Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,516
datasets available to search
ShareScore release 0.9.0
Dataset results
2,516 results for “Seri”
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>
Similarity data set used to test Synchronous Growth Changes (SGC) on dendrochronological data using tree-ring series from the ITRDB
<p>Dataset used to test the SGC, SSGC and AGC in:</p> <div> <div>Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry</em> 63(1): 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</div> </div> <p>The dataset contains the database used in this study</p> <ul> <li><em>itrdb_structure.sql</em> described the structure of the database (PostgreSQL/PostGIS)</li> <li>Tables <ul> <li><em>GC_??_tbl</em> are tables with ?? denoting the continent (see below) containg the comparisons between tree-ring series and the growth changes <ul> <li>The following columns are present: <ul> <li>ID1 and ID2: These are the ID's of the series compared.</li> <li>SGC: Synchronous Growth Changes</li> <li>SSGC: Semi Synchronous Growth Changes</li> <li>Overlap: the number of tree-rings compared</li> </ul> </li> <li>Data files with values in each table. The continents are as defined in the ITRDB (https://www.ncei.noaa.gov/access/paleo-search/?dataTypeId=18) <ul> <li>GC_af_tbl_202005 (Africa)</li> <li>GC_as_tbl_202005 (Asia)</li> <li>GC_au_tbl_202005 (Australia)</li> <li>GC_ca_tbl_202005 (Canada)</li> <li>GC_eu_tbl_202005 (Europe)</li> <li>GC_mx_tbl_202005 (Mexico)</li> <li>GC_sa_tbl_202005 (South America)</li> <li>GC_us_tbl_202005 (North America)</li> </ul> </li> </ul> </li> <li><em>headers</em>: <ul> <li>The following columns are present: <ul> <li>continent: two letter code of the continent (ITRDB)</li> <li>filename: orginal filename as deposited in the ITRDB</li> <li>line_nr: line number of the header</li> <li>header_text: text of the header related to the line number</li> </ul> </li> <li>Datafile: headers_201905222007.csv</li> </ul> </li> <li><em>names</em>: <ul> <li>The following columns are present: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>name_orig: orginal name of the tree-ring series as deposited in the ITRDB</li> <li>name_new: the IDs of the tree-ring series were replaced with a two‐letter code for the continent (AF, AS, AU, CA, EU, SA, US) and a sequence code to prevent duplicate IDs. These are used as ID1 and ID2 in the tables <em>GC_??_tbl</em></li> </ul> </li> <li>Datafile: names_201905240643.csv</li> </ul> </li> </ul> </li> <li>file: <em>geo_location_201906250635.csv</em> <ul> <li>Contains the locations related to each site in the database</li> <li>The following columns: <ul> <li>filename: orginal filename as deposited in the ITRDB</li> <li>continent: two letter code of the continent (ITRDB)</li> <li>lat: latitude</li> <li>long: longitude</li> <li>geom_point: WGS84 coordinates expressed as well-known text (WKT)</li> </ul> </li> </ul> </li> </ul> <p>For the related code, see also: </p> <p>Ronald Visser. (2022). Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7157738</p> <p>Or: https://github.com/RonaldVisser/SGC</p>
3D ED tilt series of Pigment Red 5
<p>Electron diffraction tilt series of Pigment Red 5. </p> <p><span>The experimental data were acquired using a 200 kV Glacios transmission electron microscope (Thermo Fisher) operating at liquid nitrogen temperature, employing the EPU-D (Thermo Fisher) module in continuous rotation mode. Electron diffraction patterns were collected in nanodiffraction mode, with an effective beam diameter of 1 µm on the sample. Tilt series were acquired within a goniometer tilt range of -60° to 70°, with a frame interval of 1° and a rotation speed of 0.5 s/1°. A binning factor of 4 was applied to the CETA camera, resulting in an effective pixel size of 0.002 </span><span>Å</span><span>^−1.</span></p>
Designing Smartphone Surveys: A Series of Video Tutorials
<p>A series of 8 step-by-step videos to assit in the desing and implementation of smartphone surveys.</p>
Cine y series según plataforma
<p>This dataset gives users the opportunity to examine in detail, for each streaming platform, all the series and movies available in its catalog, while providing an overview of each platform's elements, trends and audience preferences and thus being able to analyze each platform's marketing strategy.</p> <p>It also makes users spend less time searching for a spectrum of specific series or movies, and can apply filters that are not available on the site.</p> <p> </p> <p> </p>
IMDb Film & Series Data Analysis
<p><span>El conjunto de datos para este proyecto contendrá los siguientes descriptivos sobre películas y series de IMDb, lo que permitirá analizar las distintas tendencias en la industria: <em>Title, Year, Genres, Directors, Actors, Rating, Reviews, Duration, Type, Episode, Season, Budget, Revenue</em>. Estos campos creo que son lo suficientemente descriptivos como para permitirnos un análisis en profundidad de las películas, series, actores, directores, etc. a lo largo del tiempo.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Title:</span></em></strong><span> El título de la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Year:</span></em></strong><span> El año en que se lanzó la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Genres:</span></em></strong><span> El género de la película o serie (por ejemplo, drama, comedia, acción, etc.).</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Directors:</span></em></strong><span> El director de la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Actors:</span></em></strong><span> Los actores principales de la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Rating:</span></em></strong><span> La calificación de la película o serie en IMDb.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Reviews:</span></em></strong><span> El número de reseñas de usuarios para la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Duration:</span></em></strong><span> La duración de la película o serie en minutos.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Type:</span></em></strong><span> Si es una película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Episode:</span></em></strong><span> El número de episodios si es una serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Season:</span></em></strong><span> El número de temporadas si es una serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Budget:</span></em></strong><span> El presupuesto de la película o serie.</span></p> <p><span><span>·<span> </span></span></span><strong><em><span>Revenue:</span></em></strong><span> La recaudación de la película o serie.</span></p> <p><span>Los datos del conjunto abarcan un periodo de tiempo que se extiende desde el lanzamiento de IMDb en octubre de 1990 hasta el presente mes de abril de 2024.</span></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>
Long-term litter fall data series from 34 boreal forest stands in Finland
<p><strong>Introduction</strong></p> <p>Litter fall data were collected on a network of 34 forest sampling plots in Finland from late 1950s to 2010s. The data collection spanned different time periods in different sampling plots. The data have been used for studies on the flowering and seed crop of forest trees, air quality, and insect damage (see list of publications in the end of this document). They have been used to develop seed production and needle litter fall models for Scots pine (<em>Pinus sylvestris</em>) and Norway spruce (<em>Picea abies</em>), a branch litter model for pine, and total litter fall models used in greenhouse gas inventories.</p> <p><strong>Data collection</strong></p> <p>The litter fall collection was set up in mature, single species stands. The focal tree species include Scots pine (<em>Pinus sylvestris</em>), Norway spruce (<em>Picea abies</em>), Silver birch (<em>Betula pendula</em>), Downy birch (<em>Betula pubescens</em>), Grey alder (<em>Alnus</em> <em>incana</em>), European rowan (<em>Sorbus aucuparia</em>), European larch (<em>Larix decidua</em>), and Siberian larch (<em>Larix sibirica</em>). The sampling plots varied in shape and in size with a typical area of 0.1-0.25 ha. Between 6 and 30 litter collecting funnels were used per plot. The funnels were made of galvanized sheet metal and attached to cloth bags to collect the falling litter. The sampling sites were monitored for changes in conditions, such as natural disturbances, tree harvesting, forestry operations, or construction on the plot or in its immediate proximity (none observed).</p> <p>The litter samples were collected from the sampling plots, usually four to six times per year in spring to autumn. The samples were dried in room temperature (except male flowers in 1960s – 1970s, see note in Table 1) and stored in paper bags. The dried samples were sorted into litter fractions (Table 1). These fractions varied between tree species and between years to some extent. Cones and seeds were counted, and all other litter fractions were weighed to the nearest milligram.</p> <table> <caption>Table 1. Litter fractions and their codes. The code of the litter fraction is used in the data files.</caption> <thead> <tr> <th scope="col">Code</th> <th scope="col">Litter fraction</th> <th scope="col">Description / note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Male flowers</td> <td>In the 1960s – 1970s male flowers were dried in 105°C for 24 hours (Sarvas 1962, 1968).</td> </tr> <tr> <td>2</td> <td>Seeds</td> <td>Seed wings and seeds from species other than the focal one were included into the “other litter” fraction.</td> </tr> <tr> <td>3</td> <td>Female flowers</td> <td> </td> </tr> <tr> <td>4</td> <td>Needles</td> <td> </td> </tr> <tr> <td>5</td> <td>Insects and their faeces</td> <td> </td> </tr> <tr> <td>6</td> <td>Other litter</td> <td> </td> </tr> <tr> <td>7</td> <td>Lichens, branches, and tree bark</td> <td>In some years lichens, branches, and bark were combined in the same fraction, and in some they were separated in their own fractions (numbers 12-14 below).</td> </tr> <tr> <td>8</td> <td>Small cones (1 year)</td> <td>For pine stands, cones were separated into one-year old small cones and large cones. Cones were counted.</td> </tr> <tr> <td>9</td> <td>Cones</td> <td> </td> </tr> <tr> <td>10</td> <td>Cones and loose scales</td> <td>In some years loose scales were included in the same fraction as cones, and in some they were included into the “other litter” fraction.</td> </tr> <tr> <td>11</td> <td>Shifting dust</td> <td> </td> </tr> <tr> <td>12</td> <td>Branches</td> <td> </td> </tr> <tr> <td>13</td> <td>Lichens</td> <td> </td> </tr> <tr> <td>14</td> <td>Tree bark</td> <td> </td> </tr> <tr> <td>15</td> <td>Leaves</td> <td>For deciduous stands, leaves were separated into small (diameter < 1 cm) and large leaves (diameter > 1 cm), while for conifer stands all leaves were included into the “other litter” fraction.</td> </tr> <tr> <td>16</td> <td>Berries</td> <td> </td> </tr> </tbody> </table> <p>In addition to litter fall data, tree stand data were collected on most of the plots in some years. In the tree stand inventories, all trees in the sampling plot with diameter at breast height ≥ 7 cm were mapped, and all trees were counted and measured for diameter (at breast height and at 6 meters), total height, and height to first living branches. Stand basal area and dominant diameter and height were calculated. Stand age was estimated based on core samples from five trees outside but representative of the sample plot. Crown coverage was estimated with a Cajanus tube.</p> <p><strong>Description of the data files</strong></p> <p>The litter fall data is in eight csv-files, one per tree species. The files are named “Litter_Tree_species.csv”, for example “Litter_Betula_pendula.csv”.</p> <p>Variables (in columns) are consistent across the files (explained in Table 2), but note that there are varying numbers of columns between the tables in the files, as each litter collecting funnel has its own column and different maximum numbers of funnels were used in different sampling sites and tree species (see row “S1 – S30” in Table 2 for more details).</p> <table> <caption>Table 2. Variables included in the litter fall data files.</caption> <thead> <tr> <th scope="col">Variable name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Form</td> <td>Number identifying the original paper form.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Tree species code: 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch, 5 = Grey alder, 6 = Siberian larch, 7 = European larch, 8 = European rowan.</td> </tr> <tr> <td>LitterFraction</td> <td>Code for the litter fraction (1-16), explained in Table 1.</td> </tr> <tr> <td>Coefficient</td> <td>Coefficient used to transform the weight of the litter (g) to weight per square meter (g m<sup>-2</sup>). The coefficient is based on the number and area of the collection funnels.</td> </tr> <tr> <td>Date</td> <td>Date of sample collection.</td> </tr> <tr> <td>Period</td> <td>Variable used to define the time of data collection as calendar year or phenological year. The variable is based on the schedule of data collection in different years and on the focal tree species so that it corresponds to the species-specific litter fall schedule. For spruce, the peak needle fall is in the spring, so the calendar year is appropriate for describing the temporal variation in litter fall. For pine, the peak needle fall is in August–September, so a phenological year defined as July 1<sup>st</sup> – June 30<sup>th</sup> is appropriate for describing the temporal variation in litter fall. Period = -1 means the values in the row are allocated to the previous calendar year; period = 0 means the values are allocated to the current calendar year; and period = 1 means the values are allocated to the next calendar year.</td> </tr> <tr> <td>S1 – S30</td> <td>Columns S1 – S30 refer to litter collection funnels 1 – 30. The maximum number of funnels varies between tree species: for <em>silver birch</em> up to 30 funnels were used per plot, for downy birch up to 20 funnels, for spruce up to 10 funnels, for pine up to 15 funnels, for rowan 10 funnels, and for both larch species and alder 8 funnels. Missing values (NA) mean that the funnel was not used in the plot. Value -1 means that the funnel was used but the sample was missing. The values give the dry weight of the litter in mg.</td> </tr> <tr> <td>Combined</td> <td>For some samples, litter collected by different funnels has been combined and the total weight is shown in column S1. In this column, 0 = values have not been combined, and 1 = values have been combined.</td> </tr> <tr> <td>TotalWeight</td> <td>Total weight of the litter (mg).</td> </tr> <tr> <td>TotalWeightArea</td> <td>Total weight of the litter per area (mg m<sup>-2</sup>). Value is same as TotalWeight × Coefficient.</td> </tr> <tr> <td>Note</td> <td>Note</td> </tr> </tbody> </table> <p>Tree stand data is in one csv-file, named “Tree_stand_data.csv”, and can be combined with the litter fall data based on the sample plot abbreviations (variable “PlotAbbr” in both litter fall data files and the tree stand data file). Note that there is no tree stand data available for all the same years as litter fall data. There is no tree stand data available at all for one downy birch site (abbreviation HEI568), one spruce site (NOO85), one pine site (HEI566), and the alder, rowan, and larch sites. Tree stand data variables are explained in Table 3.</p> <table> <caption>Table 3. Variables included in the tree stand data file.</caption> <tbody> <tr> <td>Variable name</td> <td>Description</td> </tr> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Dominant tree species. 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch.</td> </tr> <tr> <td>Age</td> <td>Stand age (years).</td> </tr> <tr> <td>SiteType</td> <td>Forest site type describing site productivity. 2 = xeric heath forest, 3 = sub-xeric heath forest, 4 = mesic heath forest, 5 = herb-rich heath forest. </td> </tr> <tr> <td>North</td> <td>North coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>East</td> <td>East coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>Elevation</td> <td>Elevation (m above sea level).</td> </tr> <tr> <td>N</td> <td>Stem number (ha<sup>-1</sup>).</td> </tr> <tr> <td>BA</td> <td>Basal area (m<sup>2</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>DomD</td> <td>Diameter (cm) of dominant trees.</td> </tr> <tr> <td>DomH</td> <td>Height (m) of dominant trees.</td> </tr> <tr> <td>CrownLength</td> <td>Crown length (m).</td> </tr> <tr> <td>V</td> <td>Stem volume (m<sup>3</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>CrownCover</td> <td>Crown coverage (%).</td> </tr> </tbody> </table> <p> </p> <p><strong>List of publications </strong></p> <p>Hilli, A., Hokkanen, T., Hyvönen, J. & Sutinen, M.-L. 2008. Long-term variation in Scots pine seed crop size and quality in northern Finland. Scandinavian Journal of Forest Research 23(5): 395-403. </p> <p>Koski, V. & Tallqvist, R. (1978). Results of long-time measurements of the quantity of flowering and seed crop of forest trees (in Finnish with English summary). Folia Forestalia, 364, 1-60. </p> <p>Kouki, J. & Hokkanen, T. 1992. Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors. Oecologia 89: 176-181. </p> <p>Lehtonen, A., Lindholm, M., Hokkanen, T., Salminen, H. & Jalkanen, R. 2008. Testing dependence between growth and needle litterfall in Scots pine - a case study in northern Finland. Tree Physiology 28(11): 1741-1749. </p> <p>Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K.T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling 180(2-3): 305-315. </p> <p>Leikola, M., Raulo, J. & Pukkala, T. (1982). Prediction of the variations of the seed crop of Scots pine and Norway spruce (in Finnish with English summary). Folia Forestalia, 537, 1-43. </p> <p>Niemistö, P., Hokkanen T. & Varama, M. 2004. Karikemäärän muutokset 1982–2001 ja puiden kunto lumi- ja hallamittariesiintymän vaivaamissa koivikoissa Noormarkussa. Metsätieteen aikakauskirja 1/2004: 21–41. </p> <p>Poikolainen, J. & Kuusinen, M. 2000. Abundance of epiphytic lichens in litterfall during 1967-1994. In: Forest condition in a changing environment - the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers / Ed. Mälkönen, E. Sivut: 171-172. </p> <p>Pukkala, T. 1987a. A model for predicting the seed crop of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 135-144. </p> <p>Pukkala, T. 1987b. Effect of seed production on the annual growth of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 145-158. </p> <p>Pukkala, T., Hokkanen, T. & Nikkanen, T. 2010. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fennica 44(4): 629-642. </p> <p>Ranta, E., Lindström, J., Kaitala, V., Crone, E., Lundberg, P., Hokkanen, T. & Kubin, E. 2010. Life history mediated responses to weather, phenology and large-scale population patterns. In: Hudson, I. L & Keatley, M. R. (eds.). Phenological Research. Springer, Dordrecht Heidelberg London New York, Netherlands. p. 321-338. </p> <p>Raulo, J. & Hokkanen, T. 1989. Litter fall of Alnus incana and Alnus glutinosa (in Finnish with English summary). Folia Forestalia 738. 25 s.</p> <p>Saarsalmi, A., Starr, M., Hokkanen, T., Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. 2007. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 242(2-3): 578-586. </p> <p>Sarvas, R. (1962). Investigations on the flowering and seed crop of Pinus Silvestris. Communicationes Instituti Forestalis Fenniae, 53, 1-198. </p> <p>Sarvas, R. 1968. Investigation on the flowering and seed crop of Picea abies. Communicationes Instituti Forestalis Fenniae 67.5. 84 pp. </p> <p>Starr, M., Saarsalmi, A., Hokkanen, T., Merilä, P. & Helmisaari, H.-S. 2005. Models of litterfall production for Scots pine (Pinus sylvestris L.) in Finland using stand, site and climate factors. Forest Ecology and Management 205: 215-225. </p> <p>Ťupek, B., Mäkipää, R., Heikkinen J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M. & Lehtonen, A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Environment Research 20: 283–304</p>
Aeroelastic simulations of wind turbines affected by leading edge erosion: datasets for multivariate time-series classification
<p>This repository contains data generated and used for classification in the publication:<br> Duthé, G.; Abdallah, I.; Barber, S.; Chatzi, E. Modeling and Monitoring Erosion of the Leading Edge of Wind Turbine Blades. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 7262. https://doi.org/10.3390/en14217262</p> <p>The data is generated via OpenFAST aeroelastic simulations coupled with a Non-Homogeneous Compound Poisson Process for degradation modelling and was used to train a Transformer deep learning model.</p> <p>One degradation run generates 1200 samples (1 sample every 6 days corresponding to a 20 year degradation period). In total 20 degradation runs are made available (20x1200 = 24'000 multivariate time-series samples). This repo can serve to benchmark long multivariate time-series classification algorithms. There are 10 possible classes of erosion severity.</p> <p>Each sample is a multivariate time-series of length 60'000, with the following 4 channels extracted from the simulations for a section at the tip of the blade:</p> <ul> <li>Inflow velocity</li> <li>Angle of attack</li> <li>Lift coefficient</li> <li>Drag coefficient</li> </ul> <p>Please see the publication above for more information as well as the included readme for information about the data and an example of how to load it into to PyTorch.</p> <p> </p>
SEM images of PAAO samples (AJ series 1-to-5)
<p>Scanning electron microscopy (SEM) images of porous anodized aluminum oxide (PAAO) samples. The samples were made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for: 3 min 16 s (AJ-1), 3 min 37 s (AJ-2), 4 min 8 s (AJ-3), 4 min 30 s (AJ-4), 4 min 57 s (AJ-5). The imaging was performed on the surface (top) of the sample, darker parts represent pores.</p> <p>SEM: FEI Quanta 200 FEG (FEI).</p> <p>Vacuum mode: high vacuum (< 6·10<sup>-4</sup> Pa).</p> <p>Accelerating voltage: 20 kV.</p> <p>Magnification: 50 000, 100 000, 200 000.</p>
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.