Weekly Insights from My LIS Course

 From data explosion to data responsibility Almost every part of academic and public life now produces data. Universities collect research datasets,  governments release census and administrative records, and libraries continue to digitize collections  that once lived only in boxes, cabinets, and reading rooms. Creating data, however, is only the  beginning. Data becomes useful when someone else can understand it, trust it, preserve it, and use it  without having to reconstruct the whole story from scratch. In Library and Information Science (LIS), using data usually means applying it for the purpose for which it  was first collected. Reusing data means taking an existing dataset and asking new questions of it,  sometimes in a different field or with a different method. Pasquetto, Randles, and Borgman  make an important point here: reuse is not just a technical matter of downloading a file  (2017) . It depends on  context, documentation,...

You have cleaned your data, labelled every column, and written documentation you are proud of. Now comes a question that is far less glamorous but every bit as important: where are you going to put it? In the context of curation, how and where you store your data can determine whether it survives five years or fifty. A study published in Library Management found that researchers at a malawian public university stored data primarily on personal computers, flash disks, and email-all high- risk, fragile options. The data was being created, but it had nowhere safe to live. Storage Is Not The Same as Preservation Data storage is not just an IT concern. It is a core pillar of data curation. Where data is kept determines whether it can be found, accessed, verified, and reused years from now.As Hart et al. argue, poor storage contributes directly to "data entropy"- the slow, quiet decay of information that becomes less accessible over time. Good curation means choosing storage that p...

 Here is the truth nobody likes to here: not all data is worth saving. We live in a time that generates information at a breathtaking pace, yet the instinct to keep everything can do more hard than good. Without thoughtful selection, repositories become cluttered warehouses where valuable datasets get buried under mountains of noise. that is why selection and appraisal sit at the very heart of good data curation. What Do Selection and Appraisal Actually Mean? In the DCC Curation Lifecycle Model , appraisal is described as the process of evaluating data to determine what merits long-term curation and preservation. Selection is the decision that follows: which datasets stay, and which ones do not. Together, they act as a quality filter, ensuring that the data we invest in preserving is genuinely worth the effort. Think of it like editing a book. A first draft has raw material, some brilliant and some not. An editor does not keep every sentence out of loyalty to the writer. They keep ...

Let me paint you a picture. A reseacher spends two years collecting data, publishes a groundbreaking paper, and then moves on. Five years later, someone tries to build on that work. They find the dataset, download it, and open it only to discover unlabeled colomns, missing documentation, and file formats nobody usess anymore. all that effort, quietly slipping into irrelevance. This  happens far more often that we like to admit, and it is exactly why data curation deserves our attention. what data curation really means Data curation is the ongoing practice of collecting, organizing, cleaning, documenting, and preserving data so it remains useful over time. The goal is to make date FAIR  (Findable, accessible, interoperable, and reusable). It sounds technical, but at its heart, it is about respect: respect for the work that produced the data, and respect for the people who might need it next. A survey published in PLOS ONE found that 97% of researchers who used curation service...