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13,113 results for “Resistivity”
Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover
<p>Red clover (<em>Trifolium pratense</em> L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses.</p> <p>A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by <em>Colletotrichum trifolii</em>, and clover rot caused by <em>Sclerotinia trifoliorum, </em>was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide, once validated, a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.</p>
Antibiotic resistant pathogen outbreak investigation: an interdisciplinary module to teach fundamentals of evolutionary biology
<p>The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.</p>
Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures
<p><span>Characterisation dataset for “</span><span>Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures”</span><span>, DOI:</span><span>10.1039/d4nh00104d</span><span>. Data for main and supporting figures provided as *.xlsx and *.txt files.</span></p> <p> </p> <p> </p> <p> </p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
Data set of "Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"
<p>The dataset of all data presented in the article published in the virtual special issue of the Journal of Physical Chemistry Letters:</p> <p>"Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00945">https://doi.org/10.1021/acs.jpclett.4c00945</a></p> <p> </p> <p>The dataset contains the following raw data:</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2a.txt : Representative characteristic _I-V_ response of memristor (5 cycles)<br>- Fig2b.txt : Upper vertex-dependent multilevel/multistate analog resistive switching<br>- Fig2c.txt : Characteristic _I-V_ response of 20 distinct devices<br>- Fig2d.txt : Endurance measurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)</p> <p>### Figure 3<br>- Fig3a.txt : Characteristic _I-V_ response with an upper vertex of 0.25 V<br>- Fig3b.txt : Characteristic _I-V_ response with an upper vertex of 0.75 V<br>- Fig3c.txt : Characteristic _I-V_ response with an upper vertex of 1.25 V</p> <p>### Figure 4<br>- Fig4a.txt : IS spectrum under dark conditions at 0 V<br>- Fig4b.txt : IS spectrum under dark conditions at 0.2 V<br>- Fig4c.txt : IS spectrum under dark conditions at 0.3 V<br>- Fig4d.txt : IS spectrum under dark conditions at 0.4 V<br>- Fig4e.txt : IS spectrum under dark conditions at 0.6 V<br>- Fig4f.txt : IS spectrum under dark conditions at 1.0 V</p> <p>### Figure 5<br>- Fig5a.txt : Voltage-dependent transient current response of the perovskite memristor<br>- Fig5b.txt : Magnified view of the transient current response of a single voltage pulse at representative applied voltages<br>- Fig5c.txt : Pulse width-dependent transient current response<br>- Fig5d.txt : Corresponding magnified view of the first and last transient responses<br>- Fig5e.txt : Synaptic potentiation and depression characteristic response of the memristor</p> <p>### Figure 6<br>- Fig6a.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.4 V<br>- Fig6b.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.4 V<br>- Fig6c.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.8 V<br>- Fig6d.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.8 V<br>- Fig6e.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 1.2 V<br>- Fig6f.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 1.2 V</p>
Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)
<p>Dataset used to perform the following manuscripts: </p> <p>- Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. <em>JMIR mHealth and uHealth</em>, <em>4</em>(3), e112. http://doi.org/10.2196/mhealth.5716</p> <p>rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework. </p>
Identification of Genes Regulating Dexamethasone Resistance and Prognostic Model Development in Acute Lymphoblastic Leukemia
<p>This study investigates the mechanisms of dexamethasone resistance in acute lymphoblastic leukemia (ALL) and presents a prognostic model to predict patient outcomes and immunotherapy responses. By analyzing gene expression data, we identified autophagy-related genes associated with dexamethasone resistance, particularly focusing on STK38L’s role in modulating autophagy via ULK1. Our results reveal that high STK38L expression enhances dexamethasone resistance by promoting autophagy markers LC3II/LC3I and beclin-1. This study provides valuable insights into the molecular basis of dexamethasone resistance and highlights STK38L as a potential biomarker and therapeutic target for improving ALL treatment strategies.</p>
Resource use strategies, resistance and tolerance to aerial biomass removal in Argentina mid-west native plants
<p>Dataset of the PhD Thesis from Lucas D. Gorné:<br> - Gorné LD. 2018. Estrategias de uso de recursos, resistencia y tolerancia a la remoción de biomasa aérea en plantas nativas del centro-oeste de Argentina. Tesis del Doctorado en Ciencias Biológicas. Facultad de Ciencias Exactas, Físicas y Naturales. Universidad Nacional de Córdoba. Córdoba, Argentina. https://ri.conicet.gov.ar/handle/11336/87925.</p>
Resistance test to Pythium in common bean
<p>This video dhows the steps in resistance tests to Pythium ultimum in common bean in controlled conditions and it is parts of a set edited to spread the plant breeding job.</p> <p>A dissemination task developed by the Plant Genetic Group (SERIDA) for the BRESOV project</p> <p>Available in the Link : https://www.youtube.com/watch?v=YqjKjceqTGk&t=2s</p> <p>DOI :10.5281/zenodo.5557265</p>
DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset)
<p>Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database.</p>
Electrical tomography resistivity (ERT) monitoring time series
<p>Multi-temporal electrical tomography resistivity (ERT) measurements for monitoring the performance of the bio-degradable bentonite mat in OAL-Austria. The first measurement was conducted on 30 July 2020 before the implementation of the mat, afterwards seasonal measurement (except for winter due to snow cover) were obtained: 19 Oct 2020, 27 Arpil 2021, 10 August 2021, 4 October 2021, 13 April 2022. A time-lapse inversion algorithm was used to prepare the final results. See OPERANDUM deliverable 4.6 for more details.</p> <p>Device: Lippmann 4point light 10 W</p>
Repeatability of energy metabolism and resistance to dehydration in the invasive slug Limax maximus
<p>Dataset from the paper "Repeatability of energy metabolism and resistance to dehydration in the invasive slug <em>Limax maximus"</em></p> <p>It contains metabolic rates and body mass assessed on 30 individuals of L. maximus in three different trials. Metabolic rates are in CO2 ml/m</p>
Clonal heterogeneity of endocrine therapy resistance in breast cancer
<p>We barcoded endocrine therapy sensitive cell lines (MCF7 and T47D) and rendered them resistant to commonly applied first line endocrine therapeutics (Tamoxifen and estrogen deprivation). Next, we isolated single cell clones of endocrine therapy resistant populations and subjected clonal cell lines to RNA-Seq and Phosphoproteomics profiling.</p>
Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors
<p>Data required to reproduce the results/figures of the "<strong>Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors</strong>" project. </p>
Chesley et al., 2023 - Southern Hikurangi Margin Electromagnetic Data from HT-RESIST trench-crossing profile
<p>Chesley_etal_2023_South-Hikurangi-EM-data.txt contains controlled-source electromagnetic data and magnetotelluric data from the southern trench-crossing profile of the Hikurangi Trench Regional Electromagnetic Survey to Image the Subduction Thrust (HT-RESIST) project (see https://emlab.ldeo.columbia.edu/index.php/category/ht-resist/ for information regarding the survey). Chesley_etal_2023_South-Hikurangi-bathymetry.txt is the associated bathymetry file. </p>
Efficiency of the formation of acid-resistant Calcium-oxalate layers on limestone
<p><strong>Scientific background:</strong></p> <p>Carbonate-based stone monuments and buildings are susceptible to weathering in acidic environments. To combat surface corrosion and slow down material deterioration, protective coatings that inhibit calcite dissolution have been proposed. The efficiency and integrity of the coatings was studied by measuring sulfur distribution along the treated surface. Such experiment cannot be efficiently performed with standard SSD PIXE detectors due to high overlap between the strong Ca K x-ray escape peaks and S Kα. For that purpose, a new parallel-beam wavelength dispersive (PB-WDS) X-ray emission spectrometer at JSI have been used which achieves high energy resolution in the eV range and is able to measure S distribution on the surface of treated marble samples.</p> <p><strong>Measurements performed within TNA project:</strong></p> <p>The new PB-WDS X-ray emission spectrometer at J. Stefan Institute (Ljubljana, Slovenia) was used to map the presence of Sulphur on the surface of 13 marble samples treated with different coatings and after exposure to 2% sulfuric acid. Ge(111) crystal analyzer was used in the spectrometer to record the S Ka signal, the overall scan size was 5 × 5 mm<sup>2</sup>.</p> <p><strong>Data files:</strong></p> <p>We are sharing the files produced during measurements. The signal from the detector preamplifier was processed with the XIA DXP-XMAP digital pulse processor. The files are two main formats:</p> <ol> <li>Files containing mapping data. The spectrometer was set to the Bragg angle corresponding to the energy of the S Ka emission line. In a .zip folder, with 4 .mca files for every measured point (extension: _0-Si(Li) detector, _2- PB-WDS spectrometer)</li> <li>High energy resolution spectra recorded at selected points on the sample surface.</li> </ol> <p> </p> <p> </p> <table> <thead> <tr> <th> <p>#</p> </th> <th> <p>Filename</p> </th> <th> <p> </p> </th> </tr> </thead> <tbody> <tr> <td> <p>1</p> </td> <td> <p>VES_A1_5.zip</p> </td> <td> <p>Map of S on VES_A1_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S_X80_Y85.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_5 sample. Point position x = 80px, y = 85px</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>VES_A3_5.zip</p> </td> <td> <p>Map of S on VES_A3_5 sample. Ge 111, 100X100 points, 50μm step, 4s/point</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S_X95_Y33.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A3_5 sample. Point position x = 95px, y = 33px</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>VES_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 40x40 points, 125μm step, 5s/point</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S_X3_Y3.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 3px, y = 3px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S_X20_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 20px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>S_X35_Y20.zip</p> </td> <td> <p>Scan over S Ka and Kb peak on the surface of VES_A1_12 sample. Point position x = 35px, y = 20px. Z position optimized to maximum at this point</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>CAR_A1_12.zip</p> </td> <td> <p>Map of S on VES_A1_12 sample. Ge 111, 80x80 points, 65μm step, 4s/point</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>CAR_A1_12_back_side.txt</p> </td> <td> <p>Scan over S Ka and Kb peak on the back surface of CAR_A1_12 sample.</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>VES_A1_12_Washed.zip</p> </td> <td> <p>Sample washed under running water. 2250 eV - 2350 eV; stepsize = 1.00eV; 6s/point or 10s/point for back</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>VES_A2_12.zip</p> </td> <td> <p>Line map of S on VES_A2_12. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>CAR_A3_5.zip</p> </td> <td> <p>Line map of S on CAR_A3_5. 10x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>CAR_A1_5.zip</p> </td> <td> <p>Line map of S on CAR_A1_5. 10x1 points, 1mm stepsize, 10s/point1</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>VES_A3_12.zip</p> </td> <td> <p>Line map of S on VES_A3_12. 10x1 points, 1mm stepsize, 10s/point + Map of S on VES_A3_12 sample. Ge 111, 50x25 points, 200μm step, 3s/point</p> </td> </tr> <tr> <td> <p>16</p> </td> <td> <p>VES_A2_5.zip</p> </td> <td> <p>Line map of S on VES_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>17</p> </td> <td> <p>CAR_A2_5.zip</p> </td> <td> <p>Line map of S on CAR_A2_5. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>18</p> </td> <td> <p>CAR_A2_12.zip</p> </td> <td> <p>Line map of S on CAR_A2_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> <tr> <td> <p>19</p> </td> <td> <p>CAR_A3_12.zip</p> </td> <td> <p>Line map of S on CAR_A3_12. 5x1 points, 1mm stepsize, 10s/point</p> </td> </tr> </tbody> </table> <p> </p>
Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid
<p>This dataset contains measurement data from the following publication: Belt, T.; Kyyrö, S.; Kilpinen, A. T. (2023) Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid. Journal of Materials Science, 10.1007/s10853-023-08874-w. Small samples of Scots pine sapwood were modified using different concentrations of phenol formaldehyde (2.5, 5, 10, 20 and 30% resin solids content) and sorbitol-citric acid (5, 10, 20, 30 and 40% resin solids content) and then exposed to brown rot decay by <em>Coniophora puteana</em> and <em>Rhodonia placenta</em>. Sample masses and dimensions were measured at different points to determine their weight gain, anti-swelling efficiency and moisture exclusion efficiency due to modification, their mass loss due to decay and their moisture content at the end of the decay test. Fluorescence images were collected from decayed and control samples after the decay test. Further details on the experimental procedures can be found in the publication. </p> <p>The "Sample IDs and measurement data.csv" -file contains the sample IDs and all measured dimensions and mass data for every sample. Areas A<sub>dry0</sub>, Ad<sub>ry1</sub>, A<sub>wet</sub>, and A<sub>dry2</sub> are the cross-sectional areas of the samples in the dry state before modification, in the dry state after modification and before leaching, in the wet state during leaching, and in the dry state after leaching, respectively. Masses m<sub>dry0</sub>, m<sub>dry1</sub>, m<sub>dry2</sub>, m<sub>RH85</sub>, m<sub>wet</sub>, and m<sub>dry3</sub> are the masses of the samples in the dry state before modification, in the dry state after modification and before leaching, in the dry state after leaching, in the conditioned state at RH 85%, in the wet state at the end of the decay test, and in the dry state after the decay test, respectively.</p> <p>The "Fluorescence images" -folder contains fluorescence images collected from the samples. The image files are named according to the ID of the imaged sample, followed by additional tags. The samples modified using phenol formaldehyde were imaged using both green and UV excitation, and the file names contain the tag "green" or "UV" to denote the used excitation wavelengths. For all samples, the sample ID (and the excitation tag) are followed by a number to differentiate replicate images collected from the sample. </p>
Watkins natural accessions yellow rust disease resistant scores
<p>The file contains the phenotypic information from field trails conducted in Kenya at the Kenya Agriculture and Livestock Research Organisation (KALRO) and the Ethiopian Institute of Agricultural Research (EIAR). Data are separated into the three rusts (yellow rust (Yr), stem rust (Sr) and leaf rust (Lr)), although data is not complete at all locations. When possible both seedling and adult plant data is provided. Adult scores include several observation across the growing season. For scoring rust severity, the modified Cobb scale (Peterson et al. 1948) was used to determine the percentage of tissue infected (0-100%) with rust and infection response (S, MS, MR and R, corresponding to susceptible, moderately susceptible, moderately resistant and resistant). </p> <p>Accession codes relate to Watkins landraces and their country of origin and accession names are indicated. Locations, dates and disease scores are indicated. Missing data is indicated as "-". </p> <p>For more detailed passport data and access to germplasm visit the John Innes Centre Germplasm Resources Unit (<a href="https://www.seedstor.ac.uk/search-browseaccessions.php?idCollection=39">SeedStor</a>). Additional germplasm resources and populations developed from the Watkins accessions can be found here: <a href="https://wisplandracepillar.jic.ac.uk/">https://wisplandracepillar.jic.ac.uk/</a> </p> <p> </p>
Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes
Data associated with "Plant community richness and foliar fungicides impact soil Streptomyces inhibition, resistance, and resource use phenotypes" (DOI: 10.3389/fmicb.2024.1452534). These data include soil resource measurements and various phenotypic measurements of associated Streptomyces isolates/populations. Specifically, these data note population level inhibition phenotypes according to Herr's Assays, isolate level antibiotic resistance phenotypes against 9 standard antibiotics, and isolate level resource use phenotypes quantified with Biolog SF-P2 96 well plates.
WAT04 Root decomposition and nutrient dynamics are resistant to rainfall legacies in tallgrass prairie
Purpose: Litter decomposition is an important component of carbon (C) and nitrogen (N) cycling, and rates of mass loss and nutrient release are sensitive to current climate conditions. Growing evidence suggests that past climate conditions can exert legacies on soil C and N cycling, but little is known about how belowground decomposition dynamics relate to these climate legacies. Results: Root litter mass loss was resistant to most climate treatments. Contrary to expectations, decomposition rates were slowest in plots with a history of long-term irrigation and fastest under drought in lowland prairie. Similarly, mass loss rates were overall faster in the drier uplands. Changes in N concentration as a function of mass loss were similar across treatments and patterns of litter N release largely tracked mass loss. Conclusions: Changes in the decomposer community with long-term release from water stress may have led to slowed root decomposition, but these effects were subtle. Our results suggest that changes in decomposition rates are not a cause of observed climate legacy effects on C and N cycling in prairies.
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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.