# From Realistic and Automated Fish Digital Twin to Data Ethics: Building Reproducible Pipelines and Dataset for Biological AI

Bastien Lagneaux, Corentin Porcon, Jerome Keribin, Dixia Fan, Florence Razan, Gurvan Jodin

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Bastien Lagneaux, Corentin Porcon, Jerome Keribin, Dixia Fan, Florence Razan, et al.. From Realistic and Automated Fish Digital Twin to Data Ethics: Building Reproducible Pipelines and Dataset for Biological AI. DebConf25, IRISA, Jul 2025, Brest, France. pp.12. hal-05334738

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# From Realistic and Automated Fish Digital Twin to Data Ethics: Building Reproducible Pipelines and Dataset for Biological AI

Bastien LAGNEAUX *

* dept. de Mécatronique,
IETR UMR CNRS 6164,
ENS Rennes, Bruz, France

bastien.lagneaux@ens-rennes.fr

Corentin PORCON *

* dept. de Mécatronique,
IETR UMR CNRS 6164,
ENS Rennes, Bruz, France

corentin.porcon@ens-rennes.fr

Jerome KERIBIN *

* dept. de Mécatronique,
ENS Rennes, Bruz, France
jerome.kiribin@ens-rennes.fr

Dixia FAN *

* school of Engineering,
Westlake University,
Hangzhou, China

fandixia@westlake.edu.cn

Florence RAZAN *

* dept. de Mécatronique,
IETR UMR CNRS 6164,
ENS Rennes, Bruz, France

florence.razan@ens-rennes.fr

Gurvan JODIN *

* dept. de Mécatronique,
IETR UMR CNRS 6164,
ENS Rennes, Bruz, France

gurvan.jodin@ens-rennes.fr

**Abstract**—This paper presents a limited and preliminary automated pipeline for generating hydrodynamic digital twins of fish from real-world experimental data, combining computer vision, computational fluid dynamics (CFD), and ethical AI practices. Using a YOLOv8 segmentation model trained on manually labeled laboratory video recordings, the system extracts fish motion and integrates it into a dimensionless CFD simulation via the HAACHAMA framework. The resulting digital twins closely replicate vortex dynamics observed in nature and enable pressure field estimation without invasive techniques such as Particle Image Velocimetry (PIV). This work demonstrates the feasibility of automated, biologically respectful simulation in aquatic environments and contributes an open-source dataset to support reproducible research. Broader applications include ecological monitoring, bio-inspired robotics, and fish passage optimization, addressing emerging challenges in ethical, cross-disciplinary AI. Building on this proof of concept, the second part of the paper shifts focus toward the critical challenge of scaling the system through the construction of a robust and ethically grounded dataset. Given the widely reported reproducibility crisis in experimental sciences, the creation of such a dataset must go beyond quantity and embrace a reflective framework of quality. We propose key considerations for dataset design in this context: methodological transparency to ensure experimental repeatability; tooling and annotation consistency for reproducibility across computational processes; adherence to ethical standards concerning animal handling and data privacy; durable and open-access storage solutions to promote equitable availability; and careful licensing choices that balance openness with appropriate use. This discussion aims to articulate a roadmap for constructing meaningful datasets at the intersection of AI, biology, and ethics—positioning data not only as a technical resource, but as a socio-technical infrastructure that underpins trustworthy scientific advancement.

**Index Terms**—Computer Vision, Artificial Intelligence, CFD, Ethology, Ethical

This work was supported by the Human Frontier Science Program, under the project number RGY0059/2022.

## I. INTRODUCTION

Digital twin technology has become a powerful approach for modeling and analyzing complex systems, spanning domains from biomedicine to electrical engineering. In this study, a pipeline is developed to automate the generation of hydrodynamic digital twins based on real experimental data from live fish. This work extends the HAACHAMA framework [1], [2], tailoring it for biological realism and rapid deployment in dynamic environments.

In aquatic ecosystems, animals leave behind hydrodynamic wake—distinctive flow signatures rich in information about their size, trajectory, and motion history [3]. These trails are sensed by fish through canal neuromasts, which are sensitive to vortices encoding pressure and velocity information [4]–[6]. While these cues are known to enhance swimming efficiency [7], their role in guiding behavior such as obstacle avoidance and target detection remains poorly understood.

To address this, a simulation framework was constructed that integrates YOLO-based visual segmentation [8] with dimensionless CFD modeling. This approach avoids the use of Particle Image Velocimetry (PIV), which, though widely used, introduces foreign particles that could harm aquatic life [9]. By leveraging video and pressure data collected from real experiments, the model generates digital twins that respect both biological integrity and physical realism—embodying the principles of ethical and sustainable AI.

Beyond its theoretical relevance for behavioral ethology, this research also opens promising avenues for applied use cases. These include the design of more effective fish bypass passages in dams—reducing the ecological impact of such infrastructures—as well as the development of bio-inspired soft-robotics actuators that mimic aquatic locomotion [10],
<table><caption>TABLE I
PARAMETERS USED TO SET THE DIMENSIONLESS SIMULATION.</caption><thead><tr><td>Parameter</td><td>Value</td></tr></thead><tbody><tr><td>U</td><td>0.279 m/s</td></tr><tr><td>L</td><td>0.124 m</td></tr><tr><td>Re</td><td>31, 141</td></tr><tr><td>L*</td><td>31 cells</td></tr><tr><td>h</td><td>0.004 m/cell</td></tr><tr><td>ν*</td><td>9.5 × 10<sup>-4</sup></td></tr><tr><td>dt*</td><td>0.69</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>Fig. 1. Examples of complex swimming patterns, with fish of various sizes, colors and motion</figcaption></figure>

[11]. By using real experimental conditions and planning to release the data as open source, this research contributes to the development of reproducible benchmarks and addresses emerging challenges in cross-disciplinary, ethical AI, and sustainable development.

## II. SCIENTIFIC METHODS

### A. Adimensionality Parameter Selection

In computational fluid dynamics (CFD), the application of dimensionless variables is a foundational practice, and in the context of this study, the CFD solver LilyPad [12], specifically needs the kinematic viscosity and the time step. The transformation of simulated dynamic pressure into physical units is achieved via the relation $\Delta p(x, t) = 1/2 p^*(x, t)\rho U_\infty^2$, where:

* $\Delta p(x, t)$ is the physical static pressure variation at position $x$ and time $t$,

* $p^*(x, t)$ is the dimensionless pressure from the simulation,

* $\rho$ is the seawater density,

* $U_\infty$ is the free-stream velocity.

To determine the dimensionless kinematic viscosity ($\nu^*$), the Reynolds number is first calculated using $Re = \rho UL/\eta$, where $U$ is the fish's speed, $L$ its characteristic length, and $\eta$ the dynamic viscosity of seawater.

The dimensionless size of the object $L^*$ is estimated using video tracking and calibration footage. This, combined with the computed Reynolds number, yields $\nu^* = L^*/Re$. The time discretization for simulation is controlled via the dimensionless time step, derived as $dt^* = \frac{dt}{h^2\nu^*}$ with $dt$ as the time between video frames, and $h$ the grid cell size.

The final parameters employed for the simulation are summarized in table I. These calculations ensure that the simulation aligns with the dimensionless representation of the physical system.

### B. Extension to Complex Biological Models

Building on the proof-of-concept, the method was extended to real biological systems—specifically fish exhibiting diverse swimming behaviors (Fig. 1). A new dataset was curated from laboratory video recordings, capturing a wide range of locomotor patterns essential for training models under varied aquatic conditions. Vortex dynamics observed in catfish-like specimens aligned well with theoretical 2D vortex patterns [10], facilitating future comparisons with simulated outputs.

Manual segmentation ensured high-fidelity annotations, even in low-contrast settings, enhancing model convergence.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Training metrics provided by YOLOv8 for a 1,000 frames dataset of experimental fish, with a 80/20 split between train and test sets.</figcaption></figure>

This biological dataset addresses the field's overreliance on toy data, offering improved realism and supporting ethical AI applications in behavioral research.

A YOLOv8 segmentation model was trained on 1,000 manually labeled images (640×640 px), with an 80/20 train-validation split to ensure robust performance.

To evaluate the alignment between simulated and experimental pressure signals, the Fréchet distance [14], [15] was used. It captures trajectory continuity and is well-suited for time-varying pressure data. It enabled direct comparison of pressure time series from simulated digital twins with real-world sensor measurements.

### C. Training Results for Fish Segmentation

The training yielded strong results, with mAP@50 scores nearing 1, demonstrating high precision in fish detection (Fig. 2). No overfitting was detected, indicating effective generalization. However, performance across the mAP@50-95 range revealed room for improvement, likely due to the limited dataset size. Earlier proof-of-concept findings support this, highlighting the critical role of data volume in deep learning model performance.

The current dataset comprises 1,000 labeled images with metadata <fnref n="1" />, with a single instance of the target object per frame. This results in a total of 1,000 annotated instances—well below the 10,000-instance benchmark commonly recommended by the YOLO framework's authors for optimal model performance. Due to financial constraints, expanding the dataset beyond its present size was not feasible

<footnote n="1">¹width, height, frame rate, duration, focal ...</footnote>
<table><caption>TABLE II
FRECHET DISTANCE BETWEEN THE MANUALLY GENERATED DIGITAL TWIN AND THE EXPERIMENTAL PRESSURE, AND THE AUTOMATICALLY GENERATED DIGITAL TWIN AND THE EXPERIMENTAL PRESSURE</caption><thead><tr><th></th><th>Manual VS Experimental</th><th>HAACHAMA VS Experimental</th></tr></thead><tbody><tr><td>Sensor 2</td><td>0.13</td><td>0.11</td></tr><tr><td>Sensor 4</td><td>0.19</td><td>0.18</td></tr><tr><td>Sensor 7</td><td>0.29</td><td>0.32</td></tr></tbody></table>

during this stage of development. This trade-off was acknowledged as a pragmatic compromise necessary to advance the project within the existing resource limitations.

Despite these constraints, the trained model demonstrated reliable performance under test conditions. An example of successful inference is illustrated in Fig. 3. As the project continues, dataset expansion remains a priority, with dataset size recognized as a key factor influencing the effectiveness and generalizability of the simulation pipeline.

### D. Comparison Between Manual and Automated Digital Twin

To validate the automated Digital Twin pipeline, its performance was compared to a manually constructed benchmark that defined fish positions frame-by-frame.

The goal was to determine if YOLO-based segmentation with CFD synthesis could replicate the manual model's accuracy using experimental pressure data. A modified Fréchet distance—focusing only on vertical deviations—was used to quantify differences in pressure profiles. This metric, chosen for its shape sensitivity, aligned with the visual similarity of simulations (Fig. 4). Results across three sensors (Table II, see [2]) confirmed the automated model closely matched manual outcomes.

These findings validate the HAACHAMA pipeline's fidelity and its potential to significantly reduce manual effort. By supporting scalable, accurate, and less invasive simulation generation, it advances ethical, real-time digital ecological research.

## III. DISCUSSION ON THE NEED FOR REPRODUCIBLE AND GENERALIZABLE DATASET

The results presented thus far demonstrate that the proposed pipeline can automatically generate biologically realistic Digital Twins of swimming fish in controlled experimental scenarios. This validates the technical feasibility and fidelity of the method within a constrained context. However, for this approach to become a meaningful contribution to the broader scientific community, two critical extensions are necessary.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Output segmentation provided by the trained model on a test frame</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. In blue, graph of measured pressure with experimental sensors, filtered at 9Hz with a 2^nd^ order Butterworth low-pass filter and a mean value subtraction, with respect to time, in orange, graph of extracted pressure from the CFD simulation resulting from the HAACHAMA pipeline, in green, graph of extracted pressure from a manually generated DT simulation. The data displayed are related to sensor 4.</figcaption></figure>

First, the methodology must be generalized across a wider range of scenarios. This requires the construction of a comprehensive and well-annotated dataset that captures biological variability and supports robust evaluation of segmentation and simulation models.

Second, and equally important, the code and datasets used must be openly shared to support reproducibility. The ongoing crisis of reproducibility in experimental science is undermining trust and slowing progress across disciplines [16]. By making our tools and data openly accessible, we aim to enable other researchers to replicate, validate, and extend our work—thereby promoting transparent, verifiable, and cumulative scientific progress.

The next section explores key considerations in building such datasets, with an emphasis on methodological transparency, ethical integrity, and infrastructural sustainability. These insights are shaped both by lessons learned during the development of this project and by broader norms emerging at the intersection of AI, biology, and open science.

### A. Long Term Reproducibility

The long-term utility of any scientific method is fundamentally constrained by its reproducibility. As demonstrated in the previous sections, the HAACHAMA pipeline is a viable approach for generating digital twins of swimming fish. However, to ensure that these results remain obtainable even a century from now, every aspect of the workflow must be designed with enduring transparency, reproducibility, and accessibility in mind. This requires a holistic strategy spanning software environments, experimental documentation and post-processing.

1) Software-Level Reproducibility: Beyond Open Source: Open-source software is often seen as a baseline for reproducible research. While using tools like Python, YOLOv8, and LilyPad with version control ensures short-term accessibility, software environments tend to become irreproducible over time due to dependency rot and platform incompatibilities.
To mitigate this, we advocate for the use of fully declarative environments such as GNU Guix, which supports bit-reproducible builds and long-term archival of computational workflows. Tournier et al. (2023) discuss this in their work on transparent and verifiable computing infrastructures [17]. Guix enables the exact recreation of a software environment, even decades later, using a purely functional description of all dependencies. It is currently one of the most rigorous tools for scientific software preservation and should be considered foundational for any long-term research project.

2) *Experimental Setup: Documentation and Blueprint Archival:* Software alone is not sufficient. Physical reproducibility requires detailed documentation of the experimental apparatus, including sensor placements, lighting conditions, water parameters, and calibration routines. All protocols, schematics, and even metadata (e.g., animal sizes, camera frame rates, tank dimensions) must be preserved in standardized, durable formats.

Researchers have recommended open, re-compilable documentation formats (e.g., LaTeX to PDF/A) and the use of metadata standards such as the Minimum Information for Biological and Biomedical Investigations (MIBBI) guidelines [18]. These principles, in some way similar to the FAIR guidelines that came later and are more widely known [19], ensure that metadata is not only human-readable but also machine-actionable—critical for automated reproducibility in future computational ecosystems.

3) *Post-Processing and Analytic Reproducibility:* Post-processing steps, such as the application of filters, segmentation model inference, and CFD post-simulation analysis, must also be transparent. Each transformation should be logged in processing scripts using versioned software and notebooks, ideally executed inside containerized environments (e.g., Docker or Guix). The full processing pipeline should be shareable as an executable research compendium to ensure that future researchers can verify and reinterpret results with new tools or hypotheses.

## B. Ethical and Legal Considerations in International Collaborations

Ensuring that datasets are ethically reusable across borders requires careful navigation of divergent legal and cultural standards research [20]. Ethical procedures governing the use of vertebrates in scientific studies vary significantly between countries and this poses a particular challenge for international collaborations, where data gathered under one country's guidelines must remain ethically acceptable for use by institutions operating under different regulations.

In the context of this study, we adopted the ethical framework of the United Kingdom—recognized for its stringent animal welfare standards—as a baseline. Under the Animals (Scientific Procedures) Act 1986 (ASPA), any research procedure causing pain, suffering, distress, or lasting harm equivalent to or greater than a needle prick is classified as a regulated procedure. Such work requires approval at three hierarchical levels:

* A Personal Licence (PIL) for each individual researcher conducting the work,

* A Project Licence (PPL) that details and justifies the scientific protocol, and

* An Establishment Licence (PEL) for the institution hosting the animals.

Before a PPL application can be submitted, it undergoes review by the institution's Animal Welfare and Ethical Review Body (AWERB), which is responsible for evaluating the harm-benefit balance and ensuring adherence to the principles of Replacement, Reduction, and Refinement (3Rs). The UK Home Office then reviews all licence applications via the ASPeL platform and mandates ongoing monitoring of compliance, including regular inspections and progress reporting.

Even in cases where research falls below the ASPA harm threshold—classified as non-regulated procedures—ethical oversight remains obligatory. Although formal Home Office licensing is not required in such instances, institutions typically mandate review by AWERB or equivalent internal ethics committees. Researchers are still expected to justify the scientific necessity of their work, minimize animal stress, and implement 3Rs principles to the fullest extent possible.

This dual-tiered system ensures that all vertebrate animal research in the UK meets a high ethical standard, regardless of its legal status. By conforming to UK legislation and ethical review, we aim to maximize the cross-border acceptability of our dataset but also anticipate future harmonization of ethical standards across research jurisdictions.

## C. Data Distribution: Infrastructure for Inclusive Access

Even when ethical and technical criteria are met, a dataset is only useful if it can be accessed. In our case, partners operate under varied digital constraints—some in restricted environments such as China, where platforms like Dropbox or Google Drive are often inaccessible or throttled [21], [22]. Thus, we advocate for mirrored storage solutions, including institutional repositories (e.g., Zenodo, Dryad). This also supports the “desert island” scenario—making the data accessible and sharable by anybody—as well as ensuring censorship may not prevent data sharing (Dissident test).

## D. Licensing: Clarifying Rights Without Compromising Ethics

Finally, licensing is critical to ensure that open data and software are used ethically [23]. While permissive licenses such as MIT are well-suited for open-source code, they must be combined with clear documentation of ethical boundaries, especially in sensitive domains like behavioral biology or medical diagnostics.

Biological datasets, in particular, must not be licensed in ways that allow alterations of ethical declarations. For instance, attaching an ethical Open License (an example could be the Hippocratic License from https://firstdonoharm.dev) can help enforce ethical terms without giving blanket permission for commercial or misaligned reuse. Another solution could be to simply add the ethics commity decision directly inside
the MIT license instead of under it, so that it becomes one with the license.

This opens important legal and philosophical discussions regarding data sovereignty and bio-legality, which—while outside the scope of this discussion—must become central to data governance debates in the near future.

## IV. CONCLUSION

This study demonstrates the feasibility of automating the generation of biologically realistic digital twins of swimming fish by combining real-world experimental data, YOLOv8-based segmentation, and dimensionless computational fluid dynamics within the HAACHAMA framework. The resulting simulations closely replicate empirical pressure measurements and eliminate the need for invasive techniques such as PIV. These results confirm that automated hydrodynamic modeling can achieve high precision while minimizing animal disturbance and manual labor—paving the way for scalable and ethical aquatic locomotion studies.

However, the utility of such simulations extends beyond their immediate scientific outcomes. To ensure that these results remain interpretable, verifiable, and reusable even a century from now, this paper also outlines a robust framework for long-term reproducibility and ethical data stewardship. By addressing reproducibility at the software, experimental, and post-processing levels—and by embedding rigorous ethical standards based on UK regulatory frameworks—this work provides a blueprint for trustworthy scientific infrastructure in cross-disciplinary research.

We further highlight the practical importance of open-access storage, international legal compatibility, and thoughtful licensing choices, especially in the context of collaborative AI applications in biology. These considerations transform datasets from passive repositories into active, socio-technical artifacts that can sustain and accelerate knowledge production over time.

Taken together, the technical pipeline and reproducibility principles presented in this study form a cohesive contribution to both methodological innovation and scientific responsibility. They offer a replicable, ethical, and open approach to digital twin research.

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