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677 results for “time of use”
Data from: Using survival models to estimate invertebrate prey identification times in a generalist stream fish
Estimates of predator feeding rates are important for understanding trophic dynamics. One common method for quantifying feeding rates in fishes combine mass-based diet data with gastric evacuation times to estimate prey mass consumed per predator. An alternative approach is to estimate rates of prey individuals consumed using prey identification times − the time period over which prey remain identifiable in a predator's stomach. One challenge with the analysis of prey identification times, however, is that the response variable is likely to be censored because the 'true' prey identification time cannot be observed directly. Here, we applied survival analysis that can incorporate censored data to estimate the effects of predator body size, water temperature, and prey characteristics (type, count and body size) on identification times in reticulate sculpin (Cottus perplexus). We focused on seven types of prey that are common in this generalist predator's diet: mayflies (Ephemeroptera), caddisflies (Trichoptera), stoneflies (Plecoptera), flies (Diptera), beetles (Coleoptera), worms (Annelida), and sculpin eggs. An information-theoretic model comparison approach indicated that an accelerated failure-time Weibull model with all five covariates provided the best relative fit to the full dataset. Prey type had a strong effect on prey identification times, with annelid worms having the shortest times (< 1 hr) and caddisflies having the longest times (> 15 hrs). Water temperature decreased prey identification times (7.5% per 1°C increase), whereas prey count (i.e. meal size) increased prey identification times (15.5% per additional prey item). Sculpin body size had a weak negative effect on prey identification times (0.04% per 1 mm increase). Body sizes of some taxa, including mayflies, caddisflies, and stoneflies, increased prey identification times, leading to an interaction between prey type and prey size. Our study highlights the utility of survival analysis to quantify variation in prey identification times in the diets of generalist predators.
Data from: An R package for analyzing survival using continuous-time open capture-recapture models
Capture–recapture software packages have proven to be very powerful tools for analysing factors affecting survival in wild populations. However, all such packages are limited to discrete-time protocols. Appropriate survival analysis tools are still lacking for data acquired from continuous-time protocols. We have developed a statistical method and propose an r package for analysing such data based on an extension of classical survival analysis models incorporating an inhomogeneous Poisson process for modelling capture histories. First, data were simulated from a continuous-time protocol. These data were used to (i) compare survival estimation biases of discrete- and continuous-time approaches and (ii) investigate the performance and accuracy of our r package for four types of covariates: factors varying between individuals (like sex), in time (like climatic factors), both in time and between individuals (like physical condition) and age (as a categorical factor). Secondly, the r package has been applied to a real data set for survival analysis of cats in the Kerguelen archipelago (regrouping 682 cats over 20 years) as an illustrative example. Results of the simulated data analysis show that the method performs better than its discrete-time counterpart for analysing data acquired from continuous-time protocols. It provides unbiased parameter estimates for all parameters except those that vary both in time and between individuals – which is not surprising, since in our case, these factors were not updated in continuous time (i.e. only upon capture). When applied to the Kerguelen cat data set, the results suggest that survival is lower in juveniles than in adults and subadults, varies between study sites and increases with physical condition, and this latter effect being more important in females than in males. Sex, season, temporal linear trend in survival and the NDVI vegetation index were also tested but were not found to be significant. However, confidence intervals were too large (due to a low recapture rate) for excluding such effects. Further analyses are still needed for rigorous covariate testing in this context. In conclusion, continuous-time approaches – such as that presented in this paper – should be preferred when data acquired from continuous-time protocols is analysed.
Data from: Time-integrated habitat availability is a resource attribute that informs patterns of use in intertidal areas
In dynamic environments, resource availability may change by several orders of magnitude, over hours to months, but the duration of resource availability is not often included as a characteristic attribute of resources even though temporal resource dynamics might limit patterns of use. In our study of wading birds foraging in intertidal areas, tides cause large changes in the areal extent of shallow-water foraging habitat (i.e., the resource), but tides also constrain the duration of availability, which is often overlooked. We hypothesized that temporal constraints on habitat availability from tides would be reflected in patterns of habitat use by foraging birds. We estimated the time-integrated habitat availability and compared it to traditional habitat attributes (seagrass cover, substrate type, instantaneous water depth, and proximity to mangrove islands or deep water) that have strong evidential support for influencing patterns of use. To evaluate our hypotheses, we quantified habitat attributes at intertidal areas in the Florida Keys, U.S.A, where wading birds were observed foraging (Little Blue Heron, Egretta caerulea: N=183; Great White Heron, Ardea herodias occidentalis: N=162). We tested for non-random use by sampling habitat attributes at two spatial scales around the observed feeding locations and we analyzed the data using a conditional logistic regression model. There was no evidence that seagrass cover or substrate explained patterns of use. The proximity of foraging locations relative to mangroves and to deep water were important at both spatial scales but had lower effect sizes (odds-ratios) than time-integrated habitat availability and water depth, and the latter may only serve as a physical constraint on access. We found support that time-integrated habitat availability was a distinct resource attribute, had the greatest effect size (4-8-fold change in relative probability of use), and best explained patterns of habitat use at the largest spatial scale. In studies of resource use where changes in resource availability are non-linear or when strong constraints on access are imposed by behavior, incorporating time-integrated estimates of resource availability into analyses can improve insights into spatiotemporal patterns of resource use.
Data from: Biogeographic dating of speciation times using paleogeographically informed processes
Standard models of molecular evolution cannot estimate absolute speciation times alone, and require external calibrations to do so, such as fossils. Because fossil calibration methods rely on the incomplete fossil record, a great number of nodes in the tree of life cannot be dated precisely. However, many major paleogeographical events are dated, and since biogeographic processes depend on paleogeographical conditions, biogeographic dating may be used as an alternative or complementary method to fossil dating. I demonstrate how a time-stratified biogeographic stochastic process may be used to estimate absolute divergence times by conditioning on dated paleogeographical events. Informed by the current paleogeographical literature, I construct an empirical dispersal graph using 25 areas and 26 epochs for the past 540 Ma of Earth's history. Simulations indicate biogeographic dating performs well so long as paleogeography imposes constraint on biogeographic character evolution. To gauge whether biogeographic dating may be of practical use, I analyzed the well-studied turtle clade (Testudines) to assess how well biogeographic dating fares when compared to fossil-calibrated dating estimates reported in the literature. Fossil-free biogeographic dating estimated the age of the most recent common ancestor of extant turtles to be from the Late Triassic, which is consistent with fossil-based estimates. Dating precision improves further when including a root node fossil calibration. The described model, paleogeographical dispersal graph, and analysis scripts are available for use with RevBayes.
Data from: A comparison of genomic selection models across time in interior spruce (Picea engelmannii × glauca) using unordered SNP imputation methods
Genomic selection (GS) potentially offers an unparalleled advantage over traditional pedigree-based selection (TS) methods by reducing the time commitment required to carry out a single cycle of tree improvement. This quality is particularly appealing to tree breeders, where lengthy improvement cycles are the norm. We explored the prospect of implementing GS for interior spruce (Picea engelmannii × glauca) utilizing a genotyped population of 769 trees belonging to 25 open-pollinated families. A series of repeated tree height measurements through ages 3–40 years permitted the testing of GS methods temporally. The genotyping-by-sequencing (GBS) platform was used for single nucleotide polymorphism (SNP) discovery in conjunction with three unordered imputation methods applied to a data set with 60% missing information. Further, three diverse GS models were evaluated based on predictive accuracy (PA), and their marker effects. Moderate levels of PA (0.31–0.55) were observed and were of sufficient capacity to deliver improved selection response over TS. Additionally, PA varied substantially through time accordingly with spatial competition among trees. As expected, temporal PA was well correlated with age-age genetic correlation (r=0.99), and decreased substantially with increasing difference in age between the training and validation populations (0.04–0.47). Moreover, our imputation comparisons indicate that k-nearest neighbor and singular value decomposition yielded a greater number of SNPs and gave higher predictive accuracies than imputing with the mean. Furthermore, the ridge regression (rrBLUP) and BayesCπ (BCπ) models both yielded equal, and better PA than the generalized ridge regression heteroscedastic effect model for the traits evaluated.
Forecasting hourly emergency department arrival using time series analysis
<p></p> Background/aims <p>The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.</p> Methods <p>Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.</p> Results <p>The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.</p> Conclusions <p>The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.</p> <p></p><p></p><p></p>
Continuous Wavelet Transform and Short-Time Fourier Transform used to generate S1_Table2
<p>Continuous Wavelet Transform and Short-Time Fourier Transform true vs. false recognition accuracies: the t-test was used to compare CWT and STFT recognition accuracies with mean <em>f</em>NIRS classification accuracy. The result was used to generate S1_Table2.</p>
FIGURE 1. Time-calibrated phylogeny including 101 taxa built using program BEAST v1.7.5 in The phylogenetIc posItIon and taxonomIc status of the RaInbow Tree Snake Gonyophis margaritatus (Peters, 1871) (Squamata: ColubrIdae)
FIGURE 1. Time-calibrated phylogeny including 101 taxa built using program BEAST v1.7.5 (Drummond et al. 2012) with node support values representing posterior probability (left) and bootstrap support (right) from inferred Maximum Likelihood tree. Dashes for bootstrap values indicate low support or not supported by ML tree. Family Colubridae and subfamily Colubrinea are highlighted by red arrows. Taxa in genera Gonyosoma, Rhadinophis, Gonyophis, and Rhynchophis are highlighted in the red square on the tree.
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
DATA SET: Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies
<p>This repository contains the data sets of the article:</p> <p>P. Fernández Esteberena et al. (2024). Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies. <em>Biomedical Optics Express</em>. <a href="https://doi.org/10.1364/BOE.538141">https://doi.org/10.1364/BOE.538141</a></p>
Shortening the runtime using larger time steps for the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.</p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the obtained results</li> <li>Tracer concentrations obtained with different time steps for all used models and parameter vectors</li> </ul>
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
Risk perception and travel satisfaction associated with the use of public transport in the time of COVID-19. The case of Turin, Italy
<p>Dataset created from the data of a survey about risk perception and travel satisfaction associated with the use of PT in the time of Covid-19 in Turin metropolitan area (Italy).</p>
Figure 3. Time-stratified biogeographic reconstruction using a in Revision of the montane New Guinean skink genus Lobulia (Squamata: Scincidae), with the description of four new genera and nine new species
Figure 3. Time-stratified biogeographic reconstruction using a BAYAREA+J model. Tips are coloured according to the extant distribution of each species. Nodes are pie charts, where the sizes of the coloured slices correspond to the relative probability of each state at each node. Earliest dates of docking of the East-Papuan Composite Terrane (16 Mya) and the Oceanic Arc Terranes (12.5 Mya) are designated by the dashed red lines. Posterior probabilities of the nodes are listed next to each node. Four of the geological regions are illustrated in the inset map. Palau, which is further north in the Pacific Ocean, is not pictured.
Study on the Evolution Characteristics of the Return Stroke Spectra and Channel Temperatures by Using Two Spectrographs with Different Time Resolution
<p>Temperature is one of the crucial parameters reflecting the energy and current transfer characteristics in the lightning discharge channel. According to the spectra of six lightning return strokes recorded simultaneously by two high-speed slit-less spectrographs with different time resolutions, the spectral intensity and temperature evolution of the return stroke channels over time was quantitatively analyzed. The spectral characteristics show that the ionic line intensity decayed rapidly with time as the current declined, while the atomic line intensity decreased more slowly. Additionally, it is found that ionic lines existed in the spectra of continuing current process for up to hundreds of microseconds. It further indicates that the ionic line intensities are associated with the discharge currents and that their radiation mechanism is closely related to the collision excitation under the action of strong currents. The temperature calculated by the ionic lines can reflect the thermodynamic properties of the current-carrying channel. The temperature calculated using atomic lines is significantly lower than that calculated by the ionic lines in the same spectrum. The radiation mechanism for majority of the atomic lines differs from that of the ionic lines. During the continuing current, the channel temperatures calculated by both ionic lines and atomic lines showed a similar evolution feature which declined slowly or even basically unchanged. This property reflects the persistent heating effect of the current.</p>
Deaf individuals use compensatory strategies to estimate visual time events
<p>Dataset related to the manuscript entitled Deaf individuals use compensatory strategies to estimate visual time events</p>
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
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.