cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18728/igb-fred-1046.1
title: Dataset - A novel in situ experimental setup for studying the impact of bedform celerity on 2D oxygen distribution in the hyporheic zone of small streams

description: Data used for supporting the research article "A novel in situ experimental setup for studying the impact of bedform celerity on 2D oxygen distribution in the hyporheic zone of small streams" submitted to Water Resources Research.
The dataset contains:
1) Data with field notes with measurements of stream velocity, ...
2) Compilation of laser profiles data for each stream velocity
3) Morphodynamic results for each of the stream velocity: Bedform celerity, height, and length, flux calculations
4) Digital raw images of NIR camera and Optode
5) Processed image data
6) Continues measurements with the FTC at each depth profile for each velocity
Data file that have no spedified file type, e.g. planar optode processed images, can be read using Python's pickle.load() function.
DOI: 10.15468/dl.uqv3se
title: Occurrence Download

description: A dataset containing 649221 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "DatasetKey is Southern African Bird Atlas Project 2",
 "GadmGid is ZAF.3.2.1_1",
 "Year 2019-2026"
 ]
}

 The dataset includes 649221 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081318-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30585428.v2
title: Wikidata Entries For Scottish Brick History

description: This dataset was created as part of the CS4099: Major Software Project module in the School of Computer Science at the University of St Andrews in the academic year 2023-2024.This dataset relates to brick, tile, and associated works that are listed on the Scottish Brick History website. With permission, the data was web scrapped, manually matched to geographic coordinates, and stored in an Excel spreadsheet, where it was subjected to data cleaning prior to a bulk upload to Wikidata using OpenRefine.Prior to this project, there were no brick, tile, or other works on Wikidata. During this project, 761 new works were added to Wikidata, meaning that 100% of Scotland's brick, tile, and other works are on Wikidata as a result of this project.<b>Author contributions</b>Grace Young created and cleaned the original dataset built from said data on the Scottish Brick History website. Sara Thomas provided training about OpenRefine, proposed the Scottish Brick History Brick & Tileworks ID properties in Wikidata, and performed the batch uploads to Wikidata. Kirsty Ross supervised the project and uploaded the dataset to Figshare.
DOI: 10.6084/m9.figshare.30585428.v1
title: Wikidata Entries For Scottish Brick History

description: This dataset was created as part of the CS4099: Major Software Project module in the School of Computer Science at the University of St Andrews in the academic year 2023-2024.This dataset relates to brick, tile, and associated works that are listed on the Scottish Brick History website. With permission, the data was web scrapped, manually matched to geographic coordinates, and stored in an Excel spreadsheet, where it was subjected to data cleaning prior to a bulk upload to Wikidata using OpenRefine.Prior to this project, there were no brick, tile, or other works on Wikidata. During this project, 761 new works were added to Wikidata, meaning that 100% of Scotland's brick, tile, and other works are on Wikidata as a result of this project.<b>Author contributions</b>Grace Young created and cleaned the original dataset built from said data on the Scottish Brick History website. Sara Thomas provided training about OpenRefine, proposed the Scottish Brick History Brick & Tileworks ID properties in Wikidata, and performed the batch uploads to Wikidata. Kirsty Ross supervised the project and uploaded the dataset to Figshare.
DOI: 10.15468/dl.d4cpz6
title: Occurrence Download

description: A dataset containing 867 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei, Macao, Hong Kong)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chloris virgata"
 ]
}

 The dataset includes 867 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081349-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qwfsgb
title: Occurrence Download

description: A dataset containing 22486 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Alyssum montanum, Alyssum pirinicum, Amphoricarpos autariatus, Amphoricarpos neumayerianus, Androsace obtusifolia, Androsace villosa)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22486 records from 355 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081351-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kx7an4
title: Occurrence Download

description: A dataset containing 32532 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Stereum ostrea, Xylodon raduloides, Stereum hirsutum, Trametopsis brasiliensis, Basidiodendron eyrei, Sistotrema oblongisporum, Fomes fomentarius, Mycena renati, Helicogloea pellucida, Radulomyces molaris, Elmerina caryae, Phanerochaete livescens, Gloeohypochnicium analogum, Hyphoderma roseocremeum, Hyphoderma setigerum, Trametes versicolor, Peniophora versiformis, Exidia glandulosa, Peniophorella pubera, Auricularia mesenterica, Laetisaria buckii, Hericium coralloides, Xylodon flaviporus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Elevation is not null"
 ]
}

 The dataset includes 32532 records from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081350-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18184779
title: TCOM-HF :  TOMCAT CTM and Occultation Measurements based HF profile data set

description: Methodology:  

The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse and Chipperfield (2023), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data.

HF Profile Processing and Bias Correction

Collocated HF profiles are organized into five distinct latitude bins:



NH polar: 90∘N - 50∘N

NH mid-lat: 20∘N - 70∘N

Tropics: 40∘S - 40∘N

SH mid-lat: 70∘S - 20∘S

SH polar: 90∘S - 50∘S


 Initially, TOMCAT tracers are interpolated on the ACE FTS height (1km to 50km) Separate XGBoost regression models are trained to predict ACE FTS measurements for a given altitude /latitude bin if more than 2000 measurements are available. XGBoost model is then used to estimate HF volume mixing ratios for all the TOMCAT grids.  Height resolved data are then interpolated on 22-pressure levels. For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.

Dataset also includes two files containing daily mean zonal mean HF profiles on height (1-50 km) and pressure (300-1 hPa) – 22 levels (9132 days/64 latitudes). Files also include uncertainties estimated using quantile regression and uncertainty = NaN means data is from TOMCAT CTM (scientifically useful range 5 to 25km).

zmhf_tcom_hlev_2000-2024_V2.0.nc – height level data (1 to 50 km)

zmhf_tcom_plev_2000-2024_V2.0.nc – pressure level data (300 to 1 hPa)

Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

(attached pdf file gives more details. This data set improves from our previous methodology (Dhomse and Chipperfield, 2023, ESSD) and manuscript describing to those updates is submitted to ESSD as Dhomse and Chipperfield, 2026.

 

Reference Publications

This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:

Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, 2023

Dhomse, S. S. and Chipperfield, M. P.: TCOM-CFC11 and TCOM-CFC12: A Gap-Free, Observationally Constrained Global Dataset of Stratospheric CFC11 and CFC12 Profiles (V2)., submitted to Earth Syst. Sci. Data, 2026
description: This work was supported by the NERC SISLAC (NE/R001782/1), InHALE (NE/X003450/1) ( and LSO3 (NE/V0011863/1) projects
DOI: 10.5281/zenodo.7607563
title: TCOM-HF :  TOMCAT CTM and Occultation Measurements based HF profile data set

description: Methodology:  

The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse and Chipperfield (2023), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data.

HF Profile Processing and Bias Correction

Collocated HF profiles are organized into five distinct latitude bins:



NH polar: 90∘N - 50∘N

NH mid-lat: 20∘N - 70∘N

Tropics: 40∘S - 40∘N

SH mid-lat: 70∘S - 20∘S

SH polar: 90∘S - 50∘S


 Initially, TOMCAT tracers are interpolated on the ACE FTS height (1km to 50km) Separate XGBoost regression models are trained to predict ACE FTS measurements for a given altitude /latitude bin if more than 2000 measurements are available. XGBoost model is then used to estimate HF volume mixing ratios for all the TOMCAT grids.  Height resolved data are then interpolated on 22-pressure levels. For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.

Dataset also includes two files containing daily mean zonal mean HF profiles on height (1-50 km) and pressure (300-1 hPa) – 22 levels (9132 days/64 latitudes). Files also include uncertainties estimated using quantile regression and uncertainty = NaN means data is from TOMCAT CTM (scientifically useful range 5 to 25km).

zmhf_tcom_hlev_2000-2024_V2.0.nc – height level data (1 to 50 km)

zmhf_tcom_plev_2000-2024_V2.0.nc – pressure level data (300 to 1 hPa)

Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

(attached pdf file gives more details. This data set improves from our previous methodology (Dhomse and Chipperfield, 2023, ESSD) and manuscript describing to those updates is submitted to ESSD as Dhomse and Chipperfield, 2026.

 

Reference Publications

This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:

Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, 2023

Dhomse, S. S. and Chipperfield, M. P.: TCOM-CFC11 and TCOM-CFC12: A Gap-Free, Observationally Constrained Global Dataset of Stratospheric CFC11 and CFC12 Profiles (V2)., submitted to Earth Syst. Sci. Data, 2026
description: This work was supported by the NERC SISLAC (NE/R001782/1), InHALE (NE/X003450/1) ( and LSO3 (NE/V0011863/1) projects
DOI: 10.5281/zenodo.18184430
title: TCOM-HCl : TOMCAT CTM and Occultation Measurements based HCl profile data set

description: Methodology:  

The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data.

HCl Profile Processing and Bias Correction

Collocated HCl profiles are organized into five distinct latitude bins:





NH polar: 90∘N - 50∘N




NH mid-lat: 20∘N - 70∘N




Tropics: 40∘S - 40∘N




SH mid-lat: 70∘S - 20∘S




SH polar: 90∘S - 50∘S



 Initially, TOMCAT tracers are interpolated on the ACE FTS height (1km to 50km) Separate XGBoost regression models are trained to predict ACE FTS measurements for a given altitude /latitude bin if more than 2000 measurements are available. XGBoost model is then used to estimate HCl volume mixing ratios for all the TOMCAT grids.  Height resolved data are then interpolated on 22-pressure levels. For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.

Dataset also includes two files containing daily mean zonal mean HCl profiles on height (1-50 km) and pressure (300-1 hPa) – 22 levels (9132 days/64 latitudes). Files also include uncertainties estimated using quantile regression and  uncertainty = NaN means data is from TOMCAT CTM (scientifically useful range 5 to 25km).

zmhcl_tcom_hlev_2000-2024_V2.0.nc – height level data (1 to 50 km)

zmhcl_tcom_plev_2000-2024_V2.0.nc – pressure level data (300 to 1 hPa)

Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

(attached pdf file gives more details. This data set improves from our previous methodology (Dhomse and Chipperfield, 2023, ESSD) and manuscript describing to those updates is submitted to ESSD as Dhomse and Chipperfield, 2026.

 

Reference Publication

This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:

Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, 2023

Dhomse, S. S. and Chipperfield, M. P.: TCOM-CFC11 and TCOM-CFC12: A Gap-Free, Observationally Constrained Global Dataset of Stratospheric CFC11 and CFC12 Profiles (V2)., submitted to Earth Syst. Sci. Data, 2026
description: This work was supported by the NERC projects InHALE (NE/X003450/1) and LSO3 (NE/V0011863/1).
DOI: 10.5281/zenodo.7608193
title: TCOM-HCl : TOMCAT CTM and Occultation Measurements based HCl profile data set

description: Methodology:  

The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data.

HCl Profile Processing and Bias Correction

Collocated HCl profiles are organized into five distinct latitude bins:





NH polar: 90∘N - 50∘N




NH mid-lat: 20∘N - 70∘N




Tropics: 40∘S - 40∘N




SH mid-lat: 70∘S - 20∘S




SH polar: 90∘S - 50∘S



 Initially, TOMCAT tracers are interpolated on the ACE FTS height (1km to 50km) Separate XGBoost regression models are trained to predict ACE FTS measurements for a given altitude /latitude bin if more than 2000 measurements are available. XGBoost model is then used to estimate HCl volume mixing ratios for all the TOMCAT grids.  Height resolved data are then interpolated on 22-pressure levels. For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.

Dataset also includes two files containing daily mean zonal mean HCl profiles on height (1-50 km) and pressure (300-1 hPa) – 22 levels (9132 days/64 latitudes). Files also include uncertainties estimated using quantile regression and  uncertainty = NaN means data is from TOMCAT CTM (scientifically useful range 5 to 25km).

zmhcl_tcom_hlev_2000-2024_V2.0.nc – height level data (1 to 50 km)

zmhcl_tcom_plev_2000-2024_V2.0.nc – pressure level data (300 to 1 hPa)

Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

(attached pdf file gives more details. This data set improves from our previous methodology (Dhomse and Chipperfield, 2023, ESSD) and manuscript describing to those updates is submitted to ESSD as Dhomse and Chipperfield, 2026.

 

Reference Publication

This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:

Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, 2023

Dhomse, S. S. and Chipperfield, M. P.: TCOM-CFC11 and TCOM-CFC12: A Gap-Free, Observationally Constrained Global Dataset of Stratospheric CFC11 and CFC12 Profiles (V2)., submitted to Earth Syst. Sci. Data, 2026
description: This work was supported by the NERC projects InHALE (NE/X003450/1) and LSO3 (NE/V0011863/1).
DOI: 10.25345/c5xk85414
title: MassIVE MSV000100407 - Characterization of large extracellular vesicles released by apoptotic and pyroptotic cells
title: Extracellular vesicles (EVs) are emerging key factors maintaining cellular homeostasis, critical mediators of intercellular communication, potential biomarkers and therapeutic tools. While small EVs have been extensively characterized, the molecular signatures of large EVs (including those generated during regulated cell death pathways) remain poorly defined. Here, we investigated the characteristics of large EVs released during apoptosis and pyroptosis by human monocytic cell lines (THP-1 and U937). Apoptosis was induced by staurosporine and blocked with the pan-caspase inhibitor Q-VD-OPh, whereas pyroptosis was triggered by LPS/nigericin and inhibited with a selective NLRP3 inhibitor. We found that both forms of regulated cell death markedly enhanced the release of large EVs. Both apoptotic and pyroptotic large EVs showed increased Annexin V binding and decreased CD9 expression as compared to those released by healthy living cells. Apoptosis- and pyroptosis-derived large EVs exhibited distinct proteomic profiles. Pyroptotic large EVs were shown to contain interacting protein networks of RNA binding proteins and chromatin-associated proteins many of which are known as established damage associated molecular patterns or alarmins. On the other hand, we found that a subpopulation of apoptotic large EVs was characterized by the presence of dsDNA, and active caspase-3/7. Together, our data shed light on the specific protein content of large EVs released by cells undergoing apoptosis and pyroptosis. This study identifies candidate markers of large EVs released by dying cells and may enhance our understanding of the role of EVs in cell death.
DOI: 10.4121/e61b5dac-3a47-4ba7-ae98-9aaf782597ab
title: Simulation Data Belonging to: Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions

description: Download and extract the "main_folder.zip". The README.md file will then state the following:<br><strong>Simulation Data Belonging to:</strong><strong>Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions</strong><br>This dataset accompanies the paper "Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions" by V. Jelle Lagerweij, Othonas A. Moultos, and Thijs J.H. Vlugt. It is highly advised to use this data only after carefully reading both this paper (TODO INSERT DOI) and the Supporting Information.<br><br><strong>The Information contained</strong>The dataset contains the following information:The simulation input files of the LAMMPS simulations. The simulation output can be reproduced with these input files. This requires LAMMPS version August 2024, PACKMOL V20.3.1 and fftool V1.2.1. The simulation files can be ran manually in each run folder (which is listed per mixture, concentration, temperature and specific run for statistical purposes, see the described structure of the folders).The raw simulation output files of the LAMMPS simulation. These contain many properties, such as the radial distribution function (rdf.dat), the atomic positions of certain snapshots (compressed in a .dcd format), the sampled properties during all 4 phases in the simulation (initial *NPT*, main *NPT*, initial *NVT*, and main *NVT*, see 2. Structure.), and the mean-squared-displacements required to compute the viscosities, self-diffusion coefficients, and Onsager coefficients that are reported in the scientific paper.The post-processing code (in the base directory) that can be used to post-process the raw simulation data. Instructions on how to use this code are listed in section 3. Post-processing Code.The outputs of the post-processing code. This data is stored in .xslx format per chemical species and temperature. These can be found on the mixture folders level, see 2. Structure.<br>
DOI: 10.4121/e61b5dac-3a47-4ba7-ae98-9aaf782597ab.v1
title: Simulation Data Belonging to: Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions

description: Download and extract the "main_folder.zip". The README.md file will then state the following:<br><strong>Simulation Data Belonging to:</strong><strong>Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions</strong><br>This dataset accompanies the paper "Electrical Conductivity Differences Between Experiments and Classical Simulations Reveal Self-diffusion Coefficients and Ion Lifetimes of Hydroxide and Hydronium in Aqueous Solutions" by V. Jelle Lagerweij, Othonas A. Moultos, and Thijs J.H. Vlugt. It is highly advised to use this data only after carefully reading both this paper (TODO INSERT DOI) and the Supporting Information.<br><br><strong>The Information contained</strong>The dataset contains the following information:The simulation input files of the LAMMPS simulations. The simulation output can be reproduced with these input files. This requires LAMMPS version August 2024, PACKMOL V20.3.1 and fftool V1.2.1. The simulation files can be ran manually in each run folder (which is listed per mixture, concentration, temperature and specific run for statistical purposes, see the described structure of the folders).The raw simulation output files of the LAMMPS simulation. These contain many properties, such as the radial distribution function (rdf.dat), the atomic positions of certain snapshots (compressed in a .dcd format), the sampled properties during all 4 phases in the simulation (initial *NPT*, main *NPT*, initial *NVT*, and main *NVT*, see 2. Structure.), and the mean-squared-displacements required to compute the viscosities, self-diffusion coefficients, and Onsager coefficients that are reported in the scientific paper.The post-processing code (in the base directory) that can be used to post-process the raw simulation data. Instructions on how to use this code are listed in section 3. Post-processing Code.The outputs of the post-processing code. This data is stored in .xslx format per chemical species and temperature. These can be found on the mixture folders level, see 2. Structure.<br>