@article{GrothSchefflerHermanussen2023,
  author    = {Groth, Detlef and Scheffler, Christiane and Hermanussen, Michael},
  title     = {Human growth data analysis and statistics - the 5th G{\"u}lpe International Student Summer School},
  series = {Human biology and public health},
  volume    = {1},
  journal   = {Human biology and public health},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {2748-9957},
  doi       = {10.52905/hbph2023.1.70},
  pages     = {5},
  year      = {2023},
  abstract  = {The Summer School in G{\"u}lpe (Ecological Station of the University of Potsdam) offers an exceptional learning opportunity for students to apply their knowledge and skills to real-world problems. With the guidance of experienced human biologists, statisticians, and programmers, students have the unique chance to analyze their own data and gain valuable insights. This interdisciplinary setting not only bridges different research areas but also leads to highly valuable outputs. The progress of students within just a few days is truly remarkable, especially when they are motivated and receive immediate feedback on their questions, problems, and results. The Summer School covers a wide range of topics, with this year's focus mainly on two areas: understanding the impact of socioeconomic and physiological factors on human development and mastering statistical techniques for analyzing data such as changepoint analysis and the St. Nicolas House Analysis (SNHA) to visualize interacting variables. The latter technique, born out of the Summer School's emphasis on gaining comprehensive data insights and understanding major relationships, has proven to be a valuable tool for researchers in the field. The articles in this special issue demonstrate that the Summer School in G{\"u}lpe stands as a testament to the power of practical learning and collaboration. Students who attend not only gain hands-on experience but also benefit from the expertise of professionals and the opportunity to engage with peers from diverse disciplines.},
  language  = {en}
}
@article{HakeBodenbergerGroth2023,
  author    = {Hake, Tim and Bodenberger, Bernhard and Groth, Detlef},
  title     = {In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks},
  series = {Human biology and public health},
  volume    = {1},
  journal   = {Human biology and public health},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {2748-9957},
  doi       = {10.52905/hbph2023.1.63},
  pages     = {16},
  year      = {2023},
  abstract  = {The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.},
  language  = {en}
}