Umm, we're going to spend some time today talking about the NIH
policy for data management and sharing the new funding
requirement that is starting in January 2023.
Some folks who have been thinking about this and have
contributed to this presentation or also here to help us answer
questions. We have Duggan and Jerrilyn from research
development, Denise from the Daniel Dana Medical Library,
Julie from sponsored Projects administration. And my name is
Christy and I work at Hell Library.
Umm, we have a lot of content that we're going to go through
pretty quickly. The slides along with some curated resources and
the recording will be made available after today's virtual
session.
Umm, so if you'd like to receive updates on that information on
actually we will pull the attendance list from the teams
meetings and so we can let you know when all that stuff is
posted and we can share it and you can distribute to any
colleagues as you see fit.
So our goals for the day are shared general details about the
upcoming policy change. Look at The Who, the what, the when and
the where of data sharing related to the NIH policy.
I'll take a look at how this policy fits into the grant
application, and it's possible impacts. I will very quickly
review a couple data management practices and highlight a couple
of UVM resources that are available to support you.
We are planning to leave a significant chunk of time for
questions at the end, so please hold your questions for the end.
We are also recording right now so that will keep you from being
recorded in this, which we will probably be distributing.
Also, please keep in mind that this is a very new policy on
their supporting materials that are still being released in the
coming days and weeks, so.
Consider this a bit of a primer. We'll do our best to review what
we know, answer what we can and we can follow up with anything
that we can't answer today.
We're interested in knowing kind of what your concerns and
questions are so that we can better prepare to support you
through this policy implementation and beyond.
Here's a brief listing of the past and HY data sharing policy
that you may or may not be familiar with. At this point.
The new policy for data management and sharing will
replace the 2003 NIH data sharing policy.
Folks who work with genomic data are human data may be familiar
with these other 2014 and 2015 policies, which have already
required more in the way of sharing than the General 2003
policy, which is the one that's being updated.
The policy, which goes into effect January 25th, 2023, which
is alarmingly soon, requires the submission of a data management
and sharing plan, or a DMSP, and we'll talk about what that is in
a moment for all NIH funded research. So the 2003 policy
only applied to investigators that we're seeking more than
$500,000 of funding in a given year, so this will be the most
noticeable change that significantly more individuals
will need to incorporate a DSP into their application.
It's also important to know that compliance with your accepted
DMSP may affect future funding, but also know that there will be
a process to update your plan as needed. Gugan will address this
in a little bit.
So generally, what is a data management plan or a data
management and sharing plan as the NIH calls it, for anyone who
might not be formally doing this yet, a data management plan is a
formal document that outlines what you will do with your data
during and after a research project. So broadly, a data
management plan considers what kind of data will you be
generating, how will you organize it, store it, protect
it, share it and preserve it.
As for what the NIH is looking for and DMSP, it's recommended
to be 2 pages or less in length. I just want to highlight that
we're going to cover a lot of material today, so it might seem
like a lot, but just remember this is approximately 2 pages.
They provide an optional template and the template
addresses the following elements, so data type. So any
types or amount of data that you expect to generate in your
project.
Any sign tific data that will be preserved and shared, and the
rationale for doing that?
And any metadata or associated documentation to go along with
the data.
Also, addresses related tools, software and or code.
Any data standards?
PETA preservation access and associated timelines, so that
includes the repository where the data and the metadata will
be archived. How your data will be findable and identifiable.
And when and for how long it will be made available?
On access distribution and reuse considerations, so addressing
any factors that might impact subsequent access or
distribution.
On whether or not the access to your data will be controlled in
any way and addressing any protections for privacy rights
and confidentiality of human research participants and
generally or scary. Lastly, the oversight of data management and
sharing. So how are you going to make sure that what you say in
your plan is is going to happen?
And I'm going to pass it over to go in and just let me know when
you need me to change slides.
Thanks, Christy. So who needs to share data?
Umm, if you if you are work, is funded by the NIH, so any NIH
supported research that generates digital or digitized
table scientific research data.
So that leaves out training grants. It leaves out
infrastructure grants. They're mostly looking at bread and
butter research grants at this point, and that's important to
note because I'm going to be talking about that a little
later. Next slide, please.
So what do you need to share? Let's just first go over the
definition of research data per the NIH.
It is the recorded factual material commonly accepted in
sand in the scientific community as of sufficient quality, and
that's important to note that it has to be of sufficient quality
that it is reproducible or can be used to validate or replicate
research findings and what they also specify is that they don't
really care whether these data actually make it into a
scholarly publication.
So all they want is all the research data that you have
generated with taxpayer money has to be.
Uh has to be made available and accessible.
Umm.
In alignment with their expectation that the API's are
going to be doing good data stewardship, that's that's
basically in a nutshell what they want from you.
It's important to note that appropriate data sharing is
likely to be very contextual. It's going to.
Change from, you know, like a basic research or what kind of
data are they generating? That can be, you know, used by others
to replicate or validate some of their research findings. And
they also have the expectation. So the error, it has an
expectation that researchers will maximize appropriate data
sharing when they are developing plans. It's also important to
know that.
At this point, when you're submitting your grant, you're
actually just submitting a DMSP the plan.
Umm.
For the night, you actually need to share adequate data to
validate or replicate study finding. I know it sounds a
little repetitive, but I think this part bears a little
repetition and you should also try to include null findings
that do not result in publication.
Next slide please.
So what don't you need to share then? Not interested in your
back of the envelope drawings. They're not interested in your
lab notebooks. They're not interested in your preliminary
analysis. They're not interested in your peer reviews.
Uh.
So that actually not interested in any specimens. So for any new
mouse model, say that you've generated or any new fancy cell
line.
That he generated those should all be discussed in your
research resource sharing plan.
Next slide please.
So what are some acceptable reasons not to share? The very
first is when you're dealing with patient data is potentially
if if there is a potential that some of the research data could.
Even if they are, the added fight initially could be, you
know, used to reidentify individuals. So let's say, for
example, you've got like ZIP code data and you've got
specific racial, racial, demographic in there. You want
to be careful that, you know, there's no chance that somebody
could actually calculate who it was, that that that has been.
Umm.
That his provided that data right. And then of course, if
there's existing consent. So if you're using, if this is a.
Uh. A secondary study using data that we've collected earlier and
the informed consent process did not actually factor in data
sharing.
Umm, you cannot use it.
The other times that you want to be you, you can always justify
in your data sharing plan that you don't want to be disclosing
the data because there are explicit federal, state, local
or tribal law or regulations or policies that prohibit
disclosure.
And of course, the last and most important, if your datasets
cannot be practically digitized with reasonable effort or within
a reasonable budget.
Umm, that's another reason you might want to justify in your
DMSP why you will not be sharing that data.
Next slide please.
So when do you need to share this data?
Uh, before the actual article is published.
Or before the end of the performance period, whichever
comes first.
Right. And so this in a way again going back to the
principle of good data stewardship.
It answers to some of the policy calls for timely release and
sharing of data.
Next slide please.
So where does the DMSP go in your grant application? Like
Christy mentioned earlier, we are actually anticipating that
the forms H roll out will happen next week, October 25th is, I
think, the target date date that they put out, but they have also
released a lot of draft documents, you know, with high
level changes. So from that we can tell that we are expected to
add a two page research right up.
Ohh under the other plans attachment field in a regular
research plan and then if you're writing career development grant
applications, you know the other plans section has.
There is now going to be a new slot in there at position 17 in
the other research plans form, so that's where it's gonna go.
It's they've already released.
And we are going to make that available to you, but we'll also
be linking right back to the regular NIH website on this.
They've released a template and it's it's got all the prompts
and questions that you need to be thinking about and
addressing.
So it goes in as a form attachment in these places, but
then it also you're gonna be talking about it and hopefully
budgeting for it.
In your budget and in your budget justification.
In the budget, it actually goes in as a single line item and it
goes under section F other direct costs and you will be
justifying it appropriately in the budget justification.
Uh, it would go in as part of the technical evaluation in
extramural contracts it. So if you're doing any other
transactions or OTA's, it goes in as part of the technical
evaluation piece. One thing to note is that even though they do
not anticipate that training grants and infrastructure grants
are going to have.
Uh.
Data management and sharing plans. They are actually.
Preemptively putting in those slots, put those forms to be
uploaded because they they expect that they're gonna be
rolling this out in the future. Not immediately, though not in
forms age.
Next slide please.
So just a quick overview about you know.
What are we looking at when when we are talking about these new
changes with the DMSP, there's three parts to this. There's a
submission. That's when your grant is going in, then there's
the assessment at the NIH and then there's the compliance
piece where you're gonna be working with the program
officers to actually make sure that.
You know you are.
Following up with whatever it was that you proposed in your
DMS baby.
So in the submission we just talked about it, it goes, you
have to mention it in the budget justification section. If you
have budgeted for it.
And.
Uh.
As a death must be document that is going to be uploaded in the
other plan sections.
As far as assessment goes, the peer reviews peer reviewers are
not basically going to be scoring your grant on this. This
is going to be like all the other supporting documents where
they can comment on it, but they're not actually score
scoring your brand in this.
That said, the Ariet program staff are actually going to be
assessing the plans and they are going to reach out to you prior
to releasing the notice of award if they find this any
discrepancies or deficiencies. So.
Trying to be as open and thoughtful and thinking as far
ahead as possible and addressing all the points they've asked
for. I think a lot of the prompts that they have in there
are pretty self-explanatory and my sense, after having worked
with, you know, folks submitting grants is that a lot of you are
already doing this work. It's just a matter of documenting it
correctly, right? And then the plans can be updated based on.
Whatever it is that the program staff asked you to do, so if
they find that something is not.
Could be improved. They will reach out and this is all gonna
happen prior to the notice of award. This is going to actually
be entered into the terms and conditions of your notice of
award and they expect at regular intervals, which I think is
going to coincide. And again this will become clear. You know
once the final guidance is released next week.
I'll, but they will be monitoring at regular intervals,
which I think is going to align with your RP, PR's and with your
FPR. So thinking about that.
And.
This sounds a little obvious, but don't be scared. But they
also mentioned that compliance may factor into future funding
decisions, which is I think they way of telling you that they
actually expect you to follow through with whatever it is that
you propose.
Next slide please.
So.
Again, this is just a quick recap of everything that we've
discussed. Keeping in mind that they expect good data
stewardship from from the API.
The DMSB actually represents the minimum requirements for all
NYC, so if you can be more thoughtful, if you can be, you
know, think ahead a little more and try to figure out how it is,
how how can you make your data more accessible.
Two other future studies.
That would be really helpful. And I think Christie is actually
gonna be talking a lot about this in the.
Uh, in the next section. Next slide please.
So possible impacts we've already talked about budgeting.
You're going to be paying for storage for preservation. You
might also be paying for digitization, right? So let's
say that you you are a radiology lab and and you've got all these
images that you need to.
Digitize.
And that's an expense.
You can probably put that into your budget if if the budget
actually allows right so.
All of this goes hand in hand to anarchy is expecting you to do a
lot of additional work with the budgets haven't actually
increase much, so just keeping that in mind and.
As always, right, proposing something that is appropriate
for your grant and for the performance and scope of work.
Umm.
Another possible impact might be the informed consent process,
but I do think that a lot of people who are doing you know
regular human subjects research, there is a lot of rigor already
built into those proposals, so maybe maybe planning ahead a
little. And, you know, just looking at what is in the DMSP
and working that into your informed consent process, that
might be a helpful thing.
This one.
Next slide please.
And this is just going to be a very brief overview, because I
think there's a lot of information over here, probably
more than we can cover within this one hour presentation,
which is more of an overview.
Umm.
So some of the allowable cost that they mentioned?
Our data curation and the development of documentation.
Local data management.
So that is during the project before you submit to a
repository, and the preservation and sharing and repositories and
sometimes this can be an added expense as well, especially when
you're dealing with like AI type of data or or big data or
imaging data from clinical trial, right. So this could be a
rather large cost you have to think about how you're going to
budget for it.
Next slide please.
And this is where Christine takes over.
OK. Thank you. So I'm gonna spend a little bit of time very
Thank you.
briefly introducing some just some general best practices
around data management, which will make your data easier to
share, more valuable to share and also increase organization
and hopefully save time for you in the long run. So I'll touch
upon what are called fair principles, which is a general
standard for data and preparing data to share.
And the importance of documentation, metadata naming
conventions and common data elements. After this, we'll talk
a little bit about finding a repository and where to actually
put your data.
So the concept I want to introduce is what's called fair
data, so fair stands for findable, accessible,
interoperable and reusable. Applying these standards to your
data enhances the ability of machines to automatically find
and use your data, which makes it more discoverable. But it
also helps humans understand and interpret your data.
So these principles help ensure transparency, reproducibility
and also reusability.
In terms of thinking about carrots, you know, I know this
is a policy. It's things that we have to do. So it might be easy
to think about it as a stick, but a carrot here is that fair
data is more easily accessed and repurposed, which makes it a
better candidate for being cited by somebody else. And we all
like citations.
It also helps future proof loosely data, making it more
likely to age better and be readable by future technologies.
So some very basic things that can be done to make your data
fair are already done by repositories where you might
deposit your data you know well established ones, have
standards, they have good descriptive metadata fields that
you can fill out the key there is actually filling them out.
And they also make your data searchable in other places. A
huge part of being interoperable and reusable. The eye and the R
is to use the simplest and most widely accepted file formats, so
there's a lot of guidance out there that can support you in
determining what best suits your work and how to make it fair.
Uh, so next thinking about again the data that you're preparing
to share, documentation and metadata both allow users to
understand how the data were collected and how to interpret
the data, which ensures that other people can use your
datasets. But it can also prevent misuse and
misinterpretation, which is a good and important thing.
So some examples from the NIH of metadata or other things that
might be included are any methodology or procedures used
to collect the data.
Data labels, definitions of variables, and any other
information necessary to reproduce and understand the
data you might be familiar with README files. This is part of
what those are all about.
Next, thinking about naming conventions, agreeing on naming
conventions for multiple objects or files or versions of files is
very useful before starting on a project, especially ones that
generate large amounts of data that need unique names or
identifiers. But it's helpful for everyone at any scale as we
all probably know from looking at our desktops and our download
folders.
More specifically, common data elements or CDE are pieces of
data that are used across multiple data sets across
different studies. So it's they're like a standardized set
of elements. So the NIH encourages researchers to use
CD's because they improve accuracy, consistency, and
interoperability between data sets in various areas of health
and disease research.
And then each maintains a repository of the CDCDES for
standardizing purposes.
So all these things that I've kind of quickly run through will
make your data more quote UN quote fair.
And again, I just wanted to provide a little context for
these things in case there are folks who haven't already been
generating data management plans and thinking about this in a
systematic way.
Umm. But again, as Gugan mentioned, this is probably, you
know, likely a lot of the good work that you were already doing
is what we'll go into this DMSP for the night.
So where to put your data? Selecting a repository on the
NIH strongly encourages subject specific Open Access, data
sharing repositories. So here on the slide is some brief guidance
that's organized by the size of the data, the data set, so PUB
Med Central for things up to 2 gigabytes, which you're likely
already familiar with if you're doing work with the IH. There
are also many generalist repositories out there that take
larger files, and then if you're working with very large sets of
data.
There are some cloud partners that you can pay to use for
storage.
Umm. However, the NIH has some really easy to use lists for
both domain specific and generalist repositories, and
they're all listed by the particular funding institute or
center or office. So they've actually done a lot of the work
for you in terms of finding our repository. We will include the
links to these lists of repositories in our follow up
resources and if you need help figuring out where to put your
data, this is a place where the library is at UVM, can help you.
So again, Gugan touched upon this just to kind of elaborate a
little bit on in thinking about where to put your data when you
are working with data that's derived from humans, HIPAA, IRB
and patient consent always take precedence when it comes to
sharing.
Excuse me. So the NIH encourages researchers to address data
management and sharing plans during the informed consent
process.
Outline the steps that they will take for protecting privacy
rights and confidentiality of subjects.
Access.
Assess limitations on subsequent use of data and communicate any
limitations. Again, if there's something that could be
identifiable or other factors that might play, play in here.
On and lastly to consider whether or not access to the
shared data from humans should be controlled, even if it is
identified or deidentified.
And in some instances, controlled access might be
specified by certain funding opportunities or NIH groups.
I have a link on the bottom. Here we will distribute this.
Afterwards they have a couple FAQ questions that address this
specifically and provide more details. That's where this
content is coming from.
So on to our final resources section. Things that we have
here at UVM to support you.
Umm, I don't know. Have time today to do a full demo, but I
just wanted to make you aware of a resource that we have access
to it here, EVM.
Called DMP tool.
So this is actually freely available to anyone, but we have
an institutional agreement with the organization so that you can
just log in with your VM now ID instead of creating an account.
And there's some UVM specific links embedded within it.
So DMP tool is kind of like a guided wizard that walks you
through writing a data management plan. It has this
cloud based template kind of like a Google dot Google Docs
sort of thing where you can create a plan within the
specific requirements of your funder along with some
additional guidance that they provide and additional
information. So it also hosts data management plans written by
others that you can use as examples while creating it. You
can also grant read or edit privileges to others if you're
working collaborative.
Collaboratively on a DMSP.
Instead of opening a browser, I just grab some screenshots so
you can get a sense of what it's all about, but if you are
interested in using this tool and you want an overview for
yourself or.
For your project team, that's another thing that you can reach
out to. The library is for. So this is dmptool.org is the
website. This is what it looks like when you.
When you go to that address, if you look up at the top, I
highlighted the funder requirements button. So one
thing that DMP tool does that's very helpful is they pull in a
lot of the different requirements for different
funding agencies. So you can see here, here's the top of the
list. We have a search bar. So if I were to search for NIH.
This is what they have for NIH, so I've highlighted the upcoming
DMSP templates. They still have the kind of generic current one
that is in effect until January 24th, I guess.
And you'll see here they pull in associated links from the
funder. Any sample plans at the funder has provided those. I've
highlighted the little PDF there, so here's what the PDF
would look like once I open it. So it's just a nice place to go
to kind of see all these different requirements in one
place. And they are updated and checked regularly.
I mentioned that folks can also share their data management
plan, so if you use DMP tool to create one, you do not have to
share your plan. It can be private to just you or you can
share it within your project team and that's it. However,
some folks opt to share it publicly just to help other
people out and to kind of make it accessible and they can link
to it.
So at the top, if you were to click on public DMPS, you can
see and get a big list of.
Umm, specific data management plans that people have created
with DMP tool. So just keep in mind that anyone can choose to
share their plan. So this is not like from the funders is not
saying this is an excellent example of a plan. However I put
a little highlight around that featured button. Anything that
has that featured icon means that someone at the institution
has looked at it and considers it a good example. So it might
be somebody in the research office or their libraries. So
using that featured icon can be a good way to get a sense of
like things you might want to look for. And again here you can
also search for.
I hate she can see there's an option to limit on the left by
fund, or if you want to see what people have shared in terms of
NIH DMSP.
Once you log in, there's a sign in button at the top or a big
sign in box on the home page. When you go to the website, you
will have a dashboard where you can see all of your plans that
you're working on currently or in the past, and then up at the
top it adds a new little button that says create plan.
So once you click on that again, it gives this sort of like
wizard template walkthrough where you can fill out all these
information fields about our project and it will fill it in
and create a DMP for us at the end.
Here's an example of what the NIH guidance looks like. It
breaks up all of those things that they're looking for, like
you can see data type related tools, software, or code. You
get a little rich text box underneath and on the right you
get some additional guidance coming from the NIH and the DMP
tool. So there is a little bit of value added in terms of extra
information and guidance, but I think kind of the most helpful
thing about it is just that it pulls all these things together
in one place and gives you sort of some structure and it allows
you to share it.
Next, I maintain a resource page, data management research
guide.
And this is where I will be putting the NIH specific
materials after our session and after we figure out what you're
most interested in. There are a ton of support materials out
there already from the NIH. So we're going to try to go through
and pull out the things that will be most valuable to you all
so that you can save a little time and sifting through those
support materials.
Umm.
So here's the link for that. Again, we can send that out
after the session and it has some general material just about
like selecting a repository.
I'm comparison charts for that backs best practices
information. There's a tab about various supports at UVM around
different parts of the data lifecycle, so just different
campus groups that have some sort of support related to the
grant process or.
Or data in general.
So that is available for you as well.
And then there are in terms of the groups that are represented
here today, these are the different ways that they can
assist you. So your first kind of local point of contact would
be your pre award or business manager office within your unit
or college. And so that would be again your first point of
contact and kind of throughout the life cycle of your award.
The libraries, as I mentioned, we maintain DMP tool or we have
that data management research guide just general data
management plan support and assistance identifying
repositories.
The Research Development Office have, you know, extensive grant
expertise on they can provide templates and guidelines and
they will be a great asset for you in terms of like finding
specific answers in the grand development proposal phase.
And lastly, Spa, who I'm sure you're familiar with already.
Again, just extensive expertise around the grant process through
the entire life cycle of your sponsored project.
Now it's time for questions. So go again if you wanna stop the
recording.