Posts in topic: cataloging







Five data analytics questions to help secure—or increase—your e-resource budget

5 questions

By Justin Parker, Subscriptions Manager, University of Manchester Library, and
Tim O’Neill, Electronic Resources Coordinator, University of Manchester Library

As Subscriptions Manager and Electronic Resources Coordinator at the University of Manchester, part of our jobs is to make sure the university gets the best deal on its e-resource investment. But what does “best deal” really mean? Does it mean the least expensive materials? Well, an inexpensive subscription isn’t a good deal if it isn’t used at all. And even free, open source content has a cost associated with the cataloging, discovery, and course management systems we use to make it available.

The challenge is to find better ways to assess the value our students, teachers, and researchers gain from the e-resources we provide. And the end result should be a better plan for accurately conveying the importance of library collections within the larger goals of the institution. But how do you get there? Having spent some time recently tracing the pathways of e-resource usage, we have a few suggestions.

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How a network of data curators can unlock the tremendous reuse value of research data

data_reuse

Data reuse is a major focus for institutional research groups and their funders and it’s easy to see why. After the (often) expensive process of collecting, analyzing, and mining research data for valuable new knowledge, any additional attention, such as the publication, reference, or reuse of that data, multiplies its value.

But understanding researchers’ behaviors and needs when it comes to data sharing and reuse is challenging. Each discipline has unique norms and practices for how they collect and manage data, when (and if) they share their data, and how they determine a dataset’s fitness for reuse. Data curators—as information science practitioners—make a wealth of decisions and take well-informed actions to ensure that selected data have meaningful and enduring value to future research.

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Let’s cook up some metadata consistency

cooking_consistency

Let’s say you’re writing a cookbook and describing ingredients. For sure you’re going to want to be consistent from one recipe to the next. If you don’t want to confuse your readers, it’s good not to refer to one amount as “a pinch” in one recipe and “a dollop” or “a smidge” in another.

Then you look around and realize that other people are writing cookbooks and they have some standards. That’s not a pinch, to them; it’s a teaspoon to some or 5 milliliters to others. What you call a “chunk” everybody else calls “a quarter cup” or “32 grams.” So, you need to be consistent not just within your own cookbook, but with others’ cookbooks, regardless of the dish being prepared—roasts, stir fries, desserts, soups, etc.

Librarians and archivists in data repositories are learning to think like this as well. Because the data being deposited for reuse has much greater value to their institutions when the metadata attached to it are consistent at the study level, the data level, and the file level.

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Always judge a book by its cover

cover_header

We asked students to identify the types of containers from which online information is taken. Information containers can be important, obviously, because they provide critical context when evaluating the quality of sources. One said:

“This one looks like a—wait, I can’t tell what that is, but it looks like a book.”

Wait. It… looks like a book? Let’s try again:

“Pretty sure it had an ISBN number. It’s an article. Oh, no, books usually have—well, you can download the entire book or download the chapter. So, I’m thinking it’s a book. And it doesn’t have the edition, but I kind of want to say it’s a book about this book.”

That’s closer, but we can do better.

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