cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12814748
title: Table S1.xlsx

description: <b>Table S1.</b> Phenotypic data for STB, flowering time and plant height for the NIAB Elite MAGIC population collected from different datasets.
DOI: 10.6084/m9.figshare.12814751.v1
title: A Delay Equation Model for the Atlantic Multidecadal Oscillation - Supplementary Material

description: Supplementary material for manuscript:<i>S. K. J. Falkena, C. Quinn, J. Sieber, H. A. Dijkstra</i> - <b>A Delay Equation Model for the Atlantic Multidecadal Oscillation</b><br>arxiv: 2008.07219<br>Contents:<b>Figure_codes_AMO.zip (</b>Mathematica and MATLAB codes)The zip file contains the codes for all the figures produced in the above manuscript. The codes are grouped by associated figure numbers. Figure 6 was produced with Mathematica. Figures 1, 4, 5, 7, 8 and 9 were produced with MATLAB.
DOI: 10.6084/m9.figshare.12814733
title: dengue_Singapore_2020.csv

description: Dengue, a mosquito-transmitted viral infection, has posed a public health challenge to Singapore residents over years. As in this year 2020, Singapore has been experiencing the historically worst dengue outbreaks. As of 4 Aug, the cumulative number of dengue cases for this year stands at more than 22400. A fine-grained of dengue cases at an urban scale could give us a feasible way to explore the spatial and temporal dengue risk, as well as reveal reasons for the resurgence. Here, we collect a dataset of weekly dengue cases with the locality of each case. Specifically, our dataset captures all dengue cases with onset date from 15 Feb to 10 July in 2020, which covers the nationwide lockdown period for preventing the pandemics of COVID-19. The locality is matched to a spatial unit with high resolution, whose average coverage is just 1.35 km<sup>2</sup>. The resulting dengue case records with spatial locality and onset week are released in simple formats for easy access to motivating research of dengue epidemics.
DOI: 10.6084/m9.figshare.12814748.v1
title: Table S1.xlsx

description: <b>Table S1.</b> Phenotypic data for STB, flowering time and plant height for the NIAB Elite MAGIC population collected from different datasets.
DOI: 10.6084/m9.figshare.12814628
title: poi.rar

description: our poi code
DOI: 10.6084/m9.figshare.12814733.v1
title: dengue_Singapore_2020.csv

description: Dengue, a mosquito-transmitted viral infection, has posed a public health challenge to Singapore residents over years. As in this year 2020, Singapore has been experiencing the historically worst dengue outbreaks. As of 4 Aug, the cumulative number of dengue cases for this year stands at more than 22400. A fine-grained of dengue cases at an urban scale could give us a feasible way to explore the spatial and temporal dengue risk, as well as reveal reasons for the resurgence. Here, we collect a dataset of weekly dengue cases with the locality of each case. Specifically, our dataset captures all dengue cases with onset date from 15 Feb to 10 July in 2020, which covers the nationwide lockdown period for preventing the pandemics of COVID-19. The locality is matched to a spatial unit with high resolution, whose average coverage is just 1.35 km<sup>2</sup>. The resulting dengue case records with spatial locality and onset week are released in simple formats for easy access to motivating research of dengue epidemics.
DOI: 10.6084/m9.figshare.12814592
title: Self-powered analog sensor-data-logger experimental data

description: Measured data demonstrating a self-powered sensor-data-logger in operation.
DOI: 10.6084/m9.figshare.12814592.v1
title: Self-powered analog sensor-data-logger experimental data

description: Measured data demonstrating a self-powered sensor-data-logger in operation.
DOI: 10.6084/m9.figshare.12814580
title: Self-powered analog sensor-data-logger experimental data

description: Data for self-powered sensor-data-logger device based on synchronized Fowler-Nordheim tunneling dynamical systems.
DOI: 10.6084/m9.figshare.12814541
title: Population Genetics Locus typing results in Rwanda

description: Herein ,we first uploaded the biggest Population Genetics Locus typing results in Rwanda. There are 108-plex STRs and 188 SNPs‘ frequency data can be used in forensic statistic.
DOI: 10.6084/m9.figshare.12814580.v1
title: Data_Code.zip

description: Data for self-powered sensor-data-logger device based on synchronized Fowler-Nordheim tunneling dynamical systems.
DOI: 10.6084/m9.figshare.12814498
title: Additional file 1 of In silico molecular docking studies and MM/GBSA analysis of coumarin-carbonodithioate hybrid derivatives divulge the anticancer potential against breast cancer

description: Additional file 1. Prime MM-GBSA results.
DOI: 10.6084/m9.figshare.12814498.v1
title: Additional file 1 of In silico molecular docking studies and MM/GBSA analysis of coumarin-carbonodithioate hybrid derivatives divulge the anticancer potential against breast cancer

description: Additional file 1. Prime MM-GBSA results.
DOI: 10.6084/m9.figshare.12814482
title: Additional file 7 of Obsessive-compulsive disorder and attention-deficit/hyperactivity disorder: distinct associations with DNA methylation and genetic variation

description: Additional file 7: Supplementary Tables S1, Table S2, Table S3.
DOI: 10.6084/m9.figshare.12814482.v1
title: Additional file 7 of Obsessive-compulsive disorder and attention-deficit/hyperactivity disorder: distinct associations with DNA methylation and genetic variation

description: Additional file 7: Supplementary Tables S1, Table S2, Table S3.
DOI: 10.6084/m9.figshare.12814274
title: Entity Recognition

description: TBA TBA
DOI: 10.6084/m9.figshare.12814250
title: Awake Time Series

description: A cohort of 63 healthy subjects participated in the original experiment (36 females, mean ± SD age of 23.4 ± 3.3 years). Written informed consent was obtained from all subjects. The experimental protocol was approved by the local ethics committee (Goethe-Universität Frankfurt, Germany, protocol number: 305/07). The subjects were reimbursed for their participation. All experiments were conducted in accordance with the relevant guidelines and regulations, and the Declaration of Helsinki. Participants were scanned for 50 minutes using previously published acquisition parameters26. From the group of 63 subjects we selected a subgroup of 9 participants who remained awake throughout the complete duration of the scan (confirmed by assessment of the simultaneous EEG64). In this way, we obtained unusually long fMRI recordings with the purpose of robustly estimating observables related to FC dynamics
DOI: 10.6084/m9.figshare.12814307.v1
title: Multinomial Logistic Regression of increase use of bike in the next 12 months in Puerto Madryn – Analysis factor: Difficulties in cycling ed Item

description: Table with Multinomial Logistic Regression of increase use of bike in the next 12 months (dependent) with difficulties in cycling (factors). In the experiment, the dependent variable was the potential increment in the use of bike in the next 12 months.<br>The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables (namely also <i>independent</i> or <i>factors</i>) and the outcome (<i>dependent)</i>, so that the outcome of a new "experiment" can be correctly predicted for a new data point for which the explanatory variables, but not the outcome, are available. In the process, the model attempts to explain the relative effect of differing explanatory variables on the outcome.<br>
DOI: 10.6084/m9.figshare.12814250.v1
title: Awake Time Series

description: A cohort of 63 healthy subjects participated in the original experiment (36 females, mean ± SD age of 23.4 ± 3.3 years). Written informed consent was obtained from all subjects. The experimental protocol was approved by the local ethics committee (Goethe-Universität Frankfurt, Germany, protocol number: 305/07). The subjects were reimbursed for their participation. All experiments were conducted in accordance with the relevant guidelines and regulations, and the Declaration of Helsinki. Participants were scanned for 50 minutes using previously published acquisition parameters26. From the group of 63 subjects we selected a subgroup of 9 participants who remained awake throughout the complete duration of the scan (confirmed by assessment of the simultaneous EEG64). In this way, we obtained unusually long fMRI recordings with the purpose of robustly estimating observables related to FC dynamics
DOI: 10.6084/m9.figshare.12814274.v1
title: acknowledgment

description: *The dataset to estimate the fraction of papers GROBID could extract acknowledgement sections (Section 4.2).<br><br>* The dataset to compare sentence segmentation tools (Section 4.3 and Table 1).<br>* The dataset to evaluate sentence classification (Section 4.4).<br>* The dataset to evaluate entity recognition (Section 4.5 and Table 2).<br>* The dataset to evaluate the entity classification algorithm (Section 4.6 and Table 3).<br>* The dataset containing all entities extracted. I have all entities that are classified as acknowledgement entities.
DOI: 10.6084/m9.figshare.12859529
title: Knowledge, attitude and practice towards stroke and thrombolysis among students preparing for undergraduate medical entrance examination in Kathmandu, Nepal

description: Questions
DOI: 10.6084/m9.figshare.12814274.v2
title: acknowledgment

description: *The dataset to estimate the fraction of papers GROBID could extract acknowledgement sections (Section 4.2).<br><br>* The dataset to compare sentence segmentation tools (Section 4.3 and Table 1).<br>* The dataset to evaluate sentence classification (Section 4.4).<br>* The dataset to evaluate entity recognition (Section 4.5 and Table 2).<br>* The dataset to evaluate the entity classification algorithm (Section 4.6 and Table 3).<br>* The dataset containing all entities extracted. I have all entities that are classified as acknowledgement entities.
DOI: 10.6084/m9.figshare.12814136
title: Mass Spectrometry, Supplemental

description: Protein Identification from SDS-PAGE:Protein in gel bands (self-made gel), have been reduced with DTT, alkylated with iodoacetomide, subjected to 3x alternating washes, and proteolysis with Trypsin (sequential grade, Promega) in 50mM TEAB buffer at 37C overnight. Peptides extracted and desalted on u-HLB Oasis plates, eluted with 60 acetonitrile/ 0.1%TFA, dried.Dry peptides re-constituted in 20uL acetonitrile/0.1uL,and analyzed by LC/MS/MS on Orbitrap Lumos-ETD. Resolution set to 120K, 30K for the precursor and fragment ions, respectively. HCD energy at 31 with a isolation window set to 0.8Da without offset. Injected 10% for samples 3 and 4.<br>Data analysis:MS/MS raw data were searched via PD2.3 with Mascot 6.1, against RefSeq2017_83_human database (containing common contaminants). Mascot .dat files were validated with PD2.3-Percolator.
DOI: 10.6084/m9.figshare.12814274.v3
title: Acknowledgement Entity Recognition Datasets

description: *The dataset to estimate the fraction of papers GROBID could extract acknowledgement sections (Section 4.2).<br>* The dataset to compare sentence segmentation tools (Section 4.3 and Table 1).<br>* The dataset to evaluate sentence classification (Section 4.4).<br>* The dataset to evaluate entity recognition (Section 4.5 and Table 2).<br>* The dataset to evaluate the entity classification algorithm (Section 4.6 and Table 3).<br>* The dataset containing all entities extracted.
DOI: 10.6084/m9.figshare.12814103
title: Protein Identification by Mass Spectrometry, Supplemental

description: Protein Identification from SDS-PAGE:Protein in gel bands (self-made gel), have been reduced with DTT, alkylated with iodoacetomide, subjected to 3x alternating washes, and proteolysis with Trypsin (sequential grade, Promega) in 50mM TEAB buffer at 37C overnight. Peptides extracted and desalted on u-HLB Oasis plates, eluted with 60 acetonitrile/ 0.1%TFA, dried. Dry peptides re-constituted in 20uL acetonitrile/0.1uL,and analyzed by LC/MS/MS on Orbitrap Lumos-ETD. Resolution set to 120K, 30K for the precursor and fragment ions, respectively. HCD energy at 31 with a isolation window set to 0.8Da without offset. Injected 10% for samples 3 and 4.<br> Data analysis: MS/MS raw data were searched via PD2.3 with Mascot 6.1, against RefSeq2017_83_human database (containing common contaminants). Mascot .dat files were validated with PD2.3-Percolator.<br>
DOI: 10.6084/m9.figshare.12814103.v1
title: Protein Identification by Mass Spectrometry, Supplemental

description: Protein Identification from SDS-PAGE:Protein in gel bands (self-made gel), have been reduced with DTT, alkylated with iodoacetomide, subjected to 3x alternating washes, and proteolysis with Trypsin (sequential grade, Promega) in 50mM TEAB buffer at 37C overnight. Peptides extracted and desalted on u-HLB Oasis plates, eluted with 60 acetonitrile/ 0.1%TFA, dried. Dry peptides re-constituted in 20uL acetonitrile/0.1uL,and analyzed by LC/MS/MS on Orbitrap Lumos-ETD. Resolution set to 120K, 30K for the precursor and fragment ions, respectively. HCD energy at 31 with a isolation window set to 0.8Da without offset. Injected 10% for samples 3 and 4.<br> Data analysis: MS/MS raw data were searched via PD2.3 with Mascot 6.1, against RefSeq2017_83_human database (containing common contaminants). Mascot .dat files were validated with PD2.3-Percolator.<br>
DOI: 10.6084/m9.figshare.12814274.v4
title: Acknowledgement Entity Recognition Datasets

description: *The dataset to estimate the fraction of papers GROBID could extract acknowledgement sections (Section 4.2).<br>* The dataset to compare sentence segmentation tools (Section 4.3 and Table 1).<br>* The dataset to evaluate sentence classification (Section 4.4).<br>* The dataset to evaluate entity recognition (Section 4.5 and Table 2).<br>* The dataset to evaluate the entity classification algorithm (Section 4.6 and Table 3).<br>* The dataset containing all entities extracted.
DOI: 10.6084/m9.figshare.12814274.v6
title: Entity Recognition

description: TBA TBA
DOI: 10.6084/m9.figshare.12814274.v5
title: Acknowledgement Entity Recognition Datasets

description: TBA TBA
DOI: 10.6084/m9.figshare.12814055
title: 3-D bending

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.6084/m9.figshare.12814055.v1
title: 3-D bending

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.6084/m9.figshare.12814031
title: 3-D bending

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.6084/m9.figshare.12814031.v1
title: bending_3d_data.mat

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.6084/m9.figshare.12814031.v3
title: 3-D bending

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.6084/m9.figshare.12814031.v2
title: 3-D bending

description: The .mat file includes the 3-D bending deformation and yield zone distribution of the Pacific subducting plate at the Mariana Trench.Description of files:deformation ---- 3-D plate bending deformation of the Mariana subduction;longitude ----- longitude grids of study area;<br>latitude ------ latitude grids of study area;yield_zone_depth ---- calculated yield zone depth of the Mariana Trench;stress ----- calculated 3-D plate bending stress of the Mariana Trench<br>(1) You can use the script: surf (longitude, latitude, deformation/yield_zone_depth); shading interp to visualize the bending deformation/yield zone depth in Matlab<br>(2) The stress data is a 3-D matrix (m×n×p)
DOI: 10.5522/04/12813980
title: Crayfish demographic data from three field methodologies

description: Dataset detailing demographic information (carapace length, weight, gender) for signal crayfish captured using three methods. Methods were conventional baited traps, manual handsearches and a novel triple drawdown method.
DOI: 10.5522/04/12813980.v1
title: Crayfish demographic data from three field methodologies

description: Dataset detailing demographic information (carapace length, weight, gender) for signal crayfish captured using three methods. Methods were conventional baited traps, manual handsearches and a novel triple drawdown method.
DOI: 10.6084/m9.figshare.12813953
title: Microbiota composition and stroke prognosis

description: A dataset regarding the association of stroke prognosis and microbiota composition
DOI: 10.6084/m9.figshare.12859547
title: SUPPLEMENTRY_INFORMATION_3 MUTATIONS IN BANGLADESHI SEQUENCES.xlsx

description: SNP analysis of sars-cov-2 sequences from bangladesh
DOI: 10.6084/m9.figshare.12813953.v2
title: Microbiota composition and stroke prognosis

description: A dataset regarding the association of stroke prognosis and microbiota composition
DOI: 10.6084/m9.figshare.12813953.v1
title: Microbiota composition and stroke prognosis

description: A dataset regarding the association of stroke prognosis and microbiota composition
DOI: 10.6084/m9.figshare.12813944.v1
title: digital data of EOBSArray

description: 3-D-MODEL-OF-Shanxi-Rift including Shanxi_phase.tar.gz, <br>Shanxi_group.taz.gz, Shanxi_HtoV.tar.gz, COR_SXR.7z and 3-D_Vs_model_Shanxi.tar.gz. <br><br>The digital data is described in Readme<br><br>
DOI: 10.6084/m9.figshare.12859547.v3
title: SUPPLEMENTRY_INFORMATION_3 MUTATIONS IN BANGLADESHI SEQUENCES.xlsx

description: SNP analysis of sars-cov-2 sequences from bangladesh
DOI: 10.6084/m9.figshare.12859547.v2
title: SUPPLEMENTRY_INFORMATION_3 MUTATIONS IN BANGLADESHI SEQUENCES.xlsx

description: SNP analysis of sars-cov-2 sequences from bangladesh
DOI: 10.6084/m9.figshare.12813944.v2
title: 3-D Model of Shanxi Rift

description: 3-D-MODEL-OF-Shanxi-Rift including Shanxi_phase.tar.gz, <br>Shanxi_group.taz.gz, Shanxi_HtoV.tar.gz, COR_SXR.7z and 3-D_Vs_model_Shanxi.tar.gz. <br><br>The digital data is described in Readme<br><br>
DOI: 10.6084/m9.figshare.12813815
title: JVASP-40887 to JVASP-41545

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813806
title: JVASP-39375 to JVASP-40165

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813809
title: JVASP-40167 to JVASP-40866

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813800
title: JVASP-37901 to JVASP-38609

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813803
title: JVASP-38610 to JVASP-39373

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813809.v1
title: JVASP-40167 to JVASP-40866

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813800.v1
title: JVASP-37901 to JVASP-38609

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813803.v1
title: JVASP-38610 to JVASP-39373

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813806.v1
title: JVASP-39375 to JVASP-40165

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813815.v1
title: JVASP-40887 to JVASP-41545

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12813707
title: Marvel and DC Trade Mark Applications and Registrations

description: Trade mark application and registrations for superhero functional characters filed by Marvel and DC across multiple intellectual property offices.
DOI: 10.6084/m9.figshare.12813794
title: data.rar

description: 1. AAIW is defined by the low salinity tongue, and spiciness is defined by salinity anomaly along isopycnal surfaces, so that one can use salinity or spiciness to trace AAIW. Here we use WOA18 data downloaded from https://www.nodc.noaa.gov/OC5/woa18/woa18data.html, and the results are shown in Figure 2, one can trace AAIW with different tracers, including the absolute salinity (panel a), the spiciness (panel b), the distance from the origin (defined by the temperature and salinity at 30<sup>o</sup>S and 50 m below the sea surface) (panel c), and the spicity (panel d).2. As observed in Schmitt (1981), density ratio was 1.89 and 1.95 for the South and North Center Water stations. Using World Ocean Circulation Experiment (WOCE) database (https://cchdo.ucsd.edu/), we find two stations: (17.4<sup>o</sup>S, 30.8<sup>o</sup>W) in Section A17 and (24.5<sup>o</sup>N, 36.7<sup>o</sup>W) in Section A05-2, nearest to those in Schmitt (1981), to calculate the density ratio.3. In the Arctic Ocean, we used temperature and salinity data acquired with Ice-Tethered Profiler (ITP), downloaded from https://www.whoi.edu/page.do?pid=28866. With the vertical resolution of 0.25 m, the data enables us to examine the small-scale structures, i.e. diffusive convection staircases. The ITP4 station is located at (79.0<sup>o</sup>N, 142.5<sup>o</sup>W) in the Canada Basin, where a stack of staircases exists in the upper layer of a water mass, called as Antarctic Water (Figure 3(a)) (Padman and Dillon, 1987).
DOI: 10.6084/m9.figshare.12813743
title: Manuscript data.pdf

description: These data are tables of the in silico study part in the manuscript "In-vitro Cytotoxicity and In-silico Insights of the Multi-target Anticancer Candidates from Haplophyllum tuberculatum".
DOI: 10.6084/m9.figshare.12813707.v1
title: Marvel and DC Trade Mark Applications and Registrations

description: Trade mark application and registrations for superhero functional characters filed by Marvel and DC across multiple intellectual property offices.