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3,435 results for “Visualizations”
IODP Expedition 397T Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
IODP Expedition 383 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
Riassunto visuale del progetto "BIOMARCS - Riannodiamo la biodiversità marina"
<p>Questo riassunto visuale, disegnato da Jacopo Sacquegno, riassume l'iniziativa di citizen science, o scienza partecipativa "Riannodiamo la biodiversità marina" (Titolo esteso: <i>Integrating biodiversity monitoring and awareness-raising in coastal peripheries, by engaging citizen-science sailors as focal nodes in the process of local community empowerment; </i>acronimo: BIOMARCS<i>; </i>nome alternativo: Reknotting Marine Biodiversity), finanziata dal progetto europeo IMPETUS* (<a href="https://impetus4cs.eu/">https://impetus4cs.eu/</a>; Grant agreement ID: 101058677; DOI: 10.3030/101058677) </p><p>L'iniziativa coinvolge i velisti delle aree costiere periferiche nella co-progettazione, creazione e gestione di un monitoraggio opportunistico dell'ecosistema marino e della sua biodiversità attraverso tecniche di analisi del DNA ambientale (eDNA). Gli stessi velisti contribuiscono a diffondere e comunicare i dati acquisiti sulla biodiversità tra più parti interessate (ad esempio, studenti delle scuole superiori e gestori di AMP), per rendere "visibile" il patrimonio di biodiversità e rafforzare il senso del luogo e la consapevolezza dell'importanza della salute degli oceani per il benessere delle società umane.</p><p>*IMPETUS is funded by the European Union's Horizon Europe research and innovation programme under grant agreement number 101058677. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.</p>
Raw data of healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p><p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection
<p><strong>See the official website: <a href="https://autovi.utc.fr">https://autovi.utc.fr</a></strong></p> <p>Modern industrial production lines must be set up with robust defect inspection modules that are able to withstand high product variability. This means that in a context of industrial production, new defects that are not yet known may appear, and must therefore be identified.</p> <p>On industrial production lines, the typology of potential defects is vast (texture, part failure, logical defects, etc.). Inspection systems must therefore be able to detect non-listed defects, i.e. not-yet-observed defects upon the development of the inspection system. To solve this problem, research and development of unsupervised AI algorithms on real-world data is required.</p> <p>Renault Group and the Université de technologie de Compiègne (Roberval and Heudiasyc Laboratories) have jointly developed the <em>Automotive Visual Inspection Dataset (AutoVI)</em>, the purpose of which is to be used as a scientific benchmark to compare and develop advanced unsupervised anomaly detection algorithms under real production conditions. The images were acquired on Renault Group's automotive production lines, in a genuine industrial production line environment, with variations in brightness and lighting on constantly moving components. This dataset is representative of actual data acquisition conditions on automotive production lines.</p> <p>The dataset contains 3950 images, split into 1530 training images and 2420 testing images.</p> <p>The evaluation code can be found at <a href="https://github.com/phcarval/autovi_evaluation_code">https://github.com/phcarval/autovi_evaluation_code</a>.</p> <p><strong>Disclaimer</strong><br>All defects shown were intentionally created on Renault Group's production lines for the purpose of producing this dataset. The images were examined and labeled by Renault Group experts, and all defects were corrected after shooting.</p> <p><strong>License</strong><br>Copyright © 2023-2024 Renault Group</p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of the license, visit <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p> <p>For using the data in a way that falls under the commercial use clause of the license, please contact us.</p> <p><strong>Attribution</strong><br>Please use the following for citing the dataset in scientific work:</p> <p>Carvalho, P., Lafou, M., Durupt, A., Leblanc, A., & Grandvalet, Y. (2024). The Automotive Visual Inspection Dataset (AutoVI): A Genuine Industrial Production Dataset for Unsupervised Anomaly Detection [Dataset]. <a href="https://doi.org/10.5281/zenodo.10459003">https://doi.org/10.5281/zenodo.10459003</a></p> <p><strong>Contact</strong><br>If you have any questions or remarks about this dataset, please contact us at philippe.carvalho@utc.fr, meriem.lafou@renault.com, alexandre.durupt@utc.fr, antoine.leblanc@renault.com, yves.grandvalet@utc.fr.</p> <p><strong>Changelog</strong></p> <ul> <li><em>v1.0.0</em> <ul> <li>Cropped engine_wiring, pipe_clip and pipe_staple images</li> <li>Reduced tank_screw, underbody_pipes and underbody_screw image sizes</li> </ul> </li> <li><em>v0.1.1</em> <ul> <li>Added ground truth segmentation maps</li> <li>Fixed categorization of some images</li> <li>Added new defect categories</li> <li>Removed tube_fastening and kitting_cart</li> <li>Removed duplicates in pipe_clip</li> </ul> </li> </ul>
IODP Expedition 378 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
IODP Expedition 367 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
Data for "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection"
<p>The data are the full versions of tables that will be published in the manuscript titled "Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey using Deep Learning Combined with Visual Inspection" by The Astrophysical Journal Supplement Series.</p> <p>The table file named "FIRST_bt_table1.csv" is the full table for "A catalog of 4876 BTRGs identified from VLA FIRST survey". </p> <p>The table file named "FIRST_bt_table2.csv" is the full table for "Cluster details for BTRGs". </p>
Data and Videos for Argos: a toolkit for tracking multiple animals in complex visual environments
<p>Original videos used and data generated for the article "Argos: a toolkit for tracking multiple animals in complex visual environments".</p> <p>The data contains original videos used as input to the Argos Tracking tool, the generated raw tracks in Pandas-HDF5 format, and the corrected tracks after processing with Argos Review tool.</p> <p>It also includes a zip archive with ground truth tracks along with tracks detected from two videos by Argos and several other tracking tools for comparison using the HOTA metric organized in a folder structure suitable for the TrackEval tool.</p>
The gut microbiota of environmentally enriched mice regulates visual cortical plasticity
<p>ABSTRACT</p> <p>The complexity of brain circuits is sculpted both by innate genetic programs and environmental stimuli. Since the 1960s scientists have noticed that raising rodents in an enriched environment (EE) is able to improve all aspects of brain plasticity, from learning and memory to visual plasticity in adult and developing animals. Importantly, EE has also been shown to have beneficial effects on a variety of preclinical models of central nervous system diseases: Alzheimer’s and Parkinson’s disease, Rett syndrome, epilepsy etc, prompting intervention protocols in humans. However, the “enrichment derived key signals” through which this special environment performs its broad positive effects on brain health have not been completely elucidated yet. Here, we focused on signals coming from the body periphery and in particular on the gut microbiota. We found that the intestinal microbiota composition of EE mice is significantly different from the one of standard raised (ST) animals. Treatment of EE mice with an antibiotic cocktail completely prevented the EE-driven enhancement of OD plasticity. Strikingly, the fecal microbiota transplant from EE donors to adult ST mice was able to re-activate OD plasticity in the ST recipients. Thus, taken together our data suggest that experience-dependent changes in gut microbiota regulate brain plasticity.</p> <p>METHODS</p> <p>In the first dataset (Dataset1, files called zr2423) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J mice raised in EE or in ST from birth and collected at different time points during their lives.</p> <p>To analyze the composition of the microbiota of ST and EE mice at different ages, fresh faeces were collected longitudinally in the same subject at postnatal day (P)20 (n=6), P25 (n=6) and P90 (n=6). </p> <p>In the second dataset (Dataset2, files called zr2747) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J: adult donor mice living in EE (EE, n=8), adult recipient mice living in ST condition before the fecal transplantation (preFT, n=8) and 4 weeks after the fecal transplantation (postFT, n=8).</p> <p>For further details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted using a specific kit (QIAamp Powerfecal DNA kit, Qiagen) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Irvine, CA, USA). </p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). The sequencing was performed with >10% PhiX spike-in.</p> <p> </p>
Animation to visualize the effects of laser ablation on unhydrated cement clinker
<p>This animation illustrates the effect of the damage on the surface of unhydrated cement clinker caused by a pulsed laser for a LA-ICP-MS mapping. The dataset contains the raw data and the final animations.</p> <p>The images were acquired using a Thermofischer Scientific Helios G4 UX microscope at 2 kV/0.1 nA. The surface was tilted in two orientations by a few degree and an image was acquired after every tilt. The ablated area has a size of approx. 367 x 300 µm.</p> <p>A detailed description of the specimen and the parameters used for the analysis can be found in the <a href="https://doi.org/10.1016/j.cemconres.2022.106875">corresponding paper</a>.</p> <p><strong>Funding</strong></p> <p>The research was supported by the Deutsche Forschungsgemeinschaft (DFG), grant number <a href="https://gepris.dfg.de/gepris/projekt/344069666">344069666</a>.</p>
Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity
<p>Per-trial dataset accompanying the publication Stauch, Peter, Schuler, and Fries (2020): Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity.<br> Additionaly, preprocessing code is provided as Codebase.zip.</p>
Data from: Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi2Sr2CaCu2O8+δ
<p>This database contains all the necessary data of the manuscript "Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub>". Compared to the previous version (https://doi.org/10.5281/zenodo.5558592), the data of Figure S13B and Figure S13D are exchanges.</p> <p><strong>Abstract of the manuscript</strong></p> <p>In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub> (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.</p>
Xiao et al, Oligodendrocyte Precursor Cells Sculpt the Visual System by Regulating Axonal Remodeling [Dataset]
<p>Raw data from the behavior and imaging experiments of Xiao et al., Nature Neuroscience 2022. For additional details about the acquisition of each part of the dataset, refer to the methods section of the paper. From this dataset, using the published code, all the figures relative to the imaging in the optic tectum can be generated and all the cumulative statistics for the behavioural assays run with Stytra recomputed.</p> <p> </p> <p><strong>Organisation of the dataset</strong></p> <p>This dataset is organised in the following subdirectories:<br> - <em>freely_swimming</em>: contains the data for the freely swimming experiments quantifying motor activity in the various ablated groups. It contains subfolders of groups, each of which contains the Stytra raw data directories for all fish of that group. Refer to `Stytra` and `bouter` documentation for further details about the files.<br> - <em>OMR</em>: contains the data for the quantification of OMR reflex across different spatial frequencies. It contains subfolders for the control and ablated group, each of which contains the Stytra raw data directories for all fish of that group.<br> - <em>receptive_field_imaging</em>: contains the imaging data for the receptive field estimation. Subfolders contains, for each individual fish (both ablated and controls are pooled in the same directory):<br> - stytra raw output from the experiment<br> - data_from_suite2p_unfiltered.h5: `flammkuchen`-loadable `.h5` file that contains the raw fluorescent trace<br> - anatomy.mask: `flammkuchen`-loadable mask file saved by the `pypra` tool that was used for segmenting the tectum, delimiting the region of the tectum<br> The folder contains an additional file, `manual_alignment_offsets.h5`, where the offsets of the manual morphing across fish were saved.</p>
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
Interactive Visualizations for: "Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties"
<p>This deposit includes 13 HTML 3D interactive visualizations of filaments and galaxies investigated in the accepted article, "<em>Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties</em>" by Castignani et al. (accepted, 20-Oct-2021).</p> <p>The specific files correspond to the filaments listed in Table 2 of the accepted manuscript:</p> <table align="left"> <caption>Tabulated HTML files and filaments</caption> <thead> <tr> <th scope="col">HTML File</th> <th scope="col">Filament (Table 2)</th> </tr> </thead> <tbody> <tr> <td> <p>SG_cube_Virgo_Serpens_Filament.html</p> </td> <td> <p>Serpens F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Coma_Berenices_Filament.html</p> </td> <td> <p>Coma Berenices F.</p> </td> </tr> <tr> <td> <p>SG_cube_VirgoIII_Filament.html</p> </td> <td> <p>VirgoIII F.</p> </td> </tr> <tr> <td> <p>SG_cube_Ursa_Major_Cloud.html</p> </td> <td> <p>Ursa Major Cloud</p> </td> </tr> <tr> <td> <p>SG_cube_NGC5353_4_Filament.html</p> </td> <td> <p>NGC5353/4 F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_Filament.html</p> </td> <td> <p>Leo Minor F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_B_Filament.html</p> </td> <td> <p>LeoII B F.</p> </td> </tr> <tr> <td> <p>SG_cube_Canes_Venatici_Filament.html</p> </td> <td> <p>Canes Venatici F</p> </td> </tr> <tr> <td> <p>SG_cube_W-M_Sheet.html</p> </td> <td> <p>W-M Sheet</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Draco_Filament.html</p> </td> <td> <p>Draco F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Bootes_Filament.html</p> </td> <td> <p>Bootes F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_B_Filament.html</p> </td> <td> <p>Leo Minor B F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_A_Filament.html</p> </td> <td> <p>LeoII A F.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>For each visualization galaxies within 2 Mpc are color-coded by the 3D local density, and galaxies with separations greater than 2 Mpc are shown with the grey points. The filament spine is shown with the black curve.</p> <p>The files were created with plotly.js v1.58.4.</p> <p> </p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Survey data on assumed benefits of visualizations in libraries
<p>Results of a survey with 242 participants on connections between assumed benefits of visualizations in the library context and research behavior aspects (frequency of digital material usage, material focus, research subjects etc.). Additionally, participants were subjected to the visualization "An Ocean of Books" by Gaël Hugo and instructed to choose up to five adjectives out of the reaction cards subset proposed by Merčun[1].</p> <p>The head of the file contains the question ID, the question itself, and short information on the data type.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p>[1] T. Merčun. Evaluation of information visualization techniques: analysing user experience with reaction cards. In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, BELIV ’14, pp. 103–109. Association for Computing Machinery, New York, NY, USA, Nov. 2014. doi: 10.1145/2669557.2669565</p>
Dataset for Human visual gamma for color stimuli
<p>This repository contains per-trial data and R analysis code reported in Stauch, B., Peter, A., Ehrlich, I., Nolte, Z., and Fries, P. (2022), <em>Human visual gamma for color stimuli.</em> eLife 11:e75897. doi: <a href="https://doi.org/10.7554/eLife.75897"> 10.7554/eLife.75897</a>. If you want to have a look at the full analysis outcomes, start with analysis_notebook.html. The underlying code is in analysis_notebook.rmd.<br> </p> <p>Additionally, Matlab code that was used to extract per-trial data from the raw data is provided as preprocessingCode.zip.</p>
3D visualization of bioerosion in archaeological bone
<p>Set of five microCT volume images of archaeological samples. 8-bit TIFF images stacks in zipped folders.</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.