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911 results for “Temporal data”
Data from: Treefrogs exploit temporal coherence to form perceptual objects of communication signals
<p>For many animals, navigating their environment requires an ability to organize continuous streams of sensory input into discrete "perceptual objects" that correspond to physical entities in visual and auditory scenes. The human visual and auditory systems follow several Gestalt laws of perceptual organization to bind constituent features into coherent perceptual objects. A largely unexplored question is whether nonhuman animals follow similar Gestalt laws in perceiving behaviorally relevant stimuli, such as communication signals. We used females of Cope's gray treefrog (<em>Hyla chrysoscelis</em>) to test the hypothesis that temporal coherence – a powerful Gestalt principle in human auditory scene analysis – promotes perceptual binding in forming auditory objects of species-typical vocalizations. According to the principle of temporal coherence, sound elements that start and stop at the same time or that modulate coherently over time are likely to become bound together into the same auditory object. We found that the natural temporal coherence between two spectral components of advertisement calls promotes their perceptual binding into auditory objects of advertisement calls. Our findings confirm the broad ecological validity of temporal coherence as a Gestalt law of auditory perceptual organization guiding the formation of biologically relevant perceptual objects in animal behavior.</p>
Data from: Trait matching and phenological overlap increase the spatio-temporal stability and functionality of plant-pollinator interactions
<p>Morphology and phenology influence plant-pollinator network structure, but whether they generate more stable pairwise interactions with higher pollination success is unknown. Here we evaluate the importance of morphological trait matching, phenological overlap and specialisation for the spatio-temporal stability (measured as variability) of plant-pollinator interactions and for pollination success, while controlling for species abundance. To this end, we combined a six-year plant-pollinator interaction dataset, with information on species traits, phenologies, specialisation, abundance and pollination success, into structural equation models. Interactions among abundant plants and pollinators with well-matched traits and phenologies formed the stable and functional backbone of the pollination network, whereas poorly-matched interactions were variable in time and had lower pollination success. We conclude that phenological overlap could be more useful for predicting changes in species interactions than species abundances, and that non-random extinction of species with well-matched traits could decrease the stability of interactions within communities and reduce their functioning.</p>
Data from: Spatial and temporal patterns of environmental DNA detection to inform sampling protocols in lentic and lotic systems.
<p>The development of efficient sampling protocols for the capture of environmental DNA (eDNA) could greatly help improve accuracy of occupancy monitoring for species that are difficult to detect. However, the process of developing a protocol in situ is complicated for rare species by the fact that animal locations are often unknown. We tested sampling designs in lake and stream systems to determine the most effective eDNA sampling protocols for two rare species: the Sierra Nevada yellow-legged frog (<i>Rana sierrae</i>) and the foothill yellow-legged frog (<i>R. boylii</i>). We varied water volume, spatial sampling, and seasonal timing in lakes and streams; in lakes we also tested multiple filter types. We found that filtering 2 L versus 1 L increased the odds of detection in streams 5.42X (95% CI: 3.2-9.19X) in our protocol, from a probability of 0.51 to 0.85 per technical replicate. Lake sample volumes were limited by filter clogging and we found no effect of volume or filter type. Sampling later in the season increased the odds of detection in streams by 1.96X for every 30 days (95% CI: 1.3 - 2.97X) but there was no effect for lakes. Spatial autocorrelation of the quantity of yellow-legged frog eDNA captured in streams between 100 and 200 m, indicating that sampling at close intervals is important.</p>
Data: Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
<p>We make available all the brain network data, and metadata of 83 patients and 29 healthy controls included in our study.</p> <p>Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, and Peter N. Taylor "<em>Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy.</em>" Epilepsia 2021 <em>doi:10.1111/epi.16819</em>.</p> <p>Methodological details on MRI acquisition and data processing are provided in our manuscript. We request users to kindly cite our article and data appropriately.</p>
Data from: Transmission and temporal dynamics of anther-smut disease (Microbotryum) on alpine carnation (Dianthus pavonius)
1. Theory has shown that sterilizing diseases with frequency-dependent transmission (characteristics shared by many sexually transmitted diseases) can drive host populations to extinction. 2. Anther-smut disease (caused by Microbotryum sp.) has become a model plant pathogen system for studying the dynamics of vector and sexually transmitted diseases: infected individuals are sterilized, producing spores instead of pollen, and the disease is spread between reproductive individuals by insect pollinators. We investigated anther-smut disease in a heavily infected population of Dianthus pavonius (alpine carnation) over an eight-year period to determine disease impacts on host population dynamics. 3. Over the eight years, disease prevalence remained consistently high (>40%) while the host population numbers declined by over 50%. 4. The observed rate of vector transmission to reproductive, adult hosts was inadequate to explain the high disease prevalence. Additional density-dependent aerial transmission to highly susceptible juveniles, indicated from experimental field and greenhouse studies, is likely to play a key role in maintaining the high disease prevalence. 5. Epidemiological models that accounted for the mixed transmission mode predicted an eventual decline in disease. 6. Synthesis: Our results demonstrate that high prevalence of a sterilizing disease does not necessarily drive host populations towards extinction and also highlights the importance of demographic studies for establishing the presence of alternative transmission modes.
Data from: Ecological and social factors constrain spatial and temporal opportunities for mating in a migratory songbird
Many studies of sexual selection assume that individuals have equal mating opportunities and that differences in mating success result from variation in sexual traits. However, the inability of sexual traits to explain variation in male mating success suggests that other factors moderate the strength of sexual selection. Extrapair paternity is common in vertebrates and can contribute to variation in mating success and thus serves as a model for understanding the operation of sexual selection. We developed a spatially explicit, multifactor model of all possible female-male pairings to test the hypothesis that ecological (food availability) and social (breeding density, breeding distance, and the social mate's nest stage) factors influence an individual's opportunity for extrapair paternity in a socially monogamous bird, the black-throated blue warbler, Setophaga caerulescens. A male's probability of siring extrapair young decreased with increasing distance to females, breeding density, and food availability. Males on food-poor territories were more likely to sire extrapair young, and these offspring were produced farther from the male's territory relative to males on food-abundant territories. Moreover, males sired extrapair young mostly during their social mates' incubation stage, especially males on food-abundant territories. This study demonstrates how ecological and social conditions constrain the spatial and temporal opportunities for extrapair paternity that affect variation in mating success and the strength of sexual selection in socially monogamous species.
Data from: Spatio-temporally explicit model averaging for forecasting of Alaskan groundfish catch
(1) Fisheries management is dominated by the need to forecast catch and abundance of commercially and ecologically important species. The influence of spatial information and environmental factors on forecasting error is not often considered. We propose a forecasting method called spatio-temporally explicit model averaging (STEMA) to combine spatial and temporal information through model averaging. (2) We examine the performance of STEMA against two popular forecasting models and a modern spatial prediction model: the autoregressive integrated moving averages (ARIMA) model, the Bayesian hierarchical model, and the varying coefficient model. We focus on applying the methods to four species of Alaskan groundfish for which only catch data are available. (3) Our method reduces forecasting errors significantly for most of the tested models when compared to ARIMAX, Bayesian, and varying coefficient methods. We also consider the effect of sea surface temperature (SST) on the forecasting of catch, as multiple studies reveal a potential influence of water temperature on the survival and growth of juvenile groundfish. For most of the preferred models, inclusion of SST in the model improved forecasting of catch. (4) It is advisable to consider both spatial information and relevant environmental factors in forecasting models to obtain more accurate projections of population abundance. The STEMA method is capable of accounting for spatial information in forecasting and can be applied to various types of data because of its flexible varying coefficient model structure. It is therefore a suitable forecasting method for application to many fields including ecology, epidemiology, and climatology.
Data from: Spatio-temporal dynamics of impulse responses to figure motion in optic flow neurons
White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response.
Data from: How neighbourhood interactions control the temporal stability and resilience to drought of trees in mountain forests
<p>1. Over the coming decades, the predicted increase in frequency and intensity of extreme events such as droughts is likely to have a strong effect on forest functioning. Recent studies have shown that species mixing may buffer the temporal variability of productivity. However, most studies have focused on temporal stability of productivity, while species mixing may also affect forest resilience to extreme events. Our understanding of mechanisms underlying species mixing effects on forest stability and resilience remains limited because we ignore how changes from intraspecific to interspecific interactions in the neighbourhood of a given tree might affect its stability and resilience to extreme drought (i.e. response during and after this drought). This is crucial to better understand forests' response to climate change and how diversity may help maintain forest functioning.</p> <p>2. Here we analysed how local intra‐ or interspecific interactions may affect the temporal stability and resilience to drought of individual trees in French mountain<br> forests, using basal area increment data over the previous 20 years for Fagus sylvatica, Abies alba and Quercus pubescens. We analysed the effect of interspecific<br> competition on (a) the temporal stability and (b) the resilience to drought (resistance and recovery) of individual tree radial growth.</p> <p>3. We found no significant interspecific competition effect on temporal stability, but species‐specific effects on tree growth resilience to drought. There was a positive<br> effect of heterospecific proportion on the drought resilience of Q. pubescens, a negative effect for A. alba and no effect for F. sylvatica. These differences may be<br> related to interspecific differences in water use or rooting depth.</p> <p>4. Synthesis: In this study, we showed that stand composition influences individual tree growth resilience to drought, but this effect varied depending on the species<br> and its physiological responses. Our study also highlighted that a lack of biodiversity effect on long‐term stability might hide important effects on short‐term<br> resilience to extreme climatic events. This may have important implications in the face of climate change.</p>
Data from: How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands
Tropical peatlands now emit hundreds of megatons of carbon dioxide per year because of human disruption of the feedbacks that link peat accumulation and groundwater hydrology. However, no quantitative theory has existed for how patterns of carbon storage and release accompanying growth and subsidence of tropical peatlands are affected by climate and disturbance. Using comprehensive data from a pristine peatland in Brunei Darussalam, we show how rainfall and groundwater flow determine a shape parameter (the Laplacian of the peat surface elevation) that specifies, under a given rainfall regime, the ultimate, stable morphology, and hence carbon storage, of a tropical peatland within a network of rivers or canals. We find that peatlands reach their ultimate shape first at the edges of peat domes where they are bounded by rivers, so that the rate of carbon uptake accompanying their growth is proportional to the area of the still-growing dome interior. We use this model to study how tropical peatland carbon storage and fluxes are controlled by changes in climate, sea level, and drainage networks. We find that fluctuations in net precipitation on timescales from hours to years can reduce long-term peat accumulation. Our mathematical and numerical models can be used to predict long-term effects of changes in temporal rainfall patterns and drainage networks on tropical peatland geomorphology and carbon storage.
Towards the formal verification of data-intensive applications through metric temporal logic
p>The dataset consists of a set of model descriptions representingnbsp;span>Storm topologies. It is designed on purpose to show the approach presented in the paper quot;/span>span>Towards the formal verification of data-intensive applications through m/span>span>etric/span>span>nbsp;temporalnbsp;/span>span>logicquot; (F. Marconi, M.M. Bersani, M. Erascu and M. Rossi) which focuses on the analysisnbsp;/span>span>of bottleneck nodes of data intensive applications implemented with Storm./span>/p>
Data from: Evaluating temporal turnover in avian species richness in a Mediterranean semiarid region: different responses to elevation and forest cover
<p><span><strong>Aim</strong>.</span><span> When studying the effects of global change on biodiversity, it is far more common for the effects of climate change and land-use changes to be assessed separately rather than jointly. However, the effects of land-use changes in recent decades on species richness in areas affected by climate change have been less studied. </span><span>We assess the temporal turnover in species richness of an avian community between a historical period and a modern one as a consequence of global change.</span></p> <p><span><strong>Location</strong>. </span><span>Semiarid Mediterranean ecosystem (Southeastern Spain). </span></p> <p><span><strong>Method</strong>.</span><span> We fitted a hierarchical multi-species occupancy </span><span>model for each period (</span><span>1991-1992, and 2012-2017)</span><span>, obtaining avian species-specific estimates of occupancy probability in relation to environmental covariates </span><span>(elevation and forest cover)</span><span>. </span><span>We analyze the relationships between changes in the bird community and environmental variables, analysing the temporal turnover of the species richness and the richness-based species-exchange ratio.</span> </p> <p><span><strong>Results</strong>.</span> <span>The estimated species richness accounting for detectability was higher than observed species richness, and decreased in the </span><span>more recent </span><span>period. Following our hypotheses, we observed a dual pattern of species richness increase associated with different elevations, showing different species turnover rates due to the joint effects of climate change and land-use change. There is a trend toward greater species richness with higher elevations that is associated with climate change, where the species turnover rate is low. Also, species richness increased towards lower elevations, but with a high turnover rate. The latter can be due to species expansions through</span><span>ou</span><span>t new habitat configurations in bordering forest systems associated with anthropic land-use changes. </span></p> <p><span><strong>Conclusions</strong>.</span><span> Our study is of great interest to understand the temporal turnover of avian species richness associated with areas experiencing both climate and land-use change.</span></p>
Data from: Temporal dynamics in psychological assessments: a novel dataset with scales and response times
<p>This dataset is collected from February 27 to March 17, 2021, it includes responses from 24,292 students to four recognized psychological scales: PHQ-9, GAD-7, ISI, and PSS. A unique aspect of this dataset is the inclusion of response time data, which reflects the duration students took to answer each question. This temporal information offers a new dimension in understanding respondent behavior and enhances the reliability of these scales.</p>
Data from: Seeking temporal refugia to heat stress: Increasing nocturnal activity despite predation risk
<p>Flexibility in activity timing may enable organisms to quickly adapt to environmental changes. Under global warming, diurnally adapted endotherms may achieve a better energy balance by shifting their activity towards cooler nocturnal hours. However, this shift may expose animals to new or increased environmental challenges (e.g., increased predation risk, reduced foraging efficiency). We analysed a large dataset of activity data from 47 ibex (<em>Capra ibex</em>) in two protected areas, characterized by varying levels of predation risk (presence vs absence of the wolf – <em>Canis lupus</em>). We found that ibex increased nocturnal activity following warmer days and during brighter nights. Despite the considerable sexual dimorphism typical of this species and the consequent different predation-risk perception, males and females demonstrated consistent responses to heat in both predator-present and predator-absent areas. This supports the hypothesis that shifting activity towards nighttime may be a common strategy adopted by diurnal endotherms in response to global warming. As nowadays different pressures are pushing mammals towards nocturnality, our findings emphasize the urgent need to integrate knowledge of temporal behavioural modifications into management and conservation planning.</p>
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
Data from: environmental DNA reveals temporal variation in mesophotic reefs of the Humboldt upwelling ecosystems of central Chile: towards a baseline for biodiversity monitoring of unexplored marine habitats
<p>Temperate mesophotic reef ecosystems (TMREs) are among the least known marine habitats. Information on their diversity and ecology is geographically and temporally scarce, especially in highly productive large upwelling ecosystems. Lack of information remains an obstacle to understanding the importance of TMREs as habitats, biodiversity reservoirs and their connections with better-studied shallow reefs. Here, we use environmental DNA (eDNA) from water samples to characterize the community composition of TMREs on the central Chilean coast generating the first baseline for monitoring the biodiversity of these habitats. We analyzed samples from two depths (30 and 60m) over four seasons (spring, summer, autumn, and winter) and at two locations approximately 16 km apart. We used a panel of three metabarcodes, two that target all eukaryotes (18S rRNA and mitochondrial COI) and one specifically targeting fishes (16S rRNA). All panels combined encompassed eDNA assigned to 42 phyla, 90 classes, 237 orders, and 402 families. The highest family richness was found for the phyla Arthropoda, Bacillariophyta and Chordata. Overall, family richness was similar between depths but decreased during summer, a pattern consistent at both locations. Our results indicate that the structure (composition) of the mesophotic communities varied predominantly with seasons. We analyzed further the better-resolved fish assemblage and compared eDNA with other visual methods at the same locations and depths. We recovered eDNA from nineteen genera of fish, six of these have also been observed on towed underwater videos, while thirteen were unique to eDNA. We discuss the potential drivers of seasonal differences in community composition and richness. Our results suggest that eDNA can provide valuable insights for monitoring TMRE communities but highlight the necessity of completing reference DNA databases available for this region.</p>
Data for: Body mass mediates spatio-temporal responses of mammals to human frequentation across Italian protected areas
<p>Protected areas (PAs) networks are a pivotal tool to fight biodiversity loss, yet they often need to balance the mission of nature conservation with the socio-economic need of giving opportunity for outdoor recreation. Recreation in natural areas is important for human health in an urbanised society, but can prompt behavioural modifications in wild animals. Rarely, however, have these responses being studied across multiple PAs and using standardized methods. We deployed a systematic camera trapping protocol at over 200 sites to sample medium and large mammals in four PAs within the European Natura 2000 network to assess their spatio-temporal responses to human frequentation, proximity to towns, amount of open habitat, and topographical variables. By applying multi-species and single-species models on the number of diurnal, crepuscular, and nocturnal detections, and a multi-species model on nocturnality index, we estimated both species-specific and meta-community level effects, finding that increased nocturnality appeared the main strategy that the mammal meta-community used to cope with human disturbance. However, responses in the diurnal, crepuscular, and nocturnal site use were mediated by species' body mass, with larger species exhibiting avoidance of humans and smaller species more opportunistic behaviours. Our results show the effectiveness of standardised sampling and provide insights for planning the expansion of PA networks as foreseen by the Kunming-Montreal biodiversity agreement.</p>
Raw data for journal article: "Intracochlear pressure and temporal bone motion interaction under bone conduction stimulation""
<p>This is a data set containing the raw data for figures 3-6 from the journal article:</p> <p>"Intracochlear pressure and temporal bone motion interaction under bone conduction stimulation"</p> <p>Original article DOI: 10.1016/j.heares.2023.108818</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/37267833/</p> <p> </p> <p>The Fig 4-8 data are contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p>Fig 9-10 data are 3D velocity data for 3 cadaver heads (CH1-3), each recorded at the left (L) and right (R) side, all within the folder "Velocity data".</p> <p>This data are stored within a folder structure indicating the stimulation condition (defined in the journal article). For example "Velocity data\CH1-L\Stim @ BAHA" contains data for the left side of cadaver head 1 (CH1) with stimulation "Stim @ BAHA", as defined in the journal article above. </p> <p>For each combination of cadaver head and stimulation condition there is a TXT file (comma delimited) for the real and imaginary data at each stimulation frequency, and orthogonal velocity axis (X,Y,Z based on the anatomical coordinate system defined in the journal article) as well as the combined (maximum) velocity vector. The data set also includes a TXT file with the position (in same coordinate system the velocity data) of each measurement point and a list of stimulation frequencies.</p> <p>The data set also includes the geometry of the skull bone surface of each cadaver head (CH) in the form of STL file, all within the folder "Skull surface data".</p>
User evaluation results for a Wikidata-centric tool for temporal data in Humanities and Cultural Heritage (June 2024): raw tabular result data and web forms for two questionnaires from five online focus group workshops
<p><strong>Introduction</strong></p> <p>This resource is created for the article: "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" in Semantic Web Journal Special issue on the Semantic Web and Ontology Design for Cultural Heritage. It contains materials use for the user evaluation (June 2024) of a Wikidata visualization tool (<a title="ReKisstory" href="https://rekisstory.labs.vu.nl/" target="_blank" rel="noopener">ReKisstory</a>) described in the article. </p> <p><strong>Summary of the user evaluation</strong></p> <p>The structrue of the user evalution is summarized in the table below:</p> <table style="border-collapse: collapse; width: 99.9708%;"><colgroup><col style="width: 28.0622%;"><col style="width: 26.893%;"><col style="width: 27.0309%;"><col style="width: 17.9856%;"></colgroup> <tbody> <tr> <td> </td> <td>Objectives</td> <td>Setup </td> <td># of people participating</td> </tr> <tr> <td>Pre-workshop survey</td> <td>Understanding potential user profiles before workshop</td> <td>Online questionnaire (Q1)</td> <td>51</td> </tr> <tr> <td>Workshop (Focus Group)</td> <td>Introducing and testing the tool. Obtaining feedback about it</td> <td>Online demo, testing, discussion</td> <td>16</td> </tr> <tr> <td>Post-workshop survey</td> <td>Understanding user needs after testing</td> <td>Online questionnaire (Q2)</td> <td>11</td> </tr> </tbody> </table> <p> </p> <p><strong>The structure of the data is as follows:</strong></p> <p>1. The PDF and PPTX files, containing materials prepared for the pre-workshop survey, workshop, and post-workshop survey:</p> <ul> <li>Pre-workshop survey: Questionnaire (Q1) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/1stQuestionnaire.pdf?download=1&preview=1">1stQuestionnaire.pdf</a></li> <li>Post-workshop survey: Questionnaire (Q2) screenshot from GoogleForms <a href="https://zenodo.org/records/14960584/files/2ndQuestionnaire.pdf?download=1&preview=1">2ndQuestionnaire.pdf</a></li> <li>Documents distributed to the participants of the Focus Group Workshop: <ul> <li>ReKisstory Compare section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_compare_manual.pdf?download=1&preview=1">rekisstory_compare_manual.pdf</a></li> <li>ReKisstory Find section manual <a href="https://zenodo.org/records/14960584/files/rekisstory_find_manual.pdf?download=1&preview=1">rekisstory_find_manual.pdf</a></li> <li>ReKisstory Find example search patterns <a href="https://zenodo.org/records/14960584/files/rekisstory_group_search_example_search_patterns.pdf?download=1&preview=1">rekisstory_group_search_example_search_patterns.pdf</a></li> <li>Workshop slides <a href="https://zenodo.org/records/14960584/files/Focus_Group_Workshop_slides.pptx?download=1&preview=1">Focus_Group_Workshop_slides.pptx</a></li> </ul> </li> </ul> <p>2. The Excel spreadsheets consists of the results of two questionnaires (Q1 and Q2) (<a href="https://zenodo.org/records/14960584/files/Two_questionnaires_online_workshops.xlsx?download=1&preview=1">Two_questionnaires_online_workshops.xlsx</a>): </p> <ul> <li>Pre-workshop survey: Questionnaire (Q1), containing information about potential users: <ul> <li>Demographics</li> <li>Experience of <ul> <li>Time-related data</li> <li>Wikidata</li> <li>SPARQL</li> </ul> </li> </ul> </li> <li>Post-workshop survey: Questionnaire (Q2), containing information about feedback from the Workshop participants : <ul> <li>Comparison with Q1</li> <li>Questions about time-related functionalities</li> <li>Questions about Compare and Find searches</li> <li>Overall comment</li> </ul> </li> </ul> <p><em>Please see "Wikidata Visualization for Event and Temporal Data Exploration in Digital Humanities and Cultural Heritage" for more details</em></p>
Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.
<p><span><span><span>The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example:</span></span></span></p> <ul> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854).</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file </span></span></span><span><span><span>containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. </span></span></span><span><span><span> The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’</span></span></span><span><span><span>s ensemble mean</span></span></span><span><span><span>: one that bias-corrects the </span></span></span><em><span><span><span>x</span></span></span></em><span><span><span>-coordinate of the TC-center, another that bias-corrects the </span></span></span><em><span><span><span>y</span></span></span></em><span><span><span>-coordinate.</span></span></span></p> </li> <li> <p><span><span><span>wavelengths-microns=3.900-7.340-13.300/</span></span></span><span><span><span>uncertainty_calibration</span></span></span><span><span><span>/</span></span></span><span><span><span>uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the </span></span></span><span><span><span><em>x</em></span></span></span><span><span><span>-coordinate, one for spread in the </span></span></span><span><span><span><em>y</em></span></span></span><span><span><span>-coordinate.</span></span></span></p> </li> </ul> <p><span> </span></p> <p><span><span><span>As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid rows) x </span></span></span><span><span><span>3</span></span></span><span><span><span>00 (grid columns) x </span></span></span><span><span><span>9</span></span></span><span><span><span> (lag times) x 3 (wavelengths). Lag times should be in the following order: </span></span></span><span><span><span>240, 210, </span></span></span><span><span><span>180, 150, 120, 90, 60, 30, 0 min ago. Wavelengths should be in the order indicated by the subdirectory name. The numpy array itself should contain </span></span></span><em><span><span><span>normalized</span></span></span></em><span><span><span> brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a </span></span></span><em><span><span><span>plate carrée</span></span></span></em><span><span><span> grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper, </span></span></span><em><span><span><span>i.e.,</span></span></span></em><span><span><span> those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the </span></span></span><span><span><span>second-</span></span></span><span><span><span>most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-scores using the same normalization parameters as in the journal paper. See details below.</span></span></span></p> <p> </p> <p><span><span><span>Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().</span></span></span></p> <p> </p> <p><span><span><span>To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to </span></span></span><em><span><span><span>z</span></span></span></em><span><span><span>-score units).</span></span></span></p>
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.