cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25584/pnnl.data/1895153
title: FACETS Dynamical Downscaling Simulations over North America by the CAM-MPAS Variable-Resolution Model

description: Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through development of metrics and their demonstration on a hierarchy of downscaled climate datasets to quantify aspects of climate change projections that are credible, particularly for supporting decisions related to the energy-water-land nexus. As a part of this effort, we have produced a regional climate dataset using the Model for Prediction Across Scales coupled to the Community Atmosphere Model (CAM-MPAS). This global modeling framework is configured with variable-resolution meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The variable-resolution configurations allow fine-scale features to be better resolved inside the refinement and interact with global-scale circulations. The dataset includes multiple uniform- (240km and 120km) and variable-resolution (200-50km, 100-25km, and 46-12km) simulations that are designed to be compatible with other regional climate simulations that contribute to the hierarchy of downscaled climate datasets of the project. Furthermore, the dataset consists of simulations for both the present-day (1989-2010) and future (2079-2100) climate and post-processing of the model output has been coordinated across the project for consistency to facilitate common analysis across the hierarchy of datasets. Altogether, this CAM-MPAS model dataset provides a unique opportunity to assess the influence of resolutions and modeling framework on model credibility and climate change projection.
DOI: 10.5281/zenodo.7490129
title: Model input data for the FACETS downscaling simulation with the CAM-MPAS model

description: The archived file contains input data necessary to reproduce the set of simulations described in Sakaguchi et al., submitted to GWD, "Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM-MPAS variable-resolution model", using the experimental CAM-MPAS code further modified by Sakaguchi and Harrop (2022) for long-term AMIP-type simulations.
description: {"references": ["Koichi Sakaguchi, & Bryce Harrop. (2022). kosaka90/cesm1.5-mpasv4: Code version used for the FACETS downscaling data (v1.0.0-exp). Zenodo. https://doi.org/10.5281/zenodo.7262209"]}
DOI: 10.5067/aura/mls/data2519
title: MLS/Aura Level 2 Sulfur Dioxide (SO2) Mixing Ratio V005
DOI: 10.5281/zenodo.7813607
title: A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by AVHRR observations from 1981 to 2021 (2006–2021)

description: Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. We developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05°, using Advanced Very High Resolution Radiometer Level-1b Global Area Coverage data from 1981 to 2021. Validation with in situ measurements from Surface Radiation Budget sites showed that the overall root-mean-square errors of GT-LST varied from 2.0 K to 3.9 K. Inter-comparison with a common LST product (i.e., MYD11A1) revealed that the overall root-mean-square-difference was approximately 3.2 K. More details of this dataset can be seen in <em>readme.pdf.</em> This dataset provides GT-LST product from 2006 to 2021.
DOI: 10.5281/zenodo.2530835
title: Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples

description: This repository contains 411,890 unique image patches derived from histological images of colorectal cancer and gastric cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from formalin-fixed paraffin-embedded (FFPE) diagnostic slides ("DX" at the GDC data portal). This is explained well in this blog: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/

<strong>Preprocessing</strong>

All SVS slides were preprocessed as follows

1. automatic detection of tumor

2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px

4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)

5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite instable or highly mutated)

6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level

7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)

<strong>File description</strong>

1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples

2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples

3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples

4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples

5. CRC_DX_TEST_MSIMUT - test images (~30% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 29335 unique image patches; FFPE samples

6. CRC_DX_TEST_MSS - test images (~30% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 70569 unique image patches; FFPE samples

7. CRC_DX_TRAIN_MSIMUT - training images (~70% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 46704 unique image patches; FFPE samples

8. CRC_DX_TRAIN_MSS - training images (~70% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 46704 unique image patches; FFPE samples
DOI: 10.5281/zenodo.2532612
title: Histological images for MSI vs. MSS classification in gastrointestinal cancer, snap-frozen samples

description: This repository contains 218,578 unique image patches derived from histological images of colorectal cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from snap-frozen tissue slides ("TS" or "BS" at the GDC data portal).

<strong>Preprocessing</strong>

All SVS slides were preprocessed as follows

1. automatic detection of tumor

2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px

4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)

5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite unstable or hypermutated)

6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level

7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)

<strong>File description</strong>

1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples

2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples

3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples

4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples
DOI: 10.5281/zenodo.6347222
title: Data sets used in "Neural network processing of holographic images"

description: Included are the training, validation, and testing data sets for synthetic holograms (netCDF), the HOLODEC data set containing the RF07 examples (netCDF), and the two splits of manually labeled HOLODEC image tiles (numpy arrays). The source code for using the data sets can be found at https://github.com/NCAR/holodec-ml
DOI: 10.7295/w9cil38805
title: CIL_38805, Arabidopsis thaliana
DOI: 10.5258/soton/d2573
title: Dataset in support of the article 'Optical and electrical properties characterisation of photovoltaic spatial-light modulators'

description: Supporting information and data related to the paper "Optical and electrical properties characterisation of photovoltaic spatial-light modulators", The dataset contain a .pdf named "Optical and electrical properties characterisation of photovoltaic spatial-light modulators - Supporting information" that displays the methods to assemble photovoltaic units and PSLMs, along with Figure S.1, Figure S.2, Figure S.3. A .txt named "readme_SI" explains how the dataset is organised and provide information related to the publication. The dataset also contain a .zip folder named "Optical and electrical properties characterisation of photovoltaic spatial-light modulators - Data" that contain all the data measured that has been fitted and presented in the article. A .txt named "readme" explains how the folder is organised and provide information related to the publication. For any futher information, please contact me at nb1f20@soton.ac.uk.
DOI: 10.5281/zenodo.7629939
title: SAM simulation output

description: This is the simulation output of the submitted manuscript "Between Broadening and Narrowing: How Mixing Affects the Width of the Droplet Size Distribution" to the Journal of Geophysical Research: Atmospheres. tar file includes model output, including all variables used in the paper. The description of each variable is indicated in the nc file header. Each nc file is an output of a single time step in the whole model domain, where the simulation timestep is indicated as (time) at the end of each file (e.g., 000001440). The model timestep (dt) is 0.5s. Files named SCMS_REVISON_200_(time)_mod.nc are the results from simulation with a linear eddy model (LEM) and SCMS_REVISION_NOLEM_200_(time)_mod.nc are the results from the simulation without a LEM.
DOI: 10.5281/zenodo.7682217
title: The MeSS data used in "A spectral study of the Caloris basin on Mercury and the origin of associated volcanic smooth plains"

description: This document contains the dataset used in the work « A spectral study of the Caloris basin on Mercury and the origin of associated volcanic smooth plains ». In this study the authors use NASA/MESSENGER/MASCS/VIRS DDR data available at the PDS GeosciencesNode of Washington University, St. Louis, MO, USA. The dataset used contains the latest calibration provided by the MESSENGER science team. (https://ode.rsl.wustl.edu/mercury/pagehelp/Content/Missions_Instruments/MESSENGER/MASCS/VIRS/Intro.htm). The present document contains VIRS DDR reflectance spectra from PDS corrected by the methodology developed by Besse et al., 2015 (https://doi.org/10.1002/2015JE004819). Moreover, the document contains the ref_id, the latitude [deg] and longitude [deg] of every footprints used in « A spectral study of the Caloris basin on Mercury and the origin of associated volcanic smooth plains » . All spectral parameters can be calculated directly with these spectra.
DOI: 10.5281/zenodo.7725025
title: Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations"

description: Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations", including magnetic fields, number density and average velocities of particles.
DOI: 10.5281/zenodo.7804127
title: Codes for ''The lithospheric loading model for large impact basin where mantle plug presents''

description: This dataset contains the fortran codes and shell script used to generate data and figure for the article titled with "<strong>The lithospheric loading model for large impact basin where mantle plug presents</strong>". GMT and SHTOOLS is required for the operation of these code. The source code named LocalizedAdmitCorrV4.f95 is modified from the SHTOOLS example: LocalizedAdmitCorr.f95 written by Mark Wieczorek (April, 2005).
DOI: 10.5281/zenodo.7824043
title: Phanus vitreus - wing transparency optics

description: This record includes raw data underlying the publication entitled "<strong>Multi-scale dissection of wing transparency in the clearwing butterfly <em>Phanus vitreus</em></strong>" by Finet <em>et al.</em> 2023. The data are empirical (reflectance spectra, absorbance spectra, transmittance spectra, contact angles) or output files generated through optical simulations.
DOI: 10.5281/zenodo.7881492
title: Experimental Evidence for Shear-induced Melting and Generation of Stishovite in Granite at Low (&lt;18 GPa) Shock Pressure [Dataset]

description: Source data for Experimental Evidence for Shear-induced Melting and Generation of Stishovite in Granite at Low (&lt;18 GPa) Shock Pressure.
DOI: 10.5441/002/edbt.2015.07
title: Estimating Data Integration and Cleaning Effort
DOI: 10.5441/002/edbt.2019.94
title: SynthEdit: Format transformations by example using edit operations
DOI: 10.5441/002/edbt.2020.25
title: Data Curation with Deep Learning
DOI: 10.6084/m9.figshare.22153379
title: EP registre 2023 MONASTIR _ KAIROUAN final - SAFE HARBOR.sav

description: We present a dataset consisting of 255 patients included between 01/2008 and 12/2019 hospitalized in the Cardiology departments of Monastir and Kairouan with acute pulmonary embolism. It is the basis of a retrospective cohort study assessing risk levels based on multiple ECG changes.
DOI: 10.5281/zenodo.4134671
title: Mixed layer velocity and surface wind stress from numerical simulation of Super Typhoon Mangkhut

description: Mixed layer velocities and surface wind stress from a WRF-HYCOM coupled simulations of Super Typhoon Mangkhut.
DOI: 10.5281/zenodo.7536899
title: Aerosol impacts on storm electrification and lightning discharges under different thermodynamic environments

description: These are important data supporting the conclusion of the paper are available in the main text.
DOI: 10.5281/zenodo.1475458
title: Soil Organic Carbon Content In X 5 G / Kg At 6 Standard Depths (0, 10, 30, 60, 100 And 200 Cm) At 250 M Resolution

description: Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Predicted from a global compilation of soil points. Processing steps are described in detail <strong>here</strong>. Antartica is not included.

To access and visualize maps use: <strong>https://landgis.opengeohub.org</strong>

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:


	sol = theme: soil,
	organic.carbon = variable: soil organic carbon content in x 5 g / kg,
	usda.6a1c = determination method: laboratory method code,
	m = mean value,
	250m = spatial resolution / block support: 250 m,
	b10..10cm = vertical reference: 10 cm depth below surface,
	1950..2017 = time reference: period 1950-2017,
	v0.1 = version number: 0.1,
description: {"references": ["USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.", "Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagoti\u0107 A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748."]}
DOI: 10.5441/002/edbt.2020.13
title: Explaining Differences Between Unaligned Table Snapshots
DOI: 10.21335/nmdc-839276558
title: Water column data on dissolved inorganic nutrients (nitrite, nitrate, phosphate and silicic acid) from the Nansen LEGACY joint cruise KH 2018707 with R.V. Kronprins Haakon, 8-20 August 2018

description: The cruise KH 2018707 aboard the Research Vessel Kronprins Haakon is the first joint cruise of the project the Nansen LEGACY. LEGACY is the Norwegian Arctic research community’s joint effort to establish a holistic understanding of a changing marine Arctic climate and ecosystem. Water column temperature and salinity profiles were obtained with a conductivity-temperature-depth (CTD) sensor system Sea-Bird SBE 911+ mounted on a General Oceanic rosette sampler equipped with 24 Niskin bottles used for seawater sampling of chemical variables in the water column. Samples for determination of dissolved inorganic nutrients were collected from full water column at a total of 10 stations in the northern Barents Sea. The seawater samples were collected from Niskin bottles in 20 ml plastic HDPE vials (rinsed three times) and preserved with 250 µL chloroform and stored +4C and dark until post-cruise analysis of nitrite, NO2, nitrate, NO3-, phosphate, PO43-, and silicate, Si(OH)4, following standard methods (Grasshoff et al., 2009) at the chemical laboratory at Institute of Marine Research, Bergen, Norway. Three replicates were analyzed for each sample. The detection limits are 0.06 µmol/L, 0.5 µmol/L, 0.06 µmol/L and 0.7 µmol/L for NO2, NO3-, PO43-, and Si(OH)4, respectively. The study was funded by the Research Council of Norway through the project The Nansen LEGACY (RCN #276730).
DOI: 10.5063/f1c53j8x
title: Sea otter foraging data, visual observations from Prince of Wales, Alaska

description: Sea otter diets were assessed during summertime 2018 (May-August) visual observations from land. Data were collected on western Prince of Wales Island Alaska, at various sites where sea otter densities are high. These data were collected as a part of an NSF-funded project: Apex Predators, Ecosystems, and Community Sustainability (APECS, http://apecs-ak.org/). Other datasets to support this work are also archived with KNB.
DOI: 10.5063/f1q23xng
title: Macronutrient analysis of sea otter prey in Southeast Alaska

description: The goal of this study was to examine and quantify the energetic content of sea otter diets. Sea otter prey items were collected in three seasons (spring, summer, and winter) to measure caloric value and lipid and protein content. Data from this study were obtained via bomb calorimetry, sulfo-phospho-vanillin colorimetric analysis, and protein content. These data are a collaboration with NOAA ABL, Juneau, AK, and for an NSF-funded project: Apex Predators, Ecosystems, and Community Sustainability (APECS, http://apecs-ak.org/). Other datasets to support this work are also archived with KNB.Sea otter prey species information can be found in the "Sea otter foraging, Prince of Wales, Alaska" data package.
DOI: 10.5281/zenodo.7152687
title: Data and software in support of article submitted to Journal Geophy. Res. Atmos., titled "Self lofting increases altitude of black carbon in a climate model""

description: The collection provides data and software to support a journal article submission to the Journal of Geophysical Research Atmospheres. The contents will allow potential future investigators to explore the simulation data and reproduce the analyses in the submitted article. This dataset includes 7 tarred files that when expanded will contain a set of netcdf data files and a collection of python programs to read and analyze the data. The data provided in the netcdfs are model outputs from the UK Earth System Model (UKESM1) from a pair of simulations that explored the impact of black carbon aerosol on atmospheric motion.
DOI: 10.5281/zenodo.7762072
title: Effects of elevated ozone exposure on regional meteorology and air quality in China through ozone-vegetation coupling

description: Main data and code used in the study.
DOI: 10.5281/zenodo.7793110
title: Horizontal surface eddy diffusion implementation in CESM/MOM6

description: The source code, input files, main results, and plotting/analysis scripts that were used in Marques et a., 2023 submitted to JAMES. "Representing eddy diffusion in the surface boundary layer of ocean models with general vertical coordinates"
DOI: 10.5285/bd1f80ef-114a-429d-a629-20d19bacec79
title: Western Kenya soil geochemistry

description: Soil prediction maps for 56 chemical elements, pH and organic matter content have been produced using machine learning analysis in western Kenya. The predictive maps were based on 452 soil samples collected across western Kenya during field surveys carried out between 2015 and 2020. Samples were analysed by the inorganic chemistry laboratories at the British Geological Survey. The maps, created using random forest machine learning algorithms, are displayed as raster files with a spatial resolution of 500m. The samples were collected as part of a geochemistry and health project to investigate the spatial incidences of diseases in the Rift Valley (e.g. oesophageal cancer, iodine/zinc deficiency), which included a range of data and sample collections to inform sources of micronutrients or exposure to potentially harmful elements, with outputs to inform agriculture and public health practitioners. These predictive maps provide a baseline geochemistry survey for the agri-community, academics and public health officials.
DOI: 10.6073/pasta/ad8adb1329f2a75bdd522fd22f2cb201
title: Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (Instructor Materials)

description: Because of increased variability in populations, communities, and ecosystems due to land use and climate change, there is a pressing need to know the future state of ecological systems across space and time. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to preemptively prepare for fluctuations in important ecosystem services. However, forecasts must be effectively designed and communicated to those who need them to make decisions in order to realize their potential for protecting natural resources. In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations, and learn decision support techniques. Lastly, students customize a forecast visualization for a specific stakeholder's decision needs. The overarching goal of this module is for students to understand how forecasts are connected to decision-making of stakeholders, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the Zenodo data package (Woelmer et al. 2022; DOI: 10.5281/zenodo.7074674) for the R Shiny application code needed to run the module locally.
DOI: 10.17189/1520328
title: MSL MARS MAST CAMERA 4 RDR IMAGE V1.0