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911 results for “Temporal data”
Data from: The spatial distribution and temporal trends of livestock damages caused by wolves in Europe
<p>The preprint of the corresponding manuscript can be found here: doi: https://doi.org/10.1101/2022.07.12.499715</p> <p>Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. We here compiled incident-based livestock damage data caused by wolves across 21 European countries for the years 2018, 2019 and 2020.</p> <p>The file "<strong>wolf_damages_2018_2019_2020_complete_data_to_publish.csv</strong>" contains the following information per incident: country, target species, cause, number of animals killed/injured/missing, assessment level probability, reported date, number of days until inspection, location, incidentID, uniqueID, NUS1_ID, NUTS2_ID, NUTS3_ID, damage prevention measure, number of wolves attacking, latitude, longitude, comments, metadata constraints.</p> <p>The file "<strong>nuts3_regions_and_LC_where_wolves_are_present.csv</strong>" contains information of the percentage of area occupied by wolves per NUTS3 region for selected land cover variables.</p> <p>The file "<strong>prevention_measures.csv</strong>" contains information about the financial support of livestock damage prevention measure per country or NUTS region</p> <p>The file "<strong>wolf_presence_now_vs_50_years_ago_nuts3.csv</strong>" contains information on NUTS3 regions that had a documented wolf presence 50 years ago.</p> <p>The "<strong>scripts_to_publish.zip</strong>" folder contains the scripts that we used to conduct the analyses.</p>
Data of "Accurate photonic temporal mode analysis with reduced resources"
<p>Data published in "<em>Accurate photonic temporal mode analysis with reduced resources</em>".</p> <p>Phys. Rev. A <strong>101</strong>, 013801</p>
Data of "Deterministic Shaping and Reshaping of Single-Photon Temporal Wave Functions"
<p>Data published in "<em>Deterministic Shaping and Reshaping of Single-Photon Temporal Wave Functions</em>".</p> <p>Phys. Rev. Lett. <strong>123</strong>, 133602</p>
R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper
<p>This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.</p>
DATA of "Resolving the 2D temporal evolution of subglacial water flow with dense seismic array observations."
<p>The data set contains all data presented in the paper: <strong>Observing the subglacial hydrology network and its dynamics with a dense seismic array</strong> published in PNAS ( <a href="https://doi.org/10.1073/pnas.2023757118">https://doi.org/10.1073/pnas.2023757118</a> )</p> <p>See our online presentation of this dataset: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10710.html.</p> <p>The present data and code concerns the source location obtained with matched-field-processing analysis and the hydraulic potential calculation (Shreve, R. L. Movement of Water in Glaciers. <em>J. Glaciol.</em> <strong>11</strong>, 205–214 (1972)).</p> <p>We perform source location over 1-sec long signal segment of the vertical component only. We filter the signal within the [3-7] Hz frequency range and coherently apply the MFP each 0.1 Hz within this range. To maximize our algorithm efficiency and minimize computational costs we use a gradient-based minimization algorithm (Nelder-Mead optimization) to converge to the best match between the trial and the observed phase delays rather than an exhaustive grid-search exploration. The convergence criterion is reached when the variance of values obtained over the last 5 iterations of the optimization is smaller than 1e<sup>-2</sup> with a maximum of 3000 iterations. Our 29 different starting points used for optimization are located 250 m below the glacier surface and they uniformly cover an area of 800 x 800 m<sup>2</sup> centered on the array. We set the initial velocity to 1800 m.sec <sup>-1</sup>. The 29 punctual locations found per signal segment (1 sec) after convergence are located all in the same place if a clear global convergence exists (i.e. high MFP output) or at up to 29 different locations if up to 29 local minima exist (i.e. low MFP output).</p> <p>Timeseries of physical quantities can be found here <a href="https://doi.org/10.5281/zenodo.3701520">https://doi.org/10.5281/zenodo.3701520</a></p> <p>Spatial observations acquired during the same period can be found here <a href="https://doi.org/10.5281/zenodo.3971815">https://doi.org/10.5281/zenodo.3971815</a></p> <p> </p> <p>The RESOLVE project has been supported by a grant from LabEx OSUG@2020 (Investissement d’avenir – ANR10LABX56) and by the IDEX Université Grenoble Alpes. Most of the computations presented in this paper were performed using the GRICAD infrastructure (https://gricad.univ-grenoble-alpes.fr), which is supported by Grenoble research communities, and with the CiGri tool (https://github.com/oar-team/cigri) developed by Gricad, Grid5000 (https://www.grid5000.fr) and LIG (<a href="https://www.liglab.fr/">https://www.liglab.fr/</a>).</p> <p> </p> <p>You can find more information on the method and seismic dataset used in this paper here: <a href="https://zenodo.org/deposit/5645545">https://zenodo.org/deposit/5645545</a></p>
Close range hyperspectral camera dataset with high temporal resolution of strawberry with eco-physiological data of one leaf
<p>This high temporal resolution dataset of a strawberry plant was captured in two experiments, each lasting 100h. On leaf was inserted into a leaf chamber of the LI-6400XT gas exchange system, capturing information on transpiration, photosynthesis and stomatal conductance. Environmental characteristics are also captured at canopy height. These experiments were conducted in a growth chamber at ILVO (Melle, Belgium) and only covered conditions that did not result in stress in the plant. As such, this dataset attempts to capture subtle dynamic variation in the plant.</p>
Data from: DCDC2 READ1 regulatory element: how temporal processing differences may shape language
<p>Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographic isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modeled association between RU1-1 alleles of <i>DCDC2</i> and manner of articulation in 51 populations spanning five continents, and adjusting for geographic proximity, genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of <i>DCDC2</i>, appear to increase auditory processing precision that enhances stop-consonant discrimination, favoring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.</p>
Synthetic Smart Card Data for the Analysis of Temporal and Spatial Patterns
<p>This is a synthetic smart card data set that can be used to test pattern detection methods for the extraction of temporal and spatial data. The data set is tab seperated and based on a stylized travel pattern description for city of Utrecht in The Netherlands and is developed and used in Chapter 6 of the PhD Thesis of Paul Bouman. </p> <p>This dataset contains the following files:</p> <ul> <li>journeys.tsv : the actual data set of synthetic smart card data</li> <li>utrecht.xml : the activity pattern definition that was used to randomly generate the synthethic smart card data</li> <li>validate.ref : a file derived from the activity pattern definition that can be used for validation purposes. It specifies which activity types occur at each location in the smart card data set.</li> </ul>
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"
<p>This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".</p><p>The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. </p><p>The code folder contains the code to generate the results, and to generate the figures of the manuscript. </p>
Data from: Opposing responses of temporal stability of aboveground and belowground net primary productivity to water and nitrogen enrichment in a temperate grassland
<p><span>Changes in water and nitrogen availability, as important elements of global environmental change, are known to affect the temporal stability of aboveground net primary productivity (ANPP). However, evidences for their effects on the temporal stability of belowground net primary productivity (BNPP), and whether such effects are consistent between belowground and aboveground, are rather scarce. Here, we investigated the responses of temporal stability of both ANPP and BNPP to water and nitrogen addition based on a 9-year manipulative experiment in a temperate grassland in northern China. The results showed that the temporal stability of ANPP increased with water addition but decreased with nitrogen addition. By contrast, the temporal stability of BNPP decreased with water addition but increased with nitrogen enrichment. The temporal stability of ANPP was mainly determined by the soil moisture and inorganic nitrogen, which modulated species asynchrony, as well as by the stability of dominant species. On the other hand, the temporal stability of BNPP was mainly driven by the soil moisture and inorganic nitrogen that modulated ANPP of grasses, and by the direct effect of soil water availability. Our study provides the first evidence on the opposite responses of aboveground and belowground grassland temporal stability to increased water and nitrogen availability, highlighting the importance of considering both aboveground and belowground components of ecosystems for a more comprehensive understanding of their dynamics.</span></p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
<ol> <li>Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture.</li> <li>Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every six months (end of wet and dry seasons) over a four-year period across 5 sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions.</li> <li>Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons, and simulated drought conditions. Wood blocks discovered by termites decomposed 16% to 36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates.</li> <li>Taken together, we found that spatial and seasonal variation in precipitation are important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes.</li> </ol>
Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
<p>Animals rely on their experience to guide their next choice. In foraging-type tasks guided by history-dependent value, these experiences are typically integrated such that the weights of past events initially decay quickly over time but show a longer tail than expected by exponential decay. Rather, such integration is better described by a hyperbolic function. Hyperbolic integration affords sensitivity to both recent environmental dynamics and long-term trends, however the mechanism by which the brain implements this hyperbolic integration is unknown. We trained mice on a history-dependent, value-based decision task and found that the mice indeed showed hyperbolic decay on their weighting of past experience. However, the activity of history-encoding cortical neurons showed weighting with exponential decay. In resolving this apparent mismatch, we observed that cortical neurons encode history information heterogeneously across a wide variety of exponential time-constants, with the retrosplenial cortex (RSC) overrepresenting longer time-constants compared to other areas. A model that combines these diverse timescales of exponential history integration can recreate the heavy-tailed, hyperbolic history integration observed in behavior. In particular, time-constants of RSC neurons best matched the behavior, and optogenetic inactivation of RSC uniquely reduced the use of history information. These results indicate that behavior-relevant history information is maintained in neurons across multiple timescales in parallel, and suggest that the neural population in RSC is a critical reservoir of this information guiding decision-making.</p>
Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves
<p>Animal movements, needed to acquire food resources, avoid predation risk, and find breeding partners, are influenced by annual and circadian cycles. Decisions related to movement reflect a quest to maximize benefits while limiting costs, especially in heterogeneous landscapes. Predation by wolves (<em>Canis lupus</em>) has been identified as the major driver of moose (<em>Alces alces</em>) habitat selection patterns, and linear features have been shown to increase wolf efficiency to travel, hunt and kill prey. However, few studies have described moose behavioral response to roads and logging in Canada in the absence of wolves. We thus characterized temporal changes (i.e., day phases and biological periods) in eastern moose (<em>Alces alces americana</em>) habitat selection and space use patterns near a road network in a wolf-free area located south of the St. Lawrence River (eastern Canada). We used telemetry data collected on 18 females between 2017 and 2019 to build resource selection functions and mixed linear regressions to explain variations in habitat selection patterns, home-range size and movement rates. Female moose selected forest stands providing forage when movement was not impeded by snow cover (i.e., spring/green-up, summer/rearing, fall/rut) and stands offering protection against incidental predation during calving. In winter, home-range size decreased with an increasing proportion of stands providing food and shelter against harsh weather, limiting the energetic costs associated with movement. Our results reaffirmed the year-round aversive effect of roads, even in the absence of wolves, but the magnitude of this avoidance differed between day phases, being lower during the "dusk-night-dawn" phase, perhaps due to a lower level of human activity on and near roads. Female moose behavior in our study area was similar to what was observed in landscapes where moose and wolves cohabit, suggesting that the risk associated with humans, perceived as another type of predator, and with incidental predators (coyote <em>Canis latrans</em>,<em> </em>black bear <em>Ursus americanus</em>), equates that of wolf predation in heavily managed landscapes.</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Code and Data to "Quantile regression for temporal streamflow modeling"
<p>This is the accompanying code to "Quantile regression for temporal streamflow modeling", which is part of the manuscript "The Role of Process Heterogeneity in Statistical Modeling", which was submitted to the Austrian Journal of Statistics. </p> <p> </p> <p>The data used in this publication is fully accessible through the <a href="https://doi.org/10.5194/essd-13-4529-2021" target="_blank" rel="noopener">LamaH-CE</a> dataset. The two scripts "functions_create_data.R" and "create_data.R" will create the final dataset used for modelling. </p> <p>"functions_modelling.R" provide the functions for tuning the XGBoost model and computing the SHAP values. An example script is also attached (calc_predictions_shap.R). "analyzing_results.R" and "error_metrics.R" will produce the final output used in the manuscript. Finally, two plots produced in the script are added as pdf. </p> <p>All data analysis was performed in R, and we want to acknowledge the following packages: <a href="https://dplyr.tidyverse.org/">dplyr</a>, <a href="https://tidyr.tidyverse.org/">tidyr</a>, <a href="https://www.jstatsoft.org/v40/i03/">lubridate</a>, <a href="https://purrr.tidyverse.org/">purrr</a>, <a href="https://doi.org/10.18637/jss.v033.i01">glmnet</a>, <a href="https://cran.r-project.org/web/packages/xgboost/index.html">xgboost</a>, <a href="https://CRAN.R-project.org/package=shapr">shapr</a>, <a href="https://CRAN.R-project.org/package=Metrics">Metrics</a>, <a href="https://CRAN.R-project.org/package=gridExtra" target="_blank" rel="noopener">gridExtra</a>, <a href="https://doi.org/10.18637/jss.v014.i06">zoo</a> and <a href="https://CRAN.R-project.org/package=wesanderson">wesanderson</a>. </p> <p> </p>
Data sets and code for "Suprachiasmatic Nucleus-wide Estimation of Oscillatory Temporal Dynamics" (Yao et al, 2024)
<ul> <li>Data from iDISCO clearing and scanning of three adult mouse suprachaismatic nuclei. Brains are labeled as b1, b2, and b3. Each lobe of the SCN is recorded in a separate csv file. Animals were sacrificed at ZT 19. </li> <li>Data for PER2::LUC recordings of ix adult mouse suprachaismatic nuclei. For each slice there are two files: the time series data (labeled "slice-[orientation]-time-series-#" and the coordinates of the pixels represented (labeled "slice-[orientation]-pixel-coords-#."</li> <li>Code in MATLAB to perform phase extraction, linear modeling, phase estimation, and dynamical simulation.</li> </ul>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
<p>For ecosystem models to be applicable outside their context of development, temporal and spatial transferability must be demonstrated. This presents a challenge for modeling intertidal ecosystems where spatiotemporal variation arises at multiple scales. Models specializing in tidal dynamics are generally inhibited from having wider ecological applications by coarse spatiotemporal resolution or high user competency. The Tidal Inundation Model of Shallow-water Availability (TiMSA) uniquely simulates tides to empirically derive a time-integrated measure of availability for a shallow water depth range defined by the user. To evaluate temporal and spatiotemporal transferability, we employed TiMSA at the development site in the Florida Keys and at novel sub-sites in the Florida Bay (application site) under a different time period (application period). We used foraging Little Blue Herons (<em>Egretta caerulea</em>) as the ecological unit with which to constrain the model's 'water depth window', i.e., range of water depths to estimate shallow-water availability. At the development site, temporally consistent water depth windows contrasted with interannual variation in shallow-water availability which revealed short-term changes in Little Blue Heron foraging habitat. At the application site, water depth accuracy varied by sub-site and was correlated with spatial error in bathymetric elevation. Although TiMSA parameters were sensitive to environmental temporal variation and uncertainty in spatial data, a spatially-explicit water depth window generated reliable estimates of shallow-water conditions over space and time at the development and application sites. By exploring the contributing factors to model error, we provide solutions to reduce uncertainty of TiMSA parameters at potential application sites and recommendations for addressing bathymetric inaccuracy in digital elevation models. Accurately quantifying spatiotemporal changes of shallow-water has implications for monitoring habitat conditions for tidally-influenced species and projecting future changes to coastal ecosystems in response to anthropogenic stressors and natural disturbances such as sea level rise.</p>
Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss - data
<p>The dataset was analysed in the manuscript “Žagar A., Carretero, M.A., de Groot M. (accepted) Time changes everything: A multispecies analyses of temporal patterns in evaporative water loss. Oecologia”</p> <p>The dataset consisted out of water loss by 23 populations of lizards from 16 different species and three families which was compiled from several different studies. All studies used the same standardized protocols. During the experiment every hour for 12 hours, the body weight of the lizard was measured (in total 13 measurements per lizard). The species name (SP), the snout-vent length of the animal (SVL, in millimetres), altitude (m a.s.l.), sampling location (site name, latitude and longitude), weight (in grams), sex (M=male, F=female), code of the individual lizard (CODE), date of experiment (DATE_H) and the reference of the study were noted down (full references are available in the manuscript). Per column the instantaneous water loss values (EWLi) were recorded per hour measured. First hour was EWLi8, second hour was EWLi9, etc. The EWLi was calculated by the weight minus the weight in the next hour divided by the weight multiplied by 100 ((W<sub>n</sub> – W<sub>n+1 </sub>/ W<sub>n</sub>) × 100).</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.