Course: Reproducibility for Biomedical Researchers - 2021 | UCSF-CLE

  • Introduction

    Have you heard about the reproducibility crisis in the sciences? Want to know how you can make your research more transparent and rigorous? Or maybe you just want to save yourself time and effort by implementing better research workflows? 

    Then this is the workshop series for you!

    The UCSF Library and Graduate Division designed this series of workshops on research reproducibility in the biomedical sciences to be a practical guide for UCSF researchers. Our goal is to translate reproducibility recommendations and best practices from societies, articles, and funders into actionable steps and training that can be immediately implemented into your work. Together we will learn how to design rigorous experiments that reduce bias, make our work more transparent by sharing our protocols, publications, code, and data, and build a culture of reproducibility in our labs. You are welcome to register for each workshop or only the ones that interest you.

    By the end of this series, learners will be able to:

    • Define reproducibility in the context of biomedical research
    • Describe the significance of practicing reproducible and open science
    • Identify existing practices and behaviors that require modification in order to improve reproducibility
    • Apply a range of new tools, strategies, and best practices to make their research more rigorous and reproducible

    Time/Date

    Every Monday, April 5 - May 17 from 1-2:30pm via Zoom

    Click on the workshops below to read more and register for each workshop.

    If you are a UCSF trainee interested in taking the entire series for RCR or NIH reproducibility credit please register here.

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  • Instructor: Ariel Deardorff - UCSF Library Data Science Initiative

    Overview

    Join us as we launch the spring 2021 workshop series on Reproducibility for Biomedical Researchers! This session will start with an overview of the reproducibility "crisis", discuss reproducibility in light of the COVID-19 pandemic, and highlight solutions for making research more reproducible.

    Learning Objectives

    By the end of this session, learners will be able to:

    • Define reproducibility in the context of the biomedical sciences
    • Describe two causes of non-reproducible research
    • Identify one reproducible practice they want to further explore

    Instructor Bio

    Ariel Deardorff is the UCSF Data Services Librarian and member of the Library’s Data Science Team. In her role she teaches classes and provides support for research data management, open science, and reproducibility in the health sciences. She is an expert in research data publishing, and has led the Library’s involvement with several data infrastructure and policy projects. Ariel is also an open science advocate who performs research on the role of the Library in enabling open and reproducible research.

    Registration Link: https://calendars.library.ucsf.edu/event/7601864

    Course Materials

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  • Instructor: Karla Lindquist, PhD - UCSF Library Data Science Initiative

    Overview

    The scientific community, and many publishers, are increasingly demanding that experimental results be reproducible in order to be considered valid and valuable contributions. In order to achieve this, researchers must design their experiments carefully. In this workshop, we will review some of the key steps to consider in these critical planning stages.    

    Learning Objectives

    By the end of this workshop, learners will

    • Be aware of different types of experimental biases
    • Know what to consider in developing a good analysis plan
    • Be able to identify the key components of a well-written methods section

    Instructor Bio

    Karla teaches and consults on topics related to statistics and bioinformatics, as well as programming in R. She also has expertise with shell scripting and other languages.

    Registration Link: https://calendars.library.ucsf.edu/event/7601872

    Course Materials

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  • Instructors: Anneliese Taylor, UCSF Library & Jennifer Chan, UCLA Library

    Overview

    Publishing the results of scholarly research in peer-reviewed journal articles is one of the most common ways researchers get recognition and credit for their work. Yet longstanding scholarly communication practices and academic reward structures perpetuate the publishing of results that cannot be reproduced. We'll learn about transformations to publishing and peer review and how they contribute to the reproducibility of published results.

    Learning Objectives

    By the end of this workshop, learners will be able to:

    •     Define the characteristics of scholarly publishing and the causes of publication bias
    •     Explain how emerging models of scholarly communication improve reproducibility
    •     Describe online researcher identity tools and their importance for getting recognition

    Instructor bios

    Anneliese Taylor, MLIS is the Head of Scholarly Communication at the UCSF Library where she provides guidance and clarity to UCSF students, faculty, and staff on scholarly communication systems and research attention and impact models. As a co-founder of the Open Science Group at UCSF, Anneliese is an advocate for increasing openness and transparency in all scholarly research practices. She is committed to making scholarly publishing more equitable for UCSF students, early career researchers and faculty through scholarly publishing and peer review education and guidance.

    Jennifer Chan is the Scholarly Communications Librarian at the UCLA Library where she liaises with campus partners on the development of targeted outreach and programming that promote scholarly communication, open access, and open education strategies to further the campus mission of research, teaching, and public service. She administers UCLA Library’s Affordable Course Materials Initiative, which seeks to more closely align Library collections, services, and expertise with instructional needs. ACMI has helped lower the cost of course materials for thousands of UCLA students while achieving instructors’ educational objectives. Jennifer is also certified in Copyright Management and Leadership.

    Registration Link: https://calendars.library.ucsf.edu/event/7601900

    Course Materials

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  • Instructor: Ariel Deardorff, UCSF Library Data Science Initiative

    Overview

    Research data is increasingly valued as a necessary component of reproducible and open science. Initiatives focused on the usability of data like “FAIR”, coupled with mandates from funders and publishers that require data to be openly accessible, increase the need for a data savvy research workforce. This workshop will focus on organizing, preparing and sharing data for reproducibility.

    Learning Objectives

    By the end of this workshop, learners should be able to

    • Describe the link between FAIR data and reproducibility
    • Locate a data repository relevant to your research
    • Plan in advance for data sharing
    • Follow UCSF data sharing guidance

      Instructor Bio

      Ariel Deardorff is the UCSF Data Services Librarian and member of the Library’s Data Science Team. In her role she teaches classes and provides support for research data management, open science, and reproducibility in the health sciences. She is an expert in research data publishing, and has led the Library’s involvement with several data infrastructure and policy projects. Ariel is also an open science advocate who performs research on the role of the Library in enabling open and reproducible research.

      Registration Link: https://calendars.library.ucsf.edu/event/7601904

      Course Materials

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    • Instructors: Ibraheem Ali, UCLA Library & Stephen Floor, UCSF

      Overview

      One of the barriers to reproducibility of research is the lack of detailed methods in the research environment and in published articles. This workshop will examine resources and tools to simplify and improve documentation throughout the research lifecycle and to ensure that methods and protocols are findable and reproducible. 

      Learning Objectives 

      By the end of this workshop, learners will be able to:

      • Summarize tools and best practices for doumenting research methods
      • Identify resources for finding existing methods and protocols
      • Define the benefits of publishing open research protocols

      Instructor & Presenter Bios

      Ibraheem received his PhD in Biomedical Sciences at UCSF and did a brief post-doc focusing on epigenetics and transcription regulation at the Gladstone Institutes. In his current role as Sciences Data Librarian at UCLA, he teaches data visualization strategies and best-practices for preprints and methods publishing to pre-clinical and clinical researchers.
       

      Stephen Floor, PhD, is Assistant Professor in the Department of Cell & Tissue Biology. The Floor Lab studies the mechanisms by which RNA impacts human gene expression in health and disease. RNA chaperones are a major focus of the lab, which remodel RNA structures and RNA-protein interactions. We use deep sequencing, molecular biology of purified proteins, and imaging approaches to define the in vitro and cellular mechanisms of RNA chaperones. Many DEAD-box RNA chaperones are genetically altered in cancers and other diseases. We study the molecular basis of such alterations using cancer cell lines and genetically engineered stem cell models, aiming to better understand human biology and nominate new therapeutic targets or treatment regimens. 

      Registration Link: https://calendars.library.ucsf.edu/event/7601936

      Course Materials

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    • Instructor: Kat Koziar, UC Riverside Library & Ariel Deardorff, UCSF Library

      Overview

      Despite the importance of research software for science, a majority of scientists do not have sufficient training to create reproducible code. In this workshop we will discuss how to set up you work so others can easily understand and reproduce all of your computational steps. We will also cover best practices for versioning code, sharing code, and citing code for academic credit.

      Learning Objectives

      By the end of this session, participants will be able to:

      • Recognize when to cite code in your research
      • Identify ways to make their code more reproducible
      • Share and archive code associated with their research papers

      Instructor Bio

      Kat Koziar is the Data Librarian at UC Riverside where her key responsibility is to develop educational programming and hold consultations on a variety of topics related to research data, including data management, visualization, and data science.

      Ariel Deardorff is the UCSF Data Services Librarian and member of the Library’s Data Science Team. In her role she teaches classes and provides support for research data management, open science, and reproducibility in the health sciences. She is an expert in research data publishing, and has led the Library’s involvement with several data infrastructure and policy projects. Ariel is also an open science advocate who performs research on the role of the Library in enabling open and reproducible research.

      Registration Link: https://calendars.library.ucsf.edu/event/7601937

      Course Materials

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    • Instructor: Elizabeth Silva, PhD - UCSF Graduate Division

      Overview

      Aspects of the culture of academic research can be at odds with efforts to improve rigor and reproducibility, from hyper competition for jobs, funding and fame, to the everyday devaluing of “administrative tasks” as a part of scientific practice. In this workshop, participants will explore the cultural barriers to implementing rigorous and reproducible practice.

      Learning Objectives

      By the end of the session, participants will be able to:

      • Describe the broad cultural barriers to rigor and reproducibility
      • Identify at least one cultural barrier that is impeding their own practice in rigor and reproducibility
      • Develop a strategy to implement in everyday practice for improvement of rigor and reproducibility

      Instructor Bio

      Liz is a trained biomedical research scientist with interests and experience in science policy, particularly relating to publication and research ethics, reproducibility of research, and training of biomedical graduate students and postdocs.

      After completing her PhD and postdoctoral training in developmental biology and genetics (in Canada, the UK and the US), she moved to PLOS ONE as an Associate and then Senior Editor. She returned to UCSF in 2014 where she managed the Motivating INformed Decisions (MIND) program, one of 17 experimental programs across the country that aimed to bring biomedical research training in line with the realities of the career outcomes for graduate students and postdocs. In 2016 she became the Associate Dean for Graduate Programs in UCSF's Graduate Division.

      She has served as a panelist and speaker on a variety of topics in science policy, including: reproducibility in research, ethical conduct in research and publishing, research communication and publishing for scientists, career exploration and professional development for PhDs, and issues related to the roles of PhD trainees in the biomedical workforce and in academia. 

      Registration Link: https://calendars.library.ucsf.edu/event/7601939

      Course Materials


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