cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60712/si-id405212.3
title: StrainInfo SI-ID 405212.3

description: StrainInfo dataset 405212 about a strain of Rosistilla oblonga. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id405213.3
title: StrainInfo SI-ID 405213.3

description: StrainInfo dataset 405213 about a strain of Phocicoccus schoeneichii. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.15468/dl.qzvjuj
title: Occurrence Download

description: A dataset containing 1197 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Orobanche coerulescens Stephan"
 ]
}

 The dataset includes 1197 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000979-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3npnxz
title: Occurrence Download

description: A dataset containing 469 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phelipanche caesia (Rchb.) Soják"
 ]
}

 The dataset includes 469 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000980-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.98y83f
title: Occurrence Download

description: A dataset containing 152160 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Montenegro"
 ]
}

 The dataset includes 152160 records from 460 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000965-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4r8zga
title: Occurrence Download

description: A dataset containing 466 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Phelipanche aegyptiaca (Pers.) Pomel"
 ]
}

 The dataset includes 466 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000981-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkqndv
title: Occurrence Download

description: A dataset containing 1058 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grimmia reflexidens Müll.Hal.",
 "Year 1970-2025"
 ]
}

 The dataset includes 1058 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000982-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14908980
title: Spinal Vertebrae Segmentation Dataset

description: Description:







This Spinal Vertebrae Segmentation Dataset is comprised of high-resolution MRI scans of the spine, showcasing various degenerative and dystrophic changes, including vertebral fractures, osteophytes, and disc protrusions. It’s an invaluable resource for the development of machine learning models aimed at classifying and segmenting spinal abnormalities.

 







Download Dataset

 









Key Features:



Comprehensive Imaging: The dataset includes 5 studies, captured from different angles, covering a range of spinal anomalies such as a focal mass in the Th7 vertebral body, fluid masses, and spondyloarthrosis.

Clinical Annotations: All images are thoroughly labeled by experienced medical professionals and come with corresponding PDF reports, allowing for accurate diagnosis and reference.

Diverse Conditions: The scans highlight both common and complex conditions like osteophytes, fluid masses on the anterior contour of the vertebrae, and dorsal protrusions of the Th11-Th12 disc. These features make it highly applicable for detecting fractures and classifying various spine-related abnormalities.


Applications in AI and Healthcare:



Fracture Detection Models: This dataset is particularly useful for training deep learning algorithms to detect vertebral fractures, nerve compression, and other dystrophic changes.

Segmentation and Classification: The detailed imaging of vertebrae, intervertebral discs, and surrounding tissues enables the segmentation of critical regions for AI-driven diagnostic tools.

Training Data for Degenerative Disease Detection: With a focus on conditions like spondyloarthrosis, the dataset provides robust training material for detecting degenerative spinal diseases, enhancing the accuracy of automated systems.


Use Case for AI Training:

Researchers and healthcare professionals can leverage this dataset to develop machine learning models for improved diagnostics in spinal health, particularly focusing on dystrophic conditions and spine anomaly classification.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14908981
title: Spinal Vertebrae Segmentation Dataset

description: Description:







This Spinal Vertebrae Segmentation Dataset is comprised of high-resolution MRI scans of the spine, showcasing various degenerative and dystrophic changes, including vertebral fractures, osteophytes, and disc protrusions. It’s an invaluable resource for the development of machine learning models aimed at classifying and segmenting spinal abnormalities.

 







Download Dataset

 









Key Features:



Comprehensive Imaging: The dataset includes 5 studies, captured from different angles, covering a range of spinal anomalies such as a focal mass in the Th7 vertebral body, fluid masses, and spondyloarthrosis.

Clinical Annotations: All images are thoroughly labeled by experienced medical professionals and come with corresponding PDF reports, allowing for accurate diagnosis and reference.

Diverse Conditions: The scans highlight both common and complex conditions like osteophytes, fluid masses on the anterior contour of the vertebrae, and dorsal protrusions of the Th11-Th12 disc. These features make it highly applicable for detecting fractures and classifying various spine-related abnormalities.


Applications in AI and Healthcare:



Fracture Detection Models: This dataset is particularly useful for training deep learning algorithms to detect vertebral fractures, nerve compression, and other dystrophic changes.

Segmentation and Classification: The detailed imaging of vertebrae, intervertebral discs, and surrounding tissues enables the segmentation of critical regions for AI-driven diagnostic tools.

Training Data for Degenerative Disease Detection: With a focus on conditions like spondyloarthrosis, the dataset provides robust training material for detecting degenerative spinal diseases, enhancing the accuracy of automated systems.


Use Case for AI Training:

Researchers and healthcare professionals can leverage this dataset to develop machine learning models for improved diagnostics in spinal health, particularly focusing on dystrophic conditions and spine anomaly classification.

This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14909002
title: Polynomial-time solution of Hamiltonian path using dynamic inverse relation of 3D point sets and proof of P=NP

description: P=NP proof step1
DOI: 10.5281/zenodo.14909003
title: Polynomial-time solution of Hamiltonian path using dynamic inverse relation of 3D point sets and proof of P=NP

description: P=NP proof step1
DOI: 10.15468/dl.fuxfnc
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is SH1048761.09FU"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000983-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15151/esrf-es-1978056615
title: Belgium BAG

description: The Belgium BAG includes groups situated at four Flemish universities: Antwerpen, Brussels, Ghent and Leuven. The proposed projects are highly diverse, including viral, prokaryotic and eukaryotic proteins with a strong focus on targets and processes of biomedical relevance. Many projects deal with membrane proteins, cell surface receptors and protein-protein complexes. Central to the research in the BAG associated groups is the employment of integrative methods in structural biology combining X-ray crystallography, SAXS, and electron microscopy. Many crystals in the various proposed projects are small in size and radiation sensitive, requiring the use of the high brilliance MX beam lines, preferably with micro-focused X-rays.
DOI: 10.15151/esrf-es-2008566889
title: Unraveling the Crystal Structures and Electronic Properties of Novel Iron Sulfide Phases Under Martian Core Conditions

description: Iron sulphides are speculated to play a crucial role in understanding the internal structure and dynamics of Martian interiors as they are expected to crystallize and persist within the Martian core as it undergoes cooling. These compounds exhibit complex crystal chemical behaviours under high pressures, with recent studies and our preliminary findings uncovering several unexpected new phases. Building on our initial experiments, we propose to investigate the crystal structures and electronic properties of novel iron sulphide phases that are stable under conditions akin to those within the Martian core. These iron sulphide phases will be synthesized in laser-heated diamond anvil cells utilizing high pressures and temperatures up to 40 GPa and 2000 K. Characterization of the samples will be conducted using single crystal X-ray diffraction and synchrotron Mössbauer spectroscopy. The outcomes of this study will enhance our knowledge regarding the state and evolution of the Martian core.
DOI: 10.15151/esrf-es-1975515439
title: Joint-Structural Biology Group (JSBG) BAG proposal for ID29 beamline

description: The aim of the JSBG BAG proposal for ID29 beamline is to develop new methods for time-resolved serial crystallography experiments at 4th generation source and to study time dependent structural conformation of biological macromolecules that are studies at the ESRF-EMBL-GR Joint Structural Biology Group. Method developments include the study of radiation damage at high flux densities and with partially polychromatic beam (1% and 0.3%), the implementation and development of various sample delivery methods - mixing in capillary, microfluidics, tape-drive and fixed targets. This further includes development of GPU-based indexing algorithm, data reduction software, and develop new data collection strategies for pump-probe SSX experiments integrating nanosecond laser. Biological systems include the study of photoactivatable proteins, enzymatic systems through mixing experiments and multi-temperature jump experiments to study protein dynamics.
DOI: 10.15151/esrf-es-2045368343
title: Projects in protein crystallography of GSY

description: The Synchrotron Group of IBS has two activities. The first one is focused on instrumentation and methodology development, and includes developments that range
from crystallization to room temperature data collection. The second one deals with the structure elucidation of fluorescent proteins, plant proteins and proteins of
human health interest. We work on enzymes of the MEP pathway since they constitute interesting drug targets. We study also fluorescent proteins by combining Xray
diffraction and online spectro. We are involved in the characterization of the plastid-encoded prokaryotic-type RNA polymerase (PEP) and transcription factors
(ARFs) from plants. We try to improve crystal quality for X-ray and neutron diffraction.
DOI: 10.15151/esrf-es-2013610073
title: Test of beamline setup for in-situ studing zeolite crystallization

description: Traditional syntheses of zeolites convert a gel or gel-like mixture to a crystalline material under hydrothermal conditions.One of the major questions in zeolite crystallization concerns the mechanisms in the earliest stages. We plan tu use an in-situ combination of diffraction and spectroscopy to probe the crystallization. The in-situ cell will be adopted from BM26 (BUBBLE) beamline, where it has been used with sucsess for combined SAXS/WAXS measurements,
DOI: 10.15468/dl.tuxggp
title: Occurrence Download

description: A dataset containing 1142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grimmia tergestina Tomm. ex Bruch & Schimp.",
 "Year 1970-2025"
 ]
}

 The dataset includes 1142 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000984-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwcp86
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is SH1317510.09FU"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000985-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ckbwr3
title: Occurrence Download

description: A dataset containing 701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grimmia unicolor Hook.",
 "Year 1970-2025"
 ]
}

 The dataset includes 701 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000987-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wsn7e7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is SH1048761.09FU"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000988-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.trr8g3
title: Occurrence Download

description: A dataset containing 21805577 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((116.89214 19.50305,125.3422 19.50305,125.3422 27.77747,116.89214 27.77747,116.89214 19.50305))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 21805577 records from 1115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000978-250221090833381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0212744.10fu
title: SH0212744.10FU

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: 10.15156/bio/sh0212745.10fu
title: SH0212745.10FU

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: 10.15156/bio/sh0212746.10fu
title: SH0212746.10FU

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: 10.15156/bio/sh0212747.10fu
title: SH0212747.10FU

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: 10.15156/bio/sh0212748.10fu
title: SH0212748.10FU

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: 10.15156/bio/sh0212749.10fu
title: SH0212749.10FU

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: 10.15156/bio/sh0212750.10fu
title: SH0212750.10FU

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.