cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/hjrq-q088
title: CONFLICT SCENARIOS IN SMART HOMES

description: This is a repository of 104 smart home conflict scenarios, which were designated as conflict by actual human users. In other words, humans consider the scenarios below to be conflicts in a smart home environment. To see how to use this repository, and how the repository was collected, please read the following paper:Fereshteh Jadidi Miandashti, Mohammad Izadi, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi, “An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes”, IEEE Trans. on Human-Machine Systems, 2020.Please note that the scenarios in our repository help identify what is conflict from the users’ perspective. They do not tell us how to handle those conflicts. In other words, the cases below are about conflict detection, not conflict resolution.This repository can help designers of Smart Homes understand what rules (system behaviors) were perceived by users as conflict, so they can design appropriate conflict detection and conflict resolution modules for their Smart Home system. For example, in one scenario, the user might open the window to let in the fresh air, but the user has forgotten that his pet bird is roaming free in the room and might escape from the window! The system knows the latter fact and closes the window immediately. Our Smart Home users considered this scenario to be a conflict. A designer might come across a scenario similar to this. In this case, the designer can use our repository to (1) understand that his/her scenario, which is very similar to one of the scenarios in our repository, is indeed considered a conflict by smart home users; hence the designer can investigate it subjectively or with some well-known techniques, and (2) design appropriate conflict detection and resolution rules for it in order to increase user satisfaction. Of course, our repository does not contain all possible conflict scenarios in a smart home, but it gives a fairly good starting point.
DOI: 10.5281/zenodo.3526140
title: Mapping Template

description: Language-Independent Mapping Template for Knowledge Graph Creation
DOI: 10.5281/zenodo.3526141
title: Mapping Template

description: Language-Independent Mapping Template for Knowledge Graph Creation
DOI: 10.5281/zenodo.3526119
title: CESM F1850 input data for running CESM in docker container

description: This dataset contains input data for running CESM 2.1.1 with F1850 compset and resolution f09_g17. <pre>create_newcase --case /home/cesm/cases/B1850 --compset B1850 \ --res f09_g17 --machine espresso --run-unsupported && \ cd /home/cesm/cases/B1850 </pre> The case uses cesm from bioconda.
DOI: 10.5281/zenodo.3526120
title: CESM F1850 input data for running CESM in docker container

description: This dataset contains input data for running CESM 2.1.1 with F1850 compset and resolution f09_g17. <pre>create_newcase --case /home/cesm/cases/B1850 --compset B1850 \ --res f09_g17 --machine espresso --run-unsupported && \ cd /home/cesm/cases/B1850 </pre> The case uses cesm from bioconda.
DOI: 10.5281/zenodo.3526056
title: Data sets for " The nature of mean-field generation in three classes of optimal dynamos"

description: <pre>The tar archive Optimal_Dynamos.tar contains and index.html file with links to the run directories for each figure and the two tables of the paper "The nature of mean-field generation in three classes of optimal dynamos" by Axel Brandenburg (Nordita) and Long Chen (Durham University) with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/long_chen. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/Optimal_Dynamos/</pre>
description: For questions and additional requests, please email brandenb@nordita.org
DOI: 10.5281/zenodo.3526055
title: Data sets for " The nature of mean-field generation in three classes of optimal dynamos"

description: <pre>The tar archive Optimal_Dynamos.tar contains and index.html file with links to the run directories for each figure and the two tables of the paper "The nature of mean-field generation in three classes of optimal dynamos" by Axel Brandenburg (Nordita) and Long Chen (Durham University) with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/long_chen. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/Optimal_Dynamos/</pre>
description: For questions and additional requests, please email brandenb@nordita.org
DOI: 10.5281/zenodo.3526156
title: The Impact of Hurricane Harvey on Pavement Structures in the South East Texas and South West Louisiana

description: Corresponding data set for Tran-SET Project No. 18PUTA02. Abstract of the final report is stated below for reference: "This study developed a methodology to estimate the damage caused by flooding, such that caused by Hurricane Harvey, on a road or street network. The flooded street or pavement sections are identified using GIS flood maps with street GIS maps used for pavement management systems (PMS) by cities or state authorities. Then the damage caused by flooding directly through the increase moisture in foundation layers or indirectly due to the increase heavy traffic during the relief effort is estimated. An example Excel macro was created to illustrate the estimation process. The methodology estimates the increase in rehabilitation costs since the flooding imposes that many rehabilitation works must be done earlier than anticipated before the flooding. The methodology also estimated the increase in fuel consumption caused by the increased in pavement roughness if the rehabilitation works are done when anticipated before the flooding. The methodology and the Excel macro can also be used to identify the pavement structures with better resilience to the flooding by grouping sections based on the flooding duration (no flooding, single and multiple day flooding) and on design features such as pavement type, functional class, age or time from the most recent resurfacing or reconstruction, subgrade soil type, traffic volume, layer thickness."
description: Tran-SET Project No. 18PUTA02
DOI: 10.5281/zenodo.3526157
title: The Impact of Hurricane Harvey on Pavement Structures in the South East Texas and South West Louisiana

description: Corresponding data set for Tran-SET Project No. 18PUTA02. Abstract of the final report is stated below for reference: "This study developed a methodology to estimate the damage caused by flooding, such that caused by Hurricane Harvey, on a road or street network. The flooded street or pavement sections are identified using GIS flood maps with street GIS maps used for pavement management systems (PMS) by cities or state authorities. Then the damage caused by flooding directly through the increase moisture in foundation layers or indirectly due to the increase heavy traffic during the relief effort is estimated. An example Excel macro was created to illustrate the estimation process. The methodology estimates the increase in rehabilitation costs since the flooding imposes that many rehabilitation works must be done earlier than anticipated before the flooding. The methodology also estimated the increase in fuel consumption caused by the increased in pavement roughness if the rehabilitation works are done when anticipated before the flooding. The methodology and the Excel macro can also be used to identify the pavement structures with better resilience to the flooding by grouping sections based on the flooding duration (no flooding, single and multiple day flooding) and on design features such as pavement type, functional class, age or time from the most recent resurfacing or reconstruction, subgrade soil type, traffic volume, layer thickness."
description: Tran-SET Project No. 18PUTA02
DOI: 10.17632/zhpzx7f39p.1
title: Data for: The deduction of the age of the Dali Man fossil from the regional geomorphic-sedimentary evolution

description: The data is about the Dali Man site in Weihe Basin, northwester China.
DOI: 10.5281/zenodo.3526192
title: CESM FKESSLER input data for running CESM in docker container

description: This dataset contains input data for running CESM 2.1.1 with FKESSLER compset and resolution T31_g37. <pre>create_newcase --case /home/cesm/cases/fkessler --compset FKESSLER \ --res T31_g37 --compset FKESSLER --machine espresso --run-unsupported </pre>
DOI: 10.5281/zenodo.3526193
title: CESM FKESSLER input data for running CESM in docker container

description: This dataset contains input data for running CESM 2.1.1 with FKESSLER compset and resolution T31_g37. <pre>create_newcase --case /home/cesm/cases/fkessler --compset FKESSLER \ --res T31_g37 --compset FKESSLER --machine espresso --run-unsupported </pre>
DOI: 10.14272/ocswtcsejphwqb-uhfffaoysa-n.1
title: methyl 2-[bis(dimethylamino)methyleneamino]-5-(dimethylamino)benzoate
title: InChI=1S/C15H24N4O2/c1-17(2)11-8-9-13(12(10-11)14(20)21-7)16-15(18(3)4)19(5)6/h8-10H,1-7H3

description: This dataset contains experimental data from a sample (methyl 2-[bis(dimethylamino)methylideneamino]-5-(dimethylamino)benzoate, InChI=1S/C15H24N4O2/c1-17(2)11-8-9-13(12(10-11)14(20)21-7)16-15(18(3)4)19(5)6/h8-10H,1-7H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS)
DOI: 10.14272/ocswtcsejphwqb-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (methyl 2-[bis(dimethylamino)methylideneamino]-5-(dimethylamino)benzoate)
title: InChI=1S/C15H24N4O2/c1-17(2)11-8-9-13(12(10-11)14(20)21-7)16-15(18(3)4)19(5)6/h8-10H,1-7H3

description: dataset for mass spectrometry (MS) HR-MS EI: m/z (%): 292.3 [C15H24N4O2]+ (100), 277.3 [C14H21N4O2]+ (35), 261.3 [C14H21N4O]+ (8), 248.2 [C13H18N3O2]+ (13), 233.2 [C13H21N4]+ (67), 189.2 [C11H15N3]+ (44), 175.2 [C10H13N3]+ (30), 161.2 [C9H9N2O]+ (17), 147.2 [C9H9NO]+ (20), 133.2 [C8H9N2]+ (29), 116.7 [C5H14N3]+ (9), 100.2 [C5H12N2]+ (12).
DOI: 10.14272/ocswtcsejphwqb-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (methyl 2-[bis(dimethylamino)methylideneamino]-5-(dimethylamino)benzoate)
title: InChI=1S/C15H24N4O2/c1-17(2)11-8-9-13(12(10-11)14(20)21-7)16-15(18(3)4)19(5)6/h8-10H,1-7H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6, ppm) δ = 6.89–6.85 (m, 2H, HAr), 6.64–6.59 (m, 1H, HAr), 3.67 (s, 3H, OCH3), 2.80 (s, 6H, N(CH3)2), 2.54 (s, 12H, CH3).
DOI: 10.5281/zenodo.3526180
title: Inputdata for CESM2.1.1 B1850 f19_g17

description: Input data needed for use with a docker container Compset longname is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD ATM_GRID is 0.9x1.25<br> LND_GRID is 0.9x1.25<br> ICE_GRID is gx1v7<br> OCN_GRID is gx1v7 ROF_GRID is r05<br> GLC_GRID is gland4 Downloaded on 2019-11-02
DOI: 10.5281/zenodo.3526181
title: Inputdata for CESM2.1.1 B1850 f19_g17

description: Input data needed for use with a docker container Compset longname is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD ATM_GRID is 0.9x1.25<br> LND_GRID is 0.9x1.25<br> ICE_GRID is gx1v7<br> OCN_GRID is gx1v7 ROF_GRID is r05<br> GLC_GRID is gland4 Downloaded on 2019-11-02
DOI: 10.14272/ocswtcsejphwqb-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (methyl 2-[bis(dimethylamino)methylideneamino]-5-(dimethylamino)benzoate)
title: InChI=1S/C15H24N4O2/c1-17(2)11-8-9-13(12(10-11)14(20)21-7)16-15(18(3)4)19(5)6/h8-10H,1-7H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, DMSO-d6, ppm) δ = 168.2, 158.5, 143.9, 143.2, 124.7, 121.7, 118.5, 113.6, 51.1, 41.0 (2C), 38.7 (4C).
DOI: 10.25919/5dbe173bb1fb6
title: Parkes observations for project P958 semester 2019OCTS_13

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.5281/zenodo.3526371
title: FT6 Anonymous (7) 8-key tenoroon: measurements, photos

description: Dataset of FT6 Anonymous (7) 8-key tenoroon containing detailed external and internal measurements, photos.
DOI: 10.5281/zenodo.3526420
title: FT6 Anonymous (7) 8-key tenoroon: measurements, photos

description: Dataset of FT6 Anonymous (7) 8-key tenoroon containing detailed external and internal measurements, photos.
DOI: 10.6084/m9.figshare.22116323
title: Electronic supplementary information

description: Electronic supplementary information and supporting documents for network pharmacology.
DOI: 10.6084/m9.figshare.22116323.v2
title: Electronic supplementary information

description: Electronic supplementary information and supporting documents for network pharmacology.
DOI: 10.6084/m9.figshare.22116323.v1
title: Electronic supplementary information

description: Electronic supplementary information and supporting documents for network pharmacology.
DOI: 10.34740/kaggle/dsv/5093874
title: 2021 World Population (updated daily)
DOI: 10.5283/epub.53685
title: gKibra adult 2g
DOI: 10.6084/m9.figshare.21959990.v1
title: Supplementary data for MDPI 2023

description: <strong>Study on Seismicity of the Arxan Volcanic Group in Northeast China with a Dense Array</strong>
DOI: 10.6084/m9.figshare.21959990
title: Candidate 2

description: <strong>Study on Seismicity of the Arxan Volcanic Group in Northeast China with a Dense Array</strong>
DOI: 10.34740/kaggle/dsv/5094017
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.21959930
title: Candidate 3

description: A data for MDPI (2023).
DOI: 10.6084/m9.figshare.21959930.v1
title: 2023_MDPI.data

description: A data for MDPI (2023).
DOI: 10.6084/m9.figshare.22202779.v1
title: Tweet Emotion Properties

description: Properties (extracted features) for the tweet eval dataset
DOI: 10.48425/m4khli2x
title: V_rodents_cameratraps_annual_metadata_intensive_quadrats

description: Annual metadata of small mammal cameras on the Varanger peninsula, in heath and meadow habitats. The dataset contains information recorded during the annual camera check, such as the serial number of the camera, the condition of the camera and other relevant comments. The dataset includes four different types of files and all data files are saved as ;-separated txt-files: * One data file per year with metadata from the annual camera check (_YEAR.txt) * One coordinate file with coordinates of all sites (_coordinates.txt) * One auxiliary file with information about which sites are included in the study design (_aux.txt) * One readme file with additional information (_readme.pdf) Image metadata is available in the dataset 'V_rodents_cameratraps_image_metadata_heath_meadow'. Classification of all images is available in the datasets 'V_rodents_cameratraps_image_classification_heath_meadow'.
DOI: 10.5281/zenodo.7691910
title: Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application - DATASET

description: Data to support the findings reported in the journal paper Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. <em>Life</em>, <em>13</em>(3), 598. Caratterizzazione_y3b_pulito.xls: Subject ID Age at the beginning of the study Handedness Sex Score_BVSCO_pulito.xls: Subject ID columns B-D: graphemes in the three tests of the BVSCO-2 columns E-G: z-score computed on normative data for the three tests of the BVSCO-2 median z-score: computed on columns E-G at risk: if median z-score is under the -2 threshold (0=not at risk, 1=at risk) executions.mat: a matrix for each time point (y1, y2a, y2b, y3a, y3b) and game (copa_q = copy square, copia_seq = copy sequence, tunnel_q = tunnel square, tunnel_ele = tunnel word). first column of all the cell arrays: subject ID second column for the copy games: three sub-matrices with an execution for each level (spontaneous, big, small) columns 2:N for the tunnel games: an average execution for each index of difficulty for each execution, the columns of the sub-matrices are normalized time, normalized x, normalized y and pressure Copy games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to 2: First column: array with school and child code Second column: executions For the two copy games, the data has a dimension of 3 x 1, where 3 are the modalities (spontaneous, big, small) and 1 is the data matrix. By accessing the data matrix we have four variables, i.e., normalized time, normalized x, normalized y and pressure. Tunnel games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to index of difficulty number +1: First column: array with school and child code From the second column onwards: executions, each column corresponds to the "typical" execution (the average) of an ID (5 for square tunnel and 8 for ele tunnel). Sub-matrices contain the data matrix with its four variables. i.e., normalized time, normalized x, normalized y and pressure.
DOI: 10.5281/zenodo.7691909
title: Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application - DATASET

description: Data to support the findings reported in the journal paper Lomurno, E., Dui, L. G., Gatto, M., Bollettino, M., Matteucci, M., & Ferrante, S. (2023). Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application. <em>Life</em>, <em>13</em>(3), 598. Caratterizzazione_y3b_pulito.xls: Subject ID Age at the beginning of the study Handedness Sex Score_BVSCO_pulito.xls: Subject ID columns B-D: graphemes in the three tests of the BVSCO-2 columns E-G: z-score computed on normative data for the three tests of the BVSCO-2 median z-score: computed on columns E-G at risk: if median z-score is under the -2 threshold (0=not at risk, 1=at risk) executions.mat: a matrix for each time point (y1, y2a, y2b, y3a, y3b) and game (copa_q = copy square, copia_seq = copy sequence, tunnel_q = tunnel square, tunnel_ele = tunnel word). first column of all the cell arrays: subject ID second column for the copy games: three sub-matrices with an execution for each level (spontaneous, big, small) columns 2:N for the tunnel games: an average execution for each index of difficulty for each execution, the columns of the sub-matrices are normalized time, normalized x, normalized y and pressure Copy games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to 2: First column: array with school and child code Second column: executions For the two copy games, the data has a dimension of 3 x 1, where 3 are the modalities (spontaneous, big, small) and 1 is the data matrix. By accessing the data matrix we have four variables, i.e., normalized time, normalized x, normalized y and pressure. Tunnel games A matrix for each game and each time point with a number of rows equal to the number of children and a number of columns equal to index of difficulty number +1: First column: array with school and child code From the second column onwards: executions, each column corresponds to the "typical" execution (the average) of an ID (5 for square tunnel and 8 for ele tunnel). Sub-matrices contain the data matrix with its four variables. i.e., normalized time, normalized x, normalized y and pressure.
DOI: 10.26050/wdcc/icon-nwp_mh_pd
title: ICON-NWP simulations of the mid-Holocene West African Monsoon using parameterized and explicit convection and mid-Holocene land surface

description: Project: Simulations of land-atmosphere feedback during the mid-Holocene West African Monsoon with resolved deep convection - Global climate models have difficulties to simulate the northward extension of the monsoonal precipitation over north Africa during the mid-Holocene as revealed by proxy data. A common feature of these models is that they usually operate on too coarse grids to explicitly resolve convection, but convection is the most essential mechanism leading to precipitation in the West African monsoon region. Here, we investigate how the representation of tropical deep convection in the ICON climate model affects the meridional distribution of monsoonal precipitation during the mid-Holocene, by comparing regional simulations of the summer monsoon season (July to September, JAS) with parameterized (40km-P) and resolved deep convection (5km-E). Simulations with parameterized convection produce generally more rain and extend precipitation further north than simulations with resolved deep convection. The most striking difference between these simulations is the difference in the probability distribution function of precipitation and its resulting interactions with the land surface. The convective parametrization produces light and large-scale precipitation, keeping the soils moist and supporting the development of convection. In contrast, less frequent but locally intense precipitation events lead to high amounts of runoff in simulations with resolved deep convection. Hence, the stronger runoff inhibits the moistening of the soil during the monsoon season and limits the amount of water available to evaporation. This is true for simulations with present-day land surface cover and for simulations where we prescribe a higher mid-Holocene-like vegetation cover. However, in experiments with the same constant soil moisture field, precipitation expands equally far north in resolved deep convection and parameterized convection simulations. This highlights the importance of the type of rainfall in modulating land-atmosphere feedbacks, instead of only considering the amount of rainfall. Summary: The experiment aims to investigate how the representation of convection influences the West African Monsoon during the mid-Holocene. Atmospheric and SST input data originate from the MPI-ESM Holocene simulations reflecting Holocene condition. External Parameters (surface condition) were adjusted to reflect mid-Holocene vegetation conditions. We prescribe an idealized, denser vegetation cover based on the simulated desert fraction (1-vegetation fraction) of the transient mid-Holocene MPI-ESM simulations (Dallmeyer et al., 2020). We use the ICON (ICOsahedral Nonhydrostatic) model framework version 2.5.0 (see Zängl et al. (2014) for more details). The provided data covers one simulation from June to October (JJASO) for the year 7024 before present (BP) with the year 2000 as the reference year.