Data lifecycle management (DLM) is best described as?

Data lifecycle management (DLM) is best described as the tools and processes for handling data during and after a study. This concept encompasses all stages of data management, including collection, storage, processing, sharing, and disposal or archiving. Effective DLM ensures that data is not only collected efficiently but also maintained, preserved, and accessible for future use while adhering to ethical and legal standards.

DLM emphasizes the importance of managing data throughout its entire lifecycle, from its initial creation through its active use, and eventually to its disposal or archiving. This leads to better organization, less risk of data loss, and compliance with regulations. By having robust DLM practices in place, researchers can improve data quality, enhance reproducibility of research findings, and facilitate future use of data.

The other options describe concepts that do not encompass the full scope of DLM. Retaining data indefinitely is not practical for all research and does not focus on the management processes. Data visualization is a method focused on representation rather than management of the lifecycle of the data. Collecting data from previous studies pertains to secondary data use, which is only a small aspect of the broader DLM framework.