Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA. The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively. The structure of the data is as follows: 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks. 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels. Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type. 1-Panels-Ortho     |----Train     |----Test     -----Validation 1-Panels-Mask     |----Train     |----Test     -----Validation 2-NoPanels-Ortho     |----Train     |----Test     -----Validation

Extracted ASCADv2 with labels

Extracted dataset from the original ASCADv2 database (https://www.data.gouv.fr/fr/datasets/ascadv2/).   The dataset is separated in three splits: - training with 200k traces - validation with 50k traces - attack with 50k traces   For each trace, the extracted samples are  :  - samples from the masked inputs of the sboxes - samples from the input mask r_in - samples from the multiplicative mask r_m - labels for most intermediates

MORFITT : A multi-label corpus of French scientific articles in the biomedical domain

This article presents MORFITT, the first multi-label corpus in French annotated in specialties in the medical field. MORFITT is composed of 3~624 abstracts of scientific articles from PubMed, annotated in 12 specialties for a total of 5,116 annotations. We detail the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT. These preliminary results demonstrate the difficulty of the task, with a weighted average F1-score of 61.78%.

Artificial Blobs and Labels image

A groovy script to use in Fiji to generate artificial images and labels, with example images.

Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions

Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca. We use the communes administrative division defined by DANE (Departamento Administrativo Nacional de Estadística) under "municipios" in the MGN2021 at  <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a> images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: ESA WorldCover 10m V100         labels mapped to the interval [1,11] according to the following map         { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }         pixel value zero is reserved for invalid data.         see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a>                  see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a>         <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg We use the communes administrative division which is standardized across Europe by EUROSTAT at: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp. images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: ESA WorldCover 10m V100         labels mapped to the interval [1,11] according to the following map         { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }         pixel value zero is reserved for invalid data.         see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a>                  see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions

Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca. We use the communes administrative division defined by DANE (Departamento Administrativo Nacional de Estadística) under "municipios" in the MGN2021 at  <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a> images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds during the observation period according to QA60 band following the example         given in GEE dataset info page, and took the median of the resulting pixels         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: Global Human Settlement Layers, Population Grid 2015         labels range from 0 to 31, with the following meaning:            label value     original value in GEE dataset            0               0            1               1-10            2               11-20            3               21-30            ...            31              >=291          see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg We use the communes adminitrative division which is standardized across Europe by EUROSTAT at: <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a> This is roughly equivalent to the notion municipalities in most countries. From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp. images: Sentinel2 RGB from 2020-01-01 to 2020-31-12         filtered out pixels with clouds acoording to QA60 band following the example         given in GEE dataset info page at:         see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a> labels: Global Human Settlement Layers, Population Grid 2015         labels range from 0 to 31, with the following meaning:            label value     original value in GEE dataset            0               0            1               1-10            2               11-20            3               21-30            ...            31              >=291          see <a href="https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1">https://developers.google.com/earth-engine/datasets/catalog/JRC_GHSL_P2016_POP_GPW_GLOBE_V1</a>         see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/humanpop2015.py</a>   <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>

opencc-by-4.0Dec 2022View details →

Data for ms. Dairy cattle welfare – the relative effect of legislation, industry standards and labelled niche production in five European countries

Repository R 1: Scores on dimension values and weight on dimension from 38 international experts.    Repository R2: Country Benchmark scores from 38 international experts.

opencc-by-4.0Dec 2015View details →

Figure 2 in Lost and found in Ireland; how a data label resulted in a postal delivery to Metriocnemus (Inermipupa) carmencitabertarum (Orthocladiinae)

Figure 2. Back of envelope from the Northern Ireland Royal Mail Centre (N.I.M.C.) also showing the Irish "An Post" seal.

Fig. 5. Data labels. A. Xylocopamoerens Perty, 1833 in The primary types of some species of Centris bees described by European entomologists in the 18 and 20 centuries (Hymenoptera: Apidae)

Fig. 5. Data labels. A. Xylocopamoerens Perty, 1833 (lectotype ♀). B. Xylocopaxanthocnemis Perty, 1833 (lectotype ♂).

Fig. 1. Data labels. A. Centris debilis Dominique, 1898 in The primary types of some species of Centris bees described by European entomologists in the 18 and 20 centuries (Hymenoptera: Apidae)

Fig. 1. Data labels. A. Centris debilis Dominique, 1898 (lectotype ♂). B. Centris dominiquella Dominique, 1898 (lectotype ♀).

Fig 2. Data labels. A. Centris zonalis Dominique, 1898 in The primary types of some species of Centris bees described by European entomologists in the 18 and 20 centuries (Hymenoptera: Apidae)

Fig 2. Data labels. A. Centris zonalis Dominique, 1898 (lectotype ♀). B. Centris lyngbyei Jensen- Haarup, 1908 (lectotype ♂).

Fig. 4. Data labels. A in The primary types of some species of Centris bees described by European entomologists in the 18 and 20 centuries (Hymenoptera: Apidae)

Fig. 4. Data labels. A. Centrisrhodophthalma Pérez, 1911 (lectotype ♀). B. Centristransversa Pérez, 1905 (lectotype ♀).

opencc-by-4.0Jul 2023View details →

EPIC Aff: A multi-label segmentation visual affordances dataset.

This is the EPIC-Aff dataset introduced on the ICCV 2023 paper "Multi-label affordance mapping from egocentric vision".

opencc-by-4.0Jan 2022View details →

Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Zoo

# Cape Hatteras Landsat8 RGB Images and Labels for Image Segmentation using the program, Segmentation Gym ## Overview * Test datasets and files for testing the [segmentation gym](https://github.com/Doodleverse/segmentation_gym) program for image segmentation * Data set made by Daniel Buscombe, Marda Science LLC. This is version 5.0 * Dataset consists of a time-series of Landsat-8 images of Cape Hatteras National Seashore, courtesy of the U.S. Geological Survey. * Imagery spans the period February 2015 to September 2021. * Labels were created by Daniel Buscombe, Marda Science, using the labeling program [Doodler](https://github.com/Doodleverse/dash_doodler). Download this file and unzip to somewhere on your machine (although *not* inside the `segmentation_gym` folder), then see the relevant page on the [segmentation gym wiki](https://github.com/Doodleverse/segmentation_gym/wiki) for further explanation. This dataset and associated models were made by Dr Daniel Buscombe, Marda Science LLC, for the purposes of demonstrating the functionality of Segmentation Gym. The labels were created using [Doodler](https://github.com/Doodleverse/dash_doodler/). Previous versions: 1.0. https://zenodo.org/record/5895128#.Y1G5s3bMIuU original release, Oct 2021, conforming to Segmentation Gym functionality on Oct 2021 2.0 https://zenodo.org/record/7036025#.Y1G57XbMIuU, Jan 23 2022, conforming to Segmentation Gym functionality on Jan 23 2022 This is version 5.0, created 7/20/23, and has been tested with Segmentation Gym using doodleverse-utils 0.0.33 https://pypi.org/project/doodleverse-utils/0.0.33/   ## file structure ```{sh} /Users/Someone/my_segmentation_zoo_datasets                     │   ├── config                     │   |    └── *.json                     │   ├── capehatteras_data                     |   |   ├── fromDoodler                     |   |   |     ├──images                     │   |   |     └──labels                     |   |   ├──npzForModel                     │   |   └──toPredict                     │   └── modelOut                     │       └── *.png                     │   └── weights                     │       └── *.h5 ``` ## config There are 4 config files: 1. `/config/hatteras_l8_resunet.json` 2. `/config/hatteras_l8_vanilla_unet.json` 3. `/config/hatteras_l8_resunet_model2.json` 4. `/config/hatteras_l8_segformer.json`     The first two are for res-unet and unet models respectively. The third one differs from the first only with specification of kernel size. It is provided as an example of how to conduct model training experiments, modifying one hyperparameter at a time in the effort to create an optimal model. The last one is based on the new Segformer model architecture. They all contain the same essential information and differ as indicated below ``` {   "TARGET_SIZE": [768,768], # the size of the imagery you wish the model to train on. This may not be the original size   "MODEL": "resunet", # model name. Otherwise, "unet" or "segformer"   "NCLASSES": 4, # number of classes   "KERNEL":9, # horizontal size of convolution kernel in pixels   "STRIDE":2, # stride in convolution kernel   "BATCH_SIZE": 7, # number of images/labels per batch   "FILTERS":6, # number of filters   "N_DATA_BANDS": 3, # number of image bands   "DROPOUT":0.1, # amount of dropout   "DROPOUT_CHANGE_PER_LAYER":0.0, # change in dropout per layer   "DROPOUT_TYPE":"standard", # type of dropout. Otherwise "spatial"   "USE_DROPOUT_ON_UPSAMPLING":false, # if true, dropout is used on upsampling as well as downsampling   "DO_TRAIN": false, # if false, the model will not train, but you will select this config file, data directory, and the program will load the model weights and test the model on the validation subset   if true, the model will train from scratch (warning! this will overwrite the existing weights file in h5 format)   "LOSS":"dice", # model training loss function, otherwise "cat" for categorical cross-entropy   "PATIENCE": 10, # number of epochs of no model improvement before training is aborted   "MAX_EPOCHS": 100, # maximum number of training epochs   "VALIDATION_SPLIT": 0.6, #proportion to use for validation   "RAMPUP_EPOCHS": 20, # [LR-scheduler] rampup to maximim   "SUSTAIN_EPOCHS": 0.0, # [LR-scheduler] sustain at maximum   "EXP_DECAY": 0.9, # [LR-scheduler] decay rate   "START_LR":  1e-7, # [LR-scheduler] start lr   "MIN_LR": 1e-7, # [LR-scheduler] min lr   "MAX_LR": 1e-4, # [LR-scheduler] max lr   "FILTER_VALUE": 0, #if >0, the size of a median filter to apply on outputs (not recommended unless you have noisy outputs)   "DOPLOT": true, #make plots   "ROOT_STRING": "hatteras_l8_aug_768", #data file (npz) prefix string   "USEMASK": false, # use the convention 'mask' in label image file names, instead of the preferred 'label'   "AUG_ROT": 5, # [augmentation] amount of rotation in degrees   "AUG_ZOOM": 0.05, # [augmentation] amount of zoom as a proportion   "AUG_WIDTHSHIFT": 0.05, # [augmentation] amount of random width shift as a proportion   "AUG_HEIGHTSHIFT": 0.05,# [augmentation] amount of random width shift as a proportion   "AUG_HFLIP": true, #  [augmentation] if true, randomly apply horizontal flips   "AUG_VFLIP": false, #  [augmentation] if true, randomly apply vertical flips   "AUG_LOOPS": 10, #[augmentation] number of portions to split the data into (recommended > 2 to save memory)   "AUG_COPIES": 5  #[augmentation] number iof augmented copies to make   "SET_GPU": "0" #which GPU to use. If multiple, list separated by a comma, e.g. '0,1,2'. If CPU is requested, use "-1"   "WRITE_MODELMETADATA": false, #if true, the prompts `seg_images_in_folder.py` to write detailed metadata for each sample file   "LOSS_WEIGHTS": false, #if true, apply per-class weights to loss function   "SET_PCI_BUS_ID": true, #if true, make keras aware of the PCI BUS ID (advanced or nonstandard GPU usage)   "TESTTIMEAUG": true, #if true, apply test-time augmentation when model in inference mode   "WRITE_MODELMETADATA": true,# if true, write model metadata per image when model in inference mode   "OTSU_THRESHOLD": true# if true, and NCLASSES=2 only, use per-image Otsu threshold rather than decision boundary of 0.5 on softmax scores } ``` ## capehatteras_data Folder containing all the model input data ```{sh}                     │   ├── capehatteras_data: folder containing all the model input data                     |   |   ├── fromDoodler: folder containing images and labels exported from Doodler using [this program](https://github.com/dbuscombe-usgs/dash_doodler/blob/main/utils/gen_images_and_labels_4_zoo.py)                     |   |   |     ├──images: jpg format files, one per label image                     │   |   |     └──labels: jpg format files, one per image                     |   |   ├──npz4gym npz format files for model training using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/train_model.py) that have been created following the workflow [documented here](https://github.com/dbuscombe-usgs/segmentation_zoo/wiki/Create-a-model-ready-dataset) using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/make_nd_dataset.py)                     │   |   └──toPredict: a folder of images to test model prediction using [this program](https://github.com/dbuscombe-usgs/segmentation_zoo/blob/main/seg_images_in_folder.py) ``` ## modelOut PNG format files containing example model outputs from the train ('_train_' in filename) and validation ('_val_' in filename) subsets as well as an image showing training loss and accuracy curves with `trainhist` in the filename. There are two sets of these files, those associated with the residual unet trained with dice loss contain `resunet` in their name, and those from the UNet are named with `vanilla_unet`. ## weights There are model weights files associated with each config files.

opencc-by-4.0Sep 2023View details →

MarrowDLD: a microfluidic method for label-free retrieval of fragile bone marrow cells

We introduce here a label-free cytometry microsystem, MarrowDLD, based on deterministic lateral displacement. MarrowDLD enables the isolation of fragile cells based on intrinsic size properties while preserving their viability and functionality. Bone marrow adipocytes, obtained from mouse and human stromal line differentiation, as well as megakaryocytes, from primary human CD34+ hematopoietic stem and progenitor cells, were used for validation.

opencc-by-4.0Oct 2023View details →

UVA laser scanning labelled las data over tropical moist forest classified as leaf or wood points

UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022. Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction. Predicted values using SOUL model (see ref below) for a single UAV-LS acquisition are provided as separate file.

opencc-by-4.0Jun 2023View details →

Fluorescence labelled infected mouse bone tissues

This dataset comprises fluorescence-labeled images of infected mouse bone tissues obtained through a multiphoton confocal laser scanning microscope (LSM 780 META, Carl Zeiss, Jena, Germany). These images are stored in the CZI native file format and are provided for publication purposes. To know more about the images please refer to this <a href="https://doi.org/10.3390/ijms24119762">article</a>. If you find these images valuable, kindly acknowledge this dataset and the associated article:  Mandal, S.; Tannert, A.; Ebert, C.; Guliev, R.R.; Ozegowski, Y.; Carvalho, L.;Wildemann, B.; Eiserloh, S.; Coldewey, S.M.; Löffler, B.; Bastiao Silva, L.; Hoerr, V.; Tuchscherr, L.; Neugebauer, U.: Insights into S. aureus-induced bone deformation in a mouse model of chronic osteomyelitis using fluorescence and Raman imaging. Int. J. Mol. Sci. 2023, 24, 9762. https://doi.org/10.3390/ijms24119762