cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15102958
title: Additional file 3 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S3. List of drug binding hits for 6M03 –NSP5.
DOI: 10.6084/m9.figshare.15102961
title: Additional file 4 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S4. List of drug binding hits for 7JLT-NSP7-8.
DOI: 10.6084/m9.figshare.15102964
title: Additional file 5 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S5. List of drug binding hits for 6W4B-NSP9.
DOI: 10.6084/m9.figshare.15102955
title: Additional file 2 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S2. List of drug binding hits for 6WEY-NSP3.
DOI: 10.6084/m9.figshare.15102958.v1
title: Additional file 3 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S3. List of drug binding hits for 6M03 –NSP5.
DOI: 10.6084/m9.figshare.15102967.v1
title: Additional file 6 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S6. List of drug binding hits for 6ZCT-NSP10.
DOI: 10.6084/m9.figshare.15102967
title: Additional file 6 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S6. List of drug binding hits for 6ZCT-NSP10.
DOI: 10.6084/m9.figshare.15102961.v1
title: Additional file 4 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S4. List of drug binding hits for 7JLT-NSP7-8.
DOI: 10.6084/m9.figshare.15102964.v1
title: Additional file 5 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S5. List of drug binding hits for 6W4B-NSP9.
DOI: 10.6084/m9.figshare.15102970
title: Additional file 7 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S7. List of drug binding hits for 6M71-NSP7-8-12.
DOI: 10.6084/m9.figshare.15102970.v1
title: Additional file 7 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S7. List of drug binding hits for 6M71-NSP7-8-12.
DOI: 10.6084/m9.figshare.15102976.v1
title: Additional file 9 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S9. List of drug binding hits for 5C8S-NSP14.
DOI: 10.6084/m9.figshare.15102973.v1
title: Additional file 8 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S8. List of drug binding hits for 7NIO-NSP13.
DOI: 10.6084/m9.figshare.15102973
title: Additional file 8 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S8. List of drug binding hits for 7NIO-NSP13.
DOI: 10.6084/m9.figshare.15102976
title: Additional file 9 of Predicted antiviral drugs Darunavir, Amprenavir, Rimantadine and Saquinavir can potentially bind to neutralize SARS-CoV-2 conserved proteins

description: Additional file 1: S9. List of drug binding hits for 5C8S-NSP14.
DOI: 10.6084/m9.figshare.15102982.v1
title: Additional file 2 of Vanillic acid and methoxyhydroquinone production from guaiacyl units and related aromatic compounds using Aspergillus niger cell factories

description: Additional file 2: Table S1. Molar and mass yield on vanillin, vanillic acid or methoxyhydroquinone production from 5 mmol/L on guaiacyl units and related aromatic compounds obtained by ∆vdhA, ∆vhyA and ∆mhdA. Mean values and standard deviations of three biological replicates. Table S2. Transcriptome data of hqdA and NRRL3_4956. Fold change (log2) compared to a no carbon source control and p-values were calculated with DESeq2. Table S3. Primers used in this study. In red are the regions overlapping pyrG. Table S4. Fungal genomes used for the phylogenetic study of VdhA and MhdA. Genomes were obtained from JGI MycoCosm ( https://mycocosm.jgi.doe.gov/mycocosm/home ). Table S5. Amino acid sequences used for the phylogenetic analysis of MhdA and VdhA that were curated manually or with the gene prediction software Augustus.
DOI: 10.6084/m9.figshare.15102982
title: Additional file 2 of Vanillic acid and methoxyhydroquinone production from guaiacyl units and related aromatic compounds using Aspergillus niger cell factories

description: Additional file 2: Table S1. Molar and mass yield on vanillin, vanillic acid or methoxyhydroquinone production from 5 mmol/L on guaiacyl units and related aromatic compounds obtained by ∆vdhA, ∆vhyA and ∆mhdA. Mean values and standard deviations of three biological replicates. Table S2. Transcriptome data of hqdA and NRRL3_4956. Fold change (log2) compared to a no carbon source control and p-values were calculated with DESeq2. Table S3. Primers used in this study. In red are the regions overlapping pyrG. Table S4. Fungal genomes used for the phylogenetic study of VdhA and MhdA. Genomes were obtained from JGI MycoCosm ( https://mycocosm.jgi.doe.gov/mycocosm/home ). Table S5. Amino acid sequences used for the phylogenetic analysis of MhdA and VdhA that were curated manually or with the gene prediction software Augustus.
DOI: 10.6084/m9.figshare.15102996.v1
title: Additional file 1 of Quantification of spatial metal accumulation patterns in Noccaea caerulescens by X-ray fluorescence image processing for genetic studies

description: Additional file 1. List of Noccaea caerulescens accessions used.
DOI: 10.6084/m9.figshare.15102996
title: Additional file 1 of Quantification of spatial metal accumulation patterns in Noccaea caerulescens by X-ray fluorescence image processing for genetic studies

description: Additional file 1. List of Noccaea caerulescens accessions used.
DOI: 10.6084/m9.figshare.15081566.v1
title: ESM file 11 – Analysis files – 5 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Plots notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081572
title: ESM file 6 – Data file from Vocal learning in songbirds: the role of syllable order in song recognition

description: Data file (.cvs)
DOI: 10.6084/m9.figshare.15081566
title: ESM file 11 – Analysis files – 5 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Plots notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081572.v1
title: ESM file 6 – Data file from Vocal learning in songbirds: the role of syllable order in song recognition

description: Data file (.cvs)
DOI: 10.6084/m9.figshare.15081578.v1
title: ESM file 8 – Analysis files – 2 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Plots notebook (read only; html)
DOI: 10.6084/m9.figshare.15081578
title: ESM file 8 – Analysis files – 2 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Plots notebook (read only; html)
DOI: 10.6084/m9.figshare.15081584
title: ESM file 12 – Analysis files – 6 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Statistics notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081587.v1
title: ESM file 7 – Analysis files – 1 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Mixed models notebook (read only; html)
DOI: 10.6084/m9.figshare.15081584.v1
title: ESM file 12 – Analysis files – 6 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Statistics notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081587
title: ESM file 7 – Analysis files – 1 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Mixed models notebook (read only; html)
DOI: 10.6084/m9.figshare.15081596
title: ESM file 10 – Analysis files – 4 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Mixed models notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081596.v1
title: ESM file 10 – Analysis files – 4 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Mixed models notebook (.ipynb)
DOI: 10.6084/m9.figshare.15081599
title: ESM file 9 – Analysis files – 3 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Statistics notebook (read only; html)
DOI: 10.6084/m9.figshare.15081599.v1
title: ESM file 9 – Analysis files – 3 from Vocal learning in songbirds: the role of syllable order in song recognition

description: Statistics notebook (read only; html)
DOI: 10.6084/m9.figshare.15103092
title: Weakfish SIA Data_Delaware Bay 2017 and 2018

description: This dataset includes an abundance of delta 13C and delta 15N stable isotope ratios of juvenile Weakfish caught in 2017 and 2018 from the Delaware Bay, USA. The GPS coordinates of the sampling stations can be provided on an individual basis.
DOI: 10.6084/m9.figshare.15103092.v1
title: E&C_Weakfish_Data.xlsb.xlsx

description: This dataset includes an abundance of delta 13C and delta 15N stable isotope ratios of juvenile Weakfish caught in 2017 and 2018 from the Delaware Bay, USA. The GPS coordinates of the sampling stations can be provided on an individual basis.
DOI: 10.15468/2pg8cf
title: Protomauroania mikhailovi-a new species of malachite beetles (Coleoptera Dasytidae) in Rovno amber

description: This dataset contains the digitized treatments in Plazi based on the original journal article Tshernyshev, Sergei E., Perkovsky, Evgeny E. (2021): Protomauroania mikhailovi-a new species of malachite beetles (Coleoptera Dasytidae) in Rovno amber. Zootaxa 5006 (1): 189-194, DOI: https://doi.org/10.11646/zootaxa.5006.1.20
DOI: 10.15468/wb52cp
title: Resurrection of the spider genus Bucliona Benoit, 1977, with a description of a new species from Kenya (Araneae, Clubionidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Zhang, Jianshuang, Marusik, Yuri M., Oketch, Ambata D., Kioko, Esther N., Yu, Hao, Li, Shuqiang (2021): Resurrection of the spider genus Bucliona Benoit, 1977, with a description of a new species from Kenya (Araneae, Clubionidae). Zootaxa 5006 (1): 195-207, DOI: https://doi.org/10.11646/zootaxa.5006.1.21
DOI: 10.25440/smu.14705112
title: Data and code for: Two roads diverged: An alternative perspective on the EASI Model

description: This is the data and code for the published PhD Dissertation <i>Two roads diverged: An alternative perspective on the EASI model,</i> available at: https://ink.library.smu.edu.sg/etd_coll/331/<br>This research examines the relative influence of four mediators of the relationship between anger expressions and concessionary behaviour. The mediators were investigated simultaneously and perceptions of appropriateness were investigated as a moderator of these mediating relationships. Full detail of the methodology and data analytic strategies can be found in the published PhD dissertation.
DOI: 10.25440/smu.14705112.v1
title: Data and code for: Two roads diverged: An alternative perspective on the EASI Model

description: This is the data and code for the published PhD Dissertation <i>Two roads diverged: An alternative perspective on the EASI model,</i> available at: https://ink.library.smu.edu.sg/etd_coll/331/<br>This research examines the relative influence of four mediators of the relationship between anger expressions and concessionary behaviour. The mediators were investigated simultaneously and perceptions of appropriateness were investigated as a moderator of these mediating relationships. Full detail of the methodology and data analytic strategies can be found in the published PhD dissertation.
DOI: 10.25440/smu.14610096
title: Data from: Debiasing a decision maker facing supply uncertainties in a newsvendor setting

description: This is the underlying dataset for the PhD dissertation: Debiasing a decision maker facing supply uncertainties in a newsvendor setting, available at: https://ink.library.smu.edu.sg/etd_coll/347/<br>Companies must be prepared to manage uncontrollable events that will disrupt their supply chain and add uncertainty to their inventory models.<br>This thesis first studies the effect of different types of supply disruption risks on the ordering performance of profit-maximizing decision makers in a newsvendor setting.<br>Then, this thesis aims at extending the literature on the newsvendor model in studying the effect of a Decision Support System and the effect of a Secondary Task on the ordering performance of profit-maximizing decision makers who face supply uncertainties in a newsvendor setting.<br>Finally, implications for scholars and practitioners are discussed.<br>
DOI: 10.25440/smu.14610096.v1
title: Data from: Debiasing a decision maker facing supply uncertainties in a newsvendor setting

description: This is the underlying dataset for the PhD dissertation: Debiasing a decision maker facing supply uncertainties in a newsvendor setting, available at: https://ink.library.smu.edu.sg/etd_coll/347/<br>Companies must be prepared to manage uncontrollable events that will disrupt their supply chain and add uncertainty to their inventory models.<br>This thesis first studies the effect of different types of supply disruption risks on the ordering performance of profit-maximizing decision makers in a newsvendor setting.<br>Then, this thesis aims at extending the literature on the newsvendor model in studying the effect of a Decision Support System and the effect of a Secondary Task on the ordering performance of profit-maximizing decision makers who face supply uncertainties in a newsvendor setting.<br>Finally, implications for scholars and practitioners are discussed.<br>
DOI: 10.5517/ccdc.csd.cc25xk8y
title: CCDC 2023440: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xk9z
title: CCDC 2023441: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkb0
title: CCDC 2023442: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkc1
title: CCDC 2023443: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkd2
title: CCDC 2023444: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkf3
title: CCDC 2023445: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkg4
title: CCDC 2023446: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc25xkh5
title: CCDC 2023447: Experimental Crystal Structure Determination

description: Related Article: Tong Cao, Francisco Javier Valverde-Muñoz, Xiaoyi Duan, Mingjian Zhang, Ping Wang, Lingbao Xing, Fenggang Sun, Zhen Zhou, Hui Liu, Jianzhuang Jiang, M. Carmen Muñoz, José Antonio Real, Daopeng Zhang|2021|Inorg.Chem.|60|11048|doi:10.1021/acs.inorgchem.1c00802
DOI: 10.5517/ccdc.csd.cc26yp51
title: CCDC 2054313: Experimental Crystal Structure Determination

description: Related Article: Yujie Ma, Xue Han, Shaojun Xu, Zi Wang, Weiyao Li, Ivan da Silva, Sarayute Chansai, Daniel Lee, Yichao Zou, Marek Nikiel, Pascal Manuel, Alena M. Sheveleva, Floriana Tuna, Eric J. L. McInnes, Yongqiang Cheng, Svemir Rudić, Anibal J. Ramirez-Cuesta, Sarah J. Haigh, Christopher Hardacre, Martin Schröder, Sihai Yang|2021|J.Am.Chem.Soc.|143|10977|doi:10.1021/jacs.1c03036
DOI: 10.5517/ccdc.csd.cc27b42t
title: CCDC 2065315: Experimental Crystal Structure Determination

description: Related Article: Wanshu Wang, Song Zhou, Linjie Li, Yuanhang He, Xue Dong, Lu Gao, Qiantao Wang, Zhenlei Song|2021|J.Am.Chem.Soc.|143|11141|doi:10.1021/jacs.1c04667
DOI: 10.5517/ccdc.csd.cc27m5sv
title: CCDC 2074017: Experimental Crystal Structure Determination

description: Related Article: Iuliana Besleaga, Iryna Stepanenko, Tatsiana V. Petrasheuskaya, Denisa Darvasiova, Martin Breza, Marta Hammerstad, Magorzata A. Marć, Alexander Prado-Roller, Gabriella Spengler, Ana Popović-Bijelić, Eva A. Enyedy, Peter Rapta, Anatoly D. Shutalev, Vladimir B. Arion|2021|Inorg.Chem.|60|11297|doi:10.1021/acs.inorgchem.1c01275
DOI: 10.5517/ccdc.csd.cc27m5tw
title: CCDC 2074018: Experimental Crystal Structure Determination

description: Related Article: Iuliana Besleaga, Iryna Stepanenko, Tatsiana V. Petrasheuskaya, Denisa Darvasiova, Martin Breza, Marta Hammerstad, Magorzata A. Marć, Alexander Prado-Roller, Gabriella Spengler, Ana Popović-Bijelić, Eva A. Enyedy, Peter Rapta, Anatoly D. Shutalev, Vladimir B. Arion|2021|Inorg.Chem.|60|11297|doi:10.1021/acs.inorgchem.1c01275
DOI: 10.5517/ccdc.csd.cc27m5vx
title: CCDC 2074019: Experimental Crystal Structure Determination

description: Related Article: Iuliana Besleaga, Iryna Stepanenko, Tatsiana V. Petrasheuskaya, Denisa Darvasiova, Martin Breza, Marta Hammerstad, Magorzata A. Marć, Alexander Prado-Roller, Gabriella Spengler, Ana Popović-Bijelić, Eva A. Enyedy, Peter Rapta, Anatoly D. Shutalev, Vladimir B. Arion|2021|Inorg.Chem.|60|11297|doi:10.1021/acs.inorgchem.1c01275
DOI: 10.25440/smu.14916618
title: Data from: Fuelling effects of unique opinion holder’s emotions on team creativity: A collective information processing perspective

description: This is the underlying research data for the published PhD dissertation, <i>Fuelling effects of unique opinion holder’s emotions on team creativity: A collective information processing perspective</i>, available at: https://ink.library.smu.edu.sg/etd_coll/336/<br>This research examined the influence of unique opinion holder's emotions on team creativity. As compared to teams that interacted with a neutral unique opinion holder, teams working with either an angry or happy unique opinion holder were found to utilize qualitatively different ways of achieving creative ideas. The team dataset and code could be found in this page whereas the main paper provides the description of the methodology and measures.<br>
DOI: 10.25440/smu.14916618.v1
title: Data from: Fuelling effects of unique opinion holder’s emotions on team creativity: A collective information processing perspective

description: This is the underlying research data for the published PhD dissertation, <i>Fuelling effects of unique opinion holder’s emotions on team creativity: A collective information processing perspective</i>, available at: https://ink.library.smu.edu.sg/etd_coll/336/<br>This research examined the influence of unique opinion holder's emotions on team creativity. As compared to teams that interacted with a neutral unique opinion holder, teams working with either an angry or happy unique opinion holder were found to utilize qualitatively different ways of achieving creative ideas. The team dataset and code could be found in this page whereas the main paper provides the description of the methodology and measures.<br>
DOI: 10.25345/c5z84g
title: MassIVE MSV000087937 - Positive ion mode LC-MS/MS analysis for maize tissues
title: LC-MS analysis was run in positive mode on the QE-HF and TripleTOF 5600+ for maize silk, leave, and seed.
DOI: 10.25345/c5tg0s
title: MassIVE MSV000087938 - Negative ion mode LC-MS/MS analysis for maize tissues
title: LC-MS analysis was run in negative mode on the QE-HF and TripleTOF 5600+ for maize silk, leave, and seed
DOI: 10.15468/k483my
title: koellikerina_maasi

description: Koellikerina maasi materials from the following publication: Verhaegen, G, Cimoli, E, & Lindsay, D (2021). Life beneath the ice: jellyfish and ctenophores from the Ross Sea, Antarctica, with an image-based training set for machine learning. Biodiversity Data Journal.