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117 results for “Witness”
A Study of Bevacizumab (Avastin) in Combination With Rituximab (MabThera) and CHOP (Cyclophosphamide, Hydroxydaunorubicin [Doxorubicin], Oncovin [Vincristine], Prednisone) Chemotherapy in Patients Wit
ClinicalTrials.gov study NCT00486759. IPD Sharing: Not stated. Countries: 34. Publications: 2.
A Study to Evaluate Efficacy and Safety of Darunavir/Cobicistat/Emtricitabine/Tenofovir Alafenamide (D/C/F/TAF) Fixed Dose Combination (FDC) Versus a Regimen Consisting of Darunavir/Cobicistat FDC Wit
ClinicalTrials.gov study NCT02431247. IPD Sharing: Not stated. Countries: 10. Publications: 5.
A Phase 3, Long-Term Safety Study of Intravenous Epoetin Hospira in Patients With Chronic Renal Failure Requiring Hemodialysis and Receiving Epoetin Maintenance Treatment. AiME - Anemia Management Wit
ClinicalTrials.gov study NCT01628107. IPD Sharing: Not stated. Countries: 2. Publications: 1.
A Study to Investigate How Common Pancreatic Exocrine Insufficiency (PEI) is in Patients With Type 2 Diabetes and Also to Investigate the Uptake of a Single Dose of EPANOVA® or OMACOR® in Patients Wit
ClinicalTrials.gov study NCT02370537. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Deep sequencing datasets from: Witnessing the structural evolution of an RNA enzyme
Open the record for dataset details and reuse information.
Ohio Public Land Survey (PLS) Witness Tree GIS Shapefile
The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward. We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106. The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
Simulations of magnetized quark nugget dark matter in three-layer witness plate
<p>Magnetized quark nuggets (MQNs) are a recently proposed dark-matter candidate consistent with the Standard Model and with Tatsumi's theory of quark-nugget cores in magnetars. Previous publications have covered their formation in the early universe, aggregation into a broad mass distribution before they can decay by the weak force, interaction with normal matter through their magnetopause, and first observation consistent MQNs: a nearly tangential impact limiting their surface-magnetic-field parameter <i>B<sub>o</sub></i> from Tatsumi's ~10<sup>12+/-1</sup> T to 1.65 × 10<sup>12</sup> T +/- 21%. The MQN mass distribution and interaction cross section depend strongly on <i>B<sub>o</sub></i>. Their magnetopause is much larger than their geometric dimensions and can cause sufficient energy deposition to form non-meteorite craters, which are reported approximately annually. We report computer simulations of the MQN energy deposition in water-saturated peat, soft-sediments, and granite and report results from excavating such a crater. Five points of agreement between observations and hydrodynamic simulations of an MQN impact support this seocnd observation consistent with MQN dark matter and suggest a method for qualifying additional MQN events. The results also redundantly constrain <i>B<sub>o</sub></i> to ≥ 4 × 10<sup>11</sup> T.</p> <p> </p> <p>This dataset provides movies of CTH hydrodynamic simulations of a magnetized quark nugget dark matter transiting a three-layer witness plate of peat bog, soft sediments, and granite for energy/length of 1, 3, 9, 27, 81, and 243 MJ/m. The simulations, movies, and photos are explained in the paper titled <em>Results of search for magnetized quark-nugget dark matter from radial impacts on Earth</em> by J. Pace VanDevender, Robert G. Schmitt, Niall McGinley, Aaron P. VanDevender, Peter Wilson, Deborah Dixon, Helen Auer, and Jacquelyn McRae. The paper has been submitted for publication in the open-source journal Universe.</p>
Reproduction Package for Article `Fault Localization on Witnesses'
<h2>Fault Localization on Witnesses</h2> <h2>Article Abstract</h2> <p>When verifiers report an alarm, they export a violation witness (exchangeable counterexample)<br>that helps validate the reachability of that alarm.<br>Conventional wisdom says that this violation witness should be very precise:<br>the ideal witness describes a single error path for the validator to check.<br>But we claim that verifiers overshoot and produce large witnesses<br>with information that makes validation unnecessarily difficult.<br>To check our hypothesis, we reduce violation witnesses to that information<br>that automated fault-localization approaches deem relevant for triggering the reported alarm in the program.<br>We perform a large experimental evaluation on the witnesses<br>produced in the International Competition on Software Verification (SV-COMP 2023).<br>It shows that our reduction shrinks the witnesses considerably<br>and enables the confirmation of verification results that were not confirmable before.</p> <h2>VM</h2> <p>The username for the VM is `vagrant`.<br>The password for the VM is `vagrant`.</p> <h2>System Requirements</h2> <p>The artifact requires 4 CPU cores and 8 GB of RAM.</p> <p>To inspect the data, we require at least 150GB of empty disk space to run the VM.<br>To reproduce the results, we require 155GB.<br>To run the full reproduction, we require 300GB.</p> <p>The VM was tested on Ubuntu with Virtual Box Version 7.0.10 r158379 (Qt5.15.3).</p> <h2>Reproduction</h2> <p>Import the VM to VirtualBox and start it.<br>We provide symbolic links to the reproduction directory on the Desktop.<br>Please follow the instructions in the `ReadMe.md` inside the VM (located at `~/fault-localization-witnesses/ReadMe.md`).<br>The upcoming three subsections serve as a quick-start guide for our artifact.</p> <h3>Reproduce the Example</h3> <p>To reproduce the example in our paper, navigate to `~/fault-localization-witnesses` and execute `./example.sh`.</p> <h3>Reproduce the Plots</h3> <p>Execute `./reproduce.sh` from `~/fault-localization-witnesses/` to reproduce our experiments.</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>
Public data on WIT's 18 years impact survey
<p>Dataset (answers and codebooks) related to research on the impact that wrote the history of <strong>WIT during 18 years</strong>. The artifacts were used for the <strong>quantitative and qualitative analyses</strong>.</p>
Material suplementar do artigo "Gênero, parentalidade e remuneração em cargos de desenvolvimento na indústria de software" - WIT-CSBC 2024
<p>Na área de Tecnologia da Informação (TI), existe uma predominância de profissionais do gênero masculino atuando em boa parte das categorias de trabalho. Entretanto, profissionais do gênero feminino também se destacam em diferentes áreas de TI, como a área de desenvolvimento de software. Os artefatos aqui disponibilizados referem-se aos resultados de um estudo realizado com profissionais de TI que atuam com desenvolvimento de software, no contexto brasileiro, de modo a destacar a participação e o perfil de mulheres neste cenário, do ponto de vista de gênero, parentalidade e remuneração. Os resultados advindos de um Survey Exploratório com 67 profissionais desta área, indicam que há uma clara disparidade entre atividades parentais executadas por profissionais desenvolvedores, homens e mulheres; algumas diferenças salarias também são percebidas de acordo com as categorias de desenvolvimento de software; dente outros aspectos discutidos ao longo deste estudo.</p>
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>
Demonstrating Witness Visualization with the Witness Visualizer Tool
<p>We have three datasets that display visualized <a href="https://sv-comp.sosy-lab.org/">SV-COMP</a> witnesses generated with the help of the Witness Visualizer tool. Each dataset comprises two directories: <code>witnesses</code>, which contains the original witnesses provided by SVCOMP tools, and <code>visualization</code>, which contains our visual representations of the respective witnesses in HTML format. The visualization file name contains the prefix <code>error_trace-</code>, for example, <code>error_trace-witness.2ls.html</code> corresponds to a witness named <code>witness.2ls.graphml</code>.</p> <p> </p> <h3>1. Expert Evaluation of Relevant Elements for SV-COMP Properties (<a href="../records/13736385/files/dataset_1.zip?download=1">dataset_1.zip</a>)</h3> <p>This dataset comprises a selected witness for each SV-COMP property (ReachSafety, MemSafety, Termination, NoOverflow, ConcurrencySafety). The witnesses are presented in the following table:</p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Property</td> <td>Mandatory elements</td> <td>Description</td> </tr> <tr> <td>witness.memory.graphml</td> <td>CPA-BAM-SMG</td> <td>MemSafety</td> <td>Assumptions, conditions, function calls</td> <td>There is an invalid pointer being freed in this line. Using function calls to <code>append</code> helps clarify the structure of the list, while assumptions indicate which branch was chosen</td> </tr> <tr> <td>witness.overflow.graphml</td> <td>Graves-CPA</td> <td>NoOverflow</td> <td>Assumptions</td> <td>The witness showcases an explicit (<code>-2147483648</code>, which represents the minimal value for the <code>int</code> type), which has the potential to cause overflow in specific program.</td> </tr> <tr> <td>witness.termination.graphml</td> <td>CPAChecker</td> <td>NoTermination</td> <td>Assumptions, conditions</td> <td>There is a condition leading to an infinite loop.</td> </tr> <tr> <td>witness.unreach.graphml</td> <td>CPAChecker</td> <td>ReachSafety</td> <td>Function calls</td> <td>The error trace suggests that the same <code>mutex</code> was locked twice, which could result in a potential deadlock.</td> </tr> <tr> <td>witness.concurrency.graphml</td> <td>CPAChecker</td> <td>ConcurrencySafety</td> <td>Function calls, thread operations</td> <td>The error trace illustrates the creation of threads and highlights the assignments made within each thread that ultimately resulted in the violation of the property.</td> </tr> </tbody> </table> <div> </div> <p> </p> <h3>2. Overall thoroughness for all SV-COMP tools (<a href="../records/13736385/files/dataset_2.zip?download=1">dataset_2.zip</a>)</h3> <p>This dataset includes a single random witness for each SV-COMP tool, accompanied by its corresponding visualization. The visualizations showcase the various witness elements such as function calls, conditions, assumptions, thread specifics, and other operations. Cells marked with <code>+/-</code> indicate that some elements were present in the error trace, but not all of them. All witnesses are presented in the table below:</p> <table> <tbody> <tr> <td>Witness</td> <td>SV-COMP Tool</td> <td>Function calls</td> <td>Threads</td> <td>Assumptions</td> <td>Conditions</td> <td>Link to sources</td> </tr> <tr> <td>witness.2ls.graphml</td> <td>2LS</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.aprove.graphml</td> <td>AProVE (2022)</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.brick.graphml</td> <td>BRICK</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.bubaak.graphml</td> <td>Bubaak</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cbmc.graphml</td> <td>CBMC</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-bnb.graphml</td> <td>CPA-BAM-BnB</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpa-bam-smg.graphml</td> <td>CPA-BAM-SMG</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpalockator.graphml</td> <td>CPALockator</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.cpachecker.graphml</td> <td>CPAChecker</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.crux.graphml</td> <td>Crux</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.cseq.graphml</td> <td>Cseq</td> <td>+</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.dartagnan.graphml</td> <td>Dartagnan</td> <td>-</td> <td>+</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.deagle.graphml</td> <td>Deagle</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>DIVINE</td> <td>empty</td> </tr> <tr> <td>-</td> <td>EBF</td> <td>empty</td> </tr> <tr> <td>witness.esbmc-incr.graphml</td> <td>ESBMC-incr</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.esbmc-kind.graphml</td> <td>ESBMC-kind</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Frama-C-SV</td> <td>empty</td> </tr> <tr> <td>witness.gazer-theta.graphml</td> <td>Gazer-Theta</td> <td>+</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.gdart.graphml</td> <td>Gdart-LLVM</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Goblint</td> <td>empty</td> </tr> <tr> <td>witness.graves_cpa.graphml</td> <td>Graves-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.graves_par.graphml</td> <td>Graves-Par</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>-</td> <td>Infer</td> <td>empty</td> </tr> <tr> <td>witness.korn.graphml</td> <td>Korn</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lart.graphml</td> <td>LART (2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.lazy-cseq.graphml</td> <td>Lazy-CSeq</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.lfchecker.graphml</td> <td>LF-checker</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>Locksmith</td> <td>empty</td> </tr> <tr> <td>-</td> <td>Mopsa</td> <td>empty</td> </tr> <tr> <td>witness.pesco_cpa.graphml</td> <td>PeSCo-CPA</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pichecker.graphml</td> <td>PIChecker</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.pinaka.graphml</td> <td>Pinaka</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.predator.graphml</td> <td>PredatorHP</td> <td>-</td> <td>-</td> <td>-</td> <td>-</td> <td>+</td> </tr> <tr> <td>-</td> <td>SESL (2022)</td> <td>empty</td> </tr> <tr> <td>witness.smack.graphml</td> <td>SMACK (until 2022)</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.symbiotic.graphml</td> <td>Symbiotic</td> <td>-</td> <td>+</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.theta.graphml</td> <td>Theta</td> <td>different format</td> </tr> <tr> <td>witness.uatomozer.graphml</td> <td>UAutomizer</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ucutter.graphml</td> <td>UgemCutter</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.ukojak.graphml</td> <td>UKojak</td> <td>+/-</td> <td>-</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.utaipan.graphml</td> <td>UTaipan</td> <td>+/-</td> <td>+</td> <td>+</td> <td>+</td> <td>+</td> </tr> <tr> <td>witness.veriabs.graphml</td> <td>VeriAbs</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>wrong path</td> </tr> <tr> <td>witness.veriabsl.graphml</td> <td>VeriAbsL</td> <td>+</td> <td>-</td> <td>+</td> <td>+</td> <td>wrong path</td> </tr> <tr> <td>witness.verifuzz.graphml</td> <td>VeriFuzz</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> <tr> <td>witness.verioover.graphml</td> <td>VeriOover</td> <td>-</td> <td>-</td> <td>+</td> <td>-</td> <td>+</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>3. A known bug (<a href="../records/13736385/files/dataset_3.zip?download=1">dataset_3.zip</a>)</h3> <p>This dataset contains witnesses for a known bug from SVCOMP (<code>linux-3.14--drivers--usb--misc--adutux.ko.cil.i</code>) involving a data race on <code>dev->udev</code>, where concurrent writes occur without corresponding locks. Only two tools were able to solve the corresponding verification task: ESBMC-kind and CPALockator. The ESBMC error trace (<code>witness.esbmc_2020.graphml</code>) includes only thread specifics and assumptions, while the CPALockator witness (<code>witness.lockator.graphml</code>) comprises all witness elements and is presented in a human-readable format.</p> </div>
Research data used in "Quantum null dimension witness for a bipartite state"
<p>Research data used in "Quantum null dimension witness for a bipartite state"</p>
A Randomized, Open, Parallel, Controlled, Multi-center, Interventional, Cross-sectional Study to Evaluate the Detection Rate of Psoriatic Arthritis in Korean Moderate-to-severe Psoriasis Patients, Wit
ClinicalTrials.gov study NCT05758402. IPD Sharing: YES. Countries: 1. Publications: 0.
Predictive Value of Witness-derived Secondary Cincinnati Prehospital Stroke Scale Scores in Suspected Stroke
ClinicalTrials.gov study NCT07277790. IPD Sharing: YES. Countries: 1. Publications: 2.
Comparative Study of Survival and Long-term Quality of Life After Cardiac Surgery in Patients Who Are Jehovah's Witnesses
ClinicalTrials.gov study NCT03348072. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Assess the Efficacy, Safety and Tolerability of ABT-SLV187 Monotherapy in Subjects With Advanced Parkinson's Disease (PD) and Persistent Motor Complications, Despite Optimized Treatment Wit
ClinicalTrials.gov study NCT01960842. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Cardiopulmonary Resuscitation Witnessing by a Relative
ClinicalTrials.gov study NCT01009606. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Water Intake Trail and Primary Aldosteronism Postoperation(WIT-PAP)
ClinicalTrials.gov study NCT04150666. IPD Sharing: NO. Countries: 1. Publications: 4.
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.