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450 results for “Spatio-temporal”
A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides
<p>In multi-cellular organisms, cells and tissues coordinate biochemical signal propagation across length scales spanning micrometres to metres. Designing synthetic materials with similar capacities for coordinated signal propagation could allow these systems to adaptively regulate themselves across space and over time. Here, we combine ideas from cell signalling and electronic circuitry to propose a biochemical waveguide that transmits information in the form of a concentration of a DNA species on a directed path. The waveguide could be seamlessly integrated into a soft material because there is virtually no difference between the chemical or physical properties of the waveguide and the material it is embedded within. We propose the design of DNA strand displacement reactions to construct the system and, using reaction-diffusion models, identify kinetic and diffusive parameters that enable super-diffusive transport of DNA species via autocatalysis. Finally, to support experimental waveguide implementation, we propose a sink reaction and spatially inhomogeneous DNA concentrations that could mitigate the spurious amplification of an autocatalyst within the waveguide, allowing for controlled waveguide triggering. Chemical waveguides could facilitate the design of synthetic biomaterials with distributed sensing machinery integrated throughout their structure and enable coordinated self-regulating programmes triggered by changing environmental conditions.</p>
Figure 2 in Spatio-temporal variability in the Cladocera assemblage of a subtropical hypersaline lagoon
Figure 2. TS-diagram. At the 12 collection stations.
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Data from: Spatio-temporal dynamics in syntopy are driven by variability in rangeland conditions
<p>Sympatry is the most common form of niche differentiation and can exist as broad sympatry (shared geographical region) or direct sympatry (i.e., syntopy (shared resource patch)). Syntopy may be highly dynamic, particularly in environments that experience stochastic events that increase variability in abiotic conditions and vegetation. We examined how estimates of syntopy varied across space and time in a rangeland system within the Southern Great Plains, USA over a three-year period (2013-2015). We modeled annual estimates of syntopy between three functionally similar (ground-foraging Galliformes) species (northern bobwhite (<em>Colinus virginianus)</em>, scaled quail (<em>Callipepla squamata)</em>, and lesser prairie-chicken (<em>Tympanuchus pallidicinctus)</em>. Niche similarity representing increased syntopy was greatest during years with increased drought conditions (2013-2014). Niche differentiation was greatest in 2015 in which rangelands experienced the greatest amount of precipitation. Syntopy estimates were driven by variability in vegetation cover estimates, representing changes in rangeland conditions related to abiotic conditions. Our results suggest that syntopy can be highly dynamic across space and time and can be driven by variability in abiotic conditions (i.e., precipitation). Furthermore, these results suggest that habitat is in a state of non-equilibrium. Finally, we highlight that climate refuges that promote demographic resiliency through intraspecific changes in resource use are fundamental drivers of spatio-temporal patterns in community dynamics, particularly across similar functional species.</p>
Fig. 1 in Spatio-temporal variation of the invasive copepod Oithona davisae in the zooplankton community of Kavala harbour Abstract
Fig. 1: A) Map of Greece, B) Map of the sampling stations in Kavala's harbour.
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Fig. 4 in Spatio-temporal variation in prevalence and intensity of trematodes responsible for waterfowl die-offs in faucet snail-infested waterbodies of Minnesota, USA
Fig. 4. (continued).
Spatio-temporal distribution of LSD outbreaks in the Balkan region since deecember 2016 until November 2017 and cattle density (animals/square km)
<p>The European Food Safety Authority (EFSA), under request of the European Commission, performed an epidemiological analysis of the lumpy skin disease (LSD) epidemics based on the data collected from the affected and at-risk Member States and non-EU countries in south-eastern Europe. Spatial and temporal (monthly) distribution of LSD outbreaks reported in the Balkan region between December 2016 and November 2017 and cattle density (animals/square km) shown as green shade (for Bosnia and Herzegovina data at regional level are not available). LSD outbreaks were reported in this time frame only in Albania, the former Yugoslav Republic of Macedonia and Greece. Red and grey dots indicate new and past outbreaks, respectively.</p> <p>*This designation is without prejudice to positions on status and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence</p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration
<p>Values for immuohistochemical analysis or enzymatic analysis of various markers theorised to have roles in retinal dystrophies in three models of mouse retinal degeneration with a control. Analysis has been broken down by marker, mouseline, age and region of the retina recorded from</p>
Fig. 1 in Patterns of spatio-temporal distribution as criteria for the separation of planktic foraminiferal species across the Danian-Selandian transition in Spain
Fig. 1. Geographical location of the Caravaca and Zumaia sections (Spain).
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>
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>
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.
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>
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.