It’s that time of year when more and more students are asking about accessing datasets for their research through our local Research Data Centre. And a couple times now, I’ve found myself having to explain that one does not simply walk into an RDC…
Happy Spring! We’re almost there, people, I promise.
I’m beginning this post with that statement since it recalls an entry I wrote last year about taking on a limited term appointment as Wilfrid Laurier University’s Government Information Librarian. It was a rather productive year as the GovInfo Librarian, and I loved my time in the job. Moving to Ontario gave me the opportunity to meet many colleagues in Canadian LIS who I would otherwise only get the briefest introductions to at national conferences. It also meant shifting “consortial cultures” as I moved from a CAUL province to an OCUL province and had to learn a brand new vocabulary of committee names and acronyms. And it also meant having to re-learn what “hot, hazy, and humid” means, let alone the value of central air.
But I digress, it was a pretty good year. The past 12 months has been full of new colleagues and friends, introductions to new scholarly resources, publishing and speaking opportunities, and a chance to “make a difference” at the workplace. Sometimes, you leave the office later in the day than you intended, but you leave later because you really do enjoy your work. And that’s a good thing.
Like February of 2012, February of 2013 was a month of changes, and March 2013 is a month of announcements. I’ve now accepted an appointment as the Laurier Library’s Data Librarian. Needless to say, I’m quite excited by this news and can’t wait to get the ball rolling. One of my main responsibilities in this portfolio is to help develop the Library’s research data management infrastructure and to facilitate research data access, usage, and collection on campus and in the communities we serve. There are some big steps involved, but my plan is to leverage the knowledge gained at CARL’s RDMI summit in January 2013 as we roll out services and resources to students, staff, and faculty on at Laurier.
Reports will follow, as they have in the past. (I’ve thought about starting a brand new blog to collect my thoughts on data management together in one place. I’ll post a link here if I do.) In the mean time, I’ll leave you first with a link to the photoblog my spouse and I maintained while work forced us to live in different provinces for an entire year – check it out: I must say we did an awesome job. And I’m also going to leave you with some YouTube clips. It’s impossible to talk about being a data librarian without making a Star Trek reference:
And also this one. When talking about living in Waterloo, ABBA will sooner or later be mentioned. Without fail..
Last month, I attended CARL’s 4-day course on Research Data Management Services in Toronto. (Jargon alert: CARL is the Canadian Association of Research Libraries). This was an intensive week of collaborating on research data management (RDM) practices and creating a community of practice within Canadian academic librarianship. Our concern for sound RDM practices at Canadian universities brought together librarians with all kinds and levels of expertise so that we could share tools and develop action plans that will make a positive impact in this field.
1. Research Data Management, Data Lifecycles, and Research Data Lifecycles
What is research data management? I won’t go into textbook-detail suffice to say we’re talking about systematic practices that govern how research data are defined, organized, collected, used and conserved before, during, and after the research process. That sentence is a mouthful and it covers a lot of ground, so I suggest you look to Chuck Humphrey’s Research Data Management Infrastructure (RDMI) site for a more focused definition. Chuck is hailed in Canada for his data management expertise, and he led many sessions at the workshop. He explains that:
Research data management involves the practices and activities across the research lifecycle that involve the operational support of data through design, production, processing, documentation, analysis, preservation, discovery and reuse. Collectively, these data-related activities span the stages of project-based research as well as the extended stages that tend to be institutionally based. The activities are about the “what” and “how” of research data. (source)
Chuck’s website is a great introduction to the existing RDM gap in Canada, and we referred to it several times in the course. It neatly summarizes key information such as the shaky progress and history of RDM in Canada, where the Canadian RDM community stands in the world today, the differences between data management and data stewardship, and why the Canadian research community should focus its attention on building infrastructure to support RDM as opposed to building a national institution to guide it.
Beyond talking about what RDM is and isn’t, we spent a lot of time studying where RDM sits within the research lifecycle. Many people are familiar with the data lifecycle model since it introduces us to the many facets of data management, however, the CARL course proposed that we instead examine data management practices as an integral part of the larger research lifecycle. Rather than focusing only on data at the expense of the larger research project, the course facilitators asked us to apply RDM within the entire research process, using the following model from the University of Virginia:
The salient point is that research data management isn’t limited to only the data life cycle; it affects the entire research process. (A simple example: data management strategies should be discussed well before data are created or collected.). Furthermore, if we want to develop sound RDM practices, we need to think like the researcher, understand the researcher’s needs, and include our work within their processes. If you’re not working with the researcher, then your RDM plan isn’t working.
2. Local RDM Drivers and Activities
If understanding what research data management is and where it affects the research process was one takeaway of the course, analyzing our local data environments was another:
- RDM drivers, such as your library’s consortial collaborations, number of staff, existing IT relationships, administrative support, etc., are the parameters that shape and support your local RDM programme.
- The activities in your RDM programme, meanwhile can be broadly categorized into the four areas: collection, access, use, and preservation (note: activities can fall into more than one category, and the order is not linear).
Discussing the things that affect our data landscapes and the activities we could perform helped us understand what is possible at our own libraries. I think a lot of us found this useful because all of our unique circumstances (e.g., library and university sizes, existing infrastructure and knowledge, etc.) can make RDM a bit nebulous at times. Although our focus is the same – RDM – our individual goals and aims might be different – are we building our technical capability, or are we designing soft systems that focus on relationships? Are we only collecting new locally created data, or will we also gather existing, completed projects? The answers are going to depend on your local situation.
The course facilitators were careful to help participants understand RDM as a necessarily scalable enterprise. Don’t create a monster RDM plan. Instead, contextualize your local RDM drivers and your library’s capabilities and desires so that you can mitigate the risks of creating an RDM plan that doesn’t fit your organization. The aim is to create a system and process that brings clear benefits to the researchers.
3. Planning… and Doing
The final takeway from the CARL RDM course, which you may have noticed I’ve been building up to, was straight-up, no-nonsense, get’er-done planning. The course facilitators built opportunities for real action into the course, which is probably one of the best parts of the week. Generally speaking, the academic enterprise undertakes a lot of talk and high-level planning before things happen. This is often a good thing (read: I demand critical inquiry), but it can also stifle action (read: I despise institutional inertia). However, this CARL course found a way to bring together discussion and action. It gave us theory, but it demanded practice. Before the week was out, we had all talked about 3-year planning, considered how such a plan might look locally, and started to write one. Of course, these drafts aren’t ready for prime time, but my point is that before I came back to the office on Monday, I already had written the skeleton of a research data management plan that shows my library’s potential RDM activities and stakeholders, outlines activities and scopes, and offers timelines and deliverables. It didn’t make me an expert (and neither do I claim to be one), but it did offer some tools to help the library step out and make positive change.
So was the CARL RDM course money well spent? It sure was. It’s not too often you come back from an event with a new community of practice, insight on a vital part of the research enterprise, and a plan to put everything in action. Hat’s off to the course facilitators for putting on such a great week – I think you’ve started something necessary, and good, for Canadian research.
(And some time next week, I’ll start gathering up some of the key readings from some of the bibliographies they presented us… I’ll try not to turn the next post into a lit review, but it may come close to it.)
2012 has come and gone, and it’s been quite a year. If you’ve been following along on this blog or elsewhere, then you probably know that my theme for these past twelve months has been “Planes, Trains, and Automobiles.” Since starting a term position as Government Information Librarian at Wilfrid Laurier University, I split my time between Halifax, Nova Scotia, and Waterloo, Ontario. So, not only do the students at the Library’s Second Cup know my name and face, but so do some of the stewards and other professionals at Porter Airlines in Toronto. I’m now part of the jet-set, and I can also rhyme off CANSIM tables to you like nobody’s business.
Taking on a new position in a new city (and new province) means that there has been a lot of learning and adjustment. A new job brings new duties and new work cultures. And a new city means new roads and neighbourhoods, new cafés and pubs, and new local cultures. I’ve traded in a Maritime hospitality built on lobster, rum, and sea shanties for Kitchener-Waterloo’s beer, schnitzel, and breads. (and I love bread. Not kidding). Waterloo has pockets of cool, and I’m getting on quite well here.
I love my job. It has met – and exceeded – my expectations. As the Government Information Librarian, I help the university community access and use government-produced materials in their research. All of last spring’s cuts to the federal government, and especially to Statistics Canada, LAC, and to libraries within federal ministries definitely dampened the spirits of Canadian GovDoc librarians in 2012, but I’m still happy that I’ve been able to help my library’s patrons understand what the cuts mean for them and their research – today and in the future. If anything, these cutbacks have increased the need for local government publications expertise at Canadian universities, and I think the government information librarian’s role on campus is now more important than ever.
My favourite part of this position has been my work with statistics and data. Like many university libraries across Canada, responsibility for socio-economic data at the Laurier Library lies largely with the Government Information Librarian since so many of our statistical resources come from Statistics Canada. (You can read more about the relationship between StatCan and academic libraries here. This paper by Wendy Watkins and Ernie Boyko should be required reading at library schools in Canada). I’ve long wanted to practice in this field, and I saw this posting as my opportunity to work regularly with the data skills I’ve developed through the years, and to learn even more from a whole new group of data librarians. Nearly all my favourite interactions with faculty, students, and other stakeholders in 2012 are data-related, from helping students acquire data on migration to the far north, to meeting with community members and legislators to explore nation-wide open data initiatives. These are the moments where I see my skills and expertise in librarianship put to action, and the positive contribution I make on campus puts a spring in my step. Data librarianship is an essential part of the academic enterprise; I’ve given a lot of effort in this area, worked and learned from the right people, and made gains for the library and the university. So, I’m willing to smile and say “yeah, I did that, but with the help of my friends, too.”
When it comes to adjustments, I have to say that the thing that took the longest to get used to was the new jurisdiction. I say this to all librarians, young and old, green and experienced: you will never really know how important your consortium is to your daily work until you join a new one. When I moved from Nova Scotia to Ontario, I left the Council of Atlantic University Libraries, ASIN, and NovaNet, and I joined forces with the Ontario Council of University Libraries, Scholars Portal, and TUG. Now, my online resources are different. The OPAC is different. ILL is different. Committees are different. Organizational cultures and funding are different. Conferences and workshops are different. Support channels are different. Let me be clear: everything changes when your work takes you to a new consortium. Libraries really do things better when they work together. We’re stronger this way. But it’s not until you shift to a new jurisdiction that you’ll be reminded several times daily just how much effort colleagues at your library and at other institutions have put into making things work better, faster, and cheaper for everyone. We stand on the shoulders of giants.
The best example I can give to demonstrate this is <odesi>. Built and managed by Scholars Portal, ODESI is an essential part of socio-economic data discovery at Ontario universities. It is a repository of StatCan DLI-restricted surveys, and it also houses extensive polling data that stretches back decades in some cases. Using the Nesstar data dissemination platform, it helps novice and experience users find information from these surveys and polls, right down to the variable, and it also helps new users perform some statistical functions they may not otherwise have the knowledge to do. ODESI is a vital part of my work and I use it to access survey data almost daily during the school term. But prior to taking this position last winter, I had no access to it since most university libraries in Nova Scotia rely on the Equinox data delivery system out of Western Libraries. Moving to a new jurisdiction meant that not only did my committees and consortial colleagues change, but so too did my tools and resources, and I had to learn how to use new ones – fast. Today, I don’t know how I ever got on without ODESI. But last winter, ODESI was completely new to me because I hadn’t ever worked at an OCUL university. I have great colleagues at Laurier, and they gave me time to get to know this vital tool, but until I moved to Ontario and joined a new consortium, this was a foreign resource.
(For what it’s worth, ODESI, and the people behind it at Scholars Portal have done so much heavy lifting for students and faculty at Ontario university libraries, and I’m grateful I can use this resource and learn on their expertise. I’m also grateful that I can lean on province-wide and regional data committees for help and advice. This is a big shout-out and thanks to some great people out there – you know who you are.)
This is where the post peters out into vague resolutions and outlooks for the new year. How will 2013 differ from 2012? Well, I hope to not fly so much (the lustre wears off quickly), and I hope to get involved in more professional activities again. I also plan on finding new ways to up my game at work. This will involve taking some courses and hopefully using more streaming communications tools to meet with students and faculty. We’ll see where it goes. Happy 2013!.
Here’s a Monday morning link for all y’all. Dan Cohen notes an interesting way to measure a library’s holdings : by evaluating the collection’s “uniqueness.”
This may be an interesting metric that could be useful at the local-consortial level? I’ll let the Collections Librarians answer that, though. Read it here:
One of the things I’m constantly doing as a government documents librarian is giving lessons on Statistics Canada geographic areas. Census geographies can be downright confusing to the new user (and to sometimes to the seasoned expert!). The names are riddled with acronyms and jargon, and their relationships to other areas and spaces can be complicated. One legally incorporated township may be considered a census subdivision while another may be classified as only a census agglomeration. Another city may be classified as a census subdivision, and also be part of a census metropolitan area of a similar name, e.g., Toronto CSD and Toronto CMA. Or, a city may be classified as a census subdivision and exist not only in a CMA with a similar name, but also a census division (I’m looking at you, City of Waterloo CSD, Waterloo Region CD, and Kitchener-Cambridge-Waterloo CMA). And if you dare introduce census tracts the first time through, your short introduction to the “Russian dolls” nature of census geographies runs the risk of turning your lesson into an information dump about privacy and data validity when all that your first-year economics student wanted to know was why it’s so hard to get comparable income and migration numbers for Kitchener, Ontario, and The Pas in northern Manitoba.
Confusion abounds. One of the problems we encounter are the tools we use to explain these geographies, which should be easily understood but are often abstract – we may live in towns and cities, but we refer to them as census agglomerations or CMAs. What can you use to show how spaces relate to one another, or how certain concepts can be measured and expressed spatially? The answer is a map, of course. God lov’em, those maps. Maps help us express numbers – quantities, amounts, rations, proportions – with colours and shapes, and in the regions we live in and travel through each day. Face it, “big data” wouldn’t be as big as it is today if we didn’t have “big maps” to help use make sense of the numbers. However, StatCan’s digitized maps are large, layered PDFs that aren’t always user-friendly. The Standard Geographical Classification (SGC) PDFs are great reference items, but they aren’t very accessible. And this creates a learning gap for so many of our users.
To overcome this gap, I’m constantly pulling out the old SGC print maps, and I’m also cutting and pasting and hacking together magnified screenshots of the PDFs into my slide deck. Typically, if you need census help and you’ve found me in person, then there stands a good chance that I’m going to crack open the SGC and unfold a map somewhere in the office (I even keep the southern Ontario CD-CSD map posted to a wall). I started doing this last Spring after I moved to Waterloo and had to learn the region’s geography and confirm its census divisions, subdivisions, and CMAs for myself, and I realized this was a simple and effective tool that should be used more often, especially with new StatCan users.
Typically, I bring students to a nearby conference room and unfold the map on a large table. I find that being able to “walk around” the entire map and point to the places where the lines that signify the different geographies merge, separate, and then merge again, helps students understand some of the logic behind the regions (at least in terms of distance and population). They may not always be able to recall all the differences between a census division, subdivision and metropolitan area after a session, but they at least remember that there are differences, and these differences are important enough to affect their research.
The classroom is a different story, though. When working with only one person or a small group, there is a persuasive element at work that captures everyone’s attention. Carefully unfolding and presenting a map to a small group of people is like opening a box that holds a surprise. (Let’s call this surprise “knowledge” and we’ll call ourselves awesome for charming our audience so handily into learning something). But if we take that same map into the classroom or lecture hall, it risks becoming an awkward, cumbersome prop. It can become a distraction or even a failed means to demonstrate your expertise in such a short time to such a large group of people.
Maps that unfold to become wider and taller than you put the room’s attention onto your map-wrangling skills (however good or poor they might be) instead of on the knowledge you have share, so I avoid them. You’ve never caught me walking to a classroom with a print map, and I doubt many other librarians do that today.
Instead, I give the class what they want and what they expect, and that means I work that map into my PowerPoint deck. Any time I’m introducing StatCan resources and geographies to a class, I insert three images of the same PDF map, each one magnified more than the last. This helps people “zoom in” with their eyes and see the many relationships and regions that are defined in one place alone. The length of time I spend on these slides depends on the classroom’s needs: sometimes, I spend only a few moments on these slides, and other times, I’ll spend five or ten minutes. What matters is that after I’ve finished up and am headed back to the office, I know that the instructor can pass around a slide deck that always refers to all these different areas.
I know I’m not presenting anything new in this post: maps have long been a tremendous tool within government documents librarianship. Perhaps the takeaway lies more in information literacy than it does anywhere else. Is your digital resource, as presented to you, the best way to help the user understand the resource? You may want to turn to the print resource or manipulate the digital resource, as I do with StatCan maps, to improve learning and synthesis. It’s just one more tool (or two, in this case) in our IL toolbox.