cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bhfy7
title: CCDC 2159799: Experimental Crystal Structure Determination

description: Related Article: Huabin Shi, Longju Li, Dandan Song, Ya Zheng, Zhibing Wu|2023|Arab.J.Chem.|16|104884|doi:10.1016/j.arabjc.2023.104884
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.cc2d62j7
title: CCDC 2210378: Experimental Crystal Structure Determination

description: Related Article: Fabian Hartl, Veronika Brune, Sophie Lüggert, Corinna Hegemann, David van Gerven, Michael Wilhelm, Seulgi Ji, Heechae Choi, Sanjay Mathur|2023|Inorg.Chem.|62|6274|doi:10.1021/acs.inorgchem.2c04083
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.cc2d62k8
title: CCDC 2210379: Experimental Crystal Structure Determination

description: Related Article: Fabian Hartl, Veronika Brune, Sophie Lüggert, Corinna Hegemann, David van Gerven, Michael Wilhelm, Seulgi Ji, Heechae Choi, Sanjay Mathur|2023|Inorg.Chem.|62|6274|doi:10.1021/acs.inorgchem.2c04083
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.cc2d62vk
title: CCDC 2210388: Experimental Crystal Structure Determination

description: Related Article: Fabian Hartl, Veronika Brune, Sophie Lüggert, Corinna Hegemann, David van Gerven, Michael Wilhelm, Seulgi Ji, Heechae Choi, Sanjay Mathur|2023|Inorg.Chem.|62|6274|doi:10.1021/acs.inorgchem.2c04083
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.cc2df073
title: CCDC 2217034: Experimental Crystal Structure Determination

description: Related Article: Mariann Papp, Tamás Keszthelyi, Andor Vancza, Éva G. Bajnóczi, Éva Kováts, Zoltán Németh, Csilla Bogdán, Gábor Bazsó, Tamás Rozgonyi, György Vankó|2023|Inorg.Chem.|62|6397|doi:10.1021/acs.inorgchem.3c00271
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.cc2df0f9
title: CCDC 2217040: Experimental Crystal Structure Determination

description: Related Article: Mariann Papp, Tamás Keszthelyi, Andor Vancza, Éva G. Bajnóczi, Éva Kováts, Zoltán Németh, Csilla Bogdán, Gábor Bazsó, Tamás Rozgonyi, György Vankó|2023|Inorg.Chem.|62|6397|doi:10.1021/acs.inorgchem.3c00271
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.cc2df0ql
title: CCDC 2217049: Experimental Crystal Structure Determination

description: Related Article: Mariann Papp, Tamás Keszthelyi, Andor Vancza, Éva G. Bajnóczi, Éva Kováts, Zoltán Németh, Csilla Bogdán, Gábor Bazsó, Tamás Rozgonyi, György Vankó|2023|Inorg.Chem.|62|6397|doi:10.1021/acs.inorgchem.3c00271
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.cc2dfzd7
title: CCDC 2217969: Experimental Crystal Structure Determination

description: Related Article: Mariann Papp, Tamás Keszthelyi, Andor Vancza, Éva G. Bajnóczi, Éva Kováts, Zoltán Németh, Csilla Bogdán, Gábor Bazsó, Tamás Rozgonyi, György Vankó|2023|Inorg.Chem.|62|6397|doi:10.1021/acs.inorgchem.3c00271
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.cc2dn3nv
title: CCDC 2223867: Experimental Crystal Structure Determination

description: Related Article: Ya-Xuan Jing, Meng-Meng Suo, Guo-Ping Yang, Yao-Yu Wang|2023|Inorg.Chem.|62|6361|doi:10.1021/acs.inorgchem.3c00132
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.cc2dn3pw
title: CCDC 2223868: Experimental Crystal Structure Determination

description: Related Article: Ya-Xuan Jing, Meng-Meng Suo, Guo-Ping Yang, Yao-Yu Wang|2023|Inorg.Chem.|62|6361|doi:10.1021/acs.inorgchem.3c00132
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.20373801
title: Birds and Barriers: present and past seas are dominant correlates of avian turnover in the Indo-Australia Archipelago.

description: <strong>Appendix S1.</strong> R code used in analyses with associated data <br> <strong>Abstract</strong> The Indo-Australian Archipelago (IAA) is a geologically dynamic area of high biotic endemism that spans the continental shelves of Sunda and Sahul and intervening oceanic islands. We provide a comprehensive quantitative assessment of how bird communities are structured across the IAA using beta diversity indices. We focus on three key questions. Are the islands of Wallacea a biogeographically cohesive unit or a more heterogeneous transition zone? Is the rich biota of New Guinea and the East Melanesian islands most closely linked to that of Sunda, Sahul or Wallacea? What are the geographic and environmental correlates of biotic structuring across the region and how does this compare with determinants of plant diversity? We measure the dissimilarity of bird species composition between eleven major areas within the IAA using a new compilation of distribution data and calculate taxonomic beta diversity at species, genera, and family levels. To compare with recent analyses of plants, we analysed potential correlates of turnover, focusing on geographic proximity, sea barriers, land area, and climatic variation. We also used connectivity analyses to estimate the minimum number of connection events needed to explain the current distribution of shared taxa. We found that islands recently connected by land have lower beta diversity than oceanic islands. Additionally, avifaunas on the islands of Wallacea have little biotic cohesion, reflecting their complex geological history. The avifaunas of New Guinea and the Eastern Melanesian islands are very distinctive. Where New Guinea birds are most similar to Australia, East Melanesian birds are more similar to either Sahul or Wallacea, depending on taxonomic level. Isolation through space and time had the strongest influence on avifaunal turnover at all taxonomic scales, in contrast to plants for which climatic variation was the strongest predictor. Further analyses incorporating phylogeny, biome, trait, and biotic interaction data are needed to investigate the processes that have caused biotic turnover across this fascinating biogeographic region.
DOI: 10.6084/m9.figshare.20373801.v1
title: Birds and Barriers: present and past seas are dominant correlates of avian turnover in the Indo-Australia Archipelago.

description: <strong>Appendix S1.</strong> R code used in analyses with associated data <br> <strong>Abstract</strong> The Indo-Australian Archipelago (IAA) is a geologically dynamic area of high biotic endemism that spans the continental shelves of Sunda and Sahul and intervening oceanic islands. We provide a comprehensive quantitative assessment of how bird communities are structured across the IAA using beta diversity indices. We focus on three key questions. Are the islands of Wallacea a biogeographically cohesive unit or a more heterogeneous transition zone? Is the rich biota of New Guinea and the East Melanesian islands most closely linked to that of Sunda, Sahul or Wallacea? What are the geographic and environmental correlates of biotic structuring across the region and how does this compare with determinants of plant diversity? We measure the dissimilarity of bird species composition between eleven major areas within the IAA using a new compilation of distribution data and calculate taxonomic beta diversity at species, genera, and family levels. To compare with recent analyses of plants, we analysed potential correlates of turnover, focusing on geographic proximity, sea barriers, land area, and climatic variation. We also used connectivity analyses to estimate the minimum number of connection events needed to explain the current distribution of shared taxa. We found that islands recently connected by land have lower beta diversity than oceanic islands. Additionally, avifaunas on the islands of Wallacea have little biotic cohesion, reflecting their complex geological history. The avifaunas of New Guinea and the Eastern Melanesian islands are very distinctive. Where New Guinea birds are most similar to Australia, East Melanesian birds are more similar to either Sahul or Wallacea, depending on taxonomic level. Isolation through space and time had the strongest influence on avifaunal turnover at all taxonomic scales, in contrast to plants for which climatic variation was the strongest predictor. Further analyses incorporating phylogeny, biome, trait, and biotic interaction data are needed to investigate the processes that have caused biotic turnover across this fascinating biogeographic region.
DOI: 10.5281/zenodo.7869635
title: Co-activation probability between neurons in the largest brain connectome of the fruit fly

description: This is a data set containing the co-activation probability between neurons in the largest brain connectome of the fruit fly released by the FlyEM project. The co-activation probability is measured based on neural dynamics computation, where a standard leaky integrate-and-fire (LIF) model is applied on the connectome to generate neural dynamics. Please read the paper "Yang Tian, Pei Sun; <strong>Percolation may explain efficiency, robustness, and economy of the brain</strong>. <em><em>Network Neuroscience</em></em> 2022; 6 (3): 765–790. doi: https://doi.org/10.1162/netn_a_00246" for more details. <strong>This is the newest version of the data set.</strong> The following is a list of variable information: (1) SomaLocation is a 23008*3 matrix that contains the three-dimensional coordinates of neurons; (2) LambdaVector is the vector of a vector of synaptic excitation-inhibition (E/I) balance (see "Percolation may explain efficiency, robustness, and economy of the brain" for detailed explanations). (3) DirectedCoactivationPattern is a cell of co-activation probability matrices generated under each E/I balance condition, which is used in "Percolation may explain efficiency, robustness, and economy of the brain" for computational experiments. The (i,j)-th element in the matrix is the probability for neuron i to activate neuron j under the corresponding E/I balance condition. Note that the (i,j)-th element can be differnt from the (j,i)-th element. (4) SymmetricCoactivationPattern is a cell of symmetric co-activation probability matrices generated under each E/I balance condition. This is a new data that has not been used in "Percolation may explain efficiency, robustness, and economy of the brain" yet. The (i,j)-th element in the matrix is the probability for neurons i and j to be co-activated under the corresponding E/I balance condition. If we define D as the directed co-activation probability matrix and denote S as the symmetric co-activation probability matrix, then we have S(i,j)=S(j,i)=0.5*(D(i,j)+D(j,i)). The earlist version of this data can be seen in https://zenodo.org/record/5497516, which may lack detailed explanations. The second version of this data can be seen in https://zenodo.org/record/7869532, where a small mistake is found while calculating the SymmetricCoactivationPattern. This mistake is resolved in the newest version.
description: {"references": ["Yang Tian, Pei Sun; Percolation may explain efficiency, robustness, and economy of the brain. Network Neuroscience 2022; 6 (3): 765\u2013790. doi: https://doi.org/10.1162/netn_a_00246"]}
DOI: 10.5281/zenodo.5497515
title: Co-activation probability between neurons in the largest brain connectome of the fruit fly

description: This is a data set containing the co-activation probability between neurons in the largest brain connectome of the fruit fly released by the FlyEM project. The co-activation probability is measured based on neural dynamics computation, where a standard leaky integrate-and-fire (LIF) model is applied on the connectome to generate neural dynamics. Please read the paper "Yang Tian, Pei Sun; <strong>Percolation may explain efficiency, robustness, and economy of the brain</strong>. <em><em>Network Neuroscience</em></em> 2022; 6 (3): 765–790. doi: https://doi.org/10.1162/netn_a_00246" for more details. <strong>This is the newest version of the data set.</strong> The following is a list of variable information: (1) SomaLocation is a 23008*3 matrix that contains the three-dimensional coordinates of neurons; (2) LambdaVector is the vector of a vector of synaptic excitation-inhibition (E/I) balance (see "Percolation may explain efficiency, robustness, and economy of the brain" for detailed explanations). (3) DirectedCoactivationPattern is a cell of co-activation probability matrices generated under each E/I balance condition, which is used in "Percolation may explain efficiency, robustness, and economy of the brain" for computational experiments. The (i,j)-th element in the matrix is the probability for neuron i to activate neuron j under the corresponding E/I balance condition. Note that the (i,j)-th element can be differnt from the (j,i)-th element. (4) SymmetricCoactivationPattern is a cell of symmetric co-activation probability matrices generated under each E/I balance condition. This is a new data that has not been used in "Percolation may explain efficiency, robustness, and economy of the brain" yet. The (i,j)-th element in the matrix is the probability for neurons i and j to be co-activated under the corresponding E/I balance condition. If we define D as the directed co-activation probability matrix and denote S as the symmetric co-activation probability matrix, then we have S(i,j)=S(j,i)=0.5*(D(i,j)+D(j,i)). The earlist version of this data can be seen in https://zenodo.org/record/5497516, which may lack detailed explanations. The second version of this data can be seen in https://zenodo.org/record/7869532, where a small mistake is found while calculating the SymmetricCoactivationPattern. This mistake is resolved in the newest version.
description: {"references": ["Yang Tian, Pei Sun; Percolation may explain efficiency, robustness, and economy of the brain. Network Neuroscience 2022; 6 (3): 765\u2013790. doi: https://doi.org/10.1162/netn_a_00246"]}
DOI: 10.25901/kzp0-bm17
title: Far North Queensland, Soil Chemistry Data, 2019
DOI: 10.26183/mhrt-g116
title: Quality Equity and Systems Transformation in Primary Health Care (QUEST PHC) Delphi Study Quantitative Data

description: Delphi study results for QUEST PHC indicators and measures. Quest Equity and Systems Transformation in Primary Health Care (QUEST PHC) developed a suite of 79 indicators and their associated 128 measures of quality in general practice. This dataset is summary data from the Delphi study conducted to establish consensus on the relevance and feasibility of the QUEST PHC suite in Australia with general practice and Primary Health Network (PHN) experts. For more information about the data, please contact Phyllis Lau Chan Phyllis.Lau@westernsydney.edu.au ORCID 0000-0002-0665-6348.
DOI: 10.25901/e1yb-ea83
title: Far North Queensland, Montane Tree Leaf Chemistry Data, 2019
DOI: 10.25901/59d8-8x69
title: Great Western Woodlands Site, Leaf Level Physiology, Chemistry And Structural Traits Data, 2014
DOI: 10.6084/m9.figshare.6442796
title: Data on forest regeneration after catastrophic windthrow in the headwater region of the Ishikari River, Hokkaido, Japan

description: Details of the data are described in:ITÔ,Hiroki; NAKANISHI, Atsushi; TSUYAMA, Ikutaro; SEKI, Takeshi, IIDA, Shigeo; KAWAHARA, Takayuki (2018) Data on forest regeneration after catastrophic windthrow in the headwater region of the Ishikari River, Hokkaido, Japan. Bulletin of FFPRI 17: 265–272 (in Japanese with English summary).<br>Table S1.txt: Tab delimitated text file (UTF-8, LF) containing the temporal changes in vegetation. The structure is as follows:<br>Field name: DescriptionBelt: Numerical identifier of the study belt.Quadrat: Numerical identifier of the quadrat in each belt.Layer: Position in vertical layer (canopy, subcanopy, shrub, herb, vine, moss).Year: Survey year; The survey of Belt [38] in 1988 was conducted in 1989 in real.Date: Survey date; If the month or day was unclear, this filed is described up to the year or month, respectively.Scientific_name: Scientific name of the species.Japanese_name: Japanese name of the species.Cover: Rank of the vegetation coverage (+: 0–0.01, 1: 0.01–0.1, 2: 0.1–0.25, 3: 0.25–0.5, 4: 0.5–0.75, 5: 0.75–1).<br>Table S2: Tab delimitated text file (UTF-8, LF) containing the temporal changes in trees. The structure is as follows:<br>Field name: DescriptionID: Numerical identifier of the stem.Individual_ID: Numerical identifier of the individual.Belt: Numerical identifier of the survey belt.X: Location along the X axis in the belt (m).Y: Location along the Y axis in the belt (m).Scientific_name: Scientific name of the species.Japanese_name: Japanese name of the species.First: The first year that the stem was observed.Last: The last year that the stem was observed alive; for living stems, the last survey year is assigned to this field.H_1993: Height at 1993 (m); NA for missing data.D_1993: Diameter at breast height in 1993 (cm).H_1998: Height at 1998 (m); NA for missing data.D_1998: Diameter at breast height in 1998 (cm).H_2002: Height at 2002 (m).D_2002: Diameter at breast height in 2002 (cm).H_2009: Height at 2009 (m); NA for missing data.D_2009: Diameter at breast height in 2009 (cm).H_2017: Height at 2017 (m).D_2017: Diameter at breast height in 2017 (cm).<br>
DOI: 10.6084/m9.figshare.6442796.v3
title: Data on forest regeneration after catastrophic windthrow in the headwater region of the Ishikari River, Hokkaido, Japan

description: Details of the data are described in:ITÔ,Hiroki; NAKANISHI, Atsushi; TSUYAMA, Ikutaro; SEKI, Takeshi, IIDA, Shigeo; KAWAHARA, Takayuki (2018) Data on forest regeneration after catastrophic windthrow in the headwater region of the Ishikari River, Hokkaido, Japan. Bulletin of FFPRI 17: 265–272 (in Japanese with English summary).<br>Table S1.txt: Tab delimitated text file (UTF-8, LF) containing the temporal changes in vegetation. The structure is as follows:<br>Field name: DescriptionBelt: Numerical identifier of the study belt.Quadrat: Numerical identifier of the quadrat in each belt.Layer: Position in vertical layer (canopy, subcanopy, shrub, herb, vine, moss).Year: Survey year; The survey of Belt [38] in 1988 was conducted in 1989 in real.Date: Survey date; If the month or day was unclear, this filed is described up to the year or month, respectively.Scientific_name: Scientific name of the species.Japanese_name: Japanese name of the species.Cover: Rank of the vegetation coverage (+: 0–0.01, 1: 0.01–0.1, 2: 0.1–0.25, 3: 0.25–0.5, 4: 0.5–0.75, 5: 0.75–1).<br>Table S2: Tab delimitated text file (UTF-8, LF) containing the temporal changes in trees. The structure is as follows:<br>Field name: DescriptionID: Numerical identifier of the stem.Individual_ID: Numerical identifier of the individual.Belt: Numerical identifier of the survey belt.X: Location along the X axis in the belt (m).Y: Location along the Y axis in the belt (m).Scientific_name: Scientific name of the species.Japanese_name: Japanese name of the species.First: The first year that the stem was observed.Last: The last year that the stem was observed alive; for living stems, the last survey year is assigned to this field.H_1993: Height at 1993 (m); NA for missing data.D_1993: Diameter at breast height in 1993 (cm).H_1998: Height at 1998 (m); NA for missing data.D_1998: Diameter at breast height in 1998 (cm).H_2002: Height at 2002 (m).D_2002: Diameter at breast height in 2002 (cm).H_2009: Height at 2009 (m); NA for missing data.D_2009: Diameter at breast height in 2009 (cm).H_2017: Height at 2017 (m).D_2017: Diameter at breast height in 2017 (cm).<br>
DOI: 10.6084/m9.figshare.22706845
title: Thesis - Supplementary Table 4.1

description: <strong>Supplementary Table 4.1. HHsearch analysis of Hma-embedded genes.</strong>
DOI: 10.6084/m9.figshare.22706845.v1
title: Thesis - Supplementary Table 4.1

description: <strong>Supplementary Table 4.1. HHsearch analysis of Hma-embedded genes.</strong>
DOI: 10.5281/zenodo.7869590
title: Data of Numerical simulation data and experimental data for vertical mixing schemes

description: This dataset contains the Numerical simulation data and experimental data in paper_data file. The new scheme is saved in the 'mypackage' folder in paper_code file.
DOI: 10.5281/zenodo.7869589
title: Data of Numerical simulation data and experimental data for vertical mixing schemes

description: This dataset contains the Numerical simulation data and experimental data in paper_data file. The new scheme is saved in the 'mypackage' folder in paper_code file.
DOI: 10.6084/m9.figshare.22707169
title: Thesis - Supplementary Table 4.2

description: <strong>Supplementary Table 4.2. Details of Hma-embedded candidates.</strong>
DOI: 10.6084/m9.figshare.22707100
title: Spill the Chopsticks

description: Can L1-L2 overlaps faciliate L2 idiom learning?
DOI: 10.6084/m9.figshare.22707100.v1
title: Spill the Chopsticks

description: Can L1-L2 overlaps faciliate L2 idiom learning?