cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/6w3p65cjkk
title: Exocellular polysaccharides in lactic acid bacteria: matching structures and genes by combinatorial optimization

description: Supplementary data for "Exocellular polysaccharides in lactic acid bacteria: matching structures and genes by combinatorial optimization"
DOI: 10.17632/6w3p65cjkk.1
title: Exocellular polysaccharides in lactic acid bacteria: matching structures and genes by combinatorial optimization

description: Supplementary data for "Exocellular polysaccharides in lactic acid bacteria: matching structures and genes by combinatorial optimization"
DOI: 10.15468/dl.er7m88
title: Occurrence Download

description: A dataset containing 448988 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-11-24",
 "EventDate is less than or equal to 2025-12-24",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 },
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 448988 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000533-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2k35h6j6g3
title: Supplementary Tables for "The high oxygen fugacity of the Chang’e-5 lunar basalt: Implication for redox stratification of the lunar mantle"

description: This file is the Supplementary Tables for manuscript "The high oxygen fugacity of the Chang’e-5 lunar basalt: Implication for redox stratification of the lunar mantle". The file includes major element and trace element compositions of the lunar natural samples and fO2-controlled experiments, the experimentally determined partition coefficients and the crystallization modelling of the lunar magma ocean.
DOI: 10.17632/2k35h6j6g3.2
title: Supplementary Tables for "The high oxygen fugacity of the Chang’e-5 lunar basalt: Implication for redox stratification of the lunar mantle"

description: This file is the Supplementary Tables for manuscript "The high oxygen fugacity of the Chang’e-5 lunar basalt: Implication for redox stratification of the lunar mantle". The file includes major element and trace element compositions of the lunar natural samples and fO2-controlled experiments, the experimentally determined partition coefficients and the crystallization modelling of the lunar magma ocean.
DOI: 10.17632/gvd8gjm8yh
title: Parametric study on the femtosecond laser ablation of silicon nitride

description: This dataset contains a structured table from a femtosecond-laser parametric study on silicon nitride. It features input parameters alongside the measured notch geometry. This dataset supports correlation analyses, modelling, and optimisation aimed at achieving target notch geometries through parameter tuning.
• Materials and system
o Material: Silicon nitride (SL200 by CeramTec GmbH).
o Laser system: microPREP™ PRO FEMTO by 3D-Micromac AG.
o Process: Femtosecond laser ablation using rectangular area scans.
• File format and structure
o Format: Excel (.xlsx).
o Organisation: One sheet (i.e., “SL200 Parameter Study”).
o Rows: Individual experiments (one parameter setting per row & the corresponding results).
o Columns: Input parameters, and measured geometry (see “Parameters” below).
• Dataset
o Parameters: Area length | Area width | Power | Pulse frequency | DLFS | PD | LD | Repetitions | Focus shift | CO2-cleaning | Notch depth | Notch root radius | Opening angle | Width | Comments
o A full description of all parameters is given in the file “Parameter_description.pdf”.
o Units for the parameters are given in the column headers.
o Aim: The data was used to analyse the general effect of different laser parameters on the resulting notch geometry.
DOI: 10.17632/gvd8gjm8yh.1
title: Parametric study on the femtosecond laser ablation of silicon nitride

description: This dataset contains a structured table from a femtosecond-laser parametric study on silicon nitride. It features input parameters alongside the measured notch geometry. This dataset supports correlation analyses, modelling, and optimisation aimed at achieving target notch geometries through parameter tuning.
• Materials and system
o Material: Silicon nitride (SL200 by CeramTec GmbH).
o Laser system: microPREP™ PRO FEMTO by 3D-Micromac AG.
o Process: Femtosecond laser ablation using rectangular area scans.
• File format and structure
o Format: Excel (.xlsx).
o Organisation: One sheet (i.e., “SL200 Parameter Study”).
o Rows: Individual experiments (one parameter setting per row & the corresponding results).
o Columns: Input parameters, and measured geometry (see “Parameters” below).
• Dataset
o Parameters: Area length | Area width | Power | Pulse frequency | DLFS | PD | LD | Repetitions | Focus shift | CO2-cleaning | Notch depth | Notch root radius | Opening angle | Width | Comments
o A full description of all parameters is given in the file “Parameter_description.pdf”.
o Units for the parameters are given in the column headers.
o Aim: The data was used to analyse the general effect of different laser parameters on the resulting notch geometry.
DOI: 10.17632/df2xckv3fr
title: Data associated with "Oxidative Peptide Backbone Cleavage by a HEXXH Enzyme During RiPP Biosynthesis" JACS 2026 10.1021/jacs.5c19246

description: MS and NMR data associated with 10.1021/jacs.5c19246
DOI: 10.15468/dl.6wdukv
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((103.145 2.510178, 103.07467 2.504609, 103.006074 2.48804, 102.940901 2.460879, 102.880759 2.423796, 102.827127 2.377705, 102.781328 2.32374, 102.744488 2.263232, 102.717514 2.197672, 102.701068 2.128673, 102.695554 2.057936, 102.701107 1.987202, 102.717588 1.918213, 102.744591 1.852666, 102.781448 1.792176, 102.827254 1.73823, 102.880879 1.692158, 102.941004 1.655092, 103.006148 1.627946, 103.074709 1.611386, 103.145 1.60582, 103.215291 1.611386, 103.283852 1.627946, 103.348996 1.655092, 103.409121 1.692158, 103.462746 1.73823, 103.508552 1.792176, 103.545409 1.852666, 103.572412 1.918213, 103.588893 1.987202, 103.594446 2.057936, 103.588932 2.128673, 103.572486 2.197672, 103.545512 2.263232, 103.508672 2.32374, 103.462873 2.377705, 103.409241 2.423796, 103.349099 2.460879, 103.283926 2.48804, 103.21533 2.504609, 103.145 2.510178))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000552-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/df2xckv3fr.1
title: Data associated with "Oxidative Peptide Backbone Cleavage by a HEXXH Enzyme During RiPP Biosynthesis" JACS 2026 10.1021/jacs.5c19246

description: MS and NMR data associated with 10.1021/jacs.5c19246
DOI: 10.17632/rbg5f6c7wh
title: The forest canopy height data in 2021 in Shangri-La, China

description: This is a forest canopy height (FCH) inversion data from Shangri La, China. Its development process is as follows: (1) the ICESat-2 ATL08 product accuracy was validated using 10-meter resolution canopy height model (CHM) generated by airborne laser scanning (ALS) LiDAR data; (2) the canopy height information provided by ATL08 and explanatory variables provided by multi-source data were combined to construct convolutional neural network regression and extrapolation models to obtain spatially continuous FCH at 10-meter scale, with an accuracy of 0.75.
DOI: 10.17632/rbg5f6c7wh.1
title: The forest canopy height data in 2021 in Shangri-La, China

description: This is a forest canopy height (FCH) inversion data from Shangri La, China. Its development process is as follows: (1) the ICESat-2 ATL08 product accuracy was validated using 10-meter resolution canopy height model (CHM) generated by airborne laser scanning (ALS) LiDAR data; (2) the canopy height information provided by ATL08 and explanatory variables provided by multi-source data were combined to construct convolutional neural network regression and extrapolation models to obtain spatially continuous FCH at 10-meter scale, with an accuracy of 0.75.
DOI: 10.17632/6kd8ft6tjj.1
title: Survey Responses on University-Level Socioeconomic Factors in Bangladesh

description: This dataset has information from a survey about peoples economic situations. The survey was done by talking to people in person at their homes and work place. These homes are near six universities in different parts of the region. The study wants to see how having a university nearby affects the people who live in these homes. It looks at things like how money they make what kind of work they do who lives in the home and how long they have lived there. The study is really about how universities relate to the situations of the households near them including things, like income and occupation and demographic attributes and how long people have lived in the area.

We talked to 484 people in person. Wrote down what they said. Later, we put this information into a computer so we could look at it. Each line is one person from a household. We did not use their names to keep them private. The information we collected includes the age of the person, in charge of the family how money the household makes each month in BDT what they do for work how long they have lived there and which university area they are connected to.

When we worked with the data, we took out the information that could identify people. We also made sure the income numbers were all in the format. The category that said "Local" was changed to a number, 25 years because that is what we thought it meant when someone has lived somewhere for a time. If there are spaces in the data that means the person did not answer the question or we did not have that information, about them.

The university has some information about the area like how many people can read and write in different regions. This information is in a file, and it gives us an idea of what is going on in each area rather than telling us about individual people. The university is looking at things like literacy rates to get a better understanding of the situation.

This dataset is intended for academic research, teaching, and exploratory analysis in fields such as socioeconomic studies, regional development, education policy, and data science. Users should consider potential limitations related to self-reported responses, interviewer recording bias, and non-random sampling when interpreting results.
DOI: 10.17632/dnk6839b86
title: Climate-Land Cover data used by DeepEcoClimate

description: Sourced and processed from

- Climate normals: CRU TS v4.09 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.09/)
- Land cover: MCD12C1 (https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12c1-061)
- Elevation: GMTED2010 (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation)

train_data.nc and valid_data.nc are used in TorchModel.py (in the GitHub repo)
Check the GitHub repo (https://github.com/peace-Van/ClimViz) for details
DOI: 10.17632/dnk6839b86.3
title: Climate-Land Cover data used by DeepEcoClimate

description: Sourced and processed from

- Climate normals: CRU TS v4.09 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.09/)
- Land cover: MCD12C1 (https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12c1-061)
- Elevation: GMTED2010 (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-multi-resolution-terrain-elevation)

train_data.nc and valid_data.nc are used in TorchModel.py (in the GitHub repo)
Check the GitHub repo (https://github.com/peace-Van/ClimViz) for details
DOI: 10.5281/zenodo.18305022
title: agalinis_albida_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.18305023
title: agalinis_albida_zanfona_1.fasta.gz
DOI: 10.17632/ngzwcdt8xw
title: Transformation of Refractory Oceanic Lithospheric Mantle by Reactive Melt Infiltration: An Experimental Study on the Roles of Temperature, Melt Volume and Ascent Velocity

description: Supporting Data for the article named “Transformation of Refractory Oceanic Lithospheric Mantle by Reactive Melt Infiltration: An Experimental Study on the Roles of Temperature, Melt Volume and Ascent Velocity”
DOI: 10.17632/ngzwcdt8xw.1
title: Transformation of Refractory Oceanic Lithospheric Mantle by Reactive Melt Infiltration: An Experimental Study on the Roles of Temperature, Melt Volume and Ascent Velocity

description: Supporting Data for the article named “Transformation of Refractory Oceanic Lithospheric Mantle by Reactive Melt Infiltration: An Experimental Study on the Roles of Temperature, Melt Volume and Ascent Velocity”
DOI: 10.17632/4ymx977rn5
title: social survey

description: survey
DOI: 10.17632/4ymx977rn5.3
title: social survey

description: survey
DOI: 10.15468/dl.dqhggv
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galium glaucum, Galium glaucum, Galium glaucum, Asperula glauca, Asperula glauca)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 58 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000553-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/m6kgttryxt
title: JabutiVisionData - Annotated Image Dataset for Morphological Differentiation of Chelonoidis carbonaria and Chelonoidis denticulata

description: The JabutiVisionData dataset is an annotated image collection designed to support computer vision research focused on the morphological differentiation between Chelonoidis carbonaria (red-footed tortoise) and Chelonoidis denticulata (yellow-footed tortoise). These two species exhibit high morphological similarity and partial geographic overlap in South America, making visual identification challenging, particularly in fine-grained classification scenarios. The dataset consists of 1,150 RGB images collected from the citizen science platform iNaturalist, representing photographic records captured in natural environments. A rigorous manual curation process was applied to ensure taxonomic correctness, remove duplicate records, and exclude images with insufficient visual quality. The curated dataset contains a total of 1,173 annotated objects, distributed in a balanced manner between the two species. Annotations were performed manually using bounding boxes that fully encompass the visible portions of each individual, including the carapace, limbs, and head when present. Images containing multiple individuals were annotated with independent bounding boxes for each specimen. In cases of partial occlusion, only clearly visible and identifiable regions were annotated to avoid inference of non-visible areas. All annotations were reviewed to ensure spatial consistency and label accuracy.
 The dataset is provided in both COCO and YOLO annotation formats, ensuring interoperability with a wide range of deep learning frameworks and object detection architectures. The data are partitioned into training (74%), validation (14%), and test (12%) subsets, preserving class balance across all splits. Statistical analyses of bounding box dimensions reveal substantial variability in object scale and aspect ratio, reflecting realistic variations in capture distance, pose, and framing. JabutiVisionData is suitable for training and evaluating object detection and classification models, benchmarking deep learning architectures under fine-grained visual discrimination conditions, and supporting non-invasive environmental monitoring applications. Its structure also enables integration with camera trap systems and edge AI devices, facilitating real-world biodiversity monitoring and ecological research.

To organize the dataset clearly and professionally for YOLOv12 training, the folder structure should be arranged following the COCO/YOLO standard as follows:

JabutiVisionData/
├── test/
│ ├── _annotations.coco.json # train metadata
│ └── images/ # 141 images
├── train/
│ ├── _annotations.coco.json 
│ └── images/ # 849 images
└── valid/
 ├── _annotations.coco.json 
 └── images/ # 160 images
DOI: 10.17632/m6kgttryxt.1
title: JabutiVisionData - Annotated Image Dataset for Morphological Differentiation of Chelonoidis carbonaria and Chelonoidis denticulata

description: The JabutiVisionData dataset is an annotated image collection designed to support computer vision research focused on the morphological differentiation between Chelonoidis carbonaria (red-footed tortoise) and Chelonoidis denticulata (yellow-footed tortoise). These two species exhibit high morphological similarity and partial geographic overlap in South America, making visual identification challenging, particularly in fine-grained classification scenarios. The dataset consists of 1,150 RGB images collected from the citizen science platform iNaturalist, representing photographic records captured in natural environments. A rigorous manual curation process was applied to ensure taxonomic correctness, remove duplicate records, and exclude images with insufficient visual quality. The curated dataset contains a total of 1,173 annotated objects, distributed in a balanced manner between the two species. Annotations were performed manually using bounding boxes that fully encompass the visible portions of each individual, including the carapace, limbs, and head when present. Images containing multiple individuals were annotated with independent bounding boxes for each specimen. In cases of partial occlusion, only clearly visible and identifiable regions were annotated to avoid inference of non-visible areas. All annotations were reviewed to ensure spatial consistency and label accuracy.
 The dataset is provided in both COCO and YOLO annotation formats, ensuring interoperability with a wide range of deep learning frameworks and object detection architectures. The data are partitioned into training (74%), validation (14%), and test (12%) subsets, preserving class balance across all splits. Statistical analyses of bounding box dimensions reveal substantial variability in object scale and aspect ratio, reflecting realistic variations in capture distance, pose, and framing. JabutiVisionData is suitable for training and evaluating object detection and classification models, benchmarking deep learning architectures under fine-grained visual discrimination conditions, and supporting non-invasive environmental monitoring applications. Its structure also enables integration with camera trap systems and edge AI devices, facilitating real-world biodiversity monitoring and ecological research.

To organize the dataset clearly and professionally for YOLOv12 training, the folder structure should be arranged following the COCO/YOLO standard as follows:

JabutiVisionData/
├── test/
│ ├── _annotations.coco.json # train metadata
│ └── images/ # 141 images
├── train/
│ ├── _annotations.coco.json 
│ └── images/ # 849 images
└── valid/
 ├── _annotations.coco.json 
 └── images/ # 160 images
DOI: 10.48331/scielodata.cdjtuw/g8ik6g
title: README_graves.txt

description: Arquivo com descrição do conteúdo dos bancos
DOI: 10.48331/scielodata.cdjtuw/rd2z2j
title: Serie_Casos_Graves_HAV.tab

description: Banco de dados de casos graves identificados de 1/11/23 a 29/5/24
DOI: 10.48331/scielodata.cdjtuw/2a64jp
title: Dicionario_Serie_Casos_Graves_HAV.tab

description: Dicionário dados do banco Serie_Casos_Graves_HAV.csv
DOI: 10.48331/scielodata.cdjtuw/ue5d2o
title: Dicionario_BANCO_HEPATITE-A.tab

description: Dicionário dados do banco BANCO_HEPATITE-A_29052024.csv
DOI: 10.48331/scielodata.cdjtuw/a6kh06
title: BANCO_HEPATITE-A_29052024.tab

description: Banco de dados geral contendo todos os casos confirmados de hepatite A notificados no município de Curitiba entre 01 de novembro de 2023 e 29 de maio de 2024
DOI: 10.48331/scielodata.cdjtuw
title: Casos graves de hepatite A: série de casos em Curitiba, Paraná, 2023-2024

description: Trata-se de um estudo descritivo do tipo série de casos baseado em dados secundários e revisão de prontuários. O objetivo foi descrever o perfil epidemiológico do surto de hepatite A em Curitiba, Paraná, ocorrido entre novembro de 2023 e maio de 2024, com ênfase na caracterização clínica e laboratorial dos casos que evoluíram para óbito ou transplante hepático.
DOI: 10.15468/dl.f9hgkf
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-6.90617 38.16013,-7.44831 37.67133,-7.44831 37.19472,-6.92553 36.27926,-5.86062 35.91944,-5.02805 35.88807,-1.65906 36.54416,-1.90955 37.93881,-2.80989 38.26638,-4.67833 38.01512,-5.63675 38.31197,-7.05986 38.1675,-6.90617 38.16013))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Limonium malacitanum"
 ]
}

 The dataset includes 131 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000554-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18305042
title: Rhynchospora rugosa (Vahl) Gale. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53
DOI: 10.5281/zenodo.18305043
title: Rhynchospora rugosa (Vahl) Gale. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:53