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Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 5. (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after salinity increase. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Chemical profiling of milkweed and monarch butterfly wing extracts via mass spectrometry
<p>Herbivores that sequester toxins are thought to have cracked the code of plant defenses. Nonetheless, coevolutionary theory predicts that plants should evolve toxic variants that also negatively impact specialists. We propose and test the selective sequestration hypothesis, that specialists preferentially sequester compounds that are less toxic to themselves, while maintaining toxicity to enemies. Using chemically distinct plants, we show that monarch butterflies sequester only a subset of cardenolides from milkweed leaves that are less potent against their target enzyme (Na+/K+-ATPase) compared to several dominant cardenolides from leaves. However, sequestered compounds remain highly potent against sensitive Na+/K+-ATPases found in most predators. We confirmed this differential toxicity with mixtures of purified cardenolides from leaves and butterflies. The genetic basis of monarch adaptation to sequestered cardenolides was also confirmed with transgenic <em>Drosophila</em> that were CRISPR-edited with the monarch's Na+/K+-ATPase. Thus, the monarch's selective sequestration appears to reduce self-harm while maintaining protection from enemies.</p>
NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification
<p>BAM file (chunk 26-50 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.</p><p>Chunk 1-25 can be downloaded from https://zenodo.org/records/10129134.</p><p>The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:</p><blockquote><p>SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 <i>--CREATE_MD5_FILE</i></p></blockquote><p>Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):</p><blockquote><p>outdir="xxx"</p><p>args=""</p><p><i># Loop through each BAM file</i></p><p>for file in ${outdir}/*.bam; do</p><p> args+="-I $file "</p><p>done</p><p># Do the actual merging</p><p>MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE</p></blockquote><p>Picard can be downloaded from: https://github.com/broadinstitute/picard</p>
NextClone and CloneDetective: An Integrated Nextflow Pipeline and R Package for Clonal Barcode Extraction and Quantification
<p>BAM file (chunk 1-25 of 50) for the scRNAseq data required to replicate the analyses presented at: https://phipsonlab.github.io/NextClone-analysis/.</p><p>Chunk 26-50 can be downloaded from https://zenodo.org/uploads/10129625</p><p>The original BAM file was too big to fit in an entry. Thus it was split into 50 using picard:</p><blockquote><p>SplitSamByNumberOfReads -I possorted_genome_bam.bam -O split_sam/ -N_FILES 50 <i>--CREATE_MD5_FILE</i></p></blockquote><p>Before re-running all the analyses in https://phipsonlab.github.io/NextClone-analysis/, make sure you merge all 50 chunks first using picard (change xxx to point to the directory storing all the 50 chunks you have downloaded):</p><blockquote><p>outdir="xxx"</p><p>args=""</p><p><i># Loop through each BAM file</i></p><p>for file in ${outdir}/*.bam; do</p><p> args+="-I $file "</p><p>done</p><p># Do the actual merging</p><p>MergeSamFiles $args -O $outdir/merged_v2/merged_bam_v2.bam --USE_THREADING --CREATE_MD5_FILE</p></blockquote><p>Picard can be downloaded from: https://github.com/broadinstitute/picard</p>
Dereplicated vOTUs recovered from a viromics extraction buffer chemistry experiment
<p>Database of de-replicated viral operational taxonomic units recovered from a viromics (viral-size fraction metagenomics) buffer chemistry experiment. </p>
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>
Weak evidence base for bee protective pesticide mitigation measures: Data Behind Systematic Review- Raw, Extracted and Tabulated
<p>An excel file with the raw exported data from Web of Science, the data extracted from it, and the summary statistics. </p> <p>Data associated with https://doi.org/10.1093/jee/toad118</p> <p>Edward A Straw, Dara A Stanley, Weak evidence base for bee protective pesticide mitigation measures, <em>Journal of Economic Entomology</em>, Volume 116, Issue 5, October 2023, Pages 1604–1612, <a href="https://doi.org/10.1093/jee/toad118">https://doi.org/10.1093/jee/toad118</a></p> <p> </p> <p>Straw and Stanley, 2023. </p> <p> </p> <p> </p>
SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction
<p><strong>SympTEMIST</strong> stands for Symptoms TExt MIning Shared Task. It is a shared task and set of resources focused on the <strong>detection of mentions, normalization and indexing of symptoms, signs and findings in medical documents</strong> in <strong>Spanish</strong>. SympTEMIST is complementary to the DisTEMIST (https://temu.bsc.es/distemist) and MedProcNER/ProcTEMIST (https://temu.bsc.es/medprocner) corpora as they all use the same document collection.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>"Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>."</p> <p> </p> <p>This repository includes the:</p> <ul> <li><strong>Train </strong>and<strong> Test Set </strong>for the three subtasks</li> <li><strong>SYMPTEMIST gazetteer </strong>of <strong>SNOMED symptoms, signs & findings </strong></li> <li><strong>Multilingual </strong>Silver Standard<strong> </strong>in <strong>9 languages:</strong> <ul> <li><em><strong>English</strong></em></li> <li><em><strong>Portuguese</strong></em></li> <li><em><strong>French</strong></em></li> <li><em><strong>Italian</strong></em></li> <li><em><strong>Romanian</strong></em></li> <li><em><strong>Catalan</strong></em></li> <li><em><strong>Swedish</strong></em></li> <li><em><strong>Dutch</strong></em></li> <li><em><strong>Czech</strong></em></li> </ul> </li> <li><strong>Background set</strong> of over 15,000 clinical cases.</li> <li><strong>Spanish</strong> Silver Standard (predictions by participants over the background set)</li> </ul> <p> </p> <p>Please read the README file attached for more information on folder structure and file format.</p> <p> </p> <p>SympTEMIST was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioCREATIVE 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/symptemist.</p> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/symptemist/"><strong>Task web</strong></a></li> <li><a href="https://biocreative.bioinformatics.udel.edu/"><strong>BioCreative web</strong></a></li> <li><strong>Citation: </strong>Lima-López, S., Farré-Maduell, E., Gasco-Sánchez, L., Rodríguez-Miret, J. and Krallinger, M. (2023). Overview of SympTEMIST at BioCreative VIII: corpus, guidelines and evaluation of systems for the detection and normalization of symptoms, signs and findings from text. In: <em>Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models</em>.</li> <li><a href="../records/8246440"><strong>Annotation guidelines</strong></a></li> <li><a href="../records/10103191"><strong>BioCreative/AMIA workshop proceedings</strong></a></li> <li><a href="../doi/10.5281/zenodo.10104546"><strong>Overview paper</strong></a></li> <li><a href="https://www.youtube.com/playlist?list=PLyLDDulunoofv79Pci0-y2rGP5GOBoZhp"><strong>Youtube videos (overview & teams)</strong></a></li> <li><a href="https://www.slideshare.net/MartinKrallinger/symptemist-shared-task-on-symptoms-signs-and-findings-detection-and-normalization-task-overview-talk-at-biocreative-viii-workshop-amia-2023"><strong>SympTEMIST overview talk slides at BioCreative/AMIA workshop</strong></a></li> </ul> <p> </p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested in SympTEMIST, you might want to check out these corpora and resources:</p> <ul> <li><a href="../records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li> <li><a href="../records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li> <li><a href="../records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li> <li><a href="../records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li> <li><a href="../records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li> <li><a href="../records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li> <li><a href="../records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li> <li><a href="../records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li> <li><a href="../records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li> <li><a href="../records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li> <li><a href="../records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> </ul> <p> </p>
Reduced Gun Violence Frame Corpus data set for the Text2Story 2024 article: "Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing"
<p><strong>Title: </strong>Simplified Gun Violence Frame Corpus (GVFC) Subset</p> <p><strong>Description:</strong><br>This data set is a simplified subset of the Gun Violence Frame Corpus (GVFC) from Liu et al. (2019). The original GVFC consists of 1300 news articles in English from multiple U.S. based sources extracted during the year 2018, focusing on media frames commonly used when reporting the issue of Gun Violence. The original data set has 9 types of frames, including both issue-specific and generic frames. Due to high computational costs in our analysis methods, we decreased the data set size from 1300 articles to 131 articles using stratified sampling, maintaining the original distribution of the frame labels. We also manually searched for the original sources of each article based on its headline and added the missing temporal information and news source to the data set, as it was required by our algorithms.</p> <p>To further reduce the complexity of the framing model and account for the smaller data set size, we grouped the original nine frames into three higher-level frames:</p> <p>1. Frame 1: Political Issues - Combining the first, second, and third frames, which focus on political issues mostly related to gun control.<br>2. Frame 2: Public Services - Combining the fourth and fifth frames, which focus on mental healthcare issues, as well as school and public safety.<br>3. Frame 3: Cultural and Societal Issues - Combining the last four frames, which are oriented towards cultural or societal issues, including discussions around race and ethnicity, public opinion, and economic consequences.</p> <p>The resulting simplified data set contains 131 news articles, each labeled with one of the three higher-level frames, along with the necessary temporal information and news source for the narrative extraction process.</p> <p>If you use this data set, please make sure to cite the original GVFC paper and our workshop paper please. </p> <p><strong>References:</strong></p> <ol> <li>Liu et al. (2019) "Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding US Gun Violence", 23rd Conference on Computational Natural Language Learning (CoNLL 2019).</li> <li>Concha Macías, Sebastián and Keith Norambuena, Brian (2024). "Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing", Text2Story 2024 Workshop, ECIR 2024.</li> </ol>
Programs and data used for Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport
<p>This is compiled from the program and output data using in Nishizawa (2024).</p> <p> </p> <p>Nishizawa, 2024: Extracting latent variables from forecast ensembles and advancements in similarity metric utilizing optimal transport. submitted to JGR: Machine Learning and Computation.</p>
The public part of the interview extractions as thematic codings: used for the consumer behavior study
<p>Extractions from interview transcripts created by the thematic analysis, this data is part of the interview study done on consumer energy behavior during price hikes,</p>
Iodine deficiency data extraction sheet
<p><span>The contents of the Excel file reporting the studies assessing the prevalence of iodine deficiency and associated factors among school-age children in Ethiopia, 2023 are: <span>name of the authors, year of publication, study year, country, region, study design, study setting, quality of the paper, total population, iodine deficient population, prevalence of iodine deficiency, standard error of the prevalence and factors such as age, sex, goitrogenic food consumption, salt container used to store the iodized salt, salt adding time and maternal educational status and their derivatives.</span></span></p>
Electron parameters (density & temperature) extracted from MEA1 during MFBs
Open the record for dataset details and reuse information.
Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
<p>The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework.</p>
Metadata of the extracted data assessing iodine deficiency and associated factors among school-age children in Ethiopia, 2023.
<p>This is Meta-data of the extracted data assessing the prevalence of iodine deficiency and associated factors among school-age children in Ethiopia, 2023</p>
Evaluation of different methods to extract DNA from serum seeded with methicillin-resistant Staphylococcus aureus
Open the record for dataset details and reuse information.
CellMet: Extracting 3D shape metrics from cells and tissues
<p>This dataset supports the manuscript title "CellMet: Extracting 3D shape metrics from cells and tissues". It contains segmented images (mask.tif) of severals tissues. </p>
Script and Quality Métrics Extracted from CK Calculator
<p>This project involves creating a script for mining data on code metrics, tests, and bugs from Java project repositories.</p> <p><strong>Technologies Used:</strong></p> <ul> <li><strong>Pydriller</strong>: To clone repositories and traverse releases.</li> <li><strong>CK</strong>: To extract code metrics.</li> <li><strong>JaCoCo</strong>: To extract test coverage metrics.</li> <li><strong>SpotBugs</strong>: To extract bug reports.</li> <li><strong>FindSecBugs</strong>: To include security bugs in the bug reports.</li> </ul> <h3><strong>How to Run</strong></h3> <p><strong>Requirements:</strong></p> <ul> <li>Python 3 installed.</li> <li>Java 17 or higher installed.</li> <li>SpotBugs installed and configured in the PATH.</li> <li>FindSecBugs plugin installed.</li> </ul> <p><strong>Steps:</strong></p> <ol> <li>Clone this repository to your machine.</li> <li>Run the Python script.</li> </ol> <p><strong>Note:</strong><br>When executing, the script will ask if you want to traverse releases.</p> <ul> <li>Type <strong>N</strong> if you already have generated reports and want to perform only the analyses and plots.</li> <li>Type <strong>S</strong> to perform the complete operation.</li> </ul> <h3><strong>Result Organization</strong></h3> <p>The results will be organized into three different folders:</p> <ol> <li> <p><strong>Reports</strong>:<br>Contains raw data generated by third-party tools (CK, JaCoCo, SpotBugs). This is where you'll find tool-generated outputs.</p> </li> <li> <p><strong>Analysis-results</strong>:<br>Includes numerical results derived from the reports, used for plot generation. It also provides detailed numerical insights for specific queries.</p> </li> <li> <p><strong>Plots</strong>:<br>Graphical representations of the analyses.</p> </li> </ol> <h3><strong>Interpreting Metrics</strong></h3> <h4><strong>Code Metrics</strong></h4> <ol> <li> <p><strong>WMC (Weighted Methods per Class)</strong></p> <ul> <li>Measures total method complexity in a class.</li> <li><strong>Increasing Mean/Median</strong>: Classes are generally becoming more complex.</li> <li><strong>Increasing/Decreasing Variance</strong>: Complexity dispersion among classes is growing/shrinking.</li> </ul> </li> <li> <p><strong>DIT (Depth of Inheritance Tree)</strong></p> <ul> <li>Measures maximum inheritance depth of a class.</li> <li><strong>Increasing/Decreasing Mean</strong>: Average inheritance depth is increasing/reducing.</li> </ul> </li> <li> <p><strong>NOC (Number of Children)</strong></p> <ul> <li>Number of subclasses derived directly from a class.</li> <li><strong>Increasing/Decreasing Mean</strong>: More/fewer classes are being used as superclasses.</li> </ul> </li> <li> <p><strong>CBO (Coupling Between Objects)</strong></p> <ul> <li>Number of other classes a class is directly coupled to.</li> <li><strong>Increasing/Decreasing Mean</strong>: Classes are becoming more/less interdependent.</li> </ul> </li> <li> <p><strong>LCOM1 (Lack of Cohesion of Methods)</strong></p> <ul> <li>Measures cohesion among methods within a class.</li> <li><strong>Increasing Mean</strong>: Classes are becoming less cohesive.</li> </ul> </li> <li> <p><strong>RFC (Response for a Class)</strong></p> <ul> <li>Number of methods that can be invoked in response to a call.</li> <li><strong>Increasing Mean</strong>: Classes are becoming more interconnected.</li> </ul> </li> <li> <p><strong>LOC (Lines of Code)</strong></p> <ul> <li>Total lines of code in a class.</li> <li><strong>Increasing Mean</strong>: Classes are generally growing in size.</li> </ul> </li> </ol> <h4><strong>Test Metrics</strong></h4> <ol> <li> <p><strong>Instruction Missed/Covered</strong>:</p> <ul> <li>Tracks instructions not covered or covered by tests.</li> <li><strong>Increasing/Decreasing Mean</strong>: Indicates changes in overall test coverage.</li> </ul> </li> <li> <p><strong>Branch Missed/Covered</strong>:</p> <ul> <li>Tracks decision branches not covered or covered by tests.</li> </ul> </li> <li> <p><strong>Line Missed/Covered</strong>:</p> <ul> <li>Tracks lines of code not covered or covered by tests.</li> </ul> </li> <li> <p><strong>Complexity Missed</strong>:</p> <ul> <li>Cyclomatic complexity not covered by tests.</li> </ul> </li> </ol>
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.