[{"publication_status":"published","publication_identifier":{"issn":["2052-4463"]},"citation":{"apa":"Sherif, M. A., da Silva, A. A. M., Pestryakova, S., Ahmed, A. F., Niemann, S., &#38; Ngomo, A.-C. N. (2023). IngridKG: A FAIR Knowledge Graph of Graffiti. <i>Scientific Data</i>, <i>10</i>(1), Article 318. <a href=\"https://doi.org/10.1038/s41597-023-02199-8\">https://doi.org/10.1038/s41597-023-02199-8</a>","mla":"Sherif, Mohamed Ahmed, et al. “IngridKG: A FAIR Knowledge Graph of Graffiti.” <i>Scientific Data</i>, vol. 10, no. 1, 318, Springer Science and Business Media LLC, 2023, doi:<a href=\"https://doi.org/10.1038/s41597-023-02199-8\">10.1038/s41597-023-02199-8</a>.","bibtex":"@article{Sherif_da Silva_Pestryakova_Ahmed_Niemann_Ngomo_2023, title={IngridKG: A FAIR Knowledge Graph of Graffiti}, volume={10}, DOI={<a href=\"https://doi.org/10.1038/s41597-023-02199-8\">10.1038/s41597-023-02199-8</a>}, number={1318}, journal={Scientific Data}, publisher={Springer Science and Business Media LLC}, 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}, year={2023} }","short":"M.A. Sherif, A.A.M. da Silva, S. Pestryakova, A.F. Ahmed, S. Niemann, A.-C.N. Ngomo, Scientific Data 10 (2023).","ama":"Sherif MA, da Silva AAM, Pestryakova S, Ahmed AF, Niemann S, Ngomo A-CN. IngridKG: A FAIR Knowledge Graph of Graffiti. <i>Scientific Data</i>. 2023;10(1). doi:<a href=\"https://doi.org/10.1038/s41597-023-02199-8\">10.1038/s41597-023-02199-8</a>","chicago":"Sherif, Mohamed Ahmed, Ana Alexandra Morim da Silva, Svetlana Pestryakova, Abdullah Fathi Ahmed, Sven Niemann, and Axel-Cyrille Ngonga Ngomo. “IngridKG: A FAIR Knowledge Graph of Graffiti.” <i>Scientific Data</i> 10, no. 1 (2023). <a href=\"https://doi.org/10.1038/s41597-023-02199-8\">https://doi.org/10.1038/s41597-023-02199-8</a>.","ieee":"M. A. Sherif, A. A. M. da Silva, S. Pestryakova, A. F. Ahmed, S. Niemann, and A.-C. N. Ngomo, “IngridKG: A FAIR Knowledge Graph of Graffiti,” <i>Scientific Data</i>, vol. 10, no. 1, Art. no. 318, 2023, doi: <a href=\"https://doi.org/10.1038/s41597-023-02199-8\">10.1038/s41597-023-02199-8</a>."},"intvolume":"        10","date_updated":"2023-06-06T09:17:10Z","author":[{"full_name":"Sherif, Mohamed Ahmed","last_name":"Sherif","first_name":"Mohamed Ahmed"},{"first_name":"Ana Alexandra Morim","full_name":"da Silva, Ana Alexandra Morim","last_name":"da Silva"},{"full_name":"Pestryakova, Svetlana","last_name":"Pestryakova","first_name":"Svetlana"},{"last_name":"Ahmed","full_name":"Ahmed, Abdullah Fathi","first_name":"Abdullah Fathi"},{"first_name":"Sven","last_name":"Niemann","full_name":"Niemann, Sven"},{"last_name":"Ngomo","full_name":"Ngomo, Axel-Cyrille Ngonga","first_name":"Axel-Cyrille Ngonga"}],"volume":10,"doi":"10.1038/s41597-023-02199-8","type":"journal_article","status":"public","project":[{"_id":"104","name":"INGRID: INGRID: Informationssystem Graffiti in Deutschland","grant_number":"289287267"}],"_id":"45484","user_id":"6593","department":[{"_id":"574"},{"_id":"115"}],"article_number":"318","issue":"1","year":"2023","publisher":"Springer Science and Business Media LLC","date_created":"2023-06-06T09:12:39Z","title":"IngridKG: A FAIR Knowledge Graph of Graffiti","publication":"Scientific Data","abstract":[{"lang":"eng","text":"<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>"}],"keyword":["Library and Information Sciences","Statistics","Probability and Uncertainty","Computer Science Applications","Education","Information Systems","Statistics and Probability"],"language":[{"iso":"eng"}]},{"title":"A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING","doi":"10.52041/serj.v21i2.46","publisher":"International Association for Statistical Education","date_updated":"2022-07-08T12:07:46Z","volume":21,"author":[{"first_name":"SUSANNE","last_name":"PODWORNY","full_name":"PODWORNY, SUSANNE"},{"first_name":"Sven","id":"58465","full_name":"Hüsing, Sven","last_name":"Hüsing"},{"first_name":"CARSTEN","full_name":"SCHULTE, CARSTEN","last_name":"SCHULTE"}],"date_created":"2022-07-08T12:06:48Z","year":"2022","intvolume":"        21","citation":{"chicago":"PODWORNY, SUSANNE, Sven Hüsing, and CARSTEN SCHULTE. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i> 21, no. 2 (2022). <a href=\"https://doi.org/10.52041/serj.v21i2.46\">https://doi.org/10.52041/serj.v21i2.46</a>.","ieee":"S. PODWORNY, S. Hüsing, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, vol. 21, no. 2, Art. no. 6, 2022, doi: <a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>.","ama":"PODWORNY S, Hüsing S, SCHULTE C. A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>. 2022;21(2). doi:<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>","short":"S. PODWORNY, S. Hüsing, C. SCHULTE, STATISTICS EDUCATION RESEARCH JOURNAL 21 (2022).","mla":"PODWORNY, SUSANNE, et al. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, vol. 21, no. 2, 6, International Association for Statistical Education, 2022, doi:<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>.","bibtex":"@article{PODWORNY_Hüsing_SCHULTE_2022, title={A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}, volume={21}, DOI={<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>}, number={26}, journal={STATISTICS EDUCATION RESEARCH JOURNAL}, publisher={International Association for Statistical Education}, author={PODWORNY, SUSANNE and Hüsing, Sven and SCHULTE, CARSTEN}, year={2022} }","apa":"PODWORNY, S., Hüsing, S., &#38; SCHULTE, C. (2022). A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, <i>21</i>(2), Article 6. <a href=\"https://doi.org/10.52041/serj.v21i2.46\">https://doi.org/10.52041/serj.v21i2.46</a>"},"publication_identifier":{"issn":["1570-1824"]},"publication_status":"published","issue":"2","keyword":["Education","Statistics and Probability"],"article_number":"6","language":[{"iso":"eng"}],"_id":"32335","department":[{"_id":"67"}],"user_id":"58465","abstract":[{"text":"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.","lang":"eng"}],"status":"public","publication":"STATISTICS EDUCATION RESEARCH JOURNAL","type":"journal_article"},{"language":[{"iso":"ger"}],"keyword":["Strategy and Management","Applied Psychology","Social Sciences (miscellaneous)","Education","Communication","Statistics and Probability"],"abstract":[{"text":"<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>","lang":"eng"}],"publication":"Zeitschrift für Evaluation","title":"Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung","date_created":"2022-12-05T13:25:58Z","publisher":"Waxmann","year":"2022","issue":"02","quality_controlled":"1","user_id":"69383","department":[{"_id":"4"}],"_id":"34200","status":"public","type":"journal_article","doi":"10.31244/zfe.2022.02.02","author":[{"first_name":"Thiemo","last_name":"Bloh","full_name":"Bloh, Thiemo"}],"volume":2022,"date_updated":"2022-12-05T13:26:35Z","citation":{"apa":"Bloh, T. (2022). Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung. <i>Zeitschrift für Evaluation</i>, <i>2022</i>(02), 193–215. <a href=\"https://doi.org/10.31244/zfe.2022.02.02\">https://doi.org/10.31244/zfe.2022.02.02</a>","short":"T. Bloh, Zeitschrift für Evaluation 2022 (2022) 193–215.","bibtex":"@article{Bloh_2022, title={Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung}, volume={2022}, DOI={<a href=\"https://doi.org/10.31244/zfe.2022.02.02\">10.31244/zfe.2022.02.02</a>}, number={02}, journal={Zeitschrift für Evaluation}, publisher={Waxmann}, author={Bloh, Thiemo}, year={2022}, pages={193–215} }","mla":"Bloh, Thiemo. “Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung.” <i>Zeitschrift für Evaluation</i>, vol. 2022, no. 02, Waxmann, 2022, pp. 193–215, doi:<a href=\"https://doi.org/10.31244/zfe.2022.02.02\">10.31244/zfe.2022.02.02</a>.","ama":"Bloh T. Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung. <i>Zeitschrift für Evaluation</i>. 2022;2022(02):193-215. doi:<a href=\"https://doi.org/10.31244/zfe.2022.02.02\">10.31244/zfe.2022.02.02</a>","chicago":"Bloh, Thiemo. “Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung.” <i>Zeitschrift für Evaluation</i> 2022, no. 02 (2022): 193–215. <a href=\"https://doi.org/10.31244/zfe.2022.02.02\">https://doi.org/10.31244/zfe.2022.02.02</a>.","ieee":"T. Bloh, “Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung,” <i>Zeitschrift für Evaluation</i>, vol. 2022, no. 02, pp. 193–215, 2022, doi: <a href=\"https://doi.org/10.31244/zfe.2022.02.02\">10.31244/zfe.2022.02.02</a>."},"intvolume":"      2022","page":"193-215","publication_status":"published","publication_identifier":{"issn":["1619-5515","2699-5506"]}},{"abstract":[{"text":"<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>","lang":"eng"}],"status":"public","type":"journal_article","publication":"STATISTICS EDUCATION RESEARCH JOURNAL","article_number":"6","keyword":["Education","Statistics and Probability"],"_id":"48108","user_id":"30619","year":"2022","citation":{"apa":"PODWORNY, S., HÜSING, S., &#38; SCHULTE, C. (2022). A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, <i>21</i>(2), Article 6. <a href=\"https://doi.org/10.52041/serj.v21i2.46\">https://doi.org/10.52041/serj.v21i2.46</a>","bibtex":"@article{PODWORNY_HÜSING_SCHULTE_2022, title={A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}, volume={21}, DOI={<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>}, number={26}, journal={STATISTICS EDUCATION RESEARCH JOURNAL}, publisher={International Association for Statistical Education}, author={PODWORNY, SUSANNE and HÜSING, SVEN and SCHULTE, CARSTEN}, year={2022} }","short":"S. PODWORNY, S. HÜSING, C. SCHULTE, STATISTICS EDUCATION RESEARCH JOURNAL 21 (2022).","mla":"PODWORNY, SUSANNE, et al. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, vol. 21, no. 2, 6, International Association for Statistical Education, 2022, doi:<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>.","ama":"PODWORNY S, HÜSING S, SCHULTE C. A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>. 2022;21(2). doi:<a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>","chicago":"PODWORNY, SUSANNE, SVEN HÜSING, and CARSTEN SCHULTE. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i> 21, no. 2 (2022). <a href=\"https://doi.org/10.52041/serj.v21i2.46\">https://doi.org/10.52041/serj.v21i2.46</a>.","ieee":"S. PODWORNY, S. HÜSING, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,” <i>STATISTICS EDUCATION RESEARCH JOURNAL</i>, vol. 21, no. 2, Art. no. 6, 2022, doi: <a href=\"https://doi.org/10.52041/serj.v21i2.46\">10.52041/serj.v21i2.46</a>."},"intvolume":"        21","publication_status":"published","publication_identifier":{"issn":["1570-1824"]},"issue":"2","title":"A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING","doi":"10.52041/serj.v21i2.46","date_updated":"2023-10-17T06:01:58Z","publisher":"International Association for Statistical Education","author":[{"full_name":"PODWORNY, SUSANNE","last_name":"PODWORNY","first_name":"SUSANNE"},{"full_name":"HÜSING, SVEN","last_name":"HÜSING","first_name":"SVEN"},{"full_name":"SCHULTE, CARSTEN","last_name":"SCHULTE","first_name":"CARSTEN"}],"date_created":"2023-10-17T05:59:38Z","volume":21},{"doi":"10.1016/j.seps.2021.101189","title":"A private sustainable partner selection model for green public-private partnerships and regional economic development","date_created":"2024-04-04T15:50:16Z","author":[{"first_name":"Madjid","last_name":"Tavana","id":"31858","full_name":"Tavana, Madjid"},{"last_name":"Khalili Nasr","full_name":"Khalili Nasr, Arash","first_name":"Arash"},{"first_name":"Hassan","last_name":"Mina","full_name":"Mina, Hassan"},{"first_name":"Jerzy","full_name":"Michnik, Jerzy","last_name":"Michnik"}],"volume":83,"publisher":"Elsevier BV","date_updated":"2024-04-15T13:16:33Z","citation":{"chicago":"Tavana, Madjid, Arash Khalili Nasr, Hassan Mina, and Jerzy Michnik. “A Private Sustainable Partner Selection Model for Green Public-Private Partnerships and Regional Economic Development.” <i>Socio-Economic Planning Sciences</i> 83 (2022). <a href=\"https://doi.org/10.1016/j.seps.2021.101189\">https://doi.org/10.1016/j.seps.2021.101189</a>.","ieee":"M. Tavana, A. Khalili Nasr, H. Mina, and J. Michnik, “A private sustainable partner selection model for green public-private partnerships and regional economic development,” <i>Socio-Economic Planning Sciences</i>, vol. 83, Art. no. 101189, 2022, doi: <a href=\"https://doi.org/10.1016/j.seps.2021.101189\">10.1016/j.seps.2021.101189</a>.","ama":"Tavana M, Khalili Nasr A, Mina H, Michnik J. A private sustainable partner selection model for green public-private partnerships and regional economic development. <i>Socio-Economic Planning Sciences</i>. 2022;83. doi:<a href=\"https://doi.org/10.1016/j.seps.2021.101189\">10.1016/j.seps.2021.101189</a>","short":"M. Tavana, A. Khalili Nasr, H. Mina, J. Michnik, Socio-Economic Planning Sciences 83 (2022).","bibtex":"@article{Tavana_Khalili Nasr_Mina_Michnik_2022, title={A private sustainable partner selection model for green public-private partnerships and regional economic development}, volume={83}, DOI={<a href=\"https://doi.org/10.1016/j.seps.2021.101189\">10.1016/j.seps.2021.101189</a>}, number={101189}, journal={Socio-Economic Planning Sciences}, publisher={Elsevier BV}, author={Tavana, Madjid and Khalili Nasr, Arash and Mina, Hassan and Michnik, Jerzy}, year={2022} }","mla":"Tavana, Madjid, et al. “A Private Sustainable Partner Selection Model for Green Public-Private Partnerships and Regional Economic Development.” <i>Socio-Economic Planning Sciences</i>, vol. 83, 101189, Elsevier BV, 2022, doi:<a href=\"https://doi.org/10.1016/j.seps.2021.101189\">10.1016/j.seps.2021.101189</a>.","apa":"Tavana, M., Khalili Nasr, A., Mina, H., &#38; Michnik, J. (2022). A private sustainable partner selection model for green public-private partnerships and regional economic development. <i>Socio-Economic Planning Sciences</i>, <i>83</i>, Article 101189. <a href=\"https://doi.org/10.1016/j.seps.2021.101189\">https://doi.org/10.1016/j.seps.2021.101189</a>"},"intvolume":"        83","year":"2022","publication_status":"published","publication_identifier":{"issn":["0038-0121"]},"language":[{"iso":"eng"}],"article_number":"101189","keyword":["Management Science and Operations Research","Statistics","Probability and Uncertainty","Strategy and Management","Economics and Econometrics","Geography","Planning and Development"],"user_id":"51811","department":[{"_id":"277"}],"_id":"53238","status":"public","type":"journal_article","publication":"Socio-Economic Planning Sciences"},{"keyword":["Education","Statistics and Probability"],"article_number":"1","language":[{"iso":"eng"}],"_id":"34920","department":[{"_id":"363"}],"user_id":"37888","abstract":[{"text":"<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>","lang":"eng"}],"status":"public","publication":"Statistics Education Research Journal","type":"journal_article","title":"Editorial: Research on Data Science Education","doi":"10.52041/serj.v21i2.606","publisher":"International Association for Statistical Education","date_updated":"2024-04-18T09:45:53Z","volume":21,"date_created":"2022-12-23T11:20:39Z","author":[{"first_name":"Rolf","last_name":"Biehler","id":"16274","full_name":"Biehler, Rolf"},{"first_name":"Richard","last_name":"De Veaux","full_name":"De Veaux, Richard"},{"first_name":"Joachim","last_name":"Engel","full_name":"Engel, Joachim"},{"first_name":"Sibel","full_name":"Kazak, Sibel","last_name":"Kazak"},{"first_name":"Daniel","last_name":"Frischemeier","full_name":"Frischemeier, Daniel"}],"year":"2022","intvolume":"        21","citation":{"apa":"Biehler, R., De Veaux, R., Engel, J., Kazak, S., &#38; Frischemeier, D. (2022). Editorial: Research on Data Science Education. <i>Statistics Education Research Journal</i>, <i>21</i>(2), Article 1. <a href=\"https://doi.org/10.52041/serj.v21i2.606\">https://doi.org/10.52041/serj.v21i2.606</a>","mla":"Biehler, Rolf, et al. “Editorial: Research on Data Science Education.” <i>Statistics Education Research Journal</i>, vol. 21, no. 2, 1, International Association for Statistical Education, 2022, doi:<a href=\"https://doi.org/10.52041/serj.v21i2.606\">10.52041/serj.v21i2.606</a>.","short":"R. Biehler, R. De Veaux, J. Engel, S. Kazak, D. Frischemeier, Statistics Education Research Journal 21 (2022).","bibtex":"@article{Biehler_De Veaux_Engel_Kazak_Frischemeier_2022, title={Editorial: Research on Data Science Education}, volume={21}, DOI={<a href=\"https://doi.org/10.52041/serj.v21i2.606\">10.52041/serj.v21i2.606</a>}, number={21}, journal={Statistics Education Research Journal}, publisher={International Association for Statistical Education}, author={Biehler, Rolf and De Veaux, Richard and Engel, Joachim and Kazak, Sibel and Frischemeier, Daniel}, year={2022} }","ieee":"R. Biehler, R. De Veaux, J. Engel, S. Kazak, and D. Frischemeier, “Editorial: Research on Data Science Education,” <i>Statistics Education Research Journal</i>, vol. 21, no. 2, Art. no. 1, 2022, doi: <a href=\"https://doi.org/10.52041/serj.v21i2.606\">10.52041/serj.v21i2.606</a>.","chicago":"Biehler, Rolf, Richard De Veaux, Joachim Engel, Sibel Kazak, and Daniel Frischemeier. “Editorial: Research on Data Science Education.” <i>Statistics Education Research Journal</i> 21, no. 2 (2022). <a href=\"https://doi.org/10.52041/serj.v21i2.606\">https://doi.org/10.52041/serj.v21i2.606</a>.","ama":"Biehler R, De Veaux R, Engel J, Kazak S, Frischemeier D. Editorial: Research on Data Science Education. <i>Statistics Education Research Journal</i>. 2022;21(2). doi:<a href=\"https://doi.org/10.52041/serj.v21i2.606\">10.52041/serj.v21i2.606</a>"},"publication_identifier":{"issn":["1570-1824"]},"publication_status":"published","issue":"2"},{"title":"The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series","doi":"10.32614/rj-2022-017","publisher":"The R Foundation","date_updated":"2024-06-12T12:57:13Z","volume":14,"author":[{"first_name":"Yuanhua","last_name":"Feng","full_name":"Feng, Yuanhua"},{"full_name":"Gries, Thomas","last_name":"Gries","first_name":"Thomas"},{"first_name":"Sebastian","last_name":"Letmathe","full_name":"Letmathe, Sebastian"},{"last_name":"Schulz","full_name":"Schulz, Dominik","first_name":"Dominik"}],"date_created":"2023-12-21T12:09:31Z","year":"2022","intvolume":"        14","page":"182-195","citation":{"apa":"Feng, Y., Gries, T., Letmathe, S., &#38; Schulz, D. (2022). The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>, <i>14</i>(1), 182–195. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>","short":"Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.","bibtex":"@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>}, number={1}, journal={The R Journal}, publisher={The R Foundation}, author={Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}, year={2022}, pages={182–195} }","mla":"Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i>, vol. 14, no. 1, The R Foundation, 2022, pp. 182–95, doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>.","ama":"Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>. 2022;14(1):182-195. doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>","chicago":"Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i> 14, no. 1 (2022): 182–95. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>.","ieee":"Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series,” <i>The R Journal</i>, vol. 14, no. 1, pp. 182–195, 2022, doi: <a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>."},"publication_identifier":{"issn":["2073-4859"]},"publication_status":"published","issue":"1","keyword":["Statistics","Probability and Uncertainty","Numerical Analysis","Statistics and Probability"],"language":[{"iso":"eng"}],"_id":"50024","department":[{"_id":"475"},{"_id":"19"},{"_id":"200"}],"user_id":"186","status":"public","publication":"The R Journal","type":"journal_article"},{"doi":"10.52041/serj.v21i2.61","title":"Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks","date_created":"2023-01-10T08:48:23Z","author":[{"first_name":"Franz Yannik","full_name":"Fleischer, Franz Yannik","id":"42660","last_name":"Fleischer","orcid":"https://orcid.org/0000-0003-0318-0329"},{"last_name":"Biehler","full_name":"Biehler, Rolf","id":"16274","first_name":"Rolf"},{"first_name":"Carsten","last_name":"Schulte","id":"60311","full_name":"Schulte, Carsten"}],"volume":21,"publisher":"International Association for Statistical Education","date_updated":"2024-08-21T10:04:41Z","citation":{"ama":"Fleischer FY, Biehler R, Schulte C. Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. <i>Statistics Education Research Journal</i>. 2022;21(2). doi:<a href=\"https://doi.org/10.52041/serj.v21i2.61\">10.52041/serj.v21i2.61</a>","chicago":"Fleischer, Franz Yannik, Rolf Biehler, and Carsten Schulte. “Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks.” <i>Statistics Education Research Journal</i> 21, no. 2 (2022). <a href=\"https://doi.org/10.52041/serj.v21i2.61\">https://doi.org/10.52041/serj.v21i2.61</a>.","ieee":"F. Y. Fleischer, R. Biehler, and C. Schulte, “Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks,” <i>Statistics Education Research Journal</i>, vol. 21, no. 2, Art. no. 7, 2022, doi: <a href=\"https://doi.org/10.52041/serj.v21i2.61\">10.52041/serj.v21i2.61</a>.","bibtex":"@article{Fleischer_Biehler_Schulte_2022, title={Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}, volume={21}, DOI={<a href=\"https://doi.org/10.52041/serj.v21i2.61\">10.52041/serj.v21i2.61</a>}, number={27}, journal={Statistics Education Research Journal}, publisher={International Association for Statistical Education}, author={Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}, year={2022} }","mla":"Fleischer, Franz Yannik, et al. “Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks.” <i>Statistics Education Research Journal</i>, vol. 21, no. 2, 7, International Association for Statistical Education, 2022, doi:<a href=\"https://doi.org/10.52041/serj.v21i2.61\">10.52041/serj.v21i2.61</a>.","short":"F.Y. Fleischer, R. Biehler, C. Schulte, Statistics Education Research Journal 21 (2022).","apa":"Fleischer, F. Y., Biehler, R., &#38; Schulte, C. (2022). Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. <i>Statistics Education Research Journal</i>, <i>21</i>(2), Article 7. <a href=\"https://doi.org/10.52041/serj.v21i2.61\">https://doi.org/10.52041/serj.v21i2.61</a>"},"intvolume":"        21","year":"2022","issue":"2","publication_status":"published","publication_identifier":{"issn":["1570-1824"]},"language":[{"iso":"eng"}],"article_number":"7","keyword":["Education","Statistics and Probability"],"user_id":"37888","department":[{"_id":"363"},{"_id":"67"}],"_id":"35672","status":"public","abstract":[{"text":"<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>","lang":"eng"}],"type":"journal_article","publication":"Statistics Education Research Journal"},{"status":"public","type":"journal_article","publication":"The R Journal","keyword":["Statistics","Probability and Uncertainty","Numerical Analysis","Statistics and Probability"],"language":[{"iso":"eng"}],"_id":"50025","user_id":"186","year":"2022","citation":{"apa":"Feng, Y., Gries, T., Letmathe, S., &#38; Schulz, D. (2022). The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>, <i>14</i>(1), 182–195. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>","mla":"Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i>, vol. 14, no. 1, The R Foundation, 2022, pp. 182–95, doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>.","bibtex":"@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>}, number={1}, journal={The R Journal}, publisher={The R Foundation}, author={Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}, year={2022}, pages={182–195} }","short":"Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.","ieee":"Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series,” <i>The R Journal</i>, vol. 14, no. 1, pp. 182–195, 2022, doi: <a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>.","chicago":"Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i> 14, no. 1 (2022): 182–95. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>.","ama":"Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>. 2022;14(1):182-195. doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>"},"intvolume":"        14","page":"182-195","publication_status":"published","publication_identifier":{"issn":["2073-4859"]},"issue":"1","title":"The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series","doi":"10.32614/rj-2022-017","publisher":"The R Foundation","date_updated":"2025-11-10T09:32:36Z","date_created":"2023-12-21T12:09:53Z","author":[{"id":"20760","full_name":"Feng, Yuanhua","last_name":"Feng","first_name":"Yuanhua"},{"first_name":"Thomas","full_name":"Gries, Thomas","id":"186","last_name":"Gries"},{"full_name":"Letmathe, Sebastian","last_name":"Letmathe","first_name":"Sebastian"},{"last_name":"Schulz","full_name":"Schulz, Dominik","first_name":"Dominik"}],"volume":14},{"doi":"10.1088/1742-5468/abe6fd","author":[{"first_name":"Lennart","full_name":"Dabelow, Lennart","last_name":"Dabelow"},{"first_name":"Stefano","last_name":"Bo","full_name":"Bo, Stefano"},{"full_name":"Eichhorn, Ralf","last_name":"Eichhorn","first_name":"Ralf"}],"volume":2021,"date_updated":"2022-06-28T07:28:14Z","citation":{"short":"L. Dabelow, S. Bo, R. Eichhorn, Journal of Statistical Mechanics: Theory and Experiment 2021 (2021).","mla":"Dabelow, Lennart, et al. “How Irreversible Are Steady-State Trajectories of a Trapped Active Particle?” <i>Journal of Statistical Mechanics: Theory and Experiment</i>, vol. 2021, no. 3, 033216, IOP Publishing, 2021, doi:<a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">10.1088/1742-5468/abe6fd</a>.","bibtex":"@article{Dabelow_Bo_Eichhorn_2021, title={How irreversible are steady-state trajectories of a trapped active particle?}, volume={2021}, DOI={<a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">10.1088/1742-5468/abe6fd</a>}, number={3033216}, journal={Journal of Statistical Mechanics: Theory and Experiment}, publisher={IOP Publishing}, author={Dabelow, Lennart and Bo, Stefano and Eichhorn, Ralf}, year={2021} }","apa":"Dabelow, L., Bo, S., &#38; Eichhorn, R. (2021). How irreversible are steady-state trajectories of a trapped active particle? <i>Journal of Statistical Mechanics: Theory and Experiment</i>, <i>2021</i>(3), Article 033216. <a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">https://doi.org/10.1088/1742-5468/abe6fd</a>","ama":"Dabelow L, Bo S, Eichhorn R. How irreversible are steady-state trajectories of a trapped active particle? <i>Journal of Statistical Mechanics: Theory and Experiment</i>. 2021;2021(3). doi:<a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">10.1088/1742-5468/abe6fd</a>","chicago":"Dabelow, Lennart, Stefano Bo, and Ralf Eichhorn. “How Irreversible Are Steady-State Trajectories of a Trapped Active Particle?” <i>Journal of Statistical Mechanics: Theory and Experiment</i> 2021, no. 3 (2021). <a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">https://doi.org/10.1088/1742-5468/abe6fd</a>.","ieee":"L. Dabelow, S. Bo, and R. Eichhorn, “How irreversible are steady-state trajectories of a trapped active particle?,” <i>Journal of Statistical Mechanics: Theory and Experiment</i>, vol. 2021, no. 3, Art. no. 033216, 2021, doi: <a href=\"https://doi.org/10.1088/1742-5468/abe6fd\">10.1088/1742-5468/abe6fd</a>."},"intvolume":"      2021","publication_status":"published","publication_identifier":{"issn":["1742-5468"]},"article_number":"033216","user_id":"15278","department":[{"_id":"27"}],"project":[{"_id":"52","name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"_id":"32243","status":"public","type":"journal_article","title":"How irreversible are steady-state trajectories of a trapped active particle?","date_created":"2022-06-28T07:27:41Z","publisher":"IOP Publishing","year":"2021","issue":"3","language":[{"iso":"eng"}],"keyword":["Statistics","Probability and Uncertainty","Statistics and Probability","Statistical and Nonlinear Physics"],"abstract":[{"text":"<jats:title>Abstract</jats:title>\r\n               <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>","lang":"eng"}],"publication":"Journal of Statistical Mechanics: Theory and Experiment"},{"volume":4,"author":[{"first_name":"Jan","orcid":"0000-0002-8705-6992","last_name":"Kessler","id":"65425","full_name":"Kessler, Jan"},{"full_name":"Calcavecchia, Francesco","last_name":"Calcavecchia","first_name":"Francesco"},{"last_name":"Kühne","id":"49079","full_name":"Kühne, Thomas","first_name":"Thomas"}],"date_updated":"2022-10-10T08:15:37Z","doi":"10.1002/adts.202000269","publication_identifier":{"issn":["2513-0390","2513-0390"]},"publication_status":"published","intvolume":"         4","citation":{"chicago":"Kessler, Jan, Francesco Calcavecchia, and Thomas Kühne. “Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo.” <i>Advanced Theory and Simulations</i> 4, no. 4 (2021). <a href=\"https://doi.org/10.1002/adts.202000269\">https://doi.org/10.1002/adts.202000269</a>.","ieee":"J. Kessler, F. Calcavecchia, and T. Kühne, “Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo,” <i>Advanced Theory and Simulations</i>, vol. 4, no. 4, Art. no. 2000269, 2021, doi: <a href=\"https://doi.org/10.1002/adts.202000269\">10.1002/adts.202000269</a>.","ama":"Kessler J, Calcavecchia F, Kühne T. Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo. <i>Advanced Theory and Simulations</i>. 2021;4(4). doi:<a href=\"https://doi.org/10.1002/adts.202000269\">10.1002/adts.202000269</a>","apa":"Kessler, J., Calcavecchia, F., &#38; Kühne, T. (2021). Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo. <i>Advanced Theory and Simulations</i>, <i>4</i>(4), Article 2000269. <a href=\"https://doi.org/10.1002/adts.202000269\">https://doi.org/10.1002/adts.202000269</a>","mla":"Kessler, Jan, et al. “Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo.” <i>Advanced Theory and Simulations</i>, vol. 4, no. 4, 2000269, Wiley, 2021, doi:<a href=\"https://doi.org/10.1002/adts.202000269\">10.1002/adts.202000269</a>.","bibtex":"@article{Kessler_Calcavecchia_Kühne_2021, title={Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo}, volume={4}, DOI={<a href=\"https://doi.org/10.1002/adts.202000269\">10.1002/adts.202000269</a>}, number={42000269}, journal={Advanced Theory and Simulations}, publisher={Wiley}, author={Kessler, Jan and Calcavecchia, Francesco and Kühne, Thomas}, year={2021} }","short":"J. Kessler, F. Calcavecchia, T. Kühne, Advanced Theory and Simulations 4 (2021)."},"department":[{"_id":"613"}],"user_id":"71051","_id":"33649","article_number":"2000269","type":"journal_article","status":"public","date_created":"2022-10-10T08:15:23Z","publisher":"Wiley","title":"Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo","issue":"4","year":"2021","language":[{"iso":"eng"}],"keyword":["Multidisciplinary","Modeling and Simulation","Numerical Analysis","Statistics and Probability"],"publication":"Advanced Theory and Simulations"},{"publication":"Teaching Statistics","type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"<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>"}],"user_id":"30619","_id":"48109","language":[{"iso":"eng"}],"keyword":["Education","Statistics and Probability"],"issue":"S1","publication_identifier":{"issn":["0141-982X","1467-9639"]},"publication_status":"published","intvolume":"        43","citation":{"apa":"Frischemeier, D., Biehler, R., Podworny, S., &#38; Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with &#60;scp&#62;CODAP&#60;/scp&#62; using survey data. <i>Teaching Statistics</i>, <i>43</i>(S1). <a href=\"https://doi.org/10.1111/test.12283\">https://doi.org/10.1111/test.12283</a>","mla":"Frischemeier, Daniel, et al. “A First Introduction to Data Science Education in Secondary Schools: Teaching and Learning about Data Exploration with &#60;scp&#62;CODAP&#60;/Scp&#62; Using Survey Data.” <i>Teaching Statistics</i>, vol. 43, no. S1, Wiley, 2021, doi:<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>.","short":"D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43 (2021).","bibtex":"@article{Frischemeier_Biehler_Podworny_Budde_2021, title={A first introduction to data science education in secondary schools: Teaching and learning about data exploration with &#60;scp&#62;CODAP&#60;/scp&#62; using survey data}, volume={43}, DOI={<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>}, number={S1}, journal={Teaching Statistics}, publisher={Wiley}, author={Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Budde, Lea}, year={2021} }","ieee":"D. Frischemeier, R. Biehler, S. Podworny, and L. Budde, “A first introduction to data science education in secondary schools: Teaching and learning about data exploration with &#60;scp&#62;CODAP&#60;/scp&#62; using survey data,” <i>Teaching Statistics</i>, vol. 43, no. S1, 2021, doi: <a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>.","chicago":"Frischemeier, Daniel, Rolf Biehler, Susanne Podworny, and Lea Budde. “A First Introduction to Data Science Education in Secondary Schools: Teaching and Learning about Data Exploration with &#60;scp&#62;CODAP&#60;/Scp&#62; Using Survey Data.” <i>Teaching Statistics</i> 43, no. S1 (2021). <a href=\"https://doi.org/10.1111/test.12283\">https://doi.org/10.1111/test.12283</a>.","ama":"Frischemeier D, Biehler R, Podworny S, Budde L. A first introduction to data science education in secondary schools: Teaching and learning about data exploration with &#60;scp&#62;CODAP&#60;/scp&#62; using survey data. <i>Teaching Statistics</i>. 2021;43(S1). doi:<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>"},"year":"2021","volume":43,"date_created":"2023-10-17T06:06:02Z","author":[{"first_name":"Daniel","full_name":"Frischemeier, Daniel","last_name":"Frischemeier"},{"first_name":"Rolf","full_name":"Biehler, Rolf","last_name":"Biehler"},{"last_name":"Podworny","full_name":"Podworny, Susanne","first_name":"Susanne"},{"full_name":"Budde, Lea","last_name":"Budde","first_name":"Lea"}],"publisher":"Wiley","date_updated":"2023-10-17T06:13:03Z","doi":"10.1111/test.12283","title":"A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <scp>CODAP</scp> using survey data"},{"type":"journal_article","status":"public","_id":"35751","user_id":"37888","department":[{"_id":"363"}],"publication_status":"published","publication_identifier":{"issn":["0141-982X","1467-9639"]},"citation":{"ama":"Frischemeier D, Biehler R, Podworny S, Budde L. A first introduction to data science education in secondary schools: Teaching and learning about data exploration with&#60;scp&#62;CODAP&#60;/scp&#62;using survey data. <i>Teaching Statistics</i>. 2021;43(S1):S182-S189. doi:<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>","ieee":"D. Frischemeier, R. Biehler, S. Podworny, and L. Budde, “A first introduction to data science education in secondary schools: Teaching and learning about data exploration with&#60;scp&#62;CODAP&#60;/scp&#62;using survey data,” <i>Teaching Statistics</i>, vol. 43, no. S1, pp. S182–S189, 2021, doi: <a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>.","chicago":"Frischemeier, Daniel, Rolf Biehler, Susanne Podworny, and Lea Budde. “A First Introduction to Data Science Education in Secondary Schools: Teaching and Learning about Data Exploration With&#60;scp&#62;CODAP&#60;/Scp&#62;using Survey Data.” <i>Teaching Statistics</i> 43, no. S1 (2021): S182–89. <a href=\"https://doi.org/10.1111/test.12283\">https://doi.org/10.1111/test.12283</a>.","mla":"Frischemeier, Daniel, et al. “A First Introduction to Data Science Education in Secondary Schools: Teaching and Learning about Data Exploration With&#60;scp&#62;CODAP&#60;/Scp&#62;using Survey Data.” <i>Teaching Statistics</i>, vol. 43, no. S1, Wiley, 2021, pp. S182–89, doi:<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>.","short":"D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43 (2021) S182–S189.","bibtex":"@article{Frischemeier_Biehler_Podworny_Budde_2021, title={A first introduction to data science education in secondary schools: Teaching and learning about data exploration with&#60;scp&#62;CODAP&#60;/scp&#62;using survey data}, volume={43}, DOI={<a href=\"https://doi.org/10.1111/test.12283\">10.1111/test.12283</a>}, number={S1}, journal={Teaching Statistics}, publisher={Wiley}, author={Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Budde, Lea}, year={2021}, pages={S182–S189} }","apa":"Frischemeier, D., Biehler, R., Podworny, S., &#38; Budde, L. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with&#60;scp&#62;CODAP&#60;/scp&#62;using survey data. <i>Teaching Statistics</i>, <i>43</i>(S1), S182–S189. <a href=\"https://doi.org/10.1111/test.12283\">https://doi.org/10.1111/test.12283</a>"},"intvolume":"        43","page":"S182-S189","date_updated":"2024-04-18T10:12:44Z","author":[{"full_name":"Frischemeier, Daniel","last_name":"Frischemeier","first_name":"Daniel"},{"first_name":"Rolf","last_name":"Biehler","full_name":"Biehler, Rolf","id":"16274"},{"first_name":"Susanne","id":"30619","full_name":"Podworny, Susanne","orcid":"0000-0002-6313-5987","last_name":"Podworny"},{"first_name":"Lea","full_name":"Budde, Lea","id":"32443","last_name":"Budde"}],"volume":43,"doi":"10.1111/test.12283","publication":"Teaching Statistics","keyword":["Education","Statistics and Probability"],"language":[{"iso":"eng"}],"issue":"S1","year":"2021","publisher":"Wiley","date_created":"2023-01-10T10:16:44Z","title":"A first introduction to data science education in secondary schools: Teaching and learning about data exploration with<scp>CODAP</scp>using survey data"},{"volume":43,"author":[{"full_name":"Biehler, Rolf","id":"16274","last_name":"Biehler","first_name":"Rolf"},{"first_name":"Franz Yannik","last_name":"Fleischer","orcid":"https://orcid.org/0000-0003-0318-0329","id":"42660","full_name":"Fleischer, Franz Yannik"}],"date_created":"2023-01-10T10:08:32Z","publisher":"Wiley","date_updated":"2024-08-21T10:05:05Z","doi":"10.1111/test.12279","title":"Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks","publication_identifier":{"issn":["0141-982X","1467-9639"]},"publication_status":"published","page":"S133-S142","intvolume":"        43","citation":{"ama":"Biehler R, Fleischer FY. Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. <i>Teaching Statistics</i>. 2021;43:S133-S142. doi:<a href=\"https://doi.org/10.1111/test.12279\">10.1111/test.12279</a>","ieee":"R. Biehler and F. Y. Fleischer, “Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks,” <i>Teaching Statistics</i>, vol. 43, pp. S133–S142, 2021, doi: <a href=\"https://doi.org/10.1111/test.12279\">10.1111/test.12279</a>.","chicago":"Biehler, Rolf, and Franz Yannik Fleischer. “Introducing Students to Machine Learning with Decision Trees Using CODAP and Jupyter Notebooks.” <i>Teaching Statistics</i> 43 (2021): S133–42. <a href=\"https://doi.org/10.1111/test.12279\">https://doi.org/10.1111/test.12279</a>.","apa":"Biehler, R., &#38; Fleischer, F. Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. <i>Teaching Statistics</i>, <i>43</i>, S133–S142. <a href=\"https://doi.org/10.1111/test.12279\">https://doi.org/10.1111/test.12279</a>","mla":"Biehler, Rolf, and Franz Yannik Fleischer. “Introducing Students to Machine Learning with Decision Trees Using CODAP and Jupyter Notebooks.” <i>Teaching Statistics</i>, vol. 43, Wiley, 2021, pp. S133–42, doi:<a href=\"https://doi.org/10.1111/test.12279\">10.1111/test.12279</a>.","bibtex":"@article{Biehler_Fleischer_2021, title={Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks}, volume={43}, DOI={<a href=\"https://doi.org/10.1111/test.12279\">10.1111/test.12279</a>}, journal={Teaching Statistics}, publisher={Wiley}, author={Biehler, Rolf and Fleischer, Franz Yannik}, year={2021}, pages={S133–S142} }","short":"R. Biehler, F.Y. Fleischer, Teaching Statistics 43 (2021) S133–S142."},"year":"2021","department":[{"_id":"363"}],"user_id":"37888","_id":"35737","language":[{"iso":"eng"}],"keyword":["Education","Statistics and Probability"],"publication":"Teaching Statistics","type":"journal_article","status":"public"},{"author":[{"first_name":"Papatya","last_name":"Duman","id":"72752","full_name":"Duman, Papatya"}],"date_created":"2023-06-09T15:31:17Z","volume":11,"date_updated":"2023-06-09T15:33:34Z","publisher":"MDPI AG","doi":"10.3390/g11010009","title":"Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story","issue":"1","publication_status":"published","publication_identifier":{"issn":["2073-4336"]},"citation":{"apa":"Duman, P. (2020). Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story. <i>Games</i>, <i>11</i>(1), Article 9. <a href=\"https://doi.org/10.3390/g11010009\">https://doi.org/10.3390/g11010009</a>","mla":"Duman, Papatya. “Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story.” <i>Games</i>, vol. 11, no. 1, 9, MDPI AG, 2020, doi:<a href=\"https://doi.org/10.3390/g11010009\">10.3390/g11010009</a>.","short":"P. Duman, Games 11 (2020).","bibtex":"@article{Duman_2020, title={Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story}, volume={11}, DOI={<a href=\"https://doi.org/10.3390/g11010009\">10.3390/g11010009</a>}, number={19}, journal={Games}, publisher={MDPI AG}, author={Duman, Papatya}, year={2020} }","ama":"Duman P. Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story. <i>Games</i>. 2020;11(1). doi:<a href=\"https://doi.org/10.3390/g11010009\">10.3390/g11010009</a>","ieee":"P. Duman, “Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story,” <i>Games</i>, vol. 11, no. 1, Art. no. 9, 2020, doi: <a href=\"https://doi.org/10.3390/g11010009\">10.3390/g11010009</a>.","chicago":"Duman, Papatya. “Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story.” <i>Games</i> 11, no. 1 (2020). <a href=\"https://doi.org/10.3390/g11010009\">https://doi.org/10.3390/g11010009</a>."},"intvolume":"        11","year":"2020","user_id":"72752","_id":"45561","language":[{"iso":"eng"}],"article_number":"9","keyword":["Applied Mathematics","Statistics","Probability and Uncertainty","Statistics and Probability"],"type":"journal_article","publication":"Games","status":"public","abstract":[{"text":"<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>","lang":"eng"}]},{"abstract":[{"lang":"eng","text":"This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners."}],"status":"public","type":"working_paper","keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"language":[{"iso":"eng"}],"_id":"20868","department":[{"_id":"275"}],"user_id":"46447","year":"2017","page":"84","citation":{"ama":"Sievers S, Klobucnik J, Miersch D. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>.; 2017. doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>","chicago":"Sievers, Sönke, Jan Klobucnik, and David Miersch. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>, 2017. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>.","ieee":"S. Sievers, J. Klobucnik, and D. Miersch, <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017.","short":"S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017.","bibtex":"@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, DOI={<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>}, author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }","mla":"Sievers, Sönke, et al. <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. 2017, doi:<a href=\"https://doi.org/10.2139/ssrn.2237757\">10.2139/ssrn.2237757</a>.","apa":"Sievers, S., Klobucnik, J., &#38; Miersch, D. (2017). <i>Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. <a href=\"https://doi.org/10.2139/ssrn.2237757\">https://doi.org/10.2139/ssrn.2237757</a>"},"jel":["C63","C52","C53","G33","M41"],"publication_status":"published","title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence","doi":"10.2139/ssrn.2237757","main_file_link":[{"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757"}],"date_updated":"2022-01-06T06:54:41Z","date_created":"2021-01-05T11:44:45Z","author":[{"first_name":"Sönke","last_name":"Sievers","id":"46447","full_name":"Sievers, Sönke"},{"first_name":"Jan","last_name":"Klobucnik","full_name":"Klobucnik, Jan"},{"first_name":"David","last_name":"Miersch","full_name":"Miersch, David"}]},{"publication":"SSRN Electronic Journal","type":"journal_article","abstract":[{"lang":"eng","text":"This study proposes a simple theoretical framework that allows for assessing financial distress up to five years in advance. We jointly model financial distress by using two of its key driving factors: declining cash-generating ability and insufficient liquidity reserves. The model is based on stochastic processes and incorporates firm-level and industry-sector developments. A large-scale empirical implementation for US-listed firms over the period of 1980-2010 shows important improvements in the discriminatory accuracy and demonstrates incremental information content beyond state-of-the-art accounting and market-based prediction models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners. "}],"status":"public","_id":"5199","department":[{"_id":"275"}],"user_id":"64756","keyword":["Financial distress prediction","probability of default","accounting information","stochastic processes","simulation"],"language":[{"iso":"eng"}],"publication_status":"published","year":"2017","jel":["C63","C52","C53","G33","M41"],"citation":{"chicago":"Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","ieee":"J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” <i>SSRN Electronic Journal</i>, 2017.","ama":"Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>. 2017.","mla":"Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.","bibtex":"@article{Klobucnik_Miersch_Sievers_2017, title={Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence}, journal={SSRN Electronic Journal}, author={Klobucnik, Jan and Miersch, David and Sievers, Sönke}, year={2017} }","short":"J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).","apa":"Klobucnik, J., Miersch, D., &#38; Sievers, S. (2017). Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>."},"date_updated":"2022-01-06T07:01:43Z","date_created":"2018-10-31T12:19:42Z","author":[{"first_name":"Jan","last_name":"Klobucnik","full_name":"Klobucnik, Jan"},{"first_name":"David","last_name":"Miersch","full_name":"Miersch, David"},{"full_name":"Sievers, Sönke","last_name":"Sievers","first_name":"Sönke"}],"title":"Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence"},{"title":"Measuring and modeling salience with the theory of visual attention.","date_created":"2018-12-10T07:05:04Z","year":"2017","issue":"6","language":[{"iso":"eng"}],"keyword":["Salience","Visual attention","Bayesian inference","Theory of visual attention","Computational modeling","Inference","Object Recognition","Theories","Visual Perception","Visual Attention","Luminance","Perceptual Orientation","Statistical Probability","Stimulus Salience","Computational Modeling"],"abstract":[{"lang":"eng","text":"For almost three decades, the theory of visual attention (TVA) has been successful in mathematically describing and explaining a wide variety of phenomena in visual selection and recognition with high quantitative precision. Interestingly, the influence of feature contrast on attention has been included in TVA only recently, although it has been extensively studied outside the TVA framework. The present approach further develops this extension of TVA’s scope by measuring and modeling salience. An empirical measure of salience is achieved by linking different (orientation and luminance) contrasts to a TVA parameter. In the modeling part, the function relating feature contrasts to salience is described mathematically and tested against alternatives by Bayesian model comparison. This model comparison reveals that the power function is an appropriate model of salience growth in the dimensions of orientation and luminance contrast. Furthermore, if contrasts from the two dimensions are comb"}],"publication":"Attention, Perception, & Psychophysics","doi":"10.3758/s13414-017-1325-6","author":[{"first_name":"Alexander","last_name":"Krüger","full_name":"Krüger, Alexander"},{"first_name":"Jan","full_name":"Tünnermann, Jan","last_name":"Tünnermann"},{"first_name":"Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau","id":"451","full_name":"Scharlau, Ingrid"}],"volume":79,"date_updated":"2022-06-06T14:08:05Z","citation":{"ama":"Krüger A, Tünnermann J, Scharlau I. Measuring and modeling salience with the theory of visual attention. <i>Attention, Perception, &#38; Psychophysics</i>. 2017;79(6):1593-1614. doi:<a href=\"https://doi.org/10.3758/s13414-017-1325-6\">10.3758/s13414-017-1325-6</a>","ieee":"A. Krüger, J. Tünnermann, and I. Scharlau, “Measuring and modeling salience with the theory of visual attention.,” <i>Attention, Perception, &#38; Psychophysics</i>, vol. 79, no. 6, pp. 1593–1614, 2017, doi: <a href=\"https://doi.org/10.3758/s13414-017-1325-6\">10.3758/s13414-017-1325-6</a>.","chicago":"Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Measuring and Modeling Salience with the Theory of Visual Attention.” <i>Attention, Perception, &#38; Psychophysics</i> 79, no. 6 (2017): 1593–1614. <a href=\"https://doi.org/10.3758/s13414-017-1325-6\">https://doi.org/10.3758/s13414-017-1325-6</a>.","apa":"Krüger, A., Tünnermann, J., &#38; Scharlau, I. (2017). Measuring and modeling salience with the theory of visual attention. <i>Attention, Perception, &#38; Psychophysics</i>, <i>79</i>(6), 1593–1614. <a href=\"https://doi.org/10.3758/s13414-017-1325-6\">https://doi.org/10.3758/s13414-017-1325-6</a>","bibtex":"@article{Krüger_Tünnermann_Scharlau_2017, title={Measuring and modeling salience with the theory of visual attention.}, volume={79}, DOI={<a href=\"https://doi.org/10.3758/s13414-017-1325-6\">10.3758/s13414-017-1325-6</a>}, number={6}, journal={Attention, Perception, &#38; Psychophysics}, author={Krüger, Alexander and Tünnermann, Jan and Scharlau, Ingrid}, year={2017}, pages={1593–1614} }","short":"A. Krüger, J. Tünnermann, I. Scharlau, Attention, Perception, &#38; Psychophysics 79 (2017) 1593–1614.","mla":"Krüger, Alexander, et al. “Measuring and Modeling Salience with the Theory of Visual Attention.” <i>Attention, Perception, &#38; Psychophysics</i>, vol. 79, no. 6, 2017, pp. 1593–614, doi:<a href=\"https://doi.org/10.3758/s13414-017-1325-6\">10.3758/s13414-017-1325-6</a>."},"page":"1593 - 1614","intvolume":"        79","publication_status":"published","publication_identifier":{"issn":["1943-3921"]},"article_type":"original","user_id":"42165","department":[{"_id":"424"}],"_id":"6075","status":"public","type":"journal_article"},{"language":[{"iso":"eng"}],"keyword":["salience","visual attention","Bayesian inference","theory of visual attention","computational modeling","Visual Attention","Computational Modeling","Inference","Judgment","Statistical Probability"],"publication":"Advances in Cognitive Psychology","abstract":[{"lang":"eng","text":"Particular differences between an object and its surrounding cause salience, guide attention, and improve performance in various tasks. While much research has been dedicated to identifying which feature dimensions contribute to salience, much less regard has been paid to the quantitative strength of the salience caused by feature differences. Only a few studies systematically related salience effects to a common salience measure, and they are partly outdated in the light of new findings on the time course of salience effects. We propose Bundesen’s Theory of Visual Attention (TV A) as a theoretical basis for measuring salience and introduce an empirical and modeling approach to link this theory to data retrieved from temporal-order judgments. With this procedure, TV A becomes applicable to a broad range of salience-related stimulus material. Three experiments with orientation pop-out displays demonstrate the feasibility of the method. A 4th experiment substantiates its applicability t"}],"date_created":"2018-12-10T07:04:15Z","title":"Fast and conspicuous? Quantifying salience with the theory of visual attention.","issue":"1","year":"2016","department":[{"_id":"424"}],"user_id":"42165","_id":"6071","funded_apc":"1","type":"journal_article","status":"public","volume":12,"author":[{"first_name":"Alexander","last_name":"Krüger","full_name":"Krüger, Alexander"},{"first_name":"Jan","full_name":"Tünnermann, Jan","last_name":"Tünnermann"},{"id":"451","full_name":"Scharlau, Ingrid","orcid":"0000-0003-2364-9489","last_name":"Scharlau","first_name":"Ingrid"}],"date_updated":"2022-06-06T16:21:09Z","oa":"1","doi":"10.5709/acp-0184-1","main_file_link":[{"url":"http://ac-psych.org/en/download-pdf/volume/12/issue/1/id/185","open_access":"1"}],"publication_identifier":{"issn":["1895-1171"]},"publication_status":"published","intvolume":"        12","page":"20 - 38","citation":{"mla":"Krüger, Alexander, et al. “Fast and Conspicuous? Quantifying Salience with the Theory of Visual Attention.” <i>Advances in Cognitive Psychology</i>, vol. 12, no. 1, 2016, pp. 20–38, doi:<a href=\"https://doi.org/10.5709/acp-0184-1\">10.5709/acp-0184-1</a>.","bibtex":"@article{Krüger_Tünnermann_Scharlau_2016, title={Fast and conspicuous? Quantifying salience with the theory of visual attention.}, volume={12}, DOI={<a href=\"https://doi.org/10.5709/acp-0184-1\">10.5709/acp-0184-1</a>}, number={1}, journal={Advances in Cognitive Psychology}, author={Krüger, Alexander and Tünnermann, Jan and Scharlau, Ingrid}, year={2016}, pages={20–38} }","short":"A. Krüger, J. Tünnermann, I. Scharlau, Advances in Cognitive Psychology 12 (2016) 20–38.","apa":"Krüger, A., Tünnermann, J., &#38; Scharlau, I. (2016). Fast and conspicuous? Quantifying salience with the theory of visual attention. <i>Advances in Cognitive Psychology</i>, <i>12</i>(1), 20–38. <a href=\"https://doi.org/10.5709/acp-0184-1\">https://doi.org/10.5709/acp-0184-1</a>","ama":"Krüger A, Tünnermann J, Scharlau I. Fast and conspicuous? Quantifying salience with the theory of visual attention. <i>Advances in Cognitive Psychology</i>. 2016;12(1):20-38. doi:<a href=\"https://doi.org/10.5709/acp-0184-1\">10.5709/acp-0184-1</a>","chicago":"Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Fast and Conspicuous? Quantifying Salience with the Theory of Visual Attention.” <i>Advances in Cognitive Psychology</i> 12, no. 1 (2016): 20–38. <a href=\"https://doi.org/10.5709/acp-0184-1\">https://doi.org/10.5709/acp-0184-1</a>.","ieee":"A. Krüger, J. Tünnermann, and I. Scharlau, “Fast and conspicuous? Quantifying salience with the theory of visual attention.,” <i>Advances in Cognitive Psychology</i>, vol. 12, no. 1, pp. 20–38, 2016, doi: <a href=\"https://doi.org/10.5709/acp-0184-1\">10.5709/acp-0184-1</a>."}},{"publication":"IEEE Transactions on Audio, Speech, and Language Processing","type":"journal_article","status":"public","abstract":[{"text":"In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.","lang":"eng"}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11862","language":[{"iso":"eng"}],"keyword":["Bayes methods","compensation","error statistics","reverberation","speech recognition","Bayesian feature enhancement","background noise","clean speech feature vectors","compensation","connected digits recognition task","error statistics","memory requirements","noisy reverberant data","posteriori probability density function","recursive formulation","reverberant logarithmic mel power spectral coefficients","robust automatic speech recognition","signal-to-noise ratios","time-variant observation","word error rate reduction","Robust automatic speech recognition","model-based Bayesian feature enhancement","observation model for reverberant and noisy speech","recursive observation model"],"issue":"8","intvolume":"        21","page":"1640-1652","citation":{"apa":"Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>21</i>(8), 1640–1652. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>","bibtex":"@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>}, number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013}, pages={1640–1652} }","mla":"Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, 2013, pp. 1640–52, doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>.","short":"V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 21 (2013) 1640–1652.","ama":"Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2013;21(8):1640-1652. doi:<a href=\"https://doi.org/10.1109/TASL.2013.2258013\">10.1109/TASL.2013.2258013</a>","chicago":"Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 21, no. 8 (2013): 1640–52. <a href=\"https://doi.org/10.1109/TASL.2013.2258013\">https://doi.org/10.1109/TASL.2013.2258013</a>.","ieee":"V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 21, no. 8, pp. 1640–1652, 2013."},"year":"2013","volume":21,"date_created":"2019-07-12T05:29:42Z","author":[{"last_name":"Leutnant","full_name":"Leutnant, Volker","first_name":"Volker"},{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_updated":"2022-01-06T06:51:11Z","doi":"10.1109/TASL.2013.2258013","title":"Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition"}]