cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34943/9890d081-17ce-44cd-ba2b-03ec13f8894f
title: Wally Land Acoustic Doppler Current Profiler 2 MHz Deployed 2021-08-18

description: The Nortek Aquadopp Profiler A3P9315 AQD14501 was deployed on 2021-08-18 at Wally Land. Wally Land is a location within Barkley Canyon gas hydrate field, where hydrate dynamics are observed. This device is a Acoustic Doppler Current Profiler 2 MHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/f7cd8033-0b34-4643-b986-5778fe1f7eef
title: Port aux Basques Acoustic Doppler Current Profiler 600 kHz Deployed 2021-08-15

description: The Nortek Aquadopp Profiler AQD 8323 was deployed on 2021-08-15 at Port aux Basques. Port aux Basque is located on the southwestern end of Newfoundland near the Cabot Strait. This device is a Acoustic Doppler Current Profiler 600 kHz. Acoustic Doppler Current Profilers (ADCP) are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/5d8f7b90-757d-40e0-a299-bb547f6dee39
title: Fraser River Delta Upper Slope Acoustic Doppler Current Profiler 1 MHz Deployed 2022-03-11

description: The Nortek Aquadopp HR-Profiler 1 MHz 7058 was deployed on 2022-03-11 at Fraser River Delta Upper Slope. This site is located on the upper slope of the Fraser River delta, where depth is approximately 100 metres. This device is a Acoustic Doppler Current Profiler 1 MHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/ef17a265-0da1-4a94-8df5-0563659c6496
title: St. John's Bay Acoustic Doppler Current Profiler 2 MHz Deployed 2022-08-17

description: The Nortek Aquadopp Profiler AQD 8314 was deployed on 2022-08-17 at St. John's Bay. St. John's Bay is located slightly east of the city St. John's, Newfoundland. This device is a Acoustic Doppler Current Profiler 2 MHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a mobile platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/775fb80a-b29d-4968-a5c6-d185dc402e65
title: Cambridge Bay Ice Profiler Deployed 2022-09-12

description: The ASL Shallow Water Ice Profiler 53038 was deployed on 2022-09-12 at Cambridge Bay. Cambridge Bay is located on Victoria Island in the Kitikmeot Region of Nunavut, Canada. This device is a Ice Profiler. Ice Profiler instruments are upward looking single beam echosounders mounted on the ocean floor and are specialized to measure ice draft. They can estimate ice forces, determine the thickness of ice, and the detail of the underside of sea-ice. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/c52f58f3-a237-498c-89f5-d68e78b55bc5
title: Cambridge Bay Ice Profiler Deployed 2022-09-12

description: The ASL Shallow Water Ice Profiler 53038 was deployed on 2022-09-12 at Cambridge Bay. Cambridge Bay is located on Victoria Island in the Kitikmeot Region of Nunavut, Canada. This device is a Ice Profiler. Ice Profiler instruments are upward looking single beam echosounders mounted on the ocean floor and are specialized to measure ice draft. They can estimate ice forces, determine the thickness of ice, and the detail of the underside of sea-ice. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/3a3d1c92-bb6a-4b6f-9b08-298873b934a2
title: Fraser River Delta Upper Slope Acoustic Doppler Current Profiler 1 MHz Deployed 2023-03-16

description: The Nortek Aquadopp HR-Profiler 1 MHz 7058 was deployed on 2023-03-16 at Fraser River Delta Upper Slope. This site is located on the upper slope of the Fraser River delta, where depth is approximately 100 metres. This device is a Acoustic Doppler Current Profiler 1 MHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/bb7ae0f9-4c7e-4bfa-b203-7d84c07d4ab7
title: Barkley Canyon Axis Acoustic Doppler Current Profiler 55 kHz Deployed 2023-07-06

description: The Nortek Signature55 Current Profiler 200066 was deployed on 2023-07-06 at Barkley Canyon Axis. Barkley Canyon Axis is located within Barkley Canyon, where benthic processes, biodiversity, sediment dynamics are observed. This device is a Acoustic Doppler Current Profiler 55 kHz. Acoustic Doppler Current Profilers are hydroacoustic instruments, similar to sonars. ADCPs measure current speed and direction at multiple predetermined depths simultaneously. ADCPs use the Doppler effect of sound waves that are scattered by particles in seawater over a depth range. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.8187729
title: TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: U-BRITE location: /data/project/ubrite/gtkb/TCGA/GeneExp
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.5281/zenodo.8187728
title: TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Gene Expression

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: U-BRITE location: /data/project/ubrite/gtkb/TCGA/GeneExp
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.6084/m9.figshare.23786520.v1
title: Supplemental files

description: Supplemental file 1. <strong>Calculated </strong><em><strong>M</strong></em><sub><em><strong>2</strong></em></sub><strong> curve in study participants during glucose-SIB test 1 (blue), glucose-SIB test 2 (red) and glucose-SIB test 3 (green).</strong> Less than 25% decrease in concentration between the peak and the last time point were observed in subject 008 (glucose-SIB test 1), subject 019 (glucose-SIB test 2) and subject 020 (glucose-SIB test 2) disqualifying these data for computational analysis. <br> <strong> Supplemental file 2. Capillary glucose concentrations in study participants during glucose-SIB test 1 (A) and 2 (B).</strong> <br> <strong>Supplemental file 3. Calculated model parameters in the study participants. </strong>Data in italics were excluded from calculations. (A) GSDIa patients with attenuated phenotype. (B) GSDIa patients with a severe phenotype. (C) Healthy volunteers. <br>
DOI: 10.17909/gpw0-w659
title: A re-analysis of the isolated black hole candidate OGLE-2011-BLG-0462/MOA-2011-BLG-191

description: HST observations used in paper performing OB110462 reanalysis.
DOI: 10.6084/m9.figshare.22196987
title: Geoarchaeology of the Cornia river coastal plain (Piombino, southern Tuscany, Italy)

description: This paper presents a 1:20,000 scale Geoarchaeological map of the Cornia river coastal plain in southern Tuscany. The Geoarchaeological map integrates a comprehensive dataset of geomorphological and archaeological features. The spatial relationships between landforms and archaeological records show that the settlement pattern from the VII century BC to the V century AD was mostly influenced by the presence of several depositional landforms which formed before the period under consideration. Integrating the outputs of geomorphological and topographical analyses with archaeological databases is also helpful for land planning, especially in areas where present-day anthropic exploitation needs predictive and valid tools to assess the archaeological hazard.
DOI: 10.6084/m9.figshare.22196987.v1
title: Geoarchaeology of the Cornia river coastal plain (Piombino, southern Tuscany, Italy)

description: This paper presents a 1:20,000 scale Geoarchaeological map of the Cornia river coastal plain in southern Tuscany. The Geoarchaeological map integrates a comprehensive dataset of geomorphological and archaeological features. The spatial relationships between landforms and archaeological records show that the settlement pattern from the VII century BC to the V century AD was mostly influenced by the presence of several depositional landforms which formed before the period under consideration. Integrating the outputs of geomorphological and topographical analyses with archaeological databases is also helpful for land planning, especially in areas where present-day anthropic exploitation needs predictive and valid tools to assess the archaeological hazard.
DOI: 10.6084/m9.figshare.23786520
title: Supplemental files from Endogenous glucose production in patients with glycogen storage disease type Ia estimated by oral D [6,6-2H2]-glucose

description: Supplemental file 1. <strong>Calculated </strong><em><strong>M</strong></em><sub><em><strong>2</strong></em></sub><strong> curve in study participants during glucose-SIB test 1 (blue), glucose-SIB test 2 (red) and glucose-SIB test 3 (green).</strong> Less than 25% decrease in concentration between the peak and the last time point were observed in subject 008 (glucose-SIB test 1), subject 019 (glucose-SIB test 2) and subject 020 (glucose-SIB test 2) disqualifying these data for computational analysis. <br> <strong> Supplemental file 2. Capillary glucose concentrations in study participants during glucose-SIB test 1 (A) and 2 (B).</strong> <br> <strong>Supplemental file 3. Calculated model parameters in the study participants. </strong>Data in italics were excluded from calculations. (A) GSDIa patients with attenuated phenotype. (B) GSDIa patients with a severe phenotype. (C) Healthy volunteers. <br>
DOI: 10.6084/m9.figshare.23786520.v2
title: Supplemental files from Endogenous glucose production in patients with glycogen storage disease type Ia estimated by oral D [6,6-2H2]-glucose

description: Supplemental file 1. <strong>Calculated </strong><em><strong>M</strong></em><sub><em><strong>2</strong></em></sub><strong> curve in study participants during glucose-SIB test 1 (blue), glucose-SIB test 2 (red) and glucose-SIB test 3 (green).</strong> Less than 25% decrease in concentration between the peak and the last time point were observed in subject 008 (glucose-SIB test 1), subject 019 (glucose-SIB test 2) and subject 020 (glucose-SIB test 2) disqualifying these data for computational analysis. <br> <strong> Supplemental file 2. Capillary glucose concentrations in study participants during glucose-SIB test 1 (A) and 2 (B).</strong> <br> <strong>Supplemental file 3. Calculated model parameters in the study participants. </strong>Data in italics were excluded from calculations. (A) GSDIa patients with attenuated phenotype. (B) GSDIa patients with a severe phenotype. (C) Healthy volunteers. <br>
DOI: 10.17909/65zq-g239
title: Data from Mass Upper Limit on GJ 341 b paper

description: TESS data, 120s cadence, GJ341/	TIC 359271092, sectors 9, 10, 36, 37
DOI: 10.5281/zenodo.8187749
title: Albumin and fibrinogen synthesis rates in advanced chronic liver disease

description: Supplement data for the research work on "<strong>Albumin and fibrinogen synthesis rates in advanced chronic liver disease".</strong> Table S1 and table S2.
DOI: 10.5281/zenodo.8187750
title: Albumin and fibrinogen synthesis rates in advanced chronic liver disease

description: Supplement data for the research work on "<strong>Albumin and fibrinogen synthesis rates in advanced chronic liver disease".</strong> Table S1 and table S2.
DOI: 10.5281/zenodo.8187763
title: Called peaks for 8 D. melanogaster functional genomics data sets

description: Analyses were performed on chromatin accessibility (ATAC-seq, DNase-seq, FAIRE-seq), histone modification ChIP-seq (H3K4me1, H3K4me3, and H3K27ac), and direct enhancer activity reporter via an ectopic plasmid based assay (STARR-seq) data sets generated from experiments in D. melanogaster. The ATAC-seq and FAIRE-seq data sets were generated using wandering third instar larvae eye antennal imaginal disc tissue extracted from the FRT82 stock (Davie et al., 2015), while all other data sets were generated from Drosophila melanogaster S2 cells (Arnold et al., 2013; Henriques et al., 2018; de Almeida et al., 2022). Sequencing reads were downloaded from the NIH SRA, cleaned and trimmed using Trimmomatic v0.39 (Bolger et al., 2014), and aligned to the D. melanogaster r6.45 genome (Hoskins et al., 2015) using BWA (bwa aln, default settings) v0.7.17-r1188 (Li and Durbin, 2009). The D. melanogaster genome was downloaded from Fly Base (Gramates et al., 2022). Aligned reads were filtered for mapping quality (-q 10 -F 0x0200 -F 0x0100 -F 0x004) using SAMTools v1.11 (using htslib v1.11-4) (Li et al., 2009). Peaks were called using MACS2 v 2.2.7.1 (Zhang et al., 2008) with FDR correction (-q 0.01) and the preset D. melanogaster genome size (-g dm). The data sets varied in terms of read lengths, single-end or paired-end, and the availability of control data, so parameters were adjusted appropriately. MACS peak calling parameters for ATAC-seq and FAIRE-seq data were taken from Davie et al. (2015).
description: Please see the paper "Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique" by Nowling, Njoya, Peters, and Riehle (2023) published in Frontiers in Cellular and Infection Microbiology.
DOI: 10.5281/zenodo.8187764
title: Called peaks for 8 D. melanogaster functional genomics data sets

description: Analyses were performed on chromatin accessibility (ATAC-seq, DNase-seq, FAIRE-seq), histone modification ChIP-seq (H3K4me1, H3K4me3, and H3K27ac), and direct enhancer activity reporter via an ectopic plasmid based assay (STARR-seq) data sets generated from experiments in D. melanogaster. The ATAC-seq and FAIRE-seq data sets were generated using wandering third instar larvae eye antennal imaginal disc tissue extracted from the FRT82 stock (Davie et al., 2015), while all other data sets were generated from Drosophila melanogaster S2 cells (Arnold et al., 2013; Henriques et al., 2018; de Almeida et al., 2022). Sequencing reads were downloaded from the NIH SRA, cleaned and trimmed using Trimmomatic v0.39 (Bolger et al., 2014), and aligned to the D. melanogaster r6.45 genome (Hoskins et al., 2015) using BWA (bwa aln, default settings) v0.7.17-r1188 (Li and Durbin, 2009). The D. melanogaster genome was downloaded from Fly Base (Gramates et al., 2022). Aligned reads were filtered for mapping quality (-q 10 -F 0x0200 -F 0x0100 -F 0x004) using SAMTools v1.11 (using htslib v1.11-4) (Li et al., 2009). Peaks were called using MACS2 v 2.2.7.1 (Zhang et al., 2008) with FDR correction (-q 0.01) and the preset D. melanogaster genome size (-g dm). The data sets varied in terms of read lengths, single-end or paired-end, and the availability of control data, so parameters were adjusted appropriately. MACS peak calling parameters for ATAC-seq and FAIRE-seq data were taken from Davie et al. (2015).
description: Please see the paper "Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique" by Nowling, Njoya, Peters, and Riehle (2023) published in Frontiers in Cellular and Infection Microbiology.
DOI: 10.34740/kaggle/dsv/6200656
title: CStevens Peloton Data
DOI: 10.6084/m9.figshare.23618030
title: Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials

description: The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this article, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the treatment arm and then resample the EHR data to create equivalent patient populations under both the single arm trial and the resampled EHR. Resampling is implemented via a density-free importance sampling scheme. Using the synthetic control arm, inference for the treatment effect can then be carried out using any method available for RCTs. Alternatively the proposed nonparametric Bayesian model allows straightforward model-based inference. In simulation experiments, the proposed method exhibits higher power than alternative methods in detecting treatment effects, specifically for nonlinear response functions. We apply the method to supplement single arm treatment-only glioblastoma studies with a synthetic control arm based on historical trials. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23618030.v1
title: Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials

description: The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this article, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the treatment arm and then resample the EHR data to create equivalent patient populations under both the single arm trial and the resampled EHR. Resampling is implemented via a density-free importance sampling scheme. Using the synthetic control arm, inference for the treatment effect can then be carried out using any method available for RCTs. Alternatively the proposed nonparametric Bayesian model allows straightforward model-based inference. In simulation experiments, the proposed method exhibits higher power than alternative methods in detecting treatment effects, specifically for nonlinear response functions. We apply the method to supplement single arm treatment-only glioblastoma studies with a synthetic control arm based on historical trials. Supplementary materials for this article are available online.