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ShareScore release 0.9.0
Dataset results
39 results for “automated support”
Research data supporting "Single particle automated raman trapping analysis"
<p>Research raw data supporting the publication:</p> <p>Penders J., et al., Nature Communications. (2018) 9:4256 | DOI: 10.1038/s41467-018-06397</p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Datasets and codes for De Lorm et al. 2023: Optimising the automated recognition of individual animals to support population monitoring
<p>Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages.</p> <p> </p> <p>In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I<sup>3</sup>S-Pattern, and WildID. As a case study, we consider the African wild dog <em>Lycaon pictus</em>, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. </p> <p> </p> <p>The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). </p> <p> </p> <p>Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts. </p>
Supporting dataset – A systematic review of automated journalism scholarship: guidelines and suggestions for future research
<p>This dataset supports the following article, conditionally accepted for publication in Open Research Europe:</p> <p>"A systematic review of automated journalism scholarship: guidelines and suggestions for future research."</p> <p> </p> <p> </p>
Supporting Data: A large-scale dataset of solar event reports from automated feature recognition modules.
<p>This is the supporting dataset for the paper:</p> <p>A large-scale dataset of solar event reports from automated feature recognition modules. Michael A. Schuh, Rafal A. Angryk, Petrus C. Martens. Journal of Space Weather and Space Climate, 2016.</p>
Dataset supporting Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants
<p>Executable benchmarks and result data set for the experimental evaluation in our publication "Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants" published at Hybrid Systems: Computation and Control (HSCC) 2017.</p> <p> </p> <p>Abstract:</p> <p>Modern control is implemented with digital microcontrollers, embedded within a dynamical plant that represents physical components.</p> <p>We present a new algorithm based on counter-example guided inductive synthesis that automates the design of digital controllers that are correct by construction. The synthesis result is sound with respect to the complete range of approximations, including time discretization, quantization effects, and finite-precision arithmetic and its rounding errors.</p> <p>We have implemented our new algorithm in a tool called DSSynth, and are able to automatically generate stable controllers for a set of intricate plant amodels taken from the literature within minutes.</p>
Supporting information for "Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni"
<ul> <li>Files “CG_Ro_1_High Sensitivity DNA Assay_DE13805677_2019-06-20_09-10-48.pdf” and “CG_Ro_2_High Sensitivity DNA Assay_DE13805677_2019-06-20_10-12-12.pdf” uncropped files used in figure 2</li> <li>File “gel qpcr test input sds_01.tif” : uncropped file used in figure 8.</li> <li>File "20200612-1_Report.pdf" : qPCR report for inputs 1µL and available chromatine ("Row7"), figure 6 </li> <li>File "20200919-1_Report.pdf" : qPCR report for for testing SDS after Tn5, figure 7B and figure 8</li> <li>File "20200921-1_Report.pdf" : qPCR report for for comparing inputs with enzyme of Diagenode kit and our protocol with other enzyme Tn5 </li> <li>File "wilcoxon_curves.tgz" contains compressed versions of R-script "wilcoxon_curves.Rmd" that was used to compared metagene profiles and generate "wilcoxon_curves.html" and underlying ressources that are also in this compressed archive</li> </ul> <p>Produced at IHPE (http://ihpe.univ-perp.fr)</p>
Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset
<h1>Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset</h1> <h2> </h2> <h2>Dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a></h2> <div>This is the dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a> "Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery" with the goal of providing a systematic method to discover state-of-the-art security knowledge, focused on threats, measures, and properties from science and project literature with minimal manual effort, and providing software engineers with the awareness and means they need to apply the knowledge to their projects.</div> <p> </p> <h2>Structure of the files</h2> <div>The dataset contains the extraction prompt, the extraction results, and metadata.</div> <div>- <strong>ACM_IEEE_EU_Results.zip</strong> is an archive file with the metadata and extraction results with the following folder structure: </div> <div> - <strong>Meta/ </strong>contains the DOI, title, publication date, and pages (omitted for IEEE since it may contain intellectual property).</div> <div> - <strong>Extract/</strong> contains extraction results generated by the LLM in response to the extraction_prompt.txt file.</div> <div> - <strong>ExtractMeta/</strong> contains the metadata for the extraction, such as execution time.</div> <div>- <strong>extraction_prompt.txt</strong> is the prompt used to extract information from the science and project publications.</div>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Research Data Supporting "Coupling Lipid Nanoparticle Structure and Automated Single Particle Composition Analysis to Design Phospholipase Responsive Nanocarriers"
<p>Raw research data supporting Barriga, Pence, et al. 2022, Advanced Materials. <a href="https://doi.org/10.1002/adma.202200839">https://doi.org/10.1002/adma.202200839</a></p>
Supporting Information for the Journal Article "The electrostatic potential as a descriptor for the protonation propensity in automated exploration of reaction mechanisms"
<p>This dataset contains the supporting information published together with the article "The electrostatic potential as a descriptor for the protonation propensity in automated exploration of reaction mechanisms" (<a href="https://doi.org/10.1039/C9FD00061E"><em>Faraday Discuss.</em>, <strong>2019</strong>, <em>220</em>, 443</a>).</p>
Design and Implementation of a flexible Node for IoT supporting 6loWPAN and a Sensor Shield for Home Automation Application
<p>Simulation data and measurment of the developed flexible IoT board.</p>
Supporting material for "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test"
<p>The data were uploaded to support the manuscript "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test" for the submission to Journal of Pharmacological and Toxicological Methods.</p>
Supporting Information for Automated and Efficient Sampling of Chemical Reaction Space
<p>These datasets include structures from normal mode sampling, reaction pathway sampling, and transition states for validation (originally from Grambow et al.), all computed using the ωB97X/6-31G(d) method. The corresponding energies and forces are compiled in the Atomic Simulation Environment database format. </p>
Supporting Information for the Journal Article "Automated Construction of Quantum–Classical Hybrid Models"
<p>This dataset contains the supporting information published together with the article "Automated Construction of Quantum–Classical Hybrid Models" (<a href="https://doi.org/10.1021/acs.jctc.1c00178"><em>J. Chem. Theory Comput.</em>, <strong>2022</strong>, <em>17</em>, 3797</a>).</p>
Data Supporting GoodVibes: automated thermochemistry for heterogeneous computational chemistry data
<p>These files are provided in support of the use case in a recent manuscript showing the use of the Python package <a href="https://github.com/bobbypaton/GoodVibes">GoodVibes</a>.</p> <p>This data set contains Gaussian optimization and frequency calculations on 25 molecules, along with 25 corresponding ORCA single point energy calculations, a YAML file to dictate and format the reaction pathway, and example outputs of the tabulated thermochemistry and a PNG of the potential energy surface graph output.<br> To generate these output files, the command:</p> <pre><code class="language-bash">python -m goodvibes *.log --spc DLPNO --pes PhPy.yaml --graph PhPy.yaml -t 353.15 --imag --invertifreq -5 --media ethanol -c 1 </code></pre> <p>was run in the directory containing the calculation output (.log and .out) files.</p>
Datasets for experiments in support of "HADA: an Automated Tool for Hardware Dimensioning of AI Applications"
<p>The zip archive contains three datasets used during the experimental phase of the paper:</p> <ul> <li><em>ANTICIPATE_trainDataset.csv</em>: used in order to train the ML models for the ANTICIPATE algorithm (Section 4.1);</li> <li><em>CONTINGENCY_trainDataset.csv</em>: used in order to train the ML models for the CONTINGENCY algorithm (Section 4.1);</li> <li><em>EmpiricalValidationSet.csv</em>: used for validating the EML optimization model (Section 4.2)</li> </ul>
Supporting Information for "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"
<p><strong>Introduction</strong></p> <p>The supporting information for this paper consists of a movie version of Figure 2 in the main paper, comparing the high frequency (HF) amateur radio observations to differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements. In this revised version of the repository, the TEC data has been reprocessed such that all GNSS data with elevation angles down to 10 degrees were kept when applying the Savitzky–Golay (SG) filter. After the SG filter was applied, data with elevation angles below 30 degrees were discarded. This was done to remove artifacts observed in the original movies.</p> <p> </p> <p><strong>Movie S1 (20171103 Ham and TEC LSTID.mp4)</strong></p> <p>(Top Panel) Time series showing the TX-RX distance for 14 MHz amateur radio spots in 2 min by 25 km bins from 1200 UT 3 Nov 2017 - 0000 UT 4 Nov 2017. (Bottom Panel) differential Global Navigation Satellite System (GNSS) Total Electron Content (TEC) measurements over the Continental United States corresponding to the times indicated by the moving white vertical line in the top panel.</p> <p> </p> <p><strong>Movie S2 (20171103 Ham and TEC LSTID – With Arrow.mp4)</strong></p> <p>Same as movie S1, but with a fiducial black arrow indicating an estimated LSTID horizontal wavelength of 1681 km and propagation azimuth of 163°.</p>
Evaluating a Semantic-based Automated Approach to Task-Relevant Text Identification: Supporting Material
<p>This is the supplementary material for the evaluation of a semantic-based automated approach to task-relevant text identification.</p> <p> </p> <p><strong>Where:</strong></p> <ul> <li><strong>ds-python:</strong> contains the dataset produced as part of this study</li> <li><strong>output:</strong> contains the results from our data analysis</li> <li><strong>responses:</strong> contains the participants' responses to each of the major questions we asked them during our experiment</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.