Datasets
A Dataset describes one or more related data files produced by an experiment, and the variables in them.
What is a dataset?
A dataset typically corresponds to a single data file (e.g., one CSV of CTD profiles), but it may include multiple files when they share the same platform, instruments, and data submitter. The key organizing principle is that a dataset represents data collected on a single platform using a consistent set of instruments, managed by a single data submitter. If data come from different platforms or instrument configurations, they should be separate datasets.
Dataset Types
- FieldDataset: data collected during in-situ experiments, such as CTD casts, bottle samples and sensor deployments. It records the platform that collected the data, using the NERC L06 platform types (see Platform).
- ModelOutputDataset: output from a model experiment. How the output was produced is described here, through the simulation type, period, output frequency and hardware.
Rules
These apply on top of each field's own requirements:
- Data access (both dataset types):
open_accessis for data you can reach today and requiresdata_access_link. Usescheduled_accessfor anything not openly available yet; it requiresdata_access_date, the date the data will open.data_access_linkis optional for scheduled and conditional access. - Perturbation runs (model output):
simulation_typecan list several values. If it includesperturbation, describe the forcing inmcdr_forcing_description.
Linking Datasets to Experiments
Each dataset names the experiment it belongs to with experiment_id, so one experiment can have several
datasets, such as CTD profiles, bottle samples and sensor time series.
Variables
Each dataset lists its variables, metadata for each measurement or column in its files. Field
datasets use the field variable classes, with specific classes for pH, total alkalinity, DIC, CO₂ and
other common measurements. Model output datasets use ModelOutputVariable.
See Variables for how to choose a class.