![]() In common usage by the research community crystallographic information file (CIF) in chemistry), and instrument specific.įormats more likely to be accessible in the future are: ![]() Research data comes in many varied formats: text, numeric, multimedia, models, software languages, discipline specific (e.g. Examples: gene sequence databanks, chemical structures, census data, spatial data portals. Static or organic collection datasets, most probably published and/or curated. Examples: climate models, economic models, biogeochemical models Models and metadata, where the input can be more important than output data Results from using a model to study the behavior and performance of an actual or theoretical system Examples: text and data mining, derived variables, compiled database, 3D models Reproducible, but can be very expensive Examples: gene sequences, chromatograms, spectroscopy, microscopy Should be reproducible, but can be expensive Data collected under controlled conditions, in situ or laboratory-based Examples: Sensor readings, sensory (human) observations, survey results Can’t be recaptured, recreated or replaced Your data stewardship practices will be dictated by the types of data that you work with, and what format they are in.ĭata types generally fall into five categories: After defining what we mean by data, it is helpful to consider what types of data you create and/or work with, and what format those data take.
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