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37 results for “spatial-temporal”
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
<p>Distance measures are widely used for examining genetic structure in datasets that comprise many individuals scored for a very large number of attributes. Genotype datasets composed of single nucleotide polymorphisms (SNPs) typically contain bi-allelic scores for tens of thousands if not hundreds of thousands of loci.</p> <p>We examine the application of distance measures to SNP genotypes and sequence tag presence-absences (SilicoDArT) and use real datasets and simulated data to illustrate pitfalls in the application of genetic distances and their visualization.</p> <p>The datasets used to illustrate points in the associated review are provided here together with the R script used to analyse the data. Data are either simulated internal to this script or are SNP data generated as part of other studies and included as compressed binary files readily accessable by reading into R using R base function readRDS(). Refer to the analysis script for examples.</p>
Supplementary: Spatial-Temporal Variation in Erosion Rate of Lohit Bomi-Chayu Batholith around Eastern Himalayan Syntaxis (SE of Namche Barwa massif)
<p>It is supplementary material of Borgohain et al. (2025) in Tectonic journal and contains:</p> <ol> <li> <p>Method descriptions (Low-temperature thermochronology, Bayesian erosion model, mixing model, mineral concentration factor, HeFTy thermal modeling, age2exhume thermal modeling, and topographic analysis).</p> </li> <li> <p>Dataset of detrital low-temperature thermochronology (AFT, ZFT), insitu low-temperature thermochronology (AFT, ZHe, ZFT, apatite track length).</p> </li> <li> <p>Modeling outputs.</p> </li> <li> <p>Sample codes and locations (latitude, longitude).</p> </li> </ol> <p> </p>
Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
<p>The quality of cultivated land determines the production capacity of cultivated land and the level of regional development, and also directly affects the food security and ecological safety of the country. This paper starts from the perspective of spatial pattern of cultivated land quality and uses spatial autocorrelation analysis to study the spatial aggregation characteristics and differences of cultivated land quality in Henan Province at the county level scale, and also uses bivariate spatial autocorrelation to analyze the influence of neighboring influences on the quality of cultivated land in the target area. The spatial autoregressive model was used to further analyze the driving factors affecting the quality of cultivated land, and the influence of cultivated land area index was coupled in the process of rating analysis, which was finally used as a basis to propose more precise measures for the protection of cultivated land zoning. The results show that: (1) The quality of cultivated land in Henan Province has a strong spatial correlation (global Moran's I≈0.710) and shows an obvious aggregation pattern in spatial distribution; positive correlation types (high-high and low-low) are concentrated in north-central and western mountainous areas of Henan Province, respectively; negative correlation types are discrete. The negative correlation types are distributed in a discrete manner. (2) The bivariate spatial autocorrelation results show that Slope (Moran's I≈-0.505), Irrigation guarantee rate (IGR, 0.354), Urbanization rate (-0.255), Total agricultural machinery power (TAMP, 0.331) and Pesticide use (0.214) are the main influencing factors. (3) According to the absolute values of the regression coefficients, it can be seen that the magnitude of the influence of different factors on the quality of cultivated land is: Slope (0.089) >IGR (0.025) > Urbanization rate (0.002) > TAMP (0.001) > Pesticide use (1.96e-006). (4) Based on the spatial pattern presented by the spatial autocorrelation results, we proposed corresponding protection zoning measures to provide more scientific reference decisions and technical support for the implementation of refined cultivated land management in Henan Province. </p>
Determinism and stochasticity in the spatial-temporal continuum of ecological communities: the case of tropical mountains
<p>Ecological communities are assembled in a spatial-temporal continuum. However, we still have a poor understanding of the relative importance of different mechanisms structuring community composition (i.e., beta-diversity) in space and time. In this study, we start by introducing a conceptual model that capitalizes upon the core-occasional species concept to predict that the assembly process in tropical mountains is driven by the deterministic turnover of core species in space via habitat sorting, but the turnover of occasional species through time via stochastic events of colonization and local extinctions. We then propose a general analytical framework that allows assessing these predictions by partitioning the total variance of a species-by-site-by-time matrix (i.e., total beta-diversity) among its purely spatial (variation in space independent of time), purely temporal (variation in time independent of space), and spatiotemporal (i.e., variation across different sites across different moments in time) components. Through simulation models, we provided theoretical support that the proposed analytical framework is suitable to test the predictions derived from our conceptual model. We then used this framework to identify general patterns and quantify the relative importance of processes underlying the spatial and temporal organization of ten distinct insect metacommunities along a tropical elevational gradient. As predicted, we found that, across taxa, spatial beta-diversity was mainly explained by environmental variation alone: a pattern that indicates the spatial turnover of core species. In contrast, temporal beta-diversity could not be distinguished from the expectation of null models where communities are simply represented by random draws from species pools: a pattern that indicates a temporal turnover of occasional species within communities. Taken together, our findings illustrate how our conceptual model and quantitative framework can articulate a better understanding of community assembly in space and time.</p>
Data for "From pandemic to endemic: Spatial-temporal patterns of influenza-like-illness incidence in a Swiss canton, 1918-1924"
<p>Datasets underlying the analysis of the paper: "From pandemic to endemic: Spatial-temporal patterns of influenza incidence in a Swiss canton, 1918-1924"</p> <ul> <li><strong>Cofactors_1918.xlsx</strong> : Ecological cofactors for each bernese municipility</li> <li><strong>Data_Influenza_Bern.xlsx: </strong>Influenza data (1920-1924)</li> <li><strong>Data_Spanishflu.xlsx: </strong>"Spanish flu" data (1918-1919)</li> <li><strong>Data_Population.xlsx: </strong>Population for each municipality</li> <li><strong>Tb_death.xlsx : </strong>Number of tuberculosis death</li> <li><strong>Fabrik_Statistik_1929.xlsx : </strong>Information about the factories</li> </ul>
Determinism and stochasticity in the spatial-temporal continuum of ecological communities: the case of tropical mountains
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Data from: Distances and their visualization in studies of spatial-temporal genetic variation using single nucleotide polymorphisms (SNPs)
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Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
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Spatial-temporal gradient variation patterns of fish trophic guilds in a freshwater river wetland ecosystem of northeastern China
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Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
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Spatial-temporal expression analysis of lineage-restricted shell matrix proteins in the slipper snail Crepidula atrasolea reveals shell field regionalization and distinct cell populations
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Spatial-temporal change of climate in relation to urban fringe development in central Arizona-Phoenix
Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear re
Spatial-temporal growth, distribution, and diffusion of marine microplastic research and national plastic policies
<p>Plastic accounts for 80% of material waste in the ocean. The field of marine microplastic research is relatively new and is growing rapidly, in terms of published papers as well as institutions and countries conducting research. To combat plastic pollution, there is sufficient evidence that policies can lead to reduced plastic production and consumption both locally and globally. We aim to understand how marine plastics research and policies have grown and spread. Specifically, we used scientometric and spatial diffusion methods to best explain how ideas (in this case science and policy) clustered and spread geographically through time. We performed systematic literature searches to determine the spatial and temporal growth of marine microplastic publications and national plastic policies from 1900-2019. We found that more countries adopted national plastic policies than those that have conducted marine plastic research. Doubling times of each temporal growth rate analyzed (research paper, institution, country, and national policy) ranged from 1.1 – 4.05 years. Further, each temporal growth rate had a break point where doubling time changed significantly. Marine microplastic research has grown exponentially since 2006, and the topics of inquiry have increased steadily. Marine microplastic publication spread at the institution level is best explained by a hybrid of expansion and relocation diffusion while national plastic policy spread is best explained by expansion diffusion. Marine microplastic research activity was not a good indicator of a country's resources or motivation toward national plastic policies.</p>
Data from: Spatial-temporal dynamics of Neotropical velvet ant (Hymenoptera: Mutillidae) communities along a forest-savanna gradient
Understanding how and why biological communities are organized over space and time is a major challenge and can aid biodiversity conservation in times of global changes. Herein, spatial-temporal variation in the structure of velvet ant communities was examined along a forest-savanna gradient in the Brazilian Cerrado to assess the roles of environmental filters and interspecific interactions upon community assembly. Velvet ants were sampled using 25 arrays of Y-shaped pitfall traps with drift fences for one year along an environmental gradient from cerrado sensu stricto (open canopy, warmer, drier) to cerradão (closed canopy, cooler, moister). Dataloggers installed on each trap recorded microclimate parameters throughout the study period. The effects of spatial distances, microclimate parameters and shared ancestry on species abundances and turnover were assessed with canonical correspondence analysis, generalized dissimilarity modelling and variance components analysis. Velvet ant diversity and abundance were higher in the cerrado sensu stricto and early in the wet season. There was pronounced compositional turnover along the environmental gradient, and temporal variation in richness and abundance was stronger than spatial variation. The dry season blooming of woody plant species fosters host abundance and, subsequently, velvet ant captures. Species were taxonomically clustered along the gradient with Sphaeropthalmina (especially Traumatomutilla spp.) and Pseudomethocina more associated, respectively, with cerrado sensu stricto and cerradão. This suggests a predominant role of environmental filters on community assemble, with physiological tolerances and host preferences being shared among members of the same lineages. Induced environmental changes in Cerrado can impact communities of wasps and their hosts with unpredictable consequences upon ecosystem functioning and services.
Dataset for "Enhancing Cloud Detection in Sentinel-2 Imagery: A Spatial-Temporal Approach and Dataset"
<p>This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).</p> <p>Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.</p> <p><strong>Dataset </strong><strong> </strong><strong>Structure</strong>:</p> <p>Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.</p> <p>For each sample, it includes:</p> <ol> <li>Sample ID;</li> <li>Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);</li> <li>Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;</li> <li>Ordinal list for each timestamp;</li> <li>Sample weight list (reserved);</li> </ol> <p>Here is a demonstration function for parsing the TFRecord file:</p> <pre><code>import tensorflow as tf # init Tensorflow Dataset from file name def parseRecordDirect(fname): sep = '/' parts = tf.strings.split(fname,sep) tn = tf.strings.split(parts[-1],sep='_')[-2] nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64) t = tf.data.Dataset.from_tensors(tn).repeat().take(nn) t1 = tf.data.TFRecordDataset(fname) ds = tf.data.Dataset.zip((t, t1)) return ds keys_to_features_direct = { 'localid': tf.io.FixedLenFeature([], tf.int64, -1), 'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''), 'labels': tf.io.FixedLenFeature((), tf.string, ''), 'dates': tf.io.FixedLenFeature((), tf.string, ''), 'weights': tf.io.FixedLenFeature((), tf.string, '') } # The Decoder (Optional) class SeriesClassificationDirectDecorder(decoder.Decoder): """A tf.Example decoder for tfds classification datasets.""" def __init__(self) -> None: super().__init__() def decode(self, tid, ds): parsed = tf.io.parse_single_example(ds, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) sample_dict = { 'tid': tid, # tile ID 'dates': dates, # Date list 'localid': parsed['localid'], # sample ID 'imgs': decoded, # image array 'labels': label, # label list 'weights': weight } return sample_dict # simple function def preprocessDirect(tid, record): parsed = tf.io.parse_single_example(record, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) return tid, dates, parsed['localid'], decoded, label, weight t1 = parseRecordDirect('filename here') dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE) # </code></pre> <p><strong>Class Definition:</strong></p> <ul> <li>0: clear</li> <li>1: opaque cloud</li> <li>2: thin cloud</li> <li>3: haze</li> <li>4: cloud shadow</li> <li>5: snow</li> </ul> <p><strong>Dataset Construction:</strong></p> <p>First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products. <br>It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution. </p> <p>Then, the time series image patches of two shapes are cropped with each point as the center.<br>The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.<br>And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.</p> <p>The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.</p>
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Motivation: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This data set builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent General Circulation Models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution. Main types of variable contained: 19 bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables. Spatial location and grain: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator). Time period and grain: The last 5.4 million years. The grain is 10 kyr (= 539 time periods). Level of measurement: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account. Software format: All data are available as ASCII (ESRI) grid files.
Figure 2 in Spatial-temporal composition of Chrysomelidae (Insecta: Coleoptera) communities in southern Brazil
Figure 2. Species accumulation curves of Chrysomelidae at different sites in Paraná State, Brazil. ANT, Antonina; SJ, São José dos Pinhais; CO, Colombo; PG, Ponta Grossa; TB, Telêmaco Borba; GUA, Guarapuava; JU, Jundiaí do Sul; FE, Fênix.
Figure 3 in Spatial-temporal composition of Chrysomelidae (Insecta: Coleoptera) communities in southern Brazil
Figure 3. Cluster analyses of Chrysomelidae at different sites in Paraná State, Brazil (Jaccard coefficient; cc = 0.71).
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.