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.
131
datasets available to search
ShareScore release 0.9.0
Dataset results
131 results for “queries”
Query datasets used in ProjectSVR paper
<p>Query datasets used in ProjectSVR paper</p>
PrOVE QUERI Project #1
ClinicalTrials.gov study NCT02765412. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
Open the record for dataset details and reuse information.
Results of Neuron Phenotype Ontology competency queries
<p>Full list of neurons with human readable labels returned from competency queries against the Neuron Phenotype Ontology as described in Gillespie et al. (2020). Three .CSV files are included with the number of competency query (Q1, Q2 and Q3) appended. The DL query used for the query is given in the column header of column A. The numbers provided for Q1 and Q2 in columns B-E represent the count of neurons returned for each of the 3 evidence based models (EBMs) described in the paper and the Common Usage Types (CUTs) The total number of neurons returned across all categories is shown in column F.</p>
NCBI bacterial query sequences
<p>Bacterial sequences downloaded from the NCBI assembly database. The file qseqs78M.fna includes Escherichia coli, Klebsiella, and Streptococcus sequences that were aligned to E.coli pan-genome. The file qseqs29M.fna includes randomly selected sequences from the GenBank assemblies that were aligned to GenBank pan-genome including all bacterial species.</p>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Pre-trained Weights for the Baseline System
<p><strong>== Descriptions ==</strong></p> <p>We trained the AudioSep [1] model using the <a href="../records/10887496">development set</a> (Clotho and augmented FSD50K datasets) for 200k steps with a batch size of 16 using one Nvidia A100 GPU (around 1 day). Model details can be found in the <a href="https://arxiv.org/abs/2308.05037">AudioSep paper</a>.</p> <p>Pre-trained weights for the baseline system:</p> <ul> <li>audiosep_16k,baseline,step=200000.ckpt</li> </ul> <p>Baseline codebase:</p> <ul> <li>GitHub: <a href="https://github.com/Audio-AGI/dcase2024_task9_baseline">https://github.com/Audio-AGI/dcase2024_task9_baseline</a></li> </ul> <p><strong>== Reference ==</strong></p> <p>[1] Liu X, Kong Q, Zhao Y, et al. Separate anything you describe. arXiv:2308.05037, 2023.</p> <p><strong>== Contact ==</strong></p> <p>Xubo Liu, xubo.liu@surrey.ac.uk</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA
<p><strong>Summary</strong><br>This artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface.</p> <p><strong>Relation to paper</strong><br>This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:<br>- The toy examples presented in Example 12, Example 14, Example 22 and Example 34<br>- Table 3 in Section 6<br>- Table 4 in Section 6<br>- Table 5 in Section 6<br>- Table 8 in Section 6<br>- Figure 9 in Section 6<br>- Figure 10 in Section 6<br>- Figure 11 in Section 6</p> <p><strong>Structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<br>- <code>switss-multi/experiments</code>: Contains the PRISM models, PRISM properties (queries) and scripts for running the experiments.<br>- <code>switss-multi</code>: The source code of the implementation of our presented techniques.<br>- <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>.<br>- <code>peva-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data.<br>- <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.<br>- <code>LICENSE</code>: The license of this artifact (MIT license).<br>- <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).<br>- <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.</p>
Optimizing Within-Distance Queries by Approximating Shapes with Maximal Bounded Boxes - Datasets
<p>Csv and sql files for the underlying data.<br> Figure 9: Calculating the distance between various number and type of shapes, i.e. from polygons, rotated and axis aligned rectangles, to points, polygons, rotated and axis aligned rectangles.<br> Table 1: Calculating distance between shapes of various complexity, i.e. points, lines, quadrilaterals, hexagons, dodecagons, icosagons, pentacontagons.<br> Table 2: Comparing the ordering of the shapes when distance operation is calculated, (a) from polygons to rectangles, (b) from rectangles to polygons.<br> Table 5 & 6: Within-distance and distance queries between (a) actual polygons, (b) their bounded rectangles, (c) actual polygons and points, (d) their bounded rectangles and points.</p>
Queries and graphs for sequence to graph alignment method evaluation
<p>The tar contains two directories for queries and graphs used to evaluate the sequence to graph alignment methods. </p>
TopicTracker Medline files and query logs generated retrieving papers on autonomy, equity, privacy, proportionality and trust in the context of Covid-19
<p>To determine the core areas of discussion about the interplay between the Core Five and the Covid-19 pandemic, we ran a set of five queries in the TopicTracker. Each query collects articles regarding Covid-19 and one of the Core Five Enduring Values, published between January 2019 and March 2022.</p>
CUDASW4 - queries
Open the record for dataset details and reuse information.
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - QEST 2024 Artefact
<p>This artifact accompanies the QEST+FORMATS 2024 paper "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes" (<a href="https://arxiv.org/abs/2406.08175">arXiv</a>). It contains the implementation (<code>switss-multi</code>) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the results presented in Section 5 and Appendix D. Additionally, it also contains the original raw experimental data presented in Section 5 and Appendix D and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation <code>switss-multi</code> and describe how to use our tool via its command-line and programmatically via its Python interface.</p> <p><strong>Relation to paper</strong><br>This artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:<br>- The toy examples presented in Example 1 and Example 2<br>- Table 1 in Section 5<br>- Table 3 in Appendix D<br>- Figure 5 in Appendix D<br>- Figure 6 in Appendix D<br>- Table 4 in Appendix D</p> <p><strong>Aritfact structure</strong><br>This artifact consists of the following files and folders:<br>- <code>data</code>: Contains the PRISM models, PRISM properties (queries) and original raw experimental data presented in Section 5 and Appendix D. Additionally, the log files and scripts for summarizing the raw experimental data are provided.<br>- <code>switss-multi</code>: The source code of the implementation of our presented techniques.<br>- <code>switss-multi-docs</code>: A documentation of the Python API of <code>switss-multi</code>.<br>- <code>qest-docker-image.tar.gz</code>: The compressed Docker image, with the installed implementation (<code>switss-multi</code>), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the <code>data</code> folder, in case you want to run the analysis scripts on the original data.<br>- <code>docker-results</code>: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.<br>- <code>LICENSE</code>: The license of this artifact (MIT license).<br>- <code>GUROBI-EULA</code>: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).<br>- <code>GPL-3.0</code>: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.</p> <p><strong>Note on the versions:</strong> The first version (v1) contains the data and implementation at the point of the paper submission. The second version (v2) contains a Docker image and more detailed documentation and was evaluated by the QEST+FORMATS 2024 Artifact Evaluation Comittee and awarded the artifact evaluation badge. This version (v3) incorporates the feedback of the QEST+FORMATS 2024 artifact evaluation and contains improvements on the second version (v2).</p> <p><strong>Acknowledgments:</strong> We would like to thank the anonymous reviewers in the QEST+FORMATS Artifact Evaluation Committee for their valuable feedback. The authors were supported by the German Federal Ministry of Education and Research (BMBF) within the project SEMECO Q1 (03ZU1210AG) and by the German Research Foundation (DFG) through the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy) and the DFG Grant 389792660 as part of TRR 248 (Foundations of Perspicuous Software System).</p>
EditQL: A Textual Query Language for Evolving Models (Reproducibility Package)
Open the record for dataset details and reuse information.
Improve your Galaxy text life: The Query Tabular Tool
<p>Sample PSM input file for metaproteomics galaxy workflow to describe Query Tabular tool</p>
Absorption-Based Query Answering for Expressive Description Logics : Evaluation Data
<p>Sources ( <code>Sources.zip</code> ), evaluation data ( <code>Evaluation.zip</code> ) with all ontologies and queries, as well as the evaluation results ( <code>Results.zip</code> ) of the publication "<em>Absorption-Based Query Answering for Expressive Description Logics</em>" from the 18th International Semantic Web Conference (ISWC), October 26-30, 2019, Auckland, New Zealand. For easily reproducing the evaluation, you can also use the Docker image <em>koncludeeval/abqa</em> (available at <a href="https://hub.docker.com/r/koncludeeval/abqa">https://hub.docker.com/r/koncludeeval/abqa</a>). See also readme files in archives for more details.</p> <p>You may only use/share/etc. the included reasoners/ontologies as restricted by their licences (see <code>Sources.zip</code>). In particular, Konclude and OWL BGP are available under the LGPLv3 (<a href="https://www.gnu.org/licenses/lgpl-3.0.en.html">https://www.gnu.org/licenses/lgpl-3.0.en.html</a>), Pellet under the AGPLv3 (<a href="https://www.gnu.org/licenses/agpl-3.0.en.html">https://www.gnu.org/licenses/agpl-3.0.en.html</a>), and PAGOdA under an Academic Licence (<a href="http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt">http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt</a>).</p>
Artifacts for "On Hardware Security Bug Code Fixes By Querying Large Language Models""
<p>This repository contains the benchmarks and results obtained for the work "On Hardware Security Bug Code Fixes<br> By Querying Large Language Models".<br> Follow the README.md file for more information on how to use the tools yourself.</p>
Instagram: #blackoutpoetry query
<p><strong>Tags: #erasurepoetry vs. #blackoutpoetry on Instagram (Nov. 19, 2020)</strong></p> <p><br> As of November 19, 2020, searching on Instagram for the tags “#erasurepoetry” and “#blackoutpoetry” shows some overlapping of techniques in both tags.</p>
Deprecated - VAMDC extraction with query token = tipbase:ccfb5ac9-82d6-4ab2-9ec0-3aa8051a32c1:head
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://tipbase.obspm.fr/12.07/vamdc/tap/ by doing these queries: (query=select * where ( atomsymbol = 'fe' ); ) . The corresponding dataset is versioned on 2017-01-30 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=a9a38253-7788-496f-b12c-b5c44751535f. Bibliographic references are: N.N. (2018). Storey, P. J. and Zeippen, C. J. and Le Dourneuf, M. (2002). Atomic data from the IRON Project. LI. Electron impact excitation of Fe IX. Astronomy and Astrophysics. Butler, K. and Zeippen, C. J. (2001). Atomic data from the IRON Project. L. Electron impact excitation of Fe xix. Astronomy and Astrophysics. Butler, K. and Zeippen, C. J. (2001). Atomic data from the IRON Project. XLIX. Electron impact excitation of Fexx. Astronomy and Astrophysics. Pelan, J. C. and Berrington, K. A. (2001). Atomic data from the IRON project. XLVI. Electron excitation of 3s3p6 and 3s23p43d fine-structure transitions in Fe x. Astronomy and Astrophysics. Chen, Guo Xin and Pradhan, Anil K. (2000). Atomic data from the Iron Project. XLIV. Transition probabilities and line ratios for Fe VI with fluorescent excitation in planetary nebulae. Astronomy and Astrophysics Supplement Series. Chidichimo, M. C. and Zeman, V. and Tully, J. A. and Berrington, K. A. (2000). Erratum: Atomic Data from the IRON Project. Astronomy and Astrophysics Supplement Series. Storey, P. J. and Mason, H. E. and Young, P. R. (2000). Atomic data from the IRON Project. XL. Electron impact excitation of the Fe XIV EUV transitions. Astronomy and Astrophysics Supplement Series. Chidichimo, M. C. and Zeman, V. and Tully, J. A. and Berrington, K. A. (1999). Atomic data from the IRON Project. XXXVI. Electron excitation of Be-like Fe XXIII between 1s(2 2l_1 2l_2 SLJ) and 1s(2 2l_3 2l_4 S'L'J'). Astronomy and Astrophysics Supplement Series. Eissner, W. and Galavis, M. E. and Mendoza, C. and Zeippen, C. J. (1999). Atomic data from the IRON Project. XXXVIII. Electron impact excitation of the fine-structure transitions in the n=3 complex of Fe XV. Astronomy and Astrophysics Supplement Series. Chen, G. X. and Pradhan, A. K. (1999). Atomic data from the Iron Project. XXXVII. Electron impact excitation collision strengths and rate coefficients for Fe VI. Astronomy and Astrophysics Supplement Series. Eissner, W. and Galavis, M. E. and Mendoza, C. and Zeippen, C. J. (1999). Atomic data from the IRON Project. XXXIV. Electron impact excitation of Fe XVI. Astronomy and Astrophysics Supplement Series. Nahar, S. N. and Pradhan, A. K. (1999). Atomic data from the Iron Project. XXXV. Relativistic fine structure oscillator strengths for Fe XXIV and Fe XXV. Astronomy and Astrophysics Supplement Series. Galavis, M. E. and Mendoza, C. and Zeippen, C. J. (1998). Atomic data from the IRON Project. XXXII. On the accuracy of the effective collision strength for the electron impact excitation of the quadrupole transition in AR III. Astronomy and Astrophysics Supplement Series. Binello, A. M. and Mason, H. E. and Storey, P. J. (1998). Atomic data from the IRON project. XXXI. Electron impact excitation of optically allowed and intercombination electric dipole transitions in Fe XII. Astronomy and Astrophysics Supplement Series. Berrington, K. A. and Saraph, H. E. and Tully, J. A. (1998). Atomic data from the IRON Project. XXVIII. Electron excitation of the ^2P_(3/2) deg -> ^2P_(1/2) deg fine structure transition in fluorine-like ions at higher temperatures. Astronomy and Astrophysics Supplement Series. Binello, A. M. and Mason, H. E. and Storey, P. J. (1998). Atomic data from the IRON Project. XXV. Electron impact excitation of fine-structure transitions in the ground configuration of Fe XII. Astronomy and Astrophysics Supplement Series. Zhang, H. L. and Pradhan, A. K. (1997). Atomic data from the Iron Project. XXVII. Electron impact excitation collision strengths and rate coefficients for Fe IV. Astronomy and Astrophysics Supplement Series. Berrington, K. A. and Tully, J. A. (1997). Atomic data from the IRON Project. XXIV. Electron excitation of Li-like Fe XXIV between the N = 2 and n' = 2, 3, 4 fine-structure levels. Astronomy and Astrophysics Supplement Series. Zhang, H. L. and Pradhan, A. K. (1997). Atomic data from the IRON Project. XXIII. Relativistic excitation rate coefficients for Fe XXII with inclusion of radiation damping. Astronomy and Astrophysics Supplement Series. Zhang, H. (1996). Atomic data from the Iron Project. XVIII. Electron impact excitation collision strengths and rate coefficients for Fe III.. Astronomy and Astrophysics Supplement Series. Berrington, K. A. and Pelan, J. (1996). Erratum: "Atomic data from the IRON Project. XII. Electron excitation of forbidden transitions in V-like ions Mn III, Fe IV, Co V and Ni VI" [Astron. Astrophys., Suppl. Ser., Vol. 114, No. 2, p. 367 - 371 (Dec 1995)].. Astronomy and Astrophysics Supplement Series. Storey, P. J. and Mason, H. E. and Saraph, H. E. (1996). Atomic data from the IRON Project. XIV. Electron impact excitation of the Fe XIV fine-structure transition ^2^P^o^_1/2_-^2^P^o^_3/2_. Astronomy and Astrophysics. Bautista, M. A. and Pradhan, A. K. (1996). Atomic data from the Iron project. XIII. Electron excitation rates and emissivity ratios for forbidden transitions in NI II and Fe II.. Astronomy and Astrophysics Supplement Series. Berrington, K. A. and Pelan, J. C. (1995). Atomic data from the IRON Project. XII. Electron excitation of forbidden transitions in V-like ions MN III, Fe IV, CO V and NI VI.. Astronomy and Astrophysics Supplement Series. Galavis, M. E. and Mendoza, C. and Zeippen, C. J. (1995). Atomic data from the IRON Project. X. Effective collision strengths for infrared transitions in silicon- and sulphur-like ions.. Astronomy and Astrophysics Supplement Series. Pelan, J. and Berrington, K. A. (1995). Atomic data from the IRON Project. IX. Electron excitation of the ^2^P^0^_3/2_-^2^P^0^_1/2_ fine-structure transition in chlorine-like ions, from AR II to NI XII.. Astronomy and Astrophysics Supplement Series. Berrington, K. A. (1995). Atomic data from the IRON Project. VIII. Electron excitation of the 3d^4^ ^5^D_J_ ground state fine-structure transitions in Ti-like ions V II, CR III, MN IV, Fe V, CO VI and NI VII.. Astronomy and Astrophysics Supplement Series. Zhang, H. L. and Pradhan, A. K. (1995). Atomic data from the Iron Project. VI. Collision strengths and rate coefficients for Fe II.. Astronomy and Astrophysics. Edlen, Bengt (1983). Comparison of Theoretical and Experimental Level Values of the n = 2 Configurations in the Boron Isoelectronic Sequence. Physica Scripta. Jupen, C. and Isler, R. C. and Trabert, E. (1993). Solar Identifications of Fex-Fexiv Based on Comparison with Beam-Foil Tokamak and Laser-Produced Plasma Spectra. Monthly Notices of the Royal Astronomical Society. Shirai T. and Sugar J. and Musgrove (2000). Spectral Data for Highly Ionized Atoms: TI, V, Cr, Mn, Fe, Ni, Cu, Kr, and Mo. Journal of Physical and Chemical Reference Data monograph 8. Shirai T. and Sugar J. and Musgrove (1985). Atomic Energy Levels of the Iron Period Elements: Potassium through Nickel. J. Phys. Chem. Ref. Data 14, Supplement No. 2. Reader, Joseph and Sugar, Jack and Acquista, Nicolo and Bahr, Raymond (1994). Laser-produced and tokamak spectra of lithiumlike iron, Fe23 +. Journal of the Optical Society of America B: Optical Physics. Ballance, C. P. and Badnell, N. R. and Berrington, K. A. (2002). Electron-impact excitation of H-like Fe at high temperatures. Journal of Physics B Atomic Molecular Physics. Ekberg, J. O. (1981). Term Analysis of Fe VII. Physica Scripta. Churilov, S. S. and Levashov, V. E. (1993). The 3p23d and 3s3d2 configurations in aluminium-like KV VII-Ni XVI. Physica Scripta. Churilov, S. S. and Levashov, V. E. and Wyart, J. F. (1989). Extended analysis of the 3d2-3p3d transitions in the sequence K VIII-Cu XVIII and isoelectronic trends in Mg-like ions through Kr XXV. Physica Scripta. Churilov, S. S. and Kononov, E. Ya and Ryabtsev, A. N. and Zayikin, Yu F. (1985). A Detailed Analysis of the n=3 - n'=3 Transitions in the Mg-like Ions FeXV, CoXVI and NiXVII. Physica Scripta. Whiteford, A. D. and Badnell, N. R. and Ballance, C. P. and O'Mullane, M. G. and Summers, H. P. and Thomas, A. L. (2001). A radiation-damped <strong>R</strong>-matrix approach to the electron-impact excitation of helium-like ions for diagnostic application to fusion and astrophysical plasmas. Journal of Physics B Atomic Molecular Physics. Redfors, Andreas and Litzèn, Ulf (1989). Extended analysis of spectra and term systems in aluminumlike Ca viii-Ni xvi. Journal of the Optical Society of America B Optical Physics. Redfors, Andreas (1988). The 3d2 configuration in Ca IX-Zn XIX. Physica Scripta. Litzèn, Ulf and Redfors, Andreas (1987). Revised and extended analysis of transitions and energy levels in the n = 3 complex of Mg-like Ca IX-Ge XXI. Physica Scripta.</p>
Datasets of Time- and Space-Efficient Regular Path Queries
<p>Datasets that were used in the experiments of our work <em>Time- and Space-Efficient Regular Path Queries.</em></p>
RDF models and SPARQL queries for decoupled analytics demonstration
<p>This directory contains the following:</p> <p>- Two sets of RDF triples using different ontologies, modeling the same "MZVAV-2" air handling unit from the data inventory by Granderson et al. [1].</p> <p>- SPARQL queries for retrieving inputs to APAR [2] rules.</p> <p>- SPARQL queries for discovering "data links" from the models, connecting data points to time series providers.</p> <p>The models were created by manually writing the triples. For details, see included README.md</p> <p> </p> <p>[1] J. Granderson, G. Lin, A. Harding, P. Im, Y. Chen, Building fault detection data to aid diagnostic algorithm creation and performance testing, Scientific Data. 7 (2020) 65. https://doi.org/10.1038/s41597-020-0398-6.</p> <p>[2] J.M. House, H. Vaezi-Nejad, J.M. Whitcomb, An expert rule set for fault detection in air-handling units, ASHRAE Transactions. 107 (2001) 858–871.</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.