cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.7uemnu
title: Occurrence Download

description: A dataset containing 4076 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyoscyamus niger L.",
 "Year 2023-2025"
 ]
}

 The dataset includes 4076 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014793-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/3py-q0j-370
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/99v-q1a-t1p
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/0zb-ycd-rmg
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/q05-00a-9h8
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/n6j-wzf-8sj
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/q9x-26x-0cr
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/2ch-4l9-xtr
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/awm-kah-qez
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/enj-egn-wrl
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/q8v-k0e-5vb
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/r4k-1ce-he9
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/mtm-rac-aax
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/rt3-3de-qt0
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/z7e-05w-fba
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/ax7-vbx-4w2
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/rf2-1cr-k2n
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/642-93c-l43
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/jxf-4mq-czw
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/8s6-hz1-84r
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/crv-q2g-hks
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/33v-esk-6c1
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/jd1-fnr-9td
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/p19-hba-av3
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/zxe-c9x-h8g
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/dld-0a6-9n3
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/mxn-6na-y1e
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/nnf-jdr-0q5
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/xzw-nwg-7he
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/2l0-qb9-bp3
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/zsk-hcf-w5n
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/scm-6sg-y9y
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/73v-wdx-fdw
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/mr6-ed3-ydh
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.3535/3c3-6tb-151
title: Graphite

description: Digital Specimen for the physical specimen hosted at Tallinn University of Technology.
DOI: 10.15156/bio/sh1236509.10fu
title: SH1236509.10FU

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/sh1236511.10fu
title: SH1236511.10FU

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/sh1236515.10fu
title: SH1236515.10FU

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.31047504
title: A multiscale framework combining pore-level thermodynamics and process simulation via Bayesian-informed deep learning for CO<sub>2</sub> adsorption

description: Accurate modeling of carbon dioxide (CO<sub>2</sub>) adsorption units under high-pressure conditions requires the integration of mass, energy, and momentum balances with a rigorous thermodynamic description of adsorption equilibrium. In this context, this work proposes a multiscale framework in which pore-level thermodynamics are provided by an extended Peng–Robinson equation of state for confined fluids (PR-C), supported by high-pressure adsorption data experimentally obtained using a Magnetic Suspension Balance (MSB). The PR-C confinement parameters are estimated through Bayesian inference, enabling the quantification of uncertainty and the generation of statistically significant adsorption equilibrium datasets. These datasets are then used to train a Deep Neural Network (DNN) surrogate model that preserves the physical behavior captured by the PR-C formulation while introducing uncertainty quantification through dropout-based neural networks within macroscopic fixed-bed simulations. The proposed framework achieved good agreement with experimental isotherms and benchmark breakthrough data, reaching a coefficient of determination (R2) of 0.9986 across cross-validation folds. Simulation results highlight the influence of operating conditions, such as pressure, temperature, and feed composition on adsorption equilibrium and system dynamics. Overall, the methodology offers a robust and efficient platform for adsorption process modeling, with direct applicability to process optimization, risk assessment, and the development of CO<sub>2</sub> capture technologies.
DOI: 10.6084/m9.figshare.31047504.v1
title: A multiscale framework combining pore-level thermodynamics and process simulation via Bayesian-informed deep learning for CO<sub>2</sub> adsorption

description: Accurate modeling of carbon dioxide (CO<sub>2</sub>) adsorption units under high-pressure conditions requires the integration of mass, energy, and momentum balances with a rigorous thermodynamic description of adsorption equilibrium. In this context, this work proposes a multiscale framework in which pore-level thermodynamics are provided by an extended Peng–Robinson equation of state for confined fluids (PR-C), supported by high-pressure adsorption data experimentally obtained using a Magnetic Suspension Balance (MSB). The PR-C confinement parameters are estimated through Bayesian inference, enabling the quantification of uncertainty and the generation of statistically significant adsorption equilibrium datasets. These datasets are then used to train a Deep Neural Network (DNN) surrogate model that preserves the physical behavior captured by the PR-C formulation while introducing uncertainty quantification through dropout-based neural networks within macroscopic fixed-bed simulations. The proposed framework achieved good agreement with experimental isotherms and benchmark breakthrough data, reaching a coefficient of determination (R2) of 0.9986 across cross-validation folds. Simulation results highlight the influence of operating conditions, such as pressure, temperature, and feed composition on adsorption equilibrium and system dynamics. Overall, the methodology offers a robust and efficient platform for adsorption process modeling, with direct applicability to process optimization, risk assessment, and the development of CO<sub>2</sub> capture technologies.
DOI: 10.15468/dl.4fgmf6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hong Kong",
 "OccurrenceStatus is Present",
 "TaxonKey is Paraploactis hongkongiensis"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003503-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1236516.10fu
title: SH1236516.10FU

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/sh1236517.10fu
title: SH1236517.10FU

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/sh1236520.10fu
title: SH1236520.10FU

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: 10.60534/7de3v-7mk38
title: Biosensor-based growth-coupling as an evolutionary strategy towards elevated hrtB expression in Corynebacterium glutamicum

description: The iron-containing porphyrin heme is of high interest for the food industry for the production of artificial meat as well as for medical applications. Recently, the biotechnological platform strain Corynebacterium glutamicum has emerged as a promising host for animal-free heme production. Beyond engineering of complex heme biosynthetic pathways, improving heme export offers significant yet untapped potential for enhancing production strains. In this study, a growth-coupled biosensor was designed to impose a selection pressure on the increased expression of the hrtBA operon encoding an ABC-type heme exporter in C. glutamicum. For this purpose, the promoter region of the growth-regulating genes pfkA (phosphofructokinase) and aceE (pyruvate dehydrogenase) was replaced with that of PhrtB, creating biosensor strains with a selection pressure for hrtBA activation. Resulting sensor strains were used for plate-based selections and for a repetitive batch f(luorescent)ALE using a fully automated laboratory platform. Genome sequencing of isolated clones featuring increased hrtBA expression revealed three distinct mutational hotspots: (i) chrS, (ii) chrA, and (iii) cydD. Mutations in the genes of the ChrSA two-component system, which regulates hrtBA in response to heme levels, were identified as a promising target to enhance export activity. Furthermore, causal mutations within cydD, encoding an ABC-transporter essential for cytochrome bd oxidase assembly, were confirmed by the construction of a deletion mutant. Reversely engineered strains showed strongly increased hrtBA expression as well as increased cellular heme levels. These results further support the proposed role of CydDC as a heme transporter in bacteria. Mutations identified in this study therefore underline the potential of biosensor-based growth coupling and provide promising engineering targets to improve microbial heme production.

hosted on: https://git.nfdi4plants.org/projects/1582
DOI: 10.60534/9cw4g-zqm55
title: Biosensor-based growth-coupling as an evolutionary strategy towards elevated hrtB expression in Corynebacterium glutamicum

description: The iron-containing porphyrin heme is of high interest for the food industry for the production of artificial meat as well as for medical applications. Recently, the biotechnological platform strain Corynebacterium glutamicum has emerged as a promising host for animal-free heme production. Beyond engineering of complex heme biosynthetic pathways, improving heme export offers significant yet untapped potential for enhancing production strains. In this study, a growth-coupled biosensor was designed to impose a selection pressure on the increased expression of the hrtBA operon encoding an ABC-type heme exporter in C. glutamicum. For this purpose, the promoter region of the growth-regulating genes pfkA (phosphofructokinase) and aceE (pyruvate dehydrogenase) was replaced with that of PhrtB, creating biosensor strains with a selection pressure for hrtBA activation. Resulting sensor strains were used for plate-based selections and for a repetitive batch f(luorescent)ALE using a fully automated laboratory platform. Genome sequencing of isolated clones featuring increased hrtBA expression revealed three distinct mutational hotspots: (i) chrS, (ii) chrA, and (iii) cydD. Mutations in the genes of the ChrSA two-component system, which regulates hrtBA in response to heme levels, were identified as a promising target to enhance export activity. Furthermore, causal mutations within cydD, encoding an ABC-transporter essential for cytochrome bd oxidase assembly, were confirmed by the construction of a deletion mutant. Reversely engineered strains showed strongly increased hrtBA expression as well as increased cellular heme levels. These results further support the proposed role of CydDC as a heme transporter in bacteria. Mutations identified in this study therefore underline the potential of biosensor-based growth coupling and provide promising engineering targets to improve microbial heme production.

hosted on: https://git.nfdi4plants.org/projects/1582
DOI: 10.15156/bio/sh1236521.10fu
title: SH1236521.10FU

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/sh1236524.10fu
title: SH1236524.10FU

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/sh1236525.10fu
title: SH1236525.10FU

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.