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Dataset results
278 results for “Validated dataset”
Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America : Dataset
<p>This dataset is linked to the paper “Validation of a new spatially-explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex stands in Eastern North-America" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-16-1661-2023). It contains the installer of the model, its user guide, as well as all the input files (inventory, thinning, meteorology and soil horizons files for each stand used in the evaluation and calibration steps), the R scripts and associated data used to analyse the model outputs.</p>
Dataset supporting publication: "Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers"
<p>Dataset supporting publication: “Development and Validation of Analytical Solutions for Earth Basket (Spiral) Heat Exchangers” (publication available for download:<a href="https://zenodo.org/record/7274096#.Y2JpLnbMJPY">GEOFIT Zenodo</a>).</p> <p>This paper presents an analytical solution and its validation for earth basket (vertical spiral) ground heat<br> exchangers. The model, based on the well known Finite Line Source Equation, accounts for the heat exchanger pipe diameter and seasonally varying near surface temperature. For computational efficiency the standard approach of using G-functions has been implemented as well. The analytical model is validated based on laboratory experiments and extensive CFD analysis.</p>
Spatial Plus Cross-Validation experiments datasets and codes
<p>This zip file includes all materials of Spatial Plus Cross-Validation experiments. </p> <p>They are ordered by the first number of folder's name.</p> <p>In each folder, the order of running code scripts are labeled by the first number of code's name.</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Dataset for the Indonesian Newly Validated Collaborative Practice Assessment Tool
<p>The dataset is used for the analysis of an article titled: A psychometric evaluation of the Indonesian version of the Collaborative Practice Assessment Tool (CPAT) for assessing interprofessional collaborative practice in health practitioners and students.</p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
SQAT v1_0: dataset of validation sounds
<p>Dataset of test sounds used to verify the psychoacoustic metrics implemented in the first release of the sound quality analysis toolbox (SQAT), version 1.0.</p> <p>In order to reproduce the verification codes in the <validation> folder of SQAT, this repository of test sounds needs to be downloaded and the paste <validation_SQAT_v1_0> has to be included in the <sound_files> folder of the toolbox.</p>
Campype Validation Dataset
<p>Here you will find the raw reads and assembled genomes that were used to test CamPype (<a href="https://github.com/JoseBarbero/CamPype">https://github.com/JoseBarbero/CamPype</a>).</p>
Immunoassay and proteomics dataset: Identification and validation of urine CXCL-9 as a biomarker for diagnosis of acute interstitial nephritis
<p>Background: Acute tubulointerstitial nephritis (AIN) is one of the few causes of acute kidney injury with diagnosis-specific treatment options. However, due to the need to obtain a kidney biopsy for histological confirmation, AIN diagnosis can be delayed, missed, or incorrectly assumed. Here we identify and validate urine CXCL-9, an interferon-γ-induced chemokine involved in lymphocyte chemotaxis, as a diagnostic biomarker for AIN.</p> <p>Methods: In a prospectively-enrolled cohort with pathologist-adjudicated histological diagnoses (<em>discovery cohort</em>), we tested the association of 180 immune proteins measured by an aptamer-based assay with AIN and validated the top protein, CXCL-9, using sandwich immunoassay. We externally validated these findings in 2 cohorts with biopsy-confirmed diagnoses (<em>validation cohorts</em>) and examined mRNA expression differences in kidney tissue from patients with AIN and controls.</p> <p>Results: In aptamer-based assay, urine CXCL-9 was 7.6-fold higher in AIN than controls (<em>P</em>=1.23·10<sup>-5</sup>). Urine CXCL-9 measured by sandwich immunoassay was associated with AIN in the discovery cohort (n=204; 15% AIN) independently of currently available clinical tests for AIN (adjusted odds ratio for highest vs lowest quartile: 6.0; 95% CI: 1.8-20). Similar findings were noted in external validation cohorts, where CXCL-9 had an AUC of 0.94 (0.86-1.00) for AIN diagnosis. <em>CXCL9</em> mRNA expression was 3.9-fold higher in kidney tissue from patients with AIN (n=19) as compared with controls (n=52; P=5.8·10<sup>-6</sup>).</p> <p>Conclusion: We identified CXCL-9 as a biomarker for AIN diagnosis using aptamer-based urine proteomics, confirmed this association using sandwich immunoassays in discovery and validation cohorts, and observed higher expression of this protein in kidney biopsies with AIN. </p>
Dataset for Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire
<p>These are the datasets that we used for evaluating the construct validity of the Charité Alarm Fatigue Questionnaire (CAFQa) in a forthcoming publication. All items were answered on a 5-point Likert scale and were scored by us as follows: -2/“I do not agree at all”, -1/“I do not agree”, 0/“I agree in part”, 1/“I agree”, 2/"I very much agree".</p> <p>A previous version of this upload included only the data of Study 1. A new version provides the data of Study 2. Please refer to the methods section of the forthcoming publication for more details.</p> Variable names and their corresponding item. Items marked with <table><tbody><tr> <th>Variable Name</th> <th>CAFQa Item</th> </tr> </tbody><tbody> <tr> <td>procedural_instruction</td> <td>In my ward, procedural instruction on how to deal with alarms is regularly updated and shared with all staff.<sup>a</sup></td> </tr> <tr> <td>respond_quickly</td> <td>Responsible personnel respond quickly and appropriately to alarms.<sup>a</sup></td> </tr> <tr> <td>motivation_decrease</td> <td>With too many alarms on my ward, my work performance, and motivation decrease.</td> </tr> <tr> <td>physical_symptoms</td> <td>Too many alarms trigger physical symptoms for me, e.g., nervousness, headaches, and sleep disturbances.</td> </tr> <tr> <td>ward_floor</td> <td>The acoustic and visual monitor alarms used on my ward floor and in my nurse station allow me to assign the patient, the device, and the situation clearly.<sup>a</sup></td> </tr> <tr> <td>reduce_concentration</td> <td>Alarms reduce my concentration and attention.</td> </tr> <tr> <td>alarm_limits</td> <td>Alarm limits are regularly adjusted based on patients' clinical pictures (e.g., blood pressure limits for conditions after bypass surgery).<sup>a</sup></td> </tr> <tr> <td>interrupt_workflow</td> <td>My or neighboring patients' alarms or crisis alarms frequently interrupt my workflow.</td> </tr> <tr> <td>alarms_confuse</td> <td>There are situations when alarms confuse me.</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> Other variables in the data set. <table><tbody><tr> <th>Variable Name</th> <th>Explanation</th> </tr> </tbody><tbody> <tr> <td>self_reported_AF</td> <td>self-estimated alarm fatigue in percent</td> </tr> <tr> <td>estimated_false_alarms</td> <td>perceived rate of false alarms in the participant's ICU</td> </tr> <tr> <td>monthly_time_on_ICU</td> <td>the average number of workdays per month in an intensive care or monitoring area</td> </tr> <tr> <td>ICU_experience</td> <td>number of years/months of ICU experience</td> </tr> <tr> <td>profession</td> <td>physician, nurse, or supporting nurse</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p><strong>Members of the Study Group</strong> <strong>in alphabetical order</strong>: <em>Dr. med. Mirza Aghamov</em><sup><em>1</em></sup><em>, Prof. Dr. med. Manfred Blobner<sup>2</sup>, Prof. Dr. med. Ulrich Frey<sup>3</sup></em><em>, Prof. Dr. Christian von Heymann<sup>4</sup></em><em>, Prof. Dr. med. Bettina Jungwirth</em><sup><em>1</em></sup><em>, Dr. med. Dragutin Popovic<sup>4</sup></em><em>, Prof. Dr. med. Michael Sander<sup>5</sup></em><em>. </em></p> <p>1: <em>Department of Anesthesiology and Intensive Care Medicine, University Hospital Ulm, Ulm University, Ulm, Germany</em></p> <p>2: <em>Technical University Munich, School of Medicine, Klinikum Rechts der Isar, Department of Anaesthesiology & Intensive Care Medicine, Munich, Germany</em></p> <p>3: <em>Department for Anesthesiology, Surgical Intensive Care, Pain and Palliative Medicine, Marien Hospital Herne – Universitätsklinikum der Ruhr-Universität Bochum, Herne, Germany</em></p> <p>4: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Vivantes Klinikum im Friedrichshain, Berlin, Germany</em></p> <p>5: <em>Department for Anaesthesiology, Intensive Care Medicine and Pain Therapy, Justus Liebig University, Giessen, Germany</em></p> <p> </p>
choderalab/geometry-benchmark-espaloma: Small molecule geometry benchmark dataset to validate espaloma-0.3
<p>This is a collection of preprocessed QM and MM optimized structures needed to perform the small molecule geometry benchmark study, described in the <strong>espaloma-0.3</strong> paper:</p> <p>Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara, Mike Henry, Hugo MacDermott Opeskin, John D. Chodera, Yuanqing Wang. "Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond" (<a href="https://arxiv.org/abs/2307.07085">arXiv:2307.07085</a>)</p> <p>This benchmark study calculates and compares the RMSD, TFD, and ddE metrics for a specified set of MM force fields. The initial optimized structures were sourced from the <a href="https://github.com/openforcefield/qca-dataset-submission/tree/master/submissions/2021-06-04-OpenFF-Industry-Benchmark-Season-1-v1.1">OpenFF Industry Benchmark Season 1 v1.1</a> dataset, which is available through <a href="https://qcarchive.molssi.org/">QCArchive</a>. More details about the preprocessing steps is available at <a href="https://github.com/choderalab/geometry-benchmark-espaloma/tree/main/qc-opt-geo">https://github.com/choderalab/geometry-benchmark-espaloma/tree/main/qc-opt-geo</a>.</p> <ul> <li><strong>02-chunks.tar.gz</strong>: QM optimized structures chunked into small file sizes.</li> <li><strong>02-outputs-openff-2.0.0-espaloma-0.3.0rc1.tar.gz</strong>: MM optimized structures using openff-2.0.0 and espaloma-0.3.0rc1 force field (former release candidate of espaloma-0.3)</li> <li><strong>02-outputs-gaff2.11.tar.gz</strong>: MM optimized structures using gaff-2.11 force field</li> <li><strong>02-outputs-espaloma-0.3.0rc6.tar.gz</strong>: MM optimized structures using espaloma-0.3.0rc6 (espaloma-0.3) force field</li> <li><strong>02-outputs-openff-2.1.0.tar.gz</strong>: MM optimized structures using openff-2.1.0 force field</li> </ul>
Collocated model and observation datasets for the validation of the Copernicus Mediterranean Sea Waves Analysis and Forecast for the period 2018-2020.
<p>The collocated values are used for skill evaluation of the Mediterranean Sea Waves Analysis and Forecast system for a three-year-long period (Korres et al., 2022). The list of datasets includes:</p> <ol> <li>The collocated model (analysis) – buoy values for significant wave height Hs (Insitu_Hs.mat)</li> <li>The collocated model (analysis) – buoy values for spectral moments (0,2) wave period Tm (Insitu_Tm.mat)</li> <li>The collocated model (first – guess) – satellite values for significant wave height Hs (Satellite_Hs.mat)</li> <li>The collocated model – satellite values for wind speed U10 (Satellite_U10.mat)</li> </ol> <p>Each .mat file contains a header for the variables included. Collocations can be used to estimate standard quality metrics (e.g. scatter index, bias, root-mean-squared-difference). Procedures to produce these model-observation collocated datasets and determine the overall skill assessment are described in detail in Ravdas et al. (2018) and Oikonomou et al. (2022). The buoy (in-situ) measurements are obtained from the product INSITU_GLO_WAV_DISCRETE_MY_013_045 (EU Copernicus Marine Service Product, 2022a), and associated variables contain a quality flag (“time_qc”, “position_qc”, “buoy_Hs_qc”, “buoy_Tm_qc”) (de Alfonso et al., 2022a,b). In addition, the model first-guess significant wave height and the wind speed forcing (Hersbach et al., 2023) are collocated with available satellite observations (EU Copernicus Marine Service Product, 2022b) over the entire model domain.</p> <p>References</p> <p>de Alfonso, M., Manzano, F., and Gallardo, A. (2022a): EU Copernicus Marine Service Quality Information Document for the In Situ TAC Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 5.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-045.pdf</a></p> <p>de Alfonso, M., Manzano, F., Gallardo, A., and In Situ TAC (2022b): EU Copernicus Marine Service Product User Manual for Multi-Year WAVE In Situ Product, INSITU_GLO_WAV_DISCRETE_MY_013_045, Issue 2.0, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf">https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-INS-PUM-013-045.pdf</a></p> <p>EU Copernicus Marine Service Product (2022a): Multi-Year WAVE In Situ Product, Mercator Ocean International, [dataset], <a href="https://doi.org/10.17882/70345">https://doi.org/10.17882/70345</a></p> <p>EU Copernicus Marine Service Product (2022b): Global Ocean L 3 Significant Wave Height From Reprocessed Satellite Measurements, Mercator Ocean International, [dataset], <a href="https://doi.org/10.48670/moi-00176">https://doi.org/10.48670/moi-00176</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed on 26-09-2023)</p> <p>Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2022): Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves, MEDWAΜ4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). <a href="https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4">https://doi.org/10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4</a></p> <p>Oikonomou, C., Denaxa D., and Korres, G. (2022): EU Copernicus Marine Service Quality Information Document for the Mediterranean Sea Waves Reanalysis, MEDSEA_ANALYSISFORECAST_WAV_006_017, Issue 2.2, Mercator Ocean International, <a href="https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf">https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-MED-QUID-006-017.pdf</a>.</p> <p>Ravdas, M., Zacharioudaki, A., and Korres, G. (2018): Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service, Nat. Hazards Earth Syst. Sci., 18, 2675–2695, <a href="https://doi.org/10.5194/nhess-18-2675-2018">https://doi.org/10.5194/nhess-18-2675-2018</a></p> <p> </p>
Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology
Open the record for dataset details and reuse information.
Immunoassay and proteomics dataset: Identification and validation of urine CXCL-9 as a biomarker for diagnosis of acute interstitial nephritis
Open the record for dataset details and reuse information.
Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning
<p><strong>Th</strong>e dataset is composed of the RSSI fingerprinting calibration of two malls employing four different smartphones with the random walking survey method. Moreover, the dataset includes validation routes performed with ten smartphones from various brands and labeled with the code of the shops where they were registered. For evaluation purposes, together with the samples, information related to the shopping center like the Euclidean distance between shops is provided. The files are described as follows:</p> <ul> <li> <p>building.csv’: two rows with the id of mall 1 and mall 2.</p> </li> <li> <p>‘floor.csv’: there is a row for each of the floors of the two malls. Three floors for mall 1 and two floors for mall 2 </p> </li> <li> <p>‘zones.csv’: for each zone is indicated the id of the mall and the id floor. There are 69 zones from mall 1 and 124 for mall 2 </p> </li> <li> <p>‘training.csv’: in this file are registered the ids and the description of the five different calibrations performed (one per smartphone) in each of the malls. </p> </li> <li> <p>‘training_sample.csv’: each row of the file has the RSSI in dBm recorded during the off-line calibration phase together with the timestamp, channel, and MAC of the AP.</p> </li> <li> <p>‘route.csv’: the id’s and the description of each of the validation routes are recorded in this file. </p> </li> <li> <p>‘route_sample.csv’: with a similar structure than the training_sample.csv, the RSSI, timestamp, channel, and MAC acquired during the validation routes are stored in this file.</p> </li> <li> <p>‘euclidea_distance.csv”: to evaluate the error of the different algorithms, this file recorded the euclidean distance from the center of a shop to each other in meters.</p> </li> </ul> <p>The dataset is to be cited as follows:<br> J. A. López-Pastor, A.J. Ruiz-Ruiz, A.J. García-Sánchez, J.L. Gómez-Tornero. Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning. Zenodo repository. DOI: 10.5281/zenodo.3698238</p>
Dataset from UNIBO for the deliverables 7.1 and 7.2, and SydILUC model setup, calibration and validation
<p>STAR-ProBio_ILUCLiteratureReview_v1.0 _Spreadsheet: Inventory of existing key drivers and parameters for ILUC quantification and future strategies to reduce ILUC risks, and collection of standardisation work related to the sustainability of biofuels and biomaterials.</p> <p>STAR-ProBio_ModelParameters_v13.0 _Spreadsheet: Inventory of parameters and relationships used in the SydILUC model, with related metadata, i.e. source (literature), units, suggested update period, etc... </p> <p>STAR-ProBio_CalibrationGlobalFAOSTAT_v3.0_Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model</p> <p>STAR-ProBio_MaizeNationalFAOSTAT_v1.0 _Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model – for the regional version of the model</p> <p>STAR-ProBio_BioplasticProductionSydILUC_v1.0 _Spreadsheet: Collection of all the yields for different bioplastics from different sources (sugar, starch, oil) – from the documents by “Biopolymers facts and statistics” by IfBB</p>
Datasets used for validating AHR activation
<p>This .tar file contains the datasets the raw data of the datasets that were downloaded for validations performed in the manuscript " IL4I1 Is a Metabolic Immune Checkpoint that Activates the AHR and Promotes Tumor Progression". The directory structure of the files is necessary for running the validation scripts.</p>
FD-detector: Dataset gathered running measurements in the wild and Emulations for validation
<p>This file contains two folders: the dataset and emulation setup.</p> <p>The dataset is composed of warts files, resulting from traceroutes run with Scamper on NLNOG RING nodes. On the other hand, the emulations were performed on GNS3.</p>
Datasets from: Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode
<p>Metabarcoding of Metazoa using mitochondrial genes may be confounded by both the accumulation of PCR and sequencing artefacts and the co-amplification of nuclear mitochondrial pseudogenes (NUMTs). The application of read abundance thresholds and denoising methods is efficient in reducing noise accompanying authentic mitochondrial amplicon sequence variants (ASVs). However, these procedures do not fully account for the complex nature of concomitant sequences and the highly variable DNA contribution of specimens in a metabarcoding sample. We propose, as a complement to denoising, the metabarcoding Multidimensional Abundance Threshold Evaluation (<i>metaMATE</i>) framework, a novel approach that allows comprehensive examination of multiple dimensions of abundance filtering and the evaluation of the prevalence of unwanted concomitant sequences in denoised metabarcoding datasets. <i>metaMATE</i> requires a denoised set of ASVs as input, and designates a subset of ASVs as being either authentic (mtDNA haplotypes) or non-authentic ASVs (NUMTs and erroneous sequences) by comparison to external reference data and by analysing nucleotide substitution patterns. <i>metaMATE</i> (i) facilitates the application of read abundance filtering strategies, which are structured with regard to sequence library and phylogeny and applied for a range of increasing abundance threshold values, and (ii) evaluates their performance by quantifying the prevalence of non-authentic ASVs and the collateral effects on the removal of authentic ASVs. The output from <i>metaMATE</i> facilitates decision-making about required filtering stringency and can be used to improve the reliability of intraspecific genetic information derived from metabarcode data. The framework is implemented in the <i>metaMATE</i> software, available at https://github.com/tjcreedy/metamate).</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.