Best practices in workflow operate within the wider context of the social science community's activities in information and knowledge. One way of viewing these activities is to organize them according to a life cycle model consisting of identifiable stages. Each stage is discrete and contains within it a set of related activities. How these activities are organized, managed, and carried out is in part dependent on the workflow of the organization or agency doing the work.
Modern research is conducted within a community of agencies, organizations, and individuals that together support the generation of new data and knowledge. Even though individual research investigators receive substantial portions of funds from funding agencies and councils, the large investments in science are made in research teams or programs, which often are interdisciplinary and international in composition.
The management of research resources and materials within a research community involves stewardship responsibilities by all of the stakeholders making up the community. From a life cycle perspective, each stakeholder will have primary custodial responsibility for research resources within or across stages of the life cycle. A major challenge in the overall stewardship of research data and metadata is coordinating the transfer of custodial responsibilities of the metadata and data as they pass from stage to stage in the data life cycle.
The DDI community believes that the best practices in metadata creation and use take place when the following ideals are observed:
> Metadata production should not be considered solely as an afterthought.
> Metadata should facilitate other activities in the overall workflows of the data life cycle.
> Metadata that are generated throughout the life cycle should be integrated with other metadata.
> Published metadata should be versioned and preserved to maintain transparency, but never discarded – if it were, this would obscure earlier stages of data development.
> Metadata producers must be aware of their role in the larger research picture. At each stage of the life cycle, the metadata produced/prioritized may not meet the needs of others in the life cycle. This underscores the need for better communication, awareness, and articulation of metadata across the entire data life cycle.
> Key stakeholders like funding agencies need to be educated about the metadata requirements of all stages of the data life cycle and adopt measures to ensure that good stewardship and citizenship are practiced. They become the standard bearers for metadata best practices.