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Figure 3 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 3 - Example of identifying trees in a forest from LiDAR data. Illustrated is a small plot of poplar trees in Flevoland, The Netherlands, for which tree crowns and tree tops have been calculated.
Figure 2 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 2 - Generic workflow for object-based image analysis (OBIA) of LiDAR point clouds and proposed ecological applications. A workbench (blue) will be developed to handle the data storage, data exploration, and interactive OBIA of the massive LiDAR point clouds. Combined with datasets of bird distributions, climate, and other remote sensing layers (orange), the LiDAR data will be applied to several ecological case studies, e.g. by using species distribution modelling of birds and insect pollinators (green).
Figure 1 from: Kissling WD, Seijmonsbergen AC, Foppen RPB, Bouten W (2017) eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales. Research Ideas and Outcomes 3: e14939. https://doi.org/10.3897/rio.3.e14939
Figure 1 - The vertical and horizontal distribution of plants influences habitat structure and 3D characteristics of vegetation for animals. Illustrated are examples for (a) forests, (b) agricultural and open landscapes, and (c) reedbeds and marshlands. The height, openness and density of vegetation as well as specific habitat features (e.g. tree species, hedges etc.) are key aspects of animal habitat and space use.
Figure 5 from: Catenazzi A, Ttito A, Diaz MI, Shepack A (2017) Bryophryne phuyuhampatu sp. n., a new species of Cusco Andes frog from the cloud forest of the eastern slopes of the Peruvian Andes (Amphibia, Anura, Craugastoridae). ZooKeys 685: 65-81. https://doi.org/10.3897/zookeys.685.12152
Figure 5 - Collection localities of Bryophryne phuyuhampatu sp. n. in the upper Quispillomayo River valley A lookout from 3050 m a.s.l.: frogs were found under mosses, leaves and rocks in the cloud forest along the Quispillomayo River B such as the type locality at 2850 m a.s.l. C and disturbed forest at the confluence with a stream at 2795 m a.s.l. D Photographs by A. Catenazzi.
Figure 4 from: Catenazzi A, Ttito A, Diaz MI, Shepack A (2017) Bryophryne phuyuhampatu sp. n., a new species of Cusco Andes frog from the cloud forest of the eastern slopes of the Peruvian Andes (Amphibia, Anura, Craugastoridae). ZooKeys 685: 65-81. https://doi.org/10.3897/zookeys.685.12152
Figure 4 - Dorsolateral and ventral views of four paratypes of Bryophryne phuyuhampatu sp. n. showing variation in dorsal and ventral coloration. Male CORBIDI 18225, SVL = 14.2 mm A, B Male MUBI 14655, SVL = 15.9 mm C, D Female CORBIDI 18224, SVL = 22.6 mm E, F. Female MUBI 14654, SVL = 22.2 mm G, H Photographs by A. Catenazzi.
Figure 3 from: Catenazzi A, Ttito A, Diaz MI, Shepack A (2017) Bryophryne phuyuhampatu sp. n., a new species of Cusco Andes frog from the cloud forest of the eastern slopes of the Peruvian Andes (Amphibia, Anura, Craugastoridae). ZooKeys 685: 65-81. https://doi.org/10.3897/zookeys.685.12152
Figure 3 - Ventral views of hand A and foot B of holotype, CORBIDI 18226 (hand length 4.1 mm, foot length 6.6 mm) of Bryophryne phuyuhampatu sp. n. Photographs by A. Catenazzi.
Figure 2 from: Catenazzi A, Ttito A, Diaz MI, Shepack A (2017) Bryophryne phuyuhampatu sp. n., a new species of Cusco Andes frog from the cloud forest of the eastern slopes of the Peruvian Andes (Amphibia, Anura, Craugastoridae). ZooKeys 685: 65-81. https://doi.org/10.3897/zookeys.685.12152
Figure 2 - Live (left column) and preserved (right column) specimen of the holotype of Bryophryne phuyuhampatu sp. n., male CORBIDI 18226 (SVL 16.9 mm) in dorsolateral A, B dorsal C, D and ventral E, F views. Photographs by A. Catenazzi.
FIGURE 2 in Bedazzled: a new, striking species of Corades from the outskirts of Quito questions our knowledge of Andean cloud forest butterflies (Lepidoptera, Nymphalidae, Satyrinae)
FIGURE 2. Corades yanacocha sp. n. male, left: dorsal, right: ventral.
FIGURE 3 in Bedazzled: a new, striking species of Corades from the outskirts of Quito questions our knowledge of Andean cloud forest butterflies (Lepidoptera, Nymphalidae, Satyrinae)
FIGURE 3. Corades yanacocha sp. n. in the field (phot. Abigail de Pozo).
Finger clouds simulated by two-level nested SHiELD
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Dataset: Big Tree Cloud Holdings Limited (DSYWW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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>
SI_Robust Increase in South Asian Monsoon Rainfall Under Global Warming Driven by Southern Ocean Heat Uptake and Eurasia Cloud Changes
Open the record for dataset details and reuse information.
Senator Beck Basin Lidar Point Cloud 2017-02-21
<p>This lidar dataset was collected using an airborne Riegl Q1560 dual laser scanner flown aboard a King Air A90 as part of the Airborne Snow Observatory (ASO), a now complete NASA-JPL project. The data acquisition took place on February 21, 2017, over the Senator Beck Basin Study Area in the San Juan Mountains, Colorado as part of the NASA SnowEx campaign (Year 1). Ackroyd et al. (under review) utilized this point cloud for a lidar intensity correction to retrieve snow cover extent and grain size in GIScience and Remote Sensing.</p>
The TRAPUM Large Magellanic Cloud pulsar survey with MeerKAT I: Survey setup and first seven pulsar discoveries (Archive files)
<p>PSRCHIVE archive files of the seven new LMC radio pulsars as described in the paper <em>The TRAPUM Large Magellanic Cloud pulsar survey with MeerKAT I: Survey setup and first seven pulsar discoveries </em>(Prayag et al. 2024).</p>
Aircraft observations of warm clouds in Jiangxi,China
<p>There are cloud spectrum data of four warm cloud processes, which named by its date. Bin particle size have been uploded before.</p>
FIGURE 1 in A new species of planthopper in the genus Myxia (Hemiptera: Auchenorrhyncha Fulgoromorpha: Delphacoidea: Cixiidae) from the cloud forest in Costa Rica
FIGURE 1. Habitat and locality of Myxia villaloboi sp. n.
FIGURE 3 in A new species of planthopper in the genus Myxia (Hemiptera: Auchenorrhyncha Fulgoromorpha: Delphacoidea: Cixiidae) from the cloud forest in Costa Rica
FIGURE 3. Adult male habitus of Myxia villaloboi sp. n.; A) lateral view and B) dorsal view.
Updated JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
Open the record for dataset details and reuse information.
InLUT3D: Indoor Lodz University of Technology Point Cloud Dataset
<h2>Background</h2> <p>This resource contains Indoor Lodz University of Technology Point Cloud Dataset (<strong>InLUT3D</strong>) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of <strong>321</strong> scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.</p> <h2>Train/test split</h2> <p>The datset's authors impose the following train-test split:</p> <table> <tbody> <tr> <td><strong>Split</strong></td> <td><strong>Setup range<br></strong></td> </tr> <tr> <td>train</td> <td>from <em>setup_0 </em>to <em>setup_300</em></td> </tr> <tr> <td>test</td> <td>from <em>setup_301 </em>to <em>setup_320</em></td> </tr> </tbody> </table> <p>The corresponding setups' objects are recommended for splits for the classificcation task.</p> <h2>Available categories</h2> <p>The points are divided into <strong>18 </strong>distinct categories outlined in the <em>label.yaml</em> file along with their respective codes and colors. Among categories you will find:</p> <ul> <li>ceiling,</li> <li>floor,</li> <li>wall,</li> <li>stairs,</li> <li>column,</li> <li>chair,</li> <li>sofa,</li> <li>table,</li> <li>storage,</li> <li>door,</li> <li>window,</li> <li>plant,</li> <li>dish,</li> <li>wallmounted,</li> <li>device,</li> <li>radiator,</li> <li>lighting,</li> <li>other.</li> </ul> <h2>Challenges</h2> <p>Several challenges are intrinsic to the presented dataset:</p> <ol> <li>Extremely non-uniform categories distribution across the dataset.</li> <li>Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.</li> <li>Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).</li> <li>High point density throughout the dataset.</li> </ol> <h2>Dataset structure</h2> <p>The structure of the dataset is the following:</p> <p>inlut3d.tar.gz/<br>├─ setup_0/<br>│ ├─ projection.jpg<br>│ ├─ segmentation.jpg<br>│ ├─ setup_0.pts<br>├─ setup_1/<br>│ ├─ projection.jpg<br>│ ├─ segmentation.jpg<br>│ ├─ setup_1.pts<br>...</p> <table> <tbody> <tr> <td><strong>projection.jpg</strong></td> <td>A file containing a spherical projection of a corresponding PTS file.</td> </tr> <tr> <td><strong>segmentation.jpg</strong></td> <td>A file with objects marked with unique colours.</td> </tr> <tr> <td><strong>setup_x.pts</strong></td> <td>A file with point cloud int the textual PTS format.</td> </tr> </tbody> </table> <h2>Point characteristic</h2> <p>Each PTS file contains 8 columns:</p> <table> <tbody> <tr> <td><strong>Column ID</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1</td> <td>X Cartesian coordinate</td> </tr> <tr> <td>2</td> <td>Y Cartesian coordinate</td> </tr> <tr> <td>3</td> <td>Z Cartesian Coordinate</td> </tr> <tr> <td>4</td> <td>Red colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>5</td> <td>Green colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>6</td> <td>Blue colour in RGB space in the range [0, 255]</td> </tr> <tr> <td>7</td> <td>Category code</td> </tr> <tr> <td>8</td> <td>Instance ID</td> </tr> </tbody> </table> <p> </p>
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.