cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14943879
title: ATAC-seq count data from primary pediatric AML samples

description: ATAC-seq Data from Primary Pediatric AML Samples

Data Source

Primary pediatric AML patient ATAC-seq data were obtained from Yokohama City University (YCU) and published in:

Yamato G, Kawai T, Shiba N, Ikeda J, Hara Y, Ohki K, Tsujimoto S. I., Kaburagi T, Yoshida K, Shiraishi Y, Miyano S, Kiyokawa N, Tomizawa D, Shimada A, Sotomatsu M, Arakawa H, Adachi S, Taga T, Horibe K, Ogawa S, Hata K, Hayashi Y. Genome-wide DNA methylation analysis in pediatric acute myeloid leukemia. Blood Adv. 2022 Jun 14;6(11):3207-3219. doi: 10.1182/bloodadvances.2021005381. PMID: 35008106; PMCID: PMC9198913.

Bioinformatic Processing

A modified version of the ATAC-seq Data Processing Pipeline (Reichl, S. et al. Ultimate ATAC-seq Data Processing & Quantification Pipeline. (2024)) was applied to the raw BAM files, accessible at: https://github.com/epigen/atacseq_pipeline.

The pipeline utilized fastp (Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018)) for adapter removal and Bowtie2 (Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)) for read alignment to the GRCh38 (hg38) human reference genome.

Duplicate marking was performed with samblaster (Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014)). The aligned BAM files were sorted, indexed, and filtered for ENCODE blacklisted regions using samtools (Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)).

Counts over exons were obtained using featureCounts (Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014)).

Data Structure

The table contains the following columns:




Column Name
Description




NCBI_id
RefSeq (NCBI Reference Sequence) accession number for a specific mRNA transcript


Gene_symbol
Official gene symbol


ENTREZ_id
Entrez Gene ID


YCU_NUP98-NSD1+PRDM16high-AM
Read counts per gene for this sample


YCU_NUP98-NSD1+PRDM16high-HR
Read counts per gene for this sample


YCU_RUNX1-RUNX1T1-SR
Read counts per gene for this sample


YCU_t11-19MLL_KA
Read counts per gene for this sample


YCU_t11-19MLL-NR
Read counts per gene for this sample
DOI: 10.5281/zenodo.14943880
title: ATAC-seq count data from primary pediatric AML samples

description: ATAC-seq Data from Primary Pediatric AML Samples

Data Source

Primary pediatric AML patient ATAC-seq data were obtained from Yokohama City University (YCU) and published in:

Yamato G, Kawai T, Shiba N, Ikeda J, Hara Y, Ohki K, Tsujimoto S. I., Kaburagi T, Yoshida K, Shiraishi Y, Miyano S, Kiyokawa N, Tomizawa D, Shimada A, Sotomatsu M, Arakawa H, Adachi S, Taga T, Horibe K, Ogawa S, Hata K, Hayashi Y. Genome-wide DNA methylation analysis in pediatric acute myeloid leukemia. Blood Adv. 2022 Jun 14;6(11):3207-3219. doi: 10.1182/bloodadvances.2021005381. PMID: 35008106; PMCID: PMC9198913.

Bioinformatic Processing

A modified version of the ATAC-seq Data Processing Pipeline (Reichl, S. et al. Ultimate ATAC-seq Data Processing & Quantification Pipeline. (2024)) was applied to the raw BAM files, accessible at: https://github.com/epigen/atacseq_pipeline.

The pipeline utilized fastp (Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018)) for adapter removal and Bowtie2 (Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)) for read alignment to the GRCh38 (hg38) human reference genome.

Duplicate marking was performed with samblaster (Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014)). The aligned BAM files were sorted, indexed, and filtered for ENCODE blacklisted regions using samtools (Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)).

Counts over exons were obtained using featureCounts (Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014)).

Data Structure

The table contains the following columns:




Column Name
Description




NCBI_id
RefSeq (NCBI Reference Sequence) accession number for a specific mRNA transcript


Gene_symbol
Official gene symbol


ENTREZ_id
Entrez Gene ID


YCU_NUP98-NSD1+PRDM16high-AM
Read counts per gene for this sample


YCU_NUP98-NSD1+PRDM16high-HR
Read counts per gene for this sample


YCU_RUNX1-RUNX1T1-SR
Read counts per gene for this sample


YCU_t11-19MLL_KA
Read counts per gene for this sample


YCU_t11-19MLL-NR
Read counts per gene for this sample
DOI: 10.7274/28510379
title: Detection of Single Electron Charges in Nanoscale Dipoles and Anyon-Type Quantum Dots

description: In topological quantum computation, quantum information is encoded in a deco-herence free and fault-tolerant manner using exotic Majorana fermion quasiparticles. Recent advancements have revealed solid-state quantum dots hosting non-Abelian anyons, where a single electron contributes to the formation of a Majorana-bound vortex. This property makes them highly attractive for charge sensing applications. To evaluate charge sensing fidelity before detecting unit charges in candidate quantum dots, we fabricated and tested various nano-scale dipole structures consisting of two sub-20nm metal dots separated by a tunnel barrier. We experimentally demonstrate robust detection of single-electron switching within the various dipole structures using both single-electron transistors (SETs) and radio-frequency gate reflectometry. Single-electron switching detection within a dipole structure of this length scale has not been reported before. Additionally, we present a composite simulation frame-work combining COMSOL Multiphysics and SPICE to calculate the induced charge in the SET due to electron switching in the dipole, achieving excellent agreement with experimental results. Furthermore, we fabricated site-controlled non-Abelian anyon-type InP quantum dots and attempted spatial sensing of unit charges by positioning SETs in close proximity to the dots. However, our experimental observations are primarily attributed to substrate effects under light illumination. These findings highlight the necessity of carefully accounting for substrate effects in quantum dot experiments to ensure accurate and measurable outcomes.
DOI: 10.7274/28510379.v1
title: Detection of Single Electron Charges in Nanoscale Dipoles and Anyon-Type Quantum Dots

description: In topological quantum computation, quantum information is encoded in a deco-herence free and fault-tolerant manner using exotic Majorana fermion quasiparticles. Recent advancements have revealed solid-state quantum dots hosting non-Abelian anyons, where a single electron contributes to the formation of a Majorana-bound vortex. This property makes them highly attractive for charge sensing applications. To evaluate charge sensing fidelity before detecting unit charges in candidate quantum dots, we fabricated and tested various nano-scale dipole structures consisting of two sub-20nm metal dots separated by a tunnel barrier. We experimentally demonstrate robust detection of single-electron switching within the various dipole structures using both single-electron transistors (SETs) and radio-frequency gate reflectometry. Single-electron switching detection within a dipole structure of this length scale has not been reported before. Additionally, we present a composite simulation frame-work combining COMSOL Multiphysics and SPICE to calculate the induced charge in the SET due to electron switching in the dipole, achieving excellent agreement with experimental results. Furthermore, we fabricated site-controlled non-Abelian anyon-type InP quantum dots and attempted spatial sensing of unit charges by positioning SETs in close proximity to the dots. However, our experimental observations are primarily attributed to substrate effects under light illumination. These findings highlight the necessity of carefully accounting for substrate effects in quantum dot experiments to ensure accurate and measurable outcomes.
DOI: 10.15468/dl.6whfgu
title: Occurrence Download

description: A dataset containing 2242 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "TaxonKey is Meles meles (Linnaeus, 1758)"
 ]
}

 The dataset includes 2242 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007250-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.udt7e3
title: Occurrence Download

description: A dataset containing 5725 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "TaxonKey is Lophornis chalybeus (Temminck, 1821)",
 "Year 1000-2024"
 ]
}

 The dataset includes 5725 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007299-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vpd5n9
title: Occurrence Download

description: A dataset containing 507 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Brazil, Argentina)",
 "TaxonKey is Discosura langsdorffi (Temminck, 1821)",
 "Year 1000-2024"
 ]
}

 The dataset includes 507 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007251-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57833/cidacs/njo159
title: Replication Data for: Registro de Ocupação Hospitalar COVID-19 - 2020 a 2022

description: Este dataset foi desenvolvido pelo Ministério da Saúde, a partir da incorporação do sistema e-SUS Notifica- Módulo internações SUS, em vigor a partir de abril de 2020, em decorrência da epidemia de Covid-19. O módulo Internações foi desenvolvido para registro da ocupação de leitos clínicos e de Unidade de Terapia Intensiva (UTI) SUS destinados para atendimento aos pacientes com casos suspeitos ou confirmados da COVID-19 (ocupação SRAG / COVID-19). (incluindo por Covid-19) oriundos do sistema SIM, desenvolvido pelo Ministério da Saúde, contendo dados socioeconômicos, local de residência e ocorrência, óbitos fetais e não fetais, condições e causas do óbito, e informações de causas externas. Este datatset contém dados prévios e está sujeito à alteração.
DOI: 10.15468/dl.uk6nh6
title: Occurrence Download

description: A dataset containing 4392 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Brazil, Paraguay, Argentina)",
 "TaxonKey is Chlorestes notata (G.C.Reich, 1793)",
 "Year 1000-2024"
 ]
}

 The dataset includes 4392 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007252-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2sffh
title: Occurrence Download

description: A dataset containing 1076 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((-94.47563 37.16331,-94.47396 37.16324,-94.47382 37.1668,-94.47823 37.16703,-94.47837 37.16344,-94.47826 37.16343,-94.478 37.16342,-94.47799 37.16342,-94.47563 37.16331))",
 "Geometry POLYGON((-94.47371 37.17003,-94.47369 37.17037,-94.47664 37.17054,-94.47809 37.17062,-94.47816 37.16883,-94.47823 37.16703,-94.47382 37.1668,-94.47369 37.17,-94.47369 37.17,-94.47369 37.17001,-94.47369 37.17003,-94.47371 37.17003))",
 "Geometry POLYGON((-94.48099 37.16663,-94.48064 37.16686,-94.4806 37.16715,-94.48275 37.16726,-94.48289 37.16365,-94.48104 37.16356,-94.48087 37.16355,-94.48065 37.1636,-94.48057 37.16372,-94.48015 37.16406,-94.48046 37.16441,-94.48077 37.16476,-94.48086 37.16493,-94.4809 37.16514,-94.48106 37.16544,-94.48116 37.16608,-94.48112 37.16636,-94.48099 37.16663))",
 "Geometry POLYGON((-94.48268 37.16907,-94.48394 37.16912,-94.48271 37.16846,-94.48268 37.16907))",
 "Geometry POLYGON((-94.48505 37.16918,-94.48268 37.16907,-94.48262 37.17087,-94.48498 37.17101,-94.48561 37.17104,-94.48565 37.16996,-94.48567 37.16969,-94.48568 37.1692,-94.48505 37.16918))",
 "Geometry POLYGON((-94.48271 37.16846,-94.48275 37.16726,-94.4806 37.16715,-94.48161 37.16788,-94.48161 37.16788,-94.48202 37.1681,-94.48271 37.16846))",
 "Geometry POLYGON((-94.4806 37.16715,-94.47823 37.16703,-94.47816 37.16883,-94.47809 37.17062,-94.48262 37.17087,-94.48268 37.16907,-94.48268 37.16903,-94.48271 37.16846,-94.48202 37.1681,-94.48161 37.16788,-94.48161 37.16788,-94.4806 37.16715))",
 "Geometry POLYGON((-94.48464 37.16815,-94.48446 37.16735,-94.48561 37.16741,-94.48561 37.16678,-94.48561 37.16672,-94.48741 37.16654,-94.48817 37.16621,-94.4885 37.16607,-94.48875 37.16599,-94.4891 37.16593,-94.48927 37.16591,-94.48966 37.16592,-94.49017 37.16594,-94.49068 37.16596,-94.49096 37.16597,-94.49096 37.16597,-94.49098 37.1654,-94.4917 37.16542,-94.49172 37.16496,-94.49176 37.16405,-94.48958 37.16395,-94.4874 37.16385,-94.48514 37.16375,-94.48448 37.16372,-94.48394 37.16369,-94.48361 37.16368,-94.48289 37.16365,-94.48275 37.16726,-94.48271 37.16846,-94.48394 37.16912,-94.48482 37.16916,-94.48482 37.16916,-94.48464 37.16815))",
 "Geometry POLYGON((-94.48057 37.16372,-94.48065 37.1636,-94.48087 37.16355,-94.48038 37.16353,-94.48004 37.16352,-94.47979 37.1635,-94.47953 37.16349,-94.47919 37.16348,-94.47898 37.16347,-94.47869 37.16345,-94.47866 37.16345,-94.47845 37.16344,-94.47837 37.16344,-94.47823 37.16703,-94.4806 37.16715,-94.48064 37.16686,-94.48099 37.16663,-94.48112 37.16636,-94.48116 37.16608,-94.48106 37.16544,-94.4809 37.16514,-94.48086 37.16493,-94.48077 37.16476,-94.48046 37.16441,-94.48015 37.16406,-94.48057 37.16372))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1076 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007254-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c3fdjw
title: Occurrence Download

description: A dataset containing 1085 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Trillium pusillum Michx."
 ]
}

 The dataset includes 1085 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007255-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qf8f85
title: Occurrence Download

description: A dataset containing 237709 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Abundance of Rocky Reef Fishes, Invertebrates and Algae, Reef Check California (RCCA), 2006 - 2017",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 237709 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007248-250227182400228/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.28063052
title: Spatial Modeling of Graffiti as a Function of Street Network Centrality: A Case Study in San Francisco

description: Graffiti presents serious urban concerns, often signaling urban decay. This study uses open spatial data to analyze and model graffiti occurrences in terms of street network centrality measures. In particular, betweenness centrality, closeness centrality, and degree centrality are evaluated using San Francisco, California, as the case study area, with data from OpenStreetMap and reported graffiti from 2008 to 2023 from the San Francisco nonemergency municipal service (denoted as 311) as the data sets. The spatial error model was found to outperform both ordinary least squares tests and the spatial lag model. The model could further explain graffiti spatiality. Graffiti writers were observed to favor street segments that are close to the downtown and well-connected to other streets, often having high accessibility, visibility, and accommodating street furniture. The results indicate that bridges and highway segments that are difficult to stop and tag were typically avoided. In addition, for a given street, the model error in adjacent streets significantly (<i>p</i> &lt; 0.001) affected the observed value.
DOI: 10.6084/m9.figshare.28063052.v1
title: Spatial Modeling of Graffiti as a Function of Street Network Centrality: A Case Study in San Francisco

description: Graffiti presents serious urban concerns, often signaling urban decay. This study uses open spatial data to analyze and model graffiti occurrences in terms of street network centrality measures. In particular, betweenness centrality, closeness centrality, and degree centrality are evaluated using San Francisco, California, as the case study area, with data from OpenStreetMap and reported graffiti from 2008 to 2023 from the San Francisco nonemergency municipal service (denoted as 311) as the data sets. The spatial error model was found to outperform both ordinary least squares tests and the spatial lag model. The model could further explain graffiti spatiality. Graffiti writers were observed to favor street segments that are close to the downtown and well-connected to other streets, often having high accessibility, visibility, and accommodating street furniture. The results indicate that bridges and highway segments that are difficult to stop and tag were typically avoided. In addition, for a given street, the model error in adjacent streets significantly (<i>p</i> &lt; 0.001) affected the observed value.
DOI: 10.6084/m9.figshare.28385526
title: Unraveling the COVID-19 Impact on Spatiotemporal Dynamics of U.S. Domestic Migration: A Network Perspective

description: Understanding the impacts of the COVID-19 pandemic on domestic migration patterns is crucial for addressing resource needs and migration forecasting. Only a limited number of studies, however, have explored these dynamics through the lens of network analysis. Using Internal Revenue Service (IRS) county-to-county migration data, this article conceptualizes the U.S. domestic migration system as complex networks and employs tools such as backbone extraction, centrality analysis, and community detection to examine the spatiotemporal shifts in the migration landscape before and during the pandemic. Our findings reveal a diversification in migration destinations, yet the overall network structure exhibits stability, with central hubs maintaining their pivotal roles and regional communities showing substantial resilience over time. Disruptions, when observed, were generally regional and modest in magnitude. Further, an analysis of adjusted gross income data within the IRS data sets uncovers a pronounced spatial clustering of migrant wealth, suggesting a deliberate selection of destinations by wealthier migrants. This research offers a novel perspective on understanding domestic migration in the face of external shocks, such as the COVID-19 pandemic, enriching the discourse on migration studies and providing invaluable insights for policy formulation and urban development.
DOI: 10.6084/m9.figshare.28385526.v1
title: Unraveling the COVID-19 Impact on Spatiotemporal Dynamics of U.S. Domestic Migration: A Network Perspective

description: Understanding the impacts of the COVID-19 pandemic on domestic migration patterns is crucial for addressing resource needs and migration forecasting. Only a limited number of studies, however, have explored these dynamics through the lens of network analysis. Using Internal Revenue Service (IRS) county-to-county migration data, this article conceptualizes the U.S. domestic migration system as complex networks and employs tools such as backbone extraction, centrality analysis, and community detection to examine the spatiotemporal shifts in the migration landscape before and during the pandemic. Our findings reveal a diversification in migration destinations, yet the overall network structure exhibits stability, with central hubs maintaining their pivotal roles and regional communities showing substantial resilience over time. Disruptions, when observed, were generally regional and modest in magnitude. Further, an analysis of adjusted gross income data within the IRS data sets uncovers a pronounced spatial clustering of migrant wealth, suggesting a deliberate selection of destinations by wealthier migrants. This research offers a novel perspective on understanding domestic migration in the face of external shocks, such as the COVID-19 pandemic, enriching the discourse on migration studies and providing invaluable insights for policy formulation and urban development.
DOI: 10.15468/dl.7kxepb
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-105.41848748253486 20.483662010841254,-105.30862420128486 20.483662010841254,-105.30862420128486 20.593525292091254,-105.41848748253486 20.593525292091254,-105.41848748253486 20.483662010841254))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Obelia Péron & Lesueur, 1810"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007256-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zuwr7e
title: Occurrence Download

description: A dataset containing 698969 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, National Coral Reef Monitoring Program: Stratified random surveys (StRS) of reef fish in the U.S. Pacific Islands",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 698969 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007249-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5n5sv3
title: Occurrence Download

description: A dataset listing the 503 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Stenotritidae, Melittidae, Megachilidae, Halictidae, Colletidae, Andrenidae, Apidae)"
 ]
}

 The dataset's 503 records were derived from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007298-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bfpyyw
title: Occurrence Download

description: A dataset containing 348268 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Reef Life Survey: Global reef fish dataset",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 348268 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007253-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.