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opencc-by-4.0May 2024View details →

SUPER MoRRI - Secondary datasets for RRI indicators

This file lists the secondary data that was compiled and produced as part of the Horizon 2020 SUPER MoRRI project (Scientific understanding and provision of an enhanced and robust monitoring system for RRI). The data is contained in the file: “SUPER_MoRRI secondary data.xls” Descriptions of each variable can be found in  “Appendix A – Secondary Data Fiches” in  SUPER MoRRI Deliverable 2.3: Second Responsible Research and Innovation Monitoring Report (MR2) In the excel file, data for each variable is included in a separate sheet. The list below links each sheet name with the variables (see the data fiches in SUPER MoRRI Deliverable 2.3 for detailed information on each variable) Sheet name: Sh_Fem_Res All sectors; Variable: Share of female researchers by sectors of performance (all sectors) Sheet name: Sh_Fem_Res Business; Variable: Share of female researchers by sectors of performance (business enterprise sector Sheet name: Sh_Fem_Res Higher education; Variable: Share of female researchers by sectors of performance (higher education sector) Sheet name: Sh_Fem_Res Government; Variable: Share of female researchers by sectors of performance (government sector) Sheet name: IntraRD per inhabitant; Variable: Intramural R&D expenditure per inhabitant Sheet name: IntraRD percent GDP;  Variable: Intramural R&D expenditure as a percentage of GDP Sheet name: EPO patent applications; Variable: Patent applications to the EPO by priority year per million inhabitants     Sheet name: Glass Ceiling Index;  Variable: The Glass Ceiling Index Sheet name: Diss index, Higher education; Variable: Dissimilarity Index (higher education sector) Sheet name: Diss index, Government; Variable: Dissimilarity Index (government sector) Sheet name: Gender dimension in research ; Variable: Percentage of publications with a sex or gender dimension     Sheet name: Gender pay gap; Variable: Gender pay gap within scientific research & development     Sheet name: WtM, Inventorships; Variable: The women to men ratio in number of inventorships Sheet name: WtM, Auhtorship ; Variable: The women to men ratio in number of corresponding authorships Sheet name: Interest scientific discoveries; Variable: Interest in scientific discoveries     Sheet name: Feeling science efficacy ; Variable: Feeling of science efficacy Sheet name: Variable: Scientific literacy     Sheet name: Variable: Trust in scientists     Sheet name: Engagement meetings debates; Variable: Engagement and co-creation (meetings and debates) Sheet name: Engagement demonstrations ; Variable: Engagement and co-creation (petitions and demonstrations) Sheet name: OA, All pubs; Variable: Percentage of open access publications     Sheet name: OA, Green pubs; Variable: Percentage of open access publications (Green) Sheet name: OA, Gold pubs; Variable: Percentage of open access publications (Gold) Sheet name:  OA, Hybrid pubs; Variable: Percentage of open access publications (Hybrid) Sheet name: OA, Bronze pubs; Variable: Percentage of open access publications (Bronze) Sheet name: Industry Collaboration;  Variable: Percentage of co-publications with industry

opencc-by-4.0Jun 2024View details →

Sequential Synthesis and Secondary Structure Analysis of Two Classes of Perylene Bisimide Oligomers

Data to report <a title="DOI URL" href="https://doi.org/10.1021/acs.orglett.4c01928">https://doi.org/10.1021/acs.orglett.4c01928</a><a title="DOI URL" href="https://doi.org/10.1021/acs.orglett.4c01928">:</a> An iterative step-by-step synthetic approach is employed to form perylene bisimide (PBI) oligomers with defined size by connecting the PBI units through their imide positions via a benzyl linker. The versatility of this approach was showcased by its successful implementation on two different PBI building blocks to achieve two separate series of oligomers (up to the pentamer) with modulated conformations; the one with an open random coil oligomer and the other with an H-type foldamer architecture.

Fig. 9 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 9. Evolutionary hypothesis of interrelationships among the four free-living litostomatean lineages studied. This scenario was suggested on the basis of morphology and the consensus secondary structure of the ITS2 molecules. CK – circumoral kinety, DB – dorsal brush, OB – oral bulge, OO – oral bulge opening, P – proboscis, PE – perioral kinety, PR – preoral kineties, SK – somatic kineties.

Fig. 5 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 5. Quartet likelihood-mapping showing distribution of phylogenetic signal in the 18S-A and the CON-1 alignment for three possible relationships among the four main free-living litostomatean lineages studied. The corners of the triangles show the percentage of fully resolved trees, i.e., phylogenetically informative signal. The rectangular areas show the percentage of trees that are in conflict. The central triangle shows the percentage of unresolved star-like trees, i.e., phylogenetically uninformative signal. Coding of free-living litostomatean lineages: H – Haptorida, P – Pleurostomatida, R – Rhynchostomatia, S – Spathidiida.

Fig. 4 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 4. Super-network of 66 free-living litostomatean taxa constructed from 80 randomly selected post-burn-in trees from the Bayesian inference of the 18S-A–D, ITSR-C and ITSR-D as well as the CON-1 and CON-2 alignments. The super-network was constructed in the program SplitsTree, using the Z-closure option, tree size weighted mean, ten runs, and the refined heuristic technique. For details on taxa and characteristics of the alignments analyzed, see Supplementary Table S1 and S2.

Fig. 3 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 3. Phylogeny based on the 18S rRNA gene and the ITS1-5.8S-ITS2 region of 56 free-living litostomatean taxa (alignment CON-1). Posterior probabilities for the Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule ML tree. Dashes indicate posterior probabilities below 0.50 and ML bootstrap values below 50%. The scale bar indicates five substitutions per ten nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the CON-1 alignment, see Supplementary Table S1 and S2.

Fig. 1 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 1. Phylogeny based on the 18S rRNA gene of 64 free-living litostomatean taxa (alignment 18S-A). Posterior probabilities for Bayesian inference and bootstrap values for maximum likelihood were mapped onto the 50% majority rule Bayesian consensus tree. Dashes indicate ML bootstrap values below 50%. Sequences in bold were obtained during this study. The scale bar indicates two substitutions per one hundred nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the 18S-A alignment, see Supplementary Table S1 and S2.

Fig. 8 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 8. Structure logo of ITS2 helices II and III in various higher litostomatean taxa. The height of a base is proportional to its frequency in multiple sequence alignments.

Fig. 2 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 2. Phylogeny based on the ITS1-5.8S-ITS2 region of 60 free-living litostomatean taxa (alignment ITSR-A). Posterior probabilities for Bayesian inference and bootstrap values for maximum likelihood were mapped onto the best ML tree. Dashes indicate posterior probabilities below 0.50 and ML bootstrap values below 50%. Sequences in bold were obtained during this study. The scale bar indicates nine substitutions per one hundred nucleotide positions. For details on taxa, evolutionary model used, and characteristics of the ITSR-A alignment, see Supplementary Table S1 and S2.

Fig. 7 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule

Fig. 7. Consensus secondary structure of ITS2 helices II and III in various higher litostomatean taxa.

opencc-by-4.0Dec 2020View details →

Fig. 3 in Formation Dynamics Of Herpetocomplexes On Sections Of Secondary Succession In Terrestrial Ecosystems Of Belarus

Fig. 3. Changes in species diversity (D, Simpson index of diversity) of herpetocomplexes at sites of secondary succession.

opencc-by-4.0Dec 2020View details →

Fig. 1 in Formation Dynamics Of Herpetocomplexes On Sections Of Secondary Succession In Terrestrial Ecosystems Of Belarus

Fig. 1. Formation the species composition of amphibians and reptiles faunal complexes on sections restoration succession (deforestation site).

opencc-by-4.0Jul 2024View details →

Rays of the secondary fan of the Hypersimplex(2,7)

This dataset contains four files describing the rays of secondary fan of the hypersimplex(2,7). <ul> <li>hypersimplex_2_7_idmap.dat.xz maps IDs to the rays of the secondary fan, such that we may refer to the rays by that ID.</li> <li>hypersimplex_2_7_orbit_representatives.dat.xz maps orbit sizes to sets of IDs of rays with that orbit size, but only one ray per orbit</li> <li>hypersimplex_2_7_histogram.dat.xz maps spreads to sets of IDs of rays with that spread</li> <li>hypersimplex_2_7_orbit_histogram.dat.xz maps orbit sizes to sets of IDs of rays with that orbit size.</li> </ul> The datasets are compressed using xz and can be read using polymake. The datasets were computed from the regular triangulations of the hypersimplex(2,7). Since these triangulations only came up to group action, first the secondary cone was computed for such a triangulation, then the rays were computed and subsequently the orbits of the rays were expanded.

opencc-by-4.0Jul 2024View details →

Beyond gene flow: (non)-parallelism of secondary contact in a pair of highly differentiated sibling species

Replicated secondary contact zones can provide insights on the barriers to gene flow that are important during speciation and can reveal to which degree secondary contact may result in similar evolutionary outcomes. Here, we studied two secondary contact zones between highly differentiated Alpine butterflies of the genus Erebia using whole-genome re-sequencing data. We assessed the genomic relationships between populations and species and find hybridization to be rare, with no to little current or historical introgression in either contact zone. There are large similarities between the contact zones, consistent with an allopatric origin of interspecific differentiation, with no indications for ongoing reinforcing selection. Consistent with expected reduced effective population size, we further find that scaffolds related to the Z-chromosome show increased differentiation compared to the already high levels across the entire genome, which could also hint towards a contribution of the Z chromosome to species divergence in this system. Finally, we detected the presence of the endosymbiont Wolbachia, which can cause reproductive isolation between its hosts, in all E. cassioides, while it appears to be fully or largely absent in contact zone populations of E. tyndarus. We discuss how this rare pattern may have arisen and how it may have affected the dynamics of speciation upon secondary contact.

Fig. 11 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 11 Rates of chatter calls in different magpie populations and individuals. a Each mark represents average chattering rate for a single bird from five populations indicated by colours. Figures are numbers for the outliers: 1, 2—jankowskii from the mixed population of Argun'; 3, 4, 5—hybrid birds from the hybridogeneous population of Kerulen. b Each mark represents average chattering rate for a series of chatterings of one selected individual representing jankowskii, leucoptera, and hybrid birds, respectively. Green mark—pair #6 jankowskii from Vladivostok; gray—pair #43 leucoptera from Tsasuchei, Transbaikalia; blue—pair #24 hybrids from Kerulen, eastern Mongolia. X-axis—number of elements per second in a total series of chattering; Y-axis— number of elements per second in a series of 5 elements of chattering

Fig. 12 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 12 Violin plot diagram of the chatter call speed (elements per second) of Eurasian magpie populations across regions. X-axis presents a set of populations; Y-axis—elements per second. Box outlines the interquantile range (25%, 75%), whiskers represent range without outliers, central bar is the median, red dot is the mean, and figure shape is the probability density. The brackets on the top denote statistically significant pairwise differences (GamesHowell test, p<0.05)

Fig. 9 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 9 Population genetic structure based on unlinked SNP markers. Scatter plots of principal component analysis (PCA) show individual variation in components one and two (a) and three and four (b). The amount of variance explained by each PC is shown in parentheses. I—leucoptera,

Fig. 7 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 7 Bayesian skyline plots (BSPs) for effective female population sizes for haplogroups, subspecies, and populations of Pica pica. a Comparison of 6 haplogroups, depicted in the network Fig. 4. b Comparison of 6 subspecies. c Comparison of 4 populations of P. p. jankowskii. d Comparison of 3 populations of P. p. leucoptera.

Fig. 6 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 6 Mismatch distribution of nucleotide differences in populations representing different haplogroups as at Figs. 4 and 5. X-axis— number of nucleotide differences; Y-axis—proportion (frequency). Solid lines—expected distributions (under expectation of population growth); dashed lines—observed distributions. a Haplogroup 1:

Fig. 5 in Introgression at the emerging secondary contact zone of magpie Pica pica subspecies (Aves: Corvidae): integrating data on nuclear and mitochondrial markers, vocalizations, and field observations

Fig. 5 Time-calibrated Bayesian tree based on mitochondrial control region sequences of Pica pica. Numbers at the branches indicate Bayesian posterior probability values (left) and bootstrap values of the ML analysis (right, in percent). Triangle widths are proportional to specimen numbers. Blue bars next to nodes indicate 95% credibility intervals for their age estimates. The figures in bold and the time scale below are in million years (Ma) before present