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Dataset results
400 results for “fingerprints”
Dataset for the manuscript "Barium stable isotopes as a fingerprint of biological cycling in the Amazon River Basin" submitted to Biogeosciences Discussions
<p>This file contains the dataset of the manuscript submitted to Biogeosciences Discussions entitled "Barium stable isotopes as a fingerprint of biological cycling in the Amazon River Basin" by Charbonnier et al. It consists in three spreadsheets: (1) dissolved species concentration and dissolved Ba isotope data on river dissolved load; (2) main characteristics and Ba isotope data of river sediments; (3) compilation of major element and Ba and Li concentration in rocks (compilation made from the GEOROC database) and in the dissolved load of river draining single rock types (compilation made from previously published data and new data).</p>
Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice
<p class="western"><span><span><span>Polychlorinated biphenyls (PCBs) are ubiquitously detected in the environment and have been linked to metabolic diseases. The liver serves as a central hub for the metabolism of xenobiotics and endogenous metabolites. Gut dysbiosis is recognized as a critical regulator of disease susceptibility, however, little is known regarding how PCBs and gut microbiome interact to modulate the interface between xenobiotic and intermediary metabolism. We hypothesized that the gut microbiome regulates PCBs-mediated changes in the metabolic fingerprints and hepatic transcriptome. Ninety-day-old female conventional (CV) and germ-free (GF) C57BL/6 mice were orally exposed to the PCB Fox River Mixture (synthetic PCB mixture, 6 or 30 mg/kg) or corn oil (vehicle control, 10 ml/kg), once daily for 3 consecutive days. Organs were collected 24 hours after the final dose. RNA-Seq was conducted on liver, and endogenous aqueous metabolites (amino acids, carbohydrates, and nucleotides) were measured in liver and serum by LC-MS. The primary factor in clustering the transcriptomic and metabolomic signatures within the same exposure was by enterotype. The numbers of PCB-regulated genes were higher in CV than in GF conditions. The prototypical target genes of the major xenobiotic-sensing transcription factors AhR, PXR, and CAR were more readily up-regulated by PCBs in CV than in GF conditions, indicating the effect of PCBs on the hepatic transcriptome act partly through the gut microbiome. Xenobiotic and steroid metabolism pathways were up-regulated, whereas response to incorrect proteins pathway was down-regulated by PCBs in a gut microbiome-dependent manner. At the high PCB dose, NADP and arginine appear to interact with drug-metabolizing enzymes (Cyp1-3 family, DhcR7, and Nqo1), which are highly correlated with <i>Anaerotruncus</i> and <i>Roseburia</i> in CV mice, providing a novel explanation of gut-liver interaction in toxicant exposures. In GF exposure groups, hepatic glucose was down-regulated, whereas fructose 6-phosphate and glucose 6-phosphate were up-regulated, indicating increased glucose utilization potentiated by lack of gut microbiota. Through querying the LINCS L1000 chemical database, Enrichr predicted that therapeutic drugs targeting the anti-inflammatory and ER stress pathways are potential remedies to mitigate PCB toxicity. In conclusion, our findings demonstrate that habitation of the gut microbiota drives PCBs-mediated hepatic responses, possibly due to crosstalk between gut and liver. </span></span></span></p>
Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry
<p>Carbon stable isotope ratios (δ<sup>13</sup>C) for protein fractions extracted from a mixture of cultured cells of <em>Allochromatium vinosum </em>DSM 180 and <em>Synechocystis</em> sp. PCC6803, as well as from extracts of each pure culture.</p> <p>Citation: Mohr W, Tang T, Sattin SR, Bovee RJ, Pearson A. (2014) Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry. Analytical Chemistry 86, 8514-8520.</p> <p>Contact: Ann Pearson (pearson@eps.harvard.edu)</p>
Protein stable isotope fingerprinting (P-SIF): Chocolate Pots (Yellowstone) biomass
<p>Stable carbon isotope data for proteins extracted from a Yellowstone microbial mat, Chocolate Pots hot springs.</p>
Protein stable isotope fingerprinting (P-SIF): Mahoney Lake (Canada) 7-m biomass
<p>Carbon isotope data for proteins extracted from a biomass pellet from a sulfide-oxidizing phototroph layer (7m); Mahoney Lake, British Columbia, Canada.</p>
Non-cooperative 802.11 MAC layer fingerprinting and tracking of mobile devices
<p>This archive contains the datasets used for the experiments in the paper "Non-cooperative 802.11 MAC layer fingerprinting and tracking of mobile devices", namely:</p> <ul> <li><em>Glimps 2015 dataset (mac_info collection)</em>: A collection of 122,989 Probe Request frames captured by 8 monitoring stations at the Glimps music festival in Ghent, Belgium (10 - 12 December 2015). To minimize overhead, each monitoring station individually stored only one Probe Request per unique MAC. The dataset was used to show that the high entropy in Probe Requests can be used to deanonymize devices that use MAC address randomization. Only the source MAC and Information Elements (IEs) were captured for this purpose. </li> <li><em>Research center 2016 dataset (mac_research collection)</em>: A complete collection of all management and control frames (including Radiotap headers) observed at our research lab from 28 January to 8 Febuary 2016. This dataset was used to calculate the "stability" and "variability" of Probe Request IEs (see our paper for more details on these metrics).</li> <li><em>Transmission rate datasets (mac_research_0 - mac_research_4 collections)</em>: Observations of mobile devices when actively instigated for extra transmissions. These observations were used in the paper to calculate the effectiveness of the various stimulus frame techniques. This dataset should only be used to verify the results in the paper. The other datasets could be used for related experiments.</li> </ul> <p>All datasets were anonymized by applying the following rules:</p> <ul> <li>The 3 least significant bytes of each MAC address were uniquely and consistently mapped to a different value, with exception of "ff:ff:ff" and "00:00:00".</li> <li>The SSID IE has its SSID field replaced with the string "Hidden", with exception of the wildcard (empty) SSID.</li> <li>The Vendor Specific WPS IE was replaced with a hash of its payload given the amount of sensitive information (device serial / model number, UUID, etc.) contained within it, and the length of the IE was updated accordingly. Unfortunately, Wireshark stops parsing the remainder of Probes containing this anonymized IE, so it should be noted that further parsing beyond the WPS IE must be done manually (e.g. by using Scapy or by changing the Wireshark dissector).</li> </ul> <p>The datasets are provided as MongoDB collections with the following document format:</p> <ul> <li>_id: ObjectID of the document</li> <li>info_length: Length of the binary blob</li> <li>info: Binary blob of the Radiotap frame (mac_research) or only the Information Elements (mac_info)</li> <li>mac_addr: Transmitter of the frame</li> </ul> <p>To install the dataset, execute the command "mongorestore --gzip -d anonymized ./anonymized" after extracting the .tar.xz file.</p> <p>A .pcap format of the <em>mac_info </em>(wrapped in a dummy Radiotap frame) and <em>mac_research</em> datasets is additionally provided at crawdad.org.</p> <p>The <em>mac_info</em> dataset can be visually explored on https://wicability.net/datasets (Glimps 2015 dataset).</p>
Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning
<p>This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows:</p> <ul> <li>_id: Unique MongoDB document ID</li> <li>chirp: Base 64 encoded binary float complex I/Q data</li> <li>field: Symbol location inside a LoRa frame</li> <li>tag: Name of the device that sent the frame</li> <li>date: Time and date of reception</li> <li>fn: Frame number</li> <li>rand: Random number for sorting</li> </ul> <p><strong>How to import</strong></p> <p>Extract the tar archive. Inside the directory, run the following command to import the lora2msps database:</p> <p><em>mongorestore --gzip -d lora2msps ./lora2msps</em></p> <p>This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing:</p> <p><em>mongorestore --gzip . </em></p> <p><strong>How to use</strong></p> <p>After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.:</p> <p><em>./tf_train.py train conf/experiment_lora2msps_mlp.conf</em></p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Crystallographic Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of crystallographic mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Parallel Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of parallel mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Open-Source Cardiac MR Fingerprinting
<p>Magnetic Resonance (MR) raw data acquired with an open-source cardiac MR Fingerprinting (cMRF) sequence of a phantom at four different MR scanners. More details can be found here: https://github.com/PTB-MR/cMRF. The colormaps are taken from https://zenodo.org/records/11185704 because zenodo_get failed on trying to download this record in a jupyter notebook.</p> <p>Additionally cMRF data was acquired in three volunteers who were scanned at two different scanners. Cartesian and golden radial cine data was acquired to verify the anatomical features seen in the quantitative maps.</p>
Radionuclide, organic matter, geochemical and colorimetric properties of potential source material and target sediment for conducing sediment fingerprinting approaches in the Dzoumogné reservoir, Mayotte Island, France
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Colorimetric properties analysed with a portable diffuse reflectance spectrophotometer (Konica Minolta CM-700d) and geochemical contents obtained with an energy dispersive X-ray fluorescence spectrometer (ED-XRF Epsilon 4), organic matter and stable isotopes were analysed by EA-IRMS and radionuclides using coaxial N- and P- type HPGe detectors (Canberra/Ortec). These properties were analysed in potential source material that may supply sediment to the Dzoumogné reservoir, Mayotte island, France. Three potential soil source materials (n = 57) were considered: cropland (n = 29), forest (n = 13) and subsurface material originating from channel bank collapse, landslides, badlands (n = 16). A sediment core was collected in the Dzoumogné reservoir (Target) on the 8th October 2021 and 20 layers were sampled.</p><p>The current dataset comprises two Excel files including the metadata description and the data itself.</p>
CCycDB (an integrative knowledgebase to fingerprint microbial carbon cycling processes)
<p>CCycDB - version 2.0 (Please see<strong> </strong><a href="https://ccycdb.github.io/"><strong>https://ccycdb.github.io/</strong></a> for more details)</p><p><strong>CCycDB</strong> is a knowledge-based functional gene database for accurate metagenomic profiling of carbon cycling microbial communities. CCycDB contains 4,676 gene families within 6 categories. These gene families are further categorized into 45 sub-categories within sub-category I and 188 sub-categories within sub-category II with a total of 10,991,724 targeted sequences. A series of validations demonstrated that CCycDB outperformed large public orthology databases in terms of coverage, specificity, and accuracy, and can be used to accurately profile carbon cycling microbial communities in real metagenomic datasets.</p>
Data for Ocean biogeochemical fingerprints of fast-sinking tunicate and fish detritus
<p>Model results for the manuscript, "Ocean biogeochemical fingerprints of fast-sinking tunicate and fish detritus", under peer review at <em>Geophysical Research Letters</em>.</p> <p> </p> <p>Model outputs for: <br>1) the GZ-COBALT control simulation, <br>2) the GZ-COBALT simulation with fast-sinking tunicate detritus only, <br>3) the GZ-COBALT simulation with fast-sinking fish ("hp") detritus only, and<br>4) the GZ-COBALT simulation with both fast-sinking tunicate and fish ("hp") detritus.</p> <p> </p> <p>The following files are included for all 4 simulations:</p> <p> </p> <p>Model grid and area fields</p> <ul> <li><em>ocean_annual_static.nc</em></li> <li><em>ocean_static.nc</em></li> </ul> <p>Monthly 100-m integrated fluxes (in Nitrogen unless specified otherwise; a Redfield C:N ratio is used)</p> <ul> <li>Aggregation loss from: <ul> <li>Small and large phytoplankton, large tunicates</li> </ul> </li> <li>Detritus production by: <ul> <li>Small, medium, and large zooplankton, small and large tunicates, higher predators (hp)</li> </ul> </li> <li><em>[expt]_ocean_cobalt_fluxes_int_1988-2007.clim.tar.gz</em></li> </ul> <p>Other monthly 100-m integrated fluxes</p> <ul> <li>Carbon detritus sinking flux past 100-m</li> <li>Integrated primary production</li> <li><em>[expt]_ocean_cobalt_omip_2d.1988-2007.clim.tar.gz</em></li> </ul> <p>Monthly detritus fluxes past 100-m:</p> <ul> <li>Nitrogen detritus sinking flux past 100-m</li> <li><em>[expt]_ocean_cobalt_fdet_100.1988-2007.clim.tar.gz</em></li> </ul> <p>Monthly bottom fluxes:</p> <ul> <li>Nitrogen detritus sinking flux to bottom</li> <li>Nitrogen detritus burial flux</li> <li>Sediment oxic remineralization flux of nitrogen detritus</li> <li><em>[expt]_ocean_cobalt_btm.1988-2007.clim.tar.gz</em></li> </ul> <p>Annual 3-D tracers:</p> <ul> <li>Nitrate</li> <li>Dissolved oxygen</li> <li>Phosphate</li> <li><em>[expt]_ocean_cobalt_omip_tracers_year_z_1988-2007.nc</em></li> </ul> <p>Annual 3-D fluxes:</p> <ul> <li>Carbon detritus sinking flux</li> <li><em>[expt]_ocean_cobalt_omip_rates_year_z_1988-2007.nc</em></li> </ul> <p>Hypoxic volume time series:</p> <ul> <li>Total volume of water below 60 mmol O2</li> <li>Total volume of water below 5 mmol O2</li> <li><em>[expt]_hypoxicVolume.ts.tar.gz</em></li> </ul> <p>Note that files ending in <em>.tar.gz</em> need to be unzipped and extracted first. All data files are in netCDF format.</p> <p> </p> <p>Python codes for reproducing the figures in the manuscript are available on github: <a href="https://github.com/jessluo/gz_COBALT_fastPOC_analysis">https://github.com/jessluo/gz_COBALT_fastPOC_analysis</a></p>
Outdoor fingerprint localization with BLE beacons
<p><strong>Introduction</strong></p><p>The data set contains received signal strength (RSS) measurements made with Bluetooth Low Energy (BLE) technology, which can be used for outdoor fingerprint-based localization applications, as presented in an article "<a href="https://ieeexplore.ieee.org/document/9900607">LOG-a-TEC Testbed Outdoor Localization Usign BLE Beacons</a>".</p><p><strong>Measurement setup</strong></p><p>The measurements were created with WL1837MOD radio connected to a <a href="https://log-a-tec.eu/hw-lgtc.html">in-house embedded device</a>. The data set was collected with 40 nodes of the <a href="https://log-a-tec.eu">LOG-a-TEC testbed</a> positioned at the campus of the Jožef Stefan Institute, Ljubljana. The experimentation area is composed of 5 x 26 positions separated by 1.2 m covering 150 square meters. On each position a mobile phone was broadcasting BLE advertising beacons with power of -2 dBm in interval of 100 ms. Surrounding testbed nodes were collecting the beacons for approximately a minute for each position.</p><p><strong>Data set</strong></p><p>Measurements are stored in JSON format where each object contains rss measurement (in dBm) with corresponding timestamp (in seconds). The folder contains two JSON files:</p><ul><li>spring_data.json - measurements made in May 2022,</li><li>winter_data.json - smaller measurements made in December 2021. This data set contains only the measurements from the middle row of the campus park.</li></ul>
Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products
<p><span>Quantifying changes in phytoplankton communities using ocean color is essential for predicting ocean food resources, occurrences of harmful algal blooms, and carbon and other elemental cycles, among other predictions. Here we present a dataset of greater than fifty strains of phytoplankton, from a range of taxonomic lineages, geographic locations, and time in culture, alone and in mixtures, grown to exponential and/or stationary phase for determination of hyperspectral UV-VIS absorption coefficients, multi-angle and multi-spectral backscatter coefficients, volume scattering functions, particle size distributions, fluorescence, and hyperspectral remote sensing reflectance. The measurements obtained from these experiments are valuable to facilitate development of new global and/or regional ocean color models by the broader scientific community. </span></p>
Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting
<p><span>Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. However, few studies have tested species identification across different orders and classes. In this study, we collected data from 1,246 specimens and 198 species to test species identification in a diverse dataset. We also evaluated different specimen preparation and data processing approaches for machine learning and developed a workflow to optimize classification using random forest. Our results showed high success rates of over 90%, but we also found that the size of the reference library affects classification error. Additionally, we demonstrated the ability of the method to differentiate marine cryptic-species complexes and to distinguish sexes within species.</span></p>
MALDI-TOF MS data: Species delimitation of Hexacorallia and Octocorallia around Iceland using nuclear and mitochondrial DNA and proteome fingerprinting
<p>Cold-water corals build up reef structures or coral gardens and play an important role for many organisms in the deep sea. Climate change, deep-sea mining, and bottom trawling are severely compromising these ecosystems, making it all the more important to document the diversity, distribution, and impacts on corals. This goes hand in hand with species identification, which is morphologically and genetically challenging for Hexa- and Octocorallia. Morphological variation and slowly evolving molecular markers both contribute to the difficulty of species identification. In this study, a fast and cheap species delimitation tool for Octocorallia and Scleractinia of the Northeast Atlantic was tested based on 49 specimens. Two nuclear markers (ITS2 and 28S rDNA) and two mitochondrial markers (COI and mtMutS) were sequenced. The sequences formed the basis of a reference library for comparison to the results of species delimitation based on proteomic analysis using the MALDI-TOF MS method. The genetic methods were able to distinguish 17 of 18 presumed species. The MALDI-TOF MS method was able to distinguish 7 species. Species that could not be distinguished from one another still achieved good signals but were not represented by enough specimens for comparison. Therefore, it is predicted that with an extensive reference library of proteome spectra for Scleractinia and Octocorallia, MALDI-TOF MS may provide a rapid and cost-effective alternative for species discrimination in corals.</p>
Rock magnetic fingerprint of Mt. Etna volcanic ash: the dataset
<p>This dataset refers to the article: "Rock magnetic fingerprint of Mt. Etna volcanic ash" by the same authors, published in Geophysical Journal International, https://doi.org/10.1093/gji/ggac213.</p> <p>A detailed rock magnetic study was conducted on ash samples collected from different products erupted during explosive activity of Mount Etna, Italy, in order to test the use of magnetic properties as discriminating factors among them, and their explosive character in particular.<br> Samples include tephra emplaced during the last 18 ka: the benmoreitic Plinian eruptions of the Pleistocene Ellittico activity from marine core ET97-70 (Ionian Sea) and the basaltic Holocene FG eruption (122 BC), the Strombolian/Phreatomagmatic/sub-Plinian eruptions (namely, the Holocene TV, FS, FL, ETP products, and the 1990, 1998 eruptions) collected from the slope of the volcano, and the Recent explosive activity (lava fountains referred to as “Ash Rich Jets and Plumes”, or ARJP) that occurred in the 2001-2002 period, related to flank eruptions.<br> A full set of rock magnetic experiments were carried out to determine the magnetic mineralogy and the magnetic grain size at the Institute for Rock Magnetism at the University of Minnesota, including First-Order Reversal Curves (FORCs), hysteresis loops and backfield DC demagnetization remanence curves (DCD or Backfield curves) at room temperature on Princeton Measurements Corporation (Princeton, NJ) Vibrating Sample Magnetometers (VSMs).<br> Low temperature (LT) experiments were conducted on Quantum Design (San Diego, CA) Magnetic Properties Measurement Systems (MPMS-XL and 5S). LT experiments were carried out by measuring the magnetic remanence on warming from 10 K to room temperature (300 K) after cooling in a 2.5 T field (field cooled remanence, FC), as well as after cooling in zero field and applying a saturation isothermal remanent magnetization (SIRM) of 2.5 T at 10 K (zero-field cooled remanence, ZFC). A room temperature (RT) 2.5 T SIRM was also applied at 300 K and the remanence was measured upon temperature cycling to 10 K and back (RTSIRM). AC susceptibility as a function of temperature and frequency (1, 10, 100 Hz or 1, 5, 32, 178, 1000 Hz) was also measured for selected specimens from the three groups of samples.<br> Room temperature susceptibility measurements as a function of field amplitude (10, 20, 40, 80, 120, 200, 400 A/m) were carried out on the Late Pleistocene samples using a Magnon susceptibility system. Saturation magnetization on warming between room temperature and 700°C (Ms-T) was measured on selected specimens using a horizontal Curie balance with Argon gas circulation to limit oxidation processes during heating. Likewise, magnetic susceptibility on warming between room temperature and 700°C (<em>X</em>-T) was measured on a Kappabridge KLY-2 (Brno, Czech Republic) using fields of 300 A/m and 920 Hz. Ms-T and <em>X</em>-T curves are collectively referred to as thermomagnetic curves.</p>
Supplementary material - Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising
<p>Supplementary material</p>
Datasets for "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt" Coulson et al., 2022, Science
<p>Sea surface height (SSH) change altimetry-derived datasets and model predictions for:<br> "A Detection of the Sea Level Fingerprint of Greenland Ice Sheet Melt", Submitted to Science January 2022. Sophie Coulson, Sönke Dangendorf, Jerry X. Mitrovica, Mark E. Tamisiea, Linda Pan, David T. Sandwell.<br> Email: slcoulson@lanl.gov, sdangendorf1@tulane.edu</p> <p>See README for file details.</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.