cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1481528.10fu
title: SH1481528.10FU

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/sh1481554.10fu
title: SH1481554.10FU

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.fcj3u3
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"TUR.1.10_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 24 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006441-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1481555.10fu
title: SH1481555.10FU

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/sh1481661.10fu
title: SH1481661.10FU

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/sh1481755.10fu
title: SH1481755.10FU

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/sh1483001.10fu
title: SH1483001.10FU

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.nsap6u
title: Occurrence Download

description: A dataset containing 746 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pongo abelii"
 ]
}

 The dataset includes 746 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006442-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1483044.10fu
title: SH1483044.10FU

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.d3enav
title: Occurrence Download

description: A dataset containing 439506 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Columba livia",
 "Country is Spain",
 "HasCoordinate is true"
 ]
}

 The dataset includes 439506 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086872-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zesqab
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippobroma longiflora",
 "Country is Tonga",
 "HasCoordinate is true"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086910-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8u8gvh
title: Occurrence Download

description: A dataset containing 301086 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Pitangus sulphuratus)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 301086 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0086878-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5523/bris.1wg5kdkjh2q2j2wszc7p81ygt3
title: Online stories of self-harm Restricted

description: Self-harm is an important public health priority. It comprises both suicidal and non-suicidal behaviours, and reliably predicts future suicide attempts. Self-harm prevalance is also on the rise showing steady increases over the last two decades. This situation is likely to have worsened following the COVID-19 pandemic, with support agencies such as the Samaritans have seen a marked increase in demand. The internet offers an important avenue for supporting people who self-harm, and who may not have the opportunity or capability to access. With more people searching for help online, it is vital that support messages provided are effective and evidence-based. This study aims to provide insight into effective forms of online support messaging for improving mood immediately and in the longer-term, for instance by increasing help-seeking intentions and addressing important psychological factors implicated within the suicidal pathway, such as sense of entrapment. In an online experiment, individuals with a recent history of self-harm, were randomised to read stories that either mentioned: i) self-help strategies, ii) seeking help from informal and formal sources, or iii) did not mention help-seeking. Help-seeking intentions, mood, entrapment, and expectations of future self-harm was measured, and participants provided feedback on the stories.

There is an Open dataset available at: https://doi.org/10.5523/bris.ayitflrm2w3r24o6xpwexcjpw. The restricted dataset contains additional free-text demographic information.
DOI: 10.15468/dl.wsn6at
title: Occurrence Download

description: A dataset containing 308 records in GBIF matching the query:

SELECT
 GBIF_ISEA3HCode(
 8, 
 decimalLatitude,
 decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)
 ) AS geogrid_id,
 CASE 
 WHEN classKey IN ('359') THEN 'Mammals'
 WHEN classKey IN ('212') THEN 'Birds'
 WHEN familyKey IN ('1103', '1104', '494', '495', '1105', '496', '497', '1106', '498', '499', '1107', '537', '538', '1153', '547', '1162', '548', '549', '550', '1163', '1164', '1165', '1166', '1167', '1305', '1067', '1306', '1307', '1308', '1068', '1069', '587', '1310', '588', '589', '1311', '1312', '1313', '590', '708', '890', '774', '889', '773', '772', '888', '765', '879') THEN 'Bony Fish'
 WHEN classKey IN ('131') THEN 'Amphibians'
 WHEN classKey IN ('216') THEN 'Insects'
 WHEN classKey IN ('11418114', '11569602', '11592253', '11493978') THEN 'Reptiles'
 WHEN phylumKey IN ('52') THEN 'Molluscs'
 WHEN classKey IN ('367') THEN 'Arachnids'
 WHEN classKey IN ('220', '196') THEN 'Flowering Plants'
 WHEN classKey IN ('194', '244', '228', '282') THEN 'Gymnosperms'
 WHEN orderKey IN ('392') THEN 'Ferns'
 WHEN phylumKey IN ('35') THEN 'Mosses'
 WHEN phylumKey IN ('95') THEN 'Sac Fungi'
 WHEN phylumKey IN ('34') THEN 'Basidiomycota'
 END AS taxonomic_group,
 COUNT(DISTINCT speciesKey) AS species_count
FROM occurrence
WHERE countryCode = 'GA'
 AND hasCoordinate = TRUE
 AND hasGeospatialIssues = FALSE
 AND coordinateUncertaintyInMeters &lt;= 2000
 AND occurrenceStatus = 'PRESENT'
 AND speciesKey IS NOT NULL
 AND basisOfRecord != 'FOSSIL_SPECIMEN'
 AND taxonRank = 'SPECIES'
 AND (
 classKey IN ('359', '212', '131', '216', '11418114', '11569602', '11592253', '11493978', '367', '220', '196', '194', '244', '228', '282')
 OR familyKey IN ('1103', '1104', '494', '495', '1105', '496', '497', '1106', '498', '499', '1107', '537', '538', '1153', '547', '1162', '548', '549', '550', '1163', '1164', '1165', '1166', '1167', '1305', '1067', '1306', '1307', '1308', '1068', '1069', '587', '1310', '588', '589', '1311', '1312', '1313', '590', '708', '890', '774', '889', '773', '772', '888', '765', '879')
 OR phylumKey IN ('52', '35', '95', '34')
 OR orderKey IN ('392')
 )
GROUP BY geogrid_id, taxonomic_group;


 The dataset's 308 records were derived from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002213-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6avy3h
title: Occurrence Download

description: A dataset containing 241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.29_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Scutiger boulengeri"
 ]
}

 The dataset includes 241 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002231-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3a6v3
title: Occurrence Download

description: A dataset containing 550 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Festuca richardsonii",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 550 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002232-260226173443078/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31431069
title: Bringing <i>The Conversation</i> into the Geography Classroom: Public Storytelling as Authentic Learning

description: Public storytelling is increasingly important for geographers as higher education faces increased scrutiny, politicized attacks on truth-telling, and pressure to show broader relevance. This article argues for integrating more public-facing science communication into geographic education and introduces <i>The Conversation</i> as a classroom tool. A vehicle for university scholar-driven explanatory journalism, <i>The Conversation</i> publishes short, open-access articles that demonstrate the value of academic insights to real-world issues. Based on our experiences teaching with and contributing to the platform, we created two classroom exercises aligned with an authentic learning framework. First, curated articles from <i>The Conversation</i> serve as authentic class content, enabling students to evaluate how university scholars apply disciplinary ideas to contemporary problems; how knowledge is produced, translated, and evidenced for broader audiences; and whose perspectives are included (or missing) within stories. Second, the media outlet serves as an authentic practice space where students simulate writing in <i>The Conversation</i> style, from pitching and drafting to receiving feedback and revising an explainer article for general audiences. These classroom activities invite educators and students to address the rewards, challenges, and ethics of science communication, as well as the practical skills, cognitive and emotional labor, and power relations embedded in <i>The Conversation</i>’s expert-centered storytelling.
DOI: 10.6084/m9.figshare.31431069.v1
title: Bringing <i>The Conversation</i> into the Geography Classroom: Public Storytelling as Authentic Learning

description: Public storytelling is increasingly important for geographers as higher education faces increased scrutiny, politicized attacks on truth-telling, and pressure to show broader relevance. This article argues for integrating more public-facing science communication into geographic education and introduces <i>The Conversation</i> as a classroom tool. A vehicle for university scholar-driven explanatory journalism, <i>The Conversation</i> publishes short, open-access articles that demonstrate the value of academic insights to real-world issues. Based on our experiences teaching with and contributing to the platform, we created two classroom exercises aligned with an authentic learning framework. First, curated articles from <i>The Conversation</i> serve as authentic class content, enabling students to evaluate how university scholars apply disciplinary ideas to contemporary problems; how knowledge is produced, translated, and evidenced for broader audiences; and whose perspectives are included (or missing) within stories. Second, the media outlet serves as an authentic practice space where students simulate writing in <i>The Conversation</i> style, from pitching and drafting to receiving feedback and revising an explainer article for general audiences. These classroom activities invite educators and students to address the rewards, challenges, and ethics of science communication, as well as the practical skills, cognitive and emotional labor, and power relations embedded in <i>The Conversation</i>’s expert-centered storytelling.
DOI: 10.18150/fnxktc
title: Datasets from "Imaging of retinal ganglion cells and photoreceptors using Spatio-Temporal Optical Coherence Tomography (STOC-T) without hardware-based adaptive optics"

description: This dataset accompanies the study demonstrating an experimental Spatio-Temporal Optical Coherence Tomography (STOC-T) system featuring optimized illumination and an increased lateral resolution of approximately 3 μm. The integration of high-speed phase randomization with numerical magnitude averaging enables a significant improvement in the signal-to-noise ratio (SNR). The datasets contain calculations for selected ND filters and calculated SNR in four configurations:with 2D FFT filtering,without filtering,with 2D FFT filtering combined with an active deformable membrane,without 2D FFT filtering and without the membranefor reflective and scattering objects.
DOI: 10.5281/zenodo.17626504
title: Programming code for: Supporting ecological restoration: leveraging satellite data and machine learning to map invasive alien tree biomass

description: Programming code for: Supporting ecological restoration: leveraging satellite data and machine learning to map invasive alien tree biomass

This repository hosts the code for the processing of raw biomass and satellite-derived datasets to develop an invasive alien tree biomass model.

SummaryIn this study we aim to test the performance of multi-source remote sensing-derived datasets for the estimation of biomass of invasive alien trees in South Africa.

LiDAR and field-measured invasive alien tree metrics were used to develop a regional biomass model for an alien infested region of the Aguhlas Plain, in the Cape Florisitc Region of South Africa. This study leveraged satellite-derived datasets (Sentinel-1, Sentinel-2, SRTM, GEDI-L2A, and Global Canopy Height) and machine learning to develop a regional-scale model for estimating invasive alien tree biomass at a 25m resolution. Performance of the models were assessed using the coeffiecent of determination (R-squared), Spearman correlation coeffiecent (rs), PBias, and RMSE. Predictor variable importance were also tested for. Once the models were developed, they were validated with an independant alien infested ground truth site. Additionally, the most effective model was applied to ecologically distinct catchments to test model transferability and to demonstrate how the model could be used in a practical management scenario.



Note: 'Invasive alien trees' refer to taxa such as Pinus, Eucalyptus, Neltuma, and Acacia spp that are non-native to South Africa that can displace native vegetation and cause environmental and economic harm.


Repository ComponentsThe components of this repository include five primary phases each including a set of method workflow notes and code scriptsThe five phases include the following:

(1) Develop a target variable dataset

(2) Develop a predictor variable dataset containing different data types

(3) Model Validation

(4) Ground-truthing

(5) Model application

 

Google Earth Engine App

The invasive alien tree biomass maps for each catchment can be found here:



Tugela: https://ee-lscogill4.projects.earthengine.app/view/tugela-iat-biomass

Luvuvhu: https://ee-lscogill4.projects.earthengine.app/view/luvuvhu-iat-biomass

Olifants-Doring: https://ee-lscogill4.projects.earthengine.app/view/olifantsdoring-iatbiomass