cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17741556
title: Supporting Data for: Assessment of Single-Nucleotide Variant Detection Strategies in the Green Sea Turtle (Chelonia mydas) for Conservation Genomics Applications

description: This Zenodo repository contains all processed single-nucleotide variant (SNV) call files, the assembly-derived SNV truth set, and supporting genomic resources generated in the study:

“Assessment of Single-Nucleotide Variant Detection Strategies in the Green Sea Turtle (Chelonia mydas) for Conservation Genomics Applications.”

The dataset includes SNV call sets produced using five variant calling tools DeepVariant, GATK HaplotypeCaller, FreeBayes, VarScan, and BCFtools along with their corresponding TABIX index files. An assembly-derived SNV truth set, generated through minimap2 alignment and bcftools normalization, is provided for benchmarking purposes.

To ensure full reproducibility, the POLCA-corrected ABySS de novo genome assembly used for truth-set construction is also included.

These resources enable complete replication of the SNV benchmarking analyses and facilitate transparent evaluation of variant detection performance in a non-model marine vertebrate species.

📦 Repository Contents





SNV VCF files (DeepVariant, GATK, FreeBayes, VarScan, BCFtools)




TABIX index files (.tbi)




Assembly-derived SNV truth set (minimap2-aligned and bcftools-normalized VCF)




POLCA-corrected ABySS de novo assembly (FASTA format)
DOI: 10.22008/fk2/iw73uu/wzyuby
title: THU_U2_hour.nc

description: Uploaded with pyDataverse 2024-04-11 15:22
DOI: 10.22008/fk2/iw73uu/tu74l3
title: THU_U2_day.nc

description: Uploaded with pyDataverse 2024-04-11 15:22
DOI: 10.22008/fk2/iw73uu/d3c9qo
title: TUN_day.csv

description: Uploaded with pyDataverse 2024-04-11 15:22
DOI: 10.22008/fk2/iw73uu/kniegb
title: THU_U2_month.nc

description: Uploaded with pyDataverse 2024-04-11 15:22
DOI: 10.22008/fk2/iw73uu/ofw0ie
title: TUN_hour.csv

description: Uploaded with pyDataverse 2024-04-11 15:22
DOI: 10.15156/bio/sh1400127.10fu
title: SH1400127.10FU

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/sh1400128.10fu
title: SH1400128.10FU

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: 10.17026/ar/2y9yxb
title: Drone magnetometeropnamen Almere Overgooi
title: Geofysisch inventariserend veldonderzoek

description: In opdracht van de gemeente Almere heeft Periplus Archeomare B.V. in samenwerking met DEEP B.V. een geofysisch veldonderzoek uitgevoerd op een kavel bij Almere-Overgooi.
DOI: 10.24435/materialscloud:f8-1n
title: Simultaneous learning of static and dynamic charges

description: Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems.
DOI: 10.24435/materialscloud:fs-8h
title: Simultaneous learning of static and dynamic charges

description: Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems.
DOI: 10.15156/bio/sh1400129.10fu
title: SH1400129.10FU

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/sh1400131.10fu
title: SH1400131.10FU

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/sh1400138.10fu
title: SH1400138.10FU

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.18231307
title: Statistical Analysis: SINGLE CELL PROTEIN PRODUCTION FROM TOMATO LEAVES AND STEMS BY SUBMERGED FERMENTATION

description: The document is the 'Statistical Analysis' part of a thesis named 'Single Cell Protein Production from Tomato Leaves and Stems by Submerged Fermentation'.
DOI: 10.5281/zenodo.18231308
title: Statistical Analysis: SINGLE CELL PROTEIN PRODUCTION FROM TOMATO LEAVES AND STEMS BY SUBMERGED FERMENTATION

description: The document is the 'Statistical Analysis' part of a thesis named 'Single Cell Protein Production from Tomato Leaves and Stems by Submerged Fermentation'.
DOI: 10.15156/bio/sh1400141.10fu
title: SH1400141.10FU

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/sh1400148.10fu
title: SH1400148.10FU

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/sh1400151.10fu
title: SH1400151.10FU

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/sh1400155.10fu
title: SH1400155.10FU

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: 10.3886/e246231v1
title: College of Nursing Sciences, Itigidi, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online.

description: College of Nursing Sciences, Itigidi, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online. The Management Of The School Hereby Inform The general Public On The Sales Of The General Nursing Admission Form Into The School Of Nursing, And Also The Sales Of Two (2) Years Post Basic Nursing Program. Contact The Educational Board Admin On (09024168245) For More Details On How To Purchase The Form And Register Online. REQUIREMENT FOR THE THREE YEARS GENERAL NURSING PROGRAMMES1. GENERAL NURSING PROGRAMME Admission Requirements Candidates For Admission Into The General Nursing Program Of The Nursing And Midwifery Council Of Nigeria Must Posses The …
DOI: 10.3886/e246232v1
title: College of Nursing Sciences, Federal Neuropsychiatric Hospital, Calabar, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online.

description: College of Nursing Sciences, Federal Neuropsychiatric Hospital, Calabar, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online. The Management Of The School Hereby Inform The general Public On The Sales Of The General Nursing Admission Form Into The School Of Nursing, And Also The Sales Of Two (2) Years Post Basic Nursing Program. Contact The Educational Board Admin On (09024168245) For More Details On How To Purchase The Form And Register Online. REQUIREMENT FOR THE THREE YEARS GENERAL NURSING PROGRAMMES1. GENERAL NURSING PROGRAMME Admission Requirements Candidates For Admission Into The General Nursing Program Of The Nursing And Midwifery Council Of Nigeria Must Posses The …
DOI: 10.15468/7gudyo
title: CAS Botany (BOT)

description: The electronic catalog of the botanical collection at the California Academy of Sciences, San Francisco.
DOI: 10.15468/dl.nfawyj
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pseudomyrmex evitus"
 ]
}

 The dataset includes 36 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004726-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18750477
title: Demonstration of an instream flow model to accommodate a threatened tiger beetle in riverine dune habitat

description: Datasets for "Demonstration of an instream flow model to accommodate a threatened tiger beetle in riverine dune habitat." ESM 1 provides the field collected data for Gibson's Big Sand Tiger Beetle surveys, ESM 2 provides the summary data for the surveys that is presented in Table 1 of the paper, ESM 3 presents the elevations of burrows found along the river, ESM 4 contains the flow data used to generate Figure 5, ESM 5 contains the flow data used to generate Figure 6, and ESM 6 contains the flow data used to generate Figure 7.
DOI: 10.5281/zenodo.18750476
title: Demonstration of an instream flow model to accommodate a threatened tiger beetle in riverine dune habitat

description: Datasets for "Demonstration of an instream flow model to accommodate a threatened tiger beetle in riverine dune habitat." ESM 1 provides the field collected data for Gibson's Big Sand Tiger Beetle surveys, ESM 2 provides the summary data for the surveys that is presented in Table 1 of the paper, ESM 3 presents the elevations of burrows found along the river, ESM 4 contains the flow data used to generate Figure 5, ESM 5 contains the flow data used to generate Figure 6, and ESM 6 contains the flow data used to generate Figure 7.
DOI: 10.15468/dl.nntme5
title: Occurrence Download

description: A dataset containing 217689 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bubulcus ibis",
 "Country is Colombia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 217689 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004666-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nvryp2
title: Occurrence Download

description: A dataset containing 351956 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Observation)",
 "Geometry POLYGON((-95.58 34.42,-76.37 34.42,-76.37 37.29,-95.58 37.29,-95.58 34.42))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY-NC 4.0, CC-BY 4.0, CC0 1.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi"
 ]
}

 The dataset includes 351956 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004651-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqhytb
title: Occurrence Download

description: A dataset containing 7078 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amalda",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7078 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004727-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4u76yu
title: Occurrence Download

description: A dataset containing 10674 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Natatolana",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 10674 records from 178 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004728-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e246233v1
title: College of Nursing Sciences, UCTH, Calabar, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online.

description: College of Nursing Sciences, UCTH, Calabar, Cross River State.2026/2027 Admission Form/Application Form Is Out Call the Registrar (09024168245)09024168245(09024168245) For More Details On How To Apply And Register Online. The Management Of The School Hereby Inform The general Public On The Sales Of The General Nursing Admission Form Into The School Of Nursing, And Also The Sales Of Two (2) Years Post Basic Nursing Program. Contact The Educational Board Admin On (09024168245) For More Details On How To Purchase The Form And Register Online. REQUIREMENT FOR THE THREE YEARS GENERAL NURSING PROGRAMMES1. GENERAL NURSING PROGRAMME Admission Requirements Candidates For Admission Into The General Nursing Program Of The Nursing And Midwifery Council Of Nigeria Must Posses The …