Metadata itself could be quite broad; from provenance of a study material, biological and experimental metadata, to technology based information settings, protocols and parameters [ 1, 2].
See the section below on "Submitting experimental metadata" for more information.
Here, we present the ontologies we use to annotate experimental metadata to provide improved searching capabilities across the data and to build a technical framework for ensuring the accuracy of the metadata.
This simplified system of storing free text and reserved tag and value pairs hence provides a consistent way to summarize the associated experimental metadata for each data file, and give sufficient details for any user to understand the enclosed data.
For example, without a significant amount of experimental metadata, a string of numbers (i.e. '42') found in a cryptic column titled 'GExp' may mean that a gene is expressed or not, because numerical data without context is utterly meaningless.
PHENOPSIS DB is a publicly available (URL: http://bioweb.supagro.inra.fr/phenopsis/) information system developed for storage, browsing and sharing of online data generated by the PHENOPSIS platform and offline data collected by experimenters and experimental metadata.
Partitioning of variance components of clinical, sample and experimental metadata revealed the largest components of array hybridization variability to be metrics of mRNA quality, in accord with recent studies of post-mortem brain tissue [70].
Summary: The first open source software suite for experimentalists and curators that (i) assists in the annotation and local management of experimental metadata from high-throughput studies employing one or a combination of omics and other technologies; (ii) empowers users to uptake community-defined checklists and ontologies; and (iii) facilitates submission to international public repositories.
In a field where complex data sets containing hundreds of thousands of spectra, generated under multiple conditions, support scientific conclusions at the molecular level, the sufficient reporting of the experimental metadata is essential for quality assessment of these conclusions, as well as for a potential reanalysis of the valuable data.
Improved incorporation of experimental "metadata" will increase annotation successes further, especially for large candidate sets.
