cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6949508
title: Nuclear genotypes from Coryphaena hippurus

description: This dataset comprises genotypes from nuclear microsatellites of Coryphaena hippurus samples. We amplified 14 nuclear microsatellites; five of which were designed by Chapmann (pers. comm) and obtained from GenBank accession numbers: AY135025-AY132028, and AY189832. The rest nine loci were designed by Bayona-Vásquez et al. (2015). Microsatellites loci were amplified from 311 tissue samples from Coryphaena hippurus commonly know as mahi-mahi. Samples were obtained across nine localities within the Tropical Eastern Pacific: Bahía Magdalena (BM); Punta Lobos (PL); Cabo San Lucas (CSL); Guaymas (GU), Mazatlan (MZ); Chiapas (PM); Ecuador (EC); Perú (PE); and ocean sample (OC). Genotypes were recorded in three and two digits. Missing data coded as -9. Geographic coordinates of sample localities: Bahía Magdalena (BM): 24.579118, -111.998283<br> Punta lobos (PL): 23.413503, -110.234702<br> Cabo San Lucas (CSL) : 22.869653, -109.898667<br> Guaymas, Sonora (GU): 27.825287, -110.933325<br> Oceanic sample (OC): 11.886755, -122.880087<br> Mazatlán, Sinaloa (MZ): 23.192460, -106.471719<br> Puerto Madero, Chiapas (PM):14.664185, -92.482619<br> Ecuador (EC):-1.484832, -81.457839<br> Perú (PE): -12.184145, -77.250531
description: {"references": ["Ochoa-Zavala et al., 2022. Genetic divergence at species boundaries of the dolphinfish (Coryphaena hippurus) in the Tropical Eastern Pacific.10.7717/peerj.14389"]}
DOI: 10.5281/zenodo.6949509
title: Nuclear genotypes from Coryphaena hippurus

description: This dataset comprises genotypes from nuclear microsatellites of Coryphaena hippurus samples. We amplified 14 nuclear microsatellites; five of which were designed by Chapmann (pers. comm) and obtained from GenBank accession numbers: AY135025-AY132028, and AY189832. The rest nine loci were designed by Bayona-Vásquez et al. (2015). Microsatellites loci were amplified from 311 tissue samples from Coryphaena hippurus commonly know as mahi-mahi. Samples were obtained across nine localities within the Tropical Eastern Pacific: Bahía Magdalena (BM); Punta Lobos (PL); Cabo San Lucas (CSL); Guaymas (GU), Mazatlan (MZ); Chiapas (PM); Ecuador (EC); Perú (PE); and ocean sample (OC). Genotypes were recorded in three and two digits. Missing data coded as -9. Geographic coordinates of sample localities: Bahía Magdalena (BM): 24.579118, -111.998283<br> Punta lobos (PL): 23.413503, -110.234702<br> Cabo San Lucas (CSL) : 22.869653, -109.898667<br> Guaymas, Sonora (GU): 27.825287, -110.933325<br> Oceanic sample (OC): 11.886755, -122.880087<br> Mazatlán, Sinaloa (MZ): 23.192460, -106.471719<br> Puerto Madero, Chiapas (PM):14.664185, -92.482619<br> Ecuador (EC):-1.484832, -81.457839<br> Perú (PE): -12.184145, -77.250531
description: {"references": ["Ochoa-Zavala et al., 2022. Genetic divergence at species boundaries of the dolphinfish (Coryphaena hippurus) in the Tropical Eastern Pacific.10.7717/peerj.14389"]}
DOI: 10.26179/vn9p-bz20
title: Rare Earth Elements concentrations measured in detrital sediment samples collected during the IN2017-V01 voyage of the RV Investigator
DOI: 10.25919/nf8v-p205
title: Soil and Landscape Grid National Soil Attribute Maps - Soil Organic Carbon Fractions (3" resolution) - Release 1

description: This is Version 1 of the Soil Organic Carbon Fractions product of the Soil and Landscape Grid of Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This product contains six digital soil attribute maps for each of three depth intervals, 0-5cm, 5-15cm, 15-30cm These depths are consistent with the specifications of the GlobalSoilMap.net project (http://www.globalsoilmap.net/). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). These maps are generated using Digital Soil Mapping methods Attribute Definition: Soil Organic Carbon Fractions :- mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC) Units: Various; Period (temporal coverage; approximately): 1950-2022; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Format: Cloud Optimised GeoTIFF.
DOI: 10.34740/kaggle/dsv/4573807
title: Photovoltaic modules corrective maintenance
DOI: 10.57777/kgm2022000091
title: 지붕011(붉은색)
DOI: 10.22002/d1.20295
title: Dataset: The SSU Processome Component Utp25p is a Pseudohelicase

description: Extended figure 4: Dali search using the yeast and human AlphaFold Utp25 structures robustly identifies known DEAD-box helicases.
description: Related Publication:
DOI: 10.22002/d1.20183
title: Lemon Creek Glacier 2017 Seismic Full Resolution

description: Full-resolution seismic data from the July 2017 nodal deployment on Lemon Creek Glacier, Alaska
DOI: 10.22002/d1.20291
title: Data and code for figures of "Nano-electromechanical spatial light modulator enabled by asymmetric resonant dielectric metasurfaces" by Hyounghan Kwon et al

description: This upload contains the main dataset and codes represented in the figures of the following publication:

"Nano-electromechanical spatial light modulator enabled by asymmetric
resonant dielectric metasurfaces" by Hyounghan Kwon et al.
DOI: 10.22002/d1.20287
title: Indy Old Records

description: indy data before Venice change. Book 1 and Book 2 page 73
DOI: 10.22002/d1.933
title: aquaplanet simulations with seasonal cycle

description: This dataset includes the idealized aquaplanet simulations with different mixed layer depths (Aqua20m, Aqua10m, and Aqua0.2m) and the code and run scripts corresponding to the model simulations.
(The output data is averaged in pentad.)
DOI: 10.5281/zenodo.7352846
title: Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"

description: Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".
DOI: 10.5281/zenodo.7352845
title: Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"

description: Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".
DOI: 10.26179/qgd3-qc54
title: Rare Earth Elements concentrations measured in sediment leachates from samples collected during the IN2017-V01 voyage of the RV Investigator
DOI: 10.57777/kgm2022000092
title: 화성암(트라이아스기 엽리상조립질화강암(Trhygr))
DOI: 10.22002/d1.2103
title: Montage cryo-ET of the thin edge of a HeLa cell, dataset 3

description: Montage tomography dataset collected from the thin edge of a HeLa cell. The file nomenclature for the images is: CG_{tilt_angle}_{tile_number}.mrc. The accompanying text file defines both the order of data collection and the relative tile positions, with format: tilt_angle_0 \n x_0 y_0 z_0 \n .... \n x_n y_n z_n \n ... \n tilt_angle_m \n x_0 y_0 z_0 \n .... \n x_n y_n z_n \n where the subscript n indicates the nth tile collected at the corresponding tilt angle. The variables x_i and y_i correspond to positions in the plane of the detector, while z_i corresponds to the tile's predicted height in the microscope. For this dataset, 37 tiles were collected per tilt-angle. Data were acquired using a grouped dose-symmetric tilt scheme ranging from +/-60 degrees with 2 degree increments between tilt angles.
DOI: 10.57777/kgm2022000093
title: 벼004
DOI: 10.22002/d1.2099
title: Montage cryo-ET of the thin edge of a HeLa cell, dataset 2

description: Montage tomography dataset collected from the thin edge of a HeLa cell. The file nomenclature for the images is: CG_{tilt_angle}_{tile_number}.mrc. The accompanying text file defines both the order of data collection and the relative tile positions, with format: 
tilt_angle_0 \n 
x_0 y_0 z_0 \n
.... \n
x_n y_n z_n \n
... \n
tilt_angle_m \n
x_0 y_0 z_0 \n
.... \n 
x_n y_n z_n \n
where the subscript n indicates the nth tile collected at the corresponding tilt angle. The variables x_i and y_i correspond to positions in the plane of the detector, while z_i corresponds to the tile's predicted height in the microscope. For this dataset, 37 tiles were collected per tilt-angle. Data were acquired using a grouped dose-symmetric tilt scheme ranging from +/-60 degrees with 2 degree increments between tilt angles.
DOI: 10.57777/kgm2022000094
title: 화성암(백악기 언양화강암(Kegr))
DOI: 10.5517/ccdc.csd.cc1ndl8x
title: CCDC 1531439: Experimental Crystal Structure Determination

description: Related Article: J. Christina Jebapriya, Johanan Christian Prasana, U. Mohamooda Sumaya, Parutagouda Shankaragouda Patil|2022|J.Phys.Chem.Solids|173|111091|doi:10.1016/j.jpcs.2022.111091
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1ndl9y
title: CCDC 1531440: Experimental Crystal Structure Determination

description: Related Article: J. Christina Jebapriya, Johanan Christian Prasana, U. Mohamooda Sumaya, Parutagouda Shankaragouda Patil|2022|J.Phys.Chem.Solids|173|111091|doi:10.1016/j.jpcs.2022.111091
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cnq6z
title: CCDC 2194651: Experimental Crystal Structure Determination

description: Related Article: Ye Lu, Ting-Jun Gu, Rui-Ning Shen, Kou-Lin Zhang|2022|J.Mol.Struct.|1274|134550|doi:10.1016/j.molstruc.2022.134550
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dks3w
title: CCDC 2221618: Experimental Crystal Structure Determination

description: Related Article: Jun'an Lai|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.57777/kgm2022000095
title: 화성암(백악기 석영안산암(Kda))
DOI: 10.57777/kgm2022000096
title: 지붕006(유약기와-청색)
DOI: 10.5281/zenodo.4603597
title: CENP-F-dependent DRP1 function regulates APC/C activity during oocyte meiosis I

description: The raw data of MALD/MS after cenp-f CO-IP
DOI: 10.57777/kgm2022000097
title: 화성암(쥬라기 육십령화강암(Jygr))
DOI: 10.57777/kgm2022000098
title: 화성암(쥬라기 흑운모화강암(Jbgr))
DOI: 10.57777/kgm2022000099
title: 건설관련001(타일-흰색)
DOI: 10.57777/kgm2022000100
title: 운동장002
DOI: 10.5281/zenodo.7352659
title: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.</strong></em> These Residual-UNet model data are based on 1-band NDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} 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 <br> References *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, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571
DOI: 10.5281/zenodo.7352658
title: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.</strong></em> These Residual-UNet model data are based on 1-band NDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} 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 <br> References *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, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571
DOI: 10.5281/zenodo.7352859
title: Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.

description: <em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 1-band NDWI images of coasts.</strong></em> These Residual-UNet model data are based on 1-band NDWI images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.5281/zenodo.7344571 Classes: {0=water, 1=whitewater, 2=sediment, 3=other} 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 <br> References *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, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571
DOI: 10.57777/kgm2022000101
title: 담배003
DOI: 10.15151/esrf-es-998430389
title: iNEXT Discovery: Structural biology for translational research and discovery.

description: The aim of this proposal is to provide user access to Online crystallography and fragment screening pipelines based on the combination of EMBL’s HTX Lab services and the CrystalDirect Technology and automated data collection at ESRF’s MX beamlines, supported through the collaborative EMBL-ESRF Joint Structural Biology Group (JSBG). This will include three different user access modalities: 1) Large-Scale Fragment Screening, 2) Structural characterization of ligand-target complexes and 3) Online Crystallography (automated protein-to-structure pipeline). This work will be carried out in the context of international collaborative projects. Access to these modalities will require competitive application and peer-review through projects like the E.C. funded iNEXT Discovery or the recently awarded ISIDORe.