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8,038 results for “validation”
Linked collectors and determiners for: Bombus johanseni, a valid North American bumble bee species.
Natural history specimen data linked to collectors and determiners held within, "Bombus johanseni, a valid North American bumble bee species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/90d8babc-685f-449e-a4ec-7275ca7655c7">https://bionomia.net/dataset/90d8babc-685f-449e-a4ec-7275ca7655c7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/90d8babc-685f-449e-a4ec-7275ca7655c7">https://gbif.org/dataset/90d8babc-685f-449e-a4ec-7275ca7655c7</a>. Formatted as a Frictionless Data package.
Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading
<p>This data set contains all the images, DIC data and python scripts and FE input files that allows the reproduction of all results in the paper "Image-based deformation measurements for validation of fusion divertor armour under high heat flux loading" that this data is linked to.</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>
Data for Manuscript: Instrumental Validity of the Motion Detection Accuracy of a Smartphone Based Training Game
<p><strong>Background: </strong>In the project TRIMOTEP we developed a low-cost augmented reality training game. Aim of the training game ist to support patients after total hip replacement in their rehabilitation. The project was funded by the Austrian Research Promotion Agency (FFG, grant number 862050). As hardware the training game uses a headset, an android smartphone and a step board. The goal of the training game is to dodge animals and objects while performing exercises. A current version of the training game can be downloaded here: https://trimotep.fh-joanneum.at/exer-game-ar_walker/ . The training game is based on Google ARCore and uses a movement detection approach to recognise different exercises. To detect movements ARCore uses the smartphone inbuilt inertial measurement unit and the front camera (https://developers.google.com/ar/discover). In order to investigate the possibilities of the training game, it is necessary to examine the accuracy of movement detection in more detail.</p> <p><strong>Data: </strong>To investigate the accuracy, comparative measurements were carried out with 30 healthy subjects. During the measurements, the subjects motion was recorded simultaneously with the training game and an optoelectronic motion capture system (Vicon). Two trials were recorded with each subject.</p> <p>First Trial: subjects followed a protocol</p> <p>Second Trial: subjects played the training game for one minute</p> <p>The training game measures the movement of the smartphone (and therefore of the headset and the head). The optoelectronic motion capture system uses a marker set consisting of four markers. Those markers are labeled HMD_F, HMD_B, HMD_R, HMD_L. Markers HMD_R and HMD_L as well as HMD_B and HMD_F form an axis in a karthesian coordinate system. This coordinate system is rotated by 8 degrees compared to the training game along the transversal axis.</p> <p><strong>Structure of the Data Set:</strong> The data set includes an excel sheet with general data of the subjects and a figure showing the tilt between the two coordinate systems. Further one folder contains the measurement data of the training game as json files. Another folder contains the measurement data of the optoelectronic motion capturing system as csv files.</p> <p> </p> <p>For further information or help to process the data please contact:</p> <p>Bernhard Guggenberger, bernhard.guggenberger2@fh-joanneum.at</p>
Validation of heart rate measurement of Fitbit Charge 4 and Xiaomi Mi Band 5
<p>Database containig data from heart rate validation study of 2 wristbands: Fitbit Charge 4 and Xiaomi Mi Band 5.</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>
Validation of ESA CCI SM active v06.1 vs ISMN 20210131 global
QA4SM validation of soil moisture data: ESA CCI SM active v06.1 vs ISMN 20210131 global. URL: https://qa4sm.eu/result/4fc5718a-db09-4748-953d-341f5127485e/. Produced on QA4SM (https://qa4sm.eu)
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>
Raw data: Validity of fitness trackers when worn by older adults
<p>Co-authors of the dataset:</p> <p>Marina Dobnik <sup>2</sup> , Stefan Loefler <sup>3,4,6</sup> , Christian Hofer <sup>4</sup> and Nejc Šarabon <sup>2,5</sup></p> <p><sup>1</sup> University of Primorska, Andrej Marušič Institute, Muzejski trg 2, 6000 Koper, Slovenia; kaja.kastelic@iam.upr.si</p> <p><sup>2</sup> University of Primorska, Faculty of Health Sciences, Polje 42, 6310 Izola, Slovenia; <a href="mailto:nejc.sarabon@fvz.upr.si">nejc.sarabon@fvz.upr.si</a></p> <p><sup>3</sup> Physiko- & Rheumatherapie, Institute for Physical Medicine and Rehabilitation, 3100 St. Pölten, Austria; stefan.loefler@kern-reha.at</p> <p><sup>4</sup> Ludwig Boltzmann Institute for Rehabilitation Research, Neugebäudeplatz 1, 3100 St. Pölten, Austria; christian.hofer@rehabilitationresearch.eu</p> <p><sup>5</sup> InnoRenew CoE, Livade 6, 6310 Izola, Slovenia</p>
Figure 81-93. Hyperaspis esmeraldas. 81-84 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 81-93. Hyperaspis esmeraldas. 81-84) Habitus views. 85) Habitus views variations. 86) Abdomen. 87-91) Male genitalia. 87) Sipho. 88) Enlarged siphonal apex. 89-90) Lateral and ventral views of phallobase. 91) Enlarged view of basal lobe. 92-93) Female genitalia. 92) Genital plates. 93) Espermatheca.
Figure 1-10. Hyperaspis corcovado. 1-4 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 1-10. Hyperaspis corcovado. 1-4) Habitus views. 5) Abdomen. 6-9) Male genitalia. 6) Sipho. 7) Enlarged siphonal apex. 8-9) Lateral and ventral views of phallobase. 10) Antenna.
Figure 41-49. Hyperaspis mimica. 41-44 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 41-49. Hyperaspis mimica. 41-44) Habitus views. 45) Abdomen. 46-48) Male genitalia. 46) Sipho (apex lost). 47) Ventral view of phallobase. 48) Trabes. 49) Antenna.
Figure 94-103. Hyperaspis pectoralis. 94-97 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 94-103. Hyperaspis pectoralis. 94-97) Habitus views. 98) Aabdomen. 99-103) Male genitalia. 99) Sipho. 100) Enlarged siphonal apex. 101) Lateral view of phallobase. 102) Oblique view of phallobase. 103) Ventral view of phallobase.
Figure 59-69. Hyperaspis unimaculosa. 59-62 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 59-69. Hyperaspis unimaculosa. 59-62) Habitus views. 63) Female pronotum. 64) Abdomen. 65) Female genitalia. 66-69) Male genitalia. 66) Sipho. 67) Enlarged siphonal apex. 68-69) Lateral and ventral views of phallobase.
Figure 20-29. Hyperaspis humboldti. 20-23 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 20-29. Hyperaspis humboldti. 20-23) Habitus views. 24) Abdomen. 25-28) Male genitalia. 25) Sipho. 26) Enlarged siphonal apex. 27-28) Lateral and ventral views of phallobase. 29) Antenna.
Figure 11-19. Hyperaspis divaricata. 11-14 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 11-19. Hyperaspis divaricata. 11-14) Habitus views. 15) Abdomen. 16-19) Male genitalia. 16) Sipho. 17) Enlarged siphonal apex. 18-19) Lateral and ventral views of phallobase.
Figure 70-80. Hyperaspis drechseli. 70-73 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 70-80. Hyperaspis drechseli. 70-73) Habitus views. 74) Abdomen. 75-79) Male genitalia. 75) Sipho. 75) Enlarged siphonal apex. 77-78) Lateral and ventral views of phallobase. 79) Enlarged view of basal lobe. 80) Antenna.
Figure 30-40. Hyperaspis luciae. 30-33 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 30-40. Hyperaspis luciae. 30-33) Habitus views. 34) Abdomen. 36-40) Male genitalia. 36) Sipho. 37) Enlarged siphonal apex. 38) Lateral view of phallobase. 39) Oblique view of phallobase. 40) Ventral view of phallobase.
Figure 50-58. Hyperaspis praecipua. 50-53 in Additions to the Hyperaspis Chevrolat (Coleoptera: Coccinellidae) fauna of South American, descriptions of nine new species, and recognition of Hyperaspis pectoralis Crotch as a valid species
Figure 50-58. Hyperaspis praecipua. 50-53) Habitus views. 54) Abdomen. 55-58) Male genitalia. 55) Sipho. 56) Enlarged siphonal apex. 57-58) Lateral and ventral views of phallobase.
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.