cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30830651.v3
title: Indonesian Part-of-Speech Tagging Corpus

description: This dataset is a refined version of the Indonesian POS-tagged corpus by Fu et al. (2018). The corpus was filtered to include only complete single-clause sentences, identified through rule-based syntactic analysis using UPOS tags and dependency parsing.Annotation inconsistencies were detected using word-level entropy. From 7,388 word types analyzed, 617 words with H&gt;0 were identified as candidates and reviewed by 15 linguistic experts. Using majority voting, experts corrected 550 inconsistent word labels, while the remaining words were confirmed as valid ambiguities. All corrected labels were reintegrated into the corpus to produce the revised version.Evaluation comparing the original filtered corpus (V1) and the corrected version (V2) shows that V2 improves model performance across all metrics, especially on ambiguous words (≈14% accuracy and ≈15% macro F1 improvement). Average entropy also decreases from 0.07 to 0.04, indicating higher annotation consistency.This dataset provides a cleaner, more homogeneous POS-tagged resource for Indonesian language processing and improved model reliability on ambiguous and OOV cases.
DOI: 10.15468/dl.aa5dk3
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.514 -1.436819, 103.443715 -1.442385, 103.375159 -1.458945, 103.31002 -1.486093, 103.249901 -1.523159, 103.196281 -1.569232, 103.15048 -1.623179, 103.113627 -1.68367, 103.086628 -1.749217, 103.07015 -1.818207, 103.0646 -1.888942, 103.070114 -1.959679, 103.08656 -2.028677, 103.113533 -2.094237, 103.15037 -2.154744, 103.196165 -2.208708, 103.24979 -2.254799, 103.309926 -2.291881, 103.375091 -2.319041, 103.443679 -2.33561, 103.514 -2.341179, 103.584321 -2.33561, 103.652909 -2.319041, 103.718074 -2.291881, 103.77821 -2.254799, 103.831835 -2.208708, 103.87763 -2.154744, 103.914467 -2.094237, 103.94144 -2.028677, 103.957886 -1.959679, 103.9634 -1.888942, 103.95785 -1.818207, 103.941372 -1.749217, 103.914373 -1.68367, 103.87752 -1.623179, 103.831719 -1.569232, 103.778099 -1.523159, 103.71798 -1.486093, 103.652841 -1.458945, 103.584285 -1.442385, 103.514 -1.436819))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040163-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fjwhjj
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aspidosiphon mexicanus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 20 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040164-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.63ame4
title: Occurrence Download

description: A dataset containing 85 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (CAST(occurrence.taxonkey AS INTEGER) = 4363583 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 4363583 OR CAST(occurrence.kingdomkey AS INTEGER) = 4363583 OR CAST(occurrence.phylumkey AS INTEGER) = 4363583 OR CAST(occurrence.classkey AS INTEGER) = 4363583 OR CAST(occurrence.orderkey AS INTEGER) = 4363583 OR CAST(occurrence.familykey AS INTEGER) = 4363583 OR CAST(occurrence.genuskey AS INTEGER) = 4363583 OR CAST(occurrence.specieskey AS INTEGER) = 4363583)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 85 records were derived from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040139-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/ilsohj
title: PAN-00180605 - Roman smooth ware jug or jug-amphora (handle)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00180605&lt;/p&gt;
description: Modified: 2025-10-20T16:37:35+02:00
DOI: 10.25345/c57941739
title: MassIVE MSV000100157 - Screening the MMV Pathogen Box reveals the mitochondrial bc1-complex as a drug target in mature Toxoplasma gondii bradyzoites.
title: Tachyzoites and Bradyzoites have been exposed to select, dually active compounds from the MMV Pathogen Box and responses were recorded. Data are in part stable isotope resolved.
DOI: 10.15468/dl.sxb8sv
title: Occurrence Download

description: A dataset containing 9759 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lamprohiza splendidula"
 ]
}

 The dataset includes 9759 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040165-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/nwqqk0
title: PAN-00163607 - terra sigillata kom type Drag. 37 (EG)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00163607&lt;/p&gt;
description: Modified: 2025-11-02T12:09:14+01:00
DOI: 10.5281/zenodo.17864506
title: Supporting material for  "Towards Understanding  Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones" journal paper

description: This is a comprehensive and anonymized dataset of features extracted from GNSS RINEX data from 409 user tracks collected by 18 volunteer users (user indices 1 to 7 and 9 to 19) during the years 2024 and 2025 across five European countries and under 10 transportation modes.  The features extracted from the Android data are detailed below, under the Data Description section. The data from user 8 was unusable, so it was removed from this dataset.  Volunteers used their own Android phones to run the GNSSlogger app and were asked to send each recorded track via the app’s semi-automatic email feature, including the transport mode in the email subject. Volunteers were instructed to record only one transport mode per track (e.g., to start a new track when switching from bus to walking or from walking to tram, etc.). Therefore, each track corresponds to a unique transportation mode, reflected in the file name. The data can serve, for example,  as useful training data for multi-modal classification using pseudorange-based information.

Details of the data found can be found in the Readme file uploaded in the same *zip file as the 409 tracks (the track data is provided in *csv format). The anonymous and volunteer data collectors are warmly thanked for their contributions.

How to cite this data:

Yelyzaveta Pervysheva, Jari Nurmi, Oana Cramariuc, and Elena Simona Lohan, "Supporting material for  “Towards Understanding  Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones” journal paper", open-access dataset on Zenodo, DOI: 10.5281/zenodo.17864507
DOI: 10.5281/zenodo.17864507
title: Supporting material for  "Towards Understanding  Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones" journal paper

description: This is a comprehensive and anonymized dataset of features extracted from GNSS RINEX data from 409 user tracks collected by 18 volunteer users (user indices 1 to 7 and 9 to 19) during the years 2024 and 2025 across five European countries and under 10 transportation modes.  The features extracted from the Android data are detailed below, under the Data Description section. The data from user 8 was unusable, so it was removed from this dataset.  Volunteers used their own Android phones to run the GNSSlogger app and were asked to send each recorded track via the app’s semi-automatic email feature, including the transport mode in the email subject. Volunteers were instructed to record only one transport mode per track (e.g., to start a new track when switching from bus to walking or from walking to tram, etc.). Therefore, each track corresponds to a unique transportation mode, reflected in the file name. The data can serve, for example,  as useful training data for multi-modal classification using pseudorange-based information.

Details of the data found can be found in the Readme file uploaded in the same *zip file as the 409 tracks (the track data is provided in *csv format). The anonymous and volunteer data collectors are warmly thanked for their contributions.

How to cite this data:

Yelyzaveta Pervysheva, Jari Nurmi, Oana Cramariuc, and Elena Simona Lohan, "Supporting material for  “Towards Understanding  Multimodal Transport Classification Using Features From RINEX Data Extracted From Android Phones” journal paper", open-access dataset on Zenodo, DOI: 10.5281/zenodo.17864507
DOI: 10.15468/dl.ut3sku
title: Occurrence Download

description: A dataset containing 638 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d', "year", "month") yearmonth,
 countrycode countrycode,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, countrycode, PRINTF('%04d-%02d', "year", "month"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, countrycode, PRINTF('%04d-%02d', "year", "month"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, countrycode, PRINTF('%04d-%02d', "year", "month"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, countrycode, PRINTF('%04d-%02d', "year", "month"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, countrycode, PRINTF('%04d-%02d', "year", "month"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, countrycode, PRINTF('%04d-%02d', "year", "month"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (CAST(occurrence.taxonkey AS INTEGER) = 4363583 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 4363583 OR CAST(occurrence.kingdomkey AS INTEGER) = 4363583 OR CAST(occurrence.phylumkey AS INTEGER) = 4363583 OR CAST(occurrence.classkey AS INTEGER) = 4363583 OR CAST(occurrence.orderkey AS INTEGER) = 4363583 OR CAST(occurrence.familykey AS INTEGER) = 4363583 OR CAST(occurrence.genuskey AS INTEGER) = 4363583 OR CAST(occurrence.specieskey AS INTEGER) = 4363583)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL AND occurrence.hascoordinate = TRUE)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d', "year", "month"),
 occurrence.countrycode

 The dataset's 638 records were derived from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040141-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9ettyc
title: Occurrence Download

description: A dataset listing the 3 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Luidia foliolata"
 ]
}

 The dataset's 3 records were derived from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040150-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dd44gp
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gentiana siphonantha",
 "Year 1950-2025"
 ]
}

 The dataset includes 128 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040166-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17569944
title: Full Reproduction Package: Inputs, Configurations, and Monitoring Data for TETIS Computational Scalability Study

description: This repository provides the complete data package necessary to fully reproduce the computational performance analysis of the distributed hydrological model, TETIS v9.1, as presented in the associated publication, "Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Pre-diction"

The package is organized into key directories to support both model execution and data analysis:





Modelos (TETIS Configuration Files):





Contains 30 compressed archives (e.g., 001_Po_200m_Base.7z) corresponding to the 30 experimental basin configurations (2 catchments $\times$ 5 spatial resolutions $\times$ 3 reconditioning schemes).




Each archive includes the TETIS execution files (Tetis.exe, Hantec.exe), the necessary ASCII parameter maps (dem_200b.asc, slope_200b.asc), and all simulation configuration files (Control.exe, Settings.txt).


Each model contains folfer FE includes the input files to execute the model scenarios





Monitor (Hardware & Execution Data):





Includes the raw data logs from the hardware monitoring tool (e.g., equipo.csv, monitoreo.csv, HWINFO64.exe).




These files contain the Key Performance Indicators (KPIs) measured during the experiments, such as execution time, CPU utilization, Max Turbo Frequency, and memory data, which were used to train the Random Forest predictive models.





This comprehensive package ensures the highest level of reproducibility, allowing researchers to either re-run the TETIS simulations or re-validate the Machine Learning models based on the collected execution metrics.

Cite the associated article when using this repository:



Cortés-Torres, N., Salazar Galán, S., & Francés García, F. (2025). Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Prediction. (Submitted for publication).

DOI: [Submitted for publication]
DOI: 10.5281/zenodo.17569945
title: Full Reproduction Package: Inputs, Configurations, and Monitoring Data for TETIS Computational Scalability Study

description: This repository provides the complete data package necessary to fully reproduce the computational performance analysis of the distributed hydrological model, TETIS v9.1, as presented in the associated publication, "Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Pre-diction"

The package is organized into key directories to support both model execution and data analysis:





Modelos (TETIS Configuration Files):





Contains 30 compressed archives (e.g., 001_Po_200m_Base.7z) corresponding to the 30 experimental basin configurations (2 catchments $\times$ 5 spatial resolutions $\times$ 3 reconditioning schemes).




Each archive includes the TETIS execution files (Tetis.exe, Hantec.exe), the necessary ASCII parameter maps (dem_200b.asc, slope_200b.asc), and all simulation configuration files (Control.exe, Settings.txt).


Each model contains folfer FE includes the input files to execute the model scenarios





Monitor (Hardware & Execution Data):





Includes the raw data logs from the hardware monitoring tool (e.g., equipo.csv, monitoreo.csv, HWINFO64.exe).




These files contain the Key Performance Indicators (KPIs) measured during the experiments, such as execution time, CPU utilization, Max Turbo Frequency, and memory data, which were used to train the Random Forest predictive models.





This comprehensive package ensures the highest level of reproducibility, allowing researchers to either re-run the TETIS simulations or re-validate the Machine Learning models based on the collected execution metrics.

Cite the associated article when using this repository:



Cortés-Torres, N., Salazar Galán, S., & Francés García, F. (2025). Scalability and Computational Performance of the TETIS Eco-Hydrological Model Using Machine Learning-Based Prediction. (Submitted for publication).

DOI: [Submitted for publication]
DOI: 10.15468/dl.c44d9z
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gentiana robusta",
 "Year 1950-2025"
 ]
}

 The dataset includes 54 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040169-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hhgpnq
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aspidosiphon misakiensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 29 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040168-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6q392
title: Occurrence Download

description: A dataset containing 319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((0.34482 40.96289,0.38787 40.96289,0.38787 41.00464,0.34482 41.00464,0.34482 40.96289))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year 2015-2024"
 ]
}

 The dataset includes 319 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040171-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tjr2hu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.213 0.7261840000000001, 101.142731 0.720617, 101.074194 0.704052, 101.009074 0.676897, 100.948977 0.639821, 100.895382 0.593737, 100.849609 0.53978, 100.812785 0.479279, 100.785815 0.413724, 100.769364 0.344728, 100.763837 0.273992, 100.76937 0.203255, 100.785825 0.134261, 100.812798 0.068707, 100.849625 0.008208999999999999, 100.895399 -0.045745, 100.948993 -0.09182700000000001, 101.009088 -0.1289, 101.074203 -0.156054, 101.142737 -0.172618, 101.213 -0.178185, 101.283263 -0.172618, 101.351797 -0.156054, 101.416912 -0.1289, 101.477007 -0.09182700000000001, 101.530601 -0.045745, 101.576375 0.008208999999999999, 101.613202 0.068707, 101.640175 0.134261, 101.65663 0.203255, 101.662163 0.273992, 101.656636 0.344728, 101.640185 0.413724, 101.613215 0.479279, 101.576391 0.53978, 101.530618 0.593737, 101.477023 0.639821, 101.416926 0.676897, 101.351806 0.704052, 101.283269 0.720617, 101.213 0.7261840000000001))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040170-251120083545085/datasets/export for details.
DOI: 10.17026/ar/tflrgc
title: PAN-00165155 - terra sigillata bowl type Drag. 29

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00165155&lt;/p&gt;
description: Modified: 2025-10-14T11:11:21+02:00
DOI: 10.5281/zenodo.17751041
title: BASCOE CTM simulations driven by ERA5, MERRA2 and JRA‑3Q monthly zonal means

description: These data sets contain monthly zonal mean (mzm) model output from three simulations performed with the Belgian Assimilation System for Chemical ObsErvations (BASCOE) chemistry transport model (CTM), dynamically driven by ERA5, MERRA2 and JRA-3Q respectively. The BASCOE CTM was run on reduced vertical grids with 61 (ERA5), 49 (MERRA2) and 53 (JRA-3Q) levels (see Vervalcke et al., https://doi.org/10.5194/egusphere-2025-3597).
DOI: 10.5281/zenodo.17751040
title: BASCOE CTM simulations driven by ERA5, MERRA2 and JRA‑3Q monthly zonal means

description: These data sets contain monthly zonal mean (mzm) model output from three simulations performed with the Belgian Assimilation System for Chemical ObsErvations (BASCOE) chemistry transport model (CTM), dynamically driven by ERA5, MERRA2 and JRA-3Q respectively. The BASCOE CTM was run on reduced vertical grids with 61 (ERA5), 49 (MERRA2) and 53 (JRA-3Q) levels (see Vervalcke et al., https://doi.org/10.5194/egusphere-2025-3597).