When we start thinking of our data as corporate assets with value beyond our immediate need, the idea of managing data through a whole lifecycle becomes more relevant. All of the questions of documentation, storage, quality assurance, and ownership then need to be answered for each stage of the data lifecycle.
Standards make it easier to create, share, and integrate data by making sure that there is a clear understanding of how the data are represented and that the data you receive are in a form that you expected.
If your data source is well documented, you know how and where to look for your information and the results you return will be what you expect. In addition, accurate data are legally and scientifically defensible. Such data may aid the agency by reducing litigations and appeals.
Data Processing covers any set of structured activities resulting in the alteration or integration of data. Process components support validation, transformation, subsetting, summarizing, integration, and derivation, among others.
The Analyze stage of the Science Data Lifecycle represents activities associated with the exploration and assessment of data, where hypotheses are tested, discoveries are made, and conclusions are drawn.
Data citation refers to the process of citing a dataset in the same way that books or journal articles are referenced in research publications. In general data citation is a good practice that benefits the researcher, data repositories and stewards, the scientific community, and the general public.
Throughout the data lifecycle process, documentation must be updated to reflect actions taken upon the data. This includes acquisition, processing, and analysis, but may touch upon any stage of the lifecycle. Updated and complete metadata are critical to maintaining data quality.
A documented sequence of intended actions to identify and secure resources and gather, maintain, secure, and utilize data holdings comprise a Data Management Plan. This also includes the procurement of funding and the identification of technical and staff resources for full lifecycle data management. Once the data needs are determined, a...
Data-quality management is a process where protocols and methods are employed to ensure that data are properly collected, handled, processed, used, and maintained at all stages of the scientific data lifecycle.
Use domains that are already developed in your subject areaUse domains that are available and supported by an authoritative sourceIf you create your own domain, document how items are distinguished from each other, and give each value a definition/descriptionInclude listings of domain table contents in your metadata or associated documentation,...
Overview of Data ManagementOverview of Data Management > Why Manage Your DataChatfield, T., Selbach, R. February, 2011. Data Management for Data Stewards. Data Management Training Workshop. Bureau of Land Management (BLM).Overview of Data Management > Data Lifecycle OverviewFaundeen, J.L., Burley, T.E., Carlino, J.A., Govoni, D.L., Henkel, H...