cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10371737
title: Dataset for the Paper: "Mining Architectural Information: A Systematic Mapping Study"

description: The dataset is recorded in a Microsoft Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.
(1) Architecting Activities
contains the 11 architecting activities, mined architectural information, datasets and sources used in the selected studies, and relevant studies.
(2) Automatic Approaches
Contains tasks, automatic approaches, used sources, mined architectural information, and relevant studies.
 (3) Semi-automatic Approaches
Contains tasks, semi-automatic approaches, used sources, mined architectural information, and relevant studies.
 (4) Manual Approaches
Contains tasks, manual approaches, used sources, mined architectural information, and relevant studies.
 (5) General Tools 
Contains general tool names, description of each tool, URL of each tool (if provided), and relevant studies.
(6) Automatic Tools
Contains tasks, automatic tool names, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.
(7) Semi-automatic Tools
Contains tasks, semi-automatic tool, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.
DOI: 10.5281/zenodo.10636664
title: Supplementary Material for the Paper: "Mining Architectural Information: A Systematic Mapping Study"

description: The supplementary material is composed of three files:

1. SelectedStudies.pdfcontains the 104 studies selected in this systematic mapping study.

2. PublicationVenues.pdf

contains the major publication venues of the selected studies.

3. Dataset.xlsx

contains the following Excel sheets:

(1) Architecting Activities

contains the 11 architecting activities, mined architectural information, datasets and sources used in the selected studies, and relevant studies.

(2) Automatic Approaches

contains tasks, automatic approaches, used sources, mined architectural information, and relevant studies.

(3) Semi-automatic Approaches

contains tasks, semi-automatic approaches, used sources, mined architectural information, and relevant studies.

(4) Manual Approaches

contains tasks, manual approaches, used sources, mined architectural information, and relevant studies.

(5) General Tools 

contains general tool names, description of each tool, URL of each tool (if provided), and relevant studies.

(6) Automatic Tools

contains tasks, automatic tool names, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.

(7) Semi-automatic Tools

contains tasks, semi-automatic tool, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.
DOI: 10.5281/zenodo.10354001
title: Dataset for the Paper: "Mining Architectural Information: A Systematic Mapping Study"

description: The dataset is recorded in a Microsoft Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below.
(1) Architecting Activities
contains the 11 architecting activities, mined architectural information, datasets and sources used in the selected studies, and relevant studies.
(2) Automatic Approaches
Contains tasks, automatic approaches, used sources, mined architectural information, and relevant studies.
 (3) Semi-automatic Approaches
Contains tasks, semi-automatic approaches, used sources, mined architectural information, and relevant studies.
 (4) Manual Approaches
Contains tasks, manual approaches, used sources, mined architectural information, and relevant studies.
 (5) General Tools 
Contains general tool names, description of each tool, URL of each tool (if provided), and relevant studies.
(6) Automatic Tools
Contains tasks, automatic tool names, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.
(7) Semi-automatic Tools
Contains tasks, semi-automatic tool, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.
DOI: 10.5281/zenodo.10389595
title: XPS spectra for Li intercalated few-layer MoS2 films

description: Description

Dataset for synchrotron-based x-ray photoelectron (XPS) spectra of Li doped MoS2 nanofilms grown on c-plane sapphire substrate by two techniques: thermally assisted conversion (TAC) and pulsed laser deposition (PLD). Reference spectra for undoped MoS2 were measured on a commercial MoS2 powder sample.

 

 Data formats

The XPS datasets are available in two formats.



VAMAS (ASCII ISO 14976).

Plain text column files: Double-column files (dat) and corresponding metadata files (txt).
description: Sample preparation

MoS2 nanofilms were synthesized using two techniques:



Thermally assisted conversion (TAC) of 1-nm-thick Mo by one-zone sulfurization.

Pulsed laser deposition (PLD).


Lithium was incorporated into the as-prepared MoS2 films by solid-state diffusion during the subsequent sulfurization process, where a part of sulfur powder was replaced by lithium sulfide (Li2S) as a source of Li.
description: Data were acquired at BACH beamline, Elettra synchrotron facility. Access was provided by IOM-CNR in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID101.
DOI: 10.5281/zenodo.10389596
title: XPS spectra for Li intercalated few-layer MoS2 films

description: Description

Dataset for synchrotron-based x-ray photoelectron (XPS) spectra of Li doped MoS2 nanofilms grown on c-plane sapphire substrate by two techniques: thermally assisted conversion (TAC) and pulsed laser deposition (PLD). Reference spectra for undoped MoS2 were measured on a commercial MoS2 powder sample.

 

 Data formats

The XPS datasets are available in two formats.



VAMAS (ASCII ISO 14976).

Plain text column files: Double-column files (dat) and corresponding metadata files (txt).
description: Sample preparation

MoS2 nanofilms were synthesized using two techniques:



Thermally assisted conversion (TAC) of 1-nm-thick Mo by one-zone sulfurization.

Pulsed laser deposition (PLD).


Lithium was incorporated into the as-prepared MoS2 films by solid-state diffusion during the subsequent sulfurization process, where a part of sulfur powder was replaced by lithium sulfide (Li2S) as a source of Li.
description: Data were acquired at BACH beamline, Elettra synchrotron facility. Access was provided by IOM-CNR in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID101.
DOI: 10.5281/zenodo.7547438
title: HTR model for Latin and French Medieval Documentary Manuscripts (12th-15th)

description: This is the best HTR model for documentary Latin and French manuscripts presented in the paper: Sergio Torres Aguilar, Vincent Jolivet. <strong>Handwritten Text Recognition for Documentary Medieval<br> Manuscripts. </strong>2022. https://hal.science/hal-03892163 The model was trained on a charters and registers dataset from the Late-medieval period (12th-15th). The training and evaluation, entailing 1855 pages, 120k lines of text and almost 1M tokens, were conducted using three freely available ground-truth corpora : <strong>The Alcar-HOME database </strong>: https://zenodo.org/record/5600884 <strong>The e-NDP corpus </strong>: https://zenodo.org/record/7575693 <strong>The Himanis project </strong>: https://zenodo.org/record/5535306 This final model operates in a multilingual environment (Latin and Old French) and it is able to recognize several Latin script families (mostly <em>Textualis</em> and <em>Cursiva</em>) in documents produced in ca. 12th - 15th centuries. During the evaluation the models shows an accuracy of <strong>94.1%</strong> on the validation set and a CER (character error ratio) of about <strong>0.12</strong> to <strong>0.17</strong> on four external unseen datasets. A fine-tuning exercise using 10 ground-truth pages can raise these results to a CER between <strong>0.06</strong> to <strong>0.10</strong> respectively.
DOI: 10.5281/zenodo.10803068
title: Receiver functions in eastern Tibet

description: This data set consist of P-wave receiver functions (PRFs) collected from the seismic stations in eastern Tibet that are used in the study of Zhang et al. (2024). Recordings are from the ZJU-Tibet seismological array between June 2021 and July 2022 operated by Zhejiang University. The picked arrivals of Moho P-to-S converted phases on PRFs are also provided. Please note that this data is provided for reproduction purposes only.
DOI: 10.5281/zenodo.10803069
title: Receiver functions in eastern Tibet

description: This data set consist of P-wave receiver functions (PRFs) collected from the seismic stations in eastern Tibet that are used in the study of Zhang et al. (2024). Recordings are from the ZJU-Tibet seismological array between June 2021 and July 2022 operated by Zhejiang University. The picked arrivals of Moho P-to-S converted phases on PRFs are also provided. Please note that this data is provided for reproduction purposes only.
DOI: 10.5281/zenodo.10498926
title: DOES OBESITY DRIVE THE DEVELOPMENT OF POLYCYSTIC OVARY SYNDROME? AN EPIDEMIOLOGIC STUDY (SUPPLEMENTARY TABLES)

description: These are the Supplementary tables for manuscript entitled 'Does Obesity Drive the Development of Polycystic Ovary Syndrome? An Epidemiologic Study', denoting: Table 1: the search strategy for the systematic review; Table 2: the NOS quality scoring of included studies; and Table 3: the association between mean BMI or obesity prevalence and PCOS prevalence by PCOS diagnostic criteria.
DOI: 10.5281/zenodo.10790723
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2011_2015)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.

This data repository is linked to https://zenodo.org/records/10815170
DOI: 10.5281/zenodo.10790724
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2011_2015)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.

This data repository is linked to https://zenodo.org/records/10815170
DOI: 10.5281/zenodo.10815623
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. 

This data repository is linked to https://zenodo.org/records/10815170
DOI: 10.5281/zenodo.10790682
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. 

This data repository is linked to https://zenodo.org/records/10815170
DOI: 10.5281/zenodo.10815657
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. 

This data repository is linked to https://zenodo.org/records/10815170
DOI: 10.5281/zenodo.10815658
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)

description: Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. 

This data repository is linked to https://zenodo.org/records/10815170