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Journal articles on the topic 'Research Data Services'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Lassi, Monica, Maria Johnsson, and Koraljka Golub. "Research data services." IFLA Journal 42, no. 4 (November 30, 2016): 266–77. http://dx.doi.org/10.1177/0340035216671963.

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The paper reports on an exploratory study of researchers’ needs for effective research data management at two Swedish universities, conducted in order to inform the ongoing development of research data services. Twelve researchers from diverse fields have been interviewed, including biology, cultural studies, economics, environmental studies, geography, history, linguistics, media and psychology. The interviews were structured, guided by the Data Curation Profiles Toolkit developed at Purdue University, with added questions regarding subject metadata. The preliminary analysis indicates that the research data management practices vary greatly among the respondents, and therefore so do the implications for research data services. The added questions on subject metadata indicate needs of services guiding researchers in describing their datasets with adequate metadata.

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Silver, Breezy. "Wharton Research Data Services (WRDS)." Charleston Advisor 22, no. 3 (January 1, 2021): 46–51. http://dx.doi.org/10.5260/chara.22.3.46.

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Wharton Research Data Services (WRDS) is a unique, complex data resource popular with academia but also utilized in government agencies, nonprofits, and corporations. It contains financial and accounting related data and tools but also includes limited data from some other subject areas like healthcare and marketing. WRDS provides the interface to access datasets with limited datasets included and then individual datasets may also be purchased. WRDS makes accessing and using data easier for users, but it also depends on users having enough experience in the field to be familiar with the various vendors and data coverage. Within academia, faculty and graduate students may have an easier time even though undergraduates are permitted access. While it is complicated, it does ease some of the burdens of working with data. Because it is focused on data, it allows users to find and use data with less limitations that users may encounter with regular business databases.

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Cox, Andrew M., Mary Anne Kennan, Elizabeth Josephine Lyon, Stephen Pinfield, and Laura Sbaffi. "Progress in Research Data Services." International Journal of Digital Curation 14, no. 1 (September 11, 2019): 126–35. http://dx.doi.org/10.2218/ijdc.v14i1.595.

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University libraries have played an important role in constructing an infrastructure of support for Research Data Management at an institutional level. This paper presents a comparative analysis of two international surveys of libraries about their involvement in Research Data Services conducted in 2014 and 2018. The aim was to explore how services had developed over this time period, and to explore the drivers and barriers to change. In particular, there was an interest in how far the FAIR data principles had been adopted. Services in nearly every area were more developed in 2018 than before, but technical services remained less developed than advisory. Progress on institutional policy was also evident. However, priorities did not seem to have shifted significantly. Open ended answers suggested that funder policy, rather than researcher demand, remained the main driver of service development and that resources and skills gaps remained issues. While widely understood as an important reference point and standard, because of their relatively recent publication date, FAIR principles had not been widely adopted explicitly in policy.

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Tenopir, Carol, Sanna Talja, Wolfram Horstmann, Elina Late, Dane Hughes, Danielle Pollock, Birgit Schmidt, Lynn Baird, Robert J. Sandusky, and Suzie Allard. "Research Data Services in European Academic Research Libraries." LIBER QUARTERLY 27, no. 1 (February 2017): 23–44. http://dx.doi.org/10.18352/lq.10180.

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Sesartic, Ana, and Matthias Töwe. "Research Data Services at ETH-Bibliothek." IFLA Journal 42, no. 4 (November 30, 2016): 284–91. http://dx.doi.org/10.1177/0340035216674971.

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The management of research data throughout its life-cycle is both a key prerequisite for effective data sharing and efficient long-term preservation of data. This article summarizes the data services and the overall approach to data management as currently practised at ETH-Bibliothek, the main library of ETH Zürich, the largest technical university in Switzerland. The services offered by service providers within ETH Zürich cover the entirety of the data life-cycle. The library provides support regarding conceptual questions, offers training and services concerning data publication and long-term preservation. As research data management continues to play a steadily more prominent part in both the requirements of researchers and funders as well as curricula and good scientific practice, ETH-Bibliothek is establishing close collaborations with researchers, in order to promote a mutual learning process and tackle new challenges.

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Akers, Katherine G. "Going beyond data management planning: Comprehensive research data services." College & Research Libraries News 75, no. 8 (September 1, 2014): 435–36. http://dx.doi.org/10.5860/crln.75.8.9176.

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Bailit, H. L. "Health Services Research." Advances in Dental Research 17, no. 1 (December 2003): 82–85. http://dx.doi.org/10.1177/154407370301700119.

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The major barriers to the collection of primary population-based dental services data are: (1) Dentists do not use standard record systems; (2) few dentists use electronic records; and (3) it is costly to abstract paper dental records. The value of secondary data from paid insurance claims is limited, because dentists code only services delivered and not diagnoses, and it is difficult to obtain and merge claims from multiple insurance carriers. In a national demonstration project on the impact of community-based dental education programs on the care provided to underserved populations, we have developed a simplified dental visit encounter system. Senior students and residents from 15 dental schools (approximately 200 to 300 community delivery sites) will use computers or scannable paper forms to collect basic patient demographic and service data on several hundred thousand patient visits. Within the next 10 years, more dentists will use electronic records. To be of value to researchers, these data need to be collected according to a standardized record format and to be available regionally from public or private insurers.

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Kerby, Erin E. "Research data services in veterinary medicine libraries." Journal of the Medical Library Association : JMLA 104, no. 4 (October 2016): 305–8. http://dx.doi.org/10.3163/1536-5050.104.4.010.

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Bowles, K. A., E. H. Skinner, D. Mitchell, R. Haas, M. Ho, K. Salter, K. May, et al. "Data Collection Methods in Health Services Research." Applied Clinical Informatics 06, no. 01 (2015): 96–109. http://dx.doi.org/10.4338/aci-2014-10-ra-0097.

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Summary Background: Hospital length of stay and discharge destination are important outcome measures in evaluating effectiveness and efficiency of health services. Although hospital administrative data are readily used as a data collection source in health services research, no research has assessed this data collection method against other commonly used methods. Objective: Determine if administrative data from electronic patient management programs are an effective data collection method for key hospital outcome measures when compared with alternative hospital data collection methods. Method: Prospective observational study comparing the completeness of data capture and level of agreement between three data collection methods; manual data collection from ward-based sources, administrative data from an electronic patient management program (i.PM), and inpatient medical record review (gold standard) for hospital length of stay and discharge destination. Results: Manual data collection from ward-based sources captured only 376 (69%) of the 542 in-patient episodes captured from the hospital administrative electronic patient management program. Administrative data from the electronic patient management program had the highest levels of agreement with inpatient medical record review for both length of stay (93.4%) and discharge destination (91%) data. Conclusion: This is the first paper to demonstrate differences between data collection methods for hospital length of stay and discharge destination. Administrative data from an electronic patient management program showed the highest level of completeness of capture and level of agreement with the gold standard of inpatient medical record review for both length of stay and discharge destination, and therefore may be an acceptable data collection method for these measures. Citation: Sarkies MN, Bowles K-A, Skinner EH, Mitchell D, Haas R, Ho M, Salter K, May K, Markham D, O’Brien L, Plumb S, Haines T.P. Data collection methods in health services research – hospital length of stay and discharge destination. Appl Clin Inf 2015; 6: 96–109http://dx.doi.org/10.4338/ACI-2014-10-RA-0097

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Curdt, Constanze, and Dirk Hoffmeister. "Research data management services for a multidisciplinary, collaborative research project." Program: electronic library and information systems 49, no. 4 (September 1, 2015): 494–512. http://dx.doi.org/10.1108/prog-02-2015-0016.

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Purpose – Research data management (RDM) comprises all processes, which ensure that research data are well-organized, documented, stored, backed up, accessible, and reusable. RDM systems form the technical framework. The purpose of this paper is to present the design and implementation of a RDM system for an interdisciplinary, collaborative, long-term research project with focus on Soil-Vegetation-Atmosphere data. Design/methodology/approach – The presented RDM system is based on a three-tier (client-server) architecture. This includes a file-based data storage, a database-based metadata storage, and a self-designed user-friendly web-interface. The system is designed in cooperation with the local computing centre, where it is also hosted. A self-designed interoperable, project-specific metadata schema ensures the accurate documentation of all data. Findings – A RDM system has to be designed and implemented according to requirements of the project participants. General challenges and problems of RDM should be considered. Thus, a close cooperation with the scientists obtains the acceptance and usage of the system. Originality/value – This paper provides evidence that the implementation of a RDM system in the provided and maintained infrastructure of a computing centre offers many advantages. Consequently, the designed system is independent of the project funding. In addition, access and re-use of all involved project data is ensured. A transferability of the presented approach to another interdisciplinary research project was already successful. Furthermore, the designed metadata schema can be expanded according to changing project requirements.

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Barbrow, Sarah, Denise Brush, and Julie Goldman. "Research data management and services: Resources for novice data librarians." College & Research Libraries News 78, no. 5 (May 11, 2017): 274. http://dx.doi.org/10.5860/crln.78.5.274.

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Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.

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Kroeker, Brian. "Data Services Assists Teaching and Research: Delivery of Data Services via the World Wide Web." IASSIST Quarterly 21, no. 1 (July 31, 1997): 25. http://dx.doi.org/10.29173/iq571.

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Rasmussen, Karsten Boye. "Sharing qualitative research data, improving data literacy and establishing national data services." IASSIST Quarterly 43, no. 4 (January 2, 2020): 1–2. http://dx.doi.org/10.29173/iq972.

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Welcome to the fourth issue of volume 43 of the IASSIST Quarterly (IQ 43:4, 2019). The first article is authored by Jessica Mozersky, Heidi Walsh, Meredith Parsons, Tristan McIntosh, Kari Baldwin, and James M. DuBois – all located at the Bioethics Research Center, Washington University School of Medicine, St. Louis, Missouri in USA. They ask the question “Are we ready to share qualitative research data?”, with the subtitle “Knowledge and preparedness among qualitative researchers, IRB Members, and data repository curators.” The subtitle indicates that their research includes a survey of key personnel related to scientific data sharing. The report is obtained through semi-structured in-depth interviews with 30 data repository curators, 30 qualitative researchers, and 30 IRB staff members in the USA. IRB stands for Institutional Review Board, which in other countries might be called research ethics committee or similar. There is generally an increasing trend towards data sharing and open science, but qualitative data are rarely shared. The dilemma behind this reluctance to share is exemplified by health data where qualitative methods explore sensitive topics. The sensitivity leads to protection of confidentiality, which hinders keeping sufficient contextual detail for secondary analyses. You could add that protection of confidentiality is a much bigger task in qualitative data, where sensitive information can be hidden in every corner of the data, that consequently must be fine-combed, while with quantitative data most decisions concerning confidentiality can be made at the level of variables. The reporting in the article gives insights into the differences between the three stakeholder groups. An often-found answer among researchers is that data sharing is associated with quantitative data, while IRB members have little practice with qualitative. Among curators, about half had curated qualitative data, but many only worked with quantitative data. In general, qualitative data sharing lacks guidance and standards. The second article also raises a question: “How many ways can we teach data literacy?” We are now in Asia with a connection to the USA. The author Yun Dai is working at the Library of New York University Shanghai, where they have explored many ways to teach data literacy to undergraduate students. These initiatives, described in the article, included workshops and in-class instruction - which tempted students by offering up-to-date technology, through online casebooks of topics in the data lifecycle, to event series with appealing names like “Lying with Data.” The event series had a marketing mascot - a “Lying with Data” Pinocchio - and sessions on being fooled by advertisements and getting the truth out of opinion surveys. Data literacy has a resemblance to information literacy and in that perspective, data literacy is defined as “critical thinking applied to evaluating data sources and formats, and interpreting and communicating findings,” while statistical literacy is “the ability to evaluate statistical information as evidence.” The article presents the approaches and does not conclude on the question, “How many?” No readers will be surprised by the missing answer, and I am certain readers will enjoy the ideas of the article and the marketing focus. With the last article “Examining barriers for establishing a national data service,” the author Janez Štebe takes us to Europe. Janez Štebe is head of the social science data archives (Arhiv Družboslovnih Podatkov) at the University of Ljubljana, Slovenia. The Consortium of European Social Science Data Archives (CESSDA) is a distributed European social science data infrastructure for access to research data. CESSDA has many - but not all - European countries as members. The focus is on the situation in 20 non-CESSDA member European countries, with emerging and immature data archive services being developed through such projects as the CESSDA Strengthening and Widening (SaW 2016 and 2017) and CESSDA Widening Activities (WA 2018). By identifying and comparing gaps and differences, a group of countries at a similar level may consider following similar best practice examples to achieve a more mature and supportive open scientific data ecosystem. Like the earlier articles, this article provides good references to earlier literature and description of previous studies in the area. In this project 22 countries were selected, all CESSDA non-members, and interviewees among social science researchers and data librarians were contacted with an e-mail template between October 2018 and January 2019. The article brings results and discussion of the national data sharing culture and data infrastructure. Yes, there is a lack of money! However, it is the process of gradually establishing a robust data infrastructure that is believed to impact the growth of a data sharing culture and improve the excellence and the efficiency of research in general. Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the IQ. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author login to https://www.iassistquarterly.com (our Open Journal System application). We permit authors to “deep link” into the IQ as well as to deposit the paper in your local repository. Chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the IASSIST Quarterly website at https://www.iassistquarterly.com. Authors are very welcome to take a look at the instructions and layout: https://www.iassistquarterly.com/index.php/iassist/about/submissions Authors can also contact me directly via e-mail: kbr@sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you. Karsten Boye Rasmussen - December 2019

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Ozaydin, Bunyamin, Ferhat Zengul, Nurettin Oner, and Sue S. Feldman. "Healthcare Research and Analytics Data Infrastructure Solution: A Data Warehouse for Health Services Research." Journal of Medical Internet Research 22, no. 6 (June 4, 2020): e18579. http://dx.doi.org/10.2196/18579.

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Background Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. Objective This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. Methods The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. Results The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. Conclusions To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

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Martin, Elaine. "Targeting and Customizing Research Data Management Services (RDM)." Journal of eScience Librarianship 4, no. 1 (2015): e1086. http://dx.doi.org/10.7191/jeslib.2015.1086.

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Witt, Michael, and Wolfram Horstmann. "International approaches to research data services in libraries." IFLA Journal 42, no. 4 (November 30, 2016): 251–52. http://dx.doi.org/10.1177/0340035216678726.

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Garnick, Deborah W., Dominic Hodgkin, and Constance M. Horgan. "Selecting data sources for substance abuse services research." Journal of Substance Abuse Treatment 22, no. 1 (January 2002): 11–22. http://dx.doi.org/10.1016/s0740-5472(01)00208-2.

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Marlina, Ekawati, and Betty Purwandari. "Strategy for Research Data Management Services in Indonesia." Procedia Computer Science 161 (2019): 788–96. http://dx.doi.org/10.1016/j.procs.2019.11.184.

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Else, Barbara A., Edward P. Armstrong, and Emily R. Cox. "Data sources for pharmacoeconomic and health services research." American Journal of Health-System Pharmacy 54, no. 22 (November 15, 1997): 2601–8. http://dx.doi.org/10.1093/ajhp/54.22.2601.

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Niu, Jinfang. "Diffusion and adoption of research data management services." Global Knowledge, Memory and Communication 69, no. 3 (September 6, 2019): 117–33. http://dx.doi.org/10.1108/gkmc-05-2019-0057.

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Purpose This paper aims to identify the diffusion patterns, especially the communication channels, in the diffusion and adoption of research data management services (RDMS) among libraries. Design/methodology/approach Literature about the RDMS in individual libraries was gathered and analyzed. Data relevant to the research questions were extracted and analyzed. Findings Early adopters conduct much original research to create RDMS and they often serve as change agents in diffusing their RDMS and related innovations to other libraries. In contrast, late adopters usually learn from early adopters and use their innovations for establishing their own RDMS. Communication channels used in diffusing RDMS deviate slightly from those reported in general diffusion of innovations (DOI) theories. Research limitations/implications Gathered literature provides incomplete and uneven information for RDMS adopters. This makes it difficult to identify adopter categories and test many generalizations in DOI theories. To overcome these limitations, surveys and interviews will be conducted in the future. Originality/value Findings from this project contribute to general DOI theories because RDMS is unique compared with many other innovations. The diffusion of RDMS is a decentralized process that involves a high-degree of reinvention and it involves the generation and diffusion of many relevant innovations. The project also identified scholarly communication and inter-organization networks as new types of communication channels that are not well accounted for in existing DOI theories.

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Warner, Ross M., Christopher Bastianpillai, Benjamin W. Lamb, Nick Sevdalis, and James SA Green. "Health services research." Journal of Clinical Urology 11, no. 5 (January 22, 2018): 357–60. http://dx.doi.org/10.1177/2051415818754578.

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Health services research (HSR) is the science of the evaluation and improvement of health care provision. HSR can be used by health care organisations to develop and understand administrative, financial and organisational processes in order to improve efficacy of clinical treatments, efficiency of their delivery, and ultimately patient outcomes. HSR applies both quantitative and qualitative methodology, utilising data collected from real-life clinical practice, rather than the confines of more controlled traditional efficacy trials. With new and developing therapies, HSR provides an opportunity to enhance the delivery of cost-effective and efficient health care. Level of evidence: Not applicable (educational article).

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Burgi, Pierre-Yves, Eliane Blumer, and Basma Makhlouf-Shabou. "Research data management in Switzerland." IFLA Journal 43, no. 1 (January 9, 2017): 5–21. http://dx.doi.org/10.1177/0340035216678238.

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In this article, the authors report on an ongoing data life cycle management national project realized in Switzerland, with a major focus on long-term preservation. Based on an extensive document analysis as well as semi-structured interviews, the project aims at providing national services to respond to the most relevant researchers’ data life cycle management needs, which include: guidelines for establishing a data management plan, active data management solutions, long-term preservation storage options, training, and a single point of access and contact to get support. In addition to presenting the different working axes of the project, the authors describe a strategic management and lean startup template for developing new business models, which is key for building viable services.

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Barnes, Craig, Binam Bajracharya, Matthew Cannalte, Zakir Gowani, Will Haley, Taha Kass-Hout, Kyle Hernandez, et al. "The Biomedical Research Hub: a federated platform for patient research data." Journal of the American Medical Informatics Association 29, no. 4 (November 22, 2021): 619–25. http://dx.doi.org/10.1093/jamia/ocab247.

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Abstract Objective The objective was to develop and operate a cloud-based federated system for managing, analyzing, and sharing patient data for research purposes, while allowing each resource sharing patient data to operate their component based upon their own governance rules. The federated system is called the Biomedical Research Hub (BRH). Materials and Methods The BRH is a cloud-based federated system built over a core set of software services called framework services. BRH framework services include authentication and authorization, services for generating and assessing findable, accessible, interoperable, and reusable (FAIR) data, and services for importing and exporting bulk clinical data. The BRH includes data resources providing data operated by different entities and workspaces that can access and analyze data from one or more of the data resources in the BRH. Results The BRH contains multiple data commons that in aggregate provide access to over 6 PB of research data from over 400 000 research participants. Discussion and conclusion With the growing acceptance of using public cloud computing platforms for biomedical research, and the growing use of opaque persistent digital identifiers for datasets, data objects, and other entities, there is now a foundation for systems that federate data from multiple independently operated data resources that expose FAIR application programming interfaces, each using a separate data model. Applications can be built that access data from one or more of the data resources.

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Vejvoda, Berenica, Rong Luo, and Selinda Berg. "Virtualization of Research Data Services during the COVID-19 Pandemic as an Opportunity to Enhance Research Data Support." International Journal of Librarianship 6, no. 2 (December 22, 2021): 49–62. http://dx.doi.org/10.23974/ijol.2021.vol6.2.204.

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For Canadian academic libraries, the emergence of the COVID-19 pandemic necessitated an unprecedented switch to virtual services. An abrupt halt to in-person activities required almost all libraries to utilize new technologies in order to continue serving patrons. While the Academic Data Centre (ADC) has traditionally offered both physical and online services, with the emergence of the pandemic, the ADC pivoted to exclusively online service provision. Through new initiatives such as remote desktop access to statistical software, embedded virtual spaces for consultation and breakout discussions, online workshops and teaching, and the use of social media--the Academic Data Centre emerged successful in supporting student and faculty data needs. While virtually scaling up data services was essential to avoid disrupting researchers working with data, the shift to online services also presented an unexpected opportunity to reflect meeting the data needs of users and, in turn, strategize innovative future data service delivery. Three themes emerged from our reflection: emphasis on greater accessibility; more flexible instruction; and the benefits for cultivating a data community. As emerge from the pandemic, the ADC expects to further embrace newly implemented technologies and virtual services to further scale and augment research data service support.

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Meurisch, Christian, and Max Mühlhäuser. "Data Protection in AI Services." ACM Computing Surveys 54, no. 2 (April 2021): 1–38. http://dx.doi.org/10.1145/3440754.

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Advances in artificial intelligence (AI) have shaped today’s user services, enabling enhanced personalization and better support. As such AI-based services inevitably require user data, the resulting privacy implications are de facto the unacceptable face of this technology. In this article, we categorize and survey the cutting-edge research on privacy and data protection in the context of personalized AI services. We further review the different protection approaches at three different levels, namely, the management, system, and AI levels—showing that (i) not all of them meet our identified requirements of evolving AI services and that (ii) many challenges are addressed separately or fragmentarily by different research communities. Finally, we highlight open research challenges and future directions in data protection research, especially that comprehensive protection requires more interdisciplinary research and a combination of approaches at different levels.

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Higman, Rosie, and Stephen Pinfield. "Research data management and openness." Program: electronic library and information systems 49, no. 4 (September 1, 2015): 364–81. http://dx.doi.org/10.1108/prog-01-2015-0005.

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Purpose – The purpose of this paper is to investigate the relationship between research data management (RDM) and data sharing in the formulation of RDM policies and development of practices in Higher Education Institutions (HEIs). Design/methodology/approach – Two strands of work were undertaken sequentially: first, content analysis of 37 RDM policies from UK HEIs; and second, two detailed case studies of institutions with different approaches to RDM based on semi-structured interviews with staff involved in the development of RDM policy and services. The data are interpreted using insights from Actor Network Theory. Findings – RDM policy formation and service development has created a complex set of networks within and beyond institutions involving different professional groups with widely varying priorities shaping activities. Data sharing is considered an important activity in the policies and services of HEIs studied, but its prominence can in most cases be attributed to the positions adopted by large research funders. Research limitations/implications – The case studies, as research based on qualitative data, cannot be assumed to be universally applicable but do illustrate a variety of issues and challenges experienced more generally, particularly in the UK. Practical implications – The research may help to inform development of policy and practice in RDM in HEIs and funder organisations. Originality/value – This paper makes an early contribution to the RDM literature on the specific topic of the relationship between RDM policy and services, and openness – a topic which to date has received limited attention.

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Kotarski, Rachael, and Torsten Reimer. "Making Everything Available. British Library Research Services and Research Data Strategy." International Journal of Digital Curation 13, no. 1 (December 27, 2018): 161–69. http://dx.doi.org/10.2218/ijdc.v13i1.605.

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The way that researchers generate, analyse and share information keeps evolving at a rapid pace. To ensure that it is well equipped to serve its global user base for years to come, the British Library is transforming the way it works too, from the physical buildings to its digital service portfolio. One key programme, Everything Available, will ensure the Library’s continued support for research with services to enable access to information in an open and timely manner. This paper will describe the activities planned within Everything Available, with a particular focus on the aims of the Library’s recently refreshed Research Data Strategy. It will give an insight into the challenges and opportunities faced by a National Library in providing relevant services in an ‘open’ world.

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Joo, Soohyung, and Christie Peters. "User needs assessment for research data services in a research university." Journal of Librarianship and Information Science 52, no. 3 (July 1, 2019): 633–46. http://dx.doi.org/10.1177/0961000619856073.

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This study assesses the needs of researchers for data-related assistance and investigates their research data management behavior. A survey was conducted, and 186 valid responses were collected from faculty, researchers, and graduate students across different disciplines at a research university. The services for which researchers perceive the greatest need include assistance with quantitative analysis and data visualization. Overall, the need for data-related assistance is relatively higher among health scientists, while humanities researchers demonstrate the lowest need. This study also investigated the data formats used, data documentation and storage practices, and data-sharing behavior of researchers. We found that researchers rarely use metadata standards, but rely more on a standard file-naming scheme. As to data sharing, respondents are likely to share their data personally upon request or as supplementary materials to journal publications. The findings of this study will be useful for planning user-centered research data services in academic libraries.

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Blaiszik, B., K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, and I. Foster. "The Materials Data Facility: Data Services to Advance Materials Science Research." JOM 68, no. 8 (July 6, 2016): 2045–52. http://dx.doi.org/10.1007/s11837-016-2001-3.

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Palsdottir, Agusta. "Data literacy and management of research data – a prerequisite for the sharing of research data." Aslib Journal of Information Management 73, no. 2 (January 20, 2021): 322–41. http://dx.doi.org/10.1108/ajim-04-2020-0110.

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PurposeThe purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research.Design/methodology/approachThis is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% (N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data.FindingsThe main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods.Research limitations/implicationsThe survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it.Practical implicationsAwareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management.Originality/valueThe study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management.

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Koltay, Tibor. "Research 2.0 and Research Data Services in academic and research libraries: priority issues." Library Management 38, no. 6/7 (August 15, 2017): 345–53. http://dx.doi.org/10.1108/lm-11-2016-0082.

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Purpose The purpose of this paper is to examine the role of Research Data Services (RDSs), consisting of research data management, data curation and data stewardship, and data literacy education in supporting Research 2.0. Besides this, theory and principles, as well as selected examples of best practices in the relevant fields are presented. Design/methodology/approach A literature-based overview of actual insights on tasks and roles that academic and research libraries have to fulfil in order to react to the developments generated by the appearance and growing importance of Research 2.0 is provided. Taking the wide spectre of related issues into account, the discussion is limited to RDSs. Findings Even though Research 2.0 is evolving in different countries and some local environments in dissimilar ways, its data-intensive nature requires the helping presence of academic libraries and librarians. Being an emerging phenomenon, it will undoubtedly take several different shapes as it works itself out in time, but librarians should try to discover service niches, which may not be covered by other academic organisations, or their coverage is only partial or even unsatisfactory. Research limitations/implications Taking the wide spectre of issues into account, the review of literature is limited to the period between 2014 and 2016. Originality/value The paper intends to add to the body of knowledge about the relationship between RDSs and Research 2.0, as well as about the association between the components of the former.

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Treloar, Andrew, and Ross Wilkinson. "Access to Data for eResearch: Designing the Australian National Data Service Discovery Services." International Journal of Digital Curation 3, no. 2 (December 2, 2008): 151–58. http://dx.doi.org/10.2218/ijdc.v3i2.66.

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Much work on data repositories has derived from effort on document repositories. It is our contention that people do not access research data for the same reasons that they access research publications. We argue that it is valuable to understand information needs, both immediate and contextual, in establishing both what information should be collected, what metadata are captured, and what discovery services should be established. We report on the information needs that we have collected in our efforts in establishing the Australian National Data Service. These needs cover much more than data – there are needs for information about the data, their creators, a need for overviews, and further requirements to do with proof, collaboration, and innovation. We provide an analysis of those needs, and a set of conclusions that has led to some implementation decisions for ANDS.

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Su, Qian. "Research of instrument to measure customer perceived service quality of mobile data services." International Journal of Networking and Virtual Organisations 14, no. 1/2 (2014): 176. http://dx.doi.org/10.1504/ijnvo.2014.065101.

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Li, Qin. "Research on Cloud Computing Services in Data Security Issues." Applied Mechanics and Materials 687-691 (November 2014): 3032–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3032.

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This paper describes cloud computing services, analyzes the types of services and cloud computing services corresponding structural model; research focus on cloud computing data security issues were discussed in the service mode.

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Joo, Soohyung, and Gisela M. Schmidt. "Research data services from the perspective of academic librarians." Digital Library Perspectives 37, no. 3 (March 8, 2021): 242–56. http://dx.doi.org/10.1108/dlp-10-2020-0106.

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Purpose This study aims to investigate the perceptions of academic librarians regarding research data services (RDS) in academic library environments. This study also examines a range of challenges in RDS from the perspectives of academic librarians. Design/methodology/approach A nationwide online survey was administered to academic librarians engaged in data services at research universities around the USA. The collected survey responses were analyzed quantitatively using descriptive statistics, hierarchical clustering and multidimensional scaling. Findings Academic librarians perceived that consultation services would be more valuable to users than technical services in offering RDS. Accordingly, skills associated with consultation services such as instructional skills and data management planning were perceived by participants to be more important. The results revealed that academic libraries would need to seek collaboration opportunities with other units on campus to develop and offer RDS, especially technical services. Originality/value This study contributes to the existing body of research on the topic of data services in research universities. The study investigated various types of specific professional competencies and used clustering analysis to identify closely associated groups of service types. In addition, this study comprehensively examined both relevant resources for and barriers to RDS.

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Minor, David, Matt Critchlow, Arwen Hutt, Declan Fleming, Mary Linn Bergstrom, and Don Sutton. "Research Data Curation Pilots: Lessons Learned." International Journal of Digital Curation 9, no. 1 (June 17, 2014): 220–30. http://dx.doi.org/10.2218/ijdc.v9i1.313.

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In the spring of 2011, the UC San Diego Research Cyberinfrastructure (RCI) Implementation Team invited researchers and research teams to participate in a research curation and data management pilot program. This invitation took the form of a campus-wide solicitation. More than two dozen applications were received and, after due deliberation, the RCI Oversight Committee selected five curation-intensive projects. These projects were chosen based on a number of criteria, including how they represented campus research, varieties of topics, researcher engagement, and the various services required. The pilot process began in September 2011, and will be completed in early 2014. Extensive lessons learned from the pilots are being compiled and are being used in the on-going design and implementation of the permanent Research Data Curation Program in the UC San Diego Library. In this paper, we present specific implementation details of these various services, as well as lessons learned. The program focused on many aspects of contemporary scholarship, including data creation and storage, description and metadata creation, citation and publication, and long term preservation and access. Based on the lessons learned in our processes, the Research Data Curation Program will provide a suite of services from which campus users can pick and choose, as necessary. The program will provide support for the data management requirements from national funding agencies.

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Akers, Katherine G., Fe C. Sferdean, Natsuko H. Nicholls, and Jennifer A. Green. "Building Support for Research Data Management: Biographies of Eight Research Universities." International Journal of Digital Curation 9, no. 2 (November 6, 2014): 171–91. http://dx.doi.org/10.2218/ijdc.v9i2.327.

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Academic research libraries are quickly developing support for research data management (RDM), including both new services and infrastructure. Here, we tell the stories of how eight different universities have developed programs of RDM support, focusing on the prominent role of the library in educating and assisting researchers with managing their data throughout the research lifecycle. Based on these stories, we construct timelines for each university depicting key steps in building support for RDM, and we discuss similarities and dissimilarities among universities in motivation to provide RDM support, collaborations among campus units, assessment of needs and services, and changes in staffing.

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Chiware, Elisha R. T. "Open research data in African academic and research libraries: a literature analysis." Library Management 41, no. 6/7 (June 1, 2020): 383–99. http://dx.doi.org/10.1108/lm-02-2020-0027.

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PurposeThe paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data.Design/methodology/approachThe literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list.FindingsThe review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught.Originality/valueThe role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures.

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Kang, Hosung, and Shannon Sibbald. "Challenges to Using Big Data in Health Services Research." University of Western Ontario Medical Journal 87, no. 2 (March 12, 2019): 18–20. http://dx.doi.org/10.5206/uwomj.v87i2.1140.

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Given the shift in current healthcare trends toward digitization of storing information, there has been an increase in the number of studies using administrative databases. These databases provide a powerful tool to conduct research on outcomes, health services, and epidemiology. However, these databases have limitations and biases that should be considered. Given the sensitive information regarding patients’ health in the database, security clearances must be granted before data is accessed. Furthermore, algorithms to link the different variables to create a cohort of people with specific disease are imperfect and may not yield an accurate representation. Due to a large volume of records, a statistically significant finding may be observed, but may provide insignificant clinical results. Despite the current limitations, administrative databases provide powerful data that researchers can use to identify gaps in performance to improve the healthcare system.

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Katz, Slava, Zeynal Karaca, Weihuang Wong, Brandon Hesgrove, Herbert Wong, Don Jang, and Dean Resnick. "Using National Synthetic Data to Conduct Health Services Research." Health Services Research 56, S2 (September 2021): 26. http://dx.doi.org/10.1111/1475-6773.13754.

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Blewett, Lynn A., Kathleen Thiede Call, Joanna Turner, and Robert Hest. "Data Resources for Conducting Health Services and Policy Research." Annual Review of Public Health 39, no. 1 (April 2018): 437–52. http://dx.doi.org/10.1146/annurev-publhealth-040617-013544.

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Russell, Fiona. "Delivering Research Data Management Services: Fundamentals of Good Practice." Australian Academic & Research Libraries 45, no. 4 (October 2, 2014): 332–33. http://dx.doi.org/10.1080/00048623.2014.974239.

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Tenopir, Carol, Robert J. Sandusky, Suzie Allard, and Ben Birch. "Academic librarians and research data services: preparation and attitudes." IFLA Journal 39, no. 1 (March 2013): 70–78. http://dx.doi.org/10.1177/0340035212473089.

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Moss, Michael. "Delivering research data management services: fundamentals of good practice." Archives and Records 35, no. 2 (July 3, 2014): 164–66. http://dx.doi.org/10.1080/23257962.2014.947924.

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Wang, Minglu. "Supporting the research process through expanded library data services." Program 47, no. 3 (July 19, 2013): 282–303. http://dx.doi.org/10.1108/prog-04-2012-0010.

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Gunaseelan, Vidhya, Brooke Kenney, Jay Soong-Jin Lee, and Hsou Mei Hu. "Databases for surgical health services research: Clinformatics Data Mart." Surgery 165, no. 4 (April 2019): 669–71. http://dx.doi.org/10.1016/j.surg.2018.02.002.

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Grosse, Scott D., Sheree L. Boulet, Djesika D. Amendah, and Suzette O. Oyeku. "Administrative Data Sets and Health Services Research on Hemoglobinopathies." American Journal of Preventive Medicine 38, no. 4 (April 2010): S557—S567. http://dx.doi.org/10.1016/j.amepre.2009.12.015.

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Chung, Joaquin, Sean Donovan, Jeronimo Bezerra, Heidi Morgan, Julio Ibarra, Russ Clark, and Henry Owen. "Novel network services for supporting big data science research." Future Generation Computer Systems 98 (September 2019): 512–21. http://dx.doi.org/10.1016/j.future.2019.03.047.

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Wunsch, Hannah, David A. Harrison, and Kathryn Rowan. "Health services research in critical care using administrative data." Journal of Critical Care 20, no. 3 (September 2005): 264–69. http://dx.doi.org/10.1016/j.jcrc.2005.08.002.

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Matusiak, Krystyna K., and Frank A. Sposito. "Types of research data management services: An international perspective." Proceedings of the Association for Information Science and Technology 54, no. 1 (January 2017): 754–56. http://dx.doi.org/10.1002/pra2.2017.14505401144.

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