Campus-Institut Data Science (CIDAS)

Explorable Data Science

Research Focus

CIDAS members conduct research in various areas of Data Science and digitization, addressing both methodological and applied research questions. Social science perspectives on digitalization are also explored. Below, you will find a selection of projects and publications in which CIDAS members play a significant role. If you have questions regarding individual projects, you are invited to contact the respective project leads.

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Lectures
CIDAS Colloquium in Winter Semester 2024/25
24.10.2024, 14:15 Andreas Stephan From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Task Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143 21.11.2024, 14:15 Dr. Daniel Schanz Advances in Experimental Fluid Dynamics from a Data Science perspective Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143 28.11.2024, 14:15 Dr. Hossein Estiri A general approach for identifying profile shifts in functional genomics data Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143 05.12.2024, 14:15 Prof. Dr. Caroline Friedel A general approach for identifying profile shifts in functional genomics data Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143 19.12.2024, 14:15 Prof. Dr. Julian Kunkel Provision of AI Services for Research and Teaching with GWDG and KISSKI and Efficient Storage Systems Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143 23.01.2025, 14:15 Josh Moore Scalable strategies for a next-generation of FAIR bioimaging Goldschmidtstraße 1, Room 1.130 Get-Together: Room 2.143



Selected Projects

This project is funded by the German Research Foundation (DFG) as part of the Priority Program 2267 “The Digitalisation of Working Worlds” (11/23 to 10/26), carried out in cooperation with ISF Munich and ZZF Potsdam. Project duration: 11/2023 – 10/2026.

Technological Inscription as a Field of Performance Politics
Building on the first project “Politics of Performance,” which explored the interplay between broader corporate digitalization strategies and performance politics, this follow-up project focuses on technological inscription and appropriation in interaction with digitalization strategies on the one hand and performance policies on the other. Specifically, the project investigates how corporate digitalization strategies and technological innovations mutually influence each other. It examines how companies shape technology design and how digital technologies both expand corporate control options and require organizational adaptations that can limit autonomy. Furthermore, the project looks at how digital technology is utilized in concrete work processes. As with performance conditions and performance politics, technology development is understood as a contested field, shaped by diverse actors and their (sometimes contradictory) interests. Thus, the “politics of performance” extends to the “politics of inscription,” where users, providers, and industrial relations actors negotiate the concrete design of technology.

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Contact: Prof. Dr. Sarah Nies

Funded by the German Research Foundation (DFG) within the Priority Program “The Digitalisation of Working Worlds. Conceptualising and Capturing a Systemic Transformation” (SPP 2267). Project duration: 11/2020 – 10/2023.

Performance Politics in the Digital Transformation of Work
Digital technologies create new conditions for the control and management of labor and performance, including the shop floor. Automated data acquisition and processing increase transparency and surveillance potential, while the integration of physical components (Internet of Things) allows more direct interventions across entire value chains. In addition, self-learning systems, lightweight robotics, and digital assistance systems expand possibilities for flexible automation. Nevertheless, performance management extends beyond mere control and restrictions; it also involves motivating and activating employees. Furthermore, companies often pursue digital transformation strategies that go beyond labor rationalization alone, targeting broader system-level changes or reorganization across entire value chains (systemic rationalization).

Goals and Research Questions
This project examines how performance management in industrial work changes amid diverse and partly contradictory digitalization strategies pursued by companies and stakeholders, and how digitalization influences negotiations over working conditions, labor processes, and performance standards.

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Contact: Prof. Dr. Sarah Nies

Abstract: The project addresses the necessity of developing systems for automatic behavior classification as a basis for research in livestock science and a future key competency for early-career researchers. Understanding animal behavior is crucial for designing future-oriented husbandry systems, breeding, and nutrition. Automation and especially machine learning methods offer the possibility to generate larger volumes of data in shorter time frames, enabling more contemporary research questions. This project aims to strengthen data science competencies among young livestock researchers for behavior classification and the creation of individual temporal and spatial behavior profiles of pigs using video recordings and innovative machine learning approaches. Establishing such an innovative data culture for automated gathering of ethological traits is a fundamental skill for the next generation of researchers, supporting sustainable husbandry systems and robust animal breeding. Specifically, using video data from research projects focused on improving pig husbandry systems (with emphasis on animal welfare and tail-biting), the project will identify a set of methods that automatically classify specific pig behaviors and derive behavioral profiles. These profiles can then be used in ethological research, automatic phenotyping, or animal monitoring systems. The project’s innovative aspect lies in the lasting implementation of data science methods, solidly anchoring these competencies within future agricultural science curricula. This, in turn, promotes not only future livestock research but also the digital transformation of livestock farming.

Contact: Prof. Dr. Imke Traulsen or Prof. Dr. Thomas Kneib

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Abstract: The goal of this project is to provide GPU resources for scientific applications in the area of Machine Learning or Artificial Intelligence, with a focus on image analysis in life science applications, particularly neuroscience and bioimaging. The system is intended to significantly accelerate existing processes and enable new methods and applications, while optimizing resource utilization.

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Abstract: The central aim of KISSKI is to research AI methods and provide them via a high-availability AI service center for critical and sensitive infrastructures. The focus lies on the socially highly relevant fields of medicine and energy. These are key areas of applied AI research in Germany.

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Abstract: The CIDAS/Sartorius Quantitative Cell Analytics Initiative is a partnership between academic partners from the Göttingen Campus and Sartorius. The aim is to combine live cell imaging with state-of-the-art artificial intelligence to advance our understanding of fundamental biological processes, examine therapeutic interventions, and ultimately foster the development of new therapies.

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Selected Publications
2024
Bachanek, S., Wuerzberg, P., Biggemann, L., Janssen, T.Y., Nietert, M., Lotz, J., Zeuschner, P., Maßmann, A., Uhlig, A. & Uhlig, J. Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection. European Radiology, 2024 DOI: https://doi.org/10.1007/s00330-024-11026-6
Lu, Z., Siemer, S., Jha, P., Day, J.D., Manea, F. & Ganesh, V. Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis Proc. IJCAI 2024 More Information
Manea, F., Richardsen, J. & Schmid, M.L. Subsequences With Generalised Gap Constraints: Upper and Lower Complexity Bounds Proc. CPM 2024 More Information
Draghici, A., Haase, C. & Manea, F. Semënov Arithmetic, Affine VASS, and String Constraints Proc. STACS 2024 More Information
Adamson, D., Gawrychowski, P. & Manea, F. Enumerating m-Length Paths in Directed Graphs with Constant Delay Proc. LATIN 2024 More Information
2023
Day, J., Ganesh, V., Grewal, N. & Manea, F. On the Expressive Power of String Constraints Proceedings of the 50th ACM SIGPLAN Symposium on Principles of Programming Languages POPL 2023, Proc. ACM Program. Lang. (ACM) More Information
Kulczynski, M., Manea, F., Nowotka, D. & Bøgsted Poulsen, D. ZaligVinder: A generic test framework for string solvers J. Softw. Evol. Process. 35(4), 2023 More Information
Berzish, M., Day, J. D., Ganesh, V., Kulczynski, Manea, F., Mora, F. & Nowotka, D. Towards more efficient methods for solving regular-expression heavy string constraints Theor. Compute. Sci. 943: 50-72, 2023 More Information
Adamson, D., Kosche, M., Koß, T., Manea, F. & Siemer, S. Longest Common Subsequence with Gap Constraints Proc. WORDS 2023: 60-76 More Information
Adamson, D., Fleischmann, P., Huch, A., Koß, T., Manea, F. & Nowotka, D. k-Universality of Regular Languages Proc. ISAAC 2023: 4:1-4:21 More Information
Fleischmann, P., Kim, S., Koß, T., Manea, F., Nowotka, D., Siemer, S. & Wiedenhöft, M. Matching Patterns with Variables Under Simon's Congruence Proc. RP 2023: 155-170 More Information
Gawrychowski, P., Kosche, M. & Manea, F. On the Number of Factors in the LZ-End Factorization Proc. SPIRE 2023: 253-259 More Information
2022
Axenbeck, J., Berner, A. & Kneib, T. What Drives the Relationship Between Digitalization and Industrial Energy Demand? Exploring Firm-Level Heterogeneity ZEW - Centre for European Economic Research Discussion Paper No. 059, 2022 Available at SSRN
Gawrychowski, P., Manea, F. & Serafin, R. Fast and Longest Rollercoasters Algorithmica 84, 1081-1106. (Springer) DOI: https://doi.org/10.1007/s00453-021-00908-6
Kosche, M., Koß, T., Manea, F. & Pak, V. Subsequences in Bounded Ranges: Matching and Analysis Problems Proceedings of the International Conference on Reachability Problems RP 2022, LNCS (Springer) DOI: https://doi.org/10.1007/978-3-031-19135-0_10
Gawrychowski, P., Manea, F. & Siemer, S. Matching Patterns with Variables Under Edit Distance Proceedings of the International Symposium on String Processing and Information Retrieval SPIRE 2022, LNCS (Springer) DOI: https://doi.org/10.1007/978-3-031-20643-6_20
Kosche, M., Koß, T., Manea, F. & Siemer, S. Combinatorial Algorithms for Subsequence Matching: A Survey Proceedings of the International Workshop on Non-Classical Models of Automata and Applications NCMA 2022 (Invited Paper), EPTCS DOI: https://doi.org/10.48550/arXiv.2208.14722
Day, J., Kosche, M., Manea, F. & Schmid, M.L. Subsequences With Gap Constraints: Complexity Bounds for Matching and Analysis Problems Proceedings of the 33rd International Symposium on Algorithms and Computation ISAAC 2022, LIPICS More Information
Franke, K., Willeke, K.F., Ponder, K., Galdamez, M., Zhou, N., Muhammad, T., et al. State-dependent pupil dilation rapidly shifts visual feature selectivity Nature DOI: https://www.nature.com/articles/s41586-022-05270-3
Weisser, C., Gerloff, C., Thielmann, A., Python, A., Reuter, A., Kneib, T. & Säfken, B. Pseudo-Document Simulation for Comparing LDA, GSDMM and GPM Topic Models on Short and Sparse Text using Twitter Data Computational Statistics DOI: https://doi.org/10.1007/s00180-022-01246-z
Seufert, J., Python, A., Weisser, C., Cisneros, E., Kis-Katos, K. & Kneib, T. Mapping ex-ante spatial risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data Journal of the Royal Statistical Society: Series A DOI: http://doi.org/10.1111/rssa.12866
Soltau, J., Osterhoff M. & Salditt, T. Coherent diffractive imaging with diffractive optics Phys. Rev. Lett., Vol. 128, Iss. 22 DOI: https://journals.aps.org/prl/accepted/92074Y4cZ8510b70943290056cd810d85f966689f
Kant, G., Wiebelt, L., Weisser, C., Kis-Katos, K., Luber, M., Säfken, B. An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter International Journal of Data Science and Analytics DOI: https://doi.org/10.1007/s41060-022-00321-4
Rieseberg, T.P., Dadras, A., Fürst-Jansen, J.M.R., Dhabalia Ashok, A., Darienko, T., de Vries, S., Irisarri, I. & de Vries, J. Crossroads in the evolution of plant specialized metabolism. invited review Sem Cell Dev Biol In press
de Vries, S. & de Vries, J. Evolutionary genomic insights into cyanobacterial symbioses in plants. invited review Quant Plant Biol., 3:e16, 1–13 DOI: https://dx.doi.org/10.1017/qpb.2022.3
Pucker, B., Irisarri, I., de Vries, J. & Xu, B. Plant genome sequence assembly 3.0: progress, challenges, and future directions. invited review Quant Plant Biol., 3:e5, 1–14 DOI: https://dx.doi.org/10.1017/qpb.2021.18
von Pappenheim, F., Wensien M., Ye, J., Uranga, J., Irisarri, I., de Vries, J., Funk, L.-M., Mata, R., Tittmann, K. Widespread occurrence of covalent lysine–cysteine redox switches in proteins Nat Chem Biol DOI: https://doi.org/10.1038/s41589-021-00966-5
Formenti, G., Theissinger, K., Fernandes, C., Bista, I., Bombarely, A., Bleidorn, C., Ciofi, C., Crottini, A., Godoy, J.A., Höglund, J., Malukiewicz, J., Mouton, A., Oomen, R.A., Paez, S., Palsbøll, P.J., Pampoulie, C., Ruiz-Lopez, M.J., Svardal, H., Theofanopoulou, C., de Vries, J., Waldvogel, A.-M., Zhang, G., Mazzoni, C.J., Jarvis, E.D. & Bálint, M. The European Reference Genome Atlas Consortium. Bridging the gap between genomics and biodiversity conservation Trends Ecol Evol In press
2021
M. Reichardt, P.M. Jensen, V.A. Dahl, A.B. Dahl, M. Ackermann, H. Shah, F. Länger, C. Werlein, M.P. Kuehnel, D. Jonigk & T. Salditt 3D virtual histopathology of cardiac tissue from Covid-19 patients based on phase-contrast X-ray tomography eLife, 10:e71359 DOI: http://doi.org/10.7554/eLife.71359
J. Soltau, L. M. Lohse, M. Osterhoff & T. Salditt Finite-difference propagation for the simulation of x-ray multilayer optics Opt. Express, 25, 29, 41932-41953 DOI: http://doi.org/10.1364/OE.445300
M. Eckermann, B. Schmitzer, F. van der Meer, J. Franz, O. Hansen, C. Stadelmann & T. Salditt Three-dimensional virtual histology of the human hippocampus based on phase-contrast computed tomography Proc. Natl. Acad. Sci., 118, 48, e2113835118 DOI: http://doi.org/10.1073/pnas.2113835118
Fürst-Jansen, J.M.R, de Vries, S., Lorenz, M., von Schwartzenberg, K., Archibald, J.M. & de Vries, J. Submergence of the filamentous Zygnematophyceae Mougeotia induces differential gene expression patterns associated with core metabolism and photosynthesis Protoplasma DOI: https://doi.org/10.1007/s00709-021-01730-1
Irisarri, I., Darienko, T., Pröschold, T., Fürst-Jansen, J.M.R., Jamy, M. & de Vries, J. Unexpected cryptic species among streptophyte algae most distant to land plants Proc R Soc B 288:20212168 Cover Contribution
Wickell, D., Kuo, L.-Y., Yang, H.-P., Dhabalia Ashok, A., Irisarri, I., Dadras, A., de Vries, S., de Vries, J., Huang, Y.-M., Li, Z., Barker, M., Hartwick, N., Michael, T. & Li, F.-W. Underwater CAM photosynthesis elucidated by Isoetes genome Nat Commun 12:6348 More Information
Völkner, C., Holzer, L.J., Day, P.M., Dhabalia Ashok, A., de Vries, J., Bölter, B. & Kunz, H.-H. Two plastid POLLUX ion channel-like proteins are required for stress-triggered stromal Ca2+ release Plant Physiol 187:2110-2125 More Information
de Vries, S., Fürst-Jansen, J.M.R., Irisarri, I., Dhabalia Ashok, A., Ischebeck, T., Feussner, K., Abreu, I.N., Petersen, M., Feussner, I. & de Vries, J. The evolution of the phenylpropanoid pathway entailed pronounced radiation and divergence of enzyme families Plant J 107:975-1002 Research Highlight | Cover Contribution
Pyc, M., Gidda, S., Seay, D., Esnay, N., Kretzschmar, F., Cai, Y., Doner, N., Greer, M., Hull, J., Coulon, D., Bréhélin, C., Yurchenko, O., de Vries, J., Valerius, O., Braus, G., Ischebeck, T., Chapman, K., Dyer, J. & Mullen, R. LDIP cooperates with SEIPIN and LDAP to facilitate lipid droplet biogenesis in plants Plant Cell 33:3076-3103 More Information
Resemann, H., Herrfurth, C., Feussner, K., Hornung, E., Ostendorf, A., Gömann, J., Mittag, J., van Gessel, N., de Vries, J., Ludwig-Müller, J., Markham, J., Reski, R. & Feussner, I. Convergence of sphingolipid desaturation across over 500 million years of plant evolution Nat Plants 7: 219-232 More Information
Buchmueller, A., G. Kant, C. Weisser, B. Saefken, T. Kneib & K. Kis-Katos Twitmo: Twitter Topic Modeling and Visualization for R (R package version 0.1.2) 2021 More Information
Weisser, C., Lenel, F., Lu, Y., Kis-Katos, K. & Kneib, T. Using solar panels for business purposes: Evidence based on high-frequency power usage data Development Engineering DOI: https://doi.org/10.1016/j.deveng.2021.100074
Nietert, M., Vinnhoven, L., Auer, F., Hafkemeyer, S. & Stanke, F. Comprehensive analysis of chemical structures that have been tested as CFTR activating substances in a publicly available database CandActCFTR Frontiers in Pharmacology Accepted
Vinnhoven, L., Voskamp, M. & Nietert, M. Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase J. Pers. Med., 11(11), 1072 DOI: https://www.mdpi.com/2075-4426/11/11/1072
Tillmann, A., Thielmann, A., Kant, G., Weisser, C., Säfken, B., Silbersdorff, A. & Kneib, T. AuDoLab Automatic document labelling and classification for extremely unbalanced data Journal of Open Source Software, 6 (66), 3719 DOI: https://doi.org/10.21105/joss.03719
Kruse, R., Säfken, B., Silbersdorff, A. & Weisser, C. (Eds.) Learning Deep Textwork: Perspectives on Natural Language Processing and Artificial Intelligence Göttingen University Press, pages. 1-181 DOI: http://dx.doi.org/10.17875/gup2021-1608
Vinhoven, L., Stanke, F., Hafkemeyer, S., & Nietert, M.M. CFTR Lifecycle Map - A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations International Journal of Molecular Science, 22, 14 More Information
Gawrychowski, P., Manea, F. & Siemer, S. Matching Patterns with Variables under Hamming Distance Proc. MFCS 2021, LIPIcs To appear
Thielmann, A., Weisser, C., Krenz, A. & Säfken, B. Unsupervised Document Classification integrating Web-Mining, One-Class SVM and LDA Topic Modeling Journal of Applied Statistics (Special Issue: Statistical Approaches for Big Data and Machine Learning) DOI: https://www.tandfonline.com/doi/pdf/10.1080/02664763.2021.1919063
Berzish, M., Kulczynski, M., Mora, F., Manea, F., Day, J., Nowotka, D. & Ganesh, V. An SMT Solver for Regular Expressions and Linear Arithmetic over String Length Proceedings of CAV 2021, LNCS More Information
Gawrychowski, P., Kosche, M., Koss, T., Manea, F. & Siemer, S. Efficiently Testing Simon's Congruence Proc. STACS 2021, LIPIcs More Information
Day, J., Fleischmann, P., Kosche, M., Koss, T., Manea, F. & Siemer, S. The Edit Distance to k-Subsequence Universality Proc. STACS 2021, LIPIcs More Information