Find research datasets worth reusing

opencc-by-4.0Sep 2023View details →

Great Lakes Coordinated Monthly Precipitation Data

Coordinated Monthly Precipitation Data Information (1900-2023) This dataset consists of the coordinated amount of monthly precipitation that has fallen over the basin areas of the different Great Lakes.  The data are presented in both mm and inches.  Most of the processing is done by the US Army Corps of Engineers Detroit Office using models developed by that agency and the Great Lakes Environmental Research Lab (GLERL) of NOAA.  The data is then verified by Environment and Climate Change Canada. Different methods used to calculate monthly precipitation for certain periods in record period. For more information on data sources reference this paper: *<a href="http://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf">https://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf</a> An advancement made in most recent period, beginning in 1948. Data from 1948 to present calculated through AHPS model with GLERL DTP method. GLERL DTP method uses a group of meteorological stations surrounding the Great Lakes basin to calculate precipitation. In a recent study, it was found that some of the stations in the group were reporting erroneous data in recent years. Therefore, station list updated. Also, AHPS model replaced with GLSHFS model. GLSHFS uses same method for calculating precipitation. With the model change to GLSHFS and the method's (GLERL DTP) improved station list, precipitation data updated back to 1948.  Period  Current Method**  Previous Method**  1900-1930  USACE-AWD  USACE-AWD 1931-1947  GLERL-MTP  GLERL-MTP 1948-2017  GLERL-DTP (GLSHFS)  GLERL-DTP (AHPS) 2018-YYYY  GLERL-DTP (GLSHFS)  *Citation: Hunter, T. S., Clites, A. H., Campbell, K. B., & Gronewold, A. D. (2015). Development and application of a North American Great Lakes hydrometeorological database—Part I: Precipitation, evaporation, runoff, and air temperature. Journal of Great Lakes Research, 41(1), 65-77. **Acronyms AHPS: Advanced Hydrologic Prediction System AWD: Areally Weighted District DTP: Daily Thiessen Program GLERL: Great Lakes Environmental Research Laboratory GLSHFS: Great Lakes Seasonal Hydrological Forecasting System MTP: Monthly Thiessen Polygon USACE: United States Army Corps of Engineers

opencc-by-4.0May 2024View details →

NAHosMIP - monthly surface air temperature and precipitation v3

This dataset is for data generated from the North Atlantic Hosing Model Intercomparison Project (NAHosMIP), which is documented in <a href="https://gmd.copernicus.org/articles/16/1975/2023/gmd-16-1975-2023.html" target="_blank" rel="noopener">Jackson et al, 2023</a>.  Data used in that paper (including AMOC streamfunctions) can be found <a href="https://zenodo.org/records/7643437">here</a>  Experiments <ul> <li>picon - preindustrial control which was run as part of CMIP6 (<a href="https://gmd.copernicus.org/articles/9/1937/2016/">Eyring et al., 2016</a>),</li> <li>u03-hos - constant uniform hosing of 0.3 Sv. </li> <li>u03-r50 - experiment with no hosing initialised 50 years into u03-hos</li> <li>u03-r100 - experiment with no hosing initialised 100 years into u03-hos</li> </ul> Models Eight CMIP6 models took part: CanESM5, CESM2, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR   Variables <ul> <li>tas - surface air temperature (monthly resolution)</li> <li>pr - precipitation (monthly resolution)</li> <li>evspsbl - surface evaporation (including sublimation and transpiration)</li> <li>psl - sea level pressure</li> </ul> File name convention We use the CMIP file naming convention, so for example: tas_Amon_HadGEM3-GC31-LL_u03-r100_r1i1p1f1_gn_215001-215912.nc <pre><code>$variable_$timeresolution_$model_$experiment_$version_$grid_$date.nc</code></pre> Files with the same variable and model are combined in a tar file: <pre><code>$variable_$timeresolution_$model_$version_$grid.tar</code> AMOC timeseries These are included in the file M26.tar. This is the maximum streamfunction at 26.5N Additional precip and wind files Also included are files used by <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003959">Ben-Yami et al, 2024</a> who examined the impacts of an AMOC collapse on monsoons. The files contain monthly mean (mmean) or annual mean (ymean) of precipitation (prcp) and surface winds (ua and va) for the experiments u03-r50 (for HadGEM3-GC31-MM, CanESM5, CESM2) or u03-r100 (for IPSL-CM6A-LR) Files are named </pre> <pre><code>BY24_$model_$exp.tar</code></pre> <pre> </pre>

openother-openApr 2022View details →

Dataset : Predictors of global monthly precipitation

The predictability of global monthly precipitation. Currently, only one site in Beijing is included.

opencc-by-4.0Jun 2021View details →

Monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany

This dataset contains monthly precipitation intensity maxima for 14 aggregation times at 132 stations in Germany that serve as the basis of the study Modeling seasonal variations of extreme rainfall on different time scales in Germany.   Generation of the dataset: We use precipitation measurements at 132 stations in Germany that provide a temporal resolution of one minute.  The majority (129) of these stations are operated by the  German Meteorological Service (DWD) and were obtained via ftp://ftp-cdc.dwd.de/climate_environment/CDC/observations_germany/climate. The available time series at these stations range from 19 to 28 years. Additionally we use three stations operated by the  Wupperverband (https://www.wupperverband.de) with time series of more than 43 years. The observations were accumulated to the following durations: $d \in 2^{\left\lbrace 0,1,2,..,13 \right\rbrace}\,\text{min} = \left\lbrace 1,2,4,...,8192 \right\rbrace\,\text{min}$. Thus, resulting in 14 time series per station.   Files and variables meta_data_seasonal_variations_IDF_germany.csv <ul> <li>Meta information about the stations</li> <li>Variables: station name, station id (as provided by DWD), position (longitude, latitude), length of timeseries available</li> <li>Variable names: "StationName", "StationID", "Longitude", "Latitude", "NumberYears"</li> </ul> monthly_maxima_seasonal_variations_IDF_germany.csv <ul> <li>Monthly maxima for different durations (aggregation times)</li> <li>Variables: station id (as in meta file), year and month of observation, duration [h], observed monthly intensity maximum [mm/h]</li> <li>Variable names: "StationID", "Year", "Month", "Duration [h]", "MonthlyIntensityMaximum [mm/h]"</li> </ul>   Abstract of the study We model monthly precipitation maxima at 132 stations in Germany for a wide range of durations from one minute to about six days using a duration-dependent generalized extreme value (d-GEV) distribution with monthly varying parameters. This allows for the estimation of both monthly and annual intensity--duration--frequency (IDF) curves: (1) The monthly IDF curves are steeper in summer and exhibit higher intensities for short durations than in the rest of the year. Thus, everywhere in Germany short convective extreme events occur very likely in summer. In contrast, extreme events with a duration of several hours up to about one day are more likely to occur within a longer period or even spread throughout the whole year, depending on the station. There are major differences within Germany with respect to the months in which long-lasting stratiform extreme events are more likely to occur. At some stations the IDF curves (for a given quantile) for different months intersect. The meteorological interpretation of this intersection is that the season at which a certain extreme event is most likely to occur shifts from summer towards autumn or winter for longer durations. (2) We compare the annual IDF curves resulting from the monthly model with those estimated conventionally, that is, based on modeling annual maxima. We find that adding information in the form of smooth variations during the year leads to a considerable reduction of uncertainties. We additionally observe that at some stations, the annual IDF curves obtained by modeling monthly maxima deviate from the assumption of scale invariance, resulting in a flattening in the slope of the IDF curves for long durations.   Acknowledgements The authors would like to thank the Wupperverband, and in particular Marc Scheibel, as well as the Climate Data Center of the DWD, for providing and maintaining the precipitation time series.

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Bulk precipitation collected during summer months on a per rain event basis at Toolik Field Station, North Slope of Alaska, Arctic LTER 1988 to 2007.

Bulk precipitation was collected during summer months (June, July and August) on a per rain event basis at the University of Alaska Fairbanks Toolik Field Station, North Slope of Alaska (68 degrees 37' 42"N, 149 degrees 35' 46"W). Analysis of pH, NH4-N and phosphorus were performed at the field station. NO3-N were frozen and analyzed in Woods Hole, MA

openOpenDec 2015View details →

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Prairie du Sac - Sauk City, Wisconsin, monthly precipitation data

This is a compilation of total monthly precipitation data in total inches for two NOAA weather stations. The Prairie du Sac station data located at the Prairie du Sac dam on the Wisconsin River (43.31 , -89.7283) started with full monthly records being recorded in 1912 with complete monthly records through 2007. In mid-2007 a nearby station was established in Sauk City at the wastewater treatment plant (43.262 , -89.7349) with continuous data from 2008 through the present. The two stations are relatively close (about 3.25 miles apart), and both are slightly more than 4 miles to the west of the centroid of Fish Lake (Dane Co.) a core study lake in the North Temperate Lakes Long-Term Ecological Research Project conducted by the Center for Limnology at the University of Wisconsin-Madison. The compiled monthly records are based on daily NOAA precipitation records available electronically to the public. As a general practice, daily precipitation over weekends and holidays was not regularly recorded at the stations such that cumulative totals were recorded the following workweek day. As such, while the records for each single day were not always accurately recorded, the monthly totals were generally accurate. However, starting in 1996 at the Prairie du Sac station, because the cumulative weekend/holiday precipitation didn’t allow known daily totals, those cumulate weekend/holiday records were not submitted to NOAA so they were recorded as missing data in NOAA’s electronic dataset. To rectify the many months of missing data, pdf’s of the original hand-written monthly submissions were retrieved from NOAA’s archives such that the monthly precipitation totals could be calculated. In the process a few transcription errors in the electronic records of other months were also corrected in this dataset as well as determining a few other monthly records that were missing. Thus, this dataset of monthly precipitation at the two nearby weather stations is complete and hopefully accura

openCC (other)Nov 2022View details →

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SGS-LTER Standard Met Data: Monthly precipitation totals and temperatures from the Central Plains Experimental Range, Nunn, CO 1941-1973

This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83446. The objective of this study is to collect baseline meteorological data for the CPER. Datasets auto12_climdb and man11_climdb have been processed for quality and missing values.

opencc-by-4.0Apr 2020View details →

Analysis Of Monthly And Daily Annual Extreme Precipitation For Vadodara

Processed data for analysis 1 Monthly One day Extreme Rainfall IMD 2 Station wise Average Annual Rainfall SWDC 3 Station wise Number of Extreme Events (P95) SWDC 4 Urban Rural Moving Average Rainfall Ratio SWDC 5  Generalized extreme value distribution (IMD)

opencc-by-4.0Nov 2020View details →

1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_timeseries)

ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.

opencc-by-4.0Nov 2020View details →

1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series)

ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.

opencc-by-4.0Aug 2022View details →

Monthly water storage anomalies, precipitation, and groundwater recharge in two karstic basins, southwest China (2003-2014)

This dataset includes the regionally-averaged monthly hydrological data in two karstic basins, southwest China which are processed or estimated for the manuscript entitled "A novel approach for assessing groundwater recharge by combining GRACE and baseflow with case studies in karst areas of southwest China" by Huang et al., 2022 (submitted to Water Resources Research, Major Revision). Basin description: The Wujiang River Basin (WRB, ~87,900 km2, ~70% karstification) and Xijiang River Basin (XRB, ~360,000 km2, ~44% karstification) are two typical karstic basins in southwest China. The Wujiang River is the largest tributary in the southern part of the upper Yangtze River. It is originated from the Wumeng Mountain in the Yunnan-Guizhou Plateau and flows from Guizhou to Chongqing. The Xijiang River is the largest tributary of the Pearl River (the largest river in southern China). It is originated from the mountains in eastern Yunnan and flows to Guangxi, Guangdong, and finally into the South China Sea. Data description: 1. TWSA (unit: mm in equivalent water thickness) is the average terrestrial water storage anomaly data obtained from three release 6 GRACE (Gravity Recovery and Climate Experiment)  mascon solutions, i.e., the Center for Space Research (CSR) at the University of Texas (<a href="http://www2.csr.utexas.edu/grace/RL06_mascons.html">http://www2.csr.utexas.edu/grace/RL06_mascons.html</a>), Jet Propulsion Laboratory (JPL, <a href="https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2">https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2</a>), and Goddard Space Flight Center (GSFC, <a href="https://earth.gsfc.nasa.gov/geo/data/grace-mascons">https://earth.gsfc.nasa.gov/geo/data/grace-mascons</a>). The anomalies were estimated by removing a mean background value during 2006-2012. 2. SMSA and SWSA (unit: mm in equivalent water thickness) are the soil moisture storage anomaly and surface water storage anomaly data based on the model simulations by WGHM (v2.2d) provided by Dr. Hannes Müller Schmied (email: hannes.mueller.schmied@em.uni-frankfurt.de) at Institute of Physical Geography, Goethe-University Frankfurt. The anomalies were estimated by removing a mean background value during 2006-2012. 3. Precipitation data (unit: mm/month) were based on the monthly gridded (0.5×0.5 degree) data product obtained from China Meteorological Administration (CMA, https://data.cma.cn/) which was interpolated from ground-based data measured by meteorological stations. 4. Groundwater recharge (unit: mm/month) was estimated based on the groundwater budget method, i.e., the summation of groundwater storage change (GWSC) and baseflow. GRACE-based recharge was estimated using the GWSC derived from GRACE TWSA, WGHM-simulated SMSA and SWSA and in situ-based reservoir storage data. Observation-based recharge was estimated using the GWSC based on in situ groundwater-level data and specific yield (or storage coefficient). Both GRACE- and observation-based recharge were estimated using the baseflow separated by a multiple linear regression using the in situ streamflow as predictand, and precipitation and water table depth data as predictors. Time span: 2003-2014. The time lable like "200301" means January, 2003. "200312" means December, 2003.

opencc-by-4.0May 2019View details →

High-spatial-resolution monthly precipitation dataset over China during 1901–2017

The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution. /*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12. /*************/ The future 1km dataset from 2021-2100 is published. The data provider recommended the below publication as the reference. Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.

California Current Ecosystem site, station Lindbergh Field Airport, San Diego, CA, study of precipitation in units of centimeter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from California Current Ecosystem (CCE) contains precipitation measurements in centimeter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of calcium in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains calcium in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of chloride in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains chloride in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of nitrogen from ammonium in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains nitrogen from ammonium in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of ammonium in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains ammonium in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of nitrogen from nitrate in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains nitrogen from nitrate in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of nitrate in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains nitrate in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.

Kellogg Biological Station site, station NADP Station MI26, Kellogg Biological Station, MI, study of sulfur from sulfate in precipitation (volume-weighted concentration) in units of milligramsPerLiter on a monthly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Kellogg Biological Station (KBS) contains sulfur from sulfate in precipitation (volume-weighted concentration) measurements in milligramsPerLiter units and were aggregated to a monthly timescale.