cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6hww5r
title: Occurrence Download

description: A dataset containing 130 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
 occurrence.countrycode = 'CN'
 AND GBIF_STRINGARRAYCONTAINS(occurrence.classifications['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c'], '2879787', TRUE)
 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.classificationdetails['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c']['specieskey'] &lt;&gt; '' 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 130 records were derived from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002680-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18550314
title: research data
DOI: 10.5281/zenodo.18550313
title: research data
DOI: 10.6084/m9.figshare.30491783
title: U.S. Political Environment Table (2018-2024)

description: Data and Dofile for the Political Environment Table.
DOI: 10.6084/m9.figshare.30491783.v9
title: U.S. Political Environment Table (2018-2024)

description: Data and Dofile for the Political Environment Table.
DOI: 10.6084/m9.figshare.30491783.v8
title: U.S. Political Environment Table (2018-2023)

description: Data and Dofile for the Political Environment Table.
DOI: 10.15468/dl.qabgb8
title: Occurrence Download

description: A dataset containing 351 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ipomoea wightii",
 "HasCoordinate is true"
 ]
}

 The dataset includes 351 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002747-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18538236
title: Neva River Estuary (Middle Part) Aggregated Vertical Profiles Dataset (2014–2024)

description: Processed, quality-controlled, analysis-ready dataset of aggregated vertical profiles from the middle part of the Neva Estuary (Neva River estuary) (eastern Gulf of Finland, Baltic Sea). Data were collected at seven stations during late July – early August (2014–2024). Measurements include temperature, practical salinity, PAR, and chlorophyll a (from calibrated fluorescence), provided on a 0.1-m depth grid.

For each station and sampling event, up to three replicate casts were performed. After depth-binning and quality control, replicate casts were averaged within each 0.1-m depth bin to produce a single aggregated profile per station-event. Raw 1-second instrument time series and original cast files are not included.

Companion analysis code:





GitHub: https://github.com/Mikhail-Golubkov/neva-chl-pipeline




Zenodo (software snapshot): https://doi.org/10.5281/zenodo.18540702
DOI: 10.5281/zenodo.18538237
title: Neva River Estuary (Middle Part) Aggregated Vertical Profiles Dataset (2014–2024)

description: Processed, quality-controlled, analysis-ready dataset of aggregated vertical profiles from the middle part of the Neva Estuary (Neva River estuary) (eastern Gulf of Finland, Baltic Sea). Data were collected at seven stations during late July – early August (2014–2024). Measurements include temperature, practical salinity, PAR, and chlorophyll a (from calibrated fluorescence), provided on a 0.1-m depth grid.

For each station and sampling event, up to three replicate casts were performed. After depth-binning and quality control, replicate casts were averaged within each 0.1-m depth bin to produce a single aggregated profile per station-event. Raw 1-second instrument time series and original cast files are not included.

Companion analysis code:





GitHub: https://github.com/Mikhail-Golubkov/neva-chl-pipeline




Zenodo (software snapshot): https://doi.org/10.5281/zenodo.18540702
DOI: 10.15468/dl.6fkftg
title: Occurrence Download

description: A dataset containing 20153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Aristida ramosa"
 ]
}

 The dataset includes 20153 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002749-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15478571
title: EEG data analysis repository for shalu et al. Similar does not mean same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions

description: EEG data analysis repository for Shalu et al. Similar does not mean the same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions https://doi.org/10.3389/fnhum.2025.1632844. The preprocessed data from https://doi.org/10.5281/zenodo.14986232 was imported in R (Version 4.4.2; R Core Team, 2024) using the eeguana package (Version 0.1.11.9001; Nicenboim, 2018) for epoching and statistical analysis.  Single trial EEG epochs were used for statistically analysing the mean amplitudes in selected time-windows of interest by fitting linear mixed effects models (LMEM) using the lme4 package (Bates et al., 2015) in R.

The names of the zipped folders describe their respective contents. The Code_In_Context_Analysis_Output_R_Notebooks folder contains the R Notebooks of the LMEM analyses of the behavioural and ERP data. These notebooks show the code, data and output in context, and provide full model summaries and details for all the models reported in the article. The R scripts themselves reside in a folder of their own, which also includes the version of the eeguana package we used for the analyses, and the custom-made helper scripts for processing the EEG data, epoching them and extracting mean amplitudes from them. The EEG and Epochs data resulting from the ERP analysis, as well as the single-trial mean amplitudes and prestimulus mean amplitudes extracted for each time-window of interest are in EEG_And_Epochs_Data_Files. Since the LMEM models computed were quite complex, the model objects are provided as RDS files for easily importing them into R without having to compute the models again. The plots generated at various stages of the analysis are in the Plots folder. 

References

Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1. doi: 10.18637/jss.v067.i01.

Nicenboim, B. (2018). eeguana: A package for manipulating EEG data in R. Version 0.1.11.9001. Retrieved from https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138.

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.

Shalu, S., Choudhary, K. K., & Muralikrishnan, R. (2025). Dataset with EEG data from Malayalam speakers. (Shalu et al., 2025) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14986232
DOI: 10.5281/zenodo.15478572
title: EEG data analysis repository for shalu et al. Similar does not mean same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions

description: EEG data analysis repository for Shalu et al. Similar does not mean the same: ERP correlates of processing mental and physical experiencer verbs in Malayalam complex constructions https://doi.org/10.3389/fnhum.2025.1632844. The preprocessed data from https://doi.org/10.5281/zenodo.14986232 was imported in R (Version 4.4.2; R Core Team, 2024) using the eeguana package (Version 0.1.11.9001; Nicenboim, 2018) for epoching and statistical analysis.  Single trial EEG epochs were used for statistically analysing the mean amplitudes in selected time-windows of interest by fitting linear mixed effects models (LMEM) using the lme4 package (Bates et al., 2015) in R.

The names of the zipped folders describe their respective contents. The Code_In_Context_Analysis_Output_R_Notebooks folder contains the R Notebooks of the LMEM analyses of the behavioural and ERP data. These notebooks show the code, data and output in context, and provide full model summaries and details for all the models reported in the article. The R scripts themselves reside in a folder of their own, which also includes the version of the eeguana package we used for the analyses, and the custom-made helper scripts for processing the EEG data, epoching them and extracting mean amplitudes from them. The EEG and Epochs data resulting from the ERP analysis, as well as the single-trial mean amplitudes and prestimulus mean amplitudes extracted for each time-window of interest are in EEG_And_Epochs_Data_Files. Since the LMEM models computed were quite complex, the model objects are provided as RDS files for easily importing them into R without having to compute the models again. The plots generated at various stages of the analysis are in the Plots folder. 

References

Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1. doi: 10.18637/jss.v067.i01.

Nicenboim, B. (2018). eeguana: A package for manipulating EEG data in R. Version 0.1.11.9001. Retrieved from https://github.com/bnicenboim/eeguana. http://doi.org/10.5281/zenodo.2533138.

R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.

Shalu, S., Choudhary, K. K., & Muralikrishnan, R. (2025). Dataset with EEG data from Malayalam speakers. (Shalu et al., 2025) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14986232
DOI: 10.5281/zenodo.18423191
title: Research data related to a qualitative study on digital competencies and teacher professional identity in higher education

description: This dataset contains the research data produced in a qualitative study conducted in the field of higher education. The materials include: (i) the final anonymized textual corpus used for lexical analysis with the Iramuteq software; (ii) anonymized interview transcripts made available for transparency purposes; (iii) analytical tables resulting from the literature review; (iv) a PRISMA 2020 flow diagram documenting the study selection process; and (v) a table describing the search strategies used in the consulted databases.

All data were organized, anonymized, and shared in accordance with ethical research standards and Open Science principles, aiming to ensure transparency, methodological rigor, and responsible data sharing.
DOI: 10.5281/zenodo.18423192
title: Research data related to a qualitative study on digital competencies and teacher professional identity in higher education

description: This dataset contains the research data produced in a qualitative study conducted in the field of higher education. The materials include: (i) the final anonymized textual corpus used for lexical analysis with the Iramuteq software; (ii) anonymized interview transcripts made available for transparency purposes; (iii) analytical tables resulting from the literature review; (iv) a PRISMA 2020 flow diagram documenting the study selection process; and (v) a table describing the search strategies used in the consulted databases.

All data were organized, anonymized, and shared in accordance with ethical research standards and Open Science principles, aiming to ensure transparency, methodological rigor, and responsible data sharing.
DOI: 10.15468/dl.entvgd
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gentianella hispanica"
 ]
}

 The dataset includes 27 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002752-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/cdc.542
title: Chandra X-ray Observatory Chandra Data Collection for request #542

description: Chandra Data Collection for request #542
DOI: 10.5281/zenodo.18550370
title: LINF_320037200

description: EMC6-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18258565
title: Dataset: Being K. Malevich: A hands-on approach to compositional preference

description: This file contains the rating data and compositions corresponding to the research described in the article: Being K. Malevich: A hands-on approach to compositional preference. Published in i-perception
DOI: 10.5281/zenodo.18550369
title: LINF_320037200

description: EMC6-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18258564
title: Dataset: Being K. Malevich: A hands-on approach to compositional preference

description: This file contains the rating data and compositions corresponding to the research described in the article: Being K. Malevich: A hands-on approach to compositional preference. Published in i-perception
DOI: 10.15468/dl.2buekc
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ipomoea kotschyana",
 "HasCoordinate is true"
 ]
}

 The dataset includes 84 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002756-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m6458z
title: Occurrence Download

description: A dataset containing 290 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-101.72703 -8.89949,-52.27297 -8.89949,-52.27297 10.89949,-101.72703 10.89949,-101.72703 -8.89949))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dysithamnus occidentalis"
 ]
}

 The dataset includes 290 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002753-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10850079
title: Data for the paper "Fock State Sampling Method – Characteristic temperature of maximal fluctuations for interacting bosons in box potentials"
DOI: 10.5281/zenodo.10850078
title: Data for the paper "Fock State Sampling Method – Characteristic temperature of maximal fluctuations for interacting bosons in box potentials"
DOI: 10.15468/dl.7jftu3
title: Occurrence Download

description: A dataset containing 205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Viburnum sambucinum",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 205 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002755-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15125/bath-01532
title: Dataset for a framework for assessing the impact of geometric imperfections in concrete shell structures using deep learning

description: This dataset contains scripts and data supporting the following following thesis: Pollet, M. (2025). Rapid structural analysis of prefabricated thin concrete shells using deep learning (Thesis). University of Bath.

Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. However, geometric imperfections, which may occur during production can negatively impact their structural behaviour. While this impact can be assessed through Finite Element Analysis (FEA), a faster analysis method, such as surrogate modelling, could benefit concrete shell manufacturers.

This dataset contains deep learning models – Multilayer Perceptrons, and Convolutional Neural Networks – that have been trained to predict the buckling factor and stress fields of geometrically imperfect concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related thesis.
description: The methods used to generate this data can be found in the related thesis.
description: The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.
description: The original folder structure is given in README.md. To reproduce it, create a new folder and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside.

The path to the ConcreteShellFEA dataset needs to be specified in each script, under the DATASET_ROOT variable.
description: Further relevant documentation may be found in the following resources.

   Pollet, M.,  2025.   Rapid structural analysis of prefabricated thin concrete shells using deep learning: (Alternative Format Thesis).     Thesis (PhD).    University of Bath.   Available from: https://researchportal.bath.ac.uk/en/studentTheses/rapid-structural-analysis-of-prefabricated-thin-concrete-shells-u.
DOI: 10.15125/bath-01504
title: Dataset for "Fast structural analysis of concrete thin-shells using deep learning"

description: This dataset contains scripts and data supporting the following research article: Pollet, M., Shepherd, P., Hawkins, W., and Costa, E., 2026. Fast structural analysis of concrete thin-shells using deep learning. Computers & Structures, 320, 108042.

Concrete thin-shells are materially efficient structures, which can be used to reduce the environmental impact of concrete structures. Their shape is typically determined iteratively and evaluated through Finite Element Analysis (FEA). This research proposes the use of surrogate models as faster alternatives to FEA, thus enabling wider design space exploration.

This dataset contains deep learning models – Multilayer Perceptrons, Convolutional Neural Networks, and Graph Neural Networks – that have been trained to predict the buckling factor and stress fields of concrete thin-shells of various shapes under design loads. It also contains the Python scripts that were used to train these models and assess their performance. Running these scripts necessitates the associated ConcreteShellFEA dataset to be downloaded. Further details about this data can be found in the related research article.
description: Full details of the methodology used may be found in the associated article.
description: The data in the models and results folders was generated using the Python code in scripts folder. These scripts rely on the dependencies listed in requirements.txt.
description: The original folder structure is given in README.md. To reproduce it, create a folder "FastStructuralAnalysisOfConcreteThinShellsUsingDeepLearning" and extract the "models.zip" and "results.zip" folders inside. Additionally, create a "scripts" folder and store all Python scripts inside.

The path to the ConcreteShellFEA dataset needs to be specified in each script, under the DATASET_ROOT variable.
description: Further relevant documentation may be found in the following resources.

   Pollet, M., Shepherd, P., Hawkins, W., and Costa, E.,  2026.   Fast structural analysis of concrete thin-shells using deep learning.    Computers & Structures, 320, 108042.  Available from: https://doi.org/10.1016/j.compstruc.2025.108042.
DOI: 10.5281/zenodo.10850074
title: Data for the paper "Microcanonical and canonical fluctuations in atomic Bose-Einstein condensates – Fock state sampling approach"
DOI: 10.5281/zenodo.10850075
title: Data for the paper "Microcanonical and canonical fluctuations in atomic Bose-Einstein condensates – Fock state sampling approach"
DOI: 10.15468/dl.5v6cns
title: Occurrence Download

description: A dataset containing 148930 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sciades",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 148930 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002688-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vr3sd3
title: Occurrence Download

description: A dataset containing 222 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Aristida rhiniochloa"
 ]
}

 The dataset includes 222 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002757-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25824/redu/jsggnt
title: Conjunto de dados ópticos, estruturais, eletroquímicos e cromatográficos da dissertação de mestrado acerca da oxidação de amoxicilina de soluções aquosas utilizando eletrodos semicondutores

description: O conjunto de dados refere-se as caracterizações ópticas (microscopias), estruturais (Difratometrias de Raio x), e eletroquímicas (voltametrias e cronoamperometria)
DOI: 10.15468/dl.wk8q8s
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Speothos venaticus"
 ]
}

 The dataset includes 93 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002758-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.