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Figure 11 from: Camacho AI, Dorda BA, Chillón BS, Rey I (2017) The collection of Bathynellacea specimens of MNCN (CSIC) Madrid: microscope slices and DNA extract. ZooKeys 678: 31-63. https://doi.org/10.3897/zookeys.678.11543
Figure 11 - Number of holotypes (DNA extract) of Bathynellacea by continents and families in the MNCN collections.
USE OF TOBACCO EXTRACT AS PESTICIDE TO FIGHT CABBAGE CATERPILLARS AND APHIA: CASE STUDY FROM THE ADMINISTRATIVE POST OF MATSINHO
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The EMG source data, data processing code and feature extraction source code
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Antimicrobial, Probiotic, and Immunomodulatory Potential of Cannabis sativa Extract and Delivery Systems
<p>Article, Dataset for the article</p> <p> </p> <h2>Abstract</h2> <div>The compounds present in hemp show multidirectional biological activity. It is related to the presence of secondary metabolites, mainly cannabinoids, terpenes, and flavonoids, and the synergy of their biological activity. The aim of this study was to assess the activity of the Henola <span>Cannabis sativae</span> extract and its combinations with selected carriers (polyvinyl caprolactam–polyvinyl acetate–polyethylene glycol graft copolymer, magnesium aluminometasilicate, and hydroxypropyl-β-cyclodextrin) in terms of antimicrobial, probiotic, and immunobiological effects. As a result of the conducted research, the antimicrobial activity of the extract was confirmed in relation to the following microorganisms: <span>Clostridium difficile</span>, <span>Listeria monocytogenes</span>, <span>Enterococcus faecalis</span>, <span>Staphylococcus aureus</span>, <span>Staphylococcus pyrogenes</span>, <span>Escherichia coli</span>, <span>Klebsiella pneumoniae</span>, <span>Salmonella typhimurium</span>, <span>Pseudomonas aereuginosa</span>, and <span>Candida albicans</span> (microorganism count was reduced from ~10<sup>2</sup> CFU mL<sup>−1</sup> to <10 CFU mL<sup>−1</sup> in most cases). Additionally, for the system with hydroxypropyl-β-cyclodextrin, a significant probiotic potential against bacterial strains was established for strains <span>Lactobacillus acidophilus</span>, <span>Lactobacillus casei</span>, <span>Lactobacillus plantarum</span>, <span>Lactobacillus brevis</span>, <span>Lactobacillus rhamnosus</span>, <span>Lactobacillus reuteri</span>, <span>Pediococcus pentosaceus</span>, <span>Lactococcus lactis</span>, <span>Lactobacillus fermentum</span>, and <span>Streptococcus thermophilus</span> (microorganism count was increased from ~10<sup>2</sup> to 10<sup>4</sup>–10<sup>7</sup>). In terms of immunomodulatory properties, it was determined that the tested extract and the systems caused changes in IL-6, IL-8, and TNF-α levels.</div> <div> <div> <div>Keywords: </div> <a href="https://www.mdpi.com/search?q=Cannabis+sativa"><span>Cannabis sativa</span></a>; <a href="https://www.mdpi.com/search?q=cannabidiol">cannabidiol</a>; <a href="https://www.mdpi.com/search?q=antibacterial">antibacterial</a>; <a href="https://www.mdpi.com/search?q=probiotic">probiotic</a>; <a href="https://www.mdpi.com/search?q=immunomodulatory">immunomodulatory</a>; <a href="https://www.mdpi.com/search?q=immunostimulatory">immunostimulatory</a></div> </div>
NMR spectra of orange and lemon pastazzo extract
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Shaanxi building extraction dataset
<p>A dataset for building extraction built from Sentinel-2 imagery.</p>
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>
Fig. 3 in Protocol Optimization For Genomic Dna Extraction And Rapd-Pcr Of Alien Ponto-Caspian Amphipod Pontogammarus Robustoides
Fig. 3. RAPD fingerprints results from different samples of Pontogammarus robustoides with primers OPA-02 (M- marker, 1-15 runners- different samples of Pontogammarus robustoides; K- control) using RAPD-PCR 10 × Taq buffer with (NH4)2SO4.
Figs. 1A-D in Antiproliferative and genotoxic potential from extracts and fractions of Richardia brasiliensis Gomes (Rubiaceae) by the Allium cepa L. test system
Figs. 1A-D. Meristem cells of Allium cepa root strain submitted to treatments with extracts and fractions of Richardia brasiliensis. A. Normal cells in interphase and prophase (long black arrow); B. Normal cell in metaphase (short black arrow) and telophase (short black arrow with filling); C. Normal cell in metaphase; D. Normal cell in anaphase (long arrow unfilled). Bars = 10 µm.
Fig. 2 in Skin extract from Rhamdia quelen (Siluriformes: Heptapteridae) does not promote stress in conspecifics
Fig. 2. Epidermis of R. quelen under light microscopy, PAS and hematoxylin staining. (A) Broad view showing the different skin layers: epidermis (e), dermis (d), and skeletal muscle (m). Scale bar = 5 µm; (B) club cells within the epidermis (star). Scale bar = 3 µm; (C) larger magnification of a club cell, with the arrow indicating the presence of the two nuclei. Scale bar = 1 µm.
Bangla-REX: A Distinct Dataset for Bangla Relation Extraction
<p>The dataset is grounded in theoretical and methodological frameworks that emphasize the importance of structured knowledge bases and annotated corpora for effective relation extraction. To generate this dataset, we compiled a comprehensive Bangla Knowledge Base (KB) consisting of 63,256 entries, which serves as a foundation for automating the labeling process with relation tags. The corpus itself is extensive, comprising 90,441 text entries that have been meticulously processed to include Named Entity Recognition (NER) and Part-of-Speech (POS) tagging, ensuring that it is ready for immediate use in relation extraction tasks.<br>Additionally, we developed mnemonics for 440 distinct locations in Bangla, specifically tailored to enhance performance in location-based relation extraction. These mnemonics are particularly beneficial in the context of distant supervision-based relation extraction, where they help in establishing clear associations between locations and their corresponding entities or contexts.</p>
Figure 1 in Effect of untreated and pretreated sugarcane molasses on growth performance of Haematococcus pluvialis microalgae in inorganic fertilizer and macrophyte extract culture media
Figure 1. Diagram of Haematococcus pluviais, where: (A) maintenance of strain in 10 mL with WC culture medium; (B) initial culture in 250 mL with WC culture medium; (C) culture in 2 L with NPK culture medium; (D) experiment of mixotrophic cultivation with untreated and pretreated sugarcane molasses with two different culture media, NPK and ME (macrophyte extract).
Figure 4 in In vitro antifungal and antibacterial potentials of organic extracts of Avicennia marina collected from Rabigh Lagoon, Red Sea Coasts in Saudi Arabia
Figure 4. Comparison of the Diameters of inhibition in mm of A. marina extracts. All tests were performed in triplicates (n = 3) and the error bars represent the SD. * p <0.05, ** p <0.01, *** p <0.001 with respect to the control.
Figure 1 in Cytotoxicity of extracts and compounds isolated from Croton echioides in animal tumor cell (HTC)
Figure 1. Mean absorbance and standard deviation of tumor cells from rat liver (HTC) treated for 24, 48 and 72 hours with the concentrations (µg/mL) of the semi-purified fraction with ethyl acetate (A) and hexane (B), of Croton echioides. CO-: Negative Control; CO +: Positive Control; CS: DMSO Solvent Control; 2.0x104 cells per well. * Result statistically different from the negative control (Dunnet test, p <0.05).
Figure 2 in Cytotoxicity of extracts and compounds isolated from Croton echioides in animal tumor cell (HTC)
Figure 2. Mean absorbance and standard deviation of tumor cells from rat liver (HTC) treated for 24, 48 and 72 hours with the concentrations (µg/mL) of the compounds extracted and isolated from Croton echioides: Lupeol (A), CEH -1 (B), CEH-2 (C), CEH-3 (D) and CEH-4 (E). CO-: Negative Control; CO +: Positive Control; CS: DMSO Solvent Control; 2.0x104 cells per well. * Result statistically different from the negative control (Dunnet test, p <0.05).
Figure 1 in New antioxidant lauryl-free herbal shampoo formulation with a Brazilian plant extract
Figure 1. Chemical characterization by HPLC of H. speciosa leaf extracts obtained by Soxhlet (L_SOX) and ultrasound (L_US).
Figure 5 in The inhibitory and anticancer properties of Annona squamosa L. seed extracts
Figure 5. (A) XRD spectra of A. squamosa presentation projecting extract image. (B) ESE of A. squamosa powder measured by energy dispersive X-ray spectroscopy (C) ESE of A. squamosa powder as seen using scanning electron microscopy.
Figure 1. Serum IL-1 in Wound-healing and cytokine-modulating potential of medicinal oil formulation comprising leaf extract of Murraya koenigii and olive oil
Figure 1. Serum IL-1β level of treatment groups. *The level of IL-1β enhanced 134.6% when compared to Group 1; # The level of IL-1β declined 43.9% when compared to Group 2; $The level of IL-1β declined 51.8% when compared to Group 2.
Figure 7 in Pouteria ramiflora leaf extract on emulgel in wound healing activity in diabetic rats
Figure 7. Quantification of inflammatory cells, fibroblasts, and angiogenesis (blood vessels) in diabetic animals' skin on groups: Control, Ethanolic extract (Ext) and Gel + Ethanolic extract 2% (Ext+gel), 21 days. ns (statistically not significant). DUNN's test (p=0.05).
Figure 6 in Pouteria ramiflora leaf extract on emulgel in wound healing activity in diabetic rats
Figure 6. Photomicrograph of histological sections of rats' skin wounds of Control group (Carbopol) (A, B, and C), Ethanolic extract (Ext) (D, E, F) and Gel + Ethanolic extract 2% (Ext+gel) (G, H, I) at 21 days after the skin wound injury (D, E, F). epidermis (e); keratin (k); dermis (d); carbopol (c); granular layer (green arrow); fibroblasts (black arrow); collagen (red arrow); HE.
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.