cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18608360
title: Impact de la politique budgétaire sur la réduction de la pauvreté en République Démocratique Du Congo (2020–2024)

description: La pauvreté constitue un défi structurel majeur en République Démocratique du Congo (RDC), en dépit de l’abondance de ses ressources naturelles. Entre 2020 et 2024, le pays a été confronté à plusieurs chocs économiques et sociaux, notamment la pandémie de COVID-19, l’instabilité sécuritaire persistante dans certaines régions et des contraintes budgétaires accrues. Ces facteurs ont fortement influencé la conception et la mise en œuvre de la politique budgétaire nationale. Face à cette situation, les autorités congolaises ont adopté diverses mesures budgétaires destinées à soutenir l’activité économique et à atténuer la pauvreté, à travers l’augmentation des dépenses sociales, les investissements publics et certains programmes de soutien aux populations vulnérables. Cet article analyse l’impact de la politique budgétaire sur la réduction de la pauvreté en RDC sur la période 2020–2024, en s’appuyant sur une analyse documentaire et empirique des données budgétaires et socio-économiques. Les résultats indiquent que, malgré une hausse notable des dépenses publiques, les effets sur la réduction de la pauvreté restent limités. Cette situation s’explique principalement par des faiblesses structurelles de l’économie, des insuffisances dans la gouvernance des finances publiques ainsi qu’une faible efficacité dans l’allocation et l’exécution des ressources budgétaires.
DOI: 10.5281/zenodo.18608362
title: Impact de la politique budgétaire sur la réduction de la pauvreté en République Démocratique Du Congo (2020–2024)

description: La pauvreté constitue un défi structurel majeur en République Démocratique du Congo (RDC), en dépit de l’abondance de ses ressources naturelles. Entre 2020 et 2024, le pays a été confronté à plusieurs chocs économiques et sociaux, notamment la pandémie de COVID-19, l’instabilité sécuritaire persistante dans certaines régions et des contraintes budgétaires accrues. Ces facteurs ont fortement influencé la conception et la mise en œuvre de la politique budgétaire nationale. Face à cette situation, les autorités congolaises ont adopté diverses mesures budgétaires destinées à soutenir l’activité économique et à atténuer la pauvreté, à travers l’augmentation des dépenses sociales, les investissements publics et certains programmes de soutien aux populations vulnérables. Cet article analyse l’impact de la politique budgétaire sur la réduction de la pauvreté en RDC sur la période 2020–2024, en s’appuyant sur une analyse documentaire et empirique des données budgétaires et socio-économiques. Les résultats indiquent que, malgré une hausse notable des dépenses publiques, les effets sur la réduction de la pauvreté restent limités. Cette situation s’explique principalement par des faiblesses structurelles de l’économie, des insuffisances dans la gouvernance des finances publiques ainsi qu’une faible efficacité dans l’allocation et l’exécution des ressources budgétaires.
DOI: 10.15468/dl.gqwhfj
title: Occurrence Download

description: A dataset containing 249966 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversitätsdatenbank Nationalpark Hohe Tauern"
 ]
}

 The dataset includes 249966 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008093-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pwrj7m
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tometes kranponhah",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 14 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008107-260208012135463/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.31313995
title: Composilator_SupportingData

description: Accurate prediction of composite stiffness before fabrication remains difficult because real polymer composites deviate from classical mixture rules due to morphology, dispersion, and interphase effects. Micromechanical models provide physical bounds but cannot capture systematic morphology-induced bias, while unconstrained machine-learning models often fail to generalize across composite families.This dataset underpins <b>ComposiLator</b>, a hybrid AI–mechanistic framework that refines the Voigt–Reuss–Hill midpoint using a physically constrained machine-learning correction factor informed by structured descriptors.The curated corpus spans structurally independent polymer composite systems across diverse morphologies and reinforcement levels, enabling cross-family validation.Using this dataset, ComposiLator reduced normalized prediction error by 86.5% (MAE = 0.784 GPa), achieved positive cross-system correlation (R² = 0.645), and outperformed Halpin–Tsai formulations within physically reasonable parameter ranges.
DOI: 10.6084/m9.figshare.31313995.v1
title: Composilator_SupportingData

description: Accurate prediction of composite stiffness before fabrication remains difficult because real polymer composites deviate from classical mixture rules due to morphology, dispersion, and interphase effects. Micromechanical models provide physical bounds but cannot capture systematic morphology-induced bias, while unconstrained machine-learning models often fail to generalize across composite families.This dataset underpins <b>ComposiLator</b>, a hybrid AI–mechanistic framework that refines the Voigt–Reuss–Hill midpoint using a physically constrained machine-learning correction factor informed by structured descriptors.The curated corpus spans structurally independent polymer composite systems across diverse morphologies and reinforcement levels, enabling cross-family validation.Using this dataset, ComposiLator reduced normalized prediction error by 86.5% (MAE = 0.784 GPa), achieved positive cross-system correlation (R² = 0.645), and outperformed Halpin–Tsai formulations within physically reasonable parameter ranges.
DOI: 10.18150/op0op7
title: Chemical and magnetic properties of wood biomass growing in different environmental conditions

description: The data contain results from chemical and magnetic analyses of ash samples obtained from the combustion of wood biomass in a laboratory muffle furnace. The analyzed biomass originated from nine tree species growing under different environmental conditions (bedrock, soil, waste, dust deposition). Samples of wood, bark, and small twigs were analyzed. The aim of the study was to determine how environmental growing conditions influence the ecological quality of potential fuels derived from wood biomass.
DOI: 10.15468/dl.xewdnw
title: Occurrence Download

description: A dataset containing 3005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Bassia laniflora"
 ]
}

 The dataset includes 3005 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008109-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.31313989
title: Supplementary information files for "Simultaneous and sensitive quantification of protein and low molecular weight persulfides, polysulfides and H2S in biological samples"

description: Supplementary files for article "Simultaneous and sensitive quantification of protein and low molecular weight persulfides, polysulfides and H2S in biological samples"H<sub>2</sub>S reversibly modifies low molecular weight (L<sub>MW</sub>SH) and protein (PrSH) thiols to form persulfides (RSS<sup>−</sup> ) and polysulfides (RS(S)<sub>n</sub>S − ) for antioxidant defence and regulation of activity. However, our understanding of the biological significance of these processes is hampered by our inability to quantify these modifications. We develop a sensitive LC-MS/MS procedure that traps the sulfur atom of H 2 S, and the terminal sulfur atom of RSS − and RS(S) n S − as diagnostic products in biological samples. In parallel, we also trap internal S atoms of RS(S) n S − , enabling quantification of H<sub>2</sub>S, RSS<sup>−</sup> and RS(S)<sub>n</sub>S<sup>−</sup> . L<sub>MW</sub>S(S)<sub>n</sub>S − and PrS(S)<sub>n</sub>S<sup>−</sup> are determined simultaneously in the same sample. Glutathione (GSH) is the most abundant L MW SH so we develop an orthogonal approach to quantify GSS<sup>−</sup> , enabling corroboration of L<sub>MW</sub>SS − measurements by sulfur atom trapping. We demonstrate in systems from proteins to ex vivo tissues how these approaches enable exploration of persulfidation in biological systems.© The Authors, CC-BY 4.0 <br>
DOI: 10.17028/rd.lboro.31313989.v1
title: Supplementary information files for "Simultaneous and sensitive quantification of protein and low molecular weight persulfides, polysulfides and H2S in biological samples"

description: Supplementary files for article "Simultaneous and sensitive quantification of protein and low molecular weight persulfides, polysulfides and H2S in biological samples"H<sub>2</sub>S reversibly modifies low molecular weight (L<sub>MW</sub>SH) and protein (PrSH) thiols to form persulfides (RSS<sup>−</sup> ) and polysulfides (RS(S)<sub>n</sub>S − ) for antioxidant defence and regulation of activity. However, our understanding of the biological significance of these processes is hampered by our inability to quantify these modifications. We develop a sensitive LC-MS/MS procedure that traps the sulfur atom of H 2 S, and the terminal sulfur atom of RSS − and RS(S) n S − as diagnostic products in biological samples. In parallel, we also trap internal S atoms of RS(S) n S − , enabling quantification of H<sub>2</sub>S, RSS<sup>−</sup> and RS(S)<sub>n</sub>S<sup>−</sup> . L<sub>MW</sub>S(S)<sub>n</sub>S − and PrS(S)<sub>n</sub>S<sup>−</sup> are determined simultaneously in the same sample. Glutathione (GSH) is the most abundant L MW SH so we develop an orthogonal approach to quantify GSS<sup>−</sup> , enabling corroboration of L<sub>MW</sub>SS − measurements by sulfur atom trapping. We demonstrate in systems from proteins to ex vivo tissues how these approaches enable exploration of persulfidation in biological systems.© The Authors, CC-BY 4.0 <br>
DOI: 10.18419/darus-5705
title: Atmospheric transmission models for FIFI-LS: altitude 35kft, 39deg O3

description: &lt;p&gt;This dataset contains atmospheric transmission models calculated by a modified version of ATRAN ("SDC ATRAN"). They are to be used as the default models for FIFI-LS data reduction with &lt;a href="https://github.com/SOFIA-Data-Center/sofia_redux"&gt;SOFIA Redux&lt;/a&gt;
&lt;/p&gt;&lt;p&gt;
The models are generated from a modified "SDC ATRAN" model based on Steve Lord's ATRAN. The most significant modification is a correction of &lt;sup&gt;17&lt;/sup&gt;O&lt;sup&gt;16&lt;/sup&gt;O isotopologue abundance coefficients.
&lt;/p&gt;&lt;p&gt;
The models are stored in FITS binary table format with the columns "wavelength" and "transmission". All models of the same altitude (and same wavelength range) have the same number of wavelength elements.
&lt;/p&gt;&lt;p&gt;
File naming convention:
&lt;pre&gt;
atran_sdc_xxK_yydeg_zzpwv_39deg_2nlayer_40-300mum_bt.fits
&lt;/pre&gt;
where &lt;i&gt;xx&lt;/i&gt; is the flight altitude in kft, &lt;i&gt;yy&lt;/i&gt; is the zenith angle in degree, and &lt;i&gt;zz&lt;/i&gt; the
precipitable water vapor value in micron.
&lt;/p&gt;&lt;p&gt;
This dataset contains models calculated for a 2 layer atmospheric model with an ozone profile identified by "39deg".&lt;/p&gt;
DOI: 10.15468/dl.bfb5eb
title: Occurrence Download

description: A dataset containing 564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Simethis mattiazzii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 564 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008110-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbvv4s
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tometes siderocarajensis",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008111-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/f75943dc-4404-4784-8445-ceee37f141ad
title: Data supporting the publication: Adaptive gene transcription in Escherichia coli under environmental stress

description: This is data supporting the paper titled,"Adaptive gene transcription in <em>Escherichia coli</em> under environmental stress"it consists of:1.Raw data files of RT-qPCR experiments of the <em>hdeAB </em>and <em>proVWX </em>operon and their flanking genes in the MG1655 Δ<em>endA</em> (WT,NT331), MG1655 Δ<em>endA </em>Δ<em>stpA</em> (Δ<em>stpA, </em>NT633), MG1655 Δ<em>endA</em> Δ<em>hns</em> (Δ<em>hns, </em>NT794), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> (Δ<em>hns</em> Δ<em>stpA, </em>XT3236), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS</em> (Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS, </em>XT3238), MG1655 Δ<em>endA</em> Δ<em>rpoS </em>(<em>ΔrpoS</em>, XT3132)<em> </em>in different pH (pH6.6/2.0)or osmotic condition (NaCl 0.08 M/ 0.08 M to 0.3M).2.Raw data files of ChIP-qPCR experiments of the <em>hdeAB </em>and <em>proVWX </em>operon and its flanking genes in the MG1655 Δ<em>endA</em>; <em>hns</em>::<em>hns</em>-3XFLAG (NT796), MG1655 Δ<em>endA</em>; <em>stpA</em>::<em>stpA</em>-3XFLAG (NT797), MG1655 Δ<em>endA</em>;<em>hns</em>::<em>hns</em>-3XFLAG; <em>stpA::kanRccdB </em>(NT866), MG1655Δ<em>endA</em>; <em>stpA</em>::<em>stpA</em>-3XFLAG; <em>hns::kanRccdB </em>(NT867) at pH 6.6 and pH 2.0.3.Raw data files of 3C-qPCR experiments of the <em>hdeAB </em>and<em> proVWX </em>operon and their flanking region in different pH (pH6.6/2.0)or osmotic condition (NaCl 0.08 M/ 0.08 M to 0.3M). For hdeAB operon, we use the transcription start site fragment (fragment 10 in our study) as the anchor fragment in MG1655 Δ<em>endA</em> (WT,NT331), MG1655 Δ<em>endA </em>Δ<em>stpA</em> (Δ<em>stpA, </em>NT633), MG1655 Δ<em>endA</em> Δ<em>hns</em> (Δ<em>hns, </em>NT794), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> (Δ<em>hns</em> Δ<em>stpA, </em>XT3236), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS</em> (Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS, </em>XT3238), MG1655 Δ<em>endA</em> Δ<em>rpoS </em>(<em>ΔrpoS</em>, XT3132).4.Agarose gel images in this study. 5.All the raw data used for figure generation in RT-qPCR, 3C-qPCR, ChIP-qPCR, MST, and bridging assays.
DOI: 10.4121/f75943dc-4404-4784-8445-ceee37f141ad.v1
title: Data supporting the publication: Adaptive gene transcription in Escherichia coli under environmental stress

description: This is data supporting the paper titled,"Adaptive gene transcription in <em>Escherichia coli</em> under environmental stress"it consists of:1.Raw data files of RT-qPCR experiments of the <em>hdeAB </em>and <em>proVWX </em>operon and their flanking genes in the MG1655 Δ<em>endA</em> (WT,NT331), MG1655 Δ<em>endA </em>Δ<em>stpA</em> (Δ<em>stpA, </em>NT633), MG1655 Δ<em>endA</em> Δ<em>hns</em> (Δ<em>hns, </em>NT794), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> (Δ<em>hns</em> Δ<em>stpA, </em>XT3236), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS</em> (Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS, </em>XT3238), MG1655 Δ<em>endA</em> Δ<em>rpoS </em>(<em>ΔrpoS</em>, XT3132)<em> </em>in different pH (pH6.6/2.0)or osmotic condition (NaCl 0.08 M/ 0.08 M to 0.3M).2.Raw data files of ChIP-qPCR experiments of the <em>hdeAB </em>and <em>proVWX </em>operon and its flanking genes in the MG1655 Δ<em>endA</em>; <em>hns</em>::<em>hns</em>-3XFLAG (NT796), MG1655 Δ<em>endA</em>; <em>stpA</em>::<em>stpA</em>-3XFLAG (NT797), MG1655 Δ<em>endA</em>;<em>hns</em>::<em>hns</em>-3XFLAG; <em>stpA::kanRccdB </em>(NT866), MG1655Δ<em>endA</em>; <em>stpA</em>::<em>stpA</em>-3XFLAG; <em>hns::kanRccdB </em>(NT867) at pH 6.6 and pH 2.0.3.Raw data files of 3C-qPCR experiments of the <em>hdeAB </em>and<em> proVWX </em>operon and their flanking region in different pH (pH6.6/2.0)or osmotic condition (NaCl 0.08 M/ 0.08 M to 0.3M). For hdeAB operon, we use the transcription start site fragment (fragment 10 in our study) as the anchor fragment in MG1655 Δ<em>endA</em> (WT,NT331), MG1655 Δ<em>endA </em>Δ<em>stpA</em> (Δ<em>stpA, </em>NT633), MG1655 Δ<em>endA</em> Δ<em>hns</em> (Δ<em>hns, </em>NT794), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> (Δ<em>hns</em> Δ<em>stpA, </em>XT3236), MG1655 Δ<em>endA</em> Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS</em> (Δ<em>hns</em> Δ<em>stpA</em> Δ<em>rpoS, </em>XT3238), MG1655 Δ<em>endA</em> Δ<em>rpoS </em>(<em>ΔrpoS</em>, XT3132).4.Agarose gel images in this study. 5.All the raw data used for figure generation in RT-qPCR, 3C-qPCR, ChIP-qPCR, MST, and bridging assays.
DOI: 10.5281/zenodo.17043534
title: Electron Spectroscopy for Chemical Analysis of Liquids

description: Dataset pertaining to the article "Electron Spectroscopy for Chemical Analysis of Liquids", published in Chemical Science (https://doi.org/10.1039/D5SC09061J).

The following files are provided:

As-measured photoelectron spectra:

RAW_SPECTRA.zip

Data are sorted into two measurement campaigns (April 2024 and December 2024).

File naming code: <COMPOUND>-<SPECTRAL_REGION>-<NUMBER_OF_REPEATED_MEASUREMENT>

 

Computational input and output files.

CALCULATIONS.zip

Data are sorted into (i) calculations providing CHELPG charges, Mulliken charges, and orbital energies, and (ii) calculations providing Merz-Singh-Kollman charges.

File naming code: <COMPOUND>

 

Numeric representations (space-separated text files) of the traces shown in the article's Figures 1, 2, 5, and S1:

FIGURE_TRACES.zip

 

Version history:2: Raw experimental data, figure traces, and computational inputs and outputs uploaded.1: Initial upload

Contact person for questions regarding this dataset: Lukas Tomanik, tomanikl@vscht.cz . If you use these data for your scientific work, we are curious to learn about it.
description: Acknowledgements: We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III. Data was collected using the soft X-ray beamline P04 operated/provided by DESY Photon Science. We would like to thank the P04 beamline staff, particularly Dr Moritz Hoesch, for assistance during the experiments. We thank current group members, Dr Bruno Credidio, Dr Dominik Stemer, Dr Nicolas Velasquez, Dr Uwe Hergenhahn, Qi Zhou, Michele Pugini, and Harmanjot Kaur, as well as Henrik Haak and Sebastian Kray, for their assistance with maintaining, transporting, and setting up our EASI instrument for experiments at PETRA III. We thank Dr Ian Wilkinson for the valuable discussion on calibrating experimental data. We thank Prof. Martin Dračínský for the valuable feedback on the NMR-related parts of the manuscript. Wethank Prof. Gerard Meijer for his continuous support of the LJ-PES project at the Fritz-Haber-Institut. L.T. acknowledges the support of the Czech Science Foundation, project number 2412267O (POSTDOC INDIVIDUAL FELLOWSHIP OUTGOING). F.T. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project 509471550, Emmy Noether Programme. P.S. was supported by the Czech Science Foundation (EXPRO project no. 2126601X). B.W. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 883759, AQUACHIRAL).
DOI: 10.5281/zenodo.18608140
title: Electron Spectroscopy for Chemical Analysis of Liquids

description: Dataset pertaining to the article "Electron Spectroscopy for Chemical Analysis of Liquids", published in Chemical Science (https://doi.org/10.1039/D5SC09061J).

The following files are provided:

As-measured photoelectron spectra:

RAW_SPECTRA.zip

Data are sorted into two measurement campaigns (April 2024 and December 2024).

File naming code: <COMPOUND>-<SPECTRAL_REGION>-<NUMBER_OF_REPEATED_MEASUREMENT>

 

Computational input and output files.

CALCULATIONS.zip

Data are sorted into (i) calculations providing CHELPG charges, Mulliken charges, and orbital energies, and (ii) calculations providing Merz-Singh-Kollman charges.

File naming code: <COMPOUND>

 

Numeric representations (space-separated text files) of the traces shown in the article's Figures 1, 2, 5, and S1:

FIGURE_TRACES.zip

 

Version history:2: Raw experimental data, figure traces, and computational inputs and outputs uploaded.1: Initial upload

Contact person for questions regarding this dataset: Lukas Tomanik, tomanikl@vscht.cz . If you use these data for your scientific work, we are curious to learn about it.
description: Acknowledgements: We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III. Data was collected using the soft X-ray beamline P04 operated/provided by DESY Photon Science. We would like to thank the P04 beamline staff, particularly Dr Moritz Hoesch, for assistance during the experiments. We thank current group members, Dr Bruno Credidio, Dr Dominik Stemer, Dr Nicolas Velasquez, Dr Uwe Hergenhahn, Qi Zhou, Michele Pugini, and Harmanjot Kaur, as well as Henrik Haak and Sebastian Kray, for their assistance with maintaining, transporting, and setting up our EASI instrument for experiments at PETRA III. We thank Dr Ian Wilkinson for the valuable discussion on calibrating experimental data. We thank Prof. Martin Dračínský for the valuable feedback on the NMR-related parts of the manuscript. Wethank Prof. Gerard Meijer for his continuous support of the LJ-PES project at the Fritz-Haber-Institut. L.T. acknowledges the support of the Czech Science Foundation, project number 2412267O (POSTDOC INDIVIDUAL FELLOWSHIP OUTGOING). F.T. acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project 509471550, Emmy Noether Programme. P.S. was supported by the Czech Science Foundation (EXPRO project no. 2126601X). B.W. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 883759, AQUACHIRAL).
DOI: 10.5281/zenodo.18608430
title: phyllodesmium_magnum_spades_02.fasta.gz
DOI: 10.15468/dl.hnsq7z
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Year is greater than or equal to 1900",
 "TaxonKey is Bassia muricata"
 ]
}

 The dataset includes 346 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008113-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.