DOI: 10.5061/dryad.qrfj6q5sx
title: Data from: Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs

description: Jellyfish cyborgs present a promising avenue for soft robotic systems,
 leveraging the natural energy-efficiency and adaptability of biological
 systems. Here we present an approach for predicting and controlling
 jellyfish locomotion by harnessing the natural embodied intelligence of
 these animals. We developed an integrated muscle electrostimulation and 3D
 motion capture system to quantify both spontaneous and stimulus-induced
 behaviors in Aurelia coerulea jellyfish. Our key findings include an
 investigation of self-organized criticality in jellyfish swimming motions
 and the identification of optimal periods of electro-stimulus input signal
 (1.5 and 2.0 seconds) for eliciting coherent and predictable swimming
 behaviors. Furthermore, using Reservoir Computing, a machine learning
 framework, we successfully predicted future movements of the stimulated
 jellyfish, which also characterizes how the jellyfish swimming motions are
 synchronized with the electro-stimulus. Our findings provide a foundation
 for developing jellyfish cyborgs capable of autonomous navigation and
 environmental exploration, with potential applications in ocean monitoring
 and pollution management.
description: # Harnessing natural embodied intelligence for spontaneous jellyfish
 cyborgs (2025) A brief summary of dataset: Time series data on the
 three-dimensional swimming locomotion trajectories of an individual
 jellyfish (ID: JF41) in a water tank environment. It encompasses eight
 marker positions (R1, R2, O1, O2, Y1, Y2, B1, B3) and examines two
 distinct conditions: spontaneous swimming (free) and muscle electrical
 stimulation (stim). The analysis is conducted across five different
 stimulus parameters, specifically the burst period (tau). Journal: Nature
 Communications Title: Harnessing Natural Embodied Intelligence for
 Spontaneous Jellyfish Cyborgs Authors: Dai Owaki(1), Max Austin(2), Shuhei
 Ikeda(3), Kazuya Okuizumi(3), Kohei Nakajima(2) Affiliations:
 (1)Department of Robotics, Graduate School of Engineering,Tohoku
 University, 6-6-01 Aoba, Aramaki, Aoba-ku, Sendai, 980-8579, Japan.
 (2)Graduate School of Information Science and Technology, The University
 of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. (3)Kamo
 Aquarium, 657-1 Okubo, Imaizumi, Tsuruoka, 997-1206, Yamagata, Japan.
 *corredpondance: [owaki@tohoku.ac.jp](mailto:owaki@tohoku.ac.jp) DOI :
  10.1038/s41467-025-59889-7 Abstract: Jellyfish cyborgs present a
 promising avenue for soft robotic systems, leveraging the natural
 energy-efficiency and adaptability of biological systems. Here we present
 an approach for predicting and controlling jellyfish locomotion by
 harnessing the natural embodied intelligence of these animals. We
 developed an integrated muscle electrostimulation and 3D motion capture
 system to quantify both spontaneous and stimulus-induced behaviors in
 Aurelia coerulea jellyfish. Our key findings include an investigation of
 self-organized criticality in jellyfish swimming motions and the
 identification of optimal periods of electro-stimulus input signal (1.5
 and 2.0 seconds) for eliciting coherent and predictable swimming
 behaviors. Furthermore, using Reservoir Computing, a machine learning
 framework, we successfully predicted future movements of the stimulated
 jellyfish, which also characterizes how the jellyfish swimming motions are
 synchronized with the electro-stimulus. Our findings provide a foundation
 for developing jellyfish cyborgs capable of autonomous navigation and
 environmental exploration, with potential applications in ocean monitoring
 and pollution management. Field: Biology, Jellyfish, Locomotion, Control
 Funding: This work was supported by a JSPS KAKENHI (JP23K18472, D.O. and
 K.N.) and by JKA and its promotion funds from KEIRIN RACE (2024M-364,
 D.O.). ## Description of the data and file structure Detailed Data
 information: * The “processed” version of the file refers to a data file
 in which the lengths and CoM positions are calculated from the 3D
 coordinate data using Python code and saved. * \*** indicates the
 experiment trial ID. * The unit of “time” in the csv file is seconds. ###
 File structure: ``` | -Data/ | | -JF41/ | | | -01_Spontaneous/ | | | |
 -01_free_plot_time_series_csv_data.py | | | | -***t3D_free_i.csv | | | |
 -plot.h | | | | -processed**t3D_free_plot.png | | | |
 -processed****_t3D_free.csv | | | -02_Stimulated/ | | | | -period_05 | | |
 | | -02_stim_plot_time_series_csv_data.py | | | | | -***t3D_stim_i.csv | |
 | | | -plot.h | | | | | -processed**t3D_stim_plot.png | | | | |
 -processed****_t3D_stim.csv | | | | -period_10 | | | | |
 -02_stim_plot_time_series_csv_data.py | | | | | -***t3D_stim_i.csv | | | |
 | -plot.h | | | | | -processed**t3D_stim_plot.png | | | | |
 -processed****_t3D_stim.csv | | | | -period_15 | | | | |
 -02_stim_plot_time_series_csv_data.py | | | | | -***t3D_stim_i.csv | | | |
 | -plot.h | | | | | -processed**t3D_stim_plot.png | | | | |
 -processed****_t3D_stim.csv | | | | -period_20 | | | | |
 -02_stim_plot_time_series_csv_data.py | | | | | -***t3D_stim_i.csv | | | |
 | -plot.h | | | | | -processed**t3D_stim_plot.png | | | | |
 -processed****_t3D_stim.csv | | | | -period_wo | | | | |
 -02_stim_plot_time_series_csv_data.py | | | | | -***t3D_stim_i.csv | | | |
 | -plot.h | | | | | -processed**t3D_stim_plot.png | | | | |
 -processed****_t3D_stim.csv ``` #### Spontaneous Jellyfish Locomotion data
 file name: `***_t3D_free_i.csv` \***:trial ID data structure in *.csv 0:
 time,\ 1: xR1, x position of R1 marker 2: xR2, x position of R2 marker 3:
 xY1, x position of Y1 marker 4: xY2, x position of Y2 marker 5: xO1, x
 position of O1 marker 6: xO2, x position of O2 marker 7: xB1, x position
 of B1 marker 8: xB2, x position of B2 marker 9: yR1, y position of R1
 marker 10: yR2, y position of R2 marker 11: yY1, y position of Y1 marker
 12: yY2, y position of Y2 marker 13: yO1,　y position of O1 marker　 14:
 yO2,　y position of O2 marker 15: yB1, y position of B1 marker 16: yB2, y
 position of B2 marker 17: zR1, z position of R1 marker 18: zR2, z position
 of R2 marker 19: zY1, z position of Y1 marker 20: zY2, z position of Y2
 marker 21: zO1, z position of O1 marker 22: zO2, z position of O2 marker
 23: zB1, z position of B1 marker 24: zB2, z position of B2 marker ####
 Stimulated Jellyfish Locomotion] data file name: `***t3D_stim_i.csv`
 \***:trial ID data structure in *.csv 0: time,\ 1: xR1, x position of R1
 marker 2: xR2, x position of R2 marker 3: xY1, x position of Y1 marker 4:
 xY2, x position of Y2 marker 5: xO1, x position of O1 marker 6: xO2, x
 position of O2 marker 7: xB1, x position of B1 marker 8: xB2, x position
 of B2 marker 9: yR1, y position of R1 marker 10: yR2, y position of R2
 marker 11: yY1, y position of Y1 marker 12: yY2, y position of Y2 marker
 13: yO1,　y position of O1 marker　 14: yO2,　y position of O2 marker 15:
 yB1, y position of B1 marker 16: yB2, y position of B2 marker 17: zR1, z
 position of R1 marker 18: zR2, z position of R2 marker 19: zY1, z position
 of Y1 marker 20: zY2, z position of Y2 marker 21: zO1, z position of O1
 marker 22: zO2, z position of O2 marker 23: zB1, z position of B1 marker
 24: zB2, z position of B2 marker 25: stim, 0=OFF / 1=ON How to reproduce
 "processed_***t3D_free_plot.png" and
 "processed***_t3D_free.csv": python
 01_free_plot_time_series_csv_data.py *** or ./plot.sh How to reproduce
 "processed_***t3D_stim_plot.png" and
 "processed***_t3D_stim.csv": python
 02_stim_plot_time_series_csv_data.py *** or ./plot.sh ## Sharing/Access
 information N/A ## Code/Software macOS Sonoma, ver 14.7.4 Python 3.12.4 or
 macOS Sequoia ver 15.4 Python 3.12.7