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Figura 1 in Composición Y Variación Estacional De La Avifauna Presente En El Parque Ecológico Y Cultural Rucamanque, Región De La Araucanía, Chile
Figura 1. Ubicación del Parque Ecológico y Cultural Rucamanque y puntos de recuento.
Monthly averaged in-cloud coronas extracted from present-day (2000-2009) and projected (2091-2095) EMAC simulations (T42L90MA resolution)
<p>About Dataset</p> <p>Unscaled monthly averaged in-cloud corona data extracted from present-day (2000-2010) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Sergio Soler, Francisco J. Gordillo-Vazquez, Francisco J. Perez-Invernon, Patrick Jöckel, Torsten Neubert, Víctor Reglero, Nikolau Ostgaard.</p> <p>Example:</p> <p><br>netcdf BLUEs_10y_2l___20000201_0000_bluesbcl__\ _gp {<br>dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> lev = 90 ;<br> tbnds = 2 ;<br>variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2000-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float lev(lev) ;<br> lev:long_name = "hybrid level at layer midpoints" ;<br> lev:standard_name = "hybrid_sigma_pressure" ;<br> lev:units = "level" ;<br> lev:positive = "down" ;<br> lev:formula = "hyam hybm (press=hyam+hybm*aps)" ;<br> lev:borders = "ilev" ;<br> float hyam(lev) ;<br> hyam:long_name = "hybrid A coefficient at layer midpoints" ;<br> hyam:units = "Pa" ;<br> float hybm(lev) ;<br> hybm:long_name = "hybrid B coefficient at layer midpoints" ;<br> hybm:units = "1" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bps_std(time, lat, lon) ;<br> bps_std:long_name = "BJ flash frequency" ;<br> bps_std:units = "1/s" ;<br> bps_std:coordinates = "lon lat" ;<br> bps_std:cell_methods = "time: standard_deviation" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> float bpsm2_std(time, lat, lon) ;<br> bpsm2_std:long_name = "BJ flash density" ;<br> bpsm2_std:units = "1/s/m2" ;<br> bpsm2_std:coordinates = "lon lat" ;<br> bpsm2_std:cell_methods = "time: standard_deviation" ;<br> float npcanz(time, lat, lon) ;<br> npcanz:long_name = "no. of BLUEs events" ;<br> npcanz:units = "" ;<br> npcanz:coordinates = "lon lat" ;<br> npcanz:cell_methods = "time: point" ;<br> float npcanz_ave(time, lat, lon) ;<br> npcanz_ave:long_name = "no. of BLUEs events" ;<br> npcanz_ave:units = "" ;<br> npcanz_ave:coordinates = "lon lat" ;<br> npcanz_ave:cell_methods = "time: mean" ;<br> float npcanz_std(time, lat, lon) ;<br> npcanz_std:long_name = "no. of BLUEs events" ;<br> npcanz_std:units = "" ;<br> npcanz_std:coordinates = "lon lat" ;<br> npcanz_std:cell_methods = "time: standard_deviation" ;<br> float N2Obj(time, lat, lon) ;<br> N2Obj:long_name = "BJ N2O blue emission" ;<br> N2Obj:units = "kg(N)" ;<br> N2Obj:coordinates = "lon lat" ;<br> N2Obj:cell_methods = "time: point" ;<br> float N2Obj_ave(time, lat, lon) ;<br> N2Obj_ave:long_name = "BJ N2O blue emission" ;<br> N2Obj_ave:units = "kg(N)" ;<br> N2Obj_ave:coordinates = "lon lat" ;<br> N2Obj_ave:cell_methods = "time: mean" ;<br> float N2Obj_std(time, lat, lon) ;<br> N2Obj_std:long_name = "BJ N2O blue emission" ;<br> N2Obj_std:units = "kg(N)" ;<br> N2Obj_std:coordinates = "lon lat" ;<br> N2Obj_std:cell_methods = "time: standard_deviation" ;<br> float NObj(time, lat, lon) ;<br> NObj:long_name = "BJ NO blue emission" ;<br> NObj:units = "kg(N)" ;<br> NObj:coordinates = "lon lat" ;<br> NObj:cell_methods = "time: point" ;<br> float NObj_ave(time, lat, lon) ;<br> NObj_ave:long_name = "BJ NO blue emission" ;<br> NObj_ave:units = "kg(N)" ;<br> NObj_ave:coordinates = "lon lat" ;<br> NObj_ave:cell_methods = "time: mean" ;<br> float NObj_std(time, lat, lon) ;<br> NObj_std:long_name = "BJ NO blue emission" ;<br> NObj_std:units = "kg(N)" ;<br> NObj_std:coordinates = "lon lat" ;<br> NObj_std:cell_methods = "time: standard_deviation" ;<br> float NO2bj(time, lat, lon) ;<br> NO2bj:long_name = "BJ NO2 blue emission" ;<br> NO2bj:units = "kg(N)" ;<br> NO2bj:coordinates = "lon lat" ;<br> NO2bj:cell_methods = "time: point" ;<br> float NO2bj_ave(time, lat, lon) ;<br> NO2bj_ave:long_name = "BJ NO2 blue emission" ;<br> NO2bj_ave:units = "kg(N)" ;<br> NO2bj_ave:coordinates = "lon lat" ;<br> NO2bj_ave:cell_methods = "time: mean" ;<br> float NO2bj_std(time, lat, lon) ;<br> NO2bj_std:long_name = "BJ NO2 blue emission" ;<br> NO2bj_std:units = "kg(N)" ;<br> NO2bj_std:coordinates = "lon lat" ;<br> NO2bj_std:cell_methods = "time: standard_deviation" ;<br> float OHbj(time, lat, lon) ;<br> OHbj:long_name = "BJ OH blue emission" ;<br> OHbj:units = "kg(N)" ;<br> OHbj:coordinates = "lon lat" ;<br> OHbj:cell_methods = "time: point" ;<br> float OHbj_ave(time, lat, lon) ;<br> OHbj_ave:long_name = "BJ OH blue emission" ;<br> OHbj_ave:units = "kg(N)" ;<br> OHbj_ave:coordinates = "lon lat" ;<br> OHbj_ave:cell_methods = "time: mean" ;<br> float OHbj_std(time, lat, lon) ;<br> OHbj_std:long_name = "BJ OH blue emission" ;<br> OHbj_std:units = "kg(N)" ;<br> OHbj_std:coordinates = "lon lat" ;<br> OHbj_std:cell_methods = "time: standard_deviation" ;<br> float HO2bj(time, lat, lon) ;<br> HO2bj:long_name = "BJ HO2 blue emission" ;<br> HO2bj:units = "kg(N)" ;<br> HO2bj:coordinates = "lon lat" ;<br> HO2bj:cell_methods = "time: point" ;<br> float HO2bj_ave(time, lat, lon) ;<br> HO2bj_ave:long_name = "BJ HO2 blue emission" ;<br> HO2bj_ave:units = "kg(N)" ;<br> HO2bj_ave:coordinates = "lon lat" ;<br> HO2bj_ave:cell_methods = "time: mean" ;<br> float HO2bj_std(time, lat, lon) ;<br> HO2bj_std:long_name = "BJ HO2 blue emission" ;<br> HO2bj_std:units = "kg(N)" ;<br> HO2bj_std:coordinates = "lon lat" ;<br> HO2bj_std:cell_methods = "time: standard_deviation" ;<br> float O3bj(time, lat, lon) ;<br> O3bj:long_name = "BJ O3 blue emission" ;<br> O3bj:units = "kg(N)" ;<br> O3bj:coordinates = "lon lat" ;<br> O3bj:cell_methods = "time: point" ;<br> float O3bj_ave(time, lat, lon) ;<br> O3bj_ave:long_name = "BJ O3 blue emission" ;<br> O3bj_ave:units = "kg(N)" ;<br> O3bj_ave:coordinates = "lon lat" ;<br> O3bj_ave:cell_methods = "time: mean" ;<br> float O3bj_std(time, lat, lon) ;<br> O3bj_std:long_name = "BJ O3 blue emission" ;<br> O3bj_std:units = "kg(N)" ;<br> O3bj_std:coordinates = "lon lat" ;<br> O3bj_std:cell_methods = "time: standard_deviation" ;<br> float cth(time, lat, lon) ;<br> cth:long_name = "cloud top height" ;<br> cth:units = "m" ;<br> cth:coordinates = "lon lat" ;<br> cth:cell_methods = "time: point" ;<br> float cth_ave(time, lat, lon) ;<br> cth_ave:long_name = "cloud top height" ;<br> cth_ave:units = "m" ;<br> cth_ave:coordinates = "lon lat" ;<br> cth_ave:cell_methods = "time: mean" ;<br> float cth_std(time, lat, lon) ;<br> cth_std:long_name = "cloud top height" ;<br> cth_std:units = "m" ;<br> cth_std:coordinates = "lon lat" ;<br> cth_std:cell_methods = "time: standard_deviation" ;<br> float ctrop(time, lat, lon) ;<br> ctrop:long_name = "tropopause height" ;<br> ctrop:units = "m" ;<br> ctrop:coordinates = "lon lat" ;<br> ctrop:cell_methods = "time: point" ;<br> float ctrop_ave(time, lat, lon) ;<br> ctrop_ave:long_name = "tropopause height" ;<br> ctrop_ave:units = "m" ;<br> ctrop_ave:coordinates = "lon lat" ;<br> ctrop_ave:cell_methods = "time: mean" ;<br> float ctrop_std(time, lat, lon) ;<br> ctrop_std:long_name = "tropopause height" ;<br> ctrop_std:units = "m" ;<br> ctrop_std:coordinates = "lon lat" ;<br> ctrop_std:cell_methods = "time: standard_deviation" ;<br> float cbh(time, lat, lon) ;<br> cbh:long_name = "cloud bottom height" ;<br> cbh:units = "m" ;<br> cbh:coordinates = "lon lat" ;<br> cbh:cell_methods = "time: point" ;<br> float cbh_ave(time, lat, lon) ;<br> cbh_ave:long_name = "cloud bottom height" ;<br> cbh_ave:units = "m" ;<br> cbh_ave:coordinates = "lon lat" ;<br> cbh_ave:cell_methods = "time: mean" ;<br> float cbh_std(time, lat, lon) ;<br> cbh_std:long_name = "cloud bottom height" ;<br> cbh_std:units = "m" ;<br> cbh_std:coordinates = "lon lat" ;<br> cbh_std:cell_methods = "time: standard_deviation" ;<br> float cth_idx(time, lat, lon) ;<br> cth_idx:long_name = "cloud top index" ;<br> cth_idx:units = "" ;<br> cth_idx:coordinates = "lon lat" ;<br> cth_idx:cell_methods = "time: point" ;<br> float cth_idx_ave(time, lat, lon) ;<br> cth_idx_ave:long_name = "cloud top index" ;<br> cth_idx_ave:units = "" ;<br> cth_idx_ave:coordinates = "lon lat" ;<br> cth_idx_ave:cell_methods = "time: mean" ;<br> float cth_idx_std(time, lat, lon) ;<br> cth_idx_std:long_name = "cloud top index" ;<br> cth_idx_std:units = "" ;<br> cth_idx_std:coordinates = "lon lat" ;<br> cth_idx_std:cell_methods = "time: standard_deviation" ;<br> float trop_idx(time, lat, lon) ;<br> trop_idx:long_name = "tropopause index" ;<br> trop_idx:units = "" ;<br> trop_idx:coordinates = "lon lat" ;<br> trop_idx:cell_methods = "time: point" ;<br> float trop_idx_ave(time, lat, lon) ;<br> trop_idx_ave:long_name = "tropopause index" ;<br> trop_idx_ave:units = "" ;<br> trop_idx_ave:coordinates = "lon lat" ;<br> trop_idx_ave:cell_methods = "time: mean" ;<br> float trop_idx_std(time, lat, lon) ;<br> trop_idx_std:long_name = "tropopause index" ;<br> trop_idx_std:units = "" ;<br> trop_idx_std:coordinates = "lon lat" ;<br> trop_idx_std:cell_methods = "time: standard_deviation" ;<br> float xbjn2o(time, lev, lat, lon) ;<br> xbjn2o:long_name = "blue N2O emission" ;<br> xbjn2o:units = "kg(N)/s/m3" ;<br> xbjn2o:coordinates = "lon lat" ;<br> xbjn2o:cell_methods = "time: point" ;<br> float xbjn2o_ave(time, lev, lat, lon) ;<br> xbjn2o_ave:long_name = "blue N2O emission" ;<br> xbjn2o_ave:units = "kg(N)/s/m3" ;<br> xbjn2o_ave:coordinates = "lon lat" ;<br> xbjn2o_ave:cell_methods = "time: mean" ;<br> float xbjn2o_std(time, lev, lat, lon) ;<br> xbjn2o_std:long_name = "blue N2O emission" ;<br> xbjn2o_std:units = "kg(N)/s/m3" ;<br> xbjn2o_std:coordinates = "lon lat" ;<br> xbjn2o_std:cell_methods = "time: standard_deviation" ;<br> float telbjn2o(time, lev, lat, lon) ;<br> telbjn2o:long_name = "blue N2O emission tendency" ;<br> telbjn2o:units = "mol/mol/s" ;<br> telbjn2o:coordinates = "lon lat" ;<br> telbjn2o:cell_methods = "time: point" ;<br> float telbjn2o_ave(time, lev, lat, lon) ;<br> telbjn2o_ave:long_name = "blue N2O emission tendency" ;<br> telbjn2o_ave:units = "mol/mol/s" ;<br> telbjn2o_ave:coordinates = "lon lat" ;<br> telbjn2o_ave:cell_methods = "time: mean" ;<br> float telbjn2o_std(time, lev, lat, lon) ;<br> telbjn2o_std:long_name = "blue N2O emission tendency" ;<br> telbjn2o_std:units = "mol/mol/s" ;<br> telbjn2o_std:coordinates = "lon lat" ;<br> telbjn2o_std:cell_methods = "time: standard_deviation" ;<br> float xbjno(time, lev, lat, lon) ;<br> xbjno:long_name = "blue NO emission" ;<br> xbjno:units = "kg(N)/s/m3" ;<br> xbjno:coordinates = "lon lat" ;<br> xbjno:cell_methods = "time: point" ;<br> float xbjno_ave(time, lev, lat, lon) ;<br> xbjno_ave:long_name = "blue NO emission" ;<br> xbjno_ave:units = "kg(N)/s/m3" ;<br> xbjno_ave:coordinates = "lon lat" ;<br> xbjno_ave:cell_methods = "time: mean" ;<br> float xbjno_std(time, lev, lat, lon) ;<br> xbjno_std:long_name = "blue NO emission" ;<br> xbjno_std:units = "kg(N)/s/m3" ;<br> xbjno_std:coordinates = "lon lat" ;<br> xbjno_std:cell_methods = "time: standard_deviation" ;<br> float telbjno(time, lev, lat, lon) ;<br> telbjno:long_name = "blue NO emission tendency" ;<br> telbjno:units = "mol/mol/s" ;<br> telbjno:coordinates = "lon lat" ;<br> telbjno:cell_methods = "time: point" ;<br> float telbjno_ave(time, lev, lat, lon) ;<br> telbjno_ave:long_name = "blue NO emission tendency" ;<br> telbjno_ave:units = "mol/mol/s" ;<br> telbjno_ave:coordinates = "lon lat" ;<br> telbjno_ave:cell_methods = "time: mean" ;<br> float telbjno_std(time, lev, lat, lon) ;<br> telbjno_std:long_name = "blue NO emission tendency" ;<br> telbjno_std:units = "mol/mol/s" ;<br> telbjno_std:coordinates = "lon lat" ;<br> telbjno_std:cell_methods = "time: standard_deviation" ;<br> float xbjno2(time, lev, lat, lon) ;<br> xbjno2:long_name = "blue no2 emission" ;<br> xbjno2:units = "kg(N)/s/m3" ;<br> xbjno2:coordinates = "lon lat" ;<br> xbjno2:cell_methods = "time: point" ;<br> float xbjno2_ave(time, lev, lat, lon) ;<br> xbjno2_ave:long_name = "blue no2 emission" ;<br> xbjno2_ave:units = "kg(N)/s/m3" ;<br> xbjno2_ave:coordinates = "lon lat" ;<br> xbjno2_ave:cell_methods = "time: mean" ;<br> float xbjno2_std(time, lev, lat, lon) ;<br> xbjno2_std:long_name = "blue no2 emission" ;<br> xbjno2_std:units = "kg(N)/s/m3" ;<br> xbjno2_std:coordinates = "lon lat" ;<br> xbjno2_std:cell_methods = "time: standard_deviation" ;<br> float telbjno2(time, lev, lat, lon) ;<br> telbjno2:long_name = "blue no2 emission tendency" ;<br> telbjno2:units = "mol/mol/s" ;<br> telbjno2:coordinates = "lon lat" ;<br> telbjno2:cell_methods = "time: point" ;<br> float telbjno2_ave(time, lev, lat, lon) ;<br> telbjno2_ave:long_name = "blue no2 emission tendency" ;<br> telbjno2_ave:units = "mol/mol/s" ;<br> telbjno2_ave:coordinates = "lon lat" ;<br> telbjno2_ave:cell_methods = "time: mean" ;<br> float telbjno2_std(time, lev, lat, lon) ;<br> telbjno2_std:long_name = "blue no2 emission tendency" ;<br> telbjno2_std:units = "mol/mol/s" ;<br> telbjno2_std:coordinates = "lon lat" ;<br> telbjno2_std:cell_methods = "time: standard_deviation" ;<br> float xbjoh(time, lev, lat, lon) ;<br> xbjoh:long_name = "blue oh emission" ;<br> xbjoh:units = "kg(N)/s/m3" ;<br> xbjoh:coordinates = "lon lat" ;<br> xbjoh:cell_methods = "time: point" ;<br> float xbjoh_ave(time, lev, lat, lon) ;<br> xbjoh_ave:long_name = "blue oh emission" ;<br> xbjoh_ave:units = "kg(N)/s/m3" ;<br> xbjoh_ave:coordinates = "lon lat" ;<br> xbjoh_ave:cell_methods = "time: mean" ;<br> float xbjoh_std(time, lev, lat, lon) ;<br> xbjoh_std:long_name = "blue oh emission" ;<br> xbjoh_std:units = "kg(N)/s/m3" ;<br> xbjoh_std:coordinates = "lon lat" ;<br> xbjoh_std:cell_methods = "time: standard_deviation" ;<br> float telbjoh(time, lev, lat, lon) ;<br> telbjoh:long_name = "blue oh emission tendency" ;<br> telbjoh:units = "mol/mol/s" ;<br> telbjoh:coordinates = "lon lat" ;<br> telbjoh:cell_methods = "time: point" ;<br> float telbjoh_ave(time, lev, lat, lon) ;<br> telbjoh_ave:long_name = "blue oh emission tendency" ;<br> telbjoh_ave:units = "mol/mol/s" ;<br> telbjoh_ave:coordinates = "lon lat" ;<br> telbjoh_ave:cell_methods = "time: mean" ;<br> float telbjoh_std(time, lev, lat, lon) ;<br> telbjoh_std:long_name = "blue oh emission tendency" ;<br> telbjoh_std:units = "mol/mol/s" ;<br> telbjoh_std:coordinates = "lon lat" ;<br> telbjoh_std:cell_methods = "time: standard_deviation" ;<br> float xbjho2(time, lev, lat, lon) ;<br> xbjho2:long_name = "blue ho2 emission" ;<br> xbjho2:units = "kg(N)/s/m3" ;<br> xbjho2:coordinates = "lon lat" ;<br> xbjho2:cell_methods = "time: point" ;<br> float xbjho2_ave(time, lev, lat, lon) ;<br> xbjho2_ave:long_name = "blue ho2 emission" ;<br> xbjho2_ave:units = "kg(N)/s/m3" ;<br> xbjho2_ave:coordinates = "lon lat" ;<br> xbjho2_ave:cell_methods = "time: mean" ;<br> float xbjho2_std(time, lev, lat, lon) ;<br> xbjho2_std:long_name = "blue ho2 emission" ;<br> xbjho2_std:units = "kg(N)/s/m3" ;<br> xbjho2_std:coordinates = "lon lat" ;<br> xbjho2_std:cell_methods = "time: standard_deviation" ;<br> float telbjho2(time, lev, lat, lon) ;<br> telbjho2:long_name = "blue ho2 emission tendency" ;<br> telbjho2:units = "mol/mol/s" ;<br> telbjho2:coordinates = "lon lat" ;<br> telbjho2:cell_methods = "time: point" ;<br> float telbjho2_ave(time, lev, lat, lon) ;<br> telbjho2_ave:long_name = "blue ho2 emission tendency" ;<br> telbjho2_ave:units = "mol/mol/s" ;<br> telbjho2_ave:coordinates = "lon lat" ;<br> telbjho2_ave:cell_methods = "time: mean" ;<br> float telbjho2_std(time, lev, lat, lon) ;<br> telbjho2_std:long_name = "blue ho2 emission tendency" ;<br> telbjho2_std:units = "mol/mol/s" ;<br> telbjho2_std:coordinates = "lon lat" ;<br> telbjho2_std:cell_methods = "time: standard_deviation" ;<br> float xbjo3(time, lev, lat, lon) ;<br> xbjo3:long_name = "blue o3 emission" ;<br> xbjo3:units = "kg(N)/s/m3" ;<br> xbjo3:coordinates = "lon lat" ;<br> xbjo3:cell_methods = "time: point" ;<br> float xbjo3_ave(time, lev, lat, lon) ;<br> xbjo3_ave:long_name = "blue o3 emission" ;<br> xbjo3_ave:units = "kg(N)/s/m3" ;<br> xbjo3_ave:coordinates = "lon lat" ;<br> xbjo3_ave:cell_methods = "time: mean" ;<br> float xbjo3_std(time, lev, lat, lon) ;<br> xbjo3_std:long_name = "blue o3 emission" ;<br> xbjo3_std:units = "kg(N)/s/m3" ;<br> xbjo3_std:coordinates = "lon lat" ;<br> xbjo3_std:cell_methods = "time: standard_deviation" ;<br> float telbjo3(time, lev, lat, lon) ;<br> telbjo3:long_name = "blue o3 emission tendency" ;<br> telbjo3:units = "mol/mol/s" ;<br> telbjo3:coordinates = "lon lat" ;<br> telbjo3:cell_methods = "time: point" ;<br> float telbjo3_ave(time, lev, lat, lon) ;<br> telbjo3_ave:long_name = "blue o3 emission tendency" ;<br> telbjo3_ave:units = "mol/mol/s" ;<br> telbjo3_ave:coordinates = "lon lat" ;<br> telbjo3_ave:cell_methods = "time: mean" ;<br> float telbjo3_std(time, lev, lat, lon) ;<br> telbjo3_std:long_name = "blue o3 emission tendency" ;<br> telbjo3_std:units = "mol/mol/s" ;<br> telbjo3_std:coordinates = "lon lat" ;<br> telbjo3_std:cell_methods = "time: standard_deviation" ;<br> float precon(time, lat, lon) ;<br> precon:long_name = "convective precipitation at ground" ;<br> precon:units = "mm/s" ;<br> precon:coordinates = "lon lat" ;<br> precon:cell_methods = "time: point" ;<br> float precon_ave(time, lat, lon) ;<br> precon_ave:long_name = "convective precipitation at ground" ;<br> precon_ave:units = "mm/s" ;<br> precon_ave:coordinates = "lon lat" ;<br> precon_ave:cell_methods = "time: mean" ;<br> float precon_std(time, lat, lon) ;<br> precon_std:long_name = "convective precipitation at ground" ;<br> precon_std:units = "mm/s" ;<br> precon_std:coordinates = "lon lat" ;<br> precon_std:cell_methods = "time: standard_deviation" ;<br> float capecon(time, lat, lon) ;<br> capecon:long_name = "CAPE at ground" ;<br> capecon:units = "mm/s" ;<br> capecon:coordinates = "lon lat" ;<br> capecon:cell_methods = "time: point" ;<br> float capecon_ave(time, lat, lon) ;<br> capecon_ave:long_name = "CAPE at ground" ;<br> capecon_ave:units = "mm/s" ;<br> capecon_ave:coordinates = "lon lat" ;<br> capecon_ave:cell_methods = "time: mean" ;<br> float capecon_std(time, lat, lon) ;<br> capecon_std:long_name = "CAPE at ground" ;<br> capecon_std:units = "mm/s" ;<br> capecon_std:coordinates = "lon lat" ;<br> capecon_std:cell_methods = "time: standard_deviation" ;<br> float icecon(time, lat, lon) ;<br> icecon:long_name = "convective ice" ;<br> icecon:units = "kg/kg" ;<br> icecon:coordinates = "lon lat" ;<br> icecon:cell_methods = "time: point" ;<br> float icecon_ave(time, lat, lon) ;<br> icecon_ave:long_name = "convective ice" ;<br> icecon_ave:units = "kg/kg" ;<br> icecon_ave:coordinates = "lon lat" ;<br> icecon_ave:cell_methods = "time: mean" ;<br> float icecon_std(time, lat, lon) ;<br> icecon_std:long_name = "convective ice" ;<br> icecon_std:units = "kg/kg" ;<br> icecon_std:coordinates = "lon lat" ;<br> icecon_std:cell_methods = "time: standard_deviation" ;<br> float liquidcon(time, lat, lon) ;<br> liquidcon:long_name = "convective liquid" ;<br> liquidcon:units = "kg/kg" ;<br> liquidcon:coordinates = "lon lat" ;<br> liquidcon:cell_methods = "time: point" ;<br> float liquidcon_ave(time, lat, lon) ;<br> liquidcon_ave:long_name = "convective liquid" ;<br> liquidcon_ave:units = "kg/kg" ;<br> liquidcon_ave:coordinates = "lon lat" ;<br> liquidcon_ave:cell_methods = "time: mean" ;<br> float liquidcon_std(time, lat, lon) ;<br> liquidcon_std:long_name = "convective liquid" ;<br> liquidcon_std:units = "kg/kg" ;<br> liquidcon_std:coordinates = "lon lat" ;<br> liquidcon_std:cell_methods = "time: standard_deviation" ;<br> float snowcon(time, lat, lon) ;<br> snowcon:long_name = "convective snow" ;<br> snowcon:units = "kg/kg" ;<br> snowcon:coordinates = "lon lat" ;<br> snowcon:cell_methods = "time: point" ;<br> float snowcon_ave(time, lat, lon) ;<br> snowcon_ave:long_name = "convective snow" ;<br> snowcon_ave:units = "kg/kg" ;<br> snowcon_ave:coordinates = "lon lat" ;<br> snowcon_ave:cell_methods = "time: mean" ;<br> float snowcon_std(time, lat, lon) ;<br> snowcon_std:long_name = "convective snow" ;<br> snowcon_std:units = "kg/kg" ;<br> snowcon_std:coordinates = "lon lat" ;<br> snowcon_std:cell_methods = "time: standard_deviation" ;<br> float preclarge(time, lat, lon) ;<br> preclarge:long_name = "large scale precipitation at ground" ;<br> preclarge:coordinates = "lon lat" ;<br> preclarge:cell_methods = "time: point" ;<br> float preclarge_ave(time, lat, lon) ;<br> preclarge_ave:long_name = "large scale precipitation at ground" ;<br> preclarge_ave:coordinates = "lon lat" ;<br> preclarge_ave:cell_methods = "time: mean" ;<br> float preclarge_std(time, lat, lon) ;<br> preclarge_std:long_name = "large scale precipitation at ground" ;<br> preclarge_std:coordinates = "lon lat" ;<br> preclarge_std:cell_methods = "time: standard_deviation" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float aps_std(time, lat, lon) ;<br> aps_std:long_name = "surface pressure" ;<br> aps_std:units = "Pa" ;<br> aps_std:representation = "GP_2D_HORIZONTAL" ;<br> aps_std:grid_type = "gaussian" ;<br> aps_std:table = 128 ;<br> aps_std:code = 134 ;<br> aps_std:REFERENCE_TO = "g3b: aps" ;<br> aps_std:coordinates = "lon lat" ;<br> aps_std:cell_methods = "time: standard_deviation" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2000-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.2, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.5" ;<br> :MESSy_tracer = "version 2.7" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 4.0" ;<br> :MESSy_import = "version 1.1" ;<br> :MESSy_grid = "version 1.6" ;<br> :MESSy_rnd = "version 1.2" ;<br> :MESSy_tendency = "version 0.2" ;<br> :MESSy_aeropt = "version 2.1.0" ;<br> :MESSy_albedo = "version 1.4" ;<br> :MESSy_ch4 = "version 1.6" ;<br> :MESSy_cloud = "version 2.4" ;<br> :MESSy_cloudopt = "version 2.5" ;<br> :MESSy_convect = "version 2.1" ;<br> :MESSy_cvtrans = "version 2.5" ;<br> :MESSy_gwave = "version 1.1" ;<br> :MESSy_jval = "version 14.4" ;<br> :MESSy_lnox = "version 4.0" ;<br> :MESSy_blues = "version 1.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.3" ;<br> :MESSy_qbo = "version 2.3" ;<br> :MESSy_rad = "version 3.0.1" ;<br> :MESSy_e5vdiff = "version 1.3" ;<br> :MESSy_surface = "version 1.3" ;<br> :MESSy_tnudge = "version 3.2" ;<br> :MESSy_tropop = "version 2.2" ;<br> :MESSy_viso = "version 2.5" ;<br> :MESSy_experiment = "BLUEs_10y_2l" ;<br> :EXEC_CHECKSUM = "1e4e5004e03b26d8f74b729315612caa bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20000101 000000" ;<br> :GCM_timestep = 600.f ;<br> :F95_COMPILER_VERSION = "GNU Fortran (GCC) 11.2.0" ;<br> :F95_COMPILER_CALL = "/dragofs/sw/foss/0.2/software/OpenMPI/4.1.1-GCC-11.2.0/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-g -fbacktrace -cpp -D__linux__ -fno-second-underscore -ffree-line-length-none -fno-range-check -O2" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DPNCREGRID -DMPIOM_13B" ;<br> :F95_COMPILER_INCLUDES_01 = "-I../../mod -I/dragofs/sw/foss/0.2/software/netCDF-Fortran/4.5.3-gompi-2021b/include -I/dragofs/sw/foss/0.2/software/netCDF/4.8.1-gompi-2021b/include -DgFortran -I/dragofs/sw/foss/0.2/software/netCDF-Fortran/4.5.3-gompi-2021b/include -I/dragofs/sw/foss/0.2" ;<br> :F95_COMPILER_INCLUDES_02 = "/software/netCDF-Fortran/4.5.3-gompi-2021b/include" ;<br> :operating_date_time = "20240627 112437" ;<br> :operating_system = "Linux 4.18.0-348.7.1.el8_5.x86_64 on x86_64" ;<br> :operating_host = "drago31040118" ;<br> :operating_user = "fjperez (fjperez)" ;<br> :blues_mode = "production simulation" ;<br> :blues_r_scal_bf = 1.f ;<br> :blues_r_n2opbf = 0.f ;<br> :blues_r_nopbf = 0.f ;<br> :blues_r_no2pbf = 0.f ;<br> :blues_r_ohpbf = 0.f ;<br> :blues_r_ho2pbf = 0.f ;<br> :blues_r_o3pbf = 0.f ;<br> :channel_io_pe = 0 ;<br> :channel_name = "bluesbcl__ _gp" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "BLUEs_10y_2l___20000201_0000_bluesbcl__ _gp.nc" ;<br> :channel_netcdf_lib = "4.8.1 of May 6 2022 16:22:26 $" ;<br>}</p> <p> </p>
Figure 12 in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 12. – Ifremer survey in 2021 on board a professional vessel to assess the sea cucumbers abundance in SaintPierre and Miquelon's EEZ using a dredge equipped with an underwater camera (Photo Ifremer).
Figure 5. – The aviso Ville d in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 5. – The aviso Ville d'Ys, a stationary warship responsible for assisting French fishing vessels, was also observing hydrological conditions on the fishing Banks between the two-world war period.
Figure 3 in French research on fisheries in the Northwest Atlantic, from its origins to the present
Figure 3. – Maps of the French-Shore from 1713 to 1783 (A) and from 1783 to 1904 (B) (Source: Newfoundland and Labrador Heritage Web Site: https://www.heritage.nf.ca/).
Supplementary File 3; Figures S3.1 to S3.3: Additional photographs, complementing those presented in figure 2, to illustrate different ways in which signs of livestock, humans or wild mammal activity, as well as land cover attributes, were observed and classified
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Fig. 1 in Myrmecofauna (Hymenoptera: Formicidae) present in vineyards infested with Eurhizococcus brasiliensis (Hemiptera: Margarodidae) in southern Brazil
Fig. 1. Relative frequencies of occurrence of the main ant species in vineyards where Eurhizococcus brasiliensis occurred in Rio Grande do Sul.
Model input data, simulation output data and processed data presented in "Modelling the three-dimensional, diagnostic anisotropy field of an ice rise"
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considered that it was present. Due to the lack of recent data and the limited information sup- plied by previous authors for Angolan specimens, the presence of L. viridis in Angola is questionable. MAP 74. Distribution of Leptopelis viridis in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
considered that it was present. Due to the lack of recent data and the limited information sup- plied by previous authors for Angolan specimens, the presence of L. viridis in Angola is questionable. MAP 74. Distribution of Leptopelis viridis in Angola.
FIGURE 2. A. Eyes and ears present. B in Systematics, distribution and ecological analysis of rodents in Jordan
FIGURE 2. A. Eyes and ears present. B. Eyes and ears absent.
Virtual presentation of Collaboration with the Researchers – Content, Presentation systems and Techniques
<p>Virtual presentation of "<strong>Collaboration with the Researchers – Content, Presentation systems and Techniques</strong>" for <strong><a href="https://adholibdh.github.io/dh2019-preconference/">Libraries As Research Partner in Digital Humanities .</a></strong></p>
Present Events and Event Categories for Connecting Similar Past and Present Causalities
<p>This dataset includes present events described as news articles and their categories to connect similar past and present causalities. </p> <p>There are 2 files: 13category_crawled_news.csv and 13category_mainichi_news_events.csv. Both the files include an ID for a present event and its categories per line. The former one uses IDs defined in CD-Mainichi Newspapers 2012 data whereas the latter one uses URLs.</p> <p> </p> <p>In these files, 0 represents that its category is not assigned to the event. In contrast, 1 is used to represent that the category is assigned to the event. For example, if a cell (<em>k</em>, <em>l</em>) is 0, the <em>k</em>th event is not related to the <em>l</em>th category.</p>
Video Presentation of Sample Preparation by Easy Extraction and Digestion (SPEED) of Mouse Liver Tissue for Shotgun Proteomics
<p><em>Sample Preparation by Easy Extraction and Digestion</em> (<strong>SPEED</strong>) is a universal method for peptide generation from various sources. Due to its detergent-free chemistry, SPEED offers some inherent benefits for bottom-up proteomics sample preparation, including improved quantitative reproducibility, simple applicability and enhanced proteome coverage for lysis-resistant sample types as well as very rapid sample processing. The video shows the preparation of mouse liver tissue using the basic SPEED protocol for crude lysate injection into a nLC-MS/MS instrument</p>
Fig. 2 in Introduction to special issue The Past, Present and Future of Soil Protist Ecology
Fig. 2. The Broadbalk experiment at Rothamstaed. Wheat has been grown continually here since 1843 with different fertilizer regimes used in different parts of the field. From 1910 onwards a greater attention was given to the microbiology of the soil following the demonstration that protozoa were important in the soil ecology and hence the plant production on the field (Stevenson 1989). For example, it was shown that amoebae were more common on plots receiving farmyard manure (Russell 1967).
Fig. 3 in Introduction to special issue The Past, Present and Future of Soil Protist Ecology
Fig. 3. Examples of protist diversity in a range of soil habitats. Top Left: The summit of Green Mountain (845 m a. s. l.), Ascension Island in the tropical South Atlantic. A single soil sample from under the bamboo produced three species of flagellates, two non-testate amoebae (gymnamoeba), one ciliate and three testate amoebae species (Wilkinson and Smith 2006). In addition Landolt et al. (2005) isolated eight species of protostelid slime moulds from leaf litter samples collected near the pond shown in the photograph. All the vegetation seen in the photograph is comprised of introduced plant species (Wilkinson 2004).Top Right: Summit of Mot Sper Chamana Sevenna (2424 m a. s. l.) in the European Alps, on the Swiss/Italian boarder. Two moss samples from the summit gave a total of 17 testate amoebae species (Wilkinson and Mitchell, unpublished). Bottom Left: Island in a small lake at Mere Sands Wood nature reserve in North West England. The island is heavily used by roosting and breeding water birds, so the soils probably have a high nutrient status. Seventeen species of testate amoebae and 29 species of diatoms were found in the litter and upper soil horizons taken from within the quadrat shown in the photograph (Creevy et al., unpublished). Bottom Right: Holcroft Moss – a peat bog in Cheshire, England. Twenty four species of testate amoebae were found in the surface vegetation and underlying soil of a single sample (Valentine et al., unpublished).
Fig. 6. Polyp-like growths with adult mites present. Tissue extracted from H.g. 4 in There and back again - The return of the nasal mite Halarachne halichoeri to seals in German waters
Fig. 6. Polyp-like growths with adult mites present. Tissue extracted from H.g. 4's inferior nasal meatus adjacent to the hard palate.
Text-fig. 1. Graptolite biostratigraphy of the Silurian showing the up to present documented occurrences of scolecodonts in the Prague Basin. The graptolite zones are shown proportionally to the supposed relative time length (chart after Kříž 1992; Štorch 2001). in Revision Of Kettnerites Žebera, 1935 (Scolecodonta, Silurian Of The Barrandian Area, Czech Republic): Preliminary Results
Text-fig. 1. Graptolite biostratigraphy of the Silurian showing the up to present documented occurrences of scolecodonts in the Prague Basin. The graptolite zones are shown proportionally to the supposed relative time length (chart after Kříž 1992; Štorch 2001).
AMASS MOOC Lesson 7.3.: Mapping research on the social impact of the arts - what characterizes the field? (Presented by the Swedish School of Library and Information Science)
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AMASS MOOC Lesson 7.2.: Podcast - When Policy is turned in to Practice (Presented by the Swedish School of Library and Information Science)
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AMASS MOOC Lesson 7.1.: Cultural Policy in Europe (Presented by the Swedish School of Library and Information Science)
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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.