I’ve spent the last year maintaining a public longitudinal self-tracking archive covering wearables, sleep, recovery, training, body composition, biomarkers, and weekly reporting.<\/p>\n
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The repository includes:<\/p>\n
– raw and processed datasets<\/p>\n
– longitudinal sleep and wearable records<\/p>\n
– weekly reports<\/p>\n
– audit trails<\/p>\n
– prediction tracking and model-error analysis<\/p>\n
– changelog and governance documentation<\/p>\n
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My goal isn’t optimization as much as documenting what long-term observation of a single subject looks like when treated like a data project.<\/p>\n
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I’m particularly interested in feedback on:<\/p>\n
– dataset structure<\/p>\n
– governance<\/p>\n
– reproducibility<\/p>\n
– longitudinal analysis opportunities<\/p>\n
– potential blind spots in methodology<\/p>\n
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Current archive size: ~1 year of daily observations, weekly reports, wearable records, biomarker snapshots, and prediction-tracking artifacts.<\/p>\n
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Repository:<\/p>\n
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https:\/\/github.com\/CDHughett\/daniel-longitudinal-public<\/a><\/p>\n<\/div>\n submitted by \/u\/Intelligent-Arm-9001 <\/a>
[link]<\/a><\/span> [comments]<\/a><\/span><\/p>
