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3,121 results for “Resources”
Extended SILVA reference taxonomy resources
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Digital Preservation for the Masses: Creating Workflows for Institutions & Under-Resourced Organisations
<p>Digital preservation for the masses: Creating and publishing workflows and guidelines for institutions and smaller organisations interested in digital preservation, data management, and archiving</p> <p>Presentation given by Julia Colleen Miller at the Language Documentation and Archiving conference</p> <p>Creating public documentation is a crucial step in providing transparency of archiving processes, helping maintain consistent institutional knowledge, and making available resources for those wanting to know about digital preservation, archiving or data management. To this end, PARADISEC's pre-existing archiving workflows have been reworked and new technical workflows and archiving help-guides [1] have been developed and made available on GitHub.</p> <p>These resources can provide guidance for other archives, but perhaps more importantly, to empower smaller communities with fewer available resources wishing to preserve their own cultural materials. The archiving guides discuss topics such as data management and metadata. Technical workflows include digitising audio tapes, creating high-resolution photographs of manuscripts and notebooks, and processing born-digital audio and video files, closely following specifications [2] set out by the International Association for Sound and Audiovisual Archives and refined with input from holding institutions such as The National Library of Australia, The National Film and Sound Archive and The Language Archive based at the Max Planck Institute for Psycholinguistics.</p> <p>Cultural institutions including the Australian Institute of Aboriginal and Torres Strait Islander Studies and the National Film and Sound Archive have discussed their concerns to get their magnetic tape holdings digitised by 2025- a deadline beyond which access to functioning digitising equipment will become increasingly difficult and costly [3], [4]. Collaboration and capacity building will bring us closer to preserving at-risk cultural material before it is too late.</p> <p>References: [1] Miller, JC (2021) PARADISEC archiving workflows. https://paradisec-archive.github.io/PARADISEC_workflows/. [2] Prentice, W. and Gaustad, L. (2017) The Safeguarding of the Audiovisual Heritage: Ethics, Principles and Preservation Strategy (web edition). IASA. https://www.iasa-web.org/iasa-publications. [3] Campbell, L. (16 November 2021) Deadline 2025, the need to preserve First Nations community collections. AIATSIS. https://aiatsis.gov.au/publication/117747. [4] National Film and Sound Archive (27 October 2015) Deadline 2025. NFSA. https://www.nfsa.gov.au/corporate-information/publications/deadline-2025</p>
Planning resource adequacy of wind- and solar-based electricity systems: Input data and results files
<p>This record contains the input data and raw results files for the study titled "<a href="https://www.sciencedirect.com/science/article/pii/S2666792424000234">Planning reliable wind- and solar-based electricity systems</a>."</p> <p>Tyler H. Ruggles, Edgar Virgüez, Natasha Reich, Jacqueline Dowling, Hannah Bloomfield, Enrico G.A. Antonini, Steven J. Davis, Nathan S. Lewis, Ken Caldeira, "Planning reliable wind- and solar-based electricity systems," Advances in Applied Energy, 2024, https://doi.org/10.1016/j.adapen.2024.100185.</p> <p>Additionally, csv files are provided to recreate the associated figures in the paper in the "Figures_files.zip" file.</p> <p>The input data contains wind and solar generation availability profiles and electricity demand profiles for the contiguous US. The profiles cover the years 1950-2022 and are calculated from the ERA5 dataset. The study only used the satellite era data from the year 1979 onward. Input profiles are presented at 4 resolutions: hourly, 2-hour, 3-hour, and 4-hour resolution.</p> <p>The results files contain some keys indicating the modeling scenario used: "SWB" = "Solar+Wind+Battery"; "SWBNG" = "Solar+Wind+Battery+Natural Gas generation"; and "SWBPGP" = "Solar+Wind+Battery+Power-to-H2-to-Power Loop". The files can be grouped into multiple categories:</p> <ol> <li>The main analysis including the initial energy system optimization results and the secondary system performance testing results. <ol> <li>Initial optimization results are found in zip files titled "Initial_Optimization_Jan29v1_*.zip"</li> <li>The testing of the optimized systems are found in the zip file "Lost_Load_Decade_Testing_Jan29v1.zip"</li> </ol> </li> <li>A secondary analysis compared systems optimized on a single year of data and tested on a single other year of data. Those results are in "Matrix_Figure_NYrs1_Aug04v1.zip"</li> <li>A supplementary analysis compared the modeled results using input data with the 4 different time resolutions. These results can be found in the zip files titled "DeltaT_Test_July08v1dt*.zip"</li> </ol>
OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
HUMAN RESOURCE EVALUATION: THE INDIAN EXPERIENCE
<p>It is highly complicated in the today"s market find well knowledge, coached and <br>highly motivated people .human resource is one of the most imp back office operations of <br>any organization or business .their skills, creativity ,ability human cannot be replaced by <br>machines .we can loss efficiently in work if no qualitative people. at all levels and areas of <br>the business or firm human efficiency is required with machine but machine can"t .hence, <br>industry like advertising and direct marketing for instance human talent is more valuable <br>among other else. No machine can ever come up with a unique advertising idea without the <br>human input .thus companies must learn to recognize and appreciate the value of their <br>employees. It is worth and capital investment. that is why firms also need to search people <br>those are on capital investment along with capital (money)while many entrepreneurs are <br>focused on securing capital they have to make suite they want to attract investor create value <br>and maximum wealth. Solid management team in what set a company apart with venture <br>capitalists .this paper reviews the contribution of human resource to an organization and its <br>valuation, thereof. a detailed analysis is made of the main costs related to human resources <br>from the points of view of external and internal 9or managerial) accounting in regard to an <br>Indian scenario</p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
Ecological significance of marcescence in Himalayan plants: Why is standing dead phytomass more important in demanding, resource-limited environments?
<ol> <li>Understanding mechanisms allowing plants to thrive in challenging conditions is critical for predicting their responses to global environmental change. An often overlooked ecological adaptation is marcescence, where leaves and stems are retained beyond their typical shedding time, with implications for nutrient recycling and carbon sequestration. Marcescence may be common in plants with conservative resource-use strategies, especially in environments with limited resources and marked seasonality, such as deserts and alpine regions. However, the extent to which marcescence occurs in different taxa across different habitats and its relationship to seasonality and plant functioning as reflected in ecophysiological traits remains understudied.</li> <li>We studied 600 individuals across 40 Himalayan herb species found in desert, steppe, alpine, and subnival habitats, spanning 3000 to 5400 m elevation. Marcescence was observed in 37 out of 40 species evaluated, with 57% of the sampled individuals displaying dead phytomass, indicating the widespread nature of this phenomenon in Himalayan ecosystems.</li> <li>Environment and plant traits emerged as significant factors influencing the amount of dead-standing biomass. These findings remained robust after accounting for intraspecific variation and phylogenetic inertia. Desert and steppe species exhibited greater marcescent biomass, characterized by tenfold more dead stem biomass than alpine and subnival plants. In contrast, the proportion of dead phytomass increased with elevation due to increased dead leaf fraction.</li> <li>Tall desert species with heavily lignified stems and limited nitrogen and phosphorus content had high levels of stem marcescence, which may aid litter photodegradation and nutrient recycling but also deter herbivory and promote seed dispersal (tumbleweed). Conversely, slow-growing alpine and subnival plants with higher water-use efficiency, foliar C:N ratios, and parenchymatic storage tissue rich in mobile carbohydrates had a high proportion of marcescent leaves, which may aid survival in these resource-limited environments by providing delayed nutrients through moisture-conserving mulch that also provides insulation and protection from frost.</li> <li>This study provides new insights into the complex interplay of habitat, seasonality, and plant traits in dead phytomass retention in Himalayan plants. Marcescence is prevalent in resource-conservative species in highly seasonal, resource-limited habitats and may significantly shape the functioning and biodiversity of Himalayan ecosystems.</li> </ol>
ortho2tree data resources
<p>Data resources for the <strong>'ortho2tree'</strong> analysis described in the manuscript <em>"<a href="https://doi.org/10.1093/nargab/lqae066">Improved selection of canonical proteins for reference proteomes</a>" </em>(<em>NAR Genomics and Bioinformatics</em>, Volume 6, Issue 2, June 2024) of eight QfO (<a href="https://questfororthologs.org/">Quest for Orthologs</a>) mammal proteomes, based on <a href="https://www.uniprot.org/">UniProtKB </a>data (release UP2022_05).</p> <p>The file <a href="../records/10778115/files/qfomam.tar.gz?download=1">qfomam.tar.gz</a> is the archive required to re-run the analysis</p> <p>Code and instructions are available at: <a title="ortho2tree codebase" href="https://github.com/g-insana/ortho2tree" target="_blank" rel="noopener">https://github.com/g-insana/ortho2tree</a></p> <p>The file <a href="../records/10778115/files/qfomam_pdf_data.tar.gz?download=1">qfomam_pdf_data.tar.gz</a> contains all the pdf files generated as results, with tree and alignment for each of the orthogroups where canonicals were confirmed or changes were proposed.</p> <p>A web interface for filtering and viewing the pdf files with the trees from the result of that analysis (and subsequent ones) is available at <a href="https://fasta.bioch.virginia.edu/ortho2tree" target="_blank" rel="noopener">fasta.bioch.virginia.edu/ortho2tree</a></p>
KOnPoTe resources for boats and restaurants
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Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
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CEDAR, an online resource for the reporting and exploration of complexome profiling data
<p>Complexome profiling is an emerging ‘omics’ approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring <a title="Learn more about mass spectrometry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mass-spectrometry">mass spectrometry</a> data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the “minimum information required for a complexome profiling experiment” (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.</p>
Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
<p>This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.</p>
Krmni viri za prašiče analizirani v projektu V4-2201 in V4-1417 /Feed resources for pigs analysed in projects V4-2201 and V4-1417
<p>Hranilna vrednost krmnih virov zbranih in analiziranih v CRP projektih V4-2201 in V4-1417 (financerja ARIS in MKGP)</p>
Resources for the paper "Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training"
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Threat analysis material for unified network orchestrator across terrestrial and non-terrestrial resources
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Dataset for: Online Virtual Machine Provisioning under Uncertainty: A Robust Approach to Ultimate Resource Utilization
<div> <div>In cloud resource scheduling and management, efficiency and quality are two vital yet conflicting objectives. Cloud providers, such as Huawei, prioritize quality by avoiding hotspots and aim to optimize and increase the utilization efficiency of PM resources without compromising quality. The typical online VM scheduling problem in cloud practice can be stated as follows: given a fixed number of PMs and a queue of arriving VMs, the goal is to place as many VMs as possible onto the PMs while ensuring that no hotspots occur.</div> <br> <div>The trace consists of a total of 297 instances, where each instance is represented by a JSON file named in the format X-1.json, X-2.json, and X-3.json. Here, X represents the number of PMs capable of hosting the arriving VMs, ranging from 2 to 100. For example, 50-1.json indicates that there are 50 available PMs to host the arriving VMs. For each value of X, there are three replicas denoted by the suffixes 1, 2, and 3.</div> <br> <div>There are multiple flavors of PM available in our cloud service provider. Readers can refer to our online paper, which we will provide the link for below, to learn about the specific flavor we used for academic and testing purposes. However, they are also free to use other typical flavors as per their requirements.</div> <br> <div>Each JSON file contains information about the VMs waiting to be assigned to PMs, and the fields for these VMs are as follows (each line represents a VM):</div> <br> <div> <ul> <li><strong>Created_at_point</strong>: the timestamp when the VM arrives. The list is already sorted in ascending order based on the arrival times.</li> </ul> </div> <ul> <li><strong>memory</strong>: the memory capacity of the VM defined by its flavor, measured in gigabytes (GB).</li> <li><strong>duration_point</strong>: the timestamps indicating the duration of the VM's usage. Each timestamp represents a 5-minute interval in practice.</li> <li><strong>vm_util</strong>: the real utilized capacity of the VM at each timestamp, with a similar meaning as the Hotspot Resolution trace. The length of the list is equal to the value of "duration_point".</li> </ul> </div> <div> </div>
EBAII n1 scRNAseq : Common resources (gene lists, ...)
<p>This repository contains R objects (RDS) for</p> <ol> <li>Gene lists used for the single-cell RNAseq analysis pre-processing <ol> <li>Considered gene-lists : <ol> <li>mitochondrial genes</li> <li>ribosomal protein-coding genes</li> <li>mechanical stress response genes</li> </ol> </li> <li>Considered species : <ol> <li>homo sapiens (human)</li> <li>mus musculus (mouse)</li> <li>rattus norvegicus (rat</li> </ol> </li> </ol> </li> <li>Reference bulk RNAseq profiles from ImmGenData, for automatic cell type annotation through celldex </li> </ol>
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
Resources for the paper: "Social Context in Political Stance Detection: Impact and Extrapolation"
<p>This repository contains the resources in our paper <strong>[Social Context in Political Stance Detection: Impact and Extrapolation]</strong><br><em>Ramon Villa-Cox, Evan Williams, Kathleen M. Carley</em></p> <p>In this work, we explore the performance and extrapolation power of political stance-detection models using an existing large-scale weakly-labeled Twitter dataset collected around the 2019 South American Protests [1]. We construct transformer-based user and tweet encoders to embed users in a low-dimensional space using their text and ego-networks. We then train heterogeneous graph attention networks to predict user stances and contrast their ability to extrapolate stance predictions to different country contexts.</p> <p>The protest dataset, which was collected between September 25 and December 24 of 2019, contains 550k labeled users split unevenly across the four countries and contains over 36 million labeled tweets. It contains an additional 1.1 million unlabeled neighbors and 40 million unlabeled tweets. This repository includes the anonymized datasets necessary to reproduce the results and tables of the paper. In addition, we include the corresponding anonymized resources for the new weakly-labeled dataset around the 2020 Chilean Referendum presented in our paper.</p> <p>Following Twitter's January 2023 User Protection Policy update, tweet or user IDs related to sensitive political events cannot be publicly shared. We respect this policy, and only share:</p> <ul> <li>The anonymized user ID, their weak-stance label, the label predicted by each model and the data split (train, validation or test) the user was assigned to.</li> <li>Anonymized user network edges used by the different network classifiers</li> <li>The type of tweet the edge represents (Original, Reply, or Quote)</li> <li>The User Embeddings produced by the User Transformer and which serve as input for the different network models.</li> </ul> <p>This repository is comprised of the following files:</p> <ol> <li>Main_Predictions.7z: Compressed folder containing anonymized user IDs their stance label and each model’s prediction for the country it was trained on. The performance metrics for each model can be obtained based on the test split for each country. This folder includes the results for the Chilean Referendum.</li> <li>Cross_Predictions.7z: Compressed folder containing the results of the cross-country experiments for each anonymized user. The performance metrics for each model, when applied on a different country can be obtained based on each complete file. Users seen during the training of each model are excluded as described in the paper.</li> <li>Tweet_Level_Edgelists.7z: Compressed folder containing anonymized tweet edge lists indicating its interaction type (Original, Reply, or Quote).</li> <li>User_Networks.7z: Compressed folder containing different anonymized user edge lists for each interaction type.</li> <li>Embeddings.zip: Compressed Pytorch tensor files containing the User Embeddings produced by the User Transformer and which serve as input for the different network models. This are provided for the main results and the cross-country and referendum experiments.</li> </ol> <p>The code developed for this study is available at: https://github.com/rvillaco/Protest_Stance_Detection</p>
Resources for Anatomical Connectivity and Single-Neuron Typing/Subtyping
<p>The dataset consists of local morphology reconstructions and necessary computational material for the paper: <a title="https://www.researchsquare.com/article/rs-2960606/latest" href="https://www.researchsquare.com/article/rs-2960606/latest" target="_blank" rel="noopener">Anatomical Connectivity of Single Neurons Helps Classify Cell Subtypes in Mouse Brains</a></p> <p>File Inventory:</p> <ul> <li>SEU_10860_dendrite.zip </li> <li>Materials.zip</li> </ul> <p>The Material file includes tables for the following:</p> <ul> <li>Cell type (single-neuron)</li> <li>Connectivity matrix (single-neuron)</li> <li>Morphological feature (single-neuron)</li> <li>Projection matrix (single-neuron)</li> <li>Soma coordinates (single-neuron)</li> <li>Gene expression profile (MOs_FRP cells)</li> </ul>
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.