Scientific & Multidimensional Data

Overview

Scientific research often generates data that is too complex for simple tables. Multidimensional arrays, simulations, and experimental results require specialized binary formats that support high performance and self-description. This section focuses on curating these advanced file types.

Key Objectives:

  1. Dimension Analysis: We verify the shape and size of the data arrays (e.g., Time x Latitude x Longitude).
  2. Attribute Inspection: We check for “self-describing” metadata—internal attributes that define units, coordinate systems, and experiment parameters.
  3. Variable Inventory: We list all variables stored within the file to ensure completeness.

Supported Formats

NetCDF (.nc)

Network Common Data Form (NetCDF) is the standard for climate, oceanography, and atmospheric sciences. - Our Approach: We extract global attributes and variable dimensions to verify compliance with conventions like CF (Climate and Forecast).

HDF5 (.h5, .hdf5)

Hierarchical Data Format version 5 (HDF5) is a high-performance container used in physics, engineering, and genomics. - Our Approach: We navigate the internal groups and datasets, reporting on the hierarchy and the properties of the stored data.

MATLAB (.mat)

The standard format for MATLAB workspace variables, widely used in engineering and signal processing. - Our Approach: We list the variables saved in the workspace, their classes (e.g., double, struct), and their dimensions, allowing curators to understand the file content without needing a MATLAB license.

Common Curation Challenges

  • Missing Metadata: Binary files are “black boxes” without proper internal documentation. We emphasize the importance of checking for attributes like units and standard_name.
  • Proprietary Dependencies: While HDF5 and NetCDF are open, MATLAB files can be proprietary. We help identify the version and contents to assess long-term accessibility.
  • Large Data Volumes: These files can be massive. Our tools are designed to inspect headers and metadata without loading the entire dataset into memory.