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6,467 results for “process”
Dataset from FAPESP process: 2022/05633-0
<p>Primary data from the completed master research by Leonardo Rodrigues Moreira (FAPESP number: 2022/05633-0). Title: Surface modification of indium tin oxide for work function tailoring using organic self-assembled monolayer</p>
Raw and processed data, gating strategy, and photographs of instrumental setup of NAVETTA
<p>Supplementary Information and Raw Data for Weiss et al., Comp Struct Biotechn J: Nanosci Adv Mat, 2024</p> <p>1. xls sheet of raw and processed data for all figures</p> <p>2.-3. gating strategies for flow cytometry experiments</p> <p>4.-8. photographs of instrumental setups</p>
Processed data for "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER"
<p>This dataset contains the processed data for the figures and analyses performed in the publication "Characterising the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER" from Lucas O., Ward S., Zaidi R., Bunkum A., ..., Zaccaria S. Nature genetics, in press, 2024.</p> <p>The processed data are separated into three respective folders:</p> <ul> <li>GT contains all the data related to the analysis of the generated ground truth datasets;</li> <li>NSCLC contains all the data related to the analysis of the NSCLC dataset;</li> <li>TNBC_HGSC contains all the data related to the analysis of the TNBC and HGSC datasets. </li> </ul>
Questionnaires answers and data processing for a mixed-presence user study with two wall-sized displays
<p>Questionnaire answers and data processing tabs that was part of a mixed-presence experiment with two wall-sized displays.<br>Was used for a study in Q4 2023. Accompanies a paper.<br>Complements the protocol for that study that can be found at https://zenodo.org/doi/10.5281/zenodo.12663837 and contains answers for the questionnaires that can be found at https://zenodo.org/doi/10.5281/zenodo.12664007</p>
Dataset Public Opinion of UAE and Sentiment Analysis Process
<p>This article explains the UAE public opinion towards Indonesia in the era of President Jokowi's administration. In the digital era, in IR studies, it must be understood that public opinion influences a country's foreign policy, including in the economic field. The research team crawled data from Twitter (X), a data set (raw data), to find out the UAE's public opinion. The raw data was then processed using SVM machine learning to find out the UAE public's positive, negative, and neutral levels regarding Indonesia. After that, the results of public opinion were compared with the UAE's and Indonesia's economic cooperation to determine whether there was a relationship between public opinion and the level of economic cooperation between the two countries. So, the data in this study are the UAE public opinion data collection, which involved tracking and analyzing public sentiment in various UAE-based media outlets and sentiment analysis processes.</p>
CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGES
<p><span>This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0 through 6. Furthermore, mammography is the most widely utilized screening technique.</span></p> <p><span>This study suggests using mammography data processing to identify breast lesions. Adaptive filters are used for image cropping and contrast enhancement during the pre-processing phase. The pectoral muscle is then segmented using segmentation techniques that consider morphological and area growth factors. The lesion is then divided into sections at the muscle and breast levels using the Discrete Wavelet Transform (DWT), which finds any micro calcifications. Furthermore, to distinguish between dense lesions and other kinds of lesions, an area cultivation approach combined with multiple thresholding techniques is employed. Lastly, the obtained segmentation is used to extract textural and morphological features.</span></p> <p><span>When expert-segmented and automatically segmented images were compared, the Sorensen Decade similarity index was 0.73, indicating the effectiveness of the suggested method. Considering that the lesion area on a mammogram can only be roughly delineated by hand or automatically, this is a promising outcome.</span></p>
Dataset for Medical Image Processing in Python Carpentries lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, originally developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="https://zenodo.org/records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a> <div> </div> </li> <li>Additional data: to be added</li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
scLANE manuscript processed datasets
Open the record for dataset details and reuse information.
THE IMPACT OF LOCAL PRODUCT BRANDING ON THE ECONOMIC PERFORMANCE OF AGRICULTURAL AND LIVESTOCK AGRO-PROCESSING INDUSTRIES IN VLORA
<p>This study aims to examine the impact of local product branding on the economic performance of the agricultural and livestock agro-processing industries in the region of Vlora. Based on the data collected from local agro-processing and agro-tourism industries, the purpose of this research is to analyze how branding strategies contribute to increasing the level of sales, the level of income, creating a strong identity, and improving the performance of businesses and the territory where these businesses are concentrated. The study includes in the analysis the influence of the local brand in the creation of the identity, in the level of sales, in the income, the profit margins, and the improvement of the economic performance of the agricultural and livestock agro-processing industries. For this reason, the research was conducted with the inclusion of over 100 industries/agritourism, and the analysis of the questionnaire data was conducted with the STATA program. From the results of the research, it is clear that investment in the branding of products with local indicators is necessary to stimulate economic development and to strengthen the market positioning of businesses in the agricultural and livestock sectors. Also, this research provides important recommendations for improving branding practices as a strategic tool for the development and consolidation of agro-processing industries in the region.</p>
Linked collectors and determiners for: Epiproct And Dorsal Process Structure In The Allocapnia Forbesi Frison, A. Pygmaea (Burmeister), And A. Rickeri Frison Species Groups (Plecoptera: Capniidae), And Inclusion Of A. Minima (Newport) In A New Species Group.
Natural history specimen data linked to collectors and determiners held within, "Epiproct And Dorsal Process Structure In The Allocapnia Forbesi Frison, A. Pygmaea (Burmeister), And A. Rickeri Frison Species Groups (Plecoptera: Capniidae), And Inclusion Of A. Minima (Newport) In A New Species Group". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1abcb62d-e2d8-4daf-9d33-7acb0cbd9580">https://bionomia.net/dataset/1abcb62d-e2d8-4daf-9d33-7acb0cbd9580</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1abcb62d-e2d8-4daf-9d33-7acb0cbd9580">https://gbif.org/dataset/1abcb62d-e2d8-4daf-9d33-7acb0cbd9580</a>. Formatted as a Frictionless Data package.
Table 1 in Epiproct And Dorsal Process Structure In The Allocapnia Forbesi Frison, A. Pygmaea (Burmeister), And A. Rickeri Frison Species Groups (Plecoptera: Capniidae), And Inclusion Of A. Minima (Newport) In A New Species Group
<p><b>Table 1.</b> List of <i>Allocapnia</i> species studied with scanning electron microscopy.</p><table><tbody><tr><th><b>SPECIES</b></th><th><b>SEM STUDIES</b></th></tr></tbody><tbody><tr><th><i>Allocapnia aurora</i> Ricker 1952</th><td>Alford, 1998; Stark & Lacey, 2005; Nations et al., 2007</td></tr><tr><th><i>A. granulata</i> (Claassen 1924)</th><td>Nations et al., 2007; Grubbs & Sheldon, 2008</td></tr><tr><th><i>A. loshada</i> Ricker 1952</th><td>Kondratieff & Kirchner, 2000</td></tr><tr><th><i>A. malverna</i> Ross 1964</th><td>Grubbs, 2008</td></tr><tr><th><i>A. menawa</i> Grubbs & Sheldon 2008</th><td>Grubbs & Sheldon, 2008</td></tr><tr><th><i>A. mohri</i> Ross & Ricker 1964</th><td>Stark & Nelson, 2000; Grubbs, 2008</td></tr><tr><th><i>A. muskogee</i> Grubbs & Sheldon 2008</th><td>Grubbs & Sheldon, 2008</td></tr><tr><th><i>A. mystica</i> Frison 1929</th><td>Stark & Lacey, 2005; Nations et al., 2007; Grubbs & Sheldon, 2008</td></tr><tr><th><i>A. perplexa</i> Ross & Ricker 1971</th><td>Kirchner et al., 2002</td></tr><tr><th><i>A. polemistis</i> Ross & Ricker 1971</th><td>Stark et al., 2000</td></tr><tr><th><i>A. recta</i> (Claassen 1924)</th><td>Kondratieff & Kirchner, 2000; Stark et al., 2000; Grubbs, 2008</td></tr><tr><th><i>A. rickeri</i> Frison 1929</th><td>Nations et al., 2007</td></tr><tr><th><i>A. sano</i> Grubbs 2006</th><td>Grubbs, 2006</td></tr><tr><th><i>A. sequatchie</i> Kondratieff & Kirchner 2000</th><td>Kondratieff & Kirchner, 2000</td></tr><tr><th><i>A. starki</i> Kondratieff & Kirchner 2000</th><td>Alford, 1998 (as <i>A. recta</i>); Kondratieff & Kirchner, 2000; Stark et al., 2000 (as <i>A. recta</i>); Nations et al. 2007; Ray et al., 2012</td></tr><tr><th><i>A. tsalagi</i> Grubbs 2008</th><td>Grubbs, 2008</td></tr><tr><th><i>A. unzickeri</i> Ross & Yamamoto 1966</th><td>Grubbs & Sheldon, 2008</td></tr><tr><th><i>A. virginiana</i> Frison 1942</th><td>Alford, 1998; Stark & Lacey, 2005; Nations et al., 2007</td></tr><tr><th><i>A. wrayi</i> Ross 1964</th><td>Grubbs & Sheldon, 2008</td></tr></tbody></table>
WONDERBREAD: A Benchmark + Dataset for Business Process Management (BPM) Tasks
<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2406.13264">WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks</a></p> <h2><strong>Background</strong></h2> <p>The <em>WONDERBREAD</em> dataset contains <strong>2,928 human demonstrations</strong> of <strong>598 web navigation workflows</strong> across <strong>6 types of BPM tasks</strong>. These tasks measure the ability of a model to generate accurate documentation, assist in knowledge transfer, and improve the efficiency of workflows.</p> <p>Please see our website for more details: <a href="https://wonderbread.stanford.edu/">https://wonderbread.stanford.edu/</a></p> <h2><strong>Quick Start</strong></h2> <p>To start, download <strong>debug_demos.zip</strong> (1 GB). It contains a subset of <strong>24 demonstrations</strong> which can give you a sense of how the dataset is structured.</p> <p>To reproduce the paper, download <strong>gold_demos.zip</strong> (33 GB). It contains <strong>724 demonstrations</strong> corresponding to the 162 "Gold" tasks which were used for all the evaluations in the original paper.</p> <p>To obtain the full dataset, download <strong>demos.zip</strong> (133 GB). This contains all <strong>2,928 demonstrations</strong> and can be used for training, fine-tuning, and evaluating models.</p> <h2><strong>Dataset Structure</strong></h2> <p>The dataset contains several files, defined below.</p> <ol> <li><strong>Raw Data</strong><em> (useful for training/fine-tuning/evaluation)</em> <ol> <li><strong>debug_demos.zip </strong>(1 GB)<strong> -- </strong>a subset of only 24 demonstrations taken from the full dataset. Useful to get a sense of the dataset and for debugging.</li> <li><strong>gold_demos.zip</strong> (22 GB) -- a subset of only 724 demonstrations corresopnding to the 162 "Gold" tasks. This is the dataset that was used for all evaluations in the original <em>WONDERBREAD</em> paper.</li> <li><strong>demos.zip</strong> (133 GB) -- all 2,928 demonstrations across 598 tasks. Useful for training your own models.</li> </ol> </li> <li><strong>Modality-Specific Subsets of Raw Data </strong><em>(useful for specific types of training/fine-tuning/evaluation)</em><br> <ol> <li><strong>All Demos</strong> <ol> <li><strong>demos_sop_only.zip</strong> (4 MB)-- only the SOP <code>.txt</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_only.zip</strong> (770 MB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for all 2,928 demonstrations</li> <li><strong>demos_sop_and_trace_and_screenshots_only.zip</strong> (22 GB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for all 2,928 demonstrations</li> </ol> </li> <li><strong>"Gold" Demos</strong> <ol> <li><strong>gold_demos_sop_only.zip</strong> (1 MB)-- only the SOP <code>.txt</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_only.zip</strong> (190 MB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li><strong>gold_demos_sop_and_trace_and_screenshots_only.zip </strong>(6 GB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for the 724 demonstrations in the "Gold" tasks</li> </ol> </li> </ol> </li> <li><strong>Evaluation</strong><em> (useful for evaluation)</em> <ol> <li><strong>qa_dataset.csv -- </strong>contains all 120 questions and ground truth answers used in the "Knowlege Transfer" evaluation.<strong><br></strong></li> <li><strong>df_rankings.csv -- </strong>contains the rankings of all "Gold" tasks used in the "SOP Ranking" evaluation.<strong><br></strong></li> </ol> </li> <li><strong>Metadata</strong><em> (can be safely ignored)</em> <ol> <li><strong>Process Mining Task Demonstrations.xlsx --</strong> maps human annotators to specific demonstrations; also contains "Gold" task rankings used in the "SOP Ranking" evaluation.</li> <li><strong>metadata.json -- </strong>maps Google Drive URLs to Google Drive Folder IDs to demonstration names</li> <li><strong>df_valid.csv -- </strong>tracks assets associated with each demonstration</li> </ol> </li> </ol>
Low-Range FMCW Insect Radar - Lab Experiments Data, Results, and Data Processing Software
<p>This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity.</p>
Inventory of the analyzed processes
<p>Supplementary material<br> to the article:</p> <p>Life Cycle Assessment of Different Waste Lubrication Oil<br> Management Options in Serbia</p>
Business Process Intelligence Challenge 2019 - Processed
<p>This dataset is the pre-processed version of the BPIC19 dataset. It contains potential confounding features, the treatment indicator, and the cycle time.</p>
FIG. 8. — Auksiivik 174X Feature 567, a in To freeze or to dry: Seasonal variability in caribou processing and storage in the barrenlands of Northern Canada
FIG. 8. — Auksiivik 174X Feature 567, a marrow cracking area. Note anvil and hammer stones near centre of photo.
Pre-Processed Power Grid Frequency Time Series
<p><strong>Overview</strong><br> This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid:</p> <ul> <li>Continental Europe</li> <li>Great Britain</li> <li>Nordic</li> </ul> <p>This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper.</p> <p><strong>Data sources</strong><br> We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs).</p> <ul> <li><strong>Continental Europe </strong>[2]: We downloaded the data from the German TSO <em>TransnetBW GmbH,</em> which retains the Copyright on the data, but allows to re-publish it upon request [3].</li> <li><strong>Great Britain </strong>[4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO <em>National Grid</em>. They publish the frequency recordings under the NGESO Open License [5].</li> <li><strong>Nordic</strong> [6]: We obtained the data from the Finish TSO <em>Fingrid</em>, which provides the data under the open license CC-BY 4.0 [7].</li> </ul> <p><strong>Content of the repository</strong></p> <p><strong>A) Scripts</strong></p> <ol> <li>In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites.</li> <li>In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]).</li> <li>In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]).</li> </ol> <p>The python scripts run with Python 3.7 and with the packages found in "requirements.txt".</p> <p><strong>B) Yearly converted and cleansed data</strong><br> The folders "<year>_converted" contain the output of "convert_data_format.py" and "<year>_cleansed" contain the output of "clean_corrupted_data.py".</p> <ul> <li><strong>File type</strong>: The files are zipped csv-files, where each file comprises one year.</li> <li><strong>Data format</strong>: The files contain two columns. The second column contains the frequency values in Hz. The first one represents the time stamps in the format <em>Year-Month-Day Hour-Minute-Second</em>, which is given as naive local time. The local time refers to the following time zones and includes Daylight Saving Times (python time zone in brackets): <ul> <li>TransnetBW: Continental European Time (<em>CE)</em></li> <li>Nationalgrid: Great Britain (<em>GB</em>)</li> <li>Fingrid: Finland (<em>Europe/Helsinki</em>)</li> </ul> </li> <li><strong>NaN representation</strong>: We mark corrupted and missing data as "NaN" in the csv-files.</li> </ul> <p><strong>Use cases</strong><br> We point out that this repository can be used in two different was:</p> <ul> <li><strong>Use pre-processed data</strong>: You can directly use the converted or the cleansed data. Note however, that both data sets include segments of NaN-values due to missing and corrupted recordings. Only a very small part of the NaN-values were eliminated in the cleansed data to not manipulate the data too much.</li> </ul> <ul> <li><strong>Produce your own cleansed data</strong>: Depending on your application, you might want to cleanse the data in a custom way. You can easily add your custom cleansing procedure in "clean_corrupted_data.py" and then produce cleansed data from the raw data in "<year>_converted".</li> </ul> <p><strong>License</strong></p> <p>This work is licensed under multiple licenses, which are located in the "LICENSES" folder.</p> <ul> <li>We release the code in the folder "Scripts" under the MIT license .</li> <li>The pre-processed data in the subfolders "**/Fingrid" and "**/Nationalgrid" are licensed under CC-BY 4.0.</li> <li>TransnetBW originally did not publish their data under an open license. We have explicitly received the permission to publish the pre-processed version from TransnetBW. However, we cannot publish our pre-processed version under an open license due to the missing license of the original TransnetBW data.</li> </ul> <p><strong>Changelog</strong><br> Version 2:</p> <ul> <li>Add time zone information to description</li> <li>Include new frequency data</li> <li>Update references</li> <li>Change folder structure to yearly folders</li> </ul> <p>Version 3:</p> <ul> <li> <p>Correct TransnetBW files for missing data in May 2016</p> </li> </ul>
Sensory processing during sleep in Drosophila melanogaster - ethoscope dataset
<p>Dataset for "Sensory processing during sleep in Drosophila melanogaster" by French et al Nature 2021</p> <p>Gilestro Laboratory, Imperial College London</p> <p>https://lab.gilest.ro</p> <p> </p>
Dataset for Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation
<p>This data set was created between 01-04.2021, to study the current landscape of using web trackers in scholarly communication. The data set is part of an article (manuscript) that is intended to be published under the this title: Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation.</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.