---
_id: '45484'
abstract:
- lang: eng
text: AbstractGraffiti 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 (Ingrid) closes
this gap by dealing with graffiti image collections that have been made available
to the project for public use. Within Ingrid, the graffiti
images are collected, digitized and annotated. With this work, we aim to support
the rapid access to a comprehensive data source on Ingrid targeted
especially by researchers. In particular, we present IngridKG,
an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR
principles. We weekly update IngridKG 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 IngridKG 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.
article_number: '318'
author:
- first_name: Mohamed Ahmed
full_name: Sherif, Mohamed Ahmed
last_name: Sherif
- first_name: Ana Alexandra Morim
full_name: da Silva, Ana Alexandra Morim
last_name: da Silva
- first_name: Svetlana
full_name: Pestryakova, Svetlana
last_name: Pestryakova
- first_name: Abdullah Fathi
full_name: Ahmed, Abdullah Fathi
last_name: Ahmed
- first_name: Sven
full_name: Niemann, Sven
last_name: Niemann
- first_name: Axel-Cyrille Ngonga
full_name: Ngomo, Axel-Cyrille Ngonga
last_name: Ngomo
citation:
ama: 'Sherif MA, da Silva AAM, Pestryakova S, Ahmed AF, Niemann S, Ngomo A-CN. IngridKG:
A FAIR Knowledge Graph of Graffiti. Scientific Data. 2023;10(1). doi:10.1038/s41597-023-02199-8'
apa: 'Sherif, M. A., da Silva, A. A. M., Pestryakova, S., Ahmed, A. F., Niemann,
S., & Ngomo, A.-C. N. (2023). IngridKG: A FAIR Knowledge Graph of Graffiti.
Scientific Data, 10(1), Article 318. https://doi.org/10.1038/s41597-023-02199-8'
bibtex: '@article{Sherif_da Silva_Pestryakova_Ahmed_Niemann_Ngomo_2023, title={IngridKG:
A FAIR Knowledge Graph of Graffiti}, volume={10}, DOI={10.1038/s41597-023-02199-8},
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} }'
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.” Scientific Data 10, no. 1 (2023).
https://doi.org/10.1038/s41597-023-02199-8.'
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,” Scientific
Data, vol. 10, no. 1, Art. no. 318, 2023, doi: 10.1038/s41597-023-02199-8.'
mla: 'Sherif, Mohamed Ahmed, et al. “IngridKG: A FAIR Knowledge Graph of Graffiti.”
Scientific Data, vol. 10, no. 1, 318, Springer Science and Business Media
LLC, 2023, doi:10.1038/s41597-023-02199-8.'
short: M.A. Sherif, A.A.M. da Silva, S. Pestryakova, A.F. Ahmed, S. Niemann, A.-C.N.
Ngomo, Scientific Data 10 (2023).
date_created: 2023-06-06T09:12:39Z
date_updated: 2023-06-06T09:17:10Z
department:
- _id: '574'
- _id: '115'
doi: 10.1038/s41597-023-02199-8
intvolume: ' 10'
issue: '1'
keyword:
- Library and Information Sciences
- Statistics
- Probability and Uncertainty
- Computer Science Applications
- Education
- Information Systems
- Statistics and Probability
language:
- iso: eng
project:
- _id: '104'
grant_number: '289287267'
name: 'INGRID: INGRID: Informationssystem Graffiti in Deutschland'
publication: Scientific Data
publication_identifier:
issn:
- 2052-4463
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: 'IngridKG: A FAIR Knowledge Graph of Graffiti'
type: journal_article
user_id: '6593'
volume: 10
year: '2023'
...
---
_id: '32335'
abstract:
- lang: eng
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.
article_number: '6'
author:
- first_name: SUSANNE
full_name: PODWORNY, SUSANNE
last_name: PODWORNY
- first_name: Sven
full_name: Hüsing, Sven
id: '58465'
last_name: Hüsing
- first_name: CARSTEN
full_name: SCHULTE, CARSTEN
last_name: SCHULTE
citation:
ama: 'PODWORNY S, Hüsing S, SCHULTE C. A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL:
BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. STATISTICS EDUCATION RESEARCH
JOURNAL. 2022;21(2). doi:10.52041/serj.v21i2.46'
apa: 'PODWORNY, S., Hüsing, S., & SCHULTE, C. (2022). A PLACE FOR A DATA SCIENCE
PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. STATISTICS
EDUCATION RESEARCH JOURNAL, 21(2), Article 6. https://doi.org/10.52041/serj.v21i2.46'
bibtex: '@article{PODWORNY_Hüsing_SCHULTE_2022, title={A PLACE FOR A DATA SCIENCE
PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}, volume={21},
DOI={10.52041/serj.v21i2.46},
number={26}, journal={STATISTICS EDUCATION RESEARCH JOURNAL}, publisher={International
Association for Statistical Education}, author={PODWORNY, SUSANNE and Hüsing,
Sven and SCHULTE, CARSTEN}, year={2022} }'
chicago: 'PODWORNY, SUSANNE, Sven Hüsing, and CARSTEN SCHULTE. “A PLACE FOR A DATA
SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” STATISTICS
EDUCATION RESEARCH JOURNAL 21, no. 2 (2022). https://doi.org/10.52041/serj.v21i2.46.'
ieee: 'S. PODWORNY, S. Hüsing, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT
IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,” STATISTICS EDUCATION
RESEARCH JOURNAL, vol. 21, no. 2, Art. no. 6, 2022, doi: 10.52041/serj.v21i2.46.'
mla: 'PODWORNY, SUSANNE, et al. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN
STATISTICS AND EPISTEMIC PROGRAMMING.” STATISTICS EDUCATION RESEARCH JOURNAL,
vol. 21, no. 2, 6, International Association for Statistical Education, 2022,
doi:10.52041/serj.v21i2.46.'
short: S. PODWORNY, S. Hüsing, C. SCHULTE, STATISTICS EDUCATION RESEARCH JOURNAL
21 (2022).
date_created: 2022-07-08T12:06:48Z
date_updated: 2022-07-08T12:07:46Z
department:
- _id: '67'
doi: 10.52041/serj.v21i2.46
intvolume: ' 21'
issue: '2'
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
publication: STATISTICS EDUCATION RESEARCH JOURNAL
publication_identifier:
issn:
- 1570-1824
publication_status: published
publisher: International Association for Statistical Education
status: public
title: 'A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC
PROGRAMMING'
type: journal_article
user_id: '58465'
volume: 21
year: '2022'
...
---
_id: '34200'
abstract:
- lang: eng
text: 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.
author:
- first_name: Thiemo
full_name: Bloh, Thiemo
last_name: Bloh
citation:
ama: Bloh T. Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse.
Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung.
Zeitschrift für Evaluation. 2022;2022(02):193-215. doi:10.31244/zfe.2022.02.02
apa: Bloh, T. (2022). Rekonstruktive Evaluationsforschung im Kontext praxeologischer
Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen
Evaluationsforschung. Zeitschrift für Evaluation, 2022(02), 193–215.
https://doi.org/10.31244/zfe.2022.02.02
bibtex: '@article{Bloh_2022, title={Rekonstruktive Evaluationsforschung im Kontext
praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen
zur Dokumentarischen Evaluationsforschung}, volume={2022}, DOI={10.31244/zfe.2022.02.02},
number={02}, journal={Zeitschrift für Evaluation}, publisher={Waxmann}, author={Bloh,
Thiemo}, year={2022}, pages={193–215} }'
chicago: 'Bloh, Thiemo. “Rekonstruktive Evaluationsforschung im Kontext praxeologischer
Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen
Evaluationsforschung.” Zeitschrift für Evaluation 2022, no. 02 (2022):
193–215. https://doi.org/10.31244/zfe.2022.02.02.'
ieee: 'T. Bloh, “Rekonstruktive Evaluationsforschung im Kontext praxeologischer
Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen
Evaluationsforschung,” Zeitschrift für Evaluation, vol. 2022, no. 02, pp.
193–215, 2022, doi: 10.31244/zfe.2022.02.02.'
mla: Bloh, Thiemo. “Rekonstruktive Evaluationsforschung im Kontext praxeologischer
Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen
Evaluationsforschung.” Zeitschrift für Evaluation, vol. 2022, no. 02, Waxmann,
2022, pp. 193–215, doi:10.31244/zfe.2022.02.02.
short: T. Bloh, Zeitschrift für Evaluation 2022 (2022) 193–215.
date_created: 2022-12-05T13:25:58Z
date_updated: 2022-12-05T13:26:35Z
department:
- _id: '4'
doi: 10.31244/zfe.2022.02.02
intvolume: ' 2022'
issue: '02'
keyword:
- Strategy and Management
- Applied Psychology
- Social Sciences (miscellaneous)
- Education
- Communication
- Statistics and Probability
language:
- iso: ger
page: 193-215
publication: Zeitschrift für Evaluation
publication_identifier:
issn:
- 1619-5515
- 2699-5506
publication_status: published
publisher: Waxmann
quality_controlled: '1'
status: public
title: Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse.
Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung
type: journal_article
user_id: '69383'
volume: 2022
year: '2022'
...
---
_id: '48108'
abstract:
- lang: eng
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.
article_number: '6'
author:
- first_name: SUSANNE
full_name: PODWORNY, SUSANNE
last_name: PODWORNY
- first_name: SVEN
full_name: HÜSING, SVEN
last_name: HÜSING
- first_name: CARSTEN
full_name: SCHULTE, CARSTEN
last_name: SCHULTE
citation:
ama: 'PODWORNY S, HÜSING S, SCHULTE C. A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL:
BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. STATISTICS EDUCATION RESEARCH
JOURNAL. 2022;21(2). doi:10.52041/serj.v21i2.46'
apa: 'PODWORNY, S., HÜSING, S., & SCHULTE, C. (2022). A PLACE FOR A DATA SCIENCE
PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING. STATISTICS
EDUCATION RESEARCH JOURNAL, 21(2), Article 6. https://doi.org/10.52041/serj.v21i2.46'
bibtex: '@article{PODWORNY_HÜSING_SCHULTE_2022, title={A PLACE FOR A DATA SCIENCE
PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING}, volume={21},
DOI={10.52041/serj.v21i2.46},
number={26}, journal={STATISTICS EDUCATION RESEARCH JOURNAL}, publisher={International
Association for Statistical Education}, author={PODWORNY, SUSANNE and HÜSING,
SVEN and SCHULTE, CARSTEN}, year={2022} }'
chicago: 'PODWORNY, SUSANNE, SVEN HÜSING, and CARSTEN SCHULTE. “A PLACE FOR A DATA
SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING.” STATISTICS
EDUCATION RESEARCH JOURNAL 21, no. 2 (2022). https://doi.org/10.52041/serj.v21i2.46.'
ieee: 'S. PODWORNY, S. HÜSING, and C. SCHULTE, “A PLACE FOR A DATA SCIENCE PROJECT
IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING,” STATISTICS EDUCATION
RESEARCH JOURNAL, vol. 21, no. 2, Art. no. 6, 2022, doi: 10.52041/serj.v21i2.46.'
mla: 'PODWORNY, SUSANNE, et al. “A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN
STATISTICS AND EPISTEMIC PROGRAMMING.” STATISTICS EDUCATION RESEARCH JOURNAL,
vol. 21, no. 2, 6, International Association for Statistical Education, 2022,
doi:10.52041/serj.v21i2.46.'
short: S. PODWORNY, S. HÜSING, C. SCHULTE, STATISTICS EDUCATION RESEARCH JOURNAL
21 (2022).
date_created: 2023-10-17T05:59:38Z
date_updated: 2023-10-17T06:01:58Z
doi: 10.52041/serj.v21i2.46
intvolume: ' 21'
issue: '2'
keyword:
- Education
- Statistics and Probability
publication: STATISTICS EDUCATION RESEARCH JOURNAL
publication_identifier:
issn:
- 1570-1824
publication_status: published
publisher: International Association for Statistical Education
status: public
title: 'A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC
PROGRAMMING'
type: journal_article
user_id: '30619'
volume: 21
year: '2022'
...
---
_id: '53238'
article_number: '101189'
author:
- first_name: Madjid
full_name: Tavana, Madjid
id: '31858'
last_name: Tavana
- first_name: Arash
full_name: Khalili Nasr, Arash
last_name: Khalili Nasr
- first_name: Hassan
full_name: Mina, Hassan
last_name: Mina
- first_name: Jerzy
full_name: Michnik, Jerzy
last_name: Michnik
citation:
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.
Socio-Economic Planning Sciences. 2022;83. doi:10.1016/j.seps.2021.101189
apa: Tavana, M., Khalili Nasr, A., Mina, H., & Michnik, J. (2022). A private
sustainable partner selection model for green public-private partnerships and
regional economic development. Socio-Economic Planning Sciences, 83,
Article 101189. https://doi.org/10.1016/j.seps.2021.101189
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={10.1016/j.seps.2021.101189},
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} }'
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.” Socio-Economic Planning Sciences 83
(2022). https://doi.org/10.1016/j.seps.2021.101189.
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,” Socio-Economic Planning Sciences, vol. 83, Art. no. 101189,
2022, doi: 10.1016/j.seps.2021.101189.'
mla: Tavana, Madjid, et al. “A Private Sustainable Partner Selection Model for Green
Public-Private Partnerships and Regional Economic Development.” Socio-Economic
Planning Sciences, vol. 83, 101189, Elsevier BV, 2022, doi:10.1016/j.seps.2021.101189.
short: M. Tavana, A. Khalili Nasr, H. Mina, J. Michnik, Socio-Economic Planning
Sciences 83 (2022).
date_created: 2024-04-04T15:50:16Z
date_updated: 2024-04-15T13:16:33Z
department:
- _id: '277'
doi: 10.1016/j.seps.2021.101189
intvolume: ' 83'
keyword:
- Management Science and Operations Research
- Statistics
- Probability and Uncertainty
- Strategy and Management
- Economics and Econometrics
- Geography
- Planning and Development
language:
- iso: eng
publication: Socio-Economic Planning Sciences
publication_identifier:
issn:
- 0038-0121
publication_status: published
publisher: Elsevier BV
status: public
title: A private sustainable partner selection model for green public-private partnerships
and regional economic development
type: journal_article
user_id: '51811'
volume: 83
year: '2022'
...
---
_id: '34920'
abstract:
- lang: eng
text: 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.
article_number: '1'
author:
- first_name: Rolf
full_name: Biehler, Rolf
id: '16274'
last_name: Biehler
- first_name: Richard
full_name: De Veaux, Richard
last_name: De Veaux
- first_name: Joachim
full_name: Engel, Joachim
last_name: Engel
- first_name: Sibel
full_name: Kazak, Sibel
last_name: Kazak
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
citation:
ama: 'Biehler R, De Veaux R, Engel J, Kazak S, Frischemeier D. Editorial: Research
on Data Science Education. Statistics Education Research Journal. 2022;21(2).
doi:10.52041/serj.v21i2.606'
apa: 'Biehler, R., De Veaux, R., Engel, J., Kazak, S., & Frischemeier, D. (2022).
Editorial: Research on Data Science Education. Statistics Education Research
Journal, 21(2), Article 1. https://doi.org/10.52041/serj.v21i2.606'
bibtex: '@article{Biehler_De Veaux_Engel_Kazak_Frischemeier_2022, title={Editorial:
Research on Data Science Education}, volume={21}, DOI={10.52041/serj.v21i2.606},
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} }'
chicago: 'Biehler, Rolf, Richard De Veaux, Joachim Engel, Sibel Kazak, and Daniel
Frischemeier. “Editorial: Research on Data Science Education.” Statistics Education
Research Journal 21, no. 2 (2022). https://doi.org/10.52041/serj.v21i2.606.'
ieee: 'R. Biehler, R. De Veaux, J. Engel, S. Kazak, and D. Frischemeier, “Editorial:
Research on Data Science Education,” Statistics Education Research Journal,
vol. 21, no. 2, Art. no. 1, 2022, doi: 10.52041/serj.v21i2.606.'
mla: 'Biehler, Rolf, et al. “Editorial: Research on Data Science Education.” Statistics
Education Research Journal, vol. 21, no. 2, 1, International Association for
Statistical Education, 2022, doi:10.52041/serj.v21i2.606.'
short: R. Biehler, R. De Veaux, J. Engel, S. Kazak, D. Frischemeier, Statistics
Education Research Journal 21 (2022).
date_created: 2022-12-23T11:20:39Z
date_updated: 2024-04-18T09:45:53Z
department:
- _id: '363'
doi: 10.52041/serj.v21i2.606
intvolume: ' 21'
issue: '2'
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
publication: Statistics Education Research Journal
publication_identifier:
issn:
- 1570-1824
publication_status: published
publisher: International Association for Statistical Education
status: public
title: 'Editorial: Research on Data Science Education'
type: journal_article
user_id: '37888'
volume: 21
year: '2022'
...
---
_id: '50024'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
last_name: Feng
- first_name: Thomas
full_name: Gries, Thomas
last_name: Gries
- first_name: Sebastian
full_name: Letmathe, Sebastian
last_name: Letmathe
- first_name: Dominik
full_name: Schulz, Dominik
last_name: Schulz
citation:
ama: Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric
Modeling of Trend Stationary Time Series. The R Journal. 2022;14(1):182-195.
doi:10.32614/rj-2022-017
apa: Feng, Y., Gries, T., Letmathe, S., & Schulz, D. (2022). The smoots Package
in R for Semiparametric Modeling of Trend Stationary Time Series. The R Journal,
14(1), 182–195. https://doi.org/10.32614/rj-2022-017
bibtex: '@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in
R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={10.32614/rj-2022-017}, 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}
}'
chicago: 'Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The
Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.”
The R Journal 14, no. 1 (2022): 182–95. https://doi.org/10.32614/rj-2022-017.'
ieee: 'Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for
Semiparametric Modeling of Trend Stationary Time Series,” The R Journal,
vol. 14, no. 1, pp. 182–195, 2022, doi: 10.32614/rj-2022-017.'
mla: Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling
of Trend Stationary Time Series.” The R Journal, vol. 14, no. 1, The R
Foundation, 2022, pp. 182–95, doi:10.32614/rj-2022-017.
short: Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.
date_created: 2023-12-21T12:09:31Z
date_updated: 2024-06-12T12:57:13Z
department:
- _id: '475'
- _id: '19'
- _id: '200'
doi: 10.32614/rj-2022-017
intvolume: ' 14'
issue: '1'
keyword:
- Statistics
- Probability and Uncertainty
- Numerical Analysis
- Statistics and Probability
language:
- iso: eng
page: 182-195
publication: The R Journal
publication_identifier:
issn:
- 2073-4859
publication_status: published
publisher: The R Foundation
status: public
title: The smoots Package in R for Semiparametric Modeling of Trend Stationary Time
Series
type: journal_article
user_id: '186'
volume: 14
year: '2022'
...
---
_id: '35672'
abstract:
- lang: eng
text: 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.
article_number: '7'
author:
- first_name: Franz Yannik
full_name: Fleischer, Franz Yannik
id: '42660'
last_name: Fleischer
orcid: https://orcid.org/0000-0003-0318-0329
- first_name: Rolf
full_name: Biehler, Rolf
id: '16274'
last_name: Biehler
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
citation:
ama: Fleischer FY, Biehler R, Schulte C. Teaching and Learning Data-Driven Machine
Learning with Educationally Designed Jupyter Notebooks. Statistics Education
Research Journal. 2022;21(2). doi:10.52041/serj.v21i2.61
apa: Fleischer, F. Y., Biehler, R., & Schulte, C. (2022). Teaching and Learning
Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks. Statistics
Education Research Journal, 21(2), Article 7. https://doi.org/10.52041/serj.v21i2.61
bibtex: '@article{Fleischer_Biehler_Schulte_2022, title={Teaching and Learning Data-Driven
Machine Learning with Educationally Designed Jupyter Notebooks}, volume={21},
DOI={10.52041/serj.v21i2.61},
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} }'
chicago: Fleischer, Franz Yannik, Rolf Biehler, and Carsten Schulte. “Teaching and
Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks.”
Statistics Education Research Journal 21, no. 2 (2022). https://doi.org/10.52041/serj.v21i2.61.
ieee: 'F. Y. Fleischer, R. Biehler, and C. Schulte, “Teaching and Learning Data-Driven
Machine Learning with Educationally Designed Jupyter Notebooks,” Statistics
Education Research Journal, vol. 21, no. 2, Art. no. 7, 2022, doi: 10.52041/serj.v21i2.61.'
mla: Fleischer, Franz Yannik, et al. “Teaching and Learning Data-Driven Machine
Learning with Educationally Designed Jupyter Notebooks.” Statistics Education
Research Journal, vol. 21, no. 2, 7, International Association for Statistical
Education, 2022, doi:10.52041/serj.v21i2.61.
short: F.Y. Fleischer, R. Biehler, C. Schulte, Statistics Education Research Journal
21 (2022).
date_created: 2023-01-10T08:48:23Z
date_updated: 2024-08-21T10:04:41Z
department:
- _id: '363'
- _id: '67'
doi: 10.52041/serj.v21i2.61
intvolume: ' 21'
issue: '2'
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
publication: Statistics Education Research Journal
publication_identifier:
issn:
- 1570-1824
publication_status: published
publisher: International Association for Statistical Education
status: public
title: Teaching and Learning Data-Driven Machine Learning with Educationally Designed
Jupyter Notebooks
type: journal_article
user_id: '37888'
volume: 21
year: '2022'
...
---
_id: '50025'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Thomas
full_name: Gries, Thomas
id: '186'
last_name: Gries
- first_name: Sebastian
full_name: Letmathe, Sebastian
last_name: Letmathe
- first_name: Dominik
full_name: Schulz, Dominik
last_name: Schulz
citation:
ama: Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric
Modeling of Trend Stationary Time Series. The R Journal. 2022;14(1):182-195.
doi:10.32614/rj-2022-017
apa: Feng, Y., Gries, T., Letmathe, S., & Schulz, D. (2022). The smoots Package
in R for Semiparametric Modeling of Trend Stationary Time Series. The R Journal,
14(1), 182–195. https://doi.org/10.32614/rj-2022-017
bibtex: '@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in
R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={10.32614/rj-2022-017}, 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}
}'
chicago: 'Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The
Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.”
The R Journal 14, no. 1 (2022): 182–95. https://doi.org/10.32614/rj-2022-017.'
ieee: 'Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for
Semiparametric Modeling of Trend Stationary Time Series,” The R Journal,
vol. 14, no. 1, pp. 182–195, 2022, doi: 10.32614/rj-2022-017.'
mla: Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling
of Trend Stationary Time Series.” The R Journal, vol. 14, no. 1, The R
Foundation, 2022, pp. 182–95, doi:10.32614/rj-2022-017.
short: Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.
date_created: 2023-12-21T12:09:53Z
date_updated: 2025-11-10T09:32:36Z
doi: 10.32614/rj-2022-017
intvolume: ' 14'
issue: '1'
keyword:
- Statistics
- Probability and Uncertainty
- Numerical Analysis
- Statistics and Probability
language:
- iso: eng
page: 182-195
publication: The R Journal
publication_identifier:
issn:
- 2073-4859
publication_status: published
publisher: The R Foundation
status: public
title: The smoots Package in R for Semiparametric Modeling of Trend Stationary Time
Series
type: journal_article
user_id: '186'
volume: 14
year: '2022'
...
---
_id: '32243'
abstract:
- lang: eng
text: "Abstract\r\n 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."
article_number: '033216'
author:
- first_name: Lennart
full_name: Dabelow, Lennart
last_name: Dabelow
- first_name: Stefano
full_name: Bo, Stefano
last_name: Bo
- first_name: Ralf
full_name: Eichhorn, Ralf
last_name: Eichhorn
citation:
ama: 'Dabelow L, Bo S, Eichhorn R. How irreversible are steady-state trajectories
of a trapped active particle? Journal of Statistical Mechanics: Theory and
Experiment. 2021;2021(3). doi:10.1088/1742-5468/abe6fd'
apa: 'Dabelow, L., Bo, S., & Eichhorn, R. (2021). How irreversible are steady-state
trajectories of a trapped active particle? Journal of Statistical Mechanics:
Theory and Experiment, 2021(3), Article 033216. https://doi.org/10.1088/1742-5468/abe6fd'
bibtex: '@article{Dabelow_Bo_Eichhorn_2021, title={How irreversible are steady-state
trajectories of a trapped active particle?}, volume={2021}, DOI={10.1088/1742-5468/abe6fd},
number={3033216}, journal={Journal of Statistical Mechanics: Theory and Experiment},
publisher={IOP Publishing}, author={Dabelow, Lennart and Bo, Stefano and Eichhorn,
Ralf}, year={2021} }'
chicago: 'Dabelow, Lennart, Stefano Bo, and Ralf Eichhorn. “How Irreversible Are
Steady-State Trajectories of a Trapped Active Particle?” Journal of Statistical
Mechanics: Theory and Experiment 2021, no. 3 (2021). https://doi.org/10.1088/1742-5468/abe6fd.'
ieee: 'L. Dabelow, S. Bo, and R. Eichhorn, “How irreversible are steady-state trajectories
of a trapped active particle?,” Journal of Statistical Mechanics: Theory and
Experiment, vol. 2021, no. 3, Art. no. 033216, 2021, doi: 10.1088/1742-5468/abe6fd.'
mla: 'Dabelow, Lennart, et al. “How Irreversible Are Steady-State Trajectories of
a Trapped Active Particle?” Journal of Statistical Mechanics: Theory and Experiment,
vol. 2021, no. 3, 033216, IOP Publishing, 2021, doi:10.1088/1742-5468/abe6fd.'
short: 'L. Dabelow, S. Bo, R. Eichhorn, Journal of Statistical Mechanics: Theory
and Experiment 2021 (2021).'
date_created: 2022-06-28T07:27:41Z
date_updated: 2022-06-28T07:28:14Z
department:
- _id: '27'
doi: 10.1088/1742-5468/abe6fd
intvolume: ' 2021'
issue: '3'
keyword:
- Statistics
- Probability and Uncertainty
- Statistics and Probability
- Statistical and Nonlinear Physics
language:
- iso: eng
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: 'Journal of Statistical Mechanics: Theory and Experiment'
publication_identifier:
issn:
- 1742-5468
publication_status: published
publisher: IOP Publishing
status: public
title: How irreversible are steady-state trajectories of a trapped active particle?
type: journal_article
user_id: '15278'
volume: 2021
year: '2021'
...
---
_id: '33649'
article_number: '2000269'
author:
- first_name: Jan
full_name: Kessler, Jan
id: '65425'
last_name: Kessler
orcid: 0000-0002-8705-6992
- first_name: Francesco
full_name: Calcavecchia, Francesco
last_name: Calcavecchia
- first_name: Thomas
full_name: Kühne, Thomas
id: '49079'
last_name: Kühne
citation:
ama: Kessler J, Calcavecchia F, Kühne T. Artificial Neural Networks as Trial Wave
Functions for Quantum Monte Carlo. Advanced Theory and Simulations. 2021;4(4).
doi:10.1002/adts.202000269
apa: Kessler, J., Calcavecchia, F., & Kühne, T. (2021). Artificial Neural Networks
as Trial Wave Functions for Quantum Monte Carlo. Advanced Theory and Simulations,
4(4), Article 2000269. https://doi.org/10.1002/adts.202000269
bibtex: '@article{Kessler_Calcavecchia_Kühne_2021, title={Artificial Neural Networks
as Trial Wave Functions for Quantum Monte Carlo}, volume={4}, DOI={10.1002/adts.202000269},
number={42000269}, journal={Advanced Theory and Simulations}, publisher={Wiley},
author={Kessler, Jan and Calcavecchia, Francesco and Kühne, Thomas}, year={2021}
}'
chicago: Kessler, Jan, Francesco Calcavecchia, and Thomas Kühne. “Artificial Neural
Networks as Trial Wave Functions for Quantum Monte Carlo.” Advanced Theory
and Simulations 4, no. 4 (2021). https://doi.org/10.1002/adts.202000269.
ieee: 'J. Kessler, F. Calcavecchia, and T. Kühne, “Artificial Neural Networks as
Trial Wave Functions for Quantum Monte Carlo,” Advanced Theory and Simulations,
vol. 4, no. 4, Art. no. 2000269, 2021, doi: 10.1002/adts.202000269.'
mla: Kessler, Jan, et al. “Artificial Neural Networks as Trial Wave Functions for
Quantum Monte Carlo.” Advanced Theory and Simulations, vol. 4, no. 4, 2000269,
Wiley, 2021, doi:10.1002/adts.202000269.
short: J. Kessler, F. Calcavecchia, T. Kühne, Advanced Theory and Simulations 4
(2021).
date_created: 2022-10-10T08:15:23Z
date_updated: 2022-10-10T08:15:37Z
department:
- _id: '613'
doi: 10.1002/adts.202000269
intvolume: ' 4'
issue: '4'
keyword:
- Multidisciplinary
- Modeling and Simulation
- Numerical Analysis
- Statistics and Probability
language:
- iso: eng
publication: Advanced Theory and Simulations
publication_identifier:
issn:
- 2513-0390
- 2513-0390
publication_status: published
publisher: Wiley
status: public
title: Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
type: journal_article
user_id: '71051'
volume: 4
year: '2021'
...
---
_id: '48109'
abstract:
- lang: eng
text: AbstractIn 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.
author:
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
- first_name: Rolf
full_name: Biehler, Rolf
last_name: Biehler
- first_name: Susanne
full_name: Podworny, Susanne
last_name: Podworny
- first_name: Lea
full_name: Budde, Lea
last_name: Budde
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 <scp>CODAP</scp> using survey data. Teaching Statistics.
2021;43(S1). doi:10.1111/test.12283'
apa: 'Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first
introduction to data science education in secondary schools: Teaching and learning
about data exploration with <scp>CODAP</scp> using survey data.
Teaching Statistics, 43(S1). https://doi.org/10.1111/test.12283'
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 <scp>CODAP</scp> using survey data}, volume={43},
DOI={10.1111/test.12283}, number={S1},
journal={Teaching Statistics}, publisher={Wiley}, author={Frischemeier, Daniel
and Biehler, Rolf and Podworny, Susanne and Budde, Lea}, year={2021} }'
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 <scp>CODAP</Scp> Using Survey
Data.” Teaching Statistics 43, no. S1 (2021). https://doi.org/10.1111/test.12283.'
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 <scp>CODAP</scp> using survey data,” Teaching
Statistics, vol. 43, no. S1, 2021, doi: 10.1111/test.12283.'
mla: 'Frischemeier, Daniel, et al. “A First Introduction to Data Science Education
in Secondary Schools: Teaching and Learning about Data Exploration with <scp>CODAP</Scp>
Using Survey Data.” Teaching Statistics, vol. 43, no. S1, Wiley, 2021,
doi:10.1111/test.12283.'
short: D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43
(2021).
date_created: 2023-10-17T06:06:02Z
date_updated: 2023-10-17T06:13:03Z
doi: 10.1111/test.12283
intvolume: ' 43'
issue: S1
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
publication: Teaching Statistics
publication_identifier:
issn:
- 0141-982X
- 1467-9639
publication_status: published
publisher: Wiley
status: public
title: 'A first introduction to data science education in secondary schools: Teaching
and learning about data exploration with CODAP using survey data'
type: journal_article
user_id: '30619'
volume: 43
year: '2021'
...
---
_id: '35751'
author:
- first_name: Daniel
full_name: Frischemeier, Daniel
last_name: Frischemeier
- first_name: Rolf
full_name: Biehler, Rolf
id: '16274'
last_name: Biehler
- first_name: Susanne
full_name: Podworny, Susanne
id: '30619'
last_name: Podworny
orcid: 0000-0002-6313-5987
- first_name: Lea
full_name: Budde, Lea
id: '32443'
last_name: Budde
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<scp>CODAP</scp>using survey data. Teaching Statistics.
2021;43(S1):S182-S189. doi:10.1111/test.12283'
apa: 'Frischemeier, D., Biehler, R., Podworny, S., & Budde, L. (2021). A first
introduction to data science education in secondary schools: Teaching and learning
about data exploration with<scp>CODAP</scp>using survey data.
Teaching Statistics, 43(S1), S182–S189. https://doi.org/10.1111/test.12283'
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<scp>CODAP</scp>using survey data}, volume={43},
DOI={10.1111/test.12283}, number={S1},
journal={Teaching Statistics}, publisher={Wiley}, author={Frischemeier, Daniel
and Biehler, Rolf and Podworny, Susanne and Budde, Lea}, year={2021}, pages={S182–S189}
}'
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<scp>CODAP</Scp>using Survey
Data.” Teaching Statistics 43, no. S1 (2021): S182–89. https://doi.org/10.1111/test.12283.'
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<scp>CODAP</scp>using survey data,” Teaching
Statistics, vol. 43, no. S1, pp. S182–S189, 2021, doi: 10.1111/test.12283.'
mla: 'Frischemeier, Daniel, et al. “A First Introduction to Data Science Education
in Secondary Schools: Teaching and Learning about Data Exploration With<scp>CODAP</Scp>using
Survey Data.” Teaching Statistics, vol. 43, no. S1, Wiley, 2021, pp. S182–89,
doi:10.1111/test.12283.'
short: D. Frischemeier, R. Biehler, S. Podworny, L. Budde, Teaching Statistics 43
(2021) S182–S189.
date_created: 2023-01-10T10:16:44Z
date_updated: 2024-04-18T10:12:44Z
department:
- _id: '363'
doi: 10.1111/test.12283
intvolume: ' 43'
issue: S1
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
page: S182-S189
publication: Teaching Statistics
publication_identifier:
issn:
- 0141-982X
- 1467-9639
publication_status: published
publisher: Wiley
status: public
title: 'A first introduction to data science education in secondary schools: Teaching
and learning about data exploration withCODAPusing survey data'
type: journal_article
user_id: '37888'
volume: 43
year: '2021'
...
---
_id: '35737'
author:
- first_name: Rolf
full_name: Biehler, Rolf
id: '16274'
last_name: Biehler
- first_name: Franz Yannik
full_name: Fleischer, Franz Yannik
id: '42660'
last_name: Fleischer
orcid: https://orcid.org/0000-0003-0318-0329
citation:
ama: Biehler R, Fleischer FY. Introducing students to machine learning with decision
trees using CODAP and Jupyter Notebooks. Teaching Statistics. 2021;43:S133-S142.
doi:10.1111/test.12279
apa: Biehler, R., & Fleischer, F. Y. (2021). Introducing students to machine
learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics,
43, S133–S142. https://doi.org/10.1111/test.12279
bibtex: '@article{Biehler_Fleischer_2021, title={Introducing students to machine
learning with decision trees using CODAP and Jupyter Notebooks}, volume={43},
DOI={10.1111/test.12279}, journal={Teaching
Statistics}, publisher={Wiley}, author={Biehler, Rolf and Fleischer, Franz Yannik},
year={2021}, pages={S133–S142} }'
chicago: 'Biehler, Rolf, and Franz Yannik Fleischer. “Introducing Students to Machine
Learning with Decision Trees Using CODAP and Jupyter Notebooks.” Teaching Statistics
43 (2021): S133–42. https://doi.org/10.1111/test.12279.'
ieee: 'R. Biehler and F. Y. Fleischer, “Introducing students to machine learning
with decision trees using CODAP and Jupyter Notebooks,” Teaching Statistics,
vol. 43, pp. S133–S142, 2021, doi: 10.1111/test.12279.'
mla: Biehler, Rolf, and Franz Yannik Fleischer. “Introducing Students to Machine
Learning with Decision Trees Using CODAP and Jupyter Notebooks.” Teaching Statistics,
vol. 43, Wiley, 2021, pp. S133–42, doi:10.1111/test.12279.
short: R. Biehler, F.Y. Fleischer, Teaching Statistics 43 (2021) S133–S142.
date_created: 2023-01-10T10:08:32Z
date_updated: 2024-08-21T10:05:05Z
department:
- _id: '363'
doi: 10.1111/test.12279
intvolume: ' 43'
keyword:
- Education
- Statistics and Probability
language:
- iso: eng
page: S133-S142
publication: Teaching Statistics
publication_identifier:
issn:
- 0141-982X
- 1467-9639
publication_status: published
publisher: Wiley
status: public
title: Introducing students to machine learning with decision trees using CODAP and
Jupyter Notebooks
type: journal_article
user_id: '37888'
volume: 43
year: '2021'
...
---
_id: '45561'
abstract:
- lang: eng
text: 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.
article_number: '9'
author:
- first_name: Papatya
full_name: Duman, Papatya
id: '72752'
last_name: Duman
citation:
ama: Duman P. Does Informational Equivalence Preserve Strategic Behavior? Experimental
Results on Trockel’s Model of Selten’s Chain Store Story. Games. 2020;11(1).
doi:10.3390/g11010009
apa: Duman, P. (2020). Does Informational Equivalence Preserve Strategic Behavior?
Experimental Results on Trockel’s Model of Selten’s Chain Store Story. Games,
11(1), Article 9. https://doi.org/10.3390/g11010009
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={10.3390/g11010009},
number={19}, journal={Games}, publisher={MDPI AG}, author={Duman, Papatya}, year={2020}
}'
chicago: Duman, Papatya. “Does Informational Equivalence Preserve Strategic Behavior?
Experimental Results on Trockel’s Model of Selten’s Chain Store Story.” Games
11, no. 1 (2020). https://doi.org/10.3390/g11010009.
ieee: 'P. Duman, “Does Informational Equivalence Preserve Strategic Behavior? Experimental
Results on Trockel’s Model of Selten’s Chain Store Story,” Games, vol.
11, no. 1, Art. no. 9, 2020, doi: 10.3390/g11010009.'
mla: Duman, Papatya. “Does Informational Equivalence Preserve Strategic Behavior?
Experimental Results on Trockel’s Model of Selten’s Chain Store Story.” Games,
vol. 11, no. 1, 9, MDPI AG, 2020, doi:10.3390/g11010009.
short: P. Duman, Games 11 (2020).
date_created: 2023-06-09T15:31:17Z
date_updated: 2023-06-09T15:33:34Z
doi: 10.3390/g11010009
intvolume: ' 11'
issue: '1'
keyword:
- Applied Mathematics
- Statistics
- Probability and Uncertainty
- Statistics and Probability
language:
- iso: eng
publication: Games
publication_identifier:
issn:
- 2073-4336
publication_status: published
publisher: MDPI AG
status: public
title: Does Informational Equivalence Preserve Strategic Behavior? Experimental Results
on Trockel’s Model of Selten’s Chain Store Story
type: journal_article
user_id: '72752'
volume: 11
year: '2020'
...
---
_id: '20868'
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.'
author:
- first_name: Sönke
full_name: Sievers, Sönke
id: '46447'
last_name: Sievers
- first_name: Jan
full_name: Klobucnik, Jan
last_name: Klobucnik
- first_name: David
full_name: Miersch, David
last_name: Miersch
citation:
ama: 'Sievers S, Klobucnik J, Miersch D. Predicting Early Warning Signals of
Financial Distress: Theory and Empirical Evidence.; 2017. doi:10.2139/ssrn.2237757'
apa: 'Sievers, S., Klobucnik, J., & Miersch, D. (2017). Predicting Early
Warning Signals of Financial Distress: Theory and Empirical Evidence. https://doi.org/10.2139/ssrn.2237757'
bibtex: '@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence}, DOI={10.2139/ssrn.2237757},
author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }'
chicago: 'Sievers, Sönke, Jan Klobucnik, and David Miersch. Predicting Early
Warning Signals of Financial Distress: Theory and Empirical Evidence, 2017.
https://doi.org/10.2139/ssrn.2237757.'
ieee: 'S. Sievers, J. Klobucnik, and D. Miersch, Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence. 2017.'
mla: 'Sievers, Sönke, et al. Predicting Early Warning Signals of Financial Distress:
Theory and Empirical Evidence. 2017, doi:10.2139/ssrn.2237757.'
short: 'S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of
Financial Distress: Theory and Empirical Evidence, 2017.'
date_created: 2021-01-05T11:44:45Z
date_updated: 2022-01-06T06:54:41Z
department:
- _id: '275'
doi: 10.2139/ssrn.2237757
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757
page: '84'
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
Evidence'
type: working_paper
user_id: '46447'
year: '2017'
...
---
_id: '5199'
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. '
author:
- first_name: Jan
full_name: Klobucnik, Jan
last_name: Klobucnik
- first_name: David
full_name: Miersch, David
last_name: Miersch
- first_name: Sönke
full_name: Sievers, Sönke
last_name: Sievers
citation:
ama: 'Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial
Distress: Theory and Empirical Evidence. SSRN Electronic Journal. 2017.'
apa: 'Klobucnik, J., Miersch, D., & Sievers, S. (2017). Predicting Early Warning
Signals of Financial Distress: Theory and Empirical Evidence. SSRN Electronic
Journal.'
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}
}'
chicago: 'Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning
Signals of Financial Distress: Theory and Empirical Evidence.” SSRN Electronic
Journal, 2017.'
ieee: 'J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals
of Financial Distress: Theory and Empirical Evidence,” SSRN Electronic Journal,
2017.'
mla: 'Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress:
Theory and Empirical Evidence.” SSRN Electronic Journal, 2017.'
short: J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).
date_created: 2018-10-31T12:19:42Z
date_updated: 2022-01-06T07:01:43Z
department:
- _id: '275'
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
Evidence'
type: journal_article
user_id: '64756'
year: '2017'
...
---
_id: '6075'
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
article_type: original
author:
- first_name: Alexander
full_name: Krüger, Alexander
last_name: Krüger
- first_name: Jan
full_name: Tünnermann, Jan
last_name: Tünnermann
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: Krüger A, Tünnermann J, Scharlau I. Measuring and modeling salience with the
theory of visual attention. Attention, Perception, & Psychophysics.
2017;79(6):1593-1614. doi:10.3758/s13414-017-1325-6
apa: Krüger, A., Tünnermann, J., & Scharlau, I. (2017). Measuring and modeling
salience with the theory of visual attention. Attention, Perception, &
Psychophysics, 79(6), 1593–1614. https://doi.org/10.3758/s13414-017-1325-6
bibtex: '@article{Krüger_Tünnermann_Scharlau_2017, title={Measuring and modeling
salience with the theory of visual attention.}, volume={79}, DOI={10.3758/s13414-017-1325-6},
number={6}, journal={Attention, Perception, & Psychophysics}, author={Krüger,
Alexander and Tünnermann, Jan and Scharlau, Ingrid}, year={2017}, pages={1593–1614}
}'
chicago: 'Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Measuring and
Modeling Salience with the Theory of Visual Attention.” Attention, Perception,
& Psychophysics 79, no. 6 (2017): 1593–1614. https://doi.org/10.3758/s13414-017-1325-6.'
ieee: 'A. Krüger, J. Tünnermann, and I. Scharlau, “Measuring and modeling salience
with the theory of visual attention.,” Attention, Perception, & Psychophysics,
vol. 79, no. 6, pp. 1593–1614, 2017, doi: 10.3758/s13414-017-1325-6.'
mla: Krüger, Alexander, et al. “Measuring and Modeling Salience with the Theory
of Visual Attention.” Attention, Perception, & Psychophysics, vol.
79, no. 6, 2017, pp. 1593–614, doi:10.3758/s13414-017-1325-6.
short: A. Krüger, J. Tünnermann, I. Scharlau, Attention, Perception, & Psychophysics
79 (2017) 1593–1614.
date_created: 2018-12-10T07:05:04Z
date_updated: 2022-06-06T14:08:05Z
department:
- _id: '424'
doi: 10.3758/s13414-017-1325-6
intvolume: ' 79'
issue: '6'
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
language:
- iso: eng
page: 1593 - 1614
publication: Attention, Perception, & Psychophysics
publication_identifier:
issn:
- 1943-3921
publication_status: published
status: public
title: Measuring and modeling salience with the theory of visual attention.
type: journal_article
user_id: '42165'
volume: 79
year: '2017'
...
---
_id: '6071'
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
author:
- first_name: Alexander
full_name: Krüger, Alexander
last_name: Krüger
- first_name: Jan
full_name: Tünnermann, Jan
last_name: Tünnermann
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
citation:
ama: Krüger A, Tünnermann J, Scharlau I. Fast and conspicuous? Quantifying salience
with the theory of visual attention. Advances in Cognitive Psychology.
2016;12(1):20-38. doi:10.5709/acp-0184-1
apa: Krüger, A., Tünnermann, J., & Scharlau, I. (2016). Fast and conspicuous?
Quantifying salience with the theory of visual attention. Advances in Cognitive
Psychology, 12(1), 20–38. https://doi.org/10.5709/acp-0184-1
bibtex: '@article{Krüger_Tünnermann_Scharlau_2016, title={Fast and conspicuous?
Quantifying salience with the theory of visual attention.}, volume={12}, DOI={10.5709/acp-0184-1}, number={1},
journal={Advances in Cognitive Psychology}, author={Krüger, Alexander and Tünnermann,
Jan and Scharlau, Ingrid}, year={2016}, pages={20–38} }'
chicago: 'Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Fast and Conspicuous?
Quantifying Salience with the Theory of Visual Attention.” Advances in Cognitive
Psychology 12, no. 1 (2016): 20–38. https://doi.org/10.5709/acp-0184-1.'
ieee: 'A. Krüger, J. Tünnermann, and I. Scharlau, “Fast and conspicuous? Quantifying
salience with the theory of visual attention.,” Advances in Cognitive Psychology,
vol. 12, no. 1, pp. 20–38, 2016, doi: 10.5709/acp-0184-1.'
mla: Krüger, Alexander, et al. “Fast and Conspicuous? Quantifying Salience with
the Theory of Visual Attention.” Advances in Cognitive Psychology, vol.
12, no. 1, 2016, pp. 20–38, doi:10.5709/acp-0184-1.
short: A. Krüger, J. Tünnermann, I. Scharlau, Advances in Cognitive Psychology 12
(2016) 20–38.
date_created: 2018-12-10T07:04:15Z
date_updated: 2022-06-06T16:21:09Z
department:
- _id: '424'
doi: 10.5709/acp-0184-1
funded_apc: '1'
intvolume: ' 12'
issue: '1'
keyword:
- salience
- visual attention
- Bayesian inference
- theory of visual attention
- computational modeling
- Visual Attention
- Computational Modeling
- Inference
- Judgment
- Statistical Probability
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ac-psych.org/en/download-pdf/volume/12/issue/1/id/185
oa: '1'
page: 20 - 38
publication: Advances in Cognitive Psychology
publication_identifier:
issn:
- 1895-1171
publication_status: published
status: public
title: Fast and conspicuous? Quantifying salience with the theory of visual attention.
type: journal_article
user_id: '42165'
volume: 12
year: '2016'
...
---
_id: '11862'
abstract:
- lang: eng
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.
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation
and Noise Robust Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing. 2013;21(8):1640-1652. doi:10.1109/TASL.2013.2258013
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on
Audio, Speech, and Language Processing, 21(8), 1640–1652. https://doi.org/10.1109/TASL.2013.2258013
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={10.1109/TASL.2013.2258013},
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} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian
Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” IEEE
Transactions on Audio, Speech, and Language Processing 21, no. 8 (2013): 1640–52.
https://doi.org/10.1109/TASL.2013.2258013.'
ieee: 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.
mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
Noise Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 21, no. 8, 2013, pp. 1640–52, doi:10.1109/TASL.2013.2258013.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
and Language Processing 21 (2013) 1640–1652.
date_created: 2019-07-12T05:29:42Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2013.2258013
intvolume: ' 21'
issue: '8'
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
language:
- iso: eng
page: 1640-1652
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 21
year: '2013'
...