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5,162 results for “Daily”
ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO<sub>2</sub> dataset for China <strong>from 2008 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 5.34 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2</sub> dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019</strong><strong> </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4571660">https://doi.org/10.5281/zenodo.4571660</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
<p>GlobalHighPM<sub>2.5</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. <a href="https://weijing-rs.github.io/publications/Wei_et_al-NC-2023.pdf" target="_blank" rel="noopener">First close insight into global daily gapless 1 km PM<sub>2.5</sub> pollution, variability, and health impact</a>. <em>Nature Communications</em>, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3</p> </li> </ul> <p><strong>Input Data</strong></p> <p>Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.</p> <p><strong>Code</strong></p> <p>Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.</p> <p><strong>Generated Dataset</strong></p> <p>Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM<sub>2.5</sub> dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R<sup>2</sup>) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m<sup>-3</sup> on the daily, monthly, and annual bases, respectively.</p> <p><strong>Due to data volume limitations, </strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="../records/10795661">GlobalHighPM2.5 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2021 </strong>is accessible at: <strong><a href="../records/10398385">GlobalHighPM2.5 (2021)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2020 </strong>is accessible at: <strong><a href="../records/10402639">GlobalHighPM2.5 (2020)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2019 </strong>is accessible at: <strong><a href="../records/10402723">GlobalHighPM2.5 (2019)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2018 </strong>is accessible at: <strong><a href="../records/10402824">GlobalHighPM2.5 (2018)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2017 </strong>is accessible at: <strong><a href="../records/10403497">GlobalHighPM2.5 (2017)</a></strong></p> <p> continuously updated...</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)
<p><strong>extrAIM </strong>dataset is a <strong>new merged daily precipitation product</strong> (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:</p> <ul> <li><strong>Dataset format:</strong> NetCDF</li> <li><strong>Spatial resolution:</strong> 25 x 25 km</li> <li><strong>Temporal resolution:</strong> 1 day</li> <li><strong>Spatial coverage:</strong> Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li><strong>Temporal coverage: </strong>01-01-2007 to 30-09-2021</li> <li><strong>Merging approach: </strong>Two-step merging (classification and regression) <ul> <li><strong>Algorithm: </strong>Random Forest for both classification and regression</li> <li><strong>Training strategy:</strong> Full training strategy</li> </ul> </li> <li><strong>Merged precipitation products: </strong>SM2Rain-ASCAT and GPM Late Run</li> <li><strong>Reference precipitation product:</strong> EMO5</li> <li><strong>Static covariates: </strong>Longitude, Latitude and Elevation, in both classification and regression step <ul> <li><strong>Classification step:</strong> probability dry and probability dry of the 5 neighboring points around the target locations</li> <li><strong>Regression step:</strong> mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> <p>In addition, an <strong>ensemble of 20 synthetic realizations</strong> (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.</p>
Daily Severity Rating - ERA-Interim
<p>The Daily Severity Rating (DSR) is a numeric rating of the difficulty of controlling fires. It is based on the Fire Weather Index but more accurately reflects the expected efforts required for fire suppression.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately.</p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018).</p> <p>Details:</p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31</p> </li> </ul>
MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)
<p>This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).</p> <p>Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year.</p> <p>Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1.</p> <p>Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.</p>
Daily water temperature (C) in the Yolo Bypass and Sacramento River, 1998-2019
This data is an integration of raw logger data as well as relevant California Data Exchange Center (CDEC) data (Pien et al. 2020, hourly) and water quality data collected during the Yolo Bypass Fish Monitoring Program’s (YBFMP) fish collection (Pien and Kwan 2022) to produce a daily water temperature dataset for the Yolo Bypass and Sacramento River at Sherwood Harbor and Rio Vista Bridge. The raw YBFMP’s water temperature data was collected by loggers attached to the rotary screw trap (STTD) and Sherwood Harbor (SHR). Logger data ranged from daily means (1998) to a fifteen-minute collection interval (2013-2017 for the Yolo Bypass, 2009-2019 for Sherwood Harbor). A daily mean, maximum, minimum, standard deviation and coefficient of variation in water temperature was produced as well as columns for sample size (n, number of measurements per day), method (data collection or estimation), category, length (number of consecutive missing dates) and site. Daily water temperature data for the full extent of the YBFMP’s fish collection is valuable for a variety of purposes. For example, variation in water temperature during inundation and comparisons between temperatures in the Sacramento River and Yolo Bypass have been used as metrics of habitat complexity and linked to life history diversity in salmon (Goertler et al. 2017).
Daily river metabolism using oxygen flux at 75 sites in Mongolia or the United States in steppe ecoregions
We obtained GIS data to indicate local geomorphology and watershed-scale values for land use, climate, slope, and elevation for each sampling site. We selected our sites using the GIS-based program RESonate (Williams et al., 2013) to represent replicates in multiple watersheds of different geomorphic patches or Functional Process Zones (FPZs). The FPZs are reoccurring longitudinal geomorphic patches that are hypothesized to control biocomplexity, including community composition and system productivity (Thorp et al., 2006). A detailed description of the FPZ delineation methodology we employed has been provided previously (Maasri et al., 2019a; Erdenee et al., 2021). We classified each study site hierarchically by country, ecoregion, river basin, upper (streams higher in the watershed) or lower (low slope rivers of lower elevations), and relatively constrained valley or wide valley. This approach allowed us to assess reach-scale properties that could directly influence the physiological controls most often collected alongside metabolism data. This provided a framework to evaluate how we may understand the determinants of metabolism at multiple scales. We studied three large-scale temperate steppe ecoregions (Terminal Basin, TB; Montane Steppe, MS; and Grassland Steppe, GS) as characterized by Olson et al. (2001) and updated by Dinerstein et al. (2017) in two countries (Mongolia and the United States, Fig. 2). We aggregated our large-scale ecoregions for the US as follows: TB = Great Basin shrub steppe and Sierra Nevada forest, MS = South Central Rockies forest and Wyoming Basin shrub steppe, GS = Nebraska Sand Hills mixed grasslands and Northern Shortgrass prairie. We aggregated our large-scale ecoregions for Mongolia as follows: TB = Altai mountains forest and forest steppe, Gobi Lakes Valley desert steppe, Great Lakes Basin desert steppe, and Khangai Mountains alpine meadows, MS = Selenge-Orkhon forest steppe and Syan Mountains conifer forests, GS = Daurian Forest s
Daily rainfall series and rainfall erosivity in Mexico for three climatic normals (1968-1997, 1978-2007, and 1988-2017)
As in many countries around the world, there are some issues in the Mexican rainfall series, such as missing values, short measurement periods, and series homogeneity (breaks due to station relocation and measurement mistakes), which further compound the challenge of using climate data. Furthermore, it is necessary to develop a complete and helpful rainfall series database by following an imputing and homogenization process of the rainfall series. This research has compiled and systematized a national dataset with daily rainfall and rainfall erosivity for three climatic normals CN (1968-1997, 1978-2007, and 1988-2017). We have used the "climatol" package to fill the data. After, we calculated daily rainfall erosivity using a power law model. As a result, we obtained 1370, 1679, and 1683 rainfall series for the CNs 1968-1997, 1978-2007, and 1988-2017, respectively. The median values of the rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm/ ha h yr, respectively. We are making this database available for public consultation for researchers and students, technical assistants, decision-makers, and others interested in environmental studies in Mexico.
Middle Rio Grande New Mexico, Bernalillo 550 Bridge, Water Quality Daily Means 2010-2018
This data package includes daily mean data for streamflow, turbidity, light, and gross primary production for a site on the Middle Rio Grande located at the Bernalillo 550 Bridge in Bernalillo New Mexico, USA, from 2010-01-01 to 2018-12-31.
Marcell Experimental Forest daily peatland water table elevation, 1961 - ongoing
This data publication contains daily water table elevation data collected from 1961-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from seven peatlands instrumented for hydrologic monitoring.
Marcell Experimental Forest daily maximum and minimum air temperature, 1961 - ongoing
This data publication contains daily maximum and minimum air temperature collected from 1961-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from three long-term meteorological monitoring stations.
Caribou-Poker Creeks Research Watershed: Daily Flow Rates for C2, C3, C4
Stream discharge during the ice-free season was measured from ca. 1997 to present in three streams draining sub-catchments of the Caribou-Poker Creeks Research Watershed, which drain catchments with underlying permafrost extents ranging from 3 to 53%.
Hubbard Brook Experimental Forest: Daily Streamflow by Watershed, 1956 - present
Streamflow at 9 watersheds (12-77 Hectares) within the Hubbard Brook Experimental Forest in New Hampshire, USA has been measured continuously from as early as 1955. Streams are gaged with V-notch weirs, in some cases in combination with rectangular San Dimas flumes, at the outlet of each watershed. Through 2012, stage heights were recorded by means of a mechanical spring-wound clock and pen on a strip-chart recorder. Beginning January 1, 2013, these analog chart recorders were replaced with digital sensors. Overlap with both measurement techniques occurred for several years at all weirs. Streamflow data were gathered by the Hubbard Brook Experimental Forest and contributed to the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Daily Temperature Record, 1955 - ongoing
Air temperature at the Hubbard Brook Experimental Forest (HBEF) is measured at five locations in rain gage clearings throughout the experimental watersheds and at the Headquarters building. The oldest air temperature record dates back to October 20,1955 at Station 1. From 1955 - 2014, temperature measurements were made continuously using hygrothermographs housed in standard shelters. Beginning in 2013, digital sensors housed in solar radiation shields collected daily minimum and maximum temperature measurements. These data are gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Locally verified daily temperature and precipitation data from a NOAA weather station at USDA Jornada Experimental Range headquarters, southern New Mexico USA, 1914-2006
This data package contains locally verified daily meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Daily data has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 2006 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to. All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00294426/detail - daily and monthly data are available).
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.
High-frequency, hourly, and daily measurements from Lake Bonney Meteorological Station (BOYM), McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Lake Bonney Meteorological Station (BOYM), which was established in 1993 on the southern shore of the East Lobe of Lake Bonney in Taylor Valley. Parameters include air temperature, relative humidity, barometric pressure, photosynthetically active radiation, incoming and outgoing shortwave and longwave radiation, wind speed and direction, surface distance, as well as soil bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
High-frequency, hourly, and daily measurements from Lake Brownworth Meteorological Station (BRHM), McMurdo Dry Valleys, Antarctica (1994-2025, ongoing)
As part of the McMurdo Dry Valleys Long-Term Ecological Research program, a spatially distributed, long-term climate monitoring network was established across the McMurdo Dry Valleys region of Antarctica, consisting of fourteen research-grade weather stations that continuously measure a standard suite of environmental parameters. Ecosystem processes in this region are strongly regulated by climatic drivers that exhibit high variability across both time and space, making accurate measurement of environmental variables at high temporal and spatial resolution essential to understanding the biophysical dynamics of this polar desert ecosystem. This data package includes measurements from the Lake Brownworth Meteorological Station (BRHM), which was established in 1994 west of Lake Brownworth in Wright Valley. Parameters include air temperature, relative humidity, photosynthetically active radiation, incoming and outgoing shortwave radiation, wind speed and direction, surface distance, as well as soil bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content. Data are provided at high frequency (typically 15-minute intervals), along with hourly and daily summaries. Users should note that summary statistics may be affected by periods of missing data. Since there is no universally accepted standard for handling gaps in time-series data, users are encouraged to work with the high-frequency data and establish their own criteria for acceptable data completeness to minimize any potential bias.
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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.