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438 results for “space time”
Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
<p>The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment, which have been hypothesized to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyze three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasizes the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.</p>
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
Data, scripts and code for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts, and code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains data, scripts, and code. It is organized into two main directories: <em>Materials and Methods,</em> and <em>Results</em>.</p> <h2>Materials and Methods</h2> <p>The raw data can be accessed in the <em>Materials_and_Methods/Data directory</em>. The processed data used for modeling is available in <em>Materials_and_Methods/Data_for_modeling/Data_for_modeling.RData</em>. All scripts for data processing are located in <em>Materials_and_Methods/Processing_scripts</em>. The <em>Plots_and_Figures </em>directory<em> </em>includes illustrations of the data used for modeling, such as PCA correlation plots presented in Supplementary information. The main script for running the model for each species is <em>Main_script.R</em>.</p> <h2>Results</h2> <p>The <em>Results</em> directory contains three subdirectories and the script <em>Models_Outputs.R</em>. The <em>Results/Fitted_models</em> directory includes all .RData files with fitted models for each species. The script <em>Models_Outputs.R </em>generates all outputs (Figures, Tables, Numbers) included in the paper and additional results in the Supplementary Information, except for Figure 1 and AUC values. For these, refer to Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>. The plots are stored in the <em>Results/Plots </em>folder, while <em>Results/RData</em> contains intermediate .RData files created by <em>Models_Outputs.R</em> to manage computational costs.</p>
Data and analysis for "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models"
<p>This is an archive of the raw data and analysis source code for the paper "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models". The archive contains:</p> <ul> <li><strong>devel.Rmd : </strong>Source code for the worksheet that contains the early development and figures for the paper.</li> <li><strong>devel.html</strong> : HTML rendering of devel.Rmd</li> <li><strong>lg-ensemble-stats.Rmd </strong>: Source code for the worksheet that contains the statistical analysis described in the paper.</li> <li><strong>lg-ensemble-stats.html</strong> : HTML rendering of lg-ensemble-stats.Rmd</li> <li><strong>cc-analysis.Rmd </strong>: Analysis of the compromise conjecture raised by some readers of the paper</li> <li><strong>cc-analysis.nb.html</strong> : HTML rendering of cc-analysis.Rmd</li> <li><strong>data.tar.bz2 </strong>: Input data for the analyses above.</li> </ul> <p>The source code in this archive is written in R and requires the R runtime environment. It also uses the fldgen package, version 1.0.0, which is available at <a href="https://github.com/JGCRI/fldgen">https://github.com/JGCRI/fldgen</a></p> <p> </p>
Data for: Sex-specific ornament evolution is a consistent feature of climatic adaptation across space and time in dragonflies
<p><span>Adaptation to different climates fuels the origins and maintenance of biodiversity. Detailing how organisms optimize fitness for their local climate is therefore an essential goal in biology. Although we increasingly understand how survival-related traits evolve as organisms adapt to climatic conditions, it is unclear if organisms also optimize traits that coordinate </span><span><span>mating</span></span><span> between the sexes. Here, we show that dragonflies consistently adapt to warmer climates across space and time by evolving less male melanin ornamentation—a mating-related trait that also absorbs solar radiation and heats individuals above ambient temperatures. Continent-wide macroevolutionary analyses reveal that species inhabiting warmer climates evolve less male ornamentation. Community-science observations across ten species indicate that populations adapt to warmer parts of species' ranges </span><span><span>through microevolution of</span></span><span> smaller male ornaments. Observations from 2005-2019 detail that contemporary selective pressures oppose male ornaments in warmer years; and our climate-warming projections predict further decreases by 2070. Conversely, our analyses show that female ornamentation responds idiosyncratically to temperature across space and time, indicating the sexes evolve in different ways to meet the demands of the local climate. Overall, these macro- and microevolutionary findings demonstrate that organisms predictably optimize their mating-related traits for the climate just as they do their survival-related traits.</span></p>
We are what we eat, plus some per mill: Using stable isotopes to estimate diet composition in Gyps vultures over space and time
<p><span><span><span><span><span><span><span><span><span><span><span>1. Dietary studies in birds of prey involve direct observation and examination of food remains at resting and nesting sites. Although these methods accurately identify diet in raptors, they are time consuming, resource intensive and associated with biases that stem from the feeding ecology of raptors like <i>Gyps </i>vultures (<i>Gyps africanus</i> and <i>Gyps rueppelli</i>). Our study set out to estimate diet composition in <i>Gyps </i>vultures informed by stable isotopes that provide a good representation of assimilated diet from carrion resources in local systems. </span></span></span></span></span></span></span></span></span></span></span></p> <p>2. We hypothesized that differences in <i>Gyps </i>vulture diet composition is a function of sampling location, and that these vultures move between Serengeti National Park and Selous Game Reserve to forage. We also, theorized that grazing ungulates are the principal items in <i>Gyps</i> vulture diet. </p> <p><span><span><span><span><span><span><span><span><span><span><span>3. Through a combination of linear and Bayesian models, diet derived from d<sup>13</sup>C in <i>Gyps</i> vultures consisted of grazing herbivores across study areas, with those in Serengeti National Park consuming higher proportions of grazing herbivores (> 87%). d<sup>13</sup>C differences in vulture feather subsets per site did not indicate vulture diet change, and in combination with blood d<sup>13</sup>C, vultures fed largely on grazers for ~159 days before they were sampled in both sites. Similarly, d<sup>15</sup>N values implied that <i>Gyps </i>vultures fed largely on herbivores across space and time. d<sup>34</sup>S ratios separated prey source for vultures between the two sites. d<sup>34</sup>S variation in vultures across sites resulted from differences in baseline (plant) d<sup>34</sup>S values, though it is not possible to match d<sup>34</sup>S to specific locations. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>4. Our findings highlight the relevance of repeated sampling that considers tissues with varying isotopic turnover and emerging Bayesian techniques for dietary studies using stable isotopes. Findings also suggested limited vulture movement between the two local systems. However, more sampling coupled with telemetry is required to fully comprehend this observation and its implications to <i>Gyps</i> vulture ecology and conservation.</span></span></span></span></span></span></span></span></span></span></span></p>
Selection in space and time: individual tree growth is adapted to tropical forest gap dynamics
<p>In the present study, we assessed genotypic diversity within closely-related sympatric tree species belonging to the widespread tropical tree species complex <em>Symphonia globulifera</em>. We addressed the fine-scale spatial and temporal genetic adaptations of individuals through differential growth strategies in response to forest gap dynamics. We finally compared the breadth of successional niches encountered by <em>Symphonia</em> species to other locally abundant species. Combining tree diameter censuses, indirect measures of light environment of the recent past and present, and single nucleotide polymorphisms (SNPs), we used population genomics, environmental association analyses, genome wide association and growth modelling to address the following questions:</p> <ul> <li>Are individual genotypes structured by the mosaic of light and competition environments resulting from forest gap dynamics?</li> <li>Is the growth of individuals determined by genotypes?</li> <li>Is there an association between genotypic adaptations to gap dynamics and to growth?</li> <li>How are genotypic adaptations to gap dynamics and to growth structured in time, i.e., across life stages?</li> <li>Are breadths of successional niches for <em>Symphonia</em> species wider than those of other locally abundant species?</li> </ul> <p>Find the analyses here : https://sylvainschmitt.github.io/GOING/introduction.html</p>
Datasets of Time- and Space-Efficient Regular Path Queries
<p>Datasets that were used in the experiments of our work <em>Time- and Space-Efficient Regular Path Queries.</em></p>
Data for: Space-for-time substitution reveals a hump-shaped distribution of dung beetles
<p>Unravelling how climate change impacts the diversity and distribution patterns of organisms is a major concern in ecology, especially with climate-sensitive species, such as dung beetles. Often found in warmer weather conditions, beetles are used as bio-indicators of environmental conditions. By using an altitudinal gradient as a proxy for climate change (i.e., space-for-time substitution), we assessed how changes in climatic variables, such as temperature and precipitation, impact patterns of dung beetles diversity and distribution in the Peruvian Andes. We recorded dung beetles diversity using three different types of baits, feces, carrion, and fruits, distributed in 18 pitfall traps in five different altitudinal sites (from 900 to 2500 m, 400 m apart from each other) in the rainy and dry seasons. We found that (i) dung beetles richness and abundance were influenced by the climate gradient, (ii) seasonality influenced beetle richness, which was high in the wet season, but did not influence abundance, (iii) dung beetle richness and abundance fit to a hump-shaped distribution pattern along the altitudinal gradient, and (iv) species richness is the beta diversity component that best describes the composition of dung beetle species along the altitudinal gradient. Our data show that the distribution and diversity of dung beetles are different at larger scales, with different patterns resulting from the response of species to both abiotic and biotic factors.</p>
Modeling wetland functions: Is space-to-time substitution of perimeter-area relation appropriate?
<p>Wetlands' morphometric or shape properties such as their area and perimeter impact a multitude of ecosystem functions and services. However, current models used to quantify these functions often only use area as an independent variable, as the static area and perimeter of different wetlands have been found to be closely related. The study uses monthly satellite-based inundation maps to assess the temporal covariation of geographically isolated wetlands' perimeter and surface area. The results show that using static representations of wetlands to evaluate temporal dynamic perimeter-area relations can introduce significant discrepancies and that these discrepancies can be reduced if evaluations using static data are performed separately for each wetlandscape. The study concludes that current models that use implicit area-perimeter relations based on static wetland representations should be used with caution and that incorporating perimeter-area relations from temporally dynamic data can improve wetland function estimates.</p>
Data used for global TEC forecasting for space weather application based on deep learning techniques: a comparative study and considerations for real-time implementation
<p>This dataset contains the measurements of TEC from Global Ionospheric Maps (GIMs), provided by the International GNSS Service (IGS), as the target parameter and the global geomagnetic Kp index as the external input. This data set is composed of samples from 2005 to 2017. The datasets have been curated to obtain the same resolution (2 hs) of the two parameters.</p>
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
TIME and Space Sampling power tool (TIMESS)
<p>Self executable (based on R and shiny) to conduct power analysis to estimate the number of locations and repeated measurements necessary to detect a certain effect size at a given power. The algorithm allows to account for mosquitoes seasonal patterns and spatially clustered mosquito counts. </p>
Data from: Across space and time: a review of sampling and analytical biases in fossil data across macroecological scales
<p>Quantitative studies of fossil data have proven critical to a number of major macroevolutionary and macroecological discoveries, such as the 'Big 5' mass extinctions of the Phanerozoic. The development and easy accessibility of major meta-data sources such as the Paleobiology Database and Geobiodiversity Database have also spurred the widespread application of these data to testing ecological hypotheses at finer spatiotemporal and phylogenetic scales. However, issues of preservational/taphonomic biases, sampling/collecting biases, taxonomic issues, and analytical choice can impact the degree of interpretative resolution possible, and even obscure biological 'signal' from error/bias-introduced 'noise'. The degree to which these factors can impact analytical interpretations is not well-documented in comparison to the scale of use of these data sources. Here, we review the many forms of systematic error that can creep into a paleoecological study, from the stage of data collection to the interpretation of analytical results, and provide two case studies based upon re-analysis of previously-published datasets to illustrate the varying impacts of such biases. The first case study focuses on the Cambrian Burgess Shale, and the second on the Belly River Group, with both representing highly-sampled, taphonomically characterized, and spatiotemporally-constrained datasets developed through multiple years of sustained field collecting. In the former, we illustrate the impacts of collecting bias through quantitative comparisons of collected vs. discarded specimens over multiple field seasons, illustrating the impact of this data loss on ecological reconstructions and analysis. In the latter case study, we review the impact of preservational biases, the approaches to their quantification and mitigation, where these approaches have led to misinterpretations in the past, and the differences in ecological resolution that result from occurrence vs abundance approaches in macroecological analysis. Lastly, we synthesize these case studies with our review of past approaches to propose a series of recommendations for future paleoecological and macroecological studies, emphasizing the continued importance of high-quality primary data and ongoing need for a first-principles approach to address existing issues of missing data.</p>
Use climatic space-for-time substitutions with care: not only climate, but also local environment affect performance of the key forest species bilberry along elevation gradient
<p><span>An urgent aim of ecology is to understand how key species relate to climatic and environmental variation, to better predict their prospects under future climate change. The abundant dwarf shrub bilberry (<em>Vaccinium myrtillus</em> L.) has caught particular interest due to its uphill expansion into alpine areas. Species' performance under changing climate has been widely studied using the climatic space-for-time approach along elevation gradients, but potentially confounding, local environmental variables that vary along elevation gradients have rarely been considered. In this study, performed in ten sites along an elevation gradient (200–875 m) in W Norway, we recorded species composition and bilberry performance, both vegetative (ramet size and cover) and reproductive (berry and seed production) properties, over one to four years. We disentangled effects of local environmental variables and between-year, climatic variation (precipitation and temperature), and identified shared and unique contributions of these variables by variation partitioning. We found bilberry ramet size, cover, and berry production to peak at intermediate elevations, whereas seed production increased upwards. The peaks were less pronounced in extreme (dry or cold) summers than in normal summers. Local environmental variables explained much variation in ramet size and cover, less in berry production, and showed no relation to seed production. Climatic variables explained more of the variation in berry and seed production than in ramet size and cover, with temperature relating to vegetative performance, and precipitation to reproductive performance. Bilberry's clonal growth and effective reproduction probably explain why the species persists in the forest and at the same time invades alpine areas. Our findings raise concerns about the appropriateness of the climatic space-for-time approach. We recommend including both climatic and local environmental variables in studies of variation along elevation gradients, and conclude that variation partitioning can be a useful supplement to other methods for analysing variation in plant performance. </span></p>
Real Time 3D Navigation and Traditional US to Identify Epidural Space Depth in Obese Pregnant
ClinicalTrials.gov study NCT04395573. IPD Sharing: NO. Countries: 1. Publications: 1.
Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
Open the record for dataset details and reuse information.
Data from: Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles
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Data from: Ediacaran distributions in space and time: testing assemblage concepts of earliest macroscopic body fossils
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We are what we eat, plus some per mill: Using stable isotopes to estimate diet composition in Gyps vultures over space and time
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ScienceDex guides
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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.