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Dataset results
34 results for “daily series”
Daily GNSS time series of La Palma 2021 eruption
<p>Time series form GNSS station in La Palma (Canary Island, Spain) during 2021 eruption (19/09/2021-21/01/2022). The daily neu time series have been computed in a Double Diference method using Bernese v.5.2 software as describe in the references.</p> <p> </p>
Homogenisation of daily temperature and humidity series in the United Kingdom
<p>Data to accompany the publication "Homogenisation of daily temperature and humidity series in the United Kingdom". Building on previous experience with continental and global data sets, we use a quantile-matching approach to homogenise temperature and humidity series measured by a network of 220 stations in the United Kingdom (UK). The data set spans 160 years at daily resolution, although data coverage varies greatly in time, space, and across variables.</p>
Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)
<p> </p> <p><strong>Abstract</strong></p> <p>The <strong>MADIA daily gridded dataset</strong> provides the series of the main <strong>agro-meteorological </strong>variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period <strong>1981-2022</strong>. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in <strong>csv </strong>format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the <strong>ERA5 cell polygons </strong>covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:</p> <ul> <li>Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). <em>Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA)</em> [Data set]. Zenodo. <a href="http://10.5281/zenodo.6868944">https://doi.org/10.5281/zenodo.6868944</a> </li> </ul> <p>Further details on methods applied for data processing are available in:</p> <ul> <li>Parisse B., Alilla R., Pepe A.G., De Natale F., <em>MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area</em>, Data in Brief, 46 (2023), 108843, <a href="https://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>)</li> </ul> <p>The MADIA daily dataset will be periodically updated.</p> <p><strong>Attached content</strong></p> <p>A ZIP archive composed by the following folders:</p> <ol> <li>csv_data: daily time series for each year from 1981 to 2022 in csv format</li> <li>metadata: discovery and description metadata</li> <li>shp_data: a complementary vector layer with the ERA5 cell polygons for Italy</li> </ol> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)</p>
Apulian Aqueduct demo site: daily time series of estimated inflows (natural springs and reservoirs) for climate projections
<p>This dataset contains the daily time series of net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) computed with a lumped rainfall-runoff model starting from climate projections of precipitation and temperature.</p> <p>This dataset considers two representative concentration pathways (RCP 4.5 and 8.5) and two decades, in the medium (2050-2059) and long-term future (2090-2099).</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: Springs: Sele, Calore; Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m3/s</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>. </p>
Daily activity time series of ants, Camponotus japonicus
Open the record for dataset details and reuse information.
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 2
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p>
Long time series (2001-2018) of daily evapotranspiration in China generated based on SEBAL: Part 1
<p>The dataset named SEBAL evapotranspiration in China (SEBAL ET) characterized the daily evapotranspiration (in millimeter) of vegetation in China from 2001 to 2018, the spatial resolution is 1 km × 1km and the temporal resolution is 1 day with the coordinate system of GCS_WGS_1984. The products were generated using Surface Energy Balance Algorithm of Land (SEBAL) and multi-sources remote sensing data, including MOD43A1 daily surface albedo, MOD11A1 daily surface temperature and MOD13 vegetation indices (obtained from NASA: https://ladsweb.modaps.eosdis.nasa.gov/search/), the meteorological data obtained from GMAO (https://gmao.gsfc.nasa.gov/research/highlights/2013-2015.php), the input variables were all aggregated of resampled to 1 km × 1km. The products were evaluated using the eight flux towers observation data for point validation and water balance method for regional validation and showed R value of 0.79 and 0.98, respectively, which indicated the products have a great performance. SEBAL ET can be used for several geoscience studies, especially for global change, water resources mangement and agricultural drought monitoring, etc.</p> <p> </p>
Historic daily air temperature and precipitation series data for Wudaoliang and Tuotuohe sations (2009-2021)
<p>This dataset contains the historic daily air temperature and precipitation for Wudaoliang and Tuotuohe sations in Qinghai Province from 2009 to 2021</p>
Daily hydro power time series (1979-2016) for 43 Chinese reservoir hydro stations
<p>This is the hydro time series used in the article </p> <p><em>The role of hydro power, storage and transmission in the decarbonization of the Chinese power system</em></p> <p><a href="https://doi.org/10.1016/j.apenergy.2019.02.009">https://doi.org/10.1016/j.apenergy.2019.02.009</a></p> <p>Their calculations are detailed in this article:</p> <p><a href="https://doi.org/10.1016/j.mex.2019.05.024">https://doi.org/10.1016/j.mex.2019.05.024</a></p> <p> </p> <p>--------------------------------------------</p> <p><a href="https://zenodo.org/api/files/c3dc671c-7d64-4a5f-b535-650c7e7445b7/dams_large.csv?versionId=a2ceab14-c1e5-49be-805d-7ebce1f7b749">dams_large.csv </a> lists the names, coordinates, provinces, historical yearly inflows, historical water consumption rate and historical yearly electricity generation of the reservoir stations.</p> <p><a href="https://zenodo.org/api/files/c3dc671c-7d64-4a5f-b535-650c7e7445b7/reservoir_effective_capacity.pickle?versionId=9b4a5ca7-ae15-44ab-aa38-6554ed0d5a97">reservoir_effective_capacity.pickle </a> contains the effective capacity for the reservoirs.</p> <p><a href="https://zenodo.org/api/files/c3dc671c-7d64-4a5f-b535-650c7e7445b7/daily_hydro_inflow_per_dam_1979_2016_m3.pickle?versionId=069e756e-3fe7-4fd5-ae82-1f51618d4f77">daily_hydro_inflow_per_dam_1979_2016_m3.pickle </a> and <a href="https://zenodo.org/api/files/c3dc671c-7d64-4a5f-b535-650c7e7445b7/daily_hydro_inflow_per_dam_1979_2016_GWh.pickle?versionId=d0bc17ce-e48b-4a5c-934f-e602f8b9f797">daily_hydro_inflow_per_dam_1979_2016_GWh.pickle </a> contains the daily hydro inflow time series in terms of water (m3) and potential energy (GWh), respectively.</p> <pre><code class="language-python">import pandas data_pickle = pandas.read_pickle('*.pickle') data_csv = pandas.read_csv('dams_large.csv')</code></pre> <p> </p>
Apulian Aqueduct demo site: daily time series of simulated system behavior for future inflows conditions (RCP8.5)
<p>This dataset contains the daily time series obtained from the strategic model simulation, considering net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) considering climate projection RCP 8.5 and two decades, in the medium (2050-2059) and long-term future (2090-2099). In all simulations, a modified version of the drinking water demand is considered, following a different distribution of the populations, an increase in density in coastal areas also due to investments in the tourism sector, to the detriment of density in inland areas.</p> <p>In two simulations the current drinking water demand is considered, with or without the environmental flow constraint acting on each reservoir release activated.</p> <p>In the remaining simulations, a modified version of the drinking water demand is considered, following a different distribution of the populations, an increase in density in coastal areas also due to investments in the tourism sector, to the detriment of density in inland areas.</p> <p>For each decade, two simulations are performed, with or without the rehabilitation of some well-fields making the water withdrawn drinkable with innovative purification techniques.</p> <p>. More precisely, the dataset contains:</p> <ul> <li>the daily level of the main reservoirs;</li> <li>the water supplied to all users (drinking water users, irrigation, and industrial districts) from each reservoir;</li> <li>the corresponding single irrigation and industrial deficit;</li> <li>the total drinking water deficit;</li> <li>the aggregated distribution cost.</li> </ul> <p>Two simulations are performed, with or without the environmental flow constraint acting on each reservoir release activated.</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: <ul> <li>Springs: Sele, Calore;</li> <li>Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo;</li> <li>Users: drinking water users, irrigation, and industrial districts supplied by Apulian aqueduct.</li> </ul> </li> <li>Unit of measure: <em>m</em>, <em>m3/s</em> depending on the variable</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>.</p>
Daily Lake Ice Phenology Time Series Derived from AMSR-E and AMSR2, Version 1
The Daily Lake Ice Phenology Time Series Derived from AMSR-E and AMSR2 provides 5 km ice phenology retrievals describing daily lake ice conditions (ice-on/ice-off) over the Northern Hemisphere. This satellite-based data set allows for rapid assessment and regional monitoring of seasonal ice coverage over large lakes with resulting accuracy suitable for global change studies. Data are provided in the 5 km Northern Hemisphere Equal-Area Scalable Earth Grid 2.0 (EASE-Grid 2.0).
Ground-Based Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) IDS Earth Orientation Parameters (EOP) Time Series Product from NASA CDDIS
Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Earth Orientation Parameters Time Series Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite’s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network, including the time series of Earth orientation parameters (EOPs). The IDS Analysis Center Coordinator combines these solutions to produce an official IDS EOP product. The EOP time series are available in text format.
MEaSUREs SESES Daily GNSS Geodetic Displacement TIme Series products from NASA CDDIS
Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are daily geodetic displacement time series (compressed). They are combined, cleaned and filtered, GIPSY-GAMIT long-term time series of Continuous Global Navigation Satellite System (CGNSS) station positions (global and regional) in the latest version of ITRF
Apulian Aqueduct demo site: daily time series of reservoirs water levels and total releases 2010-2019
<p>This dataset contains the time series of water levels and total releases (including spillways) measured by the competent authority for the main reservoirs of Apulian aqueduct (demo site 1), at a daily time step.</p> <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: <ul> <li>water level: masl</li> <li>releases: m<sup>3</sup>/sec</li> </ul> </li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô Deliverable D4.1.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.