@article{45484,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (<jats:sc>Ingrid</jats:sc>) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within <jats:sc>Ingrid</jats:sc>, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on <jats:sc>Ingrid</jats:sc> targeted especially by researchers. In particular, we present <jats:sc>Ingrid</jats:sc>KG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update <jats:sc>Ingrid</jats:sc>KG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of <jats:sc>Ingrid</jats:sc>KG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.</jats:p>}},
  author       = {{Sherif, Mohamed Ahmed and da Silva, Ana Alexandra Morim and Pestryakova, Svetlana and Ahmed, Abdullah Fathi and Niemann, Sven and Ngomo, Axel-Cyrille Ngonga}},
  issn         = {{2052-4463}},
  journal      = {{Scientific Data}},
  keywords     = {{Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems, Statistics and Probability}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{IngridKG: A FAIR Knowledge Graph of Graffiti}}},
  doi          = {{10.1038/s41597-023-02199-8}},
  volume       = {{10}},
  year         = {{2023}},
}

@article{32335,
  abstract     = {{Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.}},
  author       = {{PODWORNY, SUSANNE and Hüsing, Sven and SCHULTE, CARSTEN}},
  issn         = {{1570-1824}},
  journal      = {{STATISTICS EDUCATION RESEARCH JOURNAL}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{34200,
  abstract     = {{<jats:p>Praxeologische Kompetenzansätze verstehen Kompetenz als sozial erlernt und folglich als relativ zum sozialen Kontext. Damit einher geht die Frage, wie solche praxeologisch gerahmten Kompetenzen eigentlich unabhängig von der sie hervorbringenden Praxis evaluiert werden können – und eben dadurch erst für einen breiteren Kompetenzdiskurs fruchtbar sind. Die Dokumentarische Evaluationsforschung bietet hierzu erste Anhaltspunkte, offenbart aber auch Grenzen, die mit dem Evaluationsverständnis zusammenhängen, sich jedoch in der Forschungspraxis so nicht finden lassen. Aus der Differenz zwischen Methode und Praxis dokumentarischer Evaluation lässt sich formulieren, wie eine praxeologische Evaluation gestaltet werden könnte. Dabei spielt die Formulierung von Referenzrahmen eine zentrale Rolle, welche einerseits der zu evaluierenden Praktik external sein, andererseits praktisch formuliert werden müssen, damit sie soziale Praktiken jenseits ihrer eigenen Sinnhaftigkeit evaluativ (er-)fassen können.</jats:p>}},
  author       = {{Bloh, Thiemo}},
  issn         = {{1619-5515}},
  journal      = {{Zeitschrift für Evaluation}},
  keywords     = {{Strategy and Management, Applied Psychology, Social Sciences (miscellaneous), Education, Communication, Statistics and Probability}},
  number       = {{02}},
  pages        = {{193--215}},
  publisher    = {{Waxmann}},
  title        = {{{Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung}}},
  doi          = {{10.31244/zfe.2022.02.02}},
  volume       = {{2022}},
  year         = {{2022}},
}

@article{48108,
  abstract     = {{<jats:p>Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.</jats:p>}},
  author       = {{PODWORNY, SUSANNE and HÜSING, SVEN and SCHULTE, CARSTEN}},
  issn         = {{1570-1824}},
  journal      = {{STATISTICS EDUCATION RESEARCH JOURNAL}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}}},
  doi          = {{10.52041/serj.v21i2.46}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{34920,
  abstract     = {{<jats:p>A very warm welcome to this Special Issue of the Statistics Education Research Journal (SERJ) on data science education. Our hope is to give an overview of selected theoretical thoughts and empirical studies on data science education from a statistics education research perspective. Data science education is rapidly developing but research into data science education is still in its infancy. The current issue presents a snapshot of this developing field.</jats:p>}},
  author       = {{Biehler, Rolf and De Veaux, Richard and Engel, Joachim and Kazak, Sibel and Frischemeier, Daniel}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Editorial: Research on Data Science Education}}},
  doi          = {{10.52041/serj.v21i2.606}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{50024,
  author       = {{Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}},
  issn         = {{2073-4859}},
  journal      = {{The R Journal}},
  keywords     = {{Statistics, Probability and Uncertainty, Numerical Analysis, Statistics and Probability}},
  number       = {{1}},
  pages        = {{182--195}},
  publisher    = {{The R Foundation}},
  title        = {{{The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series}}},
  doi          = {{10.32614/rj-2022-017}},
  volume       = {{14}},
  year         = {{2022}},
}

@article{35672,
  abstract     = {{<jats:p>This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}},
  doi          = {{10.52041/serj.v21i2.61}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{50025,
  author       = {{Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}},
  issn         = {{2073-4859}},
  journal      = {{The R Journal}},
  keywords     = {{Statistics, Probability and Uncertainty, Numerical Analysis, Statistics and Probability}},
  number       = {{1}},
  pages        = {{182--195}},
  publisher    = {{The R Foundation}},
  title        = {{{The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series}}},
  doi          = {{10.32614/rj-2022-017}},
  volume       = {{14}},
  year         = {{2022}},
}

@article{32243,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>The defining feature of active particles is that they constantly propel themselves by locally converting chemical energy into directed motion. This active self-propulsion prevents them from equilibrating with their thermal environment (e.g. an aqueous solution), thus keeping them permanently out of equilibrium. Nevertheless, the spatial dynamics of active particles might share certain equilibrium features, in particular in the steady state. We here focus on the time-reversal symmetry of individual spatial trajectories as a distinct equilibrium characteristic. We investigate to what extent the steady-state trajectories of a trapped active particle obey or break this time-reversal symmetry. Within the framework of active Ornstein–Uhlenbeck particles we find that the steady-state trajectories in a harmonic potential fulfill path-wise time-reversal symmetry exactly, while this symmetry is typically broken in anharmonic potentials.</jats:p>}},
  author       = {{Dabelow, Lennart and Bo, Stefano and Eichhorn, Ralf}},
  issn         = {{1742-5468}},
  journal      = {{Journal of Statistical Mechanics: Theory and Experiment}},
  keywords     = {{Statistics, Probability and Uncertainty, Statistics and Probability, Statistical and Nonlinear Physics}},
  number       = {{3}},
  publisher    = {{IOP Publishing}},
  title        = {{{How irreversible are steady-state trajectories of a trapped active particle?}}},
  doi          = {{10.1088/1742-5468/abe6fd}},
  volume       = {{2021}},
  year         = {{2021}},
}

@article{33649,
  author       = {{Kessler, Jan and Calcavecchia, Francesco and Kühne, Thomas}},
  issn         = {{2513-0390}},
  journal      = {{Advanced Theory and Simulations}},
  keywords     = {{Multidisciplinary, Modeling and Simulation, Numerical Analysis, Statistics and Probability}},
  number       = {{4}},
  publisher    = {{Wiley}},
  title        = {{{Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo}}},
  doi          = {{10.1002/adts.202000269}},
  volume       = {{4}},
  year         = {{2021}},
}

@article{48109,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.</jats:p>}},
  author       = {{Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Budde, Lea}},
  issn         = {{0141-982X}},
  journal      = {{Teaching Statistics}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{S1}},
  publisher    = {{Wiley}},
  title        = {{{A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <scp>CODAP</scp> using survey data}}},
  doi          = {{10.1111/test.12283}},
  volume       = {{43}},
  year         = {{2021}},
}

@article{35751,
  author       = {{Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Budde, Lea}},
  issn         = {{0141-982X}},
  journal      = {{Teaching Statistics}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{S1}},
  pages        = {{S182--S189}},
  publisher    = {{Wiley}},
  title        = {{{A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data}}},
  doi          = {{10.1111/test.12283}},
  volume       = {{43}},
  year         = {{2021}},
}

@article{35737,
  author       = {{Biehler, Rolf and Fleischer, Franz Yannik}},
  issn         = {{0141-982X}},
  journal      = {{Teaching Statistics}},
  keywords     = {{Education, Statistics and Probability}},
  pages        = {{S133--S142}},
  publisher    = {{Wiley}},
  title        = {{{Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks}}},
  doi          = {{10.1111/test.12279}},
  volume       = {{43}},
  year         = {{2021}},
}

@article{45561,
  abstract     = {{<jats:p>The purpose of this study is to experimentally test Trockel’s game, which is a modelling of the classical Chain Store Game (CSG), and determine whether one of the two theories of Equality and Deterrence may better account for the observed behavior. The CSG is an example of a simple game in extensive form where the actual behavior of well-informed players cannot be expected to agree with the clear results of game theoretical reasoning. To explain the disagreement between the theory and the expected behavior, Trockel’s game is proposed as an alternative modelling of the scenario. The existence of more than one equilibrium in Trockel’s game opens a door for reputation building. This study is the first attempt to experimentally test this alternative game with the same purpose. According to my data, there is some evidence in favor of both Equality and Deterrence Hypotheses. However, since the strategies compatible with the Equality Hypothesis are played more frequently, I observe some patterns which share the same intuition with the Deterrence Hypothesis.</jats:p>}},
  author       = {{Duman, Papatya}},
  issn         = {{2073-4336}},
  journal      = {{Games}},
  keywords     = {{Applied Mathematics, Statistics, Probability and Uncertainty, Statistics and Probability}},
  number       = {{1}},
  publisher    = {{MDPI AG}},
  title        = {{{Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story}}},
  doi          = {{10.3390/g11010009}},
  volume       = {{11}},
  year         = {{2020}},
}

@article{40334,
  author       = {{Kitzerow, Heinz-Siegfried and Jérôme, B. and Pieranski, P.}},
  issn         = {{0378-4371}},
  journal      = {{Physica A: Statistical Mechanics and its Applications}},
  keywords     = {{Condensed Matter Physics, Statistics and Probability}},
  number       = {{1}},
  pages        = {{163--194}},
  publisher    = {{Elsevier BV}},
  title        = {{{Strain-induced anchoring transitions}}},
  doi          = {{10.1016/0378-4371(91)90423-a}},
  volume       = {{174}},
  year         = {{1991}},
}

@article{40218,
  author       = {{Lasser, R. and Rösler, Margit}},
  issn         = {{0304-4149}},
  journal      = {{Stochastic Processes and their Applications}},
  keywords     = {{Applied Mathematics, Modeling and Simulation, Statistics and Probability}},
  number       = {{2}},
  pages        = {{279--293}},
  publisher    = {{Elsevier BV}},
  title        = {{{Linear mean estimation of weakly stationary stochastic processes under the aspects of optimality and asymptotic optimality}}},
  doi          = {{10.1016/0304-4149(91)90095-t}},
  volume       = {{38}},
  year         = {{1991}},
}