cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7352850
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 MNDWI 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 MNDWI images of coasts.</strong></em> These Residual-UNet model data are based on 1-band MNDWI 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.7352849
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 MNDWI 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 MNDWI images of coasts.</strong></em> These Residual-UNet model data are based on 1-band MNDWI 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.7352880
title: Turbulent pipe flow up to Re_tau = 1000

description: Data from the paper: Direct numerical simulation of turbulent pipe flow at moderately high Reynolds numbers GK El Khoury, P Schlatter, A Noorani, PF Fischer, G Brethouwer, ... Flow, turbulence and combustion 91 (3), 475-495
DOI: 10.5281/zenodo.7352879
title: Turbulent pipe flow up to Re_tau = 1000

description: Data from the paper: Direct numerical simulation of turbulent pipe flow at moderately high Reynolds numbers GK El Khoury, P Schlatter, A Noorani, PF Fischer, G Brethouwer, ... Flow, turbulence and combustion 91 (3), 475-495
DOI: 10.57777/kgm2022000102
title: 보리004
DOI: 10.57777/kgm2022000103
title: 변성암(선캠브리아기 흑운모화강암질편마암(PCEbggn))
DOI: 10.57777/kgm2022000104
title: 아스팔트001
DOI: 10.57777/kgm2022000105
title: 파003
DOI: 10.57777/kgm2022000106
title: 배추002
DOI: 10.57777/kgm2022000107
title: 지붕013(슁글-녹색)
DOI: 10.57777/kgm2022000108
title: 지붕003(스페니쉬 오지기와)
DOI: 10.57777/kgm2022000109
title: 변성암(선캠브리아기 편상화강암질편마암(PCEsgn))
DOI: 10.57777/kgm2022000110
title: 변성암(선캠브리아기 반상변정편마암(PCEgnp))
DOI: 10.34863/sd37-3694
title: Dataset: Informing Quantum Materials Discovery and Synthesis using X-ray Micro-Computed Tomography

description: The dataset shows how X-ray micro-computed tomography (μCT) can be used to non-destructively generate 3D representations of a material with micron/nanometer precision. This is shown through a variety of test cases, including: Compacted Cu powder, Flux grown SmB6 single crystals, Flux grown rutile GeO2, Floating zone grown YbMgGaO4, Diffraction Computed Tomography of rutile TiO2.
The collected data includes the raw μCT data along with any auxiliary data used (such as Laue diffraction).
DOI: 10.57777/kgm2022000111
title: 변성암(선캠브리아기 호상편마암 및 편암(PCEbgn))
DOI: 10.57777/kgm2022000112
title: 콘크리트003
DOI: 10.57777/kgm2022000113
title: 지붕010(시멘트 기와-무색)
DOI: 10.57777/kgm2022000114
title: 콘크리트001
DOI: 10.57777/kgm2022000115
title: 파004
DOI: 10.34740/kaggle/dsv/4574042
title: Photovoltaic modules corrective maintenance
DOI: 10.5517/ccdc.csd.cc2c97wt
title: CCDC 2183636: Experimental Crystal Structure Determination

description: Related Article: Honghan Ji, Wenjing Liu, Yanmin Huo, Miao Han, Qingxia Yao, Shuwen Gong, Wenzeng Duan|2023|Dyes Pigm.|209|110915|doi:10.1016/j.dyepig.2022.110915
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.cc2cb0k9
title: CCDC 2184370: Experimental Crystal Structure Determination

description: Related Article: Sachin S. Wazalwar, Anita R. Banpurkar, Franc Perdih|2023|J.Mol.Struct.|1274|134508|doi:10.1016/j.molstruc.2022.134508
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.cc2cb8ss
title: CCDC 2184625: Experimental Crystal Structure Determination

description: Related Article: Xinwei He, Ruxue Li, Pui Ying Choy, Jiahui Duan, Zhenzhen Yin, Keke Xu, Qiang Tang, Rong-Lin Zhong, Yongjia Shang, Fuk Yee Kwong|2022|Chemical Science|13|13617|doi:10.1039/D2SC04431E
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.cc2cb8xx
title: CCDC 2184629: Experimental Crystal Structure Determination

description: Related Article: Xinwei He, Ruxue Li, Pui Ying Choy, Jiahui Duan, Zhenzhen Yin, Keke Xu, Qiang Tang, Rong-Lin Zhong, Yongjia Shang, Fuk Yee Kwong|2022|Chemical Science|13|13617|doi:10.1039/D2SC04431E
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.cc2cb9gh
title: CCDC 2184646: Experimental Crystal Structure Determination

description: Related Article: Haoyuan Li, Wenjie Yan, Peipei Ren, Huimin Hu, Runbo Sun, Meixia Liu, Zhengjiang Fu, Shengmei Guo, Hu Cai|2022|RSC Advances|12|32350|doi:10.1039/D2RA06351D
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.cc2cb9hj
title: CCDC 2184647: Experimental Crystal Structure Determination

description: Related Article: Haoyuan Li, Wenjie Yan, Peipei Ren, Huimin Hu, Runbo Sun, Meixia Liu, Zhengjiang Fu, Shengmei Guo, Hu Cai|2022|RSC Advances|12|32350|doi:10.1039/D2RA06351D
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: 10.6084/m9.figshare.21608397
title: An Ontology for the Formal Representation of Case Knowledge in the Hydrologic Modeling Domain

description: Hydrologic Modeling Case Ontology hydrocase.owl : ontology in RDF/XML syntax hydrocase.ttl : ontology in Turtle syntax
DOI: 10.57777/kgm2022000116
title: 변성암(선캠브리아기 흑운모편마암(PCEbtgn))
DOI: 10.5519/qd.2xqes4x4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.57777/kgm2022000117
title: 도로011(보도블럭-황색)
DOI: 10.5281/zenodo.7100949
title: Data_of_Temporal_dynamics_of_the_sensorimotor_convergence_underlying_voluntary_limb_movement

description: Software and dataset used in a manuscript.
DOI: 10.5281/zenodo.7100948
title: Data_of_Temporal_dynamics_of_the_sensorimotor_convergence_underlying_voluntary_limb_movement

description: Software and dataset used in a manuscript.
DOI: 10.6084/m9.figshare.21614274
title: Understanding how grades of frailty affect the evolution of COVID-19 (Comprendiendo cómo el grado de Fragilidad afecta la evolución de COVID-19)

description: Dataset & protocol
DOI: 10.6084/m9.figshare.21614274.v1
title: Understanding how grades of frailty affect the evolution of COVID-19 (Comprendiendo cómo el grado de Fragilidad afecta la evolución de COVID-19)

description: Dataset & protocol
DOI: 10.57777/kgm2022000118
title: 화성암(쥬라기 복운모화강암(Jtgr))
DOI: 10.57777/kgm2022000119
title: 수박001
DOI: 10.57777/kgm2022000120
title: 화성암(백악기 언양화강암(Kegr))
DOI: 10.6084/m9.figshare.21614256
title: The Social Capital and Mental Health Study in Anhui Urban-Rural Community-Dwelling Older Adults (Adapted)

description: The Social Capital and Mental Health Study in Anhui Urban-Rural Community-Dwelling Older Adults collected socio-demographic, economic, and health information of older adults from 4 counties in Anhui Province, China in 2013. In this study, out of 3,452 eligible older adults aged 60 and over, a total of 3,045 older individuals participated in the survey, with a response rate of 88%.
DOI: 10.6084/m9.figshare.21614256.v1
title: The Social Capital and Mental Health Study in Anhui Urban-Rural Community-Dwelling Older Adults (Adapted)

description: The Social Capital and Mental Health Study in Anhui Urban-Rural Community-Dwelling Older Adults collected socio-demographic, economic, and health information of older adults from 4 counties in Anhui Province, China in 2013. In this study, out of 3,452 eligible older adults aged 60 and over, a total of 3,045 older individuals participated in the survey, with a response rate of 88%.
DOI: 10.57777/kgm2022000121
title: 변성암(선캠브리아기 우백질화강편마암(PCEgne))
DOI: 10.57777/kgm2022000122
title: 화성암(쥬라기 반상화강암(Jpgr))
DOI: 10.57777/kgm2022000123
title: 화성암(트라이아스기 엽리상반상화강암(Trhypgr))
DOI: 10.57777/kgm2022000124
title: 화성암(시대미상 각섬석화강섬록암(gd))
DOI: 10.57777/kgm2022000125
title: 화성암(쥬라기 세립질흑운모화강암(Jfgr))
DOI: 10.34740/kaggle/dsv/4574114
title: BBC News
DOI: 10.57777/kgm2022000126
title: 도로013(타일-연마된 화강암)
DOI: 10.57777/kgm2022000127
title: 화성암(백악기 반상흑운모 화강암(Kpbgr))
DOI: 10.57777/kgm2022000128
title: 화성암(백악기 반상화강암(Kpgr))
DOI: 10.57777/kgm2022000129
title: 화성암(쥬라기 반상흑운모화강암(Jpbgr))
DOI: 10.34740/kaggle/dsv/4574260
title: OntoUML/UFO Catalog
DOI: 10.57777/kgm2022000130
title: 지붕012(슁글-검정색)
DOI: 10.57777/kgm2022000131
title: 콘크리트004
DOI: 10.5517/ccdc.csd.cc220kpc
title: CCDC 1907172: Experimental Crystal Structure Determination

description: Related Article: Chuan Qin, Zhidao Huang, Song-Bai Wu, Zhuangxing Li, Yuhong Yang, Songgen Xu, Xin Zhang, Guixia Liu, Yun-Dong Wu, Lung Wa Chung, Zheng Huang|2022|J.Am.Chem.Soc.|144|20903|doi:10.1021/jacs.2c09356
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: 10.6084/m9.figshare.21370572.v1
title: JARVIS-SuperconDB

description: JARVIS-Superconductor database for 3D materials <br> <br> &gt;&gt;&gt; import pandas as pd &gt;&gt;&gt; df=pd.read_json('jarvis_epc_data_figshare.json') <br> <br> <br> We perform electron-phonon coupling calculations to establish a large and systematic database of BCS superconducting properties.
DOI: 10.57777/kgm2022000132
title: 도로006(고무블럭-붉은색)
DOI: 10.5281/zenodo.7058607
title: Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic Data of Thorne-Zytkow Object and Super-AGB Star Candidates

description: This dataset contains Magellan MIKE spectroscopy of a population of cool, luminous stars in the Magellanic Clouds, a sample of confirmed Magellanic Cloud red supergiants, and spectrophotometric standard stars. The spectra were analyzed in the paper "Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic and Environmental Analysis of Thorne-\.Zytkow Object and Super-AGB Star Candidates" by O'Grady et al. (2022). More details are provided in the README.
DOI: 10.5281/zenodo.7058608
title: Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic Data of Thorne-Zytkow Object and Super-AGB Star Candidates

description: This dataset contains Magellan MIKE spectroscopy of a population of cool, luminous stars in the Magellanic Clouds, a sample of confirmed Magellanic Cloud red supergiants, and spectrophotometric standard stars. The spectra were analyzed in the paper "Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic and Environmental Analysis of Thorne-\.Zytkow Object and Super-AGB Star Candidates" by O'Grady et al. (2022). More details are provided in the README.
DOI: 10.6084/m9.figshare.21614754
title: Distinct patterns of soil bacterial and fungal community assemblages in subtropical forest ecosystems under warming.

description: Data for figure construction
DOI: 10.34740/kaggle/dsv/4574376
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.34740/kaggle/dsv/4574387
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.26036/cnp0003719
title: Evaluation of cryopreservation affected the viability and functionality of porcine ear skin fibroblasts

description: Evaluate the effects of cryopreservation on proliferative and functional capacities of porcine fibroblasts.
DOI: 10.57777/kgm2022000133
title: 화성암(쥬라기 반상화강섬록암(Jpgdi))
DOI: 10.57777/kgm2022000134
title: 잔디006
DOI: 10.57777/kgm2022000135
title: 변성암(선캠브리아기 화강암질편마암(PCEggn))
DOI: 10.57777/kgm2022000136
title: 알타리 무001
DOI: 10.57777/kgm2022000137
title: 담배002
DOI: 10.57777/kgm2022000138
title: 잔디003
DOI: 10.26179/xftj-vg21
title: Rare Earth Elements concentrations in seawater samples collected during the IN2017-V01 voyage of the RV Investigator
DOI: 10.6084/m9.figshare.20485710.v1
title: Untitled Item

description: The link between open government data (OGD) policy and firm performance.
DOI: 10.6084/m9.figshare.20485710.v2
title: OGD Policy and firm performance

description: The link between open government data (OGD) policy and firm performance.
DOI: 10.6084/m9.figshare.20485710
title: OGD policy and firm performance

description: The link between open government data (OGD) policy and firm performance.
DOI: 10.6084/m9.figshare.20485710.v4
title: OGD policy and firm performance

description: The link between open government data (OGD) policy and firm performance.
DOI: 10.6084/m9.figshare.20485710.v3
title: OGD Policy and firm performance

description: The link between open government data (OGD) policy and firm performance.
DOI: 10.57777/kgm2022000139
title: 화성암(백악기 내장사화산암류(Knv))
DOI: 10.6084/m9.figshare.21614766
title: Smaller Greater India, Middle-Early Eocene Collision with Asia

description: The paleomagnetic datasets from the Member Ⅰ of Zongpu Formation on the Tingri, South Tibet.
DOI: 10.6084/m9.figshare.21614766.v1
title: Smaller Greater India, Middle-Early Eocene Collision with Asia

description: The paleomagnetic datasets from the Member Ⅰ of Zongpu Formation on the Tingri, South Tibet.
DOI: 10.6084/m9.figshare.21614778
title: scRNA seq of developing ovary

description: CellRanger out files from E11.5, E12.5, E13.5 and E14.5 C57/BL-6J mouse developing ovary.
DOI: 10.6084/m9.figshare.21614778.v1
title: scRNA seq of developing ovary

description: CellRanger out files from E11.5, E12.5, E13.5 and E14.5 C57/BL-6J mouse developing ovary.
DOI: 10.57777/kgm2022000140
title: 변성암(선캠브리아기 경기편마암복합체(PCEggn))
DOI: 10.57777/kgm2022000141
title: 변성암(선캠브리아기 석영장석질편마암(PCEqs))
DOI: 10.6084/m9.figshare.21614817
title: Comparative analyses of the molecular marks COI and ITS2 in Trichogramma (Hymenoptera, Trichogrammatidae).zip

description: Gene alignments, tree files and iTOL annotations for COI and ITS sequences in Trichogramma
DOI: 10.6084/m9.figshare.21614817.v1
title: Comparative analyses of the molecular marks COI and ITS2 in Trichogramma (Hymenoptera, Trichogrammatidae).zip

description: Gene alignments, tree files and iTOL annotations for COI and ITS sequences in Trichogramma
DOI: 10.57777/kgm2022000142
title: 화성암(쥬라기 각섬석흑운모화강암(Jhbgr))
DOI: 10.57777/kgm2022000143
title: 벼003
DOI: 10.57777/kgm2022000144
title: 변성암(선캠브리아기 함전기석편마암(PCEtgn))
DOI: 10.57777/kgm2022000145
title: 지붕001(유약기와-녹색)
DOI: 10.6084/m9.figshare.21456342.v2
title: Seed dispersal of the mushroom-like, non-photosynthetic plant Balanophora by the Amami rabbit Pentalagus furnessi

description: Dateset of ”Seed dispersal of the mushroom-like, non-photosynthetic plant Balanophora by the Amami rabbit Pentalagus furnessi”
DOI: 10.57777/kgm2022000146
title: 아스팔트003
DOI: 10.57777/kgm2022000147
title: 변성암(선캠브리아기 경기편마암복합체(PCEggn))
DOI: 10.57777/kgm2022000148
title: 담배001
DOI: 10.5281/zenodo.7352960
title: Repartition of S2TLD dataset

description: The S2TLD dataset contains five categories (including red, yellow, green, off, and waiting). It covers a wide range of road scenarios, such as busy street scenes, multiple visible traffic lights, and traffic tail lights that may be confused with traffic lights (e.g., large circular tail lights, etc.). A link to the original dataset: https://github.com/Thinklab-SJTU/S2TLD We randomly divided it into new training set, verification set and test set in a ratio close to 7:2:1.
description: {"references": ["available at: https://doi.org/10.48550/arXiv.2004.13316"]}
DOI: 10.5281/zenodo.7352959
title: Repartition of S2TLD dataset

description: The S2TLD dataset contains five categories (including red, yellow, green, off, and waiting). It covers a wide range of road scenarios, such as busy street scenes, multiple visible traffic lights, and traffic tail lights that may be confused with traffic lights (e.g., large circular tail lights, etc.). A link to the original dataset: https://github.com/Thinklab-SJTU/S2TLD We randomly divided it into new training set, verification set and test set in a ratio close to 7:2:1.
description: {"references": ["available at: https://doi.org/10.48550/arXiv.2004.13316"]}
DOI: 10.57777/kgm2022000149
title: 무001
DOI: 10.57777/kgm2022000150
title: 양파001
DOI: 10.57777/kgm2022000151
title: 운동장003
DOI: 10.57777/kgm2022000152
title: 벼005
DOI: 10.57777/kgm2022000153
title: 화성암(백악기 안산암(Kan))