I’ve just finished a three‑month deep‑dive project that merges high‑density SNP data from the Illumina Infinium Global Screening Array v3.0 with a custom GEDCOM export of my 12‑generation family tree. The goal was to produce a Bayesian phylogenetic model that can resolve branch points down to the 17th‑century parish level.

What I did

• Ran the DNA samples on the GSA v3.0 platform (≈720k markers) and filtered for autosomal SNPs with call rate > 99 %.
• Exported the pedigree from FamilySearch using the API (v2.0) into a GEDCOM 5.5 file, then cleaned it with GEDCOM‑Cleaner 2.1 to remove duplicate X‑ref IDs.
• Mapped each individual’s sample ID to the GEDCOM @I‑record using a lookup table generated in Python (pandas 1.5.3).
• Built a haplogroup assignment for Y‑DNA (using Y‑Full haplogroup predictor) and mtDNA (Phylotree Build 17) and added them as custom tags ("_YHG" and "_MTDNA") in the GEDCOM.
• Ran BEAST 2.6.5 with a strict clock model and a coalescent Bayesian Skyline prior to infer the time‑scaled tree. Calibration points were set using documented birth dates from the 1881 England Census.

Results

• The posterior distribution converged after ~10 M generations (ESS > 200 for all parameters).
• Visualised the tree in FigTree 1.4.4, highlighting branches where the autosomal ancestry proportion exceeds 0.85 for a single lineage – those correspond to the documented “founder” families in the parish registers.
• Exported the final tree back into GEDCOM as a set of "_TIME" tags, which can be read by CousinTree 3.0 for interactive browsing.

Why it matters

• Most hobbyist genealogists stop at a simple DNA match list. This workflow shows you can actually place those matches into a calibrated temporal framework.
• The approach is reproducible: I’ve uploaded the full pipeline (scripts, config files, and a sample dataset) to a public GitHub repo (https://github.com/ComfortableParrotfish302/gedcom‑dna‑pipeline).

Open questions / feedback wanted

• Anyone tried integrating the newer Illumina OmniExpress‑24 v1.2 data with a similar pipeline? I’m curious about marker overlap issues.
• Thoughts on using a relaxed clock model instead of a strict clock for families with known migration events?

Feel free to clone the repo, run the pipeline on your own data, and let me know if you hit any snags.

So I've been reading up on basic quantum mechanics, and the chapter on entanglement completely lost me. The book described particles somehow sharing states across distance — it felt like both a breakthrough idea, but also the most confusing one I've ever tried to parse.

I know I'm asking a question people here are already way better at explaining than I can be, so what resources do physics folks recommend when the going gets steep like this? Good physics-focused YouTube creators, authors who write for people new to this, or even just book titles I could track down? Something that explains entangled particles as if by analogy, maybe?

My last attempt at studying this ended in head scratching like, How is measuring one particle a dealbreakership thing at the quantum level. The whole concept smells like I've somehow not yet understood the basics yet, which would be annoying after reading the same textbook for four months. 😊

Anyone experienced the same confusion? If so, what helped your mind make this all stick better than quantum foam in a collapsing waveform?

Everyone cites Newton's cradle as a 'momentum demo' but no one mentions it's a kinetic energy illusion. The math checks out only if you assume perfectly elastic collisions between identical masses. Which no real-world material achieves. Try replacing one steel ball with a rubber one and watch the 'laws of physics' vanish. Car crashes are a better example of kinetic energy transfer. No one cares about momentum there. What other textbook examples are just lab-idealized parlor tricks?

I'm looking for some recommendations on economic models that can be used to predict market trends. I've been reading about the Gordon Growth Model and the Discounted Cash Flow model, but I'm not sure which one is more applicable to current market conditions. Can anyone with experience in this area provide some insight into which models they've found to be most effective? I'm particularly interested in hearing about any models that take into account external factors such as government policies and global events... do these models even exist??

just ran the 1m pendulum test again, got 2.006s vs 2.01 theoretical no more drift feels good rn

so last night i was streaming interstellar and on the news board they said 3020, I went looking up 3020 but that's out of scope for relativity haha my brain looped to 2030 instead tbh I realise that slip makes everyone question the film’s timing and the community lesson: jot down any off‑years in your margin doesn’t hurt if you ask for sources instead of guessing, way better than adding another physics factoid that might be wrong at home rn