 You know we're talking about use cases in generalist repositories and obtaining your feedback will be hosted by myself Rebecca Lee and Kristi Holmes who are the co-chairs of this subcommittee and we have a number of representatives here who'll be talking about use cases. Next slide please. I'll be giving a very short rundown of our agenda today we'll be giving a I'll be giving a very short introduction of Gray and our objectives. Kristi will be providing a spin through the actual community Zinodo where the use cases live and then we'll have a detailed walkthrough of the first set of use cases that we've been walking we've been working through this past year that's presented by the repositories themselves. And then a dedicated feedback section so as you listen would you please um think about your feedback as we present today. Some things we'll be asking you about are are these the right use cases? What are we missing and could they be presented differently in terms of the format? What what else could we be doing in the next year that would be useful to you and your team? Next please. So you know I'll talk a little bit about what Gray is. Many of you are familiar with this initiative as we've been putting on a number of webinars. Gray stands for the generalist repository ecosystem initiative that is funded by NIH. Our focus is really to develop collaborative approaches for data management and data sharing through funding of all of our generalist repository. We've been working together and and these are shown here and our presenters today will be myself, Christy from Zenodo, Eric Olson from OSF, Sarah, Lippincott from Dryad, Lisa Curtin from Figshare. We have Tracy and Luca from Mendeley and Julian from Harvard Dataverse. I think it'll be very informative for all of you and we're looking forward to a really great agenda. Next please. So in terms of our gray objectives there are a number of objectives that Gray has been working on today. Today we'll be really focused on what the use cases working group has been working on which is developing this catalog of use cases and diving deeper into how the repositories can be used and some of their use cases from a role's perspective. Next I'll turn it over to Christy who will walk us through Zenodo and where these use cases currently live. Christy? Okay great thanks Rebecca. So we're going to be housing all of our materials in Zenodo. We've developed a Zenodo community for all of the research outputs so not just these use cases but also a number of other key project activities and products that have been developed as part of this gray initiative things related to metadata, webinar slides and recordings and things like that. So it really does end up being a one-stop shop for you to be able to find any kind of material. So just to walk you through what that looks like if you go to Zenodo.org and click on the communities tab in the upper menu bar which you can see here with the big orange arrow you will then go to a communities page. These communities pages are really ways of bringing together different materials under a common theme or a common group and so we've done one for Zenodo here. I mean for a generalist repository ecosystem initiative here. If you are on that communities page and you type in generalist repository ecosystem initiative you can search for the community and what you'll get is this page on the left of your screen where you see it's listed here. This can be a great way to look for particular topics as well but if you click view then you get to the page that's shown on the right where you can see all of our project materials and then you can dive in. So for instance if you'd like to see the use cases for each of the repository platforms that will be discussed today you can search use cases and you'll bring them right up. So speaking of use cases we have four specific use cases that we'll be talking about today. Two of them are focused on researchers so the first of these is as an NIH researcher I want to select repository to share my data and we'll be hearing about that from both the open science framework as well as the dryad teams. We also think about researchers in the context of data reuse and so our second use case is as a researcher I want to find research data of interest so that I can validate my findings reuse data and build on work in my discipline and we'll get to hear about that from Figshare. Our third use case focuses on an institutional perspective. Institutions are often interested in reporting and understanding the work especially around data set generation so I want to report on all data sets from my institution so I can ensure compliance of research data sharing and management plan commitments and we'll be able to hear about that from our colleagues at Mendeley data and then finally putting this in the context of the funder perspective. I'm a funder from a specific NIH institute or a general funder and I want to find data sets we have funded so I can report on compliance with policies and track the impact of research finding research funding and usage of data and so our colleagues from Dataverse Julian will be sharing that so without further ado I am going to now turn it over to Eric from open science framework who will be sharing the next uh our first use case. All right perfect thank you Chrissy so um I'm here to tell you a little bit about the OSF and how we enable researchers depositing and sharing their their data so the OSF has a lot of similarities with some of the other tools you're going to see here today and some of the features it offers the key difference or the key value for OSF as it wants to offer tools across the entire research life cycle rather than any one particular phase of your work as you're managing your your studies so planning and collaborating with your peers and sharing data have features for all of that in the OSF can move forward and in each of those phases the slide can progress it might be slow um but in each of those phases you have needs for depositing data so we make that really easy to deposit any kind of file types into the OSF take you a couple of seconds to uh just drag and drop your files into any of those workflows across your your research life cycle and then when you do that if we move ahead applied again um oh we also enable you to connect all of the storage providers or many of the storage providers that you use so that you don't if you're collaborating with your peers they're on google drive and you're using dataverse that's completely fine you don't actually have to be downloading and uploading and having different versions floating around um on the OSF using those same interfaces that we just showed you you can actually connect all of your other storage providers to just that one workflow that one interface and you can all all of your collaborators no matter which institutions they're part of or or where they're based they can all interact with those files with you using those same interfaces so a lot of our our friends here in gray are integrated with the OSS and you can use them and the OSF in in tandem um so we can move forward again and it's a little hard to read here but um the metadata that we enable for specific files as well as the containers that you can build and define and and put your data into um all have extensive metadata so there's examples of both here of metadata specifically for this file of this data set here um as well as the container that was that it's built within or that it's been added to so lots of other data might be in that same uh container and you can define each of those separately so there's metadata there so you can tell your readers who what where and when um the the details of this particular data set and then if you move forward you can see on uh one of the containers there and we have identifiers for all of those key uh aspects the DOIs for the objects, funder IDs for the funders, um organ IDs for contributors, and institutional ROAR IDs for the affiliated institutions which is going to come back come back around in our next couple of talks here um but they can also you have data that's taking place at all the different parts of your project and some of it is code some of it's supplements some of its data sets you connect all of those to your research objects here as well and define those relationships so that whether they start on a data set that you have on Sonodo or if you start on this pre-registration container here or on my you know your research institutions collected aggregated data you can always get back to this package of identifiers and information about your data that you've uploaded and took a few minutes to define um so with that I will pass it back to you Christy. Okay wonderful thank you so much Eric and now I'd like to welcome Sarah Lippincott from Dryad to share Dryad's perspective of that uh researcher use case. Great uh thanks Christy um so Dryad the Dryad use case takes a perspective of a researcher who has determined that Dryad is is a good fit for their data that's one of the first things that that I think of when I think about this use case of a researcher who wants to select a repository to share data um there are a lot of of questions that go into determining what what makes a repository a good fit for an individual researcher's data so I think it's important for researchers to be thinking about and and those who work with researchers to be helping to helping them to consider um what repository is going to be a good fit so in among the gray repositories we all share a set of common features and we satisfy fundamental requirements like providing a DOI or a persistent identifier for the data collecting basic metadata to make it discoverable and reusable but each repository will have somewhat different requirements benefits and limitations so um I uh I'm using the example of Dryad here to kind of work through some of these questions to determine what is the right fit for a data set um thinking about questions like what disciplines are represented in my data um so is this repository um does this repository have data that uh that is uh or is my data appropriate for a generalist repository or might it be appropriate for a specialist repository um and does my funder specify a particular repository or does a particular repository make sense uh for my data because of its content subject matter or or file type otherwise a lot of the gray repositories are going to be a great fit where that disciplinary home is not available um the the other questions I to consider are what context is needed to reproduce replicate or analyze my data or research findings this might include um things like software or code published articles other data sets that might have been published in a specialist repository um or another data repository um as um you know as Eric mentioned there's there's more than just data sets that go into a research project so uh our repository is the repository uh able to accommodate other types of research outputs that can complement my data set and make it more reusable um researchers need to be thinking about the ethical and legal implications uh or or limitations on sharing their data so can the dry out the can the data be shared openly and without restriction on reuse or does it need some sort of embargo or managed access in the case of dry out all of our data is published under a creative commons zero license so it's not an appropriate fit for data containing personally identifiable information for or other sensitive content other gray repositories uh may be able to to accommodate data um like that um and finally um could my data and metadata benefit from quality control to ensure that I'm following best practices for discovery and reuse if that kind of quality control isn't something that researchers have access to within their organization dry repository like dry out can offer that quality control or curation support for researchers next slide please um so once uh once a researcher has determined that dry out is the right fit that really I think is the is is one of the harder parts of the process we try to make it really easy from that point for a researcher to log into dry out using their orchid ID upload their data um and receive a doi um enter metadata that makes their data uh discoverable and reusable submit it for our curators to check uh to ensure that the data are appropriate and ready for sharing and reuse and then uh uh be able to cite and promote their data um in uh in perpetuity um and that's that's all for me okay great thank you so much Sarah and so now we're going to um take a shift and think a little bit about looking for research data and so I'm delighted to welcome Lisa from Big Share who will be talking about that third use case Lisa take it away thanks Christy and yeah hi everybody um I'm happy to share how Fig Share meets the needs of this specific use case which is a researcher that's looking to find data for reuse to validate their own findings or otherwise build on existing work um so I wanted to take you through a couple key features we have for finding data at Fig Share uh first up is our browsing feature here uh when you visit figshare.com you'll encounter one of the or you'll encounter this page of featured categories these red boxes here and if you select one of them you'll come to a page of subcategories where you can select as many of these as you'd like to kind of narrow what you're browsing through and effectively find research related to your subject so next slide please and then moving on I wanted to share a bit more about our search functionality so if you want to conduct a search on Fig Share we have lots of different filters to help you narrow what you're looking for using these various facets here um you know the categories from the previous page are one of our filters as our a variety of other things content type item type um license type and um we do also um I want to point out these two outline in orange the funder and publication year uh those are the most recent additions to our search functionality and they're a direct result of our work to improve our support for these great use cases uh so continuing to build out the robustness of these search functions is key um to not just facilitating finding reusable data as in this use case but to objectives of most of the other use cases we're talking about today as well um next slide please and finally I just wanted to point out how fig share foster sharing and reuse of data once the researcher has found something they're interested in um so this is what an item looks like in fig share and of course you can visit fig share to have a clearer look at this from the full page um but if you know once you found something you want to use or share um we've included features on the item pages that streamline that process to access the data and share it um so for a lot of file types you can preview it right in the fig share platform you decide right there if it's something you even want to download to reuse um and if you do want to download it you're able to download it directly from this page or if you're downloading you know multiple files or want metadata collected for whatever reason we also allow um or offer the option to download via our api um and then we also have a citation generator you know a researcher can generate these whatever form they need and they can easily copy the doi so they're able to reference things accurately um and then if a user has a fig share account they can save any of this data that they find that they might you know want to come back to later to a personal collection for quick reference and then of course we have options for sharing on social media email and also for embedding this data or this item into whatever other page you might need um so all of these features support the reuse of data and help researchers find what they're looking for more efficiently um if you want to delve deeper into finding data on fig share and also into our advanced search functions which i'm not going to go into here um i will share some links to our most relevant health pages in the chat for those um and i just uh i want to say i think that's about it for me but fig share we're committed to providing this platform for finding sharing and reusing data and we're you know looking forward to continuing this collaboration with all the other gray repositories to support all of these use cases um and that's it for me so thank you christie okay great thanks so much lisa um now we're going to look at our third use case which is from the institutional perspective and reporting so with that i'd like to welcome tracy to the webinar who will be sharing um a perspective from mentally data excellent thank you so much christie so yes my name is tracy snoden representing mentally data today and um our use case we want to focus um a little bit more on the institution of course mentally data is a free repository for researchers um but in this particular use case we're looking at the institution itself um and their desire to be able to report on all data sets from their institution and this is for compliance purposes for data sharing purposes for their data management plan um and in order to honor their commitments to their researchers i do want to mention um that with mentally data we also have a funder filter search facet um that can be leveraged to track one step further to understand that um and mentally data connects to digital commons data as well as data monitor which i'm going to um share a little bit more about and those are offerings that can support specifically institutional collaboration at a higher level um as well as particularly the search and report function that we're focused on today so next slide please um okay so for the use case today i want to introduce a specific persona so we're going to focus at our friends at the university of kentucky and we're going to walk through um specifically three um three steps three key steps or three key functions um by which you can leverage mentally data in order to find your institutional data sets and report on them so the first thing we're going to look at is the search query itself and and how that functions secondly we're going to look at how you can manipulate that data using filter tools and finally i'll talk a little bit more about data monitor and the ability to to find that institutional data um through apis um that can can data scrape and and pull that information into this the single single search bar um final slide please okay so um following those steps if we look at in the upper left hand corner at the big red one um under in the search box um we would type in an all caps institution followed by um open parentheses quotation and then the university of your choice in this case university of kentucky um close uh quotation and parentheses and you'll see um as you look down at number um two that um you have about 3554 results that come up for university of kentucky um those can be sorted by most relevant they can be sorted by oldest newest newest to oldest um and then um with number two we can filter those results in a few different ways um so first we can filter those by publishing date we have a a slide bar here where we can um drag to constrain those date ranges or we can manually type those date ranges in um and we can also um filter those by data types a variety of different data types and then finally as I mentioned with data monitor so as it turns out roughly 90 percent of institutional data sets are not within an institution's repository so by coming to mentally data or another repository within the gray initiative that has this kind of search functionality you can um go across different repositories and this search query will pull that information in and that those are the results that are powered by data monitor as you see um and with number three in the upper right hand corner of the slide so if anyone has any questions about this functionality I'd be happy to respond to those in the chat box I also have other representatives of mentally data um luca belletti is also on the call today and we'd be happy to answer questions as well okay great thank you so much Tracy and uh now we're ready for our final use case we'll be welcoming julian from dataverse to um share the perspective from dataverse about the funder um so as a funder looking to find data sets um to understand how those are used um applied and compliance so julian please take it away thanks christy um so yeah uh I think a lot has been said about searching um in different generous repositories and the fact that you know a search works um there are things about searching that work similar across the repositories um so I won't go into detail too much detail about different ways of searching imagine all kinds of of ways but for this use case um we imagined what a fictional um administrator at at the at an NIH office would do if they needed to find um data sets deposited in Harvard Dataverse that were the outputs of research they had funded and um so these slides show you know how you know I think it's it's likely that someone might try simply you know pasting the number of the grant um in Harvard Dataverse repositories the further for the first slide it shows how uh you know that that funder administrator might have seen you know a research article associated with with the research that they funded and then in the in the data availability statement they see a citation of a data set published in Harvard Dataverse so they follow that URL to Harvard Dataverse itself and decide to search um to see what other data sets are published might be published in the repository that are associated with that grant so they um they it's really small I tried to make it bigger um but you can see uh they pasted the this the the funding ID of that grant and the search results show 10 data sets um and then from there they can use facets uh to narrow that search more um and uh you know and then uh you know of course they can use advanced search which I won't won't get into here either um if they um if they you know as they're as they're looking at how data is described in the repository um they might imagine using different fields um to search uh for the before it's published in the repository um so we hope that the this use case is a um is pretty simple but you know as we continue you know building on the on this use case and learning from from funders who need to um track uh compliance with with the policies um we can uh use that information to continue to um help funders uh find that you know the research that they're funding more easily and and how it's being used okay that's and that's it for me thank you Chrissy thank you so much Julian I appreciate that um so now um we before we think about next steps or begin to gather feedback we want to provide an opportunity for you to ask questions about anything that you have seen presented so far so I do see that there's a question in the chat from Amanda French um about um the Mendeley data monitor um and uh uh Randy from Mendeley has answered that question also in the chat but I want to welcome others to put things um in the chat in the question in a uh answer section okay we have a question from Isaac um from your perspectives is there a trade-off between publishing data sets alongside other research outputs via special versus uh specializing in publishing only data when it comes to discovery for instance and he's had an easier time locating relevant data sets on repositories that are prior prioritizing just data itself so we're um Eric is typing in an answer for that that's a great question because it kind of speaks to reproducibility too right so I think trade-off is the perfect way to describe that um and um so thank you Eric for typing an answer in there um and then Rebecca uh writes what domain specific repositories does data monitor search and are they only in the NIH research area so I'll ask uh colleagues from Mendeley to weigh in on that please and feel free to unmute yourselves if um sometimes it's easier just to answer rather than type in an answer so hi everyone I'm going to answer the question about the um disability specific repository in data monitor unfortunately I'm not a specialist on data monitor and for uh Mendeley data I know that over 2 000 repositories have been indexed and added to the data monitors data monitor corpus I don't have details about which specific institutional sorry um discipline specific ones but considering the number of repositories that are being uh indexed for data monitor I would imagine that all the major ones all the most important ones should be there and I'm going to look uh for the information in the meantime while other people answer questions and I'll post something in the chat if I find something I would thank you Luca I appreciate that and we do have a follow-up question about the Kentucky example Melissa um asks um for more clarity about does the platform unify multiple presentations of the name I know um all of our librarian friends that are on this call are seeing this and wondering about the different ways that the same institution is presented um you know which I think is a big reason why a lot of us are fans of Roar so um it could you clarify a little bit about how the query is parsed when you type in um an institution name does it pull in everything that's part of that organization Luca or Randy may I may I defer that question to either of you and I think yeah because I think they're Amanda had had asked a previous question and Melissa's question supports that okay so um thank you Tracy um circle back uh Randy says no at this time the search will require you to include each rendition of the search text um so thank you very much for that clarity I think you know speaking uh just um for myself here if you know what you need to search it's much easier just to be able to approach the the queries in a intentional manner so I think just knowing what those queries need to look like is very helpful um and let's see we do have a question from an attendee about demonstrating how to search for a funder in Dataverse what I'd like to do there because we are sharing slides right now is encourage um you to take a look at um the use cases particularly the institutional use case because those are going to be able to give you some perspective about what that searching looks like um and you can see how the different repositories have uh approached um you know kind of getting through those uh facets to be able to really drill into the data that's needed um I do notice that sherry and the chat posted an example searching for NIH and the Harvard Dataverse via the advanced search and that link is there so please do take a look at that and I'd be more than happy to grab that for our repository I'm sure others would as well so um yeah thank you all right any um any other questions okay well um why don't we go ahead and um transition to the feedback section where we actually just really want to hear from you we're working to develop things that are useful in the context of not only these different generalist repositories but really trying to understand what those primary um those needs are for people who are using the repositories so um Rebecca I don't know if you have we've got a couple of questions here um if you have anything that you'd like to should we ask first the first one which is um are these four use cases presented so we have the two for the researchers we have the funder and we also have the institutional use cases do those make sense so are there use cases that you really wish you would have seen today then that's important feedback for us or or how we can make those more useful for you and also if what you saw today is is useful for your role please let us know yes definitely I think you know um this use cases uh subcommittee has been really interesting because we um kind of approached it with um kind of an open mind and you know this is the approach we decided to take but you know as everyone on this call knows I any kind of opportunity to continuously improve is exactly what we're interested in so um we welcome all feedback um uh you know with ideas suggestions or just this isn't working for me um and I'd like you to take a look at it right so I see things coming into the chat and that's exactly where we want these this feedback okay great I I see um Chris Erdman um one comment I'll just highlight mentioned um from the funder side they're looking at the OA works and reports to scan for papers that uh that they have funded and then um and then using those papers as um a way to search for the data software protocols in data seer so that is a really interesting workflow and you know I think it really speaks to that idea of um this is a this environment there's lots of different um objects that are created lots of different people and roles in this space so um that's really great thank you Chris all right um and we have uh uh Sherry thank you for your mention of Dryad's um use case checklist yes very helpful it's it's anything that makes uh the decision making easier is always welcomed by all of us for sure um thanks Michael um your feedback is actually really um important and I think um so Michael would have liked a little more depth than I think you know it's always a balance so it's the logistical you know transitioning between different repositories and being able to actually do live demos I know I see a lot of people on the call who I know have expertise with live demos I know that they'll never go the way they should so uh but your point is very well taken and uh that's a great opportunity for us for further um engagement and reaching out and really giving you something to look at real um the community of science presentation because they had that little recording that really did I thought did a nice job of showing what those workflows look like so kudos to the team on that mm-hmm and maybe we can think about doing that more often either a live demo or a short recording good feedback yeah that's really great okay um so Heather asks an interesting question are datasets always linked to a funding number I think ideally um there are those some some unique string of characters to be able to uniquely identify a fund um our funding opportunity but I think different organizations in fact I'm confident different organizations manage that differently and different um levels of funding so for instance if you receive an NIH grant it might be different than a pilot award let's say from your local CTSA um hub so you know those are managed differently because of scope and and just uh infrastructure um okay and Eric thank you for your offer about uh talking about uh demos it was really effective so thank you um okay should we go to the next slide Rebecca okay um so as we think about this idea so we've thought a little bit about what you've seen today what do you want to see moving forward so we pulled together some areas that we thought might be of interest for use cases collaboration always the most important thing uh these days in research it seems to hold true across domains and speaking of domains any kind of specific disciplines that you'd like to see one thing that we're thinking a lot about at my institution is this issues related to security and privacy particularly with um human subjects data integrations uh we saw with the community of science team they highlighted a lot of those integrations um and so those would be I think of interest to understand what that integration looks like um any kind of trends or standards um challenges and barriers are always where the real um I think Jim's lie is looking at what's not working and then thinking about how we address it and then um a lot of our favorite topic for many of us on the call is the NIH data management and sharing plan so um so what are we oh we see um Rebecca Helio physics thank you for the suggestion of the domain I appreciate that um we can see um um I'm watching the numbers pop up in the um poll and it's actually looking um spread there's good representation across all of these different topic areas um so that's great to see I mean I think it really speaks that to the general need that all of us have no matter where we are or what repository or institution we work with that there's like a need for capacity building and community building in in all of these different areas okay Rebecca do you have anything you want to mention about any of that and then I maybe I also want to see if um Amanda will share the results of the poll when she is ready yes I I can see the poll but I'm not sure if everyone else can if the poll is visible to everybody okay great thank you okay great really appreciate everybody's feedback on this yeah so it looks like people are really interested in you know specific challenges or barriers emerging trends technologies and standards integrations security and privacy um I mean it's almost like an even even spread across all of these things maybe a little less so in terms of specific domains but many of there's not much that is not interesting to people all right we've got our work cut out for us I think so I think it'll be a busy year in terms of cases so thanks so much for your feedback on all of these things yeah yes absolutely and mora mentioned something about size um and scale so love that question that's something that I know uh colleagues at the noto are thinking a lot about so I we're happy to happy to see questions about size come in because they're uh it's a tough nut to crack okay um so um also in the context of feedback for future use cases you know so there's the kind of question or the domain of use case that you're interested in but we also want to find out more about the persona so what roles do you see in your ecosystem that we should be spending some time on as we start to dig in and really reflect these particular use cases um so these personas can be researchers students administrators any other kind of library roles community roles um so a lot of us are doing collaborations with community partners and thinking about meaningful context with data sharing there I think is is an important part of accountability and partnership so there's a lot of things that we can talk about there um to um to I guess what everyone's whistle about personas we did include a few key resources to take a look at um colleagues syragon solace who's also on on the zinodo team for gray led with a number of colleagues from ctsas and libraries a personas project and so that's been recently augmented there are 19 different clinical and translational science personas everything from ethicists to librarian to early career researcher and so on um and we've put those into a zinodo community and the paper for that for that work is list is linked below um and then speaking of the ctsas and personas we also mentioned personas in a paper that was led by robin shampoo it was a collaboration with several ctsas informatics teams around what do you need at the institutional level to um support research data sharing and so personas and user stories played a role there as well so um so check those out and if you have feedback for those teams please uh feel free to give those okay we're seeing some feedback coming in thanks lisa for posting those links repository rebecca mentions in the chat repository staff member linking to resources hosted on a generalist repository yes we're we think a lot about that at north western actually um how do you create a searchable record that actually links out to secure data that's held elsewhere so how do you show that it's part of the you know the work happening at that institution when you can't actually maybe make it available in the same ways that you typically would so thank you um and then alignment to um nist's um research data framework which is an interesting one so that's also there's quite a few interesting suggestions i think we'll we'll have to take all these up in our next work group yes absolutely this is really great everybody thank you so much for all of this wonderful feedback absolutely okay so now uh what is next for us so a big part of um of this work that we're doing is really to reflect um community needs to be able to support people as they're thinking about sharing nyh funded work um and uh sharing data more generally in the context of the nyh data sharing ecosystem so here's what we're thinking about as far as our next steps as rebecca mentioned we have our use cases um working group meeting is is coming up very soon so this will be an important part of that discussion how do we build out our existing use cases to make them more helpful also how do we incorporate real life examples so many of us zinoto included thought about this in the context of a more generalized account but our you know our investigators around the country are doing amazing work and so are there ways that we can highlight that work um we want to think a little bit about those personas and roles and how we leverage them to make sure that we're being intentional and thoughtful in how we're representing the work in these use cases of course we'll be reacting to your feedback and you'll be hearing more from us um through the gray communication channels um and then uh we will also be looking for additional opportunities to engage do not hesitate to reach out if you have any questions an idea you want to chat anything like that because that's really what we're here to do um okay and now uh i'm gonna turn it over to rebecca but we also want to invite you to have next steps too so rebecca yeah of course i mean these are all things that we would love to have you join us and um we have next steps and join us for the next webinar collaborative webinar series registering to attend and reach out to us for any additional ideas for engagement we've shared our emails with you and would love to hear from you if you think of anything else after this meeting oftentimes after a meeting i'll think of some ideas and we'd love to hear from you um if you think of anything afterwards please rush out to myself rebecca or or christie if you have any other ideas we'd love to hear from you about the use cases or any other feedback you might have christie okay wonderful thank you so much yes we're really excited um we are having a number of ways to keep this engagement up with the community um there's a metadata recommendations webinar that's coming up um in mid september and i would just like to you can register at that link that's on the slide and we'll send these links out i see that there's some challenges with pulling the links out of the chat so we'll send those up as a follow-up um the other thing i want to suggest is that if there are webinars that you think would be particularly timely or that would reflect priority areas that you and colleagues are identifying don't hesitate to let us know that as well and we'd be happy to see what we can do okay well um with that um we would like to thank everybody for your time and energy and all of this amazing feedback this has been really terrific thank you um and on behalf of rebecca and myself and our entire gray collaboration thank you for your time and your feedback and uh we'll be following up with um some additional information shortly thanks everybody