cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4tnqbc
title: Occurrence Download

description: A dataset containing 243 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scolecenchelys australis (Macleay, 1881)"
 ]
}

 The dataset includes 243 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042254-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8vgu7
title: Occurrence Download

description: A dataset containing 513803 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year is greater than or equal to 1970",
 "TaxonKey is Apis mellifera Linnaeus, 1758",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 513803 records from 849 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004540-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mbofr3
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014509-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yva7mc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000614-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxpx7p
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033291-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uc8tee
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006164-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.30qhxg
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000630-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsg5lg
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006845-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whe3on
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006156-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11622658
title: X-ray diffraction from PCMO on LSAT (011) June 2024 Mo Tube at SLS/MS

description: X-ray diffraction from PCMO on LSAT (011) June 2024 Mo Tube at SLS/MS
DOI: 10.15468/dl.mdvkfc
title: Occurrence Download

description: A dataset containing 1909343426 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1909343426 records from 17993 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018291-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbrs4k
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Chlorobium phaeobacteroides Pfennig, 1968"
 ]
}

 The dataset includes 20 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014014-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0148847.10fu
title: SH0148847.10FU

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: 10.17632/ngj26msn46
title: Ensembles and engrams in mouse cortical and sub-thalamic brain regions supporting context and memory recall

description: This spreadsheet contains data associated with https://doi.org/10.1016/j.nlm.2025.108128
DOI: 10.15156/bio/sh0148848.10fu
title: SH0148848.10FU

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: 10.17632/ngj26msn46.1
title: Ensembles and engrams in mouse cortical and sub-thalamic brain regions supporting context and memory recall

description: This spreadsheet contains data associated with https://doi.org/10.1016/j.nlm.2025.108128
DOI: 10.15156/bio/sh0148849.10fu
title: SH0148849.10FU

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/sh0148850.10fu
title: SH0148850.10FU

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/sh0148851.10fu
title: SH0148851.10FU

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/sh0148852.10fu
title: SH0148852.10FU

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: 10.17632/4wzw769tsx
title: Literature review seaweeds

description: This dataset compiles information extracted from 106 peer-reviewed journal articles published between 2013 and 2024 that apply remote sensing and machine-learning techniques to macroalgae monitoring in coastal and marine environments. The dataset was assembled through a systematic literature search using Google Scholar and related academic databases, followed by manual screening to ensure relevance to broad-class macroalgae detection and mapping.

For each study, the dataset records reported classification performance metrics (overall accuracy and Kappa coefficient, where available), sensor platforms (e.g., satellite, UAV, aerial, and underwater systems), spatial resolution, geographic region, preprocessing workflows, feature extraction methods, and classification algorithms. Machine-learning methods are categorized into Random Forest (RF), Support Vector Machine (SVM), and Deep Learning families. Sensor platforms are grouped into high-resolution satellites, medium- to low-resolution satellites, and drone/AUV systems based on nominal spatial resolution and acquisition mode.

The dataset also includes derived categorical fields used in the analysis, enabling reproducible aggregation of accuracy distributions across algorithm and sensor groups. No new observational or experimental data were generated; all entries are derived from published studies and are provided for synthesis, comparison, and methodological review purposes.

This dataset supports transparency and reproducibility of the associated review study and can be used by other researchers to explore methodological trends, performance variability, and research gaps in remote sensing-based macroalgae monitoring.
DOI: 10.17632/4wzw769tsx.1
title: Literature review seaweeds

description: This dataset compiles information extracted from 106 peer-reviewed journal articles published between 2013 and 2024 that apply remote sensing and machine-learning techniques to macroalgae monitoring in coastal and marine environments. The dataset was assembled through a systematic literature search using Google Scholar and related academic databases, followed by manual screening to ensure relevance to broad-class macroalgae detection and mapping.

For each study, the dataset records reported classification performance metrics (overall accuracy and Kappa coefficient, where available), sensor platforms (e.g., satellite, UAV, aerial, and underwater systems), spatial resolution, geographic region, preprocessing workflows, feature extraction methods, and classification algorithms. Machine-learning methods are categorized into Random Forest (RF), Support Vector Machine (SVM), and Deep Learning families. Sensor platforms are grouped into high-resolution satellites, medium- to low-resolution satellites, and drone/AUV systems based on nominal spatial resolution and acquisition mode.

The dataset also includes derived categorical fields used in the analysis, enabling reproducible aggregation of accuracy distributions across algorithm and sensor groups. No new observational or experimental data were generated; all entries are derived from published studies and are provided for synthesis, comparison, and methodological review purposes.

This dataset supports transparency and reproducibility of the associated review study and can be used by other researchers to explore methodological trends, performance variability, and research gaps in remote sensing-based macroalgae monitoring.
DOI: 10.15156/bio/sh0148853.10fu
title: SH0148853.10FU

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/sh0148854.10fu
title: SH0148854.10FU

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/sh0148855.10fu
title: SH0148855.10FU

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/sh0148856.10fu
title: SH0148856.10FU

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: 10.17632/pbf3zxbn2x
title: An ICP-OES method for the precise and accurate quantification of rare earth elements in natural water: a comparative study from mine waste sites in New Mexico, USA

description: . In this work, we develop an analysis method using an Agilent 5900 ICP-OES instrument and report an assessment on the accuracy and precision for the quantification of REE in the natural waters collected from mine adits and an acid seep of mine sites in the Steeple Rock and Hillsboro mining districts, New Mexico, USA. The total REE concentrations in the water samples were measured using the methods we developed for both ICP-OES and ICP-MS. The power of the new ICP-OES method lies in routine analysis of µg/L level concentrations normally analyzed using ICP-MS, including a U.S. Geological Survey standard reference sample, laboratory blank samples spiked with a National Institute of Standards and Technology traceable standard, and surface water samples from mine waste sites. This ICP-OES method achieves low quantification limits ranging from 0.2 to 5 µg/L and excellent analytical accuracy and precision for REE analysis. The precision of light (La-Gd) and heavy (Tb- Lu) REE analysis using this method are better than 5% at average concentrations above 5 ± 4 µg/L and 3 ± 2 µg/L, respectively, and 3% at average concentrations above 10 ± 9 µg/L and 5 ± 4 µg/L, respectively. This method also shows excellent sensitivity and reproducibility for our laboratory and field samples.