# Upload to Hugging Face This folder is the structured, no-Miqus upload tree. It is generated from the cleaned staging data via hard-links (so re-uploading is cheap and does not duplicate ~86 GB on disk). ## One-shot upload ```bash hf upload-large-folder /PULSE \ /path/to/hf_upload_pulse_structured_nomiqus \ --repo-type dataset \ --num-workers 8 \ --exclude '.cache/**' \ --exclude '*Miqus*.avi' ``` LFS rules in `.gitattributes` cover the large CSVs (IMU, MoCap, raw Qualisys TSV), the auxiliary MP4s, and all standard binary formats. **Do not delete `.gitattributes` before upload** — without it the large CSVs would be pushed as plain git blobs and the repo would exceed the per-file limit. ## Migrating from the old top-level `v*/` layout The previous upload had volunteer folders directly at the root (`/v1/s1/...`). This release moves them under `data/` (`/data/v1/s1/...`). **`hf upload-large-folder` does not delete files that are no longer present in the local tree.** If the same Hugging Face repo already contains the old layout, you must clean up the old paths explicitly so the dataset card globs and Croissant `includes` resolve unambiguously: ```bash # List old top-level v*/ paths still on the remote hf api repo-files /PULSE --repo-type dataset \ | grep -E '^v[0-9]+/' # Remove them (irrevocable; review the list first) hf repo-files delete /PULSE \ --repo-type dataset \ 'v*/s*/*' 'batch_alignment_summary.json' 'modality_coverage.xlsx' \ 'annotations/_run_summary.json' 'annotations/segment_counts.*' \ 'annotations/logs/*' ``` Note that the raw Qualisys TSV has also moved one level deeper, from `data/v*/s*/aligned_v*_s_Q.tsv` (in the previous staging tree) to `data/v*/s*/raw/aligned_v*_s_Q.tsv` here. If you re-uploaded an interim copy that still had the TSV at the old location, clean those up too: ```bash hf repo-files delete /PULSE \ --repo-type dataset 'data/v*/s*/aligned_v*_s_Q.tsv' ``` ## Verifying the upload After upload completes, sanity-check that: 1. `huggingface.co/datasets//tree/main` shows the four top-level dirs (`annotations/`, `data/`, `docs/`, `metadata/`) plus the licenses, README, and `croissant.json` — and **no** stray `v*/` at the root. 2. The dataset viewer can load each per-modality config: ```python from datasets import load_dataset for cfg in ["emg", "imu", "mocap", "eyetrack", "pressure"]: ds = load_dataset("", cfg, split="train", streaming=True) print(cfg, next(iter(ds)).keys()) ``` 3. Large files were stored via LFS: ```bash hf api file-info data/v1/s1/aligned_imu_100hz.csv \ --repo-type dataset # Should report `lfs: {...}` rather than a raw blob sha. ```