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opencc-by-4.0Dec 2002View details →

Fig. 8. A–D. Cambrotubulus conicus Missarzhevsky, specimen SMNH X3411, sample K2 in The tube wall of Cambrian anabaritids

Fig. 8. A–D. Cambrotubulus conicus Missarzhevsky, specimen SMNH X3411, sample K2/25. A. Broken wall with fibres arranged in bundles (?) and covered with a diagenetic phosphatic crust. B, C. Apertural lip with fibres below phosphatic crust. D. General view of the tube with growth lines and aperture (the arrows correspond to close−ups in A–C). E. Polished section of sample K2/26; back−scattered electron image, where brighter areas indicate higher densities. F. Close−up of E. Indicated in E and Fare: 1, matrix consisting of micritic calcite with low phosphate and argillaceous admixture; 2, matrix of internal mould of a bigger tube similar to matrix in 1, but lighter because of a higher phosphorus content; 3, matrix of internal mould of a smaller tube identical to 2; 4, protuberances of celestite (with barite) representing remains of a larger tube; 5, wall of a smaller tube consisting of celestite (with barite); 6, zone with a higher calcite content, the brightness of which is similar to that of the outside matrix in 1.

opencc-by-4.0Dec 2002View details →

Fig. 14. A–C. Sinospirifer gortanioides Grabau, 1931, sample C−H in Rugose corals and brachiopods across the Frasnian- Famennian boundary in central Hunan, South China

Fig. 14. A–C. Sinospirifer gortanioides Grabau, 1931, sample C−H, dorsal, ventral, and posterior views of PUM 98014; × 1.3. D–H. Lamarckispirifer hayasakai (Grabau, 1931), sample C−H, lateral, posterior, dorsal, ventral and anterior views of PUM 00025; × 1.3. I–M. Sinospirifer subarchiaci (Martelli, 1902), sample C−H, anterior, lateral, posterior, ventral and dorsal views of PUM 98001; all × 1.3 except K and L, × 1.5. N–P. Sinospirifer pekinensis Grabau, 1931, sample C−H, ventral, posterior and anterior views of PUM 980152; × 1.5.

FIGURE 5 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 5. The performance of different implementations of the residual diversity estimate when a specific bias is forced to be the dominant influence. (5.1) Mean Spearman's rho values of four implementations of the RDE using the Smith and McGowan method. PFORM and PTAPH are set at 0.9 to minimise their influence, PLOC is variable. PMIST set at 0.1. (5.2) Mean Spearman's rho values of four implementations of the RDE using the Smith and McGowan method. LOC and PTAPH are set at 0.9 to minimise their influence, PFORM is variable. PMIST set at 0.1. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 7 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 7. Sample simulation comparing the results of the taxic, phylogenetic and residual diversity estimates to the true diversity. PFROM, PLOC and PTAPH set at 0.25. PMIST set at 0.1. Black box highlights instance where the Signor Lipps effect has been exaggerated by the PDE; the TDE and RDE both identify the rapid diversity decrease present in the true diversity. Abbreviations as in Table 1.

FIGURE 6 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 6. The performance of the phylogenetic diversity estimate when errors are introduced to the phylogeny. Mean Spearman's rho values of the PDE, TDE and the best performing implementation of the RDE. PLOC, PFORM and PTAPH set at 0.25. PMIST variable. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 4 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 4. The performance of different implementations of the residual diversity estimate examining faunas with varying degrees of homogeneity. PFORM, PLOC and PTAPH are set at 0.25. The rate of dispersal is increased relative to the rate of local extinction to increase the homogeneity of the faunas. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 1 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 1. An illustration of the taphonomic filter in the simulation, shown applied to a single taxon in a single time bin. The taxon is originally present in every locality in each region it occupies, but the taphonomic filter removes it from randomly selected localities

FIGURE 3 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 3. The performance of different implementations of the residual diversity estimate (RDE) under different sampling regimes. (3.1) Mean Spearman's rho values of four implementations of the RDE using Formations as a proxy, with values of PFORM, PLOC and PTAPH variable but equal. (3.2) Mean Spearman's rho values of four implementations of the RDE using Localities as a proxy. (3.3) Mean Spearman's rho values of four implementations of the RDE, all using the Smith and McGowan method. (3.4) Mean Spearman's rho values of the taxic and phylogenetic diversity estimate compared to those of the optimum implementation of the RDE. The dashed red line indicates the critical value at p=0.05. Abbreviations as in Table 1.

FIGURE 2 in A simulation-based examination of residual diversity estimates as a method of correcting for sampling bias

FIGURE 2. An illustration of how sampling proxies are generated in this simulation. This schematic illustrates which formations and localities in a single time bin contain fossils of at least one species of the simulated clade after application of the taphonomic filter. Formations and localities are removed at random, representing a lack of sampling. Note that the number of clade-bearing formations and localities does not necessarily equal the number of formations and localities sampled, allowing the generation of four sampling proxies.

opencc-by-nc-4.0Nov 2023View details →

D-PLACE dataset derived from Murdock and White 1969 'Standard Cross-Cultural Sample'

Cite the source of the dataset as: <blockquote> Murdock GP & White DR. 1969. Standard Cross-Cultural Sample. Ethnology. 9:329–369. </blockquote>

opencc-by-4.0Mar 2022View details →

TELL sample forcing data

This dataset contains sample data forcing data for the Total Electricity Loads (TELL) model developed by the IM3 project. More information about the TELL model can be found at: https://immm-sfa.github.io/tell/user_guide.html. The sample dataset includes five years of historical weather data (2015-2019) and four years of sample future data (2039, 2059, 2079, and 2099). The 2015-2019 data is based on historical meteorology simulated by WRF. In contrast, the sample future data comes from IM3's future WRF runs under the RCP 8.5 climate scenario with SSP5 population forcing. The weather data was generated by the sequence of processing scripts that convert the meteorology from IM3's climate simulations using the Weather Research and Forecasting (WRF) model into input files ready for use in TELL. The first step in the processing chain spatially averages the gridded meteorology output from WRF into county mean values. The output of that processing step is a series of .csv files (one for every hour processed) with the county-mean value of six meteorological variables: T2, Q2, U10, V10, SWDOWN, and GLW. The second step then takes these county-level hourly values and population-weights them into an annual time-series for each of the balancing authorities (BAs) used in the TELL model. The code used to generate this sample weather data and more information about the wrf_to_tell processing pipeline can be found at: https://github.com/IMMM-SFA/im3components. The dataset also contains a sample output file from the United States version of the Global Change Analysis Model (GCAM-USA) and two future county-level population projections that can be used in the TELL quickstarter notebook.

opencc-by-4.0Sep 2017View details →

FIGURE 1. Elements histologically sampled for this analysis. 1 in A new multi-faceted framework for deciphering diplodocid ontogeny

FIGURE 1. Elements histologically sampled for this analysis. 1, Digital reconstruction of Apatosaurus louisae (by K. Stevens) with sampled elements highlighted in red. Approximate location of sampling in dorsal ribs (2; from Gilmore, 1936), neural spines (3; from Hatcher, 1901), and femora (4; from Gilmore, 1936).

opencc-by-4.0Jan 2018View details →

Fig. 2 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)

Fig. 2 Phylog_n_tic tr__ construct_d for Paramecium aurelia compl_x, P. jenningsi compl_x and P. schewiakoffi (two sp_ci_s: P. caudatum and P. multimicronucleatum w_r_ us_d as an outgroup). Th_ tr__ was construct_d on th_ basis of a comparison of s_qu_nc_s from th_ ribosomal ITS1-5.8S-ITS2-5'LSU fragm_nt using th_ maximum lik_lihood m_thod. Bootstrap valu_s for n_ighbor joining, maximum parsimony, maximum lik_lihood, and post_rior probabiliti_s for

opencc-by-4.0Jan 2018View details →

Fig. 3 in Worldwide sampling reveals low genetic variability in populations of the freshwater ciliate Paramecium biaurelia (P. aurelia species complex, Ciliophora, Protozoa)

Fig. 3 Phylog_n_tic tr__ construct_d for Paramecium aurelia compl_x, P. jenningsi compl_x and P. schewiakoffi (two sp_ci_s: P. caudatum and P. multimicronucleatum w_r_ us_d as an outgroup). Th_ tr__ was construct_d on th_ basis of a comparison of s_qu_nc_s from th_ mitochondrial COI fragm_nt using th_ maximum lik_lihood m_thod. Bootstrap valu_s for n_ighbor joining, maximum parsimony, maximum lik_lihood, and post_rior probabiliti_s for Bay_sian inf_r_nc_ ar_

Figure 4 in A new method for analyzing microplastic particle size distribution in marine environmental samples

Figure 4. Circularity-versus-Feret's diameter plots of the MP samples collected at four stations in Sevastopol bay (the Black Sea) in 2019. Dot numbers do not correspond to the MPs abundance in the water. Dashed line represents the upper border of fibers.

Figure 5 in A new method for analyzing microplastic particle size distribution in marine environmental samples

Figure 5. Distribution of MPs along Sevastopol bay (the Black Sea) in terms of average particle shape descriptors (left plots), overall abundance and weight of MP fragments, and percentage of fibers among them (right plots).

Figure 3 in A new method for analyzing microplastic particle size distribution in marine environmental samples

Figure 3. Distribution of the four types of MP particles (red – rounded, violet – irregular, blue – elongated, and green – fibers) in a Circularity-versus-Feret's diameter plot (scatter). Dashed line represents the upper border of fibers.

Figure 2. A in A new method for analyzing microplastic particle size distribution in marine environmental samples

Figure 2. A: Microplastic samples dried on the 'storage' filters (100-µm nylon mesh). B: Pure microplastics under an inverted microscope. C-D: Micrograph of a sample in Bogorov's camera and its b/w image processed in an image editor. E: High-contrast scan image of a microplastic sample.

Figure 6 in A new method for analyzing microplastic particle size distribution in marine environmental samples

Figure 6.Weight of a MP sample (M) as a function of the total silhouette area (S) of the MP particles.