Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks

We used twelve different datasets from Larson et al (2019) in our study. We kept the same folder identifiers used in their original data, for fast cross-reference. The filenames for each processed sample follow the structure `<NETWORK>-<Larson's sample folder>.zip`, where `<NETWORK>` can be `tiramisu`, `tiramisu_3d`, `unet`, `unet_3d`. For example, results for the sample 232p3, wet, obtained with the 2D U-net network are given in the file `unet-rec20160318_191511_232p3_2cm_cont__4097im_1500ms_ML17keV_6.h5.zip`.

The file `coefficients.zip` contains: 1. the training coefficients for each network, where filenames follow the structure `larson_<NETWORK>.hdf5`; 2. accuracy, loss, validation accuracy and validation loss we obtained during our training process; 3. filenames follow the structure `larson_<NETWORK>.hdf5-<MEASURE>.csv`, where `<MEASURE>` can be `accuracy`, `loss`, `val_accuracy`, `val_loss`, for accuracy, loss, validation accuracy and validation loss, respectively; 4. output of the training and prediction steps in our study, where filenames follow the structure `output.train_<NETWORK>.txt` and `output.predict_<NETWORK>.txt` for the training and prediction processes, respectively.

Funding provided by: Gordon and Betty Moore Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000936
Award Number: GBMF3834

Funding provided by: Alfred P. Sloan Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000879
Award Number: 2013-10-27