cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.k2enw9
title: Occurrence Download

description: A dataset containing 33933 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Ceratopogonidae"
 ]
}

 The dataset includes 33933 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016725-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tjawwx
title: Occurrence Download

description: A dataset containing 70978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Chironomidae"
 ]
}

 The dataset includes 70978 records from 111 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016724-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15505197
title: "BOBURNOMA"DAGI O'LCHOV BIRLIKLARINING MILLATGA XOS XUSUSIYATLARI .
DOI: 10.5281/zenodo.15505198
title: "BOBURNOMA"DAGI O'LCHOV BIRLIKLARINING MILLATGA XOS XUSUSIYATLARI .
DOI: 10.15468/dl.zv5kwq
title: Occurrence Download

description: A dataset containing 18630 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "SpeciesKey is Trifolium montanum L."
 ]
}

 The dataset includes 18630 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016731-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8kxybf
title: Occurrence Download

description: A dataset containing 9716083 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Peru"
 ]
}

 The dataset includes 9716083 records from 2342 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016726-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60600/yu/clawet/1naalb
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DOI: 10.60600/yu/clawet/zyhtu3
title: 2635.wav

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DOI: 10.60600/yu/clawet/tcpngd
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DOI: 10.60600/yu/clawet/c864jc
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DOI: 10.60600/yu/clawet/3lc7hr
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DOI: 10.60600/yu/clawet/uzyju2
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DOI: 10.5281/zenodo.12528776
title: Comparing the compression behavior of the antiperovskites CePt3Si, CePt3B, and YPt3B from combined X-ray diffraction experiments and density functional theory

description: DFT data for the paper:

Comparing the compression behavior of the antiperovskites CePt3Si, CePt3B, and YPt3B from combined X-ray diffraction experiments and density functional theory

Emma Ehrenreich-Petersen (a) , Morten B. Nielsen (a,b) , Davide Ceresoli (c), Martin Ottesen (a) , Paraskevas Parisiades (d) , Martin Bremholm (a)(a) Department of Chemistry and iNANO, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark(b) Danish Technological Institute, Kongsvang Allé 29, 8000, Aarhus C, Denmark(c) Consiglio Nazionale delle Ricerche - Istituto di Scienze e Tecnologie Chimiche “G. Natta” (CNR-SCITEC), via Golgi 19, 20133, Milano, Italy(d) Sorbonne Université, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, CNRS/MNHN/IRD (UMR 7590), 4 Place Jussieu, 75005, Paris, France
DOI: 10.5281/zenodo.12528777
title: Comparing the compression behavior of the antiperovskites CePt3Si, CePt3B, and YPt3B from combined X-ray diffraction experiments and density functional theory

description: DFT data for the paper:

Comparing the compression behavior of the antiperovskites CePt3Si, CePt3B, and YPt3B from combined X-ray diffraction experiments and density functional theory

Emma Ehrenreich-Petersen (a) , Morten B. Nielsen (a,b) , Davide Ceresoli (c), Martin Ottesen (a) , Paraskevas Parisiades (d) , Martin Bremholm (a)(a) Department of Chemistry and iNANO, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark(b) Danish Technological Institute, Kongsvang Allé 29, 8000, Aarhus C, Denmark(c) Consiglio Nazionale delle Ricerche - Istituto di Scienze e Tecnologie Chimiche “G. Natta” (CNR-SCITEC), via Golgi 19, 20133, Milano, Italy(d) Sorbonne Université, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, CNRS/MNHN/IRD (UMR 7590), 4 Place Jussieu, 75005, Paris, France
DOI: 10.60600/yu/clawet/pjnm5t
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DOI: 10.60600/yu/clawet/iwhh2v
title: 2703.wav

description: :unav
DOI: 10.15468/dl.3t963a
title: Occurrence Download

description: A dataset containing 1195 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Zingiber zerumbet (L.) Roscoe ex Sm.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1195 records from 77 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0092518-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60600/yu/clawet/uiaqb4
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DOI: 10.60600/yu/clawet/hhusr1
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DOI: 10.60600/yu/clawet/l4nlsf
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DOI: 10.60600/yu/clawet/agqm8q
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DOI: 10.60600/yu/clawet/nj84xz
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DOI: 10.60600/yu/clawet/9kk9dc
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DOI: 10.60600/yu/clawet/k4ouxp
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DOI: 10.60600/yu/clawet/nwah3v
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DOI: 10.60600/yu/clawet/sqt15u
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DOI: 10.60600/yu/clawet/rrszat
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DOI: 10.60600/yu/clawet/3xy0sg
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DOI: 10.60600/yu/clawet/dluivd
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DOI: 10.60600/yu/clawet/wdgemn
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DOI: 10.6084/m9.figshare.26096467
title: University of Reading Test dataset June 2024 v1.0.xlsx

description: University of Reading authored outputs for calendar year 2023 held on the University of Reading Eprints repository for use as part of the UKRN pilots
DOI: 10.60600/yu/clawet/zbwrxs
title: 2780.wav

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DOI: 10.15468/dl.bzdkvg
title: Occurrence Download

description: A dataset containing 1031 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Zingiber spectabile Griff.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1031 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0092521-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60600/yu/clawet/5pblik
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DOI: 10.25934/pr00010228
title: Data Challenge_TB-FUNGI DETECT-AI SYSTEM

description:  Background:
Antimicrobial resistance (AMR) is a critical global health issue, severely affecting healthcare provision. In Uganda, AMR impacts more than 74% of patients, resulting in over 1.27 million deaths annually. Contributing factors include self-medication, medical errors, and inappropriate antimicrobial use. Similarly, tuberculosis (TB) and various fungal infections have shown increased resistance to conventional treatments, complicating their management. Medication errors in African settings account for 8.4% of patient deaths, and over 8 million individuals in low- and middle-income countries (LMICs) die each year from treatable AMR conditions. These fatalities contradict Sustainable Development Goal 3, necessitating innovative approaches to healthcare systems and diagnostics to reduce mortality rates.

Summary of Proposed Research:
To address this urgent need, we propose leveraging machine learning (ML) and artificial intelligence (AI) for the diagnosis of tuberculosis and fungal infections. ML enables computer systems to learn and adapt without explicit instructions, while AI replicates human-like intelligence. AI-assisted approaches have shown promise in improving diagnostic accuracy, reaching rates as high as 96.7%. Our research focuses on developing an AI system, named TB-Fungi DetectAI, for quick and accurate self-diagnosis, particularly in resource-limited LMICs. Using a decision tree-based ML approach, we shall create a diagnostic system by leveraging repositories, developing databases, and designing user interfaces.
The Clinical Decision Support System (CDSS) will assist doctors in diagnosing TB and fungal infections by comparing subtle symptoms against a knowledge base, offering non-invasive diagnosis only when a match is found. This reduces false positives and negatives associated with traditional methods. Future directions involve expanding the AI system into a standalone platform to expedite diagnosis and address the scarcity of medical specialists in LMICs, including Uganda. This research will improve patient outcomes, strengthen stewardship and health systems, and inform public health strategies by showcasing the potential of AI-assisted diagnostics in combating TB, fungal infections, and AMR
DOI: 10.60600/yu/clawet/lqmjqy
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DOI: 10.15468/dl.5upqk8
title: Occurrence Download

description: A dataset containing 52 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Zingiber montanum (J.Koenig) Link ex A.Dietr.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 52 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0092522-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60600/yu/clawet/8d7vfv
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DOI: 10.5281/zenodo.12528494
title: Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_21

description: "This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023
 
The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI.
Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:
[
        {
            'name': 'orthophoto-resolution',
            'value': 1
        },
        {
            'name': 'auto-boundary',
            'value': true
        },
        {
            'name': 'dem-resolution',
            'value': '2.0'
        },
        {
            'name': 'dsm',
            'value': true
        }
    ]

The depot consists of the following elements:
- 00_: Image preview panel
- DCIM.zip: RAW images from UAV
- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table.
- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files),
- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ...
 
Original tree structure:
│  └─ 20221023_SYC-aldabra-arm06_UAV-02_21
│--------  └─ DCIM
│--------  └─ GPS
│--------  └─ METADATA
│--------  └─ PROCESSED_DATA

 
Flight survey information:
- Camera model and parameters:
 Make: Hasselblad
 Model: L1D-20c
 Width: 5472
 Height: 3648
 Focal: 28
 WhiteBalance: Manual
 ExposureMode: Auto Exposure
 ColoSpace: sRGB
 EV: -0.7
 MeteringMode: CenterWeightedAverage
 Camera Pitch: -75.00

- Survey informations:
 No Images: 84
 Median height: 70 meters
 Survey area: 18.64 hectares
 Survey from: 2022:10:23 10:55:50 to: 2022:10:23 11:06:22
 "
description: This work received funding from Monaco Exploration, Ifrermer - Indian ocean and from the European Regional Development Fund (ERDF) within the programme Interreg V 2014-2020 through the project G2OI
DOI: 10.60600/yu/clawet/802pgt
title: 2748.wav

description: :unav
DOI: 10.5281/zenodo.12522501
title: Orthophoto & DEM from drone images, UAV, Aldabra arm06, Seychelles - 20221023 - 02_21

description: "This dataset presents the results of the photogrammetry process using images collected by an Unmanned Aerial Vehicle in Aldabra arm06, Seychelles, on 20221023
 
The processing was carried out with the OpenDroneMap software from the raw images provided in the first version of this DOI.
Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

The parameters of the OpenDroneMap software are shared so that the proposed processes can be reproduced or improved:
[
        {
            'name': 'orthophoto-resolution',
            'value': 1
        },
        {
            'name': 'auto-boundary',
            'value': true
        },
        {
            'name': 'dem-resolution',
            'value': '2.0'
        },
        {
            'name': 'dsm',
            'value': true
        }
    ]

The depot consists of the following elements:
- 00_: Image preview panel
- DCIM.zip: RAW images from UAV
- GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table.
- METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images and flight statistics (html or pdf files),
- PROCESSED_DATA.zip: Orthophoto, DEM, point cloud, ...
 
Original tree structure:
│  └─ 20221023_SYC-aldabra-arm06_UAV-02_21
│--------  └─ DCIM
│--------  └─ GPS
│--------  └─ METADATA
│--------  └─ PROCESSED_DATA

 
Flight survey information:
- Camera model and parameters:
 Make: Hasselblad
 Model: L1D-20c
 Width: 5472
 Height: 3648
 Focal: 28
 WhiteBalance: Manual
 ExposureMode: Auto Exposure
 ColoSpace: sRGB
 EV: -0.7
 MeteringMode: CenterWeightedAverage
 Camera Pitch: -75.00

- Survey informations:
 No Images: 84
 Median height: 70 meters
 Survey area: 18.64 hectares
 Survey from: 2022:10:23 10:55:50 to: 2022:10:23 11:06:22
 "
description: This work received funding from Monaco Exploration, Ifrermer - Indian ocean and from the European Regional Development Fund (ERDF) within the programme Interreg V 2014-2020 through the project G2OI