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. 2018 Jul 17;5:180129. doi: 10.1038/sdata.2018.129

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Copyright © 2018, The Author(s)

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

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(a) Evaluation of a tracking algorithm. LTDB videos are provided as input to a tracking algorithm. Computed tracks can be compared with respect to the ground-truth tracks using a methodology of choice such as the complete graph comparison^15,16. In the example red arrows indicate errors where a cell was detected not sufficiently close, and when a track was interrupted. (b) Machine learning dataset. LTDB videos and tracks can potentially be used in the context of supervised machine learning as training and validation dataset. The generated predictive model can be generalized and used to track new videos. (c) Resource for big data analysis. Properties of leukocyte migration in different experimental conditions can potentially be discovered by the application of pattern recognition on LTDB metadata and tracks.