cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8378717.v1
title: The rhizosphere microbiome, a new way to control Striga hermonthica?

description: <em>Striga hermonthica </em>is a root parasitic plant, causing major damage to the production of crops and controlling Striga is hard, since the life cycle of Striga is closely linked to its host plant. Methods to control Striga have mainly focused on developing Striga-resistant or Striga-tolerant species. The rhizosphere microbiome has been the focus of a new method as the microbiome can support the plant through controlling diseases directly or influence the plants defense mechanism. <br> The goal of this research was to examine how microbes could help crops resist <em>Striga</em>. Changes in expression of the Pathogenesis-related 3 gene, the Hypersensitive response 1 gene and a cytochrome p450 gene were studied in 2 genotypes of Sorghum. This was done in a more resistant genotype, Sb13 (Debir), and a more susceptible genotype, SQR (Shanqui red), in sterile and nonsterile soil. Here, it was demonstrated that the two genotypes exhibit a different expression pattern and seemingly have different tactics in using the microbiome to resist <em>Striga</em>. <br> Moreover, a germination assay was conducted to unravel the interactions that takes place in the rhizosphere. Sorghum plants were grown for 3, 6 or 11 days after transferring the seedlings. Root exudates were collected at these timepoints and <em>Striga </em>seeds were treated with the collected root exudates. Here, it was observed that the microbiome has an influence on the development of the germinated <em>Striga </em>seeds. More haustoria structures were formed in the absence of a microbiome (in sterile soil). It was also observed that <em>Striga </em>seeds germinated more when treated with root exudates from time point 3, although not significantly. <br> This study gives insight in the potential of the rhizosphere microbiome as a new method to control <em>Striga. </em>Future research is still necessary to fully understand the effect of the microbiome and the mechanisms behind it.
DOI: 10.6084/m9.figshare.8378594
title: Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill.

description: The annotation results of Davidia involucrata whole transcriptome.NCBI BioProject PRJNA513477 and CNGB Nucleotide Sequence Archive (CNSA) project CNP0000260.Yang H, Zhou C, Li G, et al. Reference gene and small RNA data from multiple tissues of Davidia involucrata Baill. [J].
DOI: 10.6084/m9.figshare.8378594.v1
title: Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill.

description: The annotation results of Davidia involucrata whole transcriptome.NCBI BioProject PRJNA284518 and CNGB Nucleotide Sequence Archive (CNSA) project CNP0000260.Yang H, Zhou C, Li G, et al. Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill. [J]. (accepted)
DOI: 10.6084/m9.figshare.8378567
title: Phylogeny of the subgenus <i>Eumitria</i> in Tanzania

description: Several <i>Usnea</i> species in subgenus <i>Eumitria</i> (<i>Parmeliaceae</i>, lichenized Ascomycota) have been described from East Africa in the past decades. These have been based on morphology and chemistry data while molecular studies remain very limited. In this paper we are for the first time publishing phylogenetic analyses along with morphological and chemical data for <i>Eumitria</i>. ‬A total of 62 new sequences of <i>Eumitria</i> (26 ITS, 20 nuLSU, 6 MCM7, 10 RPB1) were generated in this study. nuLSU, MCM7 and RPB1 sequences are here for the first time reported for <i>U. baileyi</i>. A phylogeny of subgenus <i>Eumitria</i> from Tanzania based on Bayesian and maximum likelihood analyses of a concatenated four-loci data set is presented, confirming the monophyly of <i>Eumitria</i>. Further, secondary chemistry and variation in characters, such as the pigmentation of the central axis and branch shape were investigated.
DOI: 10.6084/m9.figshare.8378594.v3
title: Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill.

description: The annotation results of Davidia involucrata whole transcriptome.NCBI BioProject PRJNA513477 and CNGB Nucleotide Sequence Archive (CNSA) project CNP0000260.Yang H, Zhou C, Li G, et al. Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill. [J].
DOI: 10.6084/m9.figshare.8378594.v2
title: Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill.

description: The annotation results of Davidia involucrata whole transcriptome.NCBI BioProject PRJNA513477 and CNGB Nucleotide Sequence Archive (CNSA) project CNP0000260.Yang H, Zhou C, Li G, et al. Reference gene set and small RNA set construction with multiple tissues from Davidia involucrata Baill. [J].
DOI: 10.6084/m9.figshare.8378567.v1
title: Phylogeny of the subgenus <i>Eumitria</i> in Tanzania

description: Several <i>Usnea</i> species in subgenus <i>Eumitria</i> (<i>Parmeliaceae</i>, lichenized Ascomycota) have been described from East Africa in the past decades. These have been based on morphology and chemistry data while molecular studies remain very limited. In this paper we are for the first time publishing phylogenetic analyses along with morphological and chemical data for <i>Eumitria</i>. ‬A total of 62 new sequences of <i>Eumitria</i> (26 ITS, 20 nuLSU, 6 MCM7, 10 RPB1) were generated in this study. nuLSU, MCM7 and RPB1 sequences are here for the first time reported for <i>U. baileyi</i>. A phylogeny of subgenus <i>Eumitria</i> from Tanzania based on Bayesian and maximum likelihood analyses of a concatenated four-loci data set is presented, confirming the monophyly of <i>Eumitria</i>. Further, secondary chemistry and variation in characters, such as the pigmentation of the central axis and branch shape were investigated.
DOI: 10.6084/m9.figshare.8378465
title: MOESM1 of Generation of an E. coli platform strain for improved sucrose utilization using adaptive laboratory evolution

description: Additional file 1: Data file S1. All mutation list of ALE resequenced clones and populations.
DOI: 10.6084/m9.figshare.8378465.v1
title: MOESM1 of Generation of an E. coli platform strain for improved sucrose utilization using adaptive laboratory evolution

description: Additional file 1: Data file S1. All mutation list of ALE resequenced clones and populations.
DOI: 10.15131/shef.data.8378309
title: Makerspaces in the Early Years: Current Perceptions and Practices of Early Years Practitioners, Library and Museum Educators and Makerspace Staff - A Survey

description: The MakEY project explores the place of the rising ‘maker’ culture in the development of young children’s digital literacy and creative design skills.A survey was undertaken of early years professionals, including teachers, staff working in museums and libraries, and makerspace staff to identify what current understanding and practice is with regard to the use of makerspaces. The survey was developed by the project team and placed online using Qualtrics. The survey was translated into the languages of countries participating in the project: Danish, German, Icelandic, Norwegian and Romanian. At a later stage in the project, the survey was translated into Portuguese and Spanish, for future use.
DOI: 10.6084/m9.figshare.8378291
title: ECGDataDenoised.zip

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8378297
title: TiPS - Gago et al 2019

description: Photosynthesis and leaf anatomy data across land plants
DOI: 10.15131/shef.data.8378309.v1
title: Makerspaces in the Early Years: Current Perceptions and Practices of Early Years Practitioners, Library and Museum Educators and Makerspace Staff - A Survey

description: The MakEY project explores the place of the rising ‘maker’ culture in the development of young children’s digital literacy and creative design skills.A survey was undertaken of early years professionals, including teachers, staff working in museums and libraries, and makerspace staff to identify what current understanding and practice is with regard to the use of makerspaces. The survey was developed by the project team and placed online using Qualtrics. The survey was translated into the languages of countries participating in the project: Danish, German, Icelandic, Norwegian and Romanian. At a later stage in the project, the survey was translated into Portuguese and Spanish, for future use.
DOI: 10.6084/m9.figshare.8378297.v1
title: TiPS - Gago et al 2019

description: Photosynthesis and leaf anatomy data across land plants
DOI: 10.6084/m9.figshare.8362121
title: Analysis of mammalian myosin II isoforms associated with body mass

description: Multiple sequence alignment of 12 myosin II isoforms. All sequences are from mammals and separate alignments have been generated for the motor (or head domain) and the tail domain. These are indicated by tail or mot in the file name
DOI: 10.6084/m9.figshare.8362100
title: Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files

description: <b>Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files</b>
DOI: 10.6084/m9.figshare.8362100.v2
title: Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files

description: <b>Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files</b>
DOI: 10.6084/m9.figshare.8362100.v1
title: adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files

description: adenocarcinoma associated with pathological stage based on WGCNA analysis-Supplemental files
DOI: 10.6084/m9.figshare.8361890
title: Specimen IDs, metadata, and computational scores for arm 2L

description: Specimen IDs, metadata, and computational scores for arm 2L. Made available under the Ag1000G terms of use: https://www.malariagen.net/data/terms-use/ag1000g-terms-use
DOI: 10.6084/m9.figshare.8361896
title: Specimen IDs, metadata, and computational scores for arm 2R

description: Specimen IDs, metadata, and computational scores for <i>Anopheles coluzzii</i> and <i>An. gambiae</i> specimens used in the Love et al. computational karyotyping project, arm 2R. Made available under the Ag1000G terms of use: https://www.malariagen.net/data/terms-use/ag1000g-terms-use
DOI: 10.6084/m9.figshare.8361896.v1
title: Specimen IDs, metadata, and computational scores for arm 2R

description: Specimen IDs, metadata, and computational scores for <i>Anopheles coluzzii</i> and <i>An. gambiae</i> specimens used in the Love et al. computational karyotyping project, arm 2R. Made available under the Ag1000G terms of use: https://www.malariagen.net/data/terms-use/ag1000g-terms-use
DOI: 10.6084/m9.figshare.8360414
title: RhythmNames.xlsx

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8361860
title: ECGData.zip

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8361890.v1
title: Specimen IDs, metadata, and computational scores for arm 2L

description: Specimen IDs, metadata, and computational scores for arm 2L. Made available under the Ag1000G terms of use: https://www.malariagen.net/data/terms-use/ag1000g-terms-use
DOI: 10.6084/m9.figshare.8360411
title: ConditionNames.xlsx

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8360408
title: Diagnostics.xlsx

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8360405
title: AttributesDictionary.xlsx

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8357504
title: Ge_GRL_Submission_Data

description: The observational and modeling data for the submission to Geophysical Research Letters
DOI: 10.6084/m9.figshare.8357504.v1
title: Ge_GRL_Submission_Data

description: The observational and modeling data for the submission to Geophysical Research Letters
DOI: 10.6084/m9.figshare.8360405.v1
title: AttributesDictionary.xlsx

description: The newly inaugurated research database for Electrocardiogram (ECG) signals, which was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine), aims to conduct the study of cardiovascular biomedical signals via extensive computer simulations. Certain types of cardiovascular conditions, such as atrial fibrillation, have wide and severe impact on public health, quality of life, and medical expenditures. The non-invasive test, the long term ECG monitor is a major and vital diagnosis tool for detecting such conditions. However, such practice generates considerable amount of data, and presents itself as very challenging since analyzing by experts in the field can be dramatic consumption of time and cost. As such, obtaining the correct and most efficient diagnosis algorithm is of great important to public health. Thanks to the development of modern machine learning and statistical analysis, the more data with creditable labels are available, the better algorithm will be achieved. Thus, we propose this dataset contains 10,646 patients 12-lead ECGs with 500 Hz sampling rate and including 11 common rhythms and 67 conditions, which are labeled by professional experts. The dataset can be widely applied for artificial intelligence or machine learning algorithm research, arrhythmia and other cardiovascular conditions analysis, biomedical signal study, and cluster or classification statistical study.
DOI: 10.6084/m9.figshare.8357402
title: Additional file 4: of Human amyloid-β enriched extracts: evaluation of in vitro and in vivo internalization and molecular characterization

description: Table S3. Proteins identified by reverse-phase liquid chromatography coupled to tandem mass spectrometry (RP-LC-MS/MS) in AD1, AD2, non-AD1, and non-AD2 extracts. PSM, peptide-spectrum matches. Coverage: % of the protein identified. #peptides, number of the identified peptides corresponding to the identified protein. (XLSX 60 kb)
DOI: 10.6084/m9.figshare.8357402.v1
title: Additional file 4: of Human amyloid-β enriched extracts: evaluation of in vitro and in vivo internalization and molecular characterization

description: Table S3. Proteins identified by reverse-phase liquid chromatography coupled to tandem mass spectrometry (RP-LC-MS/MS) in AD1, AD2, non-AD1, and non-AD2 extracts. PSM, peptide-spectrum matches. Coverage: % of the protein identified. #peptides, number of the identified peptides corresponding to the identified protein. (XLSX 60 kb)
DOI: 10.6084/m9.figshare.8357372
title: Additional file 2: of Analysis of isolates from Bangladesh highlights multiple ways to carry resistance genes in Salmonella Typhi

description: Table S3. Ampicilin, ciprofloxacin, chloramphenicol, ceftriaxone, and cotrimoxazole resistance. (XLSX 50 kb)
DOI: 10.6084/m9.figshare.8357378
title: Additional file 4: of Analysis of isolates from Bangladesh highlights multiple ways to carry resistance genes in Salmonella Typhi

description: Table S1. Sheet NCBI ID: Accession numbers for sequences deposited on NCBI. Sheet Metadata: metadata for the isolates studied. (XLSX 15 kb)