Deborah N. Jakobi

VLDB 2026 Research / reviewers in the wild / expert

dblp:345/2232 · also Deborah Noemie Jakobi · DBLP profile ↗

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9ranked-venue papers

3as first author

9since 2021 · last 2026

0000-0002-9719-6673ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 7 · 3 first-author · 7 since 2021Human-computer interaction and ubiquitous computing · 7 · 3 first-author · 7 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021

Year	Publication	Venue	Position
2026	Preprocessing Eye-Tracking Data for Reading: A Community Survey on Practices, Challenges, and Needs abstract Preprocessing eye-tracking data is a critical yet challenging step in eye-tracking research, particularly in reading studies. Current practices are characterized by limited standardization, a strong reliance on proprietary or inflexible tools, and heterogeneous documentation routines, which hinder reproducibility, comparability, and data reuse. To systematically assess community practices and needs, we conducted a large-scale survey (N = 108) with a focus on reading research. The survey examined preprocessing workflows, tool usage, perceived challenges, data sharing and documentation strategies, and expectations regarding preprocessing tools and outputs. The findings indicate a strong demand for transparent, interoperable, and well-documented preprocessing workflows, as well as practical guidance such as tutorials and user support. Participants emphasized the importance of standardized terminology, flexible automation, and reusable output structures. These results provide an empirical basis for tool development, methodological guidelines, and infrastructure initiatives aimed at improving transparency, standardization, and long-term reusability in eye-tracking research. Marie-Luise Müller, Carlson Moses Büth, Deborah N. Jakobi, Daniel Krakowczyk, Eva Pavlinusic Vilus, Anastassia Shaitarova, Lena A. Jäger	ETRA	3
2026	The MultiplEYE Text Corpus: Towards a Diverse and Ever-Expanding Multilingual Text Corpus Ramune Kaspere, Anna Bondar, Sergiu Nisioi, Maja Stegenwallner-Schütz, Hanne B. Søndergaard Knudsen, Ana Matic Skoric, Eva Pavlinusic Vilus, Dorota Klimek-Jankowska, Chiara Tschirner, Not Battesta Soliva, Deborah N. Jakobi, Cui Ding, Dima Abu Romi, Cengiz Acartürk, Matilda Agdler, Anton Marius Alexandru, Mohd Faizan Ansari, Annalisa Arcidiacono, Elizabete Ausma Velta Barisa, Ana Bautista, Lisa Beinborn, Yevgeni Berzak, Nedeljka Bjelanovic, Anna Isabelle Bothmann, Jan Brasser, Caterina Cacioli, Anila Çepani, Ilze Ceple, Adelina Çerpja, Dalí Chirino, Jan Chromý, Alessandro Corona Mendozza, Iria de-Dios-Flores, Nazik Dinçtopal Deniz, Ana Dosen, Kristian Elersic, Inmaculada Fajardo, Zigmunds Freibergs, Angelina Ganebnaya, Jessica Gomes, Annjo Klungervik Greenall, Alba Haveriku, Anamaria Hodivoianu, Yu-Yin Hsu, Amanda Isaksen, Andreia Janeiro, Kristine M. Jensen de López, Aleksandar Jevremovic, Vojislav Jovanovic, Hanna Kedzierska, Nik Kharlamov, Sara Kosutar, Nelda Kote, Vanja Kovic, Izabela Krejtz, Thyra Krosness, Oleksandra Kuvshynova, Eilam Lavy, Ella Lion, Marta Lockiewicz, Kaidi Lõo, Paula Luegi, Mircea Mihai Marin, Clara Martin, Svitlana Matvieieva, Diane C. Mézière, Xavier Mínguez-López, Valeriia Modina, Jurgita Motiejuniene, Marie-Luise Müller, Tolgonai Nasipbek kyzy, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Patrizia Paggio, Marijan Palmovic, Maria Christina Panagiotopoulou, Alberto Parola, Helena Pérez, Klaudia Petersen, Anja Podlesek, Eva Pospísilová, Marta Praulina, Mikulás Preininger, Loredana Punga, Diego Rossini, Spela Rot, Habib Sani Yahaya, Irina A. Sekerina, Anne Gabija Skadina, Jordi Solé i Casals, Lonneke van der Plas, Saara M. Varjopuro, Spyridoula Varlokosta, João Veríssimo, Oskari Juhapekka Virtanen, Nemanja Vracar, Mila Dimitrova-Vulchanova, Ahmad Mustapha Wali, Peizheng Wu, Nilgün Yücel, Stefan Frank, Nora Hollenstein, Lena A. Jäger, Somayeh Bakhtiari	LREC	11
2025	Predicting Children's Reading Comprehension Through Eye Movements: Insights from Visual Search and Interpretable Machine Learning abstract Early identification of children at risk of reading difficulties is paramount for promoting educational success and equity, as earlier interventions are more effective. Traditional assessment methods of early reading abilities, however, are resource-intensive, and often require basic reading abilities. Predicting reading acquisition from non-reading tasks, particularly during the early stages of formal reading instruction, overcomes these limitations. In this paper, we investigate to what extent eye movements recorded during a visual search task that is hypothesized to correlate with reading ability allows to predict children’s reading comprehension scores at the time of recording as well as one year later. Using machine learning methods that allow for an evaluation of feature importance, namely Neural Additive Models and Random Forests, we explore what eye movement features obtained from a visual search task are predictive of reading comprehension, thus laying the groundwork for future research in early assessment systems based on eye movements. Jan Brasser, Chiara Tschirner, Maja Stegenwallner-Schütz, Deborah N. Jakobi, Lena A. Jäger	ETRA	4
2025	Neural Additive Models Uncover Predictive Gaze Features in Reading Deborah N. Jakobi, David R. Reich, Paul Prasse, Lena A. Jäger	ETRA	1
2025	MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus abstract Contains fulltext : 326363.pdf (Publisher’s version ) (Open Access) Deborah N. Jakobi, Maja Stegenwallner-Schütz, Nora Hollenstein, Cui Ding, Ramune Kaspere, Ana Matic Skoric, Eva Pavlinusic Vilus, Stefan Frank, Marie-Luise Müller, Kristine M. Jensen de López, Nik Kharlamov, Hanne B. Søndergaard Knudsen, Yevgeni Berzak, Ella Lion, Irina A. Sekerina, Cengiz Acartürk, Mohd Faizan Ansari, Katarzyna Harezlak, Pawel Kasprowski, Ana Bautista, Lisa Beinborn, Anna Bondar, Antonia Boznou, Leah Bradshaw, Jana Mara Hofmann, Thyra Krosness, Not Battesta Soliva, Anila Çepani, Kristina Cergol, Ana Dosen, Marijan Palmovic, Adelina Çerpja, Dalí Chirino, Jan Chromý, Vera Demberg, Iza Skrjanec, Nazik Dinçtopal Deniz, Inmaculada Fajardo, Mariola Giménez-Salvador, Xavier Mínguez-López, Maros Filip, Zigmunds Freibergs, Jessica Gomes, Andreia Janeiro, Paula Luegi, João Veríssimo, Sasho Gramatikov, Jana Hasenäcker, Alba Haveriku, Nelda Kote, Muhammad Mohsin Kamal, Hanna Kedzierska, Dorota Klimek-Jankowska, Sara Kosutar, Daniel Krakowczyk, Izabela Krejtz, Marta Lockiewicz, Kaidi Lõo, Jurgita Motiejuniene, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Mikulás Preininger, Loredana Punga, David R. Reich, Chiara Tschirner, Spela Rot, Andreas Säuberli, Jordi Solé i Casals, Ekaterina Strati, Igor Svoboda, Evis Trandafili, Spyridoula Varlokosta, Mila Dimitrova-Vulchanova, Lena A. Jäger	ETRA	1
2025	The More the Merrier: Boost Your Dataset Visibility and Discover Eye-Tracking Datasets with pymovements Daniel Krakowczyk, David R. Reich, Andreas Säuberli, Iza Skrjanec, Isabelle Caroline Rose Cretton, Deborah N. Jakobi, Sergiu Nisioi, Paul Prasse, Lena A. Jäger	ETRA	6
2024	Reporting Eye-Tracking Data Quality: Towards a New Standard abstract Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking datasets for more diverse and initially not considered use cases, this work advocates a new approach of sharing eye-tracking data. Instead of publishing filtered and pre-processed datasets, the eye-tracking data at all pre-processing stages should be published together with data quality reports. In order to transparently report data quality and enable cross-dataset comparisons, we develop data quality reporting standards and metrics that can be automatically applied to a dataset, and integrate them into the open-source Python package pymovements (https://github.com/aeye-lab/pymovements). Deborah N. Jakobi, Daniel Krakowczyk, Lena A. Jäger	ETRA	1
2023	ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts abstract Eye movements in reading play a crucial role in psycholinguistic research studying the cognitive mechanisms underlying human language processing.More recently, the tight coupling between eye movements and cognition has also been leveraged for language-related machine learning tasks such as the interpretability, enhancement, and pre-training of language models, as well as the inference of reader-and text-specific properties.However, scarcity of eye movement data and its unavailability at application time poses a major challenge for this line of research.Initially, this problem was tackled by resorting to cognitive models for synthesizing eye movement data.However, for the sole purpose of generating humanlike scanpaths, purely data-driven machinelearning-based methods have proven to be more suitable.Following recent advances in adapting diffusion processes to discrete data, we propose SCANDL, a novel discrete sequence-tosequence diffusion model that generates synthetic scanpaths on texts.By leveraging pretrained word representations and jointly embedding both the stimulus text and the fixation sequence, our model captures multi-modal interactions between the two inputs.We evaluate SCANDL within-and across-dataset and demonstrate that it significantly outperforms state-of-the-art scanpath generation methods.Finally, we provide an extensive psycholinguistic analysis that underlines the model's ability to exhibit human-like reading behavior.Our implementation is made available at https://github.com/DiLi-Lab/ScanDL. Lena S. Bolliger, David R. Reich, Patrick Haller 0001, Deborah N. Jakobi, Paul Prasse, Lena A. Jäger	EMNLP	4
2023	pymovements: A Python Package for Eye Movement Data Processing abstract We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional data into velocity data, detecting gaze events like saccades and fixations, computing event properties like saccade amplitude and fixational dispersion and visualizing data and results with several types of plotting methods. Moreover, pymovements also provides an easily accessible interface for downloading and processing publicly available datasets. Additionally, we emphasize how rigorous testing in scientific software packages is critical to the reproducibility and transparency of research, enabling other researchers to verify and build upon previous findings. Daniel Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi, Paul Prasse, Assunta Süss, Oleksii Turuta, Pawel Kasprowski, Lena A. Jäger	ETRA	4