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opencc-by-4.0Jan 2021View details →

Variational Inference for Learning Representations of Natural Language Edits

Performance Evaluation of Edit Representations (PEER), the dataset we use in the paper <a href="https://arxiv.org/abs/2004.09143">"Variational Inference for Learning Representations of Natural Language Edits"</a>.

opencc-by-4.0Jun 2019View details →

Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen (Populus tremula)

Data sets and files for linkage map construction and for inferring recombination rate variation in Populus tremula. Associated scripts for analyses can be found at <a href="https://github.com/parkingvarsson/Recombination_rate_variation">https://github.com/parkingvarsson/Recombination_rate_variation</a>

Figure 3 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP

Figure 3. Restriction patterns of HinfI inferred from D-loop digestion (M: Marker–100bp DNA Ladder, 1. Ordu, 2. Trabzon, 3. Rize, 4. Artvin, 5–6. Erzincan, 7–8. Kahramanmaraş, 9. Adıyaman, 10–11. Adana, 12. Muğla, 13. Burdur, 14. Konya, 15. Antalya, 16. Mersin, 17. Kastamonu, 18. Zonguldak, 19. Düzce, 20. Balıkesir, 21. İzmir, 22. Aydın, 23. A. uralensis, 24. A. witherbyi, 25. D-loop PCR products).

Figure 2 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP

Figure 2. Restriction patterns of MboI, HaeIII, and RsaI inferred from cytb digestion (M: Marker–100bp DNA Ladder, 1. Ordu, 2. Trabzon, 3. Rize, 4. Artvin, 5. Erzincan, 6. Kahramanmaraş, 7. Adıyaman, 8. Adana, 9. Muğla, 10. Burdur, 11. Konya, 12. Antalya, 13. Mersin, 14. Kastamonu, 15. Zonguldak, 16. Düzce, 17. Balıkesir, 18. İzmir, 19. Aydın, 20. A. uralensis, 21. A. witherbyi, 22. Cytb PCR product).

Figure 5 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP

Figure 5. PCoA analysis of A. mystacinus clades. The scatter plot is of the scores of three principal eigenvalues inferred from NTSYS software. Each scatter point represents a specimen of A. mystacinus.

Figure 1 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP

Figure 1. Sampling localities of A. mystacinus specimens. Table 2. Restriction enzymes and their digestion sites with reaction procedures.

opencc-by-4.0May 2016View details →

Figure 2. A in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations

Figure 2. A dendrogram summarizing the genetic relationships of M. tristrami subpopulations (NTSYSpc options: Coefficient: SM (SimQual), clustering method: UPGMA) (see Nei, 1978) (Mt1 = Gaziantep, Adana, Mt2 = Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir), Mt3 = Denizli, Mt4 = Şanlıurfa, Mt5 = Iğdır, Mt6 = Tosya/Kastamonu, Mt7 = Karadağ/Karaman, Mt8 = Turgutlu/Manisa).

opencc-by-4.0May 2016View details →

Figure 1 in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations

Figure 1. Map of the locations of the M. tristrami specimens in Turkey (Gaziantep, Adana: Mt1; Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir): Mt2; Denizli: Mt3; Şanlıurfa: Mt4; Iğdır: Mt5; Tosya/Kastamonu: Mt6; Karadağ/Karaman: Mt7; Turgutlu/ Manisa: Mt8).

opencc-by-4.0Dec 2022View details →

Data and Code for Publication "Inferring human neutral genetic variation from craniodental phenotypes"

Data and code for publication: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus. The repository contains: <ul> <li>“R code for DP-DG analysis.txt”: R code for testing levels of neutral evolutionary signals preserved in five craniodental data types: cranial metrics, dental metrics, cranial non-metric traits, dental non-metric traits, and craniodental metrics and non-metric traits combined.</li> </ul> <ul> <li>“Cranial metric data.csv”: Dataset consisting of 37 cranial metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, Comparison of craniofacial features of major human groups. Am. J. Phys. Anthropol. 99, 389–412 (1996) (<a href="https://doi.org/10.1002/(SICI)1096-8644(199603)99:3%3c389::AID-AJPA3%3e3.0.CO;2-S">https://doi.org/10.1002/(SICI)1096-8644(199603)99:3<389::AID-AJPA3>3.0.CO;2-S</a>).</li> </ul> <ul> <li>“Dental metric data.csv”: Dataset comprising 28 dental metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, H. Ishida, Metric dental variation of major human populations. Am. J. Phys. Anthropol. 128, 287–298 (2005) (<a href="https://doi.org/10.1002/ajpa.20080">https://doi.org/10.1002/ajpa.20080</a>).</li> </ul> <ul> <li>“Cranial non-metric trait data.csv”: Dataset consisting of 24 cranial non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected for the most part by T. Hanihara and presented in the publication titled: T. Hanihara, H. Ishida, Y. Dodo, Characterization of biological diversity through analysis of discrete cranial traits. Am. J. Phys. Anthropol. 121, 241–251 (2003) (<a href="https://doi.org/10.1002/ajpa.10233">https://doi.org/10.1002/ajpa.10233</a>).</li> </ul> <ul> <li>“Dental non-metric trait data.csv”: Dataset comprising 25 dental non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by C. G. Turner II, G. R. Scott, and J. D. Irish. This individual-level dataset was artificially created from population-level trait frequency information presented in the publications: G. R. Scott, J. D. Irish, Human Tooth Crown and Root Morphology (Cambridge University Press, 2017) (<a href="https://doi.org/10.1017/9781316156629">https://doi.org/10.1017/9781316156629</a>); and: J. D. Irish, A. Morez, L. Girdland Flink, E. L. W. Phillips, G. R. Scott, Do dental nonmetric traits actually work as proxies for neutral genomic data? Some answers from continental- and global-level analyses. Am. J. Phys. Anthropol. 172, 347–375 (2020) (<a href="https://doi.org/10.1002/ajpa.24052">https://doi.org/10.1002/ajpa.24052</a>).</li> </ul> <ul> <li>“SNP data.txt”: Dataset comprising 8,821 SNP markers for 26 worldwide modern populations, provided in a genepop file format. The data were obtained from various published sources: I. Lazaridis et al., Ancient human genomes suggest three ancestral populations for present-day Europeans. Nature 513, 409–413 (2014) (<a href="https://doi.org/10.1038/nature13673">https://doi.org/10.1038/nature13673</a>); P. Qin, M. Stoneking, Denisovan ancestry in east Eurasian and native American populations. Mol. Biol. Evol. 32, 2665–2674 (2015) (<a href="https://doi.org/10.1093/molbev/msv141">https://doi.org/10.1093/molbev/msv141</a>); P. Skoglund et al., Genomic insights into the peopling of the Southwest Pacific. Nature 538, 510–513 (2016) (<a href="https://doi.org/10.1038/nature19844">https://doi.org/10.1038/nature19844</a>); M. R. Nelson et al., The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research. Am. J. Hum. Genet. 83, 347–358 (2008) (<a href="https://doi.org/10.1016/j.ajhg.2008.08.005">https://doi.org/10.1016/j.ajhg.2008.08.005</a>); J. K. Pickrell, J. K. Pritchard, Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002967">https://doi.org/10.1371/journal.pgen.1002967</a>); A. Bergström et al., Insights into human genetic variation and population history from 929 diverse genomes. Science 367 (2020) (<a href="https://doi.org/10.1126/science.aay5012">https://doi.org/10.1126/science.aay5012</a>); B. M. Henn et al., Genomic ancestry of North Africans supports back-to-Africa migrations. PLoS Genet. 8, e1002397 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002397">https://doi.org/10.1371/journal.pgen.1002397</a>); S. Mallick et al., The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016) (<a href="https://doi.org/10.1038/nature18964">https://doi.org/10.1038/nature18964</a>); Lao et al., Correlation between genetic and geographic structure in Europe. Curr. Biol. 18, 1241–1248 (2008) (<a href="https://doi.org/10.1016/j.cub.2008.07.049">https://doi.org/10.1016/j.cub.2008.07.049</a>); and M. Lipson et al., Population Turnover in Remote Oceania Shortly after Initial Settlement. Curr. Biol. 28, 1157-1165.e7 (2018) (<a href="https://doi.org/10.1016/j.cub.2018.02.051">https://doi.org/10.1016/j.cub.2018.02.051</a>).</li> </ul> For population and variable names and abbreviations, see Supplementary Information in: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus.

opencc-by-4.0Jul 2023View details →

Experimental Results for "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models"

This package contains the raw data / logs (fetched from WandB) for the experiments of the following publication: O. Arenz, P. Dahlinger, Z. Ye, M. Volpp, and G. Neumann. A unified perspective on natural gradient variational inference with gaussian mixture models. Transactions on Machine Learning Research, 2023. URL: <a href="https://openreview.net/forum?id=tLBjsX4tjs">https://openreview.net/forum?id=tLBjsX4tjs</a>.

opencc-zeroDec 2020View details →

Migration trajectories of the diamondback moth Plutella xylostella in China inferred from population genomic variation

BACKGROUND: The diamondback moth (DBM), Plutella xylostella (Lepidoptera: Plutellidae), is a notorious pest of cruciferous plants. In temperate areas, annual populations of DBM originate from adult migrants. However, the source populations and migration trajectories of immigrants remain unclear. Here, we investigated migration trajectories of DBM in China with genome-wide single nucleotide polymorphisms (SNPs) genotyped using double-digest RAD (ddRAD) sequencing. We first analyzed patterns of spatial and temporal genetic structure among southern source and northern recipient populations, then inferred migration trajectories into northern regions using discriminant analysis of principal components (DAPC), assignment tests and spatial kinship patterns. RESULTS: Temporal genetic differentiation among populations was low, indicating sources of recipient populations and migration trajectories are stable. Spatial genetic structure indicated three genetic clusters in the southern source populations. Assignment tests linked northern populations to the Sichuan cluster, and central-eastern populations to the South and Yunnan clusters, indicating that Sichuan populations are sources of northern immigrants and South and Yunnan populations are sources of central-eastern populations. First-order (full-sib) and second-order (half-sib) kin pairs were always found within populations, but about 35-40% of third-order (cousin) pairs were found in different populations. Closely related individuals in different populations were in about 35-40% of cases found at distances of 900 to 1500 km, while some were separated by over 2000 km. CONCLUSION: This study unravels seasonal migration patterns in the DBM. We demonstrate how careful sampling and population genomic analyses can be combined to help understand cryptic migration patterns in insects.

opencc-zeroOct 2019View details →

Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation

Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a "straw-man" effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation ("background-rate variation"). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae).

zenodo36/100

Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans

Inferred ROH and IBD calls from Korunes et al (2022). bioRxiv DOI: https://doi.org/10.1101/2020.12.14.422766 Samples originally collected and analyzed in Beleza et al. 2013, PLoS Genetics. Inferred local ancestry information can be found at <a href="https://doi.org/10.5281/zenodo.4021277">https://doi.org/10.5281/zenodo.4021277</a> See README.txt in upload for more detailed information.

opencc-by-4.0Jun 2022View details →

opencc-zeroDec 2017View details →

Data from: A century of genetic variation inferred from a persistent soil-stored seed bank

Stratigraphic accretion of dormant propagules in soil can result in natural archives useful for studying ecological and evolutionary responses to environmental change. Few attempts have been made, however, to use soil-stored seed banks as natural archives, in part because of concerns over non-random attrition and mixed stratification. Here we examine the persistent seed bank of Schoenoplectus americanus, a foundational brackish marsh sedge, to determine whether it can serve as a resource for reconstructing historical records of demographic and population genetic variation. After assembling profiles of the seed bank from radionuclide dated soil cores, we germinated seeds to 'resurrect' cohorts spanning the 20th century. Using microsatellite markers, we assessed genetic diversity and differentiation among depth cohorts, drawing comparisons to extant plants at the study site and in nearby and more distant marshes. We found that seed density peaked at intermediate soil depths. We also detected genotypic differences among cohorts as well as between cohorts and extant plants. Genetic diversity did not decline with depth, indicating that the observed pattern of differentiation is not due to attrition. Patterns of differentiation within and among extant marshes also suggest that local populations persist as aggregates of small clones, likely reflecting repeated seedling recruitment and low immigration from admixed regional gene pools. These findings indicate that persistent and stratified soil-stored seed banks merit further consideration as resources for reconstructing decadal-to-century long records that can lend insight into the tempo and nature of ecological and evolutionary processes that shape populations over time.

zenodo36/100

Figure 4 in Determination of genetic variations between Apodemus mystacinus populations distributed in Turkey inferred from mtDNA PCR-RFLP

Figure 4. UPGMA dendrogram of the composite data by combining cytb and D-loop regions.

opencc-by-4.0Sep 2024View details →

zenodo36/100

Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"

Dataset accompaning the paper "Variational inference for correlated gravitational wave detector network noise"   <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5   (correlated noise)</li> <li>ET_caseB_noise.h5   (uncorrelated noise)</li> </ul> These contain: <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> Note: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset.    <h3>Result file</h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> These contain: <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>

publicOct 2025View details →

Code from: When and where do waterbirds need water? Inferring candidate restoration areas from spatio-temporal variation in surface water availability

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publicNov 2019View details →

Data from: A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation

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publicAug 2023View details →

Migration trajectories of the diamondback moth Plutella xylostella in China inferred from population genomic variation

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