cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11797803
title: Additional file 2 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 2: All cancer samples (metastatic and non-metastatic) were included in a single group and compared with normal samples.
DOI: 10.6084/m9.figshare.11797803.v1
title: Additional file 2 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 2: All cancer samples (metastatic and non-metastatic) were included in a single group and compared with normal samples.
DOI: 10.6084/m9.figshare.11797797
title: Additional file 1 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 1:. A subset of genes were detected using UPL probes in which case probe numbers are given.
DOI: 10.6084/m9.figshare.11797797.v1
title: Additional file 1 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 1:. A subset of genes were detected using UPL probes in which case probe numbers are given.
DOI: 10.6084/m9.figshare.11797653
title: Additional file 2 of The sugar transporter SWEET10 acts downstream of FLOWERING LOCUS T during floral transition of Arabidopsis thaliana

description: Additional file 2: Table S1. Primers used in this study. Figure S1. In silico analyses of co-expressed gene networks around SWEET10. (A) Gene network representation and list of genes correlated to SWEET10 during development generated by the GENEVESTIGATOR software [74]. (B) The Arabidopsis thaliana trans-factor and cis-element prediction database ATTED-II [75], implemented in www.arabidopsis.org, was used to predict and visualize co-expressed genes around SWEET10. Figure S2. Expression of pSWEET10:GUS in adult Arabidopsis plants. (A) GUS expression in a whole plant expressing pSWEET10:GUS. The T4 transgenic plant shown in (A) was grown under LDs until siliques were produced. Detail of an inflorescence (B) and a silique (C) showing GUS expression. Figure S3. Expression levels of SWEET10 in T1 transgenic lines overexpressing SWEET10. The expression levels of SWEET10 was quantified in Col-0 and 35S:SWEET10 T1 lines under LDs. Leaves were collected at ZT8. Errors bars indicate Standard Deviation. Figure S4. Effect of the overexpression of SWEET10 from SUC2 promoter on flowering time under LDs. At least 10 plants were used for each experiment. Asterisk indicates a significant different compared to Col-0 (T-test, p-Value ≤0.05). Errors bars indicate Standard Deviation. Figure S5. Analysis of amiR-SWEET10 transgenic plants. (A) Flowering time of 44 amiR-SWEET10 T1 lines compared to Col-0 under LDs. TLN: Total Leaf Number. (B) SWEET10 expression levels in a subset of T3 amiR-SWEET10 lines. Figure S6. Photoperiod-dependent expression profile of SWEET11, 12, 13 and 14. GUS expression in plants expressing pSWEET10:GUS. Plants were grown under SDs for 2 weeks, shifted to LDs and collected for GUS staining at ZT8 in different days. Scale bar = 5 mm. Figure S7. Levels of sugar during the photoperiodic induction of flowering. Col-0 and ft tsf plants were grown under SDs for 2 weeks, shifted to LDs shoot apices were harvested at ZT8 in different days. Shoot apices were harvested and used to quantify the concentration of fructose (fru), glucose (glu) and sucrose (suc). Errors bars indicate Standard Deviation.
DOI: 10.6084/m9.figshare.11797653.v1
title: Additional file 2 of The sugar transporter SWEET10 acts downstream of FLOWERING LOCUS T during floral transition of Arabidopsis thaliana

description: Additional file 2: Table S1. Primers used in this study. Figure S1. In silico analyses of co-expressed gene networks around SWEET10. (A) Gene network representation and list of genes correlated to SWEET10 during development generated by the GENEVESTIGATOR software [74]. (B) The Arabidopsis thaliana trans-factor and cis-element prediction database ATTED-II [75], implemented in www.arabidopsis.org, was used to predict and visualize co-expressed genes around SWEET10. Figure S2. Expression of pSWEET10:GUS in adult Arabidopsis plants. (A) GUS expression in a whole plant expressing pSWEET10:GUS. The T4 transgenic plant shown in (A) was grown under LDs until siliques were produced. Detail of an inflorescence (B) and a silique (C) showing GUS expression. Figure S3. Expression levels of SWEET10 in T1 transgenic lines overexpressing SWEET10. The expression levels of SWEET10 was quantified in Col-0 and 35S:SWEET10 T1 lines under LDs. Leaves were collected at ZT8. Errors bars indicate Standard Deviation. Figure S4. Effect of the overexpression of SWEET10 from SUC2 promoter on flowering time under LDs. At least 10 plants were used for each experiment. Asterisk indicates a significant different compared to Col-0 (T-test, p-Value ≤0.05). Errors bars indicate Standard Deviation. Figure S5. Analysis of amiR-SWEET10 transgenic plants. (A) Flowering time of 44 amiR-SWEET10 T1 lines compared to Col-0 under LDs. TLN: Total Leaf Number. (B) SWEET10 expression levels in a subset of T3 amiR-SWEET10 lines. Figure S6. Photoperiod-dependent expression profile of SWEET11, 12, 13 and 14. GUS expression in plants expressing pSWEET10:GUS. Plants were grown under SDs for 2 weeks, shifted to LDs and collected for GUS staining at ZT8 in different days. Scale bar = 5 mm. Figure S7. Levels of sugar during the photoperiodic induction of flowering. Col-0 and ft tsf plants were grown under SDs for 2 weeks, shifted to LDs shoot apices were harvested at ZT8 in different days. Shoot apices were harvested and used to quantify the concentration of fructose (fru), glucose (glu) and sucrose (suc). Errors bars indicate Standard Deviation.
DOI: 10.6084/m9.figshare.11797647
title: Additional file 1 of The sugar transporter SWEET10 acts downstream of FLOWERING LOCUS T during floral transition of Arabidopsis thaliana

description: Additional file 1. Results of the expression studies performed on the GeneChip Arabidopsis Tiling 1.0R Array.
DOI: 10.6084/m9.figshare.11797647.v1
title: Additional file 1 of The sugar transporter SWEET10 acts downstream of FLOWERING LOCUS T during floral transition of Arabidopsis thaliana

description: Additional file 1. Results of the expression studies performed on the GeneChip Arabidopsis Tiling 1.0R Array.
DOI: 10.6084/m9.figshare.11797599
title: Additional file 2 of The impact of diagnostic microbiology on de-escalation of antimicrobial therapy in hospitalised adults

description: Additional file 2. Questionnaire used for reviews of Prescription Episodes.
DOI: 10.6084/m9.figshare.11797599.v1
title: Additional file 2 of The impact of diagnostic microbiology on de-escalation of antimicrobial therapy in hospitalised adults

description: Additional file 2. Questionnaire used for reviews of Prescription Episodes.
DOI: 10.6084/m9.figshare.11797488
title: Additional file 2 of Dysregulation of TNF-α and IFN-γ expression is a common host immune response in a chronically infected mouse model of melioidosis when comparing multiple human strains of Burkholderia pseudomallei

description: Additional file 2 Table S1. Cytokines/chemokines in sera and spleen extracts in different groups of mice. A. Serum levels. B. Spleen extract levels.
DOI: 10.6084/m9.figshare.11797488.v1
title: Additional file 2 of Dysregulation of TNF-α and IFN-γ expression is a common host immune response in a chronically infected mouse model of melioidosis when comparing multiple human strains of Burkholderia pseudomallei

description: Additional file 2 Table S1. Cytokines/chemokines in sera and spleen extracts in different groups of mice. A. Serum levels. B. Spleen extract levels.
DOI: 10.6084/m9.figshare.11797455
title: Additional file 3 of Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

description: Additional file 3: Table S2.1H-NMR metabolites raw data.
DOI: 10.6084/m9.figshare.11797455.v1
title: Additional file 3 of Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

description: Additional file 3: Table S2.1H-NMR metabolites raw data.
DOI: 10.6084/m9.figshare.11797452
title: Additional file 2 of Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

description: Additional file 2: Table S1. GC-MS/SPME metabolites raw data.
DOI: 10.6084/m9.figshare.11797452.v1
title: Additional file 2 of Gut metabolomics profiling of non-small cell lung cancer (NSCLC) patients under immunotherapy treatment

description: Additional file 2: Table S1. GC-MS/SPME metabolites raw data.
DOI: 10.17608/k6.auckland.11797320
title: Laser ultrasonics data for Alpine Fault cataclasite

description: This item contains the raw waveform data for a cataclasite sample, 1a842. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the 1a842_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.17608/k6.auckland.11797320.v1
title: Laser ultrasonics data for Alpine Fault cataclasite

description: This item contains the raw waveform data for a cataclasite sample, 1a842. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the 1a842_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.17608/k6.auckland.11797245
title: Laser ultrasonics data for Alpine Fault schist

description: This item contains the raw waveform data for a schist sample, schist_2. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the schist_2_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.17608/k6.auckland.11797155
title: Laser ultrasonics data for Alpine Fault protomylonite

description: This item contains the raw waveform data for a protomylonite sample, pmv1_2. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the pmv1_2_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.17608/k6.auckland.11797044
title: Laser ultrasonics data for Alpine Fault mylonite

description: This item contains the raw waveform data for a mylonite sample, mv_2. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the mv_2_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.17608/k6.auckland.11797044.v1
title: Laser ultrasonics data for Alpine Fault mylonite

description: This item contains the raw waveform data for a mylonite sample, mv_2. Each .npy file contains all the experimental data, including the waveforms (of the trigger channel and data channel) and the angular positions of the rotation stage. Confining pressures for these experiments ranged from 1MPa to 16MPa, as stated in the filename. The data can be opened using the Python NumPy library.<br>Also included are csv files which contain the arrival time picks of the P-wave in each waveform for each pressure.<br>One example experimental configuration file is included as config.json. This describes the experimental setup at 1MPa, and is identical to the config files at other pressures.<br>Finally, the mv_2_rp_data.p is a Python pickle dictionary which contains values of important variables for this sample, calculated from the experimental data. These include fast and slow velocities, density, diameter, anisotropy at all pressures, etc.<br>All data viewing of data, plotting, and analysis was performed with the Python PlaceScan package, available at https://github.com/jsimpsonUoA/PlaceScan. All experimental data was acquired with PLACE, the lab automation software (see https://github.com/PALab/place).
DOI: 10.6084/m9.figshare.11796855
title: Japan Prefectural Emission Data 2007-2015

description: This work contributes to a time-series emission accounting with a record of 47 prefectures in Japan from 2007 to 2015.
DOI: 10.6084/m9.figshare.11796855.v1
title: Japan Prefectural Emission Data 2007-2015

description: This work contributes to a time-series emission accounting with a record of 47 prefectures in Japan from 2007 to 2015.
DOI: 10.6084/m9.figshare.11796726
title: Identification of key biomolecules in rheumatoid arthritis through the reconstruction of comprehensive disease-specific biological networks

description: Rheumatoid arthritis (RA) frequently seen chronic synovial inflammation causing joint destruction, chronic disability and reduced life expectancy. The pathogenesis of RA is not completely known. In this study, several gene expression data including synovial tissue and macrophages from synovial tissues were integrated with a holistic perspective and the molecular targets and signatures in RA were determined. Differentially expressed genes (DEGs) were identified from each dataset by comparing diseased and healthy samples. Afterward, the RA-specific protein–protein interaction (PPI) and the transcriptional regulatory network were reconstructed by using several biomolecule interaction data. Key biomolecules were determined through a statistical test employing the hypergeometric probability density function by using the physical interactions of transcriptional regulators and PPI. The integrative analyses of DEGs indicated that there were 110 and 494 common genes between synovial tissues and macrophages related datasets, respectively. Common DEGs of all datasets were identified as 25 genes and these core genes which might be feasible to uncover the mutual biological mechanism insights behind the RA pathogenesis were used for disease specific biological networks reconstruction. It was determined the hub proteins, novel key biomolecules (i.e. receptor, transcription factors and miRNAs) and biomolecules interactions by using the core DEGs. It was identified STAT1, RAC2 and KYNU as hub proteins, PEPD as a receptor, NR4A1, MEOX2, KLF4, IRF1 and MYB as TFs, miR-299, miR-8078, miR-146a, miR-3659 and miR-6882 as key miRNAs. It was determined that biomolecule interaction scenarios using identified key biomolecules and novel biomolecules including RAC2, PEPD, NR4A1, MEOX2, miR-299, miR-8078, miR-3659 and miR-6882 in RA. Our novel findings could be a crucial resource for the understanding of RA molecular mechanism and may be considered as drug targets and development of novel diagnostic strategies. Corresponding genes and miRNAs should be validated <i>via</i> experimental studies.
DOI: 10.6084/m9.figshare.11796726.v1
title: Identification of key biomolecules in rheumatoid arthritis through the reconstruction of comprehensive disease-specific biological networks

description: Rheumatoid arthritis (RA) frequently seen chronic synovial inflammation causing joint destruction, chronic disability and reduced life expectancy. The pathogenesis of RA is not completely known. In this study, several gene expression data including synovial tissue and macrophages from synovial tissues were integrated with a holistic perspective and the molecular targets and signatures in RA were determined. Differentially expressed genes (DEGs) were identified from each dataset by comparing diseased and healthy samples. Afterward, the RA-specific protein–protein interaction (PPI) and the transcriptional regulatory network were reconstructed by using several biomolecule interaction data. Key biomolecules were determined through a statistical test employing the hypergeometric probability density function by using the physical interactions of transcriptional regulators and PPI. The integrative analyses of DEGs indicated that there were 110 and 494 common genes between synovial tissues and macrophages related datasets, respectively. Common DEGs of all datasets were identified as 25 genes and these core genes which might be feasible to uncover the mutual biological mechanism insights behind the RA pathogenesis were used for disease specific biological networks reconstruction. It was determined the hub proteins, novel key biomolecules (i.e. receptor, transcription factors and miRNAs) and biomolecules interactions by using the core DEGs. It was identified STAT1, RAC2 and KYNU as hub proteins, PEPD as a receptor, NR4A1, MEOX2, KLF4, IRF1 and MYB as TFs, miR-299, miR-8078, miR-146a, miR-3659 and miR-6882 as key miRNAs. It was determined that biomolecule interaction scenarios using identified key biomolecules and novel biomolecules including RAC2, PEPD, NR4A1, MEOX2, miR-299, miR-8078, miR-3659 and miR-6882 in RA. Our novel findings could be a crucial resource for the understanding of RA molecular mechanism and may be considered as drug targets and development of novel diagnostic strategies. Corresponding genes and miRNAs should be validated <i>via</i> experimental studies.
DOI: 10.6084/m9.figshare.11796708
title: Lima Dolphin Microbiome

description: 454 pyrosequencing of amplicons from V1–V3 regions of the 16S rRNA gene from 24 healthy bottle nose dolphins blow samples.<br>
DOI: 10.6084/m9.figshare.11796708.v1
title: Lima Dolphin Microbiome

description: 454 pyrosequencing of amplicons from V1–V3 regions of the 16S rRNA gene from 24 healthy bottle nose dolphins blow samples.<br>
DOI: 10.6084/m9.figshare.11796498.v1
title: NEON-GEOSPATIAL-PYTHON-DATASETS.zip

description: This is a slimmed down version of the NEON Spatio-temporal Teaching Data Subset, available at https://figshare.com/articles/Spatio_temporal_Series_Teaching_Data_Subsets/2009586 . It's used in the Carpentries Geospatial Python workshop (currently in incubator stage) and removes unused hillshade files and adds WGS84 DTM and DSM files.<br>CONTENTS:NEON-DS-Met-Time-Series.zip: Precipitation, temperature and other variables collected from a flux tower at the NEON Harvard Forest site. NOTE: these data are not collected from the NEON tower.NEON-DS-Site-Layout-Files.zip: A set of shapefiles for the NEON's Harvard Forest field site and US and (some) state boundary layers.NEON-DS-Landsat-NDVI.zip: 2011 NDVI data product provided by USGS cropped to NEON's Harvard Forest and San Joaquin Experimental Range field sites.NEON-DS-Airborne-RemoteSensing.zip: LiDAR data including a canopy height model, digital elevation model and digital surface model for NEON's Harvard Forest and San Joaquin Experimental Range field sites.<br><br>
DOI: 10.6084/m9.figshare.11796468
title: Generic Feature Format "GFF3" for Salminus brasiliensis

description: Generic Feature Format file containing the genomic annotation for S. brasiliensis, from sequences above 10 kb extracted from a larger genomic assembly