Data Associted With the Paper Titled "Online biomimetic electrolocation using causal multi-electrode voltage histories"

Trial pairs are identified by matching file numbers: M_001.csv corresponds to trial01_tracks.csv, through M_010.csv and trial10_tracks.csv. Camera positions are provided in pixels and can be mapped to physical tank coordinates and interpolated to the electrode time axis using the calibration described below. A derived MATLAB file containing camera-derived reference positions and saved leave-one-trial-out predictions from the original benchmark models is also included.

No human participants or live animals were used; the moving electric emitter was an artificial signal-generator surrogate. Associated manuscript: "Online biomimetic electrolocation using causal multi-electrode voltage histories." SUGGESTED SUBJECTS Engineering; Electrical Engineering; Robotics; Machine Learning; Bio-inspired Sensing; Underwater Sensing SUGGESTED KEYWORDS biomimetic electrolocation; active electric sensing; multi-electrode sensing; voltage time series; camera tracking; sensor fusion; online position estimation; causal temporal modeling; underwater sensing; machine learning; trajectory data FULL DATASET README 1.

Overview This archive provides the empirical data used to study two-dimensional online biomimetic electrolocation from causal multi-electrode voltage histories. The experimental platform comprised a water tank, a distributed electrode array, a moving signal-generator surrogate, and an overhead camera used to obtain an independent reference trajectory. The dataset was designed as a controlled engineering benchmark.

It measures the location-dependent voltage pattern generated by an artificial moving emitter and does not represent natural animal behavior. No human participants or live animals were used. The ten trials may be used to develop and compare regression, sequence, filtering, inverse-search, and other tracking methods.

The associated study evaluates models with leave-one-trial-out validation, so each complete trajectory is held out in turn rather than splitting adjacent frames from the same trial between training and testing. 2. Archive Contents Raw electrode recordings: M_001.csv M_002.csv M_003.csv M_004.csv M_005.csv M_006.csv M_007.csv M_008.csv M_009.csv M_010.csv Raw camera tracks: trial01_tracks.csv trial02_tracks.csv trial03_tracks.csv trial04_tracks.csv trial05_tracks.csv trial06_tracks.csv trial07_tracks.csv trial08_tracks.csv trial09_tracks.csv trial10_tracks.csv Trial pairing is one-to-one by numerical identifier: M_001.csv <-> trial01_tracks.csv M_002.csv <-> trial02_tracks.csv ...

M_010.csv <-> trial10_tracks.csv 3. Trial Inventory Trial Electrode file Electrode records Camera file Camera frames Electrode duration (s) 1 M_001.csv 9,361 trial01_tracks.csv 1,500 29.973027 2 M_002.csv 9,366 trial02_tracks.csv 1,500 29.993882 3 M_003.csv 9,356 trial03_tracks.csv 1,500 29.959727 4 M_004.csv 9,355 trial04_tracks.csv 1,500 29.958234 5 M_005.csv 9,369 trial05_tracks.csv 1,500 29.996883 6 M_006.csv 9,365 trial06_tracks.csv 1,500 29.992062 7 M_007.csv 9,359 trial07_tracks.csv 1,500 29.974798 8 M_008.csv 9,367 trial08_tracks.csv 1,500 29.996128 9 M_009.csv 9,367 trial09_tracks.csv 1,500 29.994830 10 M_010.csv 9,362 trial10_tracks.csv 1,500 29.964581 Total electrode records: 93,627 Total camera frames: 15,000 Missing numeric values detected in the deposited CSV files: none 4.

Electrode CSV Data Dictionary Each M_###.csv file contains a header row and 28 columns: one recorded time column followed by three quantities for each of nine electrode channels. t_s Recorded time in seconds for the electrode stream. <channel>_counts Raw analog-to-digital converter count for the named channel. Counts are dimensionless integer-valued measurements as recorded by the acquisition system. <channel>_V Voltage corresponding to the ADC measurement, in volts. <channel>_Vpp Recorded peak-to-peak voltage quantity for the channel, in volts.

The associated electrolocation analyses use the nine _Vpp columns as the model input. The channel groups, in file order, are: ADCA2 ADCA3 ADCA4 ADCA5 ADCB2 ADCB3 ADCB4 ADCB5 ADCC2 The resulting nine-channel analysis vector is ordered as: ADCA2_Vpp, ADCA3_Vpp, ADCA4_Vpp, ADCA5_Vpp, ADCB2_Vpp, ADCB3_Vpp, ADCB4_Vpp, ADCB5_Vpp, ADCC2_Vpp The electrode files contain approximately 9,360 records over 30 s, corresponding to an average row rate of approximately 312.2 Hz.

The recorded t_s field contains repeated timestamp values in the raw files. Users should preserve row order and should not estimate the acquisition interval from the median of all timestamp differences, because many consecutive differences are zero. The original analysis uses the recorded time values for camera interpolation and the ordered samples for causal histories. 5.

Camera-Track CSV Data Dictionary Each trial##_tracks.csv file has no header and contains exactly 1,500 rows and two columns: Column 1: horizontal image coordinate p_x, in pixels Column 2: vertical image coordinate p_y, in pixels The files contain the tracked image-plane location of the moving signal-generator surrogate. Camera coordinates increase to the right and downward. The camera rate is 50 frames/s, and frame timestamps are defined as: t_camera[m] = m / 50 seconds, for m = 0, 1, ..., 1499.

Thus, each camera trajectory spans nominal times from 0 to 29.98 s. The camera rows are not one-to-one with the electrode rows; the two streams must be aligned by time. 6. Camera-to-Tank Coordinate Mapping The physical tank workspace used in the analysis is: Width W = 86 cm Height H = 80 cm The image crop/calibration bounds are: x pixels: 290 to 1190 y pixels: 1 to 720 For a camera point (p_x, p_y), physical coordinates are computed as: x_cm = (p_x - 290)

• 86 / (1190 - 290) y_cm = (720 - p_y)
• 80 / (720 - 1) The y coordinate is inverted because image y increases downward, while the tank coordinate system uses y increasing upward. After conversion, coordinates are clipped to the tank bounds: 0 <= x_cm <= 86 0 <= y_cm <= 80 These values are camera-derived reference positions. Their accuracy is limited by the camera tracking and spatial calibration; the archive does not provide a separate measurement of camera-reference uncertainty. 7. Synchronizing Camera and Electrode Streams For trial i: 1. Load the corresponding M_###.csv file and retain its t_s vector. 2. Load the camera file with the matching trial number as the pixel trajectory. 3. Convert the camera pixels to tank coordinates using the equations above. 4. Assign camera times at 50 frames/s, starting at t = 0 s. 5. Linearly interpolate x_cm and y_cm onto the electrode t_s values. 6. Use linear extrapolation only when an electrode timestamp lies slightly outside the camera time range. This procedure produces one two-dimensional camera-derived reference position for each electrode record. The deposited Python loader implements the same mapping and was checked against the reference positions stored in the MATLAB result file; differences were at numerical floating-point precision. 8. Licensing and Attribution Before publication, select and state an explicit reuse license in Dataverse. Replace the bracketed text below with the final license and publication information. If these data or the accompanying code are used in research, please cite both the Dataverse dataset DOI and the associated manuscript. Contact Ismail Uyanik Department of Electrical and Electronics Engineering Hacettepe University Ankara, Turkiye ismailuyanik@hacettepe.edu.tr