cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17711336
title: Fully Three-Dimensional Radial Visualization

description: We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package radviz3d, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we useRadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) United States regional SARS-Cov-2 variants’ prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.
DOI: 10.6084/m9.figshare.17711336.v2
title: Fully Three-Dimensional Radial Visualization

description: We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package radviz3d, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we useRadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) United States regional SARS-Cov-2 variants’ prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.
DOI: 10.6084/m9.figshare.17711336.v1
title: Fully Three-dimensional Radial Visualization

description: We develop methodology for three-dimensional (3D) radial visualization (RadViz) of multidimensional datasets. The classical two-dimensional (2D) RadViz visualizes multivariate data in the 2D plane by mapping every observation to a point inside the unit circle. Our tool, RadViz3D, distributes anchor points uniformly on the 3D unit sphere. We show that this uniform distribution provides the best visualization with minimal artificial visual correlation for data with uncorrelated variables. However, anchor points can be placed exactly equi-distant from each other only for the five Platonic solids, so we provide equi-distant anchor points for these five settings, and approximately equi-distant anchor points via a Fibonacci grid for the other cases. Our methodology, implemented in the R package radviz3d, makes fully 3D RadViz possible and is shown to improve the ability of this nonlinear technique in more faithfully displaying simulated data as well as the crabs, olive oils and wine datasets. Additionally, because radial visualization is naturally suited for compositional data, we use RadViz3D to illustrate (i) the chemical composition of Longquan celadon ceramics and their Jingdezhen imitation over centuries, and (ii) US regional SARS-Cov-2 variants’ prevalence in the Covid-19 pandemic during the summer 2021 surge of the Delta variant.
DOI: 10.6084/m9.figshare.17857542
title: Scalable Inference for Hybrid Bayesian Hidden Markov Model Using Gaussian Process Emission

description: The hidden Markov model (HMM), used with Gaussian Process (GP) as an emission model, has been widely used to model sequential data in complex form. This study introduces the hybrid Bayesian HMM with GP emission using SM kernel (HMM-GPSM) to estimate the hidden state of each time-series observation, that is, sequentially observed from a single channel. We then propose a scalable inference method to train the HMM-GPSM using large-scale sequences of time-series dataset that has (1) a large number of sequences for state transitions and (2) a large number of data points in a time-series observation for each hidden state. For state transitions with a large number of sequences, we employ stochastic variational inference (SVI) to update the parameters of HMM-GPSM efficiently. Also, for each time-series observation that has a large number of data points, we propose the approximate GP emission using the Random Fourier Feature (RFF), which is constructed by using the spectral points that are sampled from the spectral density of SM kernel. We propose the efficient inference of the kernel hyperparameters of the approximate GP emission and corresponding HMM-GPSM. Specifically, we derive the training loss, that is, the evidence lower bound of the HMM-GPSM that can be scalably computed for a large number of time-series observations by employing the regularized lower bound of GP emission likelihood with KL divergence. The proposed methods can be used together to train HMM-GPSM with the sequential time-series dataset that contains both (1) and (2). We validate the proposed method on the synthetic and real datasets using the clustering accuracy, marginal likelihood, and training time as the performance metrics.
DOI: 10.6084/m9.figshare.17857542.v1
title: Scalable Inference for Hybrid Bayesian Hidden Markov Model Using Gaussian Process Emission

description: The hidden Markov model (HMM), used with Gaussian Process (GP) as an emission model, has been widely used to model sequential data in complex form. This study introduces the hybrid Bayesian HMM with GP emission using SM kernel (HMM-GPSM) to estimate the hidden state of each time-series observation, that is, sequentially observed from a single channel. We then propose a scalable inference method to train the HMM-GPSM using large-scale sequences of time-series dataset that has (1) a large number of sequences for state transitions and (2) a large number of data points in a time-series observation for each hidden state. For state transitions with a large number of sequences, we employ stochastic variational inference (SVI) to update the parameters of HMM-GPSM efficiently. Also, for each time-series observation that has a large number of data points, we propose the approximate GP emission using the Random Fourier Feature (RFF), which is constructed by using the spectral points that are sampled from the spectral density of SM kernel. We propose the efficient inference of the kernel hyperparameters of the approximate GP emission and corresponding HMM-GPSM. Specifically, we derive the training loss, that is, the evidence lower bound of the HMM-GPSM that can be scalably computed for a large number of time-series observations by employing the regularized lower bound of GP emission likelihood with KL divergence. The proposed methods can be used together to train HMM-GPSM with the sequential time-series dataset that contains both (1) and (2). We validate the proposed method on the synthetic and real datasets using the clustering accuracy, marginal likelihood, and training time as the performance metrics.
DOI: 10.25345/c5db7vr11
title: MassIVE Reanalysis RMSV000000354.4528 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CSNK1D [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c58k74x7n
title: MassIVE Reanalysis RMSV000000354.4527 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, SCARB2 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.5336431
title: Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".

description: This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper: Ponti, M., Seredko, A. Human-machine-learning integration and task allocation in citizen science. <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.
DOI: 10.5281/zenodo.5336430
title: Supporting Information 1 to the paper "Human-machine-learning integration and task allocation in citizen science".

description: This appendix - Supporting Information 1 - is a dataset excel file directly related to the following paper: Ponti, M., Seredko, A. Human-machine-learning integration and task allocation in citizen science. <em>Humanit Soc Sci Commun</em> <strong>9, </strong>48 (2022). https://doi.org/10.1057/s41599-022-01049-z The dataset in this excel file is a detailed result of the integrative literature review conducted for the manuscript.
DOI: 10.25345/c54t6f43x
title: MassIVE Reanalysis RMSV000000354.4529 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, PHB [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5125q99g
title: MassIVE Reanalysis RMSV000000354.4530 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, SENP1 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5w950p4g
title: MassIVE Reanalysis RMSV000000354.4531 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, CTSG [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5rj48w1t
title: MassIVE Reanalysis RMSV000000354.4533 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, METTL20 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.23645/epacomptox.19216932
title: Performance Summary for Estrogen and Androgen Receptor Pathway Models and Associated Assay

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25<br><br>The US EPA has developed batteries of in vitro assays to test chemicals for their potential to interact with the androgen and estrogen receptors (AR and ER). These assays have been combined using mathematical models to provide overall scores for chemicals, indicating level of agonist and antagonist activity. The combinations of assays and models are known as the AR and ER Pathway Models (Judson, Magpantay et al. 2015, Kleinstreuer, Ceger et al. 2017) because the method for combining assays makes use of the underlying biological pathways leading from the interaction of a chemical with either AR or ER through receptor dimerization and cofactor recruitment, binding to DNA, RNA and protein production and, in the case of ER, to cell proliferation. Approximately 1800 chemicals have been tested in the AR and ER assays as part of the ToxCast and Tox21 in vitro screening programs and have model scores. The AR and ER Pathway Models and associated in vitro assay data have been described in multiple peer reviewed publications (Browne, Judson et al. 2015, Judson, Magpantay et al. 2015, Browne, Judson et al. 2017, Judson, Houck et al. 2017, Kleinstreuer, Ceger et al. 2017, Browne, Kleinstreuer et al. 2018, OECD 2019, Judson, Houck et al. 2020) and FIFRA Scientific Advisory Panel (SAP) reviews (U.S. EPA 2013, U.S. EPA 2017, US EPA 2018). In these publications, the models have been evaluated against various sets of reference chemicals. This document provides a consolidated resource containing the performance metrics for each of the individual assays as well as the AR and ER Pathway Models themselves.
DOI: 10.23645/epacomptox.19216932.v1
title: Summary of the Performance Metrics for the Androgen Receptor (AR) and Estrogen Receptor (ER) Pathway Models and Associated Individual In Vitro Assays

description: The US EPA has developed batteries of in vitro assays to test chemicals for their potential to interact with the androgen and estrogen receptors (AR and ER). These assays have been combined using mathematical models to provide overall scores for chemicals, indicating level of agonist and antagonist activity. The combinations of assays and models are known as the AR and ER Pathway Models (Judson, Magpantay et al. 2015, Kleinstreuer, Ceger et al. 2017) because the method for combining assays makes use of the underlying biological pathways leading from the interaction of a chemical with either AR or ER through receptor dimerization and cofactor recruitment, binding to DNA, RNA and protein production and, in the case of ER, to cell proliferation. Approximately 1800 chemicals have been tested in the AR and ER assays as part of the ToxCast and Tox21 in vitro screening programs and have model scores. The AR and ER Pathway Models and associated in vitro assay data have been described in multiple peer reviewed publications (Browne, Judson et al. 2015, Judson, Magpantay et al. 2015, Browne, Judson et al. 2017, Judson, Houck et al. 2017, Kleinstreuer, Ceger et al. 2017, Browne, Kleinstreuer et al. 2018, OECD 2019, Judson, Houck et al. 2020) and FIFRA Scientific Advisory Panel (SAP) reviews (U.S. EPA 2013, U.S. EPA 2017, US EPA 2018). In these publications, the models have been evaluated against various sets of reference chemicals. This document provides a consolidated resource containing the performance metrics for each of the individual assays as well as the AR and ER Pathway Models themselves.
DOI: 10.25345/c5mw28f99
title: MassIVE Reanalysis RMSV000000354.4522 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, SLC25A22 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5h41jn6z
title: MassIVE Reanalysis RMSV000000354.4536 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, KCTD9 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5cc0tv1b
title: MassIVE Reanalysis RMSV000000354.4534 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, PCBP4 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.6084/m9.figshare.3761562.v2137
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25345/c57m0416w
title: MassIVE Reanalysis RMSV000000354.4535 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, SPATA4 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c53t9d726
title: MassIVE Reanalysis RMSV000000354.4537 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, PRAP1 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5028pd97
title: MassIVE Reanalysis RMSV000000354.4538 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, POPDC3 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5v97zs5b
title: MassIVE Reanalysis RMSV000000354.4539 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of Bioplex AP-MS Dataset for Library Construction, RGS3 [launched by fce971e5ede541cca3fc5307a73294d8]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.15784/601525
title: Firn temperatures 50km upstream of South Pole

description: These data are 6-hourly measurements of temperature in the upper 40 m of firn at a site 50 km upstream of South Pole (89.54 S, 137.04 E). The measurements span the two years from January 2017 to December 2018.
DOI: 10.6084/m9.figshare.19216956.v1
title: SRR11608258.vcf.gz

description: VCF file and TBI file for UCSC Variant Annotation Integrator tool.
DOI: 10.6084/m9.figshare.19216956
title: SRR11608258.vcf.gz

description: VCF file and TBI file for UCSC Variant Annotation Integrator tool.