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470 results for “tool use”
Dataset for training SENMAP, a automatic tool to curate LTR-retrotransposons using convolutional neural networks
<p>Transposable elements (TEs) are specific structures of the genome of species, which can move from one location to another. For that reason, they can cause mutations or changes that can be negative, such as the appearance of diseases, or beneficial, such as participating in fundamental roles in the evolution of genomes and genetic diversity. Long Terminal Repeat retrotransposons (LTR-RT) are the most abundant in plant species, hence the importance of studying these structures in particular. Over the time, these elements can suffer changes called nested insertions, which can inactivate or modify the functioning of the element, for that they are no longer consider as intact element and cannot be used for identification and classification studies. We create a dataset containing 56,442 LTR-RTs targed as "non-intact" elements and 49,215 considered as "intact". </p> <p>We formated the sequences IDs in order to keep relevant information as the superfamily and the lineage, as well as the category (Negative for "non-intact" and Positive for "intact" elements). </p> <p> This dataset (the npy files obtained from the fasta file) was used for training SENMAP, a convolutional neural network architecture to obtain intact LTR-RT sequences in plant genomes, which is composed by four convolutional layers, LeakyReLU as activation function and BinaryFocalLoss as loss function. Achieving an F1-score percentage of 91.37% with test data, identifying low quality sequences rapidly and efficiently, contributing to curate libraries of LTR retrotransposons of plants genomes published in large-scale sequencing projects due to the post-genomic era.</p>
FAIRness Assessment of Biomedical Data Using Automated Tools (Dataset)
<p>The data were collected as part of a Master's thesis project aimed at evaluating various automated FAIR assessment tools, applying them to biomedical data. The data sets identifiers were gathered as part of the Open Data LoM and IoM incentivization at Charité Universitätsmedizin Berlin, available at <a title="Dataset of the results of data validation for articles from 2021" href="https://doi.org/10.5281/zenodo.8249758">https://doi.org/10.5281/zenodo.8249758</a>, and reused in this project.</p> <p>The data represents cleaned, aggregated, and transformed results obtained from the API services of the following FAIR assessment tools: F-UJI, FAIR Enough, FAIR-Checker, and FAIR EVA.</p> <p>The raw data in .Rdata format will be shared on GitHub repository at <a title="FAIR Tools Analysis" href="https://github.com/anastasiabright/fair-tools-analysis">https://github.com/anastasiabright/fair-tools-analysis</a>.</p>
AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study
<div> <p>This data represents four artifacts from our research in studying what impacts AI adoption and use in SE. It includes our interview questions, the codebook with example quotes, survey questions, and table with the code to category generation.</p> <p>This page includes supplementary materials associated with our paper entitled "<span>AI Tool Use and Adoption in Software Development by </span><span>Individuals and Organizations: A Grounded Theory Study</span>".</p> </div>
Fig. 2 in Combination of nuclear and mitochondrial markers as a useful tool to identify Ctenophthalmus species and subspecies (Siphonaptera: Ctenophthalmidae)
Fig. 2 Morphological characteristics of males of C. b. arvernus and C. n. dobyi. A—C. b. arvernus distal arm of IX sternum (arrowed); B—C. b. arvernus dorsal process basimere with only one long setae (arrowed); C—C. b. arvernus ventral process basimere with an apical slot
Fig. 1 in Combination of nuclear and mitochondrial markers as a useful tool to identify Ctenophthalmus species and subspecies (Siphonaptera: Ctenophthalmidae)
Fig. 1 Morphological characteristics of males of C. a. catalaniensis. A—Distal arm of IX sternum (arrowed); B—dorsal process basimere with two long setae (arrowed); C—ventral process basimere with an apical slot (arrowed); D—telomere carrying one curved long setae (arrowed)
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
Improving source code change set analysis by using a visual tool. Controlled experiment dataset.
<p>Modern software development is performed by developing features in isolated branches by each member of a software development team. When these branches need to be integrated, they have to be manually reviewed by an integrator. Source code reviewing can be a tedious and time consuming task, which is normally performed by hand reviewing a textual diff of the change set.</p> <p>The difficulty of reviewing source code changes can have a negative impact on the accep- tance of these changes. It has been shown that this can imply the rejection of important bug fixes for a software project.</p> <p>In order to facilitate the task of reviewing source code change sets, we designed and implemented a visual tool. Our visual tool helps in assessing source code change sets by providing different views of the change set: an overall overview of the change set with metrics, and a visualization representing the structural changes in the source code. We evaluated our visual tool by performing a controlled experiment with software practitioners.</p> <p>Our experimental results show no significant differences between using our visual tool and a textual diff tool in the following terms: time of analysis, precision of the analysis, and inference of intention of changes. However, we did find a positive user perception, and a reduced mental load when using our visual tool.</p> <p>Anonymized datasets with the answers to the controlled experiments for evaluating the Git Thermite visual tool for assessing source code change sets. Copies of these datasets are provided in both, CSV format, and OpenDocument format.</p> <p>Exact copies of the learning materials that was provided to the participants during the execution of the controlled experiments is also included along the dataset.</p>
APRSuite: A Suite of Components and Use Cases Based on Categorical Decomposition of Automatic Program Repair Techniques and Tools
<p><strong>During the last decade, we are witnessing the advent of a proliferation of techniques and associated tools for automatic program repair (APR). The current techniques and tools provide rich sources of knowledge that should be taken into consideration for future research. An overview of the current APR techniques and tools can serve the research community as a knowledge accumulator. However, APR techniques and tools differ in many aspects making knowledge accumulation challenging. To overcome this challenge, in this paper, we propose to leverage common components that constitute the APR techniques and tools. To achieve this objective, we surveyed current APR techniques and tools to identify the APR Suite of common constituent components, namely as APRSuite. Repair source and defect class are examples of identified components. We grouped these components into several categories such as patch evaluation and target defects. We have also identified some of the possible use cases per component as well as different lessons learned in studies for each component and for each use case. In addition, we developed a principled way for application of the components. The <em>APRSuite</em> and the <em>principled way</em> to apply it comprise a <em>framework</em> for knowledge accumulation, evaluation, and comparison of APR techniques and tools. The novelty of our work lies in its original viewpoint to the process of literature review in the APR research field. To demonstrate the applicability of the framework, we mapped out several concrete APR techniques, as a first instantiation of the framework. We observed that the framework brings discipline into the evaluation and/or comparison of APR techniques and tools. The framework offers these benefits objectively and systematically. We concluded that knowledge accumulation and characterization through literature reviews can be therefore facilitated through the identified suite of components while at the same time the existing component suite can be modified, augmented, or improved.</strong></p>
Merged citation library dataset of 3 bibliographic databases for use in testing deduplication tools
<p>Endnote xml file of complete search results - not de-duplicated. </p> <p>Search query (cannabis based medicines, endocannabinoid system modulators and cannabinoids tested in animal models of pathological or injury-related persistent pain) conducted on 9 April 2019. </p> <p>Searched databases: Web of Science, Embase and PubMed</p> <p>The citations retrieved from the searches of each database have been merged together, there is a total of 15216 citations. </p> <p> </p>
FIGURE 3 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 3. Morphological bone-to-bone comparison between the 'nominal' male (CP001) and 'actual' female (CP002) Xenopus laevis. The differences are colour-coded and show female (CP002) variance relative to the nominal bone of the male (CP001) which is depicted in the figure.
FIGURE 4 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 4. Morphological bone-to-bone comparison between the 'nominal' Xenopus laevis (CP001) with the 'actual' fossil Xenopus sp. (ZM 71336)
FIGURE 2 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 2. Bone cortex thickness analysis on a male Xenopus laevis (CP001) (A) and a fossil Xenopus sp. (ZM 71336) (B) depicted side by side in slice view from top view (1) and side view (3) and in a 3D colour-coded analysis (2 and 3).
FIGURE 1. A in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 1. A complete Breviceps montanus (Catalogue number ZR-050053) CT scan with segmentation of humerus and femur demonstrated.
Video Tutorials for Using the Solidipes Curation Tool
<h2>Contributions</h2> <ul> <li>Emmanuelle Denove realized the project, conceived the videos and proceeded to the curated export into Zenodo</li> <li>Guillaume Anciaux and Son Pham-Ba supervised the realization by attending brainstorming meetings and by providing feedbacks.</li> </ul> <h2>Data structure and information</h2> <p>This dataset contains 12 videos exported as .mp4 files, as well as their source .prproj (Premiere Pro Project) files and all media used in them. For each individual video, the name of the .mp4 file, raw file and media folder is identical. The structure of the data inside the "data" folder is as follows :</p> <ul> <li>Folder/files structure: <ul> <li><code>final_videos</code> - folder containing the final exported videos <ul> <li><code>solidipes-*.mp4</code> - the individual videos</li> </ul> </li> <li><code>raw_files</code> - folder containing the Premiere Pro source files <ul> <li><code>solidipes-*.prproj</code> - source files</li> </ul> </li> <li><code>media</code> - folder containing the media folder for every video <ul> <li><code>solidipes-*</code> - folders containing the raw media for each video</li> </ul> </li> </ul> </li> </ul> <h2>Funding</h2> <p><a href="https://ethrat.ch/en/measure-1-calls-for-field-specific-actions/">ETH-Board ORD, measure 1</a>, grant n°22945: DCSM - Cloud and web based platform for dissemination of computational solid mechanics</p> <h2>Video details</h2> <p>This section gives a short description of each video as well as its identifier in the repository.</p> <ul> <li>Creating a dataset curation: <ul> <li>identifier : <code>dcsm</code></li> <li>This video shows how to upload a dataset to be hosted on DCSM's internal servers, from which they can directly be curated with solidipes.</li> </ul> </li> <li>Initiate solidipes: <ul> <li>identifier : <code>solidipes-initialise</code></li> <li>This video shows how to initialise a directory as a solidipes curation from the terminal.</li> </ul> </li> <li>Intro and installation: <ul> <li>identifier : <code>solidipes-installation</code></li> <li>This video gives a brief intro to solidipes and shows how to install it from the terminal using pip.</li> </ul> </li> <li>What is solidipes ? : <ul> <li>identifier : <code>solidipes-intro</code></li> <li>This video gives a detailed introduction into what solidipes does and what its goals are.</li> </ul> </li> <li>Data curation from the terminal : <ul> <li>identifier : <code>solidipes-terminal-curation</code></li> <li>This video shows how a dataset can be curated on the terminal using solidipes.</li> </ul> </li> <li>Download from Zenodo : <ul> <li>identifier : <code>solidipes-terminal-download</code></li> <li>This video shows how to download a dataset from Zenodo from the terminal using solidipes.</li> </ul> </li> <li>Upload to Zenodo from the terminal : <ul> <li>identifier : <code>solidipes-terminal-export</code></li> <li>This video shows how to export a dataset curated with solidipes to Zenodo from the terminal.</li> </ul> </li> <li>Acquisition on the web service : <ul> <li>identifier : <code>solidipes-web-acquisition</code></li> <li>This video shows the aspects of the "acquisition" step of the solidipes web service.</li> </ul> </li> <li>Curation on the web service : <ul> <li>identifier : <code>solidipes-web-curation</code></li> <li>This video shows the aspects of the "curation" step of the solidipes web service.</li> </ul> </li> <li>Exporting from the web service : <ul> <li>identifier : <code>solidipes-web-export</code></li> <li>This video shows how to export a dataset to Zenodo from the solidipes web service.</li> </ul> </li> <li>Edit metadata on the web service : <ul> <li>identifier : <code>solidipes-web-metadata</code></li> <li>This video shows the aspects of the "metadata" step of the solidipes web service.</li> </ul> </li> <li>Starting the web service + intro : <ul> <li>identifier : <code>solidipes-web-overview</code></li> <li>This video shows how to start the web service from the terminal, and gives an overview of its aspects.</li> </ul> </li> </ul> <h2>Image credit</h2> <p>The following graphics were used in some of the videos :</p> <ul> <li><a href="https://www.svgrepo.com/svg/533602/arrow-narrow-left">https://www.svgrepo.com/svg/533602/arrow-narrow-left</a></li> <li><a href="https://www.svgrepo.com/svg/145033/text-file-document">https://www.svgrepo.com/svg/145033/text-file-document</a></li> <li><a href="https://www.svgrepo.com/svg/184892/scientist">https://www.svgrepo.com/svg/184892/scientist</a></li> <li><a href="https://icons8.com/icon/12245/image-file">https://icons8.com/icon/12245/image-file</a></li> </ul>
Dataset for paper "The naphthylated LEGO-lipophosphonoxin antibiotics used as a fluorescent tool for observation of target membrane perturbations preceding its disruption"
<p><span><span><span>The individual text files contain all the numerical data shown in Figures 2-10 in the publication.</span></span></span></p>
Data and scripts used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Images, script and data used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Fig. 3a-c in New Tools for Phylogenetic reconstruction using character state trees
Fig. 3a-c: A CST with the same structure as the one of fig. 1. The paths to the root of the states D (3a) and G (3b) are accentuated, as well as the elements of the set SDG (fig. 3c, see eq. 1).
Fig. 1 in New Tools for Phylogenetic reconstruction using character state trees
Fig. 1 (from LORENZ 1941): Resulting tree out of a species/characters-matrix of 48 mainly behavioural characters from twenty Anatid species (in fact, this graphic is matrix and tree in one).
Fig. 4a-b in New Tools for Phylogenetic reconstruction using character state trees
Fig. 4a-b: (a) Cladogram for five species (the tips T1 to T5) and the character which is coded in fig. 2b as CST. The inner nodes of the cladogram are hypothetical species. Their character state is reconstructed by the algorithm described in the text. Prominent branches denote an evolutionary step (a change of state). (b) Bifurcation of a cladogram with the character coded in fig. 3.
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.