cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19299896.v1
title: Harmonic radar calibration data, 2021

description: Approx. 18 hours of harmonic radar recordings collected under controlled conditions, used to test the performance of newly developed harmonic radar equipment and to calibrate the radar. Data were collected from June to September 2021.<br>Data consists of approx. 200GB of data files in a custom format giving incoming signal strengths and associated timestamps. Data files will be examined to check the performance of the hardware.<br>Additionally, the dataset contains a further approx. 800MB of mm-wave radar data in .mat file format, used to determine the height of harmonic radar transponders used to generate the harmonic radar data.<br><br>This data was collected as part of projects funded by Engineering and Physical Sciences Research Council, under grant no. EP/P006094/1, Brains on Board.<br>This data is currently unpublished. We have no plans to make the data publicly available since it is only of use for calibrating a unique piece of radar hardware and is of no conceivable value to other researchers. Please direct any enquiries to Joseph Woodgate, j.woodgate@qmul.ac.uk.
DOI: 10.5519/qd.jwas3ui7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (61067 records)
DOI: 10.5281/zenodo.6325746
title: Clinician's perception of healthcare artificial intelligence

description: This survey is only designed for medical professionals (doctor/nurse/physician/etc). You were selected as a possible participant in this study because of your expertise and experience in the medical field. Since artificial intelligence is growing and is likely to be an integral part of healthcare in the future, perceptions, and suggestions of clinicians is crucial as it will guide respective governing bodies and researchers develop and integrate AI into the clinical workflow without disturbing clinicians' and more importantly to provide them with assistance. This short online survey is designed to understand clinicians' overall perception of AI-based clinical decision support systems. The study will help identify clinicians' expectancy from AI in their respective fields of expertise and their willingness to use AI in their routine clinical practice. The information that will be obtained from this study shall help human factor scientists design clinician-centered AI decision support systems. In this survey, please consider AI-based clinical decision support systems as an automated system that mimics human cognition and generates results based on past data to assist you (healthcare worker) in decision making. For example, automated clinical decision support system (often embedded in electronic medical records), automated diagnosis using images (mobile app for skin cancer detection), machine learning-based prediction models (patient survival prediction, patient readmission, length of stay, etc.), and others.
DOI: 10.5281/zenodo.6325745
title: Clinician's perception of healthcare artificial intelligence

description: This survey is only designed for medical professionals (doctor/nurse/physician/etc). You were selected as a possible participant in this study because of your expertise and experience in the medical field. Since artificial intelligence is growing and is likely to be an integral part of healthcare in the future, perceptions, and suggestions of clinicians is crucial as it will guide respective governing bodies and researchers develop and integrate AI into the clinical workflow without disturbing clinicians' and more importantly to provide them with assistance. This short online survey is designed to understand clinicians' overall perception of AI-based clinical decision support systems. The study will help identify clinicians' expectancy from AI in their respective fields of expertise and their willingness to use AI in their routine clinical practice. The information that will be obtained from this study shall help human factor scientists design clinician-centered AI decision support systems. In this survey, please consider AI-based clinical decision support systems as an automated system that mimics human cognition and generates results based on past data to assist you (healthcare worker) in decision making. For example, automated clinical decision support system (often embedded in electronic medical records), automated diagnosis using images (mobile app for skin cancer detection), machine learning-based prediction models (patient survival prediction, patient readmission, length of stay, etc.), and others.
DOI: 10.5517/ccdc.csd.cc29g2xs
title: CCDC 2128705: Experimental Crystal Structure Determination

description: Related Article: Ziyao Gao, Shuyu Liu, Changquan Calvin Sun|2022|Int.J.Pharm.|617|121587|doi:10.1016/j.ijpharm.2022.121587
DOI: 10.5517/ccdc.csd.cc29g2yt
title: CCDC 2128706: Experimental Crystal Structure Determination

description: Related Article: Ziyao Gao, Shuyu Liu, Changquan Calvin Sun|2022|Int.J.Pharm.|617|121587|doi:10.1016/j.ijpharm.2022.121587
DOI: 10.5517/ccdc.csd.cc29g2zv
title: CCDC 2128707: Experimental Crystal Structure Determination

description: Related Article: Ziyao Gao, Shuyu Liu, Changquan Calvin Sun|2022|Int.J.Pharm.|617|121587|doi:10.1016/j.ijpharm.2022.121587
DOI: 10.25747/trkp-2q30
title: Daily profiles (2020) of load of a semi-urban synthetic electricity distribution network from UK – ATTEST project
DOI: 10.5522/04/18857324
title: Transforming Construction Network Plus research programme

description: This metadata relates to interviews exploring companies' business model changes under the ongoing transformation of the UK construction sector. The data are transcriptions of 43 in-depth, semi-structured interviews with informants from three companies at the vanguard of transformation. Data collection covered a period of 14 months from July 2019 to September 2020. <br><br>Please note that further metadata can be found on the UK Data Service Repository record at the link included:<br><br>https://reshare.ukdataservice.ac.uk/855441/<br>
DOI: 10.25747/rfde-c940
title: Daily profiles (2020) of load of a rural synthetic electricity distribution network from UK – ATTEST project
DOI: 10.6084/m9.figshare.17161180
title: Score Matching for Compositional Distributions

description: Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. With real data, it is often the case that many of the compositional components are highly right-skewed, with large numbers of zeros. Major limitations of currently available models for compositional data include one or more of the following: insufficient flexibility in terms of distributional shape; difficulty in accommodating zeros in the data in estimation; and lack of computational viability in moderate to high dimensions. In this article, we propose a new model, the polynomially tilted pairwise interaction (PPI) model, for analysing compositional data. Maximum likelihood estimation is difficult for the PPI model. Instead, we propose novel score matching estimators, which entails extending the score matching approach to Riemannian manifolds with boundary. These new estimators are available in closed form and simulation studies show that they perform well in practice. As our main application, we analyse real microbiome count data with fixed totals using a multinomial latent variable model with a PPI model for the latent variable distribution. We prove that, under certain conditions, the new score matching estimators are consistent for the parameters in the new multinomial latent variable model.
DOI: 10.6084/m9.figshare.17161180.v2
title: Score Matching for Compositional Distributions

description: Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. With real data, it is often the case that many of the compositional components are highly right-skewed, with large numbers of zeros. Major limitations of currently available models for compositional data include one or more of the following: insufficient flexibility in terms of distributional shape; difficulty in accommodating zeros in the data in estimation; and lack of computational viability in moderate to high dimensions. In this article, we propose a new model, the polynomially tilted pairwise interaction (PPI) model, for analysing compositional data. Maximum likelihood estimation is difficult for the PPI model. Instead, we propose novel score matching estimators, which entails extending the score matching approach to Riemannian manifolds with boundary. These new estimators are available in closed form and simulation studies show that they perform well in practice. As our main application, we analyse real microbiome count data with fixed totals using a multinomial latent variable model with a PPI model for the latent variable distribution. We prove that, under certain conditions, the new score matching estimators are consistent for the parameters in the new multinomial latent variable model.
DOI: 10.6075/j0vd6zm8
title: EarthCube Advisory Committee Report (March 11, 2016). In EarthCube Organization Materials

description: EarthCube Advisory Committee (EAC) issued this report to the EarthCube Leadership Council and NSF following a Reverse Site visit in 2016. The report contains actionable recommendations to support strategic planning and further guide the development of EarthCube; addititional details on the Reverse Site Visit and the original Committee Charge are included.
DOI: 10.6084/m9.figshare.17161180.v1
title: Score matching for compositional distributions

description: Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. With real data it is often the case that many of the compositional components are highly right-skewed, with large numbers of zeros. Major limitations of currently available models for compositional data include one or more of the following: insufficient flexibility in terms of distributional shape; difficulty in accommodating zeros in the data in estimation; and lack of computational viability in moderate to high dimensions. In this article we propose a new model, the polynomially-tilted pairwise interaction (PPI) model, for analysing compositional data. Maximum likelihood estimation is difficult for the PPI model. Instead, we propose novel score matching estimators, which entails extending the score matching approach to Riemannian manifolds with boundary. These new estimators are available in closed form and simulation studies show that they perform well in practice. As our main application we analyse real microbiome count data with fixed totals using a multinomial latent variable model with a PPI model for the latent variable distribution. We prove that, under certain conditions, the new score matching estimators are consistent for the parameters in the new multinomial latent variable model.
DOI: 10.6084/m9.figshare.19299944
title: Radar tracks of bumblebee workers, 2021

description: Approx. 200 hours of harmonic radar recordings of bumblebee workers undertaking their first flights out of the nest, recorded in an agricultural landscape at Rothamsted Research, Herts. Data were collected fron July-October 2021.<br>Data consists of approx 2.5TB of data files in a custom format giving incoming signal strengths and associated timestamps. Data files will subsequently be processed to identify signals originating from transponders attached to bees and to extract their azimuth and range.<br>This data was collected as part of projects funded by Engineering and Physical Sciences Research Council, under grant no. EP/P006094/1, Brains on Board.<br>This data is currently unpublished. All the processed track files will be publicly archived on publication. Please direct any enquiries to Joseph Woodgate, j.woodgate@qmul.ac.uk. <br><br>
DOI: 10.25345/c5cz32645
title: MassIVE MSV000088977 - Stromal Changes in The Aged Lung Induce an Emergence From Melanoma Dormancy
title: Disseminated cancer cells (DCCs) that escape the primary site can seed in distal tissues, but may take several years, or even decades to grow out into overt metastases, a phenomenon termed tumor dormancy. Despite its importance in metastasis and residual disease, few studies have been able to successfully model or characterize dormancy within melanoma. Here, we show that age-related changes in the lung microenvironment facilitate a permissive niche for efficient outgrowth of disseminated dormant tumor cells, in contrast to the aged skin, where age-related changes suppress melanoma growth but drive dissemination. A model of melanoma progression that addresses these microenvironmental complexities is the phenotype switching model, which argues that melanoma cells switch between a proliferative cell state and a slower-cycling, invasive state1-3. We have previously shown that dermal fibroblasts are key orchestrators of promoting phenotype switching in primary melanoma tumors via changes in the secretion of soluble factors during aging4-8. Our new data identifies Wnt5A as a master regulator of activating melanoma DCC dormancy within the lung, which initially enables efficient dissemination and seeding of melanoma cells in metastatic niches. Age-induced reprogramming of lung fibroblasts increases their secretion of the soluble Wnt antagonist sFRP1, which inhibits Wnt5A, enabling efficient metastatic outgrowth. Further, we have identified the tyrosine kinase receptors AXL and MER as promoting a dormancy-toreactivation axis respectively. Overall, we find that age-induced changes in distal metastatic microenvironments promotes efficient reactivation of dormant melanoma cells in the lung.
DOI: 10.3886/e163861
title: Clinical Decision Support System for Differential Diagnosis

description: Clinical Decision Support Systems (CDSS) are advanced technologies that are increasingly leveraged in health care settings. CDSS are used as expert systems by clinicians during the diagnosis process. It can improve the delivery of healthcare, reducing costs and decreasing the number of mistreatments and misdiagnosis. Centers for Medicare & Medicaid services have included CDSS capabilities into their Meaningful Use (MU) framework for the evaluation of maturity of Electronic Health Records (EHR) systems, which should drive even further adoption of this type of technology. However, CDSS systems are still expensive to build and its associated datais still not very comprehensive in terms of coverage. This dataset is the knowledge base for a differential diagnosis CDSS system. The knowledge base was built using automatic information extraction from medical journals and relevant sites, such as MedlinePlus. The dataset is represent in N3 notation and must be used in tandem with EYE, an open-source Prolog Virtual Machine, in order to produce results.
DOI: 10.3886/e163861v2
title: Clinical Decision Support System for Differential Diagnosis

description: Clinical Decision Support Systems (CDSS) are advanced technologies that are increasingly leveraged in health care settings. CDSS are used as expert systems by clinicians during the diagnosis process. It can improve the delivery of healthcare, reducing costs and decreasing the number of mistreatments and misdiagnosis. Centers for Medicare & Medicaid services have included CDSS capabilities into their Meaningful Use (MU) framework for the evaluation of maturity of Electronic Health Records (EHR) systems, which should drive even further adoption of this type of technology. However, CDSS systems are still expensive to build and its associated datais still not very comprehensive in terms of coverage. This dataset is the knowledge base for a differential diagnosis CDSS system. The knowledge base was built using automatic information extraction from medical journals and relevant sites, such as MedlinePlus. The dataset is represent in N3 notation and must be used in tandem with EYE, an open-source Prolog Virtual Machine, in order to produce results.
DOI: 10.6084/m9.figshare.12032745
title: Identification of potent inhibitors of COVID-19 main protease by using In Silico methods

description: COVID-19 pandemic caused by a newly emerged coronavirus. This severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) outbreak poses a serious public health risk. Also the global health concerns raised, because this viral infection spreads from person to person in some case with no reported symptoms. This pandemic situation encourages drug repurposing of the available drugs such as hydroxychloroquine and remdesivir for the COVID-19 treatment. Drug repurposing could improve the existing clinical management. Current study, aims to predict the viral protease inhibitor for the COVID-19 main protease (M<sup>pro</sup>). This COVID-19 M<sup>pro</sup> enzyme cuts the polyproteins translated from viral RNA to yield functional viral proteins. The crystal structure of M<sup>pro</sup> (PDB ID: 6LU7) was obtained from Protein Data bank. Molecular docking investigations between the target protein and ligands were performed using PyRx virtual Screening software. All the test compounds got docked with the targeted viral protein. Present <i>in silico</i> study suggest Notomycin, Coumermycin A1, and Suramin as potent M<sup>pro </sup>inhibitor based on the binding energy and molecular interactions. However, further research is necessary to investigate their potential therapeutic use. All relevant molecular docking data and supporting information is accessible freely from the www.findrug.org. <br> Virtual Screening PyRx is a Virtual Screening software for computational drug discovery that can be used to screen libraries of compounds against potential drug targets. Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. PyRx is open-source software with an intuitive user interface that runs on all major computer operating systems.<br>AcknowledgementVIT-SIF Lab, SAS, Chemistry Division for NMR and GC-MS Analysis.<br> <br>
DOI: 10.6084/m9.figshare.12032745.v28
title: FINDRUG: Identification of potent inhibitors of COVID-19 main protease by using PyRx virtual Screening

description: <b>COVID-19 pandemic caused by a newly emerged Wuhan’s novel coronavirus. This severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) outbreak poses a serious public health risk. Also the global health concerns raised, because this viral infection spreads from person to person in some case with no reported symptoms. This pandemic situation encourages drug repurposing of the available drugs such as hydroxychloroquine and remdesivir for the COVID-19 treatment. Drug repurposing could improve the existing clinical management. Current study, aims to predict the viral protease inhibitor for the COVID-19 main protease (M<sup>pro</sup>). This COVID-19 M<sup>pro</sup> enzyme cuts the polyproteins translated from viral RNA to yield functional viral proteins. The crystal structure of M<sup>pro</sup> (PDB ID: 6LU7) was obtained from Protein Data bank. Molecular docking investigations between the target protein and ligands were performed using PyRx virtual Screening software. All the test compounds got docked with the targeted viral protein. Present <i>in silico</i> study suggest Notomycin, Coumermycin A1, and Suramin as potent M<sup>pro </sup>inhibitor based on the binding energy and molecular interactions. However, further research is necessary to investigate their potential therapeutic use. All relevant molecular docking data and supporting information is accessible freely from the www.findrug.org. </b> <b>Virtual Screening</b> <b>PyRx is a Virtual Screening software for computational drug discovery that can be used to screen libraries of compounds against potential drug targets. Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. PyRx is open-source software with an intuitive user interface that runs on all major computer operating systems.</b> <b> </b><br>
DOI: 10.5281/zenodo.6324701
title: Human Population-Averaged dMRI Templates (FIB Files, NIFTI Files)

description: The templates were constructed by DSI Studio using q-space diffeomorphic reconstruction. Methods and Data Source: https://brain.labsolver.org/hcp_template.html
description: {"references": ["Yeh, Fang-Cheng, and Wen-Yih Isaac Tseng. \"NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction.\" Neuroimage 58.1 (2011): 91-99.", "Yeh, Fang-Cheng, et al. \"Population-averaged atlas of the macroscale human structural connectome and its network topology.\" Neuroimage 178 (2018): 57-68."]}
DOI: 10.5281/zenodo.6324700
title: Human Population-Averaged dMRI Templates (FIB Files, NIFTI Files)

description: The templates were constructed by DSI Studio using q-space diffeomorphic reconstruction. Methods and Data Source: https://brain.labsolver.org/hcp_template.html
description: {"references": ["Yeh, Fang-Cheng, and Wen-Yih Isaac Tseng. \"NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction.\" Neuroimage 58.1 (2011): 91-99.", "Yeh, Fang-Cheng, et al. \"Population-averaged atlas of the macroscale human structural connectome and its network topology.\" Neuroimage 178 (2018): 57-68."]}
DOI: 10.25345/c58911s3n
title: MassIVE MSV000088978 - GNPS Microcystis aeruginosa LEGE 91341
title: VLC fractions using a gradient of decreasing polarity - 10%, 30%, 50%, 70% MeOH (aq), 100% MeOH, 100% iPrOH and 100% CH2Cl2), yielding a total of seven fractions (A-G). Blanks used in the run was also included.
DOI: 10.5258/soton/d1842
title: Data for Experimental Demonstration of 50-Gb/s/λ O-band CWDM Direct-Detection Transmission over 100-km SMF

description: This dataset supports the publication: Yang Hong, Natsupa Taengnoi, Kyle R. H. Bottrill, Naresh K. Thipparapu, Yu Wang, Jayanta K. Sahu, David J. Richardson and Periklis Petropoulos Experimental Demonstration of 50-Gb/s/λ O-band CWDM Direct-Detection Transmission over 100-km SMF Asia Communications and Photonics Conference 2021 https://doi.org/10.1364/ACPC.2021.W1B.2
DOI: 10.5063/f1tt4pdb
title: Stream restoration and ecosystem functioning in lowland streams; Denmark; stream reach scale

description: Data used in the submitted paper "Stream restoration and ecosystem functioning in lowland streams"; Authors: Annette Baattrup-Pedersen, Anette Baisner Alnoe, Jes J. Rasmussen, Peter S. Levi, Nikolai Friberg, Tenna RiisSubmitted to Ecological Applications March 2022
DOI: 10.25810/558f-c477.46
title: Australian Newspaper Coverage of Climate Change or Global Warming, 2000-2022 - January 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 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.