---
license: cc-by-4.0
tags:
  - electron-microscopy
  - 4D-STEM
  - materials-science
  - quantem
pretty_name: quantem-data
---

# quantem-data

Reference electron-microscopy datasets for browsing and learning. Open in your browser via [quantem.widget](https://github.com/bobleesj/quantem.widget) — **no quantem.live install needed**.

Two buckets:

- **`4dstem/`** — 4D-STEM acquisitions. `_npy_bin*` variants are pre-binned NumPy files for fast workshop / Colab demos; the originals are full Arina h5 bundles.
- **`haadf/`** — HAADF survey images. `_npy` variants are pre-cooked NumPy + a `meta.json` sidecar carrying sampling + optics; the originals are full Velox EMD files.

A ready-to-run notebook sits at `notebooks/show4dstem_colab.ipynb` in this repo.

## One-click workshop notebook (Google Colab)

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/bobleesj/54864ce5b2a6f0a4fd5ae1e5d5719b45/show4dstem_colab.ipynb)

It does the full pipeline in 7 cells: install `quantem.widget` (TestPyPI rc) + `quantem` (dev fork branch), download the pre-binned NumPy bundle from this dataset, wrap it as `Dataset4dstem.from_tensor`, render with `Show4DSTEM` in your browser via WebGPU. No CUDA on Colab. No quantem.live.

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/bobleesj/a05a90185c6cddbb331342cae6d7e9c1/berk_workshop_v1.ipynb)

**Workshop v1** — real gold 4D-STEM: browse + bright field + dark field + probe + DPC, all on the Colab T4. No `quantem.live`, no local install. The notebook lives at `notebooks/berk_workshop_v1.ipynb` in this repo and on the `berk-workshop` branch of `bobleesj/quantem`.

## Workshop quick start — Show4DSTEM (any Jupyter)

```python
!pip install -q --pre --extra-index-url https://test.pypi.org/simple/ quantem.widget huggingface_hub
!pip install -q git+https://github.com/electronmicroscopy/quantem.git@dev

import os, json, numpy as np, torch
from huggingface_hub import snapshot_download
from quantem.core.datastructures import Dataset4dstem
from quantem.widget import Show4DSTEM

folder = snapshot_download("bobleesj/quantem-data", repo_type="dataset",
                          allow_patterns=["4dstem/gold_512_npy_bin8/*"])
asset = os.path.join(folder, "4dstem", "gold_512_npy_bin8")
data = np.load(os.path.join(asset, "data.npy"))
meta = json.load(open(os.path.join(asset, "meta.json")))

dset = Dataset4dstem.from_tensor(torch.from_numpy(data),
                                 sampling=meta["sampling"], units=meta["units"])
Show4DSTEM(dset)
```

## Workshop quick start — Show2D for HAADF

```python
import os, json, numpy as np, torch
from huggingface_hub import snapshot_download
from quantem.widget import Show2D

folder = snapshot_download("bobleesj/quantem-data", repo_type="dataset",
                          allow_patterns=["haadf/gold_haadf_npy/*"])
asset = os.path.join(folder, "haadf", "gold_haadf_npy")
img  = np.load(os.path.join(asset, "data.npy"))
meta = json.load(open(os.path.join(asset, "meta.json")))

Show2D(torch.from_numpy(img), sampling=meta["sampling"], units=meta["units"])
```

## Acquisition parameters

The 20260423 drift session's optics are **confirmed via the session's own HAADF EMD** (`AccelerationVoltage`, `BeamConvergence`, `CameraLength`). 4D-STEM `scan_sampling` is an **operator pattern from a sibling SSB session** — the drift acquisition itself was never per-file calibrated.

| dataset | voltage | probe | CL | scan | scan sampling | det pitch | mag |
|---|---|---|---|---|---|---|---|
| `haadf/gold_haadf_npy` | 300 kV ✓ | 30 mrad ✓ | 91 mm ✓ | 4096² image | 0.0186 nm/px | n/a | FOV 76.2 nm |
| `4dstem/gold_512_npy_bin8` | 300 kV ✓ | 30 mrad ✓ | 91 mm ✓ | 512² | 0.5 Å (op) | 3.68 mrad/px | unknown |
| `4dstem/gold_512_npy_bin4` | 300 kV ✓ | 30 mrad ✓ | 91 mm ✓ | 512² | 0.5 Å (op) | 1.84 mrad/px | unknown |
| `4dstem/gold_512` | 300 kV ✓ | 30 mrad ✓ | 91 mm ✓ | 512² | 0.5 Å (op) | 0.46 mrad/px | unknown |
| `4dstem/gold_30mrad1.3mx04`…`09` | 300 kV | 30 mrad | 91 mm | smaller | (session yaml) | 0.46 mrad/px | 1.3 Mx |
| `haadf/gold_haadf.emd` | 300 kV ✓ | 30 mrad ✓ | 91 mm ✓ | 4096² image | 0.0186 nm/px | n/a | FOV 76.2 nm |

✓ = confirmed via EMD/yaml. `(op)` = operator pattern, not file-certified.

## Datasets at a glance

| name | kind | shape | dtype | size | use |
|---|---|---|---|---|---|
| `4dstem/gold_512_npy_bin8/` | NumPy bundle | (512, 512, 24, 24) | uint16 | ~302 MB | workshop / Colab demo |
| `4dstem/gold_512_npy_bin4/` | NumPy bundle | (512, 512, 48, 48) | uint16 | ~1.2 GB | sharper workshop version |
| `4dstem/gold_512/` | Arina h5 | (512, 512, 192, 192) | uint16 | ~5 GB | power user |
| `4dstem/gold_30mrad1.3mx04` … `09` | Arina h5 | varies | uint16 | ~5 GB each | series demo |
| `haadf/gold_haadf_npy/` | NumPy bundle | (4096, 4096) | uint16 | ~34 MB | workshop / Colab Show2D |
| `haadf/gold_haadf.emd` | Velox EMD | (4096, 4096) | uint16 | a few MB | full optics carrier |

Each `_npy*` bundle ships a `meta.json` next to `data.npy`: shape, dtype, sampling, units, voltage / probe / CL (with provenance flags) when known.

## Power-user path (full data, GPU decompression)

Got an NVIDIA GPU and want the full Arina h5 / Velox EMD path? Install [`quantem.live`](https://github.com/bobleesj/quantem.live):

```python
from quantem.live import io
from quantem.widget import Show4DSTEM, Show2D
import torch

folder = io.download("gold_512")
result = io.load(io.discover_masters(folder)[0], det_bin=2)
Show4DSTEM(torch.from_dlpack(result.data))

ds = io.read_image(io.download("gold_haadf"))
Show2D(ds)
```

## Memory (VRAM) for the full h5

| `det_bin` | detector | loaded | peak VRAM | fits 16 GB? |
|---|---|---|---|---|
| 1 | 192×192 | 18 GB | ~25 GB | no |
| **2** | 96×96 | 4.5 GB | **~6.9 GB** | **yes** |
| 4 | 48×48 | 1.1 GB | ~2.2 GB | yes |
| 8 | 24×24 | 0.3 GB | ~0.5 GB | yes |

## Licence

CC-BY-4.0. Cite quantem.live / quantem.widget if you use these in a publication.