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641 results for “data validation”
Extended data table 1: Taqman array card results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing.
<p>Data from study, protocol published at 10.5281/zenodo.5081880</p> <p><strong>Extended Data Table 1: TAC results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing. </strong>(BAL: bronchoalveolar lavage.) *Not included in validation numbers as duplicate at sub-species or genus level detection, **MecA was not included in validation numbers. TAC hits which did not pass the internal quality control standards required for reporting are indicated by (not reported). Samples which did not undergo sequencing indicated by <em>ND</em>. (✓) indicates low confidence hits from sequencing.</p>
Data from: Validating marine Devonian biogeography: a study in bioregionalization
<p>The Devonian record presents an opportunity to test and validate an existing marine bioregionalisation. This study is the first to use comparative biogeography and phylogenetic data to test Devonian bioregionalisation. Proposed in the 1960's the Old World, Eastern Americas, and Malvinokaffric realms have been the functional standard within marine Devonian Biogeography. Data from 32 published phylogenies of Devonian marine taxa and a database of c.800 occurrences were analysed using phylogenetic software to test for area monophyly. The taxic occurrences within the current database were then tested against total fauna Devonian occurrences with the Palaeobiology Database to indicate differences in sampling. Results indicate that the current Devonian bioregionalisation is not representative of natural areas and requires revision. The result highlights areas that are most robust from which the study makes recommendations to improve the process and diagnosis of Devonian biogeographic areas. We found that legacy issues within palaeontology are evident within the results and their interpretation. The validation of bioregionalisation and process is critical to the advancement of biogeography and palaeontology. The sensitivity of bioregionalisation shows biotic and geographical relationships, how life and earth evolved together, and how geographic bias is evident in scientific process.</p>
Data for: Whole Animal Feed FLat (WAFFL): A complete and comprehensive validation of a novel high-throughput fly experimentation system
<p>Non-mammalian model organisms have been essential for our understanding of the mechanisms that control development, disease, and physiology, but they are underutilized in pharmacological and toxicological phenotypic screening assays due to their low throughput in comparison with cell-based screens. To increase the utility of using <em>Drosophila melanogaster</em> in screening, we designed the Whole Animal Feeding FLat (WAFFL), a novel, flexible, and complete system for feeding, monitoring, and assaying flies in a high-throughput format. Our 3-D printed system is compatible with inexpensive and readily available, commercial 96-well plate consumables and equipment. Experimenters can change the diet at will during the experiment and video record for behavior analysis, enabling precise dosing, measurement of feeding, and analysis of behavior in 96-well plate format. </p>
Woerthersee sediment core data for the publication "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria"
<p>This dataset comprises sediment core data of Wörthersee, a lake in the Eastern European Alps, Austria. Together with a dataset comprising the seismic data (10.5281/zenodo.6479186), this is the basis for the publication Daxer et al. "Validation of seismic hazard curves using a calibrated 14 ka lacustrine record in the Eastern Alps, Austria".</p> <p>The files contain the following data:</p> <ul> <li>Core images Long Cores.zip: Core images of the Wörthersee Kullenberg-type long cores acquired with an ITRAX core scanner</li> <li>Core images Short Cores.zip: Core images of the Wörthersee gravity short cores (hammer-coring or trigger cores of the Kullenberg system) acquired with an ITRAX core scanner; provided as .tif files</li> <li>CT data WOER18-L5-X-Dicom.zip: X-ray computed tomography data acquired with a Siemens SOMATOM Definition AS (voxel size 0.2 x 0.2 x 0.3 mm); provided in DICOM format</li> <li>MSCL data.zip: Data acquired with a Geotek Multi-sensor core logger (e.g. magnetic susceptibility and gamma density); provided as Excel spreadsheets</li> <li>XRF data.zip: X-ray fluorescence data acquired with a ITRAX core scanner; provided in .csv format</li> </ul>
Data from: Validation of Quality-of-Life assessment tool for Ethiopian old age people
<p><strong>Background</strong>: Reliable quality of life assessment is critical for identifying health issues, evaluating health interventions, and establishing the best health policies and care packages. The World Health Organization Quality of Life-Old Module is a tool for assessing the subjective quality of life in old age people. It's validated and available in more than 20 languages, except Amharic. Hence, this study was intended to translate it into Amharic language and validate it among old age people in Ethiopia.</p> <p><strong>Methods</strong>: A cross-sectional study was conducted among 180 community-dwelling old age people in Ethiopia, from January 16 to March 13, 2021. Psychometric validation was achieved through Cronbach's alpha of the internal consistency reliability test, and construct validity from confirmatory factor analysis.</p> <p><strong>Results</strong>: The study participants aged from 60 to 90 years old with a mean age of 69.44. Females made up 61.7% of the population, and 40% of them could not read and write. The results showed a relatively low level of quality of life, with the total transformed score of 58.58 ± 23.15. The Amharic version of the World Health Organization Quality of Life-Old Module showed a Cronbach's Alpha value of 0.96 and corrected item-total correlations of more than 0.74. Confirmatory factor analysis confirmed the six-factor model with a chi-square (X2) of 341.98 with a p-value less than 0.001. The comparative fit index (CFI) was 0.98, Tucker-Lewis's index (TCL) was 0.97, and the root mean square error of approximation (RMSEA) was 0.046.</p> <p><strong>Conclusion</strong>: The Amharic version of the World Health Organization Quality of Life-Old Module indicated good internal consistency reliability and construct validity. The tool can be utilized to provide care to Ethiopian community-dwelling old age people.</p>
Raw Data for the article: Mortality after transjugular intrahepatic portosystemic shunt in older adult patients with cirrhosis: A validated prediction model
<p><strong>Background and aims: </strong>Implantation of a transjugular intrahepatic portosystemic shunt (TIPS) improves survival in patients with cirrhosis with refractory ascites and portal hypertensive bleeding. However, the indication for TIPS in older adult patients (greater than or equal to 70 years) is debated, and a specific prediction model developed in this particular setting is lacking. The aim of this study was to develop and validate a multivariable model for an accurate prediction of mortality in older adults.</p> <p><strong>Approach and results: </strong>We prospectively enrolled 411 consecutive patients observed at four referral centers with de novo TIPS implantation for refractory ascites or secondary prophylaxis of variceal bleeding (derivation cohort) and an external cohort of 415 patients with similar indications for TIPS (validation cohort). Older adult patients in the two cohorts were 99 and 76, respectively. A cause-specific Cox competing risks model was used to predict liver-related mortality, with orthotopic liver transplant and death for extrahepatic causes as competing events. Age, alcoholic etiology, creatinine levels, and international normalized ratio in the overall cohort, and creatinine and sodium levels in older adults were independent risk factors for liver-related death by multivariable analysis.</p> <p><strong>Conclusions: </strong>After TIPS implantation, mortality is increased by aging, but TIPS placement should not be precluded in patients older than 70 years. In older adults, creatinine and sodium levels are useful predictors for decision making. Further efforts to update the prediction model with larger sample size are warranted.</p>
Training, Validation and Test Sets for paper 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning'
<p>Training, Validation and Test Data for model presented in paper 'A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', submitted to Journal of Geophysical Research: Solid Earth.</p> <p>Files:</p> <p>- train_events_2498.h5 = training set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- train_events_2498.pkl = event training set metadata (UTC P-/S-wave phase arrival times)</p> <p>- train_noise_2498.h5 = training set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- train_noise_2498.pkl = noise training set metadata (UTC time for training noise waveforms)</p> <p>- val_events.h5 = validation set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms)</p> <p>- val_events.pkl = event validation set metadata (UTC P-/S-wave phase arrival times)</p> <p>- val_noise.h5 = validation set of seismic waveforms (noise sections only, i.e., no event waveforms)</p> <p>- val_noise.pkl = noise validation set metadata (UTC time for validation noise waveforms)</p> <p>- test.h5 = test set of seismic waveforms (events and noise)</p> <p>- test_events.pkl = event test set metadata (UTC P-/S-wave phase arrival times for test event waveforms)</p> <p>- test_noise.pkl = noise test set metadata (UTC time for test noise waveforms)</p> <p>- nabro_2011-247.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-04), saved in mseed format (e.g., can be read with obspy)</p> <p>- nabro_2011-269.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-26), saved in mseed format (e.g., can be read with obspy)</p> <p> </p> <p>Further details and code for reading and using these files can be found at the GitHub repo for this paper: <a href="https://github.com/sachalapins/U-GPD">https://github.com/sachalapins/U-GPD</a></p> <p> </p>
Sample, test, and validation data for findmycells
<p>findmycells is an open source python package, developed to foster the use of deep-learning based python tools for bioimage analysis, specifically for researchers with limited python coding experience. It is developed and maintained in the following GitHub repository: https://github.com/Defense-Circuits-Lab/findmycells</p> <p><strong>Disclaimer: All data (including the model ensemble) uploaded here serve solely as a test dataset for findmycells and are not intended for any other purposes.</strong></p> <p>For instance, the group, subgroup, or subject IDs don´t refer to the actual experimental conditions. Likewise, also the included ROI-files were only created to allow the testing of findmycells and may not live up to scientific standards. Furthermore, the image data represents a subset of a dataset that is already published here:</p> <blockquote> <p>Segebarth, Dennis et al. (2020), Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses, Dryad, Dataset, <a href="https://doi.org/10.5061/dryad.4b8gtht9d">https://doi.org/10.5061/dryad.4b8gtht9d</a></p> </blockquote> <p>The model ensemble (cfos_ensemble.zip) was trained using deepflash2 (v 0.1.7)</p> <blockquote> <p>Griebel, M., Segebarth, D., Stein, N., Schukraft, N., Tovote, P., Blum, R., & Flath, C. M. (2021). Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2. <em>arXiv preprint arXiv:2111.06693</em>.</p> </blockquote> <p>The training was performed on a subset of the "lab-wue1" training dataset, using only the 27 images with IDs 0000 - 0099 (cfos_training_images.zip) and the corresponding est. GT masks (cfos_training_masks.zip). The images used in "cfos_fmc_test_project.zip" for the actual testing of findmycells are the images with the IDs 0100, 0106, 0149, and 0152 of the aforementioned "lab-wue1" training dataset. They were randomly distributed to the made-up subject folders and renamed to "dentate_gyrus_01" or "dentate_gyrus_02".</p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Data for: Validation of a nutria (Myocastor coypus) environmental DNA assay highlights considerations for sampling methodology
<p>Nutria (<em>Myocastor coypus</em>) is a semi-aquatic rodent species that is invasive across multiple regions within the United States. Here we evaluated a qPCR assay previously described for use in Japan for application across invasive populations in the United States. We also compared two environmental DNA sampling methodologies for this assay: field filtration of large volumes of water passed through filters versus direct sampling of small volumes of water. We validated assay specificity, generality, and sensitivity, compared assay performance between two independent laboratories, and successfully tested the assay<em> in situ </em>on a known wild population. The filtration method required fewer samples for environmental DNA detection than direct sampling, but the choice of methods should be assessed based on specific field conditions and time and budget considerations. Our extensive assay validation and comparison across laboratories suggests that the assay is ready to be applied in environmental DNA monitoring of nutria throughout the United States.</p>
Global reference data set for validating ESA WorldCereal temporary cropland extent
<p>IIASA team has created a new validation data set, which is completely independent from all other existing maps or reference data sets, and which is in line with the cropland definitions and mapping period of the WorldCereal products. To decide if this is an active cropland in each period, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The experts were asked to label 5 by 5 Sentinel pixels (each pixel 10m by 10m) either as winter crops, or as summer crops, or as maize (if this was possible), or as active crops (where it was not possible to confirm a growing season, e.g. overlap between seasons was too big or crop fields were too small in size), or as no crops, or as not sure where was too little information available for 2021. There was additional question on irrigation system, either circle, or other irrigation, or rainfed, or not sure. </p> <p>Fields:</p> <p>"rowid" -unique row identifier</p> <p>"submissionid" – unique submission id, which consists of 25 single pixels (rows)</p> <p>"timestamp" – time stamp of each submission</p> <p>"sampleid" – unique sample site, which could have a few submission ids</p> <p>"submission_itemid" – unique single pixel submission id</p> <p>"enhancement" – unique legend identifier in the Geo-Wiki database</p> <p>"question" – question asked (either on crop presence or irrigation)</p> <p>"answer" – answer to a question asked</p> <p>"sub_pixel_x","sub_pixel_y" – centroids of a single pixel in WGS84</p>
SRL data feedback in higher education and validation of microlearning proposal
<p>This dataset corresponds to those used for the SRL in the category Feedback. It also presents the data collected from a process of expert validation of a training proposal based on microlearning for the development of digital teacher competence related to feedback. </p>
Validation of the satellite-estimated sedimentation rates in reservoirs using 10-m Sentinel-2 satellites and water level data
<p><strong>Overview</strong>: The database contains data used for the validation of satellite-based sedimentation rates in eight reservoirs across the central and western United States using 10-m Sentinel-2 imagery and in-situ level data. Additional validation of the results from combining Sentinel-2 imagery with simulated 27-day Sentinel-3 altimetry levels is also included.</p> <p> </p> <p><strong>This dataset includes</strong>:</p> <ol> <li>Satellite-derived area-level duplets</li> <li>Bathymetry curves from satellite-based estimates</li> <li>Bathymetry curves from survey data</li> <li>Validation</li> </ol>
Validating NISAR's cropland mapping approach and the USDA/NASS Cropland Data Layer against ground truth data in a fragmented urban agricultural region
<p>Field data used in manuscript:</p> <p>1 shapefile containing the ROI outline for which Sentinel-1 data was cropped</p> <p>1 shapefile containing the 93 fields investigated with their names, types and sizes as attributes </p> <p>8 annual csv data for active fields, consisting of 3 harvest dates and 5 planting dates. This list is after translating data from Farmlogic Report (not conducive to analysis in the format) and filtering for fields greater 1 acre. The study lateron further screened to use only fields greater than 2 acreas (0.81 hectares). </p> <p> </p>
Data for the publication: "Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor"
<p>This data set contains all raw data for the publication “Development and In-Vivo Validation of a Portable Phosphorescence Lifetime-Based Fiber-Optic Oxygen Sensor”:</p> <p>- Raw sensor data</p> <p>- Python scripts</p> <p>- particle photon scripts</p> <p>- CAD Drawings</p> <p>- PCB Designs</p>
Data for validation and norming of the Danish KIDSCREEN-10 child/adolescent version in a national representative sample of school pupils in grades five through eight
<p>Data for for analyses in Measuring child and adolescent well-being in Denmark: validation and norming of the Danish KIDSCREEN-10 child/adolescent version in a national representative sample of school pupils in grades five through eight. Contains the following variables:</p> <p>Variables Kid1 to Kid10 are the KISDCREEN 10 items. Category labels are the orginal from the KIDSCREEN consortium (Danish version). Items Kid3 and Kid4 are reversed coded according to the KIDSCREEN coding manual. Valus have been recoded from 1-5 to 0-4 for the purpose of item analyses.</p> <p>Language (spoken in the home): 1 = Danish, 2 = other</p> <p>School: 1 = public, 2 = private</p> <p>Sex: 1 = boy, 2 = girl</p> <p>Grade (level): 1 = 5th grade, 2 = 6th grade, 3 = 7th grade, 4 = 8th grade</p> <p>Sample (random): 1 to 7</p>
Polish validation data of the MASC (multisource assessment of children's social competence) instrument
<p>Dataset of the published open access article: Wiza, A., Koszałka-Silska, A., Jaguszewski, M. <em>et al.</em> Polish adaptation of multisource assessment of children’s social competence. <em>Sci Rep</em> <strong>13</strong>, 12128 (2023). https://doi.org/10.1038/s41598-023-39292-2</p> <p> </p>
Validation data for macromolecular occupancy estimation
<p>Macromolecular densities used to validate OccuPy v0.1:</p> <ol> <li>Synthetic densities of pdb 1UXI with altered: <ul> <li>Occupancy of chain A</li> <li>Occupancy of NAD cofactors </li> <li>Bfactor of chain A at occupancy 0.5 of chain A</li> </ul> </li> <li>Reconstructions of rotavirus spike protein: <ul> <li>No selection for spike, subsets of various particle numbers </li> <li>Positive selection (enriched) for spike, subsets of various particle numbers</li> </ul> </li> </ol>
Data for: A catalytic model for SARS-CoV-2 reinfections: Performing simulation-based validation and extending the model to include nth infections
<p>For code and more details see: </p> <ul> <li><code>inf_for_sbv.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of reinfections. </li> <li><code>inf_for_sbv_third.RDS</code> - simluated timeseries of primary infections used in the simulation-based validation of third infections. </li> <li><code>3_posterior_90_null_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when not considering a second lambda parameter (to third infections)</li> <li><code>3_posterior_90_null_l2_correctdata.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript) when considering a second lambda parameter (to third infections)</li> <li><code>3_sim_90_null_correctdata.RDS</code> - simulation results when not considering a second lambda parameter for third infections (as used in the manuscript)</li> <li><code>3_sim_90_null_l2_correctdata.RDS</code> - simulation results when considering a second lambda parameter for third infections (as used in the manuscript)</li> </ul> <p> </p>
Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"
<p>This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".</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.