cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e233082v6
title: Digital Inclusion, Access, and Literacy

description: Digital health tools (e.g., patient portals, telehealth, mobile health applications) are increasingly essential to meaningful engagement in healthcare. These tools facilitate access (e.g., patient-provider communication, remote visits) and self-management (e.g., engagement with health information) and are associated with greater patient satisfaction, better chronic disease and behavioral health outcomes, and reduced acute care utilization. Providing personalized support may increase engagement among patients who face challenges to using digital health tools, but it is difficult to identify patients who require assistance with digital health due to the dearth of validated screening measure that are usable in a clinical setting, particularly screening tools tailored for use among patients in safety net settings at greater risk for facing digital exclusion (e.g., older adults).<br><br>The purpose of this study was to identify a brief set of measures to efficiently and accurately measure digital skills to identify and tailor approaches to support patients requiring assistance to effectively use digital health tools. The objectives were to: (1) Develop a short self-report measure (Skills Measurement and Readiness Training for Digital Health [SMART Digital Health] Scale) of digital skills needed to use digital health tools, and (2) validate the SMART Digital Health Scale in relation to observed performance completing digital health tasks among a diverse sample of patients receiving care from safety net healthcare settings.
DOI: 10.15468/dl.5j5ng4
title: Occurrence Download

description: A dataset containing 14604 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude 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 "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 46.479780028101516
 AND decimallatitude &lt;= 51.21395264452012
 AND decimallongitude &gt;= 62.94398091796209
 AND decimallongitude &lt;= 73.49581818030033
 AND GBIF_Within('POLYGON ((73.495818 46.479780, 73.495818 51.213953, 62.943981 51.213953, 62.943981 46.479780, 73.495818 46.479780))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 14604 records were derived from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003265-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/gdvlxf
title: URM - Herbário Pe. Camille Torrand

description: O Herbário Pe. Camille Torrend (URM) foi criado em 1954, sob a chefia do ilustre micologista Prof. Augusto Chaves Batista. No seu primeiro ano de funcionamento, o Herbário URM tinha 1141 exsicatas registradas. Cedo esse número foi aumentado, graças às doações feitas, entre outros, pelo Prof. Petrak (fungos da Áustria), pelas Universidades de Pavia e Universidade da Califórnia e pelo Instituto de Botânica da Universidade de Turku, Finlândia (Pucciniaceae). Entre os principais coletores nacionais, destacaram-se Bento Pickel, E.P. Heringer, Oswaldo Soares, Dárdano de Andrade Lima, Albino Vital e o próprio Prof. Augusto Chaves Batista. Dentre os importantes coletores internacionais, há de se ressaltar a colaboração dos seguintes cientistas: Pe. Camille Torrend, E. Petrak, Rolf Singer, J.L. Howard, G.W. Martin, T.H. MacBride, N. Harrington, J.P. Trace, J.A. von Arx, B. Cooke, R. Ciferri, K. Pirozinski, H. Johannes, e outros. Das principais coleções do Herbário URM, constam ainda duplicatas da Flora Santesson, Fungi Excicattii Fennici, Mycologicum Herbarum - Washington State College, Fungi of Oregon, Flora da Suécia, Herbarum Museu Botanica Upsaliensis, Fungi Bohemia, University of Toronto e Herbarium - Pennsilvania State College, entre outras. Atualmente, o Herbário URM possui mais de 90.000 registros, e a sua coleção de fungos herborizados é uma das maiores da América Latina, constando principalmente de amostras das regiões Norte e Nordeste do Brasil.
DOI: 10.5281/zenodo.18558044
title: SuperDARN data in netCDF format (2017-Oct)

description: 2017-Oct 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.6319965
title: SuperDARN data in netCDF format (2017-Oct)

description: 2017-Oct 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: 10.6084/m9.figshare.31145503
title: Data associated with "Peat fires contribute disproportionately to Siberian fire carbon emissions".

description: Arctic and boreal fires are critical threats to terrestrial carbon reservoirs, particularly peat fires that trigger long-term irrecoverable carbon losses and permafrost thaw. However, the occurrence of peat fires and their associated carbon emissions remain highly uncertain. In this study, we use 30-m Landsat-derived data to map burned areas and peat fires across Siberia (63–180°E, 58–74°N) over the 2001–2023 period. All materials supporting the findings of this study are included in this repository. High-resolution maps of burned area, peat fires, and their associated emissions (0.00025 × 0.00025°) are available through Google Earth Engine at: https://code.earthengine.google.com/ac4bd137b143724d337082d4457dd8b1
DOI: 10.6084/m9.figshare.31145503.v1
title: Data associated with "Peat fires contribute disproportionately to Siberian fire carbon emissions".

description: Arctic and boreal fires are critical threats to terrestrial carbon reservoirs, particularly peat fires that trigger long-term irrecoverable carbon losses and permafrost thaw. However, the occurrence of peat fires and their associated carbon emissions remain highly uncertain. In this study, we use 30-m Landsat-derived data to map burned areas and peat fires across Siberia (63–180°E, 58–74°N) over the 2001–2023 period. All materials supporting the findings of this study are included in this repository. High-resolution maps of burned area, peat fires, and their associated emissions (0.00025 × 0.00025°) are available through Google Earth Engine at: https://code.earthengine.google.com/ac4bd137b143724d337082d4457dd8b1
DOI: 10.3886/e233082
title: Digital Inclusion, Access, and Literacy

description: Digital health tools (e.g., patient portals, telehealth, mobile health applications) are increasingly essential to meaningful engagement in healthcare. These tools facilitate access (e.g., patient-provider communication, remote visits) and self-management (e.g., engagement with health information) and are associated with greater patient satisfaction, better chronic disease and behavioral health outcomes, and reduced acute care utilization. Providing personalized support may increase engagement among patients who face challenges to using digital health tools, but it is difficult to identify patients who require assistance with digital health due to the dearth of validated screening measure that are usable in a clinical setting, particularly screening tools tailored for use among patients in safety net settings at greater risk for facing digital exclusion (e.g., older adults).<br><br>The purpose of this study was to identify a brief set of measures to efficiently and accurately measure digital skills to identify and tailor approaches to support patients requiring assistance to effectively use digital health tools. The objectives were to: (1) Develop a short self-report measure (Skills Measurement and Readiness Training for Digital Health [SMART Digital Health] Scale) of digital skills needed to use digital health tools, and (2) validate the SMART Digital Health Scale in relation to observed performance completing digital health tasks among a diverse sample of patients receiving care from safety net healthcare settings.
DOI: 10.3886/e233082v7
title: Digital Inclusion, Access, and Literacy

description: Digital health tools (e.g., patient portals, telehealth, mobile health applications) are increasingly essential to meaningful engagement in healthcare. These tools facilitate access (e.g., patient-provider communication, remote visits) and self-management (e.g., engagement with health information) and are associated with greater patient satisfaction, better chronic disease and behavioral health outcomes, and reduced acute care utilization. Providing personalized support may increase engagement among patients who face challenges to using digital health tools, but it is difficult to identify patients who require assistance with digital health due to the dearth of validated screening measure that are usable in a clinical setting, particularly screening tools tailored for use among patients in safety net settings at greater risk for facing digital exclusion (e.g., older adults).<br><br>The purpose of this study was to identify a brief set of measures to efficiently and accurately measure digital skills to identify and tailor approaches to support patients requiring assistance to effectively use digital health tools. The objectives were to: (1) Develop a short self-report measure (Skills Measurement and Readiness Training for Digital Health [SMART Digital Health] Scale) of digital skills needed to use digital health tools, and (2) validate the SMART Digital Health Scale in relation to observed performance completing digital health tasks among a diverse sample of patients receiving care from safety net healthcare settings.
DOI: 10.15468/dl.yuzqg5
title: Occurrence Download

description: A dataset containing 9232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Russian Federation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Solidago canadensis"
 ]
}

 The dataset includes 9232 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003278-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sd49t9
title: Occurrence Download

description: A dataset containing 647 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day") yearmonthday,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 GBIF_STRINGARRAYCONTAINS(occurrence.classifications['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c'], '2424128', TRUE)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.classificationdetails['d7dddbf4-2cf0-4f39-9b2a-bb099caae36c']['specieskey'] &lt;&gt; '' AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL AND occurrence."day" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day")

 The dataset's 647 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003234-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25574/22645
title: Chandra X-ray Observatory ObsId 22645

description: Spiraling into the 'quotation mark' cluster
DOI: 10.25574/22863
title: Chandra X-ray Observatory ObsId 22863

description: Spiraling into the 'quotation mark' cluster
DOI: 10.25574/22864
title: Chandra X-ray Observatory ObsId 22864

description: Spiraling into the 'quotation mark' cluster
DOI: 10.5281/zenodo.17551048
title: Backup Cellranger out Projekt 1266 CD40 activation BM
DOI: 10.5281/zenodo.17551049
title: Backup Cellranger out Projekt 1266 CD40 activation BM
DOI: 10.15468/dl.4h5fmk
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-105.72962 43.07668,-95.62576 43.07668,-95.62576 45.64089,-105.72962 45.64089,-105.72962 43.07668))",
 "HasGeospatialIssue is false",
 "TaxonKey is Mentzelia"
 ]
}

 The dataset includes 118 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003279-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2fe83n
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heliotrygon gomesi",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 17 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003281-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zaaw7r
title: Occurrence Download

description: A dataset containing 1840 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "DatasetKey is one of (EOD – eBird Observation Dataset, iNaturalist Research-grade Observations)",
 "Geometry POLYGON((-120.23219 41.79464,-120.2279 41.63036,-120.1393 41.62609,-120.16359 41.78505,-120.23219 41.79464))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1840 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003282-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25976/tsbm-8561
title: Shediac Bay Watershed monitoring of the Shediac and Scoudouc rivers

description: Water quality monitoring began in the Shediac Bay Watershed during the water classification program in 1999-2001. Basic physical measurements were then taken at the same 18 sites from 1999 - 2005. In 2007, the sampling was restarted by analyzing E. coli, total coliforms, nitrate-nitrogen and phosphorus. The water quality monitoring program is used to support and detect the need for remediation actions and measures to help improve and protect water quality. It is also used to complete detailed sanitary surveys and establish the status of our rivers. Such monitoring helps determine if changes to the water quality occur and if sections of the stream or river remain suitable for aquatic life. It is of utmost importance to have accurate and continuous data of water parameters for the watershed. This allows for effective management strategies and the creation of strategic plans. The water monitoring program is funded by the NB department of Environment and local government through the NB Environmental Trust Fund.
description: The data is collected following recommendations issued by the NB department of Environment and local government. Basic water quality parameters (DO, temperature, pH, conductivity and salinity) are measured using a YSI- Professional Plus multi-parameter metre.
DOI: 10.5281/zenodo.18559551
title: Maintaining Genetic Fidelity in Sweetpotato: Epigenetic Insights into Propagation and Drought Tolerance

description: This is raw data used during the analysis for the given study. All datasets are in excel files.
DOI: 10.5281/zenodo.18559550
title: Maintaining Genetic Fidelity in Sweetpotato: Epigenetic Insights into Propagation and Drought Tolerance

description: This is raw data used during the analysis for the given study. All datasets are in excel files.
DOI: 10.15468/dl.r5hzt9
title: Occurrence Download

description: A dataset containing 1942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Parrotia persica"
 ]
}

 The dataset includes 1942 records from 135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003283-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqr3z7
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heliotrygon rosai",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 11 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003284-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/xmvjed
title: New Cretaceous fossil mantispids highlight the palaeodiversity of the extinct subfamily Doratomantispinae (Neuroptera: Mantispidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Li, Hongyu, Zhuo, De, Cao, Leran, Wang, Bo, Poinar, George, Ohl, Michael, Liu, Xingyue (2022): New Cretaceous fossil mantispids highlight the palaeodiversity of the extinct subfamily Doratomantispinae (Neuroptera: Mantispidae). Organisms Diversity & Evolution (New York, N. Y.) 22 (3): 681-730, DOI: 10.1007/s13127-022-00546-y, URL: http://dx.doi.org/10.1007/s13127-022-00546-yAbstractDoratomantispinae is an extinct mantispid subfamily reported only from mid-Cretaceous Myanmar amber, which includes two genera, Doratomantispa Poinar in Poinar & Buckley, 2011 and Paradoxomantispa Lu et al., 2020, and six species. Here, we present an overview of the genera and species of the subfamily, with descriptions of seven new species, namely D. arcimaculata sp. nov., D. gaoyuhei sp. nov., D. zhangwenjuni sp. nov., D. zhangzhiqiae sp. nov., D. zhuozhengmingi sp. nov., D. yumeiyingae sp. nov., and P. mahaiyingae sp. nov .. We also synonymize Lonchomantispa Shi, Yang & Ren in Shi et al., 2020 with Doratomantispa, proposing the new combination: D. longa (Shi, Yang & Ren in Shi et al., 2020) comb. nov.. The adult morphology of Doratomantispinae is studied in detail and comprehensively compared with many other mantispoids. We found some putative apomorphies supporting the monophyly of Doratomantispinae and its close relationship with the derived mantispid clade. This subfamily represents a lineage with the richest species diversity among mantispids known from Myanmar amber.
DOI: 10.15468/dl.qrp39j
title: Occurrence Download

description: A dataset containing 276 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "OccurrenceStatus is Present",
 "StateProvince is Assam",
 "TaxonKey is Elephas maximus",
 "Year 2000-2026"
 ]
}

 The dataset includes 276 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003285-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18559840
title: Data of A transformer-based language model reveals developmental constraint and network complexity during zebrafish embryogenesis

description: Datasets required to run Zebraformer, a transformer-based model designed to learn the developmental dynamics of the zebrafish (Danio rerio). See also https://github.com/juanfpoyatos/Zebraformer.
DOI: 10.5281/zenodo.18559841
title: Data of A transformer-based language model reveals developmental constraint and network complexity during zebrafish embryogenesis

description: Datasets required to run Zebraformer, a transformer-based model designed to learn the developmental dynamics of the zebrafish (Danio rerio). See also https://github.com/juanfpoyatos/Zebraformer.