cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5x63bj2c
title: MassIVE MSV000098186 - Murine Colon MS Imaging for Metabolomics, with DAN and DHB datasets
title: These data are imaging data files collected on a Bruker timsTOF fleX, focused on MS imaging metabolomic analyses of murine (WT C57BL/6) colons. 4 animal replicates were collected, with a single proximal, mid, and distal colon sample collected from each mouse (12 total). Each of these 12 samples were cryo-sectioned and mounted onto two separate slides for spraying with one of two different MALDI matrices (DHB and DAN), creating 24 total slides. The files here are the raw data files collected during MSI acquisition, as well as corresponding method files.
DOI: 10.15468/dl.ussbg2
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenophora pulchella De Toni, 1892",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043398-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wsx28s
title: Occurrence Download

description: A dataset containing 1534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dioscorea acanthogene Rusby, Dioscorea alainii Raz, Dioscorea amazonum Mart. ex Griseb.)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1534 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043401-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m8fkst
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lipocarpha chinensis (Osbeck) J.Kern",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043400-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ra863u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenophora pulchella De Toni, 1892",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043402-250525065834625/datasets/export for details.
DOI: 10.15468/dl.j8a59z
title: Occurrence Download

description: A dataset containing 4313 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lithobates vaillanti (Brocchi, 1877)"
 ]
}

 The dataset includes 4313 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043403-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25810/37f9-1j65.87
title: Spanish Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - May 2025

description: The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.15644287
title: Simulating Seasonal Evolution of Subglacial Hydrology at a Surging Glacier in the Karakoram

description: Description of files contained in this archive:

 

SHAKTI simulation code

Winter spin up: winter_spin_up.m

-              Runs winter base state simulation that serves as a basis for transient seasonal simulations

 

Transient seasonal simulation: transient_seasonal_uncoupled.m

-              Runs SHAKTI with seasonal meltwater inputs

 

Downscaling weather data & ablation model: UIBDownscaling.R

-              Downscales ERA-5 Land based on HydroBasins

 

Plotting: plots_standalone_paper.m

 

Sensitivity tests

Mesh resolution tests

1.        Results/datafiles: wsu_20m.mat, wsu_40m.mat, wsu_50m.mat, wsu_100m.mat, wsu_150m.mat, wsu_200m.mat

2.        Plotting and comparison: Compare_mesh_resolution_tests.m

 

Creep/basal temperature tests

1.        Plotting and comparison: creep_sensitivity.m

 

Parameterization Files

-              Neosha_standalone.par

o   Contains extra parameters that go into the winter spin-up

 

Exp/geometry files

-              Shishper domain outline: Neosha17_4.exp

 

Melt forcings

-              Total_melt_liquidp_mmd.mat

o   Contains snow melt, ice melt, and liquid precipitation across Shishper model domain (see UIBDownscaling.R)

 

SHAKTI model outputs

-              2017 (first and second passes): transientmelt_2017_1.mat, transientmelt_2017_2.mat

-              2018: transientmelt_2018.mat

-              2019: transientmelt_2019.mat

-              2020: transientmelt_2020.mat

-              2021: transientmelt_2021.mat

NOTE: updated code may be available at https://github.com/neoshanarayanan/subglacialhydro-surges-karakoram.
DOI: 10.5281/zenodo.15644288
title: Simulating Seasonal Evolution of Subglacial Hydrology at a Surging Glacier in the Karakoram

description: Description of files contained in this archive:

 

SHAKTI simulation code

Winter spin up: winter_spin_up.m

-              Runs winter base state simulation that serves as a basis for transient seasonal simulations

 

Transient seasonal simulation: transient_seasonal_uncoupled.m

-              Runs SHAKTI with seasonal meltwater inputs

 

Downscaling weather data & ablation model: UIBDownscaling.R

-              Downscales ERA-5 Land based on HydroBasins

 

Plotting: plots_standalone_paper.m

 

Sensitivity tests

Mesh resolution tests

1.        Results/datafiles: wsu_20m.mat, wsu_40m.mat, wsu_50m.mat, wsu_100m.mat, wsu_150m.mat, wsu_200m.mat

2.        Plotting and comparison: Compare_mesh_resolution_tests.m

 

Creep/basal temperature tests

1.        Plotting and comparison: creep_sensitivity.m

 

Parameterization Files

-              Neosha_standalone.par

o   Contains extra parameters that go into the winter spin-up

 

Exp/geometry files

-              Shishper domain outline: Neosha17_4.exp

 

Melt forcings

-              Total_melt_liquidp_mmd.mat

o   Contains snow melt, ice melt, and liquid precipitation across Shishper model domain (see UIBDownscaling.R)

 

SHAKTI model outputs

-              2017 (first and second passes): transientmelt_2017_1.mat, transientmelt_2017_2.mat

-              2018: transientmelt_2018.mat

-              2019: transientmelt_2019.mat

-              2020: transientmelt_2020.mat

-              2021: transientmelt_2021.mat

NOTE: updated code may be available at https://github.com/neoshanarayanan/subglacialhydro-surges-karakoram.
DOI: 10.15468/dl.bnskpd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lipocarpha chinensis (Osbeck) J.Kern",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043405-250525065834625/datasets/export for details.
DOI: 10.15468/dl.ngb4n4
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenophora pulchella De Toni, 1892",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043406-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15395348
title: Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19

description: Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. 

Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. 

The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.

When using the toolbox, please cite this record and the associated article.

 

 
description: The code is written in python v3. Required packages to run: PyBioC (https://github.com/2mh/PyBioC), pandas v1.4.0 or higher, spacy v2.2
DOI: 10.5281/zenodo.6642274
title: Files and code for English dictionaries, gold and silver standard corpora for biomedical natural language processing related to SARS-CoV-2 and COVID-19

description: Automated information extraction with natural language processing (NLP) tools is required to gain systematic insights from the large number of COVID-19 publications, reports and social media posts, which far exceed human processing capabilities. 

Here we present an NLP toolbox comprising COVID-19-related dictionaries and annotated corpora in English as well as useful code and workflows for their update and use. The dictionaries contain terms referring to the COVID-19 disease, the SARS-CoV-2 virus, its variants and common mutations, respectively. They were used together with the EasyNER NLP tool to extract and annotate all 764 398 abstracts in the CORD-19 dataset, creating a very large silver standard corpus (named Lund-Annotated-CORD-19 corpus). This was complemented with a small gold standard corpus consisting of PubMed abstracts manually annotated for key entity classes such as disease, virus, symptom, protein/gene, cell type, chemical and species terms. 

The toolbox can support various text analysis tasks related to COVID-19 such as named entity recognition and co-mention analysis. A preliminary version of the toolbox, which was released early in the pandemic, was for example already used to create a COVID-19 knowledge graph and study the evolution and variation of COVID-19-related terminology. In addition, the toolbox can be applied in the development of other NLP tools, for example to train and evaluate large language models.

When using the toolbox, please cite this record and the associated article.

 

 
description: The code is written in python v3. Required packages to run: PyBioC (https://github.com/2mh/PyBioC), pandas v1.4.0 or higher, spacy v2.2
DOI: 10.15468/dl.n45xgk
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lipocarpha chinensis (Osbeck) J.Kern",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((32.064 -28.9416,32.4847 -28.5786,30.5701 -28.5786,30.5701 -30.7329,30.6252 -30.6659,31.4179 -29.4257,31.8178 -29.0299,32.064 -28.9416))"
 ]
}

 The dataset includes 25 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043407-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.446xvm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenophora pulchella De Toni, 1892",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((32.064 -28.9416,32.4847 -28.5786,30.5701 -28.5786,30.5701 -30.7329,30.6252 -30.6659,31.4179 -29.4257,31.8178 -29.0299,32.064 -28.9416))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043408-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15657859
title: Kennisinventarisatie WP3.1 CAMBIUM

description: Voor het Interreg-project CAMBIUM werd de huidige kennis over agroforestry geïnventariseerd uit Vlaanderen en Nederland. 

De kennisinventarisatie is voltooid door alle partners, en zodoende hebben we ook een actueel overzicht van beschikbare kennismaterialen relevant voor boslandbouw in Vlaanderen en Nederland. Volgend op de kennisinvenstarisatie volgde een analyse voor elk thematisch kennisveld:  elke bron werd getoetst op enkele criteria, waaronder (1) open access, (2) gebruisvriendelijke en begrijpelijke inhoud, (3) correcte, lang houdbare en betrouwbare inhoud, (4) relevant voor bedrijfsbegeleiding/train the trainers (gerelateerd aan WP5 en WP6), (5) relevant voor infotheek/wegwijzer uit taak 3 binnen WP3, en (6) vertaalslag waardevol (indien bron niet in het Nederlands was). Hiervoor was een beoordelingsmatrix (schaal van 1 tot en met 3; van slecht naar matig/neutraal tot zeer goed) uitgewerkt. 



De kennisinventarisatie is te vinden in de Excel ''Kennisinventarisatie CAMBIUM januari 2025''.


Daarnaast werd de kennisinventarisatie onderworpen aan een grondige analyse, waarbij we per thema, subcategorie en subsubcategorie keken hoeveel bronnen ertoe behoorden en welke onderwerpen daarbij het meest of minst aan bod kwamen. Op die manier konden we duidelijk zien welke onderwerpen al goed gedocumenteerd zijn de afgelopen jaren en waar juist de kennishiaten nog liggen. 



De resultaten zijn gevisualiseerd in grafieken. Deze zijn terug te vinden in de Excel ''Sjabloon Kennisinventarisatie Cambium RESULTATEN JANUARI 2025''.
DOI: 10.5281/zenodo.15657858
title: Kennisinventarisatie WP3.1 CAMBIUM

description: Voor het Interreg-project CAMBIUM werd de huidige kennis over agroforestry geïnventariseerd uit Vlaanderen en Nederland. 

De kennisinventarisatie is voltooid door alle partners, en zodoende hebben we ook een actueel overzicht van beschikbare kennismaterialen relevant voor boslandbouw in Vlaanderen en Nederland. Volgend op de kennisinvenstarisatie volgde een analyse voor elk thematisch kennisveld:  elke bron werd getoetst op enkele criteria, waaronder (1) open access, (2) gebruisvriendelijke en begrijpelijke inhoud, (3) correcte, lang houdbare en betrouwbare inhoud, (4) relevant voor bedrijfsbegeleiding/train the trainers (gerelateerd aan WP5 en WP6), (5) relevant voor infotheek/wegwijzer uit taak 3 binnen WP3, en (6) vertaalslag waardevol (indien bron niet in het Nederlands was). Hiervoor was een beoordelingsmatrix (schaal van 1 tot en met 3; van slecht naar matig/neutraal tot zeer goed) uitgewerkt. 



De kennisinventarisatie is te vinden in de Excel ''Kennisinventarisatie CAMBIUM januari 2025''.


Daarnaast werd de kennisinventarisatie onderworpen aan een grondige analyse, waarbij we per thema, subcategorie en subsubcategorie keken hoeveel bronnen ertoe behoorden en welke onderwerpen daarbij het meest of minst aan bod kwamen. Op die manier konden we duidelijk zien welke onderwerpen al goed gedocumenteerd zijn de afgelopen jaren en waar juist de kennishiaten nog liggen. 



De resultaten zijn gevisualiseerd in grafieken. Deze zijn terug te vinden in de Excel ''Sjabloon Kennisinventarisatie Cambium RESULTATEN JANUARI 2025''.
DOI: 10.5285/371f680f-2c0b-4558-9a85-12aa8722be7e
title: Annual diatom abundance and diversity from the Jurassic ice core, Antarctic Peninsula (1873-2012 CE)

description: This dataset provides information on the annual abundance and diversity of diatoms preserved in ice layers from a Jurassic ice core that was drilled in 2012 from the Southern Antarctic Peninsula. The diatom record spans the entire depth of the ice core (0-140 meters) and represents the interval from 1873 to 2012 CE. Meltwater from the annual layers of the ice core was filtered, and the filters were analyzed using a scanning electron microscope for the identification and classification of diatoms. This dataset was utilized to reconstruct past changes in the westerly winds of the Southern Hemisphere, with the results presented in Tetzner et al. (2025).

Funding: 
This research was funded by CONICYT-Becas Chile and Cambridge Trust funding programme for PhD studies under grant number 72180432.
description: The Jurassic ice core was drilled in the austral summer 2012/2013 CE to a depth of 140m using the British Antarctic Survey (BAS) electromechanical, 104 mm diameter drill. An ice chronology was established for this core based on the annual cycles of hydrogen peroxide (H2O2) and the non-sea salt component of sulphate (nssSO42-). The estimated dating error was &lt;+/-7 months for each year and with no accumulated error. Diatom samples were obtained by filtering meltwater from annual layers of the Jurassic ice core. Jurassic ice core meltwater was filtered through 1.0 micro-meter pore size filters, which were later imaged using a Quanta-650F Scanning Electron Microscope (SEM) at the Earth Sciences Department, University of Cambridge. Diatom identification and ecological associations were based on published Southern Ocean databases and references therein (Armand et al., 2005; Hasle and Syvertsen, 1997; Cefarelli et al., 2010; Zielinski and Gersonde, 1997).
description: Quanta-650F Scanning Electron Microscope (SEM) at the Earth Sciences Department, University of Cambridge.
description: Diatom samples were processed and analysed according to the methods and constraints outlined in Tetzner et al. (2021) and Tetzner et al. (2025). Annual layer meltwater from Jurassic ice core was filtered using polycarbonate membrane filters with a pore diameter of 1.0 ?m. After filtration, the filters were scanned using a Scanning Electron Microscope (SEM). One-third of the filter area was scanned, in line with the observations from Tetzner et al. (2021) and Tetzner et al. (2025), which noted that diatoms tend to have a homogeneous spatial distribution when the counts over one-third of the filter area are six or more. The main diatom assemblage composition was established from the identified species and groups with abundances higher than 2.0% of the whole assemblage.
DOI: 10.25345/c5sf2mq6n
title: MassIVE MSV000098187 - Structural variability of apolipoprotein A-I amyloid fibrils across organs, mutations, and clinical presentations, revealed by cryo-EM
title: All these samples are fibrils isolated from patient tissues of different organs. Intact Mass Analysis: ID 1175196 - ApoAI-R173P - Heart ID 1175197 - ApoAI-R173P - Kidney ID 1175198 - ApoAI-R173P - Liver ID 1175199 - ApoAI-R173P - Spleen ID 1175200 - ApoAI-G26R - Kidney ID 1175201 - ApoAI-G26R - Liver ID 1175202 - ApoAI-G26R - Spleen ID 1175203 - ApoAI-L90P - Heart Trypsin digestion, LC-MS/MS: ID 1151139 - ApoAI-R173P - Heart, Contain both Wild type and mutant Arg173Pro ID 1175177 - ApoAI-R173P - Kidney, Contain both Wild type and mutant Arg173Pro ID 1175178 - ApoAI-R173P - Liver, Contain both Wild type and mutant Arg173Pro ID 1175179 - ApoAI-R173P - Spleen, Contain both Wild type and mutant Arg173Pro ID 1175180 - ApoAI-G26R - Kidney, Contain both Wild type and mutant Gly26Arg ID 1175181 - ApoAI-G26R - Liver, Contain both Wild type and mutant Gly26Arg ID 1175182 - ApoAI-G26R - Spleen, Contain both Wild type and mutant Gly26Arg ID 1175183 - ApoAI-L90P - Heart, Contain both Wild type and mutant Leu90Pro GluC digestion, LC-MS/MS ID 1160261 - ApoAI-R173P - Heart, Contain both Wild type and mutant Arg173Pro