 Good afternoon. Welcome to the session entitled, Institutional Research Data Management Policies Planning, Services, and Surveys. I'm Ricky Irwe, and I will be talking about university-wide data policy planning. And first, I want to give a tip of the hat to Beth Warner, who was all prepared to step in for me as I was racing around in air travel hell. So, even though she's very shoveled and I'm disheveled, she said, go ahead, Ricky, do it. And I also want to give a tip of the hat to Delta, whose new tagline should be, we'll try to get you there within about 24 hours of when you plan. And so, we're going to get started. Much has been written about researchers, about research funders requiring data management plans, and universities have moved quickly to meet those requirements. But we thought that now is a good time to reflect on much broader issues than those imposed by external requirements. Recently released report from OCLC Research entitled, Starting the Conversation, University-Wide Research Data Management Policy, it's a call for action. It addresses the benefits of adopting a university-wide policy regarding research data management, and it identifies various university stakeholders and suggests a conversation among them in order to get buy-in for a proactive, rather than reactive, high-level policy for responsible data planning and management that's both supported and sustainable. Now, any one of the various stakeholders can be the instigator, and I think it's a real opportunity for an entrepreneurial person to get things going and to make sure that they're at the table. So, here's the derivation of our work. I work for OCLC and I work in OCLC research. I work with the OCLC Research Library Partnership, and one of the areas of work that we're focused on is the library role in advancing the research mission. And one of the activities in that area is focusing on data curation, and we formed a working group and set about addressing the topic. And these are the people who actively contributed to the work. Chared by Daniel Tsong from UC Irvine, who recently received an ICPSR Distinguished Service Award, and you see many representatives from fine research institutions, including Beth from OSU. And this is how we're getting the word out. The working group is the first point of dissemination. People who worked on it know about it and talk about it. We also used the listservs of the Research Library Partnership to report on our progress, and the OCLC publication just came out this week. A derivation of it is in the November, December, EDUCAUSE review online, which is a special issue focused on data curation and policy and risks. And then we're here at CNI, and we're looking for other opportunities to get the word out. We've put in a proposal for the RDAP meeting in March in San Diego, the Research Data Access and Preservation Meeting, and hopefully, through a variety of means, we get the word out to the intended audience. So the working group started by looking at the benefits of a university-wide data policy. So now that universities have all this experience, preparing data management plans to meet funder's requirements, the benefits are starting to become apparent. Making data sets available can support validation of results. Data can be repurposed in new ways. Planning for data management early on eases curation throughout the data lifecycle. Efficiencies can be achieved when data curation activities are not treated as one-off occurrences. And when a university-wide data policy, you get a lot more desirable outcomes become possible, like clear expectations start to ease the way for data managers, uniform requirements, facilitate things for sharing among researchers, consistent data management standards, training and tracking start to foster harmony within the university. A standardized approach to data management will ease compliance and improve access to the university's intellectual assets, and positive impacts and efficiencies can benefit all research conducted at the university, not just that funded by agencies that require a DMP. So assuming that these benefits look at peeling, stakeholders should enter into discussions to make sure that their university realizes them. These are the obvious stakeholders on most campuses. Of course, there's local variations and we'll consider their viewpoints one at a time. So the university itself. Research data can be viewed as university assets stemming from the university's mission to support quality research. Applying best practices to safeguard such assets protects the university's intellectual, financial, human and material investment in research. The aspiration to commercialize research and patents must be balanced with the desire, and in addition to the requirement, to share data. The university will want to ensure that it's a responsible steward for the research outputs of the institution, and will want to find economical and sustainable ways to do so. Responsible data management and the resulting access to research data can contribute to an improved public understanding of the university's contributions to the public good. Public support can ensure future research funding, and the university may also wish to make a public commitment to open access. A university-wide policy should address how best practices in managing research data and making it publicly accessible contribute to high quality research, academic integrity and responsible stewardship. The next stakeholder is the Office of Research. The Office of Research has responsibility for administration of sponsored research and related policies and services. It's a key contact with funding agencies and involved in university and consortial advocacy around research funding and the conduct of research. The Office of Research may have responsibility for technology transfer, patent and other IP administration, research integrity and the institutional review board. This is usually where proposals, awards, progress reports and project completion are tracked. During the proposal stage, the Office of Research can ensure that those who will implement data management plans are involved as early as possible. They're concerned with the funding and policy and governance of data management programs and in maintaining good relations with funders. They can assist researchers with identifying data management costs for their grant proposals, and the Research Office is in the best position to embed research data management into grant management workflows. The next stakeholder is the Office of Research Compliance. It's important to recognize the particular point of view that the Compliance Office represents. It ensures that institutional policies are in compliance with sponsor policies and regulations and carefully reviews proposed institutional policies with view towards the practical and procedural issues of compliance, weighing both benefits and risks. The Office's responsibility for ensuring compliance with institutional policy through training, communication and enforcement requires their involvement in policy discussions. Some points of consideration may include uniformity of data management expectations, requirements and standards, and the measure of validation. They may monitor the responsibilities of the institution to data housed elsewhere and they will attend the impacts of changing data retention requirements. And our next stakeholder is the IT department. Of course, today's cyber infrastructure must support advanced data acquisition, storage, management, security, integration, mining and visualization, as well as other information processing services. All infrastructure must now include systems for documenting, depositing, managing, archiving and preserving data, as well as facilitating efficient search and retrieval and providing access. A coordinated cyber infrastructure environment can offer advantages such as the common economies of scale, integration and a focused approach on coordinating technology, expertise, computing power and storage space. Technology leaders may need to integrate the data management system with current research information systems, Chris, or virtual research environments, VREs, to make data management part of the researcher's workflow. And we can't forget the researchers themselves. As producers of the research data that must be managed and preserved, researchers are of course central stakeholders. They may be especially invested when their career advancement depends on their research outputs. Faculty members and other researchers confront a mix of requirements for data management and open access that are mandated by different funding agencies, national and state law and their own universities. They may negotiate publishing agreements that determine ownership of data and then may in some cases mandate or preclude open access. Some researchers already have experienced depositing data in institutional or disciplinary based data repositories. Researchers are likely to resist new administrative burdens. There's a surprise. The relationship between researchers and their data is an intimate one. Trust is critical to productively engage them. All researchers should be clearly informed of resulting decisions and procedures. And the academic units themselves are a stakeholder. While the Office of Research is the locust for policies oversight and other activities regarding research grants, the researchers themselves are generally in academic units overseen by the university provost. At the operational level, research projects are managed by the principal investigator's home department. Some academic units have support staff to help with proposal writing, administration, budget tracking and compliance. Some also may have their own technology infrastructure. They have close relationships with the researchers in their department and can serve as good conduits for communication. They may feel uncertain about new data management requirements and might welcome guidance, might including the provision of a more robust and sustainable infrastructure than they could manage independently. And the library is a stakeholder. The library has extensive experience with selection, metadata, collections, institutional repositories, preservation, curation and access. Many libraries have subject area liaisons who offer researchers assistance with their projects. They often provide functional liaisons for research support and data management is often one of those functions. Those with archival training can help address appraisal, deposit, retention and reappraisal. Technical processing staff can offer advice about metadata. The library's expertise with name authorities can help to make research easier to discover and to ensure that acknowledgement goes to the right researcher. Copyright issues related to the ownership of both source materials and research outcomes are familiar to library staff as our privacy issues. When it makes sense to put the data in an external repository, the library can provide guidance to help researchers meet those deposit requirements. In many universities, the library has already led the way in creation of data management plans. So those are our stakeholders. You may have others at your university. And now we move on to the conversation itself. To achieve maximum benefit and minimal burden, the conversation among stakeholders and the resulting policy and procedures should address the following points. Who owns the data? Many universities assert ownership of research data generated on their campuses as do some funding agencies. There is however widespread misunderstanding among researchers on this issue. Policies on data ownership must be clearly communicated and understood. What requirements are imposed by others? Funding agencies may mandate public distribution of the resulting data set and require that data management plans be incorporated in the grant proposal. Publishers may require that data supporting an article be deposited in a particular repository. Collaborative agreements with other institutions may impose stipulations. These requirements should be clarified early in the process. And what data should be retained? Of course, no university can, nor should, retain all research data generated by its researchers. Curating research data requires significant investment of staff time and financial resources. So universities should aim to ensure that they are investing only in data that is worth keeping. For example, data from a failed experiment may not merit curation, nor may that derived from a secondary analysis of large data sets publicly available and archived elsewhere. Who decides what data to keep? Is it the researcher or someone else should other domain experts or peers be consulted? Which data sets are likely to be reused in future research? Which data might must be retained to enable the validation of the findings? What data would be prohibitively expensive to recreate? What data do not merit preservation? Perhaps the data could be easily recreated or an algorithm is more significant than the data itself. How long should the data be maintained? Data may have long-term scientific or institutional value, but all preserved data should be subject to review. How will retention periods be tracked? How will it be decided whether the period should be extended? What metrics could assist with reappraisal? Who should be involved? How will reappraisal be managed in the repository? Should deaccession data be offered to others or destroyed? What records should be kept to document the disposition? How should digital data be preserved? Are there any unique digital preservation needs? Are they different from the approaches identified by the OAIS and the Open Archival Information System reference model and the trustworthy repository's audit and certification process? What are the ramifications of cloud storage? Should the data management plan be kept with the data to provide provenance and additional context? Are data files supported by the, are data file formats supported by the repository? What descriptors should be applied? What standards for identifier, citation, metadata will be required? Are there ethical considerations? Data should be kept in a way that is compliant with the institutional review board requirements, grant conditions or legal protocols. How will the institution handle IP rights and privacy issues? How will sensitive data be identified and contained? Are there access restrictions that must be enforced? How can ethical issues be identified during the proposal stage so that consent forms could be developed up front? What sort of risk management is needed for research data? How are data accessed? Accessed. Is it necessary only to make the metadata discoverable with links to the data files or is deeper support for manipulating the data needed? Which indices and catalogs should reference the data's availability? What service level assurances should be made? How will the repository monitor access to ensure restrictions are enforced? What are the possibilities for quantifying access and how might this information play into impact, promotion and tenure? Indeed, what is the measure of access? How open should the data be? An institution may decide to provide open access to its research data unless constrained by law or grant conditions or it may decide to share only on a case-by-case basis. Data may also be embargoed with a goal to share in the future. In situations where data cannot be shared, what explanation should be provided for not sharing data? And how should the costs be borne? Where will the necessary funds come from? Data curation costs could be included in the indirect costs and grant budgets or it may be permissible to include data management as a direct cost, though these possibilities have not been discussed much. If funding is project-based and therefore time-limited, how will the costs of long-term preservation be supported? Another possibility is co-investment by multiple partners. There's an excellent example in the Netherlands called 3TU where three technical universities have joined up to develop and deliver data management infrastructure and support together. It's important that the university be clear about which services it will cover and which are considered over and above. How will data curation costs be assessed and projected into the future? Can a case be made about the potential return on the investment? And what alternatives are there to local data management? In some cases, a more appropriate data center exists, a national or international or discipline-based data center. Many funders require data to be deposited in a large national or international repository that hold like data, like the National Climatic Data Center or the Protein Data Bank. In some cases, researchers in a particular field use a specific data repository and develop a disciplinary culture around data sharing. Examples are ICPSR for social science data and Open Context for archeological data. There are many. For multi-institutional collaborative research, it should be made explicit which institution will take responsibility for the data. And regardless of where the data is housed, most universities will want to include metadata with links in their local repository. In some cases, one home may be appropriate for preservation and another for access. With the recent Office of Science and Technology Policy Mandate, other players may emerge in the data management milieu. The Association of Research Libraries, the Association of American Universities and the Association of Public and Land Grant Universities have issued a proposal called Shared Access Research Ecosystem or Share that imagines a workflow architecture implemented across a network of university operated repositories fulfilling that mandate's requirement. Representatives of 30 organizations that archive scientific data have released a call for action urging the creation of sustainable funding streams for domain repositories that are closely tied to scholarly communities. Regardless of how this settles out, universities will still want to have a record of their own research output. And it could be that these data repositories will be important nodes in the evolving research data network. It's important to recognize the current uncertainty as to how data management support and services will be distributed among university, disciplinary, funder, national and international stakeholders. And in this complex environment, an institution must actively determine how it will manage and distribute data services internally. The various stakeholders are important in determining the appropriate approach to providing data management. Effective data management is just one aspect of achieving the ultimate goal of ensuring ongoing access to the outputs of academic research. So this report is available through the OCLC Research Site. There's a version of it in the current EDUCAUSE review online. And we welcome any conversation and also ideas about what needs attention next. Where is there a gap that needs addressing? And so we'd be interested to talk about those things. I believe we could take a question or two now and then move on with the rest of the program. So if you have a question, go to the mic. And if you don't, we'll go on to the next presentation. Thank you. Okay, our talk I think will be a good follow up to Ricky's because we have had opportunity to see what's really going on out there for some of these aspects of the new, somewhat new field for library of research data management services. And what we'll be reporting on is the results of a survey we did that was published through the ARL, their spec kit surveys. They do several a year. Ours was on looking at research data management services among other member academic libraries. And so we're gonna, I'm gonna give a brief overview and some of the lessons learned. This survey we conducted last year, gathering data roughly last spring. And we did the survey as a collaboration between Johns Hopkins University data management services and a couple of members of University of Virginia's data management consultant group. And both of our libraries had, at that time, finished roughly a year of these new areas of service and wanted to get a sense of what other libraries are doing out there. It seemed like a good benchmark time for looking at new services and also doing a bit of strategic look at what other ideas are out there, other models that are emerging and what might be shared with our community. So we saw the spec kit survey as a good opportunity. And as we proposed the survey, we were approached by ARL's e-science working group who had done another survey back in 2009 and thought that this might be a good followup survey to do some comparisons. We worked with the authors of the survey, ultimately ours was rather expanded and different, so it's not a direct benchmark comparison, but it does give a good sense of the rapid changes that have gone in this area of library services. So there have been, as you know, a number of new incentives for looking at data management, in particular for academic libraries. Both what has been around for a number of years are data services generally focused as the survey result shows around locating data sources, GIS, and statistical software information. But there's been new incentives for looking at the proposal stage of research, the data management plans, support in particular, and also at the dissemination and preservation stages of research access to data sharing and data repositories and archiving. So we at JHU and UVA got together and looked at some themes and interests that might reflect what's going on right now. We were, for example, interested at JHU and archiving services in particular, but we also, in addition to looking at what the spectrum of research data management services are that are out there, we also looked at some of the requirements for sustaining the services, like UVA was interested in looking at staffing and training models, also technical and administrative needs and challenges that are out there for this emerging domain. So we got pretty good results from our survey. As far as participation, we had 73 academic libraries responded on pretty much librarians answering the question, which was 59% of ARL members. And all of the groups reported having some area of data services, such as statistical software report, but almost three quarters offer data management services in the way that we defined them. And another 23% are planning on adding these services within the next few years. And as far as when these services started, you could see that some had been around for a number of years, tending to be special projects or roles added to a librarian, such as becoming a data librarian, but many, you could see a real spike around 2010, 2011, which was the time when NSF implemented their data management plan requirement, which was in January 2011. So that was a real motivator. But also we got reports that other motivators, really about half of them pointed to library-led incentives towards supporting research data management more broadly. But less common, this was more common than having the researchers or administration call for expanded services or coming up with formal data policies for schools. So it's really a library-led initiative for most schools. So looking at some of the key areas of RDM services being offered, we distinguished types of services, one being data management planning support, whether online or through direct interaction with researchers. Also there were a number of efforts around data management support more broadly such as having training sessions on particular topics, such as backups or supporting metadata or data citation, either instructions on how to help us to use metadata or actually helping researchers with their metadata. And then the third area is in looking at data sharing and actual archiving of data. And we looked at how libraries are involved in that. And I'll give a few more details on the results, looking first at data management planning. So again, with the NSF's instigating the movement, we did see 87% of libraries offering some online resources at least for data management planning. About 70% of those came up with their own resources rather than linking to others. And then we also got some results on the use of the data management planning tool that there'll be other talks on. This is an online data management planning source where about 75% are using those and a number of them are providing direct guidance on how to use a data management tool. But beyond online resources, a number of schools are doing data management training more directly in the form of workshops, some of them online, some of them in person. And also a number are doing data management plan consulting, which is what we're terming the direct interaction with the researcher, helping them with plans. Some are doing face-to-face office visits. Many are doing responses to emails and direct inquiries. So how is participation going with data management planning? Are the researchers beating down the door? We're not all of the schools did good tracking of their services, but of the 25 that did, we see that most are online. Only about half of them have had more than 10 contacts in the last two years, and only two libraries are averaging more than three consultations a month. So not pretty modest participation right now, and we could probably see a dip from the beginning of the NSS policy to where we are now. So this would be a good benchmark for the OSTP memo that Clifford Lynch talked about whether that's gonna be a new broadening, especially if other funders start requiring data management plans, there'll be some good numbers to compare for the future as to participation with data management planning. So in addition to looking at data management plans, we were also interested in data archiving services because here's another area where funders are trying to promote data sharing. In particular, through data repositories, and libraries are looking at how they might step up and assist in that process, but the implications are more resources, staff time needed to really support data archiving at school. So we wanted to see how libraries are being involved in that. We found that nearly everyone is assisting researchers with locating data repositories once they have data to share and almost half are giving direct assistance helping researchers actually deposit data. But interestingly, a good 74% of libraries are actually starting to host research data collections directly and manage those collections. So looking more closely at what's going on there with the libraries actually hosting data, one key finding there is that about 75% of libraries are using their institutional repositories for archiving data. These are repositories originally built, of course, for publications and data sets would be typically attached to a publication. Some are using digital repositories that might have been built for things like photo collections and only five of the people we surveyed are currently using data specific repositories. These are built for scientific data directly, hosting them online and with some preservation functions. So this situation can be explained a bit by looking at the platforms that are out there. Institutional repositories, many schools have already had them installed for many years. They're known technology. These are some of the platforms that are being used. What there is a lack of currently are a number of choices, especially off the shelf in public domain, for data archiving, data specific repositories. And of the five that we had in our survey, only one was currently online that was data-versed, the others are still nearly online, but in still in development. So we're really in early days of what's available, and again, this would be a good benchmark as some of these systems that are out there are coming into more public use in the next few years. So how is archiving funded? Largely, it's being done through internal budgets. Some grant projects, just a few schools are charging fees to the researcher. So looking long-term as data sets increase, there's gonna be more of an issue around how to fund data archiving. Right now, there's very modest use of few researchers or some large data set projects, but mostly small projects that may change in the future. But we are seeing quite a variety in the types of data that are being archived. And also we're seeing quite a lot of potential participation by library staff in helping researchers create data collections about three quarters are actually assisting with deposit of, more than three quarters are assisting with the deposit of data. So this is something to look at whether archiving can really scale up to higher capacity. And of course, staffing of services is a factor there. We had a couple of questions around staffing. One was looking at the organizational models or research data management services and some of the key skills and training for positions that were out there. We found that a number of schools had just single positions for these services. Some had mixed departments, library and other departments like IT, but most were staffed by two or more people within the library itself and among departments in particular. In many cases where there was not one or two people, there were usually groups of six or more people involved. Most of these were permanent positions, but less than 50% had RDMS roles as a majority of their time. Most of these were split roles. We did an analysis of job titles and found that subject librarian and liaison was common in job title names, data management and curation were other common words, digital. So there's really a variety of roles out there. As far as what skills were desired, we asked what we had them rank, what were important skills for these roles, subject domain expertise, digital curation experience, IT experience were ranked highly as far as what training people in these roles actually had. Most of them came from an MLIS background. Just a few had a data curation emphasis and this is a new field in high schools as well. But quite a significant number did have masters in another domain, speciality of science. So finally, we tried to get a sense of how these services were going. We asked a few assessment questions. We didn't really get real clear responses and not a lot of people are really tracking systematically how well they're doing with their services. But for example, are we ready to ask whether research or demand will really sustain these services in the future? And it's probably too early to say, at least, and it would be good to follow up on that. But we did get a good list of some of the challenges for implementing sustaining services. A number of the key ones were challenges in collaboration, getting buy-in from other departments on campus. Funding is a big challenge, of course, particularly for expanding things like archiving. Getting faculty engagement is a real significant challenge. And one to follow as some of the requirements by funders might increase and also issues around scaling up services for the future. Now I'll hand it over to Andrew, who will give some big picture. So I'm going to talk for a few minutes, make sure we have time for questions. I'm just gonna talk a little bit about some of the limitations of this survey that we did. As well as some of the lessons learned, some of the things that we've tried to derive from the data that we gathered. So some of the obvious points here, in the limitation of the survey and the design and distribution of it, generally, this went out through the ARL network. So we had a potential 125 institutions that this could have gone to, some of whom it didn't really apply to. But the very nature of it going out to ARL specifically obviously limited or excluded certain institutions by default. Additionally, we had the issue of sending this out through ARL, meaning out through libraries. And we recognize that some of the people and groups providing research data services within institutions are not within the libraries. And if there isn't a tight coupling of the services and the people providing those services, there may not even be very strong awareness of the services being provided in a different area of the institution. So there may be some deficiencies purely based on that or lacking of certain data or mis-weighting of data based on that. But either way, we think it's a relatively good coverage. Another area is estimations. So Dave mentioned that there were some estimates on the amount of time, the amount of contacts that have happened with certain service groups. And there was very little reporting in those areas, perhaps because things were being tracked or because we had certain ways of asking and people interpreted it a different way. One thing we know is the actual time invested in providing these services right now was something that we were not able to very well gather. Similarly, the volume of data is something that we were very interested in. We wanted to know how much data these different service groups were actually helping people collect or manage. And we simply didn't get a very good account on that. So this is something you can see further in the full report that we have. Another area is terminology. This is something that we expected was going to be a problem. It was a problem with the prior study that the East Science Working Group had done. And the simple issue is that terms are being used very widely and imprecisely right now. Some may describe it as digital services, some as research data services, some as data management services. And we weren't really sure how to clearly ask and hear how people were doing what they were doing. So very basically the variation of an interpretation probably presented some problems in the data that we've gathered. We attempted to normalize this in some ways. What Dave showed toward the end of job titles was one way of trying to understand this. But there probably are some further things that need to be done in that area to understand. Another area that the last that I'm going to mention for right now is broader analysis. One of the things that we went in really hoping to do, we had really big picture ideas when we started this and really we couldn't get all of it done, was we hoped to match up the data that we gathered against a lot of other institutional data that was already freely available or collected and look at how things compared based on that. So really sort of a broad merging and analysis. This is something we simply weren't able to get to within the amount of time we had and with other priorities. But as I note here, this is something we think would be a big future research opportunity for an iSchool student or somebody who has more time to focus on this now. And hopefully this would be a means of better understanding more of what's actually happening and maybe more where things might go. So on to some lessons. And these are purely our observed or derived lessons to try and understand and get some meaning out of what the data is that we collected. A first one is collaboration seems like a key element. I think this is sort of an implied thing here, but a lot of the data that we have shows that the libraries need to be collaborating across the institutions. Many are and this is a necessary aspect of dealing with this sort of broad and diverse aspect of research data management. Similarly though, this is seen as one of the biggest challenges. So it's necessary, but it's extremely hard. I think some of the results that we got suggest that this is difficult because it's maybe not a norm or because groups don't know where to go to collaborate. The networks haven't been well-established in all areas. I think this relates somewhat to some of the things that Ricky was talking about as well. A second area is that real costs exist in this. We're trying to understand how groups were approaching their investment in research data management services. What we saw was that necessary skills may require hiring new staff with different skills or retraining. These are things we've heard over the years, but this was confirmed. New skills may cost more, so it's a conscious investment of effort, perhaps different than just shifting people to different roles. Similarly, archiving, infrastructure, storage, curation, obviously are all going to incur real costs as well, so this has to be overall a very conscious thing that people start to invest in, and it has to be carefully weighted in that way. A third lesson is building more engagement. So we heard toward the end of the survey in the open-ended sections, we heard some of the reasons why things were or weren't happening in certain institutions and cases. Again, these are all within the full spec kit, so these are things you can look at further later. But one area and perhaps reason for poor engagement was a lack of awareness across institutions, so researchers may not have known about the services or various groups who were involved or not involved weren't aware. There hadn't been strong coordination quite happening yet. As a result of this, low perceived value and resistance to sharing and collaboration. We see this in part being perhaps a trickle down from empty mandates, so mandates are out there, requirements are out there, but when people have to comply and then aren't really forced to do those things, the effect is weakened a little bit. So that's just one impression that we had. A fourth one is growing services, so despite the challenges, many of the respondents that we had suggested that this isn't important in a necessary area for their groups to be investing in. It obviously comes at a balance of institutional funder policy, technical skills, staff, financial capabilities, so really merging a whole bunch of different types of services and skill sets. We're thinking that this could be another, one of the important areas to benchmark on, perhaps building future case studies as well, which was one of the things that the East Science Working Group, I think, recognized as well, so perhaps with the two they could be combined and enriched in the future. I'm going further on this. The top section here is showing plan services within two years, which had 63%. We're talking about adding online DMP resources, 54% in research data archiving, 46% in research data management topic training, staffing similarly, 44, 44 and 34%. So significant attention to growing more service and support in these areas. The bottom section talking about plans for RDM funding. The top point there is expecting a funding increase 66%. So there's demonstrated and sort of intentional investment that's happening. So we think that this will continue to be a growing area. Our very last point, the last lesson is that based on the data that we collected, and despite what we may have hoped or considered possible in doing a survey, there's not really any single path that became evident through the data that we collected. It looks like there will be many different models, many different solutions. I think one key thing is that cross institutional collaboration within and outside of seems to be one of the most essential and viable parts of the model. So perhaps that will be one that groups continue to develop. Just in closing, this is our overall credits. We're just two people out of a team of five at UVA and Johns Hopkins. And we thank Leanne George at ARL for all of her help in making this possible. So at this point, we will take questions. Thank you.