Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,882
datasets available to search
ShareScore release 0.9.0
Dataset results
9,882 results for “Scale”
Continental scale α- and β-diversity patterns of terrestrial eukaryotic microbes: effect of climate and microhabitat on testate amoeba assemblages in Eurasian peatlands
Open the record for dataset details and reuse information.
2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Characterization of Mg(OH)2 powders produced from real saltworks bitterns at a pilot scale
<p>The growing demand for a reliable supply of minerals has pushed the European Union toward seeking novel<br>mineral sources. In the present work, the recovery of magnesium, in the form of magnesium hydroxide, Mg<br>(OH)2, from saltworks bitterns was investigated at a pilot scale. The natural bittern collected from Margi saltworks<br>(Trapani, Italy) was treated with synthetic sodium hydroxide (NaOH) solutions. The influence of different<br>reactant concentrations, flow rates and pH environments, on Mg(OH)2 particle sizes, filtration characteristics,<br>Mg2+ extraction performance and the Mg(OH)2 solid purity was thoroughly assessed. The presence of additional<br>dissolved salts in NaOH solutions was also analyzed. The pH environment was found to be the most crucial<br>parameter. On the one hand, high pH values ensured high Mg2+ recovery and powder purities, i.e. > 98%, with<br>boron content complying with market requirements. On the other hand, filtration characteristics considerably<br>worsened due to the lyosorption phenomenon.</p>
Supplementary Figure 1 for "A Bioinformatics Protocol for Quickly Creating Large-Scale Phylogenetic Trees"
<p>Supplementary Figure 1 for "A Bioinformatics Protocol for Quickly Creating Large-Scale Phylogenetic Trees"</p>
FIGURE 7. Pored lateral-line scales. A in A review of the deepwater cardinalfish genus Epigonus (Perciformes: Epigonidae) of the Western Indian Ocean, with description of two new species
FIGURE 7. Pored lateral-line scales. A: Epigonus bispinosus n. sp., SAIAB 189476, holotype, 165.5 mm SL; B: E. idai, n. sp., SAIAB 204434, paratype, 132.3 mm SL.
Quantifying the large-scale electrification equilibrium effects in dust storms using field observations at Qingtu Lake Observatory
<p>The measured divergence of the electric field, PM<sub>10</sub> concentration, ambient temperature and relative humidity time series during dust storms (at Qingtu Lake observatory) are given in these datasets.</p>
Research data for: "Band Structure Interpolation using Optimized Local Orbitals from Linear-Scaling Density-Functional Theory"
<p>This file was created by Laura E. Ratcliff on 23rd March 2018. It contains the input files employed for the ONETEP and CASTEP calculations in the above publication.</p> <p>Directory Listing:</p> <p>castep/</p> <p>Contains the input files used to generate the CASTEP reference data.</p> <p>onetep/</p> <p>Contains the input files used to generate all of the ONETEP data, where filenames are labelled with the number of repeat CNT units, val (cond) indicates a valence (conduction) calculation and the suffixes follow a similar labelling convention to that employed in the manuscript.</p>
FIGURE 2 in A new scale insect of the extinct family Weitschatidae (Insecta: Hemiptera: Coccomorpha) from mid-Cretaceous Burmese amber
FIGURE 2. Detailed photographs of Criniverticillus longicumulus Lin, Yao & Ren gen. et sp. nov. holotype, CNU-COC- MA2018001. (A) dorsal and (B) ventral views of body; (C) left tubercular projection on apex of abdomen in (B); (D) ventral view of right mid- and hind legs; (E) claw, tarsus and part of tibia (note bifurcate setae and spine-like claw digitules) in (D); (F) ventral view of antennae; (G) apical antennal segment in (F); (H) lateral view of apex of antenna (note basiconic sensilla); (I) dorsal view of forewing; (J) dorsal view of hamulohalteres. Scale bars for A, B, D, F, I = 0.4 mm; C = 0.05 mm; E, G, H, J = 0.1 mm.
FIGURE 1 in A new scale insect of the extinct family Weitschatidae (Insecta: Hemiptera: Coccomorpha) from mid-Cretaceous Burmese amber
FIGURE 1. Holotype of Criniverticillus longicumulus Lin, Yao & Ren gen. et sp. nov. CNU-COC-MA2018001. Photomicrographs of (A) dorsal and (B) ventral views; line drawings of (C) dorsal and (D) ventral views. Scale bars = 1.0 mm. afp: anterior flexing patch; dse: dorsal simple eye; en: endophallus; mrt: median ridge on tergite; pfp: posterior flexing patch; sscr: sensorium on subcostal ridge; tp: tubercular projection.
Supplementary material 2 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Average yield (in t/ha) of agricultural crops in Schleswig-Holstein in 2010 (data from Statistikamt Nord 2010), nitrogen content (in kg N/dt fresh weight) of crop plants (data from DüV 2007) and corresponding estimated nitrogen content per hectare.
Supplementary material 3 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Relevant nitrogen application and input rates considering nitrogen losses (data from Landwirtschaftskammer Niedersachsen 2017b).
Supplementary material 1 from: Bicking S, Burkhard B, Kruse M, Müller F (2018) Mapping of nutrient regulating ecosystem service supply and demand on different scales in Schleswig-Holstein, Germany. One Ecosystem 3: e22509. https://doi.org/10.3897/oneeco.3.e22509
Nitrogen excretion (in kg N/year) of livestock (data from the Ministerium für Landwirtschaft, Umwelt und Verbraucherschutz Mecklenburg-Vorpommern 2008)
Supplementary material 2 from: Nieto-Montes de Oca A, Sánchez-Vega H, Durán-Fuentes I (2018) A new species of knob-scaled lizard (Xenosauridae, Xenosaurus) from the Sierra Madre Oriental of Puebla, Mexico. ZooKeys 737: 141-160. https://doi.org/10.3897/zookeys.737.15095
Variation in selected characters in the genus Xenosaurus : Explanation note: Xenosaurus clades after Nieto-Montes de Oca et al. (2017).
FIGURES 11–13 in A new species of myrmecophilous lady beetle in the genus Diomus (Coleoptera: Coccinellidae: Diomini) from Chiapas, Mexico that feeds on green coffee scale, Coccus viridis (Green) (Hemiptera: Coccidae)
FIGURES 11–13. Genitalia of D. lupusapudoves: 11, ventral view of male phallobase; 12, left lateral view of male penis; 13, ventral view of female genitalia showing spermatheca with accessory gland, sperm duct, and anterior part of bursa.
FIGURES 9–10 in A new species of myrmecophilous lady beetle in the genus Diomus (Coleoptera: Coccinellidae: Diomini) from Chiapas, Mexico that feeds on green coffee scale, Coccus viridis (Green) (Hemiptera: Coccidae)
FIGURES 9–10. Diagrammatic ventral view of Diomus prosterna (setae, color patterns and surface punctation not indicated): 9, D. lupusapudoves; 10, D. urban, showing central part only.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.