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”
Data of FigS7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS7.PNG). The Corresponding raw data and subsequent data analysis obtained contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt) and one file as csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv).</p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
Data files: Single-cell RNA profiling of Plasmodium vivax-infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets
<p>Scripts, preprocessed count matrices, and single-cell data objects generated in <strong>“Single-cell RNA profiling of <em>Plasmodium vivax</em><em>-</em>infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets” </strong></p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </p>
Single-cell analysis of megakaryopoiesis in peripheral CD34+ cells: insights into ETV6-related thrombocytopenia
<p>This repository contains necessary files for reproducing the analysis in Bigot et al, 2023. The instructions for reproducing the analysis are given in github (https://github.com/poggiteam/ETV6_2020).</p>
direct RNA seq data, triplicate of RSV - strain A2 in Calu-3 cells at 48 hours post infection
<p>Raw fastq data from Calue-3 cells infected with RSV strain A2</p>
Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"
<p>Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). <a href="https://doi.org/10.7554/eLife.80682"> https://doi.org/10.7554/eLife.80682</a> .</p>
Catalog of PAM and MBON cell types
<p>A catalog of some of the published anatomical findings on DAN PAM and MBON cell types in the mushroom body of <em>Drosophila melanogaster.</em> Major source is the major table in Aso <em>et al. </em>2014 (https://doi.org/10.7554/eLife.04577). Also includes results from other papers and combines into a single spreadsheet.</p>
Data: multimodal cell tracking from systemic administration to tumour growth by combining gold nanorods and reporter genes
<p>This data set includes multispectral optoacoustic tomography images supporting an article on cell tracking (preprint: bioRxiv 199836; https://doi.org/10.1101/199836). The corresponding bioluminescence results are included too, as well as the spectra used for the multispectral processing. </p>
Data for a publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance"
<p>The data set for contains data used in the article "LIPSS pattern induced by polymer surface instability for myoblast cell guidance".</p> <p><strong>Versions:</strong></p> <p><em><strong>V2: </strong>The current version contains reorganized file folders and is supplemented by the data found in the publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance". The dataset newly contains contact angle, cytocompatibility tests, FTIR analyses or Zeta potential results.</em></p> <p><strong>Article abstract</strong></p> <p>The presented study highlights the efficiency of employing a KrF excimer laser to create diverse types of periodic nanostructures (LIPSS – laser induced periodic surface structures) on polyether ether ketone (PEEK) and polyethylene naphthalate (PEN) substrates. LIPSS structures are very important both in tissue engineering and find also strong application in the field of sensor construction, and SERS analysis. By exposing the polymer films below their ablation threshold to laser fluence ranging from 4 to 16 mJ·cm<sup>-2</sup> at 6,000 pulses, we studied both single-phase exposure at beam incidence angles of 0° and 45°, and two-phase exposure. Atomic force microscopy analysis revealed that the laser-treated samples contained distinctive periodic patterns such as waves, globules, and pod-like structures each exhibiting unique surface roughness. Moreover, using analytical methods like EDS and XPS shed light on the changes in the atomic composition, specifically focusing on the C and O elements, as a result of laser exposure. Notably, in almost all cases, we observed an increase in oxygen percentage on the sample surfaces. This increase not only led to a decrease in the contact angle with water but also lowered the zeta potential value, thus showing that the modified samples have enhanced hydrophilicity of the surface and altered electrostatic properties. Last but not least, the samples were assessed for biocompatibility; we studied the interaction of the prepared replicates with mouse myoblasts (C2C12). The impact of globular/dot structures on the cell growth in comparison to pristine or linear LIPSS-patterned surfaces was determined. The linear pattern (LIPSS) induced the myoblast cell alignment along the pattern direction, while dot/globular pattern even enhanced the cytocompatibility compared to LIPSS samples. Through this comprehensive analysis, the research underscores the multifaceted implications of employing KrF excimer laser-induced nanostructures, ranging from surface morphology alterations to biocompatibility enhancements, thus, opening new avenues for advanced material engineering.</p>
Code for: MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells
<p>Contains source data file and code for publication "MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells".<br>Associated FASTQ files are deposited on the NIH SRA under accession: PRJNA1106276</p>
TCRb sequencing of CD4+ and CD8+ T cells from hematological patients (part 1)
<p>This dataset contains TCRb sequencing of 177 samples from patients with aplastic anemia, myelodysplastic syndrome, immune thrombocytopenia, immunodeficiency or graft-versus-host disease and healthy controls. Samples are separated CD4+ or CD8+ cells from peripheral blood or bone marrow. The data has been produced with immunoSEQ platform (Adaptive Biotechnologies). The details regarding sample processing, sequencing and metadata can be found from the publication <em>Somatic mutations associate with clonal expansion of CD8+ T cells</em> (Lundgren et al, Science Advances, in press).</p> <p>The dataset is divided in 2 parts containing 91 (part 1) and 86 (part 2) files. Data is in immunoSEQ format (v2).</p>
Dataset for publication "Scaling of planar sodium-metal chloride battery cells to 90 cm2 active area"
<p><span lang="EN-US">Sodium-metal chloride batteries; high-temperature ZEBRA batteries; molten-salt batteries; scalable stationary energy storage; alkali-metal anode</span></p>
Supplemental Files for Eckstein and Bates et al., Cell (2024)
<p>synister_fw_mat571_t11_synapses.feather - synapse level transmitter predictions for the FAFB dataset<br>========================================================================================</p> <p>Columns:id : pre-synapse ID<br>pre : neuron ID of pre-synaptic neuron<br>post : neuron ID of post-synaptic neuron<br>pre_pt_position_{x, y, z} : coordinates of pre-synapse in nm<br>nts_11.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>hemibrain-v1.2-tbar-neurotransmitters.feather.bz2 - synapse level transmitter predictions for the hemibrain dataset<br>========================================================================================</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron<br>nts_8.* : predicted score for being one of the six neurotransmitter types (or "neither")</p> <p> </p> <p>Neuprint_Synapses_manc_v1.ftr - synapse level transmitter predictions for the MANC dataset<br>========================================================================================</p> <p>From Takemura et al. 2023, made with the network and methods described in this repository: https://www.biorxiv.org/content/10.1101/2023.06.05.543757v1</p> <p>Columns:x, y, z : voxel coordinates of pre-synapse<br>conf : confidence value of synapse detection<br>sv : supervoxel ID of the pre-synapse<br>roi : name of brain region of pre-synapse<br>roi_label : numerical label of brain region of pre-synapse<br>body : body ID of the pre-synaptic neuron</p> <p> </p> <p>synister-master.zip - code for training and prediction with the 'synister' pipeline for neurotransmission<br>========================================================================================</p> <p>A clone of out GitHub repository at the time of paper release, found at: https://github.com/funkelab/synister, e738e22.</p> <p>Contains README with in-depth description.</p> <p> </p> <p>supplemental_data_1.csv - original ground truth data by identified cell type<br>========================================================================================</p> <p>A .csv file that provides each</p> <p>cell_type : cell type we used to generate our ground truth data,</p> <p>known_nt : the transmitter it is has been reported to express from the literature</p> <p>known_source : the study that made this report and</p> <p>evidence : the type of evidence it contained</p> <p>Note that we used this data to build our original ground truth, but have since made a wider effort to annotate known transmission for validation, reflected in Supplemental data 3 and 4, columns known_nt and known_source.</p> <p> </p> <p>supplemental_data_2.csv - original ground truth data by individual connectomic neuronal reconstruction<br>========================================================================================</p> <p>A .csv file that indicates each neuronal reconstruction used to generate our ground-truth data. Presynapses from each reconstruction were used.</p> <p>id : a unique identifier, which is the root ID for a flywire neuron, a skeleton ID for a FAFB-Catmaid neuron and a bodyid for a HemiBrain neuron,</p> <p>known_nt : the designated transmitter from the literature</p> <p>cell_type : its cell type and</p> <p>dataset : the dataset from which the neuron comes.</p> <p>FAFB-Catmaid reconstructions can be found on virtual fly brain: https://fafb.catmaid.virtualflybrain.org/. Flywire reconstructions can be found at: https://ngl.flywire.ai/. HemiBrain reconstructions can be found at: https://neuprint.janelia.org/?dataset=hemibrain. We used version 630 in this study.</p> <p> </p> <p>supplemental_data_3.csv - <br>========================================================================================</p> <p>A .csv file in which each row is a single identified HemiBrain reconstruction. Columns provide:</p> <p>bodyid : each neuron’s unique identifier </p> <p>cell_type : morphological cell type</p> <p>pre : number of output synapses</p> <p>cropped : whether or not the neuronal reconstruction is cut off by the HemiBrain volume</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p> </p> <p>supplemental_data_4.csv - <br>========================================================================================</p> <p>A .csv file where each row is a single identified FAFB-FlyWire reconstruction. Columns provide:</p> <p>root_id_630 : each neuron’s unique identifier from the 630 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>root_id_783 : each neuron’s unique identifier from the new 783 materialization of the FAFB-FlyWire dataset used in this paper</p> <p>cell_type : morphological cell type</p> <p>conf_nt : neuron-level transmitter prediction</p> <p>conf_nt_p : the confidence score for that prediction, which was calculated using the synapse-level confusion metrics and with synapse filtering</p> <p>top_nt, top_nt_p : neuron-level transmitter prediction with no synapse filtering for axon/dendrite</p> <p>{transmitter name} : the proportion of synapse-level transmitter predictions that “voted” for each transmitter </p> <p>segregation_index : the segregation index, a measure of neuronal polarity</p> <p>projection_score : the projection score, a measure of the distance between axon and dendrite</p> <p>ito_lee_hemilineage, hartenstein_hemilineage : the hemilineage to which this neuron belongs</p> <p>side, morphology group, flow, cell class, cell sub class : other metadata from Schlegel et al. 2023, https://zenodo.org/records/8077335</p> <p>in_ground_truth : whether or not presynapses from this neuron were included in our ground-truth data</p> <p>notes : extra notes on the cell type designation, including alternate names</p> <p>pos_x, pos_y, pos_z : because the root id for neurons is changing as neurons are edited in an active connectome project we also supply the position of a point in the neuron to help identify it in FAFB-FlyWire voxel space</p> <p>nucleus_id : an ID for the nucleus segmentation</p> <p> </p> <p>supplemental_data_5.zip - <br>========================================================================================</p> <p>A .zip archive containing .png files depicting each of the 183 brain hemilineages we have used from the FAFB-FlyWire dataset. Neurons in each hemilineage are colored by their neuron-level transmitter predictions, hemilineage names given in the file name. Hemilineage labels for the FAFB-FlyWire dataset are fully reported in Schlegel et al. 2023, https://zenodo.org/records/8077335.</p> <p> </p> <p>supplemental_data_6.csv - <br>========================================================================================</p> <p>A .csv containing summary results for our 183 central brain secondary hemilineages. Columns provide:</p> <p>ito_lee_hemilineage : each hemilineage’s name </p> <p>hartenstein_hemilineage : and lineage-associated tract</p> <p>left, right, center : the number of neurons on the left and right hemispheres (left, right, center),</p> <p>lineage_type : the lineage type i.e. Type I or II</p> <p>{transmitter} : the number of neurons predicted for each transmitter</p> <p>majority_nt : and the majority vote of the hemilineage.</p> <p>Note, the same information is given for HemiBrain hemilineages, with ‘hemibrain ’ added to the column name.</p> <p> </p> <p>supplemental_data_7.csv - <br>========================================================================================</p> <p>A .csv in which each row is a described cell type of the fly brain. Cell type annotations were drawn from Schlegel et al. 2023, neurons with no cell type or hemibrain type annotated are not included. Columns provide:</p> <p>cell_type : each cell type’s name</p> <p>cell_type_nt : predicted transmitter for the cell type (cell type nt)</p> <p>cell_type_confidence : the cell type level transmitter confidence score</p> <p>{transmitter}_{dataset}_confidence : the sum of neuronlevel transmitter predictions across all FAFB-FlyWire reconstructions for each transmitter</p> <p>{transmitter}_{dataset }_n : the number of neurons with that confidence,</p> <p>known_nt : the transmitters reported to be used by the cell type in the literature</p> <p>known_nt_source : the citations for that information </p> <p>ito_lee_hemilineage, hartenstein_hemilineage, morphology_group, flow, cell_class, cell_sub_class : morphological annotations from Schlegel et al. 2023</p> <p>cell_type_nt : the cell type level transmitter is determined by the highest {transmitter} cell type {dataset} confidence</p> <p>cell_type_nt_conf : The cell type level transmitter confidence score (cell type nt) was calculated using our prediction confusion matrices.</p> <p>Results for both FAFB-FlyWire (‘ fafb’) and HemiBrain (‘hemibrain’) datasets are given.</p> <p> </p> <p>supplemental_data_8.tar.gz<br>========================================================================================</p> <p>Contains PNG files of 2D images cropped around synapses, together with their<br>counterfactual translations into different neurotransmitter types and<br>highlights of the most important areas for classification.There is one PNG file for each pair of neurotransmitters, named<br>`{source}_{target}.png`. E.g., the file `ach_dop.png` contains translations of<br>real cholinergic synapses into counterfactual dopaminergic synapses.Each PNG shows images in six columns, which are:1: original raw + classifier scores<br>2: counterfactual raw + classifier scores<br>3: counterfactual raw, with original raw in mask + classifier scores<br>4: mask, shown on original + relative classifier change<br>5: mask, shown on counterfactual<br>6: difference in mask area</p> <p>Paper: https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6</p>
Correlative microscopy of mice cerebellar Purkinje cells from 20x confocal tissue imaging to super-resolution 93x 3D STED of dendritic spines
<p>This Dataset concerns the paper entitled "<em>From tissues to segmentation: a modular framework for multi-scale neuron isolation</em>" by Cauzzo et al. <strong>Nature Comm (2024).</strong></p> <p>S.Cauzzo<sup>$</sup>, E. Bruno, D. Boulet, P. Nazac, M. Basile, A. L. Callara, F. Tozzi, A. Ahluwalia, C. Magliaro, L. Danglot<sup>$</sup><sup>*</sup>, N. Vanello<sup>$</sup><sup>*</sup> *shared senior authorship: Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p><sup>$</sup> corresponding authors : cauzzo.simone@gmail.com ; Lydia.danglot@inserm.fr ; nicola.vanello@unipi.it</p> <p> </p>
microSPLiT single-cell and bulk transcriptomes analysed with STAR - Pseudomonas putida KT2440/pKJK5
<h3>Description of the data and file structure</h3> <p>Data are displayed as 2 files</p> <p><strong>1. Bulk transcriptomics results (Bulk_STAR.csv)</strong></p> <p>STAR processed data combined in a gene x sample table</p> <p><strong>2. microSPLiT single-cell results (microSPLiT_STARsolo.xlsx)</strong></p> <p>STARsolo processed data combined as sublibraries’ gene associated transcript numbers (UMIs) per cell for the control (E1) and experiment (E2) sublibraries (F1-8) - (1 sublibrary per table).</p> <div> <p> </p> </div>
Impact of paclitaxel treatment on the Triple Negative Breast Cancer Cell line HCC1143
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11237850</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate the impact of escalating paclitaxel dose on cell count, nuclear morphology and cellular outcome.</div> <div> </div> <div>Methods:</div> <div>Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were blocked with 1% BSA (A7906-100G, Millipore Sigma) in PBS for 1 hour at room temperature and then stained overnight with 1:100 anti-CDKN2A/p16INK4A+CDKN2B/p15INK4B-AF644 (#ab199756, Abcam), and 1:100 anti-cPARP-AF647 (#6987S, Cell Signaling Technology) or 1:500 anti-TUBB3-AF647 (#ab190575, Abcam) overnight at 4C. Each well was washed twice with room temp PBS then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask in PBS (Orange: #H32713, Green: #H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 45, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>Included files:</div> <div>row_#_level_1.zip : 6 zip file containing original images from InCell 6000, one zip per row</div> <div>level_2.csv : Data quantified to the nuclear level (cytoplasmic quantification is duplicates across multiplet nuclei)</div> <div>level_3.csv: Data quantified at the cellular level including number of nuclei and stain intensities for segmented compartments</div> <div>platemap.csv: Description of each well from the stained plate</div> <div>cellpose_modelz.zip: Zip file containing the two CellPose models used for segmentation</div> <div>image_quantification.rmd : R markdown file containing code for extracting and quantifying image intensities using the raw images (level_1) and segmentation masks created from cellpose.</div>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
Raw Data for the Protocol: Antibody-Assisted Selective Isolation of Purkinje Cell Nuclei
<p>Sun1/sfGFP+, Pcp2-Cre+ and Sun1/sfGFP+, Pcp2-Cre- cryosectioned cerebella immunostained for the Myc tag (files 3037, 3046), which is fused to the GFP protein, Calbindin (files 3038, 3047) and Hoechst (files 3036, 3045). </p> <p> </p> <p>Original uncropped images from western blot analysis of TOM20, Histone H3, and GAPDH.</p>
Franc̜ois Bedos de Celles (b3875)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Franc̜ois Bedos de Celles<br><u>musiXplora-ID</u>: b3875<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/b3875">https://musixplora.de/mxp/b3875</a><br><u>Gender</u>: m<br><u>Confessions</u>: römisch-katholisch<br><u>Date of Birth</u>: 24 January 1709<br><u>Place of Birth</u>: Caux<br><u>Date of Death</u>: 25 November 1779<br><u>Place of Death</u>: Paris<br><u>First Mentioned</u>: 1725<br><u>Sectors</u>: Kirche, Orgelbau<br><u>Professions (Musical)</u>: Orgelbauer, Orgelsachverständiger<br><u>Professions (Non-Musical)</u>: Mathematiker, Schriftsteller<br><u>Other Places of Activity</u>: Bordeaux, Paris, Pézenas, Toulouse<br><br><br><u>Personal:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Mitgliedschaft</td><td>Leiter</td><td>Benediktinerorden</td><td><a href="https://musixplora.de/mxp/3050010">3050010</a></td></tr></tbody></table><br><u>Tradition:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Interessensbereich</td><td>Nachlassempfänger</td><td>Orgelregister</td><td><a href="https://musixplora.de/mxp/2003643">2003643</a></td></tr><tr><td>Interessensbereich</td><td>Nachlassempfänger</td><td>Joseph Gabler</td><td><a href="https://musixplora.de/mxp/g0007">g0007</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
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.