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65 results for “Replication dataset”
Supplementary Datasets for the publication "Rousettus aegyptiacus Fruit Bats Do Not Support Productive Replication of Cedar Virus upon Experimental Challenge"
<p>Cedar henipavirus (CedV), which was isolated from the urine of pteropodid bats in Australia, belongs to the genus Henipavirus in the family of Paramyxoviridae. It is closely related to the Hendra virus (HeV) and Nipah virus (NiV), which have been classified at the highest biosafety level (BSL4) due to their high pathogenicity for humans. Meanwhile, CedV is apathogenic for humans and animals. As such, it is often used as a model virus for the highly pathogenic henipaviruses HeV and NiV. In this study, we challenged eight Rousettus aegyptiacus fruit bats of different age groups with CedV in order to assess their age-dependent susceptibility to a CedV infection. Upon intranasal inoculation, none of the animals developed clinical signs, and only trace amounts of viral RNA were detectable at 2 days post-inoculation in the upper respiratory tract and the kidney as well as in oral and anal swab samples. Continuous monitoring of the body temperature and locomotion activity of four animals, however, indicated minor alterations in the challenged animals, which would have remained unnoticed otherwise.</p>
Dataset and replication information for It's About Time: How to Study Intertemporal Choice in Systems Design
<p>Dataset and replication package for the paper <em>It's About Time: How to Study Intertemporal Choice in Systems Design</em> (Fagerholm, F., De los Ríos, A., Cárdenas Castro, C., Gil, J., Chatzigeorgiou, A., Ampatzoglou, A., Becker, C. (2023). It’s About Time: How To Study Intertemporal Choice in Systems Design. Information and Software Technology.). The dataset consists of answers to a scenario-based questionnaire that collects data on intertemporal choice in the context of software development. An analysis script is provided to show the details of the calculations and analyses performed for the paper. The replication package includes the protocol for data collection sessions and different versions of the task scenario and questionnaire. More information is given in the description file.</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
Dataset and code from the ATLAS phone survey to replicate analysis of HIVST positivity rates and linkage to confirmatory testing
<p>This dataset and code allow to replicate the analysis presented in the paper entitled "HIV self-testing positivity rate and linkage to confirmatory testing and care: a telephone survey in Côte d'Ivoire, Mali and Senegal" by Arsène Kra Kouassi et al. Preprint available at <a href="https://doi.org/10.1101/2023.06.10.23291206">https://doi.org/10.1101/2023.06.10.23291206</a></p><p>The data was collected within the ATLAS project, funded by Unitaid and coordinated by Solthis and IRD.</p><p>ATLAS website: <a href="https://atlas.solthis.org/">https://atlas.solthis.org/</a></p><p>ATLAS presentation on Ceped website: <a href="https://www.ceped.org/atlas">https://www.ceped.org/atlas</a></p><p>ATLAS publications portal: <a href="https://hal.science/ATLAS_ADVIH/">https://hal.science/ATLAS_ADVIH/</a></p><p> </p><p> </p>
Dataset and replication package for Temporal Discounting in Software Engineering: A Replication Study
<p>Dataset and replication package for the paper Temporal Discounting in Software Engineering: A Replication Study (Fagerholm, F., Becker, C., Chatzigeorgiou, A., Betz, S., Duboc, L., Penzenstadler, B., Mohanani, R., Venters, C. (2019). Temporal Discounting in Software Engineering: A Replication Study. 13th ACM/IEEE International Symposium of Empirical Software Engineering and Measurement (ESEM 2019)). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. Two questionnaire templates illustrate how to gather the data for professional and student participants. An analysis script is provided which shows the details of the calculations and analyses performed for the paper. More information is given in the description file.</p>
Studying Bug-Fixing Commits in the WoC Dataset: Replication Package
<p>A replication package for the MSR 2023 Challenge submission titled "Studying Bug-Fixing Commits in the WoC Dataset".</p>
replicAnt - Plum2023 - Detection & Tracking Datasets and Trained Networks
<p>This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for detection and tracking experiments in the<em> replicAnt - generating annotated images of animals in complex environments using Unreal Engine</em> manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt <a href="http://peerj.com/articles/11155/">peerj.com/articles/11155/</a>. All synthetic data has been generated with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>.</p> <p><strong>Abstract:</strong></p> <p>Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.</p> <p><strong>Benchmark data</strong></p> <p>Two video datasets were curated to quantify detection performance; one in laboratory and one in field conditions. The laboratory dataset consists of top-down recordings of foraging trails of <em>Atta vollenweideri</em> (Forel 1893) leaf-cutter ants. The colony was collected in Uruguay in 2014, and housed in a climate chamber at 25°C and 60% humidity. A recording box was built from clear acrylic, and placed between the colony nest and a box external to the climate chamber, which functioned as feeding site. Bramble leaves were placed in the feeding area prior to each recording session, and ants had access to the recording area at will. The recorded area was 104 mm wide and 200 mm long. An OAK-D camera (OpenCV AI Kit: OAK-D, Luxonis Holding Corporation) was positioned centrally 195 mm above the ground. While keeping the camera position constant, lighting, exposure, and background conditions were varied to create recordings with variable appearance: The “base” case is an evenly lit and well exposed scene with scattered leaf fragments on an otherwise plain white backdrop. A “bright” and “dark” case are characterised by systematic over- or underexposure, respectively, which introduces motion blur, colour-clipped appendages, and extensive flickering and compression artefacts. In a separate well exposed recording, the clear acrylic backdrop was substituted with a printout of a highly textured forest ground to create a “noisy” case. Last, we decreased the camera distance to 100 mm at constant focal distance, effectively doubling the magnification, and yielding a “close” case, distinguished by out-of-focus workers. All recordings were captured at 25 frames per second (fps).<br> <br> The field datasets consists of video recordings of <em>Gnathamitermes</em> sp. desert termites, filmed close to the nest entrance in the desert of Maricopa County, Arizona, using a Nikon D850 and a Nikkor 18-105 mm lens on a tripod at camera distances between 20 cm to 40 cm. All video recordings were well exposed, and captured at 23.976 fps.<br> <br> Each video was trimmed to the first 1000 frames, and contains between 36 and 103 individuals. In total, 5000 and 1000 frames were hand-annotated for the laboratory- and field-dataset, respectively: each visible individual was assigned a constant size bounding box, with a centre coinciding approximately with the geometric centre of the thorax in top-down view. The size of the bounding boxes was chosen such that they were large enough to completely enclose the largest individuals, and was automatically adjusted near the image borders. A custom-written Blender Add-on aided hand-annotation: the Add-on is a semi-automated multi animal tracker, which leverages blender’s internal contrast-based motion tracker, but also include track refinement options, and CSV export functionality. Comprehensive documentation of this tool and Jupyter notebooks for track visualisation and benchmarking is provided on the <a href="https://github.com/evo-biomech/replicAnt"><em>replicAnt</em></a> and <a href="https://github.com/FabianPlum/blenderMotionExport">BlenderMotionExport</a> GitHub repositories.</p> <p><strong>Synthetic data generation</strong></p> <p>Two synthetic datasets, each with a population size of 100, were generated from 3D models of \textit{Atta vollenweideri} leaf-cutter ants. All 3D models were created with the <em>scAnt</em> photogrammetry workflow. A “group” population was based on three distinct 3D models of an ant minor (1.1 mg), a media (9.8 mg), and a major (50.1 mg) (see <a href="https://zenodo.org/record/7849059">10.5281/zenodo.7849059</a>)). To approximately simulate the size distribution of <em>A. vollenweideri </em>colonies, these models make up 20%, 60%, and 20% of the simulated population, respectively. A 33% within-class scale variation, with default hue, contrast, and brightness subject material variation, was used. A “single” population was generated using the major model only, with 90% scale variation, but equal material variation settings.<br> <br> A <em>Gnathamitermes</em> sp. synthetic dataset was generated from two hand-sculpted models; a worker and a soldier made up 80% and 20% of the simulated population of 100 individuals, respectively with default hue, contrast, and brightness subject material variation. Both 3D models were created in Blender v3.1, using reference photographs.<br> <br> Each of the three synthetic datasets contains 10,000 images, rendered at a resolution of 1024 by 1024 px, using the default generator settings as documented in the Generator_example level file (see documentation on <a href="https://github.com/evo-biomech/replicAnt">GitHub</a>). To assess how the training dataset size affects performance, we trained networks on 100 (“small”), 1,000 (“medium”), and 10,000 (“large”) subsets of the “group” dataset. Generating 10,000 samples at the specified resolution took approximately 10 hours per dataset on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super).</p> <p><br> Additionally, five datasets which contain both real and synthetic images were curated. These “mixed” datasets combine image samples from the synthetic “group” dataset with image samples from the real “base” case. The ratio between real and synthetic images across the five datasets varied between 10/1 to 1/100.</p> <p><strong>Funding</strong></p> <p>This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>
replicAnt - Plum2023 - Pose-Estimation Datasets and Trained Models
<p>This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for semantic and instance segmentation experiments in the<em> replicAnt - generating annotated images of animals in complex environments using Unreal Engine</em> manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt <a href="http://peerj.com/articles/11155/">peerj.com/articles/11155/</a>. All synthetic data has been generated with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>.</p> <p><strong>Abstract:</strong></p> <p>Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.</p> <p><strong>Benchmark data</strong></p> <p>Two pose-estimation datasets were procured. Both datasets used first instar <em>Sungaya nexpectata</em> (Zompro 1996) stick insects as a model species. Recordings from an evenly lit platform served as representative for controlled laboratory conditions; recordings from a hand-held phone camera served as approximate example for serendipitous recordings in the field. </p> <p>For the platform experiments, walking <em>S. inexpectata</em> were recorded using a calibrated array of five FLIR blackfly colour cameras (Blackfly S USB3, Teledyne FLIR LLC, Wilsonville, Oregon, U.S.), each equipped with 8 mm c-mount lenses (M0828-MPW3 8MM 6MP F2.8-16 C-MOUNT, CBC Co., Ltd., Tokyo, Japan). All videos were recorded with 55 fps, and at the sensors’ native resolution of 2048 px by 1536 px. The cameras were synchronised for simultaneous capture from five perspectives (top, front right and left, back right and left), allowing for time-resolved, 3D reconstruction of animal pose.<br> <br> The handheld footage was recorded in landscape orientation with a Huawei P20 (Huawei Technologies Co., Ltd., Shenzhen, China) in stabilised video mode: <em>S. inexpectata </em>were recorded walking across cluttered environments (hands, lab benches, PhD desks etc), resulting in frequent partial occlusions, magnification changes, and uneven lighting, so creating a more varied pose-estimation dataset.<br> <br> Representative frames were extracted from videos using DeepLabCut (DLC)-internal k-means clustering. 46 key points in 805 and 200 frames for the platform and handheld case, respectively, were subsequently hand-annotated using the DLC annotation GUI.</p> <p><strong>Synthetic data</strong></p> <p>We generated a synthetic dataset of 10,000 images at a resolution of 1500 by 1500 px, based on a 3D model of a first instar <em>S. inexpectata </em>specimen, generated with the <a href="https://peerj.com/articles/11155/"><em>scAnt</em> photogrammetry workflow</a>. Generating 10,000 samples took about three hours on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super). We applied 70\% scale variation, and enforced hue, brightness, contrast, and saturation shifts, to generate 10 separate sub-datasets containing 1000 samples each, which were combined to form the full dataset.</p> <p><strong>Funding</strong></p> <p>This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>
replicAnt - Plum2023 - Segmentation Datasets and Trained Models
<p>This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for semantic and instance segmentation experiments in the<em> replicAnt - generating annotated images of animals in complex environments using Unreal Engine</em> manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt <a href="http://peerj.com/articles/11155/">peerj.com/articles/11155/</a>. All synthetic data has been generated with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>.</p> <p><strong>Abstract:</strong></p> <p>Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.</p> <p><strong>Benchmark data</strong></p> <p>Semantic and instance segmentation is used only rarely in non-human animals, partially due to the laborious process of curating sufficiently large annotated datasets. <em>replicAnt </em>can produce pixel-perfect segmentation maps with minimal manual effort. In order to assess the quality of the segmentations inferred by networks trained with these maps, semi-quantitative verification was conducted using a set of macro-photographs of <em>Leptoglossus zonatus</em> (Dallas, 1852) and <em>Leptoglossus phyllopus</em> (Linnaeus, 1767), provided by Prof. Christine Miller (University of Florida), and Royal Tyler (Bugwood.org. For further qualitative assessment of instance segmentation, we used laboratory footage, and field photographs of <em>Atta vollenweideri</em> provided by Prof. Flavio Roces. More extensive quantitative validation was infeasible, due to the considerable effort involved in hand-annotating larger datasets on a per-pixel basis.</p> <p><strong>Synthetic data</strong></p> <p>We generated two synthetic datasets from a single 3D scanned <em>Leptoglossus zonatus</em> (Dallas, 1852) specimen: one using the default pipeline, and one with additional plant assets, spawned by three dedicated scatterers. The plant assets were taken from the Quixel library and include 20 grass and 11 fern and shrub assets. Two dedicated grass scatterers were configured to spawn between 10,000 and 100,000 instances; the fern and shrub scatterer spawned between 500 to 10,000 instances. A total of 10,000 samples were generated for each sub dataset, leading to a combined dataset comprising 20,000 image render and ID passes. The addition of plant assets was necessary, as many of the macro-photographs also contained truncated plant stems or similar fragments, which networks trained on the default data struggled to distinguish from insect body segments. The ability to simply supplement the asset library underlines one of the main strengths of <em>replicAnt</em>: training data can be tailored to specific use cases with minimal effort.</p> <p><strong>Funding</strong></p> <p>This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>
Replication package: Dataset and stata-do-file for analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities"
<p>This dataset was used for the analysis in "Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities". The data was collected in Namibia in 2017 as part of the SASSCAL research project by Nils Christian Hoenow and Adrian Pourviseh as members of the Chair for Development and Cooperative Economics at the University of Marburg. Funded by the Southern African Science Service Center for Climate Change and Adaptive Land-UseManagement (SASSCAL) through the German Federal Ministry for Education and Research (Grant No. 01LG1201B).</p> <p> </p> <p>Article Title: Intragroup communication in social dilemmas: An artefactual public good field experiment in small-scale communities </p> <p>Authors: Nils Christian Hoenow* and Adrian Pourviseh**</p> <p> </p> <p>*RWI – Leibniz Institute for Economic Research, Essen, Germany and & School of Business and Economics, University of<br>Marburg, Marburg, Germany</p> <p>**School of Business and Economics, University of<br>Marburg, Marburg, Germany</p> <p>Abstract: <br>Communication is well-known to increase cooperation rates in social dilemma situations, but the exact mechanisms behind this remain largely unclear. This study examines the impact of communication on public good provisioning in an artefactual field experiment conducted with 216 villagers from small, rural communities in northern Namibia. In line with previous experimental findings, we observe a strong increase in cooperation when face-to-face communication is allowed before decision-making. We additionally introduce a condition in which participants cannot discuss the dilemma but talk to their group members about an unrelated topic prior to learning about the<br>public good game. It turns out that this condition already leads to higher cooperation rates, albeit not as high as in the condition in which discussions about the social dilemma are possible. The setting in small communities also allows investigating the effects of pre-existing social relationships between group members and their interaction with communication.We find that both types of communication are primarily effective among socially more distant group members, which suggests that communication and social ties work as substitutes in increasing cooperation. Further analyses rule out better comprehension of the game and increased mutual expectations of one’s group members’ contributions as drivers for the communication effect. Finally, we discuss the role of personal and injunctive norms to keep commitments made during discussions.</p>
Replicated radiation of a plant clade along a cloud forest archipelago dataset
<p>Raw data complementary of https://github.com/eaton-lab/Orienotinus-phylogeny repository</p> <p>Assembly output of <em>Oreinotinus </em>species (<em>Viburnum</em>, Adoxaceae) using ipyrad pipeline and morphological matrix:</p> <p>List of files:</p> <ul> <li><code>full_dataset.loci</code> Custom format that shows each individual locus with variable sites indicated.</li> <li><code>full_dataset.phy </code>Concatenated RAD loci in Phylip format</li> <li><code>full_dataset.seqs.hdf5</code> Sequences formated as HDF5 file</li> <li><code>full_dataset.snps</code> Phylip files that includes only variable sites (SNPs)</li> <li><code>full_dataset.snps.hdf5</code> SNPs formated as HDF5 file</li> <li><code>full_dataset.snpsmap</code> Information about location (locus) of SNPs</li> <li><code>full_dataset_stats.txt</code> Statistics about the assembly</li> <li><code>leaf_traits_measurements.tsv</code> TSV file that contains leaf measurments used in the discriminant analyses.</li> <li><code>canopy_data.txt</code> Canopy cover data</li> <li><code>central_plateau_data.txt</code> Macroenvironmental data for the Chiapas central plateau</li> <li><code>full_distribution.txt</code> Specimen metadata and coordinates for all the points for Oreinotinus distribution</li> <li><code>leaf_wetness_temp.txt</code> Climate station data for leaf wetness, temperature</li> <li><code>leaf_traits_measurements.tsv</code> TSV file that contains leaf measurments used in the discriminant analyses.</li> <li><code>tree_accessions.txt</code> Species-level tree accessions with specimen metadata</li> </ul>
Dataset and Replication Package for the View-Based Retriever Approach To Reverse Engineering Software Architecture Models
<div> <div><span>Dataset and replication package for the view-based Retriever approach to reverse engineering software architecture models. Each Dataset project is structured as follows:</span></div> <ul> <li><span>The .ruleengine.yml file contains the configuration for running the Retriever approach.</span> <ul> <li><span>The repository value is the ID of a GitHub repository.</span></li> <li><span>The current_version value is the latest version of the retriever approach used to build the architectural models.</span></li> <li><span>The rules values are the rules used to build the architectural models.</span></li> </ul> </li> <li><span>The model_re folder contains the architectural model of the system automatically generated by the Retriever approach.</span> <ul> <li><span>The pcm folder contains the Palladio Component Model (PCM) of the system.</span></li> <li><span>The uml folder contains the PlantUML model.</span></li> </ul> </li> <li><span>The model_gs folder contains our manual gold standards for the system.</span></li> </ul> <div><span>The easiest way to use our approach is to use the CLI application with the given parameters: ./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules</span></div> </div>
Dataset and replication information for Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future?
<p>Dataset and replication information for the paper Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future? (Becker, C., Fagerholm, F., Mohanani, R., Chatzigeorgiou, A., 2009). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. The respondents are from two companies. The raw data from the two companies is given separately. Information showing how to replicate the calculations required for analysis is given for each company. The dataset and replication information are given in both Excel (xlsx) and Open Document (ods) formats.</p>
De novo modelling of HEV replication polyprotein: Five-domain breakdown and involvement of flexibility in functional regulation - DATASET
<p>All models used for the paper "De novo modelling of HEV replication polyprotein: Five-domain breakdown and involvement of flexibility in functional regulation".</p>
Dataset to "Loneliness is associated with retrospective self-reports of adverse childhood experiences – A replication study in Ethiopia"
<p>Find our SPSS Dataset belonging to the yet to be published work <strong>“Loneliness is associated with retrospective self-reports of adverse childhood experiences – A replication study in Ethiopia”</strong>. This cross-sectional study is investigating the interplay between adverse childhood experiences, loneliness, social network size, and mental health problems in Ethiopia in a clinical and non-clinical group. The study sample consists of 125 psychiatric outpatients at Jimma University Medical Center, South-West of Ethiopia, suffering from major depressive disorder, bipolar disorder, or psychotic disorders, and 131 non-clinical study participants. Led by an interviewer, participants completed the UCLA Loneliness Scale, the Childhood Trauma Questionnaire, the Social Network Index, and the WHO-5 Well-Being Index. </p> <p>Definitions of the variable values and additional information can be found in the attached Codebook.</p>
DATASET: Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP
<p>Complete data set to support content of the study: Cui, et al (2022) <strong>Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP</strong></p>
Approaching the Unknown: Replication file and Dataset
<p>Dataset with R replication file for the paper: Approaching the Unknown. COVID-19 pandemic, political parties and digital adaptations: party élites' perceptions in Italy and Spain</p>
Reproducible Validation and Replication Studies in Nanoscale Physics (problem datasets for Rockstuhl et al. 2005 replication)
<p>Problem folders including all the input files necessary to reproduce the computations of the results related to Rockstuhl et al. 2005 on the paper: Reproducible Validation and Replication Studies in Nanoscale Physics</p>
Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases - Dataset
<p>The data part of this release support the results presented in the paper<br /> "Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases", by R. Osman, J. F. Perez, and G. Casale, accepted in IEEE International Conference on Software Quality, Reliability, and Security (QRS) 2016.</p> <p>When referring to the dataset or scripts please cite the paper above.</p> <p> </p> <p> </p>
Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"
<p>Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"</p><ul><li>SDR Results</li><li>SDR Queries</li><li>VALIDATE User Guide</li><li>Original Studies References in BibTeX</li></ul>
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.