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114 results for “Individual variability”
The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices
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Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Fig. 1 in Spatial, Temporal And Individual Variability In The Autumn Diet Of European Hare (Lepus Europaeus) In Hungary
Fig. 1. Localities of the study areas. Study areas are shown as gray patches, the capital (Budapest) by striped gray area, Lake Balaton and Lake Tisza by black ones. Black lines are Hungarian rivers and
Рис. 1. Брачные крики и их инΑивиΑуаΛьная изменчивость у разных виΑов воΛчков: a — I. m. minutes (Celmins 2008; Bruggen 2017; Maffezzoli 2021); b — I. m. dubius (Graff 2012; Davison 2020); c — I. m. payesii (Hesse 2009; Archer 2019; Cockcroft 2020); d — I. sinensis (Piot 2021; Raveendran 2021; Jeff 2022); e — I. e. exilis (Graves 2021; Overholtz 2021); f — I. e. peruvianus (Moore 2003; Krabbe 2015; Arias 2020); g — I. eurythmus (Mark 1997; Lastukhin 2015; Wulf 2016) Fig. 1. Mating calls and their individual variability in different bittern species: a — I. m.minutes Celmins 2008; Bruggen 2017; Maffezzoli 2021); b — I. m. dubius (Graff 2012; Davison 2020); c — I. m. payesii (Hesse 2009; Archer 2019; Cockcroft 2020); d — I. sinensis (Piot 2021; Raveendran 2021; Jeff 2022); e — I. e. exilis (Graves 2021; Overholtz 2021); f — I. e. peruvianus (Moore 2003; Krabbe 2015; Arias 2020); g — I. eurythmus (Mark 1997; Lastukhin 2015; Wulf 2016) in Call repertoire of Bitterns Ixobrychus in Russian Far East
Рис. 1. Брачные крики и их инΑивиΑуаΛьная изменчивость у разных виΑов воΛчков: a — I. m. minutes (Celmins 2008; Bruggen 2017; Maffezzoli 2021); b — I. m. dubius (Graff 2012; Davison 2020); c — I. m. payesii (Hesse 2009; Archer 2019; Cockcroft 2020); d — I. sinensis (Piot 2021; Raveendran 2021; Jeff 2022); e — I. e. exilis (Graves 2021; Overholtz 2021); f — I. e. peruvianus (Moore 2003; Krabbe 2015; Arias 2020); g — I. eurythmus (Mark 1997; Lastukhin 2015; Wulf 2016) Fig. 1. Mating calls and their individual variability in different bittern species: a — I. m.minutes Celmins 2008; Bruggen 2017; Maffezzoli 2021); b — I. m. dubius (Graff 2012; Davison 2020); c — I. m. payesii (Hesse 2009; Archer 2019; Cockcroft 2020); d — I. sinensis (Piot 2021; Raveendran 2021; Jeff 2022); e — I. e. exilis (Graves 2021; Overholtz 2021); f — I. e. peruvianus (Moore 2003; Krabbe 2015; Arias 2020); g — I. eurythmus (Mark 1997; Lastukhin 2015; Wulf 2016)
Text-fig. 9. Mean individual differences in non-metric and metric variables of M1 from respective mean values of extant A. flavicollis, A. sylvaticus and A. uralensis in the Recent samples (left) and fossils of particular Pleistocene biozones (right), superimposed to variation ranges and centroids of the former ones. in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 9. Mean individual differences in non-metric and metric variables of M1 from respective mean values of extant A. flavicollis, A. sylvaticus and A. uralensis in the Recent samples (left) and fossils of particular Pleistocene biozones (right), superimposed to variation ranges and centroids of the former ones.
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 7. Plot of discriminant scores (R1/R2) of individual M1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of total set of characters, both metric and non-metric).
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 8. Plot of discriminant scores (R1/R2) of individual m1 of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (standardized discrimination scores based on nine most significant variables).
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1). in Genus Apodemus In The Pleistocene Of Central Europe: When Did The Extant Taxa Appear?
Text-fig. 6. Plot of discriminant scores (R1/R2) of individual m1 and M1 teeth of Apodemus spp. from particular Pleistocene biozones superimposed onto a plot of variation ranges for the respective variables for the Recent Apodemus sample (based on the discrimination analysis of metric variables of M1 and m1).
Individual variability and versatility in an eco-evolutionary model of avian migration
<p>Seasonal migration is a complex and variable behavior with the potential to promote reproductive isolation. In Eurasian blackcaps (<em>Sylvia atricapilla</em>), a migratory divide in central Europe separating populations with southwest and southeast autumn routes may facilitate isolation, and individuals using new wintering areas in Britain show divergence from Mediterranean winterers. We tracked 100 blackcaps in the wild to characterize these strategies. Blackcaps to the west and east of the divide used predominantly SW and SE directions, respectively, but close to the contact zone many individuals took intermediate (S) routes. At 14.0ºE, we documented a sharp transition from SW to SE migratory directions across only 27 (10–86) km, implying a strong selection gradient across the divide. Blackcaps wintering in Britain took northwesterly migration routes from continental European breeding grounds. They originated from a surprisingly extensive area, spanning 2000 km of the breeding range. British winterers bred in sympatry with SW-bound migrants but arrived 9.8 days earlier on the breeding grounds, suggesting some potential for assortative mating by timing. Overall, our data reveal complex variation in songbird migration and suggest that selection can maintain variation in migration direction across short distances while enabling the spread of a novel strategy across a wide range.</p>
Data from: Drivers of intra-individual spatial variability in methane emissions from tree trunks in upland forest
<p><span>CH</span><sub><span>4</span></sub><span> emissions from </span><span>tree </span><span>trunks in upland forests should be scaled accurately</span><span> in order</span><span> to assess the role of tree trunk</span><span>s</span><span> in </span><span>the </span><span>forest CH<sub>4</sub></span><span> budget. As </span><span>the </span><span>chambers used to measure emission</span><span>s</span><span> cover</span><span> only</span><span> a small part of the tree trunks, </span><span>it is necessary to </span><span>understand the intra-individual spatial variability </span><span>in</span><span> trunk CH<sub>4</sub></span><span> emission</span><span>s.</span> <span>W</span><span>e measured trunk CH<sub>4</sub></span><span> flux at nine locations per individual </span><span>on</span><span> four trees in a cool-temperate upland forest. To </span><span>appreciate</span> <span>the origin of th</span><span>is</span><span> variability and </span><span>the underlying</span><span> processes, we also measured</span><span> the</span><span> potential</span><span> rate of </span><span>CH</span><span>4</span><span> production and CH</span><sub><span>4</span></sub><span> concentration </span><span>at</span><span> sapwood and </span><span>characterized </span><span>wood and bark. Up to 15-fold spatial </span><span>variation in CH<sub>4</sub></span><span> fluxes were observed</span><span> at</span><span> the</span><span> individual</span><span> level</span><span>. This variability </span><span>can be highlighted </span><span>by the variation </span><span>in </span><span>the sapwood CH<sub>4</sub></span><span> concentration which </span><span>was</span><span> further </span><span>explained</span> <span>by </span><span>the variation in CH<sub>4</sub></span><span> production rate. The radial CH<sub>4</sub></span><span> diffusivity calculated from concentration gradient</span><span>s</span><span> and emission</span><span>s</span><span> was not related to the </span><span>measured </span><span>characteristics of </span><span>either </span><span>wood </span><span>or</span><span> bark, </span><span>raising the question of</span><span> the diffusion pathway. We emphasize</span><span>d</span><span> the importance </span><span>of</span><span> sampl</span><span>ing</span><span> trunk CH<sub>4</sub></span><span> flux at multiple locations on the surface of a tree trunk</span> <span>to capture spatial variability, a prerequisite for estimating tree-level CH<sub>4</sub></span><span> emissions.</span></p>
Individual variability in space use near power lines by a long-lived territorial raptor
<p>Evaluating species responses to anthropogenic infrastructures and other habitat changes is often used to assess environmental impacts and to guide conservation actions. However, such studies are generally carried out at the population-level, disregarding inter-individual variability. Here, we investigate population- and individual-level responses towards power lines of a territorial raptor, the Bonelli's eagle Aquila fasciata. We used GPS-PTT tracking data of seventeen adult eagles to model space use as a function of distance to transmission and distribution lines, while accounting for other habitat features known to affect this species. At population level, eagles increased the intensity of space use in the proximity of power lines (up to 1000 m), suggesting an attraction effect. At individual level, some eagles shared the general population attraction pattern, while others showed reduced intensity of space use in the proximity of power lines. These differential responses were unrelated to the sex of individuals, but were affected by the characteristics of the power grid, with a tendency for apparent attraction to be associated with individuals occupying home ranges with a denser network of transmission lines and transmission pylons. However, the study could not rule out the operation of other potentially influential factors, such as individual idiosyncrasies, the spatial distribution of prey availability, and the availability of natural perches and nesting sites. Overall, these results suggest that power lines may drive different behaviors and have differential impacts across individuals, with those attracted to the proximity of power lines potentially facing increased risk of mortality through electrocution and collision, and those avoiding power lines being potentially subject to exclusion effects. More generally, our results reinforce the need to understand individual variability when assessing and mitigating impacts of anthropogenic infrastructures.</p>
Validation of transpulmonary thermodilution variables in hemodynamically stable patients with heart diseases - Individual patient data
<p>This dataset contains individual subject data for hemodynamic measurements assessed in the present study.</p>
Brain signal variability within individually defined alpha frequency range as a marker of cognitive performance
<p><strong>General description</strong></p> <p>Eight subjects with severe acquired brain injury and fourteen healthy participants took part in the study. EEG was recorded at 19 international standard 10-20 system locations with a sampling frequency of 256 Hz, using an ear-linked reference and the <em>Fpz</em> location as ground.</p> <p>For all participants three-minute baseline recordings were obtained, one with eyes open and one with eyes closed. Both groups participated in the same sustained attention task while EEG data was being collected. Healthy participants underwent 10 blocks of 225 trials while ABI patients were given 7 blocks of 225 trials.</p> <p> </p> <p><strong>Content of the zip files uploaded here</strong></p> <p>Each zip file contains matlab files with raw EEG data plus a channel that contains information of the triggers used in the sustained attention task. In addition, log files contain the latency of the experimental events and responses.</p> <p> </p> <p><strong>Description of the task</strong></p> <p>The Continuous Temporal Expectancy Task (CTET), developed by O’Connell <em>et al.</em> (2009), is a computerized task that measures sustained attention. Participants are required to notice and respond with a click of the mouse when an alternating pattern (Fig. 1) is randomly displayed for a longer interval than the regular interval (800ms). Participants practiced the experiment initially to ensure they obtained 100% accuracy at the end of the training trials. Longer target duration should be easily detected during the training block, but challenging when performed over an extended period of time. The healthy group detected the target difference at 1120ms (duration 40% longer than the standard), whereas the ABI group detected a difference at 1200ms latency (duration 50% longer than the standard). The hand that responded to the longer stimulus with a mouse-click was counterbalanced in each block.</p>
Thermal modulation of Zebrafish exploratory statistics reveals constraints on individual behavioral variability
<p><span><strong>Background</strong>: </span>Variability is a hallmark of animal behavior. It contributes to survival by endowing individuals and populations with the capacity to adapt to ever-changing environmental conditions. Intra-individual variability is thought to reflect both endogenous and exogenous modulations of the neural dynamics of the central nervous system. However, how variability is internally regulated and modulated by external cues remains elusive. Here we address this question by analyzing the statistics of spontaneous exploration of freely swimming zebrafish larvae, and by probing how these locomotor patterns are impacted when changing the water temperatures within an ethologically relevant range.<br> <br> <span><strong>Results</strong>: </span>We show that, for this simple animal model, five short-term kinematic parameters - interbout interval, turn amplitude, travelled distance, turn probability and orientational flipping rate - together control the long-term exploratory dynamics. We establish that the bath temperature consistently impacts the means of these parameters, but leave their pairwise covariance unchanged. These results indicate that the temperature merely controls the sampling statistics within a well-defined kinematic space delineated by this robust statistical structure. At a given temperature, individual animals explore the behavioral space over a timescale of tens of minutes, suggestive of a slow internal state modulation that could be externally biased through the bath temperature. By combining these various observations into a minimal stochastic model of navigation, we show that this thermal modulation of locomotor kinematics results in a thermophobic behavior, complementing direct gradient-sensing mechanisms.<br> <br> <span><strong>Conclusions</strong>: </span>This study establishes the existence of a well-defined locomotor space accessible to zebrafish larvae during spontaneous exploration, and quantifies self-generated modulation of locomotor patterns. Intra-individual variability reflects a slow diffusive-like probing of this space by the animal. The bath temperature in turn restricts the sampling statistics to sub-regions, endowing the animal with basic thermophobicity. This study suggests that in Zebrafish, as well as in other ectothermic animals, ambient temperature could be used to efficiently manipulate internal states in a simple and ethological way.</p>
Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis
<p>Test-retest functional connectomes for 32 Healthy Controls and 30 First Episode of Psychosis patients. This dataset was originally used in the following article:</p> <p><strong>(Preprint)</strong> Tepper, Ángeles and Núñez, Javiera Vásquez and Ramirez-Mahaluf, Juan Pablo and Aguirre, Juan Manuel and Barbagelata, Daniella and Maldonado, Elisa and Dellarossa, Camila Díaz and Nachar, Ruben and Gonzalez-Valderrama, Alfonso and Undurraga, Juan and Goñi, Joaquín and Crossley, Nicolas, Intra and Inter-Individual Variability in Functional Connectomes of Patients with First Episode of Psychosis. Available at SSRN: <a href="https://ssrn.com/abstract=4241607">https://ssrn.com/abstract=4241607</a> or <a href="http://dx.doi.org/10.2139/ssrn.4241607">http://dx.doi.org/10.2139/ssrn.4241607</a></p> <p>More details and python code used for analyses can be found in this<strong> <a href="https://github.com/angietep/Inter-and-Intra-Indiv-Variability">GitHub repository</a></strong></p>
Resource-related variables drive individual variation in flowering phenology and mediate population-level flowering responses to climate in an asynchronously reproducing palm
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Individual variability in space use near power lines by a long-lived territorial raptor
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Individual variability and versatility in an eco-evolutionary model of avian migration
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Thermal modulation of Zebrafish exploratory statistics reveals constraints on individual behavioral variability
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Consistent inter-individual variability in movement traits shapes the wild boar movement syndrome
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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.