cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26196479
title: Draft assembly of P. australis, North American invasive lineage scaffolded to chromosome level and its genome annotation

description: The draft genome of Phragmites australis ( Oh et al. 2022) representing the invasive lineage was scaffolded to chromosome level, when mapped against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns.Oh, D.H., Kowalski, K.P., Quach, Q.N., Wijesinghege, C., Tanford, P., Dassanayake, M. and Clay, K., 2022. Novel genome characteristics contribute to the invasiveness of Phragmites australis (common reed). <i>Molecular Ecology</i>, <i>31</i>(4), pp.1142-1159.
DOI: 10.6084/m9.figshare.26196479.v1
title: Chromosome level genome assembly of P. australis, North American invasive lineage

description: The draft genome of Phragmites australis ( Oh et al. 2022) representing the invasive lineage was scaffolded to chromosome level, when mapped against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns.Oh, D.H., Kowalski, K.P., Quach, Q.N., Wijesinghege, C., Tanford, P., Dassanayake, M. and Clay, K., 2022. Novel genome characteristics contribute to the invasiveness of Phragmites australis (common reed). <i>Molecular Ecology</i>, <i>31</i>(4), pp.1142-1159.
DOI: 10.6084/m9.figshare.26196785
title: Draft assembly of P. australis, North American native lineage Y17 scaffolded to chromosome level, and its genome annotation

description: The draft genome of Phragmites australis representing the native lineage in North America was first constructed with paired end short reads using Masurca software, and then scaffolded to chromosome level, by mapping against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns.
DOI: 10.6084/m9.figshare.26196785.v2
title: Draft assembly of P. australis, North American native lineage Y17 scaffolded to chromosome level, and its genome annotation

description: The draft genome of Phragmites australis representing the native lineage in North America was first constructed with paired end short reads using Masurca software, and then scaffolded to chromosome level, by mapping against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns.
DOI: 10.6084/m9.figshare.26196785.v1
title: Chromosome level genome assembly of P. australis, North American native lineage Y17

description: The draft genome of Phragmites australis representing the native lineage in North America was first constructed with paired end short reads using Masurca software, and then scaffolded to chromosome level, by mapping against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns.
DOI: 10.6084/m9.figshare.26233442
title: Draft genome of Phragmites australis, USland lineage scaffolded to chromosome level and its annotation

description: The draft genome of Phragmites australis representing the USland lineage (individual Y7) was first constructed with paired end short reads using Masurca software, and then scaffolded to chromosome level, by mapping against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns. The genome annotation was performed by lifting off from the genomic feature of the reference genome .
DOI: 10.6084/m9.figshare.26233442.v1
title: Draft genome of Phragmites australis, USland lineage scaffolded to chromosome level and its annotation

description: The draft genome of Phragmites australis representing the USland lineage (individual Y7) was first constructed with paired end short reads using Masurca software, and then scaffolded to chromosome level, by mapping against our chromosome level reference genome assembly using Ragtag. This file is the genome fasta file. The proportion of gaps were filled with Ns. The genome annotation was performed by lifting off from the genomic feature of the reference genome .
DOI: 10.6084/m9.figshare.26233478.v1
title: Supplementary Dataset3 Ks value between rice and P. australis subgenomes, representing the dataset behind Figure 2c,d,e.

description: The synonymous substitution rate was calculated between subgenome A and subgenome D of P. australis, between rice and subgenome A, as well as rice and subgenome D, so as to reveal the syntenic blocks of these genomes.
DOI: 10.6084/m9.figshare.26233478
title: Supplementary Dataset3 Ks value between rice and P. australis subgenomes, representing the dataset behind Figure 2c,d,e.

description: The synonymous substitution rate was calculated between subgenome A and subgenome D of P. australis, between rice and subgenome A, as well as rice and subgenome D, so as to reveal the syntenic blocks of these genomes.
DOI: 10.6084/m9.figshare.26356390
title: Numeric source data underlying Figure 3b-c.

description: Statistics of genes in orthologous groups for the five genetic lineages of P. australis.
DOI: 10.6084/m9.figshare.26356390.v1
title: Numeric source data underlying Figure 3b-c.

description: Statistics of genes in orthologous groups for the five genetic lineages of P. australis.
DOI: 10.6084/m9.figshare.26356621
title: Numerical source data underlying Figure 4b.

description: The GO enrichment for B chromosome genes of P. australis.
DOI: 10.6084/m9.figshare.26356621.v1
title: Numerical source data underlying Figure 4b.

description: The GO enrichment for B chromosome genes of P. australis.
DOI: 10.6084/m9.figshare.26356660
title: Numerical source data underlying Figure 4d.

description: The origination of B chromosome. The second indicating the number of fragments sourcing from the chromosome corresponding to the first column.
DOI: 10.6084/m9.figshare.26356660.v1
title: Numerical source data underlying Figure 4d.

description: The origination of B chromosome. The second indicating the number of fragments sourcing from the chromosome corresponding to the first column.
DOI: 10.6084/m9.figshare.26356684
title: Numerical source data underlying Figure 5 a-e.

description: These five files dipicts the alternative allele frequencies after mapping whole genome sequencing reads to the reference genome. It represents different individuals from different lineages, as the file name shows. The first and second column of each file showed the genomic coordinates, and the third column showed the number of reference alleles mapped to the genome, and the fourth column showed the number of alternative alleles mapped to the genome.
DOI: 10.6084/m9.figshare.26356684.v1
title: Numerical source data underlying Figure 5 a-e.

description: These five files dipicts the alternative allele frequencies after mapping whole genome sequencing reads to the reference genome. It represents different individuals from different lineages, as the file name shows. The first and second column of each file showed the genomic coordinates, and the third column showed the number of reference alleles mapped to the genome, and the fourth column showed the number of alternative alleles mapped to the genome.
DOI: 10.15468/dl.ywm8s3
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-122.6851 40.89439,-121.42204 40.89439,-121.42204 41.73164,-122.6851 41.73164,-122.6851 40.89439))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Botrychium Sw."
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039712-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u5zr2g
title: Occurrence Download

description: A dataset containing 3220611 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quiscalus mexicanus (Gmelin, 1788))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 3220611 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039711-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.grkyg6
title: Occurrence Download

description: A dataset containing 302 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Sistemática y distribución actual de los Agave spp mezcaleros"
 ]
}

 The dataset includes 302 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039713-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.af2jat
title: Occurrence Download

description: A dataset containing 7189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material citation, Material sample, Specimen, Occurrence evidence)",
 "Country is United States of America",
 "Geometry POLYGON((-102.58559 37.30904,-102.58559 35.78599,-89.74847 36.29368,-85.50569 38.54198,-81.80686 39.12219,-81.6618 48.80443,-104.07237 49.02201,-104.1449 41.26171,-102.54933 41.3705,-102.58559 37.30904))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is Geodetic datum assumed WGS84",
 "TaxonKey is Aplodinotus grunniens Rafinesque, 1819"
 ]
}

 The dataset includes 7189 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039715-240626123714530/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.26043871.v6
title: Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study

description: Here presents the dataset for the statistical analysis for the manuscript "Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study". The file "fwf_all_n216.xlsx" is the baseline dataset. The files "fwf_Ab_pos_133.xlsx" and "fwf_Ab_neg_83.xlsx" are beta-amyloid positive and negative subgroups, respectively. The file "M12_all_n76.xlsx" is the 12-month follow-up dataset. The files "M12_Ab_pos_n46.xlsx" and "M12_Ab_Neg_n30.xlsx" are beta-amyloid positive and negative subgroups, respectively.
DOI: 10.5281/zenodo.13118000
title: BotHawk: GitHub Bot Account Detection Dataset

description: The "BotHawk: GitHub Bot Account Detection Dataset" is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.

Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.

This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.
DOI: 10.5281/zenodo.13118001
title: BotHawk: GitHub Bot Account Detection Dataset

description: The "BotHawk: GitHub Bot Account Detection Dataset" is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.

Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.

This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.
DOI: 10.5281/zenodo.12770017
title: Ukraine Field Boundary Delineation 2021

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Alex Olinb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.5281/zenodo.12770018
title: Ukraine Field Boundary Delineation 2021

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Alex Olinb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.15468/dl.v8egxp
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hedyotis corymbosa (L.) Lam."
 ]
}

 The dataset's 1 records were derived from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039716-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12786398
title: Ukraine Field Boundary Delineation 2022

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Oleksandra Oliinykb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.5281/zenodo.12786399
title: Ukraine Field Boundary Delineation 2022

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Oleksandra Oliinykb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.5281/zenodo.12791902
title: Ukraine Field Boundary Delineation 2023

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Oleksandra Oliinykb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.5281/zenodo.12791903
title: Ukraine Field Boundary Delineation 2023

description: Authors:



Yuval Sadeha,b,*,  Josef Wagnerc, Shabarinath S. Nairc, Oleksandra Oliinykb, Enguerran Bellesc, Ayman Bibihc, Léna D'Harboulléc, Hamza Bendhamanec, Manav Guptac, Inbal Becker-Reshefb,c

aSchool of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.

bDepartment of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

cICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France

* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)


 

Information:

These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.
DOI: 10.15468/dl.8ju8x8
title: Occurrence Download

description: A dataset containing 423962 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rabelera holostea (L.) M.T.Sharples & E.A.Tripp)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 423962 records from 1975 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039714-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5883/bold:adz5803
title: Overview of Barcode Index Number - BOLD:ADZ5803

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/bold:adz5920
title: Overview of Barcode Index Number - BOLD:ADZ5920

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/bold:adz5979
title: Overview of Barcode Index Number - BOLD:ADZ5979

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/bold:adz6020
title: Overview of Barcode Index Number - BOLD:ADZ6020

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.
DOI: 10.5883/bold:adz6021
title: Overview of Barcode Index Number - BOLD:ADZ6021

description: DNA barcode data hosted in the Data Portal of the Barcode of Life Data Systems. Records consist of specimen metadata, specimen images, and sequence data.