cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17126849.v1
title: Right angled Anteromedial Knee arthrotomy Data.xlsx

description: Dataset of Range of motion of post-operative Total knee arthroplasties, 85 knees, with follow up duration
DOI: 10.6084/m9.figshare.22756712
title: MASTER TABLE OF WOUND HEALING OF SECTIO CAESAREA IN RATS.xlsx

description: Sectio Caesarea is delivery that makes an incision in the abdominal and uterine walls an incision, intact and carries several risks. Administration of trigona honey given twice daily was more effective in accelerating the wound healing process than 50% and 75% turmeric extract gel. Original Trigona honey is potential in post sectio secarea wound healing.
DOI: 10.6073/pasta/47423e5bee22f227b1563d12228856a7
title: MCR LTER: Coral Reef: Long-term Population Dynamics of Acanthaster planci, ongoing since 2005

description: These data describe the abundance of Acanthaster planci, Crown of Thorns Sea stars, surveyed as part of MCR LTER's annual reef fish monitoring program. This study began in 2005 and the dataset is updated annually. The abundances of A. planci observed on a five by fifty meter transect are recorded by a diver using SCUBA. Surveys are conducted between 0900 and 1600 hours (Moorea time) during late July or early August each year. Four replicate transects are surveyed in each of three habitats (forereef, backreef and fringing reef) at six locations, two on each of Moorea's three sides, on the forereef, six locations on the backreef (two on each of Moorea's three sides for a total of 72 individual transects. Transects are permanently marked using a series of small, stainless steel posts affixed to the reef. Transects on the forereef are located at a depth of approximately 12m, those on the backreef are located at a depth of approximately 1.5m and those on the fringing reef are located at a depth of approximately 10m. This monitoring program is consistent with the protocols adopted by the Global Coral Reef Monitoring Network and the Australian Institute of Marine Science for use with the Great Barrier Reef Long-term Monitoring Program. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site. These data from years 2005-2010 were published in: Adam, T.C., R.J. Schmitt, S.J. Holbrook, A.J. Brooks, P.J. Edmunds, R.C. Carpenter and G. Bernardi. 2011. Herbivory, connectivity and ecosystem resilience: response of a coral reef to a large-scale perturbation. PLoS One 6(8): e23717 DOI: 10.1371/journal.pone.0023717
DOI: 10.6084/m9.figshare.22756799
title: MOOMIE article data : Efficient multi-objective optimization methodology for industrial engineering models with uncertain parameters

description: For each problem (mechanical or modified B&K) we have one folder per MOO problem solution. Inside, 4 files exist : 1. hist_opt_obj1.txt - history of all results calculated during DOE and each mono-objective optimization (used to create the approximation model for the first NSGA-II opptimization) 2.opt_f2-f2_limits.txt - extremums of the Pareto front (results of the mono-objective optimization for each PF) 3.pareto_meta1 - temprorary output in Abq Insight from first optimisation on approximation RBF model, used in Abauqs Insight as an input for final NSGA-II 4.pareto_out_final - output from Abaqus Insight NSGA-II final solver (file used for parametrisation and Cij identification). <br>
DOI: 10.6084/m9.figshare.22756799.v2
title: MOOMIE article data : Efficient multi-objective optimization methodology for industrial engineering models with uncertain parameters

description: For each problem (mechanical or modified B&K) we have one folder per MOO problem solution. Inside, 4 files exist : 1. hist_opt_obj1.txt - history of all results calculated during DOE and each mono-objective optimization (used to create the approximation model for the first NSGA-II opptimization) 2.opt_f2-f2_limits.txt - extremums of the Pareto front (results of the mono-objective optimization for each PF) 3.pareto_meta1 - temprorary output in Abq Insight from first optimisation on approximation RBF model, used in Abauqs Insight as an input for final NSGA-II 4.pareto_out_final - output from Abaqus Insight NSGA-II final solver (file used for parametrisation and Cij identification). <br>
DOI: 10.6084/m9.figshare.22756799.v1
title: MOOMIE article submitted to Engineering Optimisation journal : Efficient multi-objective optimization methodology for industrial engineering models with uncertain parameters

description: For each problem (mechanical or modified B&K) we have one folder per MOO problem solution. Inside, 4 files exist : 1. hist_opt_obj1.txt - history of all results calculated during DOE and each mono-objective optimization (used to create the approximation model for the first NSGA-II opptimization) 2.opt_f2-f2_limits.txt - extremums of the Pareto front (results of the mono-objective optimization for each PF) 3.pareto_meta1 - temprorary output in Abq Insight from first optimisation on approximation RBF model, used in Abauqs Insight as an input for final NSGA-II 4.pareto_out_final - output from Abaqus Insight NSGA-II final solver (file used for parametrisation and Cij identification). <br>
DOI: 10.5517/ccdc.csd.cc2dqw23
title: CCDC 2226515: Experimental Crystal Structure Determination

description: Related Article: Markus Strienz, Florian Fetzer, Andreas Schnepf|2023|Chemical Science|14|4571|doi:10.1039/D2SC06908C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dqw34
title: CCDC 2226516: Experimental Crystal Structure Determination

description: Related Article: Markus Strienz, Florian Fetzer, Andreas Schnepf|2023|Chemical Science|14|4571|doi:10.1039/D2SC06908C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dqw45
title: CCDC 2226517: Experimental Crystal Structure Determination

description: Related Article: Markus Strienz, Florian Fetzer, Andreas Schnepf|2023|Chemical Science|14|4571|doi:10.1039/D2SC06908C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dqw56
title: CCDC 2226518: Experimental Crystal Structure Determination

description: Related Article: Markus Strienz, Florian Fetzer, Andreas Schnepf|2023|Chemical Science|14|4571|doi:10.1039/D2SC06908C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1fqn
title: CCDC 2235711: Experimental Crystal Structure Determination

description: Related Article: Huangting Ding, Yitong Sun, Meng Tang, Jingyi Wen, Shiwen Yue, Ye Peng, Fei Li, Liyan Zheng, Suning Wang, Yonggang Shi, Qiue Cao|2023|Chemical Science|14|4633|doi:10.1039/D3SC00568B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1frp
title: CCDC 2235712: Experimental Crystal Structure Determination

description: Related Article: Huangting Ding, Yitong Sun, Meng Tang, Jingyi Wen, Shiwen Yue, Ye Peng, Fei Li, Liyan Zheng, Suning Wang, Yonggang Shi, Qiue Cao|2023|Chemical Science|14|4633|doi:10.1039/D3SC00568B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1fsq
title: CCDC 2235713: Experimental Crystal Structure Determination

description: Related Article: Huangting Ding, Yitong Sun, Meng Tang, Jingyi Wen, Shiwen Yue, Ye Peng, Fei Li, Liyan Zheng, Suning Wang, Yonggang Shi, Qiue Cao|2023|Chemical Science|14|4633|doi:10.1039/D3SC00568B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1ftr
title: CCDC 2235714: Experimental Crystal Structure Determination

description: Related Article: Huangting Ding, Yitong Sun, Meng Tang, Jingyi Wen, Shiwen Yue, Ye Peng, Fei Li, Liyan Zheng, Suning Wang, Yonggang Shi, Qiue Cao|2023|Chemical Science|14|4633|doi:10.1039/D3SC00568B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1fvs
title: CCDC 2235715: Experimental Crystal Structure Determination

description: Related Article: Huangting Ding, Yitong Sun, Meng Tang, Jingyi Wen, Shiwen Yue, Ye Peng, Fei Li, Liyan Zheng, Suning Wang, Yonggang Shi, Qiue Cao|2023|Chemical Science|14|4633|doi:10.1039/D3SC00568B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/sh17-hr68
title: Table Detection Dataset for Visually rich Documents

description: Table Detection has become a fundamental task for visually rich document understanding with the surging number of electronic documents. There have been some open datasets widely used in many studies. However, popular available datasets have some inherent limitations, including the noisy and inconsistent samples, and the limit number of training samples, and the limit number of data-sources. These limitations make these datasets unreliable to evaluate the model performance and cannot reflect the actual capacity of models.Therefore, in this paper, we revisit some open datasets with high quality of annotations, identify and clean the noise, and align the annotation definitions of these datasets to merge a larger dataset, termed with Open-Tables. Moreover, to enrich the data sources, we propose a new dataset, termed with ICT-TD, using the PDF files of Information and communication technologies (ICT) commodities which is a different domain containing unique samples that hardly appear in open datasets. To ensure the label quality of the dataset, we annotated the dataset manually following the guidance of a domain expert. The proposed dataset has a larger intra-variance and smaller inter-variance, making it more challenging and can be a sample of actual cases in the business context. We built strong baselines using various state-of-the-art object detection models and also built the baselines in the cross-domain setting. Our experimental results show that the domain difference among existing open datasets are small, even they have different data-sources. Our proposed Open-tables and ICT-TD are more suitable for the cross domain setting, and  can provide more reliable evaluation for model because of their high quality and consistent annotations. We conduct experiments to discuss the side effects of noise in the open source datasets. Our experimental results show that in the cross-domain setting, benchmark models trained with cleaned Open-Tables dataset can achieve 0.6\%-2.6\% higher weighted average F1 than the corresponding ones trained with the noisy version of Open-Tables, demonstrating the reliability of the proposed datasets.
DOI: 10.6084/m9.figshare.21817599.v1
title: VCF containing MDS outlier sites for figure 2d

description: VCF containing SNPs located in the MDS outlier region identified using lostruct. PCA in figure 2d was generated using the accompanying R script.
DOI: 10.6084/m9.figshare.21817596.v1
title: Data set used to create genotype plot in figure 2c

description: VCF containing fixed SNPs used to generate the genotype plot in figure 2c. R script is included.
DOI: 10.6084/m9.figshare.21817590.v1
title: Data set for genotype plot in figure 2b

description: VCF of fixed SNPs in chromosome 5 used to generate the genotype plot in figure 2b. R script is provided
DOI: 10.6084/m9.figshare.21817581.v1
title: Data for Manhattan Plot - Figure 2a

description: 50 kb sliding window Fst estimates for allopatric populations of Baltimore and Bullock's orioles. Data were used to generate the Manhattan plot in figure 2b using the provided R script
DOI: 10.6084/m9.figshare.21810420.v1
title: Data for Admixture plot - Figure 1D

description: ADMIXTURE output (ancestry proportions) for 55 individuals for which we have whole genome sequences. Dataset was used to generate the plot in Figure 1D. Admixture proportions were simply plotted using a bar chart in R
DOI: 10.6084/m9.figshare.21810417.v1
title: Data for PCA in Figure 1C

description: PC1 and PC2 scores based on whole genome data from Baltimore and Bullock's orioles and hybrids. Data set is associated with Figure 1C, which is a plot of PC1 vs PC2, color-coded by plumage score. <br> Plot was generated using ggplot2 in R
DOI: 10.6084/m9.figshare.19704208.v1
title: Supplementary Table 1 and Supplementary Methods Table 1

description: <strong>Supplementary Table 1</strong>: Clinicopathological features, TERT-related molecular data and telomere lengths of analyzed thyroid tumor series expressed in a.u.f. (arbitrary units of fluorescence). Na: not available data. <br> <strong>Supplementary methods table 1</strong>: Log2CPMs (log2 counts per million) normalized gene expression matrix of 26 genes involved in telomere maintenance (rows) for the 106 tumor series (columns). Tumor/gene expression cells of <em>TERT </em>probes with more than 3 normalized reads are colored in pink. The TERT_AVG<em> </em>row corresponds to the mean expression values of the 4 <em>TERT </em>probes for each tumor. Tumor cells with at least 1 probe with more than 3 reads are colored in pink and these were considered positive for <em>TERT </em>expression in subsequent analyses. Outlier gene expression thresholds used to generate the gene expression print in Figure 1B (<em>TERC</em>, <em>TCAB1</em>, <em>DKC1</em>, <em>NAT10</em>, <em>TINF2 </em>and <em>POT1</em>) are indicated. Two-tailed Mann-Whitney U-test p-value and FDR-adjusted p-value for the differential gene expression analysis between tumors classes (DF and PP) are included (colums DF and DG, respectively). DF: tumors from disease-free patients; PP: clinically aggressive tumors (PP, n=48).
DOI: 10.6084/m9.figshare.21817617
title: Heterozygosity data used to generate plot 2e

description: Heterozygosity data for MDS outlier regions used to generate plot in figure 2e
DOI: 10.6084/m9.figshare.21817599
title: VCF containing MDS outlier sites for figure 2d

description: VCF containing SNPs located in the MDS outlier region identified using lostruct. PCA in figure 2d was generated using the accompanying R script.
DOI: 10.6084/m9.figshare.21817596
title: Data set used to create genotype plot in figure 2c

description: VCF containing fixed SNPs used to generate the genotype plot in figure 2c. R script is included.
DOI: 10.6084/m9.figshare.21817590
title: Data set for genotype plot in figure 2b

description: VCF of fixed SNPs in chromosome 5 used to generate the genotype plot in figure 2b. R script is provided
DOI: 10.6084/m9.figshare.21817581
title: Data for Manhattan Plot - Figure 2a

description: 50 kb sliding window Fst estimates for allopatric populations of Baltimore and Bullock's orioles. Data were used to generate the Manhattan plot in figure 2b using the provided R script
DOI: 10.6084/m9.figshare.21810420
title: Data for Admixture plot - Figure 1D

description: ADMIXTURE output (ancestry proportions) for 55 individuals for which we have whole genome sequences. Dataset was used to generate the plot in Figure 1D. Admixture proportions were simply plotted using a bar chart in R
DOI: 10.6084/m9.figshare.21810417
title: Data for PCA in Figure 1C

description: PC1 and PC2 scores based on whole genome data from Baltimore and Bullock's orioles and hybrids. Data set is associated with Figure 1C, which is a plot of PC1 vs PC2, color-coded by plumage score. <br> Plot was generated using ggplot2 in R
DOI: 10.5281/zenodo.7838786
title: Dataset for: "Micromagnetic Tomography: Numerical Libraries"

description: This dataset contains the codes and Jupyter notebooks to reproduce the results of the publication <em>Micromagnetic Tomography: Numerical Libraries. </em> This data is hosted in the Github repository: https://github.com/Micromagnetic-Tomography/paper-2023-mmt-numerical-libraries
DOI: 10.5281/zenodo.7838785
title: Dataset for: "Micromagnetic Tomography: Numerical Libraries"

description: This dataset contains the codes and Jupyter notebooks to reproduce the results of the publication <em>Micromagnetic Tomography: Numerical Libraries. </em> This data is hosted in the Github repository: https://github.com/Micromagnetic-Tomography/paper-2023-mmt-numerical-libraries
DOI: 10.22002/ct5kt-cv878
title: Promoter CpG content regulates DNMT-dependent silencing dynamics

description: Raw data for paper "Promoter CpG content regulates DNMT-dependent silencing dynamics"
DOI: 10.34740/kaggle/dsv/5596389
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.25901/bfan-9a18
title: Great Western Woodlands Site, Litterfall From Four 1-ha Plots, 2013-2014
DOI: 10.25345/c5h708979
title: MassIVE MSV000091849 - MCF7_CDKi_globalproteomics_phosphoproteomics
title: MCF7 cells were treated with DMSO, Palbociclib or PF3600 for 1 hour and 24 hours. Samples were multiplexed and global proteomics and phosphoproteomics analyses were performed.
DOI: 10.3886/e112301v1-138000
title: Data and Code for The Labor Market Impact of Immigration: Job Creation vs. Job Competition

description: This paper studies the labor market effects of both documented and undocumented immigration in a search model featuring non-random hiring. As immigrants accept lower wages, they are preferably chosen by firms and therefore have higher job finding rates than natives, consistent with evidence found in US data. Immigration leads to the creation of additional jobs but also raises competition for natives. Which effect dominates depends on the fall in wage costs, which is larger for undocumented than for legal immigration. The model predicts a dominating job creation effect for the former, reducing natives' unemployment rate, but not for the latter.
description: Personal probability weights provided by IPUMS are used.
description: Prime-age (25-65) high-school dropouts that are non-institutionalized and live in households in urban areas (MSAs) in the United States.
DOI: 10.5281/zenodo.7888464
title: USEPs data

description: This repository contains the shapefile files resulting from a scientific research project focused on identifying and classifying Urban and Socio-Environmental Patterns (USEPs) in the Brazilian Amazon cities of Santarém and Cametá, located in the state of Pará. The research was developed within the Master's Program in Remote Sensing of the National Institute for Space Research. To reproduce the classification methodology, follow the below steps: <em>Code 1:</em> Run this code twice, once for the environmental dimension file (base_env) and once for the urban morphological dimension (base_urb). This code performs a treatment and selection of variables and performs an unsupervised classification to identify clusters in each dimension of analysis. <em>Code 2:</em> After identifying the clusters of the environmental (cluster_env) and urban morphological (cluster_urb) dimensions, run this code to apply a new unsupervised classification and identify the final clusters (USEPs) that integrate the results obtained in the previous step. <em>Code 3:</em> With the final clusters in hand, run this notebook to socioeconomically characterize the USEPs identified previously. For any questions, please contact bruno.santos@inpe.br.
DOI: 10.6084/m9.figshare.21286518
title: <em><strong>Cordia subcordata</strong></em> genome

description: <em><strong>Cordia subcordata</strong></em> genome and annotation.