cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12142572
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142524
title: MIC measurements

description: Standard bacteriological media MHB (Sigma-Aldrich) was supplemented with 25 mg/L Ca2+ and 12.5 mg/L Mg2+ (CA-MHB). Eukaryotic cell culture media Roswell Park Memorial Institute 1640 (RPMI) (Thermo Fisher Scientific) was supplemented with 10% LB (R10LB). Broth microdilution was performed to determine the nafcillin MIC in each media condition.
DOI: 10.6084/m9.figshare.12142521
title: COVID-19: Salud Materna

description: Documentos o guías que brindan recomendaciones para el cuidado de la salud materna.
DOI: 10.6084/m9.figshare.12142572.v1
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142521.v1
title: COVID-19: Salud Materna

description: Documentos o guías que brindan recomendaciones para el cuidado de la salud materna.
DOI: 10.6084/m9.figshare.12142524.v2
title: MIC measurements

description: Standard bacteriological media MHB (Sigma-Aldrich) was supplemented with 25 mg/L Ca2+ and 12.5 mg/L Mg2+ (CA-MHB). Eukaryotic cell culture media Roswell Park Memorial Institute 1640 (RPMI) (Thermo Fisher Scientific) was supplemented with 10% LB (R10LB). Broth microdilution was performed to determine the nafcillin MIC in each media condition.
DOI: 10.6084/m9.figshare.12142572.v2
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142524.v1
title: MIC measurements

description: Standard bacteriological media MHB (Sigma-Aldrich) was supplemented with 25 mg/L Ca2+ and 12.5 mg/L Mg2+ (CA-MHB). Eukaryotic cell culture media Roswell Park Memorial Institute 1640 (RPMI) (Thermo Fisher Scientific) was supplemented with 10% LB (R10LB). Broth microdilution was performed to determine the nafcillin MIC in each media condition.
DOI: 10.6084/m9.figshare.12142572.v3
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142572.v4
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142572.v5
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.5281/zenodo.3989072
title: Benchmark Collection for A Time Leap Challenge for SAT-Solving

description: Benchmarks used for the paper Johannes K. Fichte, Markus Hecher, Stefan Szeider: A Time Leap Challenge for SAT-Solving, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020). For details, we refer to the original sources of the benchmarks (https://www.cs.uni-potsdam.de/wv/sets/ and https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz), which was authored by Holger H. Hoos, B. Kaufmann, T. Schaub, and M. Schneider in the publication Robust Benchmark Set Selection for Boolean Constraint Solvers, LION 7, DOI: 10.1007/978-3-642-44973-4_16, 2013.
DOI: 10.5281/zenodo.3989071
title: Benchmark Collection for A Time Leap Challenge for SAT-Solving

description: Benchmarks used for the paper Johannes K. Fichte, Markus Hecher, Stefan Szeider: A Time Leap Challenge for SAT-Solving, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020). For details, we refer to the original sources of the benchmarks (https://www.cs.uni-potsdam.de/wv/sets/ and https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz), which was authored by Holger H. Hoos, B. Kaufmann, T. Schaub, and M. Schneider in the publication Robust Benchmark Set Selection for Boolean Constraint Solvers, LION 7, DOI: 10.1007/978-3-642-44973-4_16, 2013.
DOI: 10.6084/m9.figshare.12142572.v6
title: A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations

description: Dataset for "A Case Study Exploring Associations Between Popular Media Attention of Scientific Research and Scientific Citations"<br>Legend: LeadH= Lead author H-index (WoS)InstH= Institutional rank (Shanghai)CorrH= Corresponding author H-index (WoS)Scopus= Scopus citation count WoS=Web of Science citation countFWCit= Field-weighted citation impact (Scopus)AbsViews= Abstract views (Scopus)Mendeley= Mendeley readersJIFPerc= Journal Impact Factor percentile (in WoS Sports sciences category)Impact1= 1 year JIF (WoS)Impact5= 5 year JIF (WoS)Altmetric= Altmetric Attention ScoreSocMedia= Social media mentions (Facebook + Twitter collected via Altmetric)
DOI: 10.6084/m9.figshare.12142113
title: Table S1

description: Table S1- a spreadsheet with multiple tabs including Supplemental data and results.
DOI: 10.6084/m9.figshare.12142098
title: Supplemental files for Henson et al. 2020 DTE cultivation and modeling paper

description: Fasta files, tree files, IQ-tree logs, database fasta files, and other associated files.
DOI: 10.6084/m9.figshare.12142101
title: Laporte 2020

description: Datasets and figures for the publication
DOI: 10.6084/m9.figshare.12142113.v1
title: Table S1

description: Table S1- a spreadsheet with multiple tabs including Supplemental data and results.
DOI: 10.6084/m9.figshare.12142077
title: SuppTab2.xlsx

description: Tead1 bound genomic regions on selective genes in adult mouse hearts. Reference: Akerberg BN, Gu F, VanDusen NJ, Zhang X, Dong R, Li K, Zhang B, Zhou B, Sethi I, Ma Q, Wasson L, Wen T, Liu J, Dong K, Conlon FL, Zhou J, Yuan GC, Zhou P, and Pu WT. A reference map of murine cardiac transcription factor chromatin occupancy identifies dynamic and conserved enhancers. Nat Commun 10: 4907, 2019. GSE: 124008 (PMID: 31659164)
DOI: 10.6084/m9.figshare.12142077.v1
title: SuppTab2.xlsx

description: Tead1 bound genomic regions on selective genes in adult mouse hearts. Reference: Akerberg BN, Gu F, VanDusen NJ, Zhang X, Dong R, Li K, Zhang B, Zhou B, Sethi I, Ma Q, Wasson L, Wen T, Liu J, Dong K, Conlon FL, Zhou J, Yuan GC, Zhou P, and Pu WT. A reference map of murine cardiac transcription factor chromatin occupancy identifies dynamic and conserved enhancers. Nat Commun 10: 4907, 2019. GSE: 124008 (PMID: 31659164)
DOI: 10.6084/m9.figshare.12142074
title: SuppTab1.docx

description: <b>Supplementary Table 1.</b> Unbiased Geneset Enrichment Analysis Listing Top 10 Gene Onotology Terms of Down-regulated Genes in Tead1-iCKO Hearts
DOI: 10.6084/m9.figshare.12142098.v1
title: Supplemental information for Henson et al. 2020 DTE cultivation and modeling paper

description: Fasta files, tree files, IQ-tree logs, database fasta files, and other associated files.
DOI: 10.6084/m9.figshare.12142074.v1
title: SuppTab1.docx

description: <b>Supplementary Table 1.</b> Unbiased Geneset Enrichment Analysis Listing Top 10 Gene Onotology Terms of Down-regulated Genes in Tead1-iCKO Hearts
DOI: 10.6084/m9.figshare.12142053
title: Proteomics Analysis of circulating extracellular vesicles isolated from nonalcoholic steatohepatitis (NASH) patients

description: Complete dataset of proteomics analysis of extracellular vesicles isolated from serum of patients with pre-cirrhotic NASH, cirrhotic NASH and healthy controls. Proteomics analysis was performed by using SOMAscan protein array and included over 1,300 unique analyses.
DOI: 10.6084/m9.figshare.12142068.v1
title: Mat Waiver Training Raw Data.xlsx

description: MAT Waiver Training during Medical School. Wayne State University School of Medicine. Dataset.
DOI: 10.6084/m9.figshare.12141897
title: COVID-Predictor: RNA Sequence based Prediction of Coronavirus

description: The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. Moreover, the detection of highly divergent or yet unknown viruses is a major challenge despite of its clinical importance. In this situation, the outbreak of novel coronavirus (SARS-CoV-2) and its susceptibility in different epidemic condition around the world clearly suggest that the virus is mutating to create divergent variants and making the task of virus prediction more challenging. On the other hand, despite of novel coronavirus, two more coronaviruses such as MERS and SARS-CoV-1 are already present. Therefore, the use of machine learning technique is highly required at this moment to predict the coronaviruses by considering their divergent genetic functional characteristics. Thus, we are proposing machine learning based coronavirus prediction technique, called COVID-Predictor, where 1000 of RNA sequences of SARS-CoV-1, MERS, SARS-CoV-2 and other virus are used to train a Naïve Bayes classifier so that it can predict any unknown sequence of these viruses. In order to develop the COVID-Predictor, the feature vector is constructed by the motifs of the sequence generated by k-mer and n-gram techniques. The model has been validated using 10 fold cross validation in comparison with other classification techniques. The results show the superiority of our predictor by achieving average 97\% accuracy on unseen validation set. The same pre-trained model has been used to design a web based application where RNA sequences of unknown viruses can be uploaded to predict class of coronavirus.The predictor, code and datasets are available here: http://www.nitttrkol.ac.in/indrajit/projects/COVID-Predictor/
DOI: 10.6084/m9.figshare.12141960
title: Nucleotide sequences (fasta files) of 47 MAGs

description: Samples were collected from two geographically separated but connected chloraminated reservoirs within a large South African DWDS, previously described by Potgieter et al. (2018). Following filtration of bulk water and DNA extraction of collected biomass, metagenomic sequencing was performed using the Illumina HiSeq 2500 sequence platform, resulting in 250 nt paired–end reads. The assembled scaffolds (&gt;2000 bp) resulting from the de novo co-assembly of all samples were then used to generate 47 high quality metagenomic assembled genomes (MAGs) representing a highly diverse assemblage of bacteria. These MAGs were investigated from their genetic potential in nitrogen biotransformation and discussed in the study titled 'Microbial Nitrogen Metabolism in Chloraminated Drinking Water Reservoirs' by Potgieter et al. 2020.
DOI: 10.6084/m9.figshare.12141960.v1
title: Nucleotide sequences (fasta files) of 47 MAGs

description: Samples were collected from two geographically separated but connected chloraminated reservoirs within a large South African DWDS, previously described by Potgieter et al. (2018). Following filtration of bulk water and DNA extraction of collected biomass, metagenomic sequencing was performed using the Illumina HiSeq 2500 sequence platform, resulting in 250 nt paired–end reads. The assembled scaffolds (&gt;2000 bp) resulting from the de novo co-assembly of all samples were then used to generate 47 high quality metagenomic assembled genomes (MAGs) representing a highly diverse assemblage of bacteria. These MAGs were investigated from their genetic potential in nitrogen biotransformation and discussed in the study titled 'Microbial Nitrogen Metabolism in Chloraminated Drinking Water Reservoirs' by Potgieter et al. 2020.
DOI: 10.6084/m9.figshare.12141897.v2
title: COVID-Predictor: RNA Sequence based Prediction of Coronavirus

description: The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. Moreover, the detection of highly divergent or yet unknown viruses is a major challenge despite of its clinical importance. In this situation, the outbreak of novel coronavirus (SARS-CoV-2) and its susceptibility in different epidemic condition around the world clearly suggest that the virus is mutating to create divergent variants and making the task of virus prediction more challenging. On the other hand, despite of novel coronavirus, two more coronaviruses such as MERS and SARS-CoV-1 are already present. Therefore, the use of machine learning technique is highly required at this moment to predict the coronaviruses by considering their divergent genetic functional characteristics. Thus, we are proposing machine learning based coronavirus prediction technique, called COVID-Predictor, where 1000 of RNA sequences of SARS-CoV-1, MERS, SARS-CoV-2 and other virus are used to train a Naïve Bayes classifier so that it can predict any unknown sequence of these viruses. In order to develop the COVID-Predictor, the feature vector is constructed by the motifs of the sequence generated by k-mer and n-gram techniques. The model has been validated using 10 fold cross validation in comparison with other classification techniques. The results show the superiority of our predictor by achieving average 97\% accuracy on unseen validation set. The same pre-trained model has been used to design a web based application where RNA sequences of unknown viruses can be uploaded to predict class of coronavirus.The predictor, code and datasets are available here: http://www.nitttrkol.ac.in/indrajit/projects/COVID-Predictor/
DOI: 10.6084/m9.figshare.12141789
title: Syntermes grandis soldiers and workers raw spectral measures

description: Spectroscopic raw data of 363 individual termites of 6 colonies in two castes in*dpt format.
DOI: 10.6084/m9.figshare.12141897.v1
title: COVID-Predictor: RNA Sequence based Prediction of Coronavirus

description: The problem of virus classification is always a subject of concern for virology or epidemiology over the decades. Moreover, the detection of highly divergent or yet unknown viruses is a major challenge despite of its clinical importance. In this situation, the outbreak of novel coronavirus (SARS-CoV-2) and its susceptibility in different epidemic condition around the world clearly suggest that the virus is mutating to create divergent variants and making the task of virus prediction more challenging. On the other hand, despite of novel coronavirus, two more coronaviruses such as MERS and SARS-CoV-1 are already present. Therefore, the use of machine learning technique is highly required at this moment to predict the coronaviruses by considering their divergent genetic functional characteristics. Thus, we are proposing machine learning based coronavirus prediction technique, called COVID-Predictor, where 1000 of RNA sequences of SARS-CoV-1, MERS, SARS-CoV-2 and other virus are used to train a Naïve Bayes classifier so that it can predict any unknown sequence of these viruses. In order to develop the COVID-Predictor, the feature vector is constructed by the motifs of the sequence generated by k-mer and n-gram techniques. The model has been validated using 10 fold cross validation in comparison with other classification techniques. The results show the superiority of our predictor by achieving average 97\% accuracy on unseen validation set. The same pre-trained model has been used to design a web based application where RNA sequences of unknown viruses can be uploaded to predict class of coronavirus.The predictor, code and datasets are available here: http://www.nitttrkol.ac.in/indrajit/projects/COVID-Predictor/
DOI: 10.6084/m9.figshare.12141270
title: Syntermes grandis soldiers and workers raw spectral measures

description: Spectroscopic raw data of 363 individual termites of 6 colonies in two castes in*dpt format.
DOI: 10.6084/m9.figshare.12141249
title: Echinoid larval barcoding datasets

description: This includes the datasets and input files for the circular tree, and the small phylogenies presented in "<b>DNA Barcoding of echinopluteus larvae uncovers cryptic diversity in Neotropical echinoids" </b>published in Invertebrate Biology in 2020.<b></b>
DOI: 10.6084/m9.figshare.12141270.v1
title: Syntermes grandis soldiers and workers raw spectral measures

description: Spectroscopic raw data of 363 individual termites of 6 colonies in two castes in*dpt format.
DOI: 10.6084/m9.figshare.12141789.v1
title: Syntermes grandis soldiers and workers raw spectral measures

description: Spectroscopic raw data of 363 individual termites of 6 colonies in two castes in*dpt format.
DOI: 10.6084/m9.figshare.12141213
title: Valvaster molecular datasets

description: These are the datasets from: <b>World travelers: DNA Barcoding unmasks the origin of cloning asteroid larvae from the Caribbean. </b>Published in 2020 in Biological Bulletin.<b></b><br>They include the dataset and file inputs for the haplotype network and the phylogenetic analyses presented in this publication.
DOI: 10.6084/m9.figshare.12141249.v1
title: Echinoid larval barcoding datasets

description: This includes the datasets and input files for the circular tree, and the small phylogenies presented in "<b>DNA Barcoding of echinopluteus larvae uncovers cryptic diversity in Neotropical echinoids" </b>published in Invertebrate Biology in 2020.<b></b>
DOI: 10.6084/m9.figshare.12141213.v1
title: Valvaster molecular datasets

description: These are the datasets from: <b>World travelers: DNA Barcoding unmasks the origin of cloning asteroid larvae from the Caribbean. </b>Published in 2020 in Biological Bulletin.<b></b><br>They include the dataset and file inputs for the haplotype network and the phylogenetic analyses presented in this publication.
DOI: 10.6084/m9.figshare.12141147
title: Syntermes grandis soldiers and workers raw spectral measures

description: Spectroscopic raw data of 363 individual termites of 6 colonies in two castes in*dpt format.<br>
DOI: 10.6084/m9.figshare.12141111.v1
title: Improving Estimation in Functional Linear Regression with Points of Impact: Insights into Google AdWords

description: The functional linear regression model with points of impact is a recent augmentation of the classical functional linear model with many practically important applications. In this work, however, we demonstrate that the existing data-driven procedure for estimating the parameters of this regression model can be very instable and inaccurate. The tendency to omit relevant points of impact is a particularly problematic aspect resulting in omitted-variable biases. We explain the theoretical reason for this problem and propose a new sequential estimation algorithm that leads to significantly improved estimation results. Our estimation algorithm is compared with the existing estimation procedure using an in-depth simulation study. The applicability is demonstrated using data from Google AdWords, today’s most important platform for online advertisements. The R-package FunRegPoI and additional R-codes are provided in the online supplementary material.
DOI: 10.6084/m9.figshare.12141042
title: Tugas Sahab Anwar.xlsx

description: Contoh kode rekap nila UN excel
DOI: 10.6084/m9.figshare.12141042.v1
title: Tugas Sahab Anwar.xlsx

description: Contoh kode rekap nila UN excel