cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4421030
title: Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"

description: <strong>Supplementary Material</strong> This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>". The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM). Please cite the related article if using the data or the script. Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.
description: {"references": ["Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322."]}
DOI: 10.5281/zenodo.4167318
title: Supplementary Material: "A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data"

description: <strong>Supplementary Material</strong> This material regards the paper entitled "<em>A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data</em>". The Readme.txt file explains all the contents of the data package, which consists of the data supporting the paper and the MATLAB script for the Individual Tree Detection and Measurement (ITDM). Please cite the related article if using the data or the script. Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322.
description: {"references": ["Latella, M., Sola, F., & Camporeale, C. (2021). A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data. Remote Sensing, 13(2), 322."]}
DOI: 10.25403/upresearchdata.13303385
title: Digital entrenchment influence towards customer attrition

description: The purpose of this study is to evaluate whether adoption of mobile banking application has any influence towards customer attrition rate. The study takes a quantitative research approach, which will be conducted to test hypotheses linked to the theoretical framework and the literature review. Secondary customer banking data from one of the major banks will be used to test the research hypotheses using SPSS tool. <br>The record contains 5 files in a Microsoft excel format as follows: - Customer banking raw data - Customer banking cleaned data suitable to test the proposed research hypotheses - SA banked customer data from FinMark- SA banked customer income sources data from FinMark- SA banked customer education data from FinMark
DOI: 10.25403/upresearchdata.13303385.v1
title: Digital entrenchment influence towards customer attrition

description: The purpose of this study is to evaluate whether adoption of mobile banking application has any influence towards customer attrition rate. The study takes a quantitative research approach, which will be conducted to test hypotheses linked to the theoretical framework and the literature review. Secondary customer banking data from one of the major banks will be used to test the research hypotheses using SPSS tool. <br>The record contains 5 files in a Microsoft excel format as follows: - Customer banking raw data - Customer banking cleaned data suitable to test the proposed research hypotheses - SA banked customer data from FinMark- SA banked customer income sources data from FinMark- SA banked customer education data from FinMark
DOI: 10.6084/m9.figshare.13699492
title: A transferrable and integrative type I-F Cascade for heterologous genome editing and transcription modulation

description: RNA-seq data for Figure S1B (PAOWT: PAO1 WT; PAO1CR:PAO1IF)
DOI: 10.6084/m9.figshare.13699492.v1
title: A transferrable and integrative type I-F Cascade for heterologous genome editing and transcription modulation

description: RNA-seq data for Figure S1B (PAOWT: PAO1 WT; PAO1CR:PAO1IF)
DOI: 10.21942/uva.13643189.v3
title: Geomorphology model, input datasets and legend symbology files for use in ArcMap

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results: Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.34740/kaggle/dsv/1905169
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.25403/upresearchdata.13303493
title: The impact of data breaches of varying severity on the customer loyalty of high net worth individuals in retail banking

description: This data set provides the results of an online experiment, which was in the form of a 2X3 factorial design executed through Google forms. It considers Locus of causality (Internal, External) and Breach Severity (Low, Medium, High) by suggesting one of six randomly assigned scenarios, followed by a series of questions to measure the response to the scenario presented. It also measures the factors of stability of cause, controllability of cause and the dependent variable of customer loyalty intention using Likert Scales.
DOI: 10.25403/upresearchdata.13303493.v1
title: The impact of data breaches of varying severity on the customer loyalty of high net worth individuals in retail banking

description: This data set provides the results of an online experiment, which was in the form of a 2X3 factorial design executed through Google forms. It considers Locus of causality (Internal, External) and Breach Severity (Low, Medium, High) by suggesting one of six randomly assigned scenarios, followed by a series of questions to measure the response to the scenario presented. It also measures the factors of stability of cause, controllability of cause and the dependent variable of customer loyalty intention using Likert Scales.
DOI: 10.25919/0gc5-hv69
title: Parkes observations for project P971 semester 2020OCTS_03

description: We propose timing observations of the relativistic pulsar - white-dwarf binary, PSR J1141$-$6545. This unique system offers access to a wide range of physics, from relativistic phenomena through pulsar emission cone studies, to the structure and distribution of interstellar plasma along our line-of-sight. The Parkes Ultra Wide Band Low Frequency (UWL) receiver has opened up the possibility of performing the first-ever studies of its orbital dynamics at high temporal resolution. Our analysis of data obtained to date suggests that the pulsar will precess away from our line-of-sight before 2023. Hence, regular observations are crucial to maximize science outcomes with this source.
DOI: 10.21942/uva.13693702.v3
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13643189.v4
title: Geomorphology model, input datasets and legend symbology files for use in ArcMap

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results: Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.21942/uva.13693702.v4
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.24378/exe.3083
title: Relative efficacy of biological control and cultural management for control of mollusc pests in cool climate vineyards: raw data and analysis code

description: Restrictions on the use of synthetic molluscicides highlight the importance of developing alternative control methods. Nevertheless, biocontrol agents can be costlier and less effective than synthetic counterparts. One mean of increasing the efficacy of population suppression is to combine inundative biological control with habitat management to reduce population growth of target pests. Vineyards in the cool, wet climate of western England can suffer from outbreak densities of mollusc pests that damage expanding shoots, developing grapes and promote the transmission of fungal pathogens. In this study we combined the biocontrol agent Nemaslug - Phasmarhabditis hermaphrodita (Schneider) -with a simple habitat management approach (regular mowing) to suppress mollusc pests in vineyards in South Western England. Two sites were treated with NemaSlug and or mowing in a factorial design in early spring coinciding with bud burst and the start of mollusc growth and feeding. Mowing was effective management and resulted in the reduction of slug and snail populations and significantly less damaged vines. Nemaslug did not reduce slug numbers overall but did reduce bud damage, snail numbers and lowered the proportion of susceptible Deroceras spp in treated plot. However, effect sizes of nematode biocontrol were small, potentially because this product could not be applied to bare soil. Management practice for cool climate vineyards varies considerably from site to site. This study shows the value of simple habitat management for controlling a novel target and emphasizes how consideration of pest biology can lead to effective alternatives.
description: Raw data (comma separated values) and R analysis / graphics code for the paper "Relative efficacy of biological control and cultural management for control of mollusc pests in cool climate vineyards" accepted in Biocontrol Science and Technology
description: The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/124591
DOI: 10.6084/m9.figshare.13700047
title: Resistance profile generated by RGI-CARD

description: Resistance gene profile generated by RGI-CARD tool.
DOI: 10.6084/m9.figshare.13700047.v1
title: Resistance profile generated by RGI-CARD

description: Resistance gene profile generated by RGI-CARD tool.
DOI: 10.21942/uva.13693702.v5
title: Geomorphology model, input datasets and legend symbology files for use in ArcGIS Pro

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results:Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.5517/ccdc.csd.cc216xgn
title: CCDC 1883481: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Lucile Chatelain, Farzaneh Fadaei-Tirani, Ivica Zivkovic, Marinella Mazzanti|2019|Chemical Science|10|3543|doi:10.1039/C8SC05721D
DOI: 10.5517/ccdc.csd.cc216xmt
title: CCDC 1883486: Experimental Crystal Structure Determination

description: Related Article: Luciano Barluzzi, Lucile Chatelain, Farzaneh Fadaei-Tirani, Ivica Zivkovic, Marinella Mazzanti|2019|Chemical Science|10|3543|doi:10.1039/C8SC05721D
DOI: 10.21942/uva.13643189.v5
title: Geomorphology model, input datasets and legend symbology files for use in ArcMap

description: Original model developed in 2016-17 in ArcGIS by Henk Pieter Sterk (www.rfase.org), with minor updates in 2021 by Stacy Shinneman and Henk Pieter Sterk. Model used to generate publication results: Hierarchical geomorphological mapping in mountainous areas Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps 2020, revisions made in 2021.<br>This model creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail. The input dataset needed to create this 'three-tier-legend' is a geomorphological map of Vorarlberg with a Tier 3 category (e.g. 1111, for glacially eroded bedrock). The model then automatically adds Tier 1, Tier 2 and Tier 3 categories based on the Tier 3 code in the 'Geomorph' field. The model replaces the input file with an updated shapefile of the geomorphology of Vorarlberg, now including three tiers of geomorphological features. Python script files and .lyr symbology files are also provided here.
DOI: 10.48370/ofd/1wxoup/x2bdae
title: FMW_Datronia_mollis_Sommerf_Donk_F_0847_4.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/1wxoup/c2dxl7
title: M-0847.jpg

description: :unav
DOI: 10.48370/ofd/1wxoup/vm2bd7
title: FMW_Datronia_mollis_Sommerf_Donk_F_0847_2.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/1wxoup/eojvsr
title: FMW_Datronia_mollis_Sommerf_Donk_F_0847_1.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/1wxoup/pulqs8
title: 847.S3Dm

description: :unav
DOI: 10.48370/ofd/1wxoup
title: Jamczatka wielkopora (Datronia mollis (Sommerf.) Donk), 847

description: Fungarium Makromycetes
DOI: 10.48370/ofd/kl9jum/isgqoy
title: FMW_Datronia_mollis_Sommerf_Donk_F_0996_3.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/kl9jum/5tjzin
title: FMW_Datronia_mollis_Sommerf_Donk_F_0996_2.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/kl9jum/ap0tek
title: m-0996.jpg

description: :unav
DOI: 10.48370/ofd/kl9jum/k22wos
title: FMW_Datronia_mollis_Sommerf_Donk_F_0996_1.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/kl9jum/td6etn
title: 996.S3Dm

description: :unav
DOI: 10.48370/ofd/kl9jum
title: Jamczatka wielkopora (Datronia mollis (Sommerf.) Donk), 996

description: Fungarium Makromycetes
DOI: 10.48370/ofd/7ghmvx/u0j4rf
title: FMW_Datronia_mollis_Sommerf_Donk_F_1950_2.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/7ghmvx/ezxpbc
title: FMW_Datronia_mollis_Sommerf_Donk_F_1950_3.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/7ghmvx/xgfan6
title: FMW_Datronia_mollis_Sommerf_Donk_F_1950_1.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/7ghmvx/mee8zt
title: FMW_Datronia_mollis_Sommerf_Donk_F_1950_4.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/7ghmvx/gub9g7
title: m-1950.jpg

description: :unav
DOI: 10.48370/ofd/7ghmvx/pqeriu
title: 1950.S3Dm

description: :unav
DOI: 10.48370/ofd/7ghmvx
title: Jamczatka wielkopora (Datronia mollis (Sommerf.) Donk), 1950

description: Fungarium Makromycetes
DOI: 10.6084/m9.figshare.14785074
title: Gouge et al_Biomass procurement

description: Dataset associated with the manuscript: "Biomass procurement in boreal forests affected by spruce budworm: Effects on regeneration, costs and carbon balance" by Daniel Gouge, Evelyne Thiffault and Nelson Thiffault, submitted to Canadian Journal of Forest Research<b></b>
DOI: 10.6084/m9.figshare.14785104
title: 2021_EM_Reconstructed_SPECT_Subset_15

description: COLLECTION TITLE:2021_MezzengaEmilio_Collection1<br>CATEGORY:Medical Physics<br>KEYWORDS:SPECT/CT; 3D-OSEM reconstruction; radiomics; lutetium; calibration phantom.<br>ARTICLE (when using these files, please, cite the following article):"E. Mezzenga et al. Reconstructed SPECT images of 177Lu homogeneous cylindrical phantom used for calibration and texture analysis. 2021"<br>FILE NAMES:All the files in this collection follow this nomenclature: 1. "Starting_data_not_reconstructed"”: the folder contains two subfolders were the SPECT and CT phantom acquisitions are separated.In particular, the CT folder is named “CT_data”, and the SPECT one is named “SPECT_data”. In the former folder, the CT sequence of the phantom is named as: “CTxxx.dcm”, with xxx=001, 002,…, 108.In the latter there are three DICOM files related to the SPECT acquisition.In particular, the triple window (TW) file (“TW.dcm”), the lower scatter window (LScW) file (“LScW.dcm”), and the higher scatter window (HScW) file (“HScW.dcm”).The TW file contains all three acquisition windows (i.e. photopeak, high and low scatter), while LScW and HScW contain the lower and higher scatter acquisition window, respectively.2. "Reconstructed_data":it contains two subfolders related to the reconstructed CT datasets (named: “Isotropic_CT”) and reconstructed SPECT acquisition (named: “Isotropic_NM”).Both reconstructions have the same voxel dimension. In the former folder, the CT reconstructed file are named as: “IsotropicCT001_CTyyy.dcm”, with yyy=001, 002,…, 182.The latter folder contains five subfolders, each one referring to the number of subsets used in the SPECT image reconstruction process, and named “zz_SUBSETS”, with zz=05, 10, 15, 20 and 30. Each subfolder contains the SPECT reconstructed images with the same number of subsets, but different number of iterations and are named as “IsotropicNMxxSyyI_DS.dcm”, with xx=05, 10, 15, 20 and 30, and yy=01, 02, 03, 04, 05, 06, 07, 10, 15, 20, 25, 30, 35, 40, 45, 50. <br>MAIN CONTACT:- Emilio Mezzenga, Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori”, Meldola, Italy.Email: emilio.mezzenga@irst.emr.it<br>COPYRIGHT:* Copyright (c) 2021,* IRCCS IRST Meldola, Italy,* All rights reserved.** This material is free; you can redistribute it and/or modify it under the terms of the CC BY 4.0.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
DOI: 10.6084/m9.figshare.14785104.v1
title: 2021_EM_Reconstructed_SPECT_Subset_15

description: COLLECTION TITLE:2021_MezzengaEmilio_Collection1<br>CATEGORY:Medical Physics<br>KEYWORDS:SPECT/CT; 3D-OSEM reconstruction; radiomics; lutetium; calibration phantom.<br>ARTICLE (when using these files, please, cite the following article):"E. Mezzenga et al. Reconstructed SPECT images of 177Lu homogeneous cylindrical phantom used for calibration and texture analysis. 2021"<br>FILE NAMES:All the files in this collection follow this nomenclature: 1. "Starting_data_not_reconstructed"”: the folder contains two subfolders were the SPECT and CT phantom acquisitions are separated.In particular, the CT folder is named “CT_data”, and the SPECT one is named “SPECT_data”. In the former folder, the CT sequence of the phantom is named as: “CTxxx.dcm”, with xxx=001, 002,…, 108.In the latter there are three DICOM files related to the SPECT acquisition.In particular, the triple window (TW) file (“TW.dcm”), the lower scatter window (LScW) file (“LScW.dcm”), and the higher scatter window (HScW) file (“HScW.dcm”).The TW file contains all three acquisition windows (i.e. photopeak, high and low scatter), while LScW and HScW contain the lower and higher scatter acquisition window, respectively.2. "Reconstructed_data":it contains two subfolders related to the reconstructed CT datasets (named: “Isotropic_CT”) and reconstructed SPECT acquisition (named: “Isotropic_NM”).Both reconstructions have the same voxel dimension. In the former folder, the CT reconstructed file are named as: “IsotropicCT001_CTyyy.dcm”, with yyy=001, 002,…, 182.The latter folder contains five subfolders, each one referring to the number of subsets used in the SPECT image reconstruction process, and named “zz_SUBSETS”, with zz=05, 10, 15, 20 and 30. Each subfolder contains the SPECT reconstructed images with the same number of subsets, but different number of iterations and are named as “IsotropicNMxxSyyI_DS.dcm”, with xx=05, 10, 15, 20 and 30, and yy=01, 02, 03, 04, 05, 06, 07, 10, 15, 20, 25, 30, 35, 40, 45, 50. <br>MAIN CONTACT:- Emilio Mezzenga, Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori”, Meldola, Italy.Email: emilio.mezzenga@irst.emr.it<br>COPYRIGHT:* Copyright (c) 2021,* IRCCS IRST Meldola, Italy,* All rights reserved.** This material is free; you can redistribute it and/or modify it under the terms of the CC BY 4.0.* This material is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
DOI: 10.48370/ofd/n7kidf/kkxgwl
title: FMW_Datronia_mollis_Sommerf_Donk_F_1908_2.JPG

description: Eksykat_makrofotografia
DOI: 10.48370/ofd/n7kidf/mbxck0
title: m-1908.jpg

description: :unav