Browsing by Subject "Research -- Data processing"

Now showing 1 - 6 of 6
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    Building the Future of Research Together: Collaborating with a Clinical and Translational Science Award (CTSA)-Funded Translational Science Institute to Provide Data Management Training
    (2014-05-19) Coates, Heather L.
    Objectives: To explore potential collaborations between academic libraries and Clinical Translational Science Award (CTSA) - funded institutes with respect to data management training and support. Methods: The National Institutes of Health CTSAs have established a well-funded, crucial infrastructure supporting large-scale collaborative biomedical research. This infrastructure is also valuable for smaller, more localized research projects. While infrastructure and corresponding support is often available for large, well-funded projects, these services have generally not been extended to smaller projects. This is a missed opportunity on both accounts. Academic libraries providing data services can leverage CTSA-based resources, while CTSA-funded institutes can extend their reach beyond large biomedical projects to serve the long tail of research data. Results: A year-long series of conversations with the Indiana CTSI Data Management Team resulted in resource sharing, consensus building about key issues in data management, provision of expert feedback on a data management training curriculum, and several avenues for future collaborations. Conclusions: Data management training for graduate students and early career researchers is a vital area of need that would benefit from the combined infrastructure and expertise of translational science institutes and academic libraries. Such partnerships can leverage the instructional, preservation, and access expertise in academic libraries, along with the storage, security, and analytical expertise in translational science institutes to improve the management, protection, and access of valuable research data.
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    E-science and libraries (for non science librarians)
    (2011-11) Snajdr, Eric
    Information Technology is rapidly changing the world of scientific research. We have entered a new era of science. Some call it e-science, while others call it the 4th paradigm of science. Scientists, with the aid of technology, are continually amassing larger and more complex datasets. These data are accumulated are at an ever-accelerating rate. How will this information be organized? What, if any of it should be preserved for future use? How will it be preserved? If it is preserved, how will it be made publically accessible? The NSF and others describe the solving of problems such as these as some of the major challenges of this scientific generation. They also state that tackling these problems will take expertise from many fields, including library and information science. A recent movement of this new era of science is an increasing requirement for scientists to archive and make their research data public. For example, the National Science Foundation (as of January 18, 2011) is requiring scientists to articulate how they will accomplish these goals within data management plans that must be submitted with each grant proposal. What role can libraries play in this new realm of science? What role are libraries already playing? Several libraries have taken the lead in initiating efforts in assisting scientists with a variety of data management needs. This presentation will include a brief overview of the current trends as well as possible future directions in librarianship that this new era of science may lead.
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    From Cultural Heritage to Research Innovations: Digital Scholarship Services for a Changing University
    (http://research.iupui.edu/events/researchday2013/documents/b4.pdf, 2013-04-05) Odell, Jere D.; Johnson, Jennifer; Coates, Heather L.
    Digital technologies are changing the character of research, scholarship and education. While some may see these changes as a threat to business as usual, others see opportunities to build stronger universities, healthier learning communities and more equitable access to knowledge and information. The Program of Digital Scholarship provides the tools and services to help the IUPUI community develop both innovative and proven projects supported by the University Library. The Program of Digital Scholarship provides the IUPUI community with a variety of services and tools for sharing and managing their digital scholarly assets including but not limited to: published articles, white papers, conference presentations, images, artifacts, reusable learning objects, theses and dissertations, historic texts, datasets, and multimedia files. By providing open access to digital resources, these services contribute to IUPUI’s success as an innovative, urban university. In this poster, we describe four use cases in which faculty or community groups have collaborated with the Program of Digital Scholarship to build and share 1) the cultural heritage of central Indiana, 2) published works of faculty and student scholarship, 3) journals published at IUPUI, and 4) data management plans for grant-funded research. In each case, the Program of Digital Scholarship improved the dissemination of education, research and culture while raising the standards for preservation, usability, and accessibility.
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    Improving data management in academic research: Assessment results for a pilot lab
    (2014-05-19) Coates, Heather L.
    Common practices for data collection, storage, organization, documentation, sharing, re-use, and preservation are often suboptimal. Issues often arising from common data practices include data loss, corruption, poor data integrity, and an inability to demonstrate the provenance (i.e., the origin) of the data. Ineffective data management can result in data that are unusable for re-use and re-analysis. However, effective data management practices exist to support data integrity, interoperability, and re-use. These practices maximize the value and potential impact of any particular dataset. In light of the gap between common practice and known effective strategies, we developed an intensive lab curriculum to train students and research support staff in implementing these strategies. This lab addresses the lack of formal data management training available on our campus and targets key processes in the data life cycle, promoting strategies that facilitate generation of quality data appropriate for re-use.
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    Teaching data literacy skills in a lab environment
    (2014-06-04) Coates, Heather L.
    Equipping researchers with the skills to effectively utilize data in the global data ecosystem requires proficiency with data literacies and electronic resource management. This is a valuable opportunity for libraries to leverage existing expertise and infrastructure to address a significant gap data literacy education. This session will describe a workshop for developing core skills in data literacy. In light of the significant gap between common practice and effective strategies emerging from specific research communities, we incorporated elements of a lab format to build proficiency with specific strategies. The lab format is traditionally used for training procedural skills in a controlled setting, which is also appropriate for teaching many daily data management practices. The focus of the curriculum is to teach data management strategies that support data quality, transparency, and re-use. Given the variety of data formats and types used in health and social sciences research, we adopted a skills-based approach that transcends particular domains or methodologies. Attendees applied selected strategies using a combination of their own research projects and a carefully defined case study to build proficiency.
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    Training researchers how to manage data to produce better results, enable reuse, and provide for long-term access
    (2016-04-15) Coates, Heather L.
    The existing academic research workforce is ill equipped to manage research data using increasingly complex computing technologies available to them. Despite the availability of ever more powerful desktops, mobile technologies, and high performance cloud computing and storage, universities are failing to provide graduate students with adequate data management skills for research in academia or industry. The challenge for mid- and late-career faculty is even greater, given that it is much more difficult to change established research practices in the midst of ongoing projects. This skills gap puts at risk billions of research dollars, the integrity of vast quantities of research data, and the quality of life for millions of people. Providing faculty and students with the skills they need to collect, manage, and share their data effectively is a challenge many academic libraries are taking on. Though libraries may provide some technological solutions, our most valuable contributions lay in expertise and trust. We have the resources to fill this skills gap using our information management expertise, teaching skills, knowledge of the scholarly ecosystem, ability to facilitate conversation across departmental and disciplinary boundaries, and a uniquely holistic understanding of the scholarly record. At IUPUI, data management training is the foundation of our data services. This perspective is informed by the recognition that many graduate programs are not sufficiently preparing students to manage research data in this digital age. Before we can expect academic researchers to share, preserve, and curate their data, they must understand the value and importance of data management. This chapter will describe our initial foray into data management training, the lessons learned, and the next phase of our educational efforts. In developing the program, we drew upon best practices in instructional design and information literacy, literature on the lab experience in science, and data management expertise from various research communities. Focusing on teaching practical techniques for responsible data management, we use the data management plan as a tool for teaching as well as for research. The initial training offered at IUPUI has reached a diverse audience, many of whom were not identified as stakeholders when developing the curriculum. Development of the lab, assessment results, and modifications made to subsequent iterations will be described as a working example of an evolving data literacy program.