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.
111,247
datasets available to search
ShareScore release 0.9.0
Dataset results
111,247 results for “cells”
Text-fig. 7. Lusitanispermum choffatii gen. et sp. nov. seeds from the Early Cretaceous Famalicão locality (sample 025), Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Holotype; seed in lateral view showing seed shape; note that the seed is broken near the lower surface of the hilum (S174345). b) Oblique apical view of micropylar-hilar region of holotype showing slightly ruptured micropylar slit (mi) in the outer integument and two bulging and abraded areas (arrow heads) close to hilum. c) Seed in oblique lateral-raphal view showing the two bulging structures (arrow heads) immediately adjacent to the lower edge of the hilum (S174472). d) Tangential, longitudinal cut (cut at yz0131) through the seed coat of seed in (7c) showing the undulate anticlinal cell walls of the exotesta cells that are thickest towards the outside and thinner towards the inside. Scale bars = 500 µm (a–c); 250 µm (d). in Extinct Taxa Of Exotestal Seeds Close To Austrobaileyales And Nymphaeales From The Early Cretaceous Of Portugal
Text-fig. 7. Lusitanispermum choffatii gen. et sp. nov. seeds from the Early Cretaceous Famalicão locality (sample 025), Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Holotype; seed in lateral view showing seed shape; note that the seed is broken near the lower surface of the hilum (S174345). b) Oblique apical view of micropylar-hilar region of holotype showing slightly ruptured micropylar slit (mi) in the outer integument and two bulging and abraded areas (arrow heads) close to hilum. c) Seed in oblique lateral-raphal view showing the two bulging structures (arrow heads) immediately adjacent to the lower edge of the hilum (S174472). d) Tangential, longitudinal cut (cut at yz0131) through the seed coat of seed in (7c) showing the undulate anticlinal cell walls of the exotesta cells that are thickest towards the outside and thinner towards the inside. Scale bars = 500 µm (a–c); 250 µm (d).
Text-fig. 8. Lusitanispermum choffatii gen. et sp. nov. seeds from the Early Cretaceous Famalicão locality (sample 025) (a, b, d, e), and the Vale de Água locality (sample 333) (c, f, g), Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Smaller seed in lateral view showing the bulging region (arrow head) close to hilum (S174467). b) Cut volume rending (cut at yz1032) of seed in (8a) showing the expanded cells of exotesta immediately adjacent to the lower edge of the hilum (hi), micropyle (mi) and the well preserved nutritive tissue. c) Small seed in lateral view showing the bulging exotesta (arrow head) close to hilum (S175046). d, e) Larger seed in lateral (d) and raphal (e) view (S174035). f) Detail of seed in (8c) showing the micropylar slit (mi) above the hilum (hi) and bulging exotestal tissue (arrow head) in which the walls of the exotestal cells have straight anticlinal walls. g) Surface of seed in (8c) showing the raised undulate anticlinal walls of the exotestal cells. Scale bars = 500 µm (a–e); 250 µm (f); 125 µm (g). in Extinct Taxa Of Exotestal Seeds Close To Austrobaileyales And Nymphaeales From The Early Cretaceous Of Portugal
Text-fig. 8. Lusitanispermum choffatii gen. et sp. nov. seeds from the Early Cretaceous Famalicão locality (sample 025) (a, b, d, e), and the Vale de Água locality (sample 333) (c, f, g), Portugal; Synchrotron radiation X-ray tomographic microscopy (SRXTM, volume renderings). a) Smaller seed in lateral view showing the bulging region (arrow head) close to hilum (S174467). b) Cut volume rending (cut at yz1032) of seed in (8a) showing the expanded cells of exotesta immediately adjacent to the lower edge of the hilum (hi), micropyle (mi) and the well preserved nutritive tissue. c) Small seed in lateral view showing the bulging exotesta (arrow head) close to hilum (S175046). d, e) Larger seed in lateral (d) and raphal (e) view (S174035). f) Detail of seed in (8c) showing the micropylar slit (mi) above the hilum (hi) and bulging exotestal tissue (arrow head) in which the walls of the exotestal cells have straight anticlinal walls. g) Surface of seed in (8c) showing the raised undulate anticlinal walls of the exotestal cells. Scale bars = 500 µm (a–e); 250 µm (f); 125 µm (g).
TIRF imaging data of neutrophils migrating underneath endothelial cells
<p>This data is used in the publication "Endothelial Focal Adhesions Are Functional Obstacles for Leukocytes During Basolateral Crawling": https://www.frontiersin.org/articles/10.3389/fimmu.2021.667213/full</p> <p> </p> <p><strong>TIRF Microscopy</strong></p> <p>Lentiviral transduction was used to generate an endothelial cell line expressing mNeonGreen-Paxillin (derived from addgene plasmid # 129604). Cells were imaged with a Nikon Ti-E microscope equipped with a motorized TIRF Illuminator unit, a 60x TIRF objective (60x Plan Apo, Oil DIC N2, NA =1.49, WD = 120 um) and Perfect Focus System. Images were acquired with an Andor iXon 897 EMCCD camera and the Nikon NIS elements software. mNeonGreen was imaged using the 488 nm laser line and calcein red-orange was imaged using the 561 nm laser line. A quad split dichroic mirror (405 nm, 488 nm, 561 nm, 640 nm) was used in combination with dual band pass emission filter (515 to 545 nm, 600 to 650 nm). To achieve a larger field of view a 3 x 3 tile scans was acquired with 15% overlap stitching on the GFP channel. Time lapse images were taken every 10 s.</p>
Fig. 4 in Response of mucous cells of the gills of traíra (Hoplias malabaricus) and jeju (Hoplerythrinus unitaeniatus) (Teleostei: Erythrinidae)
Fig. 4. Mean values (± SEM) of mucous cell density, individual surface area and mucous cell fractional area (MCFA) at filament epithelial surface of Hoplerythrinus unitaeniatus exposed to deionized water and high ion concentration water.
Fig. 2. a in Response of mucous cells of the gills of traíra (Hoplias malabaricus) and jeju (Hoplerythrinus unitaeniatus) (Teleostei: Erythrinidae)
Fig. 2. a. TEM photomicrograph of mucous cell (MC) in the filament epithelium of Hoplias malabaricus. Note pavement cells (PVC) covering MC. b. TEM photomicrograph of mucous cell (MC) in the filament epithelium of Hoplerythrinus unitaeniatus. Note different electrondensity of secretory granules (G). c. SEM photomicrograph of filament epithelium showing mucous granules releasing by MC between PVC cells, at epithelial surface. Scale bar in mm. Arrows indicate mucous granules.
Dataset part one to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>This study was supported in parts by research funding from the Swiss National Science Foundation (grant number 310030_189144), the Thurgauische Stiftung für Wissenschaft und Forschung, and the State Secretariat for Education, Research and Innovation to DFL.</p>
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
Data_Figure4_Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver
<p>Data of Figure 4 from “Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver”</p> <p>Dataset (doi: 10.1016/j.jcmgh.2021.04.003) contains the original publication as PDF-format (10.1038_s41598-020-59701-0). Corresponding raw data obtained from LC-MS/MS analysis provided as two files in CSV format (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-1.csv, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC_4-2.csv). All further experiment related information provided as two meta-data-files (31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_1.PDF, 31003A-179400_10.1016j.jcmgh.2021.04.003_CGC _4_1-2_M_2.PDF) as PDF format.</p>
Data for: Exocytosis of the silicified cell wall of diatoms involves extensive membrane disintegration
<p>Diatoms are unicellular algae, characterized by silica cell walls. The silica elements are formed intracellularly in a membrane-bound silica deposition vesicle (SDV), and are exocytosed after completion. How diatoms maintain membrane homeostasis during the exocytosis of these large and rigid silica elements is a long-standing enigma. We studied membrane dynamics during cell wall formation and exocytosis in two model diatom species, using live-cell confocal microscopy, transmission electron microscopy and cryo-electron tomography. Our results show that during the formation of the mineral phase it is in tight association with the SDV membranes, which are forming a precise mold of the delicate geometrical patterns. During exocytosis, the distal SDV membrane and the plasma membrane gradually detach from the mineral and disintegrate in the extracellular space, without any noticeable endocytic retrieval or extracellular repurposing. Within the cell, there is no evidence for the formation of a new plasma membrane, thus the proximal SDV membrane becomes the new barrier between the cell and its environment, and assumes the role of a new plasma membrane. These results provide direct structural observations of diatom silica exocytosis, and point to an extraordinary mechanism in which membrane homeostasis is maintained by discarding, rather than recycling, significant membrane patches.</p>
Imaging Mass Cytometry Images (APP1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from the APP1 FFPE block with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For APP1, a one mm<sup>2</sup> ROI containing a lymphoid follicle in its whole depth alongside a portion of lamina propria and of epithelium was selected. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p>
Populations of local direction-selective cells encode global motion patterns generated by self-motion. Data, Code and Model.
<p>Directional tuning of the population of local motion detectors T4/T5 in the visual system of the fruit fly <em>Drosophila melanogaster</em>. Direction tuning and receptive field location was measured by recording responses to visual stimuli containing dark or bright edges/stripes moving into 8 directions. All provided MATLAB scripts were used to analyze and illustrate data show in the manuscript 'Populations of local direction-selective cells encode global motion patterns generated by self-motion.'</p> <p>All data were obtained using <em>in vivo </em>two photon microscopy. Image time series were preprocessed using SIMA python software for motion alignment and further processed using custom written matlab or python code.</p> <p>Please find all relevant information to use the code in the README file.</p>
Hydraulic mixing cell simulation of sources of surface water in the Weierbach catchment
<p>The dataset contains the meteorological forcing data and results of the hydraulic mixing cell (HMC) simulation presented and discussed in the research article: </p> <p><em>Sources of surface water in space and time: Identification of delivery processes and geographical sources with hydraulic mixing-cell modeling</em> (DOI:10.1029/2021WR030332)</p> <p>The simulation was performed with the model HydroGeoSphere and the HMC modelling was used to identify the delivery processes and geographical sources of surface water at specific points of interest (POI) within the riparian-stream continuum of the Weierbach catchment (Luxembourg). Detailed information on the model setup, the location of the POIs, the considered source types and source areas, and the processing we applied to the raw simulation output is given in the research article.</p>
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
Modelling Cyclic Stretch of Patient-Derived Alveolar Epithelial Cells from Organoids using a New Alveoli-on-Chip Platform
<p><span>The data originates from lung tumor samples obtained at the University Hospital Bern. Patient-derived alveolar epithelial type 2 cells (AEC2) were isolated and cultured into organoids. Grown organoids where dissociated and seeded onto a novel alveoli-on-chip system submerged in medium. After 24 h of either no mechanical stimulus (control) or cyclic breathing stretch (stretch), RNA was isolated and sequenced. The RNA-seq data underwent quality control, alignment to the reference genome, read counting, DGE and GSEA. All analyses were run in R version 4.2.1.</span></p>
A single-cell atlas of multiple myeloma disease evolution
<p>A collection of computationally integrated (batch corrected) scRNA-seq and scTCR-seq data from a large cohort of bone marrow and peripheral blood samples from patients with multiple myeloma, myeloma precursor disease (MGUS and SMM) and non-cancer controls.</p> <h3>Datasets</h3> <ul> <li><strong>dataDescription.xlsx</strong> - Definition of dataset components.</li> <li><strong>panImmune.h5ad</strong> - All cells types integrated gene expression object.</li> <li><strong>Tcell.h5ad</strong> - T cells integrated object.</li> <li><strong>Tcell-scTCR.csv</strong> - Single-cell TCR data.</li> <li><strong>Botta_2023-predicted.h5ad</strong> - Single-cell gene expression data from Botta et al. analysed by label transfer.</li> <li><strong>metadata.donor.csv</strong> - Donor metadata.</li> </ul> <h3>Published data acquisition</h3> <p>Data shared through the gene expression omnibus (GEO) can be accessed for Maura et al. under accession GSE161195, Bailur et al. GSE163278, Oetjen et al. GSE120221, Granja et al. GSE139369, Zavidij et al. GSE124310, Kfoury et al. GSE143791, Botta et al. GSE205393, and Zheng et al. GSE156728. Data shared via dbGaP for Sklavenitis-Pistofidis et al. can be accessed under accession phs002476.v1.p1. Data shared online can be accessed for Stephenson et al. (via <a href="https://covid19cellatlas.org/">https://covid19cellatlas.org/</a>), Conde et al. (via <a href="https://www.tissueimmunecellatlas.org/">https://www.tissueimmunecellatlas.org/</a>), and Liu et al. (via <a href="https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81">https://explore.data.humancellatlas.org/projects/2ad191cd-bd7a-409b-9bd1-e72b5e4cce81</a>). Single-cell data from <a href="https://doi.org/10.1016/j.ccell.2022.10.017" target="_blank" rel="noopener">Sklavenitis-Pistofidis et al.</a> and extended clinical data from <a href="https://doi.org/10.1038/s43018-023-00657-1">Maura et al.</a> (i.e. paraprotein) are omitted and must be acquired through direct contact with these authors.</p> <h3>Contact information </h3> <ul> <li>Kane Foster - kane.foster.web@gmail.com</li> <li>Kwee Yong - kwee.yong@ucl.ac.uk</li> </ul> <p>Please cite <a href="https://www.medrxiv.org/content/10.1101/2024.06.22.24309250v1">our pre-print</a> if you use this resource in your research.</p>
Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
<p><strong>Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation</strong></p> <p>We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing <strong><em>C. glutamicum</em></strong> cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.</p> <p><strong>Data Acquisition</strong></p> <p><strong><em>Corynebacterium glutamicum</em></strong> ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.</p> <p>The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.</p>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Mastodon project containing the cell tracks of a developing Tribolium Castaneum embryo.
<p>Mastodon project containing the cell tracks of a developing <em>Tribolium Castaneum </em>embryo. </p> <p>To open it in Mastodon, download both the <em>.mastodon </em>and the <em>.xml </em>files and put them in the same folder.</p> <p>The image data is stored on a BigDataServer in the Institut Pasteur, Paris, provided by the <a href="https://research.pasteur.fr/en/team/image-analysis-hub/">Image Analysis Hub</a>. When you open this <em>.mastodon </em>file with Mastodon, it will stream the image from the server without you having to download it. The mastodon file contains just the tracks and the <em>.xml </em>file contins the address of the server from where the image data will be streamed.</p> <p>Check the <a href="https://mastodon.readthedocs.io/">Mastodon scientific software</a> for information on how to open and use this file for scientific purposes.</p> <p>The image data is the 'TRIF' training video 02 from the <a href="http://www.celltrackingchallenge.net/">CellTrackingChallenge.net</a>, with authorization. The original dataset provider is Dr. A. Jain. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.</p> <ul> <li>Microscope: Zeiss LightSheet LZ.1</li> <li>Objective lens: Plan-Apochromat 20x/1.0 (water)</li> <li>Voxel size (microns): 0.38 x 0.38 x 0.38</li> <li>Time step (min): 1.5</li> </ul>
Processed single cell data from CODEX multiplexed imaging of the human intestine
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. </p> <p>The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below.</p> <p>Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease.</p> <p>The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.</p>
Immune checkpoint molecule TIGIT regulates kidney T cell functions and mediates acute kidney injury
<p>This dataset includes the single cell RNA-seq data associated with the publication listed in the title (abstract below).<br> <br> CellRanger.zip contains the raw transcript counts as produced by CellRanger. There is one folder per sample. The samples are indicated by the folder name (e.g. KO_Ctrl). <br> <br> We have also included .h5ad files that contain of cells that passed quality control, as described in the manuscript.<br> <br> adTIGIT_raw_031422.h5ad contains all passing cells and the raw counts, as well as cell annotations ('celltype')<br> <br> adTIGIT_Tonly_091421.h5ad contains only T cells, .X contains the normalized data and T-cell sub-type ('cluster').<br> <br> Abstract: T cells mediate pathologic and reparative processes during acute kidney injury (AKI) but exact mechanisms regulating kidney T cell functions are unclear. This study identified upregulation of the novel immune checkpoint molecule, TIGIT, on mouse and human kidney T cells following AKI. TIGIT-expressing kidney T cells produced proinflammatory cytokines and had effector and central memory phenotype. Kidney Tregs were predominantly TIGIT+ and reduced after ischemia reperfusion (IR) injury. TIGIT deficient mice had protection from both ischemic and nephrotoxic AKI. Single cell RNA sequencing led to discovery of possible downstream targets of TIGIT. TIGIT mediates AKI pathophysiology, is a promising target for developing AKI therapy, and is being increasingly studied in human cancer therapy trials.</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.