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5,155 results for “resolution”
The ST simulated data and High-resolution H&E images used in Tumoroscope
<p>This dataset comprises the simulated ST dataset and H&E (Hematoxylin and Eosin) images corresponding to the spatial transcriptomics samples referenced in the paper titled <i>"</i> <a href="https://doi.org/10.1101/2022.09.22.508914">Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues</a>"</p>
A Spatially Downscaled TROPOMI SIF Product at 0.005 Degree Resolution in China during 2019-2020
<p><span>To improve the spatial resolutions of TROPOMI SIF, a 0.005-degree SIF product during 2019~2020 in China was generated based on a proposed downscaling strategy correcting the predicted bias with Random Forest (RF) model by TROPOMI SIF, MODIS reflectance, and ERA5 reanalysis datasets. This bias-corrected downscaled SIF product (namely BCSIF) has been validated with the original TROPOMI SIF and the long-term tower-based SIF, also evaluated by correlating with the GPP data, which verified its reliability and applicability for GPP monitoring.</span></p>
Modelled 1-km resolution population densities for 49 bird species in the UK between 2007 and 2009
<p>This is a dataset derived from the BTO/JNCC/RSPB Breeding Bird Survey (BBS) used to produce <a href="https://www.bto.org/our-science/projects/breeding-bird-survey/latest-results/maps-population-density-and-trends">modelled maps of population density</a> for 49 bird species at 1-km square resolution across the UK between 2007 and 2009. </p> <p>Acknowledgement: These data may be used subject to the appropriate acknowledgement of the BTO/JNCC/RSPB Breeding Bird Survey. Acknowledgements should be in the form of the most recent BBS Report citation, available <a href="https://www.bto.org/our-work/science/publications/reports/bbs-reports" target="_blank" rel="noopener">online</a>, or as follows: “<em>The BTO/JNCC/RSPB Breeding Bird Survey is a partnership jointly funded by the BTO, RSPB and JNCC, with fieldwork conducted by volunteers.</em>”</p> <p>Please refer to the metadata for a more detailed description, and for information on dataset usage.</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Front Parlor Mantel - High Resolution
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Plaque Gift to Mrs. Stanford - High Resolution
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Humility Brings Prosperity gate - low-resolution
A low-res version of the Humility Brings Prosperity gate, now installed in Gallery 200 at Mia. The gate comes from Shanxi province, which, because of its dry climate, contains the largest concentration of surviving historic wooden structures in China. An inscription on the main beam dates the gate to 1858, during the Qing dynasty (1644–1912). [More info](https://collections.artsmia.org/art/127331/humility-brings-prosperity-gate-china) *This imposing gate once formed the entrance to the main courtyard of an urban middle-class family compound. The four-character panel over the doorway reads, Humility Brings Prosperity (Lüqian Jianfeng 履謙漸豐). The gate demonstrates the hallmarks of traditional Chinese architecture: a post-and-lintel structure that supports a graceful gable roof and intricate rafters, as well as brackets that extend the eaves and provide decoration. Like other works of traditional Chinese architecture, no glue or nails were used, and the gate was assembled using only joinery and wooden pins.* Source: Objaverse 1.0 / Sketchfab
Monthly averaged in-cloud coronas extracted from EMAC simulations (T42L90MA resolution)
<p>About Dataset</p> <p>Unscaled monthly averaged in-cloud corona data extracted from 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> </p> <p>netcdf BLUEs_expr_____20040101_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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 gboxarea(lat, lon) ;<br> gboxarea:units = "m^2" ;<br> gboxarea:representation = "GP_2D_HORIZONTAL" ;<br> gboxarea:grid_type = "gaussian" ;<br> gboxarea:code = -1 ;<br> gboxarea:REFERENCE_TO = "geoloc: gboxarea" ;<br> gboxarea:coordinates = "lon lat" ;<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 d2.55.2-4244-g55c392ba0-dirty_55c392ba05ebe6c52cc7ffc7d8f02b6ca4381e70_2023-10-31T10:40:00+00:00_2023-11-02T15:38:53+01:00, 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 3.0" ;<br> :MESSy_import = "version 1.1" ;<br> :MESSy_grid = "version 1.6" ;<br> :MESSy_rnd = "version 1.2" ;<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 3.1" ;<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 2.4" ;<br> :MESSy_experiment = "BLUEs_expr" ;<br> :EXEC_CHECKSUM = "728d848933e9d082922041bd8091ddf6 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 -O3" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.2-4244-g55c392ba0-dirty_55c392ba05ebe6c52cc7ffc7d8f02b6ca4381e70_2023-10-31T10:40:00+00:00_2023-11-02T15:38:53+01:00\'" ;<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 = "20231204 092100" ;<br> :operating_system = "Linux 4.18.0-348.7.1.el8_5.x86_64 on x86_64" ;<br> :operating_host = "drago31010007" ;<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_time_slo = 5355600.f ;<br> :channel_name = "bluesbcl__ _gp" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "BLUEs_expr_____20040101_0000_bluesbcl__ _gp.nc" ;<br> :channel_netcdf_lib = "4.8.1 of May 6 2022 16:22:26 $" ;</p>
Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution
<p>Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration" </p><p><strong>Structure</strong></p><p><strong>ST_berghei_liver</strong></p><p>contains data generated during <i>stpipeline </i>analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h. </p><ul><li><i><strong>count</strong></i> contains gene expression matrix output from stpipeline in .tsv format</li><li><i><strong>spotfiles</strong> </i>contains coordinate files for count matrices</li><li><i><strong>images </strong></i>contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.</li><li><i><strong>masks </strong></i>contains image masks for hepaquery analysis</li><li><i><strong>distances </strong></i>contains distance measurements from original section sorted by timepoint as well as combined across timepoints</li><li><strong>cluster</strong> contains clustering information across spatial positions used in spatial enrichment analysis</li></ul><p><strong>STUtiility_mus_pb_ST.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>ST_berghei_liver</strong></p><p><strong>h5ad</strong></p><p>contains anndata files of ST data (normalized read counts), spot information, distance measurements, images and masks generated using the hepaquery package. </p><p><strong>visium_berghei_liver</strong></p><p>contains data generated with the <i>spaceranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h. </p><ul><li><i><strong>V10S29-135_A1</strong></i> contains spaceranger output for section 1 for infected and control sections at 38h post-infection</li><li><i><strong>V10S29-135_B1</strong></i> contains spaceranger output for section 1 for infected and control sections at 12h post-infection </li><li><i><strong>V10S29-135_C1 </strong></i>contains spaceranger output for section 1 for infected and control sections at 24h post-infection </li><li><i><strong>V10S29-135_D1 </strong></i>contains spaceranger output for section 2 for infected and control sections at 38h post-infection </li></ul><p><strong>se_visium.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>visium_berghei_liver</strong></p><p><strong>snSeq_berghei_liver</strong></p><p>contains data generated with the <i>cellranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.</p><p><i><strong>cellranger_cnt_out</strong> </i>contains feature count matrix information from cell ranger output</p><p><strong>final_merged_curated_annotations_270623.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>snSeq_berghei_liver.tar.gz</strong></p><p><strong>raw images.zip </strong>contains raw images for supplementary figures 20-22</p><p><strong>adjusted images.zip </strong>contains brightness and contrast adjusted images for supplementary figures 20-22</p>
Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3
<p>This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021.</p> <p>Contained within this archive are the following components:</p> <p>MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales.</p> <p>MPAS mesh data: The global meshes generated for the experiments.</p> <p>CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results.</p> <p>ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters.</p> <p>GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations.</p> <p>This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.</p>
Retrieval of dominant methane (CH4) emission sources, the first high resolution (1-2m) dataset of storage tanks of China in 2000-2021
<p>We presented a storage tank inventory of 92 typical cities with high volatile organic compounds, in response to the Three-Year Action Plan for Winning the Blue Sky Defense War, proposed by the State Council of China. It comprises of 14461 storage tanks, which are extracted based on high spatail resolution images of GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan satellites in year of 2021. It is the first inventory covering detailed distribution locations and boundaries of storage tanks. The inventory we aim to share provides a basic distribution of storage tanks in different sizes and will contribute to support environmentally friendly regulations proposal for more effective pollution control and energy resource management.</p>
NUUR-Texture2953: A Diverse Dataset of High Resolution Homogeneous Textures
<p>Permission to copy and use this dataset for noncommercial use is hereby granted provided this notice is retained in all copies and the dataset distribution and the paper mentioned below are clearly cited.</p> <p>Contacts:<br> Jue Lin: jue.lin@u.northwestern.edu<br> Zhiwei Xu: zhiweixu2050@u.northwestern.edu<br> Gaurav Sharma: gaurav.sharma@rochester.edu<br> Thrasyvoulos N. Pappas: pappas@ece.northwestern.edu</p> <p>"NUUR-Texture2953" is a dataset of diverse texture images created to facilitate research on texture analysis and synthesis, and is build upon our previously established dataset "NUUR-Texture500". The textures in this dataset are spatially homogeneous, ranging from regular to stochastic, typically containing repeated elements with random variations in position, shape, orientation and color. For a detailed description of the dataset construction and contents, readers should refer to the following paper:</p> <p>Jue Lin, Zhiwei Xu, Gaurav Sharma, Thrasyvoulos N. Pappas, "Texture Representation via Analysis and Synthesis with Generative Adversarial Networks", Journal of e-Prime - Advances in Electrical Engineering, Electronics and Energy (2023), https://doi.org/10.1016/j.prime.2023.100286.</p> <p>Jue Lin, Gaurav Sharma, Thrasyvoulos N. Pappas, "NUUR-Texture500: A Diverse Dataset of High Resolution Homogeneous Textures", Zenodo, https://doi.org/10.5281/zenodo.7127079</p> <p>Disclaimer: </p> <p>The dataset is provided "as is" with ABSOLUTELY NO WARRANTY expressed or implied. Use at your own risk.</p> <p>Acknowledgment: </p> <p>The NUUR-Texture2953 texture images are curated from a number of publicly accessible sources. We acknowledge and thank the original sites for their contributions:<br> Flickr www.flickr.com<br> NeedPix www.needpix.com<br> Pexels www.pexels.com<br> PickUpImage www.pickupimage.com<br> Pixabay www.pixabay.com<br> PublicDomainPictures www.publicdomainpictures.net<br> RawPixel www.rawpixel.com<br> Unsplash www.unsplash.com<br> WikimediaCommons commons.wikimedia.org</p>
A wideband, high-resolution vector spectrum analyzer for integrated photonics
<p>This data contains the raw data of the experiment and the code and corresponding data of the figures in the artical.</p>
Achieving sub-0.5-Angstrom resolution ptychography in an uncorrected electron microscope
<p><strong>Abstract:</strong> Sub-angstrom resolution has long been limited to aberration-corrected electron microscopy, where it is a powerful tool for understanding the atomic structure and properties of matter.<span> </span><span> </span>Here, we demonstrate electron ptychography in an uncorrected scanning transmission electron microscope (STEM) with deep sub-angstrom spatial resolution down to 0.44 Ångstrom, exceeding the conventional resolution of aberration-corrected tools and rivaling their highest ptychographic resolutions. Our approach, which we demonstrate on twisted 2D materials in a widely-available commercial microscope, far surpasses prior ptychographic resolutions (1 to 5 Ångstroms) of uncorrected STEMs. We further show how geometric aberrations can create optimized, structured beams for dose-efficient electron ptychography. Our results demonstrate that expensive aberration correctors are no longer required for deep sub-angstrom resolution.</p>
High-Resolution Vegetation Distribution (HRVD) dataset
<p><strong>High-Resolution Vegetation Distribution (HRVD) dataset</strong> was developed by integrating multiple datasets, including the China multi-period land use/cover change remote sensing monitoring dataset (CNLUCC), MODIS MCD12Q1 land cover product, and Vegetation Atlas of China (1:1,000,000). The HRVD dataset provided vegetation distribution data in China in 2020 with a horizontal resolution of 1 km. <br><em>The HRVD dataset includes tree, shrub, herb, and crop, corresponding to the value of 1, 2, 3, and 4 in the attribute table, respectively.</em></p>
Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle's 2015 study: "The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception"
<p>Data for the study "Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle’s 2015 study: “The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception”"</p> <p>Contents consist of curated EEG data for statistical analysis, statistical analysis workflow and code and Matlab code for the behavioural flash fusion task.</p>
Common Reasons for Conflicts and Common Conflict Resolution Techniques Used: Findings of 8 Years of Secondary and Primary Research Studies
<p>The paper, a summary of 8 years of research (2015-2023) on conflict management, presents the common reasons for conflicts and common conflict resolution techniques used by employees at all levels in an organization based on secondary research and also an empirical investigation. The Macro Objective of the Research is to Generalize the Reasons for Conflicts and Conflict Resolution Techniques Used at Employee, Manager and Top Management Level using Author’s Previous 3 Secondary Research Studies and Current Primary Research Study by finding the Top-3 Common Reasons for Conflicts and Top-3 Common Conflict Resolution Techniques Used at all levels of Employees. The 3 different secondary research studies are conducted (2015-2023) at Employee level, Manager Level, Top Management Level to identify Top-5 Reasons for Conflicts and Top-5 Conflict Resolution Techniques used by Employees, Managers and Top Management Respectively. Top-5 items are identified based on reference count (number of citations) in literature in each of these research works. Later on, in 4th Paper (2023) Common Top-3 Reasons for Conflicts, Common Top-3 Conflict Resolution Techniques are Identified based on the 3 published secondary research works. This current work is primary study conducted on 68 respondents; and Primary Research Findings are Compared with Secondary Research findings; further top-3 common reasons for conflicts and top-3 conflict resolution techniques used are identified at all levels of employees in an organization. Overall, the paper presents the Top-3 common reasons for conflicts among employees, project managers, and top management; and top-3 common conflict resolution techniques used by employees, project managers and top management. Based on Secondary & Primary Research Studies, the Top-3 common reasons for conflicts identified are Resources, Value Differences, Personality Differences; and Top-3 conflict resolution techniques used are Avoidance, Compromise, Confrontation/Problem Solving at all levels of employees. </p>
Fig. 8 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses
Fig. 8. Principal coordinate analysis (PCoA) for six traits of all Eremophila tietkensii individuals. Numbers represent geographic groups as per Fig. 2.
Fig. 5 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses
Fig. 5. Ovary indumentum of (a) Eremophila sp. Calvert Range, showing sericeous eglandu-lar trichomes; and (b) E. Hamersley Range, showing glandular and eglandular trichomes.
Fig. 4 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses
Fig. 4. Trichomes under compound microscope at 400× of Eremophila tietkensii: (a) PERTH 08316899, (b) PERTH 03881202, (c) PERTH 06752519, (d) PERTH 03851281, (e) PERTH 07324545; E. sp. Hamersley Range: (f) PERTH 06653537, (g) PERTH 0653561, (h) PERTH 06017142, (i) PERTH 8521; E. sp. Rudall River: (j) PERTH 08305447, (k) PERTH 03878740; and E. sp. Calvert Range: (l) PERTH 07765886, (m) PERTH 07512821.
Fig. 1 in Resolution of the Eremophila tietkensii (Scrophulariaceae) species complex based on congruence between morphological and molecular pattern analyses
Fig. 1. (a) Eremophila tietkensii. Photograph: R. Fowler. (b) E. sp. Rudall River. Photograph: J. Hurter. (c) E. sp. Calvert Range. Photograph: A. Brown. (d) E. sp. Hamersley Range. Photograph: J. Naaykens.
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.