cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z9jkpx
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus ponderosa Douglas ex C.Lawson",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019986-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpdzht
title: Occurrence Download

description: A dataset containing 951 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong, Macao, Chinese Taipei)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Brachiaria mutica (Forssk.) Stapf"
 ]
}

 The dataset includes 951 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019987-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7mq6ma
title: Occurrence Download

description: A dataset containing 391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Rubroshorea (Meijer) P.S.Ashton & J.Heckenhauer, 2022"
 ]
}

 The dataset includes 391 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019988-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sak6rn
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Delphinus delphis Linnaeus, 1758",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 46 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019989-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jak82s
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus pungens Lamb.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019991-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11093441
title: Safran-MNIST-DLS

description: This dataset contains images of serial numbers extracted from diverse avionic parts manufactured by SAFRAN, the international high-technology group and world leader operating in the aviation (propulsion, equipment and interiors), defense and space markets. This dataset resembles the well-known MNIST dataset, but with a focus to industrial contexts, encompassing variations in lighting conditions, orientations, writing styles and surface textures. 

The dataset contains 32 classes depicting numbers, alphabetic characters, and symbols, namely: [0, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S, T, U, W, Y, /, .]

April 30th, 2024 : Training dataset containing 9314 images without labels is released

Validation and test sets will be released at the end of the ICPR24 DAGECC Competition
DOI: 10.5281/zenodo.11093440
title: Safran-MNIST-DLS

description: This dataset contains images of serial numbers extracted from diverse avionic parts manufactured by SAFRAN, the international high-technology group and world leader operating in the aviation (propulsion, equipment and interiors), defense and space markets. This dataset resembles the well-known MNIST dataset, but with a focus to industrial contexts, encompassing variations in lighting conditions, orientations, writing styles and surface textures. 

The dataset contains 32 classes depicting numbers, alphabetic characters, and symbols, namely: [0, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S, T, U, W, Y, /, .]

April 30th, 2024 : Training dataset containing 9314 images without labels is released.

December 5th, 2024 : Testing and validation datasets released, ground-truth labels for training, validation and testing released.

This dataset has been proposed in the context of ICPR24 DAGECC Competition
DOI: 10.5281/zenodo.13321202
title: Safran-MNIST-DLS

description: This dataset contains images of serial numbers extracted from diverse avionic parts manufactured by SAFRAN, the international high-technology group and world leader operating in the aviation (propulsion, equipment and interiors), defense and space markets. This dataset resembles the well-known MNIST dataset, but with a focus to industrial contexts, encompassing variations in lighting conditions, orientations, writing styles and surface textures. 

The dataset contains 32 classes depicting numbers, alphabetic characters, and symbols, namely: [0, 1, 2, 3, 4, 5, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S, T, U, W, Y, /, .]

April 30th, 2024 : Training dataset containing 9314 images without labels is released.

December 5th, 2024 : Testing and validation datasets released, ground-truth labels for training, validation and testing released.

This dataset has been proposed in the context of ICPR24 DAGECC Competition
DOI: 10.15468/dl.h688dy
title: Occurrence Download

description: A dataset containing 610 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Icelinus borealis Gilbert, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 610 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019993-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yd5ah6
title: Occurrence Download

description: A dataset containing 253 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sardinops sagax (Jenyns, 1842)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 253 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019992-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27968115.v1
title: V4_05 Dec 2024 data .sav

description: Abstract*Background: Anxiety and obesity can have negative impacts on the health of children. Understanding the relationship between obesity and mental disorders in children and adolescents will help develop effective prevention and intervention strategies. This research investigates the impact of anxiety-related emotional disorders and self-esteem on obesity among Chinese children aged 6-12 in Anhui Province. Methods: Utilizing a non-experimental, cross-sectional study design, data were from 136 participants collected through standardized instrument measures from Chinese children: the " Chinese screening for overweight and obesity among school-age children and adolescents" in the "Health Industry Standards of the People's Republic of China" as the standard for identifying obesity, the Screen for Anxiety-Related Emotional Disorders (SCARED) for assessing anxiety disorder, and the Children's Self-Esteem Scale (CSES) for evaluating self-esteem. Statistical analysis, including descriptive statistics, Pearson correlation analysis, and multiple regression analyses was performed using SPSS version 26 to determine the predictive relationships between the variables. Results: The analysis revealed that anxiety-related emotional disorders and self-esteem contribute significantly to the prediction of obesity among Chinese children, accounting for a 13.7% variance. The study highlights the critical roles of anxiety-related emotional disorders and self-esteem in determining children’s obesity. It suggests that underscores the necessity for comprehensive interventions that address both physical and mental health aspects to combat childhood obesity effectively. The study concludes that addressing anxiety and self-esteem is essential for developing effective strategies to reduce obesity rates among Chinese children, meeting the research objectives, and contributing to the development of holistic health programs for children’s well-being.
DOI: 10.6084/m9.figshare.27968115
title: V4_05 Dec 2024 data .sav

description: Abstract*Background: Anxiety and obesity can have negative impacts on the health of children. Understanding the relationship between obesity and mental disorders in children and adolescents will help develop effective prevention and intervention strategies. This research investigates the impact of anxiety-related emotional disorders and self-esteem on obesity among Chinese children aged 6-12 in Anhui Province. Methods: Utilizing a non-experimental, cross-sectional study design, data were from 136 participants collected through standardized instrument measures from Chinese children: the " Chinese screening for overweight and obesity among school-age children and adolescents" in the "Health Industry Standards of the People's Republic of China" as the standard for identifying obesity, the Screen for Anxiety-Related Emotional Disorders (SCARED) for assessing anxiety disorder, and the Children's Self-Esteem Scale (CSES) for evaluating self-esteem. Statistical analysis, including descriptive statistics, Pearson correlation analysis, and multiple regression analyses was performed using SPSS version 26 to determine the predictive relationships between the variables. Results: The analysis revealed that anxiety-related emotional disorders and self-esteem contribute significantly to the prediction of obesity among Chinese children, accounting for a 13.7% variance. The study highlights the critical roles of anxiety-related emotional disorders and self-esteem in determining children’s obesity. It suggests that underscores the necessity for comprehensive interventions that address both physical and mental health aspects to combat childhood obesity effectively. The study concludes that addressing anxiety and self-esteem is essential for developing effective strategies to reduce obesity rates among Chinese children, meeting the research objectives, and contributing to the development of holistic health programs for children’s well-being.
DOI: 10.15468/dl.d62r7v
title: Occurrence Download

description: A dataset containing 731043 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is FishBase Database"
 ]
}

 The dataset includes 731043 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019959-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yv4tst
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-43.58277 29.04635,-4.30332 29.04635,-4.30332 51.9522,-43.58277 51.9522,-43.58277 29.04635))",
 "TaxonKey is Hygophum macrochir (Günther, 1864)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019994-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/yq4s-dz24
title: Samland et al. 2024

description: MAST dataproducts for Samland et al. 2024 on HD 172555
DOI: 10.15468/dl.4gdhsy
title: Occurrence Download

description: A dataset containing 383 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Vanilla chamissonis Klotzsch"
 ]
}

 The dataset includes 383 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019996-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10993736
title: Datasets to Transient Attracting Profiles in the Great Pacific Garbage Patch

description: Summary

This repository contains supplementary datasets to the publication Transient Attracting Profiles in the Great Pacific Garbage Patch (Kunz et al., 2024, preprint). TRansient Attracting Profiles (TRAPs, Serra et al. (2020), Serra and Haller (2016)) have been computed using the TRAPs detection software by Mattia Serra (2020). Raw TRAPs were further post-processed, tracked and analysed using software by Luca Kunz (2024). Here we provide two comprehensive datasets:





the TRAPS GPGP dataset: This dataset contains 20 years of TRAP detections with various attributes like e.g. TRAP lifetimes, propagation speeds and pattern detections in the surrounding vorticity field. It comes in 20 yearly files named MULTIOBS_24HI_e025_TRAPS_GPGP_20XX.pkl.




the TRAPS DRIFTERS HPS dataset: This dataset contains 20 years of drifter-TRAP pair detections with attributes like e.g. drifter-TRAP distances, drifter retention times and TRAP attraction strengths. It comes in one file named MULTIOBS_24HI_e025_GDP_24HI_TRAPS_DRIFTERS_HPS_0019_r075.pkl.



 

You can use these scripts to open the datasets and follow the analysis in Kunz et al. (2024):

aa_define_classes.ipynbrxa_read_TRAPS_GPGP.ipynbrxb_read_TRAPS_DRIFTERS_HPS.ipynbrxc_ANALYSIS_EXAMPLES.ipynb

Make sure you adopt the directory structure that is provided here. You can use the .yml file to set up the working environment via conda env create -f trapsgpgp_condaenvexport.yml or conda create -n trapsgpgp -f trapsgpgp_condaenvexport.yml.

For supplementary videos, see: https://doi.org/10.5281/zenodo.10943728

Source data

Raw TRAPs and relative vorticity are derived from daily snapshots of near-surface geostrophic + Ekman currents from the product Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing that is provided by the E.U. Copernicus Marine Service (CMEMS, 2022a). Surface drifter positions have been consulted from the Global Drifter Program (Lumpkin and Centurioni, 2019). This repository does not include raw TRAP detections, raw drifter data or relative vorticity fields but they can be shared upon request.

Acknowledgements

This work is a contribution to the project L3 Meso- to submesoscale turbulence in the ocean of the Collaborative Research Centre TRR 181 Energy Transfer in Atmosphere and Ocean funded by the German Research Foundation (DFG) and has been conducted in collaboration with The Ocean Cleanup.

Please cite

When using the data, please cite

Kunz, L., Griesel, A., Eden, C., Duran, R., and Sainte-Rose, B.: Transient Attracting Profiles in the Great Pacific Garbage Patch, Ocean Sci., 20, 1611–1630, https://doi.org/10.5194/os-20-1611-2024, 2024. 

and provide a link to the repository.
DOI: 10.5281/zenodo.11048146
title: Datasets to Transient Attracting Profiles in the Great Pacific Garbage Patch

description: Summary

This repository contains supplementary datasets to the publication Transient Attracting Profiles in the Great Pacific Garbage Patch (Kunz et al., 2024, preprint). TRansient Attracting Profiles (TRAPs, Serra et al. (2020), Serra and Haller (2016)) have been computed using the TRAPs detection software by Mattia Serra (2020). Raw TRAPs were further post-processed, tracked and analysed using software by Luca Kunz (2024). Here we provide two comprehensive datasets:





the TRAPS GPGP dataset: This dataset contains 20 years of TRAP detections with various attributes like e.g. TRAP lifetimes, propagation speeds and pattern detections in the surrounding vorticity field. It comes in 20 yearly files named MULTIOBS_24HI_e025_TRAPS_GPGP_20XX.pkl.




the TRAPS DRIFTERS HPS dataset: This dataset contains 20 years of drifter-TRAP pair detections with attributes like e.g. drifter-TRAP distances, drifter retention times and TRAP attraction strengths. It comes in one file named MULTIOBS_24HI_e025_GDP_24HI_TRAPS_DRIFTERS_HPS_0019_r075.pkl.



 

You can use these scripts to open the datasets and follow the analysis in Kunz et al. (2024):

aa_define_classes.ipynbrxa_read_TRAPS_GPGP.ipynbrxb_read_TRAPS_DRIFTERS_HPS.ipynbrxc_ANALYSIS_EXAMPLES.ipynb

Make sure you adopt the directory structure that is provided here. You can use the .yml file to set up the working environment via conda env create -f trapsgpgp_condaenvexport.yml or conda create -n trapsgpgp -f trapsgpgp_condaenvexport.yml.

For supplementary videos, see: https://doi.org/10.5281/zenodo.10943728

Source data

Raw TRAPs and relative vorticity are derived from daily snapshots of near-surface geostrophic + Ekman currents from the product Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing that is provided by the E.U. Copernicus Marine Service (CMEMS, 2022a). Surface drifter positions have been consulted from the Global Drifter Program (Lumpkin and Centurioni, 2019). This repository does not include raw TRAP detections, raw drifter data or relative vorticity fields but they can be shared upon request.

Acknowledgements

This work is a contribution to the project L3 Meso- to submesoscale turbulence in the ocean of the Collaborative Research Centre TRR 181 Energy Transfer in Atmosphere and Ocean funded by the German Research Foundation (DFG) and has been conducted in collaboration with The Ocean Cleanup.

Please cite

When using the data, please cite

Kunz, L., Griesel, A., Eden, C., Duran, R., and Sainte-Rose, B.: Transient Attracting Profiles in the Great Pacific Garbage Patch, Ocean Sci., 20, 1611–1630, https://doi.org/10.5194/os-20-1611-2024, 2024. 

and provide a link to the repository.
DOI: 10.15468/dl.426t4e
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus radiata D.Don",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019997-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/6d342aad-6b68-4345-beee-3cee4cb516c9
title: Particle Size Analysis and organic carbon content data from marine sediment samples within the Trevose Box, off southwest England, April 2023 (DEFRA funded FISP project)

description: Particle Size Analysis (PSA) and organic carbon content data from marine sediment sampling of two plots within the Trevose Box off south-west England between 14 - 18 April 2023 before and after fishing. This DEFRA funded FISP (Fisheries Industry Science Partnerships scheme) project aimed to understand the acute effects of scallop dredging and beam trawling on seabed carbon. The project was led by Bangor University in collaboration with Imperial College London, CEFAS, Western Fish Producers Organisation and the South Western Fish Producer Organisation.
The study was conducted in two areas offshore south-west England, UK. The areas were within the Trevose box, which is a management area closed to bottom fishing for the months of January, February and March each year. This allowed us to sample areas which had not been exposed to fishing immediately before our sampling which would reduce the chance of seeing any effect from our experimental fishing. One plot was designated an area that would be experimentally fished by a beam trawler, and another by a scallop dredger (Figure 1). The plots were chosen to minimise differences in depth, seabed habitat, sediment type, wave energy and tidal currents within and between them. Both were chosen where their respective types of fishing gear had been used previously, although scallop dredging has been less common in this area in recent years.
The sampling from the RV Prince Madog and experimental fishing from the fishing vessels took place between 14 – 18th April 2023. Before any fishing activity took place, grab sampling was conducted in the dredge plot and in the beam trawl plot that would be later fished to varying amounts, which will be referred to as ‘Times fished’ or ‘fishing intensity’. A 0.1 m2 Day grab was used. When the day grab was onboard two cores were used to subsample the grab sample as deep as possible, these were then sliced in 1 cm intervals and frozen onboard for later carbon analysis. A homogenised sample from the day grab was also taken for Particle Size Analysis. After the ‘Before’ grab samples were taken, a beam trawler and scallop dredger towed their gear along the sampled lanes in each respective plot. The 'After’ samples were taken after that fishing activity.
Please see report for full details on how the PSA and organic content values were determined. Whitton, T.A., Austin, M., Newbould, A., Kennedy, H., Allender, S., Cavan, E., Parker, R., North, C., Hatchman, J., Hiddink, J.G. (2024) The impact of mobile demersal fishing on blue carbon in seabed sediments. Report to DEFRA.
https://research.bangor.ac.uk/portal/en/researchoutputs/the-impact-of-mobile-demersal-fishing-on-blue-carbon-in-seabed-sediments(1fa1845a-6669-4b5b-83e1-234a6eafc75c).html
DOI: 10.15468/dl.bcfnga
title: Occurrence Download

description: A dataset containing 207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sardinops melanostictus (Temminck & Schlegel, 1846)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 207 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019998-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x3jcc5
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-42.59132 25.45413,-3.4542 25.45413,-3.4542 56.44507,-42.59132 56.44507,-42.59132 25.45413))",
 "TaxonKey is Ranzania laevis (Pennant, 1776)"
 ]
}

 The dataset includes 25 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019999-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14282400
title: Dataset for Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning

description: This dataset contains the data underlying the work titled "Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning". Each folder in the dataset correspond to a single round of autofluorescence measurements. Each folder may contain one or more regions of Interest (ROI) that were selected following histopathological analysis of the samples. Each ROI is labelled according to the final histopathological diagnosis and contains spectroscopic data (following initial processing) for each pixel within the ROI.