cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5594718
title: Automated application to assist in detecting novel gene-disease associations following whole genome sequencing

description: Results files, analysis scripts and original software from TierUp reanalysis performed on June 2020. These data contribute to the publication titled "Automated reanalysis application to assist in detecting novel gene-disease associations following whole genome sequencing". tierup_v0-3-0.tar.gz - source code used in the reanalysis tierup_results_summary.tar.gz - raw data and python code for publication figures
DOI: 10.6084/m9.figshare.16863670
title: Supplementary Material – Bryophyte flora in an Amazon island: structure of communities in areas of different levels of anthropization

description: <b>Table S1 – </b>Bryophytes of Mosqueiro Island, Belém, Pará,<b> </b>Brazilian Amazon. <br>see Original Paper: http://dx.doi.org/10.1590/2175-7860202172107
DOI: 10.6084/m9.figshare.16863670.v1
title: Supplementary Material – Bryophyte flora in an Amazon island: structure of communities in areas of different levels of anthropization

description: <b>Table S1 – </b>Bryophytes of Mosqueiro Island, Belém, Pará,<b> </b>Brazilian Amazon. <br>see Original Paper: http://dx.doi.org/10.1590/2175-7860202172107
DOI: 10.5281/zenodo.5594724
title: Backport Dataset

description: The dataset contains backports (10P_backport_keywords.csv) and normal pull-requests (10P_normal_PRs). Can be used for binary classification.
DOI: 10.6069/6kqh-w345
title: HDL proteomics on CPROBE samples

description: Patients with chronic kidney disease (CKD) are at high risk for cardiovascular disease (CVD). However, traditional lipid risk factors, including low HDL levels, cannot completely explain the increased risk. Altered HDL proteome is linked with both CVD and CKD, but the role of HDL proteins in incident CVD events in CKD is unknown. In this prospective case-control study, we used targeted proteomics to quantify 31 proteins in HDL from 92 subjects (46 incident new CVD and 46 one-to-one matched controls) at various stages of CKD. We tested associations of HDL proteins with incident CVD using matched logistic regression analysis. In unadjusted models, levels of six HDL proteins (APOA1, APOA4, APOC3, LCAT, PON1, and PON3) significantly associated with incident CVD. No significant associations were found for HDL-C. In the fully adjusted model for clinical confounders and lipid levels, we observed an inverse association between levels of PON1, PON3, and LCAT in HDL and incident CVD. Odds ratios (per 1-SD) were 0.38 (0.18-0.97, P=0.042), 0.42 (0.20-0.92, P=0.031), and 0.30 (0.11-0.83, P=0.020) for PON1, PON3, and LCAT, respectively. APOA4 remained associated with incident CVD in CKD patients in models adjusted for clinical confounders and lipid levels but lost significance with the addition of CRP and proteinuria in the model. In conclusion, levels of four HDL proteins, PON1, PON3, LCAT, and APOA4, were inversely associated with incident CVD events in CKD subjects. Our observations indicate that HDL’s protein cargo but not HDL-C levels can serve as a marker—and perhaps mediator—for elevated CVD risk in CKD patients.
DOI: 10.6069/sgzm-sg20
title: Gill proteome networks explain salinity tolerance of Oreochromis mossambicus

description: Acclimations of Oreochromis mossambicus to elevated salinity were conducted with multiple rates of salinity increase and duration of exposure to determine the rate-independent maximum salinity limit and the incipient lethal salinity. A data-independent acquisition (DIA) assay library was created for quantitative analysis of over 3000 gill proteins simultaneously in treatments representative of important key zones in the salinity level/duration landscape. From these DIA data, protein networks that represent complex molecular phenotypes associated with salinity acclimation were generated. Acclimations of Oreochromis mossambicus to elevated salinity were conducted with multiple rates of salinity increase and duration of exposure to determine the rate-independent maximum salinity limit and the incipient lethal salinity. A data-independent acquisition (DIA) assay library was created for quantitative analysis of over 3000 gill proteins simultaneously in treatments representative of important key zones in the salinity level/duration landscape. From these DIA data, protein networks that represent complex molecular phenotypes associated with salinity acclimation were generated. Protein fold change (FC) and organismal level performance indicators of salinity tolerance were then correlated. Gill protein networks impacted at extreme salinity levels both above and below the incipient lethal limit include increased energy metabolism, especially upregulation of electron transport chain complex proteins, and significant downregulation of a previously uncharacterized protein which bears strong amino acid sequence similarity to fucolectin. Proteins networks strongly impacted by crossing into the zone of resistance include cell adhesion and extracellular matrix regulation.
DOI: 10.6084/m9.figshare.16863682.v1
title: Additional file 1 of A citizen science-based survey of avian mortality focusing on haemosporidian infections in wild passerine birds

description: Additional file 1. Case details for all bird carcasses confirmed positive for haemosporidian parasites by PCR and sequencing.
DOI: 10.6084/m9.figshare.16863744
title: Additional file 2 of Comprehensive characterization of internal and cuticle surface microbiota of laboratory-reared F1 Anopheles albimanus originating from different sites

description: Additional file 2. Frequency and relative abundance of microbial taxa detected in internal and cuticle surface microbiota of F1 laboratory colonized An. albimanus larvae and adults.
DOI: 10.6084/m9.figshare.16863744.v1
title: Additional file 2 of Comprehensive characterization of internal and cuticle surface microbiota of laboratory-reared F1 Anopheles albimanus originating from different sites

description: Additional file 2. Frequency and relative abundance of microbial taxa detected in internal and cuticle surface microbiota of F1 laboratory colonized An. albimanus larvae and adults.
DOI: 10.6084/m9.figshare.16863753
title: Additional file 5 of Comprehensive characterization of internal and cuticle surface microbiota of laboratory-reared F1 Anopheles albimanus originating from different sites

description: Additional file 5. Results of downstream analysis including low frequency (potentially extraneous) reads.
DOI: 10.6084/m9.figshare.16863753.v1
title: Additional file 5 of Comprehensive characterization of internal and cuticle surface microbiota of laboratory-reared F1 Anopheles albimanus originating from different sites

description: Additional file 5. Results of downstream analysis including low frequency (potentially extraneous) reads.
DOI: 10.6084/m9.figshare.16863769
title: Additional file 3 of Confronting complexity and supporting transformation through health systems mapping: a case study

description: Additional file 3.
DOI: 10.6084/m9.figshare.16863769.v1
title: Additional file 3 of Confronting complexity and supporting transformation through health systems mapping: a case study

description: Additional file 3.
DOI: 10.6084/m9.figshare.16863807
title: Additional file 1 of TAGOPSIN: collating taxa-specific gene and protein functional and structural information

description: Additional file 1. List of Streptococcus species and their corresponding genome ACs and CDS counts retrieved by TAGOPSIN.
DOI: 10.6084/m9.figshare.16863807.v1
title: Additional file 1 of TAGOPSIN: collating taxa-specific gene and protein functional and structural information

description: Additional file 1. List of Streptococcus species and their corresponding genome ACs and CDS counts retrieved by TAGOPSIN.
DOI: 10.6084/m9.figshare.16863813
title: Additional file 3 of TAGOPSIN: collating taxa-specific gene and protein functional and structural information

description: Additional file 3. PDB dataset retrieved by TAGOPSIN for case study 3 [36].
DOI: 10.6084/m9.figshare.16863813.v1
title: Additional file 3 of TAGOPSIN: collating taxa-specific gene and protein functional and structural information

description: Additional file 3. PDB dataset retrieved by TAGOPSIN for case study 3 [36].
DOI: 10.6084/m9.figshare.16863814
title: Additional file 1 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 1. Literature Search Screening. It contains meta information about the abbreviations and the list of literature that was screened: 1439 rows - one row per study - and 14 columns containing information on author, title and journal as well as the inclusion and exclusion criteria.
DOI: 10.6084/m9.figshare.16863814.v1
title: Additional file 1 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 1. Literature Search Screening. It contains meta information about the abbreviations and the list of literature that was screened: 1439 rows - one row per study - and 14 columns containing information on author, title and journal as well as the inclusion and exclusion criteria.
DOI: 10.6084/m9.figshare.16863817
title: Additional file 2 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 2. Literature included in meta analysis. It contains the full reference of all the 50 studies retained after the literature search (Additional file 1) and included in the meta analysis. It is also available in the bib format (.bib) for the integration in a reference software.
DOI: 10.6084/m9.figshare.16863820
title: Additional file 3 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 3. Level of evidence assessment. Every column refers to one study. The spreadsheet template is taken from the supplementary material of [28].
DOI: 10.6084/m9.figshare.16863820.v1
title: Additional file 3 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 3. Level of evidence assessment. Every column refers to one study. The spreadsheet template is taken from the supplementary material of [28].
DOI: 10.6084/m9.figshare.16863817.v1
title: Additional file 2 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 2. Literature included in meta analysis. It contains the full reference of all the 50 studies retained after the literature search (Additional file 1) and included in the meta analysis. It is also available in the bib format (.bib) for the integration in a reference software.
DOI: 10.6084/m9.figshare.16863823
title: Additional file 4 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 4. Data for the meta-analysis on biodiversity. The file is a semicolon separated table (.csv) that can be opened in a spreadsheet. It has 29 rows and 21 columns containing information required for the meta-analysis. Meta information about the abbreviations used is found in Additional file 5.
DOI: 10.6084/m9.figshare.16863823.v1
title: Additional file 4 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 4. Data for the meta-analysis on biodiversity. The file is a semicolon separated table (.csv) that can be opened in a spreadsheet. It has 29 rows and 21 columns containing information required for the meta-analysis. Meta information about the abbreviations used is found in Additional file 5.
DOI: 10.6084/m9.figshare.16863826
title: Additional file 5 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 5. Description of data tables and R-Code of the analysis. The file is in the R-Markdown format (.rmd) and can be opened via R-Studio or any other text editor. The Appendix 5 is also provided in the html format including the R-Output of the analysis.
DOI: 10.6084/m9.figshare.16863826.v1
title: Additional file 5 of European agroforestry has no unequivocal effect on biodiversity: a time-cumulative meta-analysis

description: Additional file 5. Description of data tables and R-Code of the analysis. The file is in the R-Markdown format (.rmd) and can be opened via R-Studio or any other text editor. The Appendix 5 is also provided in the html format including the R-Output of the analysis.
DOI: 10.6084/m9.figshare.16863859
title: Additional file 3 of Self-reported knowledge, attitudes, practices and barriers in use of evidence-based medicine among resident physicians in Kenya: a mixed methods study

description: Additional file 3. EBM Quantitative dataset (XLSX 47.0 KB)
DOI: 10.6084/m9.figshare.16863859.v1
title: Additional file 3 of Self-reported knowledge, attitudes, practices and barriers in use of evidence-based medicine among resident physicians in Kenya: a mixed methods study

description: Additional file 3. EBM Quantitative dataset (XLSX 47.0 KB)
DOI: 10.6084/m9.figshare.16863915
title: Additional file 1 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 1. Accession number and information on publicly available isolates.
DOI: 10.6084/m9.figshare.16863915.v1
title: Additional file 1 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 1. Accession number and information on publicly available isolates.
DOI: 10.6084/m9.figshare.16863921.v1
title: Additional file 3 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 3. Sequence annotation of the prophages regions identified in the isolates investigated in the study.
DOI: 10.6084/m9.figshare.16863921
title: Additional file 3 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 3. Sequence annotation of the prophages regions identified in the isolates investigated in the study.
DOI: 10.6084/m9.figshare.16863945.v1
title: Additional file 9 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 9. Percentage of similarity between the outbreak isolates and the reference sequence of ST80/CT104 lineage.
DOI: 10.6084/m9.figshare.16863942.v1
title: Additional file 8 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 8. Percentage of similarity between the publicly available genomes and the concatenated reference sequence of index isolate (B11).
DOI: 10.6084/m9.figshare.16863940.v1
title: Additional file 2 of Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

description: Additional file 2. Comparison of different feature extraction methods in different species.
DOI: 10.6084/m9.figshare.16863937.v1
title: Additional file 1 of Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

description: Additional file 1. PseKNC accuracy display when K changes.
DOI: 10.6084/m9.figshare.16863945
title: Additional file 9 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 9. Percentage of similarity between the outbreak isolates and the reference sequence of ST80/CT104 lineage.
DOI: 10.6084/m9.figshare.16863942
title: Additional file 8 of Long-read sequencing-based in silico phage typing of vancomycin-resistant Enterococcus faecium

description: Additional file 8. Percentage of similarity between the publicly available genomes and the concatenated reference sequence of index isolate (B11).
DOI: 10.6084/m9.figshare.16863940
title: Additional file 2 of Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features

description: Additional file 2. Comparison of different feature extraction methods in different species.
DOI: 10.25582/data.2022-02.2441725/2031672
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445795/2031673
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2440619/2031674
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2434674/2031675
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443698/2031676
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435414/2031677
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2437242/2031678
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443756/2031679
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435365/2031680
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438631/2031681
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443845/2031682
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2434813/2031683
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435765/2031684
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442999/2031685
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445333/2031686
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2438921/2031687
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438599/2031688
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2441359/2031689
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442254/2031690
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443712/2031691
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435266/2031692
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442012/2031693
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443794/2031694
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445293/2031695
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2441795/2031696
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438894/2031697
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442993/2031698
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443748/2031699
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445872/2031700
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2438799/2031701
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2437352/2031702
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2440024/2031703
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435800/2031704
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435225/2031705
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445167/2031706
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2441886/2031707
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443608/2031708
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2440620/2031709
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438122/2031710
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435228/2031711
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2439120/2031712
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442713/2031713
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435340/2031714
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2441514/2031715
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2440970/2031716
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438653/2031717
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445788/2031718
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2438536/2031719
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2439021/2031720
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2439122/2031721
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438920/2031722
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2444391/2031723
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2440771/2031724
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443916/2031725
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445740/2031726
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2435838/2031727
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438958/2031728
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2441396/2031729
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2444372/2031730
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2443726/2031731
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2443033/2031732
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445624/2031733
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2438851/2031734
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435343/2031735
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435832/2031736
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445150/2031737
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2437700/2031738
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2439864/2031739
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442843/2031740
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435738/2031741
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2441269/2031742
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2437956/2031743
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2442114/2031744
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2438911/2031745
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445292/2031746
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-03.2445579/2031747
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2441688/2031748
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2444145/2031749
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2435277/2031750
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2437586/2031751
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-03.2445238/2031752
title: Data for EMSL Project 51127 from March 2022
DOI: 10.25582/data.2022-02.2443760/2031753
title: Data for EMSL Project 51127 from February 2022
DOI: 10.25582/data.2022-02.2434748/2031754
title: Data for EMSL Project 51127 from February 2022