Snippet - RDM training - UTS Data Training for Librarians
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 7 | |
| Author | ||
| License | CC Attribution 4.0 International: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/35985 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
|
Skills and training5 / 7
00:00
Stokes' theoremData managementData managementSoftware developerFrame problemPlanningLibrary (computing)Client (computing)Wave packetLattice (order)Computer animation
00:26
Data managementPlanningModal logicData managementArithmetic meanGroup actionUniverse (mathematics)Library (computing)Figurate numberComputer animation
00:49
PlanningData managementForm (programming)Row (database)Wave packetWordDegree (graph theory)Division (mathematics)Library (computing)Student's t-testProjective planeGroup actionComputer animation
01:48
E-learningWave packetModule (mathematics)Data managementVideo gameMatrix (mathematics)Cycle (graph theory)Dependent and independent variablesReflection (mathematics)Term (mathematics)AreaSet (mathematics)SpacetimeProjective planeSystem administratorComputer animation
03:30
Projective planeGroup actionData managementAffine spaceVisualization (computer graphics)Analytic setFaculty (division)Open setMultiplication signObservational studyMereologySet (mathematics)WordGateway (telecommunications)Staff (military)Level (video gaming)Social classComputer animation
04:45
Embedded systemData managementExpert systemForm (programming)Cartesian coordinate systemCodeRegular graphMotion captureWave packetSoftwareComputer scientistStudent's t-testBoundary value problemLaptopBuildingSeries (mathematics)Software testingProjective planeStaff (military)ComputerRevision controlOpen sourcePower (physics)Service (economics)Electric generatorLevel (video gaming)SubsetSpectrum (functional analysis)Computer animation
07:43
Translation (relic)Software bugData managementService (economics)Client (computing)Software developerSupercomputerSoftware bugReal numberWave packetPresentation of a groupStatisticsComputer animation
Transcript: English(auto-generated)
00:00
Hi everyone. Okay so research data management training. This question what are we training librarians for? It's been weighing heavily on my mind actually. So today I want to talk about how we frame, how the library community frames research data management for our own development as well as meeting our client community needs and what's been happening at UTS.
00:25
So when I first got here I thought that my plan was fairly simple. Research data management is the future of libraries. All I had to do was teach research data management to my colleagues, turn them all into librarians, data librarians, provoke the university into action on research data
00:43
management by any means necessary, figure out what works and do more of that. But it wasn't actually that simple. Research data management may be the future but it's hard to promote that future when it looks like another freaking web form. So at UTS our research data management is largely
01:01
driven by an asset management imperative which is very important actually. So we have policy which mandates data management plans for all projects including PhD students and higher degree research. We also have stash records for research data which are mandatory as well for any researchers
01:25
publishing data even as a supplementary item to a journal article that's going to be official in January next year and they'll be very interesting for us. In general the library is responsible for research data management training and the e-research group in the IT division
01:44
at UTS develops the infrastructure. But my question is how you say RDM also not just how do you say it but how we say it okay. I'm increasingly uncomfortable with these ideas around RDM and DMPs. These are terms that we've
02:04
created by and for librarians. I think that RDM is as much an artificial construct as a DMP which is more often than not an administrative requirement or matter of compliance rather than an actual thing that is supporting the doing of research. I've realized that my anxiety around
02:22
research data management has come from having the responsibility of persuading my colleagues to make space for a new area of expertise which is still as relatively abstract as the myriad training resources and online courses that are available to learn it while still remaining fully committed to business as usual. Moreover on reflection I learned how
02:46
to be a data librarian by actually doing it not through doing a course okay. It was through working on projects learning how to be a partnering with researchers that the skill set required had more in common with the
03:01
data scientists than traditional librarians. But of course I think every librarian here probably begs to differ with that straight away. But as a data librarian a growth mindset and problem solving skills are potentially more valuable than the myriad pieces of the
03:21
puzzle that make up the many matrices of research data management life cycles and modules okay. So at UTS we're kind of doing research data management by stealth okay. Internally my department is piloting a tinker time project using learning analytics expertise
03:41
from UTS data scientists to develop our growth mindset and data literacies. So for example I'm in a little affinity group of amateur game developers and my project will be creating a character called Bogan the librarian who must curate research data for a death metal cultural studies researcher. Well that's just
04:06
developing introductory data management which is mainly data cleaning but also including data visualization skills for undergraduate faculty classes. And we're also promoting open data sets for teaching as part of our open educational resources for
04:24
academic teaching staff. So these examples are not strictly in the research data management world because that tends to be skewered more towards or aimed at our research communities but I believe they're gateway drugs toward it. I guess we could call it
04:41
seeding the commons for want of a better word. So speaking of other research data management by stealth initiatives the recent res that we held in Sydney in July is an excellent example of that and that the research bizarre is a series of workshops on
05:02
coding and software skills for being able to teach non computational researchers that kind of computational thinking that comes with using R or Python to harness the power of
05:20
these new next generation research tools. So we discovered that research students and staff want things like data science training and they want supporting data management tools applications and software. So they want to know what will help them how to do research faster smarter more competitively. And we found that from our pilot in July
05:45
researchers also want to build community and learn about research tools across disciplines and institutional boundaries. So this is where we embed research data management by offering training in these open source tools
06:00
and software where things like version control are integrated in things like it hub alongside the risk as software workshops. We also provided some research data management workshops by that very name and I was I fell off my chair when I realized that that was the second most popular item in our
06:22
expression of interest form that we sent out initially and I don't know about you but we don't get more than 40 people registering for our regular research data management workshops. So there's something else that is happening there that enables people to see research data management as
06:40
something valuable and useful for them rather than just RDM on its own. This year UTS has also established a joint steering committee in e-research and research data management training and this is more at the executive level and that's going to allow us to embed research data management
07:01
across a broader spectrum of specialized research support and data science services. And finally we're integrating librarians into research e-research infrastructure projects such as the provision of project that e-research is running and giving librarians roles such as the go-to trainers for research tool
07:22
training for example lab notebooks and in the future RedCap, a research data capture application and also by giving librarians roles in say user acceptance testing of our in-house research data management tool Stash. We can engage them as experts developing those specifically
07:41
data librarian skills. So to answer the question I started with this is what we're training our librarians for. Three roles as active participants in providing enhanced research support services. We're training them as instructors in delivering real data management skills from undergraduates through to our
08:01
researcher communities. We're training them as advisors to deliver research data management by stealth in a way saying would you like research data management with that as one might offer fries or curly fries or something like that. And finally as engaged librarians collaborating in
08:21
e-research infrastructure development you can knowledgeably refer clients to more specialized data services for example statistical instruction or high performance computing. So being able to perform quite a triage role in that way and that's the end of my presentation.
Recommendations
Series of 13 media