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,”10.1038/s41597-023-02199-8__Scientific Data, vol. 10, no. 1, Art. no. 318, 2023, doi:10.1038/s41597-023-02199-8 .
LibreCat: /record/45484
DOI: 10.1038/s41597-023-02199-8

S. PODWORNY, S. Hüsing, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,”10.52041/serj.v21i2.46__STATISTICS EDUCATION RESEARCH JOURNAL, vol. 21, no. 2, Art. no. 6, 2022, doi:10.52041/serj.v21i2.46 .
LibreCat: /record/32335
DOI: 10.52041/serj.v21i2.46

T. Bloh, “Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung,”10.31244/zfe.2022.02.02__Zeitschrift für Evaluation, vol. 2022, no. 02, pp. 193–215, 2022, doi:10.31244/zfe.2022.02.02 .
LibreCat: /record/34200
DOI: 10.31244/zfe.2022.02.02

S. PODWORNY, S. HÜSING, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,”10.52041/serj.v21i2.46__STATISTICS EDUCATION RESEARCH JOURNAL, vol. 21, no. 2, Art. no. 6, 2022, doi:10.52041/serj.v21i2.46 .
LibreCat: /record/48108
DOI: 10.52041/serj.v21i2.46

Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series,”10.32614/rj-2022-017__The R Journal, vol. 14, no. 1, pp. 182–195, 2022, doi:10.32614/rj-2022-017 .
LibreCat: /record/50025
DOI: 10.32614/rj-2022-017

Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series,”10.32614/rj-2022-017__The R Journal, vol. 14, no. 1, pp. 182–195, 2022, doi:10.32614/rj-2022-017 .
LibreCat: /record/33666
DOI: 10.32614/rj-2022-017

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,”10.1016/j.seps.2021.101189__Socio-Economic Planning Sciences, vol. 83, Art. no. 101189, 2022, doi:10.1016/j.seps.2021.101189 .
LibreCat: /record/53238
DOI: 10.1016/j.seps.2021.101189

R. Biehler, R. De Veaux, J. Engel, S. Kazak, and D. Frischemeier, “Editorial: Research on Data Science Education,”10.52041/serj.v21i2.606__Statistics Education Research Journal, vol. 21, no. 2, Art. no. 1, 2022, doi:10.52041/serj.v21i2.606 .
LibreCat: /record/34920
DOI: 10.52041/serj.v21i2.606

Y. Fleischer, R. Biehler, and C. Schulte, “Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks,”10.52041/serj.v21i2.61__Statistics Education Research Journal, vol. 21, no. 2, Art. no. 7, 2022, doi:10.52041/serj.v21i2.61 .
LibreCat: /record/35672
DOI: 10.52041/serj.v21i2.61

L. Dabelow, S. Bo, and R. Eichhorn, “How irreversible are steady-state trajectories of a trapped active particle?,”10.1088/1742-5468/abe6fd__Journal of Statistical Mechanics: Theory and Experiment, vol. 2021, no. 3, Art. no. 033216, 2021, doi:10.1088/1742-5468/abe6fd .
LibreCat: /record/32243
DOI: 10.1088/1742-5468/abe6fd

J. Kessler, F. Calcavecchia, and T. Kühne, “Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo,”10.1002/adts.202000269__Advanced Theory and Simulations, vol. 4, no. 4, Art. no. 2000269, 2021, doi:10.1002/adts.202000269 .
LibreCat: /record/33649
DOI: 10.1002/adts.202000269

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 &;scp&;CODAP&;/scp&; using survey data,”10.1111/test.12283__Teaching Statistics, vol. 43, no. , 2021, doi:10.1111/test.12283 .
LibreCat: /record/48109
DOI: 10.1111/test.12283

R. Biehler and Y. Fleischer, “Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks,”10.1111/test.12279__Teaching Statistics, vol. 43, pp. 3–2, 2021, doi:10.1111/test.12279 .
LibreCat: /record/35737
DOI: 10.1111/test.12279

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&;scp&;CODAP&;/scp&;using survey data,”10.1111/test.12283__Teaching Statistics, vol. 43, no. , pp. 82–89, 2021, doi:10.1111/test.12283 .
LibreCat: /record/35751
DOI: 10.1111/test.12283

P. Duman, “Does Informational Equivalence Preserve Strategic Behavior? Experimental Results on Trockel’s Model of Selten’s Chain Store Story,”10.3390/g11010009__Games, vol. 11, no. 1, Art. no. 9, 2020, doi:10.3390/g11010009 .
LibreCat: /record/45561
DOI: 10.3390/g11010009

S. Sievers, J. Klobucnik, and D. Miersch, Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence. 2017.
LibreCat: /record/20868
DOI: 10.2139/ssrn.2237757

J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence,” SSRN Electronic Journal, 2017.
LibreCat: /record/5199

A. Krüger, J. Tünnermann, and I. Scharlau, “Measuring and modeling salience with the theory of visual attention.,”10.3758/s13414-017-1325-6__Attention, Perception, &; Psychophysics, vol. 79, no. 6, pp. 1593–1614, 2017, doi:10.3758/s13414-017-1325-6 .
LibreCat: /record/6075
DOI: 10.3758/s13414-017-1325-6

A. Krüger, J. Tünnermann, and I. Scharlau, “Fast and conspicuous? Quantifying salience with the theory of visual attention.,”10.5709/acp-0184-1__Advances in Cognitive Psychology, vol. 12, no. 1, pp. 20–38, 2016, doi:10.5709/acp-0184-1 .
LibreCat: /record/6071
DOI: 10.5709/acp-0184-1

V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 8, pp. 1640–1652, 2013.
LibreCat: /record/11862
DOI: 10.1109/TASL.2013.2258013