Decoding the Data Ecosystem

Allissa Dillman
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ABOUT THE PODCAST Host Bio Allissa Dillman, PhD, Training and Engagement Director for the CFDE Training Center, is the founder and CEO of BioData Sage LLC, a company focused on providing a holistic approach to data science integration in the biomedical and biological science fields. She works with clients in industry, academia, government, and the nonprofit sector to create and support training programs on bioinformatics, cloud computing, and the tools and standards for reproducible data science practices for scientific and lay communities. She also creates community events, such as hackathons, where broad communities work towards solving real biomedical data challenges. Dr. Dillman is a member of the adjunct faculty at Montgomery College and has over 10 years of experience working for the National Institutes of Health (NIH). Her work focuses on lowering the barriers of entry for data science and cloud computing. She received her PhD in computational neuroscience as part of the graduate partnership program between NIH and the Karolinska Institute, Sweden. Why Listen? Decoding the Data Ecosystem: A CFDE Training Center Podcast is more than just a podcast; it's a community for anyone passionate about the potential of omics research to solve complex biological puzzles and address pressing health challenges. Whether you're a seasoned researcher, a student just starting out, or simply curious about the future of biology, this podcast offers valuable insights, inspiring stories, and practical advice to guide your journey through the world of omics research training and education. Availability Find Decoding the Data Ecosystem on your favorite podcast platform, including Spotify, Apple Podcasts, Google Podcasts, and more. Subscribe today to stay updated with the latest episodes and join the conversation shaping the future of omics research training and education. This podcast is hosted by Oak Ridge Associated Universities (ORAU). Learn more at orau.org.

  1. 23 MAR ·  WIDEO

    Episode 15: Advancing Glycoscience Research: Empowering Discovery with GlyGen

    Description In this podcast, Allissa Dillman talks with Raja Mazumder, René Ranzinger, and Michael Tiemeyer about their journeys in glycoscience and the goals of the GlyGen program. GlyGen integrates glycan and glycoinformatics data from various sources, making it an accessible and comprehensive online resource for researchers. They also discuss the challenges of working with glycan data, including its structural complexity and variability. They cover several workflows using GlyGen resources, how researchers can contribute data to GlyGen and suggestions for new users on how to get started. Guests Raja Mazumder, PhD René Ranzinger, PhD Michael Tiemeyer, PhD Guest Bios Raja Mazumder, PhD is a Professor of Biochemistry and Molecular Medicine and Co-Director of The McCormick Genomic Proteomic Center at George Washington University. His background is in evolutionary biology and bioinformatics. He has previously worked at the Oak Ridge National Laboratory, Merck, NIH, and Georgetown University. While working on UniProt at Protein Information Resource, and leading projects such as BioCompute, GlyGen, OncoMX, and ARGOS, Dr. Mazumder worked closely with colleagues in developing international molecular biology resources and using these resources to identify therapeutics, diagnostics and vaccines targets. Through NIH, NSF, FDA and industry funding he is involved in genomic and bioinformatics research in cancer biology, glycobiology, metagenomics and bioinformatics standards development. René Ranzinger, PhD is an Associate Research Scientist in the Complex Carbohydrate Research Centre at the University of Georgia. His work focuses on GlycoBioinformatics. As a research scientist, he worked on the implementation of new software tools for the interpretation of glycomics mass spectrometric data. He and his team were also involved in the design and implementation of glycomics databases such as GlyTouCan and GlyGen. Another focus of his work is the standardization of glycomics data reporting as well as standardization of data representation and data exchange. In this context he was organizer and member of several international working groups including the working group to define minimum reporting standards for Glycomics (MIRAGE).  Michael Tiemeyer, PhD, is the Joint Director of the Complex Carbohydrate Research Center and the Center for Molecular Medicine at the University of Georgia, where he facilitates the research programs of 24 active faculty and 300 research trainees and staff who generate between 20-25 million USD in total grants and contracts each year. He also holds the title of Distinguished Research Professor in the Department of Biochemistry and Molecular Biology at the University of Georgia. His own active research areas are focused on the identification and characterization of bioactive glycans in neural development and disease as well as on the development of glycoinformatic strategies and platforms for capturing knowledge in the glycoscience domain.

    45 min

  2. 4 LUT ·  WIDEO

    Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies

    Episode 14: Investigating Acute to Chronic Pain Signatures (A2CPS) for Advancing Pain Prevention Strategies    Description  In this episode, Allissa Dillman chats with Margaret Taub, a Senior Scientist with the Johns Hopkins Bloomberg School of Public Health on her journey from mathematics to statistical genetics, highlighting her current role with the Acute to Chronic Pain Signatures (A2CPS) program. The A2CPS study aims to develop predictive biomarkers for chronic pain by collecting comprehensive genomic, psychosocial, and functional data from patients. To coordinate diverse data sources, Margaret emphasized the importance of collaboration and interdisciplinary teamwork, drawing parallels between her experiences in teaching, music, and Ultimate Frisbee to her approach in science. Margaret and Allissa also discuss the study's progress and future plans.    Guest Bio  Margaret Taub, PhD, is a Senior Scientist at Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health. She works collaboratively with clinicians and epidemiologists to use genetic and genomic data to understand the causes of complex diseases. She is also an Investigator with the Data Integration and Resource Center for the Acute to Chronic Pain Signatures (A2CPS) program.

    48 min

  3. 9 STY ·  WIDEO

    Episode 13: Harnessing Artificial Intelligence and Machine Learning for Statistical Analysis in Genomic Data

    Description In this episode, Allissa Dillman talks with John Kwagyan about the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in genomics and personalized medicine as well as the promise of these technologies in analyzing complex biological data to advance disease prediction, prevention, and personalized treatments. They also discuss machine learning models, the differences between machine learning and statistical learning, explainable AI, and ethical considerations, as well as the skills future researchers will need to thrive in the AI-genomics landscape. Guest Bio John Kwagyan, PhD, is a Statistician and Graduate Associate Professor of Public Health at Howard University College of Medicine, and serves as co-Director of Biostatistics, Epidemiology and Research Design (BERD) at the Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS). He is co-PI of the Public Health Informatics and Technology program for District of Columbia (PHIT4DC), and PI (Data Science Core) of the recently funded Howard-Hopkins Comprehensive Alliance in Cancer Research and Education (H2CARE). His research interests include statistical genetics and predictive modelling of clustered data with applications to clinical and public health outcomes.

    44 min

  4. 15.12.2025 ·  WIDEO

    Episode 12: Exploring the Human Reference Atlas (HRA) Organ Gallery in Immersive 3D

    Description  In this video podcast, Allissa Dillman and Andreas Bueckle discuss and demonstrate the Human Reference Atlas (HRA) Organ Gallery, a virtual reality (VR) application that lets users explore the HRA in immersive 3D. Andreas also presents the “HRA Powers of Ten,” a data integration module in development that uses the HRA Common Coordinate Framework (CCF) to harmonize, visualize, and explore data from the small/large intestine, lymph node, skin, and liver. The HRA Organ Gallery is available, for free, to anyone at https://www.meta.com/experiences/5696814507101529. A paper describing the concept of the HRA Organ Gallery is available at https://doi.org/10.3389/fbinf.2023.1162723.    Single-cell atlassing portals such as the Human BioMolecular Atlas Project (HuBMAP), the Cellular Senescence Network (SenNet), and the Human Tumor Atlas Network (HTAN) publish datasets across scales for multiple organs of the adult human body in health, aging, and disease. This data can be interrogated semantically and spatially, usually on 2D screens with limited 3D affordances. Imagine, for example, examining the 3D location of a cell in the context of a tissue or zooming across multiple orders of magnitude. To be tackled effectively, 3D problems require 3D platforms.    Guest Bio  Andreas Bueckle, Ph.D. (https://andreas-bueckle.com), is the Research Lead in the Cyberinfrastructure for Network Science Center at the Luddy School of Informatics, Computing, and Engineering (SICE) at Indiana University in Bloomington, IN. His research interest is interactive information visualization in XR. Andreas has a TEDx talk titled “Living and Learning in the Metaverse” (available on YouTube and on the TED website). From 2023-2025, he was awarded two JumpStart Fellowships by the National Institutes of Health (NIH) to advance multiscale exploration of the human body in VR with the Human Reference Atlas Organ Gallery. In 2025, he received an R03 award, also by the NIH, to advance the integration of 3D reference organs with data visualizations of cell type populations for Common Fund datasets in 3D and VR.

    47 min

  5. 10.11.2025 ·  WIDEO

    Episode 11: Reproducible Voice Data as a Biomarker for AI Models

    Episode 11: Reproducible Voice Data as a Biomarker for AI Models    Description  In this episode, Allissa Dillman chats with David Dorr and Andrea Krussel about their roles in the Bridge2AI (https://bridge2ai.org/) program, which aims to enhance AI readiness by improving data standards and developing machine learning practices. Co-PIs in B2AI Voice (https://b2ai-voice.org/voice-ai-summer-school/), Dorr and Krussel discussed their work on creating reproducible voice data as a biomarker for AI models, including training programs for researchers, successful examples of career development, publication of their competency framework (https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1610253/abstract), and upcoming symposium. You can also find the B2AI Voice Data online (https://docs.b2ai-voice.org/) and an AI curricula (https://instructure.okta.com/app/).     Guest Bios  David Dorr, MD, MS earned his bachelor's degree in economics (with minors in mathematics and psychology) and his MD from Washington University in St. Louis. He then completed his internal medicine residency at Oregon Health & Science University and earned a master's degree in medical informatics and health services administration from the University of Utah. Broadly, Dr. Dorr's interests lie in complex care management, especially for older adults and other at-risk populations, coordination of care, collaborative care, chronic disease management, quality and the requirements of clinical information systems to support these areas. From these interests, he has broadened into clinical information needs, Electronic Health Record (EHR) deployment and Health Information Exchange, as a way to expand systems-based approaches to all of health care. Finally, Dr. Dorr performs evaluations of care management and informatics initiatives using a variety of methodologies. Learn more on Dr. Dorr's OHSU profile (https://www.ohsu.edu/people/david-a-dorr-md-ms).    Andrea Krussel is the Director of Education and Workforce Development in the Office of Health Information and Data Science at WashU Medicine. In this capacity, she oversees the operations, development, and implementation of biostatistics, data science, and biomedical informatics academic programs. Andrea holds a BA in Theatre and an MA in Communication Arts and utilizes her teaching, acting, and science backgrounds to provide science communication courses and workshops for the WashU community. She also creates, leads, and evaluates competency-based workforce development initiatives in AI, informatics, and data science at WashU, nationally, and abroad. Andrea is a PhD student in Education Policy, Organization, and Leadership at the University of Illinois, Urbana-Champaign. Her research focuses on integrating artificial intelligence into medical education to enhance clinical decision-making skills in medical students and trainees while easing the administrative burden of clinical instructors.  Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

    42 min

  6. 09.10.2025 ·  WIDEO

    Episode 10: Overview of the Common Fund Data Ecosystem and how the Training Center supports the CFDE

    Episode 10: Overview of the Common Fund Data Ecosystem and how the Training Center supports the CFDE    Description  The National Institutes of Health Common Fund Data Ecosystem (CFDE) aims to enable broad use of Common Fund data to accelerate discovery. NIH Common Fund programs generate a wide range of diverse and valuable data sets and knowledge designed to be used by the research community. The CFDE aims to facilitate improved discovery, reuse, integration, and analyses of these datasets to form novel hypothesis for accelerating discoveries in biomedical research. ORAU received a contract from the NIH to develop a state-of-the-art training center for users of the CFDE. In this cross-over episode with Further Together: The ORAU Podcast, Allissa Dillman, Ph.D., co-principal investigator and owner of BioData Sage LLC, and Jennifer Burnette, ORAU project director, talk to host Michael Holtz about the importance of the CFDE Training Center and how it can help researchers mine data for omics research of various kinds.       Guest Bios  Allissa Dillman, PhD, co-PI and Training and Engagement Director for the CFDE Training Center, is the founder and CEO of BioData Sage LLC, a company focused on providing a holistic approach to data science integration in the biomedical and biological science fields. She works with clients in industry, academia, government, and nonprofit sector to create and support training programs on bioinformatics, cloud computing, and the tools and standards for reproducible data science practices for scientific and lay communities. She also creates community events, such as hackathons, where broad communities work towards solving real biomedical data challenges.    Jennifer Burnette, MPH, CFDE Training Center Director and project manager and director for ORAU expertly steers complex, interdisciplinary programs that bridge local, state, and federal stakeholders. Her career is a testament to her ability to navigate complex networks, having successfully coordinated projects for over 20 federal agency customers including the Centers for Disease Control and Prevention (CDC), Department of Energy (DOE), Department of Homeland Security (DHS), Federal Emergency Management Agency (FEMA), National Institutes of Health (NIH), National Science Foundation (NSF), and 13 DOE and other federal national laboratories. Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

    37 min

  7. 16.09.2025 ·  WIDEO

    Episode 09: MoTrPAC and the Science of Fitness

    Episode 09: MoTrPAC and the Science of Fitness Description In this episode, Allissa Dillman chats with researchers Malene Lindholm and Dan Katz about their work on MoTrPAC. Malene has a 20-year background in molecular exercise physiology and Dan started his medical training in cardiology and now uses high-dimensional data to understand heart failure. MoTrPAC (Molecular Transducers of Physical Activity Consortium) is a NIH-funded initiative studying exercise effects across multiple sites in the US using both human and animal studies. Dan and Malene explain what the dataset is all about, including what data is collected, from where, and the kinds of raw and pre-processed data available. They also describe how it can be accessed through various user-friendly tools for scientists of all technical abilities. Find out more at https://motrpac-data.org/ and contact them with questions at motrpac-helpdesk@lists.stanford.edu.  Guest Bios Daniel Katz, MD, is an Assistant Professor of Medicine in the Stanford Center for Biomedical Informatics Research (BMIR) and the Cardiovascular Medicine Divisions. He practices as an Advanced Heart Failure and Transplant Cardiologist. He completed internal medicine residency at Massachusetts General Hospital, general cardiology training at Beth Israel Deaconess Medical Center, and joined Stanford in 2021 for his advanced heart failure training. His research focuses on identifying the various pathophysiological patterns and mechanisms that lead to the heterogeneous syndrome of heart failure. His efforts leverage high dimensional data in many forms including clinical phenotypes, plasma proteomics, metabolomics, and genetics. He is presently engaged in analysis of multi-omics data from MoTrPAC, and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.  Malene Lindholm, PhD, is a senior research engineer and Director of the Human Molecular Athlete Moonshot for the Wu Tsai Human Performance Alliance at Stanford University. After earning her PhD in Medicine at the Karolinska Institute in Sweden, she performed her postdoctoral studies at the Cardiovascular Institute at Stanford University. Her current research focuses on unraveling the multi-omics adaptation mechanisms to exercise across tissues and the genetic basis of extreme human performance. The ultimate objective is to transform the findings into tangible application in the field of precision exercise health and medicine. Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

    53 min

  8. 13.08.2025 ·  WIDEO

    Episode 08: An Early Career Researcher’s Experience with the Common Fund Data Ecosystem

    Episode 08: An Early Career Researcher’s Experience with the Common Fund Data Ecosystem Description In this episode, Allissa Dillman interviews Seth Berke about his educational journey from pre-med to genomics, and the importance of public datasets and cloud computing in scientific research. Seth highlights the value of, and opportunities provided by the Common Fund Data Ecosystem for early career researchers, shares insights on integrating multiple types of biomedical data, and offers advice for students interested in the field of genomics. Seth’s recent publication referenced in the podcast can be found here: Fundamentals of FAIR biomedical data analyses in the cloud using custom pipelines. Guest Bio Seth Berke is a graduate of Johns Hopkins University with a B.S. in Molecular and Cellular Biology and an incoming Ph.D. student in Quantitative and Computational Biology at Princeton. His undergraduate research focused on leveraging biomedical cloud platforms to analyze large-scale pediatric genomics data, work that led to presentations at several international conferences and recent manuscript acceptances in leading journals. He currently works as a Research Assistant at the Johns Hopkins Bloomberg School of Public Health, contributing to the NIH Acute to Chronic Pain Signatures Consortium by integrating and analyzing multimodal data. Seth aims to advance precision health by uniting data across biopsychosocial domains to improve the prediction and treatment of complex disease phenotypes. Learn more about the CFDE podcast here (https://orau.org/cfde-trainingcenter/podcast.html).

    41 min
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O programie

ABOUT THE PODCAST Host Bio Allissa Dillman, PhD, Training and Engagement Director for the CFDE Training Center, is the founder and CEO of BioData Sage LLC, a company focused on providing a holistic approach to data science integration in the biomedical and biological science fields. She works with clients in industry, academia, government, and the nonprofit sector to create and support training programs on bioinformatics, cloud computing, and the tools and standards for reproducible data science practices for scientific and lay communities. She also creates community events, such as hackathons, where broad communities work towards solving real biomedical data challenges. Dr. Dillman is a member of the adjunct faculty at Montgomery College and has over 10 years of experience working for the National Institutes of Health (NIH). Her work focuses on lowering the barriers of entry for data science and cloud computing. She received her PhD in computational neuroscience as part of the graduate partnership program between NIH and the Karolinska Institute, Sweden. Why Listen? Decoding the Data Ecosystem: A CFDE Training Center Podcast is more than just a podcast; it's a community for anyone passionate about the potential of omics research to solve complex biological puzzles and address pressing health challenges. Whether you're a seasoned researcher, a student just starting out, or simply curious about the future of biology, this podcast offers valuable insights, inspiring stories, and practical advice to guide your journey through the world of omics research training and education. Availability Find Decoding the Data Ecosystem on your favorite podcast platform, including Spotify, Apple Podcasts, Google Podcasts, and more. Subscribe today to stay updated with the latest episodes and join the conversation shaping the future of omics research training and education. This podcast is hosted by Oak Ridge Associated Universities (ORAU). Learn more at orau.org.

Informacje

  • Twórca
    Allissa Dillman
  • Lata aktywności
    2025 - 2026
  • Odcinki
    15
  • Klasyfikacja
    Dla wszystkich
  • Prawa autorskie
    © Copyright 2026 Decoding the Data Ecosystem
  • Witryna programu
    Decoding the Data Ecosystem
  • Dostawca