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Research Data Services Maturity in Academic Libraries Kouper, Inna; Fear, Kathleen; Ishida, Mayu; Kollen, Christine; Williams, Sarah C. 2017

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153CHAPTER 6*Research Data Services Maturity in Academic LibrariesInna Kouper, Kathleen Fear, Mayu Ishida, Christine Kollen, and Sarah C. WilliamsIntroductionIn 2012 only a small number of academic libraries offered research data services (RDS), but many were planning to do so within the next two years.1 By 2013, 74 percent of respondents to an Association of Research Libraries (ARL) survey offered RDS, and an additional 23 percent were planning to do so.2 Stimulated by shifts toward computational paradigms and the issuance of federal mandates to increase access to products of federally funded research, academic libraries recognize that the landscape of services changes quickly and that they need to support the changing needs of research and instruction.To provide effective support for their constituencies, libraries must be pro-active and develop services that look forward and yet accommodate the existing human, technological, and intellectual resources accumulated over the decades. Setting the stage for data curation in libraries means creating visionary approach-es that supersede institutional differences while still enabling diversity in imple-mentation. How do academic libraries approach data curation? What constitutes * This work is licensed under a Creative Commons Attribution 4.0 License, CC BY (https://creativecommons.org/licenses/by/4.0/).154 ChApTer 6an established RDS suite in an academic library? What can help in RDS evalua-tion, comparison, and improvement?This chapter sets data curation in academic libraries within the broader con-text of RDS development and combines a historical overview of RDS thinking and implementations with an empirical analysis of libraries’ RDS goals and activ-ities. Using historical and current empirical data, the chapter synthesizes the state of RDS across academic libraries and argues that curation needs to be seen as part of a larger suite of services offered by libraries in support of the research life cycle and that the services evolve over time. To better understand this evolution and compare RDS across institutions, the chapter offers an empirically based frame-work of RDS maturity. A set of recommendations that libraries might consider to advance their RDS to the next maturity level is provided at the end.Research Data and LibrariesSince the 1950s, if not earlier, much of the work around data has been done by research communities as they grappled with global, inter-institutional data man-agement and archiving.3 North American academic libraries have also worked toward establishing research data services, though their services have often been anchored within their institutions. These early library data services were promi-nent in the areas of social science and GIS data reference and acquisition, but also in stewardship and sharing of data.4 Conversations about data stewardship and the library’s role in it tended to focus on needs within the university community. Thus, in 1965 I. de Sola Pool argued thatThe storing of basic data in retrievable and manipulable form is, indeed, a library function. The library is an archive of that type of information that is of interest to many members of the university community and that is too bulky or expensive for each to retain or own. Each member of the faculty owns some books, but no member of the faculty can afford all the books he needs. The library provides the economy of shared-book usage. If this is a function of the library in the university, then clearly data archives also belong in the library.…Obviously, many data collections are so bulky or so expensive or so private that not even a university library can hope to own them. That, however, only suggests that specialization, division of labor, and linkage among libraries in a total library system are necessary in this field, as in other fields.5 research Data Services Maturity in Academic Libraries 155The discussions of the 1970s and 1980s focused on staffing, institutional support, and computerized services to digitize and assist with machine-readable data.6 The services of early data facilities already included acquisition, preserva-tion, data cleaning, metadata, access and retrieval, reference, and data citation.7 At the same time, libraries played a smaller role; among the forty-eight data-shar-ing facilities in the North America listed by Clubb et al. in 1985, thirty-one were associated with universities, with most of those facilities operating as collabo-rations between research and computing centers and sometimes libraries.8 The Social Science Data and Program Library Service (DPLS) at the University of Wisconsin-Madison, for example, was established primarily by the faculty and could not be absorbed by the library because library staff at the time were not skilled in computers and data.9In the late 1990s–2000s, with digital data and new forms of research on the rise, discussions shifted towards e-science, cyberinfrastructure, and digital cura-tion, stimulated particularly by several seminal reports from the United States and the United Kingdom.10 ARL recognized the importance of building mem-bers’ awareness of the changes coming with the emergence of e-science and iden-tified policies, skillful workforce, and research infrastructure as the primary areas of library engagement.11 Data services have also been organized into tiers or areas that libraries could use to determine their current state, identify service gaps, and set goals and priorities.12 Guidance on the development of data curation services “downstream” and “upstream” in the research life cycle was another way to define libraries’ roles with RDS.13A number of studies that examined the state and development of RDS in academic libraries show a clear trend of more academic libraries providing a broader range of e-science support and data-related services. In 2010, among 57 ARL libraries surveyed, 21 (37%) reported providing infrastructure or support services for e-science, with the rest being in the planning or no support stages.14 Many libraries offered such services as information dissemination, consultations, and reference, as well as technology support (e.g., storage or software). A few libraries mentioned providing curation, stewardship, and preservation services. The common pressure points among the libraries included staffing and lack of infrastructure to handle, preserve, and provide access to data.In 2012, about 44 percent of academic libraries surveyed provided reference support for finding data, and 20 percent or less provided other types of data-re-lated services.15 The services offered were predominantly in the informational or consultative category, such services as outreach and collaboration, training, and consultations. Creating web guides to help users find data and relevant informa-tion was one of the most common types of RDS among academic libraries. A rather rare category of technical or hands-on RDS included creating metadata and preparing, identifying, and deaccessioning data. The report also found that institutions with external funding were more likely to be involved in RDS de-156 ChApTer 6velopment, suggesting that funding agency requirements were driving the need for RDS.By 2013, 74 percent or 54 of the ARL respondents offered RDS,16 with many of them providing guidance and assistance with data management plans (DMPs). Three challenges identified in the ARL survey were (1) hiring and retraining staff, (2) building technical infrastructure, and (3) reaching out and collaborating with other stakeholders on campus. Research data management has been argued to be a major change in most librarians’ responsibilities, as “data require different struc-tural metadata, schemas, and vocabularies. Librarians who have adapted their skills are difficult to find.”17 ARL institutions approached RDS issues in diverse ways, and it was predicted that RDS would evolve over the next several years,18 depending on institutional and funder policies as well as on financial and human resources available.The Current LandscapeTo map the current landscape of RDS in academic libraries, we conducted a study of the 124 ARL libraries (as of September 2015) as those most likely to have started providing or planning for RDS. The study included content analysis of library webpages and a series of interviews with library administrators and program leads that examined their views of RDS goals, activities, and evolution. For content analysis we identified data-related webpages on library websites and coded their content for (1) the presence or absence of references to local repos-itories and to librarians dedicated to RDS, and (2) the presence or absence of references to particular types of services. The interviews were recorded and ex-amined for common themes and specifics of RDS implementations. The results from both content analysis and interviews were used in a synthesizing depiction of the current landscape.About half of the libraries (52%) indicated that they have a dedicated RDS position or librarian role on staff. The nature of dedicated positions varied from single librarians leading data services, to liaison librarians taking on research data management consultations, to full units or departments with multiple data con-sultants or specialists. This variety is consistent with earlier studies that found a range of staffing models and diverse position titles.19The typology of services was developed using categories from the literature as well as from our own study.20 The typology distills the surveyed libraries’ service offerings into their core functional areas, such as “consultation and instruction,” “collaboration and engagement,” or “archiving and preservation” (see also appen-dix 6A for details on typology). Identifying core functional areas among varying implementations enabled us to consistently compare services across institutions and count their frequencies (see table 6.1). research Data Services Maturity in Academic Libraries 157TABLE 6.1Research Data Services in the ARL LibrariesGroupa Type of Service % Libraries Mentioning Service on Website (N = 124) Basic DMp assistance and mandate support74%Consultations and instruction 73%Best practices and information dissemination72%Intermediate Data deposit and repositories 49%Archiving and preservation 42%Collaboration and engagement 31%Metadata 30%Storage 27%Sharing and reuse 27%Advanced Data and researcher IDs 14%Data processing and analysis 13%Data curation 12%Acquisition 11%Copyright and ethics 10%Software and hardware 10%Data citation 10%policies 7%Data reference 6%a. Grouping is based on the frequency of service occurrence in the libraries, see more at the end of this section.According to the webpages, most libraries (74%) provide DMP assistance and mandate support, including links to the DMPTool, an online service that contains DMP templates and allows researchers to create DMPs according to the funding agency requirements. Consultation and instruction as well as best prac-tices and information dissemination are two other most frequent types of services (73% and 72%). Such capacity and partnership building is often mediated by subject librarians who are learning data management issues relevant to their dis-ciplines and are ready to offer guidance on data management requirements for particular funding agencies.The services of data deposit, archiving and preservation, collaboration and engagement, metadata, storage, and sharing and reuse were mentioned on fewer 158 ChApTer 6webpages, ranging from 49 percent to 27 percent. These services require a higher level of institutional engagement and more financial, technological, and human resources. At the same time, developing a repository for data, or, more frequently, adapting an existing institutional repository to accept data, is a common second step for libraries offering data services. Thus, several of our respondents noted that they plan to pilot repository software and explore consortial options for data archiving. Despite only 49 percent of the libraries referring to data deposit as a service, many more (70%) had a repository that enabled data deposits. As data deposit requires efforts that are related to archiving and preservation, data and re-searcher IDs, and data curation, the beginnings of such services could have been considered part of many RDS efforts. Nevertheless, oftentimes such services were not specified as areas of concerted effort, and activities of deposit and preserva-tion were used interchangeably.A number of services were offered in less than 15 percent of the libraries, including permanent IDs for data and researchers, data curation, data processing and analysis, software and hardware support, data reference, and data citation. These kinds of services often depend on the specific user needs; additionally, they require a higher level of skill and expertise on the part of the library staff who offer them. A data reference librarian, for example, can be expected to be familiar with statistical software such as SPSS and understand how to manipulate numer-ical data in such software.*A striking difference in preservation efforts (42%) and curation efforts (12%) can probably be attributed to the differences in terminologies that various libraries employ to describe their efforts as well as to the awareness of the fuller spectrum of data services. At earlier stages of RDS, the terms “preservation” and “curation”, for example, can be used interchangeably. At more advanced stages of RDS, terminology becomes more specific because it refers to specific goals, tasks and responsibilities within a library. While the services of storage, archiving and preservation, and curation are connected and dependent on each other,20 they be-come differentiated and sometimes specialized due to unique partnerships with IT units and commercial services.Services that were the least common across libraries included support for copyright and ethics, software and hardware, data citation, and policy develop-ment. These areas are among the most challenging to implement in the libraries, as stakeholders in data exchanges—including producers, providers, publishers, and consumers—are trying to understand the best ways to ensure open sharing while protecting ownership and to create tools for storing, analyzing, and sharing data at scale. Many respondents in our study confirmed that some work on devel-* See, for example, a data reference librarian job description: “Data reference Librarian,” job opening at harvard College Library, posted to IASSIST August 20, 2008, http://www.iassistdata.org/resources/jobs/1612. research Data Services Maturity in Academic Libraries 159oping data policies was being done, but it involved university-wide consultations and collaborations with institutional review boards, research administration, and information technology units. Some libraries, while acknowledging the need for data policies to guide their service provisioning and to enable data sharing, post-pone such work as it needs to be consistent with the funding mandates, publish-ing policies, and other areas that involve data. The early work on data policies in-cludes efforts to incorporate data management into institutional research policies and to increase awareness of the existing policies with regard to sensitive data and data ownership within universities.To provide an additional way of comparing RDS across academic libraries and to build the foundation for the discussion about RDS maturity below, the ty-pology of services is further grouped into three categories based on the frequency of service occurrence in the libraries: the basic services group includes services that exist in more than 50 percent of the libraries, the intermediate services group includes services that exist in less than 50 percent but more than 15 percent of the libraries, and the advanced services group includes services that exist in less than 15 percent of the libraries. While frequency alone cannot be an indicator of RDS maturity, such an approach has found support in our interviews and in the literature. Respondents in our interviews reflected that DMP services were typically the first type of services they offered when starting RDS at their in-stitutions, while also noting that they needed to move beyond that and basic policy compliance and informational services. Similarly, Fearon noted that many libraries started their RDS with support for DMPs, with almost 90 percent of the libraries providing DMP support and consultation services.22 The basic group of services naturally lends itself to the beginning stages of RDS development as it is a straightforward outgrowth of the work librarians do in advisory and refer-ence services and is relatively easy to implement; the intermediate and advanced groups require more skills, better stakeholder engagement and institutional sup-port, and more resources.RDS MaturityIn the previous section, we introduced a typology of data services and, based on our content analysis and interviews, posited that the most frequently offered ser-vices are those that represent a straightforward entry point into RDS, while those that are more challenging—more resource-intensive, more specialized, and more reliant on institutional support—are both rarer and more advanced. In the fol-lowing section, we develop this initial statement into a maturity model for RDS.Maturity evaluation is a common approach to determining the level of so-phistication of services or products. One of the earlier, better known examples of such models, the Capability Maturity Model for Software (CMM-SW), was 160 ChApTer 6developed in the 1990s to aid the US Department of Defense in software ac-quisition.23 The model’s goals were to appraise software processes and help orga-nizations to move from chaotic ad hoc processes of development to disciplined and optimal ones.24 The model developers distinguished between immature and mature software organizations and argued that the former are primarily reaction-ary and focus on solving immediate problems, while the latter are based on solid management techniques, such as consistent planning, communication, pilot test-ing, cost-benefit analysis, and defined roles and responsibilities.Recently, Qin, Crowston, Flynn, and Kirkland proposed using maturity lev-els similar to the CMM-SW to assess and improve research data management (RDM) practices in research projects.25 They described the five levels in appli-cation to RDM as follows. The first, initial level of RDM relies on competent individuals and heroic efforts, making the data management efforts unreliable. The second, managed level of RDM is based on the procedures and policies estab-lished in advance for each project, which makes it difficult to apply RDM across projects. The third, defined level is characterized by established and repeatable pro-cedures that can be used across projects. The fourth, quantitatively managed level adds metrics that help to evaluate processes and progress. The final, optimizing lev-el focuses on improvement and identification of weaknesses and inefficiencies that can be addressed proactively. The maturity levels are suggested to be applied to the following key process and practice areas: (1) data management in general; (2) data acquisition, processing, and quality assurance; (3) data description and representa-tion; (4) data dissemination; and (5) repository services and preservation.The capability maturity framework guide for data management proposed by the Australian National Data Service (ANDS) uses the same maturity levels as CMM-SW and CMM RDM, but it identifies different process areas: (1) in-stitutional policies and procedures; (2) IT infrastructure; (3) support services; and (4) managing metadata.26 For each of the areas, the processes move from being ad hoc and disorganized to being defined, standardized, managed, and optimized. Yet, there is one major difference. The CMM-RDM framework fits with the research life cycle approach and, with data management, can be applied to the stages of data collection, processing, dissemination, and preservation and, therefore, can be applied at the project level. On the other hand, the process areas of the ANDS model identify larger areas within the institutional context (e.g., policies, infrastructure, education, and metadata) that need to be in place before data management within the life cycle can take place.These models, and many other capability models that have been developed over the last few decades,27 provide guidance in terms of the trajectory that a team, a project, a service, or an organization can go through to become a well-managed unit with clear goals and path toward deliverable results. At the same time, the models offer rather loose definitions of each level and leave it up to the user of the model to determine whether the processes within an organization are sufficient- research Data Services Maturity in Academic Libraries 161ly organized, documented, managed, or optimized. CMM-RDM provides more guidance, but it is an outward looking model; that is, it guides the development of data management for data management “consumers,” such as researchers or data managers, rather than librarians. It is also not clear how much empirical ground-work went into the process areas development and maturity levels. An “inward” approach to maturity modeling that looks at data management “providers,” or or-ganizations supporting research in academic institutions, will better suit the needs of research data services being developed and evaluated in academic libraries.Similar to the maturity of software development or data management, RDS maturity can be defined as the extent to which specific services are defined, man-aged, and evaluated in their impact and effectiveness. Each service and the system of services as a whole can be evaluated in its richness and consistency with the overall organizational goals. To be well-developed and well-understood through-out an organization, RDS need to rely on dissemination and training, and con-stant user feedback. Maturity also implies consistent growth and improvement via a disciplined and optimized approach.The difference between software development and RDS is in how growth over time and improvements are conceptualized. In the context of software devel-opment, the goal is to improve processes in order to more quickly, reliably, and effectively turn out new products, often in a competitive market environment. For academic libraries, however, there is a complex interaction between the goals of RDS and the bigger strategic goals of the library and the institution; further, individual institutions’ RDS efforts are just one part of a complex and largely cooperative network of data support, which includes external entities such as dis-ciplinary and other repositories, funders and their initiatives, commercial services, and so on. As a result, the highest, optimized level of maturity may have a different meaning for various institutions depending on their missions and goals. Knowing where the “finish line” is in terms of the nature of services provided in a particular institutional context is as important as knowing what services to implement.The key areas and levels proposed in the maturity model in table 6.2 are based on our empirical analysis of the ARL libraries, particularly on the analysis of inter-views with library administrators and program leads regarding their views on im-mediate RDS implementation directions, short-term goals, and future plans. While analyzing the interviews and extracting common themes and approaches, we found that many interviewees agreed that in order to develop strong and mature RDS, a library needs to have the following: a mission that is consistent with the institutional mission, services that match user needs, qualified and dedicated staff, strong rela-tionships with other units on campus and with other institutions, and established policies that guide data collection, sharing, and use. The synthesis of these themes along with many other discussions mentioned above formed the basis of eight key areas of maturity: leadership, services, users and stakeholders, research life cycle sup-port, governance, cost and budgeting, cross-unit collaboration, and human capital.162 ChApTer 6TABLE 6.2Research Data Services Maturity ModelMaturity LevelsKey AreasBasic :: Foundation BuildingIntermediate :: Organization and StandardizationAdvanced :: Monitoring and OptimizationLeadership (vision, strategy, culture)response to mandates and external activitiesData strategies are coordinated with institutional strategic documents.Data strategies guide service development and assessment.Services DMp assistance, consultations and instruction, best practices and information disseminationData deposit and repositories, archiving and preservation, collaboration and engagement, metadata, storage, data sharing and reusepermanent IDs for data and researchers, data curation, data processing and analysis, software and hardware, data citationUsers and stakeholdersAddressing individual requestsUser strategy is based on needs assessment.User needs are regularly evaluated, and services and needs shape each other.Research life cycle supportSupport on one end (upstream with DMp or downstream with data deposit)Support broadens and formalizes for both upstream and downstream.Support is embedded in the life cycle.Governance No policies, or reliance on institutional policiesData mentioned in other policies or one general data policySet of policies from acquisition to storage to curation and disseminationCost and budgetingSpending is a burden; each data-related expense needs to be requested and justified.Spending brings benefits and creates opportunities.Budgeting for growth and sustainabilityCross-unit collaborationNone, or ad hoc meetings and committees within institutionJoint initiatives with other unitsFormal partnerships within and outside, support from university administrationHuman capitalOther staff, such as subject librarians, assume data responsibilities, ad hoc trainingSolo librarian or a working group, consistent professional trainingDedicated team with shared or specialized responsibilities, strong skills, continuous learning research Data Services Maturity in Academic Libraries 163The RDS maturity levels are simplified from five to three as compared to other CMMs to aid in clearer definitions and subsequent validation effort. The three levels also effectively represent the diversity of RDS approaches among the academic libraries in our study, which corresponded to the basic, intermediate, and advanced categorization and converged on the following three stages: (1) foundation building, (2) organization and standardization, and (3) monitoring and optimization.During foundation building, the library focuses on implementing services that do not require significant resources and expertise, and it is done with limited staff support. The implementation efforts are mostly driven by mandates and in-dividual user requests, and no significant cross-unit collaboration and user assess-ment exists. Each data-related expense needs to be justified because it potentially takes away from other library activities.At the level of organization and standardization, the library gets involved in strategic efforts to coordinate its activities with the institutional goals and mission. The leadership becomes less reactive and more focused on a stronger view of the future and the role the services will play in shaping it. The services are customized to meet institution-specific requirements; they are based on user needs assessment and cross-unit collaboration. Professional development becomes part of the library activities, and spending becomes more organized to spur further development.At the monitoring and optimization level, services become more diverse and become embedded in the research life cycle. The library not only engages users and stakeholders and understands their needs, but also develops an effective feed-back system. The library also develops a comprehensive set of policies and stra-tegic documents and builds formal external and internal cross-unit partnerships. The data services team structure and organization moves from solo librarians to dedicated, multifunctional, or specialized teams.28Looking into the FutureAs academic libraries continue to grow their RDS programs, there are two areas of strategic activities that are of primary importance in developing appropriately targeted, effective services. First, libraries need to continue to assess what their peer institutions currently offer and ask: How similar and different they are? What they are trying to achieve? What they have learned and would do different-ly? and, more importantly, Why they are offering those particular services? Sec-ond, libraries should also aim for service development that is not simply reactive; developing a vision for RDS is a critical precursor to selecting impactful services to implement. This study provides a baseline that can be used to trace RDS de-velopment and improvements across institutions as well as a model for evaluating and building RDS programs.164 ChApTer 6A key take-away from this study is that more advanced services are probably those that are most closely targeted to the needs of individual institutions’ com-munities but are also cognizant of the broader research communities to which individuals belong. The institutional approach is one way to address RDS needs, and academic libraries are playing an important role in the national and glob-al data ecosystem.29 More mature RDS programs are not necessarily those that offer the longest menu of services or employ the largest number of staff, but rather those whose activities are more deeply embedded in the mission and activ-ities of the library and the broader institution. Mature RDS services have strong connections within and outside the library, a plan for sustainability in place, well-developed policies, and so on. In other words, a mature RDS program is one where services are chosen carefully, and then carefully organized, monitored, and optimized.To some extent, high levels of maturity reflect a high level of organizational buy-in: a sustainable budget for RDS, for example, is not something that can be accomplished in isolation. Our maturity model for RDS serves a dual purpose; it is a useful tool for identifying gaps and setting priorities, but it can also be a valuable tool for communicating with library administration. Part of developing RDS is asking for resources and support from the library, which means it is im-portant not only to express the goals and vision for RDS specifically, but also to align them with the broader strategic goals and vision of the library and the insti-tution. Many respondents in our interviews acknowledged resource limitations and recognized the importance of such an alignment.Opportunities abound in building RDS. For libraries looking to take the next step with their services, it is critical to determine which opportunities are aligned with their priorities, whether it is developing a new service, building part-nerships, or planning for assessment of existing services. Looking at what services peers offer as well as self-assessing a library’s current RDS maturity level helps to sort out which opportunities will provide the most value in the long run. research Data Services Maturity in Academic Libraries 165Appendix 6A: Typology of Services and Their Descriptions on WebsitesType of Service ExplanationAcquisition Statements that describe acquisition and collection management with regard to dataArchiving and preservationStatements that describe long-term archiving and preservation of dataBest practices and information disseminationStatements that describe efforts to collect and disseminate information about (best) practices in data management and sharing, mostly via websites and other similar types of materialsCollaboration and engagementStatements that describe efforts to engage with faculty, other units on campus, or other organizationsConsultations and instructionStatements that describe consultation and instruction initiatives, including workshops, seminars, and so on (more active orientation than dissemination)Copyright and ethicsStatements that describe efforts to providing information about intellectual property and ethical uses of dataData processing and analysisStatements that describe assistance and guidance on data processing and analysis resources and issuesData and researcher IDsStatements about services that help to create and maintain permanent identification for people and documentsData citation Statements about guidance on how and why to cite dataData curation Statements that describe activities of curation with regard to dataData deposit and repositoriesStatements that describe assistance in finding and using appropriate repositories (disciplinary or institutional)Data reference Statements about reference-type services, including search, sources, and use of toolsDMP assistance and mandate supportStatements about assistance with DMps and compliance with funding agencies mandatesMetadata Statements about assistance with generating or structuring metadataPolicies Statements about creating, developing, or providing policies with regard to dataSharing and reuse Statements that describe support of sharing and reuseSoftware and hardwareStatements that describe efforts to provide or inform about hardware and software resources to process and analyze dataStorage Statements that describe efforts to provide short-term and long-term storage for data166 ChApTer 6Notes1. Carol Tenopir, Ben Birch, and Suzie Allard, Academic Libraries and Research Data Services (Chicago: Association of College and Research Libraries, 2012), http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf.2. David Fearon Jr., Betsy Gunia, Sherry Lake, Barbara E. Pralle, and Andrew L. Sallans, Research Data Management Services: SPEC Kit 334: (Washington, DC: Association of Research Libraries, July 2013), http://publications.arl.org/Research-Data-Manage-ment-Services-SPEC-Kit-334/.3. Mustapha Mokrane, “Global Data for Global Science: The New ICSU World Data Sys-tem,” IAHS Newsletter, April 2013, 4, http://iahs.info/uploads/dms/16101.IAHS%20Newsletter%20105%20-%20Final2.pdf; Inter-university Consortium for Political and Social Research, “About ICPSR,” accessed February 3, 2016, https://www.icpsr.umich.edu/icpsrweb/content/membership/about.html.4. Anna Gold, “Libraries and the Data Challenge: Roles and Actions for Libraries,” D-Lib Magazine 13, no. 9/10 (2007), http://www.dlib.org/dlib/september07/gold/09gold-pt2.html.5. Ithiel de Sola Pool, “Data Archives and Libraries,” In INTREX: Report of a Planning Conference on Information Transfer Experiments, ed. Carl F. J. Overhage and R. Joyce Harman (Woods Hole, MA: MIT Press, 1965), 179–80.6. Stephen E. Fienberg, Margaret E. Martin, and Miron L. Straf, eds. Sharing Research Data (Washington, DC: National Academies Press, 1985).7. Laine Ruus, “The University of British Columbia Data Library: An Overview,” Library Trends 30, no. 3 (1982): 397–407.8. Jerome M. Clubb, Erik W. Austin, Carolyn L. Geda, and Michael W. Traugott, “Sharing Research Data in the Social Sciences,” In Sharing Research Data, eds. Stephen E. Fien-berg, Margaret E. Martin, and Miron L. Straf (Washington, DC: National Academies Press, 1985), 77-79.9. Alice Robbin, “The Data and Program Library Service: A Case Study in Organizing Special Libraries for Computer-Readable Statistical Data,” Library Trends 30, no. 3 (1982): 407–32.10. Interagency Working Group on Digital Data, Harnessing the Power of Digital Data for Science and Society (Arlington, VA: The Networking and Information Technology Research and Development Program, 2009), https://www.nitrd.gov/About/Harness-ing_Power_Web.pdf; Elizabeth Yakel, “Digital Curation,” OCLC Systems and Services 23, no. 4 (2007): 335–40, doi:10.1108/10650750710831466; Cyberinfrastructure Council, Cyberinfrastructure Vision for 21st Century Discovery, NSF 07-28 (Arlington, VA: National Science Foundation, 2007), http://www.nsf.gov/pubs/2007/nsf0728/; Office of Science and Innovation e-Infrastructure Working Group, Developing the UK’s e-Infrastructure for Science and Innovation (National e-Science Centre, 2007), http://www.nesc.ac.uk/documents/OSI/report.pdf.11. Joint Task Force on Library Support for E-Science, Agenda for Developing e-Science in Research Libraries (Washington, DC: Association of Research Libraries, 2007), http://www.arl.org/storage/documents/publications/escience-report-final-2007.pdf.12. Rebecca Reznik-Zellen, Jessica Adamick, and Stephen McGinty, “Tiers of Research Data Support Services,” Journal of eScience Librarianship 1, no. 1 (2012): 27–35,  research Data Services Maturity in Academic Libraries 167doi:10.7191/jeslib.2012.1002; Kathleen Shearer, and Diego Argaez, Addressing the Research Data Gap (Ottawa: Canadian Association of Research Libraries, 2010), http://www.carl-abrc.ca/uploads/pdfs/library_roles-final.pdf.13. Gold, “Libraries and the Data Challenge”; Anna Gold, Data Curation and Libraries (San Luis Obispo, CA: California Polytechnic State University Office of the Dean [Library], 2010), http://works.bepress.com/agold01/9/.14. Catherine Soehner, Catherine Steeves, and Jennifer Ward, E-Science and Data Support Services (Washington, DC: Association of Research Libraries, 2010), http://www.arl.org/storage/documents/publications/escience-report-2010.pdf.15. Tenopir, Birch, and Allard, Academic Libraries and Research Data Services.16. Fearon et al., Research Data Management Services.17. P. Bryan Heidorn, “The Emerging Role of Libraries in Data Curation and E-sciences,” Journal of Library Administration 51, no. 7–8 (2011): 670, doi:10.1080/01930826.2011.601269.18. Fearon et al., Research Data Management Services, 20.19. Ibid., 17–18; Katherine G. Akers, Fe C. Sferdean, Natsuko H. Nicholls, and Jennifer A. Green, “Building Support for Research Data Management: Biographies of Eight Re-search Universities,” International Journal of Digital Curation 9, no. 4 (2014): 171–91, doi:10.2218/ijdc.v9i2.327.20. 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