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2,691 results for “streams”
Kreuzloch cave stream pressures
<p>Cave stream data collected in the Kreuzloch cave (Unteriberg, Switzerland).</p> <p>Water pressure and temperature were measured every 2 seconds during 8 months with RBR data loggers.</p>
Supplemental data for "The possible transition from glacial surge to ice stream on Vavilov Ice Cap"
<p>Data presented in the paper "The possible transition from glacial surge to ice stream on Vavilov Ice Cap".</p>
Rutford Ice Stream, Antarctica M_sf tidal velocity components derived from COSMO-SkyMED SAR data
<p>This repository provides rasters for velocity components of a tidal (periodic) model for Rutford Ice Stream (RIS), Antarctica. The tidal model consists of a secular (constant) term and a single sinusoidal component corresponding to the M_sf tidal cycle (14.76529 days). The tidal model is fit to time-dependent velocity fields over RIS derived from speckle tracking of COSMO-SkyMed SAR data, collected over 9 months beginning in August 2013. The original methodology and source dataset are described in the publication:</p> <p>Minchew, B. M., Simons, M., Riel, B., & Milillo, P. (2017). Tidally induced variations in vertical and horizontal motion on Rutford Ice Stream, West Antarctica, inferred from remotely sensed observations. <em>Journal of Geophysical Research: Earth Surface</em>, <em>122</em>(1), 167-190. doi: <a href="https://doi.org/10.1002/2016JF003971">10.1002/2016JF003971</a></p> <p>The rasters are provided in GeoTIFF format in the Polar Stereographic South (EPSG: 3031) coordinate system. The velocity components are also referenced to Polar Stereographic South coordinates. The pixel spacing is 400 meters (in both the X- and Y-directions). The individual files are:</p> <ol> <li>vx_secular.tif: secular velocity in X-direction in meters/day.</li> <li>vy_secular.tif: secular velocity in Y-direction in meters/day.</li> <li>vx_amp.tif: M_sf velocity amplitude in X-direction in meters/day.</li> <li>vy_amp.tif: M_sf velocity amplitude in Y-direction in meters/day.</li> <li>vx_phase.tif: M_sf velocity phase delay in X-direction in days.</li> <li>vy_phase.tif: M_sf velocity phase delay in Y-direction in days.</li> </ol>
Music Streams Labelled with Listening Situation - [User/Track/Device/Situation] Dataset
<p>This is a contextual music dataset labeled with the listening situation associated with each stream. Each stream is composed of the user, track, and device data labelled with a situation. The dataset is collected from Deezer for the period of August 2019 from France and Brazil. The dataset is composed of 3 subsets of situations corresponding to 4, 8, and 12 different situations. The situations are extracted based on keyword matching with the associated playlist title in the Deezer catalog. The full set of situational tags are: "<strong>work, gym, party, sleep | morning, run, night, dance | car, train, relax, club"</strong>.</p> <p>Each instance contains the track/user/deviice triplets, and a situational tag indicating that this user listens to the track in the associated situation wth the corresponding data recieved from the device. The device data contain: "l<strong>inear-time, linear-day, circular-time X, circular-time Y,circular-day X, circular-day Y, device-type, network-type</strong>". The users are represented as <strong>embeddings</strong> based on their listening history computed through the matrix factorization of the user/track matrix. Additionally, the users are also represented with their demographic data of : "<strong>age, country, gender</strong>".</p> <p>The creation of the dataset and our experimental results are described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Audio Autotagging as Proxy for Contextual MusicRecommendation" [Under Revision]. The source code of the paper is available here: <a href="https://github.com/KarimMibrahim/Situational_Session_Generator.git">https://github.com/KarimMibrahim/Situational_Session_Generator.git</a></p> <p>The dataset is composed of the media_id which is the ID of the track in the Deezer catalog. The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a>. Each user is represented with an <strong>anonymized</strong> <strong>user_id</strong> which is associated with the user embedding available in the user_embeddings.npy file. Note: The index of the embeddings in the user_embeddings arrary corresponds to the user_id, i.e. user_id = 100 have its embeddings at user_embeddings[100]. </p> <p>Finally, the dataset also contains the splits used in our experiments. Our splits were conditioned by one of three conditions: <em>ColdTrack</em> (no overlap of tracks between the splits), <em>ColdUser</em> (no overlap of users between the splits), and <em>WarmCase</em> (overlaps allowed). Each condition is split into 4 subsets for cross-validation marked with a "<strong>fold</strong>" number in each condition. </p>
Stream metabolism (as resazurin-resorufin transformation) along a boreal headwater stream and its relation to groundwater organic matter supply
<p>Datasets supporting the manuscript entitled "<strong><em>Groundwater-stream connections shape the spatial patterns and rates of aquatic metabolism</em></strong>", published in L<em>imnology and Oceanography Letters</em>. Three datasets are available:</p> <ul> <li>"<em><strong>Groundwater_Characterization.csv":</strong></em> dissolved organic matter characterization of and heterotrophic activity associated with, the major water sources discharging into a headwater boreal stream during summer 2017. Major water sources are: lake water and five discrete groundwater inflows (here named as <em>discrete riparian inflow points)</em>.</li> <li>"<em><strong>Raz_Additions.csv</strong></em>": Constant-rate additions of resazurin performed in a boreal headwater stream (Krycklan catchment, Sweden) during seven dates of summer 2017. Data contains resazurin and resorufin concentrations from the surface and hyporheic water at 18 stations along a 90-m long reach. </li> <li>"<em><strong>Raz_transformation_metric.csv</strong></em>": Hydrologic and metabolic characterization of the 90-m long reach for the seven resazurin additions conducted in summer 2017. </li> </ul> <p>More information about the data can be found in the document "<strong><em>Metadata.doc</em></strong>". Information about field and laboratory procedures can be found in the main manuscript or in the document "<strong><em>Supporting_Information.doc</em></strong>".</p>
Supporting materials for analysis of global emissions from rivers and streams
<p>These are the raw files to run the analysis detailed here: https://github.com/rocher-ros/RiverMethaneFlux. Further instructions of use can be find in the code repository. </p> <p>The results of this analysis are a global gridded product of methane river concentrations and fluxes, which can be found in: https://doi.org/10.5281/zenodo.8108959.</p> <p>The journal article based on the results of this analysis is currently in press in Nature (https://doi.org/10.1038/s41586-023-06344-6)</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>
Direct and indirect effects of climate and land use change on food webs in lakes and streams
<p>Here, we provide the data and code necessary to reproduce the workflow and analysis in: Barbosa and Siqueira. Direct and indirect effects of climate and land use change on food webs in lakes and streams. A preprint is available at https://doi.org/10.1101/2022.04.18.488700</p> <p>We compiled multicontinental data to investigate how climate and land use change are related to the structure of freshwater food webs, considering the inherent differences in lentic and lotic ecosystems. We analyzed the direct and indirect relationships between land use intensity, and temperature and precipitation changes, and food webs using multi-group structural equation modeling. Freshwater food webs were obtained from three sources: the Mangal interaction database, using the rmangal package in R, the GlobAl databasE of traits and food Web Architecture (GATEWAY) version 1.0, and the Interaction Web Data Base (IWDB). We also included food webs acquired from a search in the Web of Science Core Collection. Land use data was compiled from the global ESA CCI database, an annually generated land cover product at 300 m resolution for the period 1992 – 2015. Climate data was compiled from the TerraClimate database, a monthly generated product for climate and climatic water balance for global terrestrial surfaces at ~ 4 km for the period 1958 – 2015. </p>
Dataset from VR Streaming Server (Emulated) and Radio Access Network for Streaming Traffic
<p>The dataset contains an experiment in a site where UEs attach to a gNodeB that provides access to a streaming server that is stressed with high demanding transcoding workloads to emulate VR/AR processes. The UEs are realized through the Remote UE mode enabled by Amarisoft Simbox emulator, and the gNodeB is realized through the Amarisoft Callbox, which also provides the user plane function. The emulated VR streaming server is deployed as a Nginx pod in a Kubernetes cluster.</p> <p>We rely on MonB5G sampling functions that feed monitoring data (CPU and RAN parameters) to the monitoring system. A streaming video server has been deployed with the help of a NGINX server. It provides video-on-demand and video streaming, which can be accessed by any user (or UE) for real-time reproduction. This VR video streaming emulation aids to assess the performance of the network and therefore the benefits that each solution has brought. The video “Big Buck Bunny” with h.264 encoding and a resolution of 1920x1080p has been used for the experiments.The description of dataset features are:<br> 1-) Index Number,<br> 2-) Time: Time of the experiment,<br> 3-) N: number of VR streaming clients,<br> 4-) C: Average CPU of VR streaming server [mc]<br> 5-) O: Outbound traffic at the server average outbound traffic (O) flowing from the data interface of the video server. <br> 6-) R: Instantaneous downlink bit rate [Mbps],</p> <p>The original video file information:</p> <table> <tbody> <tr> <td> <p>Video codec </p> </td> <td> <p>Advanced Video Codec (AVC) </p> </td> </tr> <tr> <td> <p>Width </p> </td> <td> <p>1920 pixels </p> </td> </tr> <tr> <td> <p>Height </p> </td> <td> <p>1080 pixels </p> </td> </tr> <tr> <td> <p>Display aspect radio </p> </td> <td> <p>16:9 </p> </td> </tr> <tr> <td> <p>Duration </p> </td> <td> <p>10 min 34 s </p> </td> </tr> <tr> <td> <p>Max Bitrate </p> </td> <td> <p>16.7 Mb/s </p> </td> </tr> <tr> <td> <p>Frame rate </p> </td> <td> <p>30 FPS </p> </td> </tr> </tbody> </table>
Benne: A Modular Data Stream Clustering Algorithm with Flexible Design Choices
<p>All of the source dataset with preprocessed format [id features class] that have been used for evaluation in the paper.</p>
Dataset: Music Industry Professionals' Perspectives on Music Streaming Services and Recommendation
<p><strong>Questionnaire response data set</strong><br> Here, we include the data retrieved from participants at Eurosonic Noorderslag 2023, as described in the paper cited above.<br> When using, analyzing, or publishing this data in any way, please make sure to attribute it to the authors and cite it accordingly.<br> <br> We include the data in .xlsx, .csv format (semicolon-separated, and .tsv format (tab-separated). We suggest using the Excel file, as its layout makes it more easily readable.<br> <br> The complete question list as used in the questionnaire is published separately on <a href="https://doi.org/10.5281/zenodo.8121151">https://doi.org/10.5281/zenodo.8121151</a>.<br> <br> <strong>Paper title</strong><br> Looking at the FAccTs: Exploring Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendations<br> <br> <strong>Paper abstract</strong><br> Music recommender systems, commonly integrated into streaming services, help listeners find music. Previous research on such systems has focused on providing the best possible recommendations for these services' consumers, as well as on fairness for artists who release their music on streaming services. While those insights are imperative, another group of stakeholders has been omitted so far: the many other professionals working in the music industry. They, too, are (in)directly affected by music streaming services. Therefore, this work explores the perspective of music industry professionals. We present a study that addresses the role of streaming services and recommender systems in their jobs. Results indicate this role is significant. Furthermore, participants feel that music recommender systems lack transparency and are insufficiently controllable, for both customers and artists. Finally, participants desire that music streaming services take charge of increasing recommendation diversity, and variety in consumers' listening behavior and taste.</p> <p><strong>Citation</strong><br> Karlijn Dinnissen, Isabella Saccardi, Marloes Vredenborg, and Christine Bauer. 2023. Looking at the FAccTs: Exploring Music Industry Professionals’ Perspectives on Music Streaming Services and Recommendations. In 2nd International Conference of the ACM Greek SIGCHI Chapter (CHIGREECE 2023), September 27–28, 2023, Athens, Greece. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3609987.3610011</p>
Stream discharge and groundwater level data for the USVI
<p>.csv files containing 1) annual maximum stream discharge and corresponding cumulative precipitation data for three U.S. Virgin Islands (USVI) streams (one on St. Croix, St. Thomas, St. John) and 2) annual average groundwater levels for three monitoring wells in the USVI (one on St. Croix, St. Thomas, St. John). All of the data were collected by the U.S. Geological Survey and are available in government reports and online databases. </p>
Supplementary data for "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic sediment bacterial assemblages of an intermittent stream"
<p><strong>Supplementary data for the Pozzi <em>et al.</em> paper entitled "Ecological assessment of combined sewer overflow management practices through the analysis of benthic and hyporheic microbial assemblages and a tracking of exogenous bacterial taxa in a peri-urban intermittent stream".</strong></p> <p># Created by Dr Adrien C. MEYNIER POZZI on June, 29th, 2023<br> # Part of DOmic research project funded by the Agence de l’Eau - Rhône Méditerranée Corse [AE-RMC, Project 2020 0702 DOmic, 2020-2023], and of the DOmic extension funded by the EUR H2O'Lyon [ANR-17-EURE-0018] of Université de Lyon<br> # Part of the Chaudanne river long-term experiment site belonging to the Observatoire de Terrain en Hydrologie Urbaine (OTHU)<br> # Part of the work conducted in the team on Opportinistic Bacterial Pathogen in the Environment (BPOE) led by Dr. Benoit Cournoyer<br> # Samples were obtained in 2 campaigns, corresponding to periods before (2010-2011) or after (2018) the implementation of the 91/271/EEC European Directive that limited Combined-Sewer Overflow (CSO) discharges to the Chaudanne river<br> # Samples consisted in surface water, benthic and hyporheic sediments taken in run, riffle and pool geomorphologic features, either upstream or downstream the CSO outlet, plus positive and negative controls</p> <table> <tbody> <tr> <td><strong>Metadata. Name and description of data tables provided as supplementary information</strong></td> </tr> <tr> <td><strong>Data Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Data S1. River hydrology variables and hydraulic gradients at surveyed transects</td> <td>Array to describe the hydrologic variables and gradients at the studied transects. Top line is header, second line is metadata for each recorded variable, and third line is the unit of the variable, if any.</td> </tr> <tr> <td>Data S2. Environmental variables (water physical-chemistry, nutrients, FIBs, MTEs, PAHs) with metadata</td> <td>An array to list environmental variables for all true samples (n=90) included in the study. Sample identifiers and dates are provided. First 8 rows list the CAS number, SANDRE number, unit, method, limit of quantification and norm for each variable, if any.</td> </tr> <tr> <td>Data S3. Hydrological indices and synthetic variables computed with ClustOfVar</td> <td>Hydrological indices computed for the river flow, precipitations and CSO overflows computed over a 3-week period preceding each sampling date.</td> </tr> <tr> <td>Data S4. Discharge events selected to compute CSO dilution ratios</td> <td>An array to describe CSO events included for the computation of the CSO dilution ratio (SI Data 6A) together with 6 tables and 3 figures (SI Data 6B to 6J) describing the CSO event ratio all year round over the studied period, as well as for events that occurred before or after the CSO was modified and during low flow or high flow season. In SI Data 6A, top line is header and second line is metadata for each recorded variable.</td> </tr> <tr> <td>Data S5. Raw environmental matrix for use in R</td> <td>An array to list experimental design and environmental variables for all true samples and controls. Several environmental variables were synthetized using the ClustOfVar method (Chavent et al (2012) 10.18637/jss.v050.i13). Format is directly usable in R software.</td> </tr> </tbody> </table> <p> </p>
Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels
<p>The code and data for the paper entitled "Extra terrestrials: experimental drought creates niche space for rare invertebrates in terrestrialising stream channels" published in <em>Biology Letters</em>.</p>
Sodium, chloride and specific conductance in stream water and groundwater in New Hampshire 1991, 2000 – 2021
Stream water was collected at weekly to monthly intervals at 29 stream sites in New Hampshire (USA). Ten of the stream sites were instrumented with high‐frequency sensors. Twenty-one of the stream sites (including 5 sensor sites) are in the Lamprey River Hydrologic Observatory (LRHO; Wymore et al 2021) and two stream sites were nearby the LRHO. Groundwater was collected from two riparian well fields (JF, 14 wells and WHB, 13 wells). Wells were installed in 2004 and sampled monthly through May 2007, then quarterly until December 2009, after which a subset (JF, 6 and WHB, 5) was generally sampled quarterly. Stream and groundwater samples span a 17-year collection period and were analyzed for sodium, chloride and specific conductance. Methods and findings are described in the associated Limnology and Oceanography Letters manuscript.
Water quality measurements, stream order, channel slope and hydraulic equations of conterminous USGS sites: 1919-2009.
Streams and rivers emit petagrams of CO2 yet there is little known about how discharge (Q) variability impacts stream CO2 at broad scales. Herein, we compiled historical water quality (including pH, alkalinity and temperature) measurements for conterminous USGS sites and coupled them with daily Q for this analysis (the water_quality.csv dataset, 10,822 sites). Based on this dataset, NHDplus channel slopes (NHDplus_slopeSO.csv, 24,764 sites) and hydraulic geometry equations (lm_vQ.csv, 12,854 sites), we calculated partial pressure of dissolved CO2 (pCO2), gas transfer velocity (k) and CO2 effluxes (F) for a total of 813 USGS sites across conterminous US. We derived hydrologic responses (log-linear regressions) for pCO2, k and F versus Q at each site and explored how these responses varied across stream order and different regions. Ancillary datasets provided coordinates (coor_sites.xls), hydrologic unit code (HUC.csv), and watershed area of conterminous USGS sites (watersheds_area.csv).
Riparian remnant species registered at headwater streams in San Juan Zitácuaro, Mich.
Studying riparian vegetation allows understanding the floristic diversity patterns along the fluvial network, and because of the level of transformation they show, it is essential to generate knowledge to guide further recovery. This paper analyzed the remaining riparian tree vegetation in 30 sites in streams located in the Monarch Butterfly Biosphere Reserve, by describing the structure, species richness, and geographic setting (elevation, precipitation, hydrological order, and land cover), and by identifying possible invasive species. Elevation of the sites was associated with precipitation, hydrologic order, and land cover being crossed by the streams. Fifty-six mostly tree species were recorded, which increased in density and height with elevation. Some of the species with the highest importance value include Roldana angulifolia, Cestrum fulvescens, Ilex tolucana, Alnus acuminata, Buddleja cordata, and Fraxinus udhei. Four physiognomic groups emerged based on the number of species, genera and families, the number of branches, and the number, height, and diameter of individuals. High species turnover was found between sites, mainly with those located at higher elevations. The occurrence of potentially invasive species was shown to be associated with the density of individuals, with Shannon's diversity index (H'), and with geographic attributes such as elevation and hydrological order. The analyzed riverbanks show human intervention, being necessary to discriminate those impacts associated with flow alteration from those associated with land cover change.
Patch-level rates of nitrogen fixation and denitrification and environmental covariates from seven streams in Idaho and Michigan
We hypothesized that environmental variation at the patch scale (1 - 10’s m) would facilitate the co-occurrence of N2 fixation and denitrification through the formation of hot spots in streams. We measured rates of N2 fixation and denitrification and relative abundances of the genes nifH and nirS in patches determined by channel geomorphic units and substrate type in 4 Idaho and 3 Michigan streams encompassing a gradient of N and P concentrations. This data package includes patch-level measurements of N2 fixation and denitrification rates, relative gene abundances of nifH and nirS, and environmental covariates (nutrient concentrations, water temperature, surface and subsurface dissolved oxygen concentrations, organic matter content) that were used to explore the factors that could predict process rates and relative gene abundances across patches and streams.
CO2 concentrations and emissions from subtropical headwater streams, São Carlos, Brazil, 2018
The data were collected in the municipalities of São Carlos, Itirapina, and Brotas in the state of São Paulo, southeastern Brazil. Six sandy/rocky-bottom headwater streams (1st to 2nd order) were selected based on the main land use in the catchment. Three streams drained sugarcane plantations, and three streams drained native vegetation catchments (Cerrado vegetation). The catchment drainage areas were determined using digital elevation models. Land use was classified based on satellite images from LANDSAT using ArcGIS software. The data were collected to study the impact of different land uses (sugarcane plantations vs. native vegetation) on the headwater streams. These streams have previously been studied for methane dynamics, indicating a focus on understanding environmental and ecological impacts. Three samples were collected from each stream during spring, summer, and winter using the headspace extraction technique. Due to access issues, samples from one stream were not collected in spring and summer 2018. Syringes filled with ultrapure nitrogen were used to collect stream water samples, which were then shaken to equilibrate gases. The gas was analyzed using a Shimadzu GC-2014 gas chromatograph equipped with various detectors. Concentrations were compared with standards to calculate CO2 levels, and CO2 emissions were calculated based on gas transfer velocity and dissolved concentrations.
Field data for seasonal synoptic sampling of 100 urban streams in Boston, Massachusetts (USA) from 2021-2022
This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Boston, Massachusetts (USA) metropolitan area. Field collection took place during four synoptic sampling events (September 2021, November 2021, April 2022, and July 2022) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
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.