cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29571964.v1
title: Comparative Genomic and Transcriptomic Analysis of Aquaporin Gene Family Members in <i>Dalbergia</i> Species

description: Aquaporins (AQPs) are channel proteins that play a pivotal role in transport of water and other small neutral solutes. However, molecular aspects of AQPs in <i>Dalbergia</i> species remain scarce. A comparative genomic and transcriptomic investigation of the AQP gene family identified a total of 147 genes encoding AQPs in seven species of <i>Dalbergia</i> and these were classified into five subfamilies: 50 plasma membrane intrinsic proteins (PIPs), 44 nodulin-like intrinsic proteins (NIPs), 44 tonoplast intrinsic proteins (TIPs), 4 small basic intrinsic proteins (SIPs) and 5 X-intrinsic proteins (XIPs). Among the AQPs, analysis of conserved motifs suggested that aquaporins exhibit a high degree of genetic conservation. Protein–protein interactions have revealed that PIPs might interact with Glycerol-3-phosphate dehydrogenase protein to facilitate the transport of water and small neutral solutes across cell membranes. GO annotation suggested AQPs’ involvement in transmembrane transporter activity in response to abiotic stress signals in response to fluoride, salt, or inorganic substances. Furthermore, miR5251 and miR5234 were identified as frequently targeting AQPs to control the post-transcriptional expression of these genes. 3D structures, and post-translational modifications deduced the regulation of gene expression of aquaporins. Transcriptomic data suggested differential expression of AQPs under wounding stress in <i>D. odorifera</i>. The expression patterns of <i>DsiAQP-05</i> and <i>DsiAQP-09</i> exhibited variations in dieback resistant and susceptible <i>D. sissoo</i> plants during drought stress conditions in the RT-PCR analysis. These findings are significant for understanding the molecular functions of AQPs and basis for genetic improvement of <i>Dalbergia</i> plant species against abiotic stress tolerance in future.
DOI: 10.5281/zenodo.16572077
title: mIF_serial_slides_example

description: A dataset example of 2 serial slide of multiplex-immunofluorescence for IntegrAlign.Both slides have the same staining : DAPI, CLEC9A, BDCA2, CLEC10A, CD8, CK (Cytokeratin) and DCLAMP.The segmentation and phenotyping as been performed with InForm.The .txt is the original table of coordinates.The .csv is the processed file for IntegrAlign with chosen cell type and coordinates in pixels that match the image.
DOI: 10.5281/zenodo.16572078
title: mIF_serial_slides_example

description: A dataset example of 2 serial slide of multiplex-immunofluorescence for IntegrAlign.Both slides have the same staining : DAPI, CLEC9A, BDCA2, CLEC10A, CD8, CK (Cytokeratin) and DCLAMP.The segmentation and phenotyping as been performed with InForm.The .txt is the original table of coordinates.The .csv is the processed file for IntegrAlign with chosen cell type and coordinates in pixels that match the image.
DOI: 10.15468/dl.835hwz
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bactrocera invadens",
 "Country is Central African Republic",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085362-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/d1a-bez-0t1
title: Ostracoda Latreille, 1802

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/emp-epj-6nc
title: †Cycloclypeus eidae Tan, 1930

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/52a-c4j-8s7
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/azh-ehk-qyv
title: Cephalopoda Cuvier, 1795

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.16904438
title: بلا عنوان (1).pdf

description: Uploaded via Corsati app
DOI: 10.5281/zenodo.16904439
title: بلا عنوان (1).pdf

description: Uploaded via Corsati app
DOI: 10.3535/6l0-kwh-0x9
title: †Nummulites Lamarck, 1801

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ybs-fdf-nss
title: †Lepidocyclina parvula Cushman, 1919

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5d6-4fr-xf1
title: †Strophomenata Williams et al., 1996

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ckj-4l2-gq1
title: Rhynchonellata Williams, Carlson, Brunton, Holmer & Popov, 1996

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zv7-mmt-wn6
title: Cephalopoda Cuvier, 1795

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/e79-h15-s3l
title: Pachyseris Milne Edwards & Haime, 1849

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ljn-x7t-mbh
title: †Confusastraraea obsoleta (Gerth, 1921)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/j3k-jgn-mak
title: †Dielasma King, 1856

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/t3f-mwj-a53
title: Acrioceras

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.q4nb5u
title: Occurrence Download

description: A dataset containing 978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "OccurrenceStatus is Present",
 "TaxonKey is Delphinium cardinale"
 ]
}

 The dataset includes 978 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023337-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/7j0-shs-vyw
title: †Viviparus lentus (Solander, 1766)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/997-v1g-sx1
title: Aegocrioceras

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qmp-bhx-583
title: †Heterastridium conglobatum Reuss, 1865

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wsm-76h-s33
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wy6-r1k-bx5
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/156-3k2-0gm
title: Coleoidea Bather, 1888

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ewj-mlw-9wy
title: Ostrea Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/apq-7bc-pxb
title: Bivalvia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zca-6nj-jb4
title: Echinodermata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/fs2-l2b-dlg
title: Anatomites

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/zv7-lht-nqv
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0yn-b7a-jec
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xp1-tcf-ghq
title: Crinoidea Miller, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3fs-qlm-p87
title: †Hoplitoplacenticeras dolbergense (Schluter, 1876)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/5s0-q7h-hf0
title: Echinoidea Schumacher, 1817

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cgr-hn6-nbv
title: †Timorocidaris sphaeracantha Wanner, 1920

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0nl-elf-4lv
title: †Chonetes Fischer de Waldheim, 1830

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3d8-qaj-m1v
title: †Prosocoelus W. Keferstein, 1857

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.2hh5b6
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Burkina Faso",
 "TaxonKey is Eichhornia crassipes"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023339-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/d3s-14m-h48
title: Echinodermata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cly-0dv-hvv
title: Echinodermata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/r8g-xwa-sl0
title: †Melanopsis bonellii Manzoni, 1870

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gjx-8b0-n2p
title: †Gryphaea Lamarck, 1801

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/h3g-axj-ssm
title: †Gryphaea Lamarck, 1801

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3886/e230061
title: Department of Veterans Affairs Open Data Portal State Summary for Virginia

description: Map and tables with data describing Virginia's veteran population. Data tables cover: Veterans Affairs (VA) facilities and expenditures; veteran population including age distribution, period of service, household income, and educational attainment; veterans' use of VA healthcare and benefits; and projected veteran population changes as well as projections by age, gender, period of service, race/ethnicity, and age distribution over time.
DOI: 10.3886/e230061v1
title: Department of Veterans Affairs Open Data Portal State Summary for Virginia

description: Map and tables with data describing Virginia's veteran population. Data tables cover: Veterans Affairs (VA) facilities and expenditures; veteran population including age distribution, period of service, household income, and educational attainment; veterans' use of VA healthcare and benefits; and projected veteran population changes as well as projections by age, gender, period of service, race/ethnicity, and age distribution over time.
DOI: 10.15468/dl.jun9wz
title: Occurrence Download

description: A dataset containing 415 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carabus regalis Fischer von Waldheim, 1820"
 ]
}

 The dataset includes 415 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002077-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/rh4c-7m95
title: "FLEMS with PVZEN"

description: "Data from FLEMS execution in PVZE microgrid lab"
DOI: 10.17909/hvkk-dj46
title: JWST WCd images from paper
DOI: 10.4121/3fa366a0-20bd-4d7e-9243-78959527fb07
title: Data underlying the study on Diversity of α-acetolactate decarboxylase in the Saccharomycotina yeast subphylum: From discovery to brewing application.

description: Diacetyl, a vicinal diketone with a low sensory threshold, is a prominent off-flavour in beer, necessitating extended lagering to allow its reduction to non-flavour-active compounds. In brewing, bacterial α-acetolactate decarboxylases are commonly used to mitigate diacetyl formation by converting its precursor, α-acetolactate, directly into acetoin. Here, we report the first discovery and characterization of functional α-acetolactate decarboxylases enzymes of eukaryotic origin, specifically from yeasts within the Saccharomycotina subphylum. Using a homology-based search against fungal genomic databases, 29 candidate genes were identified across 18 yeast species from only three genera (<em>Lipomyces</em>, <em>Dipodascus</em> and <em>Wickerhamiella</em>) and classified into distinct phylogenetic groups. Phylogenetic analysis revealed both fungal and possible bacterial origins, suggesting evolutionary conservation and horizontal gene transfer events. Seven genes were heterologously expressed in <em>Saccharomyces pastorianus</em> lager brewing strains. Fermentation trials in both lab-scale septum flasks and E.B.C. tall tubes demonstrated that yeast-derived α-acetolactate decarboxylases significantly reduced diacetyl levels, with some performing comparably or superior to the benchmark <em>Brevibacillus brevis</em> enzyme. These strains also showed normal fermentation kinetics and produced beers with diacetyl concentrations below sensory thresholds, effectively eliminating the need for extended lagering. Our findings uncover a previously unrecognized enzymatic activity in budding yeasts and present yeast α-acetolactate decarboxylases as promising non-bacterial alternatives to improve process efficiency and sustainability in lager beer production.
DOI: 10.4121/3fa366a0-20bd-4d7e-9243-78959527fb07.v1
title: Data underlying the study on Diversity of α-acetolactate decarboxylase in the Saccharomycotina yeast subphylum: From discovery to brewing application.

description: Diacetyl, a vicinal diketone with a low sensory threshold, is a prominent off-flavour in beer, necessitating extended lagering to allow its reduction to non-flavour-active compounds. In brewing, bacterial α-acetolactate decarboxylases are commonly used to mitigate diacetyl formation by converting its precursor, α-acetolactate, directly into acetoin. Here, we report the first discovery and characterization of functional α-acetolactate decarboxylases enzymes of eukaryotic origin, specifically from yeasts within the Saccharomycotina subphylum. Using a homology-based search against fungal genomic databases, 29 candidate genes were identified across 18 yeast species from only three genera (<em>Lipomyces</em>, <em>Dipodascus</em> and <em>Wickerhamiella</em>) and classified into distinct phylogenetic groups. Phylogenetic analysis revealed both fungal and possible bacterial origins, suggesting evolutionary conservation and horizontal gene transfer events. Seven genes were heterologously expressed in <em>Saccharomyces pastorianus</em> lager brewing strains. Fermentation trials in both lab-scale septum flasks and E.B.C. tall tubes demonstrated that yeast-derived α-acetolactate decarboxylases significantly reduced diacetyl levels, with some performing comparably or superior to the benchmark <em>Brevibacillus brevis</em> enzyme. These strains also showed normal fermentation kinetics and produced beers with diacetyl concentrations below sensory thresholds, effectively eliminating the need for extended lagering. Our findings uncover a previously unrecognized enzymatic activity in budding yeasts and present yeast α-acetolactate decarboxylases as promising non-bacterial alternatives to improve process efficiency and sustainability in lager beer production.
DOI: 10.5281/zenodo.15446612
title: Data and Model Supplement: Driver Analysis of Subarctic Wildfire Severity over a 35-year Period

description: Large subarctic wildfires are causing environmental damage, releasing stored carbon, and forcing residents to relocate. Subarctic ecosystems are experiencing earlier, longer, and more intense wildfire seasons due in part to factors such as warmer winters and the broader spread of damaging insects. There is limited agreement within existing research on the type of drivers and degree of influence that climate, vegetation and topographic factors have on wildfire severity. As a result, existing research has been limited through using small datasets and applying a limited number of drivers. This study aims to address these gaps by improving upon existing methodological limitations and quantifying the influence of a more comprehensive list of climate, vegetation, and topographic variables on wildfire severity. An XGBoost regression model with an r2 of 0.7 was trained, and shapely values were used to investigate the combined variable contributions to predictions of wildfire severity. This research identified variable importance trends unique to predicting subarctic wildfire severity in rugged regions with cold annual temperatures and short growing seasons. Important variables not previously identified include, skin reservoir content, evaporation from vegetation transpiration, wind exposition index, soil temperature, and visible sky percentage, in addition to variables found by existing research, namely pre-fire vegetation, wind speed, topographic position index and land cover. This research helps to build consensus on the factors driving severe wildfires in subarctic ecosystems, and the methods developed could become the basis for future study.
DOI: 10.5281/zenodo.15446613
title: Data and Model Supplement: Driver Analysis of Subarctic Wildfire Severity over a 35-year Period

description: Large subarctic wildfires are causing environmental damage, releasing stored carbon, and forcing residents to relocate. Subarctic ecosystems are experiencing earlier, longer, and more intense wildfire seasons due in part to factors such as warmer winters and the broader spread of damaging insects. There is limited agreement within existing research on the type of drivers and degree of influence that climate, vegetation and topographic factors have on wildfire severity. As a result, existing research has been limited through using small datasets and applying a limited number of drivers. This study aims to address these gaps by improving upon existing methodological limitations and quantifying the influence of a more comprehensive list of climate, vegetation, and topographic variables on wildfire severity. An XGBoost regression model with an r2 of 0.7 was trained, and shapely values were used to investigate the combined variable contributions to predictions of wildfire severity. This research identified variable importance trends unique to predicting subarctic wildfire severity in rugged regions with cold annual temperatures and short growing seasons. Important variables not previously identified include, skin reservoir content, evaporation from vegetation transpiration, wind exposition index, soil temperature, and visible sky percentage, in addition to variables found by existing research, namely pre-fire vegetation, wind speed, topographic position index and land cover. This research helps to build consensus on the factors driving severe wildfires in subarctic ecosystems, and the methods developed could become the basis for future study.
DOI: 10.15468/dl.rd44w5
title: Occurrence Download

description: A dataset containing 30035 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((38.94019 -10.07047, 39.90966 -10.07047, 39.90966 -7.427288, 38.94019 -7.427288, 38.94019 -10.07047))"
 ]
}

 The dataset includes 30035 records from 188 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012777-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25451/flinders.29192594
title: Cross-sector collaboration in childhood obesity prevention initiative

description: Cross-sector collaboration in childhood obesity prevention initiative
DOI: 10.25451/flinders.29192594.v1
title: Cross-sector collaboration in childhood obesity prevention initiative

description: Cross-sector collaboration in childhood obesity prevention initiative
DOI: 10.15468/dl.97bnrc
title: Occurrence Download

description: A dataset containing 449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gentiana nipponica Maxim.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is Japan"
 ]
}

 The dataset includes 449 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001862-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jrpn8w
title: Occurrence Download

description: A dataset containing 236 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of North Carolina Asheville"
 ]
}

 The dataset includes 236 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001861-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.47z9ex
title: Occurrence Download

description: A dataset containing 1521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Didemnum vexillum Romanov, 1989, Didemnum vexillum Kott, 2002)"
 ]
}

 The dataset includes 1521 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001860-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15477913
title: HAVEN Datasets

description: This folder contains the datasets used for pre-training, fine-tuning, and evaluating HAVEN: Hierarchical Attention for Viral protEin-based host iNference. 

HAVEN is a viral protein language model pre-trained on 1.2 million protein sequences belonging to Viridae (viruses). It is fine-tuned to predict the hosts from which the viral protein sequence was sampled ("virus-host").

The viral protein sequences are downloaded from UniRef90. The host for the viral sequences are from the European Nucleotide Archive (ENA) maintained by European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI).
DOI: 10.5281/zenodo.15477914
title: HAVEN Datasets - Deprecated

description: deprecated
DOI: 10.5281/zenodo.15540019
title: HAVEN Datasets

description: This folder contains the datasets used for the pre-training, fine-tuning, and evaluation of HAVEN: Hierarchical Attention for Viral protEin-based host iNference. 

HAVEN is a viral protein language model pre-trained on 1.2 million protein sequences belonging to Viridae (viruses). It is fine-tuned to predict the hosts from which the viral protein sequence was sampled ("virus-host").

The viral protein sequences are downloaded from UniRef90. The host for the viral sequences are from the European Nucleotide Archive (ENA) maintained by European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI).