Context is crucial
(based on Rule 4 of the article doi:10.1111/2041-210X.14033)
Always pair your data points with metadata. Sampling protocols ideally also define metadata that can be considered as covariates of the measurement procedure or inform the user about the provenance of the trait data. Together with the trait measurements, metadata defines an observation and its context (Madin et al., 2008). While such metadata may already be necessary for the proximate research question, it further helps future users to understand better and reproduce the methods and correctly interpret the trait values. The reuse value of existing datasets increases with the quantity and quality of metadata, so, datasets with sufficient context information are more likely to be reused in future synthesis analyses or included in more extensive databases.
Define at least the minimum context:
Some metadata are considered essential and universal between all domains, such as an unique ID for observations to cross-references to other measures, geolocation, time and date, life stage (e.g. juvenile), health status, scale (e.g. leaf), habitat type (e.g., semi-natural grassland or botanical garden) and measurement details (e.g., following standards, devices used, etc., Schneider et al., 2019). Further metadata must include the source and authorship of the trait measurement. To permit effective reuse, authorship attributes should consist of the original data collectors and the databases where these data were gathered, as they may have undergone processing therein (Rule 2).
Cover the domain-specific standard, if possible:
Deciding which further metadata to collect often involves a trade-off between which data are commonly collected in a specific domain (e.g. plants) and the time and expense involved in collecting or processing such data. Metadata preferably includes detailed documentation and code of how traits were measured (e.g., manufacturer and version of devices used) and processed (e.g., standardizations or species means). We recommend checking existing well-used datasets and databases of the specific domains before collecting new trait data to determine which common metadata should be covered.
Link to other data by metadata:
A good practice is to link the data with publications directly (e.g., by DOI) for the scientific context and further information in the materials and methods sections, as well as identification of trait data providers (e.g. by ORCID) to provide opportunities for feedback and requests for additional information. Traits are often measured also to collect other data, such as ecosystem function (e.g., Bongers et al., 2021) or species composition or interactions (e.g., Breitschwerdt et al., 2018). In these cases, functions measured‚ and species composition recorded, would be part of the metadata or links to those data in other repositories.