cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27256126.v1
title: A comparison of branched DNA and reverse transcriptase quantitative polymerase chain reaction methodologies for quantitation of lipid nanoparticle encapsulated mRNA

description: Messenger RNA (mRNA)-based therapeutics have emerged as a promising modality for various clinical applications, necessitating robust methods for mRNA quantification. This biodistribution study compares the performance of branched DNA and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays for measuring lipid nanoparticle-encapsulated mRNA. Following intravenous administration of nascent peptide imaging luciferase mRNA (1 mg/kg) to rats, mRNA levels in various tissues and serum were quantified using both assays. Statistical analyses, including Bland–Altman, Deming regression and Passing–Bablok regression, were employed to assess method comparability and reproducibility. The results indicated that mRNA pharmacokinetics measured by branched DNA and RT-qPCR were largely consistent across tissues, with RT-qPCR showing greater reproducibility across multiple laboratories. RT-qPCR also demonstrated a wider dynamic range and higher sensitivity, making it a more versatile option for large-scale studies. Despite some differences in data due to tissue types and timepoints, both methods provided comparable pharmacokinetic profiles for mRNA quantification. This study underscores the importance of selecting an appropriate quantification method based on study requirements and highlights RT-qPCR’s adaptability for multisite research, especially for the clinical development of mRNA-based therapeutics. With the emergence of messenger RNA (mRNA) as a therapeutic modality with a vast number of clinical applications, accurate quantification methods are needed for development and of mRNA-based therapeutics. Our study demonstrated a comprehensive comparison between two bioanalytical methods – branched DNA (bDNA) and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) for quantifying lipid nanoparticle encapsulated mRNA in rat tissues and serum. RT-qPCR provided generally consistent results across laboratories, suggesting it as a more reproducible method for mRNA quantification. Given the similar results obtained by both methods, researchers and developers can choose between bDNA and RT-qPCR based on specific study requirements, such as sensitivity. This study highlighted the importance of method transferability, with RT-qPCR being more adaptable across different laboratories, which is critical for large-scale and multisite studies.
DOI: 10.6084/m9.figshare.27256126
title: A comparison of branched DNA and reverse transcriptase quantitative polymerase chain reaction methodologies for quantitation of lipid nanoparticle encapsulated mRNA

description: Messenger RNA (mRNA)-based therapeutics have emerged as a promising modality for various clinical applications, necessitating robust methods for mRNA quantification. This biodistribution study compares the performance of branched DNA and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays for measuring lipid nanoparticle-encapsulated mRNA. Following intravenous administration of nascent peptide imaging luciferase mRNA (1 mg/kg) to rats, mRNA levels in various tissues and serum were quantified using both assays. Statistical analyses, including Bland–Altman, Deming regression and Passing–Bablok regression, were employed to assess method comparability and reproducibility. The results indicated that mRNA pharmacokinetics measured by branched DNA and RT-qPCR were largely consistent across tissues, with RT-qPCR showing greater reproducibility across multiple laboratories. RT-qPCR also demonstrated a wider dynamic range and higher sensitivity, making it a more versatile option for large-scale studies. Despite some differences in data due to tissue types and timepoints, both methods provided comparable pharmacokinetic profiles for mRNA quantification. This study underscores the importance of selecting an appropriate quantification method based on study requirements and highlights RT-qPCR’s adaptability for multisite research, especially for the clinical development of mRNA-based therapeutics. With the emergence of messenger RNA (mRNA) as a therapeutic modality with a vast number of clinical applications, accurate quantification methods are needed for development and of mRNA-based therapeutics. Our study demonstrated a comprehensive comparison between two bioanalytical methods – branched DNA (bDNA) and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) for quantifying lipid nanoparticle encapsulated mRNA in rat tissues and serum. RT-qPCR provided generally consistent results across laboratories, suggesting it as a more reproducible method for mRNA quantification. Given the similar results obtained by both methods, researchers and developers can choose between bDNA and RT-qPCR based on specific study requirements, such as sensitivity. This study highlighted the importance of method transferability, with RT-qPCR being more adaptable across different laboratories, which is critical for large-scale and multisite studies.
DOI: 10.15468/dl.e9wftq
title: Occurrence Download

description: A dataset containing 1452 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (France, Spain, Andorra)",
 "TaxonKey is Petrocoptis A.Braun ex Endl."
 ]
}

 The dataset includes 1452 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025846-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1285/i30350107n3p159
title: Bibliografia
DOI: 10.25418/crick.28759961.v1
title: LATTICE: A federation solution scalable to UK research linking technical capability and information governance

description: This white paper outlines LATTICE, a proposed federation solution that the Crick has developed to enable Trusted Research Environments (TRES) and Secure Data Environments (SDEs) / Safe Havens to federate. It provides a solution to bring together data from different Data Zone curators (SDEs / Safe havens / Bespoke sets), matched to a single approach to Information Governance. This design has been shared with the UK research community through multiple channels, and which has gained traction as the target for the NHS OneLondon SN-SDE and other external environments. LATTICE is built to operate at scale, and is designed to pragmatically integrate existing investments.

The LATTICE platform integrates a small set of hyperscale, enterprise solutions to provide a tailorable, repeatable and secure fabric for projects to quickly convert approved projects into scientific outputs and societal gain. Delivering the fastest (and speeding up) “time to research” is a unique selling point and one that offers £100m in value for money returns.
DOI: 10.15468/dl.78vmze
title: Occurrence Download

description: A dataset containing 12 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'ES'
 AND (occurrence.level0gid IN ('ESP.12_1', 'ESP.17_1', 'ESP.16_1', 'ESP.6_1', 'ESP.8_1', 'ESP.5_1', 'ESP.4_1', 'ESP.3_1', 'ESP.15_1') OR occurrence.level1gid IN ('ESP.12_1', 'ESP.17_1', 'ESP.16_1', 'ESP.6_1', 'ESP.8_1', 'ESP.5_1', 'ESP.4_1', 'ESP.3_1', 'ESP.15_1') OR occurrence.level2gid IN ('ESP.12_1', 'ESP.17_1', 'ESP.16_1', 'ESP.6_1', 'ESP.8_1', 'ESP.5_1', 'ESP.4_1', 'ESP.3_1', 'ESP.15_1') OR occurrence.level3gid IN ('ESP.12_1', 'ESP.17_1', 'ESP.16_1', 'ESP.6_1', 'ESP.8_1', 'ESP.5_1', 'ESP.4_1', 'ESP.3_1', 'ESP.15_1'))
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.taxonkey = 5219173 OR occurrence.acceptedtaxonkey = 5219173 OR occurrence.kingdomkey = 5219173 OR occurrence.phylumkey = 5219173 OR occurrence.classkey = 5219173 OR occurrence.orderkey = 5219173 OR occurrence.familykey = 5219173 OR occurrence.genuskey = 5219173 OR occurrence.specieskey = 5219173)
 AND (occurrence."year" &gt;= 2014 AND occurrence."year" &lt;= 2025)
 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.specieskey IS NOT NULL AND occurrence."year" 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,
 occurrence."year"

 The dataset's 12 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016880-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2x56ja
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Simpsonichthys santanae (Shibatta & Garavello, 1992)"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012774-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a3zkhc
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CAN.2_1",
 "TaxonKey is Collomia tenella A.Gray"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058861-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtztg4
title: Occurrence Download

description: A dataset containing 2423 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echites P.Browne"
 ]
}

 The dataset includes 2423 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048457-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.49zxlt
title: Occurrence Download

description: A dataset containing 270 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation"
 ]
 },
 {
 "or" : [
 "Country is Mexico",
 "Country is Tanzania, United Republic of",
 "Country is Ethiopia",
 "Country is Benin",
 "Country is Uganda",
 "Country is Brazil",
 "Country is El Salvador",
 "Country is Malawi",
 "Country is Nicaragua",
 "Country is Zambia"
 ]
 },
 "Year 2000-2018",
 {
 "or" : [
 "DatasetKey is CABI Africa Invasive and Alien Species data",
 "DatasetKey is Tropicos MO Specimen Data",
 "DatasetKey is iNaturalist Research-grade Observations",
 "DatasetKey is Census of the species of Benin. Data mobilized in the framework of JRS Benin project and funded by JRS Biodiversity Foundation",
 "DatasetKey is ASE - Herbário da Universidade Federal de Sergipe",
 "DatasetKey is Flora y vegetación de la región terrestre prioritaria Tacaná-Boquerón, Chiapas, México",
 "DatasetKey is Census of medical plant species of Benin (West Africa) by SOS Biodiversity",
 "DatasetKey is African Plants - a photo guide",
 "DatasetKey is Asociación Jardín Botánico La Laguna - Herbarium LAGU",
 "DatasetKey is Naturalis Biodiversity Center (NL) - Botany"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Jatropha curcas L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 270 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012774-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdc1vb
title: Occurrence Download

description: A dataset containing 2662 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus densiflora Siebold & Zucc."
 ]
}

 The dataset includes 2662 records from 114 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014593-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ds4ex6
title: Occurrence Download

description: A dataset containing 217 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Azerbaijan",
 "Country is unknown or invalid",
 "Country is Iran (Islamic Republic of)",
 "Country is Georgia",
 "Country is Armenia",
 "Country is Türkiye",
 "Country is Russian Federation"
 ]
 },
 "TaxonKey is Zelkova carpinifolia (Pall.) C.Koch"
 ]
}

 The dataset includes 217 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021736-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eizqod
title: Occurrence Download

description: A dataset containing 409469 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((-180 20,180 20,180 90,-180 90,-180 20))",
 "Geometry POLYGON((-180 -20,180 -20,180 90,-180 90,-180 -20))"
 ]
 },
 "TaxonKey is Sedum L."
 ]
}

 The dataset includes 409469 records from 741 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014593-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2nyo6i
title: Occurrence Download

description: A dataset listing the 4511 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Adiantum L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 4511 records were derived from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012774-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdvcsc
title: Occurrence Download

description: A dataset containing 407096 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is BirdMap Data - GPS tracking of Storks, Cranes and birds of prey, breeding in Northern and Eastern Europe"
 ]
}

 The dataset includes 407096 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021737-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qyhzc7
title: Occurrence Download

description: A dataset containing 153582 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ligustrum vulgare L."
 ]
}

 The dataset includes 153582 records from 542 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014594-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0199120.10fu
title: SH0199120.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199121.10fu
title: SH0199121.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199122.10fu
title: SH0199122.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199123.10fu
title: SH0199123.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199124.10fu
title: SH0199124.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0199125.10fu
title: SH0199125.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.