cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vmjptvdcgw.1
title: UEQ Evaluation of "Olomouc's Changes over Time" Map

description: The aim of this evaluation was to assess the usability and overall user experience of the application using standardized UEQ measures and eye-tracking methods, and to refine the application based on the identified strengths and weaknesses.
User experience was assessed using the User Experience Questionnaire (UEQ), a standardized 26-item semantic differential instrument measuring six dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. In addition to scale-level scores, two aggregated dimensions were calculated: pragmatic quality (goal-oriented usability) and hedonic quality (engagement and innovation).
The dataset shows a very positive overall user experience, with all six scales falling into the excellent category when compared to the official UEQ benchmark database (21,175 respondents, 468 studies). The highest-rated dimension was Perspicuity (M = 2.43), followed by Attractiveness (M = 2.35), Efficiency (M = 2.30), and Stimulation (M = 2.08). Dependability (M = 1.98) and Novelty (M = 1.75) were also clearly above neutral. Aggregated scores indicated strong pragmatic quality (M = 2.23) and positive hedonic quality (M = 1.92), placing the evaluated system within approximately the top 10% of products in the benchmark dataset.
Behavioral intention data further supported these findings, with all participants indicating willingness to use the application again.
DOI: 10.5281/zenodo.18432073
title: Dataset: Late Bronze Age metalwork from the pile-dwelling settlements of Western Switzerland

description: Research database including CSV tables and a SQLite (.db) database. The files and their organization are described in the provided Readme.
DOI: 10.5281/zenodo.18432072
title: Dataset: Late Bronze Age metalwork from the pile-dwelling settlements of Western Switzerland

description: Research database including CSV tables and a SQLite (.db) database. The files and their organization are described in the provided Readme.
DOI: 10.6084/m9.figshare.31346959
title: S3: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S2
DOI: 10.6084/m9.figshare.31346959.v1
title: S3: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S2
DOI: 10.6084/m9.figshare.31346965
title: S4: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S3
DOI: 10.6084/m9.figshare.31346965.v1
title: S4: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S3
DOI: 10.6084/m9.figshare.31346968
title: S2: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S1
DOI: 10.6084/m9.figshare.31346968.v1
title: S2: High-resolution chemostratigraphy of the Bonarelli Level (OAE 2) in the Umbria–Marche Basin (central Italy)

description: Supplementary Table S1
DOI: 10.6084/m9.figshare.31346935
title: Soil bacterial diversity in Southern China

description: Database of bacterial diversity in Southern China acidic soils.
DOI: 10.6084/m9.figshare.31346935.v1
title: Soil bacterial diversity in Southern China

description: Database of bacterial diversity in Southern China acidic soils.
DOI: 10.57745/xsa0vu
title: R_script_vegetation.R

description: script R développé pour construire le jeu de données
DOI: 10.15468/dl.d28pcc
title: Occurrence Download

description: A dataset containing 406 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 Eriachne ovata"
 ]
}

 The dataset includes 406 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021872-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/mhkoiu
title: vegetation_style_raster.qml

description: style QGIS pour visualiser le fichier raster
DOI: 10.57745/8c0lp3
title: vegetation_methodologie_construction.pdf

description: Méthode utilisée pour créer le jeu de données
DOI: 10.57745/clfr6x
title: vegetation_style_vecteur.sld

description: style QGIS pour visualiser le fichier vecteur
DOI: 10.57745/6wcawg
title: vegetation_GrandLyon.gpkg

description: couche de végétation vecteur
DOI: 10.57745/yjnkjm
title: vegetation_GrandLyon.tif

description: couche de végétation raster
DOI: 10.57745/yxahik
title: vegetation_GrandLyon.tif

description: couche de végétation raster
DOI: 10.57745/ppfhfz
title: Végétation urbaine stratifiée à très haute résolution – Grand Lyon (2023)

description: Ce jeu de données propose une cartographie fine de la végétation urbaine de la métropole de Lyon (Grand Lyon) (résolution spatiale : 25 cm). Il identifie et classe les zones végétalisées selon leur hauteur à partir de données LiDAR et d’orthophotographies infrarouges à très haute résolution. La végétation est classifiée en 5 strates de hauteur (sans classification sémantique) : 1 : &lt;1 m, correspondant généralement à la végétation herbacée type pelouses, herbacées basses, praires 2 : 1m – 3m, correspondant généralement aux haies basses, buissons 3 : 3m - 5m, correspondant généralement aux haies hautes, arbustes, lisières de canopée des petits arbres 4 : 5m - 10m, correspondant généralement aux petites arbres et lisières de canopée des grands arbres 5 : &gt;10m, correspondant généralement au cœur de canopée des grands arbres Il a été produit à partir d’orthophotographies multispectrales à très haute résolution spatiale acquis en 2023 (5 cm) et du LiDAR HD de l’IGN acquis en 2021. Ces données sont ouvertes et disponibles respectivement aux adresses suivantes : https://ids.craig.fr/datahub/dataset/b2681a80-9d64-4a07-a6e4-83c7040719a7 et https://geoservices.ign.fr/lidarhd. Les détails méthodologiques de production du jeu de données sont disponibles (voir ci-après). Cette méthode est générique et peut être réutilisée dans d’autres aires urbaines disposant d’orthophotographies à très haute résolution (voir par exemple le cas de la métropole de Montpellier : https://doi.org/10.57745/3HK5JS). Ce jeu de données est particulièrement adapté aux analyses fines en milieu urbain continu ou dense (classes Urban Atlas « Continuous urban fabric » et « Discontinuous dense urban fabric »). Il permet notamment l’identification et la discrimination des structures linéaires de végétation, telles que les haies, ainsi que des petits patches végétalisés comme les buissons ou les pelouses. Selon le contexte géographique et les objectifs de l’étude, certaines classes de hauteur peuvent être regroupées, par exemple les classes 5–10 m et &gt; 10 m afin d’identifier les grands arbres dans leur ensemble. Pour d’autres échelles, usages ou milieux (par ex. urbain peu dense), des jeux de données alternatifs proposant des classifications sémantiques peuvent s’avérer plus appropriés, tel que le jeu de données « Nature en Ville » produit par l’IGN. Le jeu de données est accessible en flux WMS sur Geodata INRAE et en visualisation web mapping. En complément des données raster et vecteur de végétation, les fichiers suivants sont également proposés dans cette diffusion : le détail de la méthodologie de production du jeu de données. deux couches de style pour visualiser les données (respectivement aux formats vecteur et raster) sous QGIS le code R développé pour produire ce jeu de données (transposable sur d’autres territoires), Ce travail a reçu un soutien financier de la Fondation de l’Avenir.
DOI: 10.11588/duerer.online.699305306f44c
title: Maria mit langem Haar auf der Mondsichel, https://d-nb.info/gnd/1144991234
DOI: 10.5281/zenodo.18658989
title: LINF_330020100

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18658988
title: LINF_330020100

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.4121/995f218c-2149-4e88-bd55-d9411c4943d5
title: Data underlying Chapter 2, “ Role of shape in particle-lipid membrane interactions: from surfing to full engulfment”, in the PhD thesis: From Engulfment to Flexible Confinement: Giant Unilamellar Vesicles as Biomimetic Models

description: This dataset belongs to Chapter 2 of the PhD thesis of Stijn van der Ham (2026). <br>Authors: Stijn van der Ham, Hanumantha Rao Vutukuri<br>Active Soft Matter and Bio-inspired Materials Lab, Faculty of Science and Technology, MESA+ Institute, University of Twente, Enschede, The Netherlands<br>Corresponding Author: Hanumantha Rao Vutukuri (email: h.r.vutukuri@utwente.nl)<br><br><br>***General Introduction***<br>The dataset contains the experimental data, analysis, and figure files underlying the figures in <strong>Chapter 2</strong> of the PhD thesis of Stijn van der Ham.<br>This chapter has been published as: Role of shape in particle-lipid membrane interactions: from surfing to full engulfment (https://doi.org/10.1021/acsnano.3c11106)<br><br>***Methodology***<br>All data processing and analysis steps are described in detail in the Methods section of the corresponding publication, as well as in the thesis.<br>Analysis codes in the dataset can be run as is, with the exception of updating certain paths/parameters as indicated in the analysis codes.<br><br><br>***Organization of the dataset***<br>The data is organized per figure, numbered according to the thesis, with supporting figures preceded by the capital letter 'S'. <br>Within each figure folder, data is ordered as:<br>- <strong>Figure files:</strong> final figure files, provided in several formats (.ai file, .pdf file, .svg file, and/or powerpoint file)<br>- <strong>Raw data folder:</strong> raw data underlying the figure (microscopy images in the form of .tiff images/stacks/series)<br>- <strong>Analysis folder:</strong> analysis codes underlying the figure, data required for the analyses code, and codes for the generation of the plots (matlab codes or jupyter notebooks, .fig files for matlab generated figures, .svg files for python generated figures.)
DOI: 10.4121/995f218c-2149-4e88-bd55-d9411c4943d5.v1
title: Data underlying Chapter 2, “ Role of shape in particle-lipid membrane interactions: from surfing to full engulfment”, in the PhD thesis: From Engulfment to Flexible Confinement: Giant Unilamellar Vesicles as Biomimetic Models

description: This dataset belongs to Chapter 2 of the PhD thesis of Stijn van der Ham (2026). <br>Authors: Stijn van der Ham, Hanumantha Rao Vutukuri<br>Active Soft Matter and Bio-inspired Materials Lab, Faculty of Science and Technology, MESA+ Institute, University of Twente, Enschede, The Netherlands<br>Corresponding Author: Hanumantha Rao Vutukuri (email: h.r.vutukuri@utwente.nl)<br><br><br>***General Introduction***<br>The dataset contains the experimental data, analysis, and figure files underlying the figures in <strong>Chapter 2</strong> of the PhD thesis of Stijn van der Ham.<br>This chapter has been published as: Role of shape in particle-lipid membrane interactions: from surfing to full engulfment (https://doi.org/10.1021/acsnano.3c11106)<br><br>***Methodology***<br>All data processing and analysis steps are described in detail in the Methods section of the corresponding publication, as well as in the thesis.<br>Analysis codes in the dataset can be run as is, with the exception of updating certain paths/parameters as indicated in the analysis codes.<br><br><br>***Organization of the dataset***<br>The data is organized per figure, numbered according to the thesis, with supporting figures preceded by the capital letter 'S'. <br>Within each figure folder, data is ordered as:<br>- <strong>Figure files:</strong> final figure files, provided in several formats (.ai file, .pdf file, .svg file, and/or powerpoint file)<br>- <strong>Raw data folder:</strong> raw data underlying the figure (microscopy images in the form of .tiff images/stacks/series)<br>- <strong>Analysis folder:</strong> analysis codes underlying the figure, data required for the analyses code, and codes for the generation of the plots (matlab codes or jupyter notebooks, .fig files for matlab generated figures, .svg files for python generated figures.)
DOI: 10.5285/fa7887ea-5c4e-4d7a-b39f-456792f34b37
title: Ice surface velocity of lake- and land-terminating Greenland Ice Sheet outlet glaciers, 2000 and 2015 to 2019

description: This dataset contains ice surface velocity values for 102 lake- and land-terminating Greenland Ice Sheet outlet glaciers, years 2000 and 2015 to 2019, including the flowlines and sampling boxes used for data production. The dataset is provided in two folders: (i) Shapefiles and (ii) Velocity_Data.

Funding was provided by the NERC standard grant NE/S007458/1.
description: Shapefiles were produced in QGIS 3.40. Flowlines were manually digitised to follow the line of fastest flow at each lake- and land-terminating outlet glacier as seen in the ITS_LIVE 2017 velocity mosaic. Each flowline extends inland from the glacier terminus until the head of the glacier trunk is reached, or for a maximum of 10 km. To determine sampling locations, points were placed at 1500 m intervals between 500 m and 9500 m up-ice of the terminus. A 500 x 500 m box was then centred on each point to produce a chain of up to seven sampling boxes along each glacier flowline, depending on flowline length. ITS_LIVE annually averaged regional velocity mosaics (and associated error mosaics; Gardner et al., 2019) with a pixel resolution of 120 m x 120 m were sampled as the average pixel value within each sampling box. Velocity and associated error are both presented in metres per year. For a full methodology, please refer to Harpur et al. (2026).
description: Errors associated with each velocity value, extracted from the relevant ITS_LIVE annual velocity mosaic (Gardner et al., 2019), are presented in files LakeBoxError.csv and LandBoxError.csv. Note all shapefiles produced in QGIS under coordinate system WGS 84 NSIDC Sea Ice Polar Stereographic North EPSG: 3413

Missing values are indicated by NaN.
DOI: 10.18710/ui8xot/92lirr
title: 00_README.txt
DOI: 10.18710/ui8xot/njoxux
title: DATA_plant_growth_and_roots.csv

description: Data for the experiment
DOI: 10.18710/ui8xot
title: Data for: Shoot and root growth in response to hydrological fluctuations in the drought-tolerant Knautia arvensis and wet-tolerant Lythrum salicaria

description: This dataset represents a greenhouse experiment investigating the effects of fluctuating soil hydrology on plant growth and flowering, including root diameter and root length in raingardens. Plants in raingardens are important for evapotranspiration and maintaining infiltration properties. However, hydrological conditions, including cycles of dry and saturated conditions, influence plant performance and therefore affect plant selection. This study includes an experiment to assess whether fluctuating hydrology impacts growth, flowering, and root morphology in two species: the drought-tolerant Knautia arvensis and the wet-tolerant Lythrum salicaria. The results show that Knautia reduced growth under flooding cycles, while Lythrum reduced growth and flowering under drought cycles. Repeated consecutive drought and flooding cycles negatively influenced the growth of both species in the same way, with flooding affecting Knautia and drought affecting Lythrum. Lythrum showed more plasticity than Knautia, allocating more biomass to roots during drought cycles. Flooding cycles reduced root length in Knautia, while the drought cycles and consecutive Drought+Flooding cycles increased root diameter and decreased root length in Lythrum. Both species had coarse roots (&gt; 2 mm) to support infiltration through biopores. If we assume that raingardens have free drainage and only rarely flood, drought-tolerant traits are more important. However, wet-tolerant species can benefit the system, especially through higher water use.
DOI: 10.5517/ccdc.csd.cc2bgr7t
title: CSD 2159126: Experimental Crystal Structure Determination

description: Related Article: Kaitong Liu, Yuan Rao, Zhaoxue Li, Shihang Zhou, Zhi-Zhong Zhang, Yanhui Zhang|2026|Inorg.Chem.|||doi:10.1021/acs.inorgchem.5c04743
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.ye9ps2
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ukraine",
 "TaxonKey is Sparassis brevipes"
 ]
}

 The dataset includes 6 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021875-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/0a777982-f638-441d-b283-e8a84be71a2e
title: Data underlying Chapter 3, “From Shallow to Full Wrapping: Geometry and Deformability Dictate Lipid Vesicle Internalization”, in the PhD thesis: From Engulfment to Flexible Confinement: Giant Unilamellar Vesicles as Biomimetic Models

description: This dataset belongs to Chapter 3 of the PhD thesis of Stijn van der Ham (2026). <br>Authors: Stijn van der Ham, Hanumantha Rao Vutukuri<br>Active Soft Matter and Bio-inspired Materials Lab, Faculty of Science and Technology, MESA+ Institute, University of Twente, Enschede, The Netherlands<br>Corresponding Author: Hanumantha Rao Vutukuri (email: h.r.vutukuri@utwente.nl)<br><br><br>***General Introduction***<br>The dataset contains the experimental data, analysis, and figure files underlying the figures in <strong>Chapter 3</strong> of the PhD thesis of Stijn van der Ham.<br>This chapter has been published as: From Shallow to Full Wrapping: Geometry and Deformability Dictate Lipid Vesicle Internalization (https://doi.org/10.1021/acs.nanolett.5c04322)<br>Additional datasets and analysis codes corresponding to the simulations can be found with the corresponding publication, as linked above.<br><br><br>***Methodology***<br>All data processing and analysis steps are described in detail in the Methods section of the corresponding publication, as well as in the thesis.<br>Analysis codes in the dataset can be run as is, with the exception of updating certain paths/parameters as indicated in the analysis codes.<br><br><br>***Organization of the dataset***<br>The data is organized per figure, numbered according to the thesis, with supporting figures preceded by the capital letter 'S'. <br>Within each figure folder, data is ordered as:<br>- <strong>Figure files:</strong> final figure files, provided in several formats (.ai file, .pdf file, .svg file)<br>- <strong>Raw data folder:</strong> raw data underlying the figure (microscopy images in the form of .tiff images/stacks/series)<br>- <strong>Analysis folder:</strong> analysis codes underlying the figure, data required for the analyses code, and codes for the generation of the plots (jupyter notebooks, .svg files for python generated figures.)
DOI: 10.4121/0a777982-f638-441d-b283-e8a84be71a2e.v1
title: Data underlying Chapter 3, “From Shallow to Full Wrapping: Geometry and Deformability Dictate Lipid Vesicle Internalization”, in the PhD thesis: From Engulfment to Flexible Confinement: Giant Unilamellar Vesicles as Biomimetic Models

description: This dataset belongs to Chapter 3 of the PhD thesis of Stijn van der Ham (2026). <br>Authors: Stijn van der Ham, Hanumantha Rao Vutukuri<br>Active Soft Matter and Bio-inspired Materials Lab, Faculty of Science and Technology, MESA+ Institute, University of Twente, Enschede, The Netherlands<br>Corresponding Author: Hanumantha Rao Vutukuri (email: h.r.vutukuri@utwente.nl)<br><br><br>***General Introduction***<br>The dataset contains the experimental data, analysis, and figure files underlying the figures in <strong>Chapter 3</strong> of the PhD thesis of Stijn van der Ham.<br>This chapter has been published as: From Shallow to Full Wrapping: Geometry and Deformability Dictate Lipid Vesicle Internalization (https://doi.org/10.1021/acs.nanolett.5c04322)<br>Additional datasets and analysis codes corresponding to the simulations can be found with the corresponding publication, as linked above.<br><br><br>***Methodology***<br>All data processing and analysis steps are described in detail in the Methods section of the corresponding publication, as well as in the thesis.<br>Analysis codes in the dataset can be run as is, with the exception of updating certain paths/parameters as indicated in the analysis codes.<br><br><br>***Organization of the dataset***<br>The data is organized per figure, numbered according to the thesis, with supporting figures preceded by the capital letter 'S'. <br>Within each figure folder, data is ordered as:<br>- <strong>Figure files:</strong> final figure files, provided in several formats (.ai file, .pdf file, .svg file)<br>- <strong>Raw data folder:</strong> raw data underlying the figure (microscopy images in the form of .tiff images/stacks/series)<br>- <strong>Analysis folder:</strong> analysis codes underlying the figure, data required for the analyses code, and codes for the generation of the plots (jupyter notebooks, .svg files for python generated figures.)
DOI: 10.4121/9c1a4412-046a-4826-924b-bf65c00a89ea
title: Data underlying Chapter 4 “A colloidal viewpoint on the sausage catastrophe and the finite sphere packing problem”, in the PhD thesis: From Engulfment to Flexible Confinement: Giant Unilamellar Vesicles as Biomimetic Models

description: This dataset belongs to Chapter 4 of the PhD thesis of Stijn van der Ham (2026). <br>Authors: Stijn van der Ham, Hanumantha Rao Vutukuri<br>Active Soft Matter and Bio-inspired Materials Lab, Faculty of Science and Technology, MESA+ Institute, University of Twente, Enschede, The Netherlands<br>Corresponding Author: Hanumantha Rao Vutukuri (email: h.r.vutukuri@utwente.nl)<br><br><br>***General Introduction***<br>The dataset contains the experimental snapshots and figure files underlying the experimental data in the figures in <strong>Chapter 4</strong> of the PhD thesis of Stijn van der Ham.<br>This chapter has been published as: A colloidal viewpoint on the sausage catastrophe and the finite sphere packing problem (https://doi.org/10.1038/s41467-023-43722-0)<br>Additional datasets and analysis codes corresponding to the simulations can be found with the corresponding publication, as linked above.<br><br><br>***Methodology***<br>All data processing and analysis steps are described in detail in the Methods section of the corresponding publication, as well as in the thesis.<br><br><br>***Organization of the dataset***<br>The data is organized per figure, numbered according to the thesis, with supporting figures preceded by the capital letter 'S'. Only those figures containing experimental data are listed.<br>Within each figure folder, data is ordered as:<br><strong>- Figure files:</strong> final figure files, provided in several formats (.pdf file and powerpoint file)<br><strong>- Raw data folder:</strong> raw data underlying the figure (microscopy images in the form of .tiff images)<br><strong>- Analysis folder:</strong> experimental data and analysis underlying figure plots (excel file)