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1,118 results for “Time series”
PASTIS-R PixelSet - Radar and Optical Satellite Image Time Series in Pixel-Set format
<p>Extension of the <a href="https://zenodo.org/record/5012942#.YaUaQ7so-V6">PASTIS benchmark</a> with radar and optical image time series.<br> In this version the satellite image time series are prepared in pixel-set format.</p> <p>See associated <a href="https://arxiv.org/abs/2112.07558v1">article</a> for more details.</p>
GPS time series of CMONOC
<p>GPS time series of CMONOC, which were used in the studying of vertical land motion throughout the Tianshan and forelands. </p>
Nonlinear time series analysis of the interaction between the citrus whitefly and the whitefly-specialist ladybird
<p>A comprehensive understanding of the top-down effects of natural enemies on agricultural pests is essential for achieving effective biological control in integrated pest management. However, it is typically difficult to identify causal effects between the interacting species from time series data, which have often been monitored for pest forecasting purposes in agricultural ecosystems, as it is likely to involve nonlinear (state-dependent) population dynamics. In this study, we applied a recently developed framework of nonlinear time series analysis (empirical dynamic modeling) to determine top-down and bottom-up effects between the citrus whitefly <i>Dialeurodes citri</i> and the whitefly-specialist ladybird <i>Serangium japonicum</i>. We used weekly monitoring data for the two species collected over 4 years in pesticide-free citrus groves located in Shizuoka Prefecture, central Japan. Although we were able to identify time-delayed positive effects of <i>D. citri </i>abundance on <i>S. japonicum</i> abundance, we failed to detect any significant causal effects of <i>S. japonicum</i> abundance on <i>D. citri</i> abundance. Moreover, weather variables (temperature and rainfall) were found to have only a negligible effect on the population dynamics of the two species. Our findings indicate that bottom-up rather than top-down effects predominate in the weekly dynamics of this predator–prey system. On the basis of these observations, we discuss whether <i>S. japonicum</i> would be an effective agent for the biological control of <i>D. citri</i> in open field systems.</p>
Time-series transcriptome analysis identified differentially expressed genes in broiler chicken infected with mixed Eimeria species
<p>Coccidiosis caused by the <em>Eimeria</em> species is a highly problematic disease in the chicken industry. Here, we used RNA sequencing to observe the time-dependent host responses of <em>Eimeria</em>-infected chickens to examine the genes and biological functions associated with immunity to the parasite. Transcriptome analysis was performed at three time points: 4, 7, and 21 days post-infection (dpi). Based on the changes in gene expression patterns, we defined three groups of genes that showed differential expression. This enabled us to capture evidence of endoplasmic reticulum stress at the initial stage of <em>Eimeria</em> infection. Furthermore, we found that innate immune responses against the parasite were activated at the first exposure; they then showed gradual normalization. Although the cytokine-cytokine receptor interaction pathway was significantly operative at 4 dpi, its downregulation led to an anti-inflammatory effect. Additionally, the construction of gene co-expression networks enabled identification of immunoregulation hub genes and critical pattern recognition receptors after <em>Eimeria</em> infection. Our results provide a detailed understanding of the host-pathogen interaction between chicken and <em>Eimeria</em>. The clusters of genes defined in this study can be utilized to improve chickens for coccidiosis control.</p>
Supplementary materials to "Brakenhoff et al., Application of time series analysis to estimate drawdown from multiple well fields."
<p>This repository contains the supplementary material that allow to reproduce all the results from the study submitted to Frontiers journal.</p> <p> </p>
Time-series covering up to four decades reveals major changes and drivers of marine growth and proportion of repeat spawners in an Atlantic salmon population
<p><span>1. </span><span>Wild Atlantic salmon populations have declined in many regions and are affected by diverse natural and anthropogenic factors. To facilitate management guidelines, precise knowledge of mechanisms driving population changes in demographics and life history traits is needed. </span></p> <p><span>2. </span><span>Our analyses were conducted on a) age and growth data from scales of salmon caught by angling in the river Etneelva, Norway, covering smolt year classes from 1980 to 2018, b) extensive sampling of the whole spawning run in the fish trap from 2013 onwards, and c) time series of sea surface temperature, zooplankton biomass and salmon lice infestation intensity. </span></p> <p><span>3. </span><span>Marine growth during the first year at sea displayed a distinct stepwise decline across the four decades. Simultaneously, the population shifted from predominantly 1SW to 2SW salmon, and the proportion of repeat spawners increased from 3-7%. The latter observation most evident in females, and likely due to decreased marine exploitation. Female repeat spawners tended to be less catchable than males by anglers. </span></p> <p><span>4. </span><span>Depending on the time-period analysed, marine growth rate during the first year at sea was both positively and negatively associated with sea surface temperature. Zooplankton biomass was positively associated with growth while salmon lice infestation intensity was negatively associated with growth. </span></p> <p><span>Collectively these results are likely to be linked with both changes in oceanic conditions and harvest regimes. Our conflicting results regarding the influence of sea surface temperature on marine growth is likely to be caused by long-term increases in temperature which may have triggered (or coincided with) ecosystem shifts creating generally poorer growth conditions over time, but within shorter data sets warmer years gave generally higher growth. We encourage management authorities to expand the use of permanently monitored reference rivers with complete trapping facilities, like the river Etneelva, generating valuable long-term data for future analyses.</span></p>
data&result of Time Series Surface Water Reconstruction Method (TSWR) based on Spatial Relationship of Multi-stage Water Boundaries
<p>It is a dataset for a paper of Time Series Surface Water Reconstruction Method (TSWR) based on Spatial Relationship of Multi-stage Water Boundaries.</p>
ERA5-Land weekly: Total precipitation, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br> Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br> The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate proportion of ERA5-Land / aggregated CHELSA<br> 3. interpolate proportion with a Gaussian filter to 30 arc seconds<br> 4. multiply the interpolated proportions with CHELSA<br> Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.</p> <p>File naming:<br> Average of daily sum: <code>era5_land_prectot_avg_weekly_YYYY_MM_DD.tif</code><br> Sum of daily sum: <code>era5_land_prectot_sum_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel values:<br> mm * 10<br> Example: Value 218 = 21.8 mm</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Period:<br> 01/01/2016 - 12/31/2020</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
Crop visitation by wild bees declines over an 8‐year time series: A dramatic trend, or just dramatic between‐year variation?
<p>Despite widespread recognition of the need for long-term monitoring of pollinator abundances and pollination service provision, such studies are exceedingly rare.</p> <p>In this study, we assess changes in bee visitation and net capture rates at watermelon crop flowers at 19 farms in the mid-Atlantic region of the USA from 2005 to 201</p> <p>Over the eight years, we found a 58% decline in wild bee visitation to crop flowers, but no significant change in honeybee visitation rate. Most types of wild bees showed similar declines in both the visitation and the net capture data; bumblebees however declined by 56% in the visitation data but showed no change in net capture rates.</p> <p>While we detected large and significant declines in wild bees when using GLMM models, permutation analyses that account for non-directional variation in abundance were non-significant, demonstrating the challenge of identifying and describing trends in highly variable populations.</p> <p>As far as we are aware, this paper represents one of fewer than 10 published time series (defined as >5 years of data) studies of changes in bee abundance, and one of only two such studies conducted in an agricultural setting. More such studies are needed in order to understand the magnitude of bee decline and its ramifications for crop pollination.</p>
Size spectrum, time series and atom number distribution of macroalgal emission vapors and oxidation products
<p>We conducted oxidation and NPF experiments with vapor emissions from real-world coastal macroalgae in a bag reactor. The dataset showed the measurement results of volatile precursors and their oxidation products. The dataset was organized by the order of figures in our submitted manuscript.</p>
FrenchPiezo: the French mainland groundwater level multivariate time series
<p><strong>THIS REPOSITORY IS INCOMPLETE, THE CORRECT DATASET IS AT: <a href="https://zenodo.org/record/7193812#.Y3-ThhTMLic">FrenchPiezo| 7193812#.Y3-ThhTMLic</a> </strong></p> <p>This dataset is a multivariate time series of groundwater level (a.k.a piezometric level) measured by sensors in many cities in the French mainland from January 2015 to January 2021 (2,221 days). The dataset contains 1026 multivariate time series composed of three dimensions sampled daily:</p> <ul> <li><strong>p: </strong>groundwater level</li> <li><strong>tp: </strong>precipitation</li> <li><strong>e: </strong>evapotranspiration</li> </ul> <p>Each time series is identified by a <strong>bss code </strong> which is the identifier of the piezometer using to collect the associated groundwater level.</p> <p>The groundwater level is collected from <a href="https://hubeau.eaufrance.fr/">Hub'Eau</a>, the french service for accessing water data. The precipitation and evapotranspiration are collected from the Copernicus ERA5 climate database. </p> <p>The file <em>dataset_2015_2021.csv</em> contains the raw dataset with missing values</p> <p>The file <em>dataset_nomissing_linear.csv</em> is similar to the previous one, but the missing data have been imputed using linear interpolation.</p> <p> </p> <p>More details about this dataset with source code for collecting it can be found in our paper: </p> <p>```</p> <p>Mbouopda, Michael Franklin, et al. "Experimental study of time series forecasting methods for groundwater level prediction." <em>ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data</em>. 2022.</p> <p>```</p>
Monthly MODIS LST data related to the article: A new fully gap-free time series of land surface temperature from MODIS LST data
<p>Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps.</p> <p>[1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333</p> <p>LICENSE: Open Data Commons Open Database License (ODbL) http://opendatacommons.org/licenses/odbl/</p> <p>Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006.</p> <p><strong>The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min</strong> (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), <strong>aggregated to monthly data</strong>. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL:<br> <br> GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid",<br> DATUM["Not specified (based on Clarke 1866 spheroid)",<br> SPHEROID["Clarke 1866",6378206.4,294.9786982138982,<br> AUTHORITY["EPSG","7008"]]],<br> PRIMEM["Greenwich",0],<br> UNIT["degree",0.0174532925199433]]<br> </p> <p><strong>File name</strong> abbreviations:</p> <ul> <li>avg = average of daily averages</li> <li>min = minimum of daily minima</li> <li>max = maximum of daily maxima</li> </ul> <p>Meaning of <strong>pixel values</strong>:</p> <ul> <li>The <strong>pixel values</strong> are coded in <strong>degree Celsius * 100</strong> (hence, to obtain °C divide the pixel values by 100.0).</li> </ul> <p>Version <strong>changelog</strong>:</p> <ul> <li>V1.1.0: GeoTIFF metadata updated.</li> <li>V1.0.0: original upload</li> </ul>
Time series used in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic"
<p>These 6 files contain time series built using reanalyses datasets of the ECMWF as discussed in the manuscript "Causal dependences between the coupled ocean-atmosphere dynamics over the Tropical Pacific, the North Pacific and the North Atlantic" submitted for discussion in the journal "Earth System Dynamics".</p>
Google Trends time series for the term "topic modeling"
<p>Dataset received from Google Trends for the phrase "topic modeling'' on 31 January 2024 using the URL <a href="https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de">https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de</a></p> <p><em>Data obtained from Google LLC, which is the ultimate owner of these data. Published for academic and non-commercial replication purposes only.</em></p>
Synthetic dataset and prediction files for the paper "Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction", by Costantino et al. (2024)
<p>Synthetic database used for training and evaluation of SSEdenoiser</p>
ERA5-Land monthly: Total precipitation, monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Total precipitation:<br>Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.</p> <p>Processing steps:<br>The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate proportion of ERA5-Land / aggregated CHELSA <br>3. interpolate proportion with a Gaussian filter to 30 arc seconds <br>4. multiply the interpolated proportions with CHELSA <br>Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.</p> <p>File naming:<br><code>ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif</code> <br>e.g.:<code>ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>mm * 10<br>Scaled to Integer, example: value 218 = 21.8 mm</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28:18N<br>south: 14:42N<br>west: 17:05W<br>east: 4:49W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Lineage:<br>Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original ERA5-Land dataset license:<br><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Representation type: Grid</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
Monthly time series of spatially enhanced relative humidity for Europe at 1000 m resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>The data have been reprojected to EU LAEA.</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br>north: 6874000<br>south: -485000<br>west: 869000<br>east: 8712000</p> <p>Spatial resolution:<br>1000 m</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="https://doi.org/10.5281/zenodo.6146383">https://doi.org/10.5281/zenodo.6146383</a></p>
The GPS time series of 2005 Kashmir earthquake
<p>These data includes the near-field GPS data provided by Jouanne et al. (2011), InSAR data provided by Wang & Fialko (2014), and the far-field GPS data processed in this work. Additionally, we have uploaded the coseismic rupture models from Avouac et al. (2006) and Yan et al. (2013). The far-field GPS data includes the original time series, the fitting interseismic velocities, and the fitting time series of interseismic and postseismic. These datasets are organized into corresponding folders, with filenames that clearly describe the contents of each file.</p>
Mediterranean Sea Climatic Indices - Time Series of T/S, OHC/OSC Anomalies
<p>Climatic indices for the Mediterranean Sea (6.25W-36.5E, 30N-46N) from 1950 to 2015. The file<br> "Med_Climatic_Indices_TimesSeries.tar.gz" contains a directory named "TimeSeries" with the following 5 indices:</p> <ul> <li>TimeSeries_Annual.nc: annual time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0103.nc: winter time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0406.nc: spring time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_0709.nc: summer time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> <li>TimeSeries_Seasonal_1012.nc: autumn time series for Temperature (T), Salinity (S), Ocean Heat Content (OHC) and Ocean Salt Content (OSC) anomalies for 57 running decades (from decade 1950-1959 (Y1) to decade 20062015) and four layers from d1-d2 m (surface 5-150 m, intermediate 150 - 600 m, deep 600 - 4000 m and the whole column (5 - 4000 m).</li> </ul>
GNSS time series used for manuscript 2019JB017614R
<p>This dataset includes the raw time series used for deriving the GNSS velocity solution in manuscript 2019JB017614R submitted to Journal of Geophysical Research: Solid Earth (Strain Accommodation in the Daliangshan Mountain area, Southeastern Margin of the Tibetan Plateau).</p>
ScienceDex guides
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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.