WEBVTT

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Hi Matthew, so we're
here at a library,

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and we're here to
talk to Matthew

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who is a librarian here
about data management.

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So first off, how would you
describe data management?

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Basically it's the process
of reviewing everything

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you do around
organising, documenting,

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and working with your data.

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And the research
project, basically.

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And when we talk about
data, we can see things

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like your observational data,
you get from instruments.

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Or experimental data,
computational data,

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that comes up from research
or-- sorry, computer programmes.

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And that sort of thing.

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But also records, so it can
be the administrative records

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you're creating
for your project.

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Or it can be other records that
you are maybe using the data

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source, and that sort of thing.

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So anything that goes into
our movement analysis projects

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is classified as data.

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Yeah, basically.

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OK, that makes sense.

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So what components are
involved in data management?

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Well basically you're going
to be including things

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about-- how are you going
to be organising your files,

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organising your data?

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How are you going to be
collecting your data,

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and the different
data sets that you're

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going to be working with.

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It's also going to be
looking at where you're

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going to be storing your data.

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Is it going to be stored in the
cloud, is going to be stored

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in some other kind of solution?

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How you maintain the
quality of your data.

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So kind of the process
as you think around that.

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How are you going to comply
with legal standards,

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and requirements?

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If for example, you're working
with copyright information,

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or if you're working
with private information,

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or classified
information, to make sure

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you have a plan for that.

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And basically, how
are you going to be

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kind of-- just working with your
data, throughout the process.

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OK.

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So I'm assuming the process
is some sort of a cycle?

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I've heard what a
data lifecycle is,

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could you please explain it?

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Sure.

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Yeah so basically you're
starting with the planning

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of your project.

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So making sure that
you kind of know

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what data you're going
to be collecting,

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and what methods you're going
to be using for your project.

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And then using that to maybe
potentially search for data

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sets that might be existing
already and available.

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And then actually going out
and collecting your data.

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And what methods you're going
to be using for that, and what

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equipment maybe you
might be needing

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for collecting your data.

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So in any case, the
motion capture lab,

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so you're looking at the motion
capture cameras, and the suits,

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and that sort of thing.

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So you can also think
about documenting--

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if you're doing, for
example EEG type stuff,

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making sure that
you have the sensors

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and understanding what
sensors you're using.

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What kind of outputs
are coming from that.

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You could be-- also
from eye tracking

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with the cameras,
what kind of headsets

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you're using to track the
eyes, or what kind of outputs

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come from there.

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If it's proprietary formats,
if you're going to convert it

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to a nonproprietary format.

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So that you can then archive
that in a better way.

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We also like the MRI, that
you also have in your labs.

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That you're taking
of the fMRI images,

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and how you're going to
be dealing with the files

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afterwards, and what the
metadata is required,

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for example, for that.

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After that you're going to
be cleaning, and processing,

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and documenting that data.

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So making sure that
the quality's there,

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and it's actually going to
be suitable to analysis,

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which is the next step.

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Which also then produces
its own set of data,

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that you have to manage
appropriately, right?

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So it kind of goes back
to this whole cleaning,

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and processing, and
documenting process.

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So then after analysis, then
what you're going to be doing

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is sharing that data openly.

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Because that's more and
more of a requirement

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these days, with funders
requiring that you

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share your data sets openly.

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And then archiving
it, in somewhere

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to make it publicly available.

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Well that's-- the parts that
can be made publicly available,

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made publicly available.

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That makes sense.

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I assume there's some
specific documents?

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Now I know that each group
may have certain ones,

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or each institution might
require different ones,

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but what are our kind of
main types of documents,

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that you might have?

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As far as the data
management plan?

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Yeah.

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So that's kind of
this one document,

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that's kind of the most
important around data

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management.

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As everybody calls it
a data management plan.

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And it kind of captures all
these different components

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of the data management process,
that you're looking for.

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So if you're looking
at describing in there,

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specifically your data sets,
and then using it talking about

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organisation, and
that sort of thing.

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And it should be a live
document, something

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that's updated over time.

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You may not know at the very
beginning of your research

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what data sets or what equipment
you're going to be needing.

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But it should be a process, and
should be a planning process,

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that then leads to
creating this plan that

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gets updated over time.

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OK so if say, I win the lottery
halfway through my project,

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and--

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You quit.

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--just quit.

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Somebody else can come update
the data management plan,

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and keep continuing
on my research.

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They can basically
take your project,

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and continue with it,
because everything will be--

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well, should be
documented, hopefully.

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And that they can understand
all the different files

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and analysis you conducted on
your research up to that point.

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So they can just pick it
up, and go well, maybe

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after a month or two of
reviewing everything, but yeah.

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Yeah, they should
be able to do that.

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OK, what's the best way to get
started with data management?

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Contacting your resource person.

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So asking your colleagues,
who do should I talk to?

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Often the university library
will be a very good place

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for that, because they'll
have some aspect of training

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in data management.

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So they're usually a
good place to start.

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If not, there should
be a local data manager

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that you can usually contact.

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And also help you with
some of the more--

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or a lab manager
like yourself, which

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can help you with some
of the process as well.

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So if not, there usually is
some web pages available,

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and there's also a
bunch of resources,

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I'm sure, at the bottom of
this video where you can--

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Yeah, yeah so
we'll put resources

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in the links of our
articles, as well.

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All right great, is
there anything else

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that people need to know to get
started with data management?

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Make sure you get
that plan in place,

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and make sure you talk to
your colleagues about it.

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OK, all right, thank
you for your time.

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Thank you.

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