cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10790707
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)

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for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets.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.10815629
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)

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.10815648
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)

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.10815649
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)

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.10810060
title: BIBB Berufearchiv

description: Background

The Federal Institute for Vocational Education and Training (BIBB), founded in 1970 on the basis of the Vocational Training Act (BBiG), prepares the content of training regulations. The goal of this dataset is to digitize the historical documents contained in the vocational archive (Berufearchiv) by extracting and structuring text from scanned images. The focus here is on the processing of historical German vocational education and training (VET) and continuing vocational education and training (CVET) regulations to support educational research. This dataset contains data from 1908 to the present and includes paper documents, scanned documents as PDF, and fully converted XML documents and additional metadata. This dataset currently contains metadata.

Data

This dataset contains all documents with digitized metadata from the BIBB Berufearchiv. It contains 1,709 records with 32 properties, including, for example:




id
lfd. Nr.


Occupation
Beruf


Title
Titel


Subtitle
Titelzusatz


Editor
Herausgeber


Publisher
Verlag


Location
Ort


Year
Erscheiningsjahr


Edict year
Erlassjahr


Entry into application
Inkrafttreten


Category
Kategorie


Number of pages
Anzahl Seiten


Keywords
Schlagworte
DOI: 10.5281/zenodo.10810059
title: BIBB Berufearchiv

description: Background

The Federal Institute for Vocational Education and Training (BIBB), founded in 1970 on the basis of the Vocational Training Act (BBiG), prepares the content of training regulations. The goal of this dataset is to digitize the historical documents contained in the vocational archive (Berufearchiv) by extracting and structuring text from scanned images. The focus here is on the processing of historical German vocational education and training (VET) and continuing vocational education and training (CVET) regulations to support educational research. This dataset contains data from 1908 to the present and includes paper documents, scanned documents as PDF, and fully converted XML documents and additional metadata. This dataset currently contains metadata.

Data

This dataset contains all documents with digitized metadata from the BIBB Berufearchiv. It contains 1,709 records with 32 properties, including, for example:




id
lfd. Nr.


Occupation
Beruf


Title
Titel


Subtitle
Titelzusatz


Editor
Herausgeber


Publisher
Verlag


Location
Ort


Year
Erscheiningsjahr


Edict year
Erlassjahr


Entry into application
Inkrafttreten


Category
Kategorie


Number of pages
Anzahl Seiten


Keywords
Schlagworte
DOI: 10.5281/zenodo.10790758
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)

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: 10.18739/a2j38kk2t
title: Effective grain size and ice layer data from the top 10 meters of the 2016 - 2017 Greenland Traverse for Accumulation and Climate Studies (GreenTrACS) firn cores, western Greenland Ice Sheet

description: The Greenland and Antarctic ice sheets are covered in a layer of porous firn. Knowledge of firn structure improves our understanding of ice sheet mass balance, supra- and englacial hydrology, and ice core paleoclimate records. While macroscale firn properties, such as firn density, are relatively easy to measure in the field or lab, more intensive measurements of grain-scale properties are necessary to reduce uncertainty in remote sensing observations of mass balance, model meltwater infiltration, and constrain ice age -- gas age differences in ice cores. Additionally, as the duration and extent of surface melting increases, refreezing meltwater will greatly alter firn structure. Field observations of firn grain size and ice layer stratigraphy are required to test and validate physical models that simulate the ice sheet-wide evolution of the firn layer. However, visually measuring grain size and ice layer distributions is tedious, time-consuming, and can be subjective depending on the method. Here we systematically map firn core grain size and ice layer stratigraphy using a near-infrared hyperspectral imager (NIR-HSI; 900-1700 nm). We scanned 14 firn cores spanning ~1000 kilometers (km) across western Greenland’s percolation zone with the NIR-HSI mounted on a linear translation stage in a cold laboratory. We retrieve effective grain radii by inverting measured reflectance in each pixel of the images to produce high-resolution (0.4~millimeters, mm) maps of grain size and ice layer stratigraphy.
 This dataset contains maps of retrieved effective grain radii from 14 firn cores collected during GreenTrACS (Cores 1-8, 10-14, and 16) in Core*_grain_size_raster.csv files. The grain size data are also available in long-format .csv files as Core*_grain_size.csv. This dataset also contains the locations, thicknesses, and width of ice layers identified through visual inspection of these cores on a light table in *_ice_layers.csv files. The latitude and longitude of each core is stored in the Core_locations.csv file.
DOI: 10.5281/zenodo.10790757
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2016_2020)

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.10815662
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)

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.10815661
title: Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)

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.10813760
title: Dataset for "Transmission of Ultrasonic Wave across Moisture Rocks: Effects of Moisture Sorption on Velocity Variation and Attenuation"

description: Dataset for "Transmission of Ultrasonic Wave across Moisture Rocks: Effects of Moisture Sorption on Velocity Variation and Attenuation"
DOI: 10.5281/zenodo.10813759
title: Dataset for "Transmission of Ultrasonic Wave across Moisture Rocks: Effects of Moisture Sorption on Velocity Variation and Attenuation"

description: Dataset for "Transmission of Ultrasonic Wave across Moisture Rocks: Effects of Moisture Sorption on Velocity Variation and Attenuation"
DOI: 10.5281/zenodo.10688895
title: IMC Matched Primary and Metastatic Breast Cancer

description: This repository contains all raw MCD files and the fully annotated dataset file (backup_output.rds) that pertain to imaging mass cytometry analysis of primary and metastatic breast cancers in the manuscript by Grasset et al. "Mapping the breast tumor microenvironment: proximity analysis reveals spatial relationships between macrophage subtypes and metastasis-initiating cancer cells".

Otherlegends.zip contains the antibody panel, maps of ROI assignments for each of the 5 TMAs used, and legends/metadata related to all of the individual ROIs.

R scripts used for the analysis of this dataset are available on https://github.com/wjhlab/BreastMetIMC/