@article{AnsellStenoienGrundmannetal.2010,
  author    = {Ansell, Stephen W. and Stenoien, Hans K. and Grundmann, Michael and Schneider, Harald and Hemp, Andreas and Bauer, N. and Russell, S. J. and Vogel, Johannes C.},
  title     = {Population structure and historical biogeography of European Arabidopsis lyrata},
  issn      = {0018-067X},
  doi       = {10.1038/Hdy.2010.10},
  year      = {2010},
  abstract  = {Understanding the natural history of model organisms is important for the effective use of their genomic resourses. Arabidopsis lyrata has emerged as a useful plant for studying ecological and evolutionary genetics, based on its extensive natural variation, sequenced genome and close relationship to A. thaliana. We studied genetic diversity across the entire range of European Arabidopsis lyrata ssp. petraea, in order to explore how population history has influenced population structure. We sampled multiple populations from each region, using nuclear and chloroplast genome markers, and combined population genetic and phylogeographic approaches. Within-population diversity is substantial for nuclear allozyme markers (mean P = 0.610, A(e) = 1.580, H-e = 0.277) and significantly partitioned among populations (F- ST = 0.271). The Northern populations have modestly increased inbreeding (F-IS = 0.163 verses F-IS = 0.093), but retain comparable diversity to central European populations. Bottlenecks are common among central and northern Europe populations, indicating recent demographic history as a dominant factor in structuring the European diversity. Although the genetic structure was detected at all geographic scales, two clear differentiated units covering northern and central European areas (F-CT = 0.155) were identified by Bayesian analysis and supported by regional pairwise F-CT calculations. A highly similar geographic pattern was observed from the distribution of chloroplast haplotypes, with the dominant northern haplotypes absent from central Europe. We conclude A. l. petraea's cold-tolerance and preference for disturbed habitats enabled glacial survival between the alpine and Nordic glaciers in central Europe and an additional cryptic refugium. While German populations are probable peri-glacial leftovers, Eastern Austrian populations have diversity patterns possibly compatible with longer-term survival.},
  language  = {en}
}
@article{MunnesHarschKnoblochetal.2022,
  author    = {Munnes, Stefan and Harsch, Corinna and Knobloch, Marcel and Vogel, Johannes S. and Hipp, Lena and Schilling, Erik},
  title     = {Examining Sentiment in Complex Texts. A Comparison of Different Computational Approaches},
  series = {Frontiers in Big Data},
  volume    = {5},
  journal   = {Frontiers in Big Data},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2624-909X},
  doi       = {10.3389/fdata.2022.886362},
  pages     = {16},
  year      = {2022},
  abstract  = {Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the "gold standard " of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels-for example, a summary of the work and the reviewer's appraisal-but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)-which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.},
  language  = {en}
}