For behavioral science labs funded by the NIH, converting data into open, shareable formats is no longer optional. It’s mandatory.

But what happens when your data lives in a .nod file, the proprietary format used by EthoVision XT?

Here’s your go-to guide for understanding .nod files, the roadblocks they pose for data sharing, and how ConductVision streamlines the conversion process, from raw trial data to fully NIH-compliant exports.

What’s a .nod File, Anyway?

If you’re working with EthoVision XT, you’ve likely come across a file with the .nod extension — and maybe wondered what exactly lives inside it.

The .nod file is EthoVision’s project container, acting like the brain of your experiment. It doesn’t store your raw video footage or final results. Instead, it functions as a master blueprint that ties everything together from experimental setup to analysis configuration.

Here’s what’s typically embedded in a .nod file:

• Experimental Design Details
  These include arena layouts, calibration parameters, and zone definitions. Think of it as the virtual stage where your behavioral experiment plays out.
• Trial and Subject Structure
  Every trial you run with its subjects, start/stop times, and assigned conditions is recorded here, giving the .nod file a full timeline of your study.
• Behavioral Events and Filters
  EthoVision allows users to define specific behaviors or actions (e.g., rearing, freezing, zone entries), apply filters, and derive metrics. These configurations are saved in the .nod file.
• Reference to Raw Videos
  Important to note: the video recordings are not stored in the file. The .nod simply links to external files so if the video path changes, those links can break.
• Cached Analytical Results
  To make the software load faster, EthoVision saves some processed data for quick viewing but this cached information is limited in scope and usefulness outside the program.

In short, the .nod file is a critical but closed-off resource. It holds the entire experimental logic and links to data but unless you have access to EthoVision XT, it’s essentially a black box.

That’s why .nod files are so problematic for collaboration, review, and data sharing. Without EthoVision, you can’t access the structure, configuration, or behavior definitions, let alone convert the data to formats suitable for NIH compliance or long-term archiving.

Why .nod Files Are Difficult to Share

While EthoVision’s .nod files work well within the software environment, they’re notoriously difficult to use outside of it, especially when it comes to collaboration, publication, or NIH data sharing requirements.

Here’s why researchers often hit a wall when trying to archive or share .nod-based datasets:

1. They’re Locked into EthoVision

The .nod format is proprietary and closed, there’s no public specification, and no third-party software can open or interpret them. If you don’t have access to a licensed copy of EthoVision XT, you’re out of luck. Reviewers, collaborators, or repository managers can’t view your experimental setup, zone definitions, or behavioral events without it.

2. No Embedded Videos

Unlike other project containers that store all assets internally, .nod files only reference video files, they don’t actually contain them. That means if the videos are moved, renamed, or missing, the .nod file becomes useless. Re-linking paths is not only tedious, it’s a barrier for data portability.

3. Missing Critical Metadata

Most .nod exports lack key metadata required for NIH compliance:

• Species, strain, and sex
• Subject age and weight
• Environmental conditions
• Equipment specifications

Unless researchers manually enter this metadata elsewhere, the dataset fails to meet NIH Common Data Elements (CDE) standards making it incomplete for repository submission or reuse.

4. No Simple Export to Open Formats

There’s no “Export to JSON” or “Save as CSV with metadata” button in EthoVision. You have to:

• Open the .nod in EthoVision
• Manually export each data table (e.g., coordinate tracking, events)
• Write up your own README and variable dictionary
• Compile metadata from memory or lab notes
  This adds hours (sometimes days) of extra work and introduces major room for human error.

How to Open and Export a .nod File (The Manual Way)

There’s no viewer for .nod files outside EthoVision. To work with them, you need:

1. EthoVision XT installed
2. Access to original video files
3. Manual use of EthoVision’s export tools to generate CSVs or Excel files
4. Hand-assembled metadata from lab notebooks (species, age, conditions, etc.)
5. Manual mapping to NIH metadata formats and ontology IDs

It’s possible, but time-consuming, tedious, and error-prone.

The NIH DMS Policy: What You’re Required to Submit

The NIH expects:

• Open file formats: CSV, JSON, MP4, PNG, SVG
• Rich metadata: Common Data Elements (CDEs) for animal research
• Ontology mappings: Species, assays, behaviors, and medical terms must be mapped to trusted vocabularies (NCBI, NBO, OBI, UMLS)
• Provenance: Timestamped records of software versions, processing steps, and parameters
• Repository-ready structure: Clean folders with README, license, and method documentation

And none of that is embedded in a .nod file by default.

Enter ConductVision: Fast, Accurate .nod File Conversion

For researchers juggling NIH compliance and tight deadlines, manually converting .nod files isn’t just frustrating, it’s risky. That’s where ConductVision changes the game.

ConductVision was purpose-built to bridge the gap between proprietary behavioral research tools like EthoVision XT and the open data standards required by NIH repositories. It’s not a workaround, it’s a full upgrade to your data workflow.

From Proprietary to Public-Ready — In One Step

With ConductVision, you no longer need to worry about how to:

• Extract zone coordinates from EthoVision
• Manually rebuild metadata spreadsheets
• Map behavioral events to ontologies
• Structure your folders to match repository guidelines

ConductVision automates all of it. Once you import your .nod project, the platform reconstructs your experimental setup, maps variables to official ontologies, and exports your dataset in NIH-compliant formats all while preserving scientific integrity and reproducibility.

What Happens Under the Hood

When you upload your .nod file (along with your tracking CSVs and raw videos), ConductVision does the heavy lifting:

• Arena & Zone Definitions Rebuilt
  No need to redraw. ConductVision intelligently reconstructs your experimental geometry.
• Video Integration & Re-encoding
  Your linked videos are matched and re-encoded in open MP4 format with H.264 codec — no broken paths, no compatibility issues.
• Tracking & Event Data Standardized
  Raw behavior data is organized into tidy CSV files, using SI units and descriptive column names.
• Rich Metadata Generated Automatically
  Including:
  • Species, strain, sex, age, weight
  • Environmental settings (temperature, humidity, light/dark cycle)
  • Equipment and software version
  • Protocol reference
    All packaged in JSON-LD format and validated against NIH Common Data Elements.
• Ontology Mapping Built In
  Every variable is linked to terms from:
  • NCBI Taxonomy
  • NeuroBehavior Ontology (NBO)
  • Ontology for Biomedical Investigations (OBI)
  • UMLS (for medical terms)
• Provenance Metadata Captured
  Including ConductVision version, AI model hash, export timestamps, and processing steps.
  

What You Get

At the end of the process, ConductVision generates a repository-ready folder structure that includes

Everything is UTF-8 encoded, FAIR-compliant, and ready to upload to platforms like OSF, OpenNeuro, or SPARC.

The Bottom Line

For NIH-funded labs using EthoVision, compliance isn’t optional, and manual conversion is a bottleneck. ConductVision replaces that bottleneck with speed, structure, and scientific rigor.

No more wrestling with locked files, scattered metadata, or formatting headaches. Just import your project, and let ConductVision handle the rest.

Manual vs Automated: Why the Choice Matters

Manual conversion requires you to juggle EthoVision exports, re-enter metadata, convert formats, and build your own folder structure. It’s doable, but slow.

With ConductVision, you import once and everything else is handled for you. This is the difference between hours of work and a few clicks.

If you’re managing dozens (or hundreds) of EthoVision experiments, this isn’t just convenience, it’s sustainability.

Final Thoughts: If You Use .nod, You Need a Plan

Working with .nod files may be part of your everyday workflow but when it comes time to share, publish, or archive that data, the clock starts ticking.

The NIH’s Data Management and Sharing (DMS) Policy isn’t just a formality, it’s a clear directive for transparency, openness, and reproducibility. That means your data can’t live inside a locked file format, even if that format has served you well during analysis.

Whether you’re a PI managing multiple studies, a postdoc preparing a grant renewal, or a lab manager coordinating submissions, the message is clear:

If your research relies on .nod files, you need a strategy to convert and share that data — now, not later.

Too often, labs wait until just before a funding deadline or data deposition requirement to scramble for tools, export files manually, and reconstruct missing metadata from old notebooks. That’s not just inefficient, it’s risky. Critical information gets lost, datasets become noncompliant, and the credibility of your research may suffer.

🛠️ ConductVision = Your Plan

ConductVision offers an immediate, reliable solution:

• No tedious manual formatting
• No guesswork on compliance
• No delays in sharing

It automates every step from .nod import to NIH-ready export, helping your team stay focused on discovery, not documentation.

Ready to Convert?

Book a complimentary migration consultation to see how ConductVision can convert your existing .nod archives into clean, compliant, sharable datasets.

👉 Schedule a Demo