cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4158933
title: Sulfur in New Zealand geothermal systems : insights from stable isotope and trace element analyses of anhydrite from Rotokawa and Ngatamariki geothermal fields.

description: Anhydrite major and trace element analyses
DOI: 10.5281/zenodo.4158932
title: Sulfur in New Zealand geothermal systems : insights from stable isotope and trace element analyses of anhydrite from Rotokawa and Ngatamariki geothermal fields.

description: Anhydrite major and trace element analyses
DOI: 10.5281/zenodo.4158947
title: 1D layered structures of the accretionary prism beneath the DONET stations

description: <strong>Description</strong> We converted the smooth depth-varying velocity structure model of Tonegawa <em>et al.</em> (2017) to a 5-layer model beneath each DONET station. The physical parameters of each layer are listed in the vmodel.csv. The thicknesses of each layer were determined by fitting the depth-averaged <em>S</em>-wave velocities derived by Tonegawa <em>et al.</em> (2017). For example, if the depth-averaged <em>VS</em> of Tonegawa <em>et al.</em> (2017) become the <em>VS</em> of layer 1 at a certain depth, this depth is considered as the bottom of layer 1. In DONET_layeredData.csv, the estimated bottom depths of each layer are listed. <strong>For seismic wave propagation simulation</strong> For simulations of seismic wave propagation along the Nankai Trough, the 3D model used in the simulations was basically constructed from the Japan Integrated Velocity Structure Model (JIVSM) (Koketsu et al., 2012). The JIVSM onshore and outer-rise sedimentary structures and structures beneath bedrock were fixed. To construct 3D model of the accretionary prism from layered S wave velocity models in "DONET_layeredData.csv," each station's bottom depths were interpolated and extrapolated via the ‘<em>Surface</em>’ gridding algorithm in Generic Mapping Tools software (GMT; Wessel <em>et al.</em> 2013). Interpolation and extrapolation were only applied within the region of the accretionary prism (Figure S1 of Takemura, Yabe & Emoto 2020). By using interpolated and extrapolated data of layer bottom depths and physical parameters (vmodel.csv), we can obtain 3D model of the accretionary prism along the Nankai Trough. We confirmed very similar simulation results between smooth depth-varying and layered accretionary prism models. Smooth depth varying model case Takemura, Kubo et al., 2019 Takemura, Matsuzawa et al., 2019 Layered model case Figures S3, S4 of Takemura, Yabe & Emoto 2020 Related papers For citing general information of this dataset, please include this data DOI and the following references Tonegawa, T., Araki, E., Kimura, T. <em>et al. </em>(2017). Sporadic low-velocity volumes spatially correlate with shallow very low frequency earthquake clusters. <em>Nat Commun</em> <strong>8, </strong>2048 https://doi.org/10.1038/s41467-017-02276-8 Takemura, S., Yabe, S., & Emoto (2020), K. Modelling high-frequency seismograms at ocean bottom seismometers: effects of heterogeneous structures on source parameter estimation for small offshore earthquakes and shallow low-frequency tremors, Geophys. J. Int., 223 (3), 1708-1723, https://doi.org/10.1093/gji/ggaa404
DOI: 10.5281/zenodo.4158946
title: 1D layered structures of the accretionary prism beneath the DONET stations

description: <strong>Description</strong> We converted the smooth depth-varying velocity structure model of Tonegawa <em>et al.</em> (2017) to a 5-layer model beneath each DONET station. The physical parameters of each layer are listed in the vmodel.csv. The thicknesses of each layer were determined by fitting the depth-averaged <em>S</em>-wave velocities derived by Tonegawa <em>et al.</em> (2017). For example, if the depth-averaged <em>VS</em> of Tonegawa <em>et al.</em> (2017) become the <em>VS</em> of layer 1 at a certain depth, this depth is considered as the bottom of layer 1. In DONET_layeredData.csv, the estimated bottom depths of each layer are listed. <strong>For seismic wave propagation simulation</strong> For simulations of seismic wave propagation along the Nankai Trough, the 3D model used in the simulations was basically constructed from the Japan Integrated Velocity Structure Model (JIVSM) (Koketsu et al., 2012). The JIVSM onshore and outer-rise sedimentary structures and structures beneath bedrock were fixed. To construct 3D model of the accretionary prism from layered S wave velocity models in "DONET_layeredData.csv," each station's bottom depths were interpolated and extrapolated via the ‘<em>Surface</em>’ gridding algorithm in Generic Mapping Tools software (GMT; Wessel <em>et al.</em> 2013). Interpolation and extrapolation were only applied within the region of the accretionary prism (Figure S1 of Takemura, Yabe & Emoto 2020). By using interpolated and extrapolated data of layer bottom depths and physical parameters (vmodel.csv), we can obtain 3D model of the accretionary prism along the Nankai Trough. We confirmed very similar simulation results between smooth depth-varying and layered accretionary prism models. Smooth depth varying model case Takemura, Kubo et al., 2019 Takemura, Matsuzawa et al., 2019 Layered model case Figures S3, S4 of Takemura, Yabe & Emoto 2020 Related papers For citing general information of this dataset, please include this data DOI and the following references Tonegawa, T., Araki, E., Kimura, T. <em>et al. </em>(2017). Sporadic low-velocity volumes spatially correlate with shallow very low frequency earthquake clusters. <em>Nat Commun</em> <strong>8, </strong>2048 https://doi.org/10.1038/s41467-017-02276-8 Takemura, S., Yabe, S., & Emoto (2020), K. Modelling high-frequency seismograms at ocean bottom seismometers: effects of heterogeneous structures on source parameter estimation for small offshore earthquakes and shallow low-frequency tremors, Geophys. J. Int., 223 (3), 1708-1723, https://doi.org/10.1093/gji/ggaa404
DOI: 10.5281/zenodo.3732248
title: Spontaneous synchronization of motion in pedestrian crowds of different densities

description: Data necessary to support the findings in "Spontaneous synchronization of motion in pedestrian crowds of different densities" . "density-velocity.dat" is fundamental diagram data. "flow-df.dat" and "flow-dp.dat”are the raw data to verify that synchronization is most likely to be triggered at the determined characteristic density. "headway-span.dat" is the raw data to reveal the formation mechanism and functional benefit of synchronization.
DOI: 10.5281/zenodo.3732244
title: Spontaneous synchronization of motion in pedestrian crowds of different densities

description: Data necessary to support the findings in "Spontaneous synchronization of motion in pedestrian crowds of different densities" . "density-velocity.dat" is fundamental diagram data. "flow-df.dat" and "flow-dp.dat”are the raw data to verify that synchronization is most likely to be triggered at the determined characteristic density. "headway-span.dat" is the raw data to reveal the formation mechanism and functional benefit of synchronization.
DOI: 10.6084/m9.figshare.13166855.v1
title: input files for Qfly pop gen study

description: input files for small pop gen project
DOI: 10.6084/m9.figshare.13167174.v1
title: Additional file 1 of Genome-wide identification of MADS-box gene family in sacred lotus (Nelumbo nucifera) identifies a SEPALLATA homolog gene involved in floral development

description: Additional file 1: Table S1. MADS-box genes identified in N. nucifera. Table S2. Segmental and tandem duplications of paralogous MADS-box pairs in lotus. Table S3. Categorization of MADS-box genes family in three plant genomes. Table S4. FPKM value of NnMADSs. Table S5. The relative expressions of 14 representative NnMADS genes under qRT-PCR analysis. Table S6. The primers of NnMADS genes in lotus.
DOI: 10.6084/m9.figshare.13167174
title: Additional file 1 of Genome-wide identification of MADS-box gene family in sacred lotus (Nelumbo nucifera) identifies a SEPALLATA homolog gene involved in floral development

description: Additional file 1: Table S1. MADS-box genes identified in N. nucifera. Table S2. Segmental and tandem duplications of paralogous MADS-box pairs in lotus. Table S3. Categorization of MADS-box genes family in three plant genomes. Table S4. FPKM value of NnMADSs. Table S5. The relative expressions of 14 representative NnMADS genes under qRT-PCR analysis. Table S6. The primers of NnMADS genes in lotus.
DOI: 10.6084/m9.figshare.13167192.v1
title: Additional file 2 of A cell-of-origin epigenetic tracer reveals clinically distinct subtypes of high-grade serous ovarian cancer

description: Additional file 2: Table S1. Description of the FFPE cohort’s clinical data. Table S2. Multivariate analysis on IEO, TCGA, Tothill cohorts. Table S3. Summary table of Log Rank Tests performed to determine statistical significance of the differences in survival and progression between FI-like and OSE-like groups in the considered cohorts. Table S4. Mutation Frequency in the TCGA cohort stratified as FI-like and OSE-like HGSOC.
DOI: 10.6084/m9.figshare.13167192
title: Additional file 2 of A cell-of-origin epigenetic tracer reveals clinically distinct subtypes of high-grade serous ovarian cancer

description: Additional file 2: Table S1. Description of the FFPE cohort’s clinical data. Table S2. Multivariate analysis on IEO, TCGA, Tothill cohorts. Table S3. Summary table of Log Rank Tests performed to determine statistical significance of the differences in survival and progression between FI-like and OSE-like groups in the considered cohorts. Table S4. Mutation Frequency in the TCGA cohort stratified as FI-like and OSE-like HGSOC.
DOI: 10.6084/m9.figshare.13167224.v1
title: Additional file 6 of The selectivity filter of the mitochondrial protein import machinery

description: Additional file 6: Table S1 Individual values for experiments with n
DOI: 10.6084/m9.figshare.13167224
title: Additional file 6 of The selectivity filter of the mitochondrial protein import machinery

description: Additional file 6: Table S1 Individual values for experiments with n
DOI: 10.6084/m9.figshare.13167284
title: Additional file 1 of Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2

description: Additional file 1. Figure S1: Deep analysis of hydrophilicity and hydrophobicity of surface protein of SARS-CoV-2. The online software, ProtScale, was used to predict the hydrophilicity and hydrophobicity of the surface protein deeply. A. The S protein has a maximum score of hydrophobicity, 3.222 at the 7th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -2.589 at the 679th site, which revealed a strong hydrophilicity. The score of hydrophilicity and hydrophobicity on the polypeptide chain of S protein constantly fluctuates, with most of the scores being negative, which revealed the possibility that the protein had bisexual properties on the basis of hydrophilicity. B. The E protein has a maximum score of hydrophobicity, 3.489 at the 21st and the 25th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -1.550 at the 65th site, which revealed a strong hydrophilicity. Most of the scores of the residues being positive, which revealed the possibility that the protein has obvious hydrophobicity. C. The M protein has a maximum score of hydrophobicity, 2.978 at the 84th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -1.956 at the 211th and the 212th site, which revealed a strong hydrophilicity. The scores of hydrophilicity and hydrophobicity on the polypeptide chain of M protein showed large fluctuations, and the number of positive scores and negative scores were similar, the positive scores accounted for the majority, which revealed the possibility that the protein had bisexual properties on the basis of hydrophobicity.
DOI: 10.6084/m9.figshare.13167284.v1
title: Additional file 1 of Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2

description: Additional file 1. Figure S1: Deep analysis of hydrophilicity and hydrophobicity of surface protein of SARS-CoV-2. The online software, ProtScale, was used to predict the hydrophilicity and hydrophobicity of the surface protein deeply. A. The S protein has a maximum score of hydrophobicity, 3.222 at the 7th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -2.589 at the 679th site, which revealed a strong hydrophilicity. The score of hydrophilicity and hydrophobicity on the polypeptide chain of S protein constantly fluctuates, with most of the scores being negative, which revealed the possibility that the protein had bisexual properties on the basis of hydrophilicity. B. The E protein has a maximum score of hydrophobicity, 3.489 at the 21st and the 25th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -1.550 at the 65th site, which revealed a strong hydrophilicity. Most of the scores of the residues being positive, which revealed the possibility that the protein has obvious hydrophobicity. C. The M protein has a maximum score of hydrophobicity, 2.978 at the 84th site, which revealed a strong hydrophobicity; a minimum score of hydrophobicity, -1.956 at the 211th and the 212th site, which revealed a strong hydrophilicity. The scores of hydrophilicity and hydrophobicity on the polypeptide chain of M protein showed large fluctuations, and the number of positive scores and negative scores were similar, the positive scores accounted for the majority, which revealed the possibility that the protein had bisexual properties on the basis of hydrophobicity.
DOI: 10.6084/m9.figshare.13167360
title: Additional file 2 of The CBP KIX domain regulates long-term memory and circadian activity

description: Additional file 2 : Table S1. List of Genes Differentially Expressed in CBPKIX/KIX mutants compared to wild-type littermates after spatial learning. Table S2. List of the upstream regulators of the DEGs in CBPKIX/KIX mutants. Table S3. List of Genes Differentially Expressed in wildtype mice after spatial learning compared to homecage. Table S4. Positive controls for validation of differential gene expression analysis. Table S5. List of the upstream regulators of the DEGs in wild-type mice following learning. Table S6. High confidence (80%) changes in homecage gene expression in CBPKIX/KIX mice. Table S7. List of qPCR primers used to study gene expression.
DOI: 10.6084/m9.figshare.13167360.v1
title: Additional file 2 of The CBP KIX domain regulates long-term memory and circadian activity

description: Additional file 2 : Table S1. List of Genes Differentially Expressed in CBPKIX/KIX mutants compared to wild-type littermates after spatial learning. Table S2. List of the upstream regulators of the DEGs in CBPKIX/KIX mutants. Table S3. List of Genes Differentially Expressed in wildtype mice after spatial learning compared to homecage. Table S4. Positive controls for validation of differential gene expression analysis. Table S5. List of the upstream regulators of the DEGs in wild-type mice following learning. Table S6. High confidence (80%) changes in homecage gene expression in CBPKIX/KIX mice. Table S7. List of qPCR primers used to study gene expression.
DOI: 10.6084/m9.figshare.13167390
title: Additional file 1 of A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

description: Additional file 1. The list of radiomics features extracted from the delineated VOIs.
DOI: 10.6084/m9.figshare.13167390.v1
title: Additional file 1 of A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer

description: Additional file 1. The list of radiomics features extracted from the delineated VOIs.
DOI: 10.6084/m9.figshare.13167411
title: Additional file 1 of Glomus tumors with malignant features of the extremities: a case series

description: Additional file 1. Summary of Patient Data. This table presents the demographics, tumor characteristics, and oncological outcomes individually for each of the five patients.
DOI: 10.6084/m9.figshare.13167411.v1
title: Additional file 1 of Glomus tumors with malignant features of the extremities: a case series

description: Additional file 1. Summary of Patient Data. This table presents the demographics, tumor characteristics, and oncological outcomes individually for each of the five patients.
DOI: 10.6084/m9.figshare.13167608
title: Supporting Material - Nitrate removal uncertainty in stormwater control measures: Is the design or climate a culprit?

description: Supporting material data for the data embedded into the manuscript "Nitrate removal uncertainty in stormwater control measures: Is the design or climate a culprit?"<br>
DOI: 10.6084/m9.figshare.13166810
title: Data Set of multilingual Muslim sermons & speeches.pdf

description: This dataset comprises multilingual Muslim sermons and speeches in Makassar, Indonesian and Arabic. The data was obtained during ethnographic fieldwork for a PhD in the province of South Sulawesi, and was also used to write a journal article. Permission was granted from the speakers/preachers for recording, transcribing and analysing these texts, most of whom were interviewed subsequent to their preaching.
DOI: 10.6084/m9.figshare.13166810.v1
title: Data Set of multilingual Muslim sermons & speeches.pdf

description: This dataset comprises multilingual Muslim sermons and speeches in Makassar, Indonesian and Arabic. The data was obtained during ethnographic fieldwork for a PhD in the province of South Sulawesi, and was also used to write a journal article. Permission was granted from the speakers/preachers for recording, transcribing and analysing these texts, most of whom were interviewed subsequent to their preaching.
DOI: 10.21227/dntd-6169
title: Model2

description: We have followed the three-step approach to tackle this challenge.Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in temperature metadata, which we imputed using daily level linear interpolation.We also performed fuzzy clustering of time series using December daily consumption data then daily consumption data for the whole year for missing months were imputed using the cluster's average consumption and probability of meter-id to fall in each cluster and took summation.Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data – used as is (we have used avg. weather data only)b) Time Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the month and weekday1. Month gets mapped to Month_x = sin(2*pi*month/12) and Month_y=cos(2*pi*month/12)2. Weekday gets mapped to weekday_x = sin(2*pi*weekday/6) and weekday_y=cos(2*pi*weekday /6).Post multiple iterations, we settled with the XgBoost model since it performed the best among all. We trained the model on individual meter_ID and forecasted energy consumption at a daily level. The outcome was then rolled-up, to monthly level to get our final submission.
DOI: 10.6084/m9.figshare.11605818
title: Multi-model (VIC, NoahMP, SAC-SMA, and Catchment) parameters and representative outputs; pre-processing and post-processing data and scripts, for analyses in the Northern Sierra Nevada Basin, the Southern Sierra Nevada Basin, the Upper Colorado Basin, and the Columbia Basin, from 1916 to 2018.

description: This package includes all the representative data used in the analyses in the paper entitled "Understanding the role of asymmetrical warming on streamflow changes in the Western U.S.", submitted by Zhaoxin Ban, Tapash Das, Dan Cayan, Mu Xiao, and Dennis P. Lettenmaier to <i>Water Resources Research.</i>
DOI: 10.6084/m9.figshare.11605818.v2
title: Multi-model (VIC, NoahMP, SAC-SMA, and Catchment) parameters and representative outputs; pre-processing and post-processing data and scripts, for analyses in the Northern Sierra Nevada Basin, the Southern Sierra Nevada Basin, the Upper Colorado Basin, and the Columbia Basin, from 1916 to 2018.

description: This package includes all the representative data used in the analyses in the paper entitled "Understanding the role of asymmetrical warming on streamflow changes in the Western U.S.", submitted by Zhaoxin Ban, Tapash Das, Dan Cayan, Mu Xiao, and Dennis P. Lettenmaier to <i>Water Resources Research.</i>
DOI: 10.6084/m9.figshare.13159739
title: Supplementary Material for: Getting the Most Out of Your Zombie: Abdominal Sensors and Neural Manipulations Help Jewel Wasps Find the Roach’s Weak Spot

description: The parasitoid emerald jewel wasp (<i>Ampulex compressa</i>) subdues the American cockroach (<i>Periplaneta americana</i>) with a sting to the 1st thoracic ganglion, followed by a sting to the roach’s brain, causing long-term pacification. The wasp then leads the cockroach to a hole where it lays an egg on the roach middle leg before barricading the entrance and departing. Although many aspects of the wasp’s initial attack have been investigated, few studies have detailed the egg-laying process and the subsequent fate of the larvae. Here I show that larval survival depends on precise egg positioning on the cockroach by the female wasp. Ablation of sensory hairs on the wasp’s abdomen resulted in mislaid eggs, which seldom survived. In addition, the cockroach femur may block the oviposition site. The wasp contended with this challenge with a newly discovered suite of stings, 3 directed into the 2nd thoracic ganglion which resulted in extension of the femur, thus exposing the oviposition site and removing a potential barrier to the wasp’s successful reproduction. When the femur was glued in place, the wasp stung the cockroach over 100 times, in an apparent fixed action pattern triggered by the obscured oviposition target. These findings highlight the importance of proper egg placement by the wasp, and reveal sensors and new neural manipulations that facilitate the process.
DOI: 10.6084/m9.figshare.13159739.v1
title: Supplementary Material for: Getting the Most Out of Your Zombie: Abdominal Sensors and Neural Manipulations Help Jewel Wasps Find the Roach’s Weak Spot

description: The parasitoid emerald jewel wasp (<i>Ampulex compressa</i>) subdues the American cockroach (<i>Periplaneta americana</i>) with a sting to the 1st thoracic ganglion, followed by a sting to the roach’s brain, causing long-term pacification. The wasp then leads the cockroach to a hole where it lays an egg on the roach middle leg before barricading the entrance and departing. Although many aspects of the wasp’s initial attack have been investigated, few studies have detailed the egg-laying process and the subsequent fate of the larvae. Here I show that larval survival depends on precise egg positioning on the cockroach by the female wasp. Ablation of sensory hairs on the wasp’s abdomen resulted in mislaid eggs, which seldom survived. In addition, the cockroach femur may block the oviposition site. The wasp contended with this challenge with a newly discovered suite of stings, 3 directed into the 2nd thoracic ganglion which resulted in extension of the femur, thus exposing the oviposition site and removing a potential barrier to the wasp’s successful reproduction. When the femur was glued in place, the wasp stung the cockroach over 100 times, in an apparent fixed action pattern triggered by the obscured oviposition target. These findings highlight the importance of proper egg placement by the wasp, and reveal sensors and new neural manipulations that facilitate the process.
DOI: 10.6084/m9.figshare.13160447.v1
title: Ascaris ovis mitochondrion, complete genome

description: The mitochondrial genome of Ascaris ovis from the sheep in Southwest China was determined using Illumina sequencing technology. The assembled genome was 14,205 bp in size and encoded 36 genes, including 12 protein-coding genes, 22 transfer RNA genes and two ribosomal RNA genes.