Naomi J. Munthali

  What happens to data after research is completed? In many cases, valuable datasets are stored away and rarely revisited. Yet within data curation , data is no longer viewed as something created for a single purpose only. Instead, it is increasingly recognised as a long-term intellectual asset capable of generating new knowledge, supporting innovation, and strengthening future research. However, while the reuse of curated datasets offers many opportunities, I believe its success depends on more than simply making information available online. The use of data remains central to research, decision making, and knowledge production across different disciplines. Data allows researchers, organisations, and governments to identify patterns, evaluate problems, and make informed decisions. Boté and Térmens (2019) explain that datasets are generated throughout the research lifecycle using different tools and methods, ranging from surveys to digital devices. In my view, the value of data li...

Every day, institutions generate vast amounts of data, yet the challenge of storing it effectively remains one of the most underestimated issues in the digital age . Far from being a neutral or purely technical activity, data storage has become a strategic function central to records and archives management . Contemporary scholarship increasingly demonstrates that storage systems must support not only preservation but also accessibility, sustainability, and long-term usability (Katuu & Ngoepe, 2021). What this means in practice is that storing data is no longer about keeping information safe it is about ensuring its continued value. One of the most notable shifts in this space is the growing adoption of free and open source software (FOSS) such as DSpace and Archivematica . These platforms are widely recognised for their flexibility and cost effectiveness in managing digital repositories (Katuu, 2020). However, focusing solely on affordability risks oversimplifying their strategic...

In an era defined by data abundance, the challenge is no longer simply how to store information, but how to decide what   deserves   to be preserved. The selection and appraisal of data have become central to effective data management, shaping not only what is retained but also what knowledge remains accessible for future use. These processes are far from straightforward they are deeply influenced by institutional priorities, resource limitations, and evolving user needs . Traditionally, appraisal has been framed as a technical or archival function, primarily concerned with identifying records of long term value. Eastwood (2004) describes appraisal as a professional judgment grounded in evidential and accountability requirements. While this perspective is valuable, it tends to position appraisal as a static decision making process. In contrast, Hodge and Frangakis (2004) argue that appraisal must align with broader data management policies and anticipated patterns of reuse. B...

Data is the lifeblood of modern knowledge systems, yet its value is only realised when it is systematically collected, organised, and stored in environments that support long term access and reuse. Data collection and repositories sit at the very heart of data curation, forming the infrastructure upon which research, policy, and institutional memory depend. Data collection refers to the deliberate process of gathering raw information from various sources  surveys, sensors, experiments, administrative records, or observations in a structured manner (Corti et al., 2019). However, collecting data without a curation strategy is insufficient. Borgman (2015) argues that data does not speak for itself; it requires context, documentation, and governance to become meaningful and reusable across different communities. This insight shifts the responsibility of data professionals beyond mere collection toward stewardship an active, ongoing engagement with the data lifecycle . https://youtube.c...