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Dataset results
42 results for “Structural validity”
Data from: Genetic structuring among colonies of a pantropical seabird: Implication for subspecies validation and conservation
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Data from: Validating two-dimensional leadership models on three-dimensionally structured fish schools
Identifying leader-follower interactions is crucial for understanding how a group decides where or when to move, and how this information is transferred between members. Although many animal groups have a three-dimensional structure, previous studies investigating leader-follower interactions have often ignored vertical information. This raises the question whether commonly used two-dimensional leader-follower analyses can be used justifiably on groups that interact in three dimensions. To address this we quantified the individual movements of banded tetra fish (Astyanax mexicanus) within shoals by computing the three-dimensional trajectories of all individuals using a stereo-camera technique. We used these data firstly to identify and compare leader-follower interactions in two and three dimensions, and secondly to analyse leadership with respect to an individual's spatial position in three dimensions. We show that for 95% of all pairwise interactions leadership identified through two-dimensional analysis matches that identified through three-dimensional analysis, and we reveal that fish attend to the same shoalmates for vertical information as they do for horizontal information. Our results therefore highlight that three-dimensional analyses are not always required to identify leader-follower relationships in species that move freely in three-dimensions. We discuss our results in terms of the importance of taking species' sensory capacities into account when studying interaction networks within groups.
Data from: Validation of network communicability metrics for the analysis of brain structural networks.
Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.
Data from: An active-radio-frequency-identification system capable of identifying co-locations and social-structure: validation with a wild free-ranging animal
Behavioural events that are important for understanding sociobiology and movement ecology are often rare, transient and localised, but can occur at spatially distant sites e.g. territorial incursions and co-locating individuals. Existing animal tracking technologies, capable of detecting such events, are limited by one or more of: battery life; data resolution; location accuracy; data security; ability to co-locate individuals both spatially and temporally. Technology that at least partly resolves these limitations would be advantageous. European badgers (Meles meles L.), present a challenging test-bed, with extra-group paternity (apparent from genotyping) contradicting established views on rigid group territoriality with little social-group mixing. In a proof of concept study we assess the utility of a fully automated active-radio-frequency-identification (aRFID) system combining badger-borne aRFID-tags with static, wirelessly-networked, aRFID-detector base-stations to record badger co-locations at setts (burrows) and near notional border latrines. We summarise the time badgers spent co-locating within and between social-groups, applying network analysis to provide evidence of co-location based community structure, at both these scales. The aRFID system co-located animals within 31.5 m (adjustable) of base-stations. Efficient radio transmission between aRFIDs and base-stations enables a 20 g tag to last for 2–5 years (depending on transmission interval). Data security was high (data stored off tag), with remote access capability. Badgers spent most co-location time with members of their own social-groups at setts; remaining co-location time was divided evenly between intra- and inter-social-group co-locations near latrines and inter-social-group co-locations at setts. Network analysis showed that 20–100% of tracked badgers engaged in inter-social-group mixing per week, with evidence of trans-border super-groups, that is, badgers frequently transgressed notional territorial borders. aRFID occupies a distinct niche amongst established tracking technologies. We validated the utility of aRFID to identify co-locations, social-structure and inter-group mixing within a wild badger population, leading us to refute the conventional view that badgers (social-groups) are territorial and to question management strategies, for controlling bovine TB, based on this model. Ultimately aRFID proved a versatile system capable of identifying social-structure at the landscape scale, operating for years and suitable for use with a range of species.
Data from: Identification and validation of single nucleotide polymorphisms as tools to detect hybridization and population structure in freshwater stingrays
Single nucleotide polymorphism (SNP) markers were identified and validated for two stingrays species, Potamotrygon motoro and Potamotrygon falkneri, using double digest restriction-site associated DNA (ddRAD) reads using 454-Roche technology. A total of 226 774 reads (65.5 Mb) were obtained (mean read length 289 ± 183 bp) detecting a total of 5399 contigs (mean contig length: 396 ± 91 bp). Mining this data set, a panel of 143 in silico SNPs was selected. Eighty-two of these SNPs were successfully validated and 61 were polymorphic: 14 in P. falkneri, 21 in P. motoro, 3 in both species and 26 fixed for alternative variants in both species, thus being useful for population analyses and hybrid detection.
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.
To Develop and Validate a Structured Exercise Protocol and Its Efficacy in Sub Acromial Impingement Patients
ClinicalTrials.gov study NCT06107985. IPD Sharing: NO. Countries: 0. Publications: 5.
Data from: Identification and validation of single nucleotide polymorphisms as tools to detect hybridization and population structure in freshwater stingrays
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Data from: Validation of network communicability metrics for the analysis of brain structural networks.
Open the record for dataset details and reuse information.
Data from: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Open the record for dataset details and reuse information.
Data from: Validating two-dimensional leadership models on three-dimensionally structured fish schools
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Data from: An active-radio-frequency-identification system capable of identifying co-locations and social-structure: validation with a wild free-ranging animal
Open the record for dataset details and reuse information.
LBA-ECO TG-07 Forest Structure Measurements for GLAS Validation: Santarem 2004
This data set provides the results of a GLAS (the Geoscience Laser Altimeter System) forest structure validation survey conducted in Santarem and Sao Jorge, Para during November 2004 (Lefsky et al., 2005). DBH, total height, commercial height, canopy width and canopy class description were measured for 11 primary forest sites in Santarem along two 75m transects per GLAS measurement. For 10 secondary forest sites in Sao Jorge, the number of stems 0-2cm, 2-5cm, 5-10cm, and greater than 10cm were measured. For all stems greater than 10cm the DBH was measured, and for all sites, the maximum height was recorded. The basal area was calculated for all trees with DBH greater than 10cm within our transects, and biomass was calculated using the Brown, 1997 formula.Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage.
Validation of Retinal Oximetry in Glaucoma Patients: a Structural and Functional Correlation
ClinicalTrials.gov study NCT01391247. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Discovery and Validation of Protein Structural Complexes in Circulating Biofluids As Novel Biomarkers for Early Diagnosis, Prognosis and Therapeutic Management of Patients Affected by Neurodegenerativ
ClinicalTrials.gov study NCT06803784. IPD Sharing: NO. Countries: 1. Publications: 0.
Validity of Experts and ScanNav Anatomy PNB When Identifying Sono-anatomical Structures for Ultrasound-Guided Regional Anaesthesia
ClinicalTrials.gov study NCT04983771. IPD Sharing: NO. Countries: 1. Publications: 0.
Reliability and Validity of a Digital Version of a Structured Interview for Children and Adolescents (Kinder-eDIPS)
ClinicalTrials.gov study NCT06011642. IPD Sharing: NO. Countries: 1. Publications: 0.
Reliability and Validity of the DIPS (Structured Interview) in a Community Sample
ClinicalTrials.gov study NCT04475055. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
RNA structural models used to train, test and validate lociPARSE
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Structural Validity and Inter-rater Reliabitiliy of the Ataxia Trunk, Lower And Upper Extremity Scale (ATLAS)
ClinicalTrials.gov study NCT07079332. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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.