cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/42tyf35x96.1
title: Measuring urban sustainability through evolutionary paths in socio-environmental structures: Applications to Chinese prefecture cities

description: As of 2015, there were 657 cities in China, including four municipalities directly under the central government, 293 prefecture-level cities, and 360 county-level cities. However, comprehensive fine-scale city statistical data in China are only available at prefecture-level. In addition, there are many autonomous prefectures and leagues in western and northern China. Although they are administratively equivalent to the prefecture-level cities in eastern China, they have low urbanization rates, less developed economies and industries, and sparse population density. They lack statistical data appropriate for measuring urban sustainability. Thus, they are excluded from the case study. As a result, the final dataset includes 278 prefecture-level cities, including 251 in 2000, 267 in 2005, 276 in 2010, and 278 in 2017. The list and explanations of the constructs and measured variables are reported the following table. 

The list and explanations of measured variables*
Dimension	Variable	Meaning	Calculation method
ECO	gdp	Gross domestic product	-
	gdp_p	GDP per capita	GDP/Population
	wage	Wage	-
PAT	invg_a_p	Invention patent applications per capita	Invention patent applications/Population
	umg_a_p	Utility mode patent applications per capita	Utility mode patent applications/Population
	desg_a_p	Design mode patent applications per capita	Design mode patent applications/Population
	inva_e_p	Invention patents authorized per capita	Invention patents authorized/Population
	uma_e_p	Utility mode patents authorized per capita	Utility mode patents authorized/Population
	desa_e_p	Design mode patents authorized per capita	Design mode patents authorized/Population
INFOR	tele_in_p	Telecom business volume per capita	Telecom business volume/Population
	mobile_p	Mobile phone per capita	Mobile phone users/Population
	inter_p	Internet users per capita	Internet users/Population
SOR	crop_g	GDP per unit area of crop land	GDP/Area of crop land
	forest_g	GDP per unit area of forest land	GDP/Area of forest land
	water_g	GDP per unit area of water land	GDP/Area of water land
CP	no_g	GDP per unit NOx emission	GDP/NOx emission
	so_g	GDP per unit sulfur dioxide emission	GDP/Sulfur dioxide emission
	dust_g	GDP per unit discharge of industrial dusts	GDP/industrial dust discharge
EDU	college_p	Colleges per capita	Colleges/Population
	college_t_p	Proportion of college teachers per capita	college teachers/Population
	college_s_p	Proportion of college students per capita	college students/Population
TRANS	road_p	Road area per capita	Road areas/Population
	bus_p	Bus per capita	Buses/Population
	taxi_p	Taxi per capita	Taxies/Population

* Patent data is mainly derived from the patent cloud website (https://app.patentcloud.com/)., land use data is mainly from the land use database of the Chinese Academy of Sciences (http://www.resdc.cn/data.aspx?DATAID=184), and other data are mainly from the Online China Urban Statistics Yearbook (https://data.cnki.net/yearbook/Single/N2020050229).
DOI: 10.6084/m9.figshare.22792716
title: Additional file 1 of Breast cancer genomes from CHEK2 c.1100delC mutation carriers lack somatic TP53 mutations and display a unique structural variant size distribution profile

description: Additional file 1: Table S1. Characteristics of breast cancers from CHEK2 c.1100delC mutation carriers. Table S2. Number of variants, TMB, ID ratio, TP53 status, WGD, chromothripsis and percentage relative contribution of SBS, DSB, ID and SV signatures for primary and metastatic breast cancer genomes. Table S3. Relative contribution of major single base substitution signatures in primary and metastatic breast cancer genomes. Table S4. Relative contribution of major doublet base substitution signatures in primary and metastatic breast cancer genomes. Table S5. Relative contribution of major small indel signatures in primary and metastatic breast cancer genomes. Table S6. Relative contribution of 6 known structural variant signatures in CHEK2 versus HRD, ER− and ER+ primary breast cancer genomes. Table S7 Somatic driver gene mutation frequencies in primary and metastatic breast cancer genomes. Table S9. Genes differentially expressed between TP53 mutant and wild-type pBCs. Table S10. Clinicopathological variables of 760 ER+ lymph node negative treatment-naive breast cancer patients. Table S11. Clinicopathological variables of 323 hormone-naïve ER+ breast cancer patients treated with first-line tamoxifen for recurrent disease. Table S12. Univariable and multivariable Cox regression analysis of disease-free survival in 760 ER+ lymph node negative treatment-naive breast cancer patients. Table S13. Univariable and multivariable logistic regression analysis of overall response in 323 hormone-naïve ER+ breast cancer patients treated with first-line tamoxifen for recurrent disease. Table S14 Endocrine therapy resistance gene mutation frequencies in metastatic breast cancer genomes. Table S15. Sizes of structural variant types in primary and metastatic breast cancer genomes.
DOI: 10.6084/m9.figshare.22792716.v1
title: Additional file 1 of Breast cancer genomes from CHEK2 c.1100delC mutation carriers lack somatic TP53 mutations and display a unique structural variant size distribution profile

description: Additional file 1: Table S1. Characteristics of breast cancers from CHEK2 c.1100delC mutation carriers. Table S2. Number of variants, TMB, ID ratio, TP53 status, WGD, chromothripsis and percentage relative contribution of SBS, DSB, ID and SV signatures for primary and metastatic breast cancer genomes. Table S3. Relative contribution of major single base substitution signatures in primary and metastatic breast cancer genomes. Table S4. Relative contribution of major doublet base substitution signatures in primary and metastatic breast cancer genomes. Table S5. Relative contribution of major small indel signatures in primary and metastatic breast cancer genomes. Table S6. Relative contribution of 6 known structural variant signatures in CHEK2 versus HRD, ER− and ER+ primary breast cancer genomes. Table S7 Somatic driver gene mutation frequencies in primary and metastatic breast cancer genomes. Table S9. Genes differentially expressed between TP53 mutant and wild-type pBCs. Table S10. Clinicopathological variables of 760 ER+ lymph node negative treatment-naive breast cancer patients. Table S11. Clinicopathological variables of 323 hormone-naïve ER+ breast cancer patients treated with first-line tamoxifen for recurrent disease. Table S12. Univariable and multivariable Cox regression analysis of disease-free survival in 760 ER+ lymph node negative treatment-naive breast cancer patients. Table S13. Univariable and multivariable logistic regression analysis of overall response in 323 hormone-naïve ER+ breast cancer patients treated with first-line tamoxifen for recurrent disease. Table S14 Endocrine therapy resistance gene mutation frequencies in metastatic breast cancer genomes. Table S15. Sizes of structural variant types in primary and metastatic breast cancer genomes.
DOI: 10.6084/m9.figshare.22792722
title: Additional file 3 of Breast cancer genomes from CHEK2 c.1100delC mutation carriers lack somatic TP53 mutations and display a unique structural variant size distribution profile

description: Additional file 3: Table S8. RNA sequencing log2 GeTMM values from CHEK2 pBCs.
DOI: 10.6084/m9.figshare.22792722.v1
title: Additional file 3 of Breast cancer genomes from CHEK2 c.1100delC mutation carriers lack somatic TP53 mutations and display a unique structural variant size distribution profile

description: Additional file 3: Table S8. RNA sequencing log2 GeTMM values from CHEK2 pBCs.
DOI: 10.17632/7r2wgwdh7h.1
title: Data: Native plants diverge in resource availability response depending on whether they experienced multigenerational interactions with invasives

description: Meta-analysis: Invasive plant species critically threaten native plant communities and have devastating ecosystem impacts. They do so often with stronger belowground mechanisms. Despite the major negative impacts of invasive species, some populations of native plants have avoided extirpation from invasive species and native plants have responded through rapid trait change. Evidence shows that plants correspondingly respond rapidly in root allocation (RMF) to multigeneration invasive exposure, but whether RMF increases, decreases, or experiences little change between invader-naïve and invader-experienced native plants requires generalizations to better typify the effects invasive plants have on root traits of native species, including in the presence of resource stress.  I performed a meta-analysis on studies that use common garden methods to evaluate root allocation in invader-experienced and invader-naïve populations and found on 242 effect sizes on 17 native plants from nine studies that invader-experienced plants had greater RMF that increased under resource stress compared to invader-naïve plants and invader-experienced natives were slightly more competitive with the co-occurring invasive. These outcomes indicate adaptive nature of RMF in invasive exposure and begs a deeper dive in root trait responses influencing evolutionary responses and competition or coexistence with invasive species.
DOI: 10.6084/m9.figshare.22792724
title: Additional file 1 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 1. qPCR primer and probe sequences.
DOI: 10.6084/m9.figshare.22792724.v1
title: Additional file 1 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 1. qPCR primer and probe sequences.
DOI: 10.6084/m9.figshare.22792727
title: Additional file 2 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 2. Plant Root Simulator probe data.
DOI: 10.6084/m9.figshare.22792727.v1
title: Additional file 2 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 2. Plant Root Simulator probe data.
DOI: 10.6084/m9.figshare.22792730
title: Additional file 3 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 3. Agronomic data for each site and year.
DOI: 10.6084/m9.figshare.22792730.v1
title: Additional file 3 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 3. Agronomic data for each site and year.
DOI: 10.6084/m9.figshare.22792739
title: Additional file 6 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 6. Indicator taxa analysis for each site and location. Includes specificity, sensitivity, p value, and taxonomic classification.
DOI: 10.6084/m9.figshare.22792739.v1
title: Additional file 6 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 6. Indicator taxa analysis for each site and location. Includes specificity, sensitivity, p value, and taxonomic classification.
DOI: 10.6084/m9.figshare.22792742
title: Additional file 7 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 7. Correlation valuesbetween abudance of O. brassicae ITS sequences and yield.
DOI: 10.6084/m9.figshare.22792742.v1
title: Additional file 7 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 7. Correlation valuesbetween abudance of O. brassicae ITS sequences and yield.
DOI: 10.6084/m9.figshare.22792745
title: Additional file 8 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 8. Differential abundances of OTU determined using ALDEx2, including effect size, p value, and taxonomic classification.
DOI: 10.6084/m9.figshare.22792745.v1
title: Additional file 8 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 8. Differential abundances of OTU determined using ALDEx2, including effect size, p value, and taxonomic classification.
DOI: 10.6084/m9.figshare.22792748
title: Additional file 9 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 9. Root exudate amounts in root samples from each site, rotation, and year.
DOI: 10.6084/m9.figshare.22792748.v1
title: Additional file 9 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 9. Root exudate amounts in root samples from each site, rotation, and year.
DOI: 10.6084/m9.figshare.22792751
title: Additional file 10 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 10. Metadata for samples profiled using 16S rRNA gene sequencing.
DOI: 10.17632/rxpj7sp4yz.5
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A freeway relationship since 25 Ma.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.17632/rxpj7sp4yz.6
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A freeway relationship since 25 Ma.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.6084/m9.figshare.22792751.v1
title: Additional file 10 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 10. Metadata for samples profiled using 16S rRNA gene sequencing.
DOI: 10.6084/m9.figshare.22792757
title: Additional file 11 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 11. Metadata for samples profiled using ITS sequencing.
DOI: 10.6084/m9.figshare.22792757.v1
title: Additional file 11 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 11. Metadata for samples profiled using ITS sequencing.
DOI: 10.6084/m9.figshare.22792760
title: Additional file 12 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 12. ASV frequency table of 16S rRNA gene sequences for each sample.
DOI: 10.6084/m9.figshare.22792760.v1
title: Additional file 12 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 12. ASV frequency table of 16S rRNA gene sequences for each sample.
DOI: 10.6084/m9.figshare.22792766
title: Additional file 13 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 13. ASV frequency table of ITS sequences for each sample.
DOI: 10.6084/m9.figshare.22792766.v1
title: Additional file 13 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 13. ASV frequency table of ITS sequences for each sample.
DOI: 10.17632/rxpj7sp4yz.1
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.17632/rxpj7sp4yz.2
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.17632/rxpj7sp4yz.3
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A freeway relationship since 25 Ma.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.17632/rxpj7sp4yz.4
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A freeway relationship since 25 Ma.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.17632/rxpj7sp4yz.7
title: Supplement to The Totschunda-Denali fault intersection of Alaska: A freeway relationship since 25 Ma.

description: The herein included data serves as the supplement to the Paper title "The Totschunda-Denali fault intersection of Alaska: A 25-million-year freeway relationship."
DOI: 10.6084/m9.figshare.22792772
title: Additional file 14 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 14. Taxonomy of each 16S rRNA gene ASV.
DOI: 10.6084/m9.figshare.22792772.v1
title: Additional file 14 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 14. Taxonomy of each 16S rRNA gene ASV.
DOI: 10.6084/m9.figshare.22792775
title: Additional file 15 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 15. Taxonomy of each ITS ASV.
DOI: 10.6084/m9.figshare.22792775.v1
title: Additional file 15 of Crop rotation significantly influences the composition of soil, rhizosphere, and root microbiota in canola (Brassica napus L.)

description: Additional file 15. Taxonomy of each ITS ASV.
DOI: 10.6084/m9.figshare.22792779
title: Additional file 2 of Adverse infant outcomes following low-risk pregnancies in England: a retrospective cohort study

description: Additional file 2. List of codes used to identify adverse infant outcomes.
DOI: 10.6084/m9.figshare.22792779.v1
title: Additional file 2 of Adverse infant outcomes following low-risk pregnancies in England: a retrospective cohort study

description: Additional file 2. List of codes used to identify adverse infant outcomes.
DOI: 10.6084/m9.figshare.22792803
title: Additional file 2 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 2: Supplementary table 1. Underlying data for figure 1.
DOI: 10.6084/m9.figshare.22792803.v1
title: Additional file 2 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 2: Supplementary table 1. Underlying data for figure 1.
DOI: 10.6084/m9.figshare.22792806
title: Additional file 3 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 3: Supplementary table 2. MaGeCK analysis of the CRISPR knockout screen.
DOI: 10.6084/m9.figshare.22792806.v1
title: Additional file 3 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 3: Supplementary table 2. MaGeCK analysis of the CRISPR knockout screen.
DOI: 10.6084/m9.figshare.22792809.v1
title: Additional file 4 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 4: Supplementary tables 3–6. ED50 analysis of UBE2N inhibitor (S3), gRNA sequences used in CRISPR knockout library (S4) and for validation (S5), and PCR primers (S6).
DOI: 10.6084/m9.figshare.22792809
title: Additional file 4 of CRISPR-Cas9 knockout screen identifies novel treatment targets in childhood high-grade glioma

description: Additional file 4: Supplementary tables 3–6. ED50 analysis of UBE2N inhibitor (S3), gRNA sequences used in CRISPR knockout library (S4) and for validation (S5), and PCR primers (S6).
DOI: 10.17632/2629vdwmvh.1
title: HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

description: The images of western blot and eCLIP (RNA isolation, gel size selection, and eCT quantification) for YWHAG and YWHAH.
DOI: 10.17632/tfh8rcwk2w.1
title: The order set fot the Joint optimization of item and pod storage assignment problems in robotic mobile fulfillment systems

description: The order sets are used in the study of joint optimization of item and pod storage assignment problems in robotic mobile fulfillment systems.
The order sets include three different classes of order set , "10pods-20SKUs" is the small-scale one, "200pods-250SKUs" is the medium-scale one, and "400pods-500SKUs" is the large-scale instance
The data file is named X_Y_ABC.mat. X represnts the order size, Y represents the number of SKUs, ABC represents the SKU demands follow the ABC rule.
The order sets are generated randomly under different order sizes {20, 100, 500, 1000}. The number of SKUs per order follows U [1,3].
DOI: 10.17632/khxprpdt3z.1
title: Data and Code: Prediction of termination of atrial fibrillation

description: These data and code are covered in the article "Predicting spontaneous termination of Atrial Fibrillation based on Dual Path Network and feature selection". 
The "Data" folder contains: (a) clinical dynamic ECG data from Shandong Provincial Hospital; (b) selected data from AFDB database; (c) data from AFTDB database.
The "Code" folder contains the code for the network model, running tests, and related discussions for the work in the article.
DOI: 10.17632/twy4zk9227.1
title: Supplementary -3 FEM raw data

description: Raw data from FEM analysis of TPEE cylindrical structures
DOI: 10.17632/cm57pt579j.1
title: Differences in thickness-specific incidence of cutaneous melanoma by county type in the United States, 2010-2019 (Supplemental Material)

description: Supplemental material for manuscript entitled, "Differences in thickness-specific incidence of cutaneous melanoma by county type in the United States, 2010-2019" published in the Journal of the American Academy of Dermatology.