Update program.html

program.html

@@ -67,7 +67,7 @@ <h1>Program</h1>
 
 <tr><td colspan="2"><p>&nbsp;</p></td><tr><td class="program-date" colspan="2">Mon, Oct 7</td></tr><tr><td class="program-time"><strong>Short Talks</strong><br />10:00 AM -<br />11:00 AM</td><td class="program-details">10:00 AM<br /><strong>Welcome and Symposium Logistics</strong><ul class="vertical-list"><li>SEDLS 2024 Planning Committee</ul>
 <br /><br />10:20 AM<br /><strong>Unlocking Data Engagement: Collaborating Across Library Departments for Love Data Week</strong><ul class="vertical-list"><li>Nick Ruhs (Florida State University)<li>Crystal Mathews (Florida State University)<li>Laura Pellini (Florida State University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Promoting and engaging with students around the library's research data services often poses challenges for data librarians. At our library, we decided to use a unique and collaborative approach that brought together undergraduate data fellows, student employees from the library's Engagement and Marketing teams, and full-time library staff to plan the library's annual Love Data Week event. This presentation will showcase how the teams were able to combine their collective expertise to design an engaging, student-focused, week-long event that promoted the library's research data services to the campus community. We will discuss the development of a cross-divisional marketing strategy involving social media, newsletters, and email reminders. We will also discuss the development of activities, data-themed games and prizes, and an engaging display for tabling events at two campus libraries. We'll conclude by sharing lessons learned and strategies for all data librarians to collaborate across boundaries in promoting their services.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>1) Understand the benefits of collaborative approaches in promoting research data services to students; <li>2) Explore effective strategies for engaging students through events like Love Data Week utilizing cross-divisional collaboration and marketing tactics; <li>3) Examine the integral role of student data fellows and library employees in the planning and execution of data-themed library events gaining insights into how their involvement contributes to the success of student-focused data promotion initiatives</ul></div>
-<br /><br />10:40 AM<br /><strong>Implementation of Data Management Services at a Smaller University</strong><ul class="vertical-list"><li>Eric Resnis (Coastal Carolina University)<li>Adam Johnson</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">While data management services are ubiquitous at R1 and R2 institutions, these services are not as prevalent at smaller institutions. Smaller staff size, a deeper focus on undergraduate education, and an absence of large-scale research infrastructure all contribute to the lack of data management services. However, the need for these services exists at smaller institutions as well, with researchers often navigating complicated data management concerns themselves. Libraries can provide a framework to remedy these issues. <br /><br /> This session will detail the implementation of data management services at a mid-sized comprehensive institution due to administrative and faculty requests, in combination with a multi-year effort to increase the institution's research profile. The presenters will discuss how they developed a plan for data management services. They will also discuss the development of resources based on faculty input, including an extensive guide for data management and the rollout of individual consultations with faculty.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Attendees will understand the elements required to implement data management services at smaller institutions. <li>Attendees will recognize potential areas for assessment and planning to implement in their own data management services.</ul></div></td></tr>
+<br /><br />10:40 AM<br /><strong>Implementation of Data Management Services at a Smaller University</strong><ul class="vertical-list"><li>Eric Resnis (Coastal Carolina University)<li>Adam Johnson (Coastal Carolina University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">While data management services are ubiquitous at R1 and R2 institutions, these services are not as prevalent at smaller institutions. Smaller staff size, a deeper focus on undergraduate education, and an absence of large-scale research infrastructure all contribute to the lack of data management services. However, the need for these services exists at smaller institutions as well, with researchers often navigating complicated data management concerns themselves. Libraries can provide a framework to remedy these issues. <br /><br /> This session will detail the implementation of data management services at a mid-sized comprehensive institution due to administrative and faculty requests, in combination with a multi-year effort to increase the institution's research profile. The presenters will discuss how they developed a plan for data management services. They will also discuss the development of resources based on faculty input, including an extensive guide for data management and the rollout of individual consultations with faculty.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Attendees will understand the elements required to implement data management services at smaller institutions. <li>Attendees will recognize potential areas for assessment and planning to implement in their own data management services.</ul></div></td></tr>
 <td class="program-break"><strong>Break</strong><br />11:00 AM -<br />11:15 AM</td><td class="program-break"></td></tr>
 <tr><td class="program-time"><strong>Poster Sessions</strong><br />11:15 AM -<br />12:00 PM</td><td class="program-details"><strong>Data Services Internship for BIPOC Graduate Students</strong><ul class="vertical-list"><li>NNLM National Center for Data Services 2024 Interns</ul>
 <br /><br /><strong>Making the case for GIS in business school curricula</strong><ul class="vertical-list"><li>HD McKay (Vanderbilt University)<li>Stacy Curry-Johnson (Vanderbilt University)<li>Chuck Knight (Vanderbilt University)<li>John Paul Martinez (Vanderbilt University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Geospatial science is all about analyzing and visualizing data variables tied to locations. Markets are all about consumer and business data (activities and trends) tied to locations. Geospatial literacy is a fundamental skill for anyone engaged in business, yet it is often absent in business curricula. At the same time, the ubiquity of GIS applications and more accessible geospatial tools (hardware and software) allow GIS specialists to collaborate productively with other disciplines. Is this a missed opportunity you are seeing at your organization? This poster will help you make the case for integrating geospatial literacy into business curricula through specific market intelligence use cases. It provides a framework for thinking about GIS data in business contexts; a summary of applications, data sources (free and subscription) and their relevance to specific use cases; and a sample lesson plan based on sessions delivered with graduate business students at Vanderbilt University.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>1) Specific use cases for incorporating geospatial data literacy into business curricula; <li>2) sample lesson plan for GIS data or business librarians to start engaging business students and faculty; <li>3) understanding of relevant GIS and business data sources used in industry and academia (including their scope, strengths and weaknesses).</ul></div>
@@ -85,7 +85,7 @@ <h1>Program</h1>
 <br /><br />11:35 AM<br /><strong>Sentiment analysis on virtual reference transcripts</strong><ul class="vertical-list"><li>Kristin Calvert (Western Carolina University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Sentiment analysis uses natural language processing to determine the emotional tone of a piece of digital text. It is often used to understand user satisfaction with a service and improve the customer experience. There are many available sentiment lexicons/corpuses and Python libraries to make these analyses easy to perform even for beginning coders. However, many of these sentiment corpuses are trained on Twitter, Reddit, Yelp, or customer reviews, and may perform poorly on other types of texts, like virtual reference interactions. In this talk, we will review the performance of various lexicons (e.g., VADER, NRC Emotion Lexicon, AFINN, etc.) on library chat transcripts, and discuss how to choose one for your project.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Understand how lexicons assign sentiment values and valences. <li>Understand how to select a lexicon for a project.</ul></div></td></tr>
 <td class="program-break"><strong>Break</strong><br />11:55 AM -<br />12:00 PM</td><td class="program-break"></td></tr>
 <tr><td class="program-time"><strong>Keynote</strong><br />12:00 PM -<br />1:00 PM</td><td class="program-details">12:00 PM<br /><strong>Big Data Projects at the NIH: Report from an NIH DATA Scholar</strong><ul class="vertical-list"><li>Dr. Michelle Hribar (Oregon Health & Science University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content"><div style="text-align:center; width:100%"><img src="SEDLS_2024_keynote.png" width="400" alt="2024 keynote announncement flyer"></div><em>Abstract:</em> Access to large, diverse datasets is key for the development of AI models in biomedicine. The National Institutes of Health (NIH) recognizes this need and has several ongoing efforts to increase access to data for researchers: establishing data generation projects such as Bridge2AI and All of Us, promoting access to observational health data stored in electronic health records, and encouraging data sharing. All of these efforts require data standardization, which is currently an important focus of the NIH; common data models and common data elements (CDEs) are a few example approaches for improving data standardization and data sharing.<br /><br />The NIH started the Data and Technology Advancement (DATA) National Service Scholar Program in 2020 to help with large data science projects across the NIH. I served as a DATA Scholar at the National Eye Institute from 2022 – 2024, focusing on standardizing ocular observational health data into the Observational Medical Outcomes Partnership (OMOP) Common Data Model. This talk will discuss my DATA scholar project within the larger context of data science and standardization projects at the NIH.<br /><br /><em>Bio:</em> Dr. Michelle R. Hribar, PhD is an Associate Professor of Ophthalmology and Medical Informatics at Oregon Health & Science University. She was also very recently an NIH DATA Scholar at the National Eye Institute (NEI) where she worked in the Office of Data Science and Health Informatics. She is currently leading a national effort to improve the standardization of ophthalmic data for research, which includes co-chairing the Observational Health and Data Science Informatics (OHDSI) Eye Care and Vision Research workgroup with the goal of adding ophthalmic data to the OMOP common data model. Dr. Hribar originally trained as a computer scientist before retraining as a medical informaticist at Oregon Health & Science University. Her NIH grant funded research has focused exclusively on informatics in ophthalmology, specifically in the reuse of electronic health record data for research both in operations and clinical applications.</div></td></tr>
-<tr><td class="program-time"><strong>Workshop</strong><br />1:00 PM -<br /> 2:30 PM</td><td class="program-details">1:00 PM<br /><strong>Open data deidentification tools</strong><ul class="vertical-list"><li>Katie Pierce Farrier (University of North Texas Health Science Center)<li>Christine Neiman Hislop (University of Maryland Baltimore)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Data deidentification often poses a challenge to researchers. With more funders supporting or requiring data sharing, data deidentification is a crucial step in the data management lifecycle. This workshop will introduce and explore openly available tools and technologies to help with data deidentification, including how machine learning and natural language processing apply to deidentification.<br /><br /> We will focus on the NLM Scrubber from the National Library of Medicine and other open NLP tools that can be used for clinical text deidentification. This workshop will include a demonstration of how to run NLM Scrubber and show the types of data it is able to find and redact. There will be activities structured around data privacy terms and a "scavenger hunt" exploring different NLP tools. Finally, the presenters will share ways to promote data privacy and data deidentification tools at your institution.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Participants will understand natural language processing, a type of AI, and how they approach data deidentification. <li>Participants will learn about NLM Scrubber and other openly available deidentification tools and will explore ways to promote these tools at their institution. <li>Participants will leave the session with ideas on how to promote deidentification tools and how to incorporate them into library instruction and outreach programs.</ul></div></td></tr>
+<tr><td class="program-time"><strong>Workshop</strong><br />1:00 PM -<br /> 2:30 PM</td><td class="program-details">1:00 PM<br /><strong>Open data deidentification tools</strong><ul class="vertical-list"><li>Katie Pierce-Farrier (University of North Texas Health Science Center)<li>Christine Neiman Hislop (University of Maryland Baltimore)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Data deidentification often poses a challenge to researchers. With more funders supporting or requiring data sharing, data deidentification is a crucial step in the data management lifecycle. This workshop will introduce and explore openly available tools and technologies to help with data deidentification, including how machine learning and natural language processing apply to deidentification.<br /><br /> We will focus on the NLM Scrubber from the National Library of Medicine and other open NLP tools that can be used for clinical text deidentification. This workshop will include a demonstration of how to run NLM Scrubber and show the types of data it is able to find and redact. There will be activities structured around data privacy terms and a "scavenger hunt" exploring different NLP tools. Finally, the presenters will share ways to promote data privacy and data deidentification tools at your institution.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Participants will understand natural language processing, a type of AI, and how they approach data deidentification. <li>Participants will learn about NLM Scrubber and other openly available deidentification tools and will explore ways to promote these tools at their institution. <li>Participants will leave the session with ideas on how to promote deidentification tools and how to incorporate them into library instruction and outreach programs.</ul></div></td></tr>
 
 	<tr><td colspan="2"><p>&nbsp;</p></td><tr><td class="program-date" colspan="2">Wed, Oct 9</td></tr><tr><td class="program-time"><strong>Short Talks</strong><br />10:00 AM -<br />11:00 AM</td><td class="program-details">10:00 AM<br /><strong>Data-driven data services development: a research method for informed growth with a dynamic campus environment</strong><ul class="vertical-list"><li>Reid Boehm (Purdue University)<li>Kelly Burnes (Purdue University)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">As library specialists who support campus researchers with data management and sharing, it is difficult to identify a clear, holistic picture of researcher needs for services such as training, consultations, and the institutional data repository. Necessary growth in tandem with our campus research environment is underscored by anticipated requirement changes associated with the 2022 Office of Science and Technology Policy memo and other nascent policies and recommendations. This presentation will share a research design using formal content analysis of data management plans from grant awardees planning to use the data repository. It will provide an overview of the research and the implications of the results for our team. As a sample case study it will provide attendees: 1.) an outline of the method for collection and analysis, 2.) a way to engage with data for interpreting results, and 3.) a description of how specialists might choose to bring results into strategic and holistic service development.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>1.) an outline of the method for collection and analysis<li>2.) a way to engage with data for interpreting results, and <li>3.) a description of how specialists might choose to bring results into strategic and holistic service development.</ul></div>
 <br /><br />10:20 AM<br /><strong>Data Accessibility Checklist</strong><ul class="vertical-list"><li>Brandie Pullen (Virginia Tech)</ul><button type="button" class="collapsible">Show/Hide Abstract</button><div class="content">Want to make your data more accessible but don't know where to start? Come to this lightning talk to hear about a Data Accessibility Checklist. This checklist takes out the why and how while giving you actionable tasks to complete to make digital content more accessible. The checklist covers a few short tasks you can do for each of the following data types: text, tabular, code, and image. This checklist should be used as a starting point for researchers and curators to make their content more accessible for everyone. This checklist goes along with the DCN's Accessibility Primer.<br /><br /><strong>Learning Objectives:</strong> <ul class="vertical-list"><li>Data accessibility concerns<li>How to make your data more accessible</ul></div>