Geidel, B., Siegel, M., Steyrl, D., Goldberg, A. E., Bodenmann, G., & Zemp, M. (2025). Study Protocol for the Rainbow Austrian Longitudinal Family (RALF) study: A longitudinal, multi-method, multi-rater investigation of risk and resilience factors in Austrian LGBTQ+ parent families. BMC Psychology, 13. https://doi.org/10.31219/osf.io/p6zhj_v1, https://doi.org/10.1186/s40359-025-02828-4

Lor, C. S., Steyrl, D., Karner, A., Götzendorfer, S. J., Klimesch, A., Eder, S. J., Renz, F. M., Rother, J., Scharnowski, F., & Melinscak, F. (2025). SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli. Scientific Data, 12(1), 599. https://doi.org/10.1038/s41597-025-04908-x

Zhang, M., Karner, A., Kostorz, K., Shea, S., Steyrl, D., Melinscak, F., Sladky, R., Lor, C. S., & Scharnowski, F. (2025). SpiDa-MRI behavioral and (f)MRI data of adults with fear of spiders. Scientific Data, 12(1), 284. Article 284. https://doi.org/10.1038/s41597-025-04569-w

Nicholson, A. A., Lieberman, J. M., Hosseini-Kamkar, N., Eckstrand, K., Rabellino, D., Kearney, B., Steyrl, D., Narikuzhy, S., Densmore, M., Théberge, J., Hosseiny, F., & Lanius, R. A. (2025). Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 136, Article 111180. https://doi.org/10.1016/j.pnpbp.2024.111180

Todorova, B., Steyrl, D., Hornsey, M. J., Pearson, S., Brick, C., Lange, F., Van Bavel, J. J., Vlasceanu, M., Lamm, C., & Doell, K. C. (2025). Machine learning identifies key individual and nation-level factors predicting climate-relevant beliefs and behaviors. npj climate action, 4(1), 46. https://doi.org/10.1038/s44168-025-00251-4

Siegel, M., Steyrl, D., Goldberg, A. E., Nicholson, A., & Zemp, M. (2025). Minority stress and structural stigma predict well-being in European LGBTQ+ parents. Journal of Marriage and Family, 87(3), 1009-1037. https://doi.org/10.1111/jomf.13071

Chiappini, E., Massaccesi, C., Korb, S., Steyrl, D., Willeit, M., & Silani, G. (2024). Neural Hyperresponsivity During the Anticipation of Tangible Social and Nonsocial Rewards in Autism Spectrum Disorder: A Concurrent Neuroimaging and Facial Electromyography Study. Biological psychiatry. Cognitive neuroscience and neuroimaging, 9(9), 948-957. https://doi.org/10.1016/j.bpsc.2024.04.006

Spee, B. T. M., Leder, H., Mikuni, J., Scharnowski, F., Pelowski, M., & Steyrl, D. (2024). Using Machine Learning to Predict Judgments on Western Visual Art Along Content-Representational and Formal-Perceptual Attributes. PLoS ONE, 19(9), Article e0304285. https://doi.org/10.1371/journal.pone.0304285

Mikuni, J., Spee, B. T. M., Forlani, G., Leder, H., Scharnowski, F., Nakamura, K., Watanabe, K., Kawabata, H., Pelowski, M., & Steyrl, D. (2024). Cross-Cultural Comparison of Beauty Judgments in Visual Art Using Machine Learning Analysis of Art Attribute Predictors Among Japanese and German Speakers. Scientific Reports, 14(1), Article 15948. https://doi.org/10.1038/S41598-024-65088-Z

Thanhaeuser, M., Gsoellpointner, M., Kornsteiner-Krenn, M., Steyrl, D., Brandstetter, S., Jilma, B., Berger, A., & Haiden, N. (2024). Introduction of Solid Foods in Preterm Infants and Its Impact on Growth in the First Year of Life-A Prospective Observational Study. Nutrients, 16(13), Article 2077. https://doi.org/10.3390/nu16132077

Juergensen, V., Peter, L.-J., Steyrl, D., Lor, C. S., Bui, A. P., McLaren, T., Muehlan, H., Tomczyk, S., Schmidt, S., & Schomerus, G. (2024). The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach. Frontiers in Public Health, 12, Article 1504720. https://doi.org/10.3389/fpubh.2024.1504720

Karner, A., Zhang, M., Lor, C. S., Steyrl, D., Götzendorfer, S. J., Weidt, S., Melinscak, F., & Scharnowski, F. (2024). The "SpiDa" dataset: self-report questionnaires and ratings of spider images from spider-fearful individuals. Frontiers in Psychology, 15, Article 1327367. https://doi.org/10.3389/fpsyg.2024.1327367

Park, A. H., Patel, H., Mirabelli, J., Eder, S. J., Steyrl, D., Lueger-Schuster, B., Scharnowski, F., O'Connor, C., Martin, P., Lanius, R. A., McKinnon, M. C., & Nicholson, A. (2023). Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles. Psychological Trauma, 17(2), 372-386. https://doi.org/10.1037/tra0001602

Lieberman, J. M., Rabellino, D., Densmore, M., Frewen, P. A., Steyrl, D., Scharnowski, F., Théberge, J., Hosseini-Kamkar, N., Neufeld, R. W. J., Jetly, R., Frey, B. N., Ros, T., Lanius, R. A., & Nicholson, A. A. (2023). A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study. Frontiers in Neuroscience, 17, Article 1229729. https://doi.org/10.3389/fnins.2023.1229729

Spee, B. T. M., Mikuni, J., Leder, H., Scharnowski, F., Pelowski, M., & Steyrl, D. (2023). Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings. Scientific Reports, 13(1), Article 12966. https://doi.org/10.1038/s41598-023-39865-1