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641 results for “data validation”
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>
Validation of STS Conceptual Model Designing Approach. DMI374 and DMI747 Data Sets.
<p>Surveys of DMI374 and DMI747 student groups were conducted for the purpose of validating the STS conceptual model designing method. A 5-point and dichotomous Likert questionnaire was used for the survey. The files contain response datasets used in statistical processing.</p>
Observational data to validate an observational tool using inter-rater reliability
<p>The dataset includes a set of observations made from videos in The Observer XT software by 17 observers (16 novice observers and 1 expert observer) to validate an observation grid. The data made available are the observations, without the videos, and do not include any personal information.</p>
Fig. 2. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 2. A haplotype network based on cytochrome oxidase I (CO1) fragment using 112 previously published GenBank sequences and representing 20 genetic clades numbered as in Palandačić et al. (2017, 2020), of which 12 are considered valid species including P. chrysoprasius (Pallas, 1814) and Kuban' Phoxinus (an available species name not known). The lines carry the number of mutational steps (shown in red).
Fig. 1 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 1. Map of the localities of Phoxinus Agassiz, 1835 in river drainages of the northern and north-eastern coasts of the Black Sea and the Caspian Sea based on numerous published sources (references available in Supp. file 1) and public museum collections (Museum of Zoology, National Museum of Natural History, Kyiv, Ukraine; Natural History Museum, Vienna, Austria; Zoological Research Museum Alexander Koenig, Bonn, Germany). Coloured circles and numbers correspond to genetically examined individuals of clades specified in Palandačić et al. (2017, 2020): yellow circle = P. marsilii, Clade 9; black circles = 'Baltic Phoxinus', Clade 17; grey circles = P. colchicus, Clade 18; purple circle = 'Kuban Phoxinus', Clade 19; pink circle = 'Crimean Phoxinus', Clade 20.
Fig. 4 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 4. DFA based on 11 counts, two coded characters, and 55 relative measurements, for males and females separately (A) and for females only (B). Numbers of samples or clades as in Fig. 3. Abbreviations: f = females; m = males. DFA statistics values: A. Wilks' Lambda 0.00000, approx. F (156.189) = 11.390, p <0.0000. B. Wilks' Lambda 0.00002, approx. F (48.48) = 36.279, p <0.0000 (perfect discrimination).
Fig. 3 in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 3. DFA based on 11 counts and 2 coded characters as in Supp. file 4. Clade 5 = 5a, Danubian tributaries in Bulgaria (Nishava, Beli Vit, and Palakaria samples and P. csikii Hankó, 1922 from type locality). Clade 14 = non-Danubian rivers of the Black Sea coast in Bulgaria (14a, Izvorska, Veleka, Karaagach and Kamchiya) and Turkey (14b, Gönen and Sapanca). Clade 9 = P. marsilii Heckel, 1836. Clade 20 = Crimea, Salhir. DFA statistics values: Wilks' Lambda 0.16694, approx. F (45.1345) = 14.709, p <0.0000 (perfect discrimination).
Fig. 5. A in Novel data support validity of Phoxinus chrysoprasius (Pallas, 1814) (Actinopterygii, Leuciscidae)
Fig. 5. A. Neotype of Phoxinus chrysoprasius (Pallas, 1814), ♂ (ZFMK 93640-59). B. ♀ (ZFMK 93640- 59), 87.2 mm SL, same locality and date as the neotype.
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <p>To run the processing pipeline presented in [1], please follow the instructions on <a href="http://github.com/SetsompopLab/deli-cs">https://github.com/SetsompopLab/deli-cs</a> and download Zenodo datasets <a href="https://doi.org/10.5281/zenodo.7734431">10.5281/zenodo.7734431</a> and <a href="http://doi.org/10.5281/zenodo.7703200">10.5281/zenodo.7703200</a> with testing data and meta data needed for the reconstruction pipeline.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
Data sample INDIP Validation Paper
<p>Standardized data from Mobilise-D HYA dataset (one participant, data from both laboratory and 2.5 hours) and Mobilise-D technical validation dataset (one subject for each cohort - HOA, PD, MS, COPD, CHF, PFF; data from 2.5 hours) are provided in the shared folder, as an example of the the work proposed in the publication "A multi-sensor wearable system for the assessment of diseased gait in real-world conditions" that has been accepted for publication in Frontiers Journal. Please refer to that publication for further information. Please cite that publication if using these data. </p>
VoroIF-GNN training, validation, and testing data
<p>Data used to train, validate, and test the VoroIF-GNN method described in the paper "VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network".</p>
Data from: Spatial localization of anterior precuneus for bodily self validated with brain stimulation
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>The posteromedial cortex (PMC), comprising the precuneus, the posterior cingulate, and the retrosplenial regions, is known to be engaged in various self-referential functions. Recent observations have also suggested a link between PMC dysfunction and self-dissociation. To test the causal relevance of specific PMC locations for self- referential processing, we recruited nine neurosurgical participants with bilaterally implanted PMC electrodes. We applied focal electrical stimulation within discrete PMC sites while probing changes in the participants' subjective states. We found that, in all nine participants, the electrical perturbation of the anterior precuneus (aPCu), but not the other PMC sites, caused apparent dissociative changes in the physical and spatial bodily domain involving head, trunk, and legs. The responsive aPCu sites did not exhibit event-related activation during a cued autobiographical memory recall task. Furthermore, resting-state functional connectivity with functional Magnetic Resonance Imaging data and effective connectivity with single-pulse electrical stimulation procedures confirmed that the responsive aPCu sites were distinct from, while closely connected with, the adjacent PMC nodes of the default mode network (DMN). Based on these data, we conclude that the aPCu is a distinct functional unit within the PMC and causally important for processing self-referential information in the physical and spatial domains. Future larger-scale experimental studies are needed to explore how operations of distinct neuronal populations within the PMC are integral to various cognitive processes that require a reference to self in its various dimension.</p> </div> </div> </div> </div>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"
<p>Data and supplementary materials in support of "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213" </p> <p>It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.</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>
Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
Open the record for dataset details and reuse information.
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
Open the record for dataset details and reuse information.
Data from: Experimental validation of a linear momentum and bluff-body model for high-blockage cross-flow turbine arrays
Open the record for dataset details and reuse information.
ScienceDex guides
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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.