--- pretty_name: afMLevel Training and Test Data task_categories: - image-to-image - image-segmentation tags: - atomic-force-microscopy - afm - hs-afm - microscopy - image-levelling - biophysics size_categories: - 1K **Note:** Do not load the TIFFs through a standard Python image pipeline (PIL, > imagefolder, PNG conversion) as these will quantise the 32-bit float height > data. Use `tifffile`. ## Dataset structure ```txt train/ Image/ raw height TIFFs train_image_0000.tiff … train_image_2000.tiff Levelled/ reference-levelled TIFFs train_levelled_0000.tiff … train_levelled_2000.tiff Mask/ binary foreground masks train_mask_0000.csv … train_mask_2000.csv test/ Image/ test_image_000.tiff … test_image_163.tiff Levelled/ test_levelled_000.tiff … test_levelled_163.tiff Mask/ test_mask_000.csv … test_mask_163.csv training_data_sources.csv per-image provenance for train split test_data_sources.csv per-image provenance for test split README.md This datacard ``` Files are paired by numeric index: `train_image_0042.tiff`, `train_levelled_0042.tiff`, and `train_mask_0042.csv` all correspond to the same acquisition frame. | Split | Images | | --- | --- | | Train | 2001 | | Test | 164 | | **Total** | **2165** | Images are 32-bit float, single-channel, and range from 128×128 to 1024×1024 pixels. Some images are non-square. Z-values are in nanometres. Images are released in their **pre-augmentation** form. The models were trained on augmented versions of these images (×3 for MLBackground, ×5 for MLMask); users wishing to replicate training should apply augmentation as described in the accompanying paper. ## Loading ```python import tifffile # Load a paired sample raw = tifffile.imread("train/Image/train_image_0042.tiff") # float32, nm levelled = tifffile.imread("train/Levelled/train_levelled_0042.tiff") # float32, nm # Iterate all training pairs import os for fname in sorted(os.listdir("train/Image")): if not fname.endswith(".tiff"): continue idx = fname.replace("train_image_", "").replace(".tiff", "") raw = tifffile.imread(f"train/Image/{fname}") lev = tifffile.imread(f"train/Levelled/train_levelled_{idx}.tiff") ``` ## Derived targets used during training The model ground-truth targets are **derived from the raw and levelled images at load time** in the training scripts, not stored separately as image files. Both are deterministic and fully reproducible from this data plus the training code in the afMLevel repository. **Background model target:** the normalised difference between raw and levelled, sharing the raw image's z-scale: ```python import numpy as np def xyplanefit(im, px=1, py=1): mc = np.mean(im, axis=0); x = np.arange(len(mc)) im = im - np.polyval(np.polyfit(x, mc, px), x)[np.newaxis, :] mr = np.mean(im, axis=1); y = np.arange(len(mr)) return im - np.polyval(np.polyfit(y, mr, py), y)[:, np.newaxis] def normalise(im): im = im - im.min(); return im / (im.max() + 1e-8) raw_fit = xyplanefit(raw) imnorm = normalise(raw_fit) levnorm = (levelled - raw_fit.min()) / (raw_fit.max() - raw_fit.min()) background_target = imnorm - levnorm # float32 ``` **Mask model target:** binary foreground mask via sigmoid → Otsu: ```python from scipy.ndimage import gaussian_filter from skimage.filters import threshold_otsu slev = 1 / (1 + np.exp(-(levelled - np.mean(levelled)))) slev8 = (slev * 255).astype(np.uint8) blur = gaussian_filter(slev8, sigma=1) mask_target = (blur > threshold_otsu(blur)).astype(np.uint8) # binary {0, 1} ``` Both pipelines resize to 256×256 (nearest-neighbour) and min-max normalise before training. See the [afMLevel repository](https://github.com/mayatek1/afMLevel) for the full training scripts. ## Provenance Per-image source information is in `training_data_sources.csv` and `test_data_sources.csv` (columns: `new_name`, `Source`, `Link`, `Data Reference`, `Paper Reference`). Images were sourced from internal acquisitions, collaborating research groups, and previously published datasets. Data taken from published sources was already available under CC-BY-4.0; contributing authors of unpublished data have confirmed their agreement to release under CC-BY-4.0. Filenames are numeric; sample identities are not disclosed for unpublished internal data. Where images originate from published datasets they carry full citations in the source tables. | Contributor | Train | Test | Total | |---|---|---|---| | University of Leeds AFM Facility | 1106 | 68 | 1174 | | George Heath — Scheuring Lab — New York | 465 | 46 | 511 | | Varun Gupta — Heath Lab, University of Leeds | 210 | 21 | 231 | | Pyne Lab — University of Sheffield | 114 | 1 | 115 | | Nanobiomaterials Group — Paderborn University | 58 | 4 | 62 | | Daniel E. Rollins — Heath Lab, University of Leeds | 48 | 9 | 57 | | Abimbola Olulana — Hobbs Lab, University of Sheffield | 0 | 8 | 8 | | BioMed AFM Summer School — Sheffield | 0 | 7 | 7 | | **Total** | **2001** | **164** | **2165** | ### Published source datasets — please cite when using these images **Pyne Lab — DNA supercoiling:** Pyne et al. (2021). figshare. https://doi.org/10.6084/m9.figshare.13116890.v1 Paper: Pyne, A.L.B. et al. *Nat Commun* 12, 1053 (2021). https://doi.org/10.1038/s41467-021-21243-y **Pyne Lab — kinetoplast DNA:** Whittle et al. (2024). University of Sheffield. https://doi.org/10.15131/shef.data.26869993.v1 Paper: Diggines, B. et al. *Phys. Chem. Chem. Phys.* 26, 25798–25807 (2024). https://doi.org/10.1039/D4CP01795A **Nanobiomaterials Group Paderborn — DNA origami:** Pothineni et al. (2024). Zenodo. https://doi.org/10.5281/zenodo.14275812 Paper: Pothineni, B.K. et al. *Discover Nano* 20, 77 (2025). https://doi.org/10.1186/s11671-025-04254-2 ## Important notes **Lateral scale is intentionally omitted.** Images are released without pixel size or scan size metadata. The models are scale-agnostic by design (inputs are resized to 256×256 and min-max normalised). Images were exported via MATLAB and carry no embedded acquisition metadata. **Images are not independent.** Where images originate from HS-AFM movies, they were capped at a maximum of 20 frames per movie to reduce over-representation of individual acquisitions. Multiple frames from the same movie may still appear in the dataset. **Train/test split.** The split is defined by directory membership and corresponds exactly to the split used to produce the results in the accompanying paper. ## Licence This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). All contributing groups have confirmed their agreement to this licence. Data from published third-party datasets (Pyne Lab, Nanobiomaterials Group Paderborn) are also available under CC-BY-4.0 via their original repositories (see links above). The afMLevel code is BSD-3-Clause, see the [code repository](https://github.com/mayatek1/afMLevel). ## Citation ## Links - Code and models: - Paper: