cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7677467
title: Stowaways Supplement S6. Relative Size Index (RSI) Comparative Dataset & R Code for Mediterranean Ship Rats

description: Relative size index (RSI) comparative dataset and R code used to compare the RSI <em>sensu </em>Lomolino (1985) of the ship rats from the Ma'agan Mikhael B (MMB) shipwreck with other Mediterranean island populations. Worksheet 1: RSI_Dataframe - data frame used in R to produce RSI bar chart. Worksheet 2: RSI Data - Mainland and island samples' mean condylobasal lengths (CBL) and further calculations used to derive the RSI values per island population. Worksheet 3: CBL values for each Provençal and Corso-Sardinian Islet that was combined in the RSI analysis, taken directly from Granjon & Cheylan (1990). Data sources: MMB - Southern Levant (this study) Galite & Zembra Islands - Tunisia (Ibrahim et al., 2017) Congreso Island - Morocco (Ventura & Lopez Fuster, 2000) São Miguel & Terceira Island (Azores) - Portugal (Ramalhinho et al., 1996) Corsica & Provençal Islets - Provence (Granjon & Cheylan, 1990) Sardinia & Corso-Sardinian Islets - Tuscany (Granjon & Cheylan, 1990)
description: {"references": ["Granjon, L., & Cheylan, G. (1990b). Diff\u00e9renciation biom\u00e9trique des rats noirs (Rattus rattus) des \u00eeles ouest-m\u00e9diterran\u00e9ennes. Mammalia, 54(2), 213\u2013231.", "Ibrahim, A. ben, Salem, I. ben, Chetoui, M., & Nouira, S. (2017). Morpho-metric analysis of the insular and mainland Rattus in Tunisia. Biologia, 72(8), 927\u2013934. https://doi.org/10.1515/biolog-2017-0093", "Lomolino, M. v. (1985). Body Size of Mammals on Islands: The Island Rule Reexamined. The American Naturalist, 125(2), 310\u2013316. https://www.jstor.org/stable/2461638", "Ramalhinho, M. G., Mathias, M. L., Santos-Reis, M., Libois, R., Fons, R., Petrucci-Fonseca, F., Oom, M. M., & Collares-Pereira, M. (1996). First Approach on the Skull Morphology of the Black Rat (Rattus rattus) from the Terceira and S\u00e3o Miguel Islands (Azores Archipelago). Vie Milieu, 46(3/4), 245\u2013251.", "Ventura, J., & L\u00f3pez Fuster, M. (2000). Morphometric analysis of the black rat, Rattus rattus, from Congreso Island (Chafarinas Archipelago, Spain). Orsis, 15, 91\u2013102."]}
DOI: 10.5281/zenodo.7641708
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images</strong></em> These Segformer model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> other<br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.7641707
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images</strong></em> These Segformer model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> other<br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.6468940
title: Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe

description: Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.
DOI: 10.5281/zenodo.6468939
title: Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe

description: Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.
DOI: 10.5281/zenodo.6638505
title: Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe

description: Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.
DOI: 10.5281/zenodo.7677478
title: Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe

description: Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.
DOI: 10.5281/zenodo.7641724
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images</strong></em> These Segformer model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> marsh_vegetation<br> terrestrial_vegetation<br> agricultural<br> development File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.5281/zenodo.7641723
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for CoastTrain/8-class segmentation of RGB 768x768 NAIP images</strong></em> These Segformer model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2 Image size used by model: 768 x 768 x 3 pixels classes: water<br> whitewater<br> sediment<br> other_bare_natural_terrain<br> marsh_vegetation<br> terrestrial_vegetation<br> agricultural<br> development File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <strong>References</strong><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Buscombe, D., Wernette, P., Fitzpatrick, S. et al. A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. Sci Data 10, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2
DOI: 10.6084/m9.figshare.22151687
title: Temporarily anonymous

description: Temporarily anonymous
DOI: 10.6084/m9.figshare.22151687.v1
title: Parental material determines the biomass and diversity of soil microbial communities along an elevational gradient on a subtropical karst mountain

description: This data frame includes data used in the paper entitled Parental material determines the biomass and diversity of soil microbial communities along an elevational gradient on a subtropical karst mountain
DOI: 10.5281/zenodo.7677506
title: Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Segformer model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes:<br> water<br> tree_canopy_forest<br> low_vegetation_field<br> barren land<br> impervious_other<br> impervious_road<br> no_data <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)
DOI: 10.5281/zenodo.7677505
title: Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Segformer model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes:<br> water<br> tree_canopy_forest<br> low_vegetation_field<br> barren land<br> impervious_other<br> impervious_road<br> no_data <br> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)
DOI: 10.5281/zenodo.7677510
title: Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm

description: Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm
DOI: 10.5281/zenodo.7677509
title: Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm

description: Table 8. Calculation of regression equation of hydroxyproline standard solution At λ = 559 nm