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55 results for “gage”
Stream and air temperature data from stream gages and stream confluences in the Andrews Experimental Forest, 1950 to present
Stream and air temperature are measured in tandem at stream gauging stations and other selected locations and stream confluences within the Andrews Forest. Air temperature is generally measured over the stream or alongside. Currently, mean, max and min water and air temperature data are collected every 5 minutes at the gauging stations and instantaneous temperatures every 15 minutes at all other sites. Most measurements were collected hourly commencing in the later 1990s, but a few sites have daily data beginning in the late 1970s. Historic data collected 1949 to 1981 at Lookout Creek stream gauge are included with the daily summary data. Other Andrews Forest related databases: Long term air temperature data from the reference and benchmark climate stations are also available in MS001. Previous high resolution stream temperature data at some of the small watershed stream gages are available in HT001 and stream temperature data throughout the Andrews Forest stream networks during several years are available in HT002.
Stream discharge in gaged watersheds at the HJ Andrews Experimental Forest, 1949 to present
Streamflow from selected small watersheds has been continuously monitored at the HJ Andrews Experimental Forest since November 1952. The objectives of this research and monitoring include: (1) evaluate long-term changes in hydrology associated with various forest management treatments, notably clearcut logging, selective logging, and burning of predominantly Douglas-fir conifer forests; (2) characterize the hydrologic regimes and evaluate mechanisms influencing water availability from conifer forests; and (3) provide baseline data for affiliated precipitation and stream water chemistry and sediment transport studies. In addition to stage heights and discharge, data on stream chemistry, stream and air temperature, specific conductivity are collected at the stream gages. Models have also been extensively calibrated with these data to characterize the hydrologic regimes of forests at different elevations and following forest management. Many other studies within the watersheds also use these hydrology data. The original design for the first- and second-order small watersheds used a paired watershed technique to evaluate changes in streamflow following forest management and harvest in conifer forests. A reference watershed remained unharvested within each set (Andrews WS 1, 2, 3; WS 6, 7, 8; and WS 9, 10). Watershed details and treatments are described at https://andrewsforest.oregonstate.edu/research/infrastructure/watersheds and in the 'Gaged watershed description' PDF in Related Materials/Files. Mack Creek is a third-order watershed where the reference was upstream of the harvested section. The fifth-order Lookout Creek has a gage maintained by US Geological Survey. Early discharge data from Lookout Creek gage, including for the period when USFS maintained the gage, are available here. Measurements of stream stage heights are recorded in feet and streamflow data are available in units of cubic feet per second (cfs). Discharge is calculated using rating curves that ha
Hubbard Brook Experimental Forest: Daily Precipitation Rain Gage Measurements, 1956 - present
Precipitation has been measured at the Hubbard Brook Experimental Forest using rain gauges located in or around each watershed since 1956. Three types of rain gauges have been used: standard, mechanical weight recording, and electronic weight recording. Between 1956 and 2014, precipitation was measured weekly at standard gages located at 24 stations in or near gauged watersheds and at the headquarters building. Weight-recording gauges were located at 7 of the 24 stations and capture a continuous strip-chart record. Weekly totals were prorated using daily totals from the nearest recording gauges. Beginning in 2011, electronic weighing rain gauges were implemented to measure 15-minute precipitation. The number of precipitation stations was reduced to 10, when each station was fully converted to an electronic gauge for measuring 15-minute and daily precipitation beginning in 2015. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Geospatial data for Luquillo Mountains, Puerto Rico: Mean annual precipitation, elevation, watershed outlines, and rain gage locations
The data archive is here: https://doi.org/10.5066/F74F1PM2 please use this DOI when citing this data set. These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>
Stream gage and outlet locations, McMurdo Dry Valleys, Antarctica (1993-2020, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in the region. Measurements are regularly made to examine water quality, quantity, and chemical properties. The stream gauge and outlet locations table serves as an index of the sampling points used to collect hydrological and stream chemistry data in the McMurdo Dry Valley network of streams. It documents the latitude, longitude and elevation of all the stream sample points used for hydrological and/or chemical analysis.
HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States
<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>
F-IDF values at USGS stream gage sites
<p>This nc file contains F-IDF values at streamflow gage sites over the CONUS.</p> <p>The header of this netcdf file is shown below:</p> <p>----------------------------------------------------------------------------------------------------------</p> <p>netcdf flashiness_dataset {<br> dimensions:<br> stnid = 6987 ;<br> duration = 6 ;<br> frequency = 6 ;<br> variables:<br> double flashiness(stnid, duration, frequency) ;<br> flashiness:description = "It is calculated with the slope of a given time window (cfs/15min*len(window)), divided by the drainage area (sqkm) and convert to a standardized unit" ;<br> flashiness:units = "mm/h^2" ;<br> flashiness:_FillValue = NaN ; area (sqkm)" ;<br> string stnid(stnid) ;<br> int64 frequency(frequency) ;<br> int64 duration(duration) ;<br> double lon(stnid) ;<br> lon:_FillValue = NaN ;<br> double lat(stnid) ;<br> lat:_FillValue = NaN ;<br> double area(stnid) ;<br> area:_FillValue = NaN ;<br> area:units = "sqkm" ;<br> double data_length(stnid) ;<br> data_length:_FillValue = NaN ;<br> data_length:units = "years" ;<br> data_length:description = "years of available data from USGS 15-min observation" ;<br> double dor_pc_pva(stnid) ;<br> dor_pc_pva:_FillValue = NaN ;<br> dor_pc_pva:units = "percent" ;<br> dor_pc_pva:long_name = "degree of regulation" ;<br> dor_pc_pva:description = "degree of regulation retrieved from hydrobasin V10 level 12" ;<br> double slp_dg_uav(stnid) ;<br> slp_dg_uav:_FillValue = NaN ;<br> slp_dg_uav:units = "degree" ;<br> slp_dg_uav:long_name = "Terrain slope" ;<br> slp_dg_uav:description = "Terrain slope of total watershed upstream of a pour point" ;<br> double sgr_dk_sav(stnid) ;<br> sgr_dk_sav:_FillValue = NaN ;<br> sgr_dk_sav:units = "degree" ;<br> sgr_dk_sav:long_name = "Stream gradient" ;<br> sgr_dk_sav:description = "the stream gradient was calculated as the ratio between the elevation drop within the river reach (i.e. the difference between min. and max. elevation along the reach) and the length of the reach." ;<br> double tmp_dc_uyr(stnid) ;<br> tmp_dc_uyr:_FillValue = NaN ;<br> tmp_dc_uyr:units = "degree celsius" ;<br> tmp_dc_uyr:long_name = "Annual mean air temperature" ;<br> tmp_dc_uyr:description = "Annual mean air temperature retrieved from WorldClim, station-based monitoring network" ;<br> double pre_mm_uyr(stnid) ;<br> pre_mm_uyr:_FillValue = NaN ;<br> pre_mm_uyr:units = "mm" ;<br> pre_mm_uyr:long_name = "Annual mean precipitation" ;<br> pre_mm_uyr:description = "Annual mean precipitation retrieved from WorldClim, station-based monitoring network and interpolated by the thin-plate smoothing spline algorithm" ;<br> double pet_mm_uyr(stnid) ;<br> pet_mm_uyr:_FillValue = NaN ;<br> pet_mm_uyr:units = "mm" ;<br> pet_mm_uyr:long_name = "Annual mean potential evaporation" ;<br> pet_mm_uyr:description = "Annual mean PET based on termperature inputs from WorldClim and a simple temperature-based transfer model" ;<br> double aet_mm_uyr(stnid) ;<br> aet_mm_uyr:_FillValue = NaN ;<br> aet_mm_uyr:units = "mm" ;<br> aet_mm_uyr:long_name = "Annual mean actural evaporation" ;<br> aet_mm_uyr:description = "Annual mean AET based on the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double ari_ix_uav(stnid) ;<br> ari_ix_uav:_FillValue = NaN ;<br> ari_ix_uav:units = "" ;<br> ari_ix_uav:long_name = "Global aridity index" ;<br> ari_ix_uav:description = "The Global Aridity Index (Global-Aridity) is modeled using data from WorldClim as input parameters" ;<br> double cmi_ix_uyr(stnid) ;<br> cmi_ix_uyr:_FillValue = NaN ;<br> cmi_ix_uyr:units = "" ;<br> cmi_ix_uyr:long_name = "Global climate moisture index" ;<br> cmi_ix_uyr:description = "The Climate Moisture Index (CMI) was derived from the annual precipitation (P) and potential evapotranspiration (PET) datasets as provided by the WorldClim v1.4" ;<br> double snw_pc_uyr(stnid) ;<br> snw_pc_uyr:_FillValue = NaN ;<br> snw_pc_uyr:units = "%" ;<br> snw_pc_uyr:long_name = "snow cover extent" ;<br> snw_pc_uyr:description = "data obtained from The MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1)" ;<br> double cly_pc_uav(stnid) ;<br> cly_pc_uav:_FillValue = NaN ;<br> cly_pc_uav:units = "%" ;<br> cly_pc_uav:long_name = "clay fraction in soils" ;<br> cly_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double slt_pc_uav(stnid) ;<br> slt_pc_uav:_FillValue = NaN ;<br> slt_pc_uav:units = "%" ;<br> slt_pc_uav:long_name = "silt fraction in soils" ;<br> slt_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double snd_pc_uav(stnid) ;<br> snd_pc_uav:_FillValue = NaN ;<br> snd_pc_uav:units = "%" ;<br> snd_pc_uav:long_name = "sand fraction in soils" ;<br> snd_pc_uav:description = "Data obtained from SoilGrids1km" ;<br> double swc_pc_uyr(stnid) ;<br> swc_pc_uyr:_FillValue = NaN ;<br> swc_pc_uyr:units = "%" ;<br> swc_pc_uyr:long_name = "Soil water content" ;<br> swc_pc_uyr:description = "Soil water content is provided as part of the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;<br> double kar_pc_use(stnid) ;<br> kar_pc_use:_FillValue = NaN ;<br> kar_pc_use:units = "%" ;<br> kar_pc_use:long_name = "Karst area extent" ;<br> kar_pc_use:description = "The World Map of Carbonate Rock Outcrops represents an upper limit of the area of exposed karst terrain." ;<br> double ero_kh_uav(stnid) ;<br> ero_kh_uav:_FillValue = NaN ;<br> ero_kh_uav:units = "kg/hectare per year" ;<br> ero_kh_uav:long_name = "Soil erosion" ;<br> string ero_kh_uav:description = "GloSEM erosion estimates were produced with a high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data" ;<br> double pop_ct_usu(stnid) ;<br> pop_ct_usu:_FillValue = NaN ;<br> pop_ct_usu:units = "count" ;<br> pop_ct_usu:long_name = "Population count" ;<br> pop_ct_usu:description = "The Gridded Population of the World (GPW) database." ;<br> double urb_pc_use(stnid) ;<br> urb_pc_use:_FillValue = NaN ;<br> urb_pc_use:units = "%" ;<br> urb_pc_use:long_name = "Urban extent" ;<br> urb_pc_use:description = "The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time" ;<br> double rdd_mk_uav(stnid) ;<br> rdd_mk_uav:_FillValue = NaN ;<br> rdd_mk_uav:units = "meters per km^2" ;<br> rdd_mk_uav:long_name = "Road density" ;<br> rdd_mk_uav:description = "The Global Roads Inventory Project (GRIP) dataset" ;<br> double dis_m3_pyr(stnid) ;<br> dis_m3_pyr:_FillValue = NaN ;<br> dis_m3_pyr:units = "cms" ;<br> dis_m3_pyr:long_name = "Natural discharge" ;<br> dis_m3_pyr:description = "Simulated discharge by WaterGAP" ;<br> double run_mm_syr(stnid) ;<br> run_mm_syr:_FillValue = NaN ;<br> run_mm_syr:units = "mm" ;<br> run_mm_syr:long_name = "Land surface runoff" ;<br> run_mm_syr:description = "Simulated land surface runoff by WaterGAP" ;<br> double inu_pc_umx(stnid) ;<br> inu_pc_umx:_FillValue = NaN ;<br> inu_pc_umx:units = "%" ;<br> inu_pc_umx:long_name = "Annual maximum inundation extent " ;<br> inu_pc_umx:description = "GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds" ;<br> double ria_ha_usu(stnid) ;<br> ria_ha_usu:_FillValue = NaN ;<br> ria_ha_usu:units = "hectares" ;<br> ria_ha_usu:long_name = "River area" ;<br> ria_ha_usu:description = "River area was calculated using the the HydroSHEDS database at 15 arc-second resolution. It is based on a rating curve" ;<br> double riv_tc_usu(stnid) ;<br> riv_tc_usu:_FillValue = NaN ;<br> riv_tc_usu:units = "1000 m^3" ;<br> riv_tc_usu:long_name = "River volume" ;<br> riv_tc_usu:description = "River volume was calculated using the the HydroSHEDS database at 15 arc-second resolution." ;<br> double gwt_cm_sav(stnid) ;<br> gwt_cm_sav:_FillValue = NaN ;<br> gwt_cm_sav:units = "cm" ;<br> gwt_cm_sav:long_name = "Groundwater table depth" ;<br> gwt_cm_sav:description = "Fan et al. (2013) compiled global observations of water table depth from government archives and literature" ;<br> double ele_mt_uav(stnid) ;<br> ele_mt_uav:_FillValue = NaN ;<br> ele_mt_uav:units = "m" ;<br> ele_mt_uav:long_name = "Elevation" ;<br> ele_mt_uav:description = "Elevation above mean sea level based on EarthEnv-DEM90" ;<br> double glc_pc_u01(stnid) ;<br> glc_pc_u01:_FillValue = NaN ;<br> glc_pc_u01:units = "%" ;<br> glc_pc_u01:long_name = "Land cover area extent" ;<br> glc_pc_u01:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u02(stnid) ;<br> glc_pc_u02:_FillValue = NaN ;<br> glc_pc_u02:units = "%" ;<br> glc_pc_u02:long_name = "Land cover area extent" ;<br> glc_pc_u02:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u03(stnid) ;<br> glc_pc_u03:_FillValue = NaN ;<br> glc_pc_u03:units = "%" ;<br> glc_pc_u03:long_name = "Land cover area extent" ;<br> glc_pc_u03:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u04(stnid) ;<br> glc_pc_u04:_FillValue = NaN ;<br> glc_pc_u04:units = "%" ;<br> glc_pc_u04:long_name = "Land cover area extent" ;<br> glc_pc_u04:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u05(stnid) ;<br> glc_pc_u05:_FillValue = NaN ;<br> glc_pc_u05:units = "%" ;<br> glc_pc_u05:long_name = "Land cover area extent" ;<br> glc_pc_u05:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u06(stnid) ;<br> glc_pc_u06:_FillValue = NaN ;<br> glc_pc_u06:units = "%" ;<br> glc_pc_u06:long_name = "Land cover area extent" ;<br> glc_pc_u06:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u07(stnid) ;<br> glc_pc_u07:_FillValue = NaN ;<br> glc_pc_u07:units = "%" ;<br> glc_pc_u07:long_name = "Land cover area extent" ;<br> glc_pc_u07:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u08(stnid) ;<br> glc_pc_u08:_FillValue = NaN ;<br> glc_pc_u08:units = "%" ;<br> glc_pc_u08:long_name = "Land cover area extent" ;<br> glc_pc_u08:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u09(stnid) ;<br> glc_pc_u09:_FillValue = NaN ;<br> glc_pc_u09:units = "%" ;<br> glc_pc_u09:long_name = "Land cover area extent" ;<br> glc_pc_u09:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u10(stnid) ;<br> glc_pc_u10:_FillValue = NaN ;<br> glc_pc_u10:units = "%" ;<br> glc_pc_u10:long_name = "Land cover area extent" ;<br> glc_pc_u10:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u11(stnid) ;<br> glc_pc_u11:_FillValue = NaN ;<br> glc_pc_u11:units = "%" ;<br> glc_pc_u11:long_name = "Land cover area extent" ;<br> glc_pc_u11:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u12(stnid) ;<br> glc_pc_u12:_FillValue = NaN ;<br> glc_pc_u12:units = "%" ;<br> glc_pc_u12:long_name = "Land cover area extent" ;<br> glc_pc_u12:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u13(stnid) ;<br> glc_pc_u13:_FillValue = NaN ;<br> glc_pc_u13:units = "%" ;<br> glc_pc_u13:long_name = "Land cover area extent" ;<br> glc_pc_u13:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u14(stnid) ;<br> glc_pc_u14:_FillValue = NaN ;<br> glc_pc_u14:units = "%" ;<br> glc_pc_u14:long_name = "Land cover area extent" ;<br> glc_pc_u14:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u15(stnid) ;<br> glc_pc_u15:_FillValue = NaN ;<br> glc_pc_u15:units = "%" ;<br> glc_pc_u15:long_name = "Land cover area extent" ;<br> glc_pc_u15:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u16(stnid) ;<br> glc_pc_u16:_FillValue = NaN ;<br> glc_pc_u16:units = "%" ;<br> glc_pc_u16:long_name = "Land cover area extent" ;<br> glc_pc_u16:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u17(stnid) ;<br> glc_pc_u17:_FillValue = NaN ;<br> glc_pc_u17:units = "%" ;<br> glc_pc_u17:long_name = "Land cover area extent" ;<br> glc_pc_u17:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u18(stnid) ;<br> glc_pc_u18:_FillValue = NaN ;<br> glc_pc_u18:units = "%" ;<br> glc_pc_u18:long_name = "Land cover area extent" ;<br> glc_pc_u18:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u19(stnid) ;<br> glc_pc_u19:_FillValue = NaN ;<br> glc_pc_u19:units = "%" ;<br> glc_pc_u19:long_name = "Land cover area extent" ;<br> glc_pc_u19:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u20(stnid) ;<br> glc_pc_u20:_FillValue = NaN ;<br> glc_pc_u20:units = "%" ;<br> glc_pc_u20:long_name = "Land cover area extent" ;<br> glc_pc_u20:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u21(stnid) ;<br> glc_pc_u21:_FillValue = NaN ;<br> glc_pc_u21:units = "%" ;<br> glc_pc_u21:long_name = "Land cover area extent" ;<br> glc_pc_u21:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double glc_pc_u22(stnid) ;<br> glc_pc_u22:_FillValue = NaN ;<br> glc_pc_u22:units = "%" ;<br> glc_pc_u22:long_name = "Land cover area extent" ;<br> glc_pc_u22:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;<br> double pnv_pc_u01(stnid) ;<br> pnv_pc_u01:_FillValue = NaN ;<br> pnv_pc_u01:units = "%" ;<br> pnv_pc_u01:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u01:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u02(stnid) ;<br> pnv_pc_u02:_FillValue = NaN ;<br> pnv_pc_u02:units = "%" ;<br> pnv_pc_u02:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u02:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u03(stnid) ;<br> pnv_pc_u03:_FillValue = NaN ;<br> pnv_pc_u03:units = "%" ;<br> pnv_pc_u03:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u03:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u04(stnid) ;<br> pnv_pc_u04:_FillValue = NaN ;<br> pnv_pc_u04:units = "%" ;<br> pnv_pc_u04:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u04:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u05(stnid) ;<br> pnv_pc_u05:_FillValue = NaN ;<br> pnv_pc_u05:units = "%" ;<br> pnv_pc_u05:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u05:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u06(stnid) ;<br> pnv_pc_u06:_FillValue = NaN ;<br> pnv_pc_u06:units = "%" ;<br> pnv_pc_u06:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u06:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u07(stnid) ;<br> pnv_pc_u07:_FillValue = NaN ;<br> pnv_pc_u07:units = "%" ;<br> pnv_pc_u07:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u07:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u08(stnid) ;<br> pnv_pc_u08:_FillValue = NaN ;<br> pnv_pc_u08:units = "%" ;<br> pnv_pc_u08:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u08:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u09(stnid) ;<br> pnv_pc_u09:_FillValue = NaN ;<br> pnv_pc_u09:units = "%" ;<br> pnv_pc_u09:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u09:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u10(stnid) ;<br> pnv_pc_u10:_FillValue = NaN ;<br> pnv_pc_u10:units = "%" ;<br> pnv_pc_u10:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u10:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u11(stnid) ;<br> pnv_pc_u11:_FillValue = NaN ;<br> pnv_pc_u11:units = "%" ;<br> pnv_pc_u11:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u11:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u12(stnid) ;<br> pnv_pc_u12:_FillValue = NaN ;<br> pnv_pc_u12:units = "%" ;<br> pnv_pc_u12:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u12:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u13(stnid) ;<br> pnv_pc_u13:_FillValue = NaN ;<br> pnv_pc_u13:units = "%" ;<br> pnv_pc_u13:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u13:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double pnv_pc_u14(stnid) ;<br> pnv_pc_u14:_FillValue = NaN ;<br> pnv_pc_u14:units = "%" ;<br> pnv_pc_u14:long_name = "Natural Vegetation area extent" ;<br> pnv_pc_u14:description = "Vegetation Cover extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u01(stnid) ;<br> wet_pc_u01:_FillValue = NaN ;<br> wet_pc_u01:units = "%" ;<br> wet_pc_u01:long_name = "Wet land area extent" ;<br> wet_pc_u01:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u02(stnid) ;<br> wet_pc_u02:_FillValue = NaN ;<br> wet_pc_u02:units = "%" ;<br> wet_pc_u02:long_name = "Wet land area extent" ;<br> wet_pc_u02:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u03(stnid) ;<br> wet_pc_u03:_FillValue = NaN ;<br> wet_pc_u03:units = "%" ;<br> wet_pc_u03:long_name = "Wet land area extent" ;<br> wet_pc_u03:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u04(stnid) ;<br> wet_pc_u04:_FillValue = NaN ;<br> wet_pc_u04:units = "%" ;<br> wet_pc_u04:long_name = "Wet land area extent" ;<br> wet_pc_u04:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u05(stnid) ;<br> wet_pc_u05:_FillValue = NaN ;<br> wet_pc_u05:units = "%" ;<br> wet_pc_u05:long_name = "Wet land area extent" ;<br> wet_pc_u05:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u06(stnid) ;<br> wet_pc_u06:_FillValue = NaN ;<br> wet_pc_u06:units = "%" ;<br> wet_pc_u06:long_name = "Wet land area extent" ;<br> wet_pc_u06:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u07(stnid) ;<br> wet_pc_u07:_FillValue = NaN ;<br> wet_pc_u07:units = "%" ;<br> wet_pc_u07:long_name = "Wet land area extent" ;<br> wet_pc_u07:description = "Wetland extent for the drainage system. Data from EarthStat" ;<br> double wet_pc_u08(stnid) ;<br> wet_pc_u08:_FillValue = NaN ;<br> wet_pc_u08:units = "%" ;<br> wet_pc_u08:long_name = "Wet land area extent" ;<br> wet_pc_u08:description = "Wetland extent for the drainage system. Data from EarthStat" ;</p> <p>// global attributes:<br> :description = "Created by Zhi Li (li1995@ou.edu)\nFitted empirical F-IDF values for USGS gauges." ;<br> }<br> </p>
Experimental watershed boundaries and gaging station locations, Andrews Experimental Forest, 2011
This dataset contains locations of the HJ Andrews hydrology and watershed studies features including small experimental watersheds, all gaged watersheds, gaging station locations, and watershed boundaries generated from digital elevation models. Data sources are identified within the metadata for each GIS layer.
Storm nutrient dynamics at Andrews Experimental Forest stream gages, 2001 to 2003
This study examines headwater stream nutrients at stream gaging sites during selected storms in the Andrews Experimental Forest. High frequency storm sampling was conducted to examine flow path dynamics, basin characteristics, seasonal trends, and differing responses and mobility among analytes on streamwater nutrient concentrations and fluxes.
Hubbard Brook Experimental Forest Rain Gages: GIS Shapefile
These data replace an earlier GIS coverage. Locations have been updated with Trimble GeoXT. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
ASD01 Stream discharge for Kings Creek measured at USGS gaging station
The 1060 hectacre Kings Creek watershed (STATION NUMBER 06879650) is a U.S. Geological survey hydrologic benchmark. (cf. Cobb, e.D. and J.E. Beisecker. 1971. USGS circular 460-d) flow is measured continuously with a bubble gage. The period of record started in March, 1979. The hydrologic regime is driven by the mid-continent climate characterized by summer thunderstorms and drought. The stream, at the gaging station, is fourth order in gallery forest. The riparian vegetation in the headwaters (1st & 2nd order) is tallgrass, or true, prairie. Flow occurred in spring and early summer in all years. Late summer, fall and winter records also include periods of no flow, the longest of these was ca. nine months. The time from base flow to peak flow for typical storm flow hydrographs is an hour or so and amplitude of ca. three meters above base flow has been recorded (Data comes from USGS office in Lawrence, Kansas; Contact person:Butch Lawcock, (913) 842-9709). Organization: U.S. Geological Survey For more metadata description and data, please see:https://waterdata.usgs.gov/nwis/uv/?site_no=06879650
McMurdo Dry Valleys Garwood River Gage Measurements, 1988-91
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. These tables contain data pertaining to continuous monitored water quality and quantity parameters measured with automatic recording devices on streams in this region.The metadata here describes pre-LTER hydrology measurements at the Garwood River gage, from 1988 to 1991.
Testing a new optical strain gage for full-field strain measurement: Raw images
<pre>This dataset contains images obtained during two different tests performed to assess the response of a new optical strain gage developped for full-field strain measurement.</pre> <pre><strong>File contents:<br></strong> - SMA: Folder containing images obtained with a SMA specimen<br> - SMA_Paint_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the painted and engraved half (lower half in the images).<br> - SMA_Paint_Def: contains 100 images in the deformed state. <br> - SMA_Gage_Ref: contains 100 images in the reference state. These were taken with the optimal parameters for the gage (upper half in the images).<br> - SMA_Gage_Def: contains 100 images in the deformed state. <br> - Wood: Folder containing images obtained with a wood specimen<br> - Wood_Paint_Ref: contains 100 images in the reference state.<br> - Wood_Paint_Def: contains 100 images in the deformed state. <br> - Wood_Gage_Ref: contains 100 images in the reference state.<br> - Wood_Gage_Def: contains 100 images in the deformed state. <br><br>These images can be processed with the Python code available in the OpenLSA library: https://gitlab.ip.uca.fr/expmech/openlsa .</pre> <p>An example is provided in this library.</p>
A Study Comparing Obinutuzumab (RO5072759; GA101) 1000 Milligram (mg) Versus 2000 mg in Participants With Previously Untreated Chronic Lymphocytic Leukemia (CLL) (GAGE)
ClinicalTrials.gov study NCT01414205. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Luquillo Experimental Forest site, station Puente Roto gage, study of mean daily streamflow in units of litersPerSecond 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a monthly timescale.
Luquillo Experimental Forest site, station Puente Roto gage, study of mean daily streamflow in units of litersPerSecond on a yearly 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a yearly timescale.
Luquillo Experimental Forest site, station Quebrada Bisley gage 1, Bisley Upper Tower, study of mean daily streamflow in units of litersPerSecond 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a monthly timescale.
Luquillo Experimental Forest site, station Quebrada Bisley gage 1, Bisley Upper Tower, study of mean daily streamflow in units of litersPerSecond on a yearly 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a yearly timescale.
Luquillo Experimental Forest site, station Quebrada Bisley gage 2, Bisley Lower Tower 2, study of mean daily streamflow in units of litersPerSecond 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a monthly timescale.
Luquillo Experimental Forest site, station Quebrada Bisley gage 2, Bisley Lower Tower 2, study of mean daily streamflow in units of litersPerSecond on a yearly 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 Luquillo Experimental Forest (LUQ) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a yearly timescale.
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.