Margaret Gabanyi

We describe a new vision for a Data Management Plan (DMP) that incorporates controlled vocabularies and semantic descriptions of the scholarly objects to be produced by the proposed project. We implement this vision in an open‐source web‐based DMP tool, called ezDMP, at ezdmp.org . The integrated use of structured information in ezDMP permits several important goals. First, with minimal additional effort, researchers can create DMPs with more complete information about the scholarly objects to be produced. Second, research funders can productively query this structured information to learn about repository use and other patterns of scholarly objects creation. Finally, ezDMP puts a structure in place that can support the integration of information about digital scholars objects, in an organized and systematic way, into research data management environments.

Structural and functional studies require the development of sophisticated `Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, theProSMARTtool for conformation-independent structure comparison, theLabDB`super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

A key reason three-dimensional (3-D) protein structures are annotated with supporting or derived information is to understand the molecular basis of protein function. To this end, protein structure annotation databases curate key facts and observations, based on community-accepted standards, about the ~100,000 3-D experimental protein structures to date. This review will introduce the primary structure repositories, databases, and value-added structural annotation databases, as well as the range of information they provide. The different levels of annotation data (primary vs. derived vs. inferred) and how they should all be considered accordingly will also be described.