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A .nc file is a NetCDF dataset: self-describing, array-oriented scientific data in which every variable declares its dimensions and carries attributes such as units, a long name and a fill value. It is the standard container for climate, ocean, atmospheric and satellite data.

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Batch files can each use a different output. Nothing uploads for local conversions.

Working inputs include camera RAW, browser-local audio/video, PDF, CBZ/CBR comics, office documents, ebooks, markup, 3D models, structured text, images, and archives.

Where NC comes from

Unidata at UCAR released NetCDF in 1989, building on NASA's Common Data Format, so that atmospheric researchers could exchange gridded data without writing a reader per producer. The classic CDF-1 format gained a 64-bit-offset variant (CDF-2) for larger files, and NetCDF-4 in 2008 changed tack entirely by storing data in HDF5 while keeping the NetCDF API. The Climate and Forecast (CF) metadata conventions layered on top are what make files from different institutions genuinely interoperable, and NetCDF underpins the CMIP climate model archives.

How NC works

  • Classic files begin with 'CDF' and a version byte: 0x01 for CDF-1, 0x02 for the 64-bit-offset CDF-2. NetCDF-4 files are HDF5 and begin with the HDF5 signature instead.
  • Everything in the classic format is big-endian and padded to four-byte boundaries — names, attribute values and variable data alike.
  • The header lists dimensions, then global attributes, then variables; each variable records the byte offset where its data begins, so the offsets can only be computed once the header size is final.
  • One dimension may be unlimited (the record dimension), which is how time series grow without rewriting the file.
  • Attributes are conventional rather than enforced: units, long_name, _FillValue and scale_factor mean what CF says they mean because everybody agreed, not because the format checks.

When to use NC

  • Climate model output, reanalysis products and satellite observation archives
  • Gridded oceanographic and atmospheric measurements
  • Time-series sensor data with real units and provenance attached
  • Extracting a variable to CSV or a spreadsheet for a non-specialist audience

Strengths and limitations

Strengths

  • Genuinely self-describing: units and provenance travel with the numbers
  • Efficient subsetting of very large multidimensional arrays
  • Deep institutional support and decades of archived data

Limitations

  • Classic and NetCDF-4 are different file formats behind one API, which confuses tooling
  • CF conventions are not enforced, so a technically valid file can still be ambiguous
  • Multidimensional arrays flatten awkwardly into tables

Compatibility

The Unidata C library plus Python (netCDF4, xarray), R, MATLAB, Fortran, Panoply, QGIS and ncdump all handle NetCDF. Novus Convert decodes classic CDF-1 and CDF-2 datasets locally into structural JSON, long-form CSV, or one worksheet per variable. NetCDF-4/HDF5 files are not accepted here — they need the HDF5 stack, which remains behind its own gate rather than being half-supported.

Global and per-variable attributes, including units and fill values, are preserved into the JSON export and reported alongside the CSV and worksheet outputs.

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