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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
Updated example thaumatin data set from Diamond Light Source VMXi
<p>Example data set from the VMXi beamline at Diamond Light Source recorded in situ from a thaumatin crystal with processing results:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/NATIVE Overall Low High High resolution limit 1.85 5.02 1.85 Low resolution limit 50.49 50.50 1.88 Completeness 68.0 100.0 2.7 Multiplicity 3.3 3.9 1.0 I/sigma 12.9 24.8 1.4 Rmerge(I) 0.056 0.031 0.386 Rmerge(I+/-) 0.047 0.027 0.000 Rmeas(I) 0.065 0.035 0.545 Rmeas(I+/-) 0.061 0.035 0.000 Rpim(I) 0.032 0.017 0.386 Rpim(I+/-) 0.038 0.022 0.000 CC half 0.998 0.999 0.000 Wilson B factor 20.100 Anomalous completeness 58.0 99.8 0.1 Anomalous multiplicity 1.9 2.4 1.0 Anomalous correlation -0.015 -0.107 0.000 Anomalous slope 0.928 Total observations 53105 5240 32 Total unique 15898 1349 31 Assuming spacegroup: P 41 21 2 Other likely alternatives are: P 43 21 2 Unit cell (with estimated std devs): 58.6330(3) 58.6330(3) 151.4573(10) 90.0 90.0 90.0 </pre>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
SeSaMe: A Data Set of Semantically Similar Java Methods
<p>This is the data set presented in the paper</p> <p>Kamp, M., Kreutzer P., Philippsen M.: SeSaMe: A Data Set of Semantically<br> Similar Java Methods. 16th International Conference on Mining Software<br> Repositories (MSR 2019), Montreal, QC, Canada. 2019</p>
Example treatment of dynamic shadows in Eiger data set
<p>Following discussion in </p> <p> </p> <p>https://github.com/HDRMX/NXmx/wiki/Treatment-of-(Dynamic)-Shadows-in-Eiger-Data-Sets</p> <p> </p> <p>example Eiger data set with shadow mask added under /entry/shadow/dynamic_mask - uploading for discussion purposes only, data set may be revised in future. Clearly any and all are welcome to look at / inspect the files and provide commentary. </p> <p> </p> <p>N.B. the shadow model has not been optimised. </p>
Contextual maritime data set (RDF triples)
<p>This data set is the RDF conversion w.r.t. the datAcron ontology, of the contextual maritime data available at https://zenodo.org/record/1167595 . It has been generated by the RDF-Gen method on the data sets describing sea ports (World Port Index, Ports of Brittany, SeaDataNet fishing ports) and protected regions (fishing areas, fishing interdiction, Natura2000).</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 0
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images </strong>of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. <strong>This data set</strong> (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. The second part (part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Data Set: Evaluation of Domain Randomization Techniques for Transfer Learning - Part 1
<p><strong>Transfer Learning Data Set:</strong><br> The total data set contains <strong>1.44m synthetic images</strong> and <strong>10k real-world images</strong> of size 299x224.</p> <p>Due to file size limitation, the data set is split into two sets. The first part (part 0: DOI 10.5281/zenodo.2581311) contains the real-world images and the synthetic images of perspective 00. <strong>This data set </strong>(part 1: DOI 10.5281/zenodo.2581469) contains perspective 01 and 10 of the synthetic images.</p> <p><strong>1. 10k real world images</strong><br> the filename labels the image:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10. digit is empty (reserved for 2nd perspective)<br> -12. digit labels the graspbox: 0 -> green box, 1 -> yellow box<br> -14. digit labels the distractors : 0 -> no distractors, 1 -> distractors<br> -c in the end stands for acolor image, d is reserved for depth (not in use).<br> <br> <strong>2. 1.44m synthetic images</strong><br> the folder labels the enabled technique:<br> -first 2 digits label the perspective: 00 -> standard, 01 -> shake, 10 -> random<br> -3. digit labels the graspbox: 0 -> defaul green box, 1 -> random box<br> -4. digit labels the distractors: 0 -> no distractors, 1 -> distractors<br> -5. digit labels the lighting: 0 -> default lighting, 1 -> random lighting<br> -6. digit labels the mesh randomization: 0 -> default mesh color, 1 -> random mesh color<br> <br> the filename consists of 3 parts:<br> -first 6 digits represent the id.<br> -8. digit labels the grasp: 0 -> no grasp, 1 -> grasp<br> -10.-15. digit equals the folder name and represents the enabled technique<br> -c in the end stands for a color image, d is reserved for depth (not in use).</p>
Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"
<p>Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.</p>
Sample clean frequency set for use with LOFAR station SE607 and beamModelTester
<p>This is a pair of files each containing a list of frequencies with low RFI as observed from LOFAR Station SE607 designed for use with the tutorial for beamModelTester.</p>
Link Discovery on AIS and Contextual data sets
<p>This data set contains the links discovered between AIS synopses provided in https://zenodo.org/record/2576152 and contextual data sets available at https://zenodo.org/record/2576584 . Specifically, the detected relations and the contextual data sets used are:</p> <p>a) C1 ports of Brittany, World Port Index and SeaDataNet fishing ports for proximity relation "nearto", stored in AIS_nearto_ports.ttl.7z</p> <p>b) C4 fishing areas (European Commission) for "within" relation stored in AIS_within_fishingAreas.ttl.7z</p> <p>c) C5 fishing interdiction for "within" relation stored in AIS_within_FishingConstraints.ttl.7z</p> <p>d) C5 Natura2000 for "within" relation stored in AIS_within_natura2000.ttl.7z</p> <p>Please notice that the attached data sets contain only the detected links. Description of the resources is provided in the corresponding TTL files.</p>
On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil (Data set)
<p>Data used for "On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" by Colombi T, Walder F, Büchi L, Sommer M, Liu K, Six J, van der Heijden M, Charles R and Keller T. (2019). SOIL. 5, 91-105, https://doi.org/10.5194/soil-5-91-2019.</p> <p>.txt file "MetaInformation_On-farm study reveals positive relationship between gas transport capacity and organic carbon content in arable soil" contains all necessary meta-information </p>
Training/evaluation data sets and databases for the operation of bio-answerfinder biomedical question answering system
<p>A zip file containing training/evaluation data sets for the bio-answerfinder biomedical question answering system training and evaluation. The zip file also contains SQLite databases for named entity lookups, morphology, nominalizations, acronyms, PubMED trained GLoVe word/phrase embeddings and vocabulary with document frequencies and SciCrunch ontology data for named entities such as proteins, anatomical structures,</p>
Deformed iron data set
<p>This is a reduced (software binned) data set. The full resolution version of this data set is published here: 10.5281/zenodo.1214829</p> <p>Data from Electron Backscatter Diffraction analysis for a small (83 x 110) point map captured using a Bruker eFlash HR (1st generation) with full pattern resolution on a FEI Quanta instrument. The orientation data can be loaded using MTEX 5.0.3 (<a href="http://mtex-toolbox.github.io/">http://mtex-toolbox.github.io/</a>). The data is released to facilitate the development of new EBSD analysis methodologies, including AstroEBSD (<a href="https://github.com/benjaminbritton/AstroEBSD/">https://github.com/benjaminbritton/AstroEBSD/</a>) which has been developed by the Experimental Micromechanics Research Group (<a href="http://www.expmicromech.com/">http://www.expmicromech.com</a>) & the Oxford Micromechanics group (<a href="http://users.ox.ac.uk/~ajw/">http://users.ox.ac.uk/~ajw/</a>). The data is from a lightly deformed sample of interstitial free steel (Ferrite). Orientation analysis was performed using eSprit 2.1 and this is contained within the h5 file. Figures from this data set are provided to illustrate the correct representation of the data. The x axis points right to left, the y axis points top to bottom, and the z axis is out of the page (as per conventions described in <a href="http://dx.doi.org/10.1016/j.matchar.2016.04.008">http://dx.doi.org/10.1016/j.matchar.2016.04.008</a>). Data has been captured with a 0.15 um step size.</p> <p>This data was collected within the Harvey Flower EM Suite within the Department of Materials, Imperial College London. The equipment was funded under the Shell-Imperial Advanced Interfaces in Materials Science University Technology Center.</p> <p>Please contact Dr Ben Britton if you have any queries or require further information (b.britton@imperial.ac.uk).</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Data set for: Nano-scale magnetic skyrmions and target states in confined geometries
<p>This data set contains the libraries and scripts to completely reproduce the simulations of the publication <em>"Nanoscale magnetic skyrmions and target states in confined geometries</em>". These simulations are based on the Fidimag code [1] which can perform discrete spin simulations.</p> <p> </p> <p>The latest version of this data set can be found at:</p> <p><a href="https://github.com/davidcortesortuno/paper-2019_nanoscale_skyrmions_target_states_confined_geometries">https://github.com/davidcortesortuno/paper-2019_nanoscale_skyrmions_target_states_confined_geometries</a></p> <p> </p> <p>[1] Bisotti, M.-A. et al., (2018). Fidimag – A Finite Difference Atomistic and Micromagnetic Simulation Package. Journal of Open Research Software. 6(1), p.22.</p>
DBNL OCR Data set
<p>A set of 220 books digitised by the Dutch DBNL (<a href="https://dbnl.org/">https://dbnl.org/</a>). The set contains the original OCR output in .txt and the corrected version in TEI.</p>
A set of synthetic spectral energy distributions for "diskless", intermediate-mass young stars
<p>These models were published in conjunction with <em>The Duration of Star Formation in Galactic Giant Molecular Clouds. I. The Great Nebula in Carina, </em>by <a href="https://arxiv.org/abs/1906.01730">Povich et al. (2019)</a>. They will also be used in subsequent papers in that series.</p> <p>The format of these models conforms with the standards of <a href="https://doi.org/10.1051/0004-6361/201425486">Robitaille (2017)</a>, so they are compatible with the <a href="http://sedfitter.readthedocs.io/en/stable/installation.html">python implementation</a> of the <a href="https://doi.org/10.1086/512039">Robitaille et al. (2007)</a> SED fitting tool. We have pre-convolved these models with a number of useful filters, including Johnson/Bessel <em>UB</em><em>VRI, </em>UKIRT <em>ZYJHK, </em>VISTA <em>ZYJHK<sub>S</sub></em>, 2MASS <em>JHK<sub>S</sub></em>, <em>Spitzer/</em>IRAC and MIPS. </p> <p>A software pipeline implementing these models to constrain the age and mass distributions of young stellar populations is also <a href="https://doi.org/10.5281/zenodo.3234101">publicly available</a>.</p> <p><strong>Limitations of these models</strong></p> <p><em>These models produce the best results for stars more massive than the Sun and older than about 0.5 Myr. </em>They employ the pre-main-sequence evolutionary tracks of <a href="https://arxiv.org/abs/astro-ph/0003477">Siess et al. (2000)</a> and <a href="https://ui.adsabs.harvard.edu/abs/1996A&A...307..829B/abstract">Bernasconi & Maeder (1996)</a>. Numerous modern tracks offer significant improvement in the treatment of subsolar-mass stars. In addition, the Kurucz stellar atmospheres used in these synthetic SEDs work best for T<sub>eff </sub>> 4,000 K; for cooler temperatures other models, for example the PHOENIX photospheres, may be more appropriate.</p> <p>Newer evolutionary tracks covering the intermediate-mass range are now available, for example the Geneva pre-MS tracks of <a href="https://doi.org/10.1051/0004-6361/201935051">Haemmerlé et al. (2019)</a>. The principal innovation of these modern models is the treatment of accretion and location of the intermediate-mass stellar birthline. The coolest, most luminous models in this set are likely <em>unphysical</em>, representing fully-convective stars of >2 solar masses and <0.5 Myr isochronal age.</p>
Example data set for the ChIPexo Pipeline
<p>Example data set for the ChIP-exo Analytical Pipeline available through GitHub</p> <p>https://github.com/SysBioChalmers/ChIPexo_Pipeline</p>
Data set for "Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding"
<p>Raw data set for "Structural transitions in the RNA 7SK 5' hairpin and their effect on HEXIM binding"</p> <p> </p> <p>Version 1.0: Energy landscape data and MD trajectories for RNA+ARM peptide</p>
A set of videos recorded upon long-term thermocycling experiments carried out by using small containers filled with PCM candidates
<p> </p> <p>Electronic Supplementary Material for Deliverable 2.7 "<strong>Report on the final heat storage block design and long term reliability experiments".</strong></p> <p>A set of fast-forward videoclips compiled based on the images recorded during the long-term experiments on small containers filled with PCM candidates and subjected to thermocycling procedure including consecutive melting/solidification. Details on involved materials and applied temperature profiles are described in D2.7.</p> <p> </p> <p>The list of attached movies:</p> <p> </p> <p><strong>2908</strong>: Si+B mixture (3.2B wt%)/small container made of pure h-BN HeBoSint D100 (Henze, Germany)</p> <p><strong>2909</strong>: Si+B mixture (3.2B wt%)/small container made of h-BN composite HeBoSint O120 (Henze, Germany)</p> <p><strong>2910</strong>: Si+B mixture (3.2B wt %)/small container made of porous graphite (Kryzaplast, Poland)</p> <p><strong>2911</strong>: Si+B mixture (3.2B wt %)/small container made of h-BN composite HeBoSint O120 (Henze, Germany)</p> <p><strong>2969:</strong> Fe-Si-B alloy produced by NTNU/small container made of dense graphite IG-15 (Tanso, Sweden)</p> <p><strong>2970</strong>: Fe-Si-B alloy produced by NTNU/small container made of pure h-BN HeBoSint D100 (Henze, Germany)</p> <p><strong>2971:</strong> Fe-Si-B alloy produced by NTNU/small container made of h-BN composite HeBoSint O120 (Henze, Germany)</p> <p>A more detailed description of the experimental complex is shown elsewhere:</p> <p>Sobczak N, Nowak R, Radziwill W, Budzioch J, Glenz A, Experimental complex for investigations of high temperature capillarity phenomena, Mater Sci Eng A 495 (2008) 43–49</p>
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.