cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0172501.10fu
title: SH0172501.10FU

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/sh0172502.10fu
title: SH0172502.10FU

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/sh0172503.10fu
title: SH0172503.10FU

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/sh0172504.10fu
title: SH0172504.10FU

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/sh0172505.10fu
title: SH0172505.10FU

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/sh0172506.10fu
title: SH0172506.10FU

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: 10.5281/zenodo.18198516
title: GENDER-BASED VIOLENCE IN THE DIGITAL AGE: A SOCIOLEGAL STUDY OF INDIA'S CYBER GOVERNANCE AND RIGHTS FRAMEWORK

description: ABSTRACT:

As India's digital landscape rapidly expands so do the risks of gender-based violence taking on new and complex forms in online spaces. This research provides a comprehensive socio-legal analysis of digital gender-based violence in India examining how evolving laws, policies and global conventions shape the rights and protections of women and marginalized genders. The study begins by highlighting the stark digital gender divide revealed by data showing women comprise only about one-third of internet users in India and a 28% rise in cybercrimes against women between 2019 and 2021 as reported by the National Crime Records Bureau. Through real-life cases, it documents forms of online GBV such as cyberstalking, nonconsensual image sharing, trolling and coordinated harassment that leave lasting psychological and economic scars on victims. The research outlines key national policies like the Digital India initiative and PMGDISHA that aim to improve internet access and digital literacy foundational steps in reducing vulnerability to cyber abuse. It provides an accessible overview of India's evolving legal framework including significant amendments to the Information Technology Act, the new Bharatiya Nyaya Sanhita (2023) and the Digital Personal Data Protection Act (2023). Landmark judgments from Shreya Singhal striking down draconian content control provisions to the Puttaswamy privacy verdict and recent High Court orders on swift removal of non-consensual content are summarized to show the judiciary's growing sensitivity to digital rights and harms.
DOI: 10.5281/zenodo.18198515
title: GENDER-BASED VIOLENCE IN THE DIGITAL AGE: A SOCIOLEGAL STUDY OF INDIA'S CYBER GOVERNANCE AND RIGHTS FRAMEWORK

description: ABSTRACT:

As India's digital landscape rapidly expands so do the risks of gender-based violence taking on new and complex forms in online spaces. This research provides a comprehensive socio-legal analysis of digital gender-based violence in India examining how evolving laws, policies and global conventions shape the rights and protections of women and marginalized genders. The study begins by highlighting the stark digital gender divide revealed by data showing women comprise only about one-third of internet users in India and a 28% rise in cybercrimes against women between 2019 and 2021 as reported by the National Crime Records Bureau. Through real-life cases, it documents forms of online GBV such as cyberstalking, nonconsensual image sharing, trolling and coordinated harassment that leave lasting psychological and economic scars on victims. The research outlines key national policies like the Digital India initiative and PMGDISHA that aim to improve internet access and digital literacy foundational steps in reducing vulnerability to cyber abuse. It provides an accessible overview of India's evolving legal framework including significant amendments to the Information Technology Act, the new Bharatiya Nyaya Sanhita (2023) and the Digital Personal Data Protection Act (2023). Landmark judgments from Shreya Singhal striking down draconian content control provisions to the Puttaswamy privacy verdict and recent High Court orders on swift removal of non-consensual content are summarized to show the judiciary's growing sensitivity to digital rights and harms.
DOI: 10.15156/bio/sh0172507.10fu
title: SH0172507.10FU

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/sh0172508.10fu
title: SH0172508.10FU

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/sh0172509.10fu
title: SH0172509.10FU

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/sh0172510.10fu
title: SH0172510.10FU

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.sywymy
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "TaxonKey is Avicularia braunshauseni"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008715-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34894/4i9upp
title: A Study to Explore the Safety, Tolerability, Pharmacokinetic Profile, and Potential Efficacy of Guanabenz in Patients With Early Childhood Onset Vanishing White Matter (VWM)
title: Guanabenz in VWM

description: This clinical trial dataset comprises data from 33 patients with Vanishing White Matter who are treated with Guanabenz.
description: Castor EDC
DOI: 10.5281/zenodo.18219291
title: TORQUE2026: Can the second tower mode become significant for fatigue loads of offshore wind turbine monopiles?

description: This repository presents the results of time domain simulations performed for three different reference wind turbines; the IEA 10 MW, IEA 15 MW, and IEA 22 MW, as part of a contribution for the 2026 edition of the TORQUE conferene. The simulations were performed with the aeroelastic software ASHES 3.31 and the here included data are the raw result of these simulations. 

The following assumptions were made for the simulations:



 The turbines are clamped at the mudline in a water depth of 40 m. 



Soil-Structure interaction is thus not included.



Structural damping was included as Rayleigh damping with a damping ratio of 1 % for the first tower mode and 2 % for the second tower mode.

Simulations were performed for a length of 700 s of which the first 100 s should be taken as an initial transient period.

Simulations were performed for three wind-wave misalignment directions:



Wind-wave aligned, 0 degrees.

Wind-wave misaligned, 15 degrees.

Wind-wave misaligned, 30 degrees.



Simulations were performed with 6 seeds for wind and wave.

Simulations were performed at rated wind speed for each turbine with turbulent wind based on the normal turbulence model (NTM)

Simulations were performed with regular waves based on JONSWAP.

Results were obtained for beam, node, and fatigue sensors at various locations along the monopile as well as tower top and tower bottom. Furthermore, results were also obtained from the rotor and sea sensor. See Ashes user manual for more details about sensors in Ashes


The following metocean conditions were used for the simulations:




Turbine
Wind speed [m/s]
Significant wave height [m]
Peak wave period [s]


IEA 10 MW
11
1.24
4.97


IEA 15 MW
10.59 
1.05
4.59


IEA 22 MW
11
1.11
4.71




 

The following figure and table show the sensor locations along the monopile. 

 




Elevation [mSWL]
IEA 10 MW
IEA 15 MW
IEA 22 MW


15
-
MP1
MP1


10
MP1
-
-


0
MP10/MP11
MP15/MP16
MP15/MP16


-20
MP30/MP31
MP35/MP36
MP35/MP36


-40
MP50
MP55
MP55
DOI: 10.5281/zenodo.18219292
title: TORQUE2026: Can the second tower mode become significant for fatigue loads of offshore wind turbine monopiles?

description: This repository presents the results of time domain simulations performed for three different reference wind turbines; the IEA 10 MW, IEA 15 MW, and IEA 22 MW, as part of a contribution for the 2026 edition of the TORQUE conferene. The simulations were performed with the aeroelastic software ASHES 3.31 and the here included data are the raw result of these simulations. 

The following assumptions were made for the simulations:



 The turbines are clamped at the mudline in a water depth of 40 m. 



Soil-Structure interaction is thus not included.



Structural damping was included as Rayleigh damping with a damping ratio of 1 % for the first tower mode and 2 % for the second tower mode.

Simulations were performed for a length of 700 s of which the first 100 s should be taken as an initial transient period.

Simulations were performed for three wind-wave misalignment directions:



Wind-wave aligned, 0 degrees.

Wind-wave misaligned, 15 degrees.

Wind-wave misaligned, 30 degrees.



Simulations were performed with 6 seeds for wind and wave.

Simulations were performed at rated wind speed for each turbine with turbulent wind based on the normal turbulence model (NTM)

Simulations were performed with regular waves based on JONSWAP.

Results were obtained for beam, node, and fatigue sensors at various locations along the monopile as well as tower top and tower bottom. Furthermore, results were also obtained from the rotor and sea sensor. See Ashes user manual for more details about sensors in Ashes


The following metocean conditions were used for the simulations:




Turbine
Wind speed [m/s]
Significant wave height [m]
Peak wave period [s]


IEA 10 MW
11
1.24
4.97


IEA 15 MW
10.59 
1.05
4.59


IEA 22 MW
11
1.11
4.71




 

The following figure and table show the sensor locations along the monopile. 

 




Elevation [mSWL]
IEA 10 MW
IEA 15 MW
IEA 22 MW


15
-
MP1
MP1


10
MP1
-
-


0
MP10/MP11
MP15/MP16
MP15/MP16


-20
MP30/MP31
MP35/MP36
MP35/MP36


-40
MP50
MP55
MP55
DOI: 10.15468/dl.t69zh2
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aloiampelos commixta",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007964-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48804/3xpcae
title: A large, paired dataset of robotic and handheld lumbar spine ultrasound with ground truth CT benchmarking.

description: Musculoskeletal disorders present significant socio-economic challenges globally, requiring innovative diagnostic and therapeutic approaches. Current intraoperative imaging techniques, including computed tomography (CT) and radiography, involve high radiation exposure and limited soft tissue visualization. Ultrasound (US) offers a non-invasive, real-time alternative but is underutilized intraoperatively, as it is highly observer-dependent. US enhanced by artificial intelligence shows high potential for observer-independent surgical guidance, decision support, and robot-assisted applications in orthopedics. Given the limited availability of high-quality, in vivo ultrasound data, we introduce a comprehensive dataset from a collection of handheld 2D US (HUS) and robot-assisted US (RUS) lumbar spine imaging in 63 healthy volunteers. This dataset includes demographic data, HUS and RUS imaging with synchronized 3D positioning data of the US probe, corresponding CT data, and CT-reconstructed 3D models of the lumbar vertebrae L1 to L5, establishing a robust baseline for machine learning algorithms. This extensive collection is the first anatomy dataset for the lumbar spine that includes paired CT, HUS, and RUS imaging, supporting advancements in intraoperative imaging and robot-assisted surgery.
DOI: 10.15468/dl.68s2f5
title: Occurrence Download

description: A dataset containing 1075010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acer pseudoplatanus"
 ]
}

 The dataset includes 1075010 records from 2817 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007805-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ne44yb
title: Occurrence Download

description: A dataset containing 1736 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Kenneth S. Norris Center for Natural History, University of California, Santa Cruz (UCSC) Bird specimens (Arctos)"
 ]
}

 The dataset includes 1736 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006859-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.