RESUMO
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.
RESUMO
Currently, there are various approaches to proteomic analyses based on either 2D gel or HPLC separation platforms, generating data of different formats, structures and types. Identification of these separated proteins or peptide fragments is typically achieved by mass spectrometry (MS) measurements that use either accurate mass measurements or fragmentation (MS-MS) information. Integrating the information generated from these different platforms is essential if proteomics is to succeed. A further challenge lies in generating standards that can accept the hundreds-of-thousands of mass spectra produced per analysis based on threshold or probability measurements. Finally, peer review and electronic publication processes will be crucial to the dissemination and use of proteomic information. Merging the policy requirements of data-intensive research with information technology will enable scientists to gain real value from global proteomics information.