 Hello everyone and welcome to today's webinar. The goal of today is to discuss the new data management and sharing or DMS policy, especially as it relates to projects funded by the NHGRI Ethical Legal and Social Issues or LC program. The data management and sharing policy has really expanded the universe of applicants seeking NIH support who will need to prospectively consider data management and sharing for their research and so we thought it would be good to talk to this community about what the policy means for the LC research community. We're trying to keep this presentation brief to about 30 minutes so that there is plenty of time for Q&A. We hope to answer many of your questions today and that you'll stay in contact afterwards with any remaining questions. Before I begin, I'll introduce the team we have today. My name is Elena Gename. I am NHGRI's policy advisor for data science and sharing. I'll be giving you the initial overview of the policy. I'll then hand it over to Dave Kaufman, a program director for our LC research program who will give a more tailored presentation from the perspective of an LC program staff member. In the wings, we have our two other wonderful LC program directors, Renee Sterling and Nicole Lockhart. They'll be here to assist with fielding your questions. All of us have worked together on some FAQs, which supplement the NIH FAQs, which will be entered into the chat for today's webinar, and then these will be updated and shared after the webinar on the event page. After today, the program directors will be your go-to people for any subsequent questions you have about how to navigate the policy for your individual scenario. I am also always available to take questions you may have about the policy. So as I just mentioned, I will first provide a very brief overview of the policy with some key points we want to point out. Next Dave will be covering the application of the DMS policy as it relates to LC research and specifically will provide tips for preparing and submitting your DMS plan to NHGRI. Finally, we'll briefly touch again on some of the resources that are out there and then we'll open it up for Q&A and discussion. So let's get started on the basics of the data management and sharing policy. The overall spirit of the DMS policy is to promote the responsible sharing of scientific data. Responsible data management and sharing has many benefits, including accelerating the pace of biomedical research, enabling validation of research results, and providing accessibility to high value data sets, for instance, to more junior researchers. The DMS policy is the latest policy aimed at achieving NIH's long-standing commitment to making the results of NIH-funded research available. It applies to the vast majority of NIH-funded activities. More specifically, it applies to research that generates scientific data. If you're familiar with NIH's data sharing policies of the past, this policy applies regardless of the scientific data type and regardless of the funding amount. Therefore, nearly all applicants for NIH funding are now required to submit a data management and sharing plan, outlining how scientific data and any accompanying metadata will be managed and shared, taking into account any limitations or other restrictions. This plan will be a component of the application and will be reviewed programmatically. Dave will say more about programmatic review later. If an application is funded, then compliance with the approved plan becomes a term and condition of award. NIH has put a great deal of effort into communicating the requirements of this new policy. There is a relatively new website, sharing.nih.gov, which contains a variety of helpful information and resources, including some sample plans. NHGRI just contributed a sample plan for survey and interview data, and so we do encourage you to look at that. It might be particularly helpful for today's audience. We are not able to cover all of the information that NIH has put out on the data management and sharing policy, but we do encourage you to also watch the webinars that NIH has done. And also, for example, today we'll not be touching on the NIH genomic data sharing policy, which is still in effect and has been harmonized with the NIH DMS policy. However, sharing.nih.gov does cover the GDS policy requirements, so if you are proposing to generate large-scale genomic data, we encourage you to check that out or talk to your program director. I wanted to show the definition that the policy employs for scientific data, since this term is at the heart of this policy. Scientific data are the recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings regardless of whether the data are used to support scholarly publications. Scientific data do not include things such as laboratory notebooks, preliminary analyses, plans for future research, communication with colleagues, or physical objects such as laboratory specimens. Since today's webinar is specifically geared toward our LC researcher community, our PDs based on the review of prior awards created a list of some of the data types that appear in LC work and would likely be considered scientific data. This is not necessarily an exhaustive list, however, some of the data types that you may be working with and therefore need to address in a DMS plan would be raw encoded transcripts from interviews or focus groups, stakeholder meetings, Delphi processes, or deliberative events, data from social media, websites, public records, survey data, or ethnographic observations for some examples. Here we wanted to point out two things that we felt would be important to emphasize, and that is one that this policy only applies to new applications submitted on or after the effective date of January 25th, 2023. If you currently have an NIH grant, this does not change the expectations of your active award. Additionally, the policy expects that when planning for data sharing, researchers will maximize the sharing of data as appropriate, and we'll speak more about this in a bit. This slide has also been tailored to some of the common activity codes that NHGRI uses to support LC research. NIH does have a more comprehensive listing of the activity codes to which the DMS policy does and does not apply, and given that the policy applies to any NIH-funded activities that generate scientific data, it does apply to most but not all NIH funding mechanisms. The policy does apply to K01s, K99s, R awards, and U grants, and there are more. It does not apply to the mechanisms that support training or education of individuals such as the T32 or F99, F33 fellowship grants, for instance. Here we also just wanted to note that administrative supplements will follow the requirements of the parent award. Therefore, if your original award was issued before the effective date of the DMS policy, it will not apply to administrative supplements for that award. However, if you are seeking a supplement, including a competing revision that changes a parent award's approach to data management and sharing for which the DMS policy did apply, then the DMS plan of the parent award would need to be updated or should be updated. NIH has published materials to help guide researchers in thinking through how to protect participant privacy when sharing data, and we know this is on the mind of many LC scholars. Some of the considerations would be de-identifying data prior to sharing, establishing data sharing and use agreements, and understanding legal protections that are in place toward against disclosure and misuse such as certificates of confidentiality. One of the other tools in the toolbox, so to say, is consideration of the appropriate means of access for a given data set. The DMS policy expects researchers to consider whether access to scientific data from participants should be controlled, which is when there are measures in place such as requiring data requesters to verify their identity, and the appropriateness of the proposed use to access protected data. And this may be a good model even if data are de-identified to a regulatory standard, for instance. A controlled access model can be used in cases when there are explicit limitations on subsequent use as imposed either by laws, regulations, or policies, or by informed consent or other agreements. However, having explicit limitations on sharing is not a prerequisite for this mode of sharing. It can also be used in instances where data, even when de-identified to established standards, cannot sufficiently reduce the possibility of re-identification. If it comes up that there were previously unanticipated approaches or technologies that might impact the risk to participant privacy, updates to the DMS plan can and should be communicated to the NIH immediately. There are also going to be instances in which participant data can be shared in an unrestricted manner. For instance, when the data set can be de-identified and institutional review finds sharing poses very low risk to the privacy of individuals in the study. Also, it may be used in instances where participants were explicitly consented for unrestricted access. Again, this is just one of a few factors that NIH asks investigators to consider, and we wanted to spend some time going over that today. We also want to acknowledge that a lot of the questions that we've heard about the DMS policy, especially as it relates to data produced by LC-funded researchers, is whether all data must be shared under this new policy. While both the policy and we at NHGRI do encourage and expect that data will be shared maximally, there is a recognition that there are going to be instances in which it's most appropriate to limit data sharing. That's either entirely or by employing a controlled access model that we just discussed and enforcing use restrictions. Again, there may be cases where no individual level data will be shared due to privacy or other concerns such as stigmatization or participant community concerns. The policy itself calls out several categories of justifiable limitations to sharing scientific data. Limitations may come from the informed consent process or when privacy or safety of participants would be compromised, federal, state, local, and tribal law or policy limitations that come from existing or anticipated agreements, and other technical, ethical, or legal factors. Today, we wanted to share that even that when the proposal is for sharing data in some manner or the investigator believes it's most appropriate not to share the data at all, this should be described and justified in the DMS plan. You should consider the tools that you have to share such as de-identifying to the extent possible or exploring sharing via controlled access or whether certificates of confidentiality alleviate certain concerns, but of course that must be balanced with these other considerations listed on this slide. Okay, so finally, before I turn it over to Dave to walk through the DMS plan from an LCPD perspective, I also wanted to speak a bit to the informed consent for secondary research use with data. The DMS policy did not add any expectations for informed consent, but it does strongly encourage researchers to plan for how data management and sharing will be addressed in the informed consent process, including communicating with prospective participants about how their data are expected to be shared and used. In May of last year, NIH put out this relatively new resource for informed consent for secondary research with data and biospecimens that provides some sample language, and this may be a helpful starting point if you're going to be addressing this notion of data sharing in your consent forms for the first time or perhaps in a new way as a result of the policy. This sample language can and should be adjusted as needed. Also, it's important to note that broad consent, which is an approach for consent that permits current and future access and use of samples or data for research without necessarily specifying what the focus of such studies might be, is not a requirement of NIH's data sharing policies. It is up to the researcher and their IRB to determine whether any limitations on sharing would be appropriate and to communicate these two participants via the informed consent process. Additionally, we want to encourage researchers who may be engaging and including tribal communities to understand the relevant considerations and best practices for developing partnerships with American Indian and Alaska Natives from the outset of the research project, including around the plans for data sharing. NIH has also created a resource on this to support researchers in that endeavor. So for applicants and grantees, the policy resolves around data sharing plan and budget, which must be attached to grants. So we thought we'd take a little bit more time to talk about the basics of the plan before opening things up for questions. So I'm going to be turning it over to my colleague Dave. Bear with us for just one moment while Dave takes over screen sharing so he can advance the slides from here on out. Thanks so much, Elena. And while we're switching over, I just want to remind people that questions can go into the A tab down at the bottom of Zoom, or at least some bottom of my Zoom, and we'll address them at the end of the presentation. So Elena told you this, but if you're not sure whether you need a data sharing plan for a given application, the first thing you should do is check the notice of funding opportunity that you'll be applying to. If you don't see it there, generally speaking, there are only two types of LC applications that don't need a data sharing plan, applications where no scientific data are being collected, and applications for training grants, T32s, and fellowship series or F grants do not need a data sharing plan. On the other hand, if you are collecting scientific data, then it is your thing. You need a plan. And if you're not sure, we'll put the link in again to the complete list of activity codes that fall under the data sharing policy. So two important points of emphasis about the plan. First, the data management and sharing plan and budget will not affect the impact score of your grant. The plan itself is not seen by reviewers. It's not even included in their packet of materials. The budget for data sharing will be available to review, but it's not going to be score driving. Second, a plan is needed even if you're not sharing your data. In fact, it's probably even more important if you have data that you're not sharing as you need to account for your decisions to share or not share in the plan. Before you get started writing your plan, we thought about three things that might help you prepare. First is to check the funding announcement. I'll sound like a broken record on this. Check the funding announcement you're applying to to see if there are requirements about what repository to use, any novel sharing or budget requirements. You might also consider speaking with your IRB, with communities that might be affected by your data sharing, and of course with program officers is needed. And the third thing to do is to decide on which repository you'll be using to share data. One of the questions we're hearing really frequently is where applicants should share their data. If the funding opportunity doesn't tell you, doesn't specify something, then NIH is encouraging researchers to select the repository that's most appropriate for their data type and discipline. So if you want your data to go into a repository where other social science data are, if that's the kind of work you're doing, then that is acceptable. There are several ideas for repositories that exist in case you haven't sort of heard of or thought of repositories before. And we'll put links into the chat for NIH's resource on repositories for sharing scientific data, which has a listing of sort of general repositories except all kinds of data. The nature journals have a data repository guidance, and there's a global registry of research data repositories that might be useful. You can use more than one repository for a given study. However, whenever it's possible, we really encourage you to use a single location for all of the data and documentation you'll be sharing. When selecting a repository, you'll want to think about factors including the accessibility and stability of the repository, the privacy and security it offers, and curation services that it might provide to you. A full list of things to look for sort of characteristics of a good repository is being linked in the chat. NIH's preference is that researchers at least first consider putting data in the repository supported by an institute. The Anvil is NHGRI's primary repository. It supports sharing a variety of data types. It has controlled access and lots of other privacy features. You can propose an alternative to Anvil and justify your choice. And there are other options for sharing small data sets, including attaching the data as an appendix to a publication. However, that may not maximize access to the data, and you may be asked to revise such a plan. As you decide upon a repository or repositories, we recommend looking at the detailed information that the repository provides off and online about depositing, protecting, and sharing data there. Platforms really vary across many important features which might, excuse me, influence your choice. Also, a repository's recommendations and requirements and policies can provide you with lots of really good specific information that can be helpful when you're writing your data sharing plan. So a bit about writing the plan. NIH is asking people to strive for plans two pages in length, do your best. NIH is also providing a blank template plan, which is optional to use. You don't have to use the template, but it is very helpful to look at as it lays out a structure. It reminds you of the details that you need to include. Several institutes, including NHGRI, have posted sample plans online, and if you haven't looked at that lately, we literally today this morning posted an example of a plan that's sharing qualitative and quantitative data. It just went up this morning, so we encourage you to look at it, and hopefully it'll be helpful. But there are plans from several different institutes. These sample plans do not cover all possible data types and sharing issues. So do not copy and paste the sample plan. Consider the specifics of your application and focus on the data that you'll be generating and the circumstances under which they can be shared. There's another online resource called DMP Tool, which outlines the NIH requirements. It also shows all kinds of other funders sharing plan requirements, so that's kind of cool. But it outlines NIH's requirements and does provide some template text specific to NIH that may be useful as you write your plan. And then a few bits of specific advice. First, do not, do not, please do not include any hyperlinks in your data sharing and management plan. Get rid of them before you submit. If there are hyperlinks in your plan, it can cause your entire grant to be rejected before it's reviewed, which sounds crazy, but it's true, so make sure there are no hyperlinks. Secondly, most LC grants aren't going to be collecting genomic data, but if you are, and it's fine if you are, then the genomic data sharing plan, which is a different kind of data sharing plan, which we're not going to talk too much about, it gets embedded within this larger data management and sharing plan. And finally, you may remember resource sharing plans. Those still may be required in addition to a data sharing and management plan, and you should check your notice of funding opportunity there again to see if a resource sharing plan is required. These are now just being used to describe the dissemination of other grant products, excuse me, besides data like publications, websites or research tools. We thought it would be helpful to go through the six main elements of the data management and sharing plan. And if you want, if it's helpful, we'll put the template, link to the template, again in the chat, the blank template, if you want, you can use it to follow along, but obviously you don't need to. So there are six main elements. The first is data types, where you briefly describe the scientific data that is going to be managed and shared, and that's got three sort of subsections to it, excuse me. You need to summarize the types and amount of data you expect to generate, then talk about which data will be preserved and shared and your rationale based on ethical, legal and technical factors. Then the third thing is to talk about the metadata and associated documentation that you'll be sharing. And we know metadata might be a strange term for some of you. Metadata just includes sort of data collection instruments, interview guides, survey data dictionaries, qualitative coding schemes, analysis methods, any other important information or contextual details that people would need in order to use the data well. Second are related tools, software and code, although we think code might not be required, but here you just want to list any specialized tools or software that are needed to access or analyze the shared data. For example, if you were doing qualitative data analysis and used in vivo, you might want to say you might need in vivo or a different program to read the transcript coding files that I'm sharing. You don't have to supply access to in vivo, but you can tell people how to access the software that might be needed and whether it's open source or commercial. Third are standards, and these are not sort of best practices for sharing or ethical standards for sharing. These are literally just the file formats that you're going to use to share data in order to maximize use and interoperability. And I think many of us, I didn't know, many of us may not know that there are standards for sharing the kinds of data that LC folks tend to generate. If you're not sure, I really recommend looking again at the repositories that you're considering using. And I think as time goes on, we'll be accumulating standards that people are using and trying to keep track of them to help people out. Number four, data preservation, access and timelines. This includes information on repositories where data will be stored and shared for each data type, how data will be findable and associated with your study. Some repositories will give you a unique identifier that you can use to sort of cite your data in publications and abstracts. And then you want to talk about when data will be made available and how long it'll be available for. Section five is really important. I mean, they're all important, but section five, you're talking about the access, distribution, and reuse considerations. And here, you're going to talk about what factors are going to affect or limit subsequent access, distribution, or reuse of your data. How are you maximizing data sharing? You want to talk about the privacy and confidentiality provisions that you're using in order to protect participants so that your data can be shared. Then you need to talk about any limitations on sharing and justification for those limitations. Finally, section six, oversight of data management and sharing. You want to talk here about who on the grant is responsible for monitoring compliance and how your applicant organization is going to help you manage compliance with the plan. And this is a good place to look at the sample plans where there is some sort of standard text that might be the basis of what you put in these sections. Another question we're hearing is how much detail do I need to provide in the plan? It's difficult to say for any given application, things are going to vary. And I would encourage you to check the NIH sample plans, for examples. But the things you need to do are make sure that you're addressing each data type that you're treating differently. So if you've got some data that you're not sharing at all and other data that you're sharing widely, then throughout the plan, you want to talk about how you're treating each of those and why. Be clear, excuse me, which data will and won't be shared and provide a clear rationale for anything that's not being shared or that has limited sharing? Just quickly about submitting the plan, attaching it to your grant. You may be working with your administrative or scientific officer on this, and it may be that your administrative officer takes care of this, but it's good to be aware of so that if they have questions on this PHPS 398 form, you can see this is where you attach all the different parts of your grant before it goes to get submitted. And there's a new little field. Other plans, very nice, other plans, has been added to the form, and this is where your data management and sharing plan gets attached. You should also put in a budget for data sharing activities. You can request costs for data management and sharing that include the cost of curating and de-identifying data, of formatting to the standards that you're going to be using, costs of transmitting or storing the data. If your repository has fees for depositing data, you can budget for those for creating supporting documentation and metadata, and you may be able to charge or budget for some local infrastructure. There are things that are not allowable. You can't charge us, you can't not charge us. You can't ask for costs for infrastructure that are covered by institutional overhead costs. Don't try and double cost the routine conduct of research and don't double charge direct and indirect costs. And as you're submitting the budget for applications that require a detailed budget, you'll need to add a budget line called data management and sharing costs. And on this form, this R&R budget form, which may or may not be familiar to you, you show the amount requested under the in that funds requested column. If you're not planning, if you don't think you need any budget, any costs for data sharing, put a zero in that funds requested box. Don't leave it blank. And for modular grants, modular budgets, all you need to do, oh and sorry, back to detail budgets, details just need to be outlined in the budget justification just like you do with other budget items. And for modular grants, just elaborate in the narrative justification what costs you've got, and if no costs will be incurred, still just include a line item and enter zero for the amount that you're requesting. Your plan has to be approved by NIH prior to your receiving an award. So your plan's going to go, if you're being considered for funding, your plan will undergo an internal review by program officers and our grant management officers. And applicants will be notified by program directors and expected to communicate with us to revise the plan as needed until we come to an agreement. We also realize that plans may need to be updated or revised over the course of a project for a variety of reasons. For example, your community may change the permissions in sharing, or you might add a data set from a supplement. We recognize so and those plans, new plans and revisions will have to be agreed upon as well. The plan that gets approved at the time of award becomes a term and condition of the grant award. Grantees need to report their progress in following the plan in their annual reports, which are called RPPRs, and NIH will review your compliance with the plan annually. Just briefly about a couple of notes about communication. Contact us well in advance of your application to discuss any questions you've got. Make sure your administrative officer at your institution knows how to upload the plan and deal with the budget. And we will be contacting you after programmatic review of your grant, of your data sharing plan, sorry, if we're considering the grant for funding. But of course, you are always, always, always welcome to contact us. We started, as you've seen in the chat, Renee's been putting all kinds of amazing online resources. There are a ton of them, excuse me, that NIH has developed and NHRI has developed. Excuse me again. And a couple that that LC has developed. This is just a quick list of them. You don't need to really read these or look at them. But we're putting these again in the chat and we'll post these all to a file that you can access online. Just a couple to point out that LC created a few FAQs. And I know Elena pointed these out as well, but there's a policy on management and sharing of American Indian and Alaska Native participant data and a notice on protecting privacy while sharing. And really, of course, we hope to be a resource for you as as we all work on this together. So please feel free to contact me, Nicole and Renee as we go along. And we will work out questions and answers together. That's all I've got. And so we'd like to open it up to questions and answers. Excuse me. And just go ahead and put them in the chat and we will address them as a team and I'll stop sharing here. Hey, thank you. And when Dave says put them in the chat, he means put them in the Q&A. Oh, gosh. Yes, I do. Put them in there. I'm Nicole. I'm helping to do moderation today of the Q&A. We've had some questions come in. Renee and I have been trying to answer them live. So for those who have not put in a question, you can also go to the Q&A and see if maybe someone else has already asked something that you were wondering about. One that we had not, I was just about to start typing that I haven't gone to yet, is for qualitative data, is it necessary to share coded transcripts or is sharing just the coded transcripts enough? So we were just going back and forth a little bit in a separate channel on this one. Sharing the coded transcripts would probably allow the data to be more easily replicated and make the data more useful to others. However, we don't have any specific requirements as to what kind of sharing is needed. So when you're creating that data management sharing plan, that's where you're going to lay out what you're sharing, how you're sharing it, and importantly, the justification why. So if there is some reason where you don't think sharing coded transcripts is going to work, that's where you're going to describe that process, describe why you're making particular choices. And as Dave said, that's really where you're also going to have this back and forth with your program officer. So if your grant is going to be funded, then your program officer will review your data management sharing plan prior to funding. If anything is needed, if you forgot something, if something is unclear, you know, that's when you'll have that kind of back and forth discussion. Does anyone want to add on to that? I think you got it, Nicole. Thank you. Yep. Okay. A specific question about the upload of the plan, that it would be helpful to label the document as DMS plan instead of other documents. I believe there is a specific title that should be used when you're uploading that document. Does someone want to know what I'm referring to? I'm not sure if the person is referring to the other plans line of, I think that might be what's being referred to. And unfortunately, what is put on that form is well beyond and above all of our pay grades. That decision was made somewhere else. And I guess you, in case we were requiring other plans, that's where you would put them as well. Yeah. And I think that probably will be changed in the future. Changing the NIH application submission forms is a very arduous process. And for right now, the other plans section is where that where that goes. Basically, I'm sure down the line, the forms will be revised such that there is a specific section for data management sharing plans as there are from any other things. But submitting NIH grants is so involved that it takes a while to kind of update those forms. But we do appreciate the suggestion and we'll pass it along. I'll just say while we're waiting for other questions to come in, that I did post to the chat a Word file that you should be able to download to your computer with a list of questions that appear on some of which appear on the NIH FAQ list. But some unique questions that have come up in prior conversations about LC in particular are also in that document. So that is available for your review as well. And we will post that and send out a link to it after the webinar. And for the first question, the very first one that was asked around de-identification standards available for qualitative data, we know this is something that people are really wondering a lot about. We do recognize that this is an evolving space. And that it will also vary depending on what kind of data types you're collecting. So we included some resources that you could consider depending on whether or not they work for you or your study. And this is something we will plan to watch, update as needed. And keep in mind that part of why there is no particular NIH or NHGRI standard is because locking you into a specific standard wouldn't serve all of your studies, right? So having one specific standard is not always going to really benefit the science. Bob Cooke-Degan also posted a link to the open science framework where folks can deposit data such as research spreadsheets, anonymized transcript, interview scripts, survey instruments. And they will designate a permanent DOI, which is stable and easier to use than journal supplementary material. So that's something folks want to look at. Elena, would you like to take this next one? Is raw genomic sequence data considered identifiable or deidentifiable? What are the nuances? Maybe just a briefer answer to that one since it's not the core focus of this particular webinar or maybe some reference to other resources there? Sure. I'll do my best to be brief because there are nuances as the questioner points out. So NIH handles large-scale genomic data in a particular way according to the NIH genomic data sharing policy. So when it comes to human data, it generally needs to be deidentified per the regulatory standards whenever it is submitted to an NIH database. Because of the known small risk to re-identification, it's generally shared through a controlled access mechanism. And also one other thing to note is that, again, because of the small risk to re-identification, especially when it is paired with phenotypic information, studies generating large-scale genomic data are issued certificates of confidentiality by the NIH. So it kind of depends at what angle you're looking at the question, but you should deidentify large-scale human genomic data before updating it or uploading it to a repository. And that's a requirement of the GDS policy. Thank you, Elena. We do likely plan to post this recording unless the quality is very bad or something, or if we decide we sound ridiculous, maybe we won't. But we probably will post this. And we do have the new sample plan available on the NIH sharing page. And we also have a set of FAQs, which Renee posted above at 1.34 p.m. into the chat. Those are specific to LC research, so they hopefully will answer at least a few of your questions. Please put in additional, oh, here's another question right now. What metadata should be included with interview data and survey data? I can take a stab at that. I mean, I think that you, for interview data, you would ideally want to share your interview guide if you can, if it's shareable. You would want to talk about, you would probably want to show your codebook, how you were coding transcripts, and maybe even show examples of coding so that someone else could look and see what you've done. And then maybe a document, we know that a lot of people are very concerned about people not understanding the context in which interviews were conducted and data collected. So you might want to say something about that to help ensure that your data aren't misinterpreted. For surveys, similar kinds of things. If you can provide the survey instrument, we know sometimes you're using proprietary questions, and of course you probably can't provide those, but you can at least say what they are. And then a codebook for your data, how you may have recoded anything that would help with analysis. If you used R and you want to include the programs that you wrote in order to analyze your data, that would be helpful. I don't think that's required. Basically anything that would help people use your data well and use it correctly. I'm sure I have not thought of some things, but those are some things. And the sample plan that we've posted that does include some examples of metadata since it was for interview and survey. Again, it's just a sample. It's not meant to be a template or something you have to follow explicitly, but just to kind of spark some ideas to kind of give a feel for how a term like metadata would apply to LC research. And I'll state just, as program officers, these are things that you can discuss with us on pre-application calls. We always have a strong preference for talking to applicants prior to submission. So once you are maybe a few months away from submitting, reach out to me, David Renee, and if you're not sure which one, either email us all three or just pick one, and we'd be happy to discuss specific aims. And if you have questions about the data management sharing plan, that's a good time to ask as well too, because then we'll be able to talk in the context of your specific project, which is a little bit easier than trying to kind of answer all of these questions at a very high level. So feel free to always reach out and ask questions so that you can put in your strongest application. We really hope this was helpful and that, oh, there's another question. Sorry, I'll stop talking. Let's look at it. Okay. And I'm not sure. I think perhaps attendees cannot view the questions put in my others, so that's why I'm reading all of them aloud. So I think maybe only panelists can read them. I appreciate there are a range of repository options for sharing qualitative data from empirical LC research, both generalist repositories and those geared towards social science. It would be great to have the LC community coalesce around perhaps a few of these options to enhance discoverability. This may not come from NHGRI, but our thoughts on how this could move forward, perhaps a way to share within the LC community as we start to write and enact these plans. So I think that is a really interesting idea. I think having maybe not one, but maybe a few kind of places that worked well for LC data would be really useful. We are trying not to mandate anything right now because there's no perfect fit and it will vary a lot. LC studies themselves vary a lot in terms of what data are created and what kinds of resources or options they might need. I think as program staff, we could think about whether sharing sample plans in the future might be useful. We generally don't share any grant or grant materials until the project is no longer active, so all of our sample grants are no longer active, but people can always share voluntarily within the community and I think that would be something that would be valuable. There's another question related to Anvil. Did you say that Anvil is the primary repository that LC researchers should use if it is a different repository is used should that choice be justified? Elena, do you want to describe the role of Anvil or Dave, I know you I think you're typing maybe. Yeah, so I mean we I'll just say really briefly we're asking that NHGRI researchers consider putting data in Anvil since it's our primary repository. We realize and NIH realizes that there may be repositories that are better fits for your data and we just ask that you explain your choice. Elena, anything to add there? No, that was perfect. And Renee reminded me of another point that could be relevant to the question about the LC community coalescing around a few possible repositories or maybe having some kind of agreement or understanding the Center for LC Resources and Analysis. The concept for the renewal of that center was recently approved at February 2023 Council and the concept for the renewal includes having the Center for LC Resources and Analysis or CIRA serve as a means for pointing to where LC data is shared so that LC researchers it would help them discover where data is shared so maybe you would see an interview guide posted by CIRA and then you could see where the resulting data is housed and maybe related publications. Now that will be in the renewal period so that'll be you know a little bit down the road but that is a possible way that that could also be done. A suggestion to share the chat after the meeting we've had a number of folks have suggested that so we will find a way to pull all those links into some kind of useful resource and share that. Team where do we think we're going to share that on the LC webpage or maybe on the webpage for this webinar? I think for the immediate future maybe the webpage for this webinar will be the best place to put all the resources from this webinar and then maybe they'll be transferred over to a more permanent location. I'll also point out that within Zoom if you're joined via computer in the chat box in the upper right corner there are three dots next to the little smiley face and if you click on that you'll get a save chat option which will allow you to save the entire chat to a folder on your computer. When you click save chat it'll provide you a link to the folder where it's saved so if you have if you see that I think it's called an ellipse the little three dots there if you see that in your version of Zoom you can use that to save the chat but again we will do that and make the chat links available on the webinar webpage and as Nicole mentioned we will also add a recording as long as the quality is good and can be viewed. And we have the emails for everyone who registered so we could also perhaps send that as a follow-up and I think we could also probably pull together a list of the slides the links the recording and send that out to our LC listserv as well which is probably how many of you found out about this webinar so I think we can use a couple different strategies to try and get this information out and of course you know if you receive this information feel free to forward to other folks in the LC community LC trainees colleagues whoever you think could use it we'd be happy to have it shared. We have another question I'm thinking through consent for data sharing of qualitative data and I'm struggling with most of my research being exempt from documentation of consent versus the value of documented consent for or against data sharing as well as the choice itself of course. Any thoughts? I mean I do think that if you don't traditionally share data and it's something you're going to have to do then you might need to rethink your consent process that seems reasonable. To me I think that's what the question is asking. Yeah I'm wondering about a research study that's exempt from documentation of consent. I think from an ethical perspective and from thinking about how to fully inform the participants of a research study about what they're engaging in that obtaining and documenting some informed consent process can be valuable. This might also be something that you would talk about with your institution's IRB but I mean as part of sort of informing and perhaps even educating participants of a research study about the process informed consent can be one way of doing that. So I'm not sure if that helps answer the question. And for our anonymous attendee who put in that question if we're not really understanding feel free to put something in again and you know we might just have misunderstood what you were asking. We have about seven minutes left if there are other questions we could probably take a few more. I will say I really appreciate how many of you have turned out today. I think at the peak we had over 40 attendees not counting ourselves which is really great. We weren't sure if anyone would want to come listen to this webinar so we're excited that you all took time out of your busy schedules to join us today. I'll also just add to the chat NHGRI has a resource on informed consent which may have some helpful information but please do as Nicole mentioned reach out to us if you have additional questions. One additional question. Justin we think there's no more questions. One pops in. Yeah this is another one about Anvil. I mean to date I think it has been a repository primarily for genomic data but there is a very good sort of infrastructure around it as was mentioned previously. You can have controlled access. There's a committee established that reviews requests for use of data so it has a number of features that can be helpful but yes to date it has not been a primary place where LC resources and data have been placed but again NHGRI encourages you to consider it but if you think your data is better placed elsewhere that is also an option that you can just speak to briefly in your plan. I work on the LC project from a policy perspective and we have been communicating with them for a while about preparing for submissions of datasets particularly others that require control to access to Anvil that don't have genomic data as a feature so. And this may be a time to point out also you know there are LC studies that collect various types of data within one project so you could have an LC project that's collecting genomic data focus group interviews a national survey all as part of one study so you can propose in your plan to sort of deposit the different data types in different places as appropriate that's also an option for you. I saw some virtual applause pop up a second or two ago so we appreciate your being here and should we give it 30 seconds for another question to pop up oh and we've got some hearts that's nice thank you we'll wait another 30 seconds. We hope this was helpful and answered some questions we know there will be more and we really consider ourselves part of a team with you working through this so hopefully it demystified a few things and took some of the fear out of it. Okay so resources for working with students teaching people about the requirements would be helpful okay great thank you for that suggestion all right well then thanks everyone have a great afternoon we will close the webinar thank you so much for joining feel feel free to reach out to any of us with additional questions. Thank you. Be well.