cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bgqz7k
title: Occurrence Download

description: A dataset containing 627 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dalea lanata Spreng."
 ]
}

 The dataset includes 627 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061288-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmkr67
title: Occurrence Download

description: A dataset containing 315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dalea jamesii (Torr.) Torr. & A.Gray"
 ]
}

 The dataset includes 315 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061281-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dxgevm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bathytroctes microlepis Günther, 1878",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((26.6413 -34.1504,26.9648 -34.0826,26.9648 -25.1504,17.9648 -25.1504,17.9648 -34.1504,26.6413 -34.1504))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010599-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.18332146
title: SuperDARN data in netCDF format (2002-Sep)

description: 2002-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6787338
title: SuperDARN data in netCDF format (2002-Sep)

description: 2002-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.yruu3z
title: Occurrence Download

description: A dataset containing 2900 records in GBIF matching the query:
SELECT
 '94' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -16.25916876999866
 AND decimalLatitude &lt;= -12.166598047573723
 AND decimalLongitude &gt;= -56.27861780887036
 AND decimalLongitude &lt;= -50.59334038100958
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 2900 records were derived from 141 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004337-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y8wb8h
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "Geometry POLYGON((-138.68108 -49.69848,9.68108 -49.69848,9.68108 9.69848,-138.68108 9.69848,-138.68108 -49.69848))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pipraeidea bonariensis"
 ]
}

 The dataset includes 25 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004359-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.95s4s2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.713815 24.359779, 88.63673300000001 24.354202, 88.56156799999999 24.33761, 88.49019 24.310415, 88.424369 24.273293, 88.365736 24.227167, 88.315737 24.173182, 88.275599 24.112674, 88.246303 24.047143, 88.228554 23.978207, 88.22277200000001 23.907569, 88.22908099999999 23.836968, 88.247305 23.768142, 88.276979 23.702781, 88.317359 23.642489, 88.367442 23.588741, 88.425991 23.542854, 88.49157 23.505947, 88.56257100000001 23.478923, 88.63726 23.46244, 88.713815 23.456901, 88.79037 23.46244, 88.865059 23.478923, 88.93606 23.505947, 89.001639 23.542854, 89.060188 23.588741, 89.110271 23.642489, 89.150651 23.702781, 89.180325 23.768142, 89.198549 23.836968, 89.204858 23.907569, 89.19907600000001 23.978207, 89.181327 24.047143, 89.15203099999999 24.112674, 89.11189299999999 24.173182, 89.061894 24.227167, 89.00326099999999 24.273293, 88.93744 24.310415, 88.866062 24.33761, 88.790897 24.354202, 88.713815 24.359779))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004358-260120142942310/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.29458868
title: <sub>Raw data and images for "</sub><sub>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sub><sub><em>E. coli</em></sub><sub> O157:H7</sub><sub>"</sub>

description: <sup>This dataset contains the raw numerical data, annotated Western blot images, and original statistics files associated with the manuscript titled “</sup><sup>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sup><sup><em>E. coli</em></sup><sup> O157:H7</sup><sup>”. Data include Excel spreadsheets used to generate plots in Figures 1–6, as well as uncropped and annotated images for all immunoblots.</sup>
DOI: 10.6084/m9.figshare.29458868.v7
title: <sub>Raw data and images for "</sub><sub>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sub><sub><em>E. coli</em></sub><sub> O157:H7</sub><sub>"</sub>

description: <sup>This dataset contains the raw numerical data, annotated Western blot images, and original statistics files associated with the manuscript titled “</sup><sup>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sup><sup><em>E. coli</em></sup><sup> O157:H7</sup><sup>”. Data include Excel spreadsheets used to generate plots in Figures 1–6, as well as uncropped and annotated images for all immunoblots.</sup>
DOI: 10.6084/m9.figshare.29458868.v6
title: <sub>Raw data and images for "</sub><sub>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sub><sub><em>E. coli</em></sub><sub> O157:H7</sub><sub>"</sub>

description: <sup>This dataset contains the raw numerical data, annotated Western blot images, and original statistics files associated with the manuscript titled “</sup><sup>High-Throughput Transposon Sequencing Identifies HprR as a Key Regulator of the LEE Operon and Virulence in </sup><sup><em>E. coli</em></sup><sup> O157:H7</sup><sup>”. Data include Excel spreadsheets used to generate plots in Figures 1–6, as well as uncropped and annotated images for all immunoblots.</sup>
DOI: 10.15468/dl.ufecyc
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "OccurrenceStatus is Present",
 "TaxonKey is Chromolaena odorata"
 ]
}

 The dataset includes 359 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004360-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p82raz
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.713815 24.359779, 88.63673300000001 24.354202, 88.56156799999999 24.33761, 88.49019 24.310415, 88.424369 24.273293, 88.365736 24.227167, 88.315737 24.173182, 88.275599 24.112674, 88.246303 24.047143, 88.228554 23.978207, 88.22277200000001 23.907569, 88.22908099999999 23.836968, 88.247305 23.768142, 88.276979 23.702781, 88.317359 23.642489, 88.367442 23.588741, 88.425991 23.542854, 88.49157 23.505947, 88.56257100000001 23.478923, 88.63726 23.46244, 88.713815 23.456901, 88.79037 23.46244, 88.865059 23.478923, 88.93606 23.505947, 89.001639 23.542854, 89.060188 23.588741, 89.110271 23.642489, 89.150651 23.702781, 89.180325 23.768142, 89.198549 23.836968, 89.204858 23.907569, 89.19907600000001 23.978207, 89.181327 24.047143, 89.15203099999999 24.112674, 89.11189299999999 24.173182, 89.061894 24.227167, 89.00326099999999 24.273293, 88.93744 24.310415, 88.866062 24.33761, 88.790897 24.354202, 88.713815 24.359779))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004363-260120142942310/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.31121784
title: Bacteriophage vB_SalS_KY05 controls <i>Salmonella</i> in poultry without disrupting cecal microbiota composition

description: Salmonellosis is a major food safety challenge in poultry production, requiring effective antibiotic alternatives. We isolated a polyvalent <i>Tequintavirus</i>, vB_SalS_KY05, from a Taiwanese poultry farm and evaluated its biological properties and <i>in vivo</i> efficacy against <i>Salmonella enterica</i> serovars Typhimurium and Enteritidis, and <i>Escherichia coli</i>. The objective of this study was to comprehensively characterize vB_SalS_KY05 and assess its potential as a biocontrol agent for poultry farming. Genome analysis confirmed a lytic lifestyle with no detectable virulence or antimicrobial resistance genes. The phage remained stable at pH 4–10, at 41 °C, and in water for 7 days. For improved biosafety, large-scale amplification was achieved by switching the propagation host to non-pathogenic <i>E. coli</i> K12. <i>In vivo</i>, SPF chickens challenged with <i>S.</i> Typhimurium received low-dose (10<sup>5</sup> PFU/mL) or high-dose (10<sup>8</sup> PFU/mL) phage <i>via</i> drinking water. Low-dose treatment reduced splenic <i>Salmonella</i>, improved albumin-to-globulin ratios, and enriched beneficial taxa like <i>Lactobacillus crispatus</i> and <i>Blautia coccoides</i>. In contrast, high-dose treatment resulted in phage–bacteria coexistence and increased potentially harmful taxa including <i>Erysipelatoclostridium</i>. Overall, cecal microbiota composition remained largely unchanged. These findings indicate vB_SalS_KY05 is a promising biocontrol candidate, highlighting the importance of dose optimization to enhance pathogen control while preserving gut microbiota stability in poultry production.
DOI: 10.6084/m9.figshare.31121784.v1
title: Bacteriophage vB_SalS_KY05 controls <i>Salmonella</i> in poultry without disrupting cecal microbiota composition

description: Salmonellosis is a major food safety challenge in poultry production, requiring effective antibiotic alternatives. We isolated a polyvalent <i>Tequintavirus</i>, vB_SalS_KY05, from a Taiwanese poultry farm and evaluated its biological properties and <i>in vivo</i> efficacy against <i>Salmonella enterica</i> serovars Typhimurium and Enteritidis, and <i>Escherichia coli</i>. The objective of this study was to comprehensively characterize vB_SalS_KY05 and assess its potential as a biocontrol agent for poultry farming. Genome analysis confirmed a lytic lifestyle with no detectable virulence or antimicrobial resistance genes. The phage remained stable at pH 4–10, at 41 °C, and in water for 7 days. For improved biosafety, large-scale amplification was achieved by switching the propagation host to non-pathogenic <i>E. coli</i> K12. <i>In vivo</i>, SPF chickens challenged with <i>S.</i> Typhimurium received low-dose (10<sup>5</sup> PFU/mL) or high-dose (10<sup>8</sup> PFU/mL) phage <i>via</i> drinking water. Low-dose treatment reduced splenic <i>Salmonella</i>, improved albumin-to-globulin ratios, and enriched beneficial taxa like <i>Lactobacillus crispatus</i> and <i>Blautia coccoides</i>. In contrast, high-dose treatment resulted in phage–bacteria coexistence and increased potentially harmful taxa including <i>Erysipelatoclostridium</i>. Overall, cecal microbiota composition remained largely unchanged. These findings indicate vB_SalS_KY05 is a promising biocontrol candidate, highlighting the importance of dose optimization to enhance pathogen control while preserving gut microbiota stability in poultry production.
DOI: 10.6084/m9.figshare.31021083
title: Digital mapping of health-related quality of life research in non-communicable diseases: a topic modeling and bibliometric synthesis for policy and practice

description: Health-related quality of life (HrQoL) has emerged as a key metric in the management of non-communicable diseases (NCDs), reflecting the global shift from acute infectious conditions to chronic illnesses. This shift indicates a broader movement toward patient-centered care. Amidst global digital health transformations, a synthesized understanding of the HrQoL research landscape is essential. To address this gap, the study adopts the PRISMA framework to present a combined bibliometric and machine learning-based Latent Dirichlet Allocation (LDA) topic modeling analysis of 353 articles, retrieved from Scopus and Web of Science between 2015 and 2024. Using R Studio and VOSviewer, the study maps publication trends, influential authors, sources, and countries, revealing an expanded research interest across related disciplines. Five key research topics were identified: interventions and disease management, psychosocial wellbeing, digital health technologies, epidemiological research, and preventive healthcare. These represent a thematic evolution from clinical protocols to more integrative and digitally responsive models. The findings highlight the need for culturally sensitive, interdisciplinary approaches that bridge clinical care with social determinants of health. Through its computational approach, the study offers actionable insights for researchers, clinicians, and policymakers seeking to enhance the quality of life of individuals living with chronic conditions.
DOI: 10.6084/m9.figshare.31021083.v1
title: Digital mapping of health-related quality of life research in non-communicable diseases: a topic modeling and bibliometric synthesis for policy and practice

description: Health-related quality of life (HrQoL) has emerged as a key metric in the management of non-communicable diseases (NCDs), reflecting the global shift from acute infectious conditions to chronic illnesses. This shift indicates a broader movement toward patient-centered care. Amidst global digital health transformations, a synthesized understanding of the HrQoL research landscape is essential. To address this gap, the study adopts the PRISMA framework to present a combined bibliometric and machine learning-based Latent Dirichlet Allocation (LDA) topic modeling analysis of 353 articles, retrieved from Scopus and Web of Science between 2015 and 2024. Using R Studio and VOSviewer, the study maps publication trends, influential authors, sources, and countries, revealing an expanded research interest across related disciplines. Five key research topics were identified: interventions and disease management, psychosocial wellbeing, digital health technologies, epidemiological research, and preventive healthcare. These represent a thematic evolution from clinical protocols to more integrative and digitally responsive models. The findings highlight the need for culturally sensitive, interdisciplinary approaches that bridge clinical care with social determinants of health. Through its computational approach, the study offers actionable insights for researchers, clinicians, and policymakers seeking to enhance the quality of life of individuals living with chronic conditions.
DOI: 10.5281/zenodo.18334189
title: Ipseity Daily Data

description: At what rate do individuals say they are X?

Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence.  They responded to many items of the form: Does <signifier> describe you today?

The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28.  The columns are as follows:



signifier - a word, phrase or emoji

yes_count - the number of respondents who answered yes (aggregated all-time).

no_count - the number of respondents who answered no (aggregated all-time).

prevalence_per_10k - an estimate of the prevalence per 10,000.  If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier?  The number is calculated as yes_count / (yes_count + no_count) * 10,000.

earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent.  Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling.

latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent.


Version created on 2026-01-22 — cumulative data.
DOI: 10.15468/dl.3pctg2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.713815 24.359779, 88.63673300000001 24.354202, 88.56156799999999 24.33761, 88.49019 24.310415, 88.424369 24.273293, 88.365736 24.227167, 88.315737 24.173182, 88.275599 24.112674, 88.246303 24.047143, 88.228554 23.978207, 88.22277200000001 23.907569, 88.22908099999999 23.836968, 88.247305 23.768142, 88.276979 23.702781, 88.317359 23.642489, 88.367442 23.588741, 88.425991 23.542854, 88.49157 23.505947, 88.56257100000001 23.478923, 88.63726 23.46244, 88.713815 23.456901, 88.79037 23.46244, 88.865059 23.478923, 88.93606 23.505947, 89.001639 23.542854, 89.060188 23.588741, 89.110271 23.642489, 89.150651 23.702781, 89.180325 23.768142, 89.198549 23.836968, 89.204858 23.907569, 89.19907600000001 23.978207, 89.181327 24.047143, 89.15203099999999 24.112674, 89.11189299999999 24.173182, 89.061894 24.227167, 89.00326099999999 24.273293, 88.93744 24.310415, 88.866062 24.33761, 88.790897 24.354202, 88.713815 24.359779))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004365-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18014919
title: मध्य प्रदेश Madhya Pradesh (Gaonrī or Gonry). Gaonrī Copper Plate Inscription of Vākpatirājadeva

description: मध्य प्रदेश Madhya Pradesh (Gaonrī or Gonry). Gaonrī Copper Plate Inscription of Vākpatirājadeva
DOI: 10.5281/zenodo.18014920
title: मध्य प्रदेश Madhya Pradesh (Gaonrī or Gonry). Gaonrī Copper Plate Inscription of Vākpatirājadeva

description: मध्य प्रदेश Madhya Pradesh (Gaonrī or Gonry). Gaonrī Copper Plate Inscription of Vākpatirājadeva