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77 results for “surface patterns”
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Data for a publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance"
<p>The data set for contains data used in the article "LIPSS pattern induced by polymer surface instability for myoblast cell guidance".</p> <p><strong>Versions:</strong></p> <p><em><strong>V2: </strong>The current version contains reorganized file folders and is supplemented by the data found in the publication "LIPSS pattern induced by polymer surface instability for myoblast cell guidance". The dataset newly contains contact angle, cytocompatibility tests, FTIR analyses or Zeta potential results.</em></p> <p><strong>Article abstract</strong></p> <p>The presented study highlights the efficiency of employing a KrF excimer laser to create diverse types of periodic nanostructures (LIPSS – laser induced periodic surface structures) on polyether ether ketone (PEEK) and polyethylene naphthalate (PEN) substrates. LIPSS structures are very important both in tissue engineering and find also strong application in the field of sensor construction, and SERS analysis. By exposing the polymer films below their ablation threshold to laser fluence ranging from 4 to 16 mJ·cm<sup>-2</sup> at 6,000 pulses, we studied both single-phase exposure at beam incidence angles of 0° and 45°, and two-phase exposure. Atomic force microscopy analysis revealed that the laser-treated samples contained distinctive periodic patterns such as waves, globules, and pod-like structures each exhibiting unique surface roughness. Moreover, using analytical methods like EDS and XPS shed light on the changes in the atomic composition, specifically focusing on the C and O elements, as a result of laser exposure. Notably, in almost all cases, we observed an increase in oxygen percentage on the sample surfaces. This increase not only led to a decrease in the contact angle with water but also lowered the zeta potential value, thus showing that the modified samples have enhanced hydrophilicity of the surface and altered electrostatic properties. Last but not least, the samples were assessed for biocompatibility; we studied the interaction of the prepared replicates with mouse myoblasts (C2C12). The impact of globular/dot structures on the cell growth in comparison to pristine or linear LIPSS-patterned surfaces was determined. The linear pattern (LIPSS) induced the myoblast cell alignment along the pattern direction, while dot/globular pattern even enhanced the cytocompatibility compared to LIPSS samples. Through this comprehensive analysis, the research underscores the multifaceted implications of employing KrF excimer laser-induced nanostructures, ranging from surface morphology alterations to biocompatibility enhancements, thus, opening new avenues for advanced material engineering.</p>
3D output of idealized large-eddy simulations with varying speed and surface heating to assess Doppler lidar scan patterns
<p><span>This dataset consists of nine idealized large-eddy simulations that were designed to systematically investigate the ability of different Doppler lidar scan patterns to measure the 3-dimensional wind vector at one point or in one profile. For more information, please see the documentation.</span></p>
Supplementary material for "Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors"
<p><strong>Tables</strong></p> <p>Table S1. List of fungal genomes analyzed in this work, associated references and properties.</p> <p>Table S2. List of all secreted proteins less than 300 amino-acids from the 20 fungal genomes. The table includes Signalp4.0 output, mature sequence, Espritz % disorder, pfam domains, AlphaFold top prediction pLDDT and the associated pdb file in Dataset S1.</p> <p>Table S3. Top Hits to pdb database for all OCE structures. 'network_node_name' corresponds to the portein identifier in the OCE structure similarity network provided in Dataset S3. 'Hidef_raw_community' corresponds to groups of structural OCE analogs identified by HiDEF community detection performed on the network provided in Dataset S3.</p> <p>Table S4. Table S4. List of the 62 major OCE folds with associated statistics. Columns I to AB provide the number of occurrences per species. Note that the actual number of members per species might be underestimated due to the stringent pipeline used for OCE identification (excluding proteins larger than 300 amino acids or containing PFAMs for instance).</p> <p> </p> <p>Table S5. Relative surface exposure, conformational flexibility and conservation data mapped on residues of members of the Alt-A1 and BoNT families. RMSD, root mean square deviation for all aligned atoms; Conservation, percentage conservation in multiple structure alignment.</p> <p>Table S6. Assignment of NCBI accessions to MMseqs clusters and assignment of MMseqs clusters to HMM matching-based super-clusters.</p> <p>Table S7. Co-mutation occurrences and associated p-values in two OCE clades from the Alt-A1 and KP6 families.</p> <p>Table S8. Amino acid properties inferred from mutation scans and frustration analyses in Alt-A1 cluster yellow1 and KP6 cluster 43. 'Number of aa variants' corresponds to the number of different amino acids found at each position (deletion counts as 1). 'Alanine scan ∆Z' and 'Deletion scan ∆Z' correspond to the difference between Z-score for the native protein agains itself and Z-score for the native protein against mutant at each position (either Alanine replacement or 5-aa deletion). 'Destabilization factor' is the average of column E and F. 'Stabilization factor' corresponds to the difference between expected structural variation due to destabilization factor and the observed structural variation in multiple mutants. 'netEffect' is difference between column G and H. 'Max co-mutation %' is the highest frequency of co-mutation observed with other residues in natural variants, with 'Min co-mutation p-value (Bonferroni corrected)' the associated p-value.Table S9. Sequence and delta Z of natural variants and mutants from AA1_cl25</p> <p>Table S9. List of natural variants and <em>in silico</em> mutants from the Alt-A1 cluster 25 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S10. List of natural variants and in silico mutants from the KP6 cluster 43 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S11. Summary statistics for the phylogenetic trees of 15 OCE clades analyzed for structure and frustration evolution.</p> <p>Table S12. Mapping of structural and frustration data onto phylogenetic trees for 15 OCE clades. The corresponding trees and protein structures are provided in Dataset S7.</p> <p><strong>Datasets</strong></p> <p>Dataset S1. AlphaFold rank1 models for 3 927 OCEs (.pdb format).</p> <p>Dataset S2. Pairwise structure comparison for 3 911 OCE. DALI matrix output containing pairwise Z-scores.</p> <p>Dataset S3. Network file including 2 561 OCEs with 3 or more vertices of Z-score weight 5.2 or more, in .sif and .xgmml formats.</p> <p>Dataset S4. Videos illustrating the mapping of relative surface exposure and structural variability in Alt-A1 and BoNT groups, amino-acids conservation, co-selected mutation patches and residue net stabilization effects on Alt-A1 clade 25 ancestor and KP6 cluster 43 ancestor. Color scales are as in Figure 2 and 3 respectively (.mp4 format).</p> <p>Dataset S5. Phylogenetic trees (.nwk), ancestral (.fasta) and modern variant (.faa) sequences, and AlphaFold best protein models (.pdb) for members of KP6 cluster 43 and Alt-A1 cluster 25. The archive includes 140 Alt-A1 protein structure and 128 KP6 protein structures.</p> <p>Dataset S6. Best predicted structures for 917 natural variants and mutants of AA1_cl25 and 801 natural variants and mutants of KP6_cl43 (.pdb format).</p> <p>Dataset S7. Phylogenetic trees (.nwk) and AlphaFold best protein models (.pdb) for 15 OCE clades. The file includes 2 598 protein structures distributed from clades AA1_s (139), AA1_t (135), AA1_y1 (140), AA1_y2 (90), AA1_y3 (128), BoNT_s (291), CIP_s (167), CIP_t (231), crystallin (233), GNK2 (189), KP6_cl3 (203), KP6_cl26 (111), KP6_cl43 (123), KP6_cl96 (231), KP6_cl242 (187).</p> <p><strong>Text and Figures</strong></p> <p>Text S1. Contains supplementary methods, results and figures S1 to S13.</p>
F I G U R E 6 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 6 Back-calculated mean body length (±95% confidence interval) of Salmo salar at the midpoint of the winter annulus, following the conclusion of the post-smolt growth period
F I G U R E 1 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 1 Hierarchical cluster analysis of intercirculus spacing for scales of Salmo salar. (a) The dendrogram for k = 20 using Euclidean distance and Ward linkage for z-scored and interpolated data. The five major sub-branches (A–E) and the 20 clusters are ordered sequentially from the left. (b) The standardized intercirculus spacing plots for the 20 clusters. Clusters are colour-coded and ordered as in (a). The LOESS fits for each cluster are shown as a black line and the number of fish per cluster (n) is also shown
F I G U R E 2 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 2 Tabulation of significant under- and over-representation of the 10 most frequent growth pattern categories (and "Others") for Salmo salar scales amongst the 20 dendrogram clusters. Proportions of growth pattern frequency were compared to the overall population proportion of scales for k = 20 with Ward linkage, and clustering of the z-scored and interpolated data. Light shading (−) indicates significant under-representation and dark shading (+) indicates over-representation. Sample sizes (n) for each growth pattern across the time-series are shown
F I G U R E 4 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 4 Time-series changes in ocean surface temperature and Salmo salar scale growth pattern. (a) Changes in monthly SST anomaly for the 250 and 500 km standard deviation spatially weighted kernels in the Norwegian Sea (April 1992 – March 2011). (b) Changes in frequency (proportion within years) of selected growth patterns. The three selected pattern groupings illustrate fish showing persistent Fast growth (F) throughout the post-smolt growth season, Slow growth followed by Fast growth (SF), and all patterns pooled that displayed one or more growth Checks. The growth pattern data for each capture year (b) are aligned with the SST anomaly in April of the previous year (a), coinciding with the commencement of annual smolt emigration
F I G U R E 5 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 5 Monthly correlations between the SST anomalies throughout the post-smolt Salmo salar growth period and annual frequency of the Fast (F) and All Check growth patterns. The salmon data were lagged by -1 year to match the annual post-smolt growth seasons to the SST anomalies. Significant correlations (P <0.05; following adjustment of d.f. to allow for autocorrelation) are shown by the filled circles
Figures 2 and 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figures 2 and 3 summarize the under/over-representation of the more common growth patterns and years, respectively, amongst the k = 20 clusters. The SF growth pattern was over-represented for four of the five clusters of sub-branch A, in association with general underrepresentation of patterns showing an initial Fast (F) sequence (Figure 2). Sub-branch B revealed an essentially inverse structure to sub-branch A, with sporadic over-representation of patterns commencing with a Fast sequence and under-representation of those with an initial Slow sequence. Sub-branches C and D were heterogeneous
F I G U R E 3 in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
F I G U R E 3 Tabulation of significant under- and overrepresentation of the 20 dendrogram clusters amongst years of capture of return adult Salmo salar. Details as for Figure 2
Figure 4b in Variation in the post-smolt growth pattern of wild one sea-winter salmon (Salmo salar L.), and its linkage to surface warming in the eastern North Atlantic Ocean
Figure 4b shows that there was (a) a decrease in the frequency of fish showing consistently Fast growth throughout the post-smolt period, (b) an increase in the frequency of the SF growth pattern and (c) an increase in the frequency of growth patterns including one or more Check sequences. Furthermore, these time-series changes in circulus pattern were linked significantly to contemporaneous and anomalous warming of the Norwegian Sea (Figure 5). As shown in Figure 6, one proximate consequence of these changes is manifest in the backcalculated mean length of fish at the midpoint of the winter annulus, following the completion of the post-smolt growth season. This showed a marked and significant decrease across the final six capture years of the time series.
A Google Earth Engine code to analyze e visualize land surface temperature and thermal hot-spot patterns: a Rome (Italy) case study
<p>Link to the <strong>Google Earth Engine </strong>(GEE) code: <strong>https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608</strong></p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)</li> <li><strong>The surface thermal hot-spot pattern </strong>(raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool. </li> </ol> <p>Here attached the .txt file from the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r: in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 2. Salicaceae (a–g), Cannabaceae (h–n), cf. Betulaceae (o–r). a–g: Saxifragispermum, USNM PAL 772341. Scale bar = 5 mm except as indicated. a–b: Lateral, c: apical, and d: basal views of fruit, reflected light, palladium coated; apex at top of (a, b). e: Equatorial transverse section reflected light; arrows indicate presumed seeds, scale bar = 2 mm. f: Detail of locule contents extracted from (e), transmitted light, scale bar = 200 Μm. g: Interwoven trichomes or fibers from locule, transmitted light, scale bar = 5 Μm. h–j: Celtis. h, i: USNM PAL 772342, reflected light, palladium coated, scale bar = 5 mm. h: Lateral view parallel with plane of dehiscence. i: Lateral view perpendicular to plane of dehiscence. j: DMNH EPI.47809, Celtis in lateral view; showing reticulate sculpture and the vertically-oriented, plane of dehiscence (arrow), scale bar = 5 mm. k–m: Aphananthe. USNM PAL 772344, reflected light, palladium coated, scale bar = 5 mm. k: Apical view, note triangular cross section and apical plug (arrow). l: Lateral view, apex up. m: Lateral view at 90° to (l). n: Detail of cellular pattern at surface of endocarp, scale bar = 0.5 mm. o–r:
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings.
Figure 6 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 6. Examples of the manifestations of oil slicks from natural seeps on the seafloor in the Sefid Rud Cap area in satellite images. Parts of SAR-C Sentinel-1A images: a) February 16, 2015, 14:36 UTC; b) August 9, 2016, 14:36 UTC; c) October 27, 2017, 14:36 UTC. Parts of MSI Sentinel-2A images (color composites of 4, 3 and 2 spectral channels): d) September 14, 2015, 07:39 UTC; e) May 28, 2016, 07:29 UTC; f) June 25, 2019, 07:38 UTC.
Figure 7 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 7. Consolidated maps of oil slicks due to natural seepages on the seafloor revealed from SAR imagery: a) in the Cheleken area; b) in the Sefid Rud Cape area.
Figure 4 in Main Pattern of the Caspian Sea Surface Oil Pollution Revealed by Satellite Data
Figure 4. Statistics on the frequency of detection of oil patches in the "Oil Rocks" oil producing area in SAR images in dependence on near-surface winds.
Figure 12 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 12. Ciliated cell patterns around the nostrils. (A) B. viridis, stage 25; (B) X. laevis, stage 23/24; (C) B. bufo, stage 24; (D) P. venulosa, stage 27; (E) L. bolivianus, stage 23; (F) R. temporaria, stage 23. Scale bars: 500 Mm (A, E); 250 Mm (B); 35 Mm (C); 120 Mm (D, F).
ScienceDex guides
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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.