cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sf238jg557.2
title: GF-FRCNN MSCOCO

description: Continue from the Version 1: Geometric Features Extracted from 10-100 Bounding Boxes (Anderson et al., 2018) of Faster R-CNN (Ren et al., 2015) for each image in the MSCOCO (Lin et al., 2014) Image Captioning Dataset. This dataset, containing all the possible geometric features extracted from 123,287 images in the MSCOCO image captioning dataset (Lin et al., 2014) , provides essential spatial information about each object.



References
Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster r-cnn: Towards realtime object detection with region proposal networks. Advances in neural
information processing systems 28.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014. Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer. pp. 740–755.

Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L., 2018. Bottom-up and top-down attention for image captioning and visual question answering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6077–6086.
DOI: 10.15468/dl.qgvx4n
title: Occurrence Download

description: A dataset containing 21814 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ranunculus hederaceus L."
 ]
}

 The dataset includes 21814 records from 472 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019774-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/sf238jg557.3
title: GF-FRCNN MSCOCO

description: Correction in Version 1: Each file contains 36 x 17 dimension features extracted from fixed set 36 bounding boxes (Anderson et al., 2018) of Faster R-CNN (Ren et al., 2015) for each image in the MSCOCO (Lin et al., 2014) Image Captioning Dataset. The sequence is: relative_x1, relative_y1, relative_x2, relative_y2, relative_widthb, relative_heightb, relative_area, aspect_ratio, relative_center_x, relative_center_y, relative_perimeter, relative_diagonal, relative_left_margin, relative_top_margin, relative_right_margin, relative_bottom_margin, relative_distance_to_center.


Correction in Version 2: Wrong uploaded

Version 3 : This version contains 17 features extracted from the adaptive set 10 to 100 bounding boxes (Anderson et al., 2018) of Faster R-CNN (Ren et al., 2015) for each image in the MSCOCO (Lin et al., 2014) Image Captioning Dataset. This dataset, containing all the possible geometric features extracted from 123,287 images in the MSCOCO image captioning dataset (Lin et al., 2014) , provides essential spatial information about each object. The sequence is: relative_x1, relative_y1, relative_x2, relative_y2, relative_widthb, relative_heightb, relative_area, aspect_ratio, relative_center_x, relative_center_y, relative_perimeter, relative_diagonal, relative_left_margin, relative_top_margin, relative_right_margin, relative_bottom_margin, relative_distance_to_center.

References
Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster r-cnn: Towards realtime object detection with region proposal networks. Advances in neural
information processing systems 28.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014. Microsoft coco: Common objects in context, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, Springer. pp. 740–755.

Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L., 2018. Bottom-up and top-down attention for image captioning and visual question answering, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6077–6086.
DOI: 10.17632/h3hs3nnvbc.1
title: Video-based plastic bag grabbing action recognition

description: Plastic bag taking action recognition from the CCTV video is a niche action video classification problem. To tackle this niche research challenge, this paper introduces a new benchmark video dataset for the action recognition task of identifying plastic bag grabbing. This is the dataset used in our paper, entitled "Video-based plastic bag grabbing action recognition: A new video dataset and a comparative study of baseline models".
DOI: 10.5281/zenodo.14178976
title: Bremen Wilhadi Apotheke

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).
DOI: 10.15468/dl.7w2t67
title: Occurrence Download

description: A dataset containing 2368 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters is less than or equal to 249.0",
 "Country is one of (Switzerland, Italy)",
 "DistanceFromCentroidInMeters is greater than or equal to 2000.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gymnadenia conopsea (L.) R.Br."
 ]
}

 The dataset includes 2368 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019777-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24378/exe.5406
title: PROMETHEUS Future weather files

description: These files were created using the UKCP09 weather generator. The files are available for the current climate and 3 future time periods for 2 emission scenarios, medium emissions (a1b) and high emissions (a1fi). The files are in the Energy Plus format (.epw) which which can be imported into most building thermal simulation software packages including IES. The files can also be opened in Excel as a comma separated variable file. More details can be found in the Readme file.
description: The article associated with this dataset is available in ORE at: http://hdl.handle.net/10871/9483
DOI: 10.15468/dl.gghntq
title: Occurrence Download

description: A dataset containing 74238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dryas octopetala L., Dryadea octopetala (L.) Kuntze, 1891, Dryas babingtonii Porsild, Dryas chamaedrifolioides Pall., Dryas kamtschatica Juz., Dryas octopetala f. octopetala, Dryas octopetala f. typica (Beck) Buia, Dryas octopetala f. typica Derganc, Dryas octopetala subsp. genuina Regel & Tiling, Dryas octopetala var. octopetala, Dryas octopetala var. typica Beck, Dryas punctata subsp. punctata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1900",
 "Year is less than or equal to 2024",
 "Month is greater than or equal to January",
 "Month is less than or equal to September"
 ]
}

 The dataset includes 74238 records from 317 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019775-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14176853
title: Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"

description: This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.
DOI: 10.5281/zenodo.14176854
title: Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"

description: This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.
DOI: 10.17632/r36v7fxs3d.1
title: Ireland et al Nov 2024

description: Raw data for Ireland et al. "Narrowband UVB and Solar-Simulated UV Suppress Systemic Immune Responses Through Different Mechanisms"
DOI: 10.17632/cgwxryv8s2.1
title: KAVITASHARMA_PHD_FINAL_DATA

description: Data is collected from healthy school going children to calculate Normative values, Validity and Reliability of Multi-directional Reach test in children
DOI: 10.17632/nnh69sng8p.1
title: BDMANGO: A dataset for identifying Bangladeshi mango types through mango leaves

description: The field of agriculture, particularly in the context of machine learning applications, requires quality datasets for advancing research and development. Recognizing the diverse and unique characteristics of mango varieties in Bangladesh, we have created a comprehensive and publicly accessible dataset titled "Mango Leaf Classification". This dataset aims to address the challenges of identifying different mango leaf types by providing images essential for research. Collected from two distinct locations, the dataset includes images of six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar. Images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR, and then resized to 640 × 480 pixels. Both sides of each mango leaf were photographed, using white paper as the background, to accurately reflect real-world scenarios in mango cultivation fields. Additionally, using image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming, the dataset has been expanded from 837 original images to a total of 5,859 images. These augmented images significantly enhance the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain.
DOI: 10.17632/nnh69sng8p.2
title: BDMANGO: A dataset for identifying Bangladeshi mango types through mango leaves

description: The field of agriculture, particularly in the context of machine learning applications, requires quality datasets for advancing research and development. Recognizing the diverse and unique characteristics of mango varieties in Bangladesh, we have created a comprehensive and publicly accessible dataset titled "Mango Leaf Classification". This dataset aims to address the challenges of identifying different mango leaf types by providing images essential for research. Collected from two distinct locations, the dataset includes images of six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar. Images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR, and then resized to 640 × 480 pixels. Both sides of each mango leaf were photographed, using white paper as the background, to accurately reflect real-world scenarios in mango cultivation fields. Additionally, using image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming, the dataset has been expanded from 837 original images to a total of 5,859 images. These augmented images significantly enhance the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain.
DOI: 10.17632/nnh69sng8p.3
title: Image Dataset of Bangladeshi Mango Leaf

description: This dataset includes images from six mango varieties—Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar—collected from two different locations. Photos were taken using the rear cameras of a Google Pixel 6a and an iPhone XR, and resized to 640 × 480 pixels. Each leaf was photographed from both sides against a white paper background. To further enhance the dataset, we applied image augmentation techniques and increased the total number of images from 837 to 5,859.
DOI: 10.17632/nnh69sng8p.5
title: Image Dataset of Bangladeshi Mango Leaf

description: This dataset includes images from six mango varieties—Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar—collected from different locations. Photos were taken using the rear cameras of a Google Pixel 6a and an iPhone XR, and resized to 640 × 480 pixels. Each leaf was photographed from both sides against a white paper background. To further enhance the dataset, we applied image augmentation techniques on 837 original images and increased the total number of images to 6696 (837 original and 5,859 augmented). 

The Readme.pdf file is added to illustrate the directory structure along with general information regarding the dataset.
DOI: 10.17632/mkzyfj8bkj.1
title: Tea Leaf Diseases Dataset: Towards Accurate Field Diagnosis Using Image-Based Detection

description: The dataset comprises a total of 31,265 images, including 4,009 raw images and 27,256 augmented images, distributed across six categories. The raw dataset captures natural variability in leaf conditions under diverse environmental settings, while the augmented dataset enhances this variability by applying techniques such as rotation, scaling, flipping, and brightness adjustments to improve machine learning model generalizability.

Category Distribution:

Sunlight Scorching:
Raw: 1,712 images
Augmented: 4,009 images
Total: 5,721 images

Red Spider:
Raw: 410 images
Augmented: 4,746 images
Total: 5,156 images

Red Rust:
Raw: 184 images
Augmented: 4,849 images
Total: 5,033 images

Heliopeltis:
Raw: 281 images
Augmented: 4,825 images
Total: 5,106 images

Thrips:
Raw: 176 images
Augmented: 4,881 images
Total: 5,057 images

Normal (Healthy):
Raw: 1,246 images
Augmented: 3,946 images
Total: 5,192 images

Key Features:
Diversity: The dataset captures variability in backgrounds, lighting, and growth stages, ensuring robust training and testing data.
Augmentation: Includes 27,256 augmented images, generated using state-of-the-art techniques to simulate real-world variability.
Category Coverage: Spans six distinct leaf conditions—ranging from pest-related damage (e.g., Red Spider, Heliopeltis) to environmental stress (e.g., Sunlight Scorching) and healthy leaves.

Purpose:
This dataset is designed to aid in the development of machine learning models capable of:
Automated Leaf Condition Classification: Identifying specific conditions for improved crop health monitoring.
Ecological Research: Supporting biodiversity studies by cataloging leaf conditions.
Agricultural Applications: Enabling early detection of plant stressors, pests, and diseases to enhance sustainable farming practices.
This combined raw and augmented dataset serves as a valuable resource for researchers, ecologists, and machine learning practitioners, contributing to advancements in agriculture, botany, and AI-driven ecological monitoring.
DOI: 10.5281/zenodo.14178927
title: Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning

description: 玛湖凹陷凤城组和吉木萨尔凹陷芦草沟组样本的原始数据,以及核心 Python 代码。
DOI: 10.5281/zenodo.14178981
title: Predicting pore structure and oil-bearing characteristics in saline-alkaline lacustrine shale strata via transfer learning

description: 玛湖凹陷凤城组和吉木萨尔凹陷芦草沟组样本的原始数据,以及核心 Python 代码。
DOI: 10.15468/dl.3xkkar
title: Occurrence Download

description: A dataset containing 3041 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Allium ericetorum Thore, Asphodeline liburnica (Scop.) Rchb., Asyneuma limonifolium (L.) Janch., Asyneuma limoniifolium (L.) Bornm., 1921, Asyneuma linifolium (Boiss. & Heldr.) Bornm., Athamanta ramosissima Port., Athamanta ramosissima Link & Hoffmanns., Athamanta ramosissima Port. ex Spreng., 1820, Athamantha ramosissima Port., Athamantha ramosissima Port. ex Spreng., Aurinia sinuata (L.) Griseb., Atrina sinuata T.R.Waller, 2005)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 3041 records from 173 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019781-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ta2mk5
title: Occurrence Download

description: A dataset listing the 25 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((21.48019 -27.34894,22.34174 -27.34894,22.34174 -26.84173,21.48019 -26.84173,21.48019 -27.34894))",
 "HasGeospatialIssue is false",
 "TaxonKey is Poales"
 ]
}

 The dataset's 25 records were derived from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019773-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bw5489
title: Occurrence Download

description: A dataset containing 23952 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhytidiadelphus triquetrus (Hedw.) Warnst."
 ]
}

 The dataset includes 23952 records from 387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019782-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c8md9c
title: Occurrence Download

description: A dataset containing 16561 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Procambarus clarkii (Girard, 1852)",
 "Country is Spain",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 16561 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019780-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0193169.10fu
title: SH0193169.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/e242830
title: Weekly Influenza Vaccination Coverage Among Pregnant Women 18-49 Years, by Race and Ethnicity

description: Weekly Influenza Vaccination Coverage, Pregnant Women 18-49 Years Old by Race and Ethnicity

Weekly influenza vaccination coverage estimates for pregnant women 1849 years are based on electronic health record (EHR) data from the Vaccine Safety Datalink (VSD) (https://www.cdc.gov/vaccine-safety-systems/vsd/), a collaboration between CDCs Immunization Safety Office and multiple integrated health care organizations.
Read less
description: (Downloaded 1/8/2026)
DOI: 10.15156/bio/sh0193170.10fu
title: SH0193170.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3886/e242830v1
title: Weekly Influenza Vaccination Coverage Among Pregnant Women 18-49 Years, by Race and Ethnicity

description: Weekly Influenza Vaccination Coverage, Pregnant Women 18-49 Years Old by Race and Ethnicity

Weekly influenza vaccination coverage estimates for pregnant women 1849 years are based on electronic health record (EHR) data from the Vaccine Safety Datalink (VSD) (https://www.cdc.gov/vaccine-safety-systems/vsd/), a collaboration between CDCs Immunization Safety Office and multiple integrated health care organizations.
Read less
description: (Downloaded 1/8/2026)