Science Research Weekly

Get ready for rare-earth element binding proteins, single atom X-ray signals, Beagle Scouts on the march, the 2023 Python Language Summit, embedding code into SAS programs, ggplots with Tintin, and Gateway for Cancer Research funding opportunities. Science On.

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Get ready for non-natural amino acids, screwdriver-toothed mosasaurs, bubble-powered microbots, SAS Hackathon breakthroughs, Amazing Biochemistry Project Topics, and funding support from a little giraffe. Science On.

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Get ready for human environmental DNA, over-the-top cosmological energy, rafting microbes, bimodal biomass, Julia 1.9’s releases, SAS patents, the freeCodeCamp repository, and Department of Defense melanoma grants. Science On.

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Get ready for bacterial metabolism prediction, surprise asteroid belts, SAS-poor AI advice, Kruskal Wallis post-hoc tests, lots of new R packages on regression, and a Carnegie astrophysics fellowship. Science On.

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Get ready for planet swelling stars, genome guarding proteins, the largest mammalian genomic dataset in history, Bayesian Spatial Disaggregation Modeling, Computer Science courses, clickable word clouds, and Pre-Clinical Research on Gene Therapies for Rare Genetic Neurodevelopmental Disorders. Science On.

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Get ready for jetting black holes, cancer diagnosis with a strip of paper, the mysterious world of freshwater acoustics, frontiers in fungal biotechnology, transcribing/translating audio with Buzz, and NSF infrastructure grants. Science On.

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Get ready for space rodents microbiomes; 100-year biobatteries; huge, hot exoplanets; reasons why your Shiny App failed; and ways to use statistics to get funding.

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Get ready for chimeric crazy ants, viruses abounding in protists and infants alike, solar system generation in R, an awesome Python list, and a couple of nifty niche funding opportunities. Science On.

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Get ready for toxic birds, correlation for days, a flipper-tastic multi-tool, blazing fast Shiny development, and Department of Defense cancer grants as far as the eye can see. Science On.

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Get ready for atomic nuclei views, artificial organelles, AI article generation, PyCon, Simpson's paradox, building your favorite technologies from scratch, and funding to maintain ENCODE. Science On.

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Get ready for super microscopes, tardigrade blood stabilization, broken stick modeling, an R version codenamed ‘Already Tomorrow’, a ‘pinecone’ made of Python, and a pitch-style competition for infectious disease funding. Science On.

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Get ready for self-healing soft robots, modular NASA robots, Shiny recaps, Statistical Rethinking training, statistical kindergarten, and the NIH Pioneer Award Program. Science On.

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Get ready for technological tribology, tingible translational cell biology, flipping fossils, Volume 14/4 of the R Journal, ebook tips and tricks, and flash grants to fixate on.  Science On.

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Get ready for an exploration of the R-Universe, simulated octopus tentacles, effects of Vitamin D and exercise on health, fossil wings and fins, statistical conferences to set your calendar by, and a very special 23rd birthday.  Science On.

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Get ready for better arsenic detection, broad-spectrum antibiotics, model-breaking massive galaxies, Julia and Python working in harmony, a hard-to-pronounce R-package on Gaussian mixture models, and Department of Defense lung cancer grants aplenty.  Science On.

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Get ready for exoplanetary systems, missing Neptunes, a Dungeons & Dragons R package, GIFs for top R packages, Tidvyverse functions to save your day, and a whole host of NASA grant announcements.  Science On.

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Get ready for the top 10 errors in R, self-assembling peptides, microbiome mishaps, a theory-defying dwarf planet ring system, and a chance to save tiny hearts.  Science On.

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Get ready for heart-attack healing hydrogels, super rare supernovas, volumes of viroid sequences, stealth oceans, finding hex logos and packages by topic in CRAN, and NASA funding for exobiology.  Science On.

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Get ready for new deep sea bacterial phyla, extra ancient caecilian fossils, R Journal Volume 14/3 JuliaHub 6.0, visualizing star maps in R, and an IceCube neutrino competition!  Science Research Weekly covers 3 topic areas: Research Articles, Research Tools, and Research Funding.  Science On.

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Welcome to Science Research Weekly, the successor to my first podcast, Statistics Weekly.  This abbreviated trial run of an episode includes: a research article on microbes from a volcanic island, the largest stellar catalogue to date, Arduino project ideas for 2023, and NSF Research and Development grants.

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In this episode, I pondered the problem of false positives in publications, rotated around the JuliaHub, machinated over machine learning projects, and put R package ‘drawCell’ to the test.

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In this episode, I marched through machine learning abstractions, brought home the BQN, discovered statistics projects to work on over Christmas, and found the Gosling that lays the golden egg,

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In this episode, I got into the spirit of Sinterklaas with Python, jumpstarted Julia learning, cleared up confusion between ANOVA and regression results, and color paletted the night away.

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In this episode, I picked the choicest R journal articles, served up a digital poker hand in SAS, and tried not to let a defective R-package get me down.

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In this episode, I skimmed over a dimension estimator, skipped past a piecewise linear curve, and slipped by the launch of a simple data science ebook.

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In this episode, I dusted off my genomic gloves with the HCLC-FC and CLIMB methods, waxed poetic with Python’s latest alpha release, and solved plotting mysteries with R-package ‘sherlock’.

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In this episode, I took advantage of more than one free data science course, mused over mappings for R, and helped myself to a heap of helper functions with R package ‘mlmhelpr’.

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In this episode, I waved my hand across several financial data journal articles,  took advantage of free data science tutorials, learned how to run a dev   environment from a flash drive, and did a double take at a seeming CRAN package duplication.

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In this episode, I tried to keep my acronyms straight, dealt with big and tall (data), poked the ribs of SPSS, and contrasted the PIC with the AIC (more acronyms on the march!)

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In this episode, I spilled the secret sauce behind my research methods, predicted extreme events, pursued plots off the beaten ggplot path, and added another simulation-based power analysis tool to my belt.

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In this episode, I contributed to the Python Developers Survey, wiped a sweaty brow over the Isoband Incident, and booted up package 'bupaverse' for business process analytics.

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In this episode, I netted new ways to knock neural networks out of the park, let R do my calculus calculations for me, garnered gentle overviews to many   machine learning topics, and made sure my phylogenic trees were publication ready with the R package ‘CancerEvolutionVisualization'.

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In this episode, I kept it quick with appropriate microbial resistance data procedures, using a TI 83 calculator for statistics, and hoping for an easy entry to NEON data wrangling with package ‘neonOS’.

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In this episode, I kept it short and sweet with a new statistical journal reference, data science job scraping, and random learning for R-packages.

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In this episode, I found that Bambi (Bayesian Model Building Interface) is more than a little deer, rooted for the round-robin operator for genetic algorithms, and had my convictions that machine learning is not magic reinforced.

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In this episode, I chose mediation analysis over a hospital heist, gained additional sample size calculation insights, memorized machine learning methods, and battled my way up a text-based tower using package ‘rwarrior’.

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In this episode, I bite off more journal articles than I could chew, bookmarked how to reorder SGPLOT bars, was underwhelmed by R surprises, and muddled through the meta-package ‘easystats’.

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In this episode, I grabbed the tools to conduct mobility data analysis and interactive plots in Python, detected outliers in spherical data, considered the next programing language to learn, and readied the statistical syrup for waffle plots using package ‘baffle’.

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In this episode, I played catch-up with the Journal of Statistical Software, schemed how to snag a top spot in the XP Learning Challenge, and roared alongside the R-package ‘TRexSelector’.

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In this episode, I browsed yet more Monte Carlo simulation papers, introduced myself to various structural equation models, championed base R, and went neural to neural with the R package ‘cito’.

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In this episode, I didn’t get a lot of sleep. But I did get a lot of quirky Python, classy SAS, and ridiculous R.

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In this episode, RStudio gets a Posit-tively new name, SAS estimates area with  Monte Carlo simulation, and I take in the view of package ‘P2C2M.Skyline.’

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In this episode, I pared down three important -omics data considerations, coded my way into wave of new R packages, boasted about the Big Book of R, and havered over the ‘dots’ package.

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In this episode, I clued in on the claims that Linear Programming Discriminant analysis is effective, mused on the methods of PROC SQL in SAS, and fired up a virtual machine to use for package ‘vmr’.

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In this episode, I assessed applications for AI-generated synthetic data, read up on the radical R tutorial rflow, and stepped into the ‘multiverse’ package to manage reporting on many different statistical analyses.

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In this episode, I explored model selection methods for fending off invading   outliers, marveled at May’s Top 40 CRAN packages, refreshed my suppositions on samples space, and rallied up to replace words in text using package ‘rflashtext’.

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In this episode, I broadened up my bioinformatics background, found that the latest R-release was a Funny-Looking Kid, and extracted lobster species using package ‘OpenTreeChronograms’. 

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In this episode, I tackled ‘omics’ data with a Bayesian fire engine, soared over a timeline of statistical ideas, and decreased statistical bias by increasing bias understanding.

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In this episode, I learned there are both good and bad R books, umbrella reviews are meta on top of meta, flowcharts can be fashioned in ggplot2, and package ‘ggpie’ is more than mere fluff.

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In this episode, I chained together multiple scientific last names, found heaps of PLOS ONE journal articles, and learned how to grow a backbone in R using package ‘backbone’.

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In this episode I thought outside the Box-Pierce; fuzzed, sweated, and flustered my way through basic stats; encountered functions and AI for babies; and chronicled the R-package ‘chronicler’.

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In this episode, I got lost in among the statistical trees, flipped a Bernoulli trial coin, and wrapped myself up in package ‘ggblanket’.

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In this episode, I dived into non-normal distributions, went from rags to ridges, rode the Python Language Summit lightning, and looked at the ‘GenericML’ package.

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In this episode, I dig into the first articles of a new Journal of Statistical Software volume, try not to mix up package ‘nvmix’ with ‘nmix’, scratch my head over Navier-Stokes equations, and take the random R-package section in a metabolomics direction.

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In this episode, I stretch before vigorous calisthenics, give a thumbs up to O’Reilly coding books, get to the bottom of R vectorization, try not to panic during   debugging, and showcase the R-package ‘fauxnaif’.

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In this episode, I jump into Julia packages, estimate how to pronounce the generalized Kibria-Lukman estimator, commiserate with Python, and showcase the R-package ‘rsconnect’.

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In this episode, I play with jackknives, chuckle over Python, list upcoming R conferences, and showcase the R-package ‘RcppBDT’.

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In this episode, I drink from the firehose of CRAN packages, learn a use for ‘two truths and a lie’ in the statistics classroom, and showcase the R-package ‘pipeliner’.

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In this episode, I brace myself for the R release of Vigorous Calisthenics, scratch my head at Lyapunov Exponents, survey the brewing battlefield in the AI/ML Wars, and showcase the R-package 'numform'.

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