Seminars & Workshops

There is an urgent need to develop diverse, equitable, and collaborative research teams to improve research impact and health outcomes. However, several barriers remain to achieving this goal, including implementation of strategies for creating an equitable and inclusive research team.

Tufts CTSI invites you to join us for “Fundamentals for Building Inclusive Research Teams” on Friday, March 31  and Friday, April 7, from 11:00AM-12:00PM. This workshop will share practical skills for building collaborative and inclusive research teams.  Participants will learn about fostering teams that welcome diverse identities, working and learning styles, disciplines, and forms of knowledge. The format will involve case studies, presentations, a panel discussion, and reflection/discussion.

Topics covered will include:

• Cultural humility as a basis for equitable collaboration
• Creating, supporting, and sustaining cohesive, equitable, and collaborative research teams
• Leveraging a team’s diversity to improve translational research

Participants will leave with tools and frameworks that will help them implement a plan to operationalize skills learned.

Learning Objectives

After attending this event, participants will be able to do the following as part of dynamic research teams:

• Describe strategies that promote equitable collaboration
• Apply best practices for building diverse, collaborative research teams

Who Should Attend

Investigators with varied research collaboration experience and varied disciplinary interests, as well as research staff and others responsible for supporting collaborative research projects are encouraged to attend.

Details

Friday, March 31, 2023, 11:00AM-12:00PM

Friday, April 7, 2023, 11:00AM-12:00PM

This workshop will be held remotely via Zoom over two separate 1-hour sessions. Registrants should plan to attend both.

Registration

Click here to register. Registration for this workshop closes March 24, 2023.

Want to learn about the data and graphics behind The New York Times COVID-19 dashboard?

Don’t miss this live, online presentation and Q&A by Tiff Fehr, Rich Harris, Albert Sun, and Lisa Waananen Jones of The New York Times, moderated by Anna Haensch, PhD, Senior Data Scientist at the Data Intensive Studies Center (DISC) at Tufts University.

This event is co-sponsored by the Center for Quantitative Methods and Data Science (QM&DS) at Tufts Medical Center and by Tufts CTSI.

Details

Thursday, September 23, 4:00-5:30PM

Via Zoom

Registration

To attend, please register here.

POSTPONED

Given guidance and recent reports related to the novel coronavirus disease (COVID-19), this event has been postponed.

We hope to reschedule the symposium for later this year. As soon as new details are available, we will share them here.

We apologize for any inconvenience.

What does the p-value controversy mean for clinical research?

The deep controversy surrounding the use and misuse of p-values and statistical significance is evident in the decision by the American Statistical Association to issue a policy statement on the matter in 2016. The statement marked the first occasion the Association has taken a position on a specific matter of statistical practice since its founding in 1839.

This Tufts CTSI symposium, co-sponsored by the Tufts Data Intensive Studies Center (DISC),  aims to inform clinician researchers and statisticians regarding the principles covered in the statement as well as the controversy over the proper use and interpretation of the p-value. Distinguished panelists will speak on use of p-values from their multiple perspectives to reflect the landscape of opinions and provide guidance for investigators and educators going forward. They include scientists, statisticians, epidemiologists, and statistical advisors to prominent journals and policy organizations, with expertise in statistics, genetics, communication, nutrition, obesity, cardiovascular disease, and drug approval.

Learning objectives:

• Understand the rationale behind the ASA statement that “No single index [i.e., p-value] should substitute for scientific reasoning.
• Discuss the role that p-values have had on reproducibility and replication and the proposed remedies.
• Apply alternative remedies for dealing with uncertainty in clinical research and education.

Panelists

John P.A. Ioannidis, MD, DSC

C.F. Rehnborg Chair in Disease Prevention and Professor at Stanford University
Author of “Why Most Published Research Findings are False,” accessed more than three million times. His recent JAMA viewpoint is subtitled “Do Not Abandon Significance.” Dr. Ioannidis has published nearly 1,000 papers and is one of the 10 most-cited scientists worldwide.

David Allison, PhD

Dean and Provost of the Indiana University School of Public Health
Author of “A Tragedy of Errors: Mistakes in Peer-reviewed Papers are Easy to Find but Hard to Fix, Report” and committee member of the National Academies of Sciences, Engineering, and Medicine report, “Reproducibility and Replicability in Science.”

David Harrington, PhD

Professor of Biostatistics, Emeritus, Harvard T.H. Chan School of Public Health
Co-Author of the 2019 New England Journal of Medicine article, “New Guidelines for Statistical Reporting in the Journal.” Dr. Harrington is also the principal investigator of the Statistical Coordinating Center for the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium and the project leader of the Biostatistics Core and Director of the Biostatistics Research Program in the Dana-Farber/Harvard Cancer Center (DF/HCC).

Allen Schirm, PhD

Recently retired from Mathematica Policy Research
Co-Author of the 2019 The American Statistician editorial, “Moving to a World Beyond ‘p<0.05’.” He and Dr. Ron Wasserstein recently discussed their recommendations on statistical inference at the United States Conference on Teaching Statistics.

Details

Tuesday, April 7
8:30-11:30AM
Jean Mayer Human Nutrition Research Center on Aging (HNRCA)
711 Washington Street, Boston, MA 02111

All are welcome to attend, especially:

• Clinician researchers and investigators leading clinical trials.
• Statisticians working with and educating clinical investigators.

Registration

Space is limited. Please register to attend.

About Tufts CTSI Events

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544.  The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Which tools for experimental analysis should you choose?

High throughout RNA sequencing allows genome-wide investigation of gene expression and regulation. However, designing an experiment and choosing the right tools for analysis can be challenging. This session will introduce methods for analyzing and visualizing RNA-seq data: quality control, alignment-based quantification, transcriptome assembly and differential expression analysis.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by  Rebecca Batorsky, PhD and Albert Tai, PhD.

Rebecca Batorsky is a Senior Bioinformatics Scientist in Research Technology, part of Tufts Technology Services and a DISC fellow. She earned her PhD in Physics in 2012 from Tufts University, where she focused on mathematical and computational modeling of virus evolution. Before becoming staff at Tufts, she worked as a bioinformatics software developer at a clinical genomics start-up company. Dr. Batorsky works to enable researchers to answer biological questions with data-driven methods, such as analysis of high-throughput DNA and RNA sequencing data. She is especially interested in developing methods to use multiple `omics technologies to give insight into biological pathways and processes.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 14
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

What are methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles?

Single-cell RNA sequencing (scRNA-seq) allows for transcriptome-wide profiling of individual cells present in a tissue sample. While conceptually similar, scRNSeq and “bulk” RNAseq projects differ so greatly in their overall study design, goals, and statistical caveats that their analytical investigation is distinct. In this session, we will introduce methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles, including: data preprocessing and normalization, dimensionality reduction, clustering, and visualization.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Tanya Karagiannis, MS and Eric Reed, PhD.

Tanya Karagiannis is a Research Assistant at Tufts Medical Center in the Institute for Clinical Research and Health Policy Studies working with Dr. Paola Sebastiani. She has an MS in Bioinformatics from Boston University where she is also continuing her PhD in Bioinformatics under the advisement of Dr. Paola Sebastiani and Dr. Stefano Monti. Her research focus is in the application and development of single cell transcriptomic methods utilizing machine learning and Bayesian statistics, with interest in multi-omics as well.

Eric Reed is a Data Scientist in the Data Intensive Studies Center (DISC) at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Dr. Reed’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Dr. Reed’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 21
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

NIH Funding Opportunities for Methodological Research in Mental Health

This seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, December 1, 2:00-3:30PM via Zoom. The topic is NIH Funding Opportunities for Methodological Research in Mental Health, presented by Christine M. Ulbricht, PhD, MPH.

This session will focus on opportunities for methodological research at the National Institute of Mental Health (NIMH). Christine Ulbricht, PhD, MPH, the Chief of the Methodological Research Program in the Services Research and Clinical Epidemiology Research Branch at NIMH, will provide an overview of the NIH and discuss NIMH’s funding priorities, programs, and mechanisms. She will discuss recent advances in statistical methods for mental health services research and funding opportunities for such research, such as NIMH-funded studies of machine learning applications to prevent suicide.

Faculty

Christine M. Ulbricht, PhD, MPH is a psychiatric epidemiologist who oversees the extramural Methodological Research Program within the Services Research and Clinical Epidemiology Research branch of the Division of Services and Intervention Research at the National Institute of Mental Health (NIMH). Prior to joining NIMH, she was an assistant professor at the University of Massachusetts Medical School, where her primary research interests were in applying novel statistical methods to understand heterogeneity of treatment effects, improve mental health services, and improve suicide prevention. She has served as the principal investigator of several NIH-funded studies leveraging big data to examine major depressive disorder, serious mental illness, and suicide among younger and older long-term care residents. Additionally, Dr. Ulbricht has been a co-investigator of studies on improving suicide risk identification in healthcare systems and on examining pain among older adults. She also served as associate faculty of the UMass Medical School’s Department of Psychiatry’s Implementation Science and Practice Advances Research Center and Eunice Kennedy Shriver Center and as the faculty co-director of the student chapter of the International Society of Pharmacoepidemiology

Details

Wednesday, December 1, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

How can data help us to better understand and respond to the synchronization of infectious outbreaks?

The March seminar of the Center for Quantitative Methods and Data Sciences (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, March 31, 2:00-3:00PM via Zoom. The topic of this month’s webinar is To Everything There is a Season: Synchronization of Infectious Outbreaks, presented by Elena Naumova, PhD.

A marked seasonality in many infections, like influenza or salmonellosis, is a well-known phenomenon. When we observe a pronounced seasonal pattern, it gives us a reason to expect high predictability of high or low disease incidence periods in a calendar year. With the expansion of national and global surveillance systems, the opportunities to better understand the local, regional, and global temporal fluctuations are also growing. As we learn more about the seasonality of many infections, it is reasonable to expect that some will co-occur. Yet, patterns of co-occurrences and factors driving such synchronization remain elusive.

In this talk, Dr. Naumova will demonstrate the methodology developed to assess the extent, lag, and directionality of seasonal synchronization. Dr. Naumova will provide several examples using national databases, such as the CDC’s Foodborne Disease Active Surveillance Network (FoodNet), National Outbreak Reporting System (NORS), and the FluNet supported by the WHO to illustrate seasonal synchronizations among foodborne infections and the challenges of time-referenced surveillance data. The modeling approaches include the trend-adjusted mixed effects nonlinear harmonic regression models and the delta-method to derive the estimates and confidence intervals for the seasonal peak timing and amplitude, allowing us to build local, regional, and global disease calendars. The methodological rigor, standardization, and data harmonization across surveillance systems are enabling comprehensive characterization of disease seasonality and serve as a pathway for implementing the Precision Public Health, Nutrition, and Medicine principles to tailor prevention and intervention strategies.

Faculty

Elena Naumova, PhD is Professor and Chair of the Division of Nutrition Epidemiology and Data Science at the Friedman School of Nutrition Science and Policy at Tufts University. Dr. Naumova’s area of expertise is in developing methodology for modeling of transient processes with applications in environmental epidemiology, nutrition, infectious diseases, and public health. As a mathematician by training, she designs statistical, computational and mathematical models to characterize and forecast infectious outbreaks. Dr. Naumova is using large-scale data sources to study infections sensitive to climate variations and extreme weather. She led research programs in emerging biomedical fields of epidemiology, immunogenetics, nutrition and growth, nationally and internationally to set new standards for public health investigations. Dr. Naumova is Editor-in-Chief for the Journal of Public Health Policy (Nature Publishing Group). She is currently funded by the NSF to develop ways to train data-savvy workforce, highlight advancements and challenges of data revolution, share examples where the data analytics and data visualizations enhance our knowledge and help to find solutions to wicked problems. Dr. Naumova hopes to stir the discussion on how data scientists have to rethink and reframe the state-of-the-art methodology to enable the discovery of emerging trends in global health fields.

Details

Wednesday, March 31, 2021, 2:00-3:00PM, online via Zoom

Registration

To attend, please enroll here via Tufts CTSI I LEARN.

How can data science and machine learning be used to illuminate and reduce inequality in health care and public health?

The December seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, December 15, 2:00-3:30PM via Zoom. The topic is Using Machine Learning to Increase Equality in Health Care and Public Health, presented by Emma Pierson, PhD, MS.

Our society remains profoundly unequal. Worse, there is abundant evidence that algorithms can, improperly applied, exacerbate inequality in health care and other domains. This talk pursues a more optimistic counterpoint – that data science and machine learning can also be used to illuminate and reduce inequality in health care and public health – by presenting vignettes about women’s health, COVID-19, and pain.

Faculty

Emma Pierson, PhD, MS is an Assistant Professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and a computer science field member at Cornell University. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.

Details

Wednesday, December 15, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Tools, Methods, and Community Actions for Reproducible Neuroscience

The November seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, November 17, 2:00-3:30PM via Zoom. The topic is Tools, Methods, and Community Actions for Reproducible Neuroscience, presented by Jean-Baptiste Poline, PhD.

The credibility of scientific activity has recently been under scrutiny with reports questioning the reproducibility of results. In response to this “reproducibility crisis,” the National Institutes of Health (NIH) designed a plan for more reproducible science in 2015, but progress to curb the issue seems to be slow at best. It is possible that the solutions proposed by the NIH are targeting symptoms rather than causes. In this talk, Dr. Poline will first consider the field of neuroscience and human neuroimaging and analyze the main causes of irreproducibility, considering the statistical and computational aspects of neuroimaging or imaging genetics. He will then discuss the social components that are likely to contribute to irreproducibility. In a second part, Dr. Poline will review solutions to foster a more reproducible research at the level of the tools and the statistical methods used – for example in high dimensions. He will also consider the academic ecosystem and propose community actions that are both possible and could be effective to reshape the way we practice research.

Faculty

Jean-Baptiste Poline, PhD is an Associate Professor in the Department of Neurology and Neurosurgery at McGill; the co-Chair of the NeuroHub and Chair of the Technical Steering Committee for the Canadian Open Neuroscience Platform (CONP) at the Montreal Neurological Institute & Hospital (the NEURO); and a Primary Investigator at the Ludmer Centre for Neuroinformatics & Mental Health.

Details

Wednesday, November 17, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Want a framework for flexible joint Bayesian modeling of multiple time series where inference is fast, easy, and scalable?

The second June seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, June 16, 2:00-3:00PM via Zoom. The topic is Scalable Bayesian Flexible Joint Time Series Modeling, presented by Michael Wojnowicz, PhD.

We frequently obtain datasets containing multiple time series — that is, a collection of sequences, often corresponding to temporal data from multiple individuals. For example, consider movement patterns of soldiers during a ruck march, lesion counts of multiple sclerosis patients, or computer activity by employees at a company. In this talk, we describe a framework for flexible joint Bayesian modeling of multiple time series where inference is fast, easy, and scalable. In particular, we construct a scalable Bayesian approach to mixed HMMs, where mixed HMMs are Hidden Markov Models with multi-level generalized linear models (a.k.a. Generalized Linear Mixed Models, or Mixed Effects Models) embedded within the transitions and emissions structure. Mixed HMMs are an excellent framework for personalized time series modeling: models can be personalized, while sharing statistical strength across individuals to “fill in” knowledge as necessary, based on knowledge about other individuals, and particularly similar individuals. Moreover, the impact of dynamic covariates can be learned based on their effects across the entire population of individuals. In this talk, we will introduce mixed HMMs, and then discuss how to make inference fast, easy, and scalable.

Faculty

Michael Wojnowicz, PhD is a Data Scientist II in the Data Intensive Studies Center (DISC) at Tufts University, working with the Machine Learning Research Group. He earned his Ph.D. from Cornell University in 2012, where his work in Cognitive Science led to the Dallenbach Fellowship for Research Excellence, the Cognitive Science Dissertation Proposal Award, and the Cognitive Science Graduate Research Award. Dr. Wojnowicz also has master’s degrees in Mathematics (University of Washington) and Statistics (University of California at Irvine). Before joining Tufts University, Dr. Wojnowicz was the Distinguished Data Scientist at Cylance. At Cylance, he developed statistical machine learning models for detecting malicious computer files and anomalous user activity, leading to 10 patents (5 granted, 5 pending). Dr. Wojnowicz’ current research interests include time series modeling, variational inference, and nonparametric Bayesian modeling.

Details

Wednesday, June 16, 2:00-3:00PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

What is the long-term effect of direct antiviral agents for Hepatitis C?

The first June seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, June 9, 2:00-3:00PM via Zoom. The topic of this month’s webinar is What is the Long-term Effect of Direct Antiviral Agents for Hepatitis C? A Causal Inference Approach Using ‘Big Data,’ presented by Sara Lodi, PhD.

The advent of direct-acting antiviral agents (DAAs) in 2011 revolutionized hepatitis C virus (HCV) treatment: based on clinical trials and real world data, approximately 95% of patients treated with DAA achieved a sustained virological response equivalent to cure. However, even after cure is achieved, the risk of hepatic and extra-hepatic disease remains. Our understanding of post-DAA clinical outcomes is based on clinical trials with relatively short follow-up and selected participants. However, the extent to which DAA impacts extra-hepatic morbidity in the long-term and in heterogenous populations is unknown. Electronic health records collected in routine clinical practice provide a unique opportunity to estimate the long-term benefits of DAA treatment and to assess the need for post-DAA clinical management.

In this talk, Dr. Sara Lodi will discuss how to design an observational study to estimate the effect of DAA on kidney function using the target trial approach. She will also describe how to apply and interpret the results of the parametric g-formula, a causal inference method that provides consistent estimates in the presence of treatment-confounding feedback. Dr. Lodi will present preliminary results using electronic health records from Boston Medical Center and the HepCAUSAL collaboration.

Faculty

Sara Lodi, PhD is an Assistant Professor in Biostatistics at Boston University School of Public Health. She obtained her PhD in Medical Statistics at the London School of Hygiene and Tropical Medicine in 2009. Her research focuses on clinical trials, clinical epidemiology and comparative effectiveness research using routinely collected heath data, particularly in the area of infectious disease and substance use. Methodologically, she focuses on statistical techniques for causal inference to estimate effects of interventions along the HIV continuum of care. She has published many articles on behalf of large international collaborations of HIV cohorts such as CASCADE, COHERE, URBAN ARCH and the HIV-CAUSAL Collaboration.

Details

Wednesday, June 9, 2:00-3:00PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

How do centenarians delay and defy aging?

The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a virtual seminar series on a Wednesday each month from 2:00-3:00PM.

The session on Wednesday, January 20 will feature Research Assistant and PhD Candidate Tanya Karagiannis, MS. She will give a talk titled Analysis of Single Cell Transcriptomics Data as it Relates to Aging and Longevity.

Abstract

Studies of aging have shown a gradual decline in the immune system, such that people experience age-related disabilities and diseases as well as differences in immune population composition and functions over time. However, a rare population of individuals who reach 100 years of age known as centenarians, experience delay in age-related disabilities and diseases and in fact live the majority of their lives in good health. In order to investigate how centenarians delay and defy aging, we utilize single cell transcriptomic methods to investigate longevity related differences in the peripheral blood immune system of centenarians.

Single cell level transcriptomic data has allowed for the profiling of thousands of cells to characterize cell states and populations in specific tissues. More specifically, these methods can be used to identify rare populations and assess transcriptional similarities and differences within a population of cells. We describe integrated analyses using four single cell RNA-sequencing datasets that we conducted to investigate compositional and gene expression differences in immune populations of centenarians and younger age controls (20-80 years).

Early findings demonstrate gene expression differences between centenarians and younger age controls that are specific to populations of cells. We also find centenarians not only have cell type specific compositional differences but overall have more cell type diversity than younger age controls.

Faculty

Tanya Karagiannis is a Research Assistant at Tufts Medical Center in the Institute for Clinical Research and Health Policy Studies, working with Dr. Paola Sebastiani. She has an MS in Bioinformatics from Boston University where she is also continuing her PhD in Bioinformatics under the advisement of Dr. Paola Sebastiani and Dr. Stefano Monti.  Her research focus is in the application and development of single cell transcriptomic methods utilizing machine learning and Bayesian statistics, with interest in multi-omics as well.

Details

Date: Wednesday, January 20, 2020, 2:00-3:00PM

Registration

To attend, please enroll via Tufts CTSI I LEARN here.

How can data help us to better understand and respond to the opioid crisis?

The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a virtual seminar series on a Wednesday each month from 2:00-3:00PM.

The session on Wednesday, February 17 will feature Associate Professor Thomas Stopka, PhD, MS. He will give a talk titled Spatial Epidemiological Analysis and Modeling of Opioid Decedent Data.

Abstract

The current opioid crisis has contributed to precipitous increases in opioid use disorder, fatal overdoses, and infectious diseases. Opioid-related overdoses alone have increased five-fold during the past two decades in Massachusetts. Decedent data, available through the Massachusetts Registry for Vital Records and Statistics, provide a valuable resource to better understand and respond to the opioid crisis. Together with his GIS and spatial epidemiology team, Dr. Thomas Stopka has explored the spatial distribution of fatal overdoses across the state to identify high-risk locations and inform targeted public health responses.

In this presentation, Dr. Stopka will provide an overview of his work, with a focus on geo-mapping, spatial epidemiological analyses, statistical modeling, and geographically weighted regression analyses. He will highlight fatal overdose hotspots, factors associated with overdose, spatial access to services, and unique approaches to modeling the risk landscape.

Faculty

Thomas Stopka, MS, PhD is is an associate professor with the Department of Public Health and Community Medicine at Tufts University School of Medicine and Tufts Clinical and Translational Science Institute (CTSI). He has contributed to and led numerous mixed methods, interdisciplinary, and translational studies focused on the intersection of opioid use disorder, overdose, and infectious disease since 1999. Dr. Stopka has employed geographic information systems (GIS), spatial epidemiological, qualitative, biostatistical, and laboratory approaches in multi-site, multi-investigator studies and public health interventions to better understand and curb the opioid syndemic. He currently leads and contributes to several studies funded by the NIH, CDC, SAMHSA, state and local public health departments, and private and philanthropic agencies, with a focus on development and implementation of evidence-based interventions among opioid users in New England. Dr. Stopka is Co-Chair of the Tufts research priority group focused on equity in health, wealth, and civic engagement. He teaches courses in GIS and spatial epidemiology, research methods for public health, and epidemiology.

Details

Date: Wednesday, February 17, 2021, 2:00-3:00PM

Registration

To attend, please enroll via Tufts CTSI I LEARN here.

Methods to Handle Mixtures of (Environmental) Exposures in Health Analyses

This seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, January 26, 2:00-3:00PM via Zoom. The topic is Methods to Handle Mixtures of (Environmental) Exposures in Health Analyses, presented by Laura Corlin, PhD.

Traditionally, (environmental) epidemiology has focused on individual exposure-outcome relationships; however, many (environmental) exposures co-occur. How do we identify which of these often highly-correlated exposures most affect health outcomes? Are certain combinations or mixtures important? The methods to answer these types of questions have been rapidly evolving. In this talk, Dr. Corlin will discuss several major approaches in an environmental health context.

Faculty

Laura Corlin, PhD is an Assistant Professor in Public Health and Community Medicine. She earned her MS and PhD in Environmental Health through the Tufts School of Engineering in the Department of Civil and Environmental Engineering. She completed a post-doctoral fellowship in Cardiovascular Epidemiology at the Boston University School of Medicine. Her research focuses on developing and applying new methods to assess the health effects of environmental mixtures in observational studies. Through her exposure assessment and environmental epidemiology research, Dr. Corlin seeks to mitigate environmental health disparities. Dr. Corlin also enjoys working with students in and out of the classroom.

Details

Wednesday, January 26, 2022, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

N-of-1 and Aggregated N-of-1 Trials: Motivation, Applications and Future Directions

This seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, March 2, 11:00AM-noon via Zoom. The topic is N-of-1 and Aggregated N-of-1 Trials: Motivation, Applications and Future Directions, presented by Nicholas Schork, PhD.

There is tremendous interest in advancing ‘personalized’ or ‘precision’ medicine — the idea that one can tailor more effective health interventions to an individual’s unique genetic, physiological, behavioral and exposure profile. Although there have been major success stories in personalized medicine, particularly in cancer treatment settings, testing personalized interventions requires non-traditional study designs such as N-of-1 (single subject) and aggregated N-of-1 studies. Dr. Schork describes the principals behind N-of-1 trials as well as strategies for pursuing them in comprehensive and efficient ways. In particular, he describes study designs that consider the effect of individual components making up a multiple component intervention, the sequential analysis of aggregated N-of-1 trials, ’systems physiology’ studies of intervention effects in individuals, and the broad use of guided smart phone apps to optimize mental health interventions for individuals.

Faculty

Nicholas Schork, PhD is a Deputy Director and Distinguished Professor of Quantitative Medicine at The Translational Genomics Research Institute (TGen), an affiliate of the City of Hope (COH) National Medical Center, and an Adjunct Professor of Medicine and Population Science at COH. He is also an Adjunct Professor of Psychiatry and Biostatistics at the University of California San Diego (UCSD) as well as Adjunct Professor of Integrative Structural and Computational Biology at Scripps Research.

Prior to joining TGen, Dr. Schork held faculty positions at Scripps Research, the J. Craig Venter Institute, UCSD and Case Western Reserve University. Dr. Schork’s interests and expertise are in the quantitative aspects of human biology research, genetics, and integrated approaches to complex biological and medical problems. These interests include analyzing large biomedical data sets, developing systems-level approaches to the analysis of biomedical data, and the design of personalized clinical trials.

Dr. Schork has published more than 550 scientific articles and book chapters. He has mentored over 75 graduate students and post-doctoral fellows, has 12 patents, and has been involved in establishing over 10 different companies in the biomedical space. A member of several scientific journal editorial boards, Dr. Schork is a frequent participant in NIH-related steering committees and review boards. He is currently scientific director and a principal investigator for the NIA-sponsored Longevity Consortium and the Integrated Longevity OMICS initiative, two multi-million-dollar initiatives to identify and characterize genetically-mediated factors contributing to human longevity and healthspan. He is also a former member of the National Academy of Science, Engineering and Medicine (NASEM) Food and Nutrition Board and current member of the NASEM special emphasis panel on diet and disease relationships. Dr. Schork received a BA, MA, MS and PhD all from the University of Michigan.

Details

Wednesday, March 2, 2022, 11:00AM-noon, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Computational Cognitive Models of Behavior Change in the Real World and at Scale

This seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, March 30, 2:00-3:00PM via Zoom. The topic is Computational Cognitive Models of Behavior Change in the Real World and At Scale, presented by Peter Pirolli, PhD.

Psychology calls itself the science of behavior, but some have lamented that “cognitive psychology [has] never had much to say about the meaningful activities people perform in their daily lives, nor have they really intended to.” In this presentation, Dr. Pirolli discusses two threads of research on computational cognitive models of human behavior change in the ecology of everyday life:

• The first thread of research concerns models of health behavior change occurring in multi-week, in-the-world, experiments using mobile health applications designed to promote physical activity, stress reduction, and improved nutrition habits. These computational models, built in the ACT-R cognitive architecture, provide an integrated account of goal intentions, implementation intentions, self-efficacy, motivation, self-affirmation, and habit strengthening underlying more than a half dozen behavior change techniques.
• The second thread of research expands on ACT-R models of behavior change to address how humans responded to the COVID-19 pandemic. Heterogeneous behavioral responses over time and geographical regions depend on the individual beliefs and information consumption patterns of populations. To address the need for more precise and accurate epidemiological models, we are researching Psychologically Valid Agent models of human responses to epidemic information and non-pharmaceutical interventions during the pandemic.

Faculty

Peter Pirolli, PhD is currently a Senior Research Scientist at the Institute for Human and Machine Cognition. His research involves a mix of cognitive science, artificial intelligence, and human-computer interaction, with applications in digital health, sensemaking, and information foraging, among other things. Previously, Dr. Pirolli was at the Palo Alto Reseach Center, and was a Professor in the School of Education at UC Berkeley. He received his doctorate in cognitive psychology from Carnegie Mellon University in 1985. Dr. Pirolli received a B.Sc. in psychology and anthropology from Trent University. He has been elected as a Fellow of the National Academy of Inventors, the American Association for the Advancement of Science, the American Psychological Association (Div 3 and Div 21), the Association for Psychological Science, the National Academy of Education, and the ACM Computer-Human Interaction Academy. Please see his book titled “Information Foraging Theory: Adaptive Interaction with Information.”

Details

Wednesday, March 30, 2022, 2:00-3:00PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Interested in learning how to make your data analysis and other scientific computations reproducible?

The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a virtual seminar series on a Wednesday each month from 2:00-3:00PM.

The session on Wednesday, December 16 will feature Karl Broman, PhD. He will give a talk titled Steps Toward Reproducible Research.

Abstract

A minimal standard for data analysis and other scientific computations is that they be reproducible: that the code and data are assembled in a way so that another group can re-create all of the results (e.g., the figures and table in a paper). Adopting a workflow that will make your results reproducible will ultimately make your life easier; if a problem or question arises somewhere down the line, it will be much easier to correct or explain.

But organizing analyses so that they are reproducible is not easy. It requires diligence and a considerable investment of time: to learn new computational tools, and to organize and document analyses as you go. Nevertheless, partially reproducible is better than not at all reproducible. Just try to make your next paper or project better organized than the last. There are many paths toward reproducible research, and you shouldn’t try to change all aspects of your current practices all at once. Identify one weakness, adopt an improved approach, refine that a bit, and then move on to the next thing. Dr. Karl Broman will offer some suggestions for the initial steps to take towards making your work reproducible.

Faculty

Dr. Karl Broman is a Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin-Madison. Dr. Broman is an applied statistician working on the genetics of complex diseases in experimental organisms. He develops the R package, R/qtl, has written a number of short tutorials useful for data scientists, and is very keen to develop tools for interactive data visualization (to view an example, click here).

Details

Date: Wednesday, December 16, 2020, 2:00-3:00PM

Registration

To attend, please enroll via Tufts CTSI I LEARN here.

This event is cancelled. We apologize for any inconvenience.

The Center for Quantitative Methods and Data Science, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a Zoom seminar series on a Wednesday each month from 2:00-3:00PM.

The November session on Wednesday, November 11 will feature Tanya Karagiannis, MS. She will give a talk titled Analysis of Single Cell Transcriptomics Data as it Relates to Aging and Longevity.

Abstract

Studies of aging have shown a gradual decline in the immune system, such that people experience age-related disabilities and diseases as well as differences in immune population composition and functions over time. However, a rare population of individuals who reach 100 years of age known as centenarians, experience delay in age-related disabilities and diseases and in fact live the majority of their lives in good health. In order to investigate how centenarians delay and defy aging, we utilize single cell transcriptomic methods to investigate longevity related differences in the peripheral blood immune system of centenarians. Single cell level transcriptomic data has allowed for the profiling of thousands of cells to characterize cell states and populations in specific tissues. More specifically, these methods can be used to identify rare populations and assess transcriptional similarities and differences within a population of cells. We describe integrated analyses using four single cell RNA-sequencing datasets that we conducted to investigate compositional and gene expression differences in immune populations of centenarians and younger age controls (20-80 years). Early findings demonstrate gene expression differences between centenarians and younger age controls that are specific to populations of cells. We also find centenarians not only have cell type specific compositional differences but overall have more cell type diversity than younger age controls.

Faculty

Tanya Karagiannis is a Research Assistant at Tufts Medical Center in the Institute for Clinical Research and Health Policy Studies, working with Dr. Paola Sebastiani. She has an MS in Bioinformatics from Boston University where she is also continuing her PhD in Bioinformatics under the advisement of Dr. Paola Sebastiani and Dr. Stefano Monti. Her research focus is in the application and development of single cell transcriptomic methods utilizing machine learning and Bayesian statistics, with interest in multi-omics as well.

Details

Date: Wednesday, November 11, 2020, 2:00-3:00PM

Location: Zoom video conference.

Registration

To receive the link to the Zoom video conference, please register here.

The Center for Quantitative Methods and Data Sciences, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a Zoom seminar series on a Wednesday each month from 2:00-3:00PM.

The October session on Wednesday, October 21 will feature Cody Meissner, MD, and Norma Terrin, PhD. They will speak about the promise of COVID-19 vaccines in controlling the pandemic.

Abstract

The discussion will cover approaches to SARS-CoV-2 vaccine development; emergency use authorization (EUA) vs. biologic license application (BLA); acceptable safety and effectiveness; unanticipated serious adverse reactions that occurred following introduction of previous vaccines; and vaccine trial sample size justification.

Faculty

Dr. Meissner is Professor of Pediatrics at Tufts University School of Medicine and Head of the Pediatric Infectious Disease Service at Tufts Medical Center. He is a Consultant to the Committee on Infectious Disease and an Associate Editor of the Red Book for the American Academy of Pediatrics. He has served as a member of the Advisory Committee on Infectious Diseases (ACIP) at the Centers for Disease Control and Prevention (CDC) and continues to advise CDC Work Groups. He presently serves as a member of the National vaccine Advisory Committee (NVAC) and as a member of the Vaccines and Related Biologic Products Advisory Committee (VRBPAC) for the Food and Drug Administration. He serves as a member of the Massachusetts Vaccine Purchasing Council. He has published over 250 papers on various aspects of infectious disease.

Dr. Terrin is the Scientific Director of the BERD Center at Tufts Clinical and Translational Science Institute (CTSI) and Professor at Tufts University School of Medicine. She has collaborated with clinical investigators, including infectious disease researchers, throughout her career, and she served as Statistics Editor at Clinical Infectious Diseases for 12 years.

Details

Date: Wednesday, October 21, 2020, 2:00-3:00PM

Location: Zoom video conference. \

The Center for Quantitative Methods and Data Sciences, in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center and the Data-Intensive Studies Center (DISC) will host a Zoom seminar series on the fourth Wednesday of the month from 2:00-3:00PM.

The first session on Wednesday, September 23 will feature Karin Knudson, PhD, Senior Data Scientist with DISC. She will speak about the estimation of ataxia severity and disease classification from wearable sensor recordings.

Abstract

Wearable sensor data offer the potential for rich and interpretable descriptions of behavioral characteristics of ataxia and other neurodegenerative diseases. High quality behavioral biomarkers are important for understanding disease progression, assessing efficacy in clinical trials, and supporting early diagnosis and targeted interventions. In this talk we present methods for using accelerometer and gyroscope time series data from wearable sensors in order to accurately distinguish patients with ataxia from healthy controls and to estimate disease severity. We combine information from an autoregressive hidden Markov model variant and time-frequency analysis to create a flexible, extensible, and meaningful quantitative description of movement and to perform severity estimation and disease classification with short periods of data collection.

Faculty

Dr. Knudson is a Senior Data Scientist with the Tufts Data Intensive Studies Center (DISC). Her research has involved the development and application of methods from machine learning, Bayesian statistics, and compressive sensing, particularly to neural data. Before joining Tufts, she was a Research Fellow in the Department of Neurology at Massachusetts General Hospital and Harvard Medical School, and was previously the Chair of the Department of Mathematics, Statistics, and Computer Science at Phillips Academy. She completed her PhD in Mathematics at the University of Texas at Austin.

Details

Date: Wednesday, September 23, 2020, 2:00-3:00PM

Location: Zoom video conference.

Interested in learning how researchers can address implementation in their effectiveness studies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Wednesday, November 30, 2022, 1:00PM-2:00PM EST.

The guest speaker for the meeting will be Rachel Gold, PhD, MPH. An open discussion will follow her presentation.

Dr. Gold is an epidemiologist and health services researcher. Her work focuses on using health information technology to improve care quality in public clinics and reduce health disparities, and on the implementation methods needed to support adoption of such technologies. She has partnered with the OCHIN practice-based research network since 2005; she now has a joint appointment at the Center for Health Research, where she is a Senior Investigator, and OCHIN, where she is the lead research scientist. Her work includes studying how to implement a multi-faceted quality improvement initiative that targeted cardiovascular disease (CVD) and diabetes care in the Kaiser Permanente setting, in the context of community health centers serving socioeconomically vulnerable patient populations.

Dr. Gold is also studying the adoption and impact of an innovative point-of-care shared decision-making tool on CVD outcomes in community clinics. She also pilot-tested electronic health record-based tools for collecting and acting on patient-reported social risks (adverse social determinants of health), and is now studying how to help community clinics implement systematic social risk screening, and how to use patient-reported social risk data in clinical decision-making. Past efforts include analyzing the impact of state insurance policy changes on pediatric care in safety net clinics, and the relationship between continuous insurance coverage and receipt of diabetes care in community clinic settings.

Dr. Gold earned her MPH from Temple University and her PhD in epidemiology from the University of Washington.

Details

Live session via Zoom: Wednesday, November 30, 1:00PM-2:00PM EST

Registration

To receive the Zoom link to this event, please email Anna Thompson. All are welcome to attend and learn more about D&I!

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Tuesday, December 1, 10:00-11:00AM to present your research project, solicit feedback from other members, and receive advice from Tufts CTSI faculty leads Denise Daudelin, RN, MPH, and Sara Folta, PhD.

Even if you do not have a current project, you are welcome to join to learn more about this emerging field and hear from fellow researchers.

Details

Date: Tuesday, December 1, 10:00-11:00AM

Registration

To attend, please enroll here via Tufts CTSI I LEARN.

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Thursday, August 26, 3:00-4:00 PM.

In this session, Sara Folta, PhD will describe, from start to finish, her dissemination study: the StrongWomen – Healthy Hearts intervention. This was a community-based intervention designed to reduce the risk of cardiovascular disease among midlife and older women. Dr. Folta will discuss the considerations and challenges from writing the grant proposal and deciding on a framework through to publishing the results and figuring out next steps once an intervention has reached the “end” of the translational spectrum.

Details

Date: Thursday, August 26, 3:00-4:00PM

Registration

To receive the Zoom link to this event, please email Sarah Brewer, MPH, at sbrewer@tuftsmedicalcenter.org.

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Wednesday, February 3, 1:00-2:00 PM to discuss Dr. Jana Leary’s research on implementing a social determinants of health screening in primary care outpatient settings.

The group will learn more about the research project and engage in a discussion about the research. The conversation will be facilitated by Tufts CTSI faculty leads Denise Daudelin, RN, MPH, and Sara Folta, PhD.

Even if you do not have a current project, you are welcome to join to learn more about this emerging field and hear from fellow researchers.

Details

Date: Wednesday, February 3, 1:00-2:00PM

Registration

To attend, please enroll here via Tufts CTSI I LEARN.

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Tuesday, June 14, 2022, 3:00-4:00PM EST. We hope you will join us to discuss dealing with unintended consequences in D&I research.

Dr. Sara Folta will discuss how screening tools can adversely affect enrollment of underrepresented persons, and will also share a recent example of building in qualitative methods to detect unintended consequences as part of a proposed study to evaluate an intervention.

Dr. Jacob van den Berg will discuss how to closely monitor and respond to the community’s perception of a study through social media.

There will be plenty of time for open discussion around this topic.

Details

Date: Tuesday, June 14, 3:00-4:00PM EST, via Zoom

Registration

If you are interested in joining and do not already have the Zoom link, please email Alyssa Cabrera, MPH. All are welcome to attend and learn more about D&I!

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Monday, March 21, 2022, 10:00-11:00AM.

In this session, the guest presenter and discussion leader will be Linda Hudson, ScD, MSPH. Dr. Hudson is the Associate Director of Integrating Underrepresented Populations in Research (IUPR) and the Director of Collaboration for Research Equity, Sustainability, and Trust (CREST). She will discuss how to use an anti-racist health equity lens when approaching D&I work, as well as sharing her experience working on CREST.

The session will also include a brief discussion of Baumann’s Reframing implementation Science to Address Inequities in Healthcare Delivery article. Please review it prior to attending.

You are also invited to read an optional article, Implementation Science Should Give Higher Priority to Health Equity.

Details

Date: Monday, March 21, 10:00AM-11:00AM, via Zoom

Registration

To receive the Zoom link to this event, please email Senior Project Manager Alyssa Cabrera, MPH.

Interested in learning more about implementation science and dissemination and implementation strategies?

You are invited to a virtual Dissemination and Implementation Science Special Interest Group Meeting on Friday, November 5, 2021, noon-1:00PM.

In this session, Bethany Kwan, PhD, MSPH will give a talk, Pragmatic Trials and Hybrid Implementation-Effectiveness Designs in Real-World Clinical Settings.

Dr. Kwan is an Associate Professor in the Department of Family Medicine at the University of Colorado School of Medicine, Anschutz Medical Campus. She received her PhD in social psychology from the University of Colorado Boulder in 2010, following an MSPH from the University of Colorado Health Sciences Center in 2005. She holds a BS in Chemistry and Psychology from Carnegie Mellon University (’01). As an investigator in the University of Colorado’s Adult & Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS), she conducts pragmatic, patient-centered research and evaluation on health and health care in a variety of areas. With an emphasis on stakeholder engagement and dissemination and implementation (D&I) methods, her work addresses the integration of physical and behavioral health, chronic disease self-management, improving processes and systems of care to achieve the Quadruple Aim, pragmatic trials using electronic health data, and enhancing quality of life for patients and care partners. She works with patients and other stakeholders at all phases of research, from prioritization, to design, implementation, and dissemination of research. She mentors and teaches students, trainees, and fellow faculty on Designing for Dissemination to ensure that research innovations are efficiently and effectively adopted, used, and sustained in real world settings to improve health and well-being for all. Dr. Kwan directs the ACCORDS Education program as well as the Colorado Clinical & Translational Sciences Institute (CCTSI) Dissemination & Implementation Research Core.

Details

Date: Friday, November 5, noon-1:00PM

Registration

To receive the Zoom link to this event, please email Senior Project Manager Alyssa Cabrera, MPH.

View the slides for this seminar (PDF).

View the article that will be discussed during this seminar (PDF).

What makes a journal article successful?

Join us for Evaluating Scientific Journal Articles and learn the questions you should ask yourself, whether reviewing journal articles or writing your own.

By the end of this seminar you will be able to:

• List the questions you should ask yourself when evaluating a scientific journal article.
• Identify the specific, testable hypothesis of the paper.
• Identify what type of study design was used.
• Evaluate whether the results of the study were affected by bias.
• Explain why this study was important, what it added to the literature, or how it changed health practice.
• Appraise the compatibility of the conclusions of the study with the study objectives.

Details

Thursday, April 28th, 2016 1:00 – 2:30PM
Tufts Center for Medical Education, Room 114
145 Harrison Avenue, Boston

Or via live, interactive webcast (a link will be provided to those who register).

What are the three main types of functional analysis?

Functional and enrichment analyses are used to give a biological interpretation to a list of genes or proteins that may be produced from gene expression analysis. This session will introduce three main types of functional analysis and review common tools that are employed: Gene Ontology annotation and enrichment, Gene Set and Pathway enrichment, and network analysis.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Rebecca Batorsky, PhD, Eric Reed, PhD, and Albert Tai, PhD.

Rebecca Batorsky is a Senior Bioinformatics Scientist in Research Technology, part of Tufts Technology Services and a DISC fellow. She earned her PhD in Physics in 2012 from Tufts University, where she focused on mathematical and computational modeling of virus evolution. Before becoming staff at Tufts, she worked as a bioinformatics software developer at a clinical genomics start-up company. Dr. Batorsky works to enable researchers to answer biological questions with data-driven methods, such as analysis of high-throughput DNA and RNA sequencing data. She is especially interested in developing methods to use multiple `omics technologies to give insight into biological pathways and processes.

Eric Reed is a Data Scientist in the Data Intensive Studies Center (DISC) at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Dr. Reed’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Dr. Reed’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 28
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data

When should you use sample enrichment, sequencing format, and library preparation approaches?

RNA-Seq is a powerful technology that can be used to study transcription profile in sample of interest. Yet, the choice of sample enrichment, sequencing format, and library preparation approach all have profound impact on the usability of sequencing data for downstream intended (and intended) analysis. Thus, the parameters are important consideration when designing your own experiment, as well as utilizing data that is available in the public domain. This session aims to provide some guidance on these topics.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Eric Reed, PhD and Albert Tai, PhD.

Eric Reed is a Data Scientist at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Eric’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Eric’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, June 30
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

Overview

Are you involved in building a database for your research project?

Building an appropriate database for your study is critical to ensuring successful data collection and analysis. Learn how to build a database in this 90-minute Tufts CTSI workshop, Research Database Creation: Basics & Best Practices. This session will begin with an interactive lecture presented by Rachael Huebner, a Clinical Data Manager at Tufts CTSI, followed by a workshop in which participants will practice building a simple database in Excel.

This workshop is a prerequisite to a subsequent workshop, Research Database Creation: Building a REDCap Database.

After attending this event, you should be able to:

• Recognize database creation best practices
• Identify the clinical and demographic data needed to answer a study question
• Effectively name and code variables
• Create an Excel sheet appropriate for study data collection

Details

Date: Tuesday, October 29, 1:00-2:30PM

Location: Tufts Medical Center, IS Training Room, Ziskind Building, 1st Floor, Room 114A

This workshop is a prerequisite to a subsequent workshop, Research Database Creation: Building a REDCap Database.

Registration

This workshop is designed for research assistants, clinical research coordinators, investigators, residents, and fellows who will be creating or working with databases for research projects.

The workshop is now full. To add your name to the waitlist, please register here.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a database for your research project?

Building an appropriate database for your study is critical to ensuring successful data collection and analysis. Learn how to build a database in this 90-minute Tufts CTSI workshop, Research Database Creation: Basics & Best Practices. This session will begin with an interactive lecture presented by Rachael Huebner, a Clinical Data Manager at Tufts CTSI, followed by a workshop in which participants will practice building a simple database in Excel.

This workshop is a prerequisite to a subsequent workshop, Research Database Creation: Building a REDCap Database.  Please note, in this workshop, we will not be using REDCap, but we will be learning the fundamentals required for database creation, which will inform our second session where REDCap will be used.

After attending this event, you should be able to:

• Recognize database creation best practices
• Identify the clinical and demographic data needed to answer a study question
• Effectively name and code variables
• Create an Excel sheet appropriate for study data collection

Details

Date: Thursday, March 12, noon-1:30PM

Location: ONLINE ONLY

This workshop is a prerequisite to a subsequent workshop, Research Database Creation: Building a REDCap Database.

Registration

This workshop is designed for research assistants, clinical research coordinators, investigators, residents, and fellows who will be creating or working with databases for research projects.

To attend, please register here by March 5.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a REDCap database for your research project?

REDCap is an online application that can be used to create research databases. It has many advantages over Excel, and is often the preferred tool for database creation. Learn how to create REDCap databases for your research projects in this 90-minute Tufts CTSI workshop, Research Database Creation: Building a REDCap Database.

This session will begin with a lecture presented by Rachael Huebner, a Clinical Data Manager at Tufts CTSI, followed by a workshop in which participants will practice building a simple database in REDCap.

To attend this event, participants must attend the first workshop in this series, Research Database Creation: Basics & Best Practices.

After attending this event, you should be able to:

• Describe the differences between classic and longitudinal projects, and identify when to use each
• Create projects, forms, and fields in REDCap
• Export data from REDCap to Excel

Details

Date: Tuesday, November 12, 2:00-3:30PM

Location: Tufts Medical Center, IS Training Room, Ziskind Building, 1st Floor, Room 114A

To attend this event, participants must attend the first workshop in this series, Research Database Creation: Basics & Best Practices.

Registration

This workshop is designed for research assistants, clinical research coordinators, investigators, residents, and fellows who will be creating or working with databases for research projects.

Space is limited. To reserve your seat, please register here by October 24.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a REDCap database for your research project?

REDCap is an online application that can be used to create research databases. It has many advantages over Excel, and is often the preferred tool for database creation. Learn how to create REDCap databases for your research projects in this 90-minute Tufts CTSI workshop, Research Database Creation: Building a REDCap Database.

This session will consist of a guided tutorial, led by Rachael Huebner, Clinical Data Manager at Tufts CTSI, in which participants will build a simple database in REDCap.

To attend this event, participants must attend the first workshop in this series, Research Database Creation: Basics & Best Practices.

After attending this event, you should be able to:

• Describe the differences between classic and longitudinal projects, and identify when to use each
• Create projects, forms, and fields in REDCap
• Export data from REDCap to Excel

Details

Date: Thursday, March 26, noon-1:30PM

Location: Tufts Center for Medical Education, Room 514 (Computer Lab), 145 Harrison Avenue, Boston

To attend this event, participants must attend the first workshop in this series, Research Database Creation: Basics & Best Practices.

Registration

This workshop is designed for research assistants, clinical research coordinators, investigators, residents, and fellows who will be creating or working with databases for research projects.

Space is limited.

To reserve your seat, please register here by March 19.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a REDCap database for your research project?

Building an appropriate database for your study is critical to ensuring successful data collection and analysis. Learn how to build your database in this three-part Research Database Development course presented by Tufts CTSI. There are no pre-requisites required for any of the three course registrations, but participation in all three courses is encouraged as they build on each other.

Part 1: Basics & Best Practices

This two-hour session will begin with an interactive lecture presented by Rachael Huebner, MPH, Clinical Data Manager at Tufts CTSI, followed by a workshop in which participants will practice building a simple database in Excel. Participation will be required for the workshop portion.

Pre-requisite: none

Topics covered include:

• Database development best practices
• Determining which data to collect
• Choosing variable types
• Naming variables
• Assigning numbers to variables
• Using Excel to create a database

After attending this session, participants should be able to:

• Recognize database creation best practices
• Identify the clinical and demographic data needed to answer a study question
• Effectively name and code variables
• Create an Excel sheet appropriate for study data collection

Details

Date: Wednesday, August 26, 10:00AM-noon

Location: online via Zoom

Registration

Members of any Tufts CTSI-affiliated institution are welcome to attend.

To receive the Zoom link, please register here.

Instructors

Rachael Huebner, MPH is the Clinical Data Manager at Tufts CTSI, providing data management support and training to researchers. Prior to joining Tufts, she worked in data management for industry-sponsored clinical trials after receiving her MPH from Boston University School of Public Health.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a REDCap database for your research project?

Building an appropriate database for your study is critical to ensuring successful data collection and analysis. Learn how to build your database in this three-part Research Database Development course presented by Tufts CTSI. There are no pre-requisites required for any of the three course registrations, but participation in all three courses is encouraged as they build on each other.

Part 2: Building a REDCap Database

This two-hour session will consist of a guided tutorial, followed by a discussion on building REDCap databases for your own research. Participants should be prepared to discuss their research projects. Instructors will work with you to create REDCap accounts prior to this session.

Topics covered include:

• Introduction to REDCap
• Creating projects, forms, and fields
• Exporting data

After attending this session, participants should be able to:

• Describe the differences between classic and longitudinal projects, and identify when to use each
• Create projects, forms, and fields in REDCap
• Export data from REDCap to Excel

Details

Date: Thursday, August 27, 10:00AM-noon

Location: online via Zoom

Registration

Members of any Tufts CTSI-affiliated institution are welcome to attend.

To receive the Zoom link, please register here.

Instructors

Rachael Huebner, MPH is the Clinical Data Manager at Tufts CTSI, providing data management support and training to researchers. Prior to joining Tufts, she worked in data management for industry-sponsored clinical trials after receiving her MPH from Boston University School of Public Health.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Overview

Are you involved in building a REDCap database for your research project?

Building an appropriate database for your study is critical to ensuring successful data collection and analysis. Learn how to build your database in this three-part Research Database Development course presented by Tufts CTSI. There are no pre-requisites required for any of the three course registrations, but participation in all three courses is encouraged as they build on each other.

Part 3: Advanced REDCap Features

This two-hour session will begin with a demonstration of commonly used advanced REDCap features, Informatics Project Coordinator at Tufts CTSI, followed by an interactive conversation answering participants’ real-life REDCap questions.

Topics covered include:

• Complex piping and calculations within REDCap forms
• Establishing workflows with the Survey Queue and Alerts and Notifications sections
• Understanding how Action Tags can be used to help guide data collection
• Ensuring high-quality input by establishing Data Quality rules

After attending this session, participants should be able to:

• Identify use cases for advanced REDCap features
• Implement commonly-used advanced features in REDCap projects

Details

Date: Friday, August 28, 10:00AM-noon

Location: online via Zoom

Registration

Members of any Tufts CTSI-affiliated institution are welcome to attend.

To receive the Zoom link, please register here.

Instructor

Rachael Huebner, MPH is the Clinical Data Manager at Tufts CTSI, providing data management support and training to researchers. Prior to joining Tufts, she worked in data management for industry-sponsored clinical trials after receiving her MPH from Boston University School of Public Health.

Tufts CTSI Professional Education & Expectation for Course Participants

Tufts CTSI’s Professional Education programs provide non-degree continuing education and training for clinical and translational research professionals from all Tufts CTSI partners and beyond.

Course enrollment priority is given to researchers from Tufts CTSI partner institutions. If your participation needs to be approved by your supervisor or a person responsible for your time release, you may provide their contact information when you register for the program.

This course is provided free of charge, and was supported by the National Center for Advancing Translational Sciences, National Institutes of health, Award Number UL1TR002544. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

What are the appropriate analytical techniques for study designs and what measures of association, advantages and disadvantages should be used?

This seminar will provide an introduction to epidemiologic study design, including cross-sectional, cohort, and case-control studies. It will review the appropriate analytical techniques for each design along with their measures of association, advantages and disadvantages, and the types of biases that can be present with each. Examples from the literature will be provided to illustrate concepts and common pitfalls that may occur when analyzing data from observational studies.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Faculty

This workshop will be taught by Janis Breeze, MPH and Angie Rodday, PhD, MS

Janis Breeze is an Epidemiologist, Assistant Professor of Medicine, and Associate Director of the Biostatistics, Epidemiology, and Research Design (BERD) Center at Tufts CTSI. She has many years’ experience helping researchers in the design of observational and experimental studies, particularly in the areas of newborn medicine, obstetrics and gynecology, pulmonary medicine, and surgery.

Angie Rodday is a Biostatistician, Assistant Professor of Medicine, and Associate Director of the Clinical and Translational Science (CTS) Graduate Program at the Tufts University Graduate School of Biomedical Sciences. She has experience as a principal investigator of her own work, as well as experience as a Co-Investigator on others’ projects. Dr. Rodday teaches biostatistics courses as part of the CTS Graduate Program.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, June 23
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data