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Data Management

This guide deals with the management of research data

Research data may be created by an individual researcher, group or contributed by a third party to a research group. Some examples are:

Observational: data captured in real time that is usually unique. Eg. Sensing data, survey data, field recordings, sample data: Experimental: data captured from lab equipment that is often reproducible-examples are gene sequences, magnetic field data: Models or simulations: data generated from test models where the model and metadata may be more important than the output data from the model - examples are  climate models, economic models: Derived or complied: resulting from processing or combining “raw” data often reproducible: Reference or Canonical: a static or organic conglomeration or collection of datasets probably published and curated - examples are gene sequence databanks, collections of letters, historical images.

Examples of outputs to be considered

Documents, spreadsheets

Scanned laboratory notebooks, field notebooks, diaries

Online questionnaires, transcripts, surveys or codebooks

Digital audiotapes, videotapes or other digital media

Scanned photographs or films

Transcribed Test Responses

Database contents (video, audio, text, images)

Digital Models, algorithms, scripts

Contents of an application (input, output, log files for analysis software, simulation software, schemas)

Documented methodologies and workflows

Records of standard operating procedures and protocols