cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22033/esgf/cmip6.10748
title: MIROC MIROC-ES2L model output prepared for CMIP6 C4MIP esm-1pct-brch-750PgC

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.C4MIP.MIROC.MIROC-ES2L.esm-1pct-brch-750PgC' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The MIROC-ES2L climate model, released in 2018, includes the following components:
 aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T42; 128 x 64 longitude/latitude; 40 levels; top level 3 hPa), land: MATSIRO6.0+VISIT-e ver.1.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), ocnBgchem: OECO ver.2.0; NPZD-type with C/N/P/Fe/O cycles, seaIce: COCO4.9. 
The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, land: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.10750
title: MIROC MIROC-ES2L model output prepared for CMIP6 C4MIP esm-1pctCO2

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.C4MIP.MIROC.MIROC-ES2L.esm-1pctCO2' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The MIROC-ES2L climate model, released in 2018, includes the following components:
 aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T42; 128 x 64 longitude/latitude; 40 levels; top level 3 hPa), land: MATSIRO6.0+VISIT-e ver.1.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), ocnBgchem: OECO ver.2.0; NPZD-type with C/N/P/Fe/O cycles, seaIce: COCO4.9. 
The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, land: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.4248
title: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP esm-piControl

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.CMIP.CSIRO.ACCESS-ESM1-5.esm-piControl' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The Australian Community Climate and Earth System Simulator Earth System Model Version 1.5 climate model, released in 2019, includes the following components:
 aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). 
The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.5526
title: MIROC MIROC-ES2L model output prepared for CMIP6 C4MIP esm-ssp585

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.C4MIP.MIROC.MIROC-ES2L.esm-ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The MIROC-ES2L climate model, released in 2018, includes the following components:
 aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T42; 128 x 64 longitude/latitude; 40 levels; top level 3 hPa), land: MATSIRO6.0+VISIT-e ver.1.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), ocnBgchem: OECO ver.2.0; NPZD-type with C/N/P/Fe/O cycles, seaIce: COCO4.9. 
The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 500 km, atmos: 500 km, land: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.6084/m9.figshare.28902860.v1
title: Searching for dark matter with a spin-based interferometer

description: Name/institution/contact information for:
 Principal Investigator (or person responsible for collecting the data): Arne Wickenbrock / Johannes Gutenberg University in Mainz / wickenbr@uni-mainz.de
 Data manager or custodian: Daniel Gavilan-Martin / Johannes Gutenberg University in Mainz / gaviland@uni-maizn.de, Grzegorz Łukasiewicz / Jagiellonian University / grzegorz.lukasiewicz@doctoral.uj.edu.pl
 

Methodology: This is experimental data related to the article "Searching for dark matter with a spin-based interferometer" (https://doi.org/10.48550/arXiv.2408.02668). The data consists of discrete Fourier transforms of 25-h long time series recorded by self-compensating K-Rb-3He comagnetometers in Kraków, Poland and Mainz, Germany. The spectra are calibrated assuming neutron coupling referring to Table 1 in Mikhail Padniuk's et al. article "Response of atomic spin‑based sensors to magnetic and nonmagnetic perturbations" (https://doi.org/10.57903/UJ/J33SMI). Calibration function parameters are also available in each file.

Description of methods used for collection/generation of data: Each file corresponds to 25 h long dataset collected by comagnetometer sensor in Kraków, Poland and Mainz, Germany and has been already preprocessed according to description in Methods section of the article. 

Calibration: In order to recalibrate the data please use the "calibration" group in h5 file and refer to the methodology described in Mikhail Padniuk's et al. paper "Response of atomic spin‑based sensors to magnetic and nonmagnetic perturbations" (https://doi.org/10.57903/UJ/J33SMI) and the related repository (https://uj.rodbuk.pl/dataset.xhtml?persistentId=doi:10.57903/UJ/J33SMI). 


File name structure:
Krakow.zip and Mainz.zip - correspond to data collected in Krakow and Mainz respectively.
 .h5 files containing 'freq (Hz)' and 'neutron-calibrated fft (pT)' datasets as well as 'calibration' group. Filename correspond to unix timestamps. 
neutron_limits.txt - final ALP-neutron limits (from Fig. 4)
DOI: 10.6084/m9.figshare.29177555
title: Supplementary Materials A and C

description: Supplementary Material A & C for Jas Singh's PhD Thesis.
DOI: 10.5281/zenodo.17434015
title: From Simulations to Surveys: Domain Adaptation for Galaxy Observations

description: Abstract

Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images with ground-truth physical labels, but domain shifts in PSF, noise, backgrounds, selection, and label priors degrade transfer to real surveys. We present a preliminary domain adaptation pipeline that trains on simulated TNG50 galaxies and evaluates on real SDSS galaxies with morphology labels (elliptical/spiral/irregular). We train three backbones (CNN, $E(2)$-steerable CNN, ResNet-18) with focal loss and effective-number class weighting, and a feature-level domain loss $\mathcal{L}_D$ built from \texttt{GeomLoss} (entropic Sinkhorn OT, energy distance, Gaussian MMD, and related metrics). We show that a combination of these losses with an OT-based “top-$k$ soft matching’’ loss that focuses $\mathcal{L}_D$ on the worst-matched source–target pairs can further enhance domain alignment. With Euclidean distance, scheduled alignment weights, and top-$k$ matching, target accuracy (macro F1) rises from $\sim$46% ($\sim$30%) at no adaptation to $\sim$87% ($\sim$ 62.6%), with a domain AUC near 0.5, indicating strong latent-space mixing.

Dataset:

The dataset includes RGB galaxy images and labels for both source and target domains. The galaxy_images_rgb.zip archive contains all galaxy images from the source dataset (simulated IllustrisTNG galaxies) and the target dataset (observed SDSS Galaxy Zoo 2 galaxies). The source_galaxy_labels.csv file contains labels for the simulated galaxies, including image paths, subhalo IDs, stellar masses, star-forming flags, AGN presence flags, compactness flags, metallicity values, morphology classifications (elliptical, spiral, or irregular), and metal-rich indicators.

The target dataset labels are provided in two files: gz2_galaxy_labels.csv contains the top galaxies for morphology classification, selected based on the highest confidence metrics for each class (elliptical, spiral, or irregular), with image identifiers, SDSS object IDs, stellar masses, star-forming flags, AGN flags, and morphology classifications. The gz2_galaxy_labels_master.csv file contains the complete target dataset with the same structure, including all galaxies that passed the classification thresholds. Target galaxy images can be downloaded from the Galaxy Zoo 1 data release, images_gz2.zip.

The processing script that generated the target labels, including the classification thresholds and selection criteria, can be found at gz2-processing-script. The file AGN_GZ2_Hart_DR7_final.csv contains the results of crossmatching the SDSS DR7 object IDs of each galaxy with the AGN catalogue, which was used to extend the labels with AGN-related information
DOI: 10.5281/zenodo.17434016
title: From Simulations to Surveys: Domain Adaptation for Galaxy Observations

description: Abstract

Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images with ground-truth physical labels, but domain shifts in PSF, noise, backgrounds, selection, and label priors degrade transfer to real surveys. We present a preliminary domain adaptation pipeline that trains on simulated TNG50 galaxies and evaluates on real SDSS galaxies with morphology labels (elliptical/spiral/irregular). We train three backbones (CNN, $E(2)$-steerable CNN, ResNet-18) with focal loss and effective-number class weighting, and a feature-level domain loss $\mathcal{L}_D$ built from \texttt{GeomLoss} (entropic Sinkhorn OT, energy distance, Gaussian MMD, and related metrics). We show that a combination of these losses with an OT-based “top-$k$ soft matching’’ loss that focuses $\mathcal{L}_D$ on the worst-matched source–target pairs can further enhance domain alignment. With Euclidean distance, scheduled alignment weights, and top-$k$ matching, target accuracy (macro F1) rises from $\sim$46% ($\sim$30%) at no adaptation to $\sim$87% ($\sim$ 62.6%), with a domain AUC near 0.5, indicating strong latent-space mixing.

Dataset:

The dataset includes RGB galaxy images and labels for both source and target domains. The galaxy_images_rgb.zip archive contains all galaxy images from the source dataset (simulated IllustrisTNG galaxies) and the target dataset (observed SDSS Galaxy Zoo 2 galaxies). The source_galaxy_labels.csv file contains labels for the simulated galaxies, including image paths, subhalo IDs, stellar masses, star-forming flags, AGN presence flags, compactness flags, metallicity values, morphology classifications (elliptical, spiral, or irregular), and metal-rich indicators.

The target dataset labels are provided in two files: gz2_galaxy_labels.csv contains the top galaxies for morphology classification, selected based on the highest confidence metrics for each class (elliptical, spiral, or irregular), with image identifiers, SDSS object IDs, stellar masses, star-forming flags, AGN flags, and morphology classifications. The gz2_galaxy_labels_master.csv file contains the complete target dataset with the same structure, including all galaxies that passed the classification thresholds. Target galaxy images can be downloaded from the Galaxy Zoo 1 data release, images_gz2.zip.

The processing script that generated the target labels, including the classification thresholds and selection criteria, can be found at gz2-processing-script. The file AGN_GZ2_Hart_DR7_final.csv contains the results of crossmatching the SDSS DR7 object IDs of each galaxy with the AGN catalogue, which was used to extend the labels with AGN-related information
DOI: 10.17632/53krpc5w7j
title: Simulation Data- Normal Tension Glaucoma

description: Simulation Data- Normal Tension Glaucoma model
DOI: 10.17632/53krpc5w7j.2
title: Simulation Data- Normal Tension Glaucoma

description: Simulation Data- Normal Tension Glaucoma model
DOI: 10.15468/dl.feejps
title: Occurrence Download

description: A dataset containing 92135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "TaxonKey is Vulpes vulpes"
 ]
}

 The dataset includes 92135 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044837-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k4ehwg
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-112.9188 27.89913, -112.7688 27.89913, -112.7688 28.04913, -112.9188 28.04913, -112.9188 27.89913))"
 ]
}

 The dataset includes 10 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044919-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17647927
title: A multiproxy reconstruction of spring temperatures in south-west Finland since 1750

description: Spring temperatures were reconstructed by multiproxy database for south-west Finland since 1750. Proxy records used here were ice break-up in the Aurajoki River, the Baltic Sea ice extent, the plant phenological index and the annual varve thickness in the Pyhäjärvi Lake. Records were integrated into one palaeoclimate model using time-scale dependent calibration techniques. Reconstruction was verified with statistics showing a high degree of validation between the reconstructed and observed temperatures in Turku, south-west Finland. Reconstruction demonstrates that the springs have become warmer and reveals a warming trend since 1850s. Except for the period from 1750 to around 1850, the springs have been characterized as having a larger low-frequency variability, as well as by having a smaller range of annual temperature variations. Analyses of decadal variations revealed that the coldest springtimes occurred in the 1840s and 1850s and the first decade of the 19th century. Reconstruction was compared with the available meteorological series of central England, Stockholm, St. Petersburg, Uppsala and the spring-temperature reconstruction from western Norway. The effect of global solar, volcanic, greenhouse gases and aerosol forcings were examined together with the North Atlantic Oscillation (NAO) indices at local scale over the reconstructed period. Reconstructed spring-temperature changes have been related to changes in the atmospheric circulation, as indicated by the NAO (February-June).
description: The files contain a reconstructed spring temperaturess for the years 1750-2004 and a short summary in Finnish.
DOI: 10.5281/zenodo.17647928
title: A multiproxy reconstruction of spring temperatures in south-west Finland since 1750

description: Spring temperatures were reconstructed by multiproxy database for south-west Finland since 1750. Proxy records used here were ice break-up in the Aurajoki River, the Baltic Sea ice extent, the plant phenological index and the annual varve thickness in the Pyhäjärvi Lake. Records were integrated into one palaeoclimate model using time-scale dependent calibration techniques. Reconstruction was verified with statistics showing a high degree of validation between the reconstructed and observed temperatures in Turku, south-west Finland. Reconstruction demonstrates that the springs have become warmer and reveals a warming trend since 1850s. Except for the period from 1750 to around 1850, the springs have been characterized as having a larger low-frequency variability, as well as by having a smaller range of annual temperature variations. Analyses of decadal variations revealed that the coldest springtimes occurred in the 1840s and 1850s and the first decade of the 19th century. Reconstruction was compared with the available meteorological series of central England, Stockholm, St. Petersburg, Uppsala and the spring-temperature reconstruction from western Norway. The effect of global solar, volcanic, greenhouse gases and aerosol forcings were examined together with the North Atlantic Oscillation (NAO) indices at local scale over the reconstructed period. Reconstructed spring-temperature changes have been related to changes in the atmospheric circulation, as indicated by the NAO (February-June).
description: The files contain a reconstructed spring temperaturess for the years 1750-2004 and a short summary in Finnish.
DOI: 10.15468/dl.9mdk2e
title: Occurrence Download

description: A dataset containing 7594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-2.06634 41.35726,2.06267 41.35726,2.06267 43.21776,-2.06634 43.21776,-2.06634 41.35726))",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Amanita muscaria, Omphalotus olearius, Ursus arctos, Felis silvestris)"
 ]
}

 The dataset includes 7594 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044920-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.