cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7082061
title: Continuous millisecond conformational cycle of a DEAH box helicase reveals control of domain motions by atomic-scale transitions

description: Data sets for the manuscript with the title "Continuous millisecond conformational cycle of a DEAH box helicase reveals control of domain motions by atomic-scale transitions". For further information read README Code for the MSM construction in the jupyter notebook file. Trajectories of opening and closing in the Trajectories.zip<br>
DOI: 10.5255/ukda-sn-855318
title: Qualitative Interviews with Drug Service Users and Addiction Therapists, 2017-2022

description: This dataset includes anonymised transcripts of in-depth interviews seven individual interviews with drug service users about their experience with multiple relapses into illicit substance misuse and seven individual interviews with addiction therapists about their experience with supporting service users through multiple relapses into illicit substance misuse. The interviews were collected in order to access people's lived experience perspectives of relapse into substance misuse, with semi-structured questions focusing on the felt experience of relapse as well as the perceptions of recovery thereafter.
DOI: 10.5281/zenodo.7600381
title: Data used in Knowledge Transfer Research for Drone Navigation

description: Data used in Knowledge Transfer Research for Drone Navigation.
DOI: 10.5281/zenodo.7600380
title: Data used in Knowledge Transfer Research for Drone Navigation

description: Data used in Knowledge Transfer Research for Drone Navigation.
DOI: 10.6084/m9.figshare.21997772
title: Questionnaire and Validation of Indonesian version PISQ-12 .docx

description: Questionnaire and Validation of Indonesian version PISQ-12
DOI: 10.6084/m9.figshare.21997772.v1
title: Questionnaire and Validation of Indonesian version PISQ-12 .docx

description: Questionnaire and Validation of Indonesian version PISQ-12
DOI: 10.6084/m9.figshare.21997772.v2
title: Questionnaire and Validation of Indonesian version PISQ-12 .docx

description: Questionnaire and Validation of Indonesian version PISQ-12
DOI: 10.5281/zenodo.7600379
title: verilog files

description: verilog code of the program
DOI: 10.5281/zenodo.7600378
title: verilog files

description: verilog code of the program
DOI: 10.5281/zenodo.7599874
title: raw data for doi: 10.7150/thno.80289.

description: uncropped western for doi: 10.7150/thno.80289.
DOI: 10.5281/zenodo.7599873
title: raw data for doi: 10.7150/thno.80289.

description: uncropped western for doi: 10.7150/thno.80289.
DOI: 10.5281/zenodo.7576898
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on the [DeepGlobe dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset) Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset Image size used by model: 512 x 512 x 3 pixels <em>classes:</em><br> 1. urban<br> 2. agricultural<br> 3. rangeland<br> 4. forest<br> 5. water<br> 6. bare<br> 7. unknown <em>File descriptions</em> 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 <em>References</em><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 **Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops</em> (pp. 172-181).
DOI: 10.5281/zenodo.7576897
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on the [DeepGlobe dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset) Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset Image size used by model: 512 x 512 x 3 pixels <em>classes:</em><br> 1. urban<br> 2. agricultural<br> 3. rangeland<br> 4. forest<br> 5. water<br> 6. bare<br> 7. unknown <em>File descriptions</em> 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 <em>References</em><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 **Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops</em> (pp. 172-181).
DOI: 10.5281/zenodo.7576894
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for OpenEarthMap/9-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for OpenEarthMap/9-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on the [OpenEarthMap dataset](https://open-earth-map.org/) Models have been created using Segmentation Gym* using the following dataset**: https://zenodo.org/record/7223446#.Y9gtWHbMIuV Image size used by model: 512 x 512 x 3 pixels classes:<br> 1. bareland<br> 2. rangeland<br> 3. development<br> 4. road<br> 5. tree<br> 6. water<br> 7. agricultural<br> 8. building<br> 9. nodata 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 **Xia, Yokoya, Adriano, & Broni-Bediako. (2022). OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7223446
DOI: 10.5281/zenodo.7576893
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for OpenEarthMap/9-class segmentation of RGB 512x512 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for OpenEarthMap/9-class segmentation of RGB 512x512 high-res. images</strong></em> These Residual-UNet model data are based on the [OpenEarthMap dataset](https://open-earth-map.org/) Models have been created using Segmentation Gym* using the following dataset**: https://zenodo.org/record/7223446#.Y9gtWHbMIuV Image size used by model: 512 x 512 x 3 pixels classes:<br> 1. bareland<br> 2. rangeland<br> 3. development<br> 4. road<br> 5. tree<br> 6. water<br> 7. agricultural<br> 8. building<br> 9. nodata 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 **Xia, Yokoya, Adriano, & Broni-Bediako. (2022). OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7223446
DOI: 10.5281/zenodo.7600403
title: The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz

description: This dataset is in support of publication:<br> <em>"The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz", </em><br> D. C. Price, J. E. Enriquez et. al.<br> The Astronomical Journal, 159:86 (16pp), 2020<br> https://doi.org/10.3847/1538-3881/ab65f1 This dataset (<em>sband2019.tar.gz</em>) consists of '.dat' file outputs from the turboSETI narrowband dedoppler search code. The corresponding Filterbank files used as inputs (i.e. narrowband dynamic spectra) are available at http://seti.berkeley.edu/opendata. Further information about Breakthrough Listen data formats may be found in Lebofsky et. al. (2019). The file <em>static.tar.gz</em> contains the database of events (HDF5 files) and pregenerated images from the event_viewer used to manually inspect candidates.
DOI: 10.17876/plate/dr.3/plates/1_21624
title: Digital object identifier (DOI) for the plate 21624 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21625
title: Digital object identifier (DOI) for the plate 21625 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21626
title: Digital object identifier (DOI) for the plate 21626 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21627
title: Digital object identifier (DOI) for the plate 21627 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21628
title: Digital object identifier (DOI) for the plate 21628 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21629
title: Digital object identifier (DOI) for the plate 21629 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21630
title: Digital object identifier (DOI) for the plate 21630 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21631
title: Digital object identifier (DOI) for the plate 21631 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21632
title: Digital object identifier (DOI) for the plate 21632 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21633
title: Digital object identifier (DOI) for the plate 21633 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21634
title: Digital object identifier (DOI) for the plate 21634 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21635
title: Digital object identifier (DOI) for the plate 21635 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21636
title: Digital object identifier (DOI) for the plate 21636 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21637
title: Digital object identifier (DOI) for the plate 21637 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21638
title: Digital object identifier (DOI) for the plate 21638 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21639
title: Digital object identifier (DOI) for the plate 21639 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21640
title: Digital object identifier (DOI) for the plate 21640 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21641
title: Digital object identifier (DOI) for the plate 21641 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21642
title: Digital object identifier (DOI) for the plate 21642 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21643
title: Digital object identifier (DOI) for the plate 21643 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21644
title: Digital object identifier (DOI) for the plate 21644 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21645
title: Digital object identifier (DOI) for the plate 21645 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21646
title: Digital object identifier (DOI) for the plate 21646 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21647
title: Digital object identifier (DOI) for the plate 21647 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21648
title: Digital object identifier (DOI) for the plate 21648 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21649
title: Digital object identifier (DOI) for the plate 21649 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21650
title: Digital object identifier (DOI) for the plate 21650 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21651
title: Digital object identifier (DOI) for the plate 21651 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21652
title: Digital object identifier (DOI) for the plate 21652 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21653
title: Digital object identifier (DOI) for the plate 21653 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21654
title: Digital object identifier (DOI) for the plate 21654 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21655
title: Digital object identifier (DOI) for the plate 21655 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21656
title: Digital object identifier (DOI) for the plate 21656 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21657
title: Digital object identifier (DOI) for the plate 21657 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21658
title: Digital object identifier (DOI) for the plate 21658 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21659
title: Digital object identifier (DOI) for the plate 21659 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21660
title: Digital object identifier (DOI) for the plate 21660 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21661
title: Digital object identifier (DOI) for the plate 21661 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21662
title: Digital object identifier (DOI) for the plate 21662 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21663
title: Digital object identifier (DOI) for the plate 21663 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21664
title: Digital object identifier (DOI) for the plate 21664 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21665
title: Digital object identifier (DOI) for the plate 21665 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21666
title: Digital object identifier (DOI) for the plate 21666 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21667
title: Digital object identifier (DOI) for the plate 21667 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21668
title: Digital object identifier (DOI) for the plate 21668 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21669
title: Digital object identifier (DOI) for the plate 21669 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21670
title: Digital object identifier (DOI) for the plate 21670 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21671
title: Digital object identifier (DOI) for the plate 21671 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21672
title: Digital object identifier (DOI) for the plate 21672 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21673
title: Digital object identifier (DOI) for the plate 21673 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21674
title: Digital object identifier (DOI) for the plate 21674 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21675
title: Digital object identifier (DOI) for the plate 21675 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21676
title: Digital object identifier (DOI) for the plate 21676 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21677
title: Digital object identifier (DOI) for the plate 21677 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21678
title: Digital object identifier (DOI) for the plate 21678 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21679
title: Digital object identifier (DOI) for the plate 21679 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21680
title: Digital object identifier (DOI) for the plate 21680 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21681
title: Digital object identifier (DOI) for the plate 21681 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21682
title: Digital object identifier (DOI) for the plate 21682 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21683
title: Digital object identifier (DOI) for the plate 21683 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21684
title: Digital object identifier (DOI) for the plate 21684 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21685
title: Digital object identifier (DOI) for the plate 21685 in archive 1 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/1_21686
title: Digital object identifier (DOI) for the plate 21686 in archive 1 from the APPLAUSE DR3