Posts tagged under: Research Data Management

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

Lisa R. Johnston

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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Making friends and influencing people: research support edition

Brian Lavoie

make_friends_header

Research support services are essential to the university’s research enterprise—enhancing researcher productivity, facilitating analysis of research activity, and making research outputs visible and accessible across the scholarly community and beyond. Research support services extend over the entire research life cycle—as well as across the entire campus.

How is research support carried out?

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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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Increase data reusability and enhance your curation investments with these three tips

data reuse

In many cases, collecting and processing original research data is incredibly costly and difficult. It can involve travel, field work, painstaking examinations, and observations. Sometimes unique, expensive equipment or one-time access to materials or events that can’t be recreated is required. But it’s worth it if the data yields new scientific insights and advances.

And if that data can be reused in other studies, it makes the return on investment (ROI) much more attractive for universities and funding bodies. Professionals in libraries, archives, and museums have a unique view into the needs of researchers. We can develop and promote new services and procedures that encourage data sharing and data reuse.

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Libraries and RDM: Three decisions, three components, three realities

Brian Lavoie

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New data mandates, open science advocacy, and replication of research results have focused attention on data management practices during the research process. This, in turn, has led to the development of services, infrastructure, and other resources to support Research Data Management (RDM) needs at research universities.

But how are research universities addressing the challenge of managing research data throughout the research life cycle?

The Realities of Research Data Management is a four-part series from OCLC Research that looks at the context, influences, and choices that research universities face in acquiring RDM capacity. We launched this project to pull back the curtain a bit on how universities work through the process of acquiring RDM capacity. Our findings are derived from detailed case studies of four research universities:

  • University of Edinburgh (UK)
  • University of Illinois at Urbana–Champaign (US)
  • Monash University (Australia)
  • Wageningen University & Research (Netherlands)

Our focus is on three major decision points that universities face in acquiring RDM capacity.

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Bringing order to the chaos of digital data

digital-chaos

530 million songs. 90 years of high-definition video. 250,000 Libraries of Congress. That’s how much data we produce every day—2.5 exabytes according to Northeastern University. I guess that’s not surprising, given the amount of activity that goes on in social media, websites, email messages and texting.

Much of that data, though, is personal and ephemeral. Videos, photos, tweets and stories that can be passed along and deleted without any thought or care about accuracy or archiving.

But in the scholarly community, a similar and perhaps more significant explosion of digital data is occurring. Here the stakes may be much higher. Without trusted stewardship, data from research will not be effectively collected and preserved for reuse. And when this happens, research innovation and advancement slows significantly.

This is new territory in many ways. Data have been collected and preserved for thousands of years, but never at the volume we see today, nor with some of the deliberate (and in some cases, legally mandated) intentions for reuse.

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