{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:35:24Z","timestamp":1777646124945,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"crossref","award":["Program Grant P2-0209"],"award-info":[{"award-number":["Program Grant P2-0209"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visual exploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic data analysis.<\/jats:p>","DOI":"10.1007\/s10994-025-06734-z","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T21:07:31Z","timestamp":1738012051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Uncovering temporal patterns in visualizations of high-dimensional data"],"prefix":"10.1007","volume":"114","author":[{"given":"Pavlin G.","family":"Poli\u010dar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bla\u017e","family":"Zupan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"6734_CR1","doi-asserted-by":"publisher","unstructured":"Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2023). Visualization of time-oriented data, 2nd ed., Springer,https:\/\/doi.org\/10.1007\/978-1-4471-7527-8","DOI":"10.1007\/978-1-4471-7527-8"},{"key":"6734_CR2","doi-asserted-by":"publisher","unstructured":"Ali, M., Jones, M., Xie, X., & Williams, M. (2018). Towards Visual Exploration of Large Temporal Datasets. In 2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA) pp. 1\u20139. https:\/\/doi.org\/10.1109\/BDVA.2018.8534025","DOI":"10.1109\/BDVA.2018.8534025"},{"issue":"1","key":"6734_CR3","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1109\/TVCG.2015.2467851","volume":"22","author":"B Bach","year":"2016","unstructured":"Bach, B., Shi, C., Heulot, N., Madhyastha, T., Grabowski, T., & Dragicevic, P. (2016). Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data. IEEE Transactions on Visualization and Computer Graphics, 22(1), 559\u2013568. https:\/\/doi.org\/10.1109\/TVCG.2015.2467851","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"1","key":"6734_CR4","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/nbt.4314","volume":"37","author":"E Becht","year":"2019","unstructured":"Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019). Dimensionality reduction for visualizing single-cell data using umap. Nature Biotechnology, 37(1), 38\u201344. https:\/\/doi.org\/10.1038\/nbt.4314","journal-title":"Nature Biotechnology"},{"key":"6734_CR5","doi-asserted-by":"publisher","unstructured":"Beck, F., Burch, M., Diehl, S., & Weiskopf, D. (2014). The state of the art in visualizing dynamic graphs. In R. Borgo, R. Maciejewski, I. Viola (Eds.) EuroVis - STARs. The Eurographics Association, Eindhoven, The Netherlands. https:\/\/doi.org\/10.2312\/eurovisstar.20141174","DOI":"10.2312\/eurovisstar.20141174"},{"issue":"1","key":"6734_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-13055-y","volume":"10","author":"AC Belkina","year":"2019","unstructured":"Belkina, A. C., Ciccolella, C. O., Anno, R., Halpert, R., Spidlen, J., & Snyder-Cappione, J. E. (2019). Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature Communications, 10(1), 1\u201312.","journal-title":"Nature Communications"},{"key":"6734_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102991","volume":"157","author":"GV Benito","year":"2024","unstructured":"Benito, G. V., Goldberg, X., Brachowicz, N., Casta\u00f1o-Vinyals, G., Blay, N., Espinosa, A., Davidhi, F., Torres, D., Kogevinas, M., de Cid, R., & Petrone, P. (2024). Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency. Artificial Intelligence in Medicine, 157, 102991. https:\/\/doi.org\/10.1016\/j.artmed.2024.102991","journal-title":"Artificial Intelligence in Medicine"},{"key":"6734_CR8","doi-asserted-by":"publisher","unstructured":"Bennett, C., Ryall, J., Spalteholz, L., & Gooch, A. (2007). The Aesthetics of Graph Visualization. In: Cunningham, D.W., Meyer, G., Neumann, L. (Eds.) Computational Aesthetics in Graphics, Visualization, and Imaging. The Eurographics Association, Eindhoven, The Netherlands. https:\/\/doi.org\/10.2312\/COMPAESTH\/COMPAESTH07\/057-064","DOI":"10.2312\/COMPAESTH\/COMPAESTH07\/057-064"},{"issue":"2","key":"6734_CR9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1177\/1473871615600010","volume":"15","author":"DB Coimbra","year":"2016","unstructured":"Coimbra, D. B., Martins, R. M., Neves, T. T., Telea, A. C., & Paulovich, F. V. (2016). Explaining three-dimensional dimensionality reduction plots. Information Visualization, 15(2), 154\u2013172. https:\/\/doi.org\/10.1177\/1473871615600010","journal-title":"Information Visualization"},{"key":"6734_CR10","doi-asserted-by":"publisher","unstructured":"Crnovrsanin, T., Muelder, C., Correa, C., & Ma, K.-L. (2009). Proximity-based visualization of movement trace data. In 2009 IEEE symposium on visual analytics science and technology pp. 11\u201318. https:\/\/doi.org\/10.1109\/VAST.2009.5332593","DOI":"10.1109\/VAST.2009.5332593"},{"key":"6734_CR11","doi-asserted-by":"publisher","first-page":"11482","DOI":"10.1109\/ACCESS.2020.2964413","volume":"8","author":"MA Da Silva Lopes","year":"2020","unstructured":"Da Silva Lopes, M. A., D\u00f3ria Neto, A. D., & De Medeiros Martins, A. (2020). Parallel t-sne applied to data visualization in smart cities. IEEE Access, 8, 11482\u201311490. https:\/\/doi.org\/10.1109\/ACCESS.2020.2964413","journal-title":"IEEE Access"},{"key":"6734_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100741","author":"A Dadu","year":"2022","unstructured":"Dadu, A., Satone, V. K., Kaur, R., Koretsky, M. J., Iwaki, H., Qi, Y. A., Ramos, D. M., Avants, B., Hesterman, J., Gunn, R., et al. (2022). Application of Aligned-UMAP to longitudinal biomedical studies. Patterns. https:\/\/doi.org\/10.1016\/j.patter.2023.100741","journal-title":"Patterns"},{"issue":"6","key":"6734_CR13","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.jbi.2007.03.010","volume":"40","author":"J Dem\u0161ar","year":"2007","unstructured":"Dem\u0161ar, J., Leban, G., & Zupan, B. (2007). FreeViz-an intelligent multivariate visualization approach to explorative analysis of biomedical data. Journal of biomedical informatics, 40(6), 661\u2013671.","journal-title":"Journal of biomedical informatics"},{"issue":"4","key":"6734_CR14","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1057\/palgrave.ivs.9500081","volume":"3","author":"T Dwyer","year":"2004","unstructured":"Dwyer, T., & Gallagher, D. R. (2004). Visualising changes in fund manager holdings in two and a half-dimensions. Information Visualization, 3(4), 227\u2013244. https:\/\/doi.org\/10.1057\/palgrave.ivs.9500081","journal-title":"Information Visualization"},{"issue":"1","key":"6734_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TVCG.2015.2468078","volume":"22","author":"S Elzen","year":"2016","unstructured":"Elzen, S., Holten, D., Blaas, J., & Wijk, J. J. (2016). Reducing snapshots to points: A visual analytics approach to dynamic network exploration. IEEE Transactions on Visualization and Computer Graphics, 22(1), 1\u201310. https:\/\/doi.org\/10.1109\/TVCG.2015.2468078","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"6734_CR16","unstructured":"Fisher, D. (2010). Animation for visualization: Opportunities and drawbacks. In Beautiful visualization. O\u2019Reilly Media, Sebastopol, CA, USA."},{"key":"6734_CR17","doi-asserted-by":"crossref","unstructured":"Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179\u2013188.","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"6734_CR18","doi-asserted-by":"publisher","DOI":"10.1145\/3387165","author":"A Hinterreiter","year":"2021","unstructured":"Hinterreiter, A., Steinparz, C., Sch\u00d6fl, M., Stitz, H., & Streit, M. (2021). Projection path explorer: Exploring visual patterns in projected decision-making paths. ACM Transactions on Interactive Intelligent Systems (TiiS). https:\/\/doi.org\/10.1145\/3387165","journal-title":"ACM Transactions on Interactive Intelligent Systems (TiiS)"},{"issue":"1","key":"6734_CR19","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/TVCG.2015.2467553","volume":"22","author":"D J\u00e4ckle","year":"2016","unstructured":"J\u00e4ckle, D., Fischer, F., Schreck, T., & Keim, D. A. (2016). Temporal mds plots for analysis of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 22(1), 141\u2013150. https:\/\/doi.org\/10.1109\/TVCG.2015.2467553","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"4","key":"6734_CR20","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/0893-6080(88)90003-2","volume":"1","author":"RA Jacobs","year":"1988","unstructured":"Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation. Neural Networks, 1(4), 295\u2013307.","journal-title":"Neural Networks"},{"key":"6734_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/b98835","volume-title":"Principal Component Analysis","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe, I. T. (2002). Principal Component Analysis. Springer. https:\/\/doi.org\/10.1007\/b98835"},{"issue":"1","key":"6734_CR22","doi-asserted-by":"publisher","first-page":"5416","DOI":"10.1038\/s41467-019-13056-x","volume":"10","author":"D Kobak","year":"2019","unstructured":"Kobak, D., & Berens, P. (2019). The art of using t-sne for single-cell transcriptomics. Nature Communications, 10(1), 5416. https:\/\/doi.org\/10.1038\/s41467-019-13056-x","journal-title":"Nature Communications"},{"key":"6734_CR23","unstructured":"Koffka, K. (1935). Principles of Gestalt Psychology. Principles of Gestalt psychology. pp. 720\u2013720. Harcourt, Brace, Oxford, England."},{"issue":"12","key":"6734_CR24","doi-asserted-by":"publisher","first-page":"2003","DOI":"10.1109\/TVCG.2014.2346250","volume":"20","author":"B Kondo","year":"2014","unstructured":"Kondo, B., & Collins, C. (2014). Dimpvis: Exploring time-varying information visualizations by direct manipulation. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2003\u20132012. https:\/\/doi.org\/10.1109\/TVCG.2014.2346250","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"1","key":"6734_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF02289565","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1\u201327. https:\/\/doi.org\/10.1007\/BF02289565","journal-title":"Psychometrika"},{"issue":"2","key":"6734_CR26","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02289694","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal, J. B. (1964). Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29(2), 115\u2013129. https:\/\/doi.org\/10.1007\/BF02289694","journal-title":"Psychometrika"},{"key":"6734_CR27","doi-asserted-by":"publisher","DOI":"10.4135\/9781412985130","volume-title":"Multidimensional Scaling","author":"JB Kruskal","year":"1978","unstructured":"Kruskal, J. B., & Wish, M. (1978). Multidimensional Scaling. SAGE Publications Inc. https:\/\/doi.org\/10.4135\/9781412985130"},{"issue":"7719","key":"6734_CR28","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","volume":"560","author":"G La Manno","year":"2018","unstructured":"La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., L\u00f6nnerberg, P., Furlan, A., Fan, J., Borm, L. E., Liu, Z., Bruggen, D., Guo, J., He, X., Barker, R., Sundstr\u00f6m, E., Castelo-Branco, G., \u2026 Kharchenko, P. V. (2018). RNA velocity of single cells. Nature, 560(7719), 494\u2013498. https:\/\/doi.org\/10.1038\/s41586-018-0414-6","journal-title":"Nature"},{"key":"6734_CR29","doi-asserted-by":"publisher","unstructured":"Lee, J. A., & Verleysen, M. (2009). Quality assessment of dimensionality reduction: Rank-based criteria. Neurocomputing,72(7), 1431\u20131443. https:\/\/doi.org\/10.1016\/j.neucom.2008.12.017 Advances in Machine Learning and Computational Intelligence","DOI":"10.1016\/j.neucom.2008.12.017"},{"key":"6734_CR30","doi-asserted-by":"publisher","unstructured":"Lee, J. A., Peluffo-Ord\u00f3\u00f1ez, D. H., & Verleysen, M. (2015). Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing,169, 246\u2013261. https:\/\/doi.org\/10.1016\/j.neucom.2014.12.095 Learning for Visual Semantic Understanding in Big Data ESANN 2014 Industrial Data Processing and Analysis","DOI":"10.1016\/j.neucom.2014.12.095"},{"issue":"1","key":"6734_CR31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41524-018-0139-y","volume":"5","author":"X Li","year":"2019","unstructured":"Li, X., Dyck, O. E., Oxley, M. P., Lupini, A. R., McInnes, L., Healy, J., Jesse, S., & Kalinin, S. V. (2019). Manifold learning of four-dimensional scanning transmission electron microscopy. npj Computational Materials, 5(1), 5. https:\/\/doi.org\/10.1038\/s41524-018-0139-y","journal-title":"npj Computational Materials"},{"key":"6734_CR32","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"6734_CR33","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv e-prints arXiv:1802.03426 [stat.ML]","DOI":"10.21105\/joss.00861"},{"key":"6734_CR34","doi-asserted-by":"publisher","unstructured":"Poli\u010dar, P. G., Stanimirovi\u0107, D., & Zupan, B. (2023). Nation-wide eprescription data reveals landscape of physicians and their drug prescribing patterns in slovenia. In J. M. Juarez, M. Marcos, G. Stiglic, A. Tucker (Eds.) Artificial Intelligence in Medicine pp. 283\u2013292. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-031-34344-5_34","DOI":"10.1007\/978-3-031-34344-5_34"},{"key":"6734_CR35","doi-asserted-by":"publisher","unstructured":"Poli\u010dar, P. G., Zupan, B. (2023). Refining temporal visualizations using the directional coherence loss. In: A. Bifet, A. C. Lorena, R. P. Ribeiro,J. Gama, P. H. Abreu (Eds.) Discovery science, pp. 204\u2013215. Springer, Cham.https:\/\/doi.org\/10.1007\/978-3-031-45275-8_14","DOI":"10.1007\/978-3-031-45275-8_14"},{"key":"6734_CR36","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s10994-021-06043-1","volume":"112","author":"PG Poli\u010dar","year":"2021","unstructured":"Poli\u010dar, P. G., Stra\u017ear, M., & Zupan, B. (2021). Embedding to reference t-SNE space addresses batch effects in single-cell classification. Machine Learning, 112, 721\u2013740. https:\/\/doi.org\/10.1007\/s10994-021-06043-1","journal-title":"Machine Learning"},{"issue":"3","key":"6734_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v109.i03","volume":"109","author":"PG Poli\u010dar","year":"2024","unstructured":"Poli\u010dar, P. G., Stra\u017ear, M., & Zupan, B. (2024). openTSNE: A Modular Python Library for t-SNE Dimensionality Reduction and Embedding. Journal of Statistical Software, 109(3), 1\u201330. https:\/\/doi.org\/10.18637\/jss.v109.i03","journal-title":"Journal of Statistical Software"},{"issue":"5","key":"6734_CR38","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1006\/jvlc.2002.0232","volume":"13","author":"HC Purchase","year":"2002","unstructured":"Purchase, H. C. (2002). Metrics for graph drawing aesthetics. Journal of Visual Languages & Computing, 13(5), 501\u2013516. https:\/\/doi.org\/10.1006\/jvlc.2002.0232","journal-title":"Journal of Visual Languages & Computing"},{"key":"6734_CR39","doi-asserted-by":"publisher","unstructured":"Rauber, P. E., Falc\u00e3o, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. In EuroVis 2016 - Short Papers, pp. 73\u201377. The Eurographics Association, Eindhoven, The Netherlands. https:\/\/doi.org\/10.2312\/eurovisshort.20161164","DOI":"10.2312\/eurovisshort.20161164"},{"issue":"3","key":"6734_CR40","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1111\/cgf.12655","volume":"34","author":"B Rieck","year":"2015","unstructured":"Rieck, B., & Leitte, H. (2015). Persistent homology for the evaluation of dimensionality reduction schemes. Computer Graphics Forum, 34(3), 431\u2013440. https:\/\/doi.org\/10.1111\/cgf.12655","journal-title":"Computer Graphics Forum"},{"key":"6734_CR41","doi-asserted-by":"publisher","unstructured":"Silva, R., Spritzer, A., & Dal Sasso\u00a0Freitas, C. (2018). Visualization of roll call data for supporting analyses of political profiles. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI) pp. 150\u2013157 .https:\/\/doi.org\/10.1109\/SIBGRAPI.2018.00026","DOI":"10.1109\/SIBGRAPI.2018.00026"},{"issue":"1","key":"6734_CR42","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/21.87055","volume":"18","author":"R Tamassia","year":"1988","unstructured":"Tamassia, R., Di Battista, G., & Batini, C. (1988). Automatic graph drawing and readability of diagrams. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 61\u201379. https:\/\/doi.org\/10.1109\/21.87055","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"6734_CR43","doi-asserted-by":"publisher","unstructured":"Taylor, M., & Rodgers, P. (2005). Applying graphical design techniques to graph visualisation. In 9th International Conference on Information Visualisation (IV\u201905) pp. 651\u2013656. https:\/\/doi.org\/10.1109\/IV.2005.19","DOI":"10.1109\/IV.2005.19"},{"key":"6734_CR44","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/3-540-44668-0_68","volume-title":"Artificial Neural Networks - ICANN 2001","author":"J Venna","year":"2001","unstructured":"Venna, J., & Kaski, S. (2001). Neighborhood preservation in nonlinear projection methods: An experimental study. In G. Dorffner, H. Bischof, & K. Hornik (Eds.), Artificial Neural Networks - ICANN 2001 (pp. 485\u2013491). Berlin, Heidelberg: Springer."},{"issue":"2","key":"6734_CR45","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1057\/palgrave.ivs.9500013","volume":"1","author":"C Ware","year":"2002","unstructured":"Ware, C., Purchase, H., Colpoys, L., & McGill, M. (2002). Cognitive measurements of graph aesthetics. Information Visualization, 1(2), 103\u2013110. https:\/\/doi.org\/10.1057\/palgrave.ivs.9500013","journal-title":"Information Visualization"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-025-06734-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-025-06734-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-025-06734-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T02:00:21Z","timestamp":1739844021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-025-06734-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["6734"],"URL":"https:\/\/doi.org\/10.1007\/s10994-025-06734-z","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"27 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors report no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"35"}}