cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4060245
title: NUMERICAL INVESTIGATION OF CONDENSING FLOWS IN A SUPERSONIC SEPARATOR A LOW PRESSURE.

description: As a contribution to overcoming the challenges of numerical simulation involving the phenomenon of condensation, this work aims to investigate the nucleation of simple molecules, through the classical nucleation theory (CNT) associated with the Hill’s Moments (MoM) models . The main objective of this simulations was to investigate the numerical precision of the CFD open source code - Stanford University Unstructured(SU2) - to correctly capture the occurrence of a condensation wave, and to analyze thenucleation of liquid droplets through the moments of order zero to three
DOI: 10.6084/m9.figshare.13009997
title: VGEA: A snakemake pipeline for RNA virus genome assembly from next generation sequencing data

description: SARS-CoV-2, Lassa Virus (Human and Rodent) and HIV-1 Test Datasets for the VGEA Pipeline.
DOI: 10.6084/m9.figshare.13026854.v1
title: R0235_20181025a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.6084/m9.figshare.13030427.v1
title: JULES-Sheffield 1948-2010

description: Monthly soil moisture data from JULES-Sheffield simulation in inner East Asia over 1948-2010.
DOI: 10.6084/m9.figshare.13030427
title: JULES-Sheffield 1948-2010

description: Monthly soil moisture data from JULES-Sheffield simulation in inner East Asia over 1948-2010.
DOI: 10.5066/f75m63wj
title: Point cloud from low-altitude aerial imagery from unmanned aerial system (UAS) flights over Coast Guard Beach, Nauset Spit, Nauset Inlet, and Nauset Marsh, Cape Cod National Seashore, Eastham, Massachusetts on 1 March 2016 (LAZ file)

description: This point cloud was derived from low-altitude aerial images collected from an unmanned aerial system (UAS) flown in the Cape Cod National Seashore on 1 March, 2016. The objective of the project was to evaluate the quality and cost of mapping from UAS images. The point cloud contains 434,098,030 unclassifed and unedited geolocated points. The points have horizontal coordinates in NAD83(2011) UTM Zone 19 North meters, vertical coordinates in NAVD88 meters, and colors in the red-green-blue (RGB) schema. The points were generated in photogrammetric software (Agisoft Photoscan Professional v. 1.2.6) from 1122 digital images taken approximately 120 m above the ground with a Canon Powershot SX280 12-mexapixel digital camera mounted in a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. The photogrammetric processing incorporated 30 ground control points. The entire, un-edited unclassified point cloud is provided in standard LAZ format. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
DOI: 10.5967/anar-my48
title: Assessing the financial value to Indiana University of IU's investments in advanced cyberinfrastructure
DOI: 10.5281/zenodo.4060263
title: Major ion chemistry and environmental isotope data of groundwaters

description: Major ion chemistry and environmental isotope data of groundwaters
DOI: 10.5281/zenodo.4060262
title: Major ion chemistry and environmental isotope data of groundwaters

description: Major ion chemistry and environmental isotope data of groundwaters
DOI: 10.25810/fj1y-8c74.20
title: International Wire Services Coverage of Climate Change or Global Warming, 2004-2020 - August 2020

description:         The Media and Climate Change Observatory Data monitors 120 sources (across newspapers, radio and TV) in 54 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5519/qd.rxnxwzob
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3747 records)
DOI: 10.5281/zenodo.3459294
title: Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra

description: This is version 1, you should use the updated version 2 that was accepted for publication: 10.5281/zenodo.3768398 The Paper: https://arxiv.org/abs/1909.10963 We use 2.4 million spectroscopically labelled sources from the SDSS catalogue to train an optimized random forest classifier using photometry from the Sloan Digital Sky Survey (SDSS) and Widefield Infrared Survey Explorer (WISE). We apply this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue without existing spectroscopic observations. Our new catalogue contains 49.7 million galaxies, 2.4 million quasars, and 59.2 million stars. We provide individual classification probabilities for each source, with 6.4 million galaxies (13%), 0.35 million quasars (14%) and 44.3 million stars (75%) having classification probabilities greater than 0.99, and 34.8 million galaxies (70%), 0.77 million quasars (32%) and 55.3 million stars (93\%) having classification probabilities greater than 0.9. We determine Precision, Recall and F1 score as a function of feature selection, including scenarios with additional external variables. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We use a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised and fully-supervised schemes to visualise the separation of galaxies, quasars and stars in a two dimensional space using the 2.4 million spectroscopically labelled training sources. We confirm that when the same algorithm is applied to the 111 million sources without spectra it is in strong agreement with the class labels applied by our random forest model. When using this dataset, please cite our paper (https://arxiv.org/abs/1909.10963) and this DOI (10.5281/zenodo.3459294). If you make use of our scripts please cite our Github repository DOI ().
DOI: 10.5281/zenodo.4060252
title: data/ directory associated with https://github.com/ericagol/TRAPPIST1_Spitzer

description: Data directory for the repository associated with the paper "Refining the transit timing and photometric analysis of TRAPPIST-1: Masses, radii, densities, dynamics, and ephemerides."
DOI: 10.12751/g-node.heyl6r
title: Ephys data associated with the paper *Gamma activity accelerates during prefrontal development*

description: Electrophysiological recordings from prefrontal cortex of postnatal day 5-40 mice. Spontaneous and optogenetically stimulated activity.
description: IsSupplementTo: Gamma activity accelerates during prefrontal development. (https://doi.org/10.1101/2020.03.10.986281)
DOI: 10.5281/zenodo.4060251
title: data/ directory associated with https://github.com/ericagol/TRAPPIST1_Spitzer

description: Data directory for the repository associated with the paper "Refining the transit timing and photometric analysis of TRAPPIST-1: Masses, radii, densities, dynamics, and ephemerides."
DOI: 10.5066/p91lne8j
title: WRTDS annual concentrations, loads and statistical trend likelihoods for sites in the Spokane River watershed, water years 1990-2018

description: A long history of mining and widespread metals contamination in the Spokane River watershed have led to the area's designation as a Superfund site and extensive, ongoing remedial actions. Long-term (water years 1990 to 2018) water-quality data from twelve sites were used to develop Weighted Regressions on Time, Discharge and Season (WRTDS) and WRTDS with Kalman filtering (WRTDS_K) models for total and dissolved cadmium, zinc and lead; total phosphorus and nitrogen; and dissolved orthophosphate. The models estimated annual mean concentrations and annual total loads, and flow-normalized annual mean concentrations and flow-normalized annual total loads. Flow-normalized annual mean concentrations and flow-normalized annual total loads were evaluated for trends, and bootstrapped model replicates were used to determine the statistical likelihood of the trends. These data were used to understand how remedial actions are affecting water quality in the Spokane River watershed and will help inform future remedial actions.
DOI: 10.5066/f77s7mnb
title: Suspended sediment and water temperature in the Sauk River, Washington, water years 2012-2016

description: The Sauk River, a fedrally designated Wild and Scenic River, is one of the few large glacier-fed rivers in western Washington that is unconstrained by dams and drains a relatively pristine landscape which includes Glacier Peak, a glaciated stratovolcano. The U.S. Geological Survey (USGS) conducted a 5-year study (2012-2016 water years) of suspended-sediment and water temperature monitoring for the purpose of determining suspended-sediment loads and identifying sediment production regimes and associated seasonality of sediment transport. Monitoring occurred at three USGS streamgages on the Sauk River, including two long-term gages with more than 90 years of continuous record and a new gage installed for the purpose of the study. Turbidity sensors at each streamgage measured turbidity and water temperature at 15-minute intervals. Suspended-sediment samples were collected at each streamgage over a range of discharge and turbidity conditions. Regression equations were developed to estimate the suspended-sediment concentration (SSC) and the concentration of fine suspended-sediment (fSSC; lessthan 0.0625 mm) at each streamgage using turbidity and/or discharge as explanatory variables. A 15-minute timeseries of SSC, fSSC, and the uncertainty of individual estimates (prediction intervals with 90 percent confidence) was computed for each streamgage and is provided. A daily timeseries of suspended-sediment load (SSL) and fine suspended-sediment load (fSSL) derived from either 15-minute, or daily, SSC and discharge data was computed at each streamgage and is provided for the 5-year period. A daily timeseries of water temperature at each streamgage is also provided.
DOI: 10.5281/zenodo.4060273
title: Credit Score USP Example

description: The granting of credit, in recent years, has become one of the main sources of<br> revenues for banks. In this area, a great difficulty encountered by the manager<br> (decision maker) is the establishment of criteria that allow to carry out a credit analysis<br> that aggregates greater security for the operation and, consequently, assist in reducing<br> the default of the sector. In order to minimize this difficulty found in credit analysis, the<br> study of case of this dissertation has as its basic proposal the elaboration of a model<br> that, according to perception of the granting manager himself, help him to evaluate the<br> performance of clients credit borrowers, in those criteria deemed important by the<br> manager. For construction model, the Multicriteria Decision Support Methodology<br> (MCDA-Construtivista) was considered the most appropriate, due to its ability to<br> integrate both objective and subjective elements, thus providing an excellent<br> opportunity for generating knowledge and promotion of understanding related to<br> problems of this nature. The utilization model proposed here, it will provide an<br> opportunity to assess the borrower's performance those criteria considered important<br> by the decision-maker, thus contributing to aggregate greater agility and security for<br> the act of granting credit.
DOI: 10.5281/zenodo.4060272
title: Credit Score USP Example

description: The granting of credit, in recent years, has become one of the main sources of<br> revenues for banks. In this area, a great difficulty encountered by the manager<br> (decision maker) is the establishment of criteria that allow to carry out a credit analysis<br> that aggregates greater security for the operation and, consequently, assist in reducing<br> the default of the sector. In order to minimize this difficulty found in credit analysis, the<br> study of case of this dissertation has as its basic proposal the elaboration of a model<br> that, according to perception of the granting manager himself, help him to evaluate the<br> performance of clients credit borrowers, in those criteria deemed important by the<br> manager. For construction model, the Multicriteria Decision Support Methodology<br> (MCDA-Construtivista) was considered the most appropriate, due to its ability to<br> integrate both objective and subjective elements, thus providing an excellent<br> opportunity for generating knowledge and promotion of understanding related to<br> problems of this nature. The utilization model proposed here, it will provide an<br> opportunity to assess the borrower's performance those criteria considered important<br> by the decision-maker, thus contributing to aggregate greater agility and security for<br> the act of granting credit.
DOI: 10.6084/m9.figshare.12508907
title: Possible association of periodontal disease with oral cancer and oral potentially malignant disorders: a systematic review

description: <b>Background:</b> Periodontitis has been associated with several systemic diseases and medical conditions, including oral cancer (OC). However, most studies reporting an association between OC and periodontal disease have used different clinical and radiographic criteria to define periodontal disease. This review aimed to evaluate the currently available evidence to determine an association between periodontal disease (extension and severity), OC, and oral potentially malignant disorders (OPMDs). <b>Material and methods:</b> A systematic search of studies published up to August 2018 was performed following the PRISMA guidelines in the electronic databases MEDLINE (PubMed) and COCHRANE (OVID). A methodological evaluation was made using the <i>Strengthening the Reporting of Observational studies in Epidemiology</i> (STROBE) checklist. <b>Results:</b> Eight studies (case-control, cross-sectional and cohort) were included. An increased clinical attachment loss, plaque index, bleeding on probing, and radiographic bone loss was found in patients with OC and OPMDs. Differences in the methodological characteristics, case definition used for periodontal diseases, and OC location did not allow estimating the odds ratio required to conduct a meta-analysis. <b>Conclusion:</b> Some studies suggest a positive relationship between periodontal disease, OC, and OPMDs; however, the currently available evidence is insufficient to draw solid conclusions.
DOI: 10.6084/m9.figshare.12508907.v1
title: Possible association of periodontal disease with oral cancer and oral potentially malignant disorders: a systematic review

description: <b>Background:</b> Periodontitis has been associated with several systemic diseases and medical conditions, including oral cancer (OC). However, most studies reporting an association between OC and periodontal disease have used different clinical and radiographic criteria to define periodontal disease. This review aimed to evaluate the currently available evidence to determine an association between periodontal disease (extension and severity), OC, and oral potentially malignant disorders (OPMDs). <b>Material and methods:</b> A systematic search of studies published up to August 2018 was performed following the PRISMA guidelines in the electronic databases MEDLINE (PubMed) and COCHRANE (OVID). A methodological evaluation was made using the <i>Strengthening the Reporting of Observational studies in Epidemiology</i> (STROBE) checklist. <b>Results:</b> Eight studies (case-control, cross-sectional and cohort) were included. An increased clinical attachment loss, plaque index, bleeding on probing, and radiographic bone loss was found in patients with OC and OPMDs. Differences in the methodological characteristics, case definition used for periodontal diseases, and OC location did not allow estimating the odds ratio required to conduct a meta-analysis. <b>Conclusion:</b> Some studies suggest a positive relationship between periodontal disease, OC, and OPMDs; however, the currently available evidence is insufficient to draw solid conclusions.
DOI: 10.5517/ccdc.csd.cc24ny70
title: CCDC 1986332: Experimental Crystal Structure Determination

description: Related Article: Xinyue Zhang, Yuval Vidavsky, Sinai Aharonovich, Steven J. Yang, Michael R. Buche, Charles E. Diesendruck, Meredith N. Silberstein|2020|Soft Matter|16|8591|doi:10.1039/D0SM01115K
DOI: 10.25345/c53n34
title: MassIVE MSV000086216 - NAR-03186-F-2020: DNA damage analysis using xenopus egg extracts
title: DNA damage analysis using Xenopus egg extracts DNA damage complex was enriched using nanoclusters
DOI: 10.26300/spba-rp45
title: Data and Trained Models for MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images
DOI: 10.5281/zenodo.4060257
title: Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra - parquet format

description: This is the same as the published data available under 10.5281/zenodo.3768398, but in the format of parquet files. This means you can access it using Dask for convenience when using cloud compute facilities. Abstract: We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model. When using this dataset, please reference our paper via the journal (https://arxiv.org/abs/1909.10963) and this DOI (10.5281/zenodo.4060257). If you make use of our scripts please reference our Github repository DOI (10.5281/zenodo.3855160). File descriptions: All of these files are Pandas Dataframes, saved as uncompressed parquet files for ease of access when using cloud compute such as Dask. df_spec_classprobs.parquet contains the spectroscopically observed sources used for training and testing. This has been cleaned, and has the results of the random forest classifier added as additional columns (sources used for training have NaNs in the class_pred column). SDSS-ML-all.parquet contains the 111 million photometrically observed sources, with our class labels and probabilities added.
DOI: 10.5281/zenodo.4060256
title: Identifying galaxies, quasars and stars with machine learning: a new catalogue of classifications for 111 million SDSS sources without spectra - parquet format

description: This is the same as the published data available under 10.5281/zenodo.3768398, but in the format of parquet files. This means you can access it using Dask for convenience when using cloud compute facilities. Abstract: We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique (Uniform Manifold Approximation and Projection: UMAP) in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model. When using this dataset, please reference our paper via the journal (https://arxiv.org/abs/1909.10963) and this DOI (10.5281/zenodo.4060257). If you make use of our scripts please reference our Github repository DOI (10.5281/zenodo.3855160). File descriptions: All of these files are Pandas Dataframes, saved as uncompressed parquet files for ease of access when using cloud compute such as Dask. df_spec_classprobs.parquet contains the spectroscopically observed sources used for training and testing. This has been cleaned, and has the results of the random forest classifier added as additional columns (sources used for training have NaNs in the class_pred column). SDSS-ML-all.parquet contains the 111 million photometrically observed sources, with our class labels and probabilities added.
DOI: 10.25345/c5zz0v
title: MassIVE MSV000086217 - In situ detection of protein interactions for recombinant therapeutic enzymes
title: Despite their therapeutic potential, many protein drugs remain inaccessible to patients since they are difficult to secrete. Each recombinant protein has unique physicochemical properties and requires different machinery for proper folding, assembly, and post-translational modifications (PTMs). Here we aimed to identify the machinery supporting recombinant protein secretion by measuring the protein-protein interaction (PPI) networks of four different recombinant proteins (SERPINA1, SERPINC1, SERPING1 and SeAP) with various PTMs and structural motifs using the proximity-dependent biotin identification (BioID) method. We identified PPIs associated with specific features of the secreted proteins using a Bayesian statistical model, and found proteins involved in protein folding, disulfide bond formation and N-glycosylation were positively correlated with the corresponding features of the four model proteins. Among others, oxidative folding enzymes showed the strongest association with disulfide bond formation, supporting their critical roles in proper folding and maintaining the ER stability. Proximity-dependent labeling successfully identified the transient interactions supporting synthesis of secreted recombinant proteins and refined our understanding of key molecular mechanisms of the secretory pathway during recombinant protein production.