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3,673 results for “Temporal”
Data and codes for How, why, where and when people feed birds? - Spatio-temporal changes in bird-feeding in Finland
<p>This file contains all the codes and data used for the analysis of the manuscript titled "How, why, where and when people feed birds? - Spatio-temporal changes in bird-feeding in Finland" accepted for publication in the journal People and Nature</p>
Data from: Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress
<p>Data and codes associated with the manuscript '<span>Hyperspectral imaging reveals differential carotenoid and chlorophyll temporal dynamics and spatial patterns in Scots pine under water stress</span>'. </p>
Energy demand and its temporal flexibility: approaches, criticalities and ways forward
<p>Data set of the reviewed documents in the contribution 'Energy demand and its temporal flexibility: approaches, criticalities and ways forward' under consideration for publication in 'Renewable & Sustainable Energy Reviews'</p>
Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area
<p>This paper investigates species richness and species occupancy frequency distributions (SOFD) as well as patterns of abundance-occupancy relationship (SAOR) in Odonata (dragonflies and damselflies) in a subtropical area. A total of 82 species and 1983 individuals were noted from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa biome in southern Brazil. Odonate species occupancy ranged from 1 to 54. There were few widely distributed generalist species and several specialist species with a restricted distribution. About 70% of the species occurred in less than 10% of the water bodies, yielding a surprisingly high number of rare species, often making up the majority of the communities. No difference in species richness was found between temporal and permanent water bodies. Both temporal and permanent water bodies had odonate assemblages that fitted best with the unimodal satellite SOFD pattern. It seems that unimodal satellite SOFD pattern frequently occurred in the aquatic habitats. The SAOR pattern was positive and did not differ between permanent and temporal water bodies. Our results are consistent with a niche-based model rather than a metapopulation dynamics model.</p>
Temporally-balanced selection during development of larval Pacific oysters (Crassostrea gigas) inherently preserves genetic diversity within offspring
<p>Balancing selection is one of the mechanisms which has been proposed to explain the maintenance of genetic diversity in species across generations. For species with large populations and complex life histories, however, heterogeneous selection pressures may create a scenario in which the net effects of selection are balanced across developmental stages. With replicated cultures and a pooled sequencing approach, we show that genotype-dependent mortality in larvae of the Pacific oyster (Crassostrea gigas) is largely temporally dynamic and inconsistently in favor of a single genotype or allelic variant at each locus. Overall, the patterns of genetic change we observe to be taking place are more complex than what would be expected under classical examples of additive or dominant genetic interactions. They are also not easily explained by our current understanding of the effects of genetic load. Collectively, temporally heterogeneous selection pressures across different larval developmental stages may act to maintain genetic diversity in oysters, while also inherently sheltering genetic load within populations.</p>
Resource competition shapes biological rhythms and promotes temporal niche differentiation in a community simulation
<p>This is the code and dataset for the article entitled "Resource competition shapes biological rhythms and promotes temporal niche differentiation in a community simulation," in the journal Ecology and Evolution.</p> <ul> <li>Competition for resources often contributes strongly to defining an organism's ecological niche. Endogenous biological rhythms are important adaptations to the temporal dimension of niches, but how other organisms influence such temporal niches have not been much studied, and the role of competition in particular has been even less examined. We investigated how interspecific and intraspecific competition for resources shape an organism's activity rhythms.</li> <li>To do this, we simulated communities of one or two species in an agent-based model. Individuals in the simulation move according to a circadian activity rhythm and compete for limited resources. Probability of reproduction is proportional to an individual's success in obtaining resources. Offspring may have variance in rhythm parameters, which allow for the population to evolve over time.</li> <li>We demonstrate that when organisms are arrhythmic, one species will always be competitively excluded from the environment, but the existence of activity rhythms allows niche differentiation and indefinite coexistence of the two species. Two species which are initially active at the same phase will differentiate their phase angle of entrainment over time to avoid each other. When only one species is present in an environment, competition within the species strongly selects for niche expansion through arrhythmicity, but the addition of an interspecific competitor facilitates evolution of increased rhythmic amplitude when combined with additional adaptations for temporal specialization. Finally, if individuals preferentially mate with others who are active at similar times of day, then disruptive selection by intraspecific competition can split one population into two reproductively isolated groups separated in activity time.</li> <li>These simulations suggest that biological rhythms are an effective method to temporally differentiate ecological niches, and that competition is an important ecological pressure promoting the evolution of rhythms and sleep. This is the first study to use ecological modeling to examine biological rhythms.</li> </ul>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
Temporal and regional trends of antibiotic use in long-term aged care facilities across 39 countries, 1985-2019: systematic review and meta-analysis
<p><b>Background</b></p> <p>Antibiotic misuse is a key contributor to antimicrobial resistance and a concern in long-term aged care facilities (LTCFs). Our objectives were to: i) summarise key indicators of systemic antibiotic use and appropriateness of use, and ii) examine temporal and regional variations in antibiotic use, in LTCFs (PROSPERO registration CRD42018107125).</p> <p><b>Methods & Findings</b></p> <p>Medline and EMBASE were searched for studies published between 1990-2021 reporting antibiotic use rates in LTCFs. Random effects meta-analysis provided pooled estimates of antibiotic use rates (percentage of residents on an antibiotic on a single day [point prevalence] and over 12 months [period prevalence]; percentage of appropriate prescriptions). Meta-regression examined associations between antibiotic use, year of measurement and region. </p> <p>A total of 90 articles representing 78 studies from 39 countries with data between 1985-2019 were included. Pooled estimates of point prevalence and 12-month period prevalence were 5.2% (95% CI: 3.3-7.9; n=523,171) and 62.0% (95% CI: 54.0-69.3; n=946,127), respectively. Point prevalence varied significantly between regions (Q=224.1, df=7, p<0.001), and ranged from 2.4% (95% CI: 1.7-2.7) in Eastern Europe to 9.0% in the British Isles (95% CI: 7.6-10.5) and Northern Europe (95% CI: 7.7-10.5). Twelve-month period prevalence varied significantly between region (Q=15.1, df-3, p=0.002) and ranged from 53.9% (95% CI: 48.3-59.4) in the British Isles to 68.3% (95% CI: 63.6-72.7) in Australia. Meta-regression found no association between year of measurement and antibiotic use prevalence. The pooled estimate of the percentage of appropriate antibiotic prescriptions was 28.5% (95% CI: 10.3-58.0; n=17,245) as assessed by the McGeer criteria. Year of measurement was associated with decreasing appropriateness of antibiotic use over time (OR: 0.78, 95% CI: 0.67-0.91). The most frequently used antibiotic classes were penicillins (n=44 studies), cephalosporins (n=36), sulphonamides/trimethoprim (n=31), and quinolones (n=28). </p> <p><b>Conclusions</b></p> <p>Coordinated efforts focusing on LTCFs are required to address antibiotic misuse in LTCFs. Our analysis provides overall baseline and regional estimates for future monitoring of antibiotic use in LTCFs. </p>
Fig. 2 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 2. Camera trap locations in Thung Yai Naresuan (East) Wildlife Sanctuary.
Fig. 1 in Spatio-temporal correlations of large predators and their prey in western Thailand
Fig. 1. Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), Thailand.
Fig. 1 in Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): Influence of environmental variables
Fig. 1. Location of the sampling station.
Fig. 3 in Spatial and temporal distribution of fish eggs and larvae in a subtropical coastal lagoon, Santa Catarina State, Brazil
Fig. 3. Mean abundances · 100 m-3 (± sd) of fish eggs and larvae in Ibiraquera Lagoon, by month.
Fig. 1 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 1. Study area in middle rio Negro basin (a) and detail of the sampling sites (b).
Fig. 2 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 2. River level during annual flood cycles. Large arrows denote dates of sampling expeditions.
Spatial and temporal variation in phenotypes and fitness in response to developmental thermal environments
<p>1) Phenotypic variation within populations is influenced by the environment via plasticity and natural selection. How phenotypes respond to the environment can vary among traits, populations, and life stages in ways that can influence fitness.</p> <p>2) Plastic responses during early development are particularly important because they can affect components of fitness throughout an individual's life. Consequently, how natural selection shapes developmental plasticity could be influenced by fitness consequences across different life stages. Moreover, spatial variation in selection pressures could generate differences in plastic responses among populations.</p> <p>3) To gain insight into sources of variation in phenotypes and survival, we used a laboratory egg incubation experiment using brown anole lizards (Anolis sagrei) from mainland (ancestral) and island (descendent) populations, combined with a mark-release-recapture experiment in the field. Our study was designed to (i) quantify the effects developmental temperature on embryo development and offspring morphology, (ii) assess how developmental temperature influences offspring survival across different life stages, and (iii) quantify how thermal reaction norms vary among ancestral and descendant populations.</p> <p>4) Developmental temperature influenced offspring morphology, but thermal reaction norms of embryos showed little variation among populations. Developmental temperature influenced offspring survival, but the patterns differed between embryo and hatchling stages; the optimal temperature for embryos was about 5ºC lower than that for hatchlings. High temperatures were thermally stressful to embryos, but they reduced incubation duration and led to early hatching. In turn, earlier hatching increased the probability of survival to adulthood. Moreover, the effect of temperature on hatchling survival was most pronounced for offspring that hatched late in the season.</p> <p>5) The difference in optimal developmental temperatures between life stages may be driven by physiological tolerance for embryos and by ecological factors for hatchlings. Moreover, the fitness consequences of the developmental environment depend upon the phenology of hatching. Overall, these results highlight how the developmental environment can differentially effect fitness across life stages and show that temporal thermal heterogeneity can influence survival of embryos, but the consequences on post-hatching stages may vary at different times of the season.</p>
Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes
<p>Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management, and infodemic controlling. However, predicting the scale of an information cascade in a long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the follower-followee network, retweet network, tweet content, and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension to information diffusion process and may assist in the control of such process.</p>
Temporal stability in species richness but reordering in species abundances within avian assemblages of a Tropical Andes conservation hot spot
<p>As the pace of environmental change increases, there is an urgent need for quantitative data revealing the temporal dynamics of local communities in tropical areas. Here we quantify the stability of avian assemblages in the highly threatened, but poorly studied, Andean biodiversity hot spot. We evaluated the temporal variation in species richness and community composition of local bird assemblages in three habitat types (native forest, introduced forest, native shrub) using a unique, relatively long-term data series from Cajas National Park and Mazán Reserve in the Southern Andes of Ecuador. We sampled birds with mist nets using a standardized protocol over 11 years, from 2006 to 2016. Species richness remained stable over time across habitats, but community composition changed in the native forest. In particular, we observed taxonomic reordering in the native forest, in which the evenness in the distribution of abundances of taxa decreased over time. This finding is consistent with other studies where species richness remained constant over time while community composition changed. Our study highlights the value of long-term studies in the tropical Andes as we show that species composition of birds in a montane forest is changing, consistent with global trends in biodiversity change.</p>
Wind Spatio-Temporal Dataset1
<p>This dataset comprises the average and standard deviation of wind speed, collected from 120 turbines in an inland wind farm, for the years of 2009 and 2010. Missing data in the original dataset are imputed by using the iterative singular value decomposition. Two data files are associated with each year---one contains the hourly average wind speed, and the other contains the hourly standard deviation of wind speed. The naming convention makes it clear which year a file is associated with and whether it is for the average speed (Ave) or for the standard deviation (Stdev). The data arrangement in these four files is as follows---the columns are the 120 turbines and the rows are times, starting from 12 a.m. on January 1 of a respective year as the first data row, followed by the subsequent hours in that year. The fifth file in this dataset contains the coordinates of the 120 turbines. To protect the wind farm's identity, the coordinates have been transformed by an undisclosed mapping, so that their absolute values are no longer meaningful but the turbine-to-turbine relative distances are maintained. This dataset is used in Chapters 3 and 4 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book.</p>
Wind Spatio-Temporal Dataset2
<p>This dataset is used in Chapter 4 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. The data used in this study consists of one year of spatio-temporal measurements at 200 randomly selected turbines on a flat terrain inland wind farm, between 2010 and 2011. The data consists of turbine-specific hourly wind speeds measured by the anenometers mounted on each turbine. In addition, one year of hourly wind speed and direction measurements are available at three met masts on the same wind farm. Column B through Column OK are the wind speed and wind power associated with each turbine, followed by Column OL through Column OQ, which are for wind speed and wind direction associated with each mast. The coordinates of the turbines and masts are listed in the top rows, preceding the wind speed, direction, and power data. The coordinates are shifted by a constant, so that while the relative positions of the turbines and the met masts remain faithful to the actual layout, their true geographic information is kept confidential. This anemometer network provides a coverage of a spatial resolution of one mile and a temporal resolution of one hour.</p>
Genetic differences in the temporal and environmental stability of transgenerational environmental effects
<p><span><span><span><span><span><span><span><span><span><span><span>Environments influence the expression of phenotypes of individuals, their progeny, and even their grandprogeny. The duration of environmental effects and how they are modified by subsequent environments are predicted to be targets of natural selection in variable environments. However, little is known about the genetic basis of the temporal persistence of environmental effects and their stability of expression across subsequent environments, or even the extent to which natural genotypes differ in these attributes of environmental effects. We factorially manipulated the thermal environment experienced in three successive generations, to quantify the temporal persistence and environmental stability of temperature effects in contrasting genotypes of <i>Arabidopsis thaliana</i>. We found that genotypes differed in the manner in which environmental effects dissipated across successive generations, the manner in which responses to ancestral environments were stably expressed in present environments, the manner in which ancestral environments altered responses to present environments, and in the manner in which ancestral environments altered fitness in present conditions. Genetic variation exists in nature for these trait-specific environmental responses, suggesting that the temporal persistence and stability of environmental effects in variable environments have the potential to evolve in response to natural selection imposed by different environments and sequences of environments.</span></span></span></span></span></span></span></span></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.