Collaboration between Media, Librarians, and IT

As part of the Libraries strategic plan, the College of Saint Benedict/Saint John\u27s University is working to implement more collaboration between Media Services, Librarians, and campus IT. We held several brainstorming sessions to talk about how we might collaborate, and we are currently implementing our plan. We are holding training sessions for all librarians and media staff on various topics and have implemented librarian liaisons specializing in a particular media service, i.e. 3D printing, video editing, and data visualization. These liaisons will help tie information literacy learning goals into media instruction sessions

Author rights, open access, and you!

Librarians present a session on author rights, copyright and permissions, and best practices for curating faculty members’ online presence, and talk about the benefits of making one’s work openly available on DigitalCommons@CSB/SJU

Using Fad Diets to Teach Information Evaluation

In Spring 2021, the librarian presenters partnered with Laura Bauer, a Nutrition faculty member, to teach students in introductory Nutrition classes information evaluation skills. The instructor\u27s assignment originally asked the class of non-science major students to find a recipe online that met a personal nutrition goal based on current recommendations. The assignment evolved for a class of pre-health science students into an evaluative assignment where students were asked to research a fad diet online and compare those findings with the scholarly literature, all while considering diet culture through a social justice lens. In this session we\u27ll discuss how the librarians and professor collaborated to create an assignment that incorporated the learning objectives of the course into a real-life scenario where students would be expected to critically evaluate information. Over our two-part library instruction session with the pre-health science majors, we introduced two evaluation methods to students in the class: SIFT (Stop, Investigate the source, Find trusted coverage, Trace the claims) and TRAP (Timeliness, Relevance, Authority, Purpose). We will explain these evaluation techniques and how they can be applied to evaluate various types of information. We will also discuss our plans to incorporate the fad diet assignment into courses for non-science majors in the fall, replacing the more basic recipe assignment

Revolutionary or evolutionary? Making research data management manageable

This chapter investigates the role of academic librarians, particularly those at small liberal arts institutions, in providing research data management services. Research data management may not seem like an obvious fit for curricular libraries whose primary mission is supporting teaching rather than faculty research, nor is data curation an obvious need for schools without a data repository or staff who specialize in the preservation and dissemination of data. Yet numerous reports cite data management and data services as critical services for the future of academic libraries (ACRL Planning and Review Committee, 2013; Johnson, 2014; Cox, 2013; Tenopir, 2012). The question raised, then, is how and why are data management services important in the liberal arts context? What can librarians at these institutions do to develop expertise in this growing area of the profession? What services are college and university libraries beginning to provide, and how successfully can existing models be adapted to other institutions? Does the addition of data services transform the mission of liberal arts libraries, and if so, is that transition revolutionary or evolutionary? Liberal arts librarians, as they have with numerous other shifts and trends in librarianship, can turn to models in the literature from research universities, develop communities of practice amongst themselves, and also innovate from within their own unique contexts. The authors argue that such collaboration and innovation reflect an evolutionary process as librarians build on existing skills, strategies, workflows, and knowledge. The following pages of this chapter survey the current environment, offer case studies from two small liberal arts institutions, the College of Saint Benedict/Saint John’s University and Carleton College, and provide readers with recommended action steps to develop a path of gradual, manageable, shared, and sustainable work in research data management

Beginning to track 1000 datasets from public repositories into the published literature

Data sharing provides many potential benefits, although the amount of actual data reused is unknown. Here we track the reuse of data from three data repositories (NCBI\u27s Gene Expression Omnibus, PANGAEA, and TreeBASE) by searching for dataset accession number or unique identifier in Google Scholar and using ISI Web of Science to find articles that cited the data collection article. We found that data reuse and data attribution patterns vary across repositories. Data reuse appears to correlate with the number of citations to the data collection article. This preliminary investigation has demonstrated the feasibility of this method for tracking data reuse

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

Revolutionary or Evolutionary? Adapting Best Practices for Data Management

Looking for ways to talk to researchers about data management? Wondering whether text, video, and image collections “count” as data? Daunted by the idea of helping someone write a data management plan? Never fear! In this workshop-style session, you will learn how you can support researchers and students with their data projects by building on existing librarian knowledge, skills, and practices. Through discussion and interactive exercises, this session will familiarize you with key concepts and tools you’ll need to start assisting with and planning services for data management, curation, and data literacy

Investigating risk factors and predicting complications in deep brain stimulation surgery with machine learning algorithms

Background: Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurological symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to (1) investigate preoperative clinical risk factors, and (2) build machine learning models to predict adverse outcomes. Methods: This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n=501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity and accuracy. Results: Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (OR=0.44, confidence interval [CI]=0.25-0.78), BMI (OR=0.94,CI=0.89-0.99) and diabetes (OR=2.33,CI=1.18-4.60). Patients with diabetes were almost three times more likely to return to the operating room (OR=2.78,CI=1.31-5.88). Patients with a history of smoking were four times more likely to experience postoperative infection (OR=4.20,CI=1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC=0.86), a complication within 12 months (AUC=0.91), return to the operating room (AUC=0.88) and infection (AUC=0.97). Age, BMI, procedure side, gender and a diagnosis of Parkinson’s disease were influential features. Conclusions: Multiple significant complication risk factors were identified and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery