Datasets:

xkronosx
/

cryoem-acquisition

	---
	license: cc-by-4.0
	task_categories:
	- reinforcement-learning
	- tabular-regression
	tags:
	- cryo-em
	- cryoem
	- electron-microscopy
	- acquisition
	- active-learning
	- contextual-bandit
	- epu
	pretty_name: Cryo-EM Autonomous Acquisition
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: combined
	data_files:
	- split: targets
	path: index/combined/target_locations.parquet
	- split: labels
	path: index/combined/labels_v4_outcomes.parquet
	- split: holes
	path: index/combined/holes.parquet
	- split: geometry
	path: index/combined/square_geometry.parquet
	---

	# Cryo-EM Autonomous Acquisition Dataset

	Data for training agents that decide which grid square and foil hole to image next during a cryo-EM session, so a sample reaches its target in the fewest acquisitions.

	Four EPU sessions derived from public EMPIAR depositions, with per-acquisition outcome labels, pre-acquisition hole features, frozen image embeddings, and orientation-proxy profiles.

	\| \| \|
	\| --- \| --- \|
	\| candidate holes \| 27,944 \|
	\| acquired holes \| 3,936 \|
	\| acquisitions (label rows) \| 13,526 (10,595 usable) \|
	\| grid squares \| 1,795 \|
	\| sessions \| 4 \|
	\| size \| ~137 MB \|

	## Quick start

	```bash
	pip install huggingface_hub pandas pyarrow numpy
	huggingface-cli download xkronosx/cryoem-acquisition --repo-type dataset --local-dir cryoem-acquisition
	cd cryoem-acquisition

	python -m cryoem_au_data.validate index/combined # check the contract
	python example.py # load, features, labels
	```

	## Layout

	```
	index/
	combined/ all four sessions concatenated ← start here
	empiar-11895__epu__grid1/ individual sessions
	empiar-13296__epu__delta9/
	empiar-13296__epu__delta10/
	empiar-13296__epu__deltaRlmE/
	empiar-11895__epu__grid1_uncensored/ censoring-ablation variant
	features/
	combined/embeddings_cryo_ief.parquet 8 PCA columns, keyed by hole_uid
	<session>/raw_cryo_ief.parquet raw 768-d Cryo-IEF vectors
	bases/ frozen PCA basis for new sessions
	orientation/
	orientation_class2d.parquet 2D-class view proxy (EMPIAR-11895)
	cryoem_au_data/ loader, feature builder, validator
	example.py
	```

	Each index directory holds four parquet tables:

	\| table \| one row per \| what it is \|
	\| --- \| --- \| --- \|
	\| `target_locations.parquet` \| candidate hole \| every hole EPU's hole-finder proposed — the agent's decision space \|
	\| `holes.parquet` \| acquired hole \| the subset actually imaged \|
	\| `square_geometry.parquet` \| grid square \| shape and extent, for geometric features \|
	\| `labels_v4_outcomes.parquet` \| acquisition \| the outcomes — a hole shot 3× has 3 rows \|

	## The contract

	UIDs. `session:<name>:square:<id>:hole:<id>`. The `:square:` segment is load-bearing: cross-session validation splits on the substring before it, and without it leave-one-session-out silently degrades into leave-one-hole-out.

	`sample_id` is the micrograph stem and the join key to labels, orientation profiles and image features.

	Pre- vs post-exposure. `target_locations` holds only what is knowable before firing; `labels_v4_outcomes` holds what came back. `selected` (did a human take this hole) is a legitimate training target but never a model input — in deployment the agent is the selector.

	Utility:

	```python
	w_ctf = sigmoid((6.0 - ctf_res_adj) / 1.5)
	w_ice = 1.0 / (1.0 + max(0, ice_ring - 1.5)) # missing ice => no penalty
	utility = curated_count * w_ctf * w_ice # 0 where status != "ok"
	```

	`ctf_res_adj` is `ctf_res` with the defocus trend removed (level preserved). Those three constants were tuned on EMPIAR-11895 and do not transfer — on other sessions the CTF gate saturates or the ice term penalises everything. Re-tuning is a pure relabel; `compute_utility()` takes them as arguments.

	Censoring. Two statuses mark failures that must not be read as zeros:
	- `ctf_unfit` — CTFFIND reports its search limit when a fit fails (a sentinel, not a measurement). Those micrographs still yield ~28 real particles.
	- `curation_missing` — absent from curation is not zero particles.

	## Known limitations

	Stated plainly, because they bound what can be concluded:

	- Timestamps exist for only one session (`deltaRlmE`). The others fall back to a `sample_id` sort — usable but not true chronological order.
	- Label quality varies sharply. Only EMPIAR-11895 has genuine 2D-curated particle counts (`count_source == "class2d_curated"`); the rest use LoG picks, which carry roughly 7× less learnable signal. `deltaRlmE` has a between-hole to within-hole variance ratio of ~1.07 — its holes are barely separable from shot noise.
	- Transfer between sessions is weak and sometimes negative. Cross-session rank correlation of a feature→utility model is ≈0.04, and training on the 13296 family and testing on 11895 gives ≈−0.30. Treat this as one session plus a family of three, not four exchangeable domains.
	- Orientation labels are a 2D-class proxy, available for EMPIAR-11895 only. Class assignment conflates viewing direction with conformation, so coverage conclusions are bounds, not measurements. Real Euler angles from a consensus 3-D refinement would replace it.

	## Provenance and licence

	Derived from public EMPIAR depositions — [EMPIAR-11895](https://www.ebi.ac.uk/empiar/EMPIAR-11895/) (apoferritin; ships a complete RELION project) and [EMPIAR-13296](https://www.ebi.ac.uk/empiar/EMPIAR-13296/) (70S ribosome, three strains). Cite the original depositions in any work using this data. Derived tables released CC-BY-4.0. No raw movies or micrographs are included — this is metadata and labels only.

	## Building a compatible dataset

	If you are producing new EPU / cryoSPARC Live data for these agents, run the validator against your index before handing it over:

	```bash
	python -m cryoem_au_data.validate <your_index_dir>
	```

	It checks UID grammar, join integrity, the candidate-vs-acquired ratio, the status vocabulary, timestamp coverage, and whether the utility constants still have dynamic range on your data. Every check corresponds to a failure mode that breaks training silently rather than loudly.