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29 results for “heterogeneous detection”
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c).
Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes - Datasets
<p>Datasets associated with manuscript "Cascadia Subduction Zone Fault Heterogeneities from Newly Detected Small Earthquakes" by Morton et al. (2023), submitted to the <em>Journal of Geophysical Research: Solid Earth</em>. Three datasets are present in this upload:</p> <p><strong>ds01.xlsx</strong>: Catalog of 5,282 detected earthquakes along the Cascadia subduction margin, ordered by time. Events were located using Hypoinverse (Klein, 2002). Columns of the catalog are: Cascadia Initiative deployment year, Origin Time String (YYYYMMDDhhmmss), Origin Time (Year, Month, Day, Hour, Minute, Second), Latitude, Longitude, Event Depth (km), Duration Magnitude (Md), Number of P and S arrival picks with weights > 0.1, Maximum Azimuthal Gap (deg.), Distance to the Nearest Station (km), Travel Time Residual RMS (s), Horizontal Location Error (ERH; km), Vertical Location Error (ERZ; km), Focal Mechanism if applicable (Strike, Dip, Rake; deg.), Plate Designation (Slab, Interface, or Upper Plate), and Previous Existence in Regional Catalogs. Duration magnitudes that could not be constrained are listed as -9. Detected earthquakes that had previously been reported in other catalogs are listed as "Catalog" or "Stone" form the regional or Stone et al. (2018) catalogs, respectively; Those used as template events are marked as "T" or "ST", for those from regional catalogs or the Stone et al. (2018) catalog, respectively, in the last column.</p> <p><strong>ds02.xlsx</strong>: Table of earthquakes chosen as template events for subspace scanning from regional (NEIC, ANF, PNSN, CNDC) and Stone et al. (2018) catalogs. Columns of the template event table are: Catalog Source (T for regional, ST for Stone et al. 2018), Cascadia Initiative (CI) Deployment Year, Template Cluster ID, Date, Time (UTC), Catalog Location (Latitude, Longitude, Depth), Catalog Magnitude, and Whether the Template Event was Detected. Some of the templates were detected but were not included in the final catalog because the travel time residual RMS was greater than 1s and are noted in the table as "poorly located".</p> <p><strong>ds03.xlsx</strong>: Table of seismic stations used in subspace detection scanning, identified by the CI deployment year, Template Cluster ID, Station SEED, and Network Codes. Stations are listed with the corresponding high-pass (HP) or band-pass (BP) filter applied before scanning to maximize the signal-to-noise ratio.</p> <p>References</p> <p>Klein, F. W. (2002). <em>User’s Guide to HYPOINVERSE-2000, a Fortran Program to Solve for Earthquake Locations and Magnitudes</em> (Open File Report 02-171). U.S Geological Survey. https://doi.org/10.3133/ofr02171</p> <p>Stone, I., Vidale, J. E., Han, S., & Roland, E. (2018). Catalog of off-shore seismicity in Cascadia: Insights into the regional distribution of microseismicity and its relation to subduction processes. <em>Journal of Geophysical Research: Solid Earth, 123</em>, 1–12. https://doi.org/10.1002/2017JB014966</p>
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
<p>As species richness varies along the tree of life, there is a great interest in identifying factors that affect the rates by which lineages speciate or go extinct. To this end, theoretical biologists have developed a suit of phylogenetic comparative methods that aim to identify where shifts in diversification rates had occurred along a phylogeny and whether they are associated with some traits. Using these methods, numerous studies have predicted that speciation and extinction rates vary across the tree of life. In this study we show that asymmetric rates of sequence evolution rates lead to systematic biases in the inferred phylogeny, which in turn lead to erroneous inferences regarding lineage diversification patterns. The results demonstrate that as the asymmetry in sequence evolution rates increases, so does the tendency to select more complicated models that include the possibility of diversification rate shifts. These results thus suggest that any inference regarding shifts in diversification pattern should be treated with great caution, at least until any biases regarding the molecular substitution rate have been ruled out.</p>
Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
<p>1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.</p> <p>2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.</p> <p>3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km<sup>2</sup> sampling sites across a <em>P. montanus</em> hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.</p> <p>4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.</p>
Data from: The importance of population heterogeneities in detecting social learning as the foundation of animal cultural transmission
<p class="MsoNormal"><span>High levels of within-population behavioural variation can have drastic demographic consequences, thus changing the evolutionary fate of populations. A major source of within-population heterogeneity is personality. Nonetheless, it is still relatively rarely accounted for in social learning studies that constitute the most basic process of cultural transmission. Here, we perform in female mosquitofish (<em>Gambusia holbrooki</em>) a social learning experiment in the context of mate choice, a situation called mate copying, and for which there is strong evidence that it can lead to the emergence of persistent traditions of preferring a given male phenotype. </span><span class="TexteCourant1Car"><span>When accounting for the </span></span><span>global</span><span class="TexteCourant1Car"> <span>tendency of females to prefer lager males </span></span><span>but ignoring differences in personality we detected no evidence for mate copying. However, when accounting for the bold-shy dichotomy, we found that bold females did not show any evidence for mate copying, while shy females showed significant amounts of mate copying. This illustrates how the presence of variation in personality can hamper our capacity to detect mate copying. We conclude that mate copying may be more widespread than we thought because many studies ignored the presence of within-population heterogeneities.</span></p>
Detecting and reducing heterogeneity of error in acoustic classification: Data
<ol> <li>Passive acoustic monitoring can be an effective method for monitoring species, allowing the assembly of large audio datasets, removing logistical constraints in data collection, and reducing anthropogenic monitoring disturbances. However, the analysis of large acoustic datasets is challenging, and fully automated machine-learning processes are rarely developed or implemented in ecological field studies. One of the greatest uncertainties hindering the development of these methods is spatial generalisability – can an algorithm trained on data from one place be used elsewhere?</li> <li>We demonstrate that heterogeneity of error across space is a problem that could go undetected using common classification accuracy metrics. Secondly, we develop a method to assess the extent of heterogeneity of error in a random forest classification model for six Amazonian bird species. Finally, we propose two complementary ways to reduce heterogeneity of error, by (i) accounting for it in the thresholding process and (ii) using a secondary classifier that uses contextual data.</li> <li>We found that using a thresholding approach that accounted for heterogeneity of precision error reduced the coefficient of variation of the precision score from a mean of 0.61±0.17 (SD) to 0.41±0.25 in comparison to the initial classification with threshold selection based on F-score. The use of a secondary, contextual classification with thresholding selection accounting for heterogeneity of precision reduced it further still, to 0.16±0.13, and was significantly lower than the initial classification in all but one species. Mean average precision scores increased, from 0.66±0.4 for the initial classification, to 0.95±0.19, a significant improvement for all species.</li> <li>We recommend assessing - and if necessary correcting for - heterogeneity of precision error when using automated classification on acoustic data to quantify species presence as a function of an environmental, spatial or temporal predictor variable.</li> </ol>
Companion data of Detection, Evaluation and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters
<p>This is the companion data repository for the paper entitled <strong>Detection, Evaluation, and Mitigation of Resource Affinity and Communication Contention Problems in a Task-Based Runtime over Heterogeneous Clusters</strong> by Lucas Leandro Nesi and Lucas Mello Schnorr. The manuscript has been accepted for publication in the <a href="http://wscad.sbc.org.br/current/index.html">WSCAD 2020</a>.</p>
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
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Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
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Detecting and reducing heterogeneity of error in acoustic classification: Data
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Data and code from: Addressing widespread detection heterogeneity in avian occupancy modeling using passive acoustic surveys
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Data from: The importance of population heterogeneities in detecting social learning as the foundation of animal cultural transmission
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Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS, and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.
Dataset, models and code for "Automating global landslide detection with heterogeneous ensemble deep-learning classification"
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Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
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Data from: Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes
The spatial structure of the environment (e.g., the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multi-scale habitat structure common in nature. Here, we use simulations to pursue two goals: (1) we explore how landscape heterogeneity, dispersal ability, and selection affect the strength of local adaptation, and (2) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs, and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.
Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods
Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time-to-first-detection (TTD) occupancy models have been proposed as a cost-effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy-detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala (Phascolarctos cinereus) populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness-of-fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy-detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness-of-fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy-detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.
HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications
<p>HeteroFuzz supplementary material.</p>
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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)
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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.