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SLA 2017 Data Caucus Events at the Annual Conference in Phoenix

DATA CAUCUS events at SLA 2017

SLA 2017 Annual Conference in Phoenix, Sun-Tue Jun 18-20, 2017

 

Sunday, June 18, 2:15-3:30 (CC-224 B)

Beyond the Impact Factor with Bibliometrics and Altmetrics: Building a Case with New Research Metrics Tools

 

Presenters: Elaine Lasda Bergman, SUNY Albany; Richard Hulser

 

Identifying the ways to measure scholarly influence and research impact remains a vital skill for those who work with researchers. Attendees will hear about the latest developments in the rapidly changing area of scholarly metrics, and learn how these tools can be best applied. This session will provide an overview of advances in bibliometrics and altmetrics, and present case study examples to show how these tools help provide data analysis and value of research to senior executives at institutions, potential funders and the general public. Those with a need in proving the value of scholarly research who also have a basic understanding of traditional bibliometric indicators will benefit from this session as will others with interest in the topic.

Led by the Data Caucus in partnership with the Physics, Astronomy, and Math Division

Stream: Metrics, Analytics and Assessment; Intermediate

 

 

Monday, June 19, 3:30-5pm (CC-225 A-B)

Search: The Next Generation: AI, Aggregators, and Language Generation

 

Presenters: Laura Gordon-Murnane; JP Ratajczak, Director, Intelligence Systems, Aurora WDC; Ethan Redrup, Analyst, The Martec Group

 

The role of the information professional will change as info services and automated processes become increasingly sophisticated and able to take on more work. What if AI could fine tune an aggregator service? Could natural language generation bots hit a sweet spot and create relevant content summaries that would turn up on your aggregator? Are these systems ready to handle the workload without human intervention? Learn what the future holds for information professionals from a panel of experts.

Led by the Competitive Intelligence Division in partnership with the Data Caucus

Level: Master Class

 

Monday, June 19, 7-8:30 pm (Sheraton Laveen A)

DATA CAUCUS RECEPTION!! Ticketed Event.

 

Tuesday, June 20, 9-10 am (CC-226 B-C)

Creating a Window into Data Resources

 

Presenters: James King, Information Architect, Branch Chief, NIH Library; Delia Sawhney, Director, Economic Research Information Center, Federal Reserve Bank of Boston

 

In today’s world, with the availability of data continuing to increase at phenomenal speeds, it is not enough to point to the resources your clients have available to them. Instead, it is critical to help customers understand what datasets can be used to answer the questions they are facing. As part of the data management cycle, we use data visualization tools, like Tableau, to create a window into data resources, so that our clients can interact with the data immediately, instead of having to pore over documentation in order to glean insight into the content. If you are managing data, come see how we are using data visualization to help customers better understand the information they have at their fingertips.

Led by the Information Technology Division in partnership with the Data Caucus

Stream:  Data Management; Level: Intermediate

 

Tuesday, June 20, 1:30-2:30 pm (CC-224 A)

Information Security for Libraries

 

Presenter: Tracy Maleeff, Sherpa Intelligence LLC

 

Get basic instruction on the threats, cyber and physical, that could harm your library. Understand the terminology that is in the headlines these days: 2FA, ransomware, and more. Then, receive a broader understanding of policies and action plans that can be implemented to protect valuable resources like patron data and library servers. Attend this session to get a foundation for how to educate your library staff and patrons about being safety-savvy and to create a culture of security within your organization.

Led by the Data Caucus in partnership with the Legal Division

Stream: Data Management; Level: Fundamental

 

Tuesday, June 20, 1:30-2:30 pm (CC-229 B)

 

Open Science Framework

 

Presenters: Daureen Nesdill, Marriott Library, Univ of Utah; Matt Spitzer

Session attendees will learn about Open Science Framework (OSF)—a free, open access service of the Center for Open Science (COS), a non-profit technology company based in Charlottesville, Virginia. Rather than providing support at the end of a research project, OSF provides data management during the project. Discover how to keep all your files, data, and protocols in one centralized location. Explore useful functionality including the ability to connect your favorite third party services directly to the Open Science Framework, and the ability to control which parts of your project are public or private—making it easy to collaborate with the worldwide community or just your team.

Led by the PAM Division in partnership with the Data Caucus

Stream: Data Management; Level: Fundamental

 

 

ALSO look for DATA CAUCUS on Main Street SLA in the exhibit hall!

 

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ASEE – ELD Presentations on Data management

ASEE – Engineering Libraries Division (ELD) Presentations (Data management)

ASEE Annual Conference, New Orleans, LA, June 26-29, 2016

Susan Boyd, Santa Clara University

Sapp Nelson, M. (2016, June) A proposed scaffolding for data management skills from undergraduate education through postgraduate training and beyond. Lightning talk presented to the Engineering Libraries Division of the American Society for Engineering Annual Conference, New Orleans, LA. (slides available from the her presentation at the IASSIST conference, Bergen, Norway, May 31 – June 3, 2016) https://www.openconf.org/IASSIST16/modules/request.php?module=oc_program&action=summary.php&id=33

Three gaps were identified in efforts to scaffold training for data management skills throughout a student’s college years from their undergraduate to graduate years. 1) There needs to be systematic way to track and measure these skills. 2) A communication tool must be used to keep track of what has been taught, who taught it, and the level of the data literacy concepts that were taught. 3) How are the goals of becoming data literate assessed, and what is the evidence for reaching these goals?

To build a framework for DIL education:

Find out what has been taught, and to whom.

Integrate the parts into one, whole curriculum.

Communicate the goals of the integrated program to students, faculty and staff.

Use assessment to see if the goals of the program has been reached.

Scaffolding is a technique to follow the learner through these domains: personal, team and research. In each domain, there are characteristics the individual will possess that will enable him or her to move on to the next domain. Learning new skills is dependent on having learned the ones from the previous domain. Scaffolding includes instructional technology, hands-on exercises and in-person/online instruction.

A pilot to do scaffolding with 35 competencies identified was customized for Purdue. These were narrowed down to 12 competencies. The pilot scaffolding is available for download (no editing) at: http://docs.lib.purdue.edu/lib_fsdocs/136/

Sapp Nelson, M. and Phillips, M. (2016, June) Consulting with research groups to create project-specific data management training and protocols. Unconference presentation at the Engineering Libraries Division of the American Society for Engineering Education Annual Conference, New Orleans, LA. Slides at: http://depts.washington.edu/englib/eld/conf/2016/unconference_presentations.php

This presentation helps identify individuals and groups who need assistance developing research protocols. Questions are given for: 1) Interviewing the individuals and groups identified. 2) Focusing on the data management lifecycle.

Recommendation: Audio record the interviews with verbal permission from the participants. This allows you to form a more complete picture of the viewpoints of faculty and research assistants.

What to look for when listening to the transcripts so you can go back later to clarify and recommend best practices are: file naming conventions, file structure, storage, versioning, backup, and documentation procedures and submission.

After analysis and clarification, write a document that highlights what data management practices the group agreed upon and give it to the faculty member for approval, then distribute to the research group.

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