Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data

This is a dataset of FCS time-series with and without peak artifacts. It was created by 2D Monte Carlo simulations of diffusing particles. The provenance of the data is recorded in this file (see a rendered version here). This parent project (https://github.com/aseltmann/fluotracify]) also contains examples of how to use this data and related Python code to load it. The following connected paper is currently under review and should be cited together with this dataset: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review) Data structure inside each .csv file <header> 10 to 12 lines, contains metadata ... ... <source_1> <target_1a> <target_1b> <source_2> <target_2a> <target_2b> ... FCS time-series with artifact Artifact time series FCS time series without artifact FCS time series with artifact Artifact time series FCS time-series without artifact ... ... ... ... ... ... ... ...本数据集涵盖含峰伪影与不含峰伪影的荧光相关光谱（FCS, Fluorescence Correlation Spectroscopy）时间序列数据。该数据集通过对扩散粒子进行二维蒙特卡洛（Monte Carlo）模拟构建。数据的溯源信息已记录于本文件（可在此处查看渲染版本）。本关联项目（https://github.com/aseltmann/fluotracify]）还提供了该数据集的使用示例，以及用于加载该数据的相关Python代码。 下述关联论文目前处于审稿阶段，使用本数据集时需一并引用该文献： Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. 基于神经网络辅助的光子滤波可降低荧光相关光谱数据中的伪影. 2023年（目前处于审稿中） 每个.csv文件内部的数据结构如下： <header>：包含10至12行元数据 …… …… <source_1> <target_1a> <target_1b> <source_2> <target_2a> <target_2b> …… 含峰伪影的FCS时间序列 伪影时间序列 不含峰伪影的FCS时间序列 含峰伪影的FCS时间序列 伪影时间序列 不含峰伪影的FCS时间序列 …… …… …… …… …… …… …… ……