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125 results for “artefacts”
The apparent exponential radiation of Phanerozoic land vertebrates is an artefact of spatial sampling biases
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Data from: Pushing Raman spectroscopy over the edge: purported signatures of organic molecules in fossil animals are instrumental artefacts
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Performance evaluation artefacts for in-memory encryption using the advanced encryption standard
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Data from: Evidence of artefacts made of giant sloth bones in Central Brazil around the last glacial maximum
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Speech endpoint annotations and artefact details for ASVspoof 2017 version 2.0 dataset
<p>This repository contains speech endpoint annotations and filelists for different artefacts we found during our study on the ASVspoof 2017 v2.0 dataset as part of our work in the paper "Dataset biases in speaker verification systems: a case study on the ASVspoof 2017 benchmark" which is to be submitted to the IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM).</p> <p> </p>
3D models of moulds for Catalhoyuk artefacts
<p>3D models of moulds, e.g. the objects that once 3d printed can be used to create other objects by moulding them with different materials. These moulds are used to create copies Catalhoyuk artifacts. They have been used in the context of the EMOTIVE project (https://emotiveproject.eu/) experiences. Catalhoyuk is a neolithic archaeological site located in Konya, Turkey.</p>
Verifying OpenJDK's LinkedList using KeY: Additional artefacts belonging to master thesis
<p>The file testcases.tar.gz contains source code files w.r.t. 64 test cases carried out for this thesis. It also contains log files that contain output of these test cases. The latter are also contained in the file Appendix.pdf, which serves as an on-line appendix for the thesis. Other sources which are of importance for the thesis can be found in https://doi.org/10.5281/zenodo.3517081. Three proof files have been re-established for the purpose of describing these proofs in the thesis. Thus: they deviate from the ones that can be found in https://doi.org/10.5281/zenodo.3517081. These three can be found in the file "Three renewed proof files.zip". It concerns proof files for lastIndexOf(Object), linkFirst(Object), and addFirst(Object). https://doi.org/10.5281/zenodo.3517081 is a link that has been created to store artefacts w.r.t. a paper for the TACAS conference in April 2020 in Dublin, Ireland. The paper carries the same title as this thesis.</p>
Stacked Dense Denoise-Segmentation FBP synthetic reconstruction from 3601 projection without ring artefacts
<p>The FBP recontruction without ring artefacts from the 3601 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Datasets from: Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode
<p>Metabarcoding of Metazoa using mitochondrial genes may be confounded by both the accumulation of PCR and sequencing artefacts and the co-amplification of nuclear mitochondrial pseudogenes (NUMTs). The application of read abundance thresholds and denoising methods is efficient in reducing noise accompanying authentic mitochondrial amplicon sequence variants (ASVs). However, these procedures do not fully account for the complex nature of concomitant sequences and the highly variable DNA contribution of specimens in a metabarcoding sample. We propose, as a complement to denoising, the metabarcoding Multidimensional Abundance Threshold Evaluation (<i>metaMATE</i>) framework, a novel approach that allows comprehensive examination of multiple dimensions of abundance filtering and the evaluation of the prevalence of unwanted concomitant sequences in denoised metabarcoding datasets. <i>metaMATE</i> requires a denoised set of ASVs as input, and designates a subset of ASVs as being either authentic (mtDNA haplotypes) or non-authentic ASVs (NUMTs and erroneous sequences) by comparison to external reference data and by analysing nucleotide substitution patterns. <i>metaMATE</i> (i) facilitates the application of read abundance filtering strategies, which are structured with regard to sequence library and phylogeny and applied for a range of increasing abundance threshold values, and (ii) evaluates their performance by quantifying the prevalence of non-authentic ASVs and the collateral effects on the removal of authentic ASVs. The output from <i>metaMATE</i> facilitates decision-making about required filtering stringency and can be used to improve the reliability of intraspecific genetic information derived from metabarcode data. The framework is implemented in the <i>metaMATE</i> software, available at https://github.com/tjcreedy/metamate).</p>
Artefact - Hache taillée
**Nom** : Hache taillée. **Description** : Artefact achéologique taillé. **Dimensions** : 10 x 20 cm. **Âge** : Néolithique. **Localité** : Hardivillers, Oise, France. **Méthode** : Photogrammétrie avec Agisoft metashape. **Réalisation** : UniLaSalle / Collection du Musée Albert de Lapparent : https://www.musee-delapparent.com/fr/echantillons/fossiles/1487-hache-taillee-neolithique / Réalisé au GéoLab UniLaSalle : https://www.unilasalle.fr/geolab Source: Objaverse 1.0 / Sketchfab
Artefact urbain - Bague Jésuite
Source: Objaverse 1.0 / Sketchfab
Artefact - Biface acheuléen d'Amiens
**Nom** : Biface acheuléen d'Amiens. **Description** : Artefact achéologique taillé. **Dimensions** : 10 x 15 cm. **Âge** : Paléolithique inférieur. **Localité** : Amiens, Somme, France. **Méthode** : Photogrammétrie avec Agisoft metashape. **Réalisation** : UniLaSalle / Collection du Musée Albert de Lapparent : https://www.musee-delapparent.com/fr/echantillons/fossiles/310-biface-acheuleen / Réalisé au GéoLab UniLaSalle : https://www.unilasalle.fr/geolab Source: Objaverse 1.0 / Sketchfab
Artefact - Biface acheuléen de Cagny
**Nom** : Biface acheuléen de Cagny. **Description** : Artefact achéologique taillé. **Dimensions** : Inconnues. **Âge** : Inconnu. **Localité** : Cagny, Normandie, France. **Méthode** : Photogrammétrie avec Agisoft metashape. **Réalisation** : UniLaSalle / Collection du Musée Albert de Lapparent : https://www.musee-delapparent.com/fr/echantillons/fossiles/1265-biface-acheuleen / Réalisé au GéoLab UniLaSalle : https://www.unilasalle.fr/geolab Source: Objaverse 1.0 / Sketchfab
The Impact of Code Ownership of DevOps Artefacts on the Outcome of DevOps CI Builds
<p><strong># The Impact of Code Ownership of DevOps Artefacts on the Outcome of DevOps CI Builds</strong></p><p>This is the replication package of an empirical analysis on a dataset of 892,193 DevOps CircleCI builds spanning 1,689 Open-Source Software projects.</p><p>We employ a two-pronged approach to our study. First, we investigate the impact of chronological code ownership of DevOps artefacts on the outcome of a DevOps CI build on a build level. Second, we study the impact of the Skewness of DevOps contributions on the success rate of DevOps CI builds on a project level.</p>
Data and visualisation code from 'Effects and avoidance of photoconversion-induced artefacts in confocal and STED microscopy' by Dasgupta et al (2024)
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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>
Artefact for paper "LTL under reductions with weaker conditions than stutter-invariance"
<p><strong>LTLSensitivityToLength : LTL artefacts on length-sensitivity</strong></p> <p>Procedures and artefacts to reproduce experiments on "length" sensitivity of LTL formulas.</p> <p>We use both Tapaal <a href="https://www.tapaal.net/">https://www.tapaal.net/</a> and ITS-Tools <a href="https://lip6.github.io/ITSTools-web/">https://lip6.github.io/ITSTools-web/</a> in these experiments on models taken from the model-checking competition 2021 <a href="https://mcc.lip6.fr/">https://mcc.lip6.fr/</a>.</p> <p>See also companion GitHub project here : https://github.com/yanntm/LTLSensitivityToLength/</p> <p><strong>Workflow and steps</strong></p> <p>Please refer to the source of the "~/demo.sh" file for more details on the different steps.</p> <ul> <li>Step 0 : download the VM provided by the conference from here : <a href="https://zenodo.org/record/5562597">https://zenodo.org/record/5562597</a> Click the ".ova" to download the VM then open it and start it with VirtualBox.</li> <li>Step 1 : login to the VM with user/pass : tacas22/tacas22. You can change keyboard setting by doing : right click background, Display Setting, left in "Region & Language", "Input sources", first "Add" French/your keyboard type (it is in "other") then trash "US-en" using the garbage can icon. Right-click background->"Open in terminal" will now give us a terminal with correct keyboard.</li> <li>Step 2 : Download and deploy artefact in the VM. Use the Zenodo link at the bottom of this page to download and place it at the root of the tacas22 home: in /home/tacas22/. Then deploy with tar xvzf home.tgz.</li> <li>Step 3 : There are several Readme with more details but the rest is done using the demo.sh so simply run it. It will enact the further steps.</li> </ul> <p><strong>Steps in the demo</strong></p> <p>Much more detail is available in the ~/demo.sh file.</p> <ul> <li>Step 0 : install debian dependencies. We use R language and modern Java.</li> <li>Step 1 : Analyze sensitivity to length of LTL formulas and compute reduced models. This step is handled by ITS-tools. Because the benchmark is so huge we only reproduce using three model instances (out of 2822). More examples can be run by editing "step 1" of demo.sh to add more runs.</li> <li>Step 2 : run an MCC model-checker with both reduced and original model/formula pairs. Collect logs and compute a CSV summary file from these raw logs. We run both Tapaal and ITS-Tools. We did our best to simulate the conditions we ran in (we capture output in OAR.XX.out and OAR.XX.err files), but we use a cluster and OAR to reserve cpu on it in the full experiment so the match is not perfect. The actual scripts we used on the cluster are also part of this distribution however.</li> <li>Step 3 : build tables from the CSV and formulas of literature on sensitivity to length in practice. This corresponds to the Table in section 4.1 of the paper. For a large set of formulas we compute whether it is stutter insensitive, shortening insensitive, lengthening insensitive, or arbitrary. This step can actually be performed after or before Step 2 it only depnds on data produced at step 1.</li> <li>Step 4 : build tables and plots from the CSV of Step 2 using R. The demo script builds both the plots using the data you have just collected at step 1 and using the full data from our experiment. To make these auditable, we provide the CSV resulting from our cluster run in ~/tacas22/Rscripts/clusterLog as well as all the logs of this run in the ~/tacas22/logsCluster.tgz that were parsed to build the CSV.</li> </ul> <p><strong>Contents</strong></p> <p>We have deployed all dependencies.</p> <ul> <li>~/usr/ contains local installations of the LTL manipulation library Spot and of libraried required for R (used in analysis)</li> <li>~/tapaal/ contains Tapaal, configured for MCC mode. It was built from the bzr depot of Tapaal from source, following instructions helpfully provided by the authors Jiri Srba et al.</li> <li>~/tacas22/Spot-Binary-Builds/ is the folder used to build Spot with appropriate flags. We used "build_cluster.sh" script. You do not need to do this however it is deployed in ~/usr/local. The companion github is <a href="https://github.com/yanntm/Spot-Binary-Builds">https://github.com/yanntm/Spot-Binary-Builds</a>.</li> <li>~/tacas22/packages/ contains the apt-get debian packages needed to run our demo. Essentially we need R language and Java language support.</li> </ul> <p>There are instructions on how to rebuild these dependencies from scratch in each subfolder. Again it is not necessary to do so with the provided archive.</p> <p>Then the tools/models :</p> <ul> <li>~/tacas22/ITS-Tools-MCC contains the ITS-Tools distribution as well as the inputs from the MCC'21 edition. Both of these are extracted and installed following the instruction on the three github : <a href="https://github.com/yanntm/pnmcc-models-2021">https://github.com/yanntm/pnmcc-models-2021</a> (curated/annotated models from MCC 21) <a href="https://github.com/yanntm/pnmcc-tests">https://github.com/yanntm/pnmcc-tests</a> (For the test/runner framework) <a href="https://github.com/yanntm/ITS-Tools-MCC">https://github.com/yanntm/ITS-Tools-MCC</a> (For ITS-tools distributed for the MCC)</li> <li>~/tacas22/LTLPatterns/ contains formulas collected from the literature as well as a script to analyze their sensitiivty and compute metrics on them. See the README in that folder for more details.</li> <li>~/tacas22/Rscripts/ contains scripts to analyze the results and produce plots used in the paper. See the README in that folder for more details. It also contains the CSV produced from our cluster run (in clusterLog/) to reproduce the plots (these were built using the full logs provided in ~/tacas22/logsCluster.tgz and analyzed with the perl scripts from the ~/tacas22/ITS-Tools-MCC folder)</li> </ul> <p>The requirements for this setup include:</p> <ul> <li>A version of Spot : <a href="https://spot.lrde.epita.fr/">https://spot.lrde.epita.fr/</a> to both translate LTL to an automaton and analyze its sensitivity.</li> <li>The models and formulas from the <a href="https://mcc.lip6.fr/">Model Checking Contest 2021</a>. We grab these from our <a href="https://github.com/yanntm/pnmcc-models-2021">PNMCC Models 2021</a> repository that itself builds the files using the official distribution of the MCC.</li> <li>Our test/runner framework for these examples, available from <a href="https://github.com/yanntm/pnmcc-tests">https://github.com/yanntm/pnmcc-tests</a></li> </ul> <p>Then we need a MCC compatible tool that can compete in the LTL category of the contest. We used~:</p> <ul> <li>A version of <a href="http://ddd.lip6.fr/">ITS-tools</a> : we use the version packaged for the MCC competition, available from here : <a href="https://github.com/yanntm/ITS-commandline">ITS-tools for MCC</a></li> <li>A version of <a href="https://www.tapaal.net/">Tapaal</a> : we build it from the source repositories with flags to enable MCC mode. See repository here : <a href="https://bazaar.launchpad.net/~verifypn-maintainers/verifypn/new-trunk/files/head:/Scripts/MCC21/competition-scripts">https://bazaar.launchpad.net/~verifypn-maintainers/verifypn/new-trunk/files/head:/Scripts/MCC21/competition-scripts</a> and <a href="https://code.launchpad.net/verifypn">https://code.launchpad.net/verifypn</a></li> </ul> <p><strong>License</strong></p> <p>This work is provided under the terms of GPL v3 or more recent.</p> <p>(C) Yann Thierry-Mieg, Denis Poitrenaud, Etienne Renault, Emmanuel Paviot-Adet. Sorbonne Université, CNRS. 2021.</p>
RePAST dataset Artistic and cultural artefacts
<p>RePAST dataset Artistic and cultural artefacts</p>
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference [Artefact Evaluation]
<p>Source code of paper "Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference" submitted to FPGA'22 for artefact evaluation.</p>
Data from: The fourth dimension of tool use: temporally enduring artefacts aid primates learning to use tools
All investigated cases of habitual tool use in wild chimpanzees and capuchin monkeys include youngsters encountering durable artefacts, most often in a supportive social context. We propose that enduring artefacts associated with tool use, such as previously used tools, partly processed food items and residual material from previous activity, aid non-human primates to learn to use tools, and to develop expertise in their use, thus contributing to traditional technologies in non-humans. Therefore, social contributions to tool use can be considered as situated in the three dimensions of Euclidean space, and in the fourth dimension of time. This notion expands the contribution of social context to learning a skill beyond the immediate presence of a model nearby. We provide examples supporting this hypothesis from wild bearded capuchin monkeys and chimpanzees, and suggest avenues for future research.
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.