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66 results for “Timeseries”
Daily phenocam image data and derived timeseries for global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains daily data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data included here comes from phenocams installed in two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and this dataset includes color values extracted from shrub and grass regions in these images. Further analyses, including daily values of calculated greenness (green chromatic coordinate), precipitation, and temperature, for all the plots included in the study are in EDI dataset knb-lter-jrn.210574002. This study is ongoing.
Subject11-TimeSeries
<p>Subject11-TimeSeries dataset is provided by Xiuye Chen <em>et al.</em> and the result is a result of cross mapping from mpEDM on ABCI.</p>
Subject6-TimeSeries
<p>Subject6-TimeSeries dataset is provided by Xiuye Chen <em>et al.</em> and the result is a result of cross mapping from mpEDM on ABCI.</p>
NO2 Corrected Timeseries of AOD at 440,400 nm, Angstrom Exponent for Two Sites in Rome
<p>Dataset Created in the framework of QA4EO wp2360. Timeseries of AOD 440nm for AERONET stations SAP and ISAC in Rome, Italy, AOD 400nm for Skynet station in SAP and corresponding ånström exponents, corrected for Total NO2 effect, using PNG data. Time period 2017-2022</p>
Processed proteomic and phosphoproteomic timeseries from Ostreococcus tauri, with Gene Ontology enrichment, from "A phospho-dawn of protein modification anticipates light onset in the picoeukaryote O. tauri"
<p>Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga <em>Ostreococcus tauri </em>under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.</p>
nextGEMS: Output of the WP6 ocean vertical mixing sensitivity runs (timeseries)
<p>In work package 6 of the nextGEMS project, several ocean-only model runs were performed with FESOM (Version 2.0) and ICON-O (Version 2.6.6), to test the sensitivity of the upper tropical Atlantic to different settings of the vertical mixing scheme. Two different mixing schemes were tested: TKE and KPP. For TKE, we tested different settings of the c_k parameter (0.1, 0.2 and 0.3), and for KPP different settings of the critical bulk Richardson number (0.3 and 0.27). These runs were done with both ICON-O and FESOM, to enable a comparison of the effects of the vertical mixing settings across different models. From ICON-O only, there are some additional TKE runs available, where we increased the interior ocean background mixing, and switched on the Langmuir turbulence parameterisation. There is also an ICON-O run which uses the FESOM default forcing bulk formulae, to check how much of the differences between the models originates from their different default bulk formulae.<br> <br> All model runs are ocean only, forced with hourly ERA5 reanalysis data. The horizontal resolution is 10km (for FESOM, the extratropical regions have a coarser grid).</p> <p>Here we provide high-frequency (3 hourly) time series of the model output, at selected locations in the tropical Atlantic where observational data are available for comparison in the simulated time range (2014 and 2015). We provide the following locations here:</p> <ul> <li>0N, 10W</li> <li>0N, 23W</li> <li>11.5N, 23W</li> <li>15N, 38W</li> <li>11N, 21.2W</li> <li>17.6N, 24.3W</li> </ul>
SandyDuck 1997 timeseries
<p>Time series (2Hz) of waves and currents in 5 alongshore arrays from near the shoreline to 7 m water depth at the USACE Field Research Facility, Duck, NC, USA collected between Sep 1 and Oct 31 1997.</p> <p>The README describes the files with time series of pressure and velocity (2 Hz), the files with the X-Y-Z locations of the sensors, and provides links to the nearly daily bathymetric surveys and the incident (8-m water depth) wave conditions (frequency-directional spectra)</p>
OLIGOTREND, a global database of multi-decadal timeseries of chlorophyll-a and nutrient concentrations in inland and transitional waters, 1986-2023
The Oligotrend database is a collection of multi-decadal chlorophyll-a and nutrient timeseries in inland and transitional waters. The objective of this Data Package was to explore how inland and transitional aquatic ecosystems respond to oligotrophication trends. Overall, the Oligotrend L1 database is made of 4.3 million valid observations originating from 1,894 stations. There are 238, 687 and 969 stations located in estuaries, lakes and rivers, respectively. The top 3 largest sources of data are the French national water quality monitoring (775 stations), the global database of lake datasets from Naderian et al. 2024 (378 stations), and the Chesapeake Bay Program (199 stations). The data was harmonized through a reproducible data processing pathway. In this Data Package, quality-checked level L1 data is provided, together with data sources, geographical coordinates of the stations, and the output of a trend analysis of all timeseries (level L2).
CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
<p>Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. Since publication in 2021, CAMELS-AUS (Australia) has served as a resource for the study of hydrological change, arid-zone hydrology, and hydrological model improvement. In this update, the dataset has been significantly enhanced both temporally and spatially. The new dataset comprises information for over twice as many catchments (561 compared to 222). The streamflow and climatic information are updated a further eight years (2022 compared to 2014). Lastly, the attribute information is improved, particularly with respect to hydrological statistics (signatures) and uncertainty in streamflow. Together, these updates make CAMELS-AUS Version 2 a more comprehensive and current resource for hydrological research and applications. </p>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
Timeseries of lake margin, lake area, water level, mascon solutions of Lago Greve
<p>These are the dataset of timeseries of lake extent, lake area, water level, and mass around the lake, of Lago Greve presented in the study.</p> <p>1. Lake extent<br> Filename: LagoGreve.shp<br> Note: LagoGreve.cpg, LagoGreve.dbf, LagoGreve.prj, LagoGreve.shx are also needed.</p> <p>2. Timeseries of lake area<br> Filename: LakeArea_LagoGreve.csv</p> <p>3. Timeseries of water level<br> Filename: Lakelevel_LagoGreve.csv</p> <p>4. Timeseries of grace mascon solution<br> Filename: MeanMasconLagoGreve_CSRv06.csv</p> <p>5. Fitting model for mascon solution<br> Filename: MeanMasconFitting.csv</p>
Ground temperature timeseries (2014-2021) and cryostratigraphy from Villum Research Station, Station Nord, eastern North Greenland (81° N)
<p>This dataset contains ground temperature timeseries (2014-2021) and cryostratigraphy data from two 20 m deep boreholes located at Villum Research Station (VRS), Station Nord, eastern North Greenland. The cryostratigraphy data includes split permafrost core photographs and values for the following parameters: gravimetric moisture content, salinity, and freezing point depression. A complete sample inventory and information on sample quality and recovery is also included. Please read the file "Readme_Strandetal2021_V2" for the necessary background information and overview of the dataset contents (file structure and description of each file). This is the second version of the dataset; the ground temperature timeseries in this version are 2.5 years longer than in the first version, and meteorology data from the same period as the ground temperature data is provided.</p>
Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries
<p>Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in :</p> <p>Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. <em>International Journal of Climatology</em>, 43(1),558–574. https://doi.org/10.1002/joc.7795574 </p> <p> </p>
fET timeseries
<p>fET timeseries as calculated by the fET repo. The manuscript related to the project was submitted for publication in October 2022 by Giardina et al.</p>
Basic-ECVs for Case Studies (monthly timeseries)
<p>Monthly timeseries of basic-ecvs (.csv) spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>
Derived-ECVs for Case Studies (monthly timeseries)
<p>Monthly timeseries (.csv) of derived-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>
Timeseries of simulated glacier meltwater runoff for primary hydrological regions in Svalbard
<p>Timeseries of annual cumulative glacier meltwater runoff for 14 primary hydrological regions of Svalbard, as well as one subregion, for the period September 2003 to September 2013. Regional glacier meltwater runoff are extracted from climatic mass balance simulations for all glaciers in Svalbard published by Aas et al., 2016, "The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model", doi:10.5194/tc-10-1089-2016) and used in a manuscript by Dunse et al., 2021, "Regional-scale phytoplankton dynamics and their association with glacier meltwater runoff in Svalbard", submitted to the EGU journal Biogeosciences in July 2021</p>
Supplementary excel timeseries
<p>This suppelmentary excel contains additional information on the statistical analyses that were performed for the paper: "Field history matters: The effect of spatiotemporal dynamics and management practices on the soil bacterial and fungal communities in two agricultural fields."<strong>Joos L, Ommeslag S, Baeyen S, Asselberg W, Van Loo K, Clement L, Debode J, Vandecasteele B, De Tender C</strong></p>
InTheMED WP6 Data Archive – Tympaki, Greece: Inflow Timeseries and Pumping Rates
<p>This data archive includes the files “InTheMED_WP6_DS_InflowTimeSeriesGreece<strong>.</strong>mat” and “InTheMED_WP6_DS_PumpingRatesGreece.mat” which contain the datasets that were used as inputs for the simulation runs of the Tympaki numerical groundwater model.</p>
Virtual sensors benchmark study test timeseries tier 0 - part 1
<p>A dataset with time series for testing virtual sensor models for wind turbine aeroelastic loads. Data are in zipped format, with 100 time series available.</p> <p>The test sets in the study are organized with several levels of "difficulty" according to added uncertainties and noise with respect to the training data. This particular dataset (tier 0) is with exactly the same distribution as the training data.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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