cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5524989
title: The detonation of a sub-Chandrasekhar-mass white dwarf at the origin of the low-luminosity Type Ia supernova 1999by

description: Multi-epoch spectra of the MCh model DDC25 and the sub-MCh model SCH2p0 used to confront to SN 1999by observations in Blondin et al. 2018, MNRAS, 474, 3931. These files also include the input hydrodynamical models at 0.5 d (for DDC25) and 0.75 d (for SCH2p0) past explosion.
DOI: 10.5281/zenodo.5524988
title: The detonation of a sub-Chandrasekhar-mass white dwarf at the origin of the low-luminosity Type Ia supernova 1999by

description: Multi-epoch spectra of the MCh model DDC25 and the sub-MCh model SCH2p0 used to confront to SN 1999by observations in Blondin et al. 2018, MNRAS, 474, 3931. These files also include the input hydrodynamical models at 0.5 d (for DDC25) and 0.75 d (for SCH2p0) past explosion.
DOI: 10.6084/m9.figshare.15128973
title: User Acceptance Survey (Responses)

description: User Response.
DOI: 10.6084/m9.figshare.15128973.v1
title: User Acceptance Survey (Responses)

description: User Response.
DOI: 10.5281/zenodo.5524991
title: Explosion of red-supergiant stars: Influence of the atmospheric structure on shock breakout and early-time supernova radiation

description: Model spectra from Dessart, Hillier & Audit 2017, A&A, 605, 83.
DOI: 10.5281/zenodo.5524990
title: Explosion of red-supergiant stars: Influence of the atmospheric structure on shock breakout and early-time supernova radiation

description: Model spectra from Dessart, Hillier & Audit 2017, A&A, 605, 83.
DOI: 10.5281/zenodo.5018842
title: IODP Expedition 359, Hole U1467C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.5281/zenodo.5018841
title: IODP Expedition 359, Hole U1467C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.6084/m9.figshare.14067047
title: Chronos: Gene Fitness Effect Inference from CRISPR

description: Dataset provided in support of "Chronos: a model of cell population dynamics in CRISPR knockout screens that improves inference of gene dependency" by Dempster et al. Includes the jupyter notebook Analysis.ipynb together with all files needed to generate the panels in that manuscript. Also includes MAGeCK-MLE, CRISPRCleanR, CERES, and Chronos inference of gene fitness effects for the genome-wide pan-cancer CRISPR knockout experiments Project Achilles (public 20Q2) and Project Score.<br><br>v2: Additional analysis and supporting information.<br><br>To conserve space and speed up data loading, large matrices are provided as hdf5 files where dim_0 stores row names, dim_1 stores column names, and data stores the matrix as a 2D array. UTF8 encoding was used. Python users can load the matrices as Pandas dataframes using read_hdf5 from the Chronos package at https://github.com/broadinstitute/chronos. <br><br>
DOI: 10.5517/ccdc.csd.cc25ny3x
title: CCDC 2016119: Experimental Crystal Structure Determination

description: Related Article: Pierce Pham, Christian Hilty|2020|Chem.Commun.|56|15466|doi:10.1039/D0CC06840C
DOI: 10.5517/ccdc.csd.cc248hfc
title: CCDC 1974372: Experimental Crystal Structure Determination

description: Related Article: Ding Luo, Qiang Lin, Jin-Lin Tan, Hai-Yue Zhao, Xiao Feng, Neng-Hua Chen, Zhong-Nan Wu, Chun-Lin Fan, Yao-Lan Li, Wei-Long Ding, Fei Xiao, Guo-Cai Wang, Yu-Bo Zhang|2021|Bioorg.Chem.|116|105337|doi:10.1016/j.bioorg.2021.105337
DOI: 10.5517/ccdc.csd.cc26x3qz
title: CCDC 2052811: Experimental Crystal Structure Determination

description: Related Article: Ding Luo, Qiang Lin, Jin-Lin Tan, Hai-Yue Zhao, Xiao Feng, Neng-Hua Chen, Zhong-Nan Wu, Chun-Lin Fan, Yao-Lan Li, Wei-Long Ding, Fei Xiao, Guo-Cai Wang, Yu-Bo Zhang|2021|Bioorg.Chem.|116|105337|doi:10.1016/j.bioorg.2021.105337
DOI: 10.5517/ccdc.csd.cc26x3s1
title: CCDC 2052813: Experimental Crystal Structure Determination

description: Related Article: Ding Luo, Qiang Lin, Jin-Lin Tan, Hai-Yue Zhao, Xiao Feng, Neng-Hua Chen, Zhong-Nan Wu, Chun-Lin Fan, Yao-Lan Li, Wei-Long Ding, Fei Xiao, Guo-Cai Wang, Yu-Bo Zhang|2021|Bioorg.Chem.|116|105337|doi:10.1016/j.bioorg.2021.105337
DOI: 10.5517/ccdc.csd.cc26x3t2
title: CCDC 2052814: Experimental Crystal Structure Determination

description: Related Article: Ding Luo, Qiang Lin, Jin-Lin Tan, Hai-Yue Zhao, Xiao Feng, Neng-Hua Chen, Zhong-Nan Wu, Chun-Lin Fan, Yao-Lan Li, Wei-Long Ding, Fei Xiao, Guo-Cai Wang, Yu-Bo Zhang|2021|Bioorg.Chem.|116|105337|doi:10.1016/j.bioorg.2021.105337
DOI: 10.5517/ccdc.csd.cc26ynpj
title: CCDC 2054298: Experimental Crystal Structure Determination

description: Related Article: Zhongzheng Cai, Wenjie Tao, Curtis E. Moore, Shiyu Zhang, Casey R. Wade|2021|Angew.Chem.,Int.Ed.|60|21221|doi:10.1002/anie.202108095
DOI: 10.5517/ccdc.csd.cc26zfc0
title: CCDC 2055032: Experimental Crystal Structure Determination

description: Related Article: Ding Luo, Qiang Lin, Jin-Lin Tan, Hai-Yue Zhao, Xiao Feng, Neng-Hua Chen, Zhong-Nan Wu, Chun-Lin Fan, Yao-Lan Li, Wei-Long Ding, Fei Xiao, Guo-Cai Wang, Yu-Bo Zhang|2021|Bioorg.Chem.|116|105337|doi:10.1016/j.bioorg.2021.105337
DOI: 10.5517/ccdc.csd.cc278cws
title: CCDC 2063635: Experimental Crystal Structure Determination

description: Related Article: Dieter Klintuch, Mark V. Höfler, Till Wissel, Clemens Bruhn, Torsten Gutmann, Rudolf Pietschnig|2021|Inorg.Chem.|60|14263|doi:10.1021/acs.inorgchem.1c01775
DOI: 10.5281/zenodo.5524995
title: A study of the low-luminosity Type II-Plateau SN 2008bk

description: Spectra, bolometric light curves, and multi-band light curves of the models discussed in Lisakov et al. 2017, MNRAS, 466, 33<strong> </strong>(each zipped tar file contains the multi-epoch spectra and a list relating model index to the time since explosion and a light curve file).
DOI: 10.5281/zenodo.5524994
title: A study of the low-luminosity Type II-Plateau SN 2008bk

description: Spectra, bolometric light curves, and multi-band light curves of the models discussed in Lisakov et al. 2017, MNRAS, 466, 33<strong> </strong>(each zipped tar file contains the multi-epoch spectra and a list relating model index to the time since explosion and a light curve file).
DOI: 10.5517/ccdc.csd.cc27b3nc
title: CCDC 2065302: Experimental Crystal Structure Determination

description: Related Article: Nagender Thadem, Manda Rajesh, Saibal Das|2021|RSC Advances|11|29934|doi:10.1039/D1RA05070B
DOI: 10.5517/ccdc.csd.cc27b3pd
title: CCDC 2065303: Experimental Crystal Structure Determination

description: Related Article: Nagender Thadem, Manda Rajesh, Saibal Das|2021|RSC Advances|11|29934|doi:10.1039/D1RA05070B
DOI: 10.5517/ccdc.csd.cc27b3qf
title: CCDC 2065304: Experimental Crystal Structure Determination

description: Related Article: Nagender Thadem, Manda Rajesh, Saibal Das|2021|RSC Advances|11|29934|doi:10.1039/D1RA05070B
DOI: 10.5517/ccdc.csd.cc27j5dc
title: CCDC 2071122: Experimental Crystal Structure Determination

description: Related Article: Yong-Fei Li, Hua Yang, Haiquan Tian, Da-Cheng Li, Jian-Min Dou|2021|Transition Met.Chem.|46|503|doi:10.1007/s11243-021-00468-2
DOI: 10.5517/ccdc.csd.cc27j5fd
title: CCDC 2071123: Experimental Crystal Structure Determination

description: Related Article: Yong-Fei Li, Hua Yang, Haiquan Tian, Da-Cheng Li, Jian-Min Dou|2021|Transition Met.Chem.|46|503|doi:10.1007/s11243-021-00468-2
DOI: 10.5281/zenodo.5525005
title: A magnetar model for the hydrogen-rich super-luminous supernova iPTF14hls

description: Model spectra from Dessart 2018, A&A, 610, 10.
DOI: 10.5281/zenodo.5525004
title: A magnetar model for the hydrogen-rich super-luminous supernova iPTF14hls

description: Model spectra from Dessart 2018, A&A, 610, 10.
DOI: 10.5281/zenodo.5046165
title: 3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)

description: 3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344.<br> Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io)<br> Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem
description: {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}
DOI: 10.5281/zenodo.5046164
title: 3D geological models of dolomitized clinoforms and flow simulation results: scenario 2 in Teoh, C.P. et al (2021)

description: 3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 2 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344.<br> Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io)<br> Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem
description: {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}
DOI: 10.5281/zenodo.5042301
title: 3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)

description: 3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344.<br> Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io)<br> Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem
description: {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}
DOI: 10.5281/zenodo.5042300
title: 3D geological models of dolomitized clinoforms and flow simulation results: scenario 1 in Teoh, C.P. et al (2021)

description: 3D geological models of dolomitized clinoforms (10 different realisations) and flow simulation results according to Scenario 1 in Teoh, C.P. et al (2021) doi:10.1016/j.marpetgeo.2021.105344.<br> Models are built using surface-based modelling approach (doi:10.1007/s11004-018-9764-8). Flow simulations are run with IC-FERST, using unstructured tetrahedral meshes that adapt to geological heterogeneity and flow behaviour throughout the simulation to improve simulation quality and performance. For each of the 10 stochastic realisations, 5 geological models are available with corresponding flow simulation results:<br> - Only clinoforms and facies boundaries<br> - 1 dolomite body per clinothem (~20% dolomite)<br> - 2 dolomite bodies per clinothem (~40% dolomite)<br> - 3 dolomite bodies per clinothem (~60% dolomite)<br> - 4 dolomite bodies per clinothem (~80% dolomite)<br> <br> Input model files for simulation are provided in Exodus (.e) and GMSH (.msh) formats.<br> Flow simulation settings are provided for IC-FERST in .mpml files (multifluids.github.io)<br> Flow simulation results are provided as: 3D unstructured adaptive mesh in .vtu format, which can be opened with Paraview (www.paraview.org). Time interval between successive mesh outputs is 1 month. In- and outflow rates and volumetric proportions per phase in .csv Naming of files and folders:<br> <em>Sxxxxxx_yyyyz</em> where:<br> '<em>xxxxxx</em>' is the stochastic seed number used to sample the input statistics and create the geological model<br> '<em>yyyy</em>' is either 'clino' or 'dolo' to indicate if the model represents respectively only clinoforms, or contains dolomite bodies <br> '<em>z</em>' corresponds to the number of dolomite bodies per clinothem
description: {"references": ["https://doi.org/10.1016/j.marpetgeo.2021.105344", "https://doi.org/10.1007/s11004-018-9764-8"]}
DOI: 10.5281/zenodo.5525007
title: Supernovae from blue supergiant progenitors: What a mess!

description: Model spectra from Dessart & Hillier 2019, A&A, 622, 70.
DOI: 10.5281/zenodo.5525006
title: Supernovae from blue supergiant progenitors: What a mess!

description: Model spectra from Dessart & Hillier 2019, A&A, 622, 70.
DOI: 10.5281/zenodo.5525011
title: Simulations of light curves and spectra for superluminous Type Ic supernovae powered by magnetars

description: Model spectra from Dessart 2019, A&A, 621, 141.
DOI: 10.5281/zenodo.5525010
title: Simulations of light curves and spectra for superluminous Type Ic supernovae powered by magnetars

description: Model spectra from Dessart 2019, A&A, 621, 141.
DOI: 10.5281/zenodo.5525021
title: The explosion of 9-29Msun stars as Type II supernovae: Results from radiative-transfer modeling at one year after explosion

description: Model spectra from Dessart et al. 2021, A&A, 652, 64.
DOI: 10.5281/zenodo.5525020
title: The explosion of 9-29Msun stars as Type II supernovae: Results from radiative-transfer modeling at one year after explosion

description: Model spectra from Dessart et al. 2021, A&A, 652, 64.
DOI: 10.3886/e114016v1-97005
title: Replication data for: Social Connectedness: Measurement, Determinants, and Effects

description: Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook's scale as well as the relative representativeness of Facebook's user body, these data provide the first comprehensive measure of friendship networks at a national level.
DOI: 10.5281/zenodo.5018857
title: IODP Expedition 359, Hole U1467E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.5281/zenodo.5018856
title: IODP Expedition 359, Hole U1467E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.6084/m9.figshare.14555958
title: Data from: Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as a potential mechanism of vaccine-induced protection against HIV-1

description: Data and metadata needed to create figures in "Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as the primary mechanism of vaccine-induced protection against HIV-1" (Shangguan, S., et. al.)
DOI: 10.6084/m9.figshare.14555958.v1
title: Data from: Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as a potential mechanism of vaccine-induced protection against HIV-1

description: Data and metadata needed to create figures in "Monocyte-derived transcriptome signature indicates antibody-dependent cellular phagocytosis as the primary mechanism of vaccine-induced protection against HIV-1" (Shangguan, S., et. al.)
DOI: 10.5281/zenodo.5019032
title: IODP Expedition 359, Hole U1468B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.5281/zenodo.5019031
title: IODP Expedition 359, Hole U1468B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.5281/zenodo.5019528
title: IODP Expedition 359, Hole U1471E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.5281/zenodo.5019527
title: IODP Expedition 359, Hole U1471E - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 359: Maldives Monsoon and Sea Level
DOI: 10.6084/m9.figshare.16670506.v1
title: Data Fig 1

description: Fig 1K and M