Data Management for Wits: Data Life Cycle

Research Data Life cycle

Research data management (or RDM) is a term that describes the organization, storage,preservation, and sharing of data collected and used in a research project.It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions).It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).

”Data is whatever you use to conclude results"

Research Data Life cycle adatpted from DataOne by Wits Data services with thanks to Susie Allard

The data life cycle provides a high level overview of the stages involved in successful management and preservation of data for use and reuse. Multiple versions of a data life cycle exist with differences attributable to variation in practices across domains or communities.

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Research Data Life cycle has eight components:

Plan:Project management and action plan regarding all organisation and processing of data for the purposes of the Research project .

Collect:Systematically gather information through observations, experimentation, explorations: theoretical and artistic. It can also be a version of discovery.

Assure: Perform systematic quality processes during collection, entry, and analysis such that the data can be audited and claims made by evidence proved. It can also be an iteration of collection.

Analysis: Examine data to understand and form conclusions about the nature and relationship of its components or practically to see what effects or use can be made of the information contain therein.

Describes:Metadata : describes,annotates and provides context so data can be easily discovered, understood, and reproduced , this includes documentation and methods. Metadata in legal terms is knowhow. It can constitution an additional layer of data or a first stage of analysis via structuring and organisation

Integrate: Combine data from disparate sources to form one linked set of data that can be readily analysed, or organise and add to data such that it can speak to conclusion from other data. In systematic review meta analysis.

Discover:Locate and obtain relevant data for use in research, or discovery including academic literature and existing databases.

Preserve:Actions taken (use of sustainable formats, deposit in trusted repository) to ensure data is accessible for a set period.

Some research activities might use only part of the life cycle; for instance, a project involving meta-analysis might focus on the Discover, Integrate, and Analyze steps, while a project focused on primary data collection and analysis might bypass the Discover and Integrate steps. In addition, other projects might not follow the linear path depicted here, or multiple revolutions of the cycle might be necessary. Further, some scientists or teams (e.g. those engaged in modeling and synthesis) may create new data in the process of discovering, integrating, analyzing, and synthesizing existing data.