Publications

Presentations and Other Contributions

Schumm, I., & Murcia Serra, J. (2024). FDM-Infrastrukturen: Forschungsdatenmanagement in der Fachreferatsarbeit (Wirtschafts- und Sozialwissenschaften). https://doi.org/10.5281/ZENODO.11198018
Kamlah, J., & Shigapov, R. (2024). Forschungsdaten erheben: Forschungsdatenmanagement in der Fachreferatsarbeit (Wirtschafts- und Sozialwissenschaften). https://doi.org/10.5281/ZENODO.11197244
Dietze, S. (2024, May). Understanding Scientific and Societal Adoption of Scientific Knowledge and Resources Through NLP and Knowledge Graphs. Keynote Talk, SemTech4STLD, ESWC 2024.
Stahl, F., & Heitmann, M. (2024, March 6). BERD@NFDI – Services and Platform for Professional Management of Research Data and Algorithms in Business Studies.
Shigapov, R. (2024, February 15). ChatGPT for FAIR Research Data. Research Data Management Seminars at the University of Mannheim, Online.
Schumm, I. (2024, February 14). BERD@NFDI – Angebote für die Buch-, Bibliotheks- und Informationswissenschaft. NFDI-Workshops des FID BBI.
Stoilova, V. (2024, January 8). Data Protection in Research Data Management.
Ohnesorge, F. (2024). BERD@NFDI: A Data Marketplace to Foster Industry – Academia Collaboration. German Data Science Days, Munich.
Shigapov, R. (2023, December 15). Optimizing FAIR data sharing with ChatGPT. ENGAGE.EU Webinar on FAIR data, Online.
Haas, V. (2023, November 3). Transforming Data Management in Business, Economic, and Related Sciences. Annual Digital Catalysis & Catalysis-Related Sciences Conference 2023 (ADCR 2023), Frankfurt.
Schumm, I. (2023, September 25). Forschungsdatenmanagement-Grundlagen. Forschungsdatenmanagement in der Fachreferatsarbeit (Wirtschafts- und Sozialwissenschaften).
Giraldo, O., Dessi, D., Castro, L. J., Dietze, S., & Rebholz-Schuhmann, D. (2023, September 14). Machine-Actionable Metadata for Software and Software Management Plans for NFDI – Presentation.
Kreuter, F. (2023, July 17). Utilizing NLP in data collection projects. Words to Numbers. Using NLP Methods for Research in the Social Sciences and Humanities, München.
Pethig, F. (2023, July 4). Harmful or Helpful? Using Text Analytics to Understand the Effects of Plattform Interventions. Joint Conference on Research on Text Analytics, Mannheim.
Stahl, F. (2023, July 4). BERD@NFDI – Concept and Benefits for Empirical Researchers. Joint Conference on Research on Text Analytics, Mannheim.
Heitmann, M. (2023, July 4). Natural Language Processing in Marketing and Business Research. Joint Conference on Research on Text Analytics, Mannheim.
Shigapov, R. (2023, June 2). FAIR Data Week: R for Reusable. FAIR Data Week, Online.
Shigapov, R. (2023, June 1). FAIR Data Week: I for Interoperable. FAIR Data Week, Online.
Shigapov, R. (2023, May 31). FAIR Data Week: A for Accessible. FAIR Data Week, Online.
Shigapov, R. (2023, May 30). FAIR Data Week: F for Findable. FAIR Data Week, Online.
Kamlah, J., & Schmidt, T. (2023, May 24). Ground Truth-Erstellung und Modelltraining mit eScriptorium. 111. BiblioCon2023, Hannover.
Dietze, S. (2023, May 10). AI in between online and offline discourse – and what is the role        of ChatGPT in all of that? Denkreise 2022: Wohin Wollen Wir Leben, Center For Life Ethics, Bonn University.
Shigapov, R. (2023, May 9). Warum brauchen wir Wissensgraphen in NFDI? KIM Workshop 2023, Mannheim.
Kamlah, J., & Shigapov, R. (2023, May 4). The German Production Pipeline: Mannheim – OCR & Knowledge Graphs. Digitisation and OCR Workshop for Eurhisfirm community members, Groningen.
Kamlah, J. (2023, March 30). Möglichkeiten und Unmöglichkeiten moderner Texterkennung – Erfahrungen aus BERD@NFDI bei der Gewinnung von Forschungsdaten aus historischen Dokumenten. V. Kongress für Wirtschafts- und Sozialgeschichte, Leipzig.
Schumm, I. (2023, March 30). Von print zu digital, vom Bild zum Volltext, von unstrukturiert zu strukturiert – Forschungsdaten der Wirtschafts und Sozialgeschichte in BERD@NFDI. V. Kongress für Wirtschafts und Sozialgeschichte, Leipzig.
Oberländer, L., & Stoilova, V. (2023, March 15). Wer ist iVA? Und wozu braucht man einen Datenschutzassistenten? forschungsdaten.info Live Veranstaltungsreihe.
Shigapov, R. (2023, February 15). Knowledge graphs for interoperable NFDI: Digital editions. Text+ FAIR February Meetup – Let’s talk FAIR Digital Editions, Online.
Herklotz, M., & Stoilova, V. (2023, January 26). Privacy in Practice – Enhancing Privacy Literacy through an interactive Virtual Assistant (iVA). Quo Vadis, Digital Privacy? Perceptions, Practices, and Policies.
Shigapov, R. (2022, November 25). Knowledge graphs in BERD and in NFDI. Focused Tutorial on Capturing, Enriching, Disseminating Research Data Objects Use Cases from Text+, NFDI4Culture and BERD@NFDI, Mannheim.
Dietze, S. (2022, November 24). Research Knowledge Graphs and scholarly information extraction @ BERD@NFDI & GESIS. Focused Tutorial on Capturing, Enriching, Disseminating Research Data Objects.
Kamlah, J., Schmidt, T., & Shigapov, R. (2022, November 24). Extracting research data from historical documents with eScriptorium and Python. Focused Tutorial on Capturing, Enriching, Disseminating Research Data Objects: Use Cases from Text+, NFDI4Culture and BERD@NFDI, Mannheim.
Stoilova, V. (2022, November 23). GDPR applicability – introduction to the interactive Virtual Assistant iVA. “Data Literacy Snacks” series of the Mannheim Univ. Library.
Stoilova, V. (2022, November 16). interactive Virtual Assistant: iVA. AK-FDM Onlinekonferenz.
Stoilova, V. (2022, November 9). Data Protection Basics. “Data Literacy Snacks” series of the Mannheim Univ. Library.
Stahl, F. (2022, November 3). BERD@NFDI. Symposium Forschungsdaten in der BWL at the Jubiläumstagung des Verbands der Hochschullehrer für Betriebswirtschaft.
Schumm, I. (2022, October 13). Panel on the topic “Open Data in Action – Perspectives on the open data landscape in social science and humanities research” hosted by Taylor and Francis.
Herklotz, M., & Oberländer, L. (2022, October 5). interactive Virtual Assistant (iVA) – Ein Zugang zu datenschutzrechtlichen Regelungen für Forschende. Karlsruher RDM Forum.
Krieger, U. (2022, October 3). Daten verstehen. “Data Literacy Essentials” series of the Mannheim Univ. Library.
Schumm, I. (2022, September 27). Data Literacy Essentials: Crash Course Data Literacy. “Data Literacy Essentials” series of the Mannheim Univ. Library.
Hastik, C., & Murcia, J. S. (2022, July 7). EduTrain@BERD@NFDI: Das Trainingskonzept der UB Mannheim und ihre Rolle. Netzwerk Informaitonskompetenz-BW-Sitzung, Mannheim.
Kreuter, F. (2022, June 4). Who Decides What Counts? AI and Big Data: Applications in Economic and Social Science Research. Future Leaders Summit 2022, Michigan Data Science Institute (MIDAS).
Busch, A., Krieger, U., Latif, A., Limani, F., Schumm, I., & Saleh, A. (2022, May 19). BERD@NFDI – Structuring unstructured data for business, economic and related research. International Conference on Economics and Business Information (INCONECSS).

Publications

Gruber, C., Hechinger, K., Aßenmacher, M., Kauermann, G. & Plank, B. (2024). More Labels or Cases? Assessing Label Variation in Natural Language Inference. In Proceedings of the Third Workshop on Understanding Implicit and Underspecified Language (pp. 22–32). Association for Computational Linguistics. https://aclanthology.org/2024.unimplicit-1.2/
Mayer, L., Heumann, C. & Aßenmacher, M. (2024). Can OpenSource beat ChatGPT? – A Comparative Study of Large Language Models for Text-to-Code Generation. Accepted at the Swiss Text Analytics Conference 2024.
Aßenmacher, M., Sauter, N. & Heumann, C. (2024). Classifying multilingual party manifestos: Domain transfer across country, time, and genre. Accepted at the Swiss Text Analytics Conference 2024. https://doi.org/https://doi.org/10.48550/arXiv.2307.16511
Stahl, F., Hamann, A., Hoff, K., & Kockelmann, K. (2024). Collaboration Models Between Industry and Academia. Whitepaper on behalf of the Section Industry Engagement of the National Research Data Infrastructure (NFDI). Zenodo. https://doi.org/10.5281/zenodo.10473579
Urchs S, Thurner V., Aßenmacher M., Heumann C., Thiemichen S. (2023). How Prevalent is Gender Bias in ChatGPT? – Exploring German and English ChatGPT Responses. “1st Workshop on Biased Data in Conversational Agents” (Co-Located with ECML PKDD 2023). https://doi.org/https://doi.org/10.48550/arXiv.2310.03031
Henninger, F. (2023, September 14). Born-fair data projects using cookiecutter templates. 1st Conference on Research Data Infrastructure (CoRDI), Karlsruhe. https://doi.org/10.52825/CoRDI.v1i.331
F. Boehm; U. Sax; O. Vettermann; P. Kamocki; V. Stoilova. (2023, September 13). „Hello ELSA, how are you?“ Legal and Ethical Challenges in RDM, Current and Future Tasks of ELSA Activities Against the Background of AI and Anonymisation. 1st Conference on Research Data Infrastructure (CoRDI), Karlsruhe. https://doi.org/10.52825/CoRDI.v1i.333
Rossenova, L., Shigapov, R., Schubotz, M., & Mietchen, D. (2023). KGI4NFDI: Knowledge Graph Infrastructure for the German National Research Data Infrastructure. Zenodo. https://doi.org/10.5281/zenodo.8337432
Rossenova, L., Schubotz, M., & Shigapov, R. (2023, September 7). The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI. Vol. 1 (2023): 1st Conference on Research Data Infrastructure (CoRDI) – Connecting Communities. https://doi.org/10.52825/cordi.v1i.266
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer. (2023). Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. https://doi.org/https://doi.org/10.48550/arXiv.2305.16376
Öztürk, I. T., Nedelchev, R., Heumann, C., Arias, E. G., Roger, M., Bischl, B., & Aßenmacher, M. (2023). How Different Is Stereotypical Bias Across Languages? https://doi.org/10.48550/arXiv.2307.07331
Rauch, Lukas, Aßenmacher, Matthias, Huseljic, Denis, Wirth, Moritz, Bischl, Bernd, and Bernhard Sick. (2023). ActiveGLAE: A Benchmark for Deep Active Learning with Transformers. https://doi.org/10.48550/arXiv.2306.10087
Weber, T., Ingrisch, M., Bischl, B., Rügamer, D. (2023). Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. In: Kashima, H., Ide, T., Peng, WC. (eds). Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science, 13937. https://doi.org/https://doi.org/10.48550/arXiv.2303.11224
Jeblick, K., Schachtner, B., Dexl, J. et al. (2023). ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol. https://link.springer.com/article/10.1007/s00330-023-10213-1
Klostermann, Jan; Meißner, Martin; Max, Alexander; Decker, Reinhold. (2023). Presentation of celebrities’ private life through visual social media. Journal of Business Research, Vol. 156. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.113524
Otto, W., Zloch, M., Gan, L., Karmakar, S., Dietze, S. (2023). GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets. Findings of the Association for Computational Linguistics: EMNLP 2023, Edited by Houda Bouamor, Juan Pino, and Kalika Bali. https://aclanthology.org/2023.findings-emnlp.548
Björn Deiseroth, Max Meuer, Nikolas Gritsch, Constantin Eichenberg, Patrick Schramowski, Matthias Aßenmacher, Kristian Kersting. (2023). Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization. https://doi.org/https://doi.org/10.48550/arXiv.2311.01544
Schulze, P., Wiegrebe, S., Thurner, P.W. et al. (2023). A Bayesian approach to modeling topic-metadata relationships. AStA Adv Stat Anal. https://link.springer.com/article/10.1007/s10182-023-00485-9
Vogel M., Aßenmacher M., Gubler A., Attin T., Schmidlin P.R. (2023). Cleaning potential of interdental brushes around orthodontic brackets – an in vitro investigation. Swiss Dental Journal, 133, 576–583. https://pubmed.ncbi.nlm.nih.gov/37096739/
Ludwig Bothmann, Lisa Wimmer, Omid Charrakh, Tobias Weber, Hendrik Edelhoff, Wibke Peters, Hien Nguyen, Caryl Benjamin, Annette Menzel. (2023). Automated wildlife image classification: An active learning tool for ecological applications, Ecological Informatics. Volume 77. https://doi.org/https://doi.org/10.1016/j.ecoinf.2023.102231
Aßenmacher M., Rauch L., Goschenhofer J., Stephan A., Bischl B., Roth B., Sick B,. (2023). Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. Proceedings of the 7th Workshop on Interactive Adaptive Learning (Co-Located with ECML-PKDD 2023), 65–73. https://ceur-ws.org/Vol-3470/paper7.pdf
Esteban Garces Arias, Vallari Pai, Matthias Schöffel, Christian Heumann, and Matthias Aßenmacher. (2023). Automatic Transcription of Handwritten Old Occitan Language. Association for Computational Linguistics, 15416–15439. https://doi.org/10.18653/v1/2023.emnlp-main.953
Koch, P., Nuñez, G. V., Arias, E. G., Heumann, C., Schöffel, M., Häberlin, A., Aßenmacher, M. (2023). A tailored Handwritten-Text-Recognition System for Medieval Latin. ArXiv. https://doi.org/10.48550/arXiv.2308.09368
Stocker, M., Rossenova, L., Shigapov, R., Betancort, N., Dietze, S., Murphy, B., Bölling, C., Schubotz, M., & Koepler, O. (2022). Knowledge Graphs – Working Group Charter (NFDI section-metadata). https://doi.org/10.5281/zenodo.7228955
Boehm, F., von Francken-Welz, M., Hallinan, D., Lan Nguyen, T., Oberländer, L., Petri, G., Stoilova, V., & Vettermann, O. (2022). Stellungnahme zum EU Data Act Proposal der Sektion ELSA (Ethical, Legal & Social Aspects) des Verein Nationale Forschungsdateninfrastruktur (NFDI) e.V (No. F3258672). https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13045-Datengesetz-und-geanderte-Vorschriften-uber-den-rechtlichen-Schutz-von-Datenbanken/F3258672_de
Lebmeier E., Aßenmacher M., Heumann C. (2022). On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis. Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), vol 13714. https://link.springer.com/chapter/10.1007/978-3-031-26390-3_31
Aßenmacher M., Dietrich M., Elmaklizi A., Hemauer E.M., Wagenknecht N. (2022). Whitepaper: New Tools for Old Problems. Zenodo. https://doi.org/https://zenodo.org/records/6606451
Goschenhofer J., Ragupathy P., Heumann C., Bischl B., Aßenmacher M. (2022). CC-Top: Constrained Clustering for Dynamic Topic Discovery. Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP), 26–34. https://doi.org/https://doi.org/10.18653/v1/2022.evonlp-1.5
Koch, P., Aßenmacher, M., & Heumann, C. (2022). Pre-trained language models evaluating themselves – A comparative study. Proceedings of the Third Workshop on Insights from Negative Results in NLP, 180–187. https://aclanthology.org/2022.insights-1.25.pdf
Herklotz, M., & Oberländer, L. (2022). iVA: Ein interaktiver Virtueller Assistent von BERD@BW zur Aufbereitung von Rechtsfragen im Bereich Open Science. E-Science-Tage 2021: Share Your Research Data, 306–313. https://doi.org/10.11588/heibooks.979.c13742
Gehrlein, S., Klein, A., Schumm, Irene, & Tochtermann, Klaus. (2022). BERD@NFDI für unstrukturierte Daten in den Wirtschafts- und Sozialwissenschaften. Zeitschrift Für Bibliothekswesen Und Bibliographie, 69(1–2), 59–67. https://doi.org/10.3196/1864295020691280
Gehrlein, S., Schumm, I., & Shigapov, R. (2022). BERD@BW – A Science Data Center to foster Open Science in Business, Economics and Social Sciences. E-Science-Tage 2021: Share Your Research Data, 314–319. https://doi.org/10.11588/heibooks.979.c13743
Shigapov, R., Mechnich, J., & Schumm, I. (2021). RaiseWikibase: Fast inserts into the BERD instance. The Semantic Web: ESWC 2021 Satellite Events. ESWC 2021. Lecture Notes in Computer Science, 12739, 60–64. https://doi.org/10.1007/978-3-030-80418-3_11
Kinne, J., & Lenz, D. (2021). Predicting innovative firms using web mining and deep learning. PLOS ONE, 16(4). https://doi.org/10.1371/journal.pone.0249071
Axtmann, A., Bach, F., Bauer, J., Blessing, A., Bönisch, T., Buck, N., Gauza, H., Hess, J., Holz, A., Jung, K., Kamzelak, R. S., Kaminski, A., Kramski, H. W., Krauß, P., Kuhn, J., Kushnarenko, V., Landwehr, M., Leendertse, J., Schembera, B., … Viehhauser, G. (2021). Kriterien für die Auswahl einer Softwarelösung für den Betrieb eines Repositoriums für Forschungsdaten. Bausteine Forschungsdatenmanagement, 3. https://doi.org/10.17192/bfdm.2021.3.8348
Mirtsch, M., Kinne, J., & Blind, K. (2021). Exploring the adoption of the international information security management system standard ISO/IEC 27001: a web mining-based analysis. IEEE Transactions on Engineering Management, 68(1), 87–100. https://doi.org/10.1109/TEM.2020.2977815
Dörr, J. O., Kinne, J., Lenz, D., Licht, G., & Winker, P. (2021). An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers. PLOS ONE. https://doi.org/10.1371/journal.pone.0263898
Shigapov, R., Zumstein, P., Kamlah, J., Oberländer, L., Mechnich, J., & Schumm, I. (2020). bbw: Matching CSV to Wikidata via Meta-lookup. CEUR Workshop Proceedings, 2775. http://ceur-ws.org/Vol-2775/paper2.pdf
Kinne, J., Krüger, M., Lenz, D., Licht, G., & Winker, P. (2020). Corona-Pandemie betrifft Unternehmen unterschiedlich. Tagesaktuelle Webseiten-Analyse zur Reaktion von Unternehmen auf die Corona-Pandemie in Deutschland (Nos. 20–05; ZEW-Kurzexpertise). https://ftp.zew.de/pub/zew-docs/ZEWKurzexpertisen/ZEW_Kurzexpertise2005.pdf
Krüger, M., Kinne, J., Lenz, D., & Resch, B. (2020). The Digital Layer: How innovative firms relate on the Web. ZEW-Centre for European Economic Research Discussion Paper, 20–003. https://www.zew.de/publikationen/the-digital-layer-how-innovative-firms-relate-on-the-web/
Kinne, J., & Axenbeck, J. (2020). Web mining for innovation ecosystem mapping: a framework and a large-scale pilot study. Scientometrics, 125(3). https://doi.org/10.1007/s11192-020-03726-9
Gehrlein, S., Kamlah, J., Pintsch, M., Schumm, I., & Weil, S. (2020). Vom Papier zur Datenanalyse. “Neue” historische Forschungsdaten für die Wirtschaftswissenschaften. In E-Science-Tage 2019: Data to Knowledge. heiBOOKS. https://doi.org/10.11588/heibooks.598.c8423
Zloch, M., Dessi’ D., D’Souza J., Castro L. J., Zapilko B., Karmakar S., Mathiak B., Stocker M., & Otto W., Auer S. & Dietze S. (n.d.). Research Knowledge Graphs: the Shifting Paradigm      of Scholarly Information Representation.