cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5605707
title: Improving Formal Verification with Portfolio-based Runtime Techniques: Dataset

description: Evaluation Dataset of the Scientific Students’ Association Report titled Improving Formal Verification with Portfolio-based Runtime Techniques
DOI: 10.6084/m9.figshare.16892305.v1
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892305
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892308
title: May 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892308.v1
title: May 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892311.v1
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892311
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.15468/s97nzq
title: Fauna marina de la costa vasca - Sistema de Información de la Naturaleza de Euskadi

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.25345/c5q27c
title: MassIVE MSV000088273 - Philippine-lung-cancer-tissues_glycomics
title: Tissue glycomics of a cohort (n=5) of Philippine lung cancer patients Stages I-III. Comparisons were made between tumor (malignant) and peritumoral (non-malignant) tissue samples. This project is supported by the Philippine-California Advanced Research Institutes (PCARI-CHED) with grant number: IHITM2017-18.
DOI: 10.6073/pasta/13a38aedd80a9c849cc8424fca8ac127
title: Chemistry of rainfall and throughfall from El Verde and Bisley

description: Rain, throughfall, and stream water are collected weekly at the LEF sites listed below. These data sets begin as early as 1983; LTER sampling began in 1988. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rain samples from WDEV are wet only from an automatically-closing collector that prevents any dry deposition (Aerochem Metrics NADP collector). RCEV and RCB are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Subsamples for NH4 analysis were also preserved with 1 molar H2SO4. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Rain and Throughfall Sampling SitesDescriptions of LTER LUQ rain and throughfall weekly sample chemistry data from 1988 onwards. Chemical concentrations are recorded as mg/L or ug/L as appropriate. Values below detection limits are recorded as 1/2 the detection limit. Site Abbreviation Description Comments Rain collector Bisley. RCB Bulk collector. Rain collector El Verde RCEV Bulk collector. Wet/dry El Verde. WDEV. Wet only collector. Throughfall Bisley. TFB=TCB TF bulk 10-collector composite Bisley gap= BGAP, TF bulk 10-collector composite
DOI: 10.5517/ccdc.csd.cc282csj
title: CCDC 2087657: Experimental Crystal Structure Determination

description: Related Article: Martin L. Kirk, David A. Shultz, Patrick Hewitt, Daniel E. Stasiw, Ju Chen, Art van der Est|2021|Chemical Science|12|13704|doi:10.1039/D1SC02965G
DOI: 10.5517/ccdc.csd.cc282mbb
title: CCDC 2087891: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mcc
title: CCDC 2087892: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mdd
title: CCDC 2087893: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mff
title: CCDC 2087894: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mgg
title: CCDC 2087895: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mhh
title: CCDC 2087896: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mjj
title: CCDC 2087897: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mkk
title: CCDC 2087898: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mll
title: CCDC 2087899: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mmm
title: CCDC 2087900: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc282mnn
title: CCDC 2087901: Experimental Crystal Structure Determination

description: Related Article: Gemma F. Turner, Scott C. McKellar, David R. Allan, Anthony K. Cheetham, Sebastian Henke, Stephen A. Moggach|2021|Chemical Science|12|13793|doi:10.1039/D1SC03108B
DOI: 10.5517/ccdc.csd.cc286g0z
title: CCDC 2091570: Experimental Crystal Structure Determination

description: Related Article: Shengfei Jin, Graham C. Haug, Ramon Trevino, Viet D. Nguyen, Hadi D. Arman, Oleg V. Larionov|2021|Chemical Science|12|13914|doi:10.1039/D1SC04245A
DOI: 10.5517/ccdc.csd.cc286g21
title: CCDC 2091572: Experimental Crystal Structure Determination

description: Related Article: Shengfei Jin, Graham C. Haug, Ramon Trevino, Viet D. Nguyen, Hadi D. Arman, Oleg V. Larionov|2021|Chemical Science|12|13914|doi:10.1039/D1SC04245A
DOI: 10.5517/ccdc.csd.cc286g32
title: CCDC 2091573: Experimental Crystal Structure Determination

description: Related Article: Shengfei Jin, Graham C. Haug, Ramon Trevino, Viet D. Nguyen, Hadi D. Arman, Oleg V. Larionov|2021|Chemical Science|12|13914|doi:10.1039/D1SC04245A
DOI: 10.5517/ccdc.csd.cc286y3k
title: CCDC 2092038: Experimental Crystal Structure Determination

description: Related Article: Shengfei Jin, Graham C. Haug, Ramon Trevino, Viet D. Nguyen, Hadi D. Arman, Oleg V. Larionov|2021|Chemical Science|12|13914|doi:10.1039/D1SC04245A
DOI: 10.5517/ccdc.csd.cc287tkx
title: CCDC 2092889: Experimental Crystal Structure Determination

description: Related Article: Fan Xu, Myles W. Smith|2021|Chemical Science|12|13756|doi:10.1039/D1SC03533A
DOI: 10.6084/m9.figshare.16892344
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892350
title: March 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in March, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892356
title: May 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892356.v1
title: May 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892359
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892359.v1
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16892353
title: Dataset for effects of diet on triatomine shape

description: This dataset contains the results of an experiment where diet and the time food was provided were manipulated using Triatoma pallidipennis bugs
DOI: 10.25345/c5kc43
title: MassIVE MSV000088274 - Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level
title: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.
DOI: 10.15468/cuausz
title: Fauna marina de la costa vasca - Sistema de Información de la Naturaleza de Euskadi

description: Pendiente de cargar los datos del Endpoint.
DOI: 10.5281/zenodo.5602160
title: Temporal transitions in the post-mitotic nervous system of Caenorhabditis elegans

description: Representative image Volumes for various temporally-regulated genes in the post-mitotic C. elegans nervous system across post-embryonic development. The accompanying publication is at Nature (update link once published) or at https://www.biorxiv.org/content/10.1101/2020.10.06.328880v1 (Preprint).
DOI: 10.5281/zenodo.5602159
title: Temporal transitions in the post-mitotic nervous system of Caenorhabditis elegans

description: Representative image Volumes for various temporally-regulated genes in the post-mitotic C. elegans nervous system across post-embryonic development. The accompanying publication is at Nature (update link once published) or at https://www.biorxiv.org/content/10.1101/2020.10.06.328880v1 (Preprint).
DOI: 10.6084/m9.figshare.16592678
title: Dataset for Soft Elasticity Optimises Dissipation in 3D-Printed Liquid Crystal Elastomers

description: Contains data used and published in our paper:<br><br>
DOI: 10.5281/zenodo.5603140
title: Dataset of laboratory experiments on disks settling through density transition and wake evolution

description: Dataset was collected in laboratory experiments on the settling of solid disks in a stratified liquid column with a density transition. Four types of disks, with diameter 2 mm and 3 mm, varying in aspect ratio were considered. Experiments were carried out in a rectangular transparent tank (0.10 m × 0.10 m x 0.50 m) filled with two-layered liquid - aqueous solution of NaCl. A non-linear density transition formed between the two layers of homogeneous density. A sequence of reorientations and unsteady settling of disks translating through the density transition was observed. Settling behaviour of particles was recorded using cameras with macro lenses to measure the position, reorientation and settling velocity of disks. Moreover, a dye visualization was used to observe and measure the evolution of wake behind the disk settling through the density transition. The analysis of results and data visualizations are presented in Mrokowska MM, 2020: Influence of pycnocline on settling behaviour of non-spherical particle and wake evolution. Scientific Reports 10:20595, DOI 10.1038/s41598-020-77682-y.
description: {"references": ["Mrokowska MM, 2020: Influence of pycnocline on settling behaviour of non-spherical particle and wake evolution. Scientific Reports 10:20595, DOI 10.1038/s41598-020-77682-y"]}