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# NFDI4BioImage Training Materials
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This Jupyter Book contains 746 links to training materials in the context Research Data Management for Bioimaging / Microscopy Data and Bio-image Analysis and has been updated 2025-07-09.
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This Jupyter Book contains 747 links to training materials in the context Research Data Management for Bioimaging / Microscopy Data and Bio-image Analysis and has been updated 2025-07-09.
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This is a **preliminary** / **experimental** search index allowing us to explore how such a database could look like. We follow the principle [_release early, release often_](https://en.wikipedia.org/wiki/Release_early,_release_often) and aim at having a functional prototype of the search index at any time. We will use this resource to
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* explore what kind of meta-data we need to collect for training materials
## Expansion and fluctuations-enhanced microscopy for nanoscale molecular profiling of cells and tissues - Data processing manual
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Dominik Kylies, Heil, Hannah S., Vesga, Arturo G., Del Rosario, Mario, Maria Schwerk, Malte Kuehl, Wong, Milagros N., Victor Puelles, Ricardo Henriques
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Published 2023/2024
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Licensed CC-BY-4.0
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Here we provide test datasets and a training manual for the parameter optimization with eSRRF.
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The training manual will guide users through an eSRRF paramenter optimization routine and quantiative image quality assesment with both, the ImagJ-Plugin NanoJ-eSRRF (Chapter 1) and the python implementation NanoPyx-eSRRF (Chapter 2). By showcasing the optimization routine on three differnt test dataset (Chapter 3), providing intermediate results and expected outcome, the users can eaisily learn how to find the optimal processing parameters for eSRRF processing.
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Three samples are provided to showcase the eSRRF reconstruction process:
DNA-PAINT microscopy measurement of immunolabeled microtubules in fixed COS-7 cells, showing 0.121 localizations per frame and µm^2 (data published in Laine and Heil et al.)
3. Single emitters simulation: Set03_simulation_groundTruth_2p5Sigma - Fluorescence stack_Avg5.tif
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Simulated individual molecules emitting placed on concentric rings with radii increasing by 220 nm steps. On each ring the molecules are separated by 57.5, 115, 173, 230, 288 and 345 nm, respectively (data published in Laine and Heil et al.)
4. Test dataset for drift/vibration correction: Set04_ExSRRF_eSRRF_vibration_correction_practice_dataset.tif
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EsM of human kidney biopsies stained with nephrin (data published in Kylies et al.)
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100x100 pixels, 200 frames, pixel size: 102 nm
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5. Test dataset for photobleaching: Set05_Photobleaching.tif
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ExM of 120 nm Nanorulers (data published in Kylies et al.)
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150x150 pixels, 75 frames, pixel size: 64 nm
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Jupyter-Notebook: ridge_detection.ipynb
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With this notebook qantitative image analyis of sturctures resolved with ExSRRF can be performed.
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Such as:
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calculation of the target structure density.
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identifying areas with high inter-ridge spacing by maping the distance to the nearest ridge based on Euclidean distance transform.
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measuring the spatial uniformity of the structure of interest by examining the distribution of the local densities and the distances to the nearest ridge.
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