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3,673 results for “Temporal”
Data from: Widespread plant species temporal variation along environmental and microclimate gradients in Norwegian mountains
<p>The effect of climate change on mountain vegetation is influenced by environmental factors and site effects. To monitor the effect of climate change we therefore need to understand species sensitivity to microclimate and environmental gradients. The objective of this study is to study widespread plant species temporal and spatial variation along environmental and microclimate gradients in Norwegian mountains along a coast-inland gradient. Occurrence and abundance of plant species were surveyed in 110 study plots in four mountains at two points in time, seven years apart. Of the 222 plant species registered, <em>Salix herbacea, Phyllodoce caerulea, Carex bigelowii, Juncus trifidus, Vaccinium myrtillus, Avenella flexuosa</em>, and <em>Empetrum nigrum</em> were widespread across all mountains. These species responded differently to environmental and microclimate gradients, and abundance data was more sensitive than occurrence data. During the short time span we observed some indications of response which might support the assumption that boreal species outcompete alpine species in the forest transition zone, but our data does not indicate this effect at higher altitudes. Monitoring of climate change in mountains need to include plots along environmental and microclimate gradients as well as abundance of a set of wide-spread plant species that represent both regional and local environmental and climate gradients. However, when monitoring perennial plant species, the necessity of long-time monitoring projects is high because such species develop slowly over several decades.</p>
Spatial and temporal variability of freezing level in Patagonia's atmosphere
<p>Deprecated version. Please review the latest version: <a href="../doi/10.5281/zenodo.11397841">10.5281/zenodo.11397841 </a></p>
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
<p>Datasets provided in this storage are introduced in the paper: <em>Towards Neural Scaling Laws for Foundation Models on Temporal Graphs</em></p> <ul> <li>Each .csv file represents all transactions of the token network that has the same name as the file name (<em><tokenname.csv>)</em></li> <li>Each transaction corresponds to a row in each file.</li> <li>Each transaction has: <ul> <li> blockNumber : is the block ID of Ethereum that includes this transaction</li> <li>timestamp: time that the transaction is made in UNIX timestamp format</li> <li>tokenAddress : the address that specifies a unique ERC20 token</li> <li>from: address of sender</li> <li>to: address of receiver</li> <li>value: the amount the transaction</li> <li>fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks </li> </ul> </li> <li>To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. <ul> <li>Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to</li> <li>Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)</li> <li>We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py</li> </ul> </li> <li>However, we also provide raw <em>.csv </em> to divide into generate <em>edgeslist </em>and <em>label with a different setting.</em></li> </ul> <p><br><br></p>
BreizhSR: multi-temporal cross-sensor super-resolution of satellite imagery
<h1>BreizhSR, a super-resolution Sentinel-2 to SPOT-6/7 dataset </h1> <h2>1. Dataset motivation</h2> <p><strong>BreizhSR</strong> is a dataset targetting super-resolution of (RGB bands of) Sentinel-2 images by providing time series colocated in space and time with SPOT-6/7 acquisitions. This dataset is composed of cloud free Sentinel-2 time series (visible bands at 10m resolution) and SPOT-6/7 pansharpened color images resampled 2.5m resolution. The study area is the region of Brittany (Breizh in the local language), located on the northwestern coast of France with an oceanic climate. The dataset covers about 35 000 km² with mostly agricultural areas (about 80 %). All acquisitions are from 2018 in the Brittany region of France.</p> <h2>2. Dataset organization</h2> <p>The dataset folder follows the structure detailed below :</p> <p><code>BreizhSR</code><br><code>├── dataset_test.pkl</code><br><code>├── dataset_train.pkl</code><br><code>├── README.md</code><br><code>├── x</code><br><code>├── x_test</code><br><code>├── y</code><br><code>└── y_test</code></p> <p>The <code>README.md</code> file contains the same information as this description.</p> <p>Actual image patches are stored in the <code>x</code> and <code>x_test</code> folders for Sentinel-2 patches, and in the <code>y</code> and <code>y_test</code> folders for ground truth SPOT patches. Subfolders are organized using a integer identifier (e.g. <code>8355</code>) that denote the series identifier. Therefore, for the S2 series <code>x/8355</code>, the corresponding SPOT patch is in subfolder <code>y/8355</code>.</p> <p>This organization and additional metadata are described in two <a href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html">Pandas Dataframes</a> : <code>dataset_train.pkl</code> and <code>dataset_test.pkl</code>. These files are Dataframes serialized using the <a href="https://docs.python.org/3/library/pickle.html">pickle Python serialization protocol</a>. The columns available in these Dataframes are described in the table below.</p> <table> <tbody> <tr> <td>x</td> <td>y</td> <td>wkt</td> <td>spot6_name</td> <td>sen2_acquisitions</td> <td>dates_sen2</td> <td>dates_spot6</td> <td>split</td> </tr> <tr> <td>Latitude of the center point (expressed in Lambert 93 CRS)</td> <td>Longitude of the center point (expressed in Lambert 93 CRS)</td> <td>Area of interest geometry in well-known text format</td> <td>Path to the SPOT ground truth</td> <td>Paths to the Sentinel-2 input series</td> <td>Acquisition dates for the Sentinel-2 images</td> <td>Acquisition date for the SPOT ground truth</td> <td>`train` or `test`</td> </tr> </tbody> </table> <h2>3. Data collection and preprocessing</h2> <h3>Sentinel-2</h3> <p>Sentinel-2 constellation has twin satellites launched by the European Space Agency (ESA) in 2015 and 2017 that cover all Earth’s surfaces every five days at the equator. Level-2A images of the BreizhSR dataset are gathered via the THEIA platform, which employs the MAJA pre-processing algorithm to obtain atmospherically corrected ground reflectance. To match the SPOT-6 spectral characteristics, only RGB bands at a 10-meter spatial resolution (B4, B3,and B2) are used in the analysis. The images were collected for the nine tiles covering the Brittany region from the 1st of April 2018 to the 31st of August 2018, filtering images with a cloud cover under 5 %. Since the SPOT-6 data was acquired in the summer of 2018, the Sentinel-2 time period was chosen to include images from before and after the SPOT-6 acquisitions while staying in a range of similar seasonal and climate conditions.</p> <p>Sentinel-2 tiles are cropped into 3x74x74 patches. The dataset is preprocessed with a min-max normalization, using the 2% and 98% percentile as an estimation of minimum and maximum values of Sentinel-2 data to take into account the presence of outliers due to artifacts such as clouds and their shadows.</p> <h3>SPOT-6/7</h3> <p>Orthorectified SPOT data under the Licence Ouverte is collected from the <a href="https://openspot-dinamis.data-terra.org/">DINAMIS</a> platform. Multispectral images at 6m resolution are pansharpened using the panchromatic 1.5m reference using the RCS algorithm <a href="https://www.orfeo-toolbox.org/CookBook/Applications/app_BundleToPerfectSensor.html">Orfeo ToolBox</a>, similar to the Brovey pansharpening algorithm. The pansharpened tiles are preprocessed with a min-max normalization, downsampled at 2.5m resolution and patches are finally cropped with dimensions 3x296x296.</p> <h2>4. License</h2> <p>SPOT images and the Sentinel-2 Theia L2A products are released under the <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">Licence Ouverte 2.0</a> from the French government. This dataset contains modified Coprnicus Sentinel data from 2018, made available under free access by EU law. Other files in the dataset are licensed under Creative Commons Attribution 4.0 (CC BY 4.0).</p> <h3>Acknowledgements</h3> <p>We thank the support of GDR IASIS for funding this work under the SESURE project, the DINAMIS consortium, CNES/Airbus and IGN for access to the SPOT-6 data, and ESA for access to Sentinel-2 data. During the conduct of this research, Simon Donike received a European scholarship to engage in Master Copernicus in Digital Earth, Erasmus Mundus Joint Master Degree (EMJMD). We thank Dirk Tiede (Uni. Salzburg) for his help and feedback on BreizhSR. This work was performed using HPC resources from GENCI–IDRIS (grant 2022-AD011013003).</p>
Data results of usability evaluation of a geo-temporal crowding visualization platform
<p>Data results of usability evaluation of a geo-temporal crowding visualization platform.</p> <p>NASA-TLX was used for assessing the cognitive load of performing one task with the platform.</p> <p>SUS and UEQ were used for asessing the usability of performing three tasks with the platform.</p>
Fig. 5 in The black goby Gobius niger Linnaeus, 1758 in the Marchica Lagoon (Alboran Sea, Morocco): spatio-temporal distribution, its environmental drivers, and the site-related footprint
Fig. 5: Spatial and temporal distribution of Gobius niger in the Marchica Lagoon.
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
<p>This dataset includes estimates of transpiration, evaporation, soil respiration, and plant net photosynthesis obtained using five partitioning approaches. Flux components are available at 47 NEON sites over a period of five years. Additional meteorological inputs and water-use efficiency data are also included.</p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Data from: Positive spatial and temporal density-dependence drive early reproductive economy-of-scale effects of masting in a European old-growth forest community
<p>Masting, the spatial synchronization of interannual variation in seed production, can enhance reproductive efficiency through positive density-dependent processes (DD) that result in economies of scale (EOS), such as decreased pollen limitation and predator satiation in years of high reproduction. While the general occurrence of such EOS effects has been documented for masting species, few studies simultaneously investigated how spatial and temporal variation in reproduction affects pollination and predation. Furthermore, it is unclear whether the same mechanisms apply to co-occurring species with different levels of conspecific density, pollen limitation, and seed defenses. Here, we use a long-term data set with high spatial resolution of seed production of European beech (<em>Fagus sylvatica</em>), Norway spruce (<em>Picea abies</em>), and silver fir (<em>Abies alba</em>) in a primeval montane forest to investigate the relationship between reproductive effort, pollination efficiency, and predispersal predation by insects. We found that, along the temporal axis, the proportion of sound (fertilized and unpredated) seeds correlated positively with annual seed production over the 14-year study period in all three species, most strongly in beech and only weakly in silver fir. Moreover, the results show that in beech, spatial seed density interacts with plot-wide annual seed rain to enhance DD effects on seed predation, suggesting additive effects of synchronous reproduction on fitness benefits.</p> <p>Synthesis: For both pollination and predispersal predation in beech and spruce, the strongest DD effects occur at low levels of reproduction and quickly reach asymptotes at higher levels, suggesting the presence of thresholds in different EOS mechanisms. As variability and synchrony in mast-seeding are expected to decline with climate change, EOS effects driven by DD may remain stable until the threshold is reached, at which sudden declines would result in devastating effects on the availability of viable seeds for germination and recruitment.</p>
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>
Ziphius cavirostris presence relative to vertical and temporal variability of oceanographic conditions in the southern california bight
<p>The oceanographic conditions of the Southern California Bight (SCB) dictate the distribution and abundance of prey resources and therefore the presence of mobile predators, such as goose-beaked whales (<em>Ziphius cavirostris</em>). Goose-beaked whales are deep-diving odontocetes that spend a majority of their time foraging at depth. Due to their cryptic behavior, little is known about how they respond to seasonal and interannual changes in their environment. This study utilizes passive acoustic data recorded from two sites within the SCB to explore the oceanographic conditions that goose-beaked whales appear to favor. Utilizing optimum multiparameter analysis, modeled temperature and salinity data are used to identify and quantify these source waters: Pacific Subarctic Upper Water (PSUW), Pacific Equatorial Water (PEW), and Eastern North Pacific Central Water (ENPCW). The interannual and seasonal variability in goose-beaked whale presence was related to the variability in El Niño Southern Oscillation events and the fraction and vertical distribution of the three source waters. Goose-beaked whale acoustic presence was highest during the winter and spring and decreased during the late summer and early fall. These seasonal increases occurred at times of increased fractions of PEW in the California Undercurrent and decreased fractions of ENPCW in surface waters. Interannual increases in goose-beaked whale presence occurred during El Niño events. These results establish a baseline understanding of the oceanographic characteristics that correlate with goose-beaked whale presence in the SCB. Furthering our knowledge of this elusive species is key to understanding how anthropogenic activities impact goose-beaked whales.</p>
Code and data for spatial and temporal magnitude clustering analysis
<p>Code used for performing spatial and temporal seismic magnitude clustering analysis. Includes documentation (README.txt) with steps on how to implement the code. The public datasets used for this study can be accessed at the following locations: </p> <ul> <li><strong>Southern California Catalog: </strong> <ul> <li>SCEDC (2013): Southern California Earthquake Center.<br> Caltech.Dataset. doi:<a href="https://dx.doi.org/10.7909/C3WD3xH1">10.7909/C3WD3xH1</a></li> </ul> </li> <li><strong>Northern California Catalog:</strong> <ul> <li>NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC.</li> </ul> </li> <li><strong>Mixed-mode Laboratory Catalog:</strong> <ul> <li>Lin, Qing, et al. "Opening and mixed mode fracture processes in a quasi-brittle material via digital imaging." <em>Engineering Fracture Mechanics</em> 131 (2014): 176-193.</li> </ul> </li> <li><strong>ETAS Code:</strong> <ul> <li>Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021;<br> Embracing Data Incompleteness for Better Earthquake Forecasting. (Section 3.1)<br> <em>Journal of Geophysical Research: Solid Earth</em>; doi: <a href="https://doi.org/10.1029/2021JB022379">https://doi.org/10.1029/2021JB022379</a></li> </ul> </li> </ul>
Fig. 2 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 2. UPGMA dendrogram constructed on the basis of Nei's genetic distances
Resolving single-cell expression profiles by pseudo-temporal integration of transcriptomic and proteomic datasets.
<p>Raw and processed single cell proteomics (scp-MS) and scRNA-Seq data of HEK293-PIP-FUCCI cells which were challanged with hypoxia. The repository contains data for recreating the pseudo-temporal alignment analysis of transcription-translation profiles.</p>
Data from: Assessing temporal transition between microgranular and hyaline tests of calcareous microplankton during the Late Jurassic
<p>Calcareous microplankton increased in abundance during the latest Jurassic, coinciding with the increase in abundance of calcareous nannofossils and with the onset of deposition of pelagic calcareous oozes. However, the timing and causes of the shift from microgranular tests of the earliest microplankton (chitinoidellids) to hyaline tests of calpionellids are obscured because the ultrastructure of two-layered praecalpionellids that occur during the Tithonian is poorly documented. Here, we investigate the ultrastructure of chitinoidellids and praecalpionellids from Upper Tithonian deposits in the Western Carpathians. We show that (1) the chitinoidellid microgranular layer is formed by elongated, euhedral, densely-packed, nanometric needles rather than by fragments of calcareous nannofossils, (2) two-layered chitinoidellids (<em>Semichitinoidella</em>) are formed by an internal microgranular layer (identical to that of <em>Chitinoidella</em>) and by an external hyaline prismatic layer, and (3) two-layered <em>Praetintinnopsella</em> exhibits an internal hyaline layer (with densely-packed, equant microcrystals) and an external layer formed by a dark organic rim. The external layer in <em>Praetintinnopsella</em> thus does not have any relation to the microgranular layer in chitinoidellids and the external hyaline layer of <em>Semichitinoidella</em> is not equivalent in structure to the hyaline layer of <em>Praetintinnopsella</em>. As both single-layer and two-layered chitinoidellids appear prior to the first appearance of <em>Praetintinnopsella</em> but still co-occur with this genus in the lowermost Upper Tithonian deposits, the origin of two-layered <em>Praetintinnopsella</em> either reflects a major transformation in biomineralization towards larger and more packed crystals during their earlier divergence from the chitinoidellid lineage or an origination of two-layered tests with a hyaline layer from an independent non-chitinoidellid ancestor.</p>
Figure 5 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 5. – Annual changes in the Shannon-Wiener (A) and equitability (B) indices.
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>
Artifact for Misconceptions in Finite-Trace and Infinite-Trace Linear Temporal Logic
<p>Welcome!</p> <p>This artifact contains the survey instruments, final catalog, and labeled responses from our FM 2024 paper:</p> <p><em>Misconceptions in Finite-Trace and Infinite-Trace Linear Temporal Logic</em></p> <ul> <li>The artifact is primarily a dataset. There is no required software to run.</li> <li>The instruments and catalog are PDF files.</li> <li>The labeled responses are in spreadsheets, which we provide as open document files (`.ods`) with HTML as a backup.</li> </ul>
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.