cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/ayp-0ez-s0p
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/bja-cq5-x2y
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lmd-2js-2ff
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/rk8-xd3-2y3
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wde-h4g-t9b
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/19q-tsc-vvx
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z3l-zwe-08h
title: Parnassius

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ls6-w9k-yrq
title: Nymphalidae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/lx5-kaf-tqa
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/284-vhd-n7a
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/98a-ke1-d6f
title: Aporia agathon (Gray, 1831)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0jv-r8c-1pa
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/q7q-a1x-gbr
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qyw-lvj-gfz
title: Colotis euippe (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/xmw-lr3-hn6
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/142-afz-ftl
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ncq-h2a-h8q
title: Lepidoptera Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.5281/zenodo.17399363
title: Nanopore signals of the  DNAformer study

description: This repository contains the complete dataset of raw Nanopore signals supporting the publication:

Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. Nat Mach Intell, 7, 639–649 (2025). https://www.nature.com/articles/s42256-025-01003-z.

Dataset description

The repository includes the raw Nanopore signals in two formats: binned and original (unordered).

 

Binned format: 

Each text file corresponds to a specific cluster and is named according to the cluster’s 12-base index (see publication for details). The file contains:





All basecalled reads assigned to that cluster




Their associated read IDs (unique identifiers of the signals)




The corresponding FAST5 file in which each signal can be found



Original (unordered) format: 

The raw signals from the pilot experiments are provided directly in this repository.The raw Nanopore signals of the test datasets are available separately in the following Zenodo repository:https://zenodo.org/records/13896773.
DOI: 10.5281/zenodo.17399364
title: Nanopore signals of the  DNAformer study

description: This repository contains the complete dataset of raw Nanopore signals supporting the publication:

Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. Nat Mach Intell, 7, 639–649 (2025). https://www.nature.com/articles/s42256-025-01003-z.

Dataset description

The repository includes the raw Nanopore signals in two formats: binned and original (unordered).

 

Binned format: 

Each text file corresponds to a specific cluster and is named according to the cluster’s 12-base index (see publication for details). The file contains:





All basecalled reads assigned to that cluster




Their associated read IDs (unique identifiers of the signals)




The corresponding FAST5 file in which each signal can be found



Original (unordered) format: 

The raw signals from the pilot experiments are provided directly in this repository.The raw Nanopore signals of the test datasets are available separately in the following Zenodo repository:https://zenodo.org/records/13896773.
DOI: 10.15468/dl.bk8d4w
title: Occurrence Download

description: A dataset containing 1018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is one of (Karnataka, Maharashtra, Andhra Pradesh, Telangana)",
 "TaxonKey is Halcyon pileata"
 ]
}

 The dataset includes 1018 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006360-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17463866
title: Data and code: Mapping wild bee diversity and flower use for an effective conservation of the Doñana biodiversity hotspot

description: Final check list with all observations of wild bees in the Doñana protected area along with the code to process it and analyse it.
DOI: 10.5281/zenodo.17473301
title: Data and code: Mapping wild bee diversity and flower use for an effective conservation of the Doñana biodiversity hotspot

description: Final check list with all observations of wild bees in the Doñana protected area along with the code to process it and analyse it.
DOI: 10.15468/dl.v99zhx
title: Occurrence Download

description: A dataset containing 614 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, 0.0) eeacellcode,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, 0.0), "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, 0.0), "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 (CAST(occurrence.taxonkey AS INTEGER) = 7579898 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 7579898 OR CAST(occurrence.kingdomkey AS INTEGER) = 7579898 OR CAST(occurrence.phylumkey AS INTEGER) = 7579898 OR CAST(occurrence.classkey AS INTEGER) = 7579898 OR CAST(occurrence.orderkey AS INTEGER) = 7579898 OR CAST(occurrence.familykey AS INTEGER) = 7579898 OR CAST(occurrence.genuskey AS INTEGER) = 7579898 OR CAST(occurrence.specieskey AS INTEGER) = 7579898)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence.hascoordinate = TRUE)
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",
 GBIF_EEARGCODE(1000, decimallatitude, decimallongitude, 0.0)

 The dataset's 614 records were derived from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006339-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17473835
title: Dataset for "The effect of perfluorosulfonic acid membrane molecular structure on excessive swelling in the context of PEM water electrolysis at elevated temperature and pressure"

description: Increasing the operating temperature and pressure of proton exchange membrane water electrolysis (PEM WE) enhances energy efficiency at high hydrogen production rates, but membrane stability is a limiting factor in this approach. Excessive water uptake (excessive swelling) is an often-overlooked phenomenon in PEM water electrolysis field, despite its significant impact on membrane properties and system performance. In this study, four commercial PEMs were selected to investigate effects of side chain length and equivalent weight on their excessive swelling. Membrane samples were exposed to liquid water at 120 °C and 600 kPa for 800 hours, while in-plane resistivity was continuously monitored. All membranes showed a gradual increase in resistivity over time. Before and after the hydrothermal treatment, the membranes were characterized using small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), and atomic force microscopy (AFM). The results show that swelling is mainly governed by membrane hydrophilicity and that, unlike previous reports, exceeding the α-transition temperature is not required for excessive swelling to occur. Long side chains were associated with a lesser degree of resistivity increase, which was connected to larger hydrophilic domains that likely promote a more favourable mesoscale morphology at very high water contents. Our study showed that while excessive water uptake at elevated temperature and pressure represents serious stability issues. Careful design of the membrane's internal morphology might help mitigate these undesirable effects.
DOI: 10.5281/zenodo.17473836
title: Dataset for "The effect of perfluorosulfonic acid membrane molecular structure on excessive swelling in the context of PEM water electrolysis at elevated temperature and pressure"

description: Increasing the operating temperature and pressure of proton exchange membrane water electrolysis (PEM WE) enhances energy efficiency at high hydrogen production rates, but membrane stability is a limiting factor in this approach. Excessive water uptake (excessive swelling) is an often-overlooked phenomenon in PEM water electrolysis field, despite its significant impact on membrane properties and system performance. In this study, four commercial PEMs were selected to investigate effects of side chain length and equivalent weight on their excessive swelling. Membrane samples were exposed to liquid water at 120 °C and 600 kPa for 800 hours, while in-plane resistivity was continuously monitored. All membranes showed a gradual increase in resistivity over time. Before and after the hydrothermal treatment, the membranes were characterized using small-angle X-ray scattering (SAXS), differential scanning calorimetry (DSC), and atomic force microscopy (AFM). The results show that swelling is mainly governed by membrane hydrophilicity and that, unlike previous reports, exceeding the α-transition temperature is not required for excessive swelling to occur. Long side chains were associated with a lesser degree of resistivity increase, which was connected to larger hydrophilic domains that likely promote a more favourable mesoscale morphology at very high water contents. Our study showed that while excessive water uptake at elevated temperature and pressure represents serious stability issues. Careful design of the membrane's internal morphology might help mitigate these undesirable effects.
DOI: 10.15468/dl.jwhuv8
title: Occurrence Download

description: A dataset containing 77983 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is Geodetic datum assumed WGS84",
 "IucnRedListCategory is one of (CR, EN, VU)",
 "OccurrenceStatus is Present",
 "Year 2015-2025"
 ]
}

 The dataset includes 77983 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006308-251025141854904/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.30478505
title: Data for: Climate-driven biomass allocation patterns in herbaceous plants of Northern China’s drylands

description: Underlying data for the manuscript 'Climate-driven biomass allocation patterns in herbaceous plants of Northern China’s drylands' (accepted for publication in the Journal of Integrative Plant Biology). The study investigated divergent biomass allocation strategies (Optimal Partitioning Theory vs. Allometric Partitioning Theory) in temperate and alpine drylands across northern China. The dataset is provided as a single CSV file. Data were collected from 120 sites (62 temperate, 58 alpine drylands) across northern China between 2019 and 2022. Field sampling involved measurements of plant community biomass and collection of soil samples for laboratory analysis. Climate variables (MAT, MAP) were extracted from the China Meteorological Forcing Dataset (CMFD).
DOI: 10.6084/m9.figshare.30478505.v1
title: Data for: Climate-driven biomass allocation patterns in herbaceous plants of Northern China’s drylands

description: Underlying data for the manuscript 'Climate-driven biomass allocation patterns in herbaceous plants of Northern China’s drylands' (accepted for publication in the Journal of Integrative Plant Biology). The study investigated divergent biomass allocation strategies (Optimal Partitioning Theory vs. Allometric Partitioning Theory) in temperate and alpine drylands across northern China. The dataset is provided as a single CSV file. Data were collected from 120 sites (62 temperate, 58 alpine drylands) across northern China between 2019 and 2022. Field sampling involved measurements of plant community biomass and collection of soil samples for laboratory analysis. Climate variables (MAT, MAP) were extracted from the China Meteorological Forcing Dataset (CMFD).
DOI: 10.5281/zenodo.17472733
title: Dataset of "Navigating Chiral Spin Architectures in Non-Collinear Antiferromagnetic Thin Films"

description: Noncollinear antiferromagnets offer much promise for antiferromagnetic spintronics and neuromorphic applications with a plethora of functional properties surpassing many competing magnetic systems. Films grown on mismatched substrates may relieve strain by the creation of slip-plane defects that strongly manipulate global physical properties important for application. This work demonstrates that a post growth annealing strategy results in near-defect-free, structurally robust films that reveal the underlying thermal evolution of the magnetic order symmetry. Beyond a critical film thickness, the spin structure transitions between two right-handed irreducible chiral representations via left-handed chiral ordering producing a striking change in the sign and angular dependence of the Anomalous Hall coefficient. The previously established mechanism of spin rotations in the (111) plane cannot fully explain the transition in applied magnetic field, and using a macrospin model this work finds that rotations along the chirality-inverting [1-10] direction are energetically preferable under certain conditions. These observations suggest that both left-handed and right-handed chiral order can be accessed in a single system, opening new routes to engineer devices by controlled switching of magnetic chirality allowing selection of associated functional properties as governed by symmetry.
DOI: 10.5281/zenodo.17472734
title: Dataset of "Navigating Chiral Spin Architectures in Non-Collinear Antiferromagnetic Thin Films"

description: Noncollinear antiferromagnets offer much promise for antiferromagnetic spintronics and neuromorphic applications with a plethora of functional properties surpassing many competing magnetic systems. Films grown on mismatched substrates may relieve strain by the creation of slip-plane defects that strongly manipulate global physical properties important for application. This work demonstrates that a post growth annealing strategy results in near-defect-free, structurally robust films that reveal the underlying thermal evolution of the magnetic order symmetry. Beyond a critical film thickness, the spin structure transitions between two right-handed irreducible chiral representations via left-handed chiral ordering producing a striking change in the sign and angular dependence of the Anomalous Hall coefficient. The previously established mechanism of spin rotations in the (111) plane cannot fully explain the transition in applied magnetic field, and using a macrospin model this work finds that rotations along the chirality-inverting [1-10] direction are energetically preferable under certain conditions. These observations suggest that both left-handed and right-handed chiral order can be accessed in a single system, opening new routes to engineer devices by controlled switching of magnetic chirality allowing selection of associated functional properties as governed by symmetry.
DOI: 10.24435/materialscloud:j6-np
title: A deep learning model for chemical shieldings in molecular organic solids including anisotropy

description: Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. 
Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate, especially for nuclei such as 13C and 15N. Here, we introduce \shiftmlthree{}, a deep-learning model thatimproves the  accuracy of predictions of isotropic chemical shieldings  in molecular solids, and does so while also predicting the full shielding tensor. 
On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for 1H, 2.4 ppm for 13C, and 7.2 ppm for 15N, compared to DFT values of 0.49 ppm, 2.3 ppm, and 5.8 ppm, respectively.
DOI: 10.24435/materialscloud:kt-kd
title: A deep learning model for chemical shieldings in molecular organic solids including anisotropy

description: Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. 
Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate, especially for nuclei such as 13C and 15N. Here, we introduce \shiftmlthree{}, a deep-learning model thatimproves the  accuracy of predictions of isotropic chemical shieldings  in molecular solids, and does so while also predicting the full shielding tensor. 
On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for 1H, 2.4 ppm for 13C, and 7.2 ppm for 15N, compared to DFT values of 0.49 ppm, 2.3 ppm, and 5.8 ppm, respectively.
DOI: 10.15468/dl.htsv89
title: Occurrence Download

description: A dataset containing 2089 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((4.25573 45.8583,4.24132 45.85628,4.23584 45.84887,4.23624 45.83156,4.24349 45.81367,4.25573 45.80482,4.27128 45.80328,4.28078 45.80707,4.28827 45.81134,4.29174 45.8173,4.29536 45.82906,4.29609 45.83727,4.29069 45.85008,4.28376 45.86079,4.27764 45.86579,4.26524 45.8649,4.25896 45.86103,4.25735 45.85926,4.25573 45.8583))",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 2089 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006362-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zfu49t
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is one of (Karnataka, Maharashtra, Andhra Pradesh, Telangana)",
 "TaxonKey is Ficedula subrubra"
 ]
}

 The dataset includes 72 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006364-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5na6a
title: Occurrence Download

description: A dataset containing 129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Heliomaster constantii leocadiae"
 ]
}

 The dataset includes 129 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006365-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d6wcp5
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "StateProvince is one of (Karnataka, Maharashtra, Andhra Pradesh, Telangana)",
 "TaxonKey is Duttaphrynus brevirostris"
 ]
}

 The dataset includes 16 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006366-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pne2ma
title: Occurrence Download

description: A dataset containing 284349 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ulmus minor",
 "HasCoordinate is true"
 ]
}

 The dataset includes 284349 records from 2328 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006319-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25921/mjze-r973
title: EM712 Water Column Sonar Data Collected During W00739