</p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset)</p> <p>1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series)</p> <p><strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using <em>LabelImg </em>(https://github.com/tzutalin/labelImg).</p> <p> </p> <p><strong>YOLOv2 model</strong></p> <p>The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation.</p> <p>The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Solvent sites detection from molecular dynamics in mixed solvents
<p>In this zip file, you will find 3 molecular dynamics (MDs) that were run using the AMBER molecular dynamics package (https://ambermd.org/), and the necessary scripts to obtain the solvent sites.</p> <p>The MDs correspond to the kinase and rubredoxin domains of protein kinase G from Mycobacterium tuberculosis. </p> <p> </p> <p> </p>
Cracks in the mirror hypothesis: high specularity does not reduce detection or predation risk
<p>Some animals, including certain fish, beetles, spiders and Lepidoptera chrysalises, have such shiny or glossy surfaces that they appear almost mirror-like. A compelling but unsubstantiated hypothesis is that a highly specular or mirror-like appearance enhances survival by reflecting the surrounding environment and reducing detectability.</p> <p>We tested this hypothesis by asking human participants to wear a mobile eye-tracking device and locate highly realistic mirror-green and diffuse-green replica beetles against a variety of backgrounds in a natural forest environment. We also tested whether a mirror-like appearance enhances survival to wild predators by monitoring survival of mirror-green and diffuse-green replica beetles in a forested habitat and an open habitat.</p> <p>Human participants showed no difference in the detection probability or detection latency of mirror versus diffuse replica beetles, indicating that mirror-like appearance does not impair prey capture. The field predation experiment found no difference in survival between the mirror and diffuse replica beetles in forested environments. Similarly, there was no difference in survival when beetles were deployed in open habitat where there is no background to reflect, indicating that predators detect and do not actively avoid mirror-like beetles.</p> <p>Our results suggest that a mirror-like appearance does not reduce attack by predators. Instead, highly specular, mirror-like surfaces may have evolved for an alternate visual function or as a secondary consequence of selection for a non-visual function, such as thermoregulation.</p>
First Results on Wake Detection in SAR Images by Deep Learning
<p>Supplementary Materials of the paper "First Results on Wake Detection in SAR Images by Deep Learning" published on MDPI - Remote Sensing</p>
Datasets for ``Big bang nucleosynthesis limits and relic gravitational waves detection prospects''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table I of the paper "Big bang nucleosynthesis limits and relic gravitational waves detection prospects" by T. Kahniashvili, E. Clarke, J. Stepp, & Axel Brandenburg. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
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.