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572 results for “Monthly means”
NOAA Monthly Mean Sea Level Summary Data for the Key West Water Level Station (NOAA/NOS Co-OPS ID 8724580), Florida, USA, January 1913 - ongoing
Monthly Mean Sea Level Summary Data for the Key West, Florida, Water Level Station (NOAA/NOS CO-OPS ID 8724580). Data is in meters relative to the STND-Key West Station Datum.
SPARC Data Initiative monthly zonal mean composition measurements from stratospheric limb sounders (1978-2018)
<p>The SPARC Data Initiative dataset is the most comprehensive compilation of vertically resolved stratospheric composition measurements to date and consists of four decades of monthly zonal mean climatologies (1978-2018) from a range of satellite limb sounders including LIMS, SAGE I/II/III, HALOE, UARS-MLS, POAMII/III, OSIRIS, SMR, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, SMILES, OMPS-LP and SAGE III-ISS. The dataset includes most major long-lived trace gases (O<sub>3</sub>, H<sub>2</sub>O, N<sub>2</sub>O, CH<sub>4</sub>, CCl<sub>3</sub>F, and CCl<sub>2</sub>F<sub>2</sub>), transport tracers (HF, SF<sub>6</sub>, HCl, CO, HNO<sub>3</sub>, NOy), and shorter-lived trace gases important to stratospheric chemistry including nitrogens (NO, NO<sub>2</sub>, NOx, N<sub>2</sub>O<sub>5</sub>,and HNO<sub>4</sub>), halogens (BrO, ClO, ClONO<sub>2</sub> and HOCl), and other minor species (OH, HO<sub>2</sub>, CH<sub>2</sub>O, CH<sub>3</sub>CN). The observations considered have been compiled in units of volume mixing ratio (VMR) and on a common latitude-pressure grid, covering the region from the upper troposphere to the lower mesosphere (300-0.1 hPa) with a latitudinal resolution of 5 degrees.</p> <p> </p>
2000-2018 SUHII and Rural LST Monthly Means used in Sismanidis et al. 2022
<p>This dataset provides the 2000-2018 SUHII and rural LST monhtly means used in Sismanidis et al. (2022). The source of the LST data is the the <a href="https://climate.esa.int/en/odp/#/project/land-surface-temperature">v1.0 Terra MODIS data product</a> created by the <a href="https://climate.esa.int/en/projects/land-surface-temperature/">ESA-CCI project on Land Surface Temperature (LST_cci)</a>.</p>
Monthly mean sea level data (1921-2018) relative to NAVD88 for Boston, Massachusetts, NOAA/NOS
Monthly sea level data for NOAA/NOS station 8443970, Boston, Massachusetts. The tide station is located on the right side of the U.S. Coast Guard Building adjacent to Northern Avenue Bridge. NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS)
Monthly mean sea level data (1912-2018) relative to NAVD88 for Portland, Maine, NOAA/NOS
Monthly sea level data for NOAA/NOS station 8418150, Portland, Maine. Tidal bench marks directions from north bound Interstate 295 in Portland, take the Waterfront Exit (Alt. U.S. 1) to Commercial Street, then continue NE along Commercial Street for 2.4 km (1.5 mi) to the Maine State Pier, the last pier- warehouse along the waterfront. The bench marks are located within 1.6 km (1 mi) radius of tide station. The tide gage is located in the south corner on the off shore end of the Maine State Pier. NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS).
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.
Monthly Mean Equatorial Electrojet variations over the Indian sector for the years 2001 to 2022
<p>This database contain the monthly mean Equatorial Electrojet variations over the Indian sector for a period from 2001 to 2022. This data has been mainly used in building the Indian Equatorial Electrojet (IEEJ) Model and manuscript is submitted for possible publication in JGR-Space Physics. </p>
TROPOMI-derived ground level NO2 concentrations (2019 & 2020 Monthly Means)
<p>Monthly mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations for January-June 2019 and 2020. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.<br> </p>
Global 1-km monthly mean land surface temperature product (2003-2020)
<p>The monthly mean land surface temperature (MMLST) reflects stable intra- and inter-annual temperature variations, and has a wide range of applications in climatological and meteorological studies. We used a combination method (including a weighted average model derived from 253 flux sites and considering the influence of the count of valid observations) and MODIS instantaneous LST products (MOD11A1 and MYD11A1) to generate a global 1-km MMLST dataset for the years 2003–2020. The validation with SURFRAD stations showed a root mean square error of 1.5 K and a Bias of 0.5 K. Compared with existing MMLST product, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature, and had a better ability to retrieve spatial details of temperature variations.</p> <p> </p>
Coordinated Great Lakes lake-wide average monthly mean water levels
<div> <pre> </pre> <p>This dataset consists of lake-wide average monthly mean water levels for Lakes Superior, Michigan-Huron, Saint Clair, Erie, and Ontario. The data are presented in meters with reference to the International Great Lakes Datum (IGLD) 1985. The dataset is computed and stewarded by Environment and Climate Change Canada (ECCC) and the United States Army Corps of Engineers (USACE), under the auspices of the Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data (Coordinating Committee)<strong>1</strong>. Each agency independently computes and manages their own version of the dataset. The data are binationally coordinated annually, where ECCC and USACE compare their versions to verify that they are consistent. </p> <p> </p> </div> <div> <p>The lake-wide average monthly mean water levels are computed from daily hydrometric observations collected from a network of gauging stations located around each lake and on both sides of the Canada-United States border. The gauges are owned and maintained by the Canadian Hydrographic Service (CHS)<strong>2</strong> and the National Oceanic and Atmospheric Administration (NOAA)<strong>3</strong>. </p> <p> </p> </div> <div> <p>Detailed metadata and data files for each lake are provided within this dataset. The data files are in comma separated value format (CSV). The CSV data files contain both the lake-wide average monthly mean water level and concise metadata described in accordance with the Climate and Forecasting (CF) conventions. The detailed metadata is in PDF format and provides extended descriptions of data sources, computation methods, dataset history, and related information for each great lake. </p> <p> </p> <p>This dataset is updated annually with the addition of the most recent complete calendar year of data. The version number reflects the year of the last available data. However, note that as well as adding the most recent data, data from previous years may be adjusted based on updated information from those years.</p> </div> <div> <pre> </pre> <p><strong>1</strong>Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data <a href="https://www.greatlakescc.org/en/home/" target="_blank" rel="noreferrer noopener">https://www.greatlakescc.org/en/home/</a> </p> <p> </p> </div> <div> <p><strong>2</strong>DFO 2024. Marine Environmental Data Section Archive, <a href="https://meds-sdmm.dfo-mpo.gc.ca/" target="_blank" rel="noreferrer noopener">https://meds-sdmm.dfo-mpo.gc.ca</a>, Ecosystem and Oceans Science, Department of Fisheries and Oceans Canada. </p> <p> </p> </div> <div> <p><strong>3</strong>NOAA 2024. Tides and Currents, <a href="https://tidesandcurrents.noaa.gov/" target="_blank" rel="noreferrer noopener">https://tidesandcurrents.noaa.gov/</a>, Center for Operational Oceanographic Products and Services, National Oceanic and Atmospheric Administration. </p> </div>
Bias corrected era5 skin temperature over the Arctic sea ice – 1981 to 2018 monthly means and climatology
<p>This dataset is generated in the context of the peer-reviewed study of Zampieri et al., 2023. The users can find a detailed description of the bias correction strategy and information on the scientific value of the dataset in the paper. Please, do not hesitate to contact me to obtain further information and suggestions on how to employ this dataset for your specific purpose. </p> <p><strong>References:</strong></p> <p>Zampieri, L.,<strong> </strong>Arduini, G., Holland, M., Keeley, S., Mogensen, K., Shupe, M., Tietsche, S. (2023) A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses. <em>Monthly Weather Review</em>. DOI:<a href="https://doi-org.cuucar.idm.oclc.org/10.1175/MWR-D-22-0130.1">10.1175/MWR-D-22-0130.1</a></p> <p><strong>Acknowledgments:</strong></p> <p>As part of the Virtual Earth System Research Institute (VESRI), funding for the Multiscale Machine Learning In coupled Earth System Modeling (M2LInES) project was provided to Lorenzo Zampieri by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. </p>
IMS/IASI SO2 total column and SA OD monthly means gridded data
<p>Datasets in support of the manuscript: https://essopenarchive.org/doi/full/10.22541/essoar.169091894.48592907 (in revision for GRL).</p><p>Climatology 2008-2019: MM_ims_metopa_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc</p><p>2022: 2022MM_ims_metopb_tir_qnrt_{so2,aot0}_day_global_g0.5_qc0.nc</p><p>(MM=month; {so2,aod0}=SO2 total columns or SA OD)</p><p> </p><p> </p>
1/8˚ resolution MOM6-COBALT physical and biogeochemical diagnostics for the Gulf of Mexico, monthly means between 2008-2018
<p>The files in this dataset contain monthly mean chlorophyll (µg/kg), pH, nitrate (mol/kg), dissolved oxygen (mol/kg), potential temperature (˚C) and salinity model outputs for 2008-2018 for the region between 18-31˚N, 98-80˚W. Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, https://github.com/NOAA-GFDL/MOM6 ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at https://rda.ucar.edu/datasets/ds551.0/. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021).</p> <p><br> References:</p> <p><br> Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p><br> Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p><br> Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p><br> Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p><br> Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p><br> Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO2. Journal of Advances in Modeling Earth Systems, 12, e2019MS002043. https://doi.org/10.1029/2019MS002043</p> <p> </p>
CFS model monthly mean diurnal cycles of ocean and atmosphere variables at TAO mooring locations
<p>v0.1.3</p> <p>cfsm501_ocn_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly ocean variables: one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains water temperature with dimensions (time, depth, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v0.1.2</p> <p>cfsm501_atmo_2002_2006_TAOpoints_3D_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, plev, lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p> <p>-------------------------</p> <p>v0.1.1</p> <p>cfsm501_atmo_2002_2006_TAOpoints_hourly.tgz -- contains netCDF files of hourly atmosphere variables: one atmosphere file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, lat, lon) at TAO locations. If joining multiple files together, concatenate along the time axis.</p> <p>-------------------------</p> <p>v 0.1.0</p> <p>cfsm501_atmo_ocn_2002_2006_TAOpoints_monthlyMeanDiurnalCycle.tgz -- contains netCDF files of monthly mean diurnal cycle of ocean and atmosphere variables: one atmosphere and one ocean file per month over 4 years (2002-2006).</p> <p>Each file contains multiple variables with dimensions (time, hour, [depth,] lat, lon) at TAO locations. The dimension "time" is of length 1 in all files. If joining multiple files together, concatenate along the time axis. The "hour" dimension represents the 24 hours of the diurnal cycle.</p> <p>Note that this version of the data has NOT had the 3-day high pass filter applied before the diurnal cycle calculation.</p>
Monthly mean optical depth at 550 nm derived from AERONET data used for model evaluation in GMD-2021-357
<p>Climatological monthly means over 2000-2014 derived from Aerosol Robotic Network version 3 level 2.0 direct sun retrievals (monthly data) used for model evaluation in Myriokefalitakis et al. (2021), doi: 10.5194/gmd-2021-357</p> <p>The netCDF file includes the AOD at the 4 native AERONET wavelengths (440 nm, 670 nm, 870 nm and 1020 nm), as well as the interpolated values at 550 nm used for the evaluation. The statistics stored are calculated over the monthly values for the 15 year period and include: monthly mean, 5th, 50th, and 95th percentile, standard deviation, standard error, number of days available per station and month and number of days where coarse AOD dominates (used as proxy for dusty days). </p> <p>The AERONET retrievals of optical depth were downloaded through the AERONET data download tool (available at: https://aeronet.gsfc.nasa.gov/, last accessed March 28, 2020). We thank the principal investigators and their collaborators for their effort in establishing and maintaining all AERONET sites used in this compilation.</p> <p>The use of this dataset must follow the guidelines of the original data providers at AERONET, explained here: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html</p>
Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"
<p>These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.</p>
Transformed Eulerian mean data from the ERA5 reanalysis (monthly means)
<p>This dataset provides <strong>monthly</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Daily</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081436).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
1/8˚ resolution MOM6-COBALT physical and biogeochemical diagnostics interpolated to 1˚, monthly means between 1965-2017
<p>Data was extracted from a global grid run with coupled ocean-ice model configured using the Modular Ocean Model 6 (MOM6, <a href="https://github.com/NOAA-GFDL/MOM6">https://github.com/NOAA-GFDL/MOM6</a> ) and Sea Ice Simulator (SIS2) developed at the NOAA Geophysical Fluid Dynamics Laboratory (Adcroft et al., 2019). The horizontal resolution of the grid is 1/8˚, which is considered eddying and no eddy parameterization was included. Vertically, the model uses 75 hybrid vertical-sigma2 layer coordinates that is remapped onto 35 World Ocean Atlas/Coupled Model Intercomparison Project standard depth levels. The atmospheric forcing was derived from the Japanese 55-year Reanalysis version 1.5 (JRA55 1.5, <a href="https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55">https://jra.kishou.go.jp/JRA-55/index_en.html#jra-55</a>). The model is driven by river freshwater runoff from a monthly climatology derived from Dai and Trenberth (2002) and Dai et al. (2009), which can be assessed at <a href="https://rda.ucar.edu/datasets/ds551.0/">https://rda.ucar.edu/datasets/ds551.0/</a>. A remapping scheme was used to add freshwater into the appropriate coastal grid cells near the river mouths. The biogeochemical model used was the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALTv2, Stock et al., 2020), which uses 33 tracers for representation of coupled elemental cycles of carbon, nitrogen, phosphorus, iron, silicon, alkalinity, oxygen and lithogenic matter and associated plankton food web dynamics. More details about the model setup are described in Liu et al. (2019) and Liu et al. (2021). This work was part of a PMEL-led project "A Pilot BGC Argo Float Array in the California Current Large Marine Ecosystem" funded by NOAA Research. This dataset contains dissolved oxygen, nitrate, temperature and salinity data interpolated to monthly means with 1˚ latitude/longitude horizontal resolution, between 1965-2017.</p> <p>References:</p> <p> </p> <p>Adcroft, A., Anderson, W., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M. et al. (2019). The GFDL Global Ocean and Sea Ice Model OM4.0: Model description and simulation features. Journal of Advances in Modeling Earth System, doi: 10.1029/2019MS001726</p> <p> </p> <p>Dai, A., T. Qian, K. E. Trenberth, and J. D Milliman, 2009: Changes in continental freshwater discharge from 1948-2004. J. Climate, 22, 2773-2791</p> <p> </p> <p>Dai, A., and K. E. Trenberth, 2002: Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeorol., 3, 660-687</p> <p> </p> <p>Liu, X., Dunne, J.P., Stock, C. A., Harrison, M.J., Adcroft, A., Resplandy, L. (2019). Simulating Water Residence Time in the Coastal Ocean: A Global Perspective. Geophysical Research Letters, 46, 22, 13910-13919. Doi:10.1029/2019GL085097</p> <p> </p> <p>Liu, X., Stock, C.A., Dunne, J.P., Lee, M., Shevliakova, E., Malyshev, S., Milly, P.C.D (2021). Simulated Global Coastal Ecosystem Responses to a Half-Century Increase in River Nitrogen Loads.</p> <p> </p> <p>Stock, C. A., Dunne, J. P., Fan, S., Ginoux, P., John, J., Krasting, J. P., et al. (2020). Ocean biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric CO<sub>2</sub>. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>, e2019MS002043. https://doi.org/10.1029/2019MS002043</p> <p> </p>
Compilation of mean monthly water table depth data (2015-2023) and linkages to further published sources of water table data, from European peatlands
<p>This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.</p>
GPRChinaTemp1km: 1 km monthly mean air temperature for China from January 1951 to December 2020
<p>GPRChinaTemp1km is a new high-resolution (1-km) monthly gridded air temperature dataset for China from January 1951 to December 2020. The dataset includes monthly mean air temperature covering the main land area of China during 1951-2020, which was interpolated by the Gaussian process regression (GPR) method based on the meteorological station data. The monthly gridded temperature dataset was evaluated by the observed values of the meteorological stations from the China Meteorological Data Service Centre. The dataset is in GeoTIFF format in the WGS84 (EPSG:4326) coordinate system. The unit of the data is degree Celsius (°C).</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.