cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13467024
title: The Teachers' Occupational Wellbeing Study

description: The Teachers’ Occupational Wellbeing Study examines Finnish teachers’ occupational wellbeing, along with its predictors and outcomes, using a comprehensive survey questionnaire. Attention is also given to demographic aspects, such as regional differences and variation across educational levels and institutions. The aim is to use the findings to inform interventions, teacher training, and policy development.

The study began in spring 2020, at the onset of the COVID-19 pandemic and was started by Professor Katariina Salmela-Aro (1961-2025). Since then, data have been collected biannually every spring and autumn. In addition, the collection of longitudinal data began in spring 2024. This design enables the examination of both general trends (cross-sectional data) and individual-level changes over time (longitudinal study).

The survey data have been gathered in close collaboration with the Trade Union of Education in Finland (OAJ), consisting of responses from its members. The participants include teachers from across Finland and all educational levels.

The study is currently part of and funded by the EDUCA Flagship. Close collaboration is also done with the Finnish Principal Barometer. More information about the study and its main results can be found on the EDUCA Flagship website and in the supporting materials provided on this page. 

The project is currently led by University Lecturer Dr. Lauri Hietajärvi (lauri.hietajarvi@helsinki.fi) from University of Helsinki, Finland, and managed by Dr. Olli-Pekka Heinimäki (olli-pekka.heinimaki@helsinki.fi) from the same university. 

*******************************************************************************

Selected sections of datasets from 2020 to 2022 have been made openly available for anyone to use. Please read the relevant documentation carefully before working with the data. Should you have any questions, please contact the project management.

The attached documents includes:



Teacher occupational wellbeing study data collection report – openly distributed version: Details about the project and data collection.



Time Series 2020–2025: Key wellbeing trends across the full datasets (each time point).




Representativeness of the Data: Estimates of representativity at each measurement point.




Teacher_occupational_wellbeing_measures_overall: Summary of all measures included in the survey across timepoints.




README: Information about the open-access datasets and how they were generated from the original data.




2020_Spring.zip – 2022_Autumn.zip: Six open-access datasets collected at different time points (each provided in both .sav and .csv formats).




Scale documentation 2020–2022 open: Scale documents for each of the published datasets.



********************************************************************************
DOI: 10.5281/zenodo.18431419
title: The Teachers' Occupational Wellbeing Study

description: The Teachers’ Occupational Wellbeing Study examines Finnish teachers’ occupational wellbeing, along with its predictors and outcomes, using a comprehensive survey questionnaire. Attention is also given to demographic aspects, such as regional differences and variation across educational levels and institutions. The aim is to use the findings to inform interventions, teacher training, and policy development.

The study began in spring 2020, at the onset of the COVID-19 pandemic and was started by Professor Katariina Salmela-Aro (1961-2025). Since then, data have been collected biannually every spring and autumn. In addition, the collection of longitudinal data began in spring 2024. This design enables the examination of both general trends (cross-sectional data) and individual-level changes over time (longitudinal study).

The survey data have been gathered in close collaboration with the Trade Union of Education in Finland (OAJ), consisting of responses from its members. The participants include teachers from across Finland and all educational levels.

The study is currently part of and funded by the EDUCA Flagship. Close collaboration is also done with the Finnish Principal Barometer. More information about the study and its main results can be found on the EDUCA Flagship website and in the supporting materials provided on this page. 

The project is currently led by University Lecturer Dr. Lauri Hietajärvi (lauri.hietajarvi@helsinki.fi) from University of Helsinki, Finland, and managed by Dr. Olli-Pekka Heinimäki (olli-pekka.heinimaki@helsinki.fi) from the same university. 

*******************************************************************************

Selected sections of datasets from 2020 to 2022 have been made openly available for anyone to use. Please read the relevant documentation carefully before working with the data. Should you have any questions, please contact the project management.

The attached documents includes:



Teacher occupational wellbeing study data collection report – openly distributed version: Details about the project and data collection.



Time Series 2020–2025: Key wellbeing trends across the full datasets (each time point).




Representativeness of the Data: Estimates of representativity at each measurement point.




Teacher_occupational_wellbeing_measures_overall: Summary of all measures included in the survey across timepoints.




README: Information about the open-access datasets and how they were generated from the original data.




2020_Spring.zip – 2022_Autumn.zip: Six open-access datasets collected at different time points (each provided in both .sav and .csv formats).




Scale documentation 2020–2022 open: Scale documents for each of the published datasets.



********************************************************************************
DOI: 10.5281/zenodo.12087575
title: Template for HFLAV results in Zenodo

description: HFLAV template for results in March 2025

Cite the results presented as

S. Banerjee et al., Averages of b-hadron, c-hadron, and tau-lepton properties as of 2023, Phys. Rev. D113, (2026) 012008, arXiv:2411.18639, with specific result from doi:10.5281/zenodo.18494470.

Alternatively use the bibtex record


    @article{HFLAV:2024ctg,
        author = "Banerjee, Sw. and others",
        collaboration = "Heavy Flavor Averaging Group (HFLAV)",
        title = "{Averages of $b$-hadron, $c$-hadron, and $\tau$-lepton properties as of 2023}",
        eprint = "2411.18639",
        archivePrefix = "arXiv",
        primaryClass = "hep-ex",
        doi = "10.1103/x87q-tld5",
        journal = "Phys. Rev.",
        volume = "D113",
        number = "1",
        pages = "012008",
        year = "2026"
        note = "{with specific result from \href{https://doi.org/10.5281/zenodo.18494470}{{\texttt{doi:10.5281/zenodo.18494470}}}}"
}
DOI: 10.5281/zenodo.18494470
title: Template for HFLAV results in Zenodo

description: HFLAV template for results in March 2025

Cite the results presented as

S. Banerjee et al., Averages of b-hadron, c-hadron, and tau-lepton properties as of 2023, Phys. Rev. D113, (2026) 012008, arXiv:2411.18639, with specific result from doi:10.5281/zenodo.18494470.

Alternatively use the bibtex record


    @article{HFLAV:2024ctg,
        author = "Banerjee, Sw. and others",
        collaboration = "Heavy Flavor Averaging Group (HFLAV)",
        title = "{Averages of $b$-hadron, $c$-hadron, and $\tau$-lepton properties as of 2023}",
        eprint = "2411.18639",
        archivePrefix = "arXiv",
        primaryClass = "hep-ex",
        doi = "10.1103/x87q-tld5",
        journal = "Phys. Rev.",
        volume = "D113",
        number = "1",
        pages = "012008",
        year = "2026"
        note = "{with specific result from \href{https://doi.org/10.5281/zenodo.18494470}{{\texttt{doi:10.5281/zenodo.18494470}}}}"
}
DOI: 10.7488/502653f6-122e-400d-b4b5-c8594a70f1df
title: PacBio HiFi raw data of a Boran bull

description: As of 05/02/2026 this data will be deleted and no longer be stored in the DataVault.

Raw data following PacBio SMRT Cell runs for Boran cattle (a single animal, a bull). Data stored on DataVault due to size. Requests for access will not necessarily be granted.
description: Wragg, D. and Prendergast J. "HiFi sequence data of a Boran bull" Edinburgh DataVault (2021) https://doi.org/10.7488/502653f6-122e-400d-b4b5-c8594a70f1df
DOI: 10.15468/dl.kq9mv8
title: Occurrence Download

description: A dataset containing 134426 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coryphaena hippurus"
 ]
}

 The dataset includes 134426 records from 266 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013666-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18494393
title: Jeff Kosaka (LiDAR) June 24 2025

description: This orthomosaic of Sånta Rita-Sumai, Guam was created from 262 images taken on June 24 2025, at 150ft AGL, with a DJI Matrice 350 . The mission was planned using the DJI Pilot application and post-processed using DJI Terra.

This project was made possible through funding by the following grant(s): NACD (Award No. HQ00342020012).
description: Disclaimer: The following aerial imagery is intended solely for research purposes related to the assessment of the captured area. It is strictly prohibited to distribute this imagery to any unauthorized individuals or third-party entities without prior authorization. All drone imagery was obtained in accordance with FAA guidelines and applicable local laws, with all necessary authorizations secured to ensure safe and legal operation.

The University of Guam Drone Corps funded by NASA Guam Space Grant and NASA Guam EPSCoR retain all rights to the aerial imagery, including but not limited to copyrights and intellectual property rights. Any unauthorized modification, reproduction, or distribution of the imagery is strictly prohibited.

By using this data, you acknowledge and agree to abide by the terms and conditions set forth in this disclaimer.

 

 

Privacy Disclaimer: The aerial imagery captured for assessment may inadvertently include images of private property and individuals. While every effort has been made to respect privacy, it is possible that identifiable information or sensitive locations may be visible in the imagery. University of Guam Drone Corps and related parties disclaim any responsibility for the unintentional collection or dissemination of such information. The recipient(s) of this imagery are urged to handle it with due care and take necessary precautions to protect the privacy rights of individuals and comply with applicable privacy laws and regulations.
DOI: 10.15468/dl.njjvww
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Warburgia salutaris",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 32 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013722-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ysfnct
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Specimen)",
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "PublishingOrg is 2cd829bb-b713-433d-99cf-64bef11e5b3e",
 "TaxonKey is Myotis",
 "Year 1800-1950"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013723-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18494515
title: The Double Penalty of Fossil Fuel Imports: Unraveling the Energy-Growth-Environment Nexus in the MENA Region

description: The Middle East and North Africa (MENA) region faces a structural paradox: it holds nearly 57% of the world’s oil reserves while being highly vulnerable to climate change. This study examines the dynamic relationships between renewable energy (RES), economic growth, and environmental quality in ten MENA countries over 2000–2021 using a panel ARDL model, complemented by cross-sectional dependence and heterogeneity tests. The analysis considers both short- and long-term effects of RES, CO₂ emissions, investment (INV), and fossil fuel imports (FFI). Results show that RES significantly reduce CO₂ emissions and boost investment in the long run, highlighting their key role in sustainable growth. In contrast, fossil fuel imports create a “double penalty” by increasing emissions and constraining investment. The study concludes that energy transition can promote a decoupling of economic growth from environmental degradation, provided that policies are differentiated and adapted to national specificities.
DOI: 10.5281/zenodo.18494516
title: The Double Penalty of Fossil Fuel Imports: Unraveling the Energy-Growth-Environment Nexus in the MENA Region

description: The Middle East and North Africa (MENA) region faces a structural paradox: it holds nearly 57% of the world’s oil reserves while being highly vulnerable to climate change. This study examines the dynamic relationships between renewable energy (RES), economic growth, and environmental quality in ten MENA countries over 2000–2021 using a panel ARDL model, complemented by cross-sectional dependence and heterogeneity tests. The analysis considers both short- and long-term effects of RES, CO₂ emissions, investment (INV), and fossil fuel imports (FFI). Results show that RES significantly reduce CO₂ emissions and boost investment in the long run, highlighting their key role in sustainable growth. In contrast, fossil fuel imports create a “double penalty” by increasing emissions and constraining investment. The study concludes that energy transition can promote a decoupling of economic growth from environmental degradation, provided that policies are differentiated and adapted to national specificities.
DOI: 10.15468/dl.bv7c6m
title: Occurrence Download

description: A dataset listing the 598 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-11.93216 36.15286,-10.61407 36.15286,-10.61407 37.05301,-11.93216 37.05301,-11.93216 36.15286))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset's 598 records were derived from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013690-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v24rra
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Octopus vulgaris",
 "Year 2016-2026"
 ]
}

 The dataset includes 38 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013725-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bny263
title: Occurrence Download

description: A dataset containing 419 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Drimys winteri",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 419 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013726-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7929/issda/wty5ok
title: Healthy Ireland Survey, 2019

description: The annual Healthy Ireland Survey provides an up-to-date picture of the nation’s healthbalong with a robust and credible baseline set of data on a range of health behaviours which have significant impact on individual health outcomes. These data are being used by the Department of Health to inform current and future policy direction and programme development and implementation. The Survey reports on many lifestyle behaviours such as smoking, alcohol consumption, physical activity, active travel, weight management, diet and nutrition, health service utilization patterns, dental health, chronic disease incidence, mental health, sleep patterns, dementia awareness, caring responsibilities, women’s health and sexual health. Some modules are repeated every year (e.g. smoking, demographics, chronic conditions), however, the majority of modules are being repeated on a 2-4 year cycle, allowing the Survey to ask a wider variety of questions. The Questionnaire for each year therefore varies substantially (but not completely) from the year before; researchers should be aware of that. Since 2014, the Healthy Ireland Survey has been administered on an annual basis by Ipsos- MRBI on behalf of the Department of Health.
DOI: 10.5281/zenodo.18493109
title: A Comprehensive Dataset for Validating Predictive Models of Young's Modulus Evolution during Cement Hydration

description: A systematically integrated and standardized dataset was present, designed to provide benchmark data for developing and validating predictive models of the time-dependent mechanical properties of cementitious materials. All data underwent extraction, cleaning, and standardization, and were stored in a structured format for easy access and reuse. The dataset provides paired measurements of porosity and Young's modulus over hydration time, and can be used to evaluate the applicability of existing models, support multiscale numerical simulations, train data-driven models, and serve as a teaching case study.
DOI: 10.5281/zenodo.18493108
title: A Comprehensive Dataset for Validating Predictive Models of Young's Modulus Evolution during Cement Hydration

description: A systematically integrated and standardized dataset was present, designed to provide benchmark data for developing and validating predictive models of the time-dependent mechanical properties of cementitious materials. All data underwent extraction, cleaning, and standardization, and were stored in a structured format for easy access and reuse. The dataset provides paired measurements of porosity and Young's modulus over hydration time, and can be used to evaluate the applicability of existing models, support multiscale numerical simulations, train data-driven models, and serve as a teaching case study.
DOI: 10.15468/dl.xgj9v9
title: Occurrence Download

description: A dataset containing 434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Meles meles",
 "Year 1992-1992"
 ]
}

 The dataset includes 434 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013727-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18494076
title: Datasets supporting figures in 'Optimization of the interaction geomtery of inverse Compton scattering x-ray sources'

description: This is the data used to create figures 5, 9 and 10 in the paper 'Optimizing the interaction geomtery of inverse Compton scattering x-ray sources'. The data is structured as follows: Every curve/dots in the figure has two files. One containing the plotted data, and one containing the input parameters of the simulation.

Figure 5. red curve/dots.  Data: 'Fig5Data.csv'. Parameters: 'Fig5Parameters.csv'

Figure 9. main blue curve/dots.  Data: 'Fig9_main_Data.csv'. Parameters: 'Fig9_main_Parameters.csv'

Figure 9. inset blue curve/dots.  Data: 'Fig9_closeup_Data.csv'. Parameters: 'Fig9_closeup_Parameters.csv'

Figure 10a blue curve/dots.  Data: 'Fig10_FullOptimizationFullRange_Data.csv'. Parameters: 'Fig10_FullOptimizationFullRange_Parameters.csv'

Figure 10a green curve/dots.  Data: 'Fig10_gamma100FullRange_Data.csv'. Parameters: 'Fig10_gamma100FullRange_Parameters.csv'

Figure 10a red curve/dots.  Data: 'Fig10_headonFullRange_Data.csv'. Parameters: 'Fig10_headonFullRange_Parameters.csv'

Figure 10b blue curve/dots.  Data: 'Fig10_FullOptimizationSoftxray_Data.csv'. Parameters: 'Fig10_FullOptimizationSoftxray_Parameterss.csv'

Figure 10b green curve/dots.  Data: 'Fig10_gamma100Softxray_Data.csv'. Parameters: 'Fig10_gamma100Softxray_Parameters.csv'

Figure 10b red curve/dots.  Data: 'Fig10_headonSoftxray_Data.csv'. Parameters: 'Fig10_headonSoftxray_Parameters.csv'

 

Any questions regarding the datasets can be refered to c.w.sweers@tue.nl
DOI: 10.5281/zenodo.18494077
title: Datasets supporting figures in 'Optimization of the interaction geomtery of inverse Compton scattering x-ray sources'

description: This is the data used to create figures 5, 9 and 10 in the paper 'Optimizing the interaction geomtery of inverse Compton scattering x-ray sources'. The data is structured as follows: Every curve/dots in the figure has two files. One containing the plotted data, and one containing the input parameters of the simulation.

Figure 5. red curve/dots.  Data: 'Fig5Data.csv'. Parameters: 'Fig5Parameters.csv'

Figure 9. main blue curve/dots.  Data: 'Fig9_main_Data.csv'. Parameters: 'Fig9_main_Parameters.csv'

Figure 9. inset blue curve/dots.  Data: 'Fig9_closeup_Data.csv'. Parameters: 'Fig9_closeup_Parameters.csv'

Figure 10a blue curve/dots.  Data: 'Fig10_FullOptimizationFullRange_Data.csv'. Parameters: 'Fig10_FullOptimizationFullRange_Parameters.csv'

Figure 10a green curve/dots.  Data: 'Fig10_gamma100FullRange_Data.csv'. Parameters: 'Fig10_gamma100FullRange_Parameters.csv'

Figure 10a red curve/dots.  Data: 'Fig10_headonFullRange_Data.csv'. Parameters: 'Fig10_headonFullRange_Parameters.csv'

Figure 10b blue curve/dots.  Data: 'Fig10_FullOptimizationSoftxray_Data.csv'. Parameters: 'Fig10_FullOptimizationSoftxray_Parameterss.csv'

Figure 10b green curve/dots.  Data: 'Fig10_gamma100Softxray_Data.csv'. Parameters: 'Fig10_gamma100Softxray_Parameters.csv'

Figure 10b red curve/dots.  Data: 'Fig10_headonSoftxray_Data.csv'. Parameters: 'Fig10_headonSoftxray_Parameters.csv'

 

Any questions regarding the datasets can be refered to c.w.sweers@tue.nl
DOI: 10.15468/dl.y97d32
title: Occurrence Download

description: A dataset containing 512 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Maldives",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carcharhinus melanopterus"
 ]
}

 The dataset includes 512 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013728-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k52s8c
title: Occurrence Download

description: A dataset containing 2145 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Locality is one of (Trélazé, Trélazé, Maine-et-Loire, Pays de la Loire)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2145 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013729-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.