 Hello everyone and thank you so much for joining me for the CNI spring 2023 prerecorded project briefing session. My name is Michaela Narlock and I'm so excited to present to you today on where we've been and where we're going, reflecting on the data curation network. Before I get too far along in my presentation, I wanted to take a second to acknowledge that I'm going to use words like we and our, and I want to emphasize that this work has been the result of many individuals volunteering their time and labor. So when I say we and are I really mean a complex network of individuals who have made this work possible. Additionally, we'd like to extend our deepest gratitude to the Alfred P. Sloan Foundation and the Institute of Museum and Library Services for funding this work. In case you're not familiar with us yet here's a little bit of background about the data curation network or the DCN as you'll hear me refer to it. The DCN is a community led network of curators advancing open research by making data more ethical, reusable and understandable. There are an active community of data stewards and curation practitioners who share knowledge and time to collaboratively carry research outputs and advanced the data curation profession. The DCN has been active since 2016 and officially launched as a member funded organization in July 2021. I'm not going to go into too much more detail about the DCN here, but my contact information is on the final slide of this presentation so please feel free to reach out if you have any follow up questions. So like I mentioned, the DCN was initially conceived of in 2016 thanks to funding from the Alfred P. Sloan Foundation. After a one year planning grant the project team launched into a three year implementation grant again from the Alfred P. Sloan Foundation, both led by P. Lisa Johnston. After a one year no cost extension, the DCN concluded the grant funding phase in May 2022 and officially transitioned to be exclusively member funded. And even near the end of this grant funding period though we thought it was a really great time to reflect on where we've been and where we're going and so we brought team members together to host a project retrospective to better understand the structures that enabled the successes of the DCN, as well as the challenges that the team either overcame or is still grappling with this retrospective was held in March 2022 in Washington DC. In two and a half days team members engaged in discussions and activities to really unpack the work of the network. And we are incredibly excited to share that the result of this retrospective was published in March 2023 through the University of Michigan Press. And it's titled the art science and magic of the data curation network, a retrospective on cross institutional collaboration. This is the official DOI to the open access online version. I encourage you to read the report it's only about 50 pages of content. And in this briefing I'm going to cover some of the key components. But if you are interested in learning more I encourage you to check out the report, or you can always connect with the DCN on our website data curation network.org. Alright, so the report is structured according to the conversations we had in Washington DC. We started with our successes, what are the things that we are most proud of as a community. Then we transitioned to unpacking the structures of success. How was it that we were able to achieve what we set out to do. We then discussed our challenges, what are the things that maybe we didn't achieve the things we would change differently if we were doing it again today. Maybe how do we want to address any challenges moving forward. We ended by looking at future directions, what other research data sharing challenges might benefit from a cross institutional approach, not unlike the DCN. And then, not on the slide but there are also appendices which include the names of everyone who has contributed to the DCN over the years, as well as the agenda for our project retrospective, and some of the data that we generated at the meeting that then pulled from to create the report. So for today's presentation I'm of course not going to read the report, but I wanted to highlight some sections that I found particularly resonant. I will primarily be pulling from the successes challenges in future directions sections for a discussion of the enabling structures. I invite you to check out our CNI fall 2022 member meeting presentation, authored by Jake Carlson, when Coles Lisa Johnston and myself. So let's dive right in and talk about some of the successes that we as a group identified as particularly, particularly proud of. To start by saying our successes are 100% the result of our community. We could not have done this work without such an engaged and active community of practice, which leads me to our first success that we were really proud of was that we conducted a lot of research grounded in our experience to inform our work. This has been essential as we advocate for ourselves about the value of curation. In particular, I'm thinking of a research project that just concluded that surveyed repository managers and end users to better understand what the value of curation is to them so in other words like what do researchers think of the work that we do for them. There are two articles about this currently under peer review, but our initial takeaway from the data we've received this far that this work is essential and researchers really appreciate the support that curators provide. Another key success is the practical guidance we've created about data curation. This is an evolving field. Every day there's something new to learn in terms of different disciplines and what their practices are different formats. And so we have to keep actively working to create new practical guidance and recommended practices. In particular, I'm thinking of our curated steps, which you can check out at z.umn.eu forward slash curate. As well as what would later become a two day workshop curriculum and data curation primers, all of which are free and open access so anybody can take and run with them. Now at the start of this section I noted that our successes are the result of our community, but I think a key success is the community of practice. We really have a network that relies on radical interdependence in order to be successful. In order to keep this successful community together we've really prioritized slow, thoughtful and intentional growth. We don't want to oversell overwhelm ourselves too quickly by growing too much. So we're trying to take it slow and be really intentional about how we recruit new members. We also have a bedrock of trust. That means that each member in the network knows and relies on one another. We also have numerous models of data data services that we can learn from one another in a welcoming environment where we are trusted and empowered to say, I don't know, can you help me. And lastly, another key success is our shared curation workflow. Members can submit a data set to the DCN for expert curation so that means that when a data set falls out of an institution's expertise in terms of disciplinary knowledge or format knowledge, or even when their capacity might be limited, we can lean on one another to curate data for our researchers and ensure it is shared in high quality and fair mechanisms. So this is really essential because it means that no one institution has to have a data curation expert never refilled or format, but instead we can really rely on one another. So those are some of the challenges that I found particularly resonant, but I'm sorry the success is that I found particularly resonant, but I want to take a second to talk about some of the challenges that we faced. That shared curation workflow, one of the challenges we faced is balancing using that shared workflow with local curation. So it's been used a little less than we anticipated, but one of the key reasons for this is that need for member institutions to balance local curation because the more curation they do locally, the more they can empower their curators, helping them up skill, helping them forge those relationships with researchers. So of course, means that the shared curation workflow is used less than we anticipated, and we recognize that the benefits of the DCN are far more than just this shared curation workflow. It includes upskilling research, upskilling local curators through practice, as well as the data primers that we've made the data curation workshop, but it's just something that we've had to struggle with as the DCN has matured. We also have a pretty homogeneous set of members. So our members are well resourced institutions, this includes our one academic institutions and nonprofit data repositories. And to a large degree we understand why we formed this way. These institutions had the resources to dedicate to this project and they had curation units, and they also have the most active risk that they could take on. And so it makes total sense that they would have the resources to commit to this experiment and be among the first members. But we recognize that this reinforces and in some ways exacerbates structural inequities inherent in systems of higher education. So while we are eager to include other institution types, we want to be sure that membership in the DCN meets the needs of and is of value to non R1s minority serving institutions, and other types of research institutions, not currently represented such as disciplinary repositories or government agencies. But we have to do research on our end to better understand the needs of these institutions and what value the DCN can provide for them. Another final challenge I want to highlight is managing our community capacity to ensure the sustainability of the network. And this is very near to my heart because I'm the only full time employee of the network. I did spend all of my time helping the DCN grow. But this means that every other DCN member also has responsibilities to their home institutions engagement in the DCN comes on top of their daily work. So while we've done some really phenomenal things over the years which I just highlighted, as the DCN keeps growing there's a risk that the number of initiatives that we develop and support will outweigh the amount of time and resources available. We need to make sure that we are being realistic with our capacity, and only taking on products that align with our strategic framework and guiding values. Similarly, we need to ensure the organizational sustainability of the DCN. This is especially important as our value proposition continues to be refined. Earlier the DCN began as a means to support the work of curating data sets, but it's evolved to become a strong community engaged in advocacy, research and education, in addition to the distributed data curation work. As the DCN continues to mature, and we explore new membership models and articulate our value statement, ensuring the sustainability of the organization will necessarily have to be a community driven effort. In hindsight, here are some of the things that we wish we had done differently. So if we were building the DCN today here are some things we might take into consideration. First we would try and develop paid fellowships and other workforce development programs earlier. Learning how to be a successful curator requires hands on experience working with data sets and researchers. This invaluable experience would significantly benefit library science students, while providing additional curatorial support to DCN members. Now we've started some work in this area in partnership with the National Center for Data Services based out of the network of the National Library of Medicine. And this has been really helpful to better understand the needs of students but we wish we'd started some of this work earlier. And the one feedback that I continuously get in the DCN that we're continuously trying to make space for is incorporating more peer learning opportunities. So it's a thing we would have done differently but it's also a continued challenge, especially as we remain and will always be a distributed network. We learn a lot from information sharing sessions focused on details and specifications of curatorial actions. We learn a lot when we can take a topic and really unpack it together as a network like what does preservation of research data sets look like. All of these discussions of practices and procedures and informal information exchange opportunities are entirely meaningful and critical for members. But the challenge has been enabling these meaningful conversations virtually understanding how we can best facilitate meaningful interaction between members when we are challenged by distance and of course time zones will continue to be a hurdle as we balance providing in person and virtual opportunities to accommodate all members of our community. Now I'm going to spend a couple of seconds talking about what other research data sharing challenges that we see that could potentially benefit from a collaborative approach. This is by no means an exhaustive list and we are not necessarily experts in these field but here are some ideas that we want others to take up or maybe collaborate on. Data communities and data management. Data communities are formal or informal groups of scholars who share a certain type of data with each other regardless of disciplinary or institutional boundaries. And we think that supporting these communities could similarly span disciplinary and institutional boundaries. Another potential collaborative endeavor is reviewing and creating research data management plans so many of us at our home institutions are tasked with reviewing or helping researchers create data management plans. And so collaborative evaluable evaluative tools could be extremely useful especially for institutions that might have one or even just half a time person focused on this effort. And lastly applying metadata standards among our institutions fields like date keyword subject or even creator can be used differently depending on how a repository defines and applies them. And so by addressing issues in applying medic metadata standards through a collaborative effort and aligning our metadata academic institutions can leverage the expertise of information professionals to not only uncover the challenges of creating this more unified approach but also work towards solving the problem through a connected and interdependent community of practice. So I'm going to start wrapping up but you'll notice that was a lightning fast tour of the report again I just hit on some of the things that I found particularly impactful and I really encourage you to go check out the report for more information. So, but one of the final challenge I want to talk about, and it's also a wonderful opportunity is that the work of bringing together a community and launching a collaborative effort and finding appropriate capacity appropriate capacity levels to avoid burnout is really challenging labor. And we need to be carefully balanced as the DC and grows community development, especially for an organization grounded in radical interdependence is not particularly speedy. Work is often slow, thoughtful and deliberate as it requires careful planning to ensure the fabric of a community grows with new members. However, like I mentioned this work is critically important to the DCN, and it will remain our primary focus focus as we continue to develop our organization. I want to take a quick moment to thank everyone who made this report possible, including our colleagues at the University of Michigan press. I want to extend a special thanks to the University of Minnesota for serving as the fiscal home of the DCN. And of course, thank you to everyone who has been a part of the data curation network over the past few years. This work would not have been possible without you. If you have any follow up questions, I invite you to reach out to me at my email M N a R L O C K at um and edu, or again connect with us through our website data curation network.org. Thank you all.