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5,805 results for “Data model”
Predicting HMX bioavailability using microarray gene expression data and regression modeling
GEO Series GSE42866. Eisenia fetida. 120 samples. Type: Expression profiling by array.
SNV microarray data from "Genome-edited cardiac models reveal combinatorial genetic interactions in human cardiomyopathy" by Deacon et al
GEO Series GSE121844. Homo sapiens. 11 samples. Type: Genome variation profiling by SNP array.
Single-cell RNA-seq data of sorted bone marrow CD19+B220+ B cells from Cyldflx/flx mice and Mb1Cre-Cyldflx/flx mouse models
<p>CYLD, a protein encoded by the Cyld gene, is a deubiquitinase involved in signalling events rather than proteasomal degradation. Mutations in CYLD are associated with familial cylindromatosis, leading to benign skin tumours. CYLD downregulates various solid and haematological tumours by negatively regulating signalling pathways like NF-κB, Wnt/β-catenin, and Notch. The most common Cyld splice variant encodes a 956 amino acid polypeptide with a catalytic domain at the carboxy-terminal, CAP-Gly domains at the N-terminal, and other domains in the central region. It interacts with various proteins, including tubulin, microtubules, and NEMO. CYLD is implicated in diverse functions such as cell proliferation, apoptosis, inflammation, spermatogenesis, and immune responses. Its role in B cell lymphopoiesis is less clear, with some studies showing increased B cell activation in Cyld-/- mice, while others suggest minimal impact on B cell maturation. To clarify CYLD's role in B cell lymphopoiesis, transgenic animals with B cell-specific catalytic inactivation of CYLD were created, revealing that CYLD plays a significant role in B cell maturation and function, with its disruption severely impeding B cell responses. Mb1Cre-Cyldflx/flx mice were generated by crossing a neo- derivative (<i>Cyldfl9/fl9</i> <i>neo-</i>) of the previously developed <i>Cyldfl9/fl9</i> mice25 with <i>Mb1Cre</i> mice33 (provided by Prof. M. Reth)34. In <i>Cyldfl9/fl9</i> mice, loxP sites flank the 9th exon and when recombination occurs in the presence of Cre recombinase, the ensuing exons, including the gene region encoding for the catalytic domain, are out of frame and not expressed. All mice were maintained under specific pathogen-free (SPF) conditions at the animal laboratory facility of the IRCCS Ospedale San Raffaele, Milan, Italy. Mice of experimental groups were age-matched (3 and 6 months), of both genders and, in most cases, littermates. All animal experiments were approved by the Animal Ethics Committee of the IRCCS Ospedale San Raffaele for compliance to European regulations and licensed by the National Veterinary Administration authorities of Italy. </p><p>For single-cell RNA sequencing, sorted bone marrow CD19+B220+ B cells were collected in RPMI medium (Euroclone, ECB2000) supplemented with 10% FBS (Euroclone, ECS0180L). Cell viability was then determined using Trypan Blue exclusion (Sigma-Aldrich, T8154-EA) and TC20 Automated Cell Counter (Bio-rad). 4000 cells were encapsulated using the Chromium Controller platform (10X Genomics) and libraries were prepared using the Chromium Next GEM Single Cell 3' Kit v3.1 (10X Genomics). Final libraries were sequenced according to the manufacturer's instructions on Novaseq6000. </p><p>The pertinent code can be found here: <a href="https://github.com/BiodataAnalysisGroup/Cyld-regulation-of-bcell-maturation">https://github.com/BiodataAnalysisGroup/Cyld-regulation-of-bcell-maturation</a></p>
Data from: experiment using Large Language Models for unit testing generation
<p>IMPORTANT: Upon acceptance, all data will be available on Zenodo.org, and the link updated here.</p><p><br>This dataset represents the anonymized information from: background check form, experiment feedback form and test files produced by experiment participants</p>
Data set for Dermal Biomimicry: Human dermal decellularized ECM Hydrogels as Fidelity-Rich Scaffolds for In Vitro Skin Models.
<p>Data set for the article <strong>Dermal Biomimicry: Human dermal decellularized ECM Hydrogels as Fidelity-Rich Scaffolds for In Vitro Skin Models.</strong></p>
Data for "A unified model-based framework for doublet/multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet/Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Flight data for a conventional fixed wing and delta -wing drone applied to model-based navigation
<p>The following two bags permit the testing of the vehicle dynamic model-based navigation real-time software VDMc available <a href="https://gitlab.epfl.ch/laupre/vdm_c">here</a>. </p> <p><strong>TOPOPlane2_20221027_STIM14.bag</strong></p> <p>Data are saved in a <em>rosbag </em>while flying with the TOPOPlane2 drone on October 2nd, 2022. The <em>_tagged </em>suffix name means that the data come from the autopilot and the time is GNSS time-tagged with an internal routine. The bag contains the following topics:</p> <ul> <li>/GIINAV_POSE - solution of the INS/GNSS software (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>)</li> <li>/GPS0 - binary output of the GNSS receiver. These data need to be parsed with the function <em>GNSSReceiver::gpsCallBack</em> in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/gnssreceiver.cpp?ref_type=heads">gnssreceiver.cpp</a> (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/UInt8MultiArray.html">std_msgs/Uint8MultiArray</a>)</li> <li>/IMU0 - binary output of the IMU sensor. These data need to be parsed with the function <em>IMUReceiver::imuCallback</em> in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/imureceiver.cpp?ref_type=heads">imureceiver.cpp </a>(ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/UInt8MultiArray.html">std_msgs/Uint8MultiArray</a>)</li> <li>/airData - data from the surrey PitotTube and processed (custom ros msg type: surrey_sensor/AirData, available in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/AirData.h?ref_type=heads">AirData.h</a>) <ul> <li>GNSS time [s]</li> <li>airSpeed [m/s]</li> <li>baroAltitude [m]</li> <li>density [hPa]</li> </ul> </li> <li>/airpressure_tagged - dynamic air pressure of the PitotTube (Pa) (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a> ) </li> <li>/airspeed_tagged - transformed airspeed [m/s] and groundspeed [m/s] from Pitotube (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/VFR_HUD.html">mavros_msgs/VFR_HUD</a>)</li> <li>/cc_tagged - control commands tagged with GNSS time (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/RCOut.html">mavros_msgs/RCout</a>) <ul> <li>header, channels (autopilot value from 1000 to 2000). The conversion is done in the function CCReceiver::ccCallback in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/src/TP2/ccreceiver.cpp?ref_type=heads">ccreceiver.cpp</a> [aileron elevator rpm rudder]</li> </ul> </li> <li>/mavros/debug_value/debug_vector - This custom vector is used to exchange data from/to the Ground Control Station (GCS). It contains the solution of the INS/GNSS (Giinav) and VDMNav (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/DebugValue.html">mavros_msgs/DebugValue</a>)</li> <li>/mavros/debug_value/named_value_float - Topic used to trigger the real-time simulated GNSS outage from the GCS (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/DebugValue.html">mavros_msgs/DebugValue</a>) </li> <li>/mavros/debug_value/send - not used</li> <li>/mavros/imu/diff_pressure (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a>)</li> <li>/mavros/imu/static_pressure (ros msg type: <a href="https://docs.ros.org/en/melodic/api/sensor_msgs/html/msg/FluidPressure.html">sensor_msgs/FluidPressure</a>)</li> <li>/mavros/rc/out - control commands tagged with autopilot (system) time (ros msg type: <a href="https://docs.ros.org/en/noetic/api/mavros_msgs/html/msg/RCOut.html">mavros_msgs/RCout</a>)</li> <li>/mavros/wind_estimation - autopilot estimation of the wind (ros msg type: <a href="https://docs.ros.org/en/melodic/api/geometry_msgs/html/msg/TwistWithCovarianceStamped.html">geometry_msgs/TwistWithCovarianceStamped</a>)</li> <li>/surrey - raw data from the surrey PitotTube (custom ros msg type: surrey_sensor/Surrey, available in <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/Surrey.h?ref_type=heads">Surrey.h</a>)</li> <li>/timeSync - mapping from autopilot time and GNSS time to tag the data autopilot data (*<em>_tagged</em>) with GNSS time (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/Float32MultiArray.html">std_msgs/Float32MultiArray</a>)</li> </ul> <p><strong>concordeS_20230601.bag</strong></p> <p>These data are generated in a <em>formatted </em>version using the recorded flight with the ConcordeS1 drone on June 1st, 2023. The bag contains the following topics:</p> <ul> <li>/airData - data from the PitotTube (custom ros msg types: surrey_sensor/AirData, available from <a href="https://gitlab.epfl.ch/laupre/vdm_c/-/blob/Topoplane2/include/AirData.h?ref_type=heads">AirData.h</a>) <ul> <li>GNSS time [s]</li> <li>airSpeed [m/s]</li> <li>baroAltitude [m]</li> <li>density [hPa]</li> </ul> </li> <li>/cc_formatted - control commands (ros msg type: <a href="https://docs.ros.org/en/melodic/api/std_msgs/html/msg/Float64MultiArray.html">std_msgs/Float64MultiArray</a>) <ul> <li>data :[GNSS time [s], rpm [normalized value from autopilot channel 0-1000], LeftDeflection [degree], RightDeflection [degree]]</li> </ul> </li> <li>/giinav_formatted - INS/GNSS solution, used to initialize the in-flight VDM-based navigation filter (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>) <ul> <li>pose: <ul> <li>position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]</li> <li>orientation (NED) [x y z w] in quaternion</li> </ul> </li> <li>twist: <ul> <li>linear (NED) [x [m/s], y [m/s], z [m/s]]</li> <li>angular (body) [x [degree/s], y [degree/s], z [degree/s]]</li> </ul> </li> </ul> </li> <li>/gnss_formatted - GNSS position and velocity (ros msg type: <a href="https://docs.ros.org/en/noetic/api/nav_msgs/html/msg/Odometry.html">nav_msgs/Odometry</a>) <ul> <li>pose: <ul> <li>position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]</li> <li>orientation - null</li> </ul> </li> <li>twist: <ul> <li>linear (NED) [x [m/s], y [m/s], z [m/s]]</li> <li>angular - null</li> </ul> </li> </ul> </li> <li>/imu_formatted - a triplet of accelerometer and gyroscope (ros msg type: <a href="https://docs.ros.org/en/noetic/api/sensor_msgs/html/msg/Imu.html">sensors_msgs/IMU</a>) <ul> <li>header</li> <li>orientation (NED) (quaternion) [x y z w]</li> <li>orientation_covariance</li> <li>angular_velocity (body) [degree/s] [x y z]</li> <li>angular_velocity_covariance</li> <li>linear_acceleration (body) [m/s2] [x y z]</li> <li>linear_<em>acceleration</em>_covariance</li> </ul> </li> <li>/toggleOnOffGnss - Simulate the activation of the GNSS outage for testing purposes (ros msg type: <a href="https://docs.ros.org/en/noetic/api/std_msgs/html/msg/Bool.html">std_msgs/Bool</a>)</li> </ul> <p>In addition, there is a complete example of VDMc with TOPOPLANE2 in the <strong>TOPOPlane2_20220826_STIM13.zip</strong> file. It contains</p> <ul> <li>The VDMc code in the vdm_c folder that you can run</li> <li>The input data: TP2_20220825_outage382606_120sbaro.bag, which contains the rosbag from the STIM13 flight, performed on August 25th, 2022</li> <li>The compilation and execution of the code is explained at : https://gitlab.epfl.ch/laupre/vdm_c/-/wikis/Installation</li> </ul>
Enhancing reservoir water level time series in the Mekong river basin by improving area-elevation models and integrating multi-source satellite data
<p>This is a reservoir water surface area and water levels dataset including 32 major reservoirs in Mekong River basin. For all the 32 reservoirs, water levels were inverted by using improved DEM-derived A-E model (combined with actual reservoir parameters limitation), improved DEM-derived A-E model (combined with actual reservoir parameters limitation) or satellite-derived A-E model based on the Landsat-derived surface area. An initial time series was constructed based on the optimal improved A-E model according to their own altimetry data availability. Then all the altimetry water levels (if available) were merged into the initial time series to construct the final time series water levels.Altimetry water level was preferred when the date of two datasets was identical.</p>
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Model codes and data for ``Low-cost High-Speed Photogrammetry for Measuring Dynamic Flow Deposits"
<p>The dataset contains the raw and processed data of the experiment, and the Matlab code is used to synchronize the cameras. The content includes: </p> <p><br>1. CatchFlashlights.m: Matlab algorithm to identify flashlight time steps for camera synchronization.<br>2. cameras_01_10.rar, cameras_11_20.rar, ..., cameras_61_70.rar: videos for the experiment, including calibration, flashlights, and fan evolution for the 70 cameras.<br>3. targets.txt: xyz data for 100 reference points.<br>4. DEM_tif_600_699.rar, DEM_tif_700_799.rar, and DEM_tif_800_900.rar: DEMs for every second during 600-900 seconds for the debris flow fan experiment described in the manuscript.</p>
UM-UKCA model data for MOSAiC April 2020 warm air intrusion period
<p>Working model data output for PolarRES WP4 MOSAiC case study from the UM-UKCA simulations.</p>
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Input data for the IMACLIM-R France model
<p>Data to run the IMACLIM-R France code</p> <p>https://github.com/CIRED/IMACLIM-R_France</p>
Data set related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"
<p>This record contains raw data related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"</p>
Evaluation Data for "Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation"
<p><strong>Contents</strong><br> <br> The four CSV files are the data used for the evaluation in:</p> <ul> <li>Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035.</li> <li>DOI: <a href="http://doi.org/10.1007/978-3-030-45439-5_15">10.1007/978-3-030-45439-5_15</a></li> <li>Code: <a href="https://github.com/IllDepence/ecir2020">github.com/IllDepence/ecir2020</a></li> </ul> <p>The evaluation was conducted in a citation re-prediction setting.</p> <p><strong>CSV Format</strong></p> <ul> <li>7 columns divided by \u241E <ol> <li>cited document ID <ul> <li>for *_nomarker.csv: citation marker position ambiguous</li> <li> for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context</li> </ul> </li> <li>adjacent cited document IDs <ul> <li>only given in citrec_unarxive_*.csv</li> <li>divided by \u241F</li> <li>order matches 'CIT' markers in citation context</li> </ul> </li> <li>citing document ID</li> <li>citation context</li> <li>MAG field of study IDs <ul> <li>divided by \u241F</li> </ul> </li> <li>predicate:argument tuples generated based on PredPatt <ul> <li>JSON</li> </ul> </li> <li>noun phrases <ul> <li>for *_nomarker.csv: divided by \u241F</li> <li>for *_withmarker.csv: <ul> <li>divided by \u241D into</li> <li>noun phrases</li> <li>noun phrase directly preceding citation marker</li> </ul> </li> </ul> </li> </ol> </li> </ul> <p><strong>Data Sources</strong></p> <ol> <li>citrec_unarxive_cs_withmarker.csv <ul> <li>data set <ul> <li>unarXive</li> <li>Paper DOI: <a href="http://doi.org/10.1007/s11192-020-03382-z">10.1007/s11192-020-03382-z</a></li> <li>Data DOI: <a href="http://doi.org/10.5281/zenodo.2553522">10.5281/zenodo.2553522</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science</li> <li>cited doc is cited at least 5 times</li> </ul> </li> </ul> </li> <li>citrec_mag_cs_en.csv <ul> <li>data set <ul> <li>Microsoft Academic Graph (MAG)</li> <li>Paper DOI: <a href="http://doi.org/10.1145/2740908.2742839">10.1145/2740908.2742839</a></li> </ul> </li> <li>filter <ul> <li>citing doc from computer science and in English</li> <li>citing doc abstract in MAG given</li> <li>cited doc is cited at least 50 times</li> </ul> </li> </ul> </li> <li>citrec_refseer.csv <ul> <li>data set <ul> <li>RefSeer</li> <li>Paper URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">ojs.aaai.org/index.php/AAAI/article/view/9528</a></li> <li>Data URL: <a href="http://ojs.aaai.org/index.php/AAAI/article/view/9528">psu.app.box.com/v/refseer</a></li> </ul> </li> <li>filter <ul> <li>for citing and cited docs title, venue, venuetype, abstract, and year not NULL</li> </ul> </li> </ul> </li> <li>citrec_acl-arc_withmarker.csv <ul> <li>data set <ul> <li>ACL ARC</li> <li>Paper URL: <a href="http://aclanthology.org/L08-1005">aclanthology.org/L08-1005</a></li> <li>Data URL: <a href="http://acl-arc.comp.nus.edu.sg/">acl-arc.comp.nus.edu.sg/</a></li> </ul> </li> <li>filter <ul> <li>cited doc has a DBLP ID</li> </ul> </li> </ul> </li> </ol> <p> </p> <p><strong>Paper Citation</strong></p> <pre>@inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15}, } </pre>
Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law
<p>Data set for thermal transport study in single-band Hubbard Model using DQMC: Lorenz number and Wiedemann-Franz law</p> <p>Labeling in plots are slightly different with the preprint paper.</p>
Model America - data for every U.S. building
<p> </p> <p><strong>DATA HAS BEEN MIGRATED TO <a href="https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980">https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980</a></strong></p> <p> </p> <p>The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".</p> <p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".</p> <ol> <li><strong>Data, separated by state - minimalist list of each building (rows) for the following fields (columns)</strong> <ol> <li>ID - unique building ID</li> <li>Footprint2D - lat/lon vertices of building footprint</li> <li>State_Abbrev - Abbreviation for the from which building is located</li> <li>Area - estimate of total conditioned floor area (ft<sup>2</sup>)</li> <li>Area2D - footprint area (ft<sup>2</sup>) </li> <li>CZ - ASHRAE Climate Zone designation</li> <li>Height - building height (ft)</li> <li>NumFloors - number of floors (above-grade) </li> <li>WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)</li> <li>CZ - US climate zone designation</li> <li>BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards</li> <li>Standard - building vintage (determined by building age)</li> </ol> </li> </ol> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). </p>
Data from: Modeling 2020 regulatory changes in international shipping emissions helps explain 2023 anomalous warming
Open the record for dataset details and reuse information.
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</p>
Model code and Data for manuscript 'The Role of Wave-induced Mixing in Spring Phytoplankton Bloom in the South Yellow Sea'
<p>This repository contains four files:</p> <p>(1) Wave-induced mixing data for China seas;</p> <p>(2) Water transparency data for the South Yellow Sea;</p> <p>(3) Observation data used in this study; and</p> <p>(4) Model code for our ecological model and model experiments, including configuration data file. </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.