cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13319827
title: Water Body Checklists 2019: Greenland Sea Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Greenland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.5281/zenodo.13319828
title: Water Body Checklists 2019: Greenland Sea Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Greenland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.4121/491199a3-4ceb-4f5a-aac9-451df6c77d0b
title: Data underlying the publication "Modeling Human Driver Behavior During Highway Merging Using the Communication-Enabled Interaction Framework"

description: This dataset contains simulated merging interactions on a highway. The model presented in the paper "Modeling Human Driver Behavior During Highway Merging Using the Communication-Enabled Interaction Framework" was simulated in nine scenarios, each in 100 trials. This model is based on the Communication-Enabled Interaction framework and describes how two human drivers interact during merging in terms of low-level control inputs, safety margins, and high-level decisions (who goes first?). This dataset contains 900 trials where a simulated human-driven vehicle merges onto a single-lane highway occupied by one other simulated human-driven vehicle. The initial velocity differences and gaps between the vehicles vary, resulting in the nine scenarios. We published a software repository on GitHub (see references) containing playback and plotting scripts to use the data.
DOI: 10.4121/491199a3-4ceb-4f5a-aac9-451df6c77d0b.v1
title: Data underlying the publication "Modeling Human Driver Behavior During Highway Merging Using the Communication-Enabled Interaction Framework"

description: This dataset contains simulated merging interactions on a highway. The model presented in the paper "Modeling Human Driver Behavior During Highway Merging Using the Communication-Enabled Interaction Framework" was simulated in nine scenarios, each in 100 trials. This model is based on the Communication-Enabled Interaction framework and describes how two human drivers interact during merging in terms of low-level control inputs, safety margins, and high-level decisions (who goes first?). This dataset contains 900 trials where a simulated human-driven vehicle merges onto a single-lane highway occupied by one other simulated human-driven vehicle. The initial velocity differences and gaps between the vehicles vary, resulting in the nine scenarios. We published a software repository on GitHub (see references) containing playback and plotting scripts to use the data.
DOI: 10.5281/zenodo.13319782
title: Water Body Checklists 2019: Beaufort Sea Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Beaufort Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.5281/zenodo.13319783
title: Water Body Checklists 2019: Beaufort Sea Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Beaufort Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.5281/zenodo.13318063
title: Metrics: NCBI data coverage

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13318064
title: Metrics: NCBI data coverage

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13323203
title: NMNH Botany Elevation Data: Maxs

description: Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.
DOI: 10.5281/zenodo.13323204
title: NMNH Botany Elevation Data: Maxs

description: Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.
DOI: 10.5281/zenodo.13318129
title: Larvae of Hydrothermal Vents in the Eastern Pacific

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13318130
title: Larvae of Hydrothermal Vents in the Eastern Pacific

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13322901
title: Genus Opisthostoma (myspecies): Resource (156) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13322902
title: Genus Opisthostoma (myspecies): Resource (156) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.34740/kaggle/dsv/10738679
title: ⏳ endoflife.date database (duckdb)
DOI: 10.5281/zenodo.13319888
title: Water Body Checklists 2019: Hudson Strait Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Hudson Strait using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.5281/zenodo.13319889
title: Water Body Checklists 2019: Hudson Strait Species List

description: Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Hudson Strait using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
DOI: 10.5281/zenodo.13319045
title: Ashihara et al, 2008

description: Hiroshi Ashihara, Hiroshi Sano, Alan Crozier. 2008. Caffeine and related purine alkaloids: Biosynthesis, catabolism, function and genetic engineering. Phytochemistry 69: 841–856.  https://edisciplinas.usp.br/pluginfile.php/87480/mod_resource/content/1/cafeine%20biosynthesis.pdf
DOI: 10.5281/zenodo.13319046
title: Ashihara et al, 2008

description: Hiroshi Ashihara, Hiroshi Sano, Alan Crozier. 2008. Caffeine and related purine alkaloids: Biosynthesis, catabolism, function and genetic engineering. Phytochemistry 69: 841–856.  https://edisciplinas.usp.br/pluginfile.php/87480/mod_resource/content/1/cafeine%20biosynthesis.pdf
DOI: 10.5281/zenodo.13322888
title: Cybertruffle xml resource: Resource (322) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13322889
title: Cybertruffle xml resource: Resource (322) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13323053
title: ChEss (myspecies): Resource (144) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13323054
title: ChEss (myspecies): Resource (144) DwCA

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13323225
title: Monocotyledons and Gymnosperms of Puerto Rico and the Virgin Islands: Monocots and Gymnosperms

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.5281/zenodo.13323226
title: Monocotyledons and Gymnosperms of Puerto Rico and the Virgin Islands: Monocots and Gymnosperms

description: This is where EOL hosts source datasets (archives, dumps, etc.) from EOL content partners (especially partners without a web presence of their own).

This organization will also include the content partner utility files EOL connectors use to generate a particular content partner__s resource EOL archive or XML.

For questions or suggestions please visit the EOL Services forum at http://discuss.eol.org/c/eol-services
DOI: 10.3886/e219221
title: NCES EDGE Comparable Wage Index for Teachers (CWIFT)

description: The Comparable Wage Index for Teachers (CWIFT) is an experimental index created by the Education Demographic and Geographic Estimates (EDGE) program at the National Center for Education Statistics (NCES) to facilitate comparison of educational expenditures. The CWIFT is a measure of the systematic, regional variations in the wages and salaries of college graduates who are not PK‌-12 educators as determined by reported occupational category. It can be used by researchers to adjust school district-level finance data in order to make comparisons across geographic areas. The CWIFT is based on data from the American Community Survey (ACS), a continuous household survey conducted by the U.S. Census Bureau. The ACS provides information about the earnings, age, occupation, industry, and other demographic characteristics for millions of U.S. workers. The CWIFT measures wage and salary differences for college graduates, using an analysis that is modeled after the baseline analysis used to construct the original CWI released by NCES in 2006.
DOI: 10.3886/e219221v1
title: NCES EDGE Comparable Wage Index for Teachers (CWIFT)

description: The Comparable Wage Index for Teachers (CWIFT) is an experimental index created by the Education Demographic and Geographic Estimates (EDGE) program at the National Center for Education Statistics (NCES) to facilitate comparison of educational expenditures. The CWIFT is a measure of the systematic, regional variations in the wages and salaries of college graduates who are not PK‌-12 educators as determined by reported occupational category. It can be used by researchers to adjust school district-level finance data in order to make comparisons across geographic areas. The CWIFT is based on data from the American Community Survey (ACS), a continuous household survey conducted by the U.S. Census Bureau. The ACS provides information about the earnings, age, occupation, industry, and other demographic characteristics for millions of U.S. workers. The CWIFT measures wage and salary differences for college graduates, using an analysis that is modeled after the baseline analysis used to construct the original CWI released by NCES in 2006.
DOI: 10.5281/zenodo.14863250
title: Advancing Neurosurgical Education: The Role of 3D Printing in Training, Confidence, and Skill Development
DOI: 10.5281/zenodo.14863251
title: Advancing Neurosurgical Education: The Role of 3D Printing in Training, Confidence, and Skill Development
DOI: 10.5281/zenodo.14861967
title: Jianming Li
DOI: 10.5281/zenodo.14861968
title: Jianming Li
DOI: 10.5517/ccdc.csd.cc2hfm6s
title: CCDC 2306995: Experimental Crystal Structure Determination

description: Related Article: Guang Xu, Cuiju Zhu, Xiang Li, Kexin Zhu, Hao Xu|2025|Chin.Chem.Lett.|36|110114|doi:10.1016/j.cclet.2024.110114
description: Related Article: Guang Xu|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hfm7t
title: CCDC 2306996: Experimental Crystal Structure Determination

description: Related Article: Guang Xu|2023|CSD Communication|||
description: Related Article: Guang Xu, Cuiju Zhu, Xiang Li, Kexin Zhu, Hao Xu|2025|Chin.Chem.Lett.|36|110114|doi:10.1016/j.cclet.2024.110114
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25403/upresearchdata.28375421
title: <b>Cohesion challenges in a multicultural team in a hospital in Saudi Arabia</b>

description: The researcher opted to use a descriptive qualitative design, and the preferred data collection method was individual face-to-face interviews to explore and understand the participants’ perceptions of the cohesion challenges that their multicultural nursing team experienced. The researcher led the interviews with the open-ended question, <i>‘</i><i>What cohesion challenges does the multicultural nursing team of the cardiac centre experience, and what support should they get from management to overcome these challenges</i>?<i>’ </i>and also used probing questions that guided the participants’ responses to enable a deeper understanding of their experiences. The interviews were audio-recorded as permitted by the participants. A thematic analysis was used to analyze the data.<br>The following six categories with subcategories emerged:- Excluded from communication due to language barriersDifferences in practical experiences lead to alienationNurse managers struggling to meet the new staff needsAdvantages of multi-nationality nurses rejectedForeign nurses obliged to conformNurse managers struggling to promote unified teamwork<br>
DOI: 10.25403/upresearchdata.28375421.v1
title: <b>Cohesion challenges in a multicultural team in a hospital in Saudi Arabia</b>

description: The researcher opted to use a descriptive qualitative design, and the preferred data collection method was individual face-to-face interviews to explore and understand the participants’ perceptions of the cohesion challenges that their multicultural nursing team experienced. The researcher led the interviews with the open-ended question, <i>‘</i><i>What cohesion challenges does the multicultural nursing team of the cardiac centre experience, and what support should they get from management to overcome these challenges</i>?<i>’ </i>and also used probing questions that guided the participants’ responses to enable a deeper understanding of their experiences. The interviews were audio-recorded as permitted by the participants. A thematic analysis was used to analyze the data.<br>The following six categories with subcategories emerged:- Excluded from communication due to language barriersDifferences in practical experiences lead to alienationNurse managers struggling to meet the new staff needsAdvantages of multi-nationality nurses rejectedForeign nurses obliged to conformNurse managers struggling to promote unified teamwork<br>
DOI: 10.15161/oar.it/212420
title: Carta Geologica d'Italia alla scala 1:50.000, F. 490 Stigliano

description: Foglio geologico alla scala 1:50.000 basato su rilevamenti alla scala 1:10.000 (eseguiti tra il 2005 e il 2006) comprensivo di legenda, schemi a cornice, sezioni geologiche.
DOI: 10.15161/oar.it/212421
title: Carta Geologica d'Italia alla scala 1:50.000, F. 490 Stigliano

description: Foglio geologico alla scala 1:50.000 basato su rilevamenti alla scala 1:10.000 (eseguiti tra il 2005 e il 2006) comprensivo di legenda, schemi a cornice, sezioni geologiche.
DOI: 10.5281/zenodo.14863272
title: Fighter Aircraft Image Dataset

description: Description:







This dataset is an extensive collection of high-quality images featuring various Fighter Aircraft Image Dataset, curated specifically for machine learning tasks such as classification, image recognition, and object detection. The images are systematically organized into folders, each labeled with the corresponding aircraft model name, offering a valuable resource for researchers and developers working on AI-driven aviation projects.

 







Download Dataset

 









Aircraft Models Included

The dataset includes a wide variety of modern and vintage fighter aircraft from around the world. Examples range from early 20th-century designs to cutting-edge jet fighters, making it suitable for research across various eras of aviation technology.





Examples of Fighter Models:



F-22 Raptor

Su-30MKI

Eurofighter Typhoon

F-16 Falcon




Each model folder contains a comprehensive set of images covering key visual aspects of the aircraft, such as wing design, fuselage structure, and engine placement, allowing for high-precision classification models.

Applications in Machine Learning

This dataset is an essential tool for tasks like:



Object Detection: Training models to detect fighter aircraft in aerial images or videos.

Image Classification: Creating models that can differentiate between various aircraft types based on visual features.

Feature Extraction: Studying unique design features that distinguish different fighter jets, assisting in AI-based image feature extraction.


Dataset Specifications



Format: JPEG/PNG files

Size: The dataset contains thousands of images with varying resolutions to fit different model requirements.

Annotations: Optional annotation files provide bounding boxes and additional metadata for object detection tasks.


Conclusion

The Fighter Aircraft Dataset offers a well-rounded collection of fighter aircraft visuals, enhancing machine learning efforts in both the defense and aviation sectors. Whether you’re building an AI model for image classification or exploring aviation research, this dataset serves as a critical resource for accurate and efficient learning models.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14863273
title: Fighter Aircraft Image Dataset

description: Description:







This dataset is an extensive collection of high-quality images featuring various Fighter Aircraft Image Dataset, curated specifically for machine learning tasks such as classification, image recognition, and object detection. The images are systematically organized into folders, each labeled with the corresponding aircraft model name, offering a valuable resource for researchers and developers working on AI-driven aviation projects.

 







Download Dataset

 









Aircraft Models Included

The dataset includes a wide variety of modern and vintage fighter aircraft from around the world. Examples range from early 20th-century designs to cutting-edge jet fighters, making it suitable for research across various eras of aviation technology.





Examples of Fighter Models:



F-22 Raptor

Su-30MKI

Eurofighter Typhoon

F-16 Falcon




Each model folder contains a comprehensive set of images covering key visual aspects of the aircraft, such as wing design, fuselage structure, and engine placement, allowing for high-precision classification models.

Applications in Machine Learning

This dataset is an essential tool for tasks like:



Object Detection: Training models to detect fighter aircraft in aerial images or videos.

Image Classification: Creating models that can differentiate between various aircraft types based on visual features.

Feature Extraction: Studying unique design features that distinguish different fighter jets, assisting in AI-based image feature extraction.


Dataset Specifications



Format: JPEG/PNG files

Size: The dataset contains thousands of images with varying resolutions to fit different model requirements.

Annotations: Optional annotation files provide bounding boxes and additional metadata for object detection tasks.


Conclusion

The Fighter Aircraft Dataset offers a well-rounded collection of fighter aircraft visuals, enhancing machine learning efforts in both the defense and aviation sectors. Whether you’re building an AI model for image classification or exploring aviation research, this dataset serves as a critical resource for accurate and efficient learning models.

This dataset is sourced from Kaggle.