Yeah. Okay. So this session's should be 30
minutes and hopefully it nicely ties into the next session
. Ginny will cover and moderate a very interesting game, Bingo,
including some candy prizes. And this covers
mostly about what kind of content you would put when
you're writing a data management plan and that ties into
the data management plan bingo. So hopefully you would
. Uh Oh well hopefully it won't be too much
overlap. But I talked some in this morning and
we have a smaller audience but would like to start
with a quick survey and how? Yeah. As
I noted earlier, the federal Funding Agency and also
private foundations started governing the data management. And have
you heard or are you familiar with these policies?
Yeah, there you are very familiar with the data
management plan, requirements or policies? I've got a
lot to deal with having all the time you think
? Yes. Yeah, yep. So you're not
. Yeah. All the policies and guidelines will propose
is very complicated. But we hear when focusing on
the data management planning guidance and if you're not very
familiar, you are the right sessions and if you
have written data manage plan amazing hands which is required
now for major funding agencies as part of your grant
proposal. So if you are to write a grant
proposal, you would actually face this issue and we
have several data curation team members and helping faculty to
write a data management plan. So this is something
that we can actually assist one a one or email
. And we have also like online resources I'm going
to actually introduce at the end. So this is
sort of that the we are starting to helping faculty
but it is very important to start thinking about when
you're still in a graduate student. So what is
the data management plan? It's pretty much straight forward
. It's a two page document that specifies your plans
for managing data. And in the morning session we
talked about why it's important to manage data. Mhm
. There are different funding agencies and also a different
policies that indicates the requirements of data management plan as
part of the grant proposal. They're all slightly different
, but mostly they say toothpaste document and we see
some variation across funding, interested and we hope to
have more standard guidelines within a year or two because
the federal agency started to mandating for the researchers to
have awareness of the data management and also consider it
as part of the integrated part of research. So
, the NSF is one of the federal agencies start
started requiring a pretty much comprehensive tube page uh proposal
, uh data management plan about five years ago and
that uh NIH actually precedes that NIH started a little
before in SF, but it has been requiring researchers
to say something within a paragraph, which is almost
impossible. We helped with Ginny and other research librarians
have been helping faculty, but it's almost impossible to
say everything Within a one paragraph. So NIH is
considering to change to two pages. So within the
two pages, what researchers as a junior researchers would
do you consider adding as a content? So there
are five questions I would like uh you know,
and I'll walk you through during the session and first
one what types of data will be generated and what
data and metadata standards, standards will be used.
How will the data be shared on the other day
to be re used by others? And how will
the data be store and archaic. So you have
been hearing over and over about data management and focusing
on different segments of data research, research and data
life cycles. So throughout the cycles, you have
different ways to deal with data. You collect data
, you analyze data, you publish data, you
clean data before sharing with others. And if you're
dealing with the private information is virus information, confidential
information. You have to anonymized. So those steps
are included. But the first you want to start
thinking about what types of data which you generate during
the course of the project that there are different ways
to think about it. And for major categories are
observational derived, stimulated experimental. But there are questions
by address by many researchers that will wait a minute
. There are different definition of data and there is
certainly one particular particular definition of data. But depending
on the field you're in, you may consider text
is a major data that you will generate and deal
with or maybe the images or the water quality the
researchers have noted earlier. It would be measurement,
it would be a numerical data in a spreadsheet.
So you may want to think about before you start
research what types of data you'll be generating. And
some examples I usually give. It's not in a
um comprehensive but it could be survey could be social
media. There are so many social scientists, even
computer scientists started and analyzing big data, including social
media, twitter data and audio recordings and videos.
So we've been recording today and definitely that would be
possibly considered as a data and videos fieldwork. So
there was a lunch table here and they were actively
discussing about field work and that's something that you would
generate data through fieldwork. That could be note,
that could be audio for interviews, that could be
electronic lab notebook. And there is uh increasing need
for electronic Glad netflix. In addition, in addition
to a traditional physical notebooks and there are definitely different
fields are higher in in demand for electronic, more
digitized lab notebook. And sometimes the chemistries they want
to stick with a physical uh, biologist are already
switching to electronic lab. So there are a lot
of data is generated by different tools, different software
, different devices and for mobiles. So peter talked
about citizen science and farmers and even individuals are entering
data. The farm for seniors. And that's something
that the data will be collected through devices like mobile
devices and of course images. And then you have
uh, if you were here in the morning we
had some hands on experiences and each table had a
different pictures. And when you do sharing with the
pictures you often had. So if some of you
do flickers flicker, encouraging users to tag each photos
and that's how you actually consider as a data.
But then I'm going to talk about that. It's
a metadata is also considered as data. So the
first questions I would like you to keep in mind
for writing a data management plan is that the types
of data, But there are other ways to categorize
data. And one is that the I noted earlier
, it's a state depending on the data, each
data set has a different progress. It could be
starting with a big data as a role data but
it could be like a little bit of a text
or the interview notes. So it all varies but
it's considered as a road data that may not be
even shared. But it's important for you to right
in a plan that what kind of data you would
generate and you want to list everything and you want
to think about what progress you would you know go
through throughout the research project and think about the state
of the data and hear all these instructors are from
the university libraries and focusing on the data curation.
Data curation is something that we consider. It's like
adding a value. So real data may be just
the data but we may not see much of the
added value. But if we clean the data,
if we save the data and preserved data in nonproprietary
unencrypted format, you could openly share with the public
and that increases the value. So that's something that
you want to consider the state of the data.
And when you write what types of data you would
generate. You also want to actually include metadata because
metadata is part of the data and in one of
the D. M. P. S. I
have seen by, written by Virginia Tech University Virginia
Attack faculty. Consider even curriculum and instructional material generated
by project is considered as data and I think it
is correct. So the second question, what did
intimate a data standards will be used and as I
mentioned, you possibly tag some pictures and the tagging
is comes with a different uh schemes and different keywords
. And so that's something that you have to think
about so that it all depends on what types of
data you generate to deal with. It affects fire
, organization, it affects back of formats and the
back of formats itself is determined by So how long
you would like to retain data and it's with the
word we often use is preservation and it's usually the
time span is 5, 10, 20 years.
Even longer. So formats the typical. Oh yes
, sorry, standards with standards out there follow for
that service, the state status or? Yeah.
In the side of the Yes, this question,
Sorry, here. Yeah. What next primary yeah
. Okay. Set standards. I understand. Oh
. Oh, you mean like are there any standards
? Isn't something like? Yes, this is.
So there are, so let's say like one picture
you have like a newman or like new aerial pictures
and that could be considered as metadata. But then
for picture, image preservation, we want to start
with a date like who taking the pictures, like
author for the articles or journal articles and the books
. So and then it's like what kind of like
the formats and then when it was taken and then
who owns it? So and the location of this
picture actually all uh stored or something. So those
are like a particular metadata. And then we have
like different standards across different fields, but we can
give you a list of those standards if you're interested
. Yeah. Mhm. Okay, so the typical
questions we get from faculty and researchers and students is
that property terry versus non property terry. And in
the data management plan we uh focus mostly on reservations
and openness and public sharing. But the question is
how data or five could be usable. So I
gave an example in the earlier session that if you're
sharing data tomorrow, like daily basis and it was
in a month or two, you do not want
to convert data files or net left file to something
that open source because that would takes time and they
lose some information that you do not want to lose
. So it is important to list both. And
it's not necessarily bad that you work with property terry
format because that's something that your field is about.
I can discipline is about. So in G.
I. S. There is always a discussion about
S. Slave, which is commercial and then it
keeps very strong sort of like uh domain and the
control over specific shape file. But then the professional
society would like to come up with something more open
source or open standard. And that's always the trade
off is just coming up with a particular commercial uh
software and then we have to deal with what you
know, which is more important or which is more
useful. And the meta data is again it's a
data about data but there are certain principles that I
would like to stress here is that it's not just
for data, it's also for users. So when
you share your data, others people should not necessarily
come back to you every time you open the fly
they open the file. So you would like to
consider creating a documentations either in a code book or
read me file that people would open it and then
not necessarily coming back to you every time. So
that's the rule. And metadata is not just for
description and discovery. And people say well the better
metadata definitely helps discover your data and items, but
it's also for contextual Ization and you watched a little
bear or videos this morning and the user, I
mean creator data creators themselves forget what the table was
, what the variable name was. So that's something
that you want to contextualize within your research or was
in your like a whole small lab. It was
in the discipline. And that's something that metadata will
help you contextualized. And metadata must be machine readable
and understandable as well as a human understandable. So
what we mean by metadata helps data to be discovered
is because metadata standard helps to read by machines.
So D M P examples I we I'd like to
show share with you is that there are certain standards
and they're like 34 major ones. But depending on
the field when we work with patrons and the libraries
that definitely were like to work with you one on
one and more like a customized basis because there are
certain standards that is particular for image. For example
like we use like a Dublin core and D.
D. I. For social scientists a survey.
But then when it comes to image I have to
look into and it's called my ex metadata and metadata
for images in xml. So that's something that depends
on the types of data and the format of the
data you would deal with. So how will the
data be shared? We've been talking about the data
should be shared and the policies for access and sharing
. You would like to include information about. Where
would you present your research results and students? Researchers
have been doing this over years and years and nothing
has really changed. Everybody wants to publish and you
want to get promotion, you want to get credit
and you want to increase the impact of the research
. But then the methods, the modes of dissemination
has somewhat diverse and increased. And when you look
at some of the tweets we just look up the
blogs. You almost like articles and tweeting about your
article and through link which helps the citation. And
that's something that you know, we've been also noting
that if you have a dataset have given the permanent
object digital objects identify or D. O. I
. There is a study that supports the argument that
your citation would be increased the rate of citation.
So if you share with if you share your data
with a D. U. I. Which is
persistent so that people can actually find the data and
then you get a more credit because people set your
data so they are on a lot of ways to
disseminate your research and there is nothing wrong. If
you just say conference presentation, journal articles and project
website and octopus, their incubators, their consent would
come up and say well that's not very stable.
You may want to add your data into institutional repository
but there are different options. So in the data
management plan you are totally correct to say every possible
dissemination methods. And they keep in mind that as
a bear kept saying that oh everything is in my
science journal and the article but not necessarily everything is
in the journal. Yes. It was more of
a question about maybe like the propriety issues when you
publish a journal in an article, Well, the
Journal of Psychology, um Oftentimes I see that the
burgers will put the link or well like the pdf
of that article on their website and because anything going
to download it. Um, so just at that
point in the article launched the person The structure.
Because it depends on the publishers and sometimes healthier,
very strict. And even if they don't allow like
six months, 12 months not to post anything outside
of the LCS prescribes journals. Yes. But there's
some open, open access journals. The authors would
have keep the the copyright and then only totally open
. But it also depends even open access journals the
journal may have hold copyright and it's also like sometimes
at the time difference thank you. And also just
add onto that. Sometimes you can negotiate with the
journal to put a prior version of the journal article
openly available but the final version is owned by the
journal itself. Damn, yep. Mhm. Mhm
. And today you have more options to disseminate data
. And you have probably noticed that there are some
journal articles saying that data accessibility as part of the
references or even as part of the articles and say
it's a link Herbal. There are ways to make
data available. And one common practice these days or
best practices is to deposit your data, underlying data
to support your articles into public access is trusted secure
um public access repository. And there are about 1000
. But again the librarians can help to track which
reponse story would be most useful for you to deposit
data. And the university library as is hosting institutional
response story called detect works and and that's something that
information that you would like to include into the later
section of the data management plan. So the website
is fine but it may not be stable. So
you have options to disseminate but the email later section
you have to note that the way to preserve your
data and al Qaeda in your data. And that
comes into the question comes to the question about what
repository you would like to uh choose. And one
of the D. M. V. Example say
the data metadata and associate files will be available immediately
after. So it's a timing that you would share
is also noted here And what types of data.
And also if journal article submission requires depositing the underlying
data into trusted repository of subset of large data set
and collection will be arrived there disciplinary depository and or
Virginia tax institutional repository. And when you describe the
way to share your data and disseminate your data you
have to consider some concerns. And Ellen noted that
privacy issue ethical issues and there is a confidential concerns
if some of you deal with health sciences information it's
very protective and it's considered as a data but you
may have to start working with I. R.
B. And also Hipaa our regulations. And how
will the data be used? That's the fourth question
. So if you if the mayor comes up or
the panda bear comes up to you and how how
would you like to share your data? And it
could be email attachment or it could be a drug
box and that's totally fine. But after considering who
would be how would you share your data? You
have to think about who should have access everybody in
in the this country or the whole world or should
have some limit to access. And consider who would
find a value out of your research data to describe
how you make the data available for reuse. So
it's not just like a randomly thinking like whoever is
going to use. You have to think about who's
going to find a data useful. So that determines
how you document your data and also consider who is
expected to use the data in what context and provide
information. How users of your shared data should give
you credit. And that comes back to the question
of how data should be shared and how the data
should be preserved. If you deposit your data into
institutional repository you'll be assigned. Were maintain A.
D. O. I. The digital object in
identifying which would be unique to item and you can
share with that handle and you will be Good for
20 years, 30 years I hope. And that's
something that you would like to get credit because you
spend so much time to generate data and then even
considering writing a plan ahead and that's that you want
to get a credit for your research. In fact
, yes. If we deposit our data Virginia tab
, would would that generally suffice for for NIH agency
like that? Or would they insist on additional and
also can after you initially deposited? It's an ongoing
project can additionally can later on add. Mhm.
So NIH there's expert there. Uh NIH actually has
a list of repository encouraging researchers to pick. So
data management plan they call data sharing plan and it's
still only requires only one paragraph. And we helped
researchers right at least the page. And we often
uh guided researchers go to the NIH website and they
have about dozen jenny or two dozen sort of repositories
. Uh oh now 59. Oh my God,
Okay. So that many. And then that comes
to the question like which depository you would pick but
mhm No, no, no. That's so that's
sort of like a question that you have to uh
59 questions. 59 reports, stories are recommended by
NIH secondary. Yes, and NSF does not necessarily
require but they encourage both. And if you pick
one particular disciplinary subject based repository, that's fine.
But also you could deposit your data and the institutional
repository and to answer the second part of your questions
, if you have ongoing data, that's not something
that we would encourage because that's still called uh work
in Progress and it should be finalized but we will
help to deposit different versions but that would be totally
different items. Could have the same project different versions
. The comment. Yes, you're exactly right.
So really working on the project at all, you
have to differentiate when is appropriating storage for my work
and when is it appropriate to our network and you
really have to have defining moment. And I usually
say when you're ready to publish the work you've done
, that's a good point to go ahead and archive
work you've done on that project. And so the
people could then read the article and have the data
replicated and so you're continuing network or say, you
know, it's a research project that you can kind
of mirror in a different environment. Yes, it's
absolutely appropriate that once you reach a finishing point with
that section to add that to. Now, currently
there's some issues with how you have to admit Dy
for that new data. Um because version gets a
little tricky and repository systems, it's probably a conversation
for another time. Mhm. Yes. Thank you
. Shoot screen this war down. Oh, all
right. You wife or for your, you're shooting
how the data will be assigned. How do you
get the? Oh so the library University library has
institutional repository. And we're even building data depository and
the data services unit. And and it would be
talking about data services in an hour or so.
And we are maintaining D. O. I.
S. If you are ready to deposit into our
institutional repository and that is signed through data site and
there are different meeting organizations. But we partner with
the specific uh Oh, I'm eating organization called D
. O. I. Data site. And is
that a question? What type of or any Any
datasets? Hmm. But you have to be ready
to have like finalized version because that would be totally
discoverable if you deposit and then the deal I would
be minted because you would not be changing like,
oh, this variable is missing or the number is
missing. And then you want to get a different
deal. I once you're meant to do like that's
going to be permanent. Is it a reasonable approach
? Once you're ready to archive to come to you
folks, your Virginia tech use our depository meant to
D. O. I. And and then also
deposited in one of the 59 or whatever, um
, uh, other repositories. But presume I'm presuming
I'm asking then is it almost trivial process to to
use one of these other repositories also because there's already
A D. O. I. Yeah. They
okay. Yeah, sure. You can actually meant
a UI and you have to go through someone who
actually has the credentials to access the, oh I
see what we can do that here letters you can
absolutely go and get A. T. O.
I threw dataset without actually archiving and publishing it.
It's not recommended. Um And most repository systems actually
offer some form of dy meeting or citation through their
site. We went with data site specifically because we
trust them to be around. We trust the links
that they provide. The most possible systems work is
when you contain that y uh that's part of the
metadata fill out created deposit deposit. So if you
want to deposit in humans repositories just have extra.
Uh And uh it's easy once you once you've got
the D. O. I. Detect repository then
I gather it's very very easy at another. Yes
. Okay so as you noted that we have a
group of people in the data services and we can
help identify a depository for data deposit and plan ahead
because it's not just for requirement and meeting the requirement
for the funding agency. And it saves the time
because you would probably forget things in two years and
three years and I forgot all my uh variable names
when I did my dissertation is probably it's good to
document a lot of things and thinking about other people's
in mind. So you want to you know put
yourself as in the shoes of others and then think
about how other people would use your data and those
are the things you want to write in the two
page document which is pretty tough to include everything.
And that's something that we are here and with some
resources. And then uh consultant and one tool that
I would like to point out is a DNP tool
and the slides are available so you can actually go
through the website and there. We also created some
of the guys library guide I want to and again
I would like to emphasize it did. A management
plan is not just a static document. If you
are writing a proposal and then get approved and granted
and it's for three years, five years or during
the course of the project, you would definitely want
to revisit your data management plan and to think about
how things changed. And the federal agency has started
checking on the management of the research researchers are doing
. So that's something that it's not just a one
time deal. Okay. Thank you. Yeah. 493 00:32:46.039 --> A:middle L:90% Mhm.