Metadata include: characteristics of experiments and associated protocols, list of genotypes grown in an experiment, list of variables measured in an experiment with their definition and associated protocols, comments on the experiments, micrometeorological data and plant watering data.
Such data must be accompanied by enough contextual information (i.e. metadata; sample characteristics, technology and measurement types; instrument parameters and sample-to-data relationships) to make datasets comprehensible and reusable if they are to underpin future investigations.
Analyses of sequence characteristics and metadata were performed in ARB.
This corpus of data and metadata allows us to analyze characteristics of data from the LTER program, including temporal coverage, data format, rate of submission, volume of data, and ecological characteristics of the data (e.g., ecosystems, processes, organisms).
The authors of this paper aim to solve this mystery by defining different professional publisher groups, examining the behaviors of these user groups, and identifying the characteristics of their metadata use.
SaaS applications can be grouped into maturity models that are determined by the following characteristics: scalability, configurability via metadata, and multi-tenancy [30, 33].
Metadata is used to facilitate the understanding, characteristics, and management usage of data.
This decision can be supported by interactive exploration of metadata in such a way that different characteristics of datasets can be studied simultaneously.
Model metadata is used to facilitate the understanding, characteristics, and management of the model.
Metadata consist of protocols, descriptions of variables, genotype characteristics and online environmental data.
