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867 results for “spatiotemporal”
Spatiotemporal variations of air pollution during the COVID-19 pandemic across Tehran, Iran: Commonalities with and differ-ences from global trends
<p>Figure S1: Green space and green area per capita across Tehran; Figure S2: Temporal distribution of CO content at each station, gray rectangular shows strict social distancing time. Figure S3: Temporal distribution of NO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S4: Temporal distribution of PM10 content in all investigated stations gray rectangular shows strict social distancing time; Figure S5: Temporal distribution of O3 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S6: Temporal distribution of SO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S7: Temporal distribution of AQI indices in all investigated stations, gray rectangular shows strict social distancing time. </p>
Global spatiotemporal continuous daily high-resolution total column carbon monoxide for TROPOMI
<p>A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.</p>
Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway
<h3>Code</h3> <p><strong>MATLABWhaleDetectionCodeNWA.zip</strong></p> <p>The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway<em> </em>(Goblot et al., in review)<em> </em>to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales). </p> <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> <p>The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip).</p> <p><strong>Table S2</strong></p> <p><strong>WhaleDetectionsFeb2020Jan2022LSZ.mat</strong></p> <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> <p><strong>Table S3</strong></p> <p>a)<strong> MonthlyBlueWhaleDetectionsOct2015Jan2022.csv</strong> and b) <strong>MonthlyFinWhaleDetectionsOct2015Jan2022.csv</strong></p> <ul> <li> <div> <div> <div> <p>Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included.</p> <p>Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1.</p> </div> </div> </div> </li> </ul> <h3>Additional</h3> <p><strong>Movie S1</strong></p> <p><strong>LSLSWhales.mp4</strong></p> <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>
Spatiotemporal determination of photoinduced strain in a Weyl semimetal
<p>The raw data of project-Spatiotemporal determination of photoinduced strain in a Weyl semimetal, including UEM bright image sequence, time delay series and HRTEM of WTe2 sample.</p>
Dataset for High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating
<p>This is a dataset for the manuscript High-throughput combinatorial analysis of the spatiotemporal dynamics of nanoscale lithium metal plating. This mansucript is currently under peer-review in ACS Nano. </p>
A photo-switchable gold nanoformulation based on the dCas9 protein for spatiotemporal controlled gene editing activation in vivo
Open the record for dataset details and reuse information.
Figure S4 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S4. – Spatial-temporal correlation matrix at a 782 km2 (A) and 1043 km2 (B) scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure S2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S2. – Spatial hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 2 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 2. – Spatial correlation matrix at a 522 km2 scale displaying correlation from strongly negative (dark blue) to strongly positive (dark red).
Figure 11 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 11. – Scophthalmus rhombus from low (blue) to high (red) median densities of numbers/ km2 in log scale for 522 km2 for the Eastern English Channel.
Figure S5 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S5. – Spatial-temporal hierarchical clustering at a 782 km2 (A) and 1043 km2 (B) scale. The rectangle outlines the communities that where find statistically significant by ASTEC given the approximately unbiased p-values expressed as proportion (red).
Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.
Figure 2 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 2. Jaccard's coefficient similarity dendrograms showing the faunal similarity among the 12 sampling sites (based on presence/absence of species) and clustering relationships among the 16 ostracod species. (Species codes are given in Table 3.)
Fig. 1 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Fig. 1. Map of the eight studied coastal lagoons. Selected sampling sites at Tuzla Lake 1 (St-1), Tuzla Lake 2 (St-2), Tuzla Lake 3 (St-3), Taz (St-4), Işık (St-5), Dalyan (St-7, 8, and 9), Kuvalak (St-10), and Taşaltı (St-11 and 12) were used for comparisons of the lagoons. The sampling sites are indicated by red circles; the red arrows show the direction of water currents.
Processed Hi-C contact matrices for "Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization"
<p>Processed Hi-C interaction matrices (iterative correction) saved in .hic format (40kb bins).</p> <p>.hic files were generated by juicer pipeline using processed Hi-C interaction matrices.</p> <p>Only <em>cis </em>interactions were available.</p> <p>To extract the data, please see </p> <p>https://github.com/aidenlab/juicer/wiki/Data-Extraction</p>
Fig. 2 in (Hem.: Pentatomidae) in flooded rice crop in Southern Brazil Mapping of spatiotemporal distribution of Tibraca limbativentris Stal
Fig. 2. Interpolation maps by multiquadric equations of spatiotemporal distribution of occurrence categories of Tibraca limbativentris [I = no insect (green), II = adult (red), III = nymphs (pink), IV = adult + nymphs (blue)] in flooded rice crop in Southern Brazil, 2011/2012 crop season. *Thematic maps: (A) 11/19/11 [V4]; (B) 12/03/11 [V6]; (C) 12/17/11 [V8/V9]; (D) 01/07/12 [V11]; (E) 01/21/12 [R1]; (F) 02/02/12 [R5]; (G) 02/15/12 [R9]; (H) 02/29/12 [post-harvest = crop residues destroyed]. Phenological stage according to Counce et al. (2000).
Figure 2 in Soil BON Earthworm - A global initiative on earthworm distribution, traits, and spatiotemporal diversity patterns
Figure 2. Information on studies that will be resampled globally by the Soil BON Earthworm consortium. (A) Global distribution of studies, with the distribution of sites along longitude and latitude, (B) distribution of ecosystem types among studies, (C) localization of sites among terrestrial biomes defined by Mean Annual Temperature (MAT, °C) and Mean Annual Precipitation (MAP, mm), with the distribution of MAT and MAP values, (D) distribution of time span with blue and green colors representing the variable distribution before and after resampling, respectively, (E) Temporal coverage of individual studies.
Figure 3 in Soil BON Earthworm - A global initiative on earthworm distribution, traits, and spatiotemporal diversity patterns
Figure 3. Global distribution of Oligochaeta observations on iNaturalist (assessed on the 16th of November 2023) and longitudinal and latitudinal distribution.
Figure 3 in Spatiotemporal distribution, abundance, and species-environment relationships of Scyphozoa (Cnidaria) species in Hisarönü, Marmaris, and Fethiye bays (Muğla, Turkey
Figure 3. RDA ordination plot for Scyphozoa species, environmental parameters, sampling months, and stations. Sampling stations in RDA plot indicated with □: Hisarönü 1; ■: Hisarönü 2; O: Marmaris 1; ●: Marmaris 2; ◇: Marmaris 3; ◆: Marmaris 4; ×: Marmaris 5; ∆: Fethiye 1; △: Fethiye 2; ▲: Fethiye 3. Scyphozoa species indicated by the following abbreviations: Aa: Aurelia aurita; Ct: Cotylorhiza tuberculata; Ca: Cassiopea andromeda. Sampling months in RDA plot indicated with: 1: September 2011; 2: October 2011; 3: November 2011; 4: December 2011; 5: January 2012; 6: February 2012; 7: March 2012; 8: April 2012; 9: May 2012; 10: June 2012; 11: July 2012; 12: August 2012; 13: September 2012; 14: October 2012. See Table 4 for abbreviations of environmental variables.
Figure 4 in Spatiotemporal patterns of ground beetle diversity (Coleoptera: Carabidae) in a Ramsar wetland (Chott Tinsilt) of Algeria
Figure 4. Sample-based rarefaction (solid line) and extrapolation (dashed line) curves of species richness estimated for ground beetle communities living in the halophytic vegetation belts surrounding Chott Tinsilt in northeastern Algeria. White solid circles indicate reference samples. Light gray shaded areas represent lower and upper bounds of 95% confidence intervals for the S. Colored/shaded (est) areas indicate ±SDs.
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.