FSCI 2022

Rhiannon Cameron, Damion Dooley, William Hsiao, Emma Griffiths, Anoosha Sehar

Abstract: The ways in which people encode meaning into text are complex. It is difficult to know for sure what one means without additional context. Semantic ambiguity can impede the sharing of knowledge and impact the comparability and interoperability of datasets.

This course covers how we can use ontologies to improve the consistency and communication of ideas. Ontologies are data structures that are composed of controlled vocabularies, and the relations between them, that represent a piece of knowledge in a subject area. They are being used to support a variety of academic research, government, and commercial projects by providing findable, accessible, interoperable, and reusable (FAIR) data annotations that computers can reason over.

This is a course not on how to develop an ontology, nor on the underlying data models, but rather on how a data curator can engage in ontology practices to support their FAIR data objectives. Over three sessions we will cover what ontologies are, how to access and explore ontologies, finding and evaluating appropriate ontology terms, annotating spreadsheet data, and how to make new term requests.

We will introduce users to the Open Biological and Biomedical Ontologies (OBO) foundry community and their founding principles, as well as explore practical examples and applications using the Genomic Epidemiology Ontology (GenEpiO) and the Food Ontology (FoodOn). That being said, this course is not limited to individuals who work within genomic and epidemiological frameworks – our aim is to support users in solving practical data-quality problems using open access ontologies across disciplines.

Several free and/or open-source tools will be introduced throughout the course, including but not limited to: Ontobee, EMBL-EBI Ontology Lookup Service, Protégé, and OntoMaton.

Audience: Researchers, data curators.

LIVE ZOOM SESSION SCHEDULE (All times Pacific UTC-7)
Tuesday, July 26
4-5PM
Wednesday, July 27
4-5PM
Thursday, July 28
4-5PM

Ronald Margolis

Abstract: Complex problems, particularly in the life and natural sciences, are characterized by increasingly complex and technically challenging approaches. Often it takes a cross- or trans-disciplinary approach to first understand and then solve such problems. To do so, teams of investigators drawn from several disciplines must come together to bring their separate, and sometimes overlapping, expertise to bear on the problem.

One challenge is to assemble teams from all levels of the academy, from trainees (undergraduate and graduate students) and junior faculty, to the most senior members of a department or discipline. Melding these differing levels of experience, expertise, and working knowledge of the necessary technologies and approaches to form a cohesive team is a task for a core group of organizers who are able to manage disparate personalities and career paths. Understanding the needs of junior versus senior members of such a team involves questions of allocation of credit, openness to ideas from all contributors, and willingness to sometimes push the envelope.

The rewards both for the participants and for the field as a whole can lead to outcomes with broad impact as movement is made toward solving the initial complex problem. Catalyzing a team’s efforts toward the common goal is not easy, but the results can lead to expanded opportunities for team members as they interact with new collaborators and ideas.

This course will explore the concept of a team approach to solving a complex problem and help participants to see both how they might fit into such a concept and how they might seek to initiate a team science approach to a complex and unfulfilled problem.

Activities will include finding and focusing on a complex problem; identifying the expertise needed to address the problem and whether and which disciplines may be needed; and deciding how to identify, recruit, and meld a team of investigators, as well as how to organize the team around the goals set for addressing the problem. The final steps will include how the team can report out its findings.

Audience: Researchers, librarians, faculty/scholars, publishers, and administrators. The instructor’s background is in the natural sciences, though the concepts embodied in the course outline would apply to the physical sciences as well.

LIVE ZOOM SESSION SCHEDULE (All times Pacific UTC-7)
Tuesday, July 26
4-5:30PM
Wednesday, July 27
4-5:30PM
Thursday, July 28
4-5:30PM

Clarke Iakovakis, Kay Bjornen, Brandon Katzir, Megan Macken 

Abstract: The objectives of this course are to obtain a set of ORCID iDs for people affiliated with your institution, harvest a list of DOIs for publications associated with these iDs, and gather open access information for the articles using Sherpa Romeo and Unpaywall.

Students will work with a set of pre-written scripts in R, customizing them for their institutions to access the APIs for ORCID, Sherpa Romeo, and Unpaywall, and bring it all together into a manageable data file.
While some experience using R will be helpful, it is not required. However, although the basics of using R and understanding the code will be reviewed, the emphasis of the course will be on running the scripts and gathering and interpreting the data. In other words, this course is focused not on learning R, but rather on obtaining a dataset of publications based on institutional affiliation and open access information on those publications. It is inspired by a course taught previously at FSCI, available at https://osf.io/vpgbt/. The course will conclude with a discussion of using this data to develop outreach methods to authors to inform them of their right to deposit author manuscripts.

Audience: The course will be useful for repository managers, research information management system (RIMS) administrators, librarians, funders, publishers, and others who need to systematically gather publication and open access data.

LIVE ZOOM SESSION SCHEDULE (All times Pacific UTC-7)
Tuesday, July 26
4-6PM
Wednesday, July 27
4-6PM
Thursday, July 28
4-6PM

Matthias Liffers, Kathryn Unsworth 

Abstract: This course will focus on FAIR research data management and stewardship practices. It will provide an understanding of FAIR (findable, accessible, interoperable, and reusable) data and how it fits into scholarly communication workflows. Participants will learn about the FAIR Data Principles and how they can be implemented with regard for indigenous data sovereignty under the CARE principles.

Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. In research libraries, the principles can be used as a framework for fostering and extending research data services.

This course will provide an overview of the FAIR Data Principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to implementing FAIR principles, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; and tips for enabling data reuse (e.g., data licensing) with best-practice examples. Along the way, we will get hands-on with data and tools through self-paced exercises. There will be opportunities for participants to learn from each other and to develop skills in data management and expertise in making data FAIR.

The course will conclude with a look at applying the FAIR principles beyond data, such as vocabularies, platforms, software, and training materials.

Audience: Researchers, librarians, faculty/scholars, publishers, administrators, technical support staff, and research infrastructure project teams

LIVE ZOOM SESSION SCHEDULE (All times Pacific UTC-7)
Tuesday, July 26
5-6PM
Wednesday, July 27
5-6PM
Thursday, July 28
5-6PM