cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh2756940.08fu
title: SH2756940.08FU

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/sh2756941.08fu
title: SH2756941.08FU

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/sh489974.07fu
title: SH489974.07FU

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/sh489975.07fu
title: SH489975.07FU

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/sh2756942.08fu
title: SH2756942.08FU

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/sh2756943.08fu
title: SH2756943.08FU

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/sh2756944.08fu
title: SH2756944.08FU

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/sh489976.07fu
title: SH489976.07FU

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.zmuhee
title: Occurrence Download

description: A dataset containing 61 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dalbergia cochinchinensis Pierre)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 61 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108389-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5hxsuy
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dalbergia cultrata T.S.Ralph)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 140 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108390-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3zzr2fppnm.1
title: Genetically predicted mood swings increased risk of Cisplatin-induced hearing loss in childhood cancer: Evidence from a Mendelian randomization study

description: Original data
DOI: 10.17632/sydxb9rwyb.1
title: NFT Bidding Behaviours and Sentiment Analysis

description: This folder serves as a repository for hosting the raw data and jupyter notebook files needed to recreate the analysis for 'Dutch auction dynamics in non-fungible token (NFT) markets'.

Purpose of File 'Telegram_Sentiment_Analysis_using_VADER.ipynb':
1. Deconstructing and cleaning 33,533 Telegram Forum messages to extract further insights on the bidding and listing behaviours of investors involved in a fledgling NFT marketplace
2. Applying a Sentiment Analysis model specialising in social media communications (VADER)
3. Extracting sentiment scores, for later analysis against NFT listing and sales data
4. Visually inspecting the evolution of forum participation and investor sentiment over an 8-week study period.

Purpose of File 'NFT_Plots.ipynb':
1. Visually inspecting the evolution of 29,154 NFT listings and 5,011 NFT sales data over an 8-week period.

The data required to run both notebooks are available in the 'NFT_Dutch_Auction_Study_Data.xlsx' file

Jupyter Notebook(s) Author: Dr. Darren Shannon. All errors my own.
Journal Article Authors: Dr. Darren Shannon, Prof. Michael Dowling, Dr. Marjan Zhaf, Dr. Barry Sheehan

More information on running the files:
- Virtual environment / project generation / library / root files are not provided and will need to be created before running the Python code.
- Minor code amendments are required in each notebook to open the data file and save generated images locally.
- Information on the library packages required to run the analysis are provided in the relevant notebooks.
- Please direct any detected errors in the code / data to: darren.shannon@ul.ie
DOI: 10.17632/sydxb9rwyb.2
title: NFT Bidding Behaviours and Sentiment Analysis

description: This folder serves as a repository for hosting the raw data and jupyter notebook files needed to recreate the analysis for 'Dutch auction dynamics in non-fungible token (NFT) markets'.

Purpose of File 'Telegram_Sentiment_Analysis_using_VADER.ipynb':
1. Deconstructing and cleaning 33,533 Telegram Forum messages to extract further insights on the bidding and listing behaviours of investors involved in a fledgling NFT marketplace
2. Applying a Sentiment Analysis model specialising in social media communications (VADER)
3. Extracting sentiment scores, for later analysis against NFT listing and sales data
4. Visually inspecting the evolution of forum participation and investor sentiment over an 8-week study period.

Purpose of File 'NFT_Plots.ipynb':
1. Visually inspecting the evolution of 29,154 NFT listings and 5,011 NFT sales data over an 8-week period.

The data required to run both notebooks are available in the 'NFT_Dutch_Auction_Study_Data.xlsx' file

Jupyter Notebook(s) Author: Dr. Darren Shannon. All errors my own.
Journal Article Authors: Dr. Darren Shannon, Prof. Michael Dowling, Dr. Marjan Zhaf, Dr. Barry Sheehan

More information on running the files:
- Virtual environment / project generation / library / root files are not provided and will need to be created before running the Python code.
- Minor code amendments are required in each notebook to open the data file and save generated images locally.
- Information on the library packages required to run the analysis are provided in the relevant notebooks.
- Please direct any detected errors in the code / data to: darren.shannon@ul.ie
DOI: 10.17632/sydxb9rwyb.3
title: NFT Bidding Behaviours and Sentiment Analysis

description: This folder serves as a repository for hosting the raw data and jupyter notebook files needed to recreate the analysis for 'Dutch auction dynamics in non-fungible token (NFT) markets', published in the Economic Modelling journal.

Purpose of File 'Telegram_Sentiment_Analysis_using_VADER.ipynb':
1. Deconstructing and cleaning 30,197 Telegram Forum messages to extract further insights on the bidding and listing behaviours of investors involved in a fledgling NFT marketplace
2. Applying a Sentiment Analysis model specialising in social media communications (VADER)
3. Extracting sentiment scores, for later analysis against NFT listing and sales data
4. Visually inspecting the evolution of forum participation and investor sentiment over an 8-week study period.

Purpose of File 'NFT_Plots.ipynb':
1. Visually inspecting the evolution of 28,919 NFT listings and 4,937 NFT sales data over an 8-week period.

The data required to run both above notebooks are available in the 'NFT_Dutch_Auction_Study_Data_Regressions.xlsx' file

Purpose of File 'NFT_Dutch_Auction_Study_Network_Graph_of_Wallets.ipynb':
1. Visually inspect the inter-connections between all wallets buying and selling on the NFT marketplace, where each wallet represents a node. This is one method of revealing suspicious trading patters amongst marketplace users.

The data required to run the above notebook is available in the NFT_Dutch_Auction_Study_Data_Network_Analysis.xlsx' file

Jupyter Notebook(s) Author: Dr. Darren Shannon. All errors my own.
Journal Article Authors: Dr. Darren Shannon, Prof. Michael Dowling, Dr. Marjan Zhaf, Dr. Barry Sheehan

More information on running the files:
- Minor code amendments are required in each notebook to open the data file and save generated images locally.
- Although minimial code adjustments are required to get the code working, it is suggested that you have a beginning level of proficiency in Python to run the code.
- Virtual environment / project generation / library / root files are not provided and will need to be created before running the Python code.
- Information on the library packages required to run the analysis are provided in the relevant notebooks.
- Please direct any detected errors in the code / data to: darren.shannon@ul.ie
DOI: 10.15468/dl.qc8q25
title: Occurrence Download

description: A dataset containing 800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-119.41571 32.53139,-115.00407 32.20883,-113.69786 32.7739,-114.24106 34.88914,-116.25741 36.34007,-118.7852 36.08451,-121.1981 35.26347,-119.41571 32.53139))",
 "TaxonKey is Calliphoridae"
 ]
}

 The dataset includes 800 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108391-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kzd3fh
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dalbergia odorifera T.C.Chen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 51 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108392-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.48ca4z
title: Occurrence Download

description: A dataset containing 15574 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Montifringilla nivalis (Linnaeus, 1766)",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 15574 records from 136 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108393-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mx6nrm
title: Occurrence Download

description: A dataset containing 1634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Corytophanes cristatus (Merrem, 1820)"
 ]
}

 The dataset includes 1634 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108394-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tn3v6x
title: Occurrence Download

description: A dataset containing 1036 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dalbergia oliveri Gamble ex Prain)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1036 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108395-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a3zzrh
title: Occurrence Download

description: A dataset containing 313 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Melanosuchus niger (Spix, 1825)"
 ]
}

 The dataset includes 313 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108397-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cz256p2w3d.1
title: Bridging the Gap Survey Results

description: Survey Results from scGY participants. Group "0" represents participants who had yet to start professional school. Group "1" represents participants who had already completed at least some professional school.
DOI: 10.15468/dl.y63bua
title: Occurrence Download

description: A dataset containing 10496 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippopotamus amphibius Linnaeus, 1758"
 ]
}

 The dataset includes 10496 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108396-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3z7ss4
title: Occurrence Download

description: A dataset containing 1036 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dalbergia sissoo Roxb. ex DC.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1036 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108398-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5c4dcvxdmb.2
title: KTTS Single Speaker Dataset

description: This dataset has been primarily developed to facilitate the creation of text-to-speech systems for the Kashmiri language, a digitally underrepresented language predominantly spoken in the Jammu and Kashmir region of India. The dataset comprises 2,984 audio recordings in WAV format, each accompanied by its corresponding textual data in a separate file named ‘textcorpus.csv’. The ‘id’ column in the CSV file serves as a unique identifier, allowing users to efficiently locate the corresponding WAV files, which are systematically named according to the ‘id’ associated with the sentences they contain.
All recordings feature a single male voice with a sample rate of 48,000 Hz, ensuring high-quality audio suitable for detailed phonetic analysis and machine learning applications. This consistent audio quality across the dataset provides a reliable foundation for training and testing text-to-speech models. Furthermore, the dataset can be a valuable resource for future research and development efforts aimed at enhancing digital accessibility for the Kashmiri-speaking population.
DOI: 10.17632/nmstgnmp25.1
title: RSV and HBoV1 co-infection causes more severe pathogenic damage to human respiratory epithelium than single infection

description: To analyze the differences in HAE gene expression between RSV and HBoV1 co-infection and single infection, total RNA of HAE with “RSV single”, “HBoV1 single”, “RSV and HBoV1” infection at 0 (mock-), 2 and 6 d.p.i. was isolated using the Magzol Reagent (Magen, China) according to the manufacturer's protocol. The quantity and integrity of RNA yield was assessed by using the K5500 (Beijing Kaiao, China) and the Agilent 2200 TapeStation (Agilent Technologies, USA) separately. the mRNA was enriched by oligo dT according to instructions of NEB Next® Poly(A) mRNA Magnetic Isolation Module (NEB,USA), and then fragmented to approximately 200 bp. Subsequently, the RNA fragments were subjected to first strand and second strand cDNA synthesis followed by adaptor ligation and enrichment with a low-cycle according to instructions of NEBNext® Ultra™ RNA Library Prep Kit for Illumina. The purified library products were evaluated using the Agilent 2200 TapeStation and Qubit (Thermo Fisher Scientific, USA). The libraries were sequenced by Illumina (Illumina, USA) with paired-end 150bp.
DOI: 10.15468/dl.8at73x
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Year is 2023",
 "DatasetKey is one of (Fauna presente en los monitoreos de las fincas de Agropecuaria Aliar S.A.)",
 "GadmGid is one of (COL.21_2)",
 "TaxonKey is one of (Polyborus plancus (J.F.Miller, 1777))"
 ]
 }
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0108400-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.