Building Open, FAIR Repositories for Behavioral Time-Series Data

The scientific community stands at a pivotal crossroads in behavioral neuroscience. As video-based behavioral experiments become more complex, detailed, and high-throughput, so too does the demand for open, scalable, and FAIR (Findable, Accessible, Interoperable, Reusable) repositories that can handle the resulting terabytes of time-series data. From raw video to pose estimation outputs and unsupervised behavioral clusters, the scientific utility of these datasets depends not only on their generation, but on how they are stored, shared, and preserved.

This article addresses the architectural, technical, and ethical considerations required to build FAIR repositories for behavioral time-series data—tailored for labs using pose estimation, motif discovery, and high-resolution tracking platforms like those offered by Conduct Science. We explore metadata standards, compression protocols, and best practices that ensure reproducibility, collaboration, and scalability in behavioral research across labs and species.

Why FAIR Principles Matter for Behavioral Neuroscience

In behavioral neuroscience, the complexity, richness, and scale of data being generated have outpaced traditional storage and sharing practices. High-frame-rate video, multi-animal tracking, pose estimation, and unsupervised motif analysis routinely produce terabytes of time-series data. Yet without structured, shareable formats, much of this information remains siloed—difficult to discover, replicate, or integrate into broader scientific efforts. This is where the FAIR principles—Findable, Accessible, Interoperable, and Reusable—become essential.

FAIR data is not merely about openness—it is about functional openness. For behavioral neuroscience, this means that raw and processed datasets must be traceable, well-described, and structured in such a way that other researchers can not only find and access them but actually reuse them in meaningful ways. For example, a time series of mouse locomotion trajectories should come with metadata describing the animal’s genotype, housing conditions, arena layout, and video recording parameters. Without such contextual information, the data loses scientific value.

Findability ensures that behavioral datasets are indexed and searchable through persistent identifiers like DOIs, allowing researchers to locate relevant resources via public repositories or literature references. In practice, this helps unify studies across different labs or timeframes, enabling meta-analyses of behaviors like anxiety, sociality, or exploration.

Accessibility is critical because behavioral neuroscience data is often stored in formats too large or fragmented for easy sharing. Using open-standard file formats (e.g., HDF5, MP4, JSON) and cloud-based platforms makes it possible for collaborators—and future researchers—to retrieve full experimental records without technical barriers.

Interoperability becomes especially important in multi-species, multi-lab contexts. Behavioral datasets must be annotated using standardized ontologies and vocabularies so that a “sniffing” motif in rats can be compared to an equivalent investigatory behavior in zebrafish or mice. Without this, cross-study comparison breaks down, undermining translational relevance.

Reusability closes the loop by ensuring that datasets are accompanied by comprehensive documentation, licensing information, and analysis scripts. This enables others to build upon the work—re-analyzing behavior, benchmarking machine learning algorithms, or integrating with new modalities like calcium imaging or electrophysiology.

Behavioral neuroscience, by its nature, involves a vast space of spontaneous, often unpredictable data. Implementing FAIR principles ensures that no single experiment stands in isolation. Instead, it becomes part of a growing, verifiable, and ethically grounded body of knowledge. For institutions, journals, and researchers alike, adopting FAIR principles is not just best practice—it is an imperative for sustainable, transparent, and collaborative science.

Tools and platforms from Conduct Science help facilitate this transition by standardizing how behavioral data is acquired, tracked, and stored. Their modular arenas, camera rigs, and data-compatible designs provide the physical infrastructure necessary to support FAIR-compliant research from the ground up. With consistent metadata capture, environment logging, and compatibility with pose estimation pipelines, researchers are empowered to generate data that’s ready to be shared, validated, and reanalyzed—fulfilling both scientific and ethical responsibilities in a data-intensive era.

Data Types in Behavioral Time-Series Repositories

Behavioral time-series repositories capture an extraordinarily rich spectrum of biological information—more than just video footage or behavioral scores. These datasets are increasingly multidimensional, layered, and temporally aligned. To make them reproducible and analytically useful, it’s crucial to structure them as modular but interconnected data types, each supporting a specific role in analysis, validation, and reuse.

Below is a breakdown of the core data types that must be considered when constructing a comprehensive and FAIR-compliant behavioral repository:

1. Raw Video Recordings

The foundation of most behavioral data pipelines is high-resolution, continuous video, often recorded from multiple synchronized cameras (e.g., top-down, side view). These videos capture the complete, unfiltered behavior of the subject(s) in real time. For each session, recordings may range from several minutes to multiple hours and often span multiple gigabytes or terabytes per experiment.

• Format recommendations: .mp4, .mkv, or .avi using H.264 or H.265 codecs.
• Considerations: Frame rate, resolution, lighting conditions, and synchronization cues must be consistently documented and stored alongside the video.

2. Pose Estimation Outputs

After video acquisition, deep learning-based tools like DeepLabCut or SLEAP extract 2D or 3D coordinates of animal body parts across each frame. These pose time series provide the raw material for behavior classification and unsupervised motif discovery.

• Structure: Keypoints per frame (e.g., nose, tail base, ears, limbs), usually in pixel or real-world (cm) units.
• Format: .h5 (HDF5), .npy, or .csv (though less ideal for large datasets).
• Metadata dependencies: Body part definitions, network training details, tracking confidence scores, and camera calibration files should be included.

3. Derived Kinematic Features

From the pose data, various derived features are computed—such as speed, acceleration, joint angles, head-body orientation, and center of mass trajectory. These features serve as the quantitative backbone for behavior segmentation and classification.

• Purpose: Enables dimensionality reduction (e.g., PCA, UMAP), clustering, or HMM-based motif extraction.
• Format: Often structured as labeled NumPy arrays or pandas DataFrames with time alignment to pose data.

4. Behavioral Motifs and Cluster Labels

Using unsupervised or supervised models, researchers segment behavior into motifs or discrete action units. Each motif is defined by start and end timestamps and often linked to a latent state or cluster ID.

• Structure: Timestamped labels per motif or per frame (e.g., grooming, rearing, chase, pause).
• Format: .json, .csv, .h5, or .yaml files that include label IDs, timestamps, and, optionally, confidence scores.
• Documentation: Should specify the clustering method, number of clusters, and interpretation of each cluster (if available).

5. Experimental Metadata

This layer ties all data together, contextualizing behavior within the experiment’s biological and technical framework.

• Includes:
  • Animal metadata: ID, strain, sex, age, housing, group composition.
  • Arena metadata: dimensions, materials, lighting, and enrichment objects.
  • Camera metadata: Resolution, lens type, FPS, and infrared status.
  • Experimental metadata: treatment conditions, phase (e.g., baseline, test, recovery), and time of day.
• Format: .json or .yaml structured metadata files, ideally conforming to community standards such as NWB (Neurodata Without Borders) or BIDS-like templates for behavior.

6. Analysis Scripts and Parameter Files

To ensure reproducibility, repositories must include the exact scripts and configuration files used to process raw data into behavioral outcomes.

• Includes:
  • Pose estimation configuration and trained model files.
  • Dimensionality reduction and clustering scripts with hyperparameters.
  • Motif visualization and behavioral state decoding tools.
• Format: Python scripts, Jupyter notebooks, Conda environment files, or Docker containers.

7. Synchronization and Integration Logs

Especially in multimodal experiments (e.g., video + electrophysiology or optogenetics), time synchronization is critical. Logs or metadata files that map behavioral timestamps to neural recordings or stimuli are essential for integrative analysis.

• Includes TTL pulse logs, synchronization offsets, sampling rate metadata, and clock drift corrections.
• Format: .csv, .txt, or protocol-specific formats (e.g., Open Ephys .events files).

Metadata Standards: Structuring Behavioral Context

Metadata is the scaffolding that gives behavioral data meaning. In time-series behavioral neuroscience, where experiments involve high-speed video, pose trajectories, unsupervised clustering, and biological variation across subjects, metadata provides the context that transforms raw numbers into interpretable, reusable science. Without rigorous metadata, even the most precisely recorded data becomes an opaque archive—difficult to replicate, compare, or validate. Metadata standards ensure that behavioral datasets are not only stored and shared but also understood, trusted, and reused.

Why Behavioral Metadata Is Essential

Behavior is not just motion—it’s motion situated in time, space, physiology, and intention. To reproduce a behavioral experiment or even interpret its outcomes, researchers need to know:

• What species was recorded?
• What were the environmental conditions?
• What software and parameters were used in pose estimation?
• What behaviors were inferred, and how were they defined?

Answering these questions consistently across studies requires structured, machine-readable metadata tied to both biological and computational dimensions of the experiment.

Core Categories of Metadata for Behavioral Repositories

1. Subject-Level Metadata
   
   • Animal ID, strain, genotype, sex, age, weight
   • Housing conditions: cage mates, light/dark cycle, enrichment
   • Treatment history: pharmacological exposure, surgical interventions
   • Enables cross-subject analysis and stratified behavior comparison.
     
2. Experimental Context Metadata
   
   • Experiment date, protocol version, researcher ID
   • Session phase: habituation, baseline, treatment, test, recovery
   • Stimulus events: type, timing, duration (e.g., auditory cue, light flash)
   • Critical for reconstructing the experimental logic and temporal alignment.
     
3. Arena and Hardware Metadata
   
   • Arena dimensions, material, lighting conditions, temperature
   • Camera model, angle, frame rate, lens settings
   • Calibration files: camera matrices for pose reconstruction
   • Ensures consistent pose accuracy and interpretable spatial features.
     
4. Pose Estimation Metadata
   
   • Software tool used (e.g., DeepLabCut, SLEAP), version number
   • Body part definitions and anatomical keypoints
   • Confidence thresholds, smoothing parameters, model training set
   • Allows replication of pose outputs and cross-model comparisons.
     
5. Behavioral Annotation Metadata
   
   • Clustering method: UMAP, t-SNE, HMM, B-SOiD, etc.
   • Dimensionality reduction settings and feature sets
   • Label definitions: if clusters are named (e.g., “groom,” “rear”), documentation of criteria
   • Supports interpretability and validation of behavioral motif extraction.
     
6. Data Integration Metadata
   
   • Time synchronization with neural, physiological, or stimulus modalities
   • Sampling rates, offset corrections, and event logs
   • Required for multimodal behavioral-neural analyses and closed-loop systems.
     

Standardization Efforts and Recommendations

There is a growing movement toward shared schemas and ontologies in behavioral neuroscience:

• Neurodata Without Borders (NWB): Originally developed for neurophysiology but extensible to behavioral data, NWB provides a containerized format with metadata templates and standard fields.
• Behavioral Experimental Ontology (BEO) and OBI (Ontology for Biomedical Investigations): offer structured vocabularies for describing behavioral terms and experimental interventions.
• BIDS (Brain Imaging Data Structure): Though initially designed for imaging, efforts like BIDS-behavior extensions propose unified metadata for behavioral time series.

Behavioral labs can either adopt these frameworks or develop domain-specific templates inspired by them. In either case, metadata should be:

• Machine-readable (e.g., JSON, YAML, XML)
• Version-controlled
• Co-stored with raw and processed data
• Documented with human-readable README files
  

Role of Conduct Science Platforms

Conduct Science’s modular, camera-integrated arenas are well-suited for systematic metadata capture. Their environments are designed with:

• Stable, defined physical parameters (e.g., wall height, arena dimensions)
• Controlled lighting and camera configurations, simplifying reproducibility
• Cross-platform compatibility, ensuring metadata from software pipelines can be merged with environmental context

Researchers using these platforms can implement consistent metadata collection from the outset of an experiment, reducing friction during data analysis, sharing, and publication.

Compression and File Format Recommendations

Given the size of behavioral datasets, efficient compression is essential for sharing and long-term storage without sacrificing data fidelity. Best practices include:

• Video Compression: Use H.264 or H.265 formats in .mp4 or .mkv containers, with consistent bitrate settings across datasets.
  
• Pose Data: Save as .h5 (HDF5) or .npy files with versioned documentation; avoid CSVs for large arrays due to size and I/O performance.
  
• Clustering Results and Motif Labels: Store as timestamped JSON or HDF5 files, with clear mapping between cluster ID, temporal window, and behavior label (if applicable).
  
• Analysis Pipelines: Archive processing scripts with version-locking and environment specifications (e.g., Conda YAML or Dockerfiles).
  

Compression is not just about space—it’s about preserving fidelity while enabling accessibility, especially when repositories are accessed across institutions with varied infrastructure.

Best Practices for Reproducibility and Data Sharing

To make behavioral repositories genuinely FAIR, consider the following best practices:

1. Versioned Datasets
   Every update—whether a re-annotation, a reprocessed pose file, or an adjusted motif definition—should be tracked and timestamped. Use Git-based data versioning systems like DVC or Quilt when feasible.
   
2. Cloud-Accessible Archives
   Host datasets in accessible cloud storage (e.g., Zenodo, OpenNeuro, or OSF) with persistent DOIs. Ensure that the file structure is intuitive and accompanied by README files.
   
3. Open Licensing
   Use permissive licenses (e.g., CC-BY or CC0) when possible to maximize reuse and downstream integration with meta-analyses and AI training sets.
   
4. Reproducibility Bundles
   Include minimal working examples (MWE) for re-running pose estimation, clustering, or visualization using a few minutes of data. These bundles improve onboarding for collaborators and peer reviewers.
   
5. Cross-Lab Standardization
   When designing your experimental setup, use modular, reproducible environments such as those offered by Conduct Science. Their calibrated arenas, lighting controls, and tracking-compatible enclosures support data harmonization across sites—an essential step for cross-lab reproducibility.
   

Conduct Science Platforms and FAIR Readiness

As the behavioral neuroscience community advances toward open science and data-intensive experimentation, the need for platforms that support FAIR (Findable, Accessible, Interoperable, and Reusable) principles by design becomes more pressing. Conduct Science provides precisely this foundation. Their behavioral research platforms are not only modular and scalable—they are also engineered to streamline reproducibility, standardization, and long-term data utility, making them naturally aligned with FAIR data management strategies.

1. Findability Through Standardized Hardware and Documentation

A fundamental challenge in behavioral reproducibility is the heterogeneity of experimental environments. Variations in arena size, lighting conditions, and camera angles can drastically alter pose estimation accuracy and behavioral expression. Conduct Science addresses this with standardized arena systems, featuring:

• Precisely measured and reproducible physical dimensions
• Configurable and well-documented layouts
• Consistent integration with video tracking and IR illumination systems
  

This level of structural consistency ensures that the data generated is not just interpretable within a single lab but also easily located and understood across collaborative research networks and data repositories. Clear documentation, including CAD drawings, user manuals, and configuration files, enhances the findability of both experimental context and dataset relevance.

2. Accessibility Through Integration and Compatibility

Conduct Science platforms are designed to be immediately compatible with pose estimation tools such as DeepLabCut, SLEAP, and custom machine learning pipelines. With support for high-definition, IR-compatible cameras, top- and side-view configurations, and multi-animal tracking, these platforms ensure that high-quality behavioral data can be captured without proprietary limitations or restrictive software dependencies.

This openness enhances data accessibility by:

• Allowing raw and processed data to be saved in universally accepted formats (e.g., MP4, HDF5, JSON)
• Supporting real-time export of behavioral and environmental metadata
• Enabling cloud integration for remote data storage and retrieval

Combined, these features make it easier for researchers to share and retrieve data across institutional boundaries and technological ecosystems.

3. Interoperability Through Cross-Modal Support

Behavioral experiments are rarely conducted in isolation. Increasingly, behavioral data must be integrated with neural recordings (e.g., LFPs, calcium imaging), physiological signals, optogenetic stimulations, and pharmacological interventions. Conduct Science platforms support this multimodal convergence by:

• Offering synchronization capabilities between video data and electrophysiology systems
• Providing outputs in formats compatible with frameworks like NWB (Neurodata Without Borders) and BIDS
• Enabling time-stamped metadata logging that can be aligned with external data streams

This design facilitates interoperability, allowing datasets to serve not just as standalone records but as interoperable components in multi-domain scientific inquiry.

4. Reusability Through Metadata-Ready Design and Experiment Logging

Perhaps most importantly for FAIR compliance, Conduct Science platforms support data reusability by making it easy to capture, store, and share the experimental conditions and metadata necessary to interpret behavioral outcomes.

Features that promote reusability include

• Environmentally controlled arenas, where temperature, lighting, and spatial parameters are logged and stable
• Modular setups that allow precise documentation of object placement, wall configurations, and subject interaction zones
• Built-in support for metadata templates, enabling researchers to define animal-specific, protocol-specific, and session-specific details in machine-readable formats

This infrastructure not only improves internal lab efficiency but ensures that archived datasets retain their scientific value long after collection. Repositories built with these platforms are ready for integration into open science hubs, collaborative consortia, and machine learning training libraries.

Conclusion

Conduct Science platforms do more than enable behavioral experiments—they establish a research environment optimized for ethical, reproducible, and collaborative science. Their inherent compatibility with FAIR principles makes them particularly well-suited for labs committed to long-term data stewardship, cross-lab harmonization, and transparent scientific reporting.

In an era where behavioral data is not just recorded but reanalyzed, repurposed, and recontextualized across studies, Conduct Science offers the physical and digital infrastructure to ensure that data is not only collected but also shared, understood, and reused in a future-proof manner. This makes their platforms indispensable for researchers who view FAIR not as a burden, but as a baseline for doing better science.

Explore tutorials and lab demonstrations at the Conduct Science YouTube Channel and full platform documentation at ConductScience.com.

References

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

Mathis, A., & Mathis, M. W. (2020). Deep learning tools for the measurement of animal behavior in neuroscience. Current Opinion in Neurobiology, 60, 1–11. https://doi.org/10.1016/j.conb.2019.10.008

Berman, G. J. (2018). Measuring behavior across scales. BMC Biology, 16(1), 23. https://doi.org/10.1186/s12915-018-0494-7

Ruebel, O., Tritt, A., Dichter, B. K., et al. (2019). NWB: Neurodata Without Borders—data standard for neurophysiology. Neuron, 102(5), 917–919. https://doi.org/10.1016/j.neuron.2019.04.036

Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature Neuroscience, 23(12), 1537–1549. https://doi.org/10.1038/s41593-020-00734-x

Conduct Science. (n.d.). Behavior Analysis Systems: Open Field, Social Interaction, Home Cage, and Tracking Tools. Retrieved from https://conductscience.com/behavior/behavior-analysis/

Conduct Science YouTube Channel. (n.d.). Tracking Systems, Metadata Integration, and FAIR-Compatible Setup Tutorials. Retrieved from https://www.youtube.com/@conductscience

Open Behavior. (2021). Best practices for behavioral video sharing and compression. https://www.openbehavior.com