cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4110622
title: Sex, shells, and weaponry: Coercive reproductive tactics in the Painted Turtle, Chrysemys picta

description: <strong>Abstract. </strong>Males and females have divergent reproductive interests arising from their unequal investments in offspring. This sexual conflict drives an antagonistic arms-race that influences sex-specific reproductive success. Alternative reproductive tactics are expected in long-lived species for which the reproductive strategy that maximizes mating success could differ across body sizes. The mating strategy of the painted turtle (<em>Chrysemys picta</em>) has been characterized as an elaborate and amiable male courtship display during which males use their elongate foreclaws to stroke females, coupled with female mate choice. Contrary to this long-held understanding, <em>in situ</em> field observations and experimental trials from our long-term study in Algonquin Provincial Park, Canada, demonstrate that males also exhibit an alternative, coercive mating strategy. Males are equipped with sexually-size dimorphic tomiodonts, tooth-like cusps of the beak, as well as a weaponized anterior shell, with which they wound the head and neck of females. Behavioral trials during the breeding periods showed that male reproductive tactics shift from courtship (foreclaw display) to coercion (striking, biting, and forced submergence) across ontogeny, and male size predicts the occurrence and frequency of coercive behavior. We found phenotype-behavior matching whereby small males invest in putatively ornamental foreclaws used for courtship and large males invest in weaponry for coercion, challenging existing knowledge of this well-studied species. As a group with a long evolutionary history and varied mating systems, Testudines are a particularly interesting taxon in which to ask questions about mating system evolution.
description: The dataset deposit includes: R code (R statistical computing) related to statistical analysis, data used for analysis (.csv files), and three supplementary video files (.mov files).
DOI: 10.5281/zenodo.3694099
title: TTWISTOR files from LAPSE-RATE

description: This dataset includes measurements obtained using the University of Colorado TTwistor UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018. <strong>All parties downloading the data are asked to cite the original research team and funding body when appropriate, and when using images and data in publications, presentations or other public displays. The DOI for the current dataset, and associated articles should be referenced whenever the dataset is used for scientific analysis.</strong>
description: Funding supported by the US National Science Foundation (AGS 1807199) and the US Department of Energy (DE-SC0018985).
DOI: 10.5281/zenodo.4110644
title: Modeling ultrasound extraction of phenolic compounds from grape pomace

description: The provided data include measurements of antioxidant activity, total phenolic content, concentrations of individual phenols and individual anthocyanins of grape pomace extracts for initial (DoE1) and final (DoE2) ultrasound method optimization.
DOI: 10.5281/zenodo.4110643
title: Modeling ultrasound extraction of phenolic compounds from grape pomace

description: The provided data include measurements of antioxidant activity, total phenolic content, concentrations of individual phenols and individual anthocyanins of grape pomace extracts for initial (DoE1) and final (DoE2) ultrasound method optimization.
DOI: 10.5066/p9hx91b2
title: North Coast and Cascades Network consolidated amphibian database (1984-2005)

description: This data set is an amalgamation of twenty-nine original data sets, which represent amphibian surveys in the seven national parks comprising the North Coast and Cascades Network (NCCN) of the National Park Service. The data were collected from 1984-2005, and include the localities of 19 species of amphibians at various life stages, 18 native to the Pacific Northwest and one invasive species.
DOI: 10.5281/zenodo.3694116
title: Talon files from LAPSE-RATE

description: This dataset includes measurements obtained using the University of Colorado Talon UAS during the 2018 LAPSE-RATE (Lower Atmospheric Profiling Studies at Elevation - a Remotely-piloted Aircraft Team Experiment) field campaign. This campaign took place in the San Luis Valley of Colorado between 14-21 July, 2018. <strong>All parties downloading the data are asked to cite the original research team and funding body when appropriate, and when using images and data in publications, presentations or other public displays. The DOI for the current dataset, and associated articles should be referenced whenever the dataset is used for scientific analysis.</strong>
description: Funding provided by the US National Science Foundation (AGS 1807199) and the US Department of Energy (DE-SC0018985).
DOI: 10.25345/c5h777
title: MassIVE MSV000086334 - A Fbxo48 inhibitor prevents pAMPKa degradation and ameliorates insulin resistance
title: Beas-2b cells treated with vehicle or BC1618 compound, which targets the ubiquitin E3 ligase for targeted degradation of pAMPKa protein. Cell lysate was subjected to mass spectrometry for phosphoproteomics to uncover alterations in the phosphoproteome.
DOI: 10.17632/7jbpnxx967.2
title: Data for: Deterministic matrix-based radiative design using a new general formulation of exergy and exergy efficiency for hybrid solar collectors

description: N/A
DOI: 10.5517/ccdc.csd.cc23j774
title: CCDC 1952015: Experimental Crystal Structure Determination

description: Related Article: Kalyanmoy Jana, Ribhu Maity, Horst Puschmann, Anindita Mitra, Rita Ghosh, Subhas Chandra Debnath, Aparna Shukla, Arun Kumar Mahanta, Tithi Maity, Bidhan Chandra Samanta|2021|Inorg.Chim.Acta|515|120067|doi:10.1016/j.ica.2020.120067
DOI: 10.5517/ccdc.csd.cc25qw82
title: CCDC 2017984: Experimental Crystal Structure Determination

description: Related Article: Saurabh Kumar, Maravanji S. Balakrishna|2020|Polyhedron|190|114779|doi:10.1016/j.poly.2020.114779
DOI: 10.5517/ccdc.csd.cc25s5rx
title: CCDC 2019239: Experimental Crystal Structure Determination

description: Related Article: Yu-hui Tan|2020|CSD Communication|||
description: Related Article: Xing-Xian Dong, Ning Song, Bin Huang, Yu-Hui Tan, Yun-Zhi Tang, Wen-Juan Wei, Yu-Kong Li, Qing Shu|2021|Inorg.Chim.Acta|515|120051|doi:10.1016/j.ica.2020.120051
DOI: 10.5517/ccdc.csd.cc25wl1q
title: CCDC 2022503: Experimental Crystal Structure Determination

description: Related Article: Cory T. Kornman, Lei Li, Asmerom O. Weldeab, Ion Ghiviriga, Khalil A. Abboud, Ronald K. Castellano|2020|Chemical Science|11|10190|doi:10.1039/D0SC04409A
DOI: 10.6084/m9.figshare.13011527
title: Raw datasets for "Quantification of antibody avidities and accurate detection of SARS-CoV-2 antibodies in serum and saliva on plasmonic substrates"

description: Raw data Include 4 datasets:<b>1. </b>pGOLD COVID-19 COV_HCOV IgG avidity dataset;<br><b>2.</b> pGOLD COVID-19 IgG IgM assay result summary (RBD cutoff determination and clinical agreement);<br><b>3. </b>pGOLD COVID-19 IgG IgM assay result summary (S1 cutoff determination and clinical agreement);<br><b>4.</b> pGOLD COVID-19 saliva dataset.
DOI: 10.6084/m9.figshare.13011527.v1
title: Raw datasets for "Quantification of antibody avidities and accurate detection of SARS-CoV-2 antibodies in serum and saliva on plasmonic substrates"

description: Raw data Include 4 datasets:<b>1. </b>pGOLD COVID-19 COV_HCOV IgG avidity dataset;<br><b>2.</b> pGOLD COVID-19 IgG IgM assay result summary (RBD cutoff determination and clinical agreement);<br><b>3. </b>pGOLD COVID-19 IgG IgM assay result summary (S1 cutoff determination and clinical agreement);<br><b>4.</b> pGOLD COVID-19 saliva dataset.
DOI: 10.25345/c5cj5b
title: MassIVE MSV000086335 - GNPS Casearia arborea endophytic fungi active extracts
title: Active extracts obtained from endophytic fungi fermentation in rice and PD broth were selected to the organization of MS/MS data and dereplication using GNPS. The fungi were isolated from fresh and heathy leaves of Casearia arborea.
DOI: 10.5281/zenodo.4106680
title: Dataset: 295,416 RiPP BGCs from BiG-FAM version 1.0

description: 295,416 RiPP BGCs from BiG-FAM version 1 (https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkaa812/5917658) to be used for downstream analyses. Data includes: - all antiSMASH5 regiongbk files in one folder - GCF assignment (t=900) - BiG-SLiCE's BGC feature matrix (see https://www.biorxiv.org/content/10.1101/2020.08.17.240838v2, pandas dataframe pickled in Python 3.6.7, pandas 1.0.4) - taxonomy information (GTDB) - RiPP subclasses information
description: {"references": ["Kautsar, Satria A., et al. \"BiG-FAM: the biosynthetic gene cluster families database.\" Nucleic Acids Research (2020).", "Kautsar, Satria A., et al. \"BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters.\" BioRxiv (2020)."]}
DOI: 10.5281/zenodo.4106679
title: Dataset: 295,416 RiPP BGCs from BiG-FAM version 1.0

description: 295,416 RiPP BGCs from BiG-FAM version 1 (https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkaa812/5917658) to be used for downstream analyses. Data includes: - all antiSMASH5 regiongbk files in one folder - GCF assignment (t=900) - BiG-SLiCE's BGC feature matrix (see https://www.biorxiv.org/content/10.1101/2020.08.17.240838v2, pandas dataframe pickled in Python 3.6.7, pandas 1.0.4) - taxonomy information (GTDB) - RiPP subclasses information
description: {"references": ["Kautsar, Satria A., et al. \"BiG-FAM: the biosynthetic gene cluster families database.\" Nucleic Acids Research (2020).", "Kautsar, Satria A., et al. \"BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters.\" BioRxiv (2020)."]}
DOI: 10.5281/zenodo.4110704
title: Heusinkveld_et_al_Complementing_sparse_vascular_imaging data_JAPPL

description: Source Data belonging to the Manuscript entitled: Complementing sparse vascular imaging data by physiological adaptation rules
DOI: 10.5281/zenodo.4110703
title: Heusinkveld_et_al_Complementing_sparse_vascular_imaging data_JAPPL

description: Source Data belonging to the Manuscript entitled: Complementing sparse vascular imaging data by physiological adaptation rules
DOI: 10.5517/ccdc.csd.cc266bwn
title: CCDC 2031891: Experimental Crystal Structure Determination

description: Related Article: Soohyun Lim, Alexander T. Radosevich|2020|J.Am.Chem.Soc.|142|16188|doi:10.1021/jacs.0c07580
DOI: 10.5517/ccdc.csd.cc266bxp
title: CCDC 2031892: Experimental Crystal Structure Determination

description: Related Article: Soohyun Lim, Alexander T. Radosevich|2020|J.Am.Chem.Soc.|142|16188|doi:10.1021/jacs.0c07580
DOI: 10.5517/ccdc.csd.cc266byq
title: CCDC 2031893: Experimental Crystal Structure Determination

description: Related Article: Soohyun Lim, Alexander T. Radosevich|2020|J.Am.Chem.Soc.|142|16188|doi:10.1021/jacs.0c07580
DOI: 10.5517/ccdc.csd.cc266bzr
title: CCDC 2031894: Experimental Crystal Structure Determination

description: Related Article: Soohyun Lim, Alexander T. Radosevich|2020|J.Am.Chem.Soc.|142|16188|doi:10.1021/jacs.0c07580
DOI: 10.6084/m9.figshare.12526271.v1
title: Daily_flow county level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12526271
title: Daily_flow county level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12526262
title: Daily_flow state level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12526262.v1
title: Daily_flow state level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12520739
title: Daily_flow census tract level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12520655.v1
title: Weekly_flow census tract level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12520655
title: Weekly_flow census tract level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12520574
title: Weekly_flow county level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.
DOI: 10.6084/m9.figshare.12520574.v1
title: Weekly_flow county level

description: Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce a regularly updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.