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.
505
datasets available to search
ShareScore release 0.9.0
Dataset results
505 results for “apple”
AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots
<p>This dataset consists of three zip archives containing valuable visual and measurement data related to tree assessments conducted using the Apple Vision Pro (AVP) technology. The first zip archive, <strong>images.zip</strong>, includes images taken in the forest, presented in .PNG and .JPG formats. These images capture various aspects of the study area and the measurement process.</p> <p>The second archive, <strong>videos_app_HR.zip</strong>, features videos recorded with the AVP using the "Handsruler" app, which focuses on measuring diameter at breast height (dbh) at 22 designated sample plots. Each video file is labeled with a numeric identifier that corresponds to the specific sample plot number, allowing for easy reference and organization.</p> <p>The third archive, <strong>videos_app_TM.zip</strong>, contains videos from the "Tape Measure" app, documenting dbh measurements taken at 17 sample plots. Similar to the previous videos, the file names indicate the respective sample plot numbers.</p> <p>In addition to the visual data, the dataset includes a comma-separated values (CSV) file named <strong>information_all_trees.csv</strong>, which consolidates all reference data regarding individual trees and sample plots. Each row in this file represents a single tree and includes several columns, each providing specific details about the measurements and observations.</p> <p>The column headers in <strong>information_all_trees.csv</strong> are as follows:</p> <ul> <li><strong>PLOT_ID</strong>: The numeric identifier for each sample plot.</li> <li><strong>tree_species_short</strong>: Abbreviation of the tree species.</li> <li><strong>caliper_dbh</strong>: The manually measured dbh of the tree in centimeters.</li> <li><strong>AVP_App1_dbh</strong>: The dbh measurement obtained from the AVP app "Handsruler" in centimeters.</li> <li><strong>AVP_App2_dbh</strong>: The dbh measurement obtained from the AVP app "Tape Measure" in centimeters.</li> <li><strong>res_App1</strong>: The difference between the dbh measured by the "Handsruler" app (AVP_App1_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>res_App2</strong>: The difference between the dbh measured by the "Tape Measure" app (AVP_App2_dbh) and the manual measurement (caliper_dbh), expressed in centimeters.</li> <li><strong>tree_species</strong>: The Latin name of the tree species, with genus and species connected by an "_".</li> <li><strong>tree_class</strong>: Classification of the tree into a species-specific category.</li> <li><strong>date</strong>: The date of the recordings.</li> <li><strong>time_App_1_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Handsruler" app, in minutes.</li> <li><strong>time_App_2_min</strong>: The duration of all dbh measurements at the entire sample plot using the "Tape Measure" app, in minutes.</li> <li><strong>time_manual_caliper_min</strong>: The duration of all dbh measurements at the entire sample plot conducted manually, in minutes.</li> <li><strong>measuring_person</strong>: The individual field worker for conducting all dbh measurements (manual and both AVP apps) at the sample plot.</li> <li><strong>mean_slope_degrees</strong>: The average slope of the terrain across the sample plot, expressed in degrees.</li> </ul> <p>This comprehensive dataset provides essential insights into the effectiveness of the AVP technology for measuring tree dimensions and contributes to ongoing research in forest management and ecological studies. The included videos and images serve as a visual reference for the measurement processes, while the CSV file encapsulates the quantitative data necessary for analysis. Each row in the CSV file represents a single tree, facilitating detailed examinations of individual measurements and comparisons across different sample plots.</p>
Survey Data on Apple Farming in China: Agronomic Management, Advisory Channels, and Profitability
<p>The Survey results and original data are stored in a directory structured as the table:</p> <table style="width: 100%; height: 223.938px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"><strong>Type</strong></td> <td style="width: 21.7597%; height: 19.5938px;"><strong>File Name</strong></td> <td style="width: 59.4134%; height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 47.5938px;"> <td style="width: 18.8269%; height: 47.5938px;"> <p>Raw_Data_Spearate_Source</p> </td> <td style="width: 21.7597%; height: 47.5938px;">raw_data_english_telephone.xlsx</td> <td style="width: 59.4134%; height: 47.5938px;">Translated data in English corresponding to the Chinese telephone interview data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_wechat.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Translated data in English corresponding to the Chinese Wechat Mini Program data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Raw_Data_Total</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_total.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Combined data from raw_data_english_telephone.xlsx and raw_data_english_wechat.xlsx</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 18.8269%; height: 39.1875px;">Apple_Statistical_Data</td> <td style="width: 21.7597%; height: 39.1875px;">apple_2022_statistical_data.xlsx</td> <td style="width: 59.4134%; height: 39.1875px;">Contains data on apple planting area, production, and yield sourced from the China Statistics Bureau, along with the number of survey questionnaires collected from various provinces</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_eng.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Contains the English version of the provinces' names</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Map_Boundary_line</td> <td style="width: 21.7597%; height: 19.5938px;">national_boundary_line.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The country boundaires of China</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_boundary.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The province boundaries of China</td> </tr> </tbody> </table> <p>For privacy reasons, personally identifiable information such as respondents’ names, telephone numbers, and specific addresses has been anonymized in the dataset. The file <em>raw_data_english_total.xlsx</em> contains 96 columns, each corresponding to a question in the questionnaire.</p>
MCR LTER: Coral Reef: Landscape-scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts, Adam et al., Ecol. Appl.
These data and analyses code were generated in support of the manuscript: Adam TC, Burkepile DE, Holbrook SJ, Carpenter RC, Claudet J, Loiseau C, Thiault L, Brooks, AJ, Washburn L, and RJ Schmitt, Ecological Applications We investigated the potential role of anthropogenic nutrient loading in driving recent coral-to-macroalgae phase shifts on reefs in the lagoons surrounding Moorea, French Polynesia. We used nitrogen (N) tissue content and stable isotopes (δ15N) in an abundant macroalga (Turbinaria ornata) together with empirical models of nutrient discharge to describe spatial and temporal patterns of nutrient enrichment in the lagoons. Turbinaria ornata were collected at 190 sites around Moorea in January, May, and August 2016. These sampling periods corresponded with distinct seasonal shifts in rainfall and wave forcing. Our results revealed that patterns of N enrichment were linked to rainfall, wave-driven circulation, and distance from anthropogenic nutrient sources, especially human sewage. In addition to describing high resolution patterns of N enrichment from 2016, we also analyzed core MCR time series on N tissue content in Turbinaria ornata from three habitats (fringing reef, back reef, and reef crest) at the six core MCR LTER sites between 2007 and 2013. These data showed that fringing reefs have been consistently enriched in N relative to back reefs, which are enriched relative to the reef crest. Further, these patterns mirror long-term patterns of nitrate and nitrite concentrations in the water column. We also analyzed core MCR time series on benthic communities and fishes and found that back reef sites that were consistently enriched in N between 2007 and 2013 experienced large increases in macroalgae while macroalgae remained much less abundant at back reef sites with lower N. These phase shifts to macroalgae occurred despite island-wide increases in the density and biomass of herbivorous fishes over the time period. Together, these results indicate th
DS_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_SEM images
<p>Scanning electron microscopy images showing P3HT pillar arrays with and without living cells on top</p>
Dataset associated to Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000
<p>Dataset associated to paper published under the SINFONIA project</p> <p>Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000</p>
Data from 89 fungicide trials on apple scab across Europe 2008-2018
<p>The data set comprises records of disease incidence and yield from untreated and treated plots.</p> <p><strong>Assessments</strong><br> The disease infection was expressed as the intensity of attack (=severity) and/or the frequency of attack. Two assessment methods were used:</p> <p>1. Visual assessment:<br> • P%INF: intensity of attack was obtained as a visual estimation of the percentage of each plant part (leaves or fruits) affected by disease<br> • P%FREQ: frequency of attack was represented by the number of infected leaves or fruits. The frequency is expressed as a percentage of the number sampled.</p> <p><br> 2. Class assessment<br> The level of the attack (intensity and frequency) was evaluated and calculated by classing plants into different severity categories ranging from no disease to severe attack.<br> • BEFHKT: frequency of disease attack expressed as a percentage, considering 4 damage classes<br> • BEFWER: intensity of attack expressed as a percentage, considering 4 damage classes.</p> <p>BEFHKT = 100*(cl2 + cl3 + cl4)/(cl1 + cl2 + cl3 + cl4)<br> BEFWER = 100*(cl2 + 2*cl3 + 3*cl4)/(3*N) (the Townsend-Heuberger intensity of attck)</p> <p><strong>Table of measures</strong><br> Measure Description<br> P%FREQ Estimated frequency of attack %<br> P%INF Estimated severy of attack %<br> %ANTK1 Class 1 (%)<br> %ANTK2 Class 2 (%)<br> %ANTK3 Class 3 (%)<br> %ANTK4 Class 4 (%)<br> BEFHKT Frequency of attack<br> BEFWER Index of attack<br> INFECT Infection (F); Infestation (F)<br> KR%ABB Diseased (%)<br> WIRKGR Percent of untreated</p> <p><strong>Data format and source</strong><br> The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The plot produced is also provided as a PNG file.</p> <p>The field trials were conducted by BASF, Germany.</p> <p> </p>
Apples to Apples: Shift from Mass Ratio to Additive Molecules per Electrode Area to Optimize Li-Ion Batteries
<p>Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective interphase (SEI) atthe electrodes that aims to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focus on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (radd) of the additive's amount of substance (nadd) to the electrode area (Aelectrode). Herein we utilize our extremely accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. The data provides strong evidence that reporting the mass ratios of electrolyte components is insufficient and the mol of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt.-% - 3.0 wt.-% for VC, and 5 wt.-% - 15 wt.-% for FEC for two mass loadings of 1 mAh/cm2 and 3 mAh/cm2. To engage the community to find better descriptors, such as the proposed radd, we publish the dataset alongside this manuscript.</p> <p>Codes and mechanical parts of the project:</p> <p>AutoBASS 2.0: <a href="https://github.com/Helge-Stein-Group/AutoBASS/tree/AutoBASS_2.0">GitHub - Helge-Stein-Group/AutoBASS at AutoBASS_2.0</a></p>
Model results: Model-based decision support for the choice of active spring frost protection measures in apple production
<p><strong>Background: </strong></p> <p>Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters.</p> <p>As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region).</p> <p><strong>Repository content:</strong></p> <p>This repository contains the simulation results of 100,000 Monte Carlo runs with the model.</p> <p>The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.</p>
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Images of apples for the use of the Viola-Jones method. Data set no. 2 - grey scale.
<p>The database contains pictures of apples made at different angles, from different sides and containing different varieties. In this way, two bases of apple images were created (each database contains 1,100 images). This set is data set no. 2 - grey scale: processed images in shades of gray. The photos were prepared for the best possible detection process in the Viola-Jones method. These photo bases with apples can be used to teach machines to recognize specific varieties and count apples.</p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Auswirkungen von Google, Apple, Facebook, Amazon und Microsoft auf Schule und Unterricht in Deutschland
<p>Google, Apple, Facebook, Amazon und Microsoft (GAFAM) sind einige der größten Unternehmen der IT-Welt. Viele ihrer Produkte werden von Millionen von Personen nahezu täglich genutzt. Ihre Produkte werden unter Anderem auch in Schulen und im Unterricht eingesetzt. Um zu untersuchen, welche Auswirkungen GAFAM auf Schule und Unterricht aus Sicht der Lehrkräften haben, wurde eine Online-Befragung mit Lehrer*innen durchgeführt.</p> <p> </p>
Data and code of Teixeira, Bauer et al. (2023) Basic Appl Ecol
<p>Data and code for:</p> <p>Teixeira LH, Bauer M, Moosner M, Kollmann J (2023) <strong>River dike grasslands can reconcile different ecosystem services and provide high multifunctionality.</strong> – <em>Basic and Applied Ecology</em> 66, 22–30. <a href="https://doi.org/10.1016/j.baae.2022.12.001">DOI: 10.1016/j.baae.2022.12.001</a></p> <p><a href="https://github.com/markus1bauer/2022_dike_grasslands_opinion/blob/main/README.md">GitHub README</a></p>
Dataset of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples
<p><strong>Summary</strong></p><p>This dataset is a collection of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples.</p><p><br><strong>Description</strong></p><p><i>Sample information</i></p><p>In 2022, 120 'Kanzi' apples that had been stored under CA conditions (4 °C, 1 kPa O2, 1.5 kPa CO2) for 8 months were obtained from FruitMasters, The Netherlands. The fruit was grown in orchards surrounding Geldermalsen, the Netherlands, and harvested at physiological maturity in 2021.</p><p><i>CT acquisition</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020]. A cone beam geometry with a circular trajectory was used to acquire 1440 projection images at an exposure time of 100ms, a tube peak voltage of 90kV, a current of 550uA, and 2 times binning, halving the detector resolution. Volumes were reconstructed with the FDK algorithm and a voxel size of 129.3um. Beam hardening correction was used from the FleXbox package [Kostenko 2020]. To make sure that the grey values could be compared between scans the spectral sensitivity of the scanner was first estimated for each scan individually and the average of these estimates was used for beam hardening correction on all CT scans. All apples were scanned with the stem side on top. Moreover, a line was drawn on all apples from the stem to the calyx. The apples were put in the CT scanner so that the line was facing the X-ray source.</p><p>The CT volumes are saved as .tiff stacks. All volumes have been cropped to remove the background.</p><p><i>Slicing and photograph acquisition</i></p><p>One day after CT scanning, the apples were sliced using a modified meat-slicing machine (CaterChef, house brand of EMGA, Mijdrecht, The Netherlands), which is illustrated in the file slicing_machine_labels.png. The sliding surface of the meat-slicing machine was replaced by a transparent acrylic sheet, and a camera was placed behind the slicing surface. While in the machine, each apple was kept in place by a suction cup so that it could not rotate during the slicing. All apples were sliced from the stem end to the calyx end, with a slice thickness of roughly 4mm. Every time before slicing, a picture was taken of the remaining part of the apple through the transparent sliding surface. To ensure that all apples were roughly aligned to the CT scans, the apples were oriented so that the line drawn earlier was on top.</p><p>The slice photographs are saved as .png files. All photographs have been cropped to remove the background and to center the apple in the image.</p><p><i>Visual browning scores</i></p><p>After each apple was sliced it was also visually inspected, and a score from one to ten was given to describe the amount of browning in the apple.</p><p><strong>Related paper</strong></p><p>When using this dataset please consider citing the following paper. It explains how the dataset was collected and used for the first time:</p><p>Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg, "Detecting internal disorders in fruit by CT. Part 1: Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit", 2023, <a href="https://arxiv.org/abs/2310.01987">arXiv preprint arXiv:2310.01987</a></p><p><br><strong>Research group</strong><br>This dataset was produced in a collaboration between the Computational Imaging group at Centrum Wiskunde & Informatica (CWI), and GREEFA.</p><p><a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a><br><a href="https://www.greefa.com/nl/">https://www.greefa.com/nl/</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p>
A chromosome-level genome assembly of the woolly apple aphid, Eriosoma lanigerum (Hausman) (Hemiptera: Aphididae)
<p><strong><em>Eriosoma lanigerum</em> v1.0 frozen release</strong></p> <p>Genome assembly: Eriosoma_lanigerum.v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Eriosoma_lanigerum.v1.0.scaffolds.gff</p> <p>BRAKER2 protein sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Eriosoma_lanigerum.v1.0.scaffolds.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Eriosoma_lanigerum.v1.0.scaffolds.gff.cds.fa</p> <p><em>Buchnera aphidicola</em> scaffolds: Buchnera_aphidicola.scaffolds.fa</p> <p><strong>Aphid orthogroups</strong></p> <p>OrthoFinder run files (see for details <a href="https://github.com/davidemms/OrthoFinder/blob/master/OrthoFinder-manual.pdf">https://github.com/davidemms/OrthoFinder/blob/master/OrthoFinder-manual.pdf</a>): OrthoFinder_run.tar.gz</p>
DS3_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_neurons electrophysiology
<p>Whole-cell current clamp recordings of the electrical activity of neurons plated on P3HT flat and pillars</p>
DS5_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_immunofluorescence
<p>Neuron synaptic expression analysis; neurons and HEK cells morphological analysis</p>
DS4_LH_Tullii et al._ACS Appl. Mater. Interfaces_2019_cell viability
<p>cell viability assay on HEK cells and neurons plated on P3HT flat and pillars</p>
Maintaining habitat diversity at small scales benefits wild bees and pollination services in mountain apple orchards
<p>In 2021, we conducted our study in apple orchards in South Tyrol, an Alpine region in Italy, using pan-traps, direct observations of visitation frequency, and a pollinator exclusion experiment. We investigated the scale-dependent effects of landscape heterogeneity and other parameters on wild bee assemblages and the related pollination service they provide at five spatial scales (radius 100 – 2,000 m).</p>
Phenotypic, weather, soil, and imputed genomic data for the apple REFPOP
<p>Supporting datasets for the article "Integrative multi-environmental genomic prediction in apple" by Jung et al. (2024)<em>.</em></p> <p>Pheno_raw.xlsx – Eleven traits were assessed during up to five years from 2018 to 2022 (Year) at up to five locations* (Country). The traits evaluated were floral emergence (Flowering_begin), flowering intensity (Flowering_intensity), harvest date (Harvest_date),<strong> </strong>total fruit weight (Fruit_weight), fruit number (Fruit_number), single fruit weight (Fruit_weight_single), titratable acidity (Acidity), soluble solids content (Sugar), fruit firmness (Firmness), red over color (Color_over), and russet frequency (Russet_freq_all).</p> <p>Weather_raw.xlsx – Hourly measurements from 2018 to 2022 (Date) of temperature (Temperature), relative humidity (Humidity), and global radiation (Radiation) were obtained at five locations* (Location).</p> <p>Soil_raw.xlsx – Soil characteristics (Variable) were measured at five locations* (Group.1) and two soil depths (Group.2) in 2016.</p> <p>SNPs_final_2022.bed, SNPs_final_2022.bim, SNPs_final_2022.fam – imputed genomic dataset of 303,239 biallelic SNPs in the PLINK format.</p> <p>*The locations correspond to Belgium (BEL), Switzerland (CHE), Spain (ESP), France (FRA) and Italy (ITA).</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.