Processed ADV mean velocity and turbulence statistics at reference tidal energy sites

This dataset contains uniformly processed acoustic Doppler velocimeter (ADV) measurements from six energetic tidal energy sites located in the United States, Scotland, and Chile, compiled for the 2026 publication Comparative Turbulence Characterization at Energetic Tidal Energy Sites. The sites include:

- East River (New York, USA)
- Nodule Point and Admiralty Inlet (Puget Sound, Washington, USA)
- Western Passage (Eastport, Maine, USA)
- Sound of Islay (Scotland, UK)
- Chacao Channel (Chile)

Raw ADV time series were sourced from published open-access repositories and research collaborations, except for Sound of Islay where only processed data were available. The dataset covers diverse tidal regimes, channel geometries, and flow conditions representative of tidal energy development environments.

All raw data were reprocessed using a consistent methodology including phase-space thresholding for spike removal, Doppler noise estimation and correction, coordinate transformation to principal flood and ebb directions, and filtering of slack-tide intervals below 0.75 m/s mean velocity. Turbulence metrics reported include mean velocities, turbulence intensities, Reynolds stresses, turbulent kinetic energy, integral length scales, and velocity power spectra, evaluated with 5-minute averaging windows aligned with IEC 62600-201 and IEC 62600-2 standards.

Each site's data are provided in standardized formats with file names corresponding to site codes defined in the publication. The dataset supports cross-site comparison of tidal turbulence characteristics relevant to tidal energy converter design, certification, and resource assessment.

Comprehensive metadata, variable descriptions, and processing details are included in the dataset README. The submission complies with FAIR data principles: it is findable through the open-access PRIMRE Marine and Hydrokinetic Data Repository (MHKDR) with a DOI; accessible via self-describing netCDF files readable in open-source tools such as Python and R; interoperable for integration with other applications and databases; and reusable through comprehensive documentation.