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DIAPASON Project - Interplay between negative symptoms, time spent doing nothing, and negative emotions in patients with schizophrenia spectrum disorders: results from a 37-site study.
<p>Datasets of the paper <em>Interplay between negative symptoms, time spent doing nothing, and negative emotions in patients with schizophrenia spectrum disorders: results from a 37-site study. NPJ Schizophrenia</em>, 2023 of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-<a>2018-12365514</a>) (https://www.diapason-study.eu/)</p>
Cross-Project-Code-Context-Prediction_Dataset
<p>The dataset of 3,469 code context models (Code Context Models.zip) and 14,510 code context patterns (Topological Patterns.zip) from Platform, PDE, and ECF.</p> <p><strong>Code Context Models</strong></p> <p>There are 10 files in each directory (i.e., working period), including:</p> <p>1. interaction events.txt: the interaction events during this working period</p> <p>2. event timestamps.txt: the timestamps of this working period, including:</p> <ul> <li>start datetime of the first event</li> <li>start datetime of the last event</li> <li>end datetime of the last event</li> <li>duration of interaction events (in seconds)</li> </ul> <p>3. code elements_extracted.txt: the code elements the developer accesses during this working period, extracted from interaction events (e.g., org.eclipse.ecf.provider.irc.ui/src&lt;org.eclipse.ecf.internal.irc.ui.wizards{IRCConnectWizardPage.java[IRCConnectWizardPage~getConnectID)</p> <p>4. code elements_resolved.txt: the resolved code elements (e.g., 'org.eclipse.ecf.internal.irc.ui.wizards.IRCConnectWizardPage[getConnectID')</p> <p>5. repository urls.txt: the urls of the git repositories identified from the code elements</p> <p>6. code elements_total.txt: all the code elements extracted from the code snapshots</p> <p>7. code context model.txt: the code context model with the code elements in [4]. This file is like:</p> <p>vertices:</p> <p>A@#c0</p> <p>B@#f0</p> <p>edges:</p> <p>c0@#f0@#declare</p> <p>A and B are the code elements, while c0, f0 are their ids (c: class, f: function). The relation between them is 'declare'. </p> <p>8. code context model_total.txt: the code context model with the code elements in [6]</p> <p>9. stereotype roles_total.txt: the stereotype roles of all the code elements in [6]</p> <p>10. code context model_abstract.txt: the abstract code context model of which the code elements are abstracted as stereotype roles</p> <p><strong>Topological Patterns</strong></p> <p>There are 9, 8, and 7 pattern groups mined from Platform, PDE, and ECF respectively. Each pattern group is named 'project_X_MinSupp', where 'X' denotes the X-th cluster of code context models and 'MinSupp' = 0.02. The information about the X-th cluster can be found in the paper's appendix. There are various patterns in a pattern group. For example:</p> <p>t # 25<br> v 0 DATA_PROVIDER <br> v 1 COLLABORATOR-CONSTRUCTOR <br> e 0 1 declare<br> 12</p> <p>The id of this pattern is 25, and there are two vertices 0 and 1, corresponding to DATA_PROVIDER and COLLABORATOR-CONSTRUCTOR respectively. There is a 'declare' edge from DATA_PROVIDER to COLLABORATOR-CONSTRUCTOR. This pattern occurs 12 times in all the abstract code context models</p>
Spatial Transcriptomics Sequencing of only 2 Slides (Extraslides from Project 651)
<p>Dataset from BMFZ Spatial Transcriptomics Sequencing from aortic tissues (d7+d14, DMSO, PPE, PPE+ TrafSTOP) (Extraslides from Project 651)</p>
DIAPASON Project - Ecological monitoring of emotional intensity, variability, and instability in individuals with schizophrenia spectrum disorders (ssd): results of a multicentre study
<p>Dataset of the paper <em>Ecological monitoring of emotional intensity, variability, and instability in individuals with schizophrenia spectrum disorders (ssd): results of a multicentre study.</em> of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2018-12365514) (https://www.diapason-study.eu/)</p>
Dataset for the project "Dissecting molecular mechanisms triggered by progressive loss of progranulin and C9orf72 in frontotemporal dementia: novel therapeutic targets? (RF-2016-02361492)
<p><strong>Dataset for the project “Dissecting molecular mechanisms triggered by progressive loss of progranulin and C9orf72 in frontotemporal dementia: novel therapeutic targets?” Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361492)</strong></p> <p><strong>Specific Aim 1</strong></p> <p>To further collect and generate human cellular disease models (skin fibroblast and LCL) of progressive progranulin and C9orf72 loss.</p> <p><strong>Specific Aim 2</strong></p> <p>To test the hypothesis that EVs secreted from microglia, in which GRN or C9orf72 are downregulated, contain and deliver excessive C1q and C3 complements factors to the synapses, thereby causing aberrant synaptic pruning. Results of this aim will clarify whether microglia-derived EVs have a pathogenic role in the disease, thus indicating a novel therapeutic target to limit synaptic loss and disease progression.</p> <p><strong>Specific Aim 3</strong></p> <p>To identify sensitive biomarkers for patient stratification and treatment monitoring by a) characterization of exosomal and lysosomal pathways in human cellular disease models with progressive loss of progranulin and C9orf72; b) studying lysosomal and immune proteins in EVs circulating in body fluids of subjects with progressive loss of progranulin and C9orf72; c) studying the impact of variants in lysosomal genes on age of disease onset in subjects carrying GRN and C9orf72 mutations.</p> <p>The present dataset contains the following folders:</p> <ul> <li><strong>RawData_Aim2 [Unpublished Data]:</strong> <ul> <li>Materials and Methods_RawData_Aim2</li> <li>RawData_Aim2: a dataset (excel file) containing the following sheets: <ul> <li>Fig.1: Number of EVs from microglia (analysis with Tunable Resistive Pulse Sensing-TRPS technique, using a qNano instrument)</li> <li>Fig.2: Genetic expression levels of homeostatic and inflammatory genes in microglia</li> <li>Fig.3: WB analysis to confirm GRN silencing in microglia</li> <li>Fig.4A: Quantification of immunofluorescence analysis for Lamp-1, CD68, and Cathepsin D</li> <li>Fig.4B: WB analysis for Lamp-1, CD68, TMEM119 and Cathepsin D in microglia</li> <li>Fig.5A: WB analysis for C3, C1q, and TMEM119 in microglia</li> <li>Fig.5B: Quantification of immunofluorescence analysis for C1q and Cd11b in microglia</li> <li>Fig.5C: WB analysis for C3, C1q, Alix, Flotillin, Annexin-A2, and TOM20 in EVs from microglia</li> <li>Fig.6A-B: WB analysis for C3, C1q, Lamp1, CD68, Cathepsin D, Alix, and Annexin-A2 in EVs</li> <li>Fig.8: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (no treatment)</li> <li>Fig.9: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (treatments GW4869)</li> <li>Fig.10: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (treatments with EVs)</li> <li>Fig.11: Phagocytosis assay</li> </ul> </li> <li>Fig.4A: a folder containing original immunofluorescence pictures for Lamp-1, CD68, and Cathepsin D in microglia</li> <li>Fig.5B: a folder containing original immunofluorescence pictures for C1q and Cd11b in microglia</li> <li>Fig.11: a folder containing original pictures from phagocytosis assay</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>RawData_Aim3a_Fibroblast [Unpublished Data]</strong>: <ul> <li>Materials and Methods_Aim3a_Fibroblasts</li> <li>RawData_Aim3a_Fibroblasts_NTA_Complement_LC3_CathD: a folder containing: <ul> <li>RawData_Aim3a_Fibroblasts_NTA_Complement_LC3_CathD: a dataset (excel file) containing the following variables referred to EVs from fibroblasts: <ul> <li>Sample (Col. A) and its Category (Col. B)</li> <li>Raw NTA File name (Col. C)</li> <li>Raw Values_C1q EVs from Bioplex assay (Col. D-F)</li> <li>Raw Values_C3 EVs from Bioplex assay (Col. G-I)</li> <li>Raw Values_C4 EVs from Bioplex assay (Col. J-L)</li> <li>Raw Values_LC3 EVs from ELISA assay (Col. M-P)</li> <li>Raw Values_Cathepsin D EVs from ELISA assay (Col. Q-T)</li> </ul> </li> <li>RawData_Aim3a_Fibroblasts_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> <li>RawData_Aim3a_Fibroblasts_EVMarkers_PGRN_C9orf72_pTDP43: a folder containing: <ul> <li>WB_Aim3a_Fibroblasts_EVMarkers: a folder containing original WB pictures for EV biomarkers TSG101, Alix, CD63, Flotillin-1</li> <li>ELISA_C9orf72_Fibroblasts: a dataset containing raw C9orf72 data from ELISA assay on fibroblasts lysates and EVs lysates</li> <li>ELISA_pTDP43_Fibroblasts: a dataset containing raw pTDP43 data from ELISA assay on fibroblasts lysates and EVs lysates</li> <li>WB_Analyses_Fibroblasts_PGRN: a dataset containing densitometric analysis from WB on fibroblasts lysates and EVs lysates for PGRN and Actin/TSG101 (for cells/EVs normalization respectively)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.a RawData_Aim3a_LCL [Unpublished Data]</strong>: <ul> <li>Materials and Methods_Aim3a _LCL</li> <li>RawData_Aim3a_LCL_NTA_Complement_LC3_CathD: a folder containing: <ul> <li>RawData_Aim3a_LCL_NTA_Complement_LC3_CathD: a dataset (excel file) containing the following variables referred to EVs from LCL: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Raw NTA File name (Col. C)</li> <li>Raw Values_C1q EVs from Bioplex assay (Col. D-F)</li> <li>Raw Values_C3 EVs from Bioplex assay (Col. G-I)</li> <li>Raw Values_C4 EVs from Bioplex assay (Col. J-L)</li> <li>Raw Values_LC3 EVs from ELISA assay (Col. M-P)</li> <li>Raw Values_Cathepsin D EVs from ELISA assay (Col. Q-T)</li> </ul> </li> <li>RawData_Aim3a_LCL_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> <li>WB_Analyses_Aim3a_LCL_PGRN_C9orf72_TDP43_pTDP43_Lamp1: a dataset containing densitometric analysis from WB on LCL lysates and EVs lysates for PGRN, C9orf72, p-TDP43, TDP43, Lamp-1, Actin/TSG101 (for cells/EVs normalization respectively)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.b </strong><strong>RawData_Aim3b_Plasma_1 [Published] </strong><br> Results (NTA data) from aim 3.b are published in Bellini&Saraceno et al. Cells 2022 (doi:10.3390/cells11030488) and the raw data supporting the results are published in the Mendeley Data Repository at doi:10.17632/kds9sb4z6t.2 <ul> <li>Materials and Methods_Aim3b_Plasma</li> <li>RawData_Aim3b_Plasma: a folder containing: <ul> <li>RawData_Aim3b_Plasma: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Age (Col. C)</li> <li>Corresponding Raw NTA File name (Col. D)</li> </ul> </li> <li>RawData_Aim3b_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.b </strong><strong>RawData_Aim3b_Plasma_2 [<em>in press</em>]</strong><br> Results (NTA and Cathepsin D data) from aim 3.b have been included in an article submitted to IJMS (Manuscript ID: ijms-1899683; <em>in press</em>) and the raw data supporting the results are published in the Zenodo Data Repository at doi: 10.5281/zenodo.6958338. <ul> <li>Materials and Methods_Aim3b_Plasma</li> <li>RawData_Aim3b_Plasma: a folder containing: <ul> <li>RawData_Aim3b_Plasma: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Age (Col. C)</li> <li>Onset (Col. D)</li> <li>Corresponding Raw NTA File name (Col. E)</li> <li>Raw Values_Cathepsin D plasma EVs from ELISA assay (Col. F-J)</li> <li>Raw Values_Cathepsin D plasma from ELISA assay (Col. K-O)</li> </ul> </li> <li>RawData_Aim3b_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.c </strong><strong>RawData_Aim3c_NGS [Submitted]</strong><br> Results from aim 3.c have been included in an article submitted to IJMS (Manuscript ID: ijms-1922450; Under review) and the raw data supporting the results are openly available in the Zenodo Data Repository at doi: 10.5281/zenodo.7040532. <ul> <li>Materials and Methods_Aim3c_NGS</li> <li>RawData­_ Aim3c_NGS: a folder containing: <ul> <li>Correspondence_RawData_NGS: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Group (Col. B)</li> <li>Corresponding g.vcf File name (Col. C)</li> </ul> </li> </ul> </li> <li>Gene_Selection: a file with the selection of the 98 genes related to the lysosomal pathway included in the NGS panel</li> </ul> </li> </ul> <p> </p>
DIAPASON Project - Daily Time Use Among Individuals With Schizophrenia Spectrum Disorders and Unaffected Controls: Results From the Diapason Multicentric Project
<p>Datasets of the paper entitled "Daily Time Use Among Individuals With Schizophrenia Spectrum Disorders and Unaffected Controls: Results From the Diapason Multicentric Project" of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-<a>2018-12365514</a>) (https://www.diapason-study.eu/). Psychiatric Rehabilitation Journal, 2023</p>
Dataset for the project "Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697)
<p><strong>Dataset for the project “Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach” Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697)</strong></p> <p><strong>Aim 1</strong>: Assessment of the validity and clinical utility of a pharmacogenetic test in the selection of antidepressant treatment for patients with Major Depressive Disorder.</p> <p><strong>Aim 2</strong>: Pharmacokinetic analysis and identification of novel genetic variants linked with no response to treatment and/or with the occurrence of side effects.</p> <p><strong>Aim 3</strong>: Implementation of the combinatorial algorithm based on possible new findings.</p> <p>This dataset contains :</p> <ul> <li><strong>RawData_Aim1</strong></li> </ul> <p>· Materials and Methods for Aim 1 (Materials and methods_RawData_Aim1)</p> <p>· An Excel table (RF16 - Genotyping - Aim1) with the following sheets:</p> <p>- Sheet 1: Genotyping results</p> <p>- Sheet 2: Metabolizer phenotype results for CYP2D6 and CYP2C19 genes</p> <ul> <li><strong>RawData_Aim2</strong></li> </ul> <p>· Materials and methods Sequencing_RawData_Aim2</p> <p>· Materials and methods Pharmacokinetics_RawData_Aim2</p> <p>· VCF data from sequencing analysis (filtered_variants - Aim2)</p> <p>· Pharmacokinetics analysis (Pharmacokinetics Data_Aim2)</p> <ul> <li><strong>RawData_Aim3</strong></li> </ul> <p>· An Excel table with data from clinical assessments (Clinical Data_Aim3)</p>
BORIS behavioural project files
<p>BORIS project files with logs of each observation for each cassowary, accessible using BORIS (<a href="http://www.boris.unito.it/">http://www.boris.unito.it/</a>)</p>
MSc project Sociality zebra finch - Mul - Week 48
<p>Sociality videos</p>
MSc project Sociality zebra finch - Mul - Week 47 (2)
<p>Sociality videos</p>
MSc project Sociality zebra finch - Mul - Week 50
<p>Sociality videos</p>
MSc project Sociality zebra finch - Mul - Week 49
<p>Sociality videos</p>
Bug Fixes Projects SANER 2016
<p>corresponding projects of https://github.com/xuanbachle/data-bugfixes/blob/master/all.zip, which satisfy file level redundancy assumption</p>
AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, slow scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, slow scan</p> <p>20190312_HUTCH_2220_HT_PTB_Long_002</p> <p>PTB hole plate (6mm) - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame averaged over 16 frames, 120kVp, 83 μA, 3143 projections, 185 minutes, shuttling, stop start scanning mode </p> <p>S2O: 23.5414199829102</p> <p>S2D: 670.0553</p>
AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, slow scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, slow scan</p> <p>20190312_HUTCH_2220_NPL_Long225_001</p> <p>NPL phantom - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame averaged over 16 frames, 160kVp, 142 μA, 3143 projections, 185 minutes, shuttling, stop start scanning mode </p> <p>S2O: 255.7822265625</p> <p>S2D: 895.7499</p>
AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, PTB sample, fast scan</p> <p>20190312_HUTCH_2220_HT_PTB_fast_002</p> <p>PTB hole plate (6mm) - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 120kVp, 83 μA, 3143 projections, 7 minutes, no shuttling, continious scanning mode </p> <p>S2O: 23.5411643981934</p> <p>S2D: 670.0555</p>
AdvanCT EURAMET project, Southampton XCT scan data, Sphere sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, Double ruby sphere sample, fast scan</p> <p>20181107_HUTCH_2074_HD_6mm_ideal_1fpp_2W</p> <p>6mm ruby spheres, touching</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 90kVp, 22 μA, 3143 projections, 7 minutes, no shuttling, continuous motion scanning mode </p> <p>S2O: 27.8778991699219</p> <p>S2D: 799.6818</p>
AdvanCT EURAMET project, Southampton XCT scan data, EMPA sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, EMPA sample, fast scan</p> <p>20190314_HUTCH_2220_HT_EMPA_450fastfilter_003</p> <p>EMPA step cylinder phantom - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 450kVp reflection target microfocus source</p> <p>250ms exposure per frame, 390kVp, 242 μA, 3143 projections, 13 minutes, 8mm copper filter, no shuttling, continious scanning mode </p> <p>S2O: 652.500579833984</p> <p>S2D: 107.011</p>
AdvanCT EURAMET project, Southampton XCT scan data, Sphere sample, slow scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, Double ruby sphere sample, slow scan</p> <p>20181107_HUTCH_2074_HD_6mm_ideal_1fpp</p> <p>6mm ruby spheres, touching</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 90kVp, 155 μA, 3143 projections, 7 minutes, no shuttling, continuous motion scanning mode </p> <p>S2O: 27.8778991699219</p> <p>S2D: 799.6818</p>
AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, fast scan
<p>AdvanCT EURAMET project, Southampton XCT scan data, NPL sample, slow scan</p> <p>20190312_HUTCH_2220_NPL_Fast225_002</p> <p>NPL phantom - aluminium</p> <p>Data acquisition system: Custom Nikon metrology custom x-ray tomography system</p> <p>Detector: PerkinElmer XRD 1621 CN3 – HS, 2000x2000 pixels, 200 μm2 pixel size.</p> <p>Nikon 225kVp reflection target microfocus source</p> <p>134ms exposure per frame, 90kVp, 113 μA, 3143 projections, 7 minutes, no shuttling, continuous scanning mode </p> <p>S2O: 255.781982421875</p> <p>S2D: 895.7496</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.