cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.44xrzq
title: Occurrence Download

description: A dataset containing 982 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Cyclamen graecum Link"
 ]
}

 The dataset includes 982 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018291-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12784600
title: Duke Lung Cancer Screening Dataset 2024 - part 2

description: Note - This is part 2 of the dataset.Part 1 can be found at : https://zenodo.org/records/13799069 Part 2 can be found at : https://zenodo.org/records/12784601Part 3 can be found at : https://zenodo.org/records/14659131

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification.

Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist.

Results: The dataset contains 1613 CT volumes with 2487 annotated nodules, selected from a total dataset of 2061 patients, with the remaining data reserved for future testing. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%.

Conclusions: The Duke Lung Cancer Screening Dataset 2024 is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.Dataset part Details:Part 1: DLCS subset 1 to 7 and, metadata and Annotations.Part 2: DLCS subset 8,9 and CT image info metadata.Part 3: DLCS subset 10.

Updates and Versions:



Part 1, Version 1.0 (Published on [03/05/2024]): Released initial dataset, including partial data subsets 1 to 7 and 3D bounding box annotations of the lung nodules.

Part 1, Version 1.1 (Published on [09/19/2024]): Added metadata file (DLCSD24_metadata_v1.1.xlsx) and updated the dataset description and title. 10.5281/zenodo.13799069

Part 2, Version 1.0 (Published on [02/04/2025]): Released DLCS subset 8,9, CT image info metadata (DLCSD24_CT_ImageInfo_v1.csv and metadata documentation).

Part 3, Version 1.0 (Published on [02/04/2025]): Released DLCS subset 10.


Code Repository:To support reproducible open-access research and benchmarking, we have shared several pre-trained models and baseline results in a GitHub and GitLab repository.

GitLab: https://gitlab.oit.duke.edu/cvit-public/ai_lung_health_benchmarkingGitHub: https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets

Funding:This work was supported by the Duke Department of Radiology Charles E. Putman Vision Award, NIH/NIBIB P41-EB028744, and NIH/NCI R01-CA261457.
DOI: 10.5281/zenodo.12784601
title: Duke Lung Cancer Screening Dataset 2024 - part 2

description: Note - This is part 2 of the dataset.Part 1 can be found at : https://zenodo.org/records/13799069 Part 2 can be found at : https://zenodo.org/records/12784601Part 3 can be found at : https://zenodo.org/records/14659131

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification.

Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist.

Results: The dataset contains 1613 CT volumes with 2487 annotated nodules, selected from a total dataset of 2061 patients, with the remaining data reserved for future testing. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%.

Conclusions: The Duke Lung Cancer Screening Dataset 2024 is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.Dataset part Details:Part 1: DLCS subset 1 to 7 and, metadata and Annotations.Part 2: DLCS subset 8,9 and CT image info metadata.Part 3: DLCS subset 10.

Updates and Versions:



Part 1, Version 1.0 (Published on [03/05/2024]): Released initial dataset, including partial data subsets 1 to 7 and 3D bounding box annotations of the lung nodules.

Part 1, Version 1.1 (Published on [09/19/2024]): Added metadata file (DLCSD24_metadata_v1.1.xlsx) and updated the dataset description and title. 10.5281/zenodo.13799069

Part 2, Version 1.0 (Published on [02/04/2025]): Released DLCS subset 8,9, CT image info metadata (DLCSD24_CT_ImageInfo_v1.csv and metadata documentation).

Part 3, Version 1.0 (Published on [02/04/2025]): Released DLCS subset 10.


Code Repository:To support reproducible open-access research and benchmarking, we have shared several pre-trained models and baseline results in a GitHub and GitLab repository.

GitLab: https://gitlab.oit.duke.edu/cvit-public/ai_lung_health_benchmarkingGitHub: https://github.com/fitushar/AI-in-Lung-Health-Benchmarking-Detection-and-Diagnostic-Models-Across-Multiple-CT-Scan-Datasets

Funding:This work was supported by the Duke Department of Radiology Charles E. Putman Vision Award, NIH/NIBIB P41-EB028744, and NIH/NCI R01-CA261457.
DOI: 10.15468/dl.j43pnb
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018290-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/659e43f4796f71e3a37673a0f2d7a77a
title: North Temperate Lakes LTER: Chlorophyll - Trout Lake Area 1981 - current

description: Chlorophyll and phaeopigments are measured at our permanent sampling station in the deepest part of each lake. A profile of chlorophyll samples is collected from the seven primary study lakes: Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes and bog lakes 27-02 (Crystal Bog), and 12-15 (Trout Bog) and analyzed spectrophotometrically. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 7
DOI: 10.15468/dl.whmxpt
title: Occurrence Download

description: A dataset containing 3140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rana draytonii Baird & Girard, 1852",
 "Year 1980-2025"
 ]
}

 The dataset includes 3140 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018293-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f86dyg
title: Occurrence Download

description: A dataset containing 13611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Anodorhynchus hyacinthinus (Latham, 1790)"
 ]
}

 The dataset includes 13611 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018294-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwqwhm
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018295-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dd.rpj69c
title: Dataset generated from Esri tool

description: Derived dataset generated by Esri geoprocessing tool using GBIF API
DOI: 10.6084/m9.figshare.28710704.v1
title: Raw data for manuscript entitled "Inheritable epigenetic memory mediates transgenerational plasticity of <i>Phragmites australis</i> induced by parental salt stress"

description: A controlled interaction experiment was conducted to examine the phenotypes and DNA methylation of 9 genetically distantly related dominant genotypes of <i>Phragmites australis</i> at parental and offspring generations in response to salt stress. Another 13 genetically distantly related dominant genotypes of <i>P. australis </i>to further evaluated the inheritable stability of DNA methylation information over three generations of plants under control and salt stress conditions.
DOI: 10.5281/zenodo.15253073
title: Victim detection

description: 📦 Segmentation Dataset 

This repository contains a dataset labeled with Roboflow in YOLOv8 segmentation format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: YOLOv8 Segmentation-Number of classes: 10-Number of picture: 2276-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

 

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

 

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── data.yaml├── README.dataset.txt └── README.roboflow.txt
DOI: 10.5281/zenodo.15574656
title: Victim detection

description: 📦 Segmentation Dataset 

This repository contains a dataset labeled with Roboflow in YOLOv8 segmentation format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: YOLOv8 Segmentation-Number of classes: 10-Number of picture: 2276-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

 

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

 

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── data.yaml├── README.dataset.txt └── README.roboflow.txt
DOI: 10.15468/quejlc
title: Dry Tortugas Reef Visual Census 2000

description: In 1998, the Florida Fish and Wildlife Conservation Commission's (FWC) Fisheries Independent Monitoring (FIM) program began a long-term monitoring effort of key reef fish populations in the Florida Keys National Marine Sanctuary. This effort was aimed at evaluating the relative abundance, size structure, and habitat utilization of specific reef fish species that are targeted by commercial and recreational fisheries.
DOI: 10.7910/dvn/pxuapk
title: Time Release Ion Matrix Regenerates Dystrophic Skeletal Muscle

description: Supplementary Materials
DOI: 10.15468/g8q8ey
title: Dry Tortugas Reef Visual Census 1999

description: In 1998, the Florida Fish and Wildlife Conservation Commission's (FWC) Fisheries Independent Monitoring (FIM) program began a long-term monitoring effort of key reef fish populations in the Florida Keys National Marine Sanctuary. This effort was aimed at evaluating the relative abundance, size structure, and habitat utilization of specific reef fish species that are targeted by commercial and recreational fisheries.
DOI: 10.15468/dl.s9wssz
title: Occurrence Download

description: A dataset containing 4760 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.5_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ursus americanus Pallas, 1780",
 "Year 2001-2025"
 ]
}

 The dataset includes 4760 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018298-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/uzpt9m
title: Florida Keys Reef Visual Census 1995

description: These data are reef fish observations from surveys conducted in the Florida Keys in 1995. These surveys were part of the developmental years of what became the Reef Visual Census, described in the project metadata, Bohnsack et al. 1999 and Smith et al. 2011
DOI: 10.13012/b2idb-6566418_v1
title: Data for Analysis of Nematode Ventral Nerve Cords Suggests Multiple Instances of Evolutionary Addition and Loss of Neurons

description: This data comprises image files used in the analysis of Analysis of Nematode Ventral Nerve Cords Suggests Multiple Instances of Evolutionary Addition and Loss of Neurons by Han et al. (bioRxiv, 2025: doi: https://doi.org/10.1101/2025.03.20.644414). It is separated into two folders. The first comprise data using DAPI staining to quantify the number of VNC nuclei in diverse nematodes. The second includes dye-filling data of Mononchus aquaticus.
DOI: 10.5067/goes-17/ceres/geo_ed4_goe17_nh_v01.4
title: SatCORPS CERES GEO Edition 4 GOES-17 Northern Hemisphere Version 1.4
DOI: 10.5281/zenodo.15583105
title: Ambiguity Detection in Business Process Descriptions - Annotated Dataset

description: This dataset contains 71 process descriptions annotated for ambiguities. 

For ambiguities definition, we use the taxonomy of ambiguities for BPM in 

Franceschetti, M., Seiger, R., López, H. A., Burattin, A., García-Bañuelos, L., & Weber, B. (2023, October). A characterisation of ambiguity in bpm. In International Conference on Conceptual Modeling (pp. 277-295). Cham: Springer Nature Switzerland.

 

The dataset contains three files: 

Ambiguity Annotations - All Descriptions.csv - 71 process descriptions annotated for ambiguities

Ambiguity Annotations - Declarative Descriptions.csv - 50 process descriptions annotated for ambiguities, where most of the text in the process description is written declaratively (as in, using business rules)

Ambiguity Annotations - Imperative Descriptions.csv- 21 process descriptions annotated for ambiguities, where most of the text in the process description is written imperatively (as in, describing process flows)
DOI: 10.5281/zenodo.15586917
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250603r Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250603r Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.15586918
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250603r Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250603r Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.15586907
title: Harvesting E-Commerce Data for Multimedia Recommendation Systems in the Gaming Industry
DOI: 10.5281/zenodo.15586908
title: Harvesting E-Commerce Data for Multimedia Recommendation Systems in the Gaming Industry
DOI: 10.15468/dl.9gj5uv
title: Occurrence Download

description: A dataset containing 2764 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-4.47733 36.66056,-4.44 36.66056,-4.44 36.68572,-4.47733 36.68572,-4.47733 36.66056))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 2764 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018299-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/gaekez
title: Florida Keys Reef Visual Census 1996

description: In 1998, the Florida Fish and Wildlife Conservation Commission?s (FWC) Fisheries Independent Monitoring (FIM) program began a long-term monitoring effort of key reef fish populations in the Florida Keys National Marine Sanctuary. This effort was aimed at evaluating the relative abundance, size structure, and habitat utilization of specific reef fish species that are targeted by commercial and recreational fisheries.
DOI: 10.15468/rdkfyf
title: Florida Keys Reef Visual Census 1994

description: These data are reef fish observations from surveys conducted in the Florida Keys in 1994. These surveys were part of the developmental years of what became the Reef Visual Census, described in the project metadata, Bohnsack et al. 1999 and Smith et al. 2011
DOI: 10.15468/dl.3yhn8u
title: Occurrence Download

description: A dataset containing 1050 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Parkia pendula (Willd.) Benth. ex Walp."
 ]
}

 The dataset includes 1050 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018300-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xppam
title: Occurrence Download

description: A dataset containing 1732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Benin",
 "TaxonKey is Erythrocebus patas (Schreber, 1774)"
 ]
}

 The dataset includes 1732 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018301-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/yvgzjt
title: Fish length, weight, and abundance data from trawl surveys in the Chukchi Sea, Arctic Marine Biodiversity Observing Network (AMBON) research cruise on the vessel Norseman II, 09 August 2015 - 03 September 2015

description: This dataset is three comma-separated values (csv) files containing fish haul information collected during trawl surveys in the Chukchi Sea during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise. Tables describe haul event information, species occurrence per haul, and fish morphometric information. Fish were collected with a plump-staff beam trawl and Isaac-Kidds midwater trawl, identified to species, and their life stage, length, and weight were measured and recorded. The data was transformed from the native format into a table structure using Darwin Core terms and organized in the OBIS-ENV-DATA format.
DOI: 10.15468/dl.jvabh8
title: Occurrence Download

description: A dataset containing 30508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Scleractinia"
 ]
}

 The dataset includes 30508 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018302-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/ykbf7p
title: Benthic Epifauna Biomass and Abundance Data, Arctic Marine Biodiversity Observing Network (AMBON) research cruise on the Norseman II, 9 August 2015 - 3 September 2015

description: This dataset contains benthic epifauna biomass and abundance data collected in the Chukchi Sea, U.S. Arctic during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise aboard the vessel Norseman II. The dataset contains two comma separated values (csv) files exported from Microsoft Excel. These data were generated from epifauna samples conducted using beam trawls during the research cruise.