Check out the newly published article in the Journal of eScience Librarianship, “Planning Data Management Education Initiatives: Process, Feedback, and Future Directions.” In the article, Christopher Eaker, Data Curation Librarian at the University of Tennessee Libraries, discusses a one day Data Management Workshop that he taught to graduate science and engineering students using modules from the New England Collaborative Data Management Curriculum. As part of the workshop, Eaker asked students to take a pre-workshop survey and a series of seven post-module surveys throughout the day. In the article, Eaker discusses findings from the surveys and how they are shaping his plans for future research data management training.
The following announcement was made by George Coulbourne, Supervisory Program Specialist, Library of Congress, Office of Strategic Initiatives:
The Library of Congress Office of Strategic Initiatives, in partnership with the Institute of Museum and Library Services (IMLS), is planning for another year of the National Digital Stewardship Residency program (NDSR) to be held in the Washington, DC Metro area, starting in June, 2015. As you may know, this program is designed for recent master’s and doctoral graduates interested in the field of digital stewardship. This will be the fourth class of residents for this program overall – the first in 2013, was held in Washington, DC and the second and third, which started earlier this month, are being held concurrently in New York and Boston.
The 2015 DC Residents will each be paired with an affiliated host institution for a 12-month program that will provide them with an opportunity to develop, apply, and advance their digital stewardship knowledge and skills in real-world settings. The participating hosts and projects for the 2015 cohort will be announced in early December, and the application period will open shortly after. News and updates will be posted to the NDSR webpage (www.digitalpreservation.gov/ndsr ), and The Signal blog (http://blogs.loc.gov/digitalpreservation/).
In addition to providing great career benefits for the residents, the success of the NDSR program also provides benefits to the institutions involved as well as the library and archives field in general.
Please help us spread the word about this program, and forward this information to student groups and other organizations who might be interested. We appreciate your help very much.
To learn more about the NDSR, please visit our website at: www.digitalpreservation.gov/ndsr.
According to the US Labor Department, Labor Day ” is dedicated to the social and economic achievements of American workers. It constitutes a yearly national tribute to the contributions workers have made to the strength, prosperity, and well-being of our country.”
And what better way to celebrate Labor Day on e-Science Community than to share a few recent job announcements:
Institute for Research Design in Librarianship: Raising the Bar in Library & Information Science Research
Submitted by guest contributors: Daina Bouquin, Data & Metadata Services Librarian, Weill Cornell Medical College of Cornell University, email@example.com; Chris Eaker, Data Curation Librarian, University of Tennessee Libraries, firstname.lastname@example.org
Why do librarians need to do research? Or rather, why does anyone need to do research? Librarians conduct research to better understand the communities they serve and to develop responses that reflect their needs. Whether it be biomedical research, engineering, art history, or library science, research is imperative to developing the skills necessary to execute on innovative ideas and support decisions with data. Publication allows researchers to share their findings with the wider scholarly community and to build upon the findings of others. Research in the library and information science fields also helps increase receptivity to change in established environments; improves management skills through systematic study and data driven decision making; and helps researchers provide better service to and empathy for faculty researchers within their institutions (Black & Leysen, 1994; Montanelli & Stenstrom, 1986). Librarians who engage in research may also be better equipped to initiate new services that meet the specific needs of their communities. Furthermore, research in the academic library environment is not only useful, but expected for many academic librarians. Librarians who produce comprehensive research are better able to progress toward promotion, tenure, higher salaries, advancement in the profession, and well-warranted recognition. However, many librarians are confronted with barriers to pursuing research. Many of these obstacles have been documented in the literature and include lack of time to conduct research, unfamiliarity with the research process, lack of support for research, lack of confidence, and inadequate education in research methods (Koufogiannakis & Crumley, 2006, 333; Powell, Baker, & Mika, 2002, 50; McNicol & Nankivell, 2003). In response to these barriers, librarian researchers at Loyola Marymount University developed the Institute for Research Design in Librarianship (IRDL). The IRDL is a 9-day continuing education program designed to mitigate these obstacles and train world-class library and information science researchers.
And so this past June, starting June 16 and running through June 26, twenty-five academic librarians and information professionals participated in the first-ever Institute for Research Design in Librarianship (IRDL) at Loyola Marymount University in Los Angeles, California. The IRDL is funded for three years by the Institute of Museum and Library Services to train a total of 75 professionals (25 per year) in research methods and to support them in developing professional research networks as they embark on their first attempts at comprehensive research and publishing in peer-reviewed journals. The first set of 25 IRDL Scholars (including the two authors of this article) were chosen in a competitive application process out of 86 applicants. To apply for the IRDL, applicants had to submit a proposal for a research project they would like to conduct once IRDL was over.
During IRDL, scholars received comprehensive training in the nuts and bolts of the research process. Topics included creating research questions and hypotheses, using qualitative methods (e.g. in-depth interviews and focus groups) and quantitative methods (e.g. surveys), along with mixed-methods research. Scholars were also given hands-on training with both quantitative and qualitative data analysis techniques and software, such as SPSS and NVivo. By studying these aspects of the research process, and consulting with peers and instructors, scholars were able to start developing skills to help them become more critical consumers of published research — this skillset is key when trying to not only produce quality research, but also contribute to meaningful discussion and criticism of research in information science. Scholars were also introduced to issues regarding realistic approaches to publishing to better prepare them to share their prospective research findings in the future.
The IRDL program also reflected an emphasis on the importance of having a supportive learning environment, mentorship opportunities, and tools to jump-start a new research agenda. Additionally, the Institute gave scholars access to both qualitative and quantitative methods experts both inside and outside of library and information science fields to help address the need to improve the quality of Library and Information Science research. An article published in The Journal of Academic Librarianship analyzed the contents of 1,880 articles in library and information science journals. Of those, they found that only 16% “qualified as research,” which they defined as “an inquiry which is carried out, at least to some degree, by a systematic method with the purpose of eliciting some new facts, concepts, or ideas” (Turcios, Agarwal & Watkins, 2014). This study also found that surveys were the most commonly used research method among the studies published in the reviewed journals. These results could suggest that although there is research being done, librarians may not be making full use of all the methods they have available to them, and may not be producing as much “research” as they suspect. The goals of the IRDL are reflective of this sentiment.
During IRDL, scholars had to refine their initial proposal based on the new skills and concepts they were learning– now that the IRDL Scholars have returned to their respective institutions, the real work begins. Scholars are finalizing their research design and submitting IRB applications to begin conducting their research. Over the next several months, institute scholars will be conducting interviews and focus groups, administering surveys, and maybe even using our new favorite research method: garbology! Over the next year, keep a watch in the library and information science journals for articles from all the IRDL scholars’ many and varied research projects.
If you’re a new librarian, or a librarian who is still unsure of the research process, we encourage you to apply for next year’s IRDL. The IMLS has funded IRDL for three years, but they are working on plans to make it sustainable so many more cohorts of librarians can be trained in sound research methods and techniques. You can find out more about IRDL at http://irdlonline.org/ or on Twitter @IRDLonline and #IRDL. You will be overwhelmed with information, but that’s the price we must pay to move our research to the next level.
Black, W. K., & Leysen, J. M. (May 1994). Scholarship and the academic librarian. College & Research Libraries, 55, 229-241.
Montanelli, D. S., & Stenstrom, P. F. (September 1986). The benefits of research for academic librarians and the instititions they serve. College & Research Libraries 47, 482-485.
Koufogiannakis, D., & Crumley, E. (2006). Research in librarianship: issues to consider. Library Hi Tech, 24(3), 324-340. doi:10.1108/07378830610692109
McNicol, S., & Nankivell, C. (2003). The LIS research landscape: A review and prognosis. Centre for Information Research. Retrieved from http://www.researchgate.net/publication/228392587_The_LIS_research_landscape_a_review_and_prognosis.
Powell, R. R., Baker, L. M., & Mika, J. J. (2002). Library and information science practitioners and research. Library & Information Science Research, 24(1), 49-72. doi:10.1016/S0740-8188(01)00104-9
Turcios, M. E., Agarwal, N. K., & Watkins, L. (2014). How much of library and information science literature qualifies as research?. The Journal of Academic Librarianship. doi: 10.1016/j.acalib.2014.06.003
Submitted by guest contributor Willow Dressel, Plasma Physics/E-Science Librarian, Princeton University. email@example.com
The last week of July I attended ICPSR’s workshop Curating and Managing Research Data for Reuse at the University of Michigan in Ann Arbor. The workshop is part of ICPSR’s summer program and was started three years ago. I was interested in this workshop to try to get a firmer grasp on managing research data and begin to develop a deeper understanding of what is involved in curating.
The workshop was presented by curators from both ICPSR and the UK Data Archives and followed the ICPSR Pipeline Process for curation, with each day progressing through the issues and actions associated with Deposit, Processing, Delivery, and Access. There was a healthy mix of lecture and hands on activities. The roughly twenty or so participants were international and from diverse backgrounds including social science research, other data repositories, and libraries, which provided unique perspectives that greatly enhanced class discussions.
Like many of my colleagues, I am a science librarian who has been tasked with developing services and resources to help science researchers manage their research data. Over the last couple of years I have attended various workshops and conferences to try to get up to speed. In this time, I have learned a lot about the different issues around managing and preserving scientific research data, as well as what other libraries are doing. As a result, I have managed to put together some really basic services such as data management plan consultation and assistance depositing in disciplinary repositories.
However, as I begin to put together a data management workshop and libguide, I can feel my knowledge gaps in this area. I understand the need for things like documentation, stable file formats, storage and back-up, file cleaning, and confidentiality, but I don’t have a deep understanding of how to do these things. I am still reading and learning as I go. As an undergraduate physics and astronomy major, I worked with only a little bit of spreadsheet data, and that was ten years ago. It’s hard to feel confident in giving people advice on how to manage their data when I have worked with so little data myself. This workshop offered a lot of hands on exercises, including actually working with both quantitative and qualitative data. Prior to attending the workshop, I had been concerned that the heavily social science perspective of the workshop might not be as relevant to me as a science librarian. Now I believe this is a benefit. Who better to learn from than a field with established disciplinary repositories and a long culture of managing, curating, and reusing their data.
As for the curation aspect of the workshop, I don’t currently have data curation in my job description and my institution doesn’t currently offer data curation services. Nevertheless, it seems that this is an important aspect of dealing with research data and I believe having an understanding of the process and issues associated with data curation will help me assist researchers to deposit in a repository as well as inform the possibility of developing these services.
Data Scientist Training for Librarians or DST4L (http://altbibl.io/dst4l) is an experimental course being offered by the Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library and the Harvard Library to train librarians to respond to the growing data needs of their communities. Data science techniques are becoming increasingly important to all fields of scholarship. In this hands-on course, librarians learn the latest tools for extracting, wrangling, storing, analyzing, and visualizing data. By experiencing the research data lifecycle themselves, librarians develop the data savvy skills that can help transform the services they offer.
The DST4L course is free and open to beginners. Registration opens on August 15th and closes on August 22nd. A maximum of 40 participants will be accepted into the program and it is open to librarians outside of Harvard University. A tentative course outline can be found on the Current Course page (http://altbibl.io/dst4l/current-course/) of the DST4L website. Please review the provisional schedule to see if you can commit to the program first before registering. If you cannot attend the course, material will be made available via the DST4L website as it progresses. The course will not be live streamed or recorded. You must be physically present for the course.
Registration form: http://goo.gl/FtffdX
In addition to the course sessions, there will also be monthly Data Savvy Librarians meetups to work on projects together, share discoveries, and hone our skills using real-world problems. Meetups will be announced via the DST4L Google Group:
https://groups.google.com/forum/#!forum/dst4l (sign up required)
You do not need to be enrolled in DST4L to join the group or attend the meetups, though we recommend that you have some familiarity with data-related tools to participate in the meetups.
Submitted by Kate McNeil, Social Science Data Services and Economics Librarian, MIT
Highlights of: National Network of Libraries of Medicine Symposium: Doing It Your Way: Approaches to Research Data Management for Libraries
Rockefeller University, NY, April 28-29, 2014
In late April, the National Network of Libraries of Medicine, Middle Atlantic Region (NN/LM MAR) hosted a two-day symposium on research data management (RDM). The event garnered well over 100 participants from the mid-Atlantic and beyond, professionals both from medical libraries and a variety of other settings who are providing or exploring RDM services.
The initial keynote speaker was Paul Harris, Director, Office of Research Informatics, Vanderbilt University School of Medicine. He encouraged participants to seek out opportunities to develop tools and services immediately useful to their local researchers which also would further the goals of RDM. He profiled several tools that they provide locally, including:
- Project RedCap (Research Electronic Data Capture): This system enables the collection of metadata about active biomedical projects and associated collected data at one’s institution. It was created at Vanderbilt and since has been deployed at other institutions via the RedCap Consortium.
- StarBRITE CMS Researcher Portal: This Vanderbilt-specific platform enables the centralized collection of information about research: news, pilot funding, project information, researcher profiles.
The second keynote speaker was Keith Webster, the Dean of Libraries for Carnegie Mellon University. He provided a general overview of the importance of RDM for academic libraries (in the light of changes in the way that science is done and evolving roles for academic libraries). He then spent time situating the attendees’ work within important trends in the broader, international, professional context. He encouraged participants to develop their skills in this field and to stay aware of the very significant progress and initiatives happening internationally, particularly in Europe and Australia. He noted some key reports and articles to read (listed at the end of this posting).
The final keynote speaker was Jared Lyle, Director of Curation Services of the Inter-university Consortium for Political and Social Research (ICPSR). ICPSR, the large, long-standing social science data archive based at the University of Michigan, has a tradition working with data producers to acquire data and then curate it for optimal re-use by secondary researchers. ICPSR tools highlighted include: the data catalog (which enables discovery of datasets and granular information including variables) and the Bibliography of Data-related Literature (which links ICPSR studies to resulting publications based on the data in the archive). With ICPSR’s history of supporting data re-use, he pointed out that a well-prepared data collection should be complete and self-explanatory. However, researchers (many of whom may have a high willingness to share) rarely have sufficient time, money, or resources to prepare and document their data well for re-use. But he pointed out as well that many professionals in the field are trying to better understand this landscape and develop new services in order to improve the sharing and quality of research data. For example, one Stanford librarian works with local researchers to curate, redistribute, and archive their research data. The Stanford Social Science Data Collection is a type of intermediary repository; staff members work with researchers to capture their datasets, later moving them to a more long-term repository.
University Service Models
In the afternoon, attendees heard from practicing professionals on overviews of their RDM services. Following are highlights of the services of a selection of universities:
University of Minnesota:
The Libraries have a dedicated staff member to RDM, the Research Data Management/Curation Lead, who provides services and coordinates the work of other staff. Their RDM service is overseen by a campus advisory group with members from various stakeholder departments; the Libraries are working with this group to develop a campus-wide referral network. One significant effort of the Libraries is a pilot to have staff actively curate and upload the data associated with 30 researcher projects into their institutional repository (IR). They also have worked with researchers to self-deposit their datasets into the IR, instructing them on practices in realms such as metadata. They use DSpace for their IR and are finding that the newer version (i.e., 4.x) provides more flexible features for research data, including metadata elements beyond Dublin Core.
University of North Carolina:
This university has an RDM service group co-led by two librarians (each of whose primary focus is on other service areas). Staff members provide a range of services in cooperation with other stakeholder departments on campus to whom they reached out over time. For example, they collaboratively conducted a series of information sessions on data management for researchers. The Libraries partnered with campus stakeholders to each teach components on different topics (DMPs, repository options, sensitive research data, data security), including areas of expertise outside of the Libraries. These popular sessions, in addition to being provided in-person, were live streamed (to a large audience) and recorded for later viewing. Looking towards the future, the libraries are in the process of actively reorganizing for improved research lifecycle support.
NYU Health Sciences Libraries:
They have worked at developing RDM services, working in partnerships with staff both inside the library (e.g., subject liaisons) and throughout the university. A core challenge for this institution has been to helping to change perceptions about the scope of a library and demonstrate to researchers the library’s role in RDM services. To that end, they collaborated with various staff members to develop and distribute several quickly-popular YouTube videos on the significance of RDM. These videos are used on their own and as part of library instruction (not only at NYU but, as the symposium illustrated, by many other universities as well):
- Hanson, K, Read, K, & Surkis, A; How to Avoid a Data Management Nightmare
- Hanson, K, Surkis, A, & Yacobucci, K. “Data sharing and management snafu in 3 short acts”
Their dedicated Scientific Data Curation Specialist coordinates services and the work of other staff. She manages a collaborative consulting team, consisting of two groups: 1) a core of staff members (mostly in the Libraries) and 2) additional second-level team members from departments campus-wide whom are call upon as needed (e.g., staff members from IT and legal). In addition, their service is overseen from an upper-level management council (with membership across several university departments).
On the second day, Sherry Lake, Senior Data Consultant, and Andrea Horne Denton, Research and Data Services Manager, of the University of Virginia educated attendees on some key RDM best practices via a case study that they use in their workshops, based on a case from the Digital Curation Profiles Directory. Participants examined the profiled research group’s practices in the realms of: data collection and organization, documentation and metadata, storage and backup, and preservation/sharing/licensing. In doing so, they learned about common issues which researchers might face and how to assist them.
Regarding RDM services, UVA has two different approaches:
- operational: helping to improve researcher efficiency and good organization and documentation practices throughout the life cycle
- sharing: helping researchers to be aware of requirements and plan for downstream data sharing
UVA provides many services similar to other institutions, and like some others does a series of workshops (dubbed “Research Data Management Boot Camp”) with contributing instructors from departments across the university.
Lastly, the presenter shared two lists of resources that she maintains for keeping up-to-date on the field of RDM:
Principles for RDM Work
Over the two days, presentations highlighted various strategies that professionals utilize in providing RDM services:
- Promote curation rather than sharing; the former is more salient for researchers, and must precede the latter.
- A well-prepared data collection should be complete and self-explanatory; help researchers to meet this standard.
- Encourage best practices yet support people where they are. I.e., even if a researcher’s method of sharing data— e.g., storing on one’s hard drive and responding to requests—has significant drawbacks, help them to execute their selected method in an optimal way (i.e., in this example, help them to establish appropriate backups) while at the same time gently share concerns about their method and be available to help them consider other methods when the time is right.
- Continue outreach efforts on a regular basis; people don’t always see ads even if you do a great one-time campaign.
- Once researchers have shared their data, tell them about the ways to track use of their data.
- Develop services based on the hypothesis that researchers will do the right thing (maintain their information securely, track metadata, maintain audit trails, etc.) if provided an easy way to do it with needed tools and services.
- When developing partnerships or services, the technology is the easiest part. Relationships take time to build; be prepared to slow down to work with diverse needs
- Frame one’s services within the data curation lifecycle for staff and stakeholders with whom one communicates or partners.
- In planning collaborative services with senior administrators/department heads, make sure they are communicating plans and expectations down to the PIs.
- Track your work for assessment.
- Stay aware of RDM requirements/regulations around the world, both for professional awareness and given the fact that U.S. researchers likely are collaborating across borders.
In summary, while symposium attendees were largely focused on medical library settings, the lessons learned apply to research and libraries in all disciplinary contexts.
Suggested Reports/Articles to Read
- European Commission; Guidelines on Data Management in Horizon 2020 (PDF) (2013)
- Interagency Working Group on Digital Data of the National Science and Technology Council’s Committee on Science; Harnessing the Power of Digital Data for Science and Society (2009)
- Limor Peer, Ann Green and Libbie Stephenson; Committing to Data Quality Review (PDF) (Pre-Print paper, IDCC 2014)
- JISC; Value and Impact of Data Sharing and Curation (2014)
- National Science Board; Digital Research Data Sharing and Management (PDF) (2011)
- OECD; Principles and Guidelines for Access to Research Data from Public Funding (2007)
- RCUK; Common Principles on Data Policy
- Royal Society; Knowledge, Networks and Nations: Final Report (2011)
- Royal Society; Science as an Open Enterprise: Final Report (2012)
- Webster, Keith; eResearch and the Future of Research Libraries (2011)
The Tisch Library at Tufts University, has recently announced a job opportunity for a Science Collections Librarian. For further details: http://tufts.taleo.net/careersection/jobdetail.ftl?job=14000636&lang=en&sns_id=mailto
OpenCon 2014 will be held in Washington DC November 15-17th. This workshop is geared to students of all levels, early career researchers, and young professionals in fields related to scholarly and scientific research (e.g. librarians, professional advocates, etc.). OpenCon 2014′s theme is Open Access, Open Education and Open Data.
To attend OpenCon 2014, there is an application process instead of open registration, as a large number of participants will be offered full or partial travel scholarships. See OpenCon 2014′s application page.
Submitted by Sarah Wright, Life Sciences Librarian for Research at Cornell University’s Albert R. Mann Library
We all know that instruction is a major part of librarians’ jobs, but more specialized instruction opportunities, like educating students about data and research techniques, are often less recognized. Furthermore, librarians rarely expect to offer instruction for credit. But over the course of the last three years, I had the opportunity to pursue just this type of specialized instruction.
It began in 2012 with a grant from the Institute of Museum and Library Services (IMLS) which enabled me, in collaboration with Camille Andrews, learning technologies and assessment librarian at Cornell’s Mann Library, and Cliff Kraft, associate professor in the department of natural resources, to explore needs and develop a course to help graduate students learn to manage their data. The IMLS funded collaboration also included Purdue University, the University of Minnesota and the University of Oregon, and focused on developing data management instruction in several different STEM fields. The experiences of the collaborative effort are collected on our wiki (datainfolit.org), and explained more in-depth in a book due to be published by Purdue University Press this fall: Data Information Literacy: Librarians, Data and the Education of a New Generation of Researchers.
At Cornell, data management instruction has taken the form of a for-credit course, offered experimentally in spring 2013, and then as a formally approved course in 2014 (NTRES 6600: Managing Data to Facilitate Your Research). Cliff and I share teaching duties; I introduce research techniques and best practices, and Cliff contributes the research context. I also take advantage of the expertise of others, calling on colleagues to teach classes on metadata and relational database design.
The course is offered for 1 credit, spanning 6 sessions early in the spring, and introduces the students to a range of best practices, from initial steps like using consistent file names, to creating “readme” directory files and managing complex data documentation. The students also have the opportunity to discuss hot topics like data sharing, something that is often a topic of conversation, but rarely covered in courses.
The faculty collaborator, Cliff, was instrumental in making this course a reality. His interest in co-teaching the course came from his experience managing data in his own research group, where he had long been interested in introducing training, but didn’t feel comfortable with all the topics he felt needed to be addressed. After offering the course experimentally, Cliff took the step of submitting the course to the curriculum committee and asking that it be made a permanent part of the natural resources curriculum. It helped that the students who took the experimentally offered course were enthusiastic – when surveyed, the students said that they would recommend the course to others, reported substantial gains in confidence around the data management topics covered, and one even went so far as to say that the course filled a very important hole in their education.
We plan to continue offering (and improving) the course as long as interest from students continues. Every semester offers an opportunity for fine-tuning. We survey the students to make sure we’re focusing on important topics and the depth of coverage is appropriate. The reaction the second time the course was offered was better than the first – evidence that our choices to cut some content and provide deeper instruction and opportunities for hands-on practice around other content was well-received.
We’re also thinking of branching out into other subjects: both iterations of the course so far have drawn some social scientists, and the natural resources examples we use aren’t a great fit for them. They still get a lot out of the class, but they’d probably get even more out of it if we were able to offer a class targeted at their needs. The only limit is time and energy – it takes a lot of both to develop courses like this, along with subject expertise, so it will require more librarians and faculty who are willing and able to collaborate to develop instruction. The payoff is tremendous though. Not only was I able to develop stronger relationships with graduate students, giving me added insight into their needs, at the end of the course students enthusiastically thanked us for covering such an important topic and for making them aware of a much wider variety of resources and help available at Cornell than they had realized existed. The library benefits from greater embeddedness in the research process, and the students benefit from having us there.
I learned lessons from the process, and include a few of those here:
- Limit class size (or develop content that can easily scale up). We had prepared for a small class size, developing content around active learning and discussion. When almost 30 students showed up, we had trouble adapting. Good problem to have, but still a problem. (Colleagues at the University of Minnesota developed nice online content targeted to their engineering students that allowed them to scale up.)
- Make clear from the outset at what level you’ll teach the course. Very advanced students taking a beginning-level class will be frustrated, and vice versa.
- Know your graduate students. We timed the class to occur in early spring since many of the students in natural resources are wrapping up field work in the fall and then starting up again as the weather improves in the spring.
- Context is important. Real-life examples are a key component of the class, and Cliff pulls data from his research that resonates much more than a generic example might.
- Don’t underestimate your ability to contribute. What I considered very basic data management instruction was some of the most well-received; students also requested more best practices guidance than I expected. (Example: file-naming and organization strategies)
- If you can’t offer a for-credit course, offer what you can. Working with other liaison librarians, I’ve adapted content and offered workshops for graduate students in other fields including engineering and physical sciences and astronomy.
Submitted by Regina Fisher Raboin, Data Management Services Group Coordinator, Science Research & Instruction Librarian, Tisch Library, Tufts University
You know the oft-quoted phrase, “Whatever happens in Vegas stays in Vegas”? Well, I did hear this line many, many times while in Las Vegas for the American Library Association’s annual convention, but I can assure you that NECDMC didn’t stay in Vegas!
At this year’s convention, amidst Elvis impersonators, side-by-side hotel wedding chapels and tattoo parlors, and the ever-present din of gambling, I had the pleasure of representing the New England Collaborative Data Management Curriculum’s project coordinators at the Association for Library Collections and Technical Services (ALCTS) Scholarly Interest Group (SIG) forum. Despite the June 28, post-lunch time-slot, there were approximately 100 in attendance.
The presentation was entitled “New England Collaborative Data Management Curriculum (NECDMC): An educational program and service for best practices in research data management (RDM)” and focused on the impetus for the development of the curriculum – the need to instruct faculty/researchers, students and librarians in best practices surrounding research data management. The talk covered development and piloting of the open source curriculum, information on how the curriculum materials can be used and customized, along with how building institutional and regional partnerships leads to successful curriculum implementation, compliance with federal mandates and highlighting best practices in research data management at an institutional level. Additionally, the presentation featured the recent “Train-the-Trainer” workshops and current/future pilots of the curriculum. The presentation was well-received and resulted in questions both during and after the session, along with emails from librarians interested in implementing NECDMC.
Preceding the NECDMC presentation was one by Sherri L. Barnes, the Scholarly Communication Program Coordinator, University of California, Santa Barbara, who spoke about their new Scholarly Communication Program, Scholarly Communication Express, a service that allows campus departments to request 15-minute presentations that are delivered at department meetings. The Express offerings include altmetrics, creating data management plans for the social sciences and sciences, Creative Commons licenses, eScholarship, UC’s institutional repository, EZID accounts, the NIH Public Access Policy, UC Open Access Policy, and understanding article publication agreements. Ms. Barnes commented that the web site is designed to reach an audience that rarely has time to think about, let alone change, the way they navigate the scholarly communication system and manage their intellectual property.
Later that day I spoke to the Numeric and Geospatial Data Services in Academic Libraries Interest Group (Association of College and Research Libraries) annual meeting where I presented, “Tisch Library’s Data Management Services Group: Accomplishments, Strategic Initiatives & Sustainability”. The presentation and following round-table discussion focused on the development and launching of Tisch’s research data management services, strategic partnerships and initiatives, how the services were marketed, and the library’s plans to sustain these services. NECDMC was also highlighted in this presentation, as it is the anchor for Tisch’s Research Data Management Group’s best practices in research data management initiative.
While I attended many sessions at ALA Annual 2014, I’d like to highlight a few that I found interesting and informative. “Electronic Lab Notebooks: Managing Research from Data Collection to Publication”, offered by LITA (Library and Information Technology Association), looked at how Yale and Cornell implemented LabArchives and how this software fit into their broader data management support programs. A discussion group sponsored by the College and Research Libraries Interest Group and monitored by Buddy Pennington, Director of Collections and Access Management, University of Missouri, Kansas City and Doralyn Rossman, Head of Collection Development, Associate Professor,
Montana State University Library, focused on supporting payment of author publishing fees and negotiating author’s rights, demonstrating value of OA publications to faculty and graduate students, understanding granting agencies’ requirements to making data and findings publicly available, publishing in OA Journals, ensuring preservation and access of OA publications and advocating value beyond Impact Factor such as Altmetrics. The discussion was organized, allowing for the attendees to explore and go beyond the presented discussion topics.
A “why didn’t I think of that?” moment came during the Science and Technology Library Research Forum (ACRL STS) when Uta Hussong-Christian, Instruction & Science Librarian, Oregon State University and Rick Stoddart, Assessment Librarian, Oregon State University, presented a short paper, “STEM Learning in the Library Learning Commons: Examining Whiteboards for Evidence of Learning through Student-Generated Visualizations”. While they had their library’s learning commons usage statistics, they also wanted some type of substantive assessment to complement the data. So every Monday morning they would photograph all of the whiteboards (a ‘cognitive artifact’) in their library and then analyze the types of student-generated visualizations. The analysis coded the content by board, types of content (subjects/disciplines), drawing types (matrix, chart, diagram, etc.), and visualization-skill scores (i.e. how well drawn). They discovered the majority of whiteboards supported STEM student-learning, providing low-tech, high impact learning. Based on their research findings, Oregon State University library is going to be purchasing graphing whiteboards and checking out this new-fangled technology!
So I bet you’re wondering what I mean by “Dry Heat/Blue Legs” in the title. Don’t believe anyone who tells you that 110 degree temperatures (for 4 days I tell you!) aren’t uncomfortable since Las Vegas heat is ‘dry heat’. Well, I tested that hypothesis by wearing new dark blue capri jeans to ALA Annual 2014 – I think I’ll change my Twitter handle to “Blue Legs Raboin”.
By Andrew Creamer, Scientific Data Management Specialist, Brown University Library
Six years ago Ubogu and Sayed (2008) conducted a survey of members of the Networked Digital Library of Theses and Dissertations (NDLTD) about their handling of ETDs and their associated data. They found that most of the institutions had no policy on the stewardship of raw data related to a thesis/dissertation, and such data was only stored if provided by the author as a supplementary file along with the full text. Only one institution at that time had a relationship between its electronic theses and dissertations (ETD) program and its research data management center/program. I would love if this study could be reproduced to see if these numbers had changed.
In 2011, Collie and Witt wrote a superb article supporting the positioning of libraries to collect ETD data. Indeed, they felt, at the time and may very well still feel so today, that “Dissertation datasets represent “low-hanging fruit” for universities who are developing institutional data collections” (p.166). While I agree 100% with their proposal that these collections are valuable and should be a strategy priority for libraries, especially as my colleagues and I embark on this journey at my own institution, I am, however, finding myself more than disappointed with their choice of metaphor, one that equates collecting student ETD data with no or little effort. On the contrary, I argue that setting up a sustainable service to archive ETD data can be a lot of work (but worth it)!
Indeed, the preparation and assessment phases can take a great deal of leg work. In the spring of this year Kate Thornhill and Lisa Palmer presented on a practical project at UMass Medical School that explored graduate students’ awareness of the ability to submit separate data files along with the PDF of their electronic theses and dissertations to the institutional repository. Kate, Lisa and their project team colleagues (disclaimer: Donna and I were on that team) wanted to have an idea of the types of data that graduate students were producing and the nature of the data that they might be submitting along with their ETDs, so Kate conducted a survey and some interviews to gather very useful information on the number, formats, and sizes of the data files the graduate students were producing, and their interest in submitting data along with their ETDs. If you have not seen this poster, you should, and you should also reproduce it at your institution.
Lately I have been sharing with colleagues the potential value and opportunities for the library to archive and build searchable collections of ETD-associated data sets produced on our campus, and I have received positive responses: “What a good idea! The data associated with my thesis is in a box in my PI’s basement” and “I can see how that would be useful–I don’t even know where my data is anymore.” However, even with such buy-in, we are quickly finding we are going to need a ladder to harvest this “low” fruit because there are many hurdles to climb and aspects for us to consider before actively seeking from the students the data files associated with their ETDs.
1. What’s Out There?
Kate, Lisa and colleagues wanted to know the nature of data students might be collecting and interested in sharing because they wanted to know if the institutional repository could handle the needs and demand were they ever to actively do outreach to build an ETD data collection. Data also means different things to different people. Which data would they want to submit- raw data? Analyzed data? What about data documentation and code? What file formats? What file sizes? What is the level of description? The list goes on.
2. Where Does the Buck Stop?
Many universities have an ETD submission process that involves the graduate school and library. Yet, as my colleague Jean Bauer points out, people often overlook the local aspect of the departmental coordinators and administrative assistants that are usually the first point of contact for graduate students to get information about ETD requirements, deadlines, and reminders for getting their ETD and graduation paperwork submitted. Thus, if the library wants to begin advertising to graduate students that they can submit their data files as well, then it needs to be sure that all the nodes along this chain of communication, not just the deans in the graduate school, are aware of it and have the same information. The issue here, of course, is that this takes a lot of leg work to do outreach with the subject librarians, departmental staff, as well as finding access opportunities to share this with graduate students at some point of their studies so that they are aware of the option to submit data. One issue we are encountering here is updating all the web pages from the stakeholders so that they have the same information.
3. Collections Policy
Since data can take so many forms, a one-size-fits-all policy for ETD data would be difficult to apply. Instead there should be an appraisal process in place to prioritize the collection of ETD data sets that do not necessarily have an established disciplinary repository that would be a better and more logical home for the data, one where researchers in that field would most likely look. In the case described above, a good policy would be to look at each submission on a case-by-case basis, and then create a separate record for the data set with metadata and a minted DOI that points out to where the data is, e.g., a 40 GB file of RNA sequence data in the Sequence Read Archive (SRA) at NCBI. As a result, if a user finds the record for the ETD, then he or she can find the associated record with experiment-level metadata for the data set, and then follow the DOI out to the SRA to access the data. A 40 GB file of RNA sequence data would be better placed in the SRA than an institutional repository anyways because biomedical researchers would logically go there to access such data, and it can better accommodate the large file sizes associated with raw sequencing data. Yet, this type of attention requires an investment of energy and effort on the side of the library to mediate these submissions and dedicate staff time to assist with metadata, mint DOIs, etc. This also begs the question about scalability- if the number of data sets submitted with ETDs were to suddenly increase as a result of good buy-in, outreach, and user satisfaction, could the library then keep up the pace of that investment and maintain the same quality of individual attention?
4. Embargoes and Encumbered Data
Managing ETDs comes with its own set of issues. Libraries usually offer some sort of embargo option for students’ ETDs, for example. The question here is if an ETD is placed under an embargo, would the data set also need to be embargoed? There are also concerns about copyright and intellectual property, sensitive data and identifiers, etc. that go along with any digital object placed into an institution’s repository. Thus, a question for the library to discuss with the graduate school is whether there should be a different process for ingesting ETD data sets or should they be treated the same as the ETDs, and what policies, indemnity clauses, and deposit agreements would be appropriate. In addition, since so many graduate students have data wrapped up in the work of their advisors, the usual questions about ownership apply. Here are two examples of such deposit licenses students from the University of Virginia agree to before depositing their ETDs and related data sets:
So as you can see, the height of this fruit is relative to where you’re standing. From an informal survey of some peer institutions, here are two scenarios of how ETD data curation is currently being handled (and I would love if the E-Science Community Blog readers could chime in and share more with me):
Scenario #1: There is no direct policy or concerted outreach to obtain ETD data sets from students. On the contrary, the ingest of data related to ETDs is either student-initiated through department program admins, the graduate school or repository; students either pass on supplementary files to the repository along with the ETD submission process through departments, graduate school and/or the repository, or they submit the data set separately to the repository unattached to the ETD, which may be linked back. There is no special treatment or separate record created for this supplementary data i.e., no metadata is created for the supplementary files; and there is no involvement of the graduate school in terms of policy, but the library or graduate school may mention the option to submit supplementary data files in the submission guidelines, but there are no special ingest permissions, policies, etc. created specifically to address ETD supplementary data sets (Ex. see number 9: http://library.gwu.edu/etds/steps.php).
Scenario #2: There is communication among department program admins, the graduate school, and library that submitting a data set along with an ETD is an option, and it is described in guidelines and communicated to graduate students; the data is either collected via supplementary files along with the submission of the ETD or deposited separately and then linked back to the ETD; submitted data sets receive metadata and their own digital object records. There is some sort of official license agreement or indemnity clause that the depositor (student) agrees to saying there is no IP, PII or other sensitive data restrictions (Ex. Harvard’s Dataverse deposit agreement: http://thedata.org/book/data-deposit-terms).
If you have some more ideas on this topic, please email me at firstname.lastname@example.org.
Update: Since I posted this I have heard back from colleagues looking at this same issue. Here is a really great idea from Sarah Shreeves to evaluate the ETD supplemental files in our repositories: http://hdl.handle.net/2142/35314. Shreeves, Sarah L. 2013. Supplemental Files in Electronic Theses and Dissertations: Implications for Policy and Practice [Poster Abstract]. Poster presented at the 8th International Digital Curation Conference, Amsterdam, Netherlands, January 14-17, 2013.
Acknowledgement: Thank you to Jean Bauer for the suggested title of this post.
Submitted by Simmons GSLIS student Jennifer Chaput, recipient of the 2014 Science Boot Camp Fellows scholarship.
As a new library science student, I was intrigued when I heard about Science Boot Camp, and am grateful that I was able to attend as a Student Fellow. A chance to connect with a group of science librarians and hear talks from researchers in different fields sounded like a great opportunity to see what the field is really like. This year’s Boot Camp was held on the campus of the University of Connecticut in Storrs.
I was nervous about how much of the content might apply to me, since I am not working in the field yet, but I never found that to be a problem. Everyone was friendly and welcoming, and by the end of the first day I already felt part of the group. The sense of camaraderie continued through the week. Meeting a wide variety of librarians was one of the best parts of Boot Camp for me, and I came away with many new friends and contacts in the field. I enjoyed being able to speak to librarians who work in a variety of settings. My career goal is to work in a hospital library, but after meeting many academic science librarians, I’m interested in learning more about that aspect of librarianship as well.
The bulk of Boot Camp is based around sessions in which scientists working in various fields present their research so we can learn about current trends in the field and how librarians can assist them. This year’s sessions were about Computer Science, Evolution, and Pharmacology. Within those fields, there’s an astounding variety of research being conducted. One of my favorite sessions was on tapeworms, of all things! During the Evolution session, Janine Caira from the University of Connecticut presented an engaging and dynamic talk about her work classifying and studying tapeworms in sharks and rays. I realized that it’s increasingly important for a researcher or scientist to be able to communicate their work well to a wide variety of audiences. While she was speaking on a more scientific level to us, I thought about how I would try to explain the content to a friend or family member. Working with researchers to understand their work and their needs for information and data managment is a key role that librarians can play in the process of scientific research.
The theme of communication carried into our capstone session on Citizen Science. From ways to involve the public in scientific observation such as fish counts or birdwatching, to a rap about climate change from Dr. Jonathan Garlick of Tufts University, a variety of ways to get people involved and interested in science were presented. Dr. Robert Stevenson from University of Massachusetts Boston summed up the entire Boot Camp well when he said that “librarians are silent partners to scientists.”
I came away from Boot Camp feeling energized and excited about my new career path, and am already looking forward to attending again next year. Although it’s almost overwhelming to see what options are out there and the different ways librarians function in the scientific fields, it’s also nice to see how broad the field is and to know that almost any direction is possible.
The video recordings of this year’s New England Science Boot Camp, held at the University of Connecticut are now available. The recorded sessions include Computer Science, Evolutionary Biology, Pharmaceutical Sciences and a special Capstone session on Communicating Science. Included in each recording are the concluding question and answer portions of the science and Capstone sessions. To view the recordings, see the 2014 Science Boot Camp YouTube Channel.
Submitted by guest contributor Brianna Marshall, Digital Curation Coordinator at University of Wisconsin at Madison.
In June, I started as the Digital Curation Coordinator at the University of Wisconsin-Madison Libraries. This is my first professional position, so some of the ideas below apply to new jobs of any kind.
1. Simplify your explanation of what you do. Data services folks have incredibly varied backgrounds, titles, and responsibilities. It can be really hard to explain what we do to anyone, even our colleagues. For instance, my job is newly created and the title is a mouthful, so very few people beyond my search committee understand what I am supposed to be doing. To translate, I tell people I’m a data management librarian who works with the institutional repository. This helps both my colleagues and researchers that I work with understand my role by answering the questions I already know they’ll ask.
2. Anticipate the challenge of understanding the culture of your institution. I don’t think of myself as particularly naïve, but I will say that I thought this would be simpler. In retrospect, I think I put too much pressure on myself to know it all right away. However, that’s just not possible – a lot comes down to getting to know past events: the history of relationships, projects, and group dynamics. It takes waiting and watching, asking questions and listening carefully. Sometimes it takes stumbling upon information. It was helpful when colleagues told me it was normal to still feel somewhat out of the loop as late as a year into the job – suddenly I felt much more relaxed about my confusion!
3. Take the time to meet people one on one. At first I felt strange about this – getting coffee with a colleague felt too fun to be work. In my former life as an hourly worker I had never done this on the clock! However, I am confident this will pay dividends. Getting work done is a matter of relationships. You rarely get the real story in a committee meeting; you get it by talking to someone one on one. Coming into this job, I was worried that people may think I was trying to overshadow the existing data-related work happening across campus. Getting to know people on an individual level allowed my intentions to shine through and helped us figure out how to collaborate. As a bonus, these relationships have helped me get acclimated much quicker – rather than still feeling like the new girl, I’m starting to feel like just another member of the team at UW.
4. Re-learning how to strategize. I always prided myself on being a long-term thinker: I like to plan, execute, and enjoy the fruits of my labors. With a new job, though, it’s tougher to see how I fit into the big picture on campus. How does my work with the institutional repository affect my work with data information literacy and how will that affect my ability to get a data curation pilot up and running? When I was in grad school, my ultimate goal was to get a job, so I tailored my activities to that objective and timeline. Now, it seems that my goals are moving targets with quickly shifting timelines. The scope of my job description is broad due to its very nature as a new and exploratory role. It has been helpful for me to have a supervisor that I can pepper with questions as needed. What is appropriate? Has anything like this been done here before? Do I have resources available? This helps me focus in on how the pieces fit together. It’s tempting to try to speed up to catch up to the impressive projects undertaken by other institutions (Purdue and University of Minnesota, I’m looking at you!) but I remind myself: one bite at a time!
5. Introduce yourself to the other data librarians/ archivists/ technologists/ coordinators out there. We are a diverse group. Some of us are scientists, some of us have computer science/IT backgrounds, and others still are English majors who found themselves working with data and are still a bit in awe of how far they’ve come from the career expectations they had as 12 year olds (raises hand). We’re all different, and as someone with a mixed LIS/IT background I am truly excited about all I can learn from my peers. I’ve met exceptionally cool people through conferences and online communities like Twitter. Saying hello and perhaps asking for advice is only hard in your head – in my experience, people in this field are very receptive and friendly, so by all means go for it.
I hope these ideas help anyone out there starting a new data-related job! What are your tips?
Below are some recent job opportunities for data librarians. If you would like to disseminate news about job opportunities at your institution on e-Science Community, please contact me at email@example.com.
University of Virginia: Senior Research Data Scientist (MLS not required, MS or PhD in Science/Engineering preferred)
Submitted by guest contributor Daina Bouquin, Data & Metadata Services Librarian, Weill Cornell Medical College of Cornell University, firstname.lastname@example.org
When do you use a database, and when do you use a spreadsheet? This simple question is key when designing a data management strategy, but it is important to note neither tool is always the better choice, rather you need to determine which tool is best suited for the task at hand. Spreadsheets get a bad name because they are so easy to misuse, but following some best practices for tabular data will keep you from making the most common mistakes. Meanwhile, databases are typically seen as being overly complicated and involving difficult to learn skills, but there are more than a few resources online to get you started with them (like Stanford’s Database course). This brief walkthrough will help you determine whether to go with a spreadsheet or a database on your next project.
First, it’s important to recognize that both spreadsheets and databases can be useful in manipulating data. Where these two tools differ is in how they store and manipulate data.
In spreadsheets, data values are stored in cells, with many cells making up an array of rows and columns. Cells can “refer” to each other and carry out processes on other cell values. Spreadsheets have taken over functions that, in the past, were carried out with paper and pencil in ledgers and worksheets (like financial record keeping) because they enable data to be recalculated much more quickly and efficiently than by hand. When one value in a column changes, totals and other formulas entered into cells are automatically recalculated. It’s important to note though that spreadsheets are not ideal for long-term data storage and only offer relatively simple query options. They also do not easily guard data integrity, and offer little protection from data corruption– so spreadsheets are great for tracking simple lists, but have realistic limitations. Many people think of MS Excel when they think of spreadsheets, but other platforms like GoogleDocs have spreadsheet applications as well.
With databases, data are usually stored in multiple tables. Each table is given a name and has columns and rows. Each row in the table is called record, and each record typically has a value for each column in a table. Database tables are typically used to store raw data, meaning that data in rows are not the result of some manipulation or function like in a spreadsheet. Databases also allow you to enforce relationships between records in different tables so that the data can then be retrieved through querying. Querying is like asking questions of the data to pull information into a formatted reports (e.g. an invoice). In this way databases easily manage large amounts of data and maintain data integrity better than spreadsheets typically do. Likewise, databases are better for long-term storage of records that may change and also have a much larger storage capacity than spreadsheets. Some database tools include MySQL, SQL Server, Oracle, MS Access, and REDCap.
Some questions to ask when deciding between the two:
- If you use a spreadsheet, would changes in one spreadsheet require you to make changes in others?
- Would the amount of data be manageable in a spreadsheet?
- Would you need several spreadsheets to contain related data?
- Would the data you are looking for be easy to find in a spreadsheet?
- Do multiple people need to access the data?
Answering yes to a few of these questions means you may want to consider going with a database over a spreadsheet for your project.
In summary, you may want to go with a database if:
- Multiple people will need to access the file
- The data is subject to change
- You want to store data long term
- You need a lot of storage space
- You need to generate multiple reports based on the same data– For example: a clinical researcher wants to see a group of patients average weight by month, another researcher only wants to see that measure for a certain subset of patients, and yet another researcher may be interested in instead seeing the median weight grouped by age. Rather than build three spreadsheets with different views, it would be easier and more efficient to make a database that would allow for queries to generate all three reports from one source.
Both spreadsheets and databases have their place, just try to avoid forcing a spreadsheet to do the work of a database. It’ll save you a headache.
A friend recently shared this article. It’s an interesting read that suggests that – education and experience being equal – women are less confident than their male peers, and that this lack of confidence in women negatively impacts their careers.
As I read the piece it triggered a memory – an old but vivid one.
Years ago I worked at a biotech company. My coworkers were almost all male. We came from similar backgrounds; most of us were recent college grads with degrees in biology or chemistry. We had plenty of chances to talk and bond over what was often monotonous work, so we got to know each other quite well. Sometimes we’d chat about science to pass the time.
One day I was talking science with a coworker while we worked on a large batch of samples. (He was the odd man out in our little group, since he’d majored in physics.) He made a pronouncement on some arcane biology topic that now escapes me. I knew what he said wasn’t right, and after a pause I jokingly called him on it.
He laughed and said: “OK, you got me. I’m really not sure – you’re probably right. I’m just the physics guy, remember?”
I said: “Well, you sure sounded like you were positive about it!”
Him: “Yeah, but that’s 90% of life, isn’t it? Sounding like you know what you’re talking about.”
Lighthearted as this conversation was, I can still remember being dumbstruck by his comment – the ol’ light bulb moment. I realized how self-assured my male coworkers sounded when they talked – about science, and just about everything else. I was much more tentative, sprinkling my speech with loopholes and deflections. It was a revelation – maybe they really DIDN’T know a lot more than I did; they just sounded like they did!
Per the 2013 Demographics Survey of ALA members, over 80% of librarians are female. IF librarians are mostly women, and IF women tend to be less confident than men, and IF a lack of self-assurance hurts women in their careers – what does that mean for libraries? And in particular, what does that mean in situations where libraries are pushing boundaries, reinventing themselves, and working to insert themselves and their librarians into the research enterprise in new ways?
There are, of course, many factors that contribute to the success of new initiatives. Much can rise and fall on environmental factors, like the support of library administration, institutional aspirations, and budgetary pressures. Subject matter background certainly helps librarians work closely with their departments, though I’ve argued before that I don’t see discipline knowledge as crucial. After reading this article, I’m wondering if part of what that subject matter expertise gives librarians is confidence. An extra dose of confidence is helpful for any of us, and may be even more welcome in situations where libraries and librarians are forging new paths.
What do you think? Does this article resonate with you, or not? Do you see any connections with our library work? Please comment here and/or on Twitter – #NERescience.
Simmons GSLIS is offering a Scientific Research Data Management class, 532G-01, this upcoming fall semester at Simmons’ Boston and Simmons West (Mt Holyoke) campuses simultaneously via videocasting on Saturday mornings from 9-12 pm. The class will be taught by a team of librarians that includes Dr. Elaine Martin, Rebecca Reznik-Zellen, and Donna Kafel from UMass Medical School, Andrew Creamer from Brown University, and Regina Raboin from Tufts University. The instructors will alternate between the two campuses; one week teaching at the Simmons’ Boston campus and videocasting to the students at Mt. Holyoke, and the alternate week vice versa. The class is open to enrolled Simmons students as well as interested librarians. (Registration for non-Simmons students begins in August). The first class begins on Sept. 6th.
This course, LIS 532G, Scientific Research Data Management, uses the case study method to prepare students from all academic backgrounds for roles in scientific research data management. It explores the current and emerging roles for information professionals in managing large or small volumes of research data sets. The course provides students with the skill set relevant to that of a data librarian whose job involves helping researchers manage and curate research data sets. The course examines the data practices of researchers in scientific fields such as biomedicine and engineering as examples of how researchers produce data and how they use these data for purposes of inquiry. Students learn about the purposes and tools of research data production and data reuse, data lifecycles and data reference interviews, data management practices, and the strategies of offering data consultancy services to researchers. Current issues regarding citing datasets, Open Access policies, and embedding the librarian as a member of a research team will also be addressed. The course will feature guest lectures by data scientists, data librarians and data archivists. Assignments include a series of readings, case study assignments, data reference interviews with researchers, and the development of data reference interview tools and data management plans for real research projects.
Full tuition for this 3 credit class is $3486, plus a $50 activity fee. Auditing the class (no credit or grade earned) is a less expensive option and is half the current tuition ($1743 for non-Simmons grads, $400 for any Simmons grads). For further details, see Simmons Forms and Policies.
If you’re interested in the class, please contact the Admissions Office at Simmons at email@example.com and discuss with the Admissions Office your preference for enrolling for credit or auditing. The office will send you an application. An official copy of your master’s degree transcript is a required component of the application.
New England librarians (or those outside of NE who don’t mind a long drive!) who are interested in learning more about e-Science librarianship and the management of scientific research data may want to consider enrolling in or auditing this class.
This summer ACRL is sponsoring an e-learning online course “What You Need to Know about Writing Data Management Plans” from July 14- August 1, 2014. The course teaches participants the elements of a comprehensive data management plan and is taught by Dee Ann Allison and Kiyomi Deards of the University of Nebraska-Lincoln. See ACRL announcement for more details.