cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0017949.10fu
title: SH0017949.10FU

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: 10.3535/d0k-rqk-d6h
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/zrt-brz-epy
title: Viola rupestris subsp. rupestris

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0017950.10fu
title: SH0017950.10FU

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: 10.3535/hm7-gz9-0hc
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0017951.10fu
title: SH0017951.10FU

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: 10.3535/h2z-bx4-5kv
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/w3g-j4z-c9a
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/dnb-a1s-sc8
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/vp0-kha-66a
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0017952.10fu
title: SH0017952.10FU

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: 10.3535/f8j-x1m-w70
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/ymp-4cv-vth
title: Epilobium ciliatum subsp. ciliatum

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/vwe-crx-qs5
title: Viola rupestris subsp. rupestris

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/z0s-bmj-kaq
title: Viola rupestris subsp. rupestris

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0017953.10fu
title: SH0017953.10FU

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: 10.3535/2y3-r44-y9t
title: Viola rupestris subsp. rupestris

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/6rx-j8g-hmf
title: Viola rupestris subsp. rupestris

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0017954.10fu
title: SH0017954.10FU

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: 10.15468/dl.8msz5c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Daboecia cantabrica",
 "Country is India",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058395-250920141307145/datasets/export for details.
DOI: 10.15468/dl.uwsrfz
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Daboecia cantabrica",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 64 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058396-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ad9zfr
title: Occurrence Download

description: A dataset containing 538087 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-109.45 31.85,-109.25 31.85,-109.25 32.05,-109.45 32.05,-109.45 31.85))",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 538087 records from 241 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058337-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14834045
title: 2Danalysis-test-set

description: Data for the tutorials of 2Danalysis, a toobox to analyze simulation trajectories of lipid membranes and biopolymers projected onto a 2D plane. 

User Guide

Tutorials
DOI: 10.5281/zenodo.14834046
title: 2Danalysis-test-set

description: Data for the tutorials of 2Danalysis, a toobox to analyze simulation trajectories of lipid membranes and biopolymers projected onto a 2D plane. 

User Guide

Tutorials
DOI: 10.5281/zenodo.14834110
title: 2Danalysis-test-set

description: Data for the tutorials of 2Danalysis, a toobox to analyze simulation trajectories of lipid membranes and biopolymers projected onto a 2D plane. 

User Guide

Tutorials
DOI: 10.5281/zenodo.15239072
title: 2Danalysis-test-set

description: Data for the tutorials of 2Danalysis, a toobox to analyze simulation trajectories of lipid membranes and biopolymers projected onto a 2D plane. 

User Guide

Tutorials
DOI: 10.15468/dl.wv4qfy
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Marasmius haematocephalus (Mont.) Fr."
 ]
}

 The dataset includes 275 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018421-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kd7pbp
title: Occurrence Download

description: A dataset containing 8575 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fumana laevipes (L.) Spach",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8575 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018422-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10477564
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2020

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2020

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.10478444
title: UFLUX 100m daily photosynthesis fluxes in southeastern UK 2020

description: UFLUX Ensemble UK100mdaily (UK 100 Daily) in southeastern UK 2020

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using machine learning models. It integrates satellite-based Sentinel-1 backscatters and Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. It  includes five core flux components:





Gross Primary Production (GPP) [open access]




Ecosystem Respiration (RECO) [in progress]




Net Ecosystem Exchange (NEE) [in progress]




Sensible Heat Flux (H) [in progress]




Latent Energy Flux (LE) [in progress]



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.15468/dl.yhc8zh
title: Occurrence Download

description: A dataset containing 9217 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Kadsura Kaempf. ex Juss."
 ]
}

 The dataset includes 9217 records from 203 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070026-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.71802/0mrdijd6
title: Comparative report (Output 1)

description: The overall objective of the report is to support the library and teaching staff of universities and research organizations in deepening their knowledge about the most effective methodologies and strategies used in Europe for IL teaching in scientific and technical fields. Contents: The report analyses the state of the art of training activities on information competence in the scientific and technical sector conducted in the countries participating in the project (Italy, Spain, Belgium, Portugal, and Latvia) in relation to the existence of syllabus, curricula, and specific IL frameworks. It highlights, in particular, the experiences that refer to a theoretical model of IL as a vital and civil value connected to the activities and needs of everyday life.
DOI: 10.5281/zenodo.15176147
title: ПОСЛЕОПЕРАЦИОННЫЕ ОСЛОЖНЕНИЯ ПРИ ХИРУРГИЧЕСКОМ ЛЕЧЕНИИ ЭХИНОКОККОЗА ПЕЧЕНИ И МЕТОДЫ ИХ ПРОФИЛАКТИКИ.
DOI: 10.5281/zenodo.15176148
title: ПОСЛЕОПЕРАЦИОННЫЕ ОСЛОЖНЕНИЯ ПРИ ХИРУРГИЧЕСКОМ ЛЕЧЕНИИ ЭХИНОКОККОЗА ПЕЧЕНИ И МЕТОДЫ ИХ ПРОФИЛАКТИКИ.
DOI: 10.15156/bio/sh1049879.10fu
title: SH1049879.10FU

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/sh1049886.10fu
title: SH1049886.10FU

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: 10.6084/m9.figshare.31041946
title: A Machine Learning and Quantum Chemical Strategy to Disrupt Oncogenic Arginine Methylation

description: Protein arginine N-methyltransferase 1 (PRMT1) plays a key role in gene regulation by modifying proteins through asymmetric arginine dimethylation, a process often linked to cancer development. Due to its elevated activity in cancer, PRMT1 is a promising target for therapeutic intervention. This study used an integrated approach to identify and optimize selective inhibitors of PRMT1, starting from the known compound GSK3368715. Over 1,000 structurally related analogs were screened for their binding potential. Top candidates underwent quantum mechanical optimization and docking studies, which confirmed strong interactions with key active-site residues, including hydrogen bonding with Tyr57 and Glu171, and <i>π-π</i> stacking with Tyr53 and Phe54. Molecular dynamics simulations revealed stable binding behavior, supported by MM/GBSA binding energy calculations. One compound, 118971667, showed particularly high binding affinity and structural stability. Principal component analysis and free energy landscapes further confirmed its favorable dynamics. Additionally, a machine learning model trained on 290 known PRMT1 inhibitors predicted 118971667 to have strong inhibitory potential (pIC<sub>50</sub> of 6.66), outperforming a reference compound. This comprehensive strategy, combining structural modeling, simulations, quantum calculations, and predictive analytics, highlights 118971667 as a promising PRMT1 inhibitor and offers valuable insights into targeting arginine methylation in cancer.