ff 6.2.23

June 08, 2023

ID10045

To CiteDCA Citation Guide

00:00I am so grateful to Caitlin
00:03Thugmorton for joining us today,
00:06having just recently submitted an
00:08RO one with the new DMS policy,
00:11I can tell you how easy
00:13it is to run afoul of it.
00:15And So what we wanted to do for
00:17members of the Janeway Society is
00:19give you a little bit of education
00:21around the data management
00:23guidelines and sharing expectations.
00:25And we're lucky to have Caitlin with us,
00:28who's a data librarian for
00:29the Health Sciences.
00:30She received her Master's in
00:32Library and Information Science
00:33from the University of Washington,
00:35a BA in English and Spanish
00:37from Covenant College,
00:38and she provides really important data
00:40support services to students, faculty,
00:41staff, researchers and clinicians.
00:44Across her medical campus,
00:46her research interests include research,
00:49data management, data literacy,
00:51and Open Access.
00:52And so Caitlin,
00:53we are so grateful to you for
00:55presenting this and I hope that
00:56people come today with lots of
00:59questions to ask you as they
01:01navigate these requirements.
01:04Absolutely. Thank you for that
01:05kind introduction, Keith,
01:06and I'm actually really happy
01:07that you brought up my long ago
01:10background in English and Spanish.
01:11Sometimes I like to highlight that for
01:13anyone who feels really overwhelmed
01:15by these new data policies and
01:16all the things that go into data
01:18management and some of the things
01:20around programming and data science,
01:22I improve that.
01:23It's possible that you can come
01:24from a humanities background and
01:26really become quite data proficient.
01:29So if that's freaking you out a little
01:30bit and you want to talk to me about it,
01:32and please feel free.
01:33So I'm excited to be here to talk
01:35to you about fulfilling new data
01:37management and sharing expectations.
01:38We're mostly gonna focus on the
01:40National Institutes of Health,
01:41new data management and sharing policy.
01:43But I'm gonna talk a little bit just
01:46briefly about some other policies that
01:47are in the works that might be affecting you.
01:50And I really do like my presentations
01:52to be engaging and lots of questions.
01:54So if you have questions,
01:55please do not hesitate to stop
01:57me as I'm going along.
01:59You can put something in the chat or raise
02:01your hand or just go ahead and unmute.
02:03But I'll also save some time
02:04for questions at the end.
02:05All
02:08right. So like I said,
02:10we're going to talk about the new NIH
02:12policy and what it means for you.
02:14I'm going to give you lots of
02:16advice about how to write those data
02:17management and sharing plans when
02:19you're putting together your grant.
02:20And I'm also just going to give you
02:22some general strategies for how you
02:24can succeed once you've actually done
02:25the grant submission and you're moving
02:27on into the actual research process.
02:29And then like I said,
02:30we will also leave plenty of time for Q&amp;A.
02:34OK. So Keith just mentioned that
02:36he has worked on one of these.
02:37So I'd actually like to see a show of hands.
02:40You can use the raise hand.
02:41Feature and zoom.
02:42If you have worked on a data
02:44management plan and some capacity
02:46and perhaps you've assisted a π,
02:48perhaps you've done this yourself.
02:50But it could have been this year,
02:51it could have been formerly.
02:53It looks like there's a good number of you,
02:55so this is exciting to see.
02:57So some of you've already had some exposure.
03:00To this,
03:00and I'm actually looking through
03:02the participants now and I see a
03:04handful of familiar names and faces.
03:06So hi to you all and to those
03:08of you I haven't met yet,
03:09I hope to meet you at some point
03:11in the future, but this is great.
03:13So some of you have already
03:15had some exposure to this.
03:16So now I want to ask, now that you've had it,
03:19if you've had some exposure to this,
03:21how did it go?
03:22You can use the clapping emoji
03:24if you thought it went great.
03:26Are pretty neutral about it.
03:27You can use the thumbs up and if you
03:30didn't think that it went so great,
03:32you could use the surprise emoji.
03:35Okay.
03:35I see a few of you didn't maybe
03:37have the best time.
03:38So I hope that this will help
03:40you have a better time.
03:42And the next time you go forward with this,
03:45I know that this has been overwhelming.
03:46Some of the instructions are not so clear.
03:50Some of parts of the process can feel
03:52a little overwhelming and that's okay.
03:54So.
03:54And feel free to ask questions
03:56about that as we go along.
03:57If anything I say makes it more confusing,
04:00please jump in.
04:01And at the end,
04:02if anyone wants to talk about
04:04what their experiences have been
04:05like or what you found especially
04:07kind of difficult to wrinkle,
04:09I'd love for you all to share that.
04:10It's great for you all to learn
04:12from each other and I think more of
04:14kind of sharing in the space can
04:16only improve all of our strategies.
04:18So great,
04:19appreciate you all interacting with me there.
04:22OK,
04:23so we're going to jump in and I'm
04:25going to give you guys the Cliff
04:27notes kind of quick version of what
04:29this all means, what it all looks like,
04:31and then I'm going to get into
04:33some of the
04:34more nitty gritty.
04:36So first I just like to set the tone
04:38and kind of the baseline for why,
04:40why is all of this happening?
04:42And the thick of it, you know,
04:44it just really feels like a lot of rules
04:45and a lot of work in another document
04:47that you need to complain and submit.
04:49But there has been a lot of
04:51thinking that has gone into why
04:53these new policies have come forth.
04:55So the first one is really just this
04:57is an update to an existing policy that
05:00the NIH already had starting in 2003.
05:03Also, I see someone's hand is still up.
05:06Is that a question for me or is
05:07that left over from the previous?
05:12Give it a second if you want
05:14to jump in with a question.
05:16I think it was just left over.
05:17No worries, Okay.
05:19So this is really just a continuation of
05:22a policy that the NIH started in 2003,
05:25so that was 20 years ago.
05:27This was a much needed update.
05:30People like me and other data experts
05:32and data curators and people who
05:34run data repositories have wanted to
05:36see a stronger policy in this arena
05:38from the NIH for quite some time.
05:40NIH actually kind of started to
05:41give hints that they were going
05:43to be releasing an update to
05:45this policy in the 2018-2019.
05:47Time frame.
05:47And then they released their draft in 2020.
05:51Of course, a lot of us were
05:53overwhelmed with other things in 2020,
05:54Not exactly the best time and
05:56to release your draft policy.
05:57But they did give people about
05:59three years to kind of address,
06:01adjust to this new idea,
06:03read through the policy,
06:04provide comments and kind of come to
06:05terms with what all of this would mean.
06:07So it's not really coming out of nowhere.
06:09It's had a trajectory and kind
06:11of a history associated with it.
06:13And then Speaking of 2020,
06:15COVID has really accelerated kind
06:18of the appetite for these policies.
06:20The availability of open data,
06:22lots of data sharing between
06:25scientists accelerated a lot of
06:26things in the medical community.
06:28And I think maybe the NIH really feel
06:30like they had to capitalize on that momentum.
06:33And then finally,
06:33this just really seems to be a hot topic.
06:36It's not just the NIH.
06:37Who's coming out with data sharing policies
06:40and updated data management expectations?
06:42Lots of other federal agencies
06:44are joining forces with this.
06:46The EU has also seen quite a
06:48lot of expansion in this area,
06:50as have other countries outside of the US.
06:52So this isn't an isolated thing.
06:54If anything,
06:55it seems to be accelerating.
06:56And then finally,
06:58I just want to give you the couple
07:00of things that the NIH said about
07:01this and why they're doing it.
07:03They really want to see the
07:05acceleration of results into knowledge.
07:07They really want to maximize the value
07:10of and trust in scientific data.
07:12And they feel that, you know,
07:13making the things that the public has
07:15funded available to the public is important.
07:17And ultimately they want
07:18to improve human health,
07:19which I think is a goal that
07:21we can all rally around,
07:22even in the sick of it,
07:23even when some of these things
07:25feel a little redundant,
07:27a little overwhelming,
07:27a little intimidating.
07:32Okay, so this has already happened.
07:34In case you were not aware,
07:36the new policy has already gone into effect.
07:38It went into effect on the 25th
07:40of January and it now affects all
07:42future research new applications
07:44for grants with the NIH.
07:46So if you're doing renewals,
07:48this may not affect you,
07:49but for pretty much it.
08:07Show that my slides are made available
08:09to you all so you can get all the
08:11links to all the sources and resources
08:12and that I'm referencing here.
08:14But eventually the policy
08:15comes down to two parts.
08:17The first happens before
08:18you ever get the award,
08:19and that means that you have to submit
08:21the data management and sharing a plan.
08:23So that's step one.
08:24And then Step 2,
08:25if you are actually awarded the grant,
08:27is that you then have to implement
08:29everything that you've described in
08:30that data management and sharing plan.
08:32This is a really, really content
08:35simplified version of what the policy means.
08:38There's obviously way more work
08:39than just these two steps.
08:41Just writing the plan alone can
08:42take some time.
08:43And then of course,
08:44actually carrying out the plan
08:46over the course of a multiyear
08:47grant is quite a bit of work.
08:49But essentially this is the kind of help
08:51you see that there's really just these.
08:53That you may come out that
08:55the NIH is requiring,
08:57even though they have a lot of kind of
08:59extra cities that come alongside them,
09:01but this is just to give
09:02you the really quick,
09:03quick snapshot of what it entails.
09:06So what is the data management
09:09and sharing plan?
09:10It sounds like some of you have
09:12have experienced these before and
09:13or have worked on one or worked
09:15alongside A colleague with one,
09:17but the NIH has a really specific
09:19definition of how they want these to look.
09:21But in general,
09:23data management and sharing plans
09:25essentially encompass how you're
09:26going to handle and keep track of
09:28your data throughout the life of a
09:30project and then what you're going to
09:31do with that data at the end of the project.
09:33So that's more or less in a
09:35nutshell what these entail.
09:36But specifically,
09:37what the NIH wants to see is
09:39they want to see six elements.
09:41The first one is your data types.
09:43So what are you going to actually generate?
09:46Are you going to generate survey data?
09:48Are you going to generate clinical data?
09:49Are you going to generate Omics data?
09:51They want you to break out into
09:53categories the different kinds of
09:54data that you're going to generate.
09:56They want you to talk about
09:57approximately how many samples you're
09:59going to have of each data type.
10:00And sometimes they want to
10:01know a little bit about like,
10:02is it going to be raw data.
10:04Is it going to be processed data?
10:05What file format is the data going to be in?
10:08They described this pretty
10:09clearly on their website,
10:11but essentially they're just
10:12looking for a good overview.
10:14Can even be a bulleted list of all the
10:17data types that you plan to generate.
10:19The next thing they want you to
10:21talk about is data standards.
10:22This can really run the gamut in terms
10:24of how you decide to interpret this.
10:26But this is essentially if you're
10:27going to adhere to any standards for
10:29the data that exist in your field
10:31or in kind of the data
10:33space that you are within.
10:34So this can be things like standards for
10:36how the data is structured and formatted.
10:39There's a lot of these in like
10:40the Omics and sequencing world.
10:42It can just be that you intend
10:44to adhere to a really particular
10:46type of file format and whether
10:48that's like Csv's or Pdf's or.
10:50Image file type,
10:51and that's really standard in the field.
10:53This can also be way more specific.
10:54You can get into the world of
10:57vocabulary and taxonomy essentially
10:58like how you actually, you know,
11:00write out terms that are covered in the data.
11:02So there are a lot of options for
11:05this and you're feeling like you
11:06really want to explore this area.
11:07Please get in touch with me.
11:09I have a lot of a lot of resources to
11:12share with you on this and a lot of.
11:14Kind of guidance to offer,
11:15particularly from the library and
11:16standard because we really care about
11:19standards in our world over in the library.
11:22The next thing that I want you to
11:24talk about is any related tools,
11:25software,
11:26code used to actually generate
11:28or analyze the data.
11:29So this can be anything from like
11:31really big lab equipment that's
11:33necessary to the generation.
11:37And are they just want you to basically
11:40tell people what this involves and this
11:43is really for the reproducibility piece.
11:46So these next two bullet points look really
11:48similar and they're due to preservation
11:51access and associated timeline and access
11:54distribution and reuse considerations.
11:56But they're slightly different I
11:58think of the first one as really kind
12:00of being facing towards whoever is
12:02going to be the reusers of your data.
12:04So this is related to you know
12:06where you're going to store it,
12:07how people are going to access it and
12:09when the data is going to be available.
12:11The fifth piece, access,
12:13distribution and reuse considerations
12:14is really more facing towards.
12:17Participants,
12:17so whoever you're collecting the data from,
12:19and this is particularly oriented
12:22towards human participants.
12:23So consent factors that need to go
12:26into what you're actually releasing,
12:29thinking about,
12:30who's affected by the data release,
12:32thinking about, you know,
12:33is there any kind of confidentiality or
12:36deidentification steps that you need to take?
12:38This is really what that
12:41element is discussing.
12:42And then the final element is just
12:44who's going to oversee the data
12:46management and sharing throughout
12:47the life of the grant.
12:48And typically this is the
12:50principal investigator.
13:12Mutational Biology a few months ago.
13:14It walks you through 10 steps and 10
13:18simple rules for maximizing the plan
13:20and kind of how to go about walking
13:23through the plan and making sure
13:24that you're in compliance each step.
13:26And they have lots of resources
13:28in this article.
13:29And in this article they also have this
13:31really nice figure where they break
13:33out the six elements into 10 steps.
13:35And I personally find this
13:37language much more plain language,
13:38much more user friendly.
13:39They kind of walk you through
13:41the steps of describing the data
13:44and choosing documentation types,
13:45describing your tools and software,
13:47using standard file types,
13:49understanding your options for preservation,
13:51potentially finding a data repository,
13:53which I'm going to talk more about.
13:54Coordinating timelines for data sharing,
13:56protecting privacy,
13:57knowing approvals needed for data
14:00accessibility, and final planning.
14:02So this is, I think,
14:03a much more approachable way
14:05to approach how to do this.
14:08It's a little a little more user
14:09friendly than the language that the
14:11NIH chose for the data elements.
14:12So if you're interested in this,
14:14again,
14:14I really recommend this article
14:16and it's in the references and
14:18I'll make sure you guys get those.
14:20OK,
14:20so now I'm going to talk a little bit
14:21about some other parts of the policy.
14:23The plan is really the biggest
14:25piece and often what people want
14:27guidance on and what kind of
14:29tips for how to get through it.
14:31But I also want to talk about
14:32some other parts of the policy
14:33that are crucial for you to know
14:35about and will definitely affect
14:36the way your research process
14:37goes if you're awarded a grant.
14:39So the first one is that the policy
14:41is now going to require much more
14:44aggressive timelines in terms of
14:46when data is supposed to be shared.
14:49So the first one is at time of associated
14:52publication or at the end of the grant,
14:54whichever comes first.
14:55So what this means?
14:57And that any data that is associated
15:00with a publication that's been generated
15:03during the time of the grants,
15:05that data has to be released
15:07alongside the publication.
15:09And then whatever else doesn't get
15:11released in publications that's
15:13considered relevant findings has to be
15:15released by the time the grant is over.
15:18So what data exactly is the NIH
15:20wanting you to share and release?
15:23It covers all scientific data generated in
15:25the grants and this is their definition,
15:28not the clearest of definitions.
15:30I will be honest,
15:31I wish that they would clarify this a
15:34little bit more and lots of groups have been.
15:36Asking the NIH to do that,
15:37so maybe cross your fingers.
15:39We'll see more clarity around
15:40this in the next year or so,
15:42but the definition here is data commonly
15:44accepted in the scientific community
15:46as a sufficient quality to validate
15:48and replicate research findings,
15:50regardless of whether the data are used
15:52to support scholarly publications.
15:53So essentially.
15:54If the data is really underpins your
15:57findings and would be important for
16:00validation and replication
16:01of what you have discovered,
16:03then they want you to share it
16:05and some some way or another,
16:06whether that's through your
16:08publications or at the end of the grant.
16:10And I'm going to talk more
16:12about ways to share.
16:14So some things that the NIH has
16:17outlined for ways to share.
16:19They have, first and foremost,
16:21really encouraged you to
16:23use data repositories,
16:24and I'm going to talk a little bit
16:25about why data repositories are an
16:27ideal choice if it's available to you.
16:29But they also have other options,
16:31some of which I feel like some of
16:32this has actually been skipped over
16:34in a lot of materials that have been
16:36made available about the policy,
16:37And people have really heavily
16:39focused on data repositories,
16:40and for good reason.
16:41They are really the ideal choice.
16:44But there are other options.
16:45Things like data enclaves,
16:46which are really secure places to
16:49put data that usually have a lot
16:51of controlled access features that
16:53require identification of who's
16:54actually going to use the data.
16:56Often pretty strict use and licensing
16:59agreements are entailed with data enclaves.
17:02Sometimes data enclaves are
17:03actually physical,
17:04like you have to go to a physical
17:06location and usually disconnected
17:07from the Internet to access the data.
17:10That's a pretty extreme situation,
17:12but some people do have really sensitive.
17:14Data that would qualify for
17:16this type of situation.
17:17They've also said that it's acceptable
17:20to share data under the offices of
17:23the investigator in some situations
17:25and that really means anything that
17:27you see fit to do and that could
17:30be a lot of different situations.
17:32And then finally they've sent
17:33that it may be mixed modes.
17:34So maybe you'll put 90% of your
17:36data in the data repository,
17:38but then 10% that's highly sensitive
17:40need something else unique that
17:42you may need to put together.
17:44So there's a lot of room here
17:46for taking a lot of different
17:48approaches to data sharing.
17:49I don't think the NIH has wanted
17:51to lock people into, you know,
17:53a box when it comes to this.
17:54But I will say that data repositories
17:56are often your best choice.
17:58So I am going to talk more about those.
18:01Okay.
18:01And then just a couple of other things
18:04to keep in mind about this policy.
18:06So plans are just two pages.
18:08So the plan that you're gonna
18:10submit before you get the grant to
18:12the NIH about your data management
18:14and sharing just two pages,
18:15it's really not a lot of space.
18:19So you have to be pretty brief here,
18:20pretty concise and they are not looking for,
18:23you know, real depth.
18:24They just want you to give a good
18:26breath of what you're going to do.
18:27You're also making this plan before
18:29you even start your research.
18:30So there's only so much that
18:32you can be super descriptive
18:33and comprehensive about.
18:35You're essentially making a plan.
18:37It's not set in stone.
18:38So there's there's some room here
18:41to potentially update,
18:42which I'll talk about in a second.
18:44I also want to point out
18:45that at least at this point.
18:46Plans are not part of scored peer
18:50review criteria unless otherwise
18:51noted in the funding announcement.
18:53There's a handful of departments
18:55of the NIH that are doing that,
18:57but they're pretty rare at this point.
18:59So As for now, this isn't going
19:02to affect your grant funding.
19:04So I like to call this the
19:05anxiety reducing slide.
19:06This is the one that can hopefully help
19:08you take a deep breath and realize,
19:09but you know this is important,
19:12but it hopefully isn't going
19:13to affect your funding.
19:15And then plans can be updated,
19:16so again, not set in stone.
19:18It can be updated over the
19:20course of the grant.
19:21And the NIH has outlined lots of reasons
19:23why you might want to make updates.
19:26And then I also want to point out,
19:27even though I'm not gonna talk
19:28about this in this presentation.
19:30Budgeting for data management
19:31and sharing is really important.
19:33I've linked some resources from
19:35the office of Sponsored projects
19:37and they have a budgeting tip
19:38sheet and lots of other great
19:40resources to help you do this.
19:41And you can talk to people over in
19:43the office of Sponsored projects
19:44to get assistance with this.
19:45But the one thing I will say here
19:47is that me and basically everyone
19:49else in the office of sponsored
19:52product is really encouraging
19:53you to not list $0.00 here.
19:55Data management and sharing it does
19:57not come at no cost. It's a pretty.
20:00Labor intensive process.
20:02Even if you're not actually utilizing
20:04any services that cost dollars,
20:06you're almost assuredly going to
20:08need to cover labor costs for things
20:11like duration and data cleaning and
20:13organizing all of your resources and
20:15packaging things up for publication.
20:17There will almost assuredly be cost,
20:20so consider budgeting at
20:21least something for this,
20:23even if it's not a lot.
20:25And then there are handful of cases
20:27in which the policy doesn't apply.
20:29I have linked resources for this
20:30for you to look at.
20:32And if you think that you might be in a
20:33situation where the policy doesn't apply,
20:35feel free to reach out to me and
20:36I'm happy to help you clarify it.
20:37But it does apply in pretty much all cases.
20:41If you're generating data and
20:42you're getting money from the NIH,
20:44you're probably going to fall
20:46under this policy okay.
20:48So then I quickly want to say something
20:51about kind of what's ahead and what
20:53we might be seeing in the future.
20:55And I'm assuming many of you,
20:56if you're going to be sinking funding,
20:58will probably be getting that from the NIH.
21:00But there are lots and lots and
21:02lots of other funders out there and
21:04lots and lots of other places that
21:06require this sort of thing already.
21:07And if they don't yet,
21:09they may in the future.
21:11So just to give you kind of a quick
21:13understanding of what's been happening here.
21:15So like I said. The policy draft for
21:18the new NIH policy was released in 2020.
21:21Last year in August, the White House
21:23released something called the Nelson Memo,
21:25which came out from the Office of
21:28Science and Technology Policy.
21:29This memo states that all U.S.
21:31Federal agencies must update their
21:34public access policies by 2025,
21:36so that's about 2 1/2 years
21:39away at the latest.
21:40This new memo is going to
21:44remove publication embargoes.
21:45And it's also going to require
21:47more open data sharing.
21:48And so I'll talk a little bit more
21:50about that on the next slide.
21:52And then the new NIH policy is in effect.
21:56And then the NIH has already
21:58responded to this new White House
22:00memo about the public access policy,
22:02basically saying that their new policy
22:04that they rolled out in January 2023
22:06as their version of compliance.
22:09And then of course, by the end of 2025,
22:11we're going to see way more
22:12responses to this memo from all U.S.
22:14Federal agencies.
22:15So this is going to have a pretty big effect.
22:18If this is something that you're
22:20interested in or if you expect to
22:22receive funding from some other
22:23federal US agency,
22:25I encourage you to go to open
22:26dot science.gov They're tracking
22:28basically all the responses and
22:30various public comments and various
22:33plans that are getting released
22:35from federal agencies in response.
22:37To the new memo.
22:38So basically in short,
22:39if you take nothing away from this slide,
22:41this stuff is happening all across the
22:44country and really all across the world.
22:46There seems to be there's a much
22:48stronger trend towards the data
22:50management and data sharing and
22:52lots of funders are coming out
22:54with more policies to enforce this
22:57and to encourage compliance.
22:59So just to quickly say what
23:02the OSTP memo says about data.
23:05So for those with federally funded research,
23:07they want people to be sharing
23:09data freely and publicly at the
23:11time of publication,
23:12unless it's subject to various
23:14limitations like human subjects
23:16sensitivity or perhaps you have a
23:18consented patients or all kinds
23:20of reasons that can potentially
23:21limit you from sharing data.
23:23But in general,
23:24they want people sharing
23:26data freely and publicly.
23:27They want people sharing data
23:29even outside of publications.
23:31And they have a lot of recommendations
23:33about how they want that data to
23:34be shared and what they want those
23:36repositories to look like when the
23:38data is deposited in data repositories.
23:40These guidelines are really excellent
23:42and the NIH more or less use the
23:45same ones for their recommendations.
23:46So I'll be pointing you to those
23:49and the resources.
23:50And I also just want to highlight that
23:53this memo basically says that it's
23:54up to each federal agency that come
23:57up with their own policy and their
23:58own guidance and their own recommendations.
24:01So hopefully there will be a lot more
24:03guidance and a lot more resources out
24:04there over the next couple of years
24:06both. And I just send the example,
24:09they're not always the easiest
24:10to interpret and that's part
24:11of the reason that I'm here.
24:13So hopefully we'll see more resources,
24:15how clear they will or won't be,
24:17No promises. OK.
24:20So I'm going to start to talk about
24:22kind of how to actually do some of this.
24:24That was the overview,
24:25but I want to pass here and see if
24:28there's any questions at this point.
24:37So why do you
24:38think they took this approach rather
24:44than provide guidelines for what to
24:48do with particular types of data?
24:52Yeah, that's it's a really
24:54interesting question,
24:55and to some extent they have.
24:57A little bit. They still have the
25:00genomic data sharing policy which
25:02is specific to that kind of data.
25:05The plans are going to be harmonized
25:08more or less so that basically your
25:10plans will look the same regardless
25:12of data type that you have.
25:13But you do have more requirements
25:15if you have genomic data.
25:17So it's a great question.
25:19I mean to some extent they are doing this by
25:22letting each individual NIH center add on.
25:26Kind of their own requirements
25:27and their own recommendations,
25:29so you should read funding announcements
25:32really carefully and because they
25:34may have extra addons to this.
25:36But I think to some extent it seems
25:39like NH wants to standardize,
25:40and it sounds like they wanted to remove
25:43some of the burden of this paperwork,
25:45like they wanted to make things
25:47shorter and more consolidated.
25:48I'm not really sure that they've
25:51achieved that based on what.
25:52And I have heard from researchers,
25:54it seems like a lot of this is
25:55redundant to some of the other
25:57documents that have to be created,
25:58so there's certainly lots of
26:01room for improvement there.
26:02But I will also say that to some extent,
26:05because the plants are so short,
26:06they're only two pages and you're really
26:08only covering 6 elements in a really quick,
26:11concise summary,
26:13some summative form.
26:15To some extent they are offshoring
26:17some of that.
26:19Data type specific guidance
26:21to Data repositories,
26:23I'm going to talk a little bit more
26:24about that as I talk about Data
26:25Repositories in the next couple of slides.
26:27But most data Repositories have
26:30pretty sometimes rigorous steps
26:32as to how they want data to be
26:35prepared to deposit with them.
26:37So to some extent NIH is just not
26:39covering that at all saying you
26:41know pick a repository if you have
26:43one and then follow their guidance.
26:45So to some extent they are doing that,
26:46they're.
26:47Just handing that off to another
26:49player in the process.
26:51So if that does that help answer
26:53your question,
26:53it's kind of a wishy washy answer
26:56I am with you.
26:57And that I kind of wish that they had
26:59provided more guidance around more data.
27:01Yeah, I guess it just seems to
27:04me that just taking genomic
27:06data and you know as an example,
27:08you know if it's single cell data
27:10it goes here, if it's you know.
27:13RNA seek and chip seek and these
27:16kinds of data they go here,
27:18but maybe that feels too restrictive
27:20because there's always some sort of
27:22exception or something and so they don't
27:24want to dictate quite to that level, but.
27:28Yeah. And I will say that there
27:30are a lot of groups right now
27:32working on that kind of resource.
27:34Like you said, there's potential limitations
27:37when you get really prescriptive about
27:39things because there's always outliers.
27:41But I think that it's very likely
27:43that you're going to see some kind
27:45of resource that looks very similar
27:46to what you just described there.
27:48You say I have this kind of data and they
27:51will give you guidance on how you go
27:53forward with that when it's I I've heard of.
27:56At least three groups at this
27:58point that are trying to put
27:59together a resource like that.
28:00So when something like that's available,
28:02believe me,
28:02I will be telling everyone about it.
28:04So I would love to see something
28:06like that just as much as you do.
28:10Yeah. Shelly, hi. This is really helpful.
28:13I had a couple of questions.
28:15The first one is I had to write
28:17one of these recently and it wasn't
28:19super clear to me what kind of.
28:22The demographic data was required
28:24for so like the last person
28:26probably doing similar stuff,
28:28genomic experiments on
28:30tissue from human subjects.
28:32The sharing of the genomic data
28:34is the easy part, I think,
28:35because we've all done that before.
28:37But it seems to me like we also
28:39had to make a plan for sharing
28:41like principle demographic
28:42information on the subjects.
28:43And I don't know that's difficult
28:46to navigate given our tendencies
28:48to not share those things usually.
28:51So that's my first question.
28:53My second question was,
28:54are folks
28:55like you in the library available to help us
28:58write these plans and should we know
29:02what's the mechanism for doing that?
29:04Yeah. So I'll actually take
29:06your last question first.
29:07So I am available to help review them.
29:09I can't really help write them just
29:12because they're often so specific to
29:14the type of science that you're doing.
29:16But once you think you've gotten
29:17pretty close to a final draft,
29:19I am always happy to review them
29:20and kind of give you some final
29:22pointers for like you know,
29:23maybe you should have included this
29:25or what about this day repository.
29:27So I'm always happy to do kind
29:29of a spot check and help you
29:31with anywhere that you're stuck.
29:33As for your first question,
29:35so yeah, so clinical data,
29:37particularly clinical demographic
29:39data gonna be tricky.
29:42There are some data repositories available.
29:44Visbly is a really good example.
29:46I'm gonna have to be on one
29:48of the slides coming up,
29:51data that comes with some really nice.
29:54Controlled access features.
29:55So they do things like make sure
29:58that people are like at a research
30:00institution before they access the data.
30:02I think they also usually ask them to
30:03submit a proposal so you as the researcher
30:05can do things like, say, you know,
30:07I only want people to reuse this data
30:09if they're doing this kind of research.
30:11So there's a good bit of kind of control
30:13there to protect participants and to
30:16protect yourself and protect the research.
30:18So that's one option.
30:20Another is just doing pretty
30:23intense DE identification.
30:24So there's some repositories out there
30:26that will accept really just about
30:28any kind of data that they're going to
30:30want it to be heavily DE identified.
30:32So that is where I get into the,
30:35the place of re encouraging you
30:36again to think about what are some
30:38things that you might ask for in your
30:40budget that might make that easier.
30:42Do you need to hire a team of,
30:44you know,
30:44a couple post docs to do lots
30:46of DE identification?
30:48Do or you know what,
30:49what do you need there in terms
30:51of that to get that done in a way
30:53that feels feasible and doable?
30:55The other option is that if you
30:57have data that is like really just
30:59feel like it can't be shared,
31:00there's plenty of avenues for that.
31:02NIH has like a kind of almost like a whole,
31:04like flow chart on like,
31:05do I actually need, you know,
31:07can I actually share this data?
31:08And if I can't,
31:09what are my justifications for
31:11not sharing it?
31:12So that's always the potential as well.
31:13Maybe it just isn't a good idea to share it.
31:16In that case,
31:17you just say that in your plan and
31:18you give a justification for it.
31:23Anything else there that I can answer?
31:24If he's went up with the question, go ahead.
31:29Hi Caitlin, thanks for this talk.
31:30Really important stuff.
31:31So I have not done one of these before.
31:35And my question is in regards to is this
31:38for all data like even animal data,
31:41Western Blots, Eliza's, How,
31:43how do you see that being reported?
31:48Yes, I am. So it is all scientific data.
31:51So you know if it's underpinning findings
31:53that you're gonna release kind of as
31:55part of the main findings of the grant,
31:56then absolutely yes that is included.
31:59Pretty much at this point,
32:00the only kinds of data that are
32:02excluded or like data about students,
32:03if you're on like a training or
32:05an education grant from the NIH,
32:07there's really not a lot of exclusion.
32:10Pretty much if you're generating scientific
32:12data, it is going to be covered.
32:14So yes, animal specimens, absolutely.
32:17In fact, I would say at this point
32:18that's the majority of the plans
32:20that I have personally laid eyes on.
32:22The good news is if you're dealing
32:24with animal specimens,
32:25you're probably going to have.
32:27Less issues in the in the realm of
32:29needing to deidentify or having to
32:32deal with kind of confidentiality
32:35sensitivity concerns, but yes,
32:37if you're in the image realm.
32:38That is kind of an ongoing discussion
32:41in the image.
32:42Status here is how much should be shared.
32:44I know Western blots in particular are huge,
32:47and sometimes it's, you know,
32:49uncertain what's really useful
32:51to share for reuse.
32:53So if you want to talk more
32:55about that after today's session,
32:56I'm happy to point you to some resources.
32:58There's some good.
32:59Discussions and good guidance kind
33:01of forming around some of the
33:03stuff and related to image data,
33:05but none of it I would say it's
33:07like cut and dry at this point.
33:08So some of that's just you know
33:10describing to the NIH what you can
33:13do that's reasonable and potentially
33:15coming up with justifications if you
33:17feel there's data that shouldn't
33:19be shared or is you know just
33:21it wouldn't be useful to reshare
33:23and to make available for reuse.
33:30Hope that helps.
33:33Okay other question.
33:36All
33:43right, well with that I think I'm
33:44going to head into a couple more tips,
33:46a few of which I hope will make some
33:48of this a little bit easier for some
33:50of the questions that you all have
33:52brought up or at least give you a
33:54couple of avenues to follow and.
33:56So one of the first things for any
33:57of you who are here who haven't
33:59done one of these before,
34:01I really recommend that you use
34:03some of the tools and templates
34:05that are available out there.
34:07One of the first ones that I'm
34:08gonna recommend here is DMP Tool.
34:10I'm actually going to click on
34:11this and take you out and show
34:12you what this looks like.
34:14So I'm actually logged in right now
34:16to DMP tool and I'm gonna pop in here.
34:20So you log into this,
34:21we license this,
34:22and this is a free resource for
34:24you here at Yale.
34:25You log in with your Yale address and
34:28you are basically able to use the for
34:31free with a yale.edu e-mail address.
34:33And essentially what this is,
34:34is the data management plan generator.
34:38And what's beautiful about this
34:40is if you had over here to
34:42this middle button right plan.
34:44You'll notice that I've already
34:46selected that this is an NIH
34:48General Data Management Engineering
34:50plan for the 2023 new policy.
34:54And because it's selected that it
34:56already knows what the six elements
34:58are that the NIH requires for this
35:00plan and if we click into one of these,
35:02like data type for instance.
35:05And it gives you these really nice,
35:07fill in the blank prompts and I
35:09find these incredibly useful.
35:11But when I'm giving advice to
35:12people on how to write these and
35:14I I've found that a lot of people,
35:15particularly early career people
35:17who maybe haven't had the chance
35:19to do one of these yet,
35:20that this can really help you
35:21go through the process.
35:23They also give you sample answers.
35:25They also give you guidance from the NIH.
35:27They link you out to all the
35:29different parts of the guidance.
35:30They also have their own guidance
35:32that people at CMP Tool have written.
35:35This is run by quite a few of the
35:37universities in California and
35:39the California Digital Library,
35:41although they've made it available
35:42to basically everyone,
35:44and they also have really nice guidance
35:45here on how to go about doing this.
35:47I find in a lot of ways easier to
35:50digest them with the NIH has provided,
35:53so this is really nice.
35:54You can also invite collaborators into
35:56this and when you're done you can export it,
35:59and lots of different formats of the
36:02PDFI can keep them at the CFB if
36:04you really wanted to do it that way.
36:05You can also make it public,
36:07or you can make it private to all
36:09your collaborators.
36:10So there's a lot of kind of extra
36:11built in features here that are really nice.
36:13So this is a good thing to know about.
36:16I totally encourage you to use.
36:19I hear really positive things about
36:21it from people who've tried it,
36:23so if that sounds like it could help,
36:25I definitely recommend using it.
36:28The other thing is that the NIH keeps
36:29making more and more sample plans available.
36:31I think right now they have about
36:34over a dozen available and there
36:36are lots of other ones out and
36:39about on the Internet to review.
36:41DMP tool itself also has lots of
36:43plans that people have made public
36:45available for you to review,
36:46so there's a lot floating about.
36:49I also recommend just consulting
36:51with your colleagues.
36:52You've already heard on this call that
36:53some people have already done this,
36:55so you know,
36:56if you have colleagues who are amenable
36:58to sharing their plans with you,
37:00that can be really helpful to borrow
37:01language from people who've already done it,
37:03particularly if they're researching
37:05really similar things to you.
37:07And I also just point out that your
37:09School of Medicine has a grant library.
37:11And I think Nick probably knows a
37:13little bit more about that than I do.
37:14It probably doesn't have any of
37:16the new NIH grants and it yet,
37:18but eventually it will.
37:20So that's also something to
37:22check out and take a look at.
37:24I also want to just talk a little
37:26bit about kind of this part that
37:29isn't explicitly stated in the plans,
37:32but will definitely be part of the
37:34process and can really catch you
37:36up for sharing data well later.
37:39And that's the data contextualization.
37:41So keeping good notes and good
37:44track of what your data means,
37:46so that when you're asked to share it,
37:49sometimes a year, two years,
37:51three years from when you
37:53actually generated it,
37:54you know what it means and you can
37:56describe it really well to another reuser.
37:59So this includes things like documenting
38:01your data using metadata standards.
38:04And ensuring to data standards
38:06and best practices.
38:07I have a lot of resources for this at
38:09the end of the slides and this could
38:11be a whole presentation unto itself,
38:14so I won't get too far into the weeds here.
38:15But just a note to say that it's really
38:18important to do this one for yourself,
38:19just so that you understand the
38:21data later when you come back to it,
38:22sometimes many months later,
38:24and also for the people who might
38:27be using your data again and future.
38:30And I also want to talk a little
38:31bit about this.
38:32It's kind of already been mentioned a
38:34little bit and some of your questions.
38:37But how do you protect participants
38:39through this process,
38:40particularly human participants?
38:43So one of the first things to really
38:45think about and I think one of the kind of.
38:47Instigation through why this policy
38:49has come about is to really think
38:51about consenting early on.
38:52You want to make sure that you're
38:54really explicit and the consent
38:56documents about what's going to
38:57happen with patient data,
38:59whether they want it to be shared,
39:00how it's going to be shared,
39:01what that sharing is going to look like,
39:03who it's going to be made available to you.
39:04And IH has a lot of good documentation and
39:08guidance on how to go about doing this.
39:11They even have sample consent
39:12language to include on your forms.
39:15But you want to make sure that you're as
39:17upfront about this as possible to protect
39:19your participants and make sure they
39:20understand what they're getting into.
39:22And you also want to go ahead and make plans
39:24for date of the identification if that's
39:26going to be something that you have to do.
39:28That can be a pretty time intensive
39:31process that usually has statistical
39:33components involved with it.
39:34So make sure that you involve the right
39:37people in this and that you budget
39:39accordingly if you're going to need to do it.
39:42And then I also talk a little bit about
39:44this because I think sometimes these
39:45can just feel like requirement after
39:47requirement and just another thing to do.
39:50But there's there's potential kind of
39:53benefit here for you and what this means
39:56for kind of your research outputs.
39:58But first I do, I forgot,
39:59I also want to talk about this to you.
40:01There's some things that you can do
40:03if you're kind of concerned about
40:05your research getting out and about
40:06and potentially into the wrong hands,
40:08particularly if you have really
40:10sensitive research, so.
40:11One of the first things that you can do
40:13is you can look for data repositories
40:15that have access controls or this is
40:17something that you can kind of retrofit
40:18yourself in the data sharing process.
40:20But essentially you kind of,
40:22you know,
40:22keep some of the data back based on criteria.
40:25So maybe you only hand it over to
40:27academic researchers,
40:28maybe you only hand it over to people
40:30doing certain kinds of research.
40:31There can be reasons for that,
40:33particularly if you have really
40:35sensitive data.
40:36I'm thinking of like recently I
40:38heard from somebody with intimate
40:39partner violence data.
40:41I'm covering really sensitive groups.
40:42You know,
40:43that's the kind of data that you
40:45don't want to just openly share unless
40:46it's been heavily be identified.
40:48So if you're going to share it and
40:50it's a real granular format that's
40:52probably going to come as pretty
40:54tight access controls.
40:55You can also utilize data licensing and
40:57data use agreements to really carefully
40:59specify how the data should be reused.
41:02So that's another way to go
41:04about thinking about this.
41:04And most data repositories will already
41:06have lots of data licenses for you to
41:09choose from to add to your data that
41:11users have to agree to before downloading.
41:14And then this is the piece that
41:16I want to talk about that's kind
41:17of the benefit for you.
41:19When we do all these sorts of things
41:21like deposit data in a data repository
41:24and put it in a place that's, you know,
41:26somewhat curated and controlled,
41:28that's going to come with
41:29lots of extra features.
41:30Things like persistent identifiers,
41:32so digital object identifiers or DOI,
41:35something that you're probably used
41:36to seeing on your publications.
41:38Data sets can get Doi's as well and those
41:41are trackable and they can be cited.
41:43So there's some benefits
41:44here and that you know.
41:45You can, while you're releasing
41:47your data set down into the
41:49world for other people to use,
41:51you can kind of track what their
41:52reuse was like much in the same way
41:53that you would with a publication.
41:55And that can go into your metrics
41:57and your impact and go into kind
41:58of what you cite is the output
42:00of your work and your research.
42:04OK, so finally I want to talk about this.
42:07Again, this could probably be its own
42:09whole workshop, and I do actually
42:11have a whole workshop on this.
42:13And so if you're interested,
42:14keep an eye out for that
42:16on the library calendar.
42:17But I really want to encourage you,
42:18if possible, where feasible
42:20and where one exists for you to
42:23take advantage of repositories.
42:24They can really simplify the longterm
42:27preservation piece that the NIH is
42:29asking for you to describe in those plans.
42:32It really takes a lot of the gas
42:34work out of curation and kind of
42:36longterm sustainability of the
42:38data and lots of things that the
42:39NIH wants you to think about,
42:40like how are people going to find it,
42:42how are they going to access it,
42:43how are they going to download it?
42:45Repositories already have all
42:46of that stuff built in.
42:48NIH also has a lot of repositories listed.
42:52So I encourage you to check those out.
42:54And then there's a few more that
42:55I want you to know about.
42:57The first one is Ryan.
42:59We are members of Dryad and Yale
43:02affiliates with the Yale dot Edu e-mail
43:05address get up to 300 gigs of data
43:08with Dryad for deposits and that's per
43:11project and you can have unlimited projects.
43:14So there is quite a bit of room there.
43:17They take just about any data type.
43:19They're really only caveats to Dryad or
43:22that it has to be public domain data.
43:25So you basically have to
43:27release it completely open.
43:28So if you have really sensitive
43:30data that may not work,
43:32but otherwise they are good
43:34for a lot of data types.
43:36This other one is ICPSR which
43:38we are also members of.
43:39This one is more for like public
43:41health data and kind of social justice
43:44oriented health data and they're
43:46originally official sciences repository,
43:48but they have multiple NIH data collections,
43:51so it's possible that you might
43:53find a fit there.
43:54And then a few more that I want to mention,
43:55I already said Bisley is 1/1 that I would
43:58definitely encourage you to check out.
44:00If you're on the clinical front,
44:02this one in the top right corner is
44:04how you get to basically all of the
44:07National Center for Biotechnology
44:09information repositories,
44:10including like SRA, Geo,
44:13lots of Omec resources.
44:15So that's where you can find
44:16all of those in one place.
44:17And in the bottom left corner of these
44:20are all the repositories that I each have.
44:23It's it's a very long list.
44:24They have hundreds and then OSF
44:26is another one that I like to plug
44:29the home for lots of different data
44:31types and also a good one to know
44:33about if you're going to be required
44:35to do pre registration for your
44:37study if they handle that as well,
44:40Okay.
44:40So just a few final things that
44:42I want to say and then I'm going
44:44to open it up again for question.
44:46This is something that the
44:47NIH has on their website
44:48right now, which is that they really
44:50want investigators to benefit from
44:52first and continuing use of their data,
44:55but not from prolonged exclusive use.
44:58So this is really something to think
44:59about as you're thinking about,
45:01well, how do I share?
45:02What do I share and when do I share it?
45:05The truth is that most researchers
45:07want to hold on to their data for
45:09a while because often you can get.
45:10Several you know and continuing
45:12uses out of it and then I had
45:14supports that they want you to be
45:16able to get that continuing use.
45:18What they don't want you to do is to hold
45:20on the onto it for you know a really
45:22long time and never let anyone else.
45:24Get access to it.
45:26So finding the balance between that
45:28is something that I think the NIH
45:29could do a better job of describing
45:31and being much more clear on.
45:33But that is what they're saying
45:34is their intent.
45:35So I think that you're well within
45:37your rights to hold them to that,
45:38both in your plan and how you go
45:41through your entire research project.
45:44And then I also just want to say this
45:46is a bit of an ambitious goal of my own,
45:49but something that I know that the
45:51NIH was hoping for in that lots of
45:53other people who contributed to this
45:54policy and kind of heard about this
45:56policy and have encouraged similar
45:58policies want to see is that the real
46:01goal here is that hopefully eventually
46:03we will start to see data sets.
46:05As a primary research output,
46:06Just as important, just as valuable,
46:09just as prioritize.
46:10Just as held up for promotion
46:13and tenure as publications are.
46:15Because data sets are a ton of work.
46:18I mean,
46:18whether it's the generation of
46:20actually creating and collecting them,
46:21whether it's the analysis that goes into it,
46:23whether it's the cleaning and the
46:26identification that's required
46:27to actually make them usable and
46:29depositable and a new location.
46:31They are a lot of work,
46:33a lot of time, a lot of Labor.
46:36So they deserve to be,
46:37you know,
46:37treated just the same as your publications
46:40are held up very much in the same way.
46:43And that's something that I hope we're
46:44going to see more of in the future,
46:46that they're very much seen as just as
46:48worthy of a product as all the publications
46:51that you hope to get on your CV.
46:53So again,
46:53bit of a rosy outlook there.
46:55I don't know how long it will take
46:56for us to get to that point where
46:58if we'll ever get to that point,
46:59but that is one of the goals.
47:02So trying to see,
47:03just to sum up a little bit,
47:05data management and turning
47:07expectations are increasing not just
47:08at the NIH but at lots of other U.S.
47:11Federal agencies and at lots of
47:12other places around the globe.
47:14So I really don't think this is going away.
47:16If anything this is kind of turning up.
47:19And something to be really
47:21thinking about in your future as a
47:23researcher and as a career academic.
47:27And finally, I hope the main thing that
47:29you take away from this is that there are
47:31a lot of resources available out there.
47:33That's actually when I'm going
47:35to show you next.
47:36I've got all the references here from
47:38things that were mentioned in the slides.
47:41There are quite a lot of resources
47:42at the library.
47:43We have a whole site with lots of
47:45resources that were discussed today,
47:47but many more.
47:48There's an asynchronous e-mail course
47:49that you can take on data management
47:51and how to write data management plans.
47:54It takes about two to three weeks to take.
47:55It's completely asynchronous that just
47:57emails that get delivered to you.
47:59So if you're interested in that,
48:00sign up and then Ioffer lots of trainings
48:04on this and I am available if you
48:06want help with your data management plans.
48:08Once you're kind of towards the final stages,
48:10always happy to do a quick review for you
48:13and give you text on how to potentially
48:15make it better and answer your questions.
48:18Also, these are some more resources
48:20to help you write your plans,
48:22some of which I already covered today.
48:24Here's resources from the Office of
48:27Sponsored Projects about budgeting,
48:29and then finally my e-mail address and
48:31for any questions that I can answer.
48:33Office Sponsored Projects is
48:34there for you to help as well.
48:36They're definitely the ones
48:37you want to go to.
48:38If you have specific questions
48:39about budgeting or some of
48:40the more kind of like policy,
48:42upload questions,
48:43and they're the ones who
48:44will help you with that.
48:46So that is all I have for you all.
48:48I'm going to stop it right here
48:50on my e-mail address and I will
48:52open the floor for any additional
48:55questions that you all have.
48:56And like I said,
48:57I will make sure these slides get to you.
48:59I'm not sure who I need to send them to,
49:00but I'll make sure that
49:02they're available for everyone.
49:17Yeah, Question. Go ahead.
49:19I was wondering, do you know
49:22if they're actually going to
49:24be checking in on whether we
49:26follow up with these plans?
49:28And if so, however in the world,
49:30would that possibly happen?
49:33I can't imagine them, you know,
49:35that would take so much effort and
49:37it would be no fun for anybody,
49:38I would imagine. Yeah. So they.
49:44I have you know,
49:45suggested that they might in the
49:47various webinars and trainings that
49:48they've that they've had about this,
49:51but I think your intuition is spot on.
49:53I mean, how how could they?
49:56I don't think the end of the.
49:58The NH as well enough resource to really
50:01could be doing lots of check up about this.
50:03I mean in a lot of ways you're very
50:05much on the honor system here.
50:07Although I will say that you know
50:09data repositories are definitely
50:10going to check up if you submit to
50:12the most of them have the readers
50:14on staff if you were going to you
50:15know look through your data trying
50:17to make sure that it's actually in
50:18a pretty good state for publishing.
50:20Much in the same way that when
50:21you publish you know a manuscripts
50:23you're usually.
50:24I don't have peer review and
50:25editors looking at things.
50:26So data repositories are really
50:27similar in that in that sense.
50:29But is the NIH going to be, you know,
50:32coming around doing audits on
50:34that kind of stuff?
50:35I'm just not so sure.
50:36But I will say this is a good
50:38opportunity to show you the various
50:40resources the NIH has about this.
50:42These are in the resources
50:43that I just showed you.
50:44But this is sharing.nih.gov It's a
50:47new website that they have produced,
50:48support the policy and they
50:51do actually have a section.
50:53About this,
50:54so I'm going to head over here to writing
50:56that data management and sharing plan.
50:59And down here they have assessment
51:02and this is where they basically
51:04talk about how it's going to be
51:06reviewed and they also talk about
51:08that they are going to expect you to
51:10update on this during your RPP R's,
51:12I think I said that acronym, right?
51:14I hope so that those are the two
51:16things that they've said about
51:18it that they will be.
51:19Reviewing the plan during the pre
51:22award process and that they will
51:23also be expecting you to kind of talk
51:25about this in your updates to the NIH.
51:27But other than that no particular
51:30enforcement has yet been announced.
51:33So we'll see maybe that's coming,
51:35maybe it's not,
51:37but for now it's pretty light touch
51:42other questions.
51:45I will just go back and say
51:47this is a really nice resource.
51:49Some of it is done,
51:52but a lot of questions can definitely
51:54be answered on sharing.nih.gov But
51:57you're always welcome to e-mail me
51:59and I'm happy to kind of point you
52:01directly to the resource that you need.
52:02I feel like I know this website by
52:04the back of my hand at this point.
52:21Question.
52:28Actually I have another question. It's
52:32directly related to to this
52:34particular document, but on the R35
52:36that I just recently submitted,
52:39I had to, I was told at the very
52:41last minute I had to do this thing.
52:43It's called a plan for
52:46enhancing diverse perspectives.
52:47Are you aware of this?
52:49And unlike the data management plan,
52:51I was unable to find any templates.
52:54Or any sort of real sense of
52:56what they were actually after.
52:58And I basically had to write
53:00something that I have no idea if it
53:03was along the right lines or not.
53:07You said it was called the plan
53:09that for enhancing the diverse
53:11perspectives. So it's essentially ADEI plan.
53:16I am not familiar with that,
53:18but I'll do some looking around see if
53:21that's something that maybe I should
53:22be talking about in these books.
53:24Presentations. I also wonder if you'd
53:25be able to get more information
53:27about that, perhaps from Y FM,
53:30the diversity office or the library
53:33also has the diversity office,
53:35but I don't know about that offhand.
53:37I am sorry to say
53:40I didn't either until the
53:41day I submitted my grant,
53:42and then I realized I found out
53:44I had to write this in one day.
53:46What an unwelcome surprise.
53:48That is So frustrating.
53:50That's why I thought I'd mention
53:51it now, just in case other people
53:53might run into the same thing.
53:56Well, I appreciate you bringing that up.
53:57I hope that's a good potential, kind of,
54:00you know, people will know that maybe
54:02they need to look around for that,
54:03and I'll definitely keep my eye
54:04out for resources on that as well.
54:15All right. If there's no other questions,
54:18thank you very much, Caitlin.
54:19We will make the recording in the
54:21slides available up on our website.