cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48690/1524230
title: LandScan HD Russia v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1528975
title: LandScan HD Slovakia v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524231
title: LandScan HD South Korea v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524232
title: LandScan HD South Sudan v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1529405
title: LandScan HD South Sudan v2.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1529415
title: LandScan HD Sudan v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524233
title: LandScan HD Syria v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524234
title: LandScan HD Taiwan v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1529406
title: LandScan HD Syria v2.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524250
title: LandScan HD Ukraine v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1528645
title: LandScan HD Turkiye v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524235
title: LandScan HD Venezuela v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524236
title: LandScan HD West Bank v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524237
title: LandScan HD Yemen v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.48690/1524238
title: LandScan HD Zambia v1.0

description: LandScan HD is developed for individual countries around the world and provides gridded population estimates at 3 arc-second resolution. The LandScan HD model incorporates current land use and infrastructure data from a variety of sources, applies facility occupancy estimates from ORNL's Population Density Tables (PDT) project, and leverages novel image processing algorithms developed at ORNL to rapidly map building structures and neighborhood areas using high-performance computing environments. In this way, LandScan HD is developed using a 'bottom-up' approach where high resolution population estimates are not dependent on a recently conducted, high quality census. This approach is particularly useful for parts of the world that regularly experience large changes in population distribution due to rapid growth, natural hazards, or conflict.
DOI: 10.5258/soton/d2728
title: Dataset supporting the report "Use and acceptability of digital remote monitoring for older people research study".

description: The dataset contains three interview transcripts, a summary of participant characteristics and a small quantitative dataset. Please complete the attached request form for access Participant consent allows sharing of interview transcripts and participant characteristic information with bona fide academic researchers only. The quantitative data cannot be shared due to the final dataset being much smaller than anticipated at the outset of the research, increasing potential for identification of individuals from the data. This data was analysed with data collected for other components of the NHS Insights Prioritisation Programme (NIPP) project and the findings from this analysis are reported in the NIPP 2022-23 Final Report This dataset contains: CE01.pdf, CE02.pdf, UE01.pdf These are anonymised interview transcripts. Summary of participant characteristics.pdf This contains information about the interview participants, such as gender and age range. Quantitative data.xlsx This contains the raw data and data produced as part of data analysis.
DOI: 10.21227/m3wr-hq64
title: DANGCEM2014-2021

description: The stock market is a volatile and nonlinear environment, making it difficult to predict returns accurately. However,machine learning and deep learning models have been able toachieve some degree of accuracy in predicting financial timeseries. The recurrent neural networks (RNN) are derived fromthe feedforward neural networks, a deep learning algorithm.The cases of gradient vanishing and explosion are commonlyassociated with the traditional RNNs. The Long-Short TermMemory (LSTM) model is capable of eliminating the problemswith RNNs and this has made the LSTM to have become famousin the modeling of data with some complex sequences. The goal oft his research is to improve the accuracy of stock market forecastsu sing machine learning and classification algorithms of neural network LSTM. The historical stock prices data (2nd January, 2014 – 22nd September, 2021) of the selected Nigerian company, Dangote Cement Plc. was subjected to data mining and information extraction for the purpose of understanding the hiddenp atterns and forecast the behavior trend in the future. Resultsr evealed that the proposed RNN-LSTM outperformed ANN and Fuzzy-GA models deployed for the same stock price movement’s datasets using MSE and RMSE as 0.1942 to 0.6889/0.7192, and 0.4990 to 0.8300/0.8481 respectively. The minimal MSE and RMSE obtained shows the efficacy of the LSTM model in thep  rediction of stock market returns.
DOI: 10.5281/zenodo.8238149
title: Data and code for the publication "Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil"

description: <strong>Background</strong> The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141) published in Science of the Total Environment. <strong>Description of the dataset</strong> The zipped folder <strong>data.zip</strong> contains subfolders <strong>imagelab_data</strong> (contains microplastics data by depth and the blanks), <strong>additional_data</strong> (weights of aliquots and polymer densities) and <strong>soil_data</strong> (data on soil properties and images of soil profiles). <strong>images.zip</strong> contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd. <strong>results.zip</strong> contains the results of reading and wrangling of microplastics data. <strong>Description of the code</strong> The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd). <strong>Disclaimer</strong> The data and code are provided as is without any warranty. <strong>Funding</strong> Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357. <strong>References</strong> M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141 R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/
DOI: 10.5281/zenodo.6487652
title: Data and code for the publication "Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil"

description: <strong>Background</strong> The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141) published in Science of the Total Environment. <strong>Description of the dataset</strong> The zipped folder <strong>data.zip</strong> contains subfolders <strong>imagelab_data</strong> (contains microplastics data by depth and the blanks), <strong>additional_data</strong> (weights of aliquots and polymer densities) and <strong>soil_data</strong> (data on soil properties and images of soil profiles). <strong>images.zip</strong> contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd. <strong>results.zip</strong> contains the results of reading and wrangling of microplastics data. <strong>Description of the code</strong> The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd). <strong>Disclaimer</strong> The data and code are provided as is without any warranty. <strong>Funding</strong> Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357. <strong>References</strong> M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” https://doi.org/10.1016/j.scitotenv.2022.155141 R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/
DOI: 10.5067/measures/wvcc/data210
title: Aqua AIRS-MODIS 1-km Matchup Indexes V1
DOI: 10.5067/measures/wvcc/data211
title: SNPP CrIS-VIIRS 750-m Matchup Indexes V1
DOI: 10.6084/m9.figshare.22189705
title: Heeringa_etal_ANF-RLF-aging

description: This dataset belongs to the manuscript entitled: "Cochlear aging disrupts the correlation between spontaneous rate and sound level coding in auditory nerve fibers" by Amarins Heeringa, Fiona Teske, Go Ashida, and Christine Köppl from the University of Oldenburg. The pdf file shows Supplementary Figures S1 and S2 and the Supplementary Table S1. Each excel file contains the data for the listed figure panel(s).
DOI: 10.5517/ccdc.csd.cc2fskjb
title: CCDC 2257932: Experimental Crystal Structure Determination

description: Related Article: Yi-Fan Wang|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ftbvg
title: CCDC 2258686: Experimental Crystal Structure Determination

description: Related Article: Hannah Clara|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2ftfd3
title: CCDC 2258765: Experimental Crystal Structure Determination

description: Related Article: Arto Valkonen, Reijo Sillanpää|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fthrj
title: CCDC 2258838: Experimental Crystal Structure Determination

description: Related Article: Jie Li|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fthsk
title: CCDC 2258839: Experimental Crystal Structure Determination

description: Related Article: Jie Li|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.