Experimental, computational and robotic data from: "Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish"

Experimental, computational and robotic data from: "Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish"with authors: Liu Lei, Ramón Escobedo, Clément Sire and Guy Theraulaz.There are three zip files:1. Fish.zip contains a single file with the data of experiments with 5 fish (H. rhodostomus) in a tank of 25 cm of radius.2. Agents.zip contains 11 files with the data of 5 agents in an arena of radius 25 cm, for the respective interaction strategies described in the article.3. Robots.zip contains 10 files with the data of 5 robots (cuboids) in an arena of radius 42 cm, for the respective interaction strategies described in the article except one.All dataset have the same structure of 5 columns containing the following information, in columns:#1: Experiment_ID#2: fish/agent/robot identity#3: time of kick of individual nº1#4: x-coordinate of the individual with ID shown in column #2#5: y-coordinate of the individual with ID shown in column #2Time is in seconds, distances are in meters.Data correspond to segmented trajectories, where instants of time denote the kicking time of individual #1. Data of individuals from #2 to #5 correspond to the position of the individual at the kicking instant of individual #1.The 11 conditions are specified in the name of each file:Strategy 0: no interaction between individuals (k = 0)Strategy 1: "NEAREST": interaction with the k = 1, 2, 3 nearest neighborsStrategy 2: "RANDOM": interaction with k = 1, 2, 3 neighbors selected randomlyStrategy 3: "MOST INFLUENTIAL": interaction with the k = 1, 2, 3 most influential neighborsStrategy 4: interaction with all the neighbors (k=4)For example, for the agents simulated with the model derived in [1], the 11 files are:----------------------------------------0.agent-no-interaction-k0.dat1.agent-nearest-k1.dat1.agent-nearest-k2.dat1.agent-nearest-k3.dat2.agent-random-k1.dat2.agent-random-k2.dat2.agent-random-k3.dat3.agent-mostinfluential-k1.dat3.agent-mostinfluential-k2.dat3.agent-mostinfluential-k3.dat4.agent-all-neighbors-k4.dat----------------------------------------[1] D.S. Calovi, A. Litchinko, V. Lecheval, U. Lopez, A. Pérez Escudero, H. Chaté, C. Sire, G. Theraulaz (2018) Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors. PLoS Comput. Biol. 14(1):e1005933.

本数据集源自论文"计算与机器人建模揭示群游鱼类个体间交互的简约组合模式"，作者为Liu Lei、Ramón Escobedo、Clément Sire与Guy Theraulaz，涵盖实验、计算与机器人学三类数据。

本数据集包含三个压缩包：
1. Fish.zip：内含单个数据文件，记录了在半径25cm的水槽中开展的5条红鼻鱼（H. rhodostomus）的实验数据。
2. Agents.zip：内含11个数据文件，对应论文中描述的各类交互策略下，在半径25cm的竞技场内运行的5个智能体（Agent）的相关数据。
3. Robots.zip：内含10个数据文件，对应论文中除某一种策略外的其余交互策略下，在半径42cm的竞技场内运行的5个长方体机器人的相关数据。

所有数据集均采用统一的5列结构，各列信息如下：
第1列：实验编号（Experiment_ID）
第2列：鱼/智能体/机器人的个体标识
第3列：个体1的kick时刻
第4列：第2列所标识个体的x轴坐标
第5列：第2列所标识个体的y轴坐标

时间单位为秒，距离单位为米。所有数据均为分段轨迹，各时间节点均对应个体1的kick时刻；第2至第5列的个体数据，为个体1触发kick时刻时，对应编号个体的空间位置。

11种实验/仿真条件由各数据文件的文件名指定，具体策略如下：
- 策略0：个体间无交互（k=0）
- 策略1：「最近邻（NEAREST）」：与k=1、2、3个最近邻产生交互
- 策略2：「随机（RANDOM）」：与随机选取的k=1、2、3个相邻个体产生交互
- 策略3：「最具影响力（MOST INFLUENTIAL）」：与k=1、2、3个最具影响力的相邻个体产生交互
- 策略4：与所有相邻个体产生交互（k=4）

以基于文献[1]所提模型仿真的智能体数据为例，其11个数据文件命名如下：
0.agent-no-interaction-k0.dat
1.agent-nearest-k1.dat
1.agent-nearest-k2.dat
1.agent-nearest-k3.dat
2.agent-random-k1.dat
2.agent-random-k2.dat
2.agent-random-k3.dat
3.agent-mostinfluential-k1.dat
3.agent-mostinfluential-k2.dat
3.agent-mostinfluential-k3.dat
4.agent-all-neighbors-k4.dat

[1] D.S. Calovi, A. Litchinko, V. Lecheval, U. Lopez, A. Pérez Escudero, H. Chaté, C. Sire, G. Theraulaz (2018) Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors. PLoS Comput. Biol. 14(1):e1005933.