cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/6m3v4ksd7y.4
title: Dataset on eco-friendly cement production using iron ore tailings from Brazil and evaluation of grinding process on the synthesis of geopolymers

description: The use of iron ore tailings in Brazil is an important topic after two recent tragedies. The data were collected in order to understand the geomechanical feasibility of eco-friendly cements using iron ore tailings and evaluate the impact of the grinding process on the compressive strength of geopolymers. The data are based on two major studies using the same methodology. The first one is the examination of the material best composition in terms of uniaxial compression resistance by varying its proportions of iron ore tailings (IOT), metakaolin (MK) and alkaline solution (composed by 1:3 ratio of sodium hydroxide (NaOH) and sodium silicate (Na2SiO3)). The second one is the evaluation of the effects in compressive strength when grinding the iron ore tailings for 1, 2 and 3 hours for producing the geopolymer. The best proportion consisted in 50% of iron ore tailings, 25% of metakaolin and 25% of a alkaline solution obtained an uniaxial compressive strength resistance of 23.5 MPa in 3 days. Also, the gain obtained with the ground tailings after 3 hours only occurs in the firsts days of cure, which does not justify grinding for this iron ore tailings due to the high cost of this operation. Tests with 1 and 2 hours of tailings grinding showed lower results of UCS when compared with IOT without milling, which is unexpected and needs further investigation to understand this behavior. One possible explanation is material agglomeration during the initial grinding times, and it is feasible that the clustering of particles limited the reaction. Finally, it appears that geopolymer mortars seem to be a viable alternative for the sustainable use of IOT in the state of Minas Gerais, in Brazil, since they present satisfactory results of compressive strength.
DOI: 10.17632/frv6nwf5zr.1
title: Fuzzy DEMATEL Datasets on the Interdependence of Barriers to Sustainable Urban Water Management

description: Supplementary Material for “An integrated MCDM approach for analyzing manifold and interdependent barriers to sustainable urban water management” This supplementary material contains the tables associated with the MFDEMATEL applied to the system elements, which are not presented in the manuscript for purposes of brevity. This document is provided, therefore, for computation tractability. The contents of this document are arranged as follows: Table 1. Fuzzy direct relation matrix for Expert 1 Table 2. Fuzzy direct relation matrix for Expert 2 Table 3. Fuzzy direct relation matrix for Expert 3 Table 4. Fuzzy direct relation matrix for Expert 4 Table 5. Average fuzzy direct relation matrix for barriers
DOI: 10.17632/xzznxxr99b.1
title: Double TFN-Fuzzy DELPHI Datasets in the Selection of Barriers to Sustainable Urban Water Management

description: Supplemental Document for “An integrated MCDM approach for analyzing manifold and interdependent barriers to sustainable urban water management” This supplemental document presents the following: initial list of barriers to Sustainable Urban Water Management (SUWM), complete table pertaining to the data from the results of the Double TFN-Fuzzy Delphi.
DOI: 10.17632/xzznxxr99b.2
title: Double TFN-Fuzzy DELPHI Datasets in the Selection of Barriers to Sustainable Urban Water Management

description: Supplemental Document for “An integrated MCDM approach for analyzing manifold and interdependent barriers to sustainable urban water management” This supplemental document presents the following: initial list of barriers to Sustainable Urban Water Management (SUWM), complete table pertaining to the data from the results of the Double TFN-Fuzzy Delphi.
DOI: 10.17632/xzznxxr99b.3
title: Double TFN-Fuzzy DELPHI Datasets in the Selection of Barriers to Sustainable Urban Water Management

description: This data set serves as a supplementary document for the study: “An integrated MCDM approach for analyzing manifold and interdependent barriers to sustainable urban water management.” It contains results from an expert survey conducted in the Philippines. The survey intends to draw information from experts on the relevance of barriers to sustainable urban water management obtained from the literature in the context of a developing country like the Philippines. The survey results were processed using the Double TFN-Fuzzy Delphi Method. This supplemental document presents the following: an initial list of barriers to Sustainable Urban Water Management (SUWM), a complete table pertaining to the data from the results of the Double TFN-Fuzzy Delphi Method.
DOI: 10.17632/frv6nwf5zr.2
title: Fuzzy DEMATEL Datasets on the Interdependence of Barriers to Sustainable Urban Water Management

description: This data set serves as a supplementary document for the study: “An integrated MCDM approach for analyzing manifold and interdependent barriers to sustainable urban water management.” It contains results from an expert survey conducted in the Philippines. The survey intends to draw information from experts on the causal relationship of barriers to sustainable urban water management obtained from the literature in the context of a developing country like the Philippines. The survey results were processed using the Fuzzy DEMATEL methodology. This supplementary material contains the tables associated with the MFDEMATEL applied to the system elements, which are not presented in the manuscript for purposes of brevity. This document is provided, therefore, for computation tractability. The contents of this document are arranged as follows: Table 1. Fuzzy direct relation matrix for Expert 1 Table 2. Fuzzy direct relation matrix for Expert 2 Table 3. Fuzzy direct relation matrix for Expert 3 Table 4. Fuzzy direct relation matrix for Expert 4 Table 5. Average fuzzy direct relation matrix for barriers
DOI: 10.17632/g7n88kf7hk.1
title: India NSE Deepak Nitrite 24 Months Stock data

description: Deepak Nitrite Stock data from 16-08-2018 TO 14-08-2020 from NSE
DOI: 10.17632/9kgxzwd6y3.1
title: Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing

description: This dataset contains the percentage change in PM2.5, NO2, Tropospheric ozone, and AOD over 93 major megacities of the world using cloud computing enabled satellite data-based remote sensing. NO2 and Tropospheric ozone are used using Google Earth Engine from Sentinel-5P, AOD from MODIS and PM2.5 is the data-assimilated product from ECMWF.
DOI: 10.17632/gpcj4b3wtk.1
title: Disseminated intravascular coagulation in Stevens-Johnson syndrome and toxic epidermal necrolysis

description: Supplemental information Tables: 2 Figures: 3 Supplement Table 1. Demographic data of patients with SJS/TEN enrolled in this study (n=150) Supplement Table 2. Microorganisms in SJS/TEN patients with DIC (n=32) Supplement Fig 1. Flow chart of patient enrollment in the study. Supplement Fig 2. Clinical presentation of two toxic epidermal necrolysis (TEN) patients with and without disseminated intravascular coagulation (DIC) Supplement Fig 3. Demonstration of the disseminated intravascular coagulation score in 7 deceased and 7 survived Stevens-Johnson syndrome/toxic epidermal necrolysis patients.
DOI: 10.17632/fnr3mkxmk9.1
title: Esieh lake seepage HESS

description: Movie showing intense ebullition on Esieh lake, in support to "Mobile open dynamic chamber measurement of methane macroseeps in lakes"
DOI: 10.17632/7nhzscvwj4.1
title: Data for: Assessing the Effects of Engine Load on Compression Ignition Engines Using Biodiesel Blends

description: Abstract This study evaluated the performance of a diesel engine operated with waste plastic biodiesel fuel blends. At all engine loads the emissions of carbon monoxide (CO), unburnt hydrocarbons (UHC) and carbon dioxide (CO2) were low compared to conventional petro-diesel (PD), but the emissions of NOX were higher. The brake specific fuel consumption (BSFC) for the blends dropped while the brake thermal efficiency (BTE) continued to increase with load for all blends until intermediate load when it decreased. Compared to PD fuel, CO emissions for blend 95/WPPO5 at all engine speed idling modes were 285 ppm, 298 ppm, 320 ppm, and 388 ppm while PD emissions were 270 ppm, 295 ppm, 315 ppm and 365 ppm respectively. The values for UHC for blend 95/WPPO5 at all engine speed idling modes were 35 ppm, 28 ppm, 22 ppm, and 18 ppm compared to PD fuel with 20 ppm, 25 ppm, 30 ppm, and 40 ppm respectively. The NOx emissions for PD fuel at all engine speed idling modes were 175 ppm, 225 ppm, 300 ppm and 375 ppm compared to blend 95/WPPO5 at 195 ppm, 245 ppm, 335 ppm, and 397 ppm. The BSFC values for blend 95/WPPO5 at all engine idling speed modes were 0.48 g/kW.h, 0.41 g/kW.h, 0.35 g/kW.h and 0.4 g/kW.h compared to PD at 0.45 g/kW.h , 0.39 g/kW.h , 0.33 g/kW.h and 035 g/kW.h respectively. Keywords: Engine loads, Emissions, Higher viscosity, Spray characteristics
DOI: 10.17632/7vmjpddjck.1
title: Apuntes de programación UDEMY

description: En esta base de datos se almacenarán apuntes referentes al desarrollo de software.
DOI: 10.5517/ccdc.csd.cc24nn7q
title: CCDC 1986053: Experimental Crystal Structure Determination

description: Related Article: Kenichiro Omoto, Toyotaka Nakae, Masaki Nishio, Yoshinori Yamanoi, Hidetaka Kasai, Eiji Nishibori, Takaki Mashimo, Tomohiro Seki, Hajime Ito, Kazuki Nakamura, Norihisa Kobayashi, Naofumi Nakayama, Hitoshi Goto, Hiroshi Nishihara|2020|J.Am.Chem.Soc.|142|12651|doi:10.1021/jacs.0c03643
DOI: 10.5517/ccdc.csd.cc23bsgq
title: CCDC 1946783: Experimental Crystal Structure Determination

description: Related Article: Kenichiro Omoto, Toyotaka Nakae, Masaki Nishio, Yoshinori Yamanoi, Hidetaka Kasai, Eiji Nishibori, Takaki Mashimo, Tomohiro Seki, Hajime Ito, Kazuki Nakamura, Norihisa Kobayashi, Naofumi Nakayama, Hitoshi Goto, Hiroshi Nishihara|2020|J.Am.Chem.Soc.|142|12651|doi:10.1021/jacs.0c03643
DOI: 10.5517/ccdc.csd.cc252zw4
title: CCDC 1998876: Experimental Crystal Structure Determination

description: Related Article: Haixiang Han, Jesse C. Corozza, Zheng Zhou, Yuxuan Zhang, Zheng Wei, Artem M. Abakumov, Alexander S. Filatov, Yu-Sheng Chen, Daniel J. SantaLucia, John F. Berry, Evgeny V. Dikarev|2020|J.Am.Chem.Soc.|142|12767|doi:10.1021/jacs.0c05139
DOI: 10.5517/ccdc.csd.cc252zx5
title: CCDC 1998877: Experimental Crystal Structure Determination

description: Related Article: Haixiang Han, Jesse C. Corozza, Zheng Zhou, Yuxuan Zhang, Zheng Wei, Artem M. Abakumov, Alexander S. Filatov, Yu-Sheng Chen, Daniel J. SantaLucia, John F. Berry, Evgeny V. Dikarev|2020|J.Am.Chem.Soc.|142|12767|doi:10.1021/jacs.0c05139
DOI: 10.5517/ccdc.csd.cc252zy6
title: CCDC 1998878: Experimental Crystal Structure Determination

description: Related Article: Haixiang Han, Jesse C. Corozza, Zheng Zhou, Yuxuan Zhang, Zheng Wei, Artem M. Abakumov, Alexander S. Filatov, Yu-Sheng Chen, Daniel J. SantaLucia, John F. Berry, Evgeny V. Dikarev|2020|J.Am.Chem.Soc.|142|12767|doi:10.1021/jacs.0c05139
DOI: 10.5517/ccdc.csd.cc252zz7
title: CCDC 1998879: Experimental Crystal Structure Determination

description: Related Article: Haixiang Han, Jesse C. Corozza, Zheng Zhou, Yuxuan Zhang, Zheng Wei, Artem M. Abakumov, Alexander S. Filatov, Yu-Sheng Chen, Daniel J. SantaLucia, John F. Berry, Evgeny V. Dikarev|2020|J.Am.Chem.Soc.|142|12767|doi:10.1021/jacs.0c05139
DOI: 10.5517/ccdc.csd.cc25f942
title: CCDC 2008773: Experimental Crystal Structure Determination

description: Related Article: Yi-Zhu Zhang, Feng-Tao Sheng, Zuoquan Zhu, Zhi-Ming Li, Shu Zhang, Wei Tan, Feng Shi|2020|Org.Biomol.Chem.|18|5688|doi:10.1039/D0OB01230K
DOI: 10.5517/ccdc.csd.cc23w8kv
title: CCDC 1962627: Experimental Crystal Structure Determination

description: Related Article: Wen-Fang Liu, Qi-Ming Qiu, Mo Zhang, Zhao-Min Su, Qingqing An, Hongjin Lv, Zhiyu Jia, Guo-Yu Yang|2020|Dalton Trans.|49|10156|doi:10.1039/D0DT01994A
DOI: 10.5517/ccdc.csd.cc2305y8
title: CCDC 1936599: Experimental Crystal Structure Determination

description: Related Article: Xuebin Xu, Meiying Liu, Zhiliang Liu|2020|Dalton Trans.|49|10402|doi:10.1039/D0DT01323D
DOI: 10.5517/ccdc.csd.cc2586xl
title: CCDC 2003899: Experimental Crystal Structure Determination

description: Related Article: Wen-Fang Liu, Qi-Ming Qiu, Mo Zhang, Zhao-Min Su, Qingqing An, Hongjin Lv, Zhiyu Jia, Guo-Yu Yang|2020|Dalton Trans.|49|10156|doi:10.1039/D0DT01994A
DOI: 10.5517/ccdc.csd.cc23061d
title: CCDC 1936602: Experimental Crystal Structure Determination

description: Related Article: Xuebin Xu, Meiying Liu, Zhiliang Liu|2020|Dalton Trans.|49|10402|doi:10.1039/D0DT01323D
DOI: 10.5517/ccdc.csd.cc23m5jg
title: CCDC 1954845: Experimental Crystal Structure Determination

description: Related Article: Ge Li, Xiaoxi Zhao, Qingxin Han, Li Wang, Weisheng Liu|2020|Dalton Trans.|49|10120|doi:10.1039/D0DT01711F
DOI: 10.5517/ccdc.csd.cc23m4qm
title: CCDC 1954820: Experimental Crystal Structure Determination

description: Related Article: Ge Li, Xiaoxi Zhao, Qingxin Han, Li Wang, Weisheng Liu|2020|Dalton Trans.|49|10120|doi:10.1039/D0DT01711F
DOI: 10.5517/ccdc.csd.cc23m5nl
title: CCDC 1954849: Experimental Crystal Structure Determination

description: Related Article: Ge Li, Xiaoxi Zhao, Qingxin Han, Li Wang, Weisheng Liu|2020|Dalton Trans.|49|10120|doi:10.1039/D0DT01711F
DOI: 10.5517/ccdc.csd.cc23m4rn
title: CCDC 1954821: Experimental Crystal Structure Determination

description: Related Article: Ge Li, Xiaoxi Zhao, Qingxin Han, Li Wang, Weisheng Liu|2020|Dalton Trans.|49|10120|doi:10.1039/D0DT01711F
DOI: 10.5676/gruan/dpkg-2020-1
title: Data package related to the article 'Managing the transition from Vaisala RS92 to RS41 radiosondes within the Global Climate Observing System Reference Upper-Air Network (GRUAN): a progress report'

description: This repository contains the data files that were used to produce Figure 8 for the manuscript "Progress in managing the transition from the RS92 to the Vaisala RS41 as the operational radiosonde within the GCOS Reference Upper-Air Network" by Dirksen et al. (10.5194/gi-2019-36)
DOI: 10.34740/kaggle/dsv/1423977
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.3905166
title: Raw data of oesophageal length in Sprague Dawley Rats by sex and age

description: The dataset comprises of 41 (11 males) oesophageal length measurements of Sprague Dawley rats aged between 57 and 59 days separated by sex. Age is provided in days of life, oesophageal length in centimetres, and bodyweight in grams.
DOI: 10.5281/zenodo.3905165
title: Raw data of oesophageal length in Sprague Dawley Rats by sex and age

description: The dataset comprises of 41 (11 males) oesophageal length measurements of Sprague Dawley rats aged between 57 and 59 days separated by sex. Age is provided in days of life, oesophageal length in centimetres, and bodyweight in grams.
DOI: 10.5281/zenodo.3987104
title: Data Repository

description: Data repository of the Bachelor Thesis project 'Differences in the number and type of Gustatory Receptor genes between generalist and specialist lepidopteran species'.
DOI: 10.5281/zenodo.3987103
title: Data Repository

description: Data repository of the Bachelor Thesis project 'Differences in the number and type of Gustatory Receptor genes between generalist and specialist lepidopteran species'.
DOI: 10.21227/zpbh-sa51
title: RSRPSet: test dataset of the 16th CNGMCM

description: All data used for model evaluation and training are from the 16th Chinese National Graduate Mathematical Contest in Modeling (CNGMCM). The dataset is measured by HUAWEI TECHNOLOGIES CO., LTD, which includes engineering parameter data, map data and RSRP label data of multiple communities. And the dataset used in this letter is from the public dataset of CNGMCM, which is sponsored by the Ministry of Education's Degree and Graduate Education Development Center. The purpose of building the dataset is to find the mapping model between engineering parameters, geographical environment and RSRP, so as to quickly predict the RSRP value of a specific geographical location in the new environment.ATTENTION: This is Not the orginal dataset of CNGMCM. The data have beenpreprocessed according to the requested privacy rules.P.S.: Sorry for updating introduction and analysis of this dataset late, because I am bothered from the COVID-19in our city. I have removed some infomation about this dataset since Privacy Requirements of a paper submitted.
DOI: 10.21227/cf1k-g481
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/s26f-1g96
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/98jb-sb63
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/97jv-eg82
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/4e5a-5v93
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/s8m6-4v38
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/bswt-x147
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/h86q-w950
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/6fq6-dj93
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.21227/8nbg-se81
title: Experiment on bearing under variable working conditions

description: Experiment on bearing under variable working condition
DOI: 10.5281/zenodo.3987384
title: Supplemental data

description: Datasets for machine learning as described in "Genome-wide predictions of genetic redundancy in <em>Arabidopsis thaliana</em>" (https://doi.org/10.1101/2020.08.13.250225): Dataset_1.txt: Training set only, RD4 Dataset_2.txt: Training set only, RD9 Dataset_3.txt: Training set = RD4, testing set = whole genome and tandem duplicates (WG/TD set) + RD9 pairs not included in RD4 Dataset_4.txt: Training set = RD4, testing set = kinases (Kin set) + RD9 pairs not included in RD4
DOI: 10.5281/zenodo.3987383
title: Supplemental data

description: Datasets for machine learning as described in "Genome-wide predictions of genetic redundancy in <em>Arabidopsis thaliana</em>" (https://doi.org/10.1101/2020.08.13.250225): Dataset_1.txt: Training set only, RD4 Dataset_2.txt: Training set only, RD9 Dataset_3.txt: Training set = RD4, testing set = whole genome and tandem duplicates (WG/TD set) + RD9 pairs not included in RD4 Dataset_4.txt: Training set = RD4, testing set = kinases (Kin set) + RD9 pairs not included in RD4
DOI: 10.7910/dvn/harm6x/4kswrh
title: class_skiff_OB_stars_clusters_564.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/ngtjqr
title: class_skiff_OB_stars_clusters_271.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/elhd2g
title: class_skiff_OB_stars_clusters_357.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/n6nqni
title: class_skiff_OB_stars_clusters_617.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/e9sofd
title: class_skiff_OB_stars_clusters_199.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/fcl0dz
title: class_skiff_OB_stars_clusters_56.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/rfvfme
title: class_skiff_OB_stars_clusters_7.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/5bndnl
title: class_skiff_OB_stars_clusters_593.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/rvqgli
title: class_skiff_OB_stars_clusters_266.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/n3azea
title: class_skiff_OB_stars_clusters_551.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/4vfh3s
title: class_skiff_OB_stars_clusters_407.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/2uunt7
title: class_skiff_OB_stars_clusters_319.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/x7tvxo
title: class_skiff_OB_stars_clusters_176.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/kzucvk
title: class_skiff_OB_stars_clusters_536.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/gdzgul
title: class_skiff_OB_stars_clusters_537.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/aq4y6d
title: class_skiff_OB_stars_clusters_491.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/eyf4x9
title: class_skiff_OB_stars_clusters_198.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/pvsfit
title: class_skiff_OB_stars_clusters_473.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/9rq30a
title: class_skiff_OB_stars_clusters_428.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/ujga9d
title: class_skiff_OB_stars_clusters_362.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/l3cxsq
title: class_skiff_OB_stars_clusters_138.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/gy0reg
title: class_skiff_OB_stars_clusters_393.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/mwqejl
title: class_skiff_OB_stars_clusters_36.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/bhwmgn
title: class_skiff_OB_stars_clusters_446.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/esfydt
title: class_skiff_OB_stars_clusters_132.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/jhtqha
title: class_skiff_OB_stars_clusters_423.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/dnb7lf
title: class_skiff_OB_stars_clusters_489.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/uodps8
title: class_skiff_OB_stars_clusters_504.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/4soq0l
title: class_skiff_OB_stars_clusters_131.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/1vcvlv
title: class_skiff_OB_stars_clusters_508.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/xz2qvk
title: class_skiff_OB_stars_clusters_22.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/fgokuz
title: class_skiff_OB_stars_clusters_457.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/2p0jzf
title: class_skiff_OB_stars_clusters_558.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/cwnpjk
title: class_skiff_OB_stars_clusters_376.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/njvyjf
title: class_skiff_OB_stars_clusters_455.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/e8h9nt
title: class_skiff_OB_stars_clusters_249.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/q25v1y
title: class_skiff_OB_stars_clusters_582.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/bhyrjz
title: class_skiff_OB_stars_clusters_337.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/sds7dd
title: class_skiff_OB_stars_clusters_303.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/tg5y3n
title: class_skiff_OB_stars_clusters_520.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/ofhezc
title: class_skiff_OB_stars_clusters_116.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/jrq1jf
title: class_skiff_OB_stars_clusters_411.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/p53exl
title: class_skiff_OB_stars_clusters_74.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/z2qvhl
title: class_skiff_OB_stars_clusters_605.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/x9akr0
title: class_skiff_OB_stars_clusters_507.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/lbh9jl
title: class_skiff_OB_stars_clusters_426.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/lnsikc
title: class_skiff_OB_stars_clusters_237.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/hzo206
title: class_skiff_OB_stars_clusters_425.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/u9javw
title: class_skiff_OB_stars_clusters_560.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/khrzhq
title: class_skiff_OB_stars_clusters_529.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/acupun
title: class_skiff_OB_stars_clusters_147.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/wgkdxs
title: class_skiff_OB_stars_clusters_611.pdf

description: :unav
DOI: 10.7910/dvn/harm6x/0tsoyy
title: class_skiff_OB_stars_clusters_345.pdf

description: :unav