Podcasts about plos computational biology

Die Themen in den Wissensnachrichten: +++ Wie Gesichtserkennung und Schubladendenken im Gehirn zu einer Sinnestäuschung führen könnten +++ Sigma-Content humorvoll, aber gefährlich +++ Narwal jagen, fühlen und spielen mit ihrem langen Stoßzahn +++**********Weiterführende Quellen zu dieser Folge:Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition/ Plos Computational Biology, 27.01.2025Toxic Communication on TikTok: Sigma Masculinities and Gendered Disinformation/ Social Media + Society, 22.01.2025Exploring the nature of multilingual input to infants in multiple caregiver families in an African city: The case of Accra (Ghana)/ Cognitive Development, 21.02.2025Use of tusks by narwhals, Monodon monoceros, in foraging, exploratory, and play behavior/ Frontiers in Marine Science, 28.02.2025A dataset of laymen olfactory perception for 74 mono-molecular odors/ Scientific Data, 26.02.2025Alle Quellen findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok auf&ab , TikTok wie_geht und Instagram .

Analysis of image classifiers demonstrates that it is possible to understand backprop networks at the task-relevant run-time algorithmic level. In these systems, at least, networks gain their power from deploying massive parallelism to check for the presence of a vast number of simple, shallow patterns. https://betterwithout.ai/images-surface-features This episode has a lot of links: David Chapman's earliest public mention, in February 2016, of image classifiers probably using color and texture in ways that "cheat": twitter.com/Meaningness/status/698688687341572096 Jordana Cepelewicz's “Where we see shapes, AI sees textures,” Quanta Magazine, July 1, 2019: https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/ “Suddenly, a leopard print sofa appears”, May 2015: https://web.archive.org/web/20150622084852/http://rocknrollnerd.github.io/ml/2015/05/27/leopard-sofa.html “Understanding How Image Quality Affects Deep Neural Networks” April 2016: https://arxiv.org/abs/1604.04004   Goodfellow et al., “Explaining and Harnessing Adversarial Examples,” December 2014: https://arxiv.org/abs/1412.6572 “Universal adversarial perturbations,” October 2016: https://arxiv.org/pdf/1610.08401v1.pdf “Exploring the Landscape of Spatial Robustness,” December 2017: https://arxiv.org/abs/1712.02779 “Overinterpretation reveals image classification model pathologies,” NeurIPS 2021: https://proceedings.neurips.cc/paper/2021/file/8217bb4e7fa0541e0f5e04fea764ab91-Paper.pdf “Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet,” ICLR 2019: https://openreview.net/forum?id=SkfMWhAqYQ Baker et al.'s “Deep convolutional networks do not classify based on global object shape,” PLOS Computational Biology, 2018: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006613 François Chollet's Twitter threads about AI producing images of horses with extra legs: twitter.com/fchollet/status/1573836241875120128 and twitter.com/fchollet/status/1573843774803161090 “Zoom In: An Introduction to Circuits,” 2020: https://distill.pub/2020/circuits/zoom-in/ Geirhos et al., “ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness,” ICLR 2019: https://openreview.net/forum?id=Bygh9j09KX Dehghani et al., “Scaling Vision Transformers to 22 Billion Parameters,” 2023: https://arxiv.org/abs/2302.05442 Hasson et al., “Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks,” February 2020: https://www.gwern.net/docs/ai/scaling/2020-hasson.pdf

Robert (Bob) Wilson is an Associate Professor of Psychology at Georgia Tech. We talk about his tutorial paper (w/ Anne Collins) on computational modelling, and some of his recent work on detecting phishing.BJKS Podcast is a podcast about neuroscience, psychology, and anything vaguely related, hosted by Benjamin James Kuper-Smith.Support the show: https://geni.us/bjks-patreonTimestamps0:00:00: Bob's strange path through computational cognitive neuroscience0:07:37: Phishing: a computational model with real-life applications0:25:46: Start discussing Bob's paper 10 simple rules for computational modeling of behavioral data0:32:15: Rule 0: Why even do computational modelling?0:46:24: Rules 1 & 2: Design a good experiment & Design a good model1:02:51: Rule 3: Simulate!1:05:48: Rules 4 & 5: Parameter estimation and recovery1:18:28: Rule 6: Model recovery1:25:55: Rules 7 & 8: Collect data and validate the model1:33:15: Rule 9: Latent variable analysis1:36:24: Rule 10: Report your results1:37:46: Computational modelling and the open science movement1:40:17: A book or paper more people should read1:43:35: Something Bob wishes he'd learnt sooner1:47:18: Advice for PhD students/postdocsPodcast linksWebsite: https://geni.us/bjks-podTwitter: https://geni.us/bjks-pod-twtRobert's linksWebsite: https://geni.us/wilson-webGoogle Scholar: https://geni.us/wilson-scholarTwitter: https://geni.us/wilson-twtBen's linksWebsite: https://geni.us/bjks-webGoogle Scholar: https://geni.us/bjks-scholarTwitter: https://geni.us/bjks-twtReferencesEpisodes w/ Paul Smaldino: https://geni.us/bjks-smaldinohttps://geni.us/bjks-smaldino_2Bechara, Damasio, Damasio, & Anderson (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition.Feng, Wang, Zarnescu & Wilson (2021). The dynamics of explore–exploit decisions reveal a signal-to-noise mechanism for random exploration. Scientific Reports.Grilli, ... & Wilson (2021). Is this phishing? Older age is associated with greater difficulty discriminating between safe and malicious emails. The Journals of Gerontology: Series B.Hakim, Ebner, ... & Wilson (2021). The Phishing Email Suspicion Test (PEST) a lab-based task for evaluating the cognitive mechanisms of phishing detection. Behavior research methods.Harootonian, Ekstrom & Wilson (2022). Combination and competition between path integration and landmark navigation in the estimation of heading direction. PLoS Computational Biology.Hopfield (1982). Neural networks and physical systems with emergent collective computational abilities. PNAS.MacKay (2003). Information theory, inference and learning algorithms.Miller, Eugene & Pribram (1960). Plans and the Structure of Behaviour.Sweis, Abram, Schmidt, Seeland, MacDonald III, Thomas, & Redish (2018). Sensitivity to “sunk costs” in mice, rats, and humans. Science.Walasek & Stewart (2021). You cannot accurately estimate an individual's loss aversion using an accept–reject task. Decision.Wilson & Collins (2019). Ten simple rules for the computational modeling of behavioral data. Elife.

In the first episode of 2024, Shobita and Jack reflect on the first CRISPR therapy approved by drug regulators around the world, for sickle cell disease. We also talk about the safety issues plaguing Boeing, and the Post Office scandal roiling the UK and why it matters for regulating AI. And, we reconnect with Alondra Nelson, one of The Received Wisdom's first guests! Alondra Nelson is the Harold F. Linder Professor at the Institute for Advanced Study and previously as deputy assistant to President Joe Biden and acting director of the White House Office of Science and Technology Policy(OSTP). References:- Elish, M. (2019, March 23). Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction. Engaging Science, Technology, and Society. - Lazar, S and A. Nelson (2023, July 13). "AI safety on whose terms?" Science. 381 (6654): 138- Zook, M, S. Barocas, d. boyd, K. Crawford, E. Keller, S. P. Gangadharan, A. Goodman, R. Hollander, B.A. Koenig, J. Metcalf, A. Narayanan, A. Nelson, and F. Pasquale (2017, March 30). "Ten simple rules for responsible big data research." PLOS Computational Biology. - Nelson, A. (2016). The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome. Beacon Press.- Nelson, A, C. Marcum, J. Isler (2022, Fall). "Public Access to Advance Equity." Issues in Science and Technology. - White House (2022, Oct 4). Blueprint for an AI Bill of Rights.

Wer forscht, muss publizieren. Wissenschaftsverlage verdienen daran gut. Und nicht alles läuft optimal bei der Qualitätssicherung. Wenn in der Corona-Pandemie eine Studie öffentlich kritisiert wurde, sorgte das immer für Aufsehen. Dahinter steckt ein komplexes System. Es geht um Qualitätssicherung, aber auch um viel Geld. Denn wer forscht, muss veröffentlichen. Bevor ein Journal einen Artikel annimmt, wird er von anderen Wissenschaftler*innen begutachtet. Doch wie funktioniert das eigentlich? Warum wird dafür kein Honorar gezahlt, und wieso läuft das anonym? Yasmin Appelhans erklärt im Gespräch mit Host Lucie Kluth, warum manche Wissenschaftsverlage eine größere Gewinnmarge als Amazon haben und was das für den Zugang zu Wissen bedeutet. Aus ihrer eigenen Zeit als Meeresbiologin berichtet Appelhans von ihren Erfahrungen mit dem mysteriösen "Reviewer No. 2" - und sie bringt Musik mit, die sich mit dem Thema beschäftigt. DIE HINTERGRUNDINFORMATIONEN • Liedparodie Bohemian Rhapsody: Bohemian Rhapsody (aka ‘The tale of a Post Doc') - Laboratory Parody. 2013. https://www.youtube.com/watch?v=Q1YIYx8VBkI [Aufgerufen am 6. Dezember 2022].  • Was Übersichtsarbeiten und Metaanalysen leisten können am Beispiel der Medizin: Ressing M, Blettner M, Klug SJ. Systematische Übersichtsarbeiten und Metaanalysen. Deutsches Ärzteblatt. 2009;106(27): 456–463. https://www.aerzteblatt.de/archiv/65225/Systematische-Uebersichtsarbeiten-und-Metaanalysen • Meinungsartikel in der Wissenschaft: Goh HH, Bourne P. Ten simple rules for writing scientific op-ed articles. PLOS Computational Biology. 2020;16(9): e1008187. https://doi.org/10.1371/journal.pcbi.1008187 • Wie der Journal Impact Factor berechnet wird: DeGroote S. Subject and Course Guides: Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Journal Impact Factor (IF). https://researchguides.uic.edu/if/impact [Aufgerufen am 4. Januar 2023].  • Wie der H-Index berechnet wird: Universität Zürich UZH. What is your h-index?. Blog der Hauptbibliothek. https://www.uzh.ch/blog/hbz/2018/05/29/what-is-your-h-index/ [Aufgerufen am 4. Januar 2023].  • Gründe, warum viele medizinsche Studien nicht veröffentlicht warden: Song F, Loke Y, Hooper L. Why Are Medical and Health-Related Studies Not Being Published? A Systematic Review of Reasons Given by Investigators. PLOS ONE. 2014;9(10): e110418. https://doi.org/10.1371/journal.pone.0110418.  • Unbewusste Ungleichheit im Begutachtungssystem: Kuehn BM. Rooting out bias. eLife. 2017;6: e32014. https://doi.org/10.7554/eLife.32014.  • Die Geschichte des wissenschaftlichen Begutachtungsprozesses: Spier R. The history of the peer-review process. Trends in Biotechnology. 2002;20(8): 357–358. https://doi.org/10.1016/S0167-7799(02)01985-6.  • Wie sich das wissenschaftliche Publikationswesen entwickelt hat: Bargheer M. Historische Umbrüche im wissenschaftlichen Publikationswesen und ihr Widerhall in heutigen Techniken. In: Lackner K, Schilhan L, Kaier C (eds.) Publikationsberatung an Universitäten. 1st ed. Bielefeld, Germany: transcript Verlag; 2020. p. 21–52. https://doi.org/10.14361/9783839450727-003. [Aufgerufen am 2. Dezember 2022].  • Podcast des ZBW - Leibniz-Informationszentrums Wirtschaft: Podcast der ZBW. https://podcast.zbw.eu/ [Aufgerufen am 7. Dezember 2022].   • Umsatz Elsevier 2021: Online iXBRL Viewer. https://reports.relx.com/2021/esef-ar-uk/549300WSX3VBUFFJOO66-2021-12-31-Viewer.html [Aufgerufen am 7. Dezember 2022].  • Gebühren für Open-Access schrecken Forschende aus dem globalen Süden ab: Kwon D. Open-access publishing fees deter researchers in the global south. Nature. 2022; https://doi.org/10.1038/d41586-022-00342-w.  • Liste der Zeitschriften mit Diamond Open Access in Deutschland: Bruns A, Taubert NC, Cakir Y, Kaya S, Beidaghi S. Diamond Open Access Journals Germany (DOAG). 2022; https://pub.uni-bielefeld.de/record/2963331  • Positionspapier der Deutschen Forschungsgemeinschaft (DFG) zum wissenschaftlichen Publikationswesen: Deutsche Forschungsgemeinschaft | AG Publikationswesen. Wissenschaftliches Publizieren als Grundlage und Gestaltungsfeld der Wissenschaftsbewertung. 2022 May [Accessed 4th January 2023]. https://zenodo.org/record/6538163 [Aufgerufen am 4. Januar 2023]. 

Wer forscht, muss publizieren. Wissenschaftsverlage verdienen daran gut. Und nicht alles läuft optimal bei der Qualitätssicherung. Wenn in der Corona-Pandemie eine Studie öffentlich kritisiert wurde, sorgte das immer für Aufsehen. Dahinter steckt ein komplexes System. Es geht um Qualitätssicherung, aber auch um viel Geld. Denn wer forscht, muss veröffentlichen. Bevor ein Journal einen Artikel annimmt, wird er von anderen Wissenschaftler*innen begutachtet. Doch wie funktioniert das eigentlich? Warum wird dafür kein Honorar gezahlt, und wieso läuft das anonym? Yasmin Appelhans erklärt im Gespräch mit Host Lucie Kluth, warum manche Wissenschaftsverlage eine größere Gewinnmarge als Amazon haben und was das für den Zugang zu Wissen bedeutet. Aus ihrer eigenen Zeit als Meeresbiologin berichtet Appelhans von ihren Erfahrungen mit dem mysteriösen "Reviewer No. 2" - und sie bringt Musik mit, die sich mit dem Thema beschäftigt. DIE HINTERGRUNDINFORMATIONEN • Liedparodie Bohemian Rhapsody: Bohemian Rhapsody (aka ‘The tale of a Post Doc') - Laboratory Parody. 2013. https://www.youtube.com/watch?v=Q1YIYx8VBkI [Aufgerufen am 6. Dezember 2022].  • Was Übersichtsarbeiten und Metaanalysen leisten können am Beispiel der Medizin: Ressing M, Blettner M, Klug SJ. Systematische Übersichtsarbeiten und Metaanalysen. Deutsches Ärzteblatt. 2009;106(27): 456–463. https://www.aerzteblatt.de/archiv/65225/Systematische-Uebersichtsarbeiten-und-Metaanalysen • Meinungsartikel in der Wissenschaft: Goh HH, Bourne P. Ten simple rules for writing scientific op-ed articles. PLOS Computational Biology. 2020;16(9): e1008187. https://doi.org/10.1371/journal.pcbi.1008187 • Wie der Journal Impact Factor berechnet wird: DeGroote S. Subject and Course Guides: Measuring Your Impact: Impact Factor, Citation Analysis, and other Metrics: Journal Impact Factor (IF). https://researchguides.uic.edu/if/impact [Aufgerufen am 4. Januar 2023].  • Wie der H-Index berechnet wird: Universität Zürich UZH. What is your h-index?. Blog der Hauptbibliothek. https://www.uzh.ch/blog/hbz/2018/05/29/what-is-your-h-index/ [Aufgerufen am 4. Januar 2023].  • Gründe, warum viele medizinsche Studien nicht veröffentlicht warden: Song F, Loke Y, Hooper L. Why Are Medical and Health-Related Studies Not Being Published? A Systematic Review of Reasons Given by Investigators. PLOS ONE. 2014;9(10): e110418. https://doi.org/10.1371/journal.pone.0110418.  • Unbewusste Ungleichheit im Begutachtungssystem: Kuehn BM. Rooting out bias. eLife. 2017;6: e32014. https://doi.org/10.7554/eLife.32014.  • Die Geschichte des wissenschaftlichen Begutachtungsprozesses: Spier R. The history of the peer-review process. Trends in Biotechnology. 2002;20(8): 357–358. https://doi.org/10.1016/S0167-7799(02)01985-6.  • Wie sich das wissenschaftliche Publikationswesen entwickelt hat: Bargheer M. Historische Umbrüche im wissenschaftlichen Publikationswesen und ihr Widerhall in heutigen Techniken. In: Lackner K, Schilhan L, Kaier C (eds.) Publikationsberatung an Universitäten. 1st ed. Bielefeld, Germany: transcript Verlag; 2020. p. 21–52. https://doi.org/10.14361/9783839450727-003. [Aufgerufen am 2. Dezember 2022].  • Podcast des ZBW - Leibniz-Informationszentrums Wirtschaft: Podcast der ZBW. https://podcast.zbw.eu/ [Aufgerufen am 7. Dezember 2022].   • Umsatz Elsevier 2021: Online iXBRL Viewer. https://reports.relx.com/2021/esef-ar-uk/549300WSX3VBUFFJOO66-2021-12-31-Viewer.html [Aufgerufen am 7. Dezember 2022].  • Gebühren für Open-Access schrecken Forschende aus dem globalen Süden ab: Kwon D. Open-access publishing fees deter researchers in the global south. Nature. 2022; https://doi.org/10.1038/d41586-022-00342-w.  • Liste der Zeitschriften mit Diamond Open Access in Deutschland: Bruns A, Taubert NC, Cakir Y, Kaya S, Beidaghi S. Diamond Open Access Journals Germany (DOAG). 2022; https://pub.uni-bielefeld.de/record/2963331  • Positionspapier der Deutschen Forschungsgemeinschaft (DFG) zum wissenschaftlichen Publikationswesen: Deutsche Forschungsgemeinschaft | AG Publikationswesen. Wissenschaftliches Publizieren als Grundlage und Gestaltungsfeld der Wissenschaftsbewertung. 2022 May [Accessed 4th January 2023]. https://zenodo.org/record/6538163 [Aufgerufen am 4. Januar 2023]. 

 Revealing atomistic structures behind AFM imaginghttps://nanolsi.kanazawa-u.ac.jp/en/achievements/revealing-atomistic-structures-behind-afm-imaging/Atomic force microscopy (AFM) enables the visualization of the dynamics of single biomolecules during their functional activity. However, all observations are restricted to regions that are accessible by a fairly big probing tip during scanning. Hence, the AFM only records images of biomolecular surfaces with limited spatial resolution, thereby missing important information that is required for a detailed understanding of the observed phenomena.To facilitate the interpretation of experimental imaging, Romain Amyot and Holger Flechsig from the Kanazawa NanoLSI have developed the mathematical framework and computational methods to reconstruct 3D atomistic structures from AFM surface scans. ==Transcript of this podcastHello and welcome to the NanoLSI podcast. In this episode we feature the latest research published by Romain Amyot and Holger Flechsig of the Computational Science group at the Kanazawa University NanoLSI.The research described in this podcast was published in the journal PLOS Computational Biology in March 2022. Revealing atomistic structures behind AFM imaginghttps://nanolsi.kanazawa-u.ac.jp/en/achievements/revealing-atomistic-structures-behind-afm-imaging/Atomic force microscopy (AFM) enables the visualization of the dynamics of single biomolecules during their functional activity. However, all observations are restricted to regions that are accessible by a fairly big probing tip during scanning. Hence, the AFM only records images of biomolecular surfaces with limited spatial resolution, thereby missing important information that is required for a detailed understanding of the observed phenomena.To facilitate the interpretation of experimental imaging, Romain Amyot and Holger Flechsig from the Kanazawa NanoLSI have developed the mathematical framework and computational methods to reconstruct 3D atomistic structures from AFM surface scans. In this paper they describe applications for high-speed AFM imaging ranging from single molecular machines, protein filaments, to even large-scale assemblies of protein lattices, and demonstrate how the full atomistic information advances the molecular understanding beyond topographic images.Their approach employs simulation AFM, which was previously established by Amyot and Flechsig and distributed within the free BioAFMviewer software package. Simulation AFM computationally emulates experimental scanning of biomolecules to translate structural data into simulation AFM topographic images that can be compared to real AFM images. The researchers implemented a procedure of automated fitting to identify the high-resolution molecular structure behind a limited-resolution experimental AFM image. It is therefore possible to retrieve full 3D atomistic information from just a scan of the protein surface obtained under AFM observations. To illustrate the potential of this achievement, Flechsig says: “Imagine that instead of just seeing the tip of an iceberg, you are now able to see everything hidden under the sea, to the extent that you can even detect impurities or density differences within its structure, helping you to explain the icebergs' coloration.”To share these developments with the global Bio-AFM community, all computational methods are embedded within the user-friendly BioAFMviewer interactive software interface. The new methods have already been applied in numerous interdisciplinary collaborations to understand expe

In this episode, Kris and Paul discuss the process they use to choose and read research papers, both for Breaking Beta and for their own interests as climbers and coaches.   *Additional studies/resources mentioned in this episode: Beall's List of Potential Predatory Journals and Publishers How to (seriously) read a scientific paper by Elisabeth Pain; published on Science; March 21, 2016. Ten simple rules for reading a scientific paper by Maureen A. Carey, Kevin L. Steiner, and William A. Petri Jr.; published on PLOS COMPUTATIONAL BIOLOGY; July 30, 2020. How to read and understand a scientific paper: a guide for non-scientists by Jennifer Raff; published on Violent Metaphors; August 25, 2013. How to read scientific papers quickly (and effectively organize them for a literature review) published on Genius Lab Gear. Make sure you're subscribed, leave us a review, and share! And please tell all of your friends who are confused and overwhelmed by the amount of jumbled and conflicting training info out there, that you have the perfect podcast for them.   Better Call Paul | Breaking Beta is brought to you by Power Company Climbing and Crux Conditioning, and is a proud member of the Plug Tone Audio Collective. Find full episode transcripts, citations, and more at our website. Follow Kris and Breaking Beta on Instagram  Follow Paul and Crux Conditioning on Instagram  If you have questions, comments, or want to suggest a paper we should cover, find us at our Community + Knowledge Hub. Our music is from legendary South Dakota band Rifflord.

Aaron Bornstein, cognitive sciences assistant professor at UC Irvine, studies human memory and the ways memory guides decisions. Aaron Bornstein, cognitive sciences assistant professor at UC Irvine, studies human memory and the ways memory guides decisions. Whether subconsciously or consciously, when a new situation is encountered, memory helps us to generalize from past experiences to make sense of our current environment, he says. No two people have the exact same experienced memories - not even identical twins raised together - so studying how memory influences behavior can help us understand why behaviors change so dramatically from person to person. He’s interested in further studying how memory impacts economic decisions, impulsivity, disorders of choice such as addiction, as well as everyday choices about what to buy, eat, or how to interpret the world around us.His research on the topic has been funded by the National Institutes of Health and published in Nature Communications; Nature Neuroscience; Cognitive, Affective & Behavioral Neuroscience; PLoS Computational Biology; the European Journal of Neuroscience; and the Journal of Cognitive Neuroscience.

Our guest for this episode is Dr. Holly Bik. Holly obtained her PhD in Molecular Phylogenetics at the University of Southampton, working with John Lambshead at the Natural History Museum of London in conjunction with the UK National Oceanography Center. She completed postdoctoral appointments with Dr. Kelley Thomas at the University of New Hampshire and Dr. Jonathan Eisen at UC Davis before starting her faculty position. In addition to her research, Holly is invested in science communication. She serves as an associate editor for the popular marine blog Deep-Sea News and maintains an active presence on Twitter (@hollybik). Holly has co-authored a number of peer-reviewed articles on the use of social media and online tools in academia, including “An Introduction to Social Media for Scientists” in PLoS Biology and “Ten Simple Rules for Effective Online Outreach” in PLoS Computational Biology. In this episode, we discuss the first paper to come out of Holly’s lab at UC Riverside , entitled “Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats” (Schuelke et al., 2018). Holly elaborates on the unexpected results from this paper and talks about the many challenges associated with collecting and analyzing marine sediments. In addition to the technical aspects of this paper, we also talk about time management and how Holly set aside time to write a draft in one week. She tells us about her 6-month-long personal work/life balance experiment in time-tracking and shares what she learned from this experience. We discuss the concept of Deep Work and why she continues to fill out weekly review worksheets to help manage stress and productivity. At the time of this recording, Holly was in the process of moving her lab from UC Riverside to The University of Georgia where she is currently an Assistant Professor in the Department of Marine Sciences. We talk about the process of moving and the factors Holly considered when making this important career decision. Holly explains that it's important for early career researchers to understand how long things take, and also be okay with the fact that some things are just going to take way longer than you expect. SHOW NOTES: Paper: Schuelke, T., Pereira, T. J., Hardy, S. M., & Bik, H. M. (2018). Nematode‐associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Molecular Ecology, 27(8), 1930-1951. A few of Holly’s Twitter threads: Data-driven time management https://twitter.com/hollybik/status/1133750210331496450 Concept of ‘deep work’ https://twitter.com/hollybik/status/1133751166091685888?s=20 Work life balance: https://twitter.com/hollybik/status/1133751739599876106?s=20 The Monday Motivator - weekly emails that provides positive energy, good vibes, and a productivity tip from the National Center of Faculty Development and Diversity Cal Newport (author of Deep Work) https://www.calnewport.com/about/ @hollybik @ehaswell @baxtertwi @taprootpodcast

Jon and Jeff do a deep-dive into the PLOS: Computational Biology paper "Ten Simple Rules for Better Figures." This one is kind of nerdy, but at this point, isn't that what y'all want?

John is defending his dissertation, so he prepares by describing it to you! Meseum of Osteology XKCD Thesis Defense Origin of the Thesis NZ Earthquake Lights Hydraulic Press Channel Biaxal deformation press Fun Paper Friday How can we understand the brain? Is a processor the same thing? This study examines the classic 6502. Economist Column Jonas, Eric, and Konrad Paul Kording. “Could a neuroscientist understand a microprocessor?.” PLOS Computational Biology 13.1 (2017): e1005268. Contact us: Show - www.dontpanicgeocast.com - SWUNG Slack - @dontpanicgeo - show@dontpanicgeocast.com John Leeman - www.johnrleeman.com - @geo_leeman Shannon Dulin - @ShannonDulin  

Marcel is a digital epidemiologist working at the interface of population biology, computational sciences, and the social sciences. He obtained his PhD at ETH Zurich and spent two years as a postdoc in Stanford before joining the faculty at Penn State in 2010. In 2014, he spent half a year at Stanford as visiting assistant professor. In the summer of 2015, Marcel became an Associate Professor at EPFL where he heads the Digital Epidemiology Lab at the new Campus Biotech.He has published papers in a variety of fields and recently wrote a book called "Nature, in Code". He led the development of the MOOC “Epidemics - The Dynamics of Infectious Disease”, a popular large-scale online course and co-founded PlantVillage, a knowledge exchange platform for growers of all kinds of plants. He also is deputy editor of PLOS Computational Biology.Marcel has spend a few years in the tech industry as a web app developer. He was part of the renowned Y Combinator startup accelerator’s class of 2014.