cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/z04k-gyv
title: Structural and functional connectivity data of the ARCHI database in the Constellation atlas

description: Structural connectivity matrices and resting-state functional data have been collected on the 78 subjects of the ARCHI database. Connectivity data have been mapped on the Constellation atlas, which is derived from the Desikan as follows. The Constellation atlas has been built as a subdivision of the regions of the Desikan atlas, based on the structural connectivity data of this specific group of subjects. The subdivision has been computed with the Constellation toolbox, in such a way that the resulting regions have a uniform connectivity profile across subjects. This atlas is thus specific to this group of subjects, but is likely to differ only slightly for other groups because it takes into account the inter-subject variability and the group is large enough to represent the main variability of a normal population. Individual connectivity matrices have been built for diffusion-MR-based structural connectivity (connection strength resulting from fibers reconstructed using a tractography algorithm). Resting-state functional data have been reduced into this atlas. More precisely the dataset includes: * The Constellation parcellation atlas, as a texture (.gii), with nomenclatures as text files and regions surface areas (.csv). * Individual matrices of structural connectivity for all subjects of ARCHI. * Individual rsfMRI time series reduced on the Constellation atlas. * Individual cortical meshes in .gii format. * An average mesh built for visualization purposes from half of the ARCHI subjects, as a mesh (.gii).
DOI: 10.6084/m9.figshare.18881372
title: iHypoxia: An Integrative Database of the Expression Dynamics of Proteins in Response to Hypoxia in Animals

description: we developed an integrated resource for the expression dynamics of proteins and candidate genes in response to hypoxia (iHypoxia), and this database contains 1629 expression events of 1215 proteins identified by low-throughput experiments (LTEs) and 456,852 quantitative expression events of 39,483 proteins identified by high-throughput experiments (HTEs) from five mammals that exhibit a response to hypoxia. Various experimental details such as the hypoxic experimental conditions, expression patterns, and samples were carefully collected and integrated. Furthermore, ~10,000 candidate genes were also integrated from diverse species inhabiting low oxygen environment. In addition, we conducted an orthologous search and identified 432,298 computationally detected proteins that may respond to hypoxia among 48 animals.
DOI: 10.6084/m9.figshare.18881372.v2
title: iHypoxia.rar

description: we developed an integrated resource for the expression dynamics of proteins and candidate genes in response to hypoxia (iHypoxia), and this database contains 1629 expression events of 1215 proteins identified by low-throughput experiments (LTEs) and 456,852 quantitative expression events of 39,483 proteins identified by high-throughput experiments (HTEs) from five mammals that exhibit a response to hypoxia. Various experimental details such as the hypoxic experimental conditions, expression patterns, and samples were carefully collected and integrated. Furthermore, ~10,000 candidate genes were also integrated from diverse species inhabiting low oxygen environment. In addition, we conducted an orthologous search and identified 432,298 computationally detected proteins that may respond to hypoxia among 48 animals.
DOI: 10.25493/2v62-ttg
title: Ultrahigh resolution 3D cytoarchitectonic map of Area hOc5 (LOC) created by a Deep-Learning assisted workflow

description: This dataset contains automatically created cytoarchitectonic maps of Area hOc5 (LOC) in the BigBrain. Mappings were created using Deep Convolutional Neural networks trained on delineations on every 60th section using multivariate statistical image analysis, applied to GLI-images of coronal histological sections of 1 micron resolution. Resulting mappings are available on every section. Maps were transformed to the 3D reconstructed BigBrain space. Individual sections were used to assemble a 3D volume of the area, low quality results were replaced by interpolations between nearest neighboring sections. The volume was then smoothed using an 11³ median filter and largest connected components were identified to remove false positive results. The dataset consists of a HDF5 file containing the volume in RAS dimension ordering (20 micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset “interpolation_info” contains an integer vector for each section which indicates if a section was interpolated due to low quality results (value 2) or not (value 1).
DOI: 10.25493/dbzg-qz
title: Reference delineations of Area hOc3v (LingG) in individual sections of the BigBrain

description: This dataset contains cytoarchitectonic maps of area hOc3v (LingG) in the BigBrain. The mappings were created using multivariate statistical image analysis, applied to GLI-images of coronal histological sections of 1 micron resolution. Mappings are available on 38 sections of the occipital lobe of the BigBrain and have been transformed to the 3D reconstructed BigBrain space. For this brain area, a highly detailed 3D map has been computed based on automatic delineations on every histological section from a novel Deep Learning algorithm. This dataset can be accessed here: [Ultrahigh resolution 3D cytoarchitectonic map of Area hOc3v (LingG) created by a Deep-Learning assisted workflow](https://kg.ebrains.eu/search/live/minds/core/dataset/v1.0.0/f746514d-b79a-48e2-9c07-39f7c62459cf) This ultrahigh resolution 3D cytoarchitectonic map of Area hOc3v can be explored in the HBP interactive Atlas Viewer.
DOI: 10.25493/ph0c-ekx
title: Ultrahigh resolution 3D cytoarchitectonic map of Area hOc3v (LingG) created by a Deep-Learning assisted workflow

description: This dataset contains automatically created cytoarchitectonic maps of Area hOc3v (LingG) in the BigBrain. Mappings were created using Deep Convolutional Neural networks trained on delineations on every 60th section using multivariate statistical image analysis, applied to GLI-images of coronal histological sections of 1 micron resolution. Resulting mappings are available on every section. Maps were transformed to the 3D reconstructed BigBrain space. Individual sections were used to assemble a 3D volume of the area, low quality results were replaced by interpolations between nearest neighboring sections. The volume was then smoothed using an 11³ median filter and largest connected components were identified to remove false positive results. The dataset consists of a HDF5 file containing the volume in RAS dimension ordering (20 micron isotropic resolution, dataset “volume”) and an affine transformation matrix (dataset “affine”). An additional dataset “interpolation_info” contains an integer vector for each section which indicates if a section was interpolated due to low quality results (value 2) or not (value 1).
DOI: 10.25493/k4jr-5q7
title: Dictionary of simulated tissue samples mimicking white matter

description: In order to go beyond current state-of-the-art analytical models for the local orientation distribution functions (ODFs), a simulator have been developed to generate white matter virtual tissues that are usable by diffusion MRI (dMRI) and/or 3D-Polarized Light Imaging (3D-PLI) simulations. These tissue simulators are referred to as the Microstructure Environment Designer with Unified Sphere Atoms (MEDUSA). MEDUSA enables the generation of highly detailed numerical white matter phantoms biomimicking realistic geometric fiber configurations at different levels of disorder. By this means, the influence of various geometrical parameters present in white matter — such as global angular dispersion, tortuosity, presence of Ranvier nodes, or beading — on the measurement signal can be investigated. With MEDUSA, we have generated the here provided dictionary of white matter phantoms to study the impact of the listed parameters on the extra-cellular perpendicular diffusivity frequency in dMRI by simulating the diffusion process within in the phantoms of increasing complexity. These models are geometrically generalized, therefore they can be used across species, e.g. human or rodents.
DOI: 10.5517/ccdc.csd.cc22k5xr
title: CCDC 1923144: Experimental Crystal Structure Determination

description: Related Article: Sunglim Choi, Jihyun Shim, Philjae Kang, Soo Hyuk Choi|2022|Org.Biomol.Chem.|20|613|doi:10.1039/D1OB02223G
DOI: 10.5517/ccdc.csd.cc22k5ys
title: CCDC 1923145: Experimental Crystal Structure Determination

description: Related Article: Sunglim Choi, Jihyun Shim, Philjae Kang, Soo Hyuk Choi|2022|Org.Biomol.Chem.|20|613|doi:10.1039/D1OB02223G
DOI: 10.5281/zenodo.5895155
title: Experimental data set of manuscript

description: All experimental data of a manuscript submitted to GRL
DOI: 10.5281/zenodo.5895154
title: Experimental data set of manuscript

description: All experimental data of a manuscript submitted to GRL
DOI: 10.6084/m9.figshare.18956402
title: Fieldnotes-Focus Groups

description: Feedback notes on the observations during the Focus Group in Drieziek, Orange Farm.
DOI: 10.6084/m9.figshare.18956402.v1
title: Fieldnotes-Focus Groups

description: Feedback notes on the observations during the Focus Group in Drieziek, Orange Farm.
DOI: 10.6084/m9.figshare.18910619.v1
title: Changes in plant detritus inputs alter soil carbon and microbial communities in forest ecosystems

description: Global changes can alter plant inputs from both above- and below-ground, which thus may differently affect soil carbon and microbial communities. However, the general patterns of how detritus input changes affect them in forests remain unclear. By conducting a meta-analysis of 3193 observations from 165 experiments worldwide, we found that alterations in aboveground litter and/or root inputs had profound effects on soil carbon and microbial communities in forest ecosystems. Litter addition stimulated soil organic carbon (SOC) pools and microbial biomass, while removal of litter, root or both (no inputs) decreased them. The increased SOC under litter addition suggested that aboveground litter inputs benefit SOC sequestration despite accelerated decomposition. Unlike root removal, litter alterations and no inputs altered particulate organic carbon, while all detrital treatments had minor effects on mineral-associated organic carbon. In addition, detrital treatments contrastingly altered soil microbial community, with litter addition or removal shifting it towards fungi, while root removal shifting it towards bacteria. Furthermore, the responses of soil C and microbial biomass to litter alterations positively correlated with litter input rate and total litter input, suggesting that litter input quantity is a critical controller of belowground processes. Taken together, these findings provide critical insights into understanding how altered plant productivity and allocation affects soil carbon cycling, microbial communities and functioning of forest ecosystems under global changes. Future studies can take full advantage of the existing plant detritus experiments, and should focus on the relative roles of litter and roots in forming SOC and its fractions.
DOI: 10.6084/m9.figshare.18910619.v2
title: Data for the responses of soil carbon and microbial community in forests

description: Global changes can alter plant inputs from both above- and below-ground, which thus may differently affect soil carbon and microbial communities. However, the general patterns of how detritus input changes affect them in forests remain unclear. By conducting a meta-analysis of 3193 observations from 165 experiments worldwide, we found that alterations in aboveground litter and/or root inputs had profound effects on soil carbon and microbial communities in forest ecosystems. Litter addition stimulated soil organic carbon (SOC) pools and microbial biomass, while removal of litter, root or both (no inputs) decreased them. The increased SOC under litter addition suggested that aboveground litter inputs benefit SOC sequestration despite accelerated decomposition. Unlike root removal, litter alterations and no inputs altered particulate organic carbon, while all detrital treatments had minor effects on mineral-associated organic carbon. In addition, detrital treatments contrastingly altered soil microbial community, with litter addition or removal shifting it towards fungi, while root removal shifting it towards bacteria. Furthermore, the responses of soil C and microbial biomass to litter alterations positively correlated with litter input rate and total litter input, suggesting that litter input quantity is a critical controller of belowground processes. Taken together, these findings provide critical insights into understanding how altered plant productivity and allocation affects soil carbon cycling, microbial communities and functioning of forest ecosystems under global changes. Future studies can take full advantage of the existing plant detritus experiments, and should focus on the relative roles of litter and roots in forming SOC and its fractions.
DOI: 10.5281/zenodo.5895163
title: Database VitD

description: VitD, database
DOI: 10.5281/zenodo.5895162
title: Database VitD

description: VitD, database
DOI: 10.34740/kaggle/dsv/3084219
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.5281/zenodo.5830989
title: Chromatin-IP-MS raw data

description: Immunoprecipitated proteins by Flag-Polη were resolved by SDS-PAGE, excised and in-gel digested with trypsin, and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a Thermo Orbitrap Fusion Lumos mass spectrometer.
DOI: 10.5281/zenodo.5830988
title: Chromatin-IP-MS raw data

description: Immunoprecipitated proteins by Flag-Polη were resolved by SDS-PAGE, excised and in-gel digested with trypsin, and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a Thermo Orbitrap Fusion Lumos mass spectrometer.
DOI: 10.5281/zenodo.5895177
title: Lithosphere tearing and foundering during continental subduction: insights from Oligocene-Miocene magmatism in southern Tibet

description: Table S1. Major and trace elements and age data summary. Table S2. Zircon U-Pb ages. Table S3. Mineral major elements. Table S4. Zircon trace elements. Table S5. Published ages of the Oligocene to Miocene igneous rocks in southern Tibet Table S6. Calculated primary magmas and P estimates of the ultrapotassic rocks in southern Tibet.
DOI: 10.5281/zenodo.3782319
title: Lithosphere tearing and foundering during continental subduction: insights from Oligocene-Miocene magmatism in southern Tibet

description: Table S1. Major and trace elements and age data summary. Table S2. Zircon U-Pb ages. Table S3. Mineral major elements. Table S4. Zircon trace elements. Table S5. Published ages of the Oligocene to Miocene igneous rocks in southern Tibet Table S6. Calculated primary magmas and P estimates of the ultrapotassic rocks in southern Tibet.
DOI: 10.5517/ccdc.csd.cc28gj2c
title: CCDC 2099322: Experimental Crystal Structure Determination

description: Related Article: N. W. G. Smits, D. Rademaker, A. I. Konovalov, M. A. Siegler, D. G. H. Hetterscheid|2022|Dalton Trans.|51|1206|doi:10.1039/D1DT03296H
DOI: 10.5517/ccdc.csd.cc28kp0k
title: CCDC 2102358: Experimental Crystal Structure Determination

description: Related Article: Marie Vayer, Miryam Pastor, Christiane Kofink, Nuno Maulide|2021|Org.Lett.|24|27|doi:10.1021/acs.orglett.1c03569
DOI: 10.5517/ccdc.csd.cc28nj9s
title: CCDC 2105095: Experimental Crystal Structure Determination

description: Related Article: Arshia Sulaiman, Yi-Zhan Jiang, Mohammad Khurram Javed, Shu-Qi Wu, Zhao-Yang Li, Xian-He Bu|2021|Inorg.Chem.Front.|9|241|doi:10.1039/D1QI01086G
DOI: 10.5517/ccdc.csd.cc28py31
title: CCDC 2106453: Experimental Crystal Structure Determination

description: Related Article: Meng-Ying Hao, Min-Hua Huang, Xin-Yu Gu, Tian-Shu Zhang, Xiao-Tong Zhu, Wen-Juan Hao, Bo Jiang|2022|J.Org.Chem.|87|1518|doi:10.1021/acs.joc.1c02299
DOI: 10.5517/ccdc.csd.cc28q3y2
title: CCDC 2106634: Experimental Crystal Structure Determination

description: Related Article: Zhiyi Song, Hiroyasu Sato, Adriana Pietropaolo, Qingyu Wang, Shuhei Shimoda, Heshuang Dai, Yoshitane Imai, Hayato Toda, Takunori Harada, Yukatsu Shichibu, Katsuaki Konishi, Masayoshi Bando, Naofumi Naga, Tamaki Nakano|2022|Chem.Commun.|58|1029|doi:10.1039/D1CC05633F
DOI: 10.5517/ccdc.csd.cc28s025
title: CCDC 2108436: Experimental Crystal Structure Determination

description: Related Article: Yong Ai, Peng-Fei Li, Meng-Juan Yang, Yu-Qiu Xu, Meng-Zhen Li, Ren-Gen Xiong|2022|Chemical Science|13|748|doi:10.1039/D1SC06781H
DOI: 10.5517/ccdc.csd.cc28s07b
title: CCDC 2108441: Experimental Crystal Structure Determination

description: Related Article: Yong Ai, Peng-Fei Li, Meng-Juan Yang, Yu-Qiu Xu, Meng-Zhen Li, Ren-Gen Xiong|2022|Chemical Science|13|748|doi:10.1039/D1SC06781H
DOI: 10.5517/ccdc.csd.cc28syfg
title: CCDC 2109346: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28sygh
title: CCDC 2109347: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28syhj
title: CCDC 2109348: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28syjk
title: CCDC 2109349: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28sykl
title: CCDC 2109350: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28sylm
title: CCDC 2109351: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Shina Quek, Ming-Chung Yang, Zheng-Feng Zhang, Ming-Der Su, Cheuk-Wai So|2022|Chem.Commun.|58|1033|doi:10.1039/D1CC05391D
DOI: 10.5517/ccdc.csd.cc28tk0p
title: CCDC 2109922: Experimental Crystal Structure Determination

description: Related Article: Ai-Huan Sun, Xi-Xi Liu, Rong Sun, Jin Xiong, Hao-Ling Sun, Song Gao|2021|Inorg.Chem.Front.|9|231|doi:10.1039/D1QI01173A
DOI: 10.6084/m9.figshare.14936685
title: Genomic regions controlling yield-related traits in spring wheat: a mini review and a case study for rainfed environments in Australia and China

description: <b>Abstract</b> Breeding high-yield wheat varieties performing well in target environment is economically important. This study conducted a mini review of genome-wide association study (GWAS) outcomes on wheat yield-related traits reported in recent years, and performed GWAS in six individual environments to identify major alleles and their candidate genes responsible for wheat yield-related traits in Australia and North China where rainfed farming system is adopted. A panel of 228 spring wheat varieties were used. A double digest restriction-site associated DNA (ddRAD) genotyping-by-sequencing (GBS) protocol was performed to generate single nucleotide polymorphism (SNP) marker data. A total of 223 significant marker-trait association (MTAs) for yield traits, and 46 candidate genes for the major or consistent MTAs were identified. A phenomenon seldom reported in previous studies was that MTA clustered chromosome segments and gene clusters responsible for the trait were found across the genome, which suggested that marker-assisted selection (MAS) or transgenic method targeting a single gene might not be as effective as MAS targeting a much larger genomic region (GR) where all the genes or gene clusters underlying the GR play important roles.<br>
DOI: 10.5519/qd.j4zg6ovx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.5880941
title: Data in "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study"

description: The files contain the data of results in the paper "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study".
DOI: 10.5281/zenodo.4604212
title: Data in "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study"

description: The files contain the data of results in the paper "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study".
DOI: 10.6084/m9.figshare.18961994
title: T2D benchmarking output 2 -- Supp Fig 1 D-E data

description: These are the data used to generate the outputs described in the upset plots in Supp Fig 1D-E, which were generated as a response to reviewers. If you want to see how these are plotted, check out the Rmd here (https://github.com/chiragjp/ubiome_robustness). If you want to understand more about how they are generated, they are processed outputs from the quantvoe pipeline. The code to run this analysis is also in the above Git repo, in the revisions folder (analysis code: https://github.com/chiragjp/ubiome_robustness/blob/main/revisions/parse_revisions2_analysis.R processing code: https://github.com/chiragjp/ubiome_robustness/blob/main/revisions/parse_revisions2_analysis.R)
DOI: 10.34740/kaggle/dsv/3084452
title: Our World in Data - COVID-19
DOI: 10.5517/ccdc.csd.cc28tk1q
title: CCDC 2109923: Experimental Crystal Structure Determination

description: Related Article: Ai-Huan Sun, Xi-Xi Liu, Rong Sun, Jin Xiong, Hao-Ling Sun, Song Gao|2021|Inorg.Chem.Front.|9|231|doi:10.1039/D1QI01173A
DOI: 10.5517/ccdc.csd.cc28tk2r
title: CCDC 2109924: Experimental Crystal Structure Determination

description: Related Article: Ai-Huan Sun, Xi-Xi Liu, Rong Sun, Jin Xiong, Hao-Ling Sun, Song Gao|2021|Inorg.Chem.Front.|9|231|doi:10.1039/D1QI01173A
DOI: 10.5517/ccdc.csd.cc28tk3s
title: CCDC 2109925: Experimental Crystal Structure Determination

description: Related Article: Ai-Huan Sun, Xi-Xi Liu, Rong Sun, Jin Xiong, Hao-Ling Sun, Song Gao|2021|Inorg.Chem.Front.|9|231|doi:10.1039/D1QI01173A
DOI: 10.25505/fiz.icsd.cc28zdj6
title: CSD 2114588: Experimental Crystal Structure Determination

description: Related Article: Sylvia Lorraine Kunz, Subrata Chandra Roy, Thomas Bredow, Ulrich Schwarz, Robert Glaum|2021|Inorg.Chem.|61|507|doi:10.1021/acs.inorgchem.1c03070
DOI: 10.25505/fiz.icsd.cc28zfj7
title: CSD 2114619: Experimental Crystal Structure Determination

description: Related Article: Sylvia Lorraine Kunz, Subrata Chandra Roy, Thomas Bredow, Ulrich Schwarz, Robert Glaum|2021|Inorg.Chem.|61|507|doi:10.1021/acs.inorgchem.1c03070
DOI: 10.25505/fiz.icsd.cc28z8g0
title: CSD 2114462: Experimental Crystal Structure Determination

description: Related Article: Sylvia Lorraine Kunz, Subrata Chandra Roy, Thomas Bredow, Ulrich Schwarz, Robert Glaum|2021|Inorg.Chem.|61|507|doi:10.1021/acs.inorgchem.1c03070
DOI: 10.25505/fiz.icsd.cc2900kx
title: CSD 2115178: Experimental Crystal Structure Determination

description: Related Article: Sylvia Lorraine Kunz, Subrata Chandra Roy, Thomas Bredow, Ulrich Schwarz, Robert Glaum|2021|Inorg.Chem.|61|507|doi:10.1021/acs.inorgchem.1c03070
DOI: 10.25505/fiz.icsd.cc2900ly
title: CSD 2115179: Experimental Crystal Structure Determination

description: Related Article: Sylvia Lorraine Kunz, Subrata Chandra Roy, Thomas Bredow, Ulrich Schwarz, Robert Glaum|2021|Inorg.Chem.|61|507|doi:10.1021/acs.inorgchem.1c03070