cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12278279.v1
title: Brain extraction (average BOLD) weights

description: Brain extraction for 3-D bold volume<br>
DOI: 10.6084/m9.figshare.12278405.v4
title: disintegrating bullet visualiser

description: data visualiser for simulation data<br>
DOI: 10.6084/m9.figshare.12278276.v1
title: Comparative mesocarp transcriptome of twelve oil palm accessions with different vitamin E content

description: The gene details and annotation information, including GO, KEGG, PRGdb, and transcription factor (TF)
DOI: 10.6084/m9.figshare.12278132
title: Metabolite BridgeDb ID Mapping Database (20200413)

description: BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of April 2020), ChEBI 186 (Release of April 2020), and Wikidata (13 April 2020) as data sources.
DOI: 10.6084/m9.figshare.12278132.v1
title: Metabolite BridgeDb ID Mapping Database (20200413)

description: BridgeDb ID mapping database for metabolites, using HMDB 4.0 (Release of April 2020), ChEBI 186 (Release of April 2020), and Wikidata (13 April 2020) as data sources.
DOI: 10.6084/m9.figshare.12278249.v3
title: SARS-CoV-2 RNA in blood

description: This dataset provides supplementary material for a report of the prevalence of SARS-CoV-2 RNA in blood and blood products.<br><b>S1 Table: </b>Metadata table providing data for prevalence of SARS-CoV-2 RNA in blood and blood products based on a systematic literature review. Details of 28 citations are shown, and the 22 studies included in quantitative meta-analysis are indicated. <br> <b>S2 Table: </b>Metadata table providing underlying data for serum samples from adults with COVID-19 infection. Sheet 1: samples obtained through patients recruited into a UK clinical cohort at Oxford University Hospitals NHS Foundation Trust (n=212). Cells highlighted in blue show follow-up samples collected from the same individual at different time points. Cells highlighted in orange show serum PCR positives. Sheet 2: samples obtained from convalescent donors a minimum of 28 days post resolution of symptoms, via NHS Blood and Transfusion, NHSBT (n=212). <br> <b>S3 Methods: </b>Details of RT-PCR reagents and cycling conditions for detection of SARS CoV-2 RNA.<br> <b>S4 Methods and data:</b> qRT-PCR quantification of vRNA from sera and viral culture assays. File contains methods for qRT-PCR and calculation of vRNA copy numbers, and qRT-PCR results in figure and table format.<br><b>PRISMA checklist:</b> formal report for systematic literature review
DOI: 10.6084/m9.figshare.12278129
title: Review Data ( GBD).csv

description: Data collated from VizHub ( Global Burden of Disease 2017)<br><br><br>Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Reference Life Table. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.<br>
DOI: 10.6084/m9.figshare.12278249.v4
title: SARS-CoV-2 RNA in blood

description: This dataset provides supplementary material for a report of the prevalence of SARS-CoV-2 RNA in blood and blood products.<br><b>S1 Table: </b>Metadata table providing data for prevalence of SARS-CoV-2 RNA in blood and blood products based on a systematic literature review. Details of 28 citations are shown, and the 22 studies included in quantitative meta-analysis are indicated. <br> <b>S2 Table: </b>Metadata table providing underlying data for serum samples from adults with COVID-19 infection. Sheet 1: samples obtained through patients recruited into a UK clinical cohort at Oxford University Hospitals NHS Foundation Trust (n=212). Cells highlighted in blue show follow-up samples collected from the same individual at different time points. Cells highlighted in orange show serum PCR positives. Sheet 2: samples obtained from convalescent donors a minimum of 28 days post resolution of symptoms, via NHS Blood and Transfusion, NHSBT (n=212). <br> <b>S3 Methods: </b>Details of RT-PCR reagents and cycling conditions for detection of SARS CoV-2 RNA.<br> <b>S4 Methods and data:</b> qRT-PCR quantification of vRNA from sera and viral culture assays. File contains methods for qRT-PCR and calculation of vRNA copy numbers, and qRT-PCR results in figure and table format.<br><b>PRISMA checklist:</b> formal report for systematic literature review
DOI: 10.6084/m9.figshare.12278084
title: Parameters that impact clinical and genetic outcome of PGT-A in young patients with unexplained recurrent miscarriag

description: Data set of our manuscript
DOI: 10.6084/m9.figshare.12278084.v1
title: Parameters that impact clinical and genetic outcome of PGT-A in young patients with unexplained recurrent miscarriag

description: Data set of our manuscript
DOI: 10.6084/m9.figshare.12278249.v5
title: SARS-CoV-2 RNA in blood

description: This dataset provides supplementary material for a report of the prevalence of SARS-CoV-2 RNA in blood and blood products.<br> <b>Extended Data File 1: Metadata table providing data for prevalence of SARS-CoV-2 RNA in blood and blood products based on a systematic literature review. </b>Details of 28 citations are presented, and the 22 studies included in quantitative meta-analysis are indicated.<br> <b>Extended Data File 2: qRT-PCR quantification of vRNA from sera and viral culture assays.</b> Calculation of vRNA copy numbers, and qRT-PCR results in figure and table format.<br> <b>Underlying Data File 1: Metadata table providing underlying data for serum samples from adults with confirmed SARS-CoV-2 infection, based on RT-PCR nose/throat swab. </b>Sheet 1: samples obtained through patients recruited into a UK clinical cohort at Oxford University Hospitals NHS Foundation Trust (n=212 samples from 167 unique individuals). Cells highlighted in blue show follow-up samples collected from the same individual at different time points. Cells highlighted in orange show serum PCR positives. Sheet 2: samples obtained from convalescent donors a minimum of 28 days post resolution of symptoms, via NHS Blood and Transplant, NHSBT (n=142 samples from 142 individuals).<br><b>RT-PCR Primer sequences: </b>provided in a .xlsx file.<br><b> </b><b>PRISMA checklist: </b>reporting for systematic review and meta-analysis<br><b> </b><b>STROBE checklist: </b>reporting for cohort studies
DOI: 10.6084/m9.figshare.12278051
title: Swimming behaviour of Yangtze voles is key to understanding the mechanisms of their population dynamics

description: This data comes from our research team's experimental work and field investigation. This dataset is divided into five parts, which are recorded in five Excel files. No. 1 Excel shows the lake water surface temperature of Dongting Lake during the flood season.No. 2 Excel shows 2 changes in individual characteristics of the voles ( <i>Microtus fortis</i>) in Dongting Lake before and after flood.<br>No. 3 Excel shows the inter-annual variation of flood events and inter-annual variation of <i>Microtus fortis </i>populations in the Dongting Lake wetland.No. 4 Excel shows the swimming ability and fur waterproof performance of the voles ( <i>Microtus fortis</i>) of different age groups in water temperature of 20 ℃.No. 5 Excel shows the swimming ability and fur waterproof performance of the voles ( <i>Microtus fortis</i>) of different age groups in different water temperature.
DOI: 10.6084/m9.figshare.12278051.v1
title: Swimming behaviour of Yangtze voles is key to understanding the mechanisms of their population dynamics

description: This data comes from our research team's experimental work and field investigation. This dataset is divided into five parts, which are recorded in five Excel files. No. 1 Excel shows the lake water surface temperature of Dongting Lake during the flood season.No. 2 Excel shows 2 changes in individual characteristics of the voles ( <i>Microtus fortis</i>) in Dongting Lake before and after flood.<br>No. 3 Excel shows the inter-annual variation of flood events and inter-annual variation of <i>Microtus fortis </i>populations in the Dongting Lake wetland.No. 4 Excel shows the swimming ability and fur waterproof performance of the voles ( <i>Microtus fortis</i>) of different age groups in water temperature of 20 ℃.No. 5 Excel shows the swimming ability and fur waterproof performance of the voles ( <i>Microtus fortis</i>) of different age groups in different water temperature.
DOI: 10.6084/m9.figshare.12278249.v6
title: SARS-CoV-2 RNA in blood

description: This dataset provides supplementary material for a report of the prevalence of SARS-CoV-2 RNA in blood and blood products.<br> <b>Extended Data File 1: Metadata table providing data for prevalence of SARS-CoV-2 RNA in blood and blood products based on a systematic literature review. </b>Details of 28 citations are presented, and the 22 studies included in quantitative meta-analysis are indicated.<br> <b>Extended Data File 2: qRT-PCR quantification of vRNA from sera and viral culture assays.</b> Calculation of vRNA copy numbers, and qRT-PCR results in figure and table format.<br> <b>Underlying Data File 1: Metadata table providing underlying data for serum samples from adults with confirmed SARS-CoV-2 infection, based on RT-PCR nose/throat swab. </b>Sheet 1: samples obtained through patients recruited into a UK clinical cohort at Oxford University Hospitals NHS Foundation Trust (n=212 samples from 167 unique individuals). Cells highlighted in blue show follow-up samples collected from the same individual at different time points. Cells highlighted in orange show serum PCR positives. Sheet 2: samples obtained from convalescent donors a minimum of 28 days post resolution of symptoms, via NHS Blood and Transplant, NHSBT (n=142 samples from 142 individuals).<br><b>RT-PCR Primer sequences: </b>provided in a .xlsx file.<br><b> </b><b>PRISMA checklist: </b>reporting for systematic review and meta-analysis<br><b> </b><b>STROBE checklist: </b>reporting for cohort studies<br><b>Fig 4A-D:</b> raw, unedited microscope images of cell cultures. Dates of each image stored in the format YYYYMMDD. (A) and (B) are controls:(A) Negative control Vero E6 cells in media; (B) Cytopathic effect (CPE) in Vero E6 cells spiked with Victoria/01/2020 SARS-CoV-2.(C) and (D) are Vero E6 cells inoculated with 1/10 dilution of serum sample from sample VC12 (patient ID UKCOV040), that tested positive for SARS-CoV-2 RNA by RT-PCR:(C) Aberrant cellular effects at day 4 in a culture inoculated with VC12 at day 0; (D) Normal appearance of cells at day 7 inoculated with 1/10 dilution of the culture supernatant of the VC12-challenged culture, illustrated in (C).<br>
DOI: 10.6084/m9.figshare.12278006
title: Targeted next-generation sequencing for congenital hypothyroidism with positive neonatal TSH screening

description: We performed genetic analysis of 24 causative genes using next-generation sequencing in 167 patients with congenital hypothyroidism
DOI: 10.6084/m9.figshare.12277934
title: Vc and starch content

description: Ascorbic acid and starch content in different fruit developmental stages in guava. There are six samples in each stage.
DOI: 10.6084/m9.figshare.12277037
title: Additional file 3 of Aromatic clusters in protein–protein and protein–drug complexes

description: Additional file 3. Protein-drug interactions with PDBID, chain, residue name and Ki data if available.
DOI: 10.6084/m9.figshare.12277034
title: Additional file 2 of Aromatic clusters in protein–protein and protein–drug complexes

description: Additional file 2. Protein-protein interactions with PDBID, chain and UniProtID.
DOI: 10.6084/m9.figshare.12277037.v1
title: Additional file 3 of Aromatic clusters in protein–protein and protein–drug complexes

description: Additional file 3. Protein-drug interactions with PDBID, chain, residue name and Ki data if available.
DOI: 10.6084/m9.figshare.12277034.v1
title: Additional file 2 of Aromatic clusters in protein–protein and protein–drug complexes

description: Additional file 2. Protein-protein interactions with PDBID, chain and UniProtID.
DOI: 10.6084/m9.figshare.12276980
title: Additional file 2 of Sub-national stratification of malaria risk in mainland Tanzania: a simplified assembly of survey and routine data

description: Additional file 2: Table S1. The maximum of the annual mean values per indicator and resulting overall risk strata assigned per council.
DOI: 10.6084/m9.figshare.12276980.v1
title: Additional file 2 of Sub-national stratification of malaria risk in mainland Tanzania: a simplified assembly of survey and routine data

description: Additional file 2: Table S1. The maximum of the annual mean values per indicator and resulting overall risk strata assigned per council.
DOI: 10.6084/m9.figshare.12276920
title: Additional file 2 of TERT expression is susceptible to BRAF and ETS-factor inhibition in BRAFV600E/TERT promoter double-mutated glioma

description: Table S4. Dataset of 103 BRAFV600E-mutated glioma with information on tumor grade and TERT promoter mutation. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.12276920.v1
title: Additional file 2 of TERT expression is susceptible to BRAF and ETS-factor inhibition in BRAFV600E/TERT promoter double-mutated glioma

description: Table S4. Dataset of 103 BRAFV600E-mutated glioma with information on tumor grade and TERT promoter mutation. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.12276875
title: Additional file 1 of The impact of terrestrial protected areas on vegetation extent and condition: a systematic review protocol

description: Additional file 1. Completed ROSES form.
DOI: 10.6084/m9.figshare.12276875.v1
title: Additional file 1 of The impact of terrestrial protected areas on vegetation extent and condition: a systematic review protocol

description: Additional file 1. Completed ROSES form.
DOI: 10.6084/m9.figshare.12276857
title: Additional file 4 of Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles

description: Additional file 4.
DOI: 10.6084/m9.figshare.12276851
title: Additional file 2 of Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles

description: Additional file 2.
DOI: 10.6084/m9.figshare.12276854
title: Additional file 3 of Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles

description: Additional file 3.
DOI: 10.6084/m9.figshare.12276857.v1
title: Additional file 4 of Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles

description: Additional file 4.
DOI: 10.6084/m9.figshare.12276851.v1
title: Additional file 2 of Alzheimer’s disease progression characterized by alterations in the molecular profiles and biogenesis of brain extracellular vesicles

description: Additional file 2.
DOI: 10.6084/m9.figshare.12276788
title: Additional file 2 of SMN-deficiency disrupts SERCA2 expression and intracellular Ca2+ signaling in cardiomyocytes from SMA mice and patient-derived iPSCs

description: Additional file 2. Table 1 Transcriptome analysis.
DOI: 10.6084/m9.figshare.12276788.v1
title: Additional file 2 of SMN-deficiency disrupts SERCA2 expression and intracellular Ca2+ signaling in cardiomyocytes from SMA mice and patient-derived iPSCs

description: Additional file 2. Table 1 Transcriptome analysis.
DOI: 10.6084/m9.figshare.12276761
title: Additional file 3 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 3. Matlab data table in .mat format containing geometric, material property, bending strength, rind puncture and CT data obtained in this study.
DOI: 10.6084/m9.figshare.12276755
title: Additional file 2 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 2. Data table in .csv format containing geometric, material property, bending strength, and rind puncture data obtained in this study.
DOI: 10.6084/m9.figshare.12276755.v1
title: Additional file 2 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 2. Data table in .csv format containing geometric, material property, bending strength, and rind puncture data obtained in this study.
DOI: 10.6084/m9.figshare.12276761.v1
title: Additional file 3 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 3. Matlab data table in .mat format containing geometric, material property, bending strength, rind puncture and CT data obtained in this study.
DOI: 10.6084/m9.figshare.12276749
title: Additional file 1 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 1. Text file providing an in depth explanation of the data contained in Additional Files 2 and 3.
DOI: 10.6084/m9.figshare.12276749.v1
title: Additional file 1 of The effect of probe geometry on rind puncture resistance testing of maize stalks

description: Additional file 1. Text file providing an in depth explanation of the data contained in Additional Files 2 and 3.
DOI: 10.6084/m9.figshare.12276626
title: Cycle data for "Growth and arrest of topological cycles in small physical networks"

description: The chordless cycles data given here is required to reproduce the random and Monte Carlo data in Fig. 1 and 2 of "Growth and arrest of topological cycles in small physical networks" in PNAS. <br>Random graphs were generated from the following method, as implemented in Networkx (https://networkx.github.io/):<br>A. Steger and N. Wormald, Generating random regular graphs quickly, Probability and Computing 8 (1999), 377-396, 1999.<br>The vitroid code for cycle counting: https://github.com/vitroid/CountRings<br>LAMMPS was used for molecular dynamics simulations: https://lammps.sandia.gov/<br>The python library SciPy was used for numerical fits of a Guassian to this data: https://www.scipy.org/
DOI: 10.6084/m9.figshare.12276626.v1
title: Cycle data for "Growth and arrest of topological cycles in small physical networks"

description: The chordless cycles data given here is required to reproduce the random and Monte Carlo data in Fig. 1 and 2 of "Growth and arrest of topological cycles in small physical networks" in PNAS. <br>Random graphs were generated from the following method, as implemented in Networkx (https://networkx.github.io/):<br>A. Steger and N. Wormald, Generating random regular graphs quickly, Probability and Computing 8 (1999), 377-396, 1999.<br>The vitroid code for cycle counting: https://github.com/vitroid/CountRings<br>LAMMPS was used for molecular dynamics simulations: https://lammps.sandia.gov/<br>The python library SciPy was used for numerical fits of a Guassian to this data: https://www.scipy.org/
DOI: 10.17608/k6.auckland.12276452
title: Supplementary_spreadsheet_8

description: Supplementary_spreadsheet_8
DOI: 10.23641/asha.12276458
title: Measuring early vocalizations (McDaniel et al., 2020)

description: <b>Purpose: </b>This study was designed to test the incremental validity of more expensive vocal development variables relative to less expensive variables for predicting later expressive language in children with autism spectrum disorder (ASD). We devote particular attention to the added value of coding the quality of vocalizations over the quantity of vocalizations because coding quality adds expense to the coding process. We are also interested in the added value of more costly human-coded vocal variables relative to those generated through automated analyses.<b>Method: </b>Eighty-seven children with ASD aged 13–30 months at study initiation participated. For quantity of vocalizations, we derived one variable from human coding of brief communication samples and one from an automated process for daylong naturalistic audio samples. For quality of vocalizations, we derived four human-coded variables and one automated variable. A composite expressive language measure was derived at study entry, and 6 and 12 months later. The 12 months–centered intercept of a simple linear growth trajectory was used to quantify later expressive language.<b>Results: </b>When statistically controlling for human-coded or automated quantity of vocalization variables, human-coded quality of vocalization variables exhibited incremental validity for predicting later expressive language skills. Human-coded vocal variables also predicted later expressive language skills when controlling for the analogous automated vocal variables.<b>Conclusion:</b> In sum, these findings support devoting resources to human coding of the quality of vocalizations from communication samples to predict later expressive language skills in young children with ASD despite the greater costs of deriving these variables.<br><b>Supplemental Material S1.</b> Descriptive statistics for vocal variables at Time 1.<br><b>Supplemental Material S2.</b> Correlations between vocal variables.<br><b>Supplemental Material S3. </b>Growth curve model results.<br>McDaniel, J., Yoder, P., Estes, A., & Rogers, S. J. (2020). Predicting expressive language from early vocalizations in young children with autism spectrum disorder: Which vocal measure is best? <i>Journal of Speech, Language, and Hearing Research.</i> Advance online publication. https://doi.org/10.1044/2020_JSLHR-19-00281
DOI: 10.17608/k6.auckland.12276440
title: Supplementary_spreadsheet_6

description: Supplementary_spreadsheet_6
DOI: 10.17608/k6.auckland.12276449
title: Supplementary_spreadsheet_7

description: Supplementary_spreadsheet_7
DOI: 10.17608/k6.auckland.12276452.v1
title: Supplementary_spreadsheet_8

description: Supplementary_spreadsheet_8
DOI: 10.23641/asha.12276458.v1
title: Measuring early vocalizations (McDaniel et al., 2020)

description: <b>Purpose: </b>This study was designed to test the incremental validity of more expensive vocal development variables relative to less expensive variables for predicting later expressive language in children with autism spectrum disorder (ASD). We devote particular attention to the added value of coding the quality of vocalizations over the quantity of vocalizations because coding quality adds expense to the coding process. We are also interested in the added value of more costly human-coded vocal variables relative to those generated through automated analyses.<b>Method: </b>Eighty-seven children with ASD aged 13–30 months at study initiation participated. For quantity of vocalizations, we derived one variable from human coding of brief communication samples and one from an automated process for daylong naturalistic audio samples. For quality of vocalizations, we derived four human-coded variables and one automated variable. A composite expressive language measure was derived at study entry, and 6 and 12 months later. The 12 months–centered intercept of a simple linear growth trajectory was used to quantify later expressive language.<b>Results: </b>When statistically controlling for human-coded or automated quantity of vocalization variables, human-coded quality of vocalization variables exhibited incremental validity for predicting later expressive language skills. Human-coded vocal variables also predicted later expressive language skills when controlling for the analogous automated vocal variables.<b>Conclusion:</b> In sum, these findings support devoting resources to human coding of the quality of vocalizations from communication samples to predict later expressive language skills in young children with ASD despite the greater costs of deriving these variables.<br><b>Supplemental Material S1.</b> Descriptive statistics for vocal variables at Time 1.<br><b>Supplemental Material S2.</b> Correlations between vocal variables.<br><b>Supplemental Material S3. </b>Growth curve model results.<br>McDaniel, J., Yoder, P., Estes, A., & Rogers, S. J. (2020). Predicting expressive language from early vocalizations in young children with autism spectrum disorder: Which vocal measure is best? <i>Journal of Speech, Language, and Hearing Research.</i> Advance online publication. https://doi.org/10.1044/2020_JSLHR-19-00281
DOI: 10.17608/k6.auckland.12276383
title: Supplementary_spreadsheet_5

description: Supplementary_spreadsheet_5