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1,118 results for “Time series”
Time series of epileptic seizures in a 8yo male on a cannabidiol trial
<p>Time series of epileptic seizures in a 8yo male with idiopathic catastrophic epilepsy (probably of mitochondrial origin) in a cannabidiol (CBD) trial.</p> <p>The data are of <strong>observed seizures </strong>during each day by the patient's parents and caregivers and thus are not exact values for total daily seizure activity; the values in this dataset are best considered a lower bound. However, observation effort was essentially constant over the time series, so the data likely reflect overall trends even though precise daily seizure counts are not available.</p> <p>The time series includes a 21-day baseline period and a 36-day trial period. The CBD was administered via g-tube in an olive oil base. Dosage was approximately 7.5 mg/kg/day for two weeks, and approximately 8 mg/kg/day for three weeks.</p> <p>Each 1ml of oil contained 10.00mg of total CBD and 0.42mg of total THC, for a CBD:THC ratio of approximately 20:1 (analysis by HPLC).</p> <p><strong><em>Data Dictionary</em></strong></p> <ul> <li><strong>Date</strong>: YYYY-MM-DD</li> <li><strong>Tonic</strong>: tonic seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-seizures)</li> <li><strong>Tonic-Clonic</strong>: tonic-clonic (aka "grand mal") seizure count (http://www.epilepsy.com/learn/types-seizures/tonic-clonic-seizures)</li> <li><strong>Spasms</strong>: myoclonic seizure count (http://www.epilepsy.com/learn/types-seizures/myoclonic-seizures)</li> <li><strong>Total</strong>: total number of seizures (sum of above)</li> <li><strong>Notes</strong>: notes by parents</li> </ul>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo CSV - Presentamos una nueva serie de extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite</p> <p>CSV file - We present a new time series of September Arctic sea ice extent from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014 - A new time series of September Arctic sea ice extent: 1935-2014
<p>Archivo NetCDF- Presentamos una nueva serie de datos raster con la extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite.</p> <p>NetCDF file - We present a new gridded September Arctic sea ice extent dataset from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.</p> <p> </p>
Hashtag adoption time series
<p>The dataset released here has been used in our paper "#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags." [link to the paper](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6083/6376)</p> <p>In this study, we examine the growth, survival, and context of over 250 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between the hashtags' contextual features and the relative success of hashtags. This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks, which has implications for understanding social influence and collective action in social media more generally.</p> <p>The dataset was the hashtag adoption time sequences during the four debates. They are used to create **Figure 1** in the paper (Cumulative tweet volume of hashtags over time, starting from each debate). </p> <p>Each csv file has three columns:<br> time (in UTC), y (the minute-by-minute cumulative tweet count), and tag (the hashtag name).</p> <p>The onset of the four debates are:<br> Debate 1: 2012-10-04 00:00:00 UTC<br> Debate 2: 2012-10-12 00:00:00 UTC<br> Debate 3: 2012-10-17 00:00:00 UTC<br> Debate 4: 2012-10-23 00:00:00 UTC</p> <p>The raw tweet data have been released via the ICWSM Data Sharing Service. See: <br> [http://www.icwsm.org/2013/datasets/datasets/](http://www.icwsm.org/2013/datasets/datasets/)</p> <p> </p> <p><strong>Publication</strong><br> If you make use of these data sets and code, please cite:</p> <p>Lin, Y.-R., Margolin, D., Keegan, B., Baronchelli, A., Lazer, D. (2013). #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM 2013) </p>
Three-dimensional GNSS Time Series Data for Terrestrial Water Storage Changes Inversion in Yunnan, China
<p>The dataset includes three-dimensional GNSS time series data featured in the publication "Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China", published in 'Water Resources Research'.</p> <p>Reference:<br>Zhu, H., Chen, K., Hu, S., Liu, J.,Shi, H., Wei, G., et al. (2023). Using the global navigation satellite system and precipitation data to establish the propagation characteristics of meteorological and hydrological drought in Yunnan, China. Water Resources Research, 59, e2022WR033126. https:// doi.org/10.1029/2022WR033126</p> <p><br>The sitelist file lists basic information about all the utilized stations, including their names and geographic coordinates. <br>The Time.mat file contains the time vectors of the data employed. <br>The Filter_time_series_N/E/U.mat files showcase the filtered time series, which have been processed using Independent Component Analysis (ICA) for the inversion of terrestrial water storage in Yunnan, after removing the effects of outliers, steps, and non-tidal atmospheric/oceanic loading.</p>
CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany
<h2>Description</h2> <p>CAMELS-DE provides a comprehensive collection of hydro-meteorological timeseries data (e.g. discharge, water level, precipitation, air temperature) and catchment attributes for 1582 streamflow gauges across Germany. The time series data is in daily resolution and spans up to 70 years, from January 1951 to December 2020. The static catchment attributes include information on topography, soils, land cover, hydrogeology and human influences. Additionally, the dataset includes discharge simulations from a regional Long-Short Term Memory (LSTM) network and a conceptual hydrological model (HBV), providing benchmark data for future hydrological modelling studies in Germany.</p> <p>The accompanying data description gives information on data sources, the structure of the data set and contains extensive information on time series and catchment attribute variables. In addition, up-to-date benchmark results of the LSTM and HBV are provided.</p> <blockquote> <p><strong><strong>Important: As CAMELS-DE is continuously developed and updated, please ensure that you cite the correct version of the dataset that you are using.<br><br></strong></strong><strong>The CAMELS-DE data description paper is available here: <a href="https://doi.org/10.5194/essd-16-5625-2024">https://doi.org/10.5194/essd-16-5625-2024</a>.</strong></p> </blockquote> <p>Information about the code and methods for generating CAMELS-DE can be found here: <a title="CAMELS-DE Processing Pipeline" href="https://doi.org/10.5281/zenodo.12760336" target="_blank" rel="noopener">CAMELS-DE Processing Pipeline</a>.</p> <p>CAMELS-DE is also part of the Caravan project, a global hydrological dataset. Due to the use of data products that are available beyond the Germany national boundaries, Caravan-DE includes 305 additional streamflow gauges, resulting in a total of 1887 streamflow gauges: <a href="https://doi.org/10.5281/zenodo.13320514">https://doi.org/10.5281/zenodo.13320514</a>.</p> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <h3>Changelog</h3> <ul> <li><strong>v1.1.0</strong> <ul> <li>LSTM benchmark results are now based on a <strong>LSTM with 10 ensemble members</strong>, changing the median NSE in the testing period from 0.83 to 0.85 <div> <ul> <li>The columns <em>discharge_spec_sim_lstm</em> and <em>discharge_vol_sim_lstm</em> in <em>timeseries_simulated</em> are now based on the median values of the 10 ensemble members.</li> <li>The column <em>NSE_lstm</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> is now calculated from the median simulations of the 10 ensemble members</li> <li><em>model_parameters/LSTM/CAMELS_DE_epochs_training_lstm.zip</em> now contains the epochs of the 10 ensemble members</li> </ul> </div> </li> <li>The columns <em>NSE_lstm</em>, <em>NSE_hbv</em> and <em>training_perc_complete</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> were calculated from 2001 - 2020, now corrected to 2000 - 2020</li> <li>Fixed a bug in the calculation of <em>high_prec_dur</em> and <em>low_prec_dur</em> in <em>CAMELS_DE_climatic_attributes.csv</em> calculation (thank you to Bastian Klein from BfG for reporting this issue)</li> <li>Bayern: removed blank space after gauge and water body name and removed water body name from some gauge names, where it was included as "[gauge_name]_[water_body_name]", e.g. "Würzburg_Main" -> "Würzburg", water body name is now only included in the `water_body_name` column in<code> </code><em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Nordrhein-Westfalen: corrected some wrong river names in <em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Sachsen: added `gauge_elevation_metadata` information to <em>CAMELS_DE_topographic_attributes.csv</em></li> </ul> </li> </ul> <ul> <li><strong>v1.0.0</strong> <ul> <li>CAMELS-DE v1.0.0 is the version of the dataset that is described by the <a href="https://doi.org/10.5194/essd-2024-318">CAMELS-DE data description paper</a>.</li> <li>Addition of the federal state of Saarland, resulting in 27 additional catchments and coverage of all federal states except the city states of Berlin, Bremen and Hamburg. This also leads to a change in the title of the dataset from 1555 catchments to 1582 catchments.</li> <li>Addition of HBV model parameters and LSTM model training period epochs.</li> <li>Catchment DE911970: Removal of erroneous zero discharge values at the beginning of the measurement period.</li> <li>Minor fixes such as the elimination of discrepancies between the variable names in the dataset and in the data description.</li> <li>We were able to identify and fix some of these problems based on the feedback from the community, thank you very much!</li> </ul> </li> </ul>
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
<p>We present a <strong>multi-temporal</strong>, <strong>multi-modal</strong> remote-sensing dataset for predicting <strong>how active wildfires will spread</strong> at a resolution of 24 hours. The dataset consists of <strong>13.607 images</strong> across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a <strong>full time series of daily observations</strong>, containing detected active fires and variables related to <strong>fuel, topography and weather conditions</strong>.</p><h2>Documentation</h2><p><i><strong>WildfireSpreadTS_Documentation.pdf</strong></i> includes further details about the dataset, following Gebru et al.'s <strong>"Datasheets for Datasets"</strong> framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. </p><h2>Code: Getting started</h2><p>Get started working with the dataset at <a href="https://github.com/SebastianGer/WildfireSpreadTS">https://github.com/SebastianGer/WildfireSpreadTS</a>. </p><p>The code includes a <strong>PyTorch Dataset</strong> and <strong>Lightning DataModule </strong>to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository.</p><p> </p><p>This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.</p>
Soil study results at Vallon de Nant : Soil moisture and soil temperature time series and granulometry results
<p>Dataset:</p><ul><li><a href="https://zenodo.org/api/records/10136586/draft/files/Particule_size_distribution.csv/content">Particule_size_distribution.csv : </a><br>Particle size distribution obtained for 34 samples in Vallon de Nant. Please refer to the pdf report for technical information.<br>Columns description : <br>point,depth= identification of the point. Please refer to the pdf notice<br>size (in micrometers) : Particle size ranging from 0.003mum to 2 mm<br>value (in %) : Fraction of the material volume corresponding to the size<br>USDAclass : Soil class according to the USDA classification for each sample<br> </li><li><a href="https://zenodo.org/api/records/10136586/draft/files/measure_T_HU_5TM_3stations.csv/content">measure_T_HU_5TM_3stations.csv :</a><br>Hourly soil moisture and soil temperature measurements at 3 points and different depths in the catchment. Please refer to the pdf report for technical information.<br>Columns description : <br>Time,Hour : recording time stamp<br>portX_HU : Soil moisture recorded at the X slot. Please refer to the notice for sensor depth.<br>portX_T : Soil temperature recorded at the X slot.<br>Station : Name of the measurement point (Auberge, Chalet or LaChaux). Auberge and LaChaux are at the exact same location than the corresponding weather stations. Chalet point is on the left bank on the river, near little bridge. Please refer to the pdf notice.</li></ul>
PV-gradient (PVG) tropopause: Time series 1980--2017 in four reanalyses
<h1>PV-gradient tropopause time series</h1> <h2>General description</h2> <p>These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, <a href="https://doi.org/10.1029/2010JD014343">doi:10.1029/2010JD014343</a>) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471).</p> <h2>Data and methods</h2> <p>The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published):</p> <ul> <li>from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55</li> <li>for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily at 00h, 06h, 12h and 18h</li> <li>on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses.</li> </ul> <h2>Contents</h2> <p>Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension:</p> <ul> <li>time in seconds since 2000/01/01 00:00 UTC</li> <li>u_lim: Zonal wind speed at the PVG tropopause</li> <li>vh_lim: Horizontal wind speed at the PVG tropopause</li> <li>q_lim: Maximum of Q = vh * Grad PV</li> <li>eqlat_lim: Location of the PVG tropopause in equivalent latitudes</li> <li>latmean_lim: Location of the PVG tropopause in latitudes</li> <li>pv_lim: PV value at the PVG tropopause</li> </ul> <p>In this upload, the PVG tropopause time series are included as *.zip files:</p> <ul> <li>ERA5 dataset: "pvg-tp_era5_ts.zip"</li> <li>ERA-Interim dataset: "pvg-tp_eraint_ts.zip"</li> <li>MERRA-2 dataset: "pvg-tp_merra2_ts.zip"</li> <li>JRA-55 dataset: "pvg-tp_jra55_ts.zip"</li> <li>Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip".</li> </ul> <h2>How to use</h2> <p>The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows:</p> <pre><code>import netCDF4 as nc file="<insert file path here>" d = nc.Dataset(file) # read in a variable. Syntax: d["group name"]["variable name"][:]. For example: latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:] # test print print(f"First value of latmean_lim in NH: {latmean_lim[0]}")</code></pre> <p>If you would like to read in all variables in both hemispheres, you can loop e.g. as follows:</p> <pre><code>import netCDF4 as nc file = "<insert file path here>" d = nc.Dataset(file) # iterate through both hemispheres for hem in ["NorthernHemisphere", "SouthernHemisphere"]: # select the group to each hemisphere in the netCDF file g = d.groups[hem] # iterate through variables in each hemisphere. "v" is the name of each variable in the group. for v in g.variables: # read in the data for variable 'v' in hemisphere 'hem' as an array var = g[v][:] # just a test print, optional print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}")</code></pre> <h2>Funding</h2> <p>This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange: The Tropopause Region in a Changing Atmosphere (<a href="https://tpchange.de/">https://tpchange.de/</a>).</p>
Long time series (2001-2015) high-resolution crop yield and water productivity dataset of China
<p>A long-term data series, at 1-km resolution, of crop yield (kg/ha) and crop water productivity (kg/m3) for maize and wheat across China, based on the MOD16 ET product, multiple remotely sensed crop physiological and environmental indicators, and crop phenological information, using a random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. These high-resolution crop yield and CWP datasets generated in this study revealed spatiotemporal patterns of agricultural production in China and may be applied to many scenarios, including understanding effects of climate change on agricultural production capacity in China under increasing demand for food security to optimize agricultural production strategies.</p>
Testing alternative hypotheses for the decline of cichlid fish in Lake Victoria using fish fossils time series from sediment cores
<p>Lake Victoria is well known for its high diversity of endemic fish species that provide livelihoods for millions of people. The lake garnered widespread attention during the twentieth century as major environmental and ecological changes modified the fish community with the extinction of ~40% of endemic cichlid species by the 1980s. Suggested causal factors include anthropogenic eutrophication, fishing, and introduced non-native species but their relative importance remains unresolved because monitoring data started in the 1970s when changes were already underway. Here, for the first time, we reconstruct two time series, covering the last ~200 years, of fish assemblage using fish teeth preserved in lake sediments. Two sediment cores Lake Victoria (Mwanza Gulf), were subsampled continuously at intra-decadal resolution, and teeth were identified to major taxa: Cyprinoidea, Haplochromini, Mochokidae, and Oreochromini. None of the fossils could be confidently assigned to non-native Nile Perch. Our data show significant decreases in haplochromine and oreochromine cichlid fish abundances began long before Nile Perch's arrival, while cyprinoids have generally been increasing. Our study is the first to reconstruct a time series of fish assemblage in Lake Victoria extending deeper back in time than the past 50 years, helping shed light on processes underlying Lake Victoria's biodiversity loss.</p>
Subtidal ocean temperature time series for Maxwell Bay for the period 2017-2022
<div>The data set contains daily averages of subtidal temperature records at Maxwell Bay and CTD observations at the surface and 10 m, for summer campaigns at Maxwell Bay between 2017-2020 and 2022.</div>
Hourly time series of soil and atmosphere variables at the experimental site of El Cautivo, Tabernas Desert, Almeria, Spain (February 2018 to December 2019)
<p>Measurements were performed along a hypothetical succession of biological soil crusts. Main studied variables were the soil-atmosphere CO2 and water vapor fluxes. This dataset was used by Lopez-Canfin et al. (2022) and Kim and al. (2024) at the time of publication.</p>
Fluxes of particulate organic carbon, nitrogen and mass from the Station M abyssal time series in the northeast Pacific, (1989-2022)
<p>Overview:</p> <p>This dataset provides particulate fluxes to Station M in the NE Pacific, from 1989 to 2022. Samples were collected with McLane Parflux sequencing sediment traps deployed on moorings. Data are provided for traps 50 m above bottom and 600 m above bottom, with deployment bottom depths ranging from approximately 3900 m to 4500 m. Gaps reflect lapses in funding, weather disruptions, clogs in sediment traps, or the occasional spilled sample. Where available, GPS coordinates and ship-recorded bottom depth at deployment location are given. Where these are not available, approximate location and depth are given and noted.</p> <p> </p> <p>Methods:</p> <p>This program used McLane Parflux sequencing sediment traps. Attempts to avoid sediment trap clogs, which increasingly became an issue, included replacing manufacture-supplied plastic funnels with Teflon-coated fiberglass funnels (October 2014), doubling the size of sediment trap collection cups (from 250 ML to 500 ML starting in October 2014), and adding a function that periodically agitated material in the funnel constriction (starting in June 2015).</p> <p>Before deployment, sediment trap cups were acid-washed and filled with a preservative (mercuric chloride from 1989 to 2009, 3%–5% buffered formalin from 2009 to 2022). Formalin brine recipe followed that recommended by McLane. Following sample recovery, zooplankton that many have swum into the traps were identified visually and manually removed (KLS). Samples were returned to the lab, freeze-dried, and weighed to calculate mass flux. The freeze-dried sample was analyzed for inorganic carbon content using a coulometer (UIC), and total carbon, hydrogen, and nitrogen using an elemental analyzer (Perkin-Elmer or Exeter Analytical, University of California Santa Barbara Marine Science Institute Analytical Laboratory). Dry mass was corrected for salt content using a AgNO<sub>3</sub> titration (<a href="https://www.sciencedirect.com/science/article/pii/S0967064519302395#bib99">Strickland and Parsons, 1972</a>). Data [mass flux, particulate organic carbon flux, and total nitrogen flux] from the 600 mab trap were used. Gaps in this data set were infilled using the linear relationship between data from the 600 mab and 50 mab traps. Full details of these methods can be found in Baldwin et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL101018#grl65243-bib-0002">1998</a>).</p> <p>Data provided have been quality-controlled, and only usable data are included here.</p> <p> </p> <p>References:</p> <p>Baldwin, R. J., Glatts, R. C., & Smith Jr, K. L. (1998). Particulate matter fluxes into the benthic boundary layer at a long time-series station in the abyssal NE Pacific: composition and fluxes. Deep Sea Research Part II: Topical Studies in Oceanography, 45(4-5), 643-665.</p> <p>Strickland, J.D.H., Parsons, T.R. (1972) A Practical Handbook of Seawater Analysis. Fisheries Research Board of Canada, Ottawa </p> <p>Smith, K. L., Huffard, C. L., & Ruhl, H. A. (2020). Thirty-year time series study at a station in the abyssal NE Pacific: An introduction. <em>Deep Sea Research Part II: Topical Studies in Oceanography</em>, <em>173</em>, 104764.</p>
Input GNSS time series data for Tanaka et al. (2024), JGR Solid Earth
<p>Detail explanatios are in the uploaded README file. </p>
Time series generated by nonlinear Langevin equation
<p>Datasets used in papers:</p> <p>Telesca L. and Z. Czechowski, Fisher–Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Entropy 2023, 25, 1650.</p> <p>Czechowski Z. and L. Telesca, Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Chaos, Solitons and Fractals 183 (2024), 114927</p>
CAMELS-IND: hydrometeorological time series and catchment attributes for 472 catchments in Peninsular India
<p>We introduce <strong>CAMELS-IND</strong> (<em><strong>C</strong>atchment <strong>A</strong>ttributes and <strong>ME</strong>teorology for <strong>L</strong>arge-sample <strong>S</strong>tudies – <strong>India</strong></em>), a dataset containing hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India, of which 242 catchments have observed streamflow data available for over 30% of the period between 1980 to 2020. This dataset aims to foster large-sample hydrological studies within India and encourage the inclusion of Indian catchments in global hydrological research.</p> <p>The data set covers <strong>41 years</strong> of data between <em><strong>1st January 1980</strong></em> and <em><strong>31st December 2020</strong></em> for each catchments: daily time series of available streamflow observations, meteorological data such as precipitation, air temperature, solar radiation, relative humidity, wind speed, potential and actual evapotranspiration, and soil moisture. Additionally, CAMELS-IND includes regionally trained LSTM-model predicted streamflow for all 472 catchments. The static catchment attributes includes location and topography, climate, hydrological signatures, land-use, land cover, soil, geology, and anthropogenic influences.</p> <p>The corresponding manuscript is published in the Journal "Earth System Science Data" (ESSD).<br>Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, <a href="https://doi.org/10.5194/essd-17-461-2025" target="_blank" rel="noopener">https://doi.org/10.5194/essd-17-461-2025</a>, 2025.</p> <p>The data description file (<strong><em>CAMELS_IND_Data_Description.pdf</em></strong>) contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources.</p> <p> </p> <h3><strong>### CAMELS-IND attributes/forcings history</strong></h3> <p>--------------------------------------<br><strong>Version 2.2: March 2025</strong><br>--------------------------------------<br><strong>Major changes/additions:</strong><br>- Updated streamflow observations: CAMELS-IND now includes 242 catchments with streamflow observations for more than 30% of the period between 1980 and 2020.<br>- "<em>CAMELS_IND_Catchments_Streamflow_Sufficient.zip</em>" contains a subset of 242 catchments with observed streamflow data available for more than 30% of the duration between 1980 and 2020.</p> <p><strong>Minor changes:</strong><br>- Correction to the forcing column headings for 'evap_canopy (kg/m²/s)' and 'evap_surface (kg/m²/s)': the units have been updated to (mm/day).<br>- Corrections made to the gauge_id mapping for basin codes 12 and 15.</p> <p> </p> <p>--------------------------------------<br><strong>Version 2.1: October 2024</strong><br>--------------------------------------<br>Data described in revised ESSD paper - <br><strong>Major changes/additions:</strong><br>- The dataset name "<em>CAMELS-INDIA</em>" has been changed back to "<em>CAMELS-IND</em>" to align with the naming convention of other CAMELS datasets.<br>- A Python script file “<em>filter_catchment.py</em>” is added to filter out the subset of the dataset based on flow data availability.<br>- <strong>"<em>CAMELS_IND_Catchments_Streamflow_Sufficient.zip</em>" contains a subset of 228 catchments with observed streamflow data available for more than 30% of the duration between 1980 and 2020.</strong></p> <p> </p> <p>-----------------------------------<br><strong>Version 2: August 2024</strong><br>-----------------------------------</p> <p><strong>Major changes/additions:</strong><br>- The dataset name "<em>CAMELS-IND</em>" has beed changed to "<em>CAMELS-INDIA</em>".<br>- All forcing time series have been extended from 01/01/1980 to 31/12/2020.<br>- Two new forcings, "<em>pet_gleam</em>" and "<em>aet_gleam</em>", have been added.<br>- Several attributes have been added, including gauge elevation, mean drainage path slopes, precipitation uniformity, asynchronicity, gini coefficient, base flow index, stream elasticity, slope of FDC, water table depth, and anthropogenic influence.<br>- Available observed streamflow time series has been added for all 472 catchments for the period 01/01/1980 to 31/12/2020.<br>- Regionally trained LSTM model-predicted streamflow has been added for all 472 catchments for the period 01/01/1980 to 31/12/2020.</p> <p><strong>Minor changes:</strong><br>- Attribute files have been renamed to "<em>camels_India_XXXX</em>"</p> <p>A short descriptions of all attributes and time series is provided in "<em>camels_India_data_description.pdf"</em>.</p> <p><strong>The following attributes are included in CAMELS-INDIA v2:</strong><br>07 attributes : camels_India_name<br>16 attributes : camels_India_topo (topography and location)<br>42 attributes : camels_India_clim (climate indices)<br>73 attributes : camels_India_hydro (hydrological signatures)<br>13 attributes : camels_India_land (land cover characteristics)<br>28 attributes : camels_India_soil (soil characteristics)<br>07 attributes : camels_India_geol (geological characteristics)<br>25 attributes : camels_India_anth (anthropogenic influences)<br>---------------<br><strong>Total:</strong> 211 attributes, 19 catchment mean forcings, and available observed and LSTM-based predicted streamflow time series.</p> <p> </p> <p>--------------------------------<br><strong>Version 1 : April 2024</strong><br>--------------------------------<br>A short descriptions of all attributes and forcings are described in "<em>camels_ind_attributes.xlsx</em>" and "<em>camels_ind_forcings.xlsx</em>"<br><em>Following attributes were included in CAMELS-IND 1.0:</em><br>06 attributes : camels_ind_name<br>14 attributes : camels_ind_topo (topography and location)<br>36 attributes : camels_ind_clim (climate indices)<br>64 attributes : camels_ind_hydro (hydrological signatures)<br>13 attributes : camels_ind_land (land cover characteristics)<br>27 attributes : camels_ind_soil (soil characteristics)<br>07 attributes : camels_ind_geol (geological characteristics)<br>13 attributes : camels_ind_anth (anthropogenic influences)<br>-------------<br><strong>Total:</strong> 180 attributes & 17 catchment mean forcings. </p> <p> </p> <p>-----------------------------------------------------<br><strong>### CONTRIBUTE TO CAMELS-IND</strong><br>-----------------------------------------------------</p> <p>If you are working with a data set covering Indian catchments and would like to contribute catchment averages to <em>CAMELS-IND</em>, please get in touch.</p> <p>We are committed to identifying and correcting errors. If you encounter any unrealistic or suspicious values, please notify us as soon as possible. Thank you for your assistance.</p> <p><strong>Contacts:</strong><br>- Nikunj K. Mangukiya (<em>nikk.mangukiya@gmail.com</em>)<br>- Ashutosh Sharma (<em>ashutosh.sharma@hy.iitr.ac.in</em>)</p> <p> </p> <p>--------------------------------<br><strong>### Acknowledgments</strong><br>--------------------------------</p> <p>The authors gratefully acknowledge the Central Water Commission (CWC), the National Water Informatics Centre (NWIC), and the Ministry of Jal Shakti (MoJS) for providing the streamflow dataset through the online portal, India – Water Resources Information System (India-WRIS; <a href="https://indiawris.gov.in/wris/#/">https://indiawris.gov.in/wris/#/</a>). The authors also extend their gratitude to the India Meteorological Department (IMD), Ministry of Earth Sciences, Government of India, for providing the gridded rainfall and temperature datasets through their respective websites. Additionally, the authors gratefully acknowledge the National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Government of India, for the Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis. The IMDAA reanalysis was produced under the collaboration between UK Met Office, NCMRWF, and IMD, with financial support from the Ministry of Earth Sciences under the National Monsoon Mission programme. The authors utilized numerous publicly available datasets for compiling catchment attributes and meteorological forcing time series, duly acknowledging and citing them where applicable. The authors extend their gratitude to all the researchers and contributing authors of these open-source datasets.</p> <p> </p> <p>--------------------------------<br><strong>### Disclaimer</strong><br>--------------------------------</p> <p>The CAMELS-IND dataset provided on this webpage is openly accessible for academic and research purposes. While efforts have been made to ensure data accuracy, the authors do not take any responsibility for errors, omissions, or misuse of the data. Users must cite the following paper when utilizing the dataset and acknowledge that all interpretations and conclusions drawn from the data are their own. We encourage users to cite/acknowledge the original data sources wherever required based on the source data usage policies. The dataset is provided "as is" without any warranties, and users are advised to check for updates. It is strongly recommended that users exercise caution and verify the data before using it for any purpose. The authors assume no responsibility for any consequences arising from the use or misuse of this dataset.</p> <p><strong>How to cite:</strong> Mangukiya, N. K., Kumar, K. B., Dey, P., Sharma, S., Bejagam, V., Mujumdar, P. P., and Sharma, A.: CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India, Earth Syst. Sci. Data, 17, 461–491, <a href="https://doi.org/10.5194/essd-17-461-2025" target="_blank" rel="noopener">https://doi.org/10.5194/essd-17-461-2025</a>, 2025.</p>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil - link to files.
<blockquote> <h3><strong>Version 1.2 (March 2025): </strong>Now with longer time series, expanded stream gauge coverage, meteorological data from additional sources, soil moisture time series, and observed rainfall time series from 11,853 rain gauges.</h3> </blockquote> <p> </p> <p>This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies – Brazil) accompanying the paper: Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, <a href="https://doi.org/10.5194/essd-12-2075-2020" target="_blank" rel="noopener">https://doi.org/10.5194/essd-12-2075-2020</a>, 2020.</p> <p>CAMELS-BR provides daily observed streamflow time series for 4,025 stream gauges, daily observed rainfall for 11,853 rain gauges, daily meteorological time series and 65 attributes for 897 catchments in Brazil.</p> <p>The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from five products, (iii) actual evapotranspiration extracted from three products, (iv) potential evapotranspiration extracted from two products, (v) reference evapotranspiration extracted from one product, (vi) minimum, mean, and maximum temperature extracted from three products, and (vii) soil moisture extracted from two products.</p> <p>The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.</p> <p>The data follow the same standards as other CAMELS datasets such as for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).</p> <p><strong>How to cite:</strong> Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.</p> <p> </p> <h3><strong>Changes in CAMELS-BR version 1.2:</strong></h3> <p><strong>Major changes</strong></p> <ul> <li>Updated streamflow time series up to February 2025 (where available), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Some historical records have changed slightly due to ANA's quality control procedures. For eight gauges (see the readme.txt file), data are merged from 2025 and 2019 records (i.e. from CAMELS-BR version 1.1).</li> <li>Increased the stream gauge coverage to 4025 stream gauges (including both quality-controlled and non-quality-controlled series), up from 3679 in version 1.1.</li> <li>Added daily observed rainfall time series for 11853 rain gauges (not catchment averages), as obtained from ANA's website on 27 February 2025 (ANA – Brazilian National Water and Sanitation Agency – http://www.snirh.gov.br/hidroweb/). Data include quality flags but are mostly not quality-controlled.</li> <li>Added a GeoPackage file with coordinates for 11853 rain gauges.</li> <li>Updated precipitation time series (catchment averages) up to October 2024 (where available). Now derived from: CHIRPS v2.0; CPC; ERA5-Land; MSWEP v2.8; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil – 864 catchments).</li> <li>Updated actual evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from: GLEAM v4.2a; ERA5-Land; and MGB-SA.</li> <li>Updated potential evapotranspiration time series (catchment averages) up to October 2024 (where available). Now derived from GLEAM v4.2a and ERA5-Land.</li> <li>Added reference evapotranspiration time series (catchment averages). Derived from BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Updated daily maximum, mean, and minimum temperature time series (catchment averages) up to October 2024 (where available). Now derived from: CPC; ERA5-Land; and BR-DWGD v3.2.3 (when at least 95% of the catchment area lies within Brazil).</li> <li>Added daily soil moisture time series (catchment averages) up to December 2024 (where available). Computed from GLEAM v4.2a and ERA5-Land.</li> <li>Improved meteorological data processing. Catchment averages now account for pixel fraction coverage.</li> <li>Reformatted meteorological time series files. Files now includes data from different products, with columns renamed for clarity.</li> <li>Hydrological and climatic indices were not updated, despite the new streamflow and meteorological data.</li> </ul> <p><strong>Minor changes</strong></p> <ul> <li>Updated stream gauge coordinates based on ANA's website on 27 February 2025. Coordinates were updated for 73 gauges in the 897 selected catchments and for 298 gauges across all catchments.</li> <li>Streamflow time series now include quality flag values from 0 to 7 (see the readme.txt file), previously from 0 to 4 in CAMELS-BR version 1.1. Flags from 5 to 7 may be present only in the last few years of data.</li> <li>Streamflow time series files for the 897 selected gauges now include values in both millimeters per day and cubic meters per second.</li> <li>Removed streamflow time series with fewer than 180 days of measurement.</li> <li>Converted gauge and catchment spatial data from Shapefile (.shp) to GeoPackage (.gpkg).</li> <li>Catchment areas computed by GSIM (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") flagged as "caution" for quality were set to "nan" due to low reliability.</li> <li>Updated catchment areas computed by ANA (in files "camels_br_location.txt" and "location_gauges_streamflow.gpkg") to reflect the newest ANA's data from 27 February 2025. Streamflow values in millimeters per day remain unchanged because unit conversions rely on GSIM areas.</li> <li>Set catchment areas with zero squared kilometers, as computed by ANA, to "nan".</li> <li>Removed CPC daily mean temperature time series (catchment averages) because they were a simple average of minimum and maximum temperatures. For daily mean temperatures, refer to ERA5-Land data (now included) as they are computed from hourly data.</li> </ul> <p> </p>
Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data
<p>This dataset contains the latest version of a selection of result data of the paper "Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data" (DOI: https://doi.org/10.1016/j.rse.2025.114740 )</p> <p>The dataset contains:</p> <ol> <li>A geopackage of training points of pure tree species</li> <li>The resulting 12-band tree species fraction map of Rhineland-Palatinate</li> <li>HSV-colored map of dominant tree species. For information which tree species are represented by the different colors, refer to the Supplemental in the original paper.</li> <li>CSV-table of predicted and reference propotion of the tree species in the validation polygon (the original polygon data can not be published due to data privacy regulations) </li> </ol> <p> </p>
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