cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/htvrjyd3tk.1
title: Data set for: On the role of informational privacy in connected vehicles: A privacy-aware acceptance modelling approach for connected vehicular services

description: This data set contains the questionnaire raw data underlying the journal paper "On the role of informational privacy in connected vehicles: A privacy-aware acceptance modelling approach for connected vehicular services". It comprises the responses of 116 participants who took part in a driving simulator study. The data were assessed with an online questionnaire after the participants had interacted with a click-dummy of a connected parking service.
DOI: 10.7910/dvn/uul8wj/fssklx
title: 003_dataset_PROMIS-Mali_Followup_ChildMonth.tab

description: :unav
DOI: 10.7910/dvn/uul8wj/u34mww
title: 001_questionnaire_PROMIS-Mali_Follow-up.pdf

description: :unav
DOI: 10.7910/dvn/uul8wj/y2nl60
title: 002_codebook_PROMIS-Mali_Followup_ChildMonth.pdf

description: :unav
DOI: 10.17632/tybx7gmw5s.3
title: Dataset on Analysis of Users and Nonusers (Acceptance of Public Cloud Storage Services in South Korea)

description: This is raw data for empirical study, “Acceptance of Public Cloud Storage Services in South Korea: A multi-Group Analysis”. This study amied to explore the differences and similarities between users and nonusers in the context of public cloud storage services of South Korea. 526 samples in South Korea were collected through an online survey. Data (users = 210; nonusers = 187) were analyzed using structural equation modeling. SmartPLS 3.0 tool was used for data analysis with a proposed research model. The data is uniquely characterized by focusing on individual-level voluntary technology acceptance within a non-organizational context. The data set is available for other researchers. There are some tips useful. ○ The file 2 is raw data about previous studies regarding cloud computing adoption at individual level. This raw data is summarized in Table 1 of this research paper, depending on study focus and model used. ○ To conduct exprimental repetability, you just follow "steps to reproduce" mentioned the below.
DOI: 10.17632/tybx7gmw5s.4
title: Dataset on Analysis of Users and Nonusers (Acceptance of Public Cloud Storage Services in South Korea)

description: This is raw data for empirical study, “Acceptance of Public Cloud Storage Services in South Korea: A multi-Group Analysis”. This study amied to explore the differences and similarities between users and nonusers in the context of public cloud storage services of South Korea. 526 samples in South Korea were collected through an online survey. Data (users = 210; nonusers = 187) were analyzed using structural equation modeling. SmartPLS 3.0 tool was used for data analysis with a proposed research model. The data is uniquely characterized by focusing on individual-level voluntary technology acceptance within a non-organizational context. The data set is available for other researchers. There are some tips useful. ○ The file 2 is raw data about previous studies regarding cloud computing adoption at individual level. This raw data is summarized in Table 1 of this research paper, depending on study focus and model used. ○ To conduct exprimental repetability, you just follow "steps to reproduce" mentioned the below.
DOI: 10.17632/tybx7gmw5s.5
title: Dataset on Analysis of Users and Nonusers (Acceptance of Public Cloud Storage Services in South Korea)

description: This is raw data for empirical study, “Acceptance of Public Cloud Storage Services in South Korea: A multi-Group Analysis”. This study amied to explore the differences and similarities between users and nonusers in the context of public cloud storage services of South Korea. 526 samples in South Korea were collected through an online survey. Data (users = 210; nonusers = 187) were analyzed using structural equation modeling. SmartPLS 3.0 tool was used for data analysis with a proposed research model. The data is uniquely characterized by focusing on individual-level voluntary technology acceptance within a non-organizational context. The data set is available for other researchers. There are some tips useful. ○ The file 2 is raw data about previous studies regarding cloud computing adoption at individual level. This raw data is summarized in Table 1 of this research paper, depending on study focus and model used. ○ To conduct exprimental repetability, you just follow "steps to reproduce" mentioned the below.
DOI: 10.7910/dvn/9wsfar/2wespi
title: 002_codebook_PROMIS-Mali_Enrollment.pdf

description: :unav
DOI: 10.7910/dvn/9wsfar/dwiepw
title: 001_questionnaire_PROMIS-Mali_Longitudinal_enrollmentFollow-up.pdf

description: :unav
DOI: 10.7910/dvn/9wsfar/fbfd6n
title: 003_dataset_PROMIS-Mali_Enrollment.tab

description: :unav
DOI: 10.7910/dvn/nkh8ju/dhcxis
title: 001_questionnaire_PROMIS-Mali_Endline.pdf

description: :unav
DOI: 10.7910/dvn/nkh8ju/eeklev
title: 003_dataset_PROMIS-Mali_Endline_childPlatform.tab

description: :unav
DOI: 10.7910/dvn/nkh8ju/kirlkc
title: 002_codebook_PROMIS-Mali_Endline_childPlatform.pdf

description: :unav
DOI: 10.21227/9fb7-wd10
title: NLOS Identification and Mitigation Using Low-Cost UWB Devices

description: Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.
DOI: 10.7910/dvn/myabwr/fofgtp
title: 002_codebook_PROMIS-Mali_Endline_child.pdf

description: :unav
DOI: 10.7910/dvn/myabwr/thedjr
title: 003_dataset_PROMIS-Mali_Endline_child.tab

description: :unav
DOI: 10.7910/dvn/myabwr/xqthnv
title: 001_questionnaire_PROMIS-Mali_Endline.pdf

description: :unav
DOI: 10.7910/dvn/x4y2xb/bcdxqy
title: 001_questionnaire_PROMIS-Mali_Baseline.pdf

description: :unav
DOI: 10.7910/dvn/x4y2xb/po5hrb
title: 002_codebook_PROMIS-Mali_Baseline_child.pdf

description: :unav
DOI: 10.7910/dvn/x4y2xb/vizkig
title: 003_dataset_PROMIS-Mali_Baseline_child.tab

description: :unav
DOI: 10.5281/zenodo.3364032
title: MD simulation data: Intrinsic disorder is a conserved feature of hepatitis C virus E2 glycoprotein

description: <strong>Background</strong> Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. The GLYCAM_06j-1 force field was used for the simulations of the glycosylated E2. All software is available from http://ambermd.org/. <strong>Contents</strong> There are three tarball (<strong>.tar.gz</strong>) files, one for each of the HCV strain investigated. The contents of each tarball is as follows: 1. a source PDB (<strong>.pdb</strong>) file 2. <strong>leap.scr</strong> - a script used to create the .prmtop and .inpcrd files 3. Two AMBER parameter/topology (<strong>.prmtop</strong>) (one with hydrogen mass repartitioning) and an AMBER coordinate (<strong>.inpcrd</strong>) file 4. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model 5. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps 6. File <strong>parmed.txt</strong> used to repartition the hydrogen atom mass (for the unglycosylated simulations) 7. control file <strong>prod.ctl</strong> used for the production run 8. Executable <strong>run_prod</strong> that was used to perform the production run 9. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs 10. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs. 10. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.
description: This work was supported by the Wellcome Trust (grant 109162/Z/15/Z to LS and grant 107653/Z/15/Z to JG).
DOI: 10.5281/zenodo.3364033
title: MD simulation data: Intrinsic disorder is a conserved feature of hepatitis C virus E2 glycoprotein

description: <strong>Background</strong> Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. The GLYCAM_06j-1 force field was used for the simulations of the glycosylated E2. All software is available from http://ambermd.org/. <strong>Contents</strong> There are three tarball (<strong>.tar.gz</strong>) files, one for each of the HCV strain investigated. The contents of each tarball is as follows: 1. a source PDB (<strong>.pdb</strong>) file 2. <strong>leap.scr</strong> - a script used to create the .prmtop and .inpcrd files 3. Two AMBER parameter/topology (<strong>.prmtop</strong>) (one with hydrogen mass repartitioning) and an AMBER coordinate (<strong>.inpcrd</strong>) file 4. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model 5. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps 6. File <strong>parmed.txt</strong> used to repartition the hydrogen atom mass (for the unglycosylated simulations) 7. control file <strong>prod.ctl</strong> used for the production run 8. Executable <strong>run_prod</strong> that was used to perform the production run 9. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs 10. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs. 10. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.
description: This work was supported by the Wellcome Trust (grant 109162/Z/15/Z to LS and grant 107653/Z/15/Z to JG).
DOI: 10.5519/qd.f2sa6wcv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (39 records)
DOI: 10.5281/zenodo.3364071
title: CIViCmine

description: This describes the output files for the CIViCmine project. These files are loaded directly by the CIViCmine viewer. The code for this viewer is available in the CIViCmine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>civicmine_collated.tsv</strong> if you just want the list of cancer biomarkers. If you want the supporting sentences, look at <strong>civicmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>civicmine_unfiltered.tsv</strong>. <strong>civicmine_collated.tsv:</strong> This contains the cancer biomarkers with citation counts supporting them. It contains the normalized cancer and gene names along with IDs for HUGO, Entrez Gene and the Disease Ontology. <strong>civicmine_sentences.tsv:</strong> This contains the supporting sentences for the cancer biomarker in the collated file. Each row is a single supporting sentence for one cancer biomarker. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the cancer biomarker extracted. <strong>civicmine_unfiltered.tsv:</strong> This is the raw output of the applyModelsToSentences.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific cancer biomarker extracted (with HUGO, Entrez Gene and Disease Ontology IDs). This file is further processed to create the other two.
DOI: 10.26186/5d4cb60d15392
title: Beagle Marine Park bathymetry data 2018

description: This resource includes bathymetry data for Beagle Marine Park (Bass Strait) collected by Geoscience Australia (GA) and the Institute for Marine & Antarctic Studies (University of Tasmania; UTAS) during the period 17 - 26 June 2018 on the RV Bluefin. The survey was undertaken as a collaborative project funded through the National Environmental Science Program Marine Biodiversity Hub, with co-investment by GA and UTAS. The purpose of the project was to build baseline information for benthic habitats in the Beagle Marine Park that will support ongoing environmental monitoring within the South-east Marine Park Network as part of the 10-year management plan (2013-2023). Data acquisition for the project was completed during three separate voyages: Phase 1 - Seabed mapping by multibeam sonar; Phase 2 - Seabed imagery acquisition by Autonomous Underwater Vehicle, and sediment sampling; Phase 3 - Survey of demersal fish communities using Baited Remote Underwater Video (BRUVs). This dataset from Phase 1 comprises 11 bathymetry grids derived from multibeam sonar data gridded at 1 m spatial resolution, covering a combined area of 364 km2. A detailed report on the survey is provided in: Falster, G., Monk, J., Carroll, A., Siwabessy, J., Deane, A., Picard, K., Dando, N., Hulls, J., Nichol, S., Barrett, N. 2019. Australian Marine Park Baseline and Monitoring Survey: Post Survey Report, Beagle Marine Park South-east Marine Park Network. Report to the National Environmental Science Program, Marine Biodiversity Hub.
DOI: 10.5281/zenodo.3364128
title: DWD European Weather 2

description: opendata.dwd.de - OpenData by Deutscher Wetter Dienst Conditions: https://www.dwd.de/EN/service/copyright/copyright_node.html dates and times are UTC.
DOI: 10.25345/c5fd2f
title: MassIVE MSV000084186 - Deep Neural Networks for Classification of LC-MS Spectral Peaks
title: Files associated with the manuscript "Deep Neural Networks for Classification of LC-MS Spectral Peaks"
DOI: 10.25739/7shy-wr69
title: ParFlow CONUS1.0 Groundwater Depletions
title: Data to support Condon and Maxwell (2019)

description: This dataset contains the model simulations results behind all of the figures in Condon and Maxwell, 2019. The ReadMe file describes all of the files and how they were combined to create the figures in the paper. All of the figures can be viewed using the free software program QGIS and the qgs file in the main directory of this repo. Alternatively, all underlying tif, shape and csv files are also included here.
description: If you are using this dataset please refer to and cite: Condon, L., and R. Maxwell (2019), Simulating the sensitivity of evapotranspiration and streamflow to large-scale groundwater depletion, Sci Adv, 5(6), eaav4574, doi:10.1126/sciadv.aav4574.
DOI: 10.7910/dvn/ickbvs/9jytjs
title: Dataset-menopause.obesity.tab

description: :unav
DOI: 10.21227/ccss-n573
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.21227/gck5-mt39
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation and testing of algorithm robustness under a wide variety of conditions. It includes multiple sensor modalities and covers various environmental conditions as well as diverse changes to illumination, scene structure, and pedestrian/vehicle traffic volumes. The dataset also includes a semantic segmentation dataset with different illumination conditions and camera perspectives, and a ‘Road’ class image dataset which is automatically generated using lidar. To facilitate the dataset use by researchers and practitioners, we also provide a set of development tools for the analysis and visualisation of the dataset.
DOI: 10.5281/zenodo.3364139
title: CMS 2011A Simulation | Pythia 6 QCD 600-800 | pT &gt; 375 GeV | MOD HDF5 Format

description: Simulated QCD jets from the Simulated QCD 600-800 Dataset of the CMS 2011 Open Data reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have \(p_T^\text{jet}&gt;375\) GeV, where \(p_T^\text{jet}\) includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files. For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have \(|\eta^\text{jet}|&lt;1.9\) to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets. The supported method for downloading, reading, and using this dataset is through the EnergyFlow Python package, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please submit an issue on GitHub. For reference, the other corresponding datasets of simulated jets available on Zenodo are: SIM/GEN QCD Jets 170-300 GeV SIM/GEN QCD Jets 300-470 GeV SIM/GEN QCD Jets 470-600 GeV SIM/GEN QCD Jets 600-800 GeV SIM/GEN QCD Jets 800-1000 GeV SIM/GEN QCD Jets 1000-1400 GeV SIM/GEN QCD Jets 1400-1800 GeV SIM/GEN QCD Jets 1800-\(\infty\) GeV There is an associated dataset of jets recorded by the CMS detector available on Zenodo: CMS 2011A Jets, pT &gt; 375 GeV Changes: v1 - Uploaded missing file.
description: {"references": ["P. T. Komiske, E. M. Metodiev, EnergyFlow, https://energyflow.network."]}
DOI: 10.5061/dryad.mq385f0/1
title: Gee & Reisz_Supplemental Figure 1

description: Supplemental Figure 1. Results of phylogenetic analysis of Nanobamus macrorhinus in the matrix of Schoch (2019) (35 taxa, 108 characters); tree visually pruned to focus on Amphibamiforms. (1) majority rule consensus of the first 107-character (removal of character 17) sampling permutation; (2) strict consensus tree of the same permutation with bootstrap support and Bremer decay index (the latter in parentheses); (3) majority rule consensus of the second 107-character sampling (removal of character 30) permutation; (4) strict consensus tree of the same permutation with bootstrap support and Bremer decay index.
DOI: 10.21227/6ffr-kr31
title: Correlation matrix of the items of the Six Sigma CSF survey in HEIs

description: The correlation matrix and the survey items are provided for the design and validation of an instrument to evaluate the implementation of six sigma critical success factors during the realization of improvement projects in higher education institutions.