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23,670 results for “Site”
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Datasets generated by rurAllure project - promotion of rural museums and heritage sites in the vicinity of European pilgrimage routes
<p>These datasets have been generated as part of rurAllure project (funded by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004887). Main goal of rurAllure is the promotion of rural museums and heritage sites in the vicinity of European pilgrimage routes: https://rurallure.eu/project/about/</p>
Water chemistry of LTER-Europe research site Lake Paione Superiore LTER_EU_IT_089 (1984-2013)
<p>This dataset provides information about water chemical parameters for Lake Paione Superiore LTER_EU_IT_089: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013.</p> <p>Lake Paione Superiore (LPS) is a high altitude Alpine lake, located at 2269 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.68 ha and a maximum depth of 11.5 m. The Lake, together with Lake Paione Inferiore (LPI), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy.</p> <p>This dataset includes the following files: Metadata LTER_EU_IT_089.xls and per each parameter one xls file with data records.</p> <p>Detailed description of the site LPS is available at https://deims.org/7e5837a9-ee27-4e27-822a-f50e5217c313</p> <p>Dataset for water chemistry of LPS for the period 2014-2020 is available at https://doi.org/10.5281/zenodo.10519126</p>
Precipitation and fire history at landslide sites
<p>These data include precipitation and burned area histories for events listed in the NASA Global Landslide Catalog. Each landslide includes a location uncertainty estimate. Precipitation values are the mean of all values within the uncertainty radius, while the fraction burned is computed for burned area.</p> <p>These data are intended to be used with the an RMarkdown notebook available at <a href="http://doi.org/10.5281/zenodo.7653683">this GitHub repository</a></p>
Data for: The function of stilt roots in the growth strategy of Socratea exorrhiza (Arecaceae) at two neotropical sites
<p>We provide the raw and processed data used in the following study: Goldsmith, G. R., & Zahawi, R. A. (2007). The function of stilt roots in the growth strategy of Socratea exorrhiza (Arecaceae) at two neotropical sites. <em>Revista de Biología Tropical</em>, <em>55</em>(3-4), 787-793.</p> <p><strong>Methods</strong> can be found in the file entitled "README-GoldsmithZahawi-SocrateaData-5Mar23F.txt," while metadata for data columns can be found in the file entitled: "GoldsmithZahawi-SocrateaMetaData-5March2023.csv."</p> <p><strong>Original Published Abstract</strong>: Arboreal palms have developed a variety of structural root modifications and systems to adapt to the<br> harsh abiotic conditions of tropical rain forests. Stilt roots have been proposed to serve a number of functions<br> including the facilitation of rapid vertical growth to the canopy and enhanced mechanical stability. To examine<br> whether stilt roots provide these functions, we compared stilt root characteristics of the neotropical palm tree<br> Socratea exorrhiza on sloped (>20º) and flat locations at two lowland neotropical sites. S. exorrhiza (n=80 trees)<br> did not demonstrate differences in number of roots, vertical stilt root height, root cone circumference, root cone<br> volume, or location of roots as related to slope. However, we found positive relationships between allocation<br> to vertical growth and stilt root architecture including root cone circumference, number of roots, and root cone<br> volume. Accordingly, stilt roots may allow S. exorrhiza to increase height and maintain mechanical stability<br> without having to concurrently invest in increased stem diameter and underground root structure. This strategy<br> likely increases the species ability to rapidly exploit light gaps as compared to non-stilt root palms and may also<br> enhance survival as mature trees approach the theoretical limits of their mechanical stability.</p>
Binding site plasticity regulation of the FimH catch-bond mechanism: Molecular Dynamics dataset
<p>Dataset of Molecular Dynamics simulations and analysis scripts used in the article "Binding site plasticity regulation of the FimH catch-bond mechanism" [<a href="https://doi.org/10.1016/j.bpj.2023.05.029">paper</a>][<a href="https://doi.org/10.1101/2022.11.15.516604">bioRxiv</a>].</p> <p>Contains:</p> <ul> <li>Replica Exchange with Solute Scaling (REST2) simulations of the FimH protein lectin domain in its two main allosteric states (Associated and Separated), in presence and absence of its synthetic ligand heptyl α-ᴅ-mannose (input files and trajectories of the unscaled replicas)</li> <li>Replica Exchange Umbrella Sampling (REUS) simulations of the liganted systems along a collective variable (CV) describing binding site opening (input files and trajectories)</li> <li>REUS simulations in presence of a pulling force on the protein-ligand complex.</li> </ul> <p>See the article for more details.</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>
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Microstructure of experimental faults in Pāpaku Fault core samples (IODP site U1518)
<p>Backscattered electron images of experimentally deformed samples from the Pāpaku Fault, Hikurangi Margin, New Zealand (International Ocean Discovery Program, Site U1518). In the experiments, intact mini-cores extracted from drill core samples were deformed in a single-direct shear box at MARUM, University of Bremen. Details of the experiments and the experimental data are available from the Pangea data publisher at: </p> <p>The data set contains original mosaics of whole thin sections, cut parallel to the shear direction and perpendicular to the experimental fault. These are in TIFF format and named SAMPLE#.tif.</p> <p>Annotated images include interpretations of the deformed zone (in red shading) superimposed on images that have been enhanced for better contrast. These are in Adobe Illustrator format and named SAMPLE#_annotated.ai.</p> <p>For image analysis, traces of the inferred deformed zone were extracted and scaled in ImageJ. These traces are available in TIFF format and named SAMPLE#_dz_trace.tif. Our measurements based on these traces are tabulated in Papaku_experiments_microstructure_data.csv.<br> </p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
Simulation output for seven simulations with CLM-FATES in the Land Sites Platform
<p>Supporting data for a manuscript involving simulation of vegetation with the<a href="https://www.cesm.ucar.edu/models/clm"> Community Land Model </a>and<a href="https://github.com/NGEET/fates"> FATES</a>. Simulations were run using the <a href="https://noresmhub.github.io/noresm-land-sites-platform/">Land Sites Platform </a>on two virtual linux machines provided by <a href="https://nrec.no/">NREC</a>. The manuscript is part of my (EL) PhD thesis. <br> <br> See the associated GitHub repository for notebooks and workflow documentation, and the thesis chapter or manuscript for further information.</p> <p>In this dataset:</p> <ul> <li>Concatenated model history files for the entire simulation period (e.g. "alp4-1500-cosmo-IA.0-1500.nc")</li> <li>Readme files per simulation specifying the variables passed to the Land Sites Platform to start the simulation (e.g. "readme.md")</li> <li>Zipped case folders, containing the standard CLM case folder structure. Individual monthly history files are found under /archive/lnd/hist. E.g. "2985cbd23a2e3d7b1ac057abd862abb7_alp4-1500-cosmo-ia.zip"</li> </ul>
Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes
<p>Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles:</p> <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> <p>The spatial reference system is EPSG 25830.</p>
Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology
<p>Zip folder conaining the data and code that support the findings of <em>Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology.</em></p> <p>The <em>Data_code.zip</em> folder contains four subfolders with the following files:</p> <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>
Archaeological sites in Taiwan during the Neolithic and Metal Age
<p>Coordinates marked by asterisks were added in the current study (see Data and methods section for details). Chinese site names are romanised following the Wade-Giles system. Site names in indigenous peoples' languages are given in romanised form followed by the Chinese name in parentheses, if available. Archaeological cultures represented by the respective site are indicated by the value "1".</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
Ground surface temperature data 2007-2021 at different sites of the PERMATHERMAL monitoring network in Livingston and Deception Islands, SouthShetland Archipelago, Antarctica.
<p>Ground Surface Temperature (GST) corrected data adquired between 2007 and 2021 at different stations of the PERMATHERMAL monitoring network at Livingston and Deception Islands, South Shetland Archipelago, Antarctica.</p> <p>(To be completed)</p>
SNOWISO model snow- and firn core simulations for the EastGRIP drilling site in Greenland
<p>This dataset (.csv) includes four SNOWISO v2 snowpack simulations of the stable water isotopes (δ<sup>18</sup>O, δD, d-excess) and is the result of snowpack simulations in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters</em>, <a href="https://doi.org/10.1029/2023GL104249">http</a><a href="https://doi.org/10.1029/2023GL104249">s://doi.org/10.1029/2023GL104249</a></p> <p>The SNOWISO model is a 1-D isotope-enabled snowpack and surface exchange model. The model accumulates snowfall (input) and applies water vapor exchange (input) at the snow surface with subsequent isotopic fractionation of the surface snow. In addition, diffusion of water isotopes in the accumulated snowpack is applied. This dataset is simulated in a 1 cm vertical layer resolution.</p> <p>The scientific theory of the SNOWISO model is described in:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>The documentation of the SNOWISO model v2 operational set-up is given in:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></p> <p>This model dataset consists of simulations for two model configurations each, with (control) and without (no_frac) fractionation during vapor exchange: </p> <ol> <li>daily average isotopes in the <strong>surface snow</strong> (top 2 cm) for the periods 11/05/2018-5/8/2018 and 17/5/2019-31/7/2019 <ul> <li>surface_snow_simulation_2018-2019_control.csv</li> <li>surface_snow_simulation_2018-2019_no_frac.csv</li> </ul> </li> <li>three 1-m long <strong>snow cores </strong>ending in 2017, 2018, and 2019, respectively <ul> <li>snowpack_core_simulation_2017_control.csv</li> <li>snowpack_core_simulation_2018_control.csv</li> <li>snowpack_core_simulation_2019_control.csv</li> <li>snowpack_core_simulation_2017_no_frac.csv</li> <li>snowpack_core_simulation_2018_no_frac.csv</li> <li>snowpack_core_simulation_2019_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2011 (~6 m) <ul> <li>snowiso_model_1990-2012_control.csv</li> <li>snowiso_model_1990-2012_no_frac.csv</li> </ul> </li> <li>one <strong>firn core </strong>simulation in the period 1990-2020 (~8.5 m) <ul> <li>snowiso_model_1990-2020_control.csv</li> <li>snowiso_model_1990-2020_no_frac.csv</li> </ul> </li> </ol> <p>Model input:</p> <ul> <li>6-hourly precipitation rate, vapor, and precipitation water stable isotopes from ECHAM6-wiso simulation nudged to the ERA-5 reanalysis (https://zenodo.org/record/8341390)</li> <li>hourly latent heat flux, near-surface meteorological variables, and snowpack variables from MARv3.12 simulation driven by the ERA-5 reanalysis (https://zenodo.org/record/8335402)</li> </ul> <p>Please be encouraged to contact me (Laura.Dietrich@uib.no) if you have any questions or ideas regarding these SNOWISO model simulations.<br><br><strong>Data usage notice:</strong></p> <p>When using the <strong>SNOWISO model</strong>, you should refer to:<br><em>Wahl, S., Steen‐Larsen, H.C., Hughes, A.G., Dietrich, L.J., Zuhr, A., Behrens, M., Faber, A.K. and Hörhold, M., 2022. Atmosphere‐Snow Exchange Explains Surface Snow Isotope Variability. Geophysical Research Letters, 49(20), p.e2022GL099529.</em></p> <p>If you use <strong>any of these simulations</strong>, you should refer to:<br><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, <a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a></em></p> <p> </p>
Database 2 - Second-year samplings in the CodeRe-farm pilot sites in Greece
<p>This database contains the results of the second year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.</p>
Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.</p><p><strong>Contents</strong></p><ul><li><i>Track-3_TA-2023.pdf: </i>Technical annexe describing the competition (Version 2)</li><li><i>01 Logfiles: </i>This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors.</li><li><i>02 Supplementary_Materials: </i>This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization.</li><li><i>03 Evaluation: </i>This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results.</li></ul><p>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competition</p><p><strong>Citation Policy</strong> </p><p>Please cite the following works when using the datasets included in this package:</p><p><i>Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2023</i><br><i>Competition Track 3 (Smartphone-based, off-site), Zenodo 2023</i><br><i>http://dx.doi.org/10.5281/zenodo.8362205</i></p><p>Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.8362205</p><p><strong>Contact</strong></p><p>For any further questions about the database and this competition track, please contact: </p><p>Joaquín Torres-Sospedra <br>Centro ALGORITMI,<br>Universidade do Minho, Portugal<br>info@jtorr.es - jtorres@algoritmi.uminho.pt<br> <br>Antonio R. Jiménez <br>Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain <br>antonio.jimenez@csic.es</p><p>Antoni Pérez-Navarro<br>Faculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spain<br>aperezn@uoc.edu</p><p><strong>Acknowledgements</strong></p><p>We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points.</p><p>We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.</p><p>We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition.</p><p>This work and competition belong to the IPIN 2023 Conference in Nuremberg (Germany). </p><p>Parts of this work received the financial support received from projects and grants: </p><ul><li>ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072)</li><li>GeoLibero (from CYTED)</li><li>INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE)</li><li>MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)</li><li>TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)</li><li>SmartLoc(CSIC-PIE Ref.201450E011)</li><li>LORIS (TIN2012-38080-C04-04)</li></ul>
Quantity and composition of POM and MAOM in 156 soil samples collected from 20 National Ecological Observatory Network (NEON) sites in 2019
While it is generally assumed that particulate organic matter (POM) and mineral associated organic matter (MAOM) have distinct biogeochemical characteristics, it remains unresolved where and why POM and MAOM differ in their composition and relationships to total SOM decomposition among heterogenous soils. To address these questions, we analyzed elemental, isotopic, and chemical composition, including diffuse reflectance infrared Fourier transform (DRIFT) spectra, of POM and MAOM in 156 soil samples collected from 20 National Ecological Observatory Network (NEON) sites spanning diverse ecosystems (tundra to tropics) across North America in 2019. We used a classic size separation method for POM (53–2000 µm) and MAOM (< 53 µm) following chemical dispersion.
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.