在生命科学中，跟踪电影中的物体使研究人员能够量化单个粒子、细胞器、细菌、细胞甚至整个动物的行为。虽然现在有许多工具允许自动跟踪视频，但编译、分析和探索这些方法生成的大型数据集仍然存在重大挑战。在这里，我们介绍 CellTracksColab，这是一个专为简化细胞跟踪数据的探索和分析而定制的平台。 CellTracksColab 有助于跨多个视野、条件和重复的结果的编译和分析，确保整体数据集概览。 CellTracksColab 还利用高维数据缩减和聚类的力量，使研究人员能够无偏见地识别不同的行为模式和趋势。最后，CellTracksColab 还包括专门的分析模块，可实现空间分析（聚类、接近特定感兴趣区域）。我们通过 3 个用例展示了 CellTracksColab 的功能，包括 T 细胞和癌细胞迁移以及丝状伪足动力学。 CellTracksColab 可供更广泛的科学界使用，网址为 https://github.com/CellMigrationLab/CellTracksColab。版权所有：© 2024 Gómez-de-Mariscal 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.Copyright: © 2024 Gómez-de-Mariscal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.