cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/edw8-86hz
title: Measurement of Inorganics in precipitation at Diabla Gora

description: Measurement of Inorganics in precipitation at Diabla Gora
DOI: 10.48597/gk4v-rc4f
title: Measurement of Inorganics in precipitation at Diabla Gora

description: Measurement of Inorganics in precipitation at Diabla Gora
DOI: 10.15468/dl.rha8f8
title: Occurrence Download

description: A dataset containing 6803 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pucrasia macrolopha (R.Lesson, 1829)"
 ]
}

 The dataset includes 6803 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017479-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10025849
title: Reproduction package for the paper: "The effects of planetary day-night temperature gradients on He 1083 nm transit spectra"

description: Material for the publication "The effects of planetary day-night temperature gradients on He 1083 nm transit spectra". Hydrodynamic model snapshots (in Athena++ hdf5 format), as well as 3D post-processed species number densities (hdf5 format), and the synthetic spectra (in ascii format) of models A100%, A50%, and A10%. Also included, the radiative transfer post-processing software used in our study, the modified Athena++ setup files, and a notebook to replicate the presented figures.
DOI: 10.5281/zenodo.10025850
title: Reproduction package for the paper: "The effects of planetary day-night temperature gradients on He 1083 nm transit spectra"

description: Material for the publication "The effects of planetary day-night temperature gradients on He 1083 nm transit spectra". Hydrodynamic model snapshots (in Athena++ hdf5 format), as well as 3D post-processed species number densities (hdf5 format), and the synthetic spectra (in ascii format) of models A100%, A50%, and A10%. Also included, the radiative transfer post-processing software used in our study, the modified Athena++ setup files, and a notebook to replicate the presented figures.
DOI: 10.15468/dl.rfa2jh
title: Occurrence Download

description: A dataset containing 4095 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2017",
 "DecimalLongitude is greater than or equal to 10.04",
 "DecimalLongitude is less than or equal to 10.15",
 "DecimalLatitude is greater than or equal to 55.84",
 "DecimalLatitude is less than or equal to 55.87"
 ]
}

 The dataset includes 4095 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017480-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.snxtsd
title: Occurrence Download

description: A dataset containing 11096 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-49.08822 -26.94397,-49.03827 -26.94397,-49.03827 -26.89318,-49.08822 -26.89318,-49.08822 -26.94397))"
 ]
}

 The dataset includes 11096 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017481-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14024692
title: Dataset for fracture topology in mafic formations

description: This dataset is part of the GEOMIMIC project, funded by the European Union’s Horizon Europe Research and Innovation program, which aims to improve carbon storage and mineralization efficiency in fractured mafic reservoirs. It combines fracture property data from previous studies with new data gathered in this project, along with results from fracture network simulations exploring reactive fluid flow near injection wells in mafic formations.
DOI: 10.5281/zenodo.14024693
title: Dataset for fracture topology in mafic formations

description: This dataset is part of the GEOMIMIC project, funded by the European Union’s Horizon Europe Research and Innovation program, which aims to improve carbon storage and mineralization efficiency in fractured mafic reservoirs. It combines fracture property data from previous studies with new data gathered in this project, along with results from fracture network simulations exploring reactive fluid flow near injection wells in mafic formations.
DOI: 10.5281/zenodo.13985968
title: Reproduction package for the paper: "The overflowing atmosphere of WASP-121 b"
title: High-resolution He I 10833 transmission spectroscopy with VLT/CRIRES+

description: Supplementary Material for the paper "The overflowing atmosphere of WASP-121 b: High-resolution He i 10833 transmission spectroscopy with VLT/CRIRES+" by Czesla et al. (2024).

The material is related to the 3D modeling part of the paper (Sect. 3.5), specifically focused on the atmospheric escape simulations and radiative transfer calculations for the exoplanet WASP-121 b.

We provide the results of the 3D hydrodynamic simulation with Athena++, with key files including the input file and a snapshot of the simulation after 8 orbits.Post-processing is done with a radiation transfer code located in the folder "post-process_W121b". This code, based on MacLeod & Oklopcic (2022), generates synthetic spectra for two density scenarios (scaling factors 0.5 and 1.0) and has been modified for accurate calculations in high density regions. A Jupyter notebook in the same folder illustrates how to analyze the data and generate the corresponding figures.

Detailed descriptions can be found in the README.txt file.
DOI: 10.5281/zenodo.13985969
title: Reproduction package for the paper: "The overflowing atmosphere of WASP-121 b"
title: High-resolution He I 10833 transmission spectroscopy with VLT/CRIRES+

description: Supplementary Material for the paper "The overflowing atmosphere of WASP-121 b: High-resolution He i 10833 transmission spectroscopy with VLT/CRIRES+" by Czesla et al. (2024).

The material is related to the 3D modeling part of the paper (Sect. 3.5), specifically focused on the atmospheric escape simulations and radiative transfer calculations for the exoplanet WASP-121 b.

We provide the results of the 3D hydrodynamic simulation with Athena++, with key files including the input file and a snapshot of the simulation after 8 orbits.Post-processing is done with a radiation transfer code located in the folder "post-process_W121b". This code, based on MacLeod & Oklopcic (2022), generates synthetic spectra for two density scenarios (scaling factors 0.5 and 1.0) and has been modified for accurate calculations in high density regions. A Jupyter notebook in the same folder illustrates how to analyze the data and generate the corresponding figures.

Detailed descriptions can be found in the README.txt file.
DOI: 10.15468/dl.97d45q
title: Occurrence Download

description: A dataset containing 2493 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((29.27643 -5.27446,183.13474 -5.27446,183.13474 56.24252,29.27643 56.24252,29.27643 -5.27446))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aythya baeri (Radde, 1863)"
 ]
}

 The dataset includes 2493 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017482-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.net2vz
title: Occurrence Download

description: A dataset containing 3882 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2018",
 "DecimalLongitude is greater than or equal to 10.04",
 "DecimalLongitude is less than or equal to 10.15",
 "DecimalLatitude is greater than or equal to 55.84",
 "DecimalLatitude is less than or equal to 55.87"
 ]
}

 The dataset includes 3882 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017483-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1123055
title: Digital Archive from a Historic Building Survey and Archaeological Evaluation on the Land at 29 Brook Street, Elsworth, Cambridgeshire, March - July 2022

description: This digital archive comprises images, gis and site record data from a historic building survey and a programme of archaeological evaluation by trial trenching on land at 29 Brooke Street, Elsworth, Cambridgeshire, as part of planning consent for the construction of two detached dwellings.Thirteen One Limited commissioned Allen Archaeology Limited to undertake the works in March and July 2022
DOI: 10.35003/j8thc5
title: Replication Data for: Blaubeuren, Cloppenburg, and Machtenstein—Three recently recognized H-group chondrite finds in Germany with distinct terrestrial ages and weathering effects

description: Key points: Three meteorites (Blaubeuren, Cloppenburg, and Machtenstein) found in Germany were recently identified as H-group ordinary chondrites. Two of the meteorites had been previously recovered but were only identified as meteorites after further analysis. The study aims to fully characterize these meteorites based on their mineral composition, primarily olivine and * low-Ca pyroxene. Blaubeuren and Cloppenburg have a visible brecciated texture, common in chondrites, while Machtenstein’s texture is less apparent. Shock levels were classified as S2 for Blaubeuren and Machtenstein and S3 for Cloppenburg.Weathering has significantly impacted all three meteorites, eroding their fusion crust and metal content.* Oxygen isotope compositions align with other H chondrites, though Cloppenburg shows disturbance from terrestrial weathering.Cloppenburg contains vivianite, a rare hydrated iron phosphate mineral, indicating weathering in a wet environment.Terrestrial ages for Blaubeuren (~9.2 ka), Cloppenburg (~5.4 ka), and Machtenstein (~1.8 ka) show similarity to meteorites from wet environments. Overall, metal elements such as Fe, Co, and Ni have been altered, except for Co and Ni in Machtenstein, likely due to its shorter terrestrial age.
DOI: 10.35003/j8thc5/ojsl4b
title: Tables_1-6.tab

description: :unav
DOI: 10.35003/j8thc5/osmbmb
title: Tables_1-5.tab

description: :unav
DOI: 10.35003/j8thc5/rmnbtp
title: Figures_1-9_&_captions.pptx

description: :unav
DOI: 10.5285/10394c6c-ebb9-4826-8de2-4f3bf7b9f25d
title: Rapid light curves of Greenland Ice Sheet glacier algal communities, 2024

description: Dataset includes 2 sets of data on Streptophyte glacier algal photophysiology as assessed through Pulse Amplitude Modulation (PAM) fluorometry of communities sampled from the surface of the Greenland Ice Sheet during summer 2024. The first set of responses are of communities sampled from fully exposed versus shaded regions of ice measured through rapid light curve techniques after 5 minutes of dark adaptation. The other set of responses are the same surface ice samples measured after 24 hours of dark-adaptation and melt. Analysis of rapid light curve datasets to highlight fine-scale heterogeneity in glacier algal in-situ photophysiology are published in Williamson et al. (2024). This marks the first time an in-situ method has been developed to map glacier algal photophysiology relative to dominant abiotic stressors.
description: Surface ice samples containing glacier algal communities were sampled and analysed through rapid light curve techniques to study their photophysiology. Experiments were carried out in situ. Please refer to Williamson et al. (2024) as this methods paper further describes data collection methods.

A comparison was made between samples analysed within 5 minutes of sampling from fully exposed versus shaded patches of surface ice, to samples taken from the same surface ice locations but melted in the dark for 24 hours prior to measurements. Replication of N = 3 was used per treatment (Total n = 12). Rapid light curves were performed with a Walz WATER-PAM fluorometer with a blue-light emitter detector unit attached.
description: Walz WATER-PAM fluorometer with a blue-light emitter detector unit attached.
description: N = 3 replicates of each treatment condition are apparent, for a total of N = 12 samples
DOI: 10.6084/m9.figshare.27448881
title: Adenocarcinoma cell line ENCODE files

description: RNA-seq and HM ChIP-seq data of the Adenocarcinoma cell lines A549, Caco-2, PC-9, HeLa-S3, and PC-3 from ENCODE.
DOI: 10.6084/m9.figshare.27448881.v1
title: Adenocarcinoma cell line ENCODE files

description: RNA-seq and HM ChIP-seq data of the Adenocarcinoma cell lines A549, Caco-2, PC-9, HeLa-S3, and PC-3 from ENCODE.
DOI: 10.5281/zenodo.11431852
title: FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting

description: Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.

 

This dataset comprises three folders:  the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.

 

FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.
DOI: 10.5281/zenodo.11431853
title: FloodCastBench: A Large-Scale Dataset and Foundation Models for Flooding Modeling and Forecasting

description: Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML. This dataset comprises three folders: the study regions folder, containing terrain data for the four study areas; the low-fidelity flood forecasting folder; and the high-fidelity flood forecasting folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data within the dataset are in TIFF format and have a temporal resolution of 300 seconds.

 

FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.
description: This version is for peer-review.
DOI: 10.5281/zenodo.14017092
title: FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting

description: Effective flood forecasting is crucial for informed decision-making and emergency response. Existing flood datasets mainly describe flood events but lack dynamic process data suitable for machine learning (ML). This work introduces the FloodCastBench dataset, designed for ML-based flood modeling and forecasting, featuring four major flood events: Pakistan 2022, UK 2015, Australia 2022, and Mozambique 2019. FloodCastBench provides comprehensive low-fidelity and high-fidelity flood forecasting datasets specifically for ML.

 

This dataset comprises three folders:  the low-fidelity flood forecasting folder, the high-fidelity flood forecasting folder, and the relevant data folder. The low-fidelity flood forecasting folder includes data on the 2022 Pakistan flood and the 2019 Mozambique flood, both with a spatial resolution of 480 m. The high-fidelity flood forecasting folder contains two subfolders: one for the 2022 Australia flood and the 2015 UK flood with a spatial resolution of 30 m, and another for the same floods with a spatial resolution of 60 m. All data files are stored in TIFF format, with a temporal resolution of 300 seconds, and file names are numbered sequentially, incremented every 300 seconds until the simulation endpoint. The relevant data folder includes five subfiles: DEM, land use and land cover, rainfall data, georeferenced files, and initial condition files. The DEM, land use and land cover, rainfall, and initial condition data are all provided in TIFF format. The rainfall data is organized in a format of year-month-day-hour-minute-second. Georeferenced files provide geographic extent and spatial reference to support viewing and analysis of the associated TIFF files in GIS.

 

FloodCastBench details the process of flood dynamics data acquisition, starting with input data preparation (e.g., topography, land use, rainfall) and flood measurement data collection (e.g., SAR-based maps, surveyed outlines) for hydrodynamic modeling. We deploy a widely recognized finite difference numerical solution to construct high-resolution spatiotemporal dynamic processes with 30-m spatial and 300-second temporal resolutions. Flood measurement data are used to calibrate the hydrodynamic model parameters and validate the flood inundation maps. Furthermore, we establish a benchmark of foundational models for neural flood forecasting using FloodCastBench, validating its effectiveness in supporting ML models for spatiotemporal, cross-regional, and downscaled flood forecasting.
DOI: 10.5281/zenodo.13242014
title: A comprehensive model for separating systematic bias and noise in metabolomic timecourse data -- A nonlinear B-spline mixed effect approach

description: Code for the analysis and validation of the systematic error correction model presented.
DOI: 10.5281/zenodo.13242015
title: A comprehensive model for separating systematic bias and noise in metabolomic timecourse data -- A nonlinear B-spline mixed effect approach

description: Code for the analysis and validation of the systematic error correction model presented.
DOI: 10.15468/dl.kn7gse
title: Occurrence Download

description: A dataset containing 1714 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2019",
 "DecimalLongitude is greater than or equal to 10.04",
 "DecimalLongitude is less than or equal to 10.15",
 "DecimalLatitude is greater than or equal to 55.84",
 "DecimalLatitude is less than or equal to 55.87"
 ]
}

 The dataset includes 1714 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017486-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vu28jq
title: Occurrence Download

description: A dataset containing 252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aedes aegypti (Linnaeus, 1762)",
 "Country is Puerto Rico",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 252 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017490-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3sp9u
title: Occurrence Download

description: A dataset containing 13731 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Bubalus bubalis (Linnaeus, 1758)"
 ]
}

 The dataset includes 13731 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017491-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.25hyqf
title: Occurrence Download

description: A dataset containing 2493 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((29.27643 -5.27446,183.13474 -5.27446,183.13474 56.24252,29.27643 56.24252,29.27643 -5.27446))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aythya baeri (Radde, 1863)"
 ]
}

 The dataset includes 2493 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017493-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.