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278 results for “Validated dataset”
UML Diagram Dataset from the paper Creating and Validating a Ground Truth Dataset of UML Diagrams Using Deep Learning Techniques
<p>Dataset of six UML diagram classes, comprising a total of 2,626 images (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams). Importantly, unlike other existing datasets, ours contains no duplicate elements and all diagrams are correctly classified.</p>
Syntalos Sync Validation Example Datasets
<p>This dataset contains some data used to evaluate the performance of the <a href="https://github.com/bothlab/syntalos">Syntalos</a> data acquisition platform in various scenarios.</p> <p>The "Marathon" dataset contains one run over multiple hours to evaluate data synchrony after a long experiment, while the "LaunchSyncVarSteps*" sets contain Syntalos starting and stopping the same experiment many times to determine the inherent start latency jitter.</p> <p>For every run, Syntalos was tasked with acquiring a variety of data from an electrophysiology amplifier, UCLA Miniscope and two different types of cameras, while also responding to incoming periodic events via a Python script.</p> <p>Used PC Systems for data acquisition:</p> <ul> <li>HP DV7-7147SG laptop with an Intel i5-3210M and 8 GB of memory</li> <li>Lenovo E495 laptop</li> <li>PC with an AMD Ryzen 5 5600G CPU and 16.0 GB of RAM</li> </ul> <p>Data Acquisition Hardware:</p> <ul> <li>Intan Technologies RHD USB Interface Board with an RHD 64-Channel Recording headstage (Intan Technologies, Los Angeles, California)</li> <li>The Imaging Source Cameras: DFK 37BUX462 and DFK 37BUX290 (The Imaging Source LLC, Charlotte, United States)</li> <li>Basler ace Classic acA1920-25um camera (Basler AG, Ahrensburg, Germany)</li> <li>UVC Webcam, AnkePower Computer Webcam V-24 1080p (generic camera Amazon online shop)</li> <li>UCLA Miniscope v4 (Los Angeles, California)</li> <li>Arduino Uno R3</li> <li>Raspberry Pi Pico</li> </ul>
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Tour planning validation dataset
<p>This is the dataset produced during the validation of the Viarota scenario for developing personalised city break tours. The mocked data collected were generated during the RADON validation activities, using the Viarota application in a controlled testing environment.</p>
ValLAI_Crop: Validation dataset for coarse-resolution satellite LAI product over Chinese Cropland
<p>Numerous validation campaigns have been conducted over the last decade to assess the accuracy of the global leaf area index (LAI) products. Accurate and comprehensive validations for coarse-resolution LAI products are still very difficult due to lack of enough high-quality field measurements. Here we developed a fine resolution LAI dataset, consisting of 80 sample plots with an area of 3 km × 3 km in four major agricultural regions in China collected from 2003 to 2017. Instead of the indirect optical measurement method employed in most validation campaigns, the direct destructive method was employed to measure LAI of cropland for all the field experiments to avoid the measurement uncertainties, especially for crops at early growth stages with low height. Fine resolution reference LAI maps were derived from Landsat-5 TM and Landsat-8 OLI surface reflectance products based on the semi-empirical inversion model, which were calibrated using field measurements for each growth stage with an RMSE ranging from 0.22 to 0.95, and a relative root mean square error (RRMSE) ranging from 7.58% to 44.42%. Then, 80 sample plots with an area of 3 km × 3 km were selected as the fine resolution validation dataset from the fine resolution reference LAI maps with a proportion of cropland larger than 75% and one or more in-situ samples were contained in each 3 km × 3 km reference map.</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
Decomposition Tool Validation Dataset 2
<p>This dataset provides artifacts, benchmarks and models used to evaluate the utility of the optimization approach as well as the corresponding results:</p> <ul> <li>Base TOSCA models for generating the considered model set</li> <li>TOSCA models obtained through the optimization approach and the naive one</li> <li>Analytics of the resulting models in terms of memory, concurrency and costs</li> </ul>
Diarrhea etiology prediction validation dataset - Bangladesh and Mali
<p>Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.</p> <p>Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ("App") for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used previously derived and internally validated models using combinations of "patient-intrinsic" information (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference), pre-test odds using location-specific historical prevalence and recent patients, climate, and viral seasonality. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.</p> <p>Results:<b> </b>Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large α=-0.393 (-0.455 – -0.331) and calibration slope β=1.287 (1.207 – 1.367). By site, the pre-test odds model performed best in Mali with an AUC of 0.783 (0.705 - 0.86); the viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595 - 0.825).</p> <p>Conclusion: The app accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the app's potential use in diagnostic and antimicrobial stewardship are underway.</p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
Dataset for "Radiation environment at the surface and subsurface of the Moon: Model development and validation" publication in Journal of Geophysical Research: Planets
<p>data set used for plots in manuscript "Radiation environment at the surface and subsurface of the Moon: Model development and validation" submitted to GRL</p>
Dataset supporting publication: "Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings"
<p>Dataset supporting publication: “Geofit: Experimental Investigations and Numerical Validation of Shallow Spiral Collectors as a Basis for Development of a Design Tool for Geothermal Retrofitting of Existing Buildings” (publication available in <a href="https://zenodo.org/record/7273966#.Y2JgBnbMJPY">GEOFIT Zenodo</a>)</p> <p>The H2020 GEOFIT (grant no. 792210) project will implement and demonstrate easy-to-install and economical geothermal systems in combination with heat pumps for energy-efficient building retrofits at five pilot sites across Europe - a historic building (ITA), a school (ESP), an indoor swimming pool (IRL), an office building (FRA) and a single-family house (IRL) (GEOFIT,2018). Heat pump tests and experimental laboratory tests with shallow geothermal heat collector types are carried out in climate chambers at the AIT. Material data of different soil types are determined in the thermophysics laboratory. Furthermore, CFD simulations of the conducted experiments are calculated with ANSYS Fluent. All this provides data and know-how for the development of a design tool for ground collector configurations such as helices and slinky loops, which are particularly relevant for building retrofits in GEOFIT. Experimental work focused on near-surface spiral geothermal heat exchanger configurations that can be installed at a maximum depth of five metres. Real-scale experiments were carried out for vertically oriented spiral collectors (helix) in real soil. One objective was to develop a measurement concept in the laboratory environment to create the framework for a reliable database. This database is used as a basis for the further development or new development of engineering design tools. Distributed resistance temperature sensors and a fibre-optic temperature measurement system (DTS) were used. The moisture content of the soil was recorded using soil moisture sensors. A heat flow was conditioned by means of a helix shaped electric heating cable in a 1m³ cuboid soil container. The measurements were carried out in a climate chamber at a defined constant temperature of 10 °C. The evaluation of the transient response behaviour is spatially resolved. This results in coordinate-related temperature points, which describe temperature gradients in all axes of the container over time. Three different types of soil were investigated. The temperature behaviour of humus soil, sand and a mixture of these was investigated experimentally in smaller experiments and the material data such as heat capacity, thermal conductivity and density were determined thermophysically in the laboratory. Based on this data, a CFD model was developed which can be used to modify the geometry parameters of the helix.</p>
Dataset for the publication "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities"
<p>This is dataset for paper published:</p> <p>G. D’Avanzo <em>et al</em>., "Theory and Experimental Validation of Two Techniques for Compensating VT Nonlinearities," in <em>IEEE Transactions on Instrumentation and Measurement</em>, vol. 71, pp. 1-12, 2022, Art no. 9001312, doi: 10.1109/TIM.2022.3147883.</p>
Dataset used for the study of "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence"
<p>Datasets used to create the figures and statistical study in "Validation of Aeolus wind profiles using ground-based lidar and radiosonde observations at La Réunion Island and the Observatoire de Haute Provence" </p>
Supplementary data for A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms
<p>The attached Microsoft Excel files contain the data and diagrams of the paper:</p> <p><strong>A systematic literature review on the code smells datasets and validation mechanisms</strong></p> <p>The article is under review in the ACM Computing Surveys.</p> <p> </p>
Datasets for validating Harmony
<p>Harmony is a data harmonisation project that uses Natural Language Processing to help researchers make better use of existing data from different studies by supporting them with the harmonisation of various measures and items used in different studies. Harmony is a collaboration project between the University of Ulster, University College London, the Universidade Federal de Santa Maria in Brazil, and Fast Data Science Ltd.</p> <p>You can read more at <a href="https://harmonydata.org/">https://harmonydata.org</a>.</p> <p>There is a live demo at: <a href="https://app.harmonydata.org/">https://app.harmonydata.org/</a></p> <p>These are the datasets used to validate Harmony. The Excel file is McElroy et al's data, and the zip file contains the English and Portuguese GAD-7s.</p>
Unlabeled Sentinel 2 time series dataset (validation): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only validation data</strong> are available. To download the full pretraining dataset, see <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UVU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
SynthRAD2023 Grand Challenge validation dataset: synthetizing computed tomography for radiotherapy
<p><strong>Version 1.1</strong>, updated on 2023-06-04 --> the task2_val.zip has been modified with a new cbct file for patient 2BA078.<br> <br> The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.7868169">10.5281/zenodo.7868169</a> and a detailed description is offered at <a href="https://doi.org/10.5281/zenodo.7260704">https://doi.org/10.5281/zenodo.7260704</a> in the "synthRAD2023_dataset_description.pdf".</p> <p>The<strong> </strong>input of the<strong> validation datasets</strong> for Task1 is in Task1_val.zip, while for Task2 in Task2_val.zip. After unzipping, each Task is organized according to the following folder structure:</p> <p>Task1_val.zip/</p> <p>├── Task1</p> <p> ├── brain</p> <p> ├── 1Bxxxx</p> <p> ├── mr.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_brain_val.xlsx</p> <p> ├── 1Bxxxx_val.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 1Pxxxx</p> <p> ├── mr.nii.gz</p> <p> ├── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 1_pelvis_val.xlsx</p> <p> ├── 1Pxxxx_val.png</p> <p> └── ....</p> <p>Task2_val.zip/</p> <p>├──Task2</p> <p> ├── brain</p> <p> ├── 2Bxxxx</p> <p> ├── cbct.nii.gz</p> <p> └── mask.nii.gz</p> <p> ├── ...</p> <p>└── overview</p> <p> ├── 2_brain_val.xlsx</p> <p> ├── 2Bxxxx_val.png</p> <p> └── ... </p> <p> └── pelvis</p> <p> ├── 2Pxxxx</p> <p> ├── cbct.nii.gz</p> <p> ├── mask.nii.gz</p> <p>├── ...</p> <p>└── overview</p> <p> ├── 2_pelvis_val.xlsx</p> <p> ├── 2Pxxxx_val.png</p> <p> └── ....</p> <p>Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below:</p> <p>[Task] [Anatomy] [Center] [PatientID]</p> <p>1 B A 001</p> <p>In each patient folder, two files can be found: </p> <ul> <li> <p>mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image</p> </li> <li> <p>mask.nii.gz: image containing a binary mask of the dilated patient outline </p> </li> </ul> <p>For each task and anatomy, an overview folder is provided which contains the following files:</p> <ul> <li> <p>[task]_[anatomy]_val.xlsx: This file contains information about the image acquisition protocol for each patient.</p> </li> <li> <p>[task][anatomy][center][PatientID]_val.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data.</p> </li> </ul> <p><strong>DATASET DESCRIPTION</strong></p> <p>This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks.</p> <p>Data was collected at 3 Dutch university medical centers:</p> <ul> <li> <p>Radboud University Medical Center;</p> </li> <li> <p>University Medical Center Utrecht;</p> </li> <li> <p>University Medical Center Groningen.</p> </li> </ul> <p>For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to.</p> <p>The following number of patients is available in the validation set.</p> <p><strong>Validation</strong></p> <table> <tbody> <tr> <td> </td> <td> <p><strong>Brain</strong></p> </td> <td> <p><strong>Pelvis</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Total</strong></p> </td> <td> <p><strong>Center A</strong></p> </td> <td> <p><strong>Center B</strong></p> </td> <td> <p><strong>Center C</strong></p> </td> <td> <p><strong>Tota</strong>l</p> </td> </tr> <tr> <td> <p><strong>Task 1</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>20</p> </td> <td> <p>0</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> <tr> <td> <p><strong>Task 2</strong></p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>10</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p>In total, for all tasks and anatomies combined, 120 image pairs are available in this dataset. <strong>This repository only contains the validation data. </strong>The training data is provided at: h<a href="https://doi.org/10.5281/zenodo.7260704">ttps://doi.org/10.5281/zenodo.7260704</a>.</p> <p>All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure).</p> <p>Data was acquired with the following scanners:</p> <ul> <li> <p>Center A:</p> <ul> <li> <p>MRI: Philips Ingenia 1.5T/3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> <li> <p>Center B:</p> <ul> <li> <p>MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T</p> </li> <li> <p>CT: Siemens SOMATOM Definition AS</p> </li> <li> <p>CBCT: IBA Proteus+ or Elekta XVI</p> </li> </ul> </li> <li> <p>Center C:</p> <ul> <li> <p>MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T</p> </li> <li> <p>CT: Philips Brilliance Big Bore</p> </li> <li> <p>CBCT: Elekta XVI</p> </li> </ul> </li> </ul> <p>For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast.</p> <p>For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT.</p> <p>The following pre-processing steps were performed on the data:</p> <ul> <li> <p>Conversion from dicom to compressed nifti (nii.gz)</p> </li> <li> <p>Rigid registration between CT and MR/CBCT</p> </li> <li> <p>Anonymization (face removal, only for brain patients)</p> </li> <li> <p>Patient outline segmentation (provided as a binary mask)</p> </li> <li> <p>Crop MR/CBCT, CT and mask to remove background and reduce file sizes</p> </li> </ul> <p>The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics.</p> <p><strong>ETHICAL APPROVAL</strong></p> <p>Each institution received ethical approval from their internal review board/Medical Ethical committee:</p> <ul> <li> <p>UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”.</p> </li> <li> <p>UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”.</p> </li> <li> <p>Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”.</p> </li> </ul> <p><strong>CHALLENGE DESIGN</strong></p> <p>The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746019">https://doi.org/10.5281/zenodo.7746019</a>.</p>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Lung CT Deformable Image Registration Validation Dataset
<p>This dataset contains 30 different cases of CT image pairs, with a high number of vessel bifurcation landmark pairs identified in each case. These landmarks can be used for deformable image registration (DIR) algorithm validation and quality assurance. Images are obtained from several publicly available image repositories as well as clinical scans from Barnes Jewish Hospital.</p> <p> </p> <p>Guidelines for loading and visualizing data can be found on our Github at </p> <p>https://github.com/deshanyang/Lung-DIR-QA</p> <p> </p> <p>If you use our dataset, please cite the paper at </p> <p>https://doi.org/10.1002/mp.17026</p>
Observational datasets for validation of Mediterranean Biogeochemical Copernicus Modelling System, period 2018-2020
<p>Datasets used for the validation of the biogeochemical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020.</p> <p>The list of datasets includes:</p> <p>1) the Delay Mode Satellite chlorophyll from https://data.marine.copernicus.eu/product/OCEANCOLOUR_MED_BGC_L3_NRT_009_141/description after interpolation to the 1/24° horizontal resolution, weekly averages and quality check with internal climatology</p> <p>2) the BGC-Argo float profiles of nitrate, chlorophyll and oxygen from Coriolis DAC (ftp://ftp.ifremer.fr/ifremer/argo; https://doi.org/10.17882/42182#76230) after an internal quality check procedure which is described in Salon et al., 2019. </p> <p>3) the climatological profiles for 16 subbasins of nitrate, phosphate, silicate, oxygen, DIC, alkalinity, pCO2 and pH computed from the Emodnet 2018 data collection and additional scientific datasets as described in Salon et al., 2019.</p> <p> </p> <p>Ref.: Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C. and Crise, A., 2019. Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. <em>Ocean Science</em>, <em>15</em>(4), pp.997-1022.</p> <p> </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.