Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
610
datasets available to search
ShareScore release 0.9.0
Dataset results
610 results for “Static”
Replication Package for "On the correlation between Architectural Smells and Static Analysis Warnings"
<div> <div> <div> <div> <div> <h1>Replication Package for the Paper: "On the Relation between Architectural Smells and Static Analysis Warnings"</h1> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Mikel Robredo, University of Oulu, Finland</li> <li>Francesca Arcelli Fontana, University of Milano-Bicocca, Italy</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> <p>This replication package contains the following materials:</p> <ul> <li><strong>Tables:</strong> Excel files that include all hypothesis testing data, including normality tests for each hypothesis.</li> <li><strong>Data:</strong> RAW Qualitas Corpus dataset and aggregated SAT output.</li> <li><strong>SAT Instructions:</strong> A PDF file providing detailed instructions for the SAT setup and execution.</li> </ul> <h2>Contact Information</h2> <p>For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help!</p> </div> </div> </div> </div> </div>
From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil
<p>Dataset belonging to publication 'From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil', <a href="https://doi.org/10.3390/foods13091372">https://doi.org/10.3390/foods13091372</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>PO = palm oil</p> <p>POE = palm oil + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p>
Results of quasi static test on CFRP/CFRP joints and CFRP/Al joint with different surface treatments within project _Spoke11_WP2_Task2.3_MOST
<p>Quasi-static single lap joint test on CFRP CFRP joints and CFRP aluminum joints. Two different epoxy adhesives have been used and different chemical surface treatments used in order to try to enhance the mechanical resistance of the single lap joint.</p>
Demo of Impatto: A Static Analyzer for Quantitative Input Data Usage
<p><strong>Impatto</strong> is a sound fully-automatic and always-terminating static analysis tool based on the quantitative framework for input data usage properties proposed by Mazzucato (https://hal.science/hal-04339001).<strong>Impatto</strong> leverages an underlying backward analyzer to compute the set of input-output relations of the program under analysis. This backward analyzer is a parameter of the tool, allowing different kind of analyses such as program or neural network analysis. Furthermore, the choice of the impact definition is also a parameter of the tool to better suit several factors, such as the program structure, the environment, and the intuition of the researcher.</p> <p>GitHub repository at https://github.com/denismazzucato/impatto</p>
Fig. 2 in First attempt to understand the effect of pingers on static fishing gear in Bulgarian Black Sea coast
Fig. 2. Comparison of number of attacks on active and control dalyans.
Fig. 1 in First attempt to understand the effect of pingers on static fishing gear in Bulgarian Black Sea coast
Fig. 1. Correlation between the numbers of observations and attacks on active dalyans.
Differential static light scattering (DSLS) of full-length huntingtin samples of different polyQ lengths (Q19, Q23, Q42 and Q54) – 2018/05/09
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment</strong> - Differential static light scattering (DSLS) of full-length huntingtin samples of different polyQ lengths (Q19, Q23, Q42 and Q54) – 2018/05/09. To investigate how increased polyQ length of the huntingtin protein affects its biophysical properties. </p> <p> </p>
Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations
<p>This link provides the data from the playback experiment to determine effective detection areas for porpoises recorded by C-POD acoustic dataloggers. The firstdata set includes the artifically created porpoises click trains captured by the C-PODs and the second is the record of the rate of re-capture for the real, recorded porpoise clicks used in the experiment. </p>
static stress-strain behaviour of hardened and tempered steel 52100 (100Cr6)
<p>this file contains the recorded data (time, nominal stress, strain, actuator position) of a tensile test as long as the used strain gauge worked</p>
Experimental–Computational Analysis of Nucleation Sites for Primary Static Recrystallization
<p>This repository contains supplementary material to our paper. Specifically, the Matlab, Python,a and Shell scripts and cellular automaton source code we used to run and post-process the simulations as well as the simulation results:</p> <p><strong>MTEXEBSDMappingStructureInitialization.zip</strong><br> Specifies, using MTex v5.0.3, how we converted the measured SEM/EBSD mapping to a synthetic 2d microstructure.</p> <p><strong>SCORESourceCode.zip</strong><br> Specifies the source code of SCORE. Version 1.2.1. Demands a local HDF5 installation. MPI/OpenMP parallelized.<br> Inspect www.github.com/mkuehbach/SCORE for further details on how to compile and background to the model<br> an implementation.</p> <p><strong>ExecuteSimulations.zip</strong><br> Specifies shell scripts and UDS input files to execute the simulations. Details via these UDS files also all parameter<br> settings we used to reproduce the runs.</p> <p><strong>ComparisonXaXv.tar.gz</strong><br> Compares in summarized form, and extracted from the RXAreaFractionDepthProfile folder files, the area vs<br> volume fraction at specified time snapshots for the z= [0.0, 0.5, 1.0] RDTD section.<br> <br> <strong>Inherited_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei inherited the orientation from their site.</p> <p><strong>Random_GrainSizeMicrostructure.zip</strong><br> ANG-like serial sectioning snapshot results and IPF visualization of microstructure evolution for those<br> simulation cases in which the nuclei had random orientations form the SO3.</p> <p><strong>RXAreaFractionDepthProfile.zip</strong><br> Specifies the evolution of the area fraction recrystallized with grains in cross-sectional area >=13px<br> for every RDTD layer.</p> <p>The corresponding parameterization is detailed in the *.uds input file which specifies all constitutive parameter<br> and log settings of the automaton. The simulation is executed by compiling the program and linking to<br> HDF5. The OMP_NUM_THREADS environment variable should be set to not more than 10.<br> The SCORE is executed as follows:<br> mpirun -np 1 ./score <simid> <udsfile> <KAM Ang EBSD file> 1>STDOUT.txt 2>STDERR.txt<br> <br> <strong>Profiling.zip</strong><br> Details the execution log of the automaton ie runtime individual composition of nuclei volume transformation<br> progression, interfacial area evolution, etc.</p> <p><strong>SingleGrainData.zip</strong><br> Details the volume consumption / volume gain kinetics of every single deformed / recrystallized grain.</p> <p><strong>TemperatureTimeProfile.zip</strong><br> Details the time/temperature and step profile of the numerical integration.<br> This allows to map integration time steps to simulated microstructural states.</p> <p><strong>ThreadProfilingGrowth.zip</strong><br> Details the evolution of the recrystallized volume versus time and number of active cells per thread sub-domain.</p> <p><strong>MartinPostprocessingScripts.zip</strong><br> Is a collection of Python and MTex scripts to compile the area size distribution and compute ODFs.</p>
A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT1-3144 Q23 - 2019/01/28
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>A time-course analysis using Differential Static Light Scattering (DSLS) of purified HTT<sup>1-3144</sup>Q23 </p> <p><strong>Date completed:­ </strong>2019/01/28</p> <p><strong>Rationale: </strong>Time and resources in the HD field have been primarily focussed on understanding HTT aggregation looking as caspase cleavage products spanning aa. 1-586 or exon 1 spanning aa. 1-90. However, we know that HTT protein purified in its apo form is able to self-associate into larger oligomeric species and that monomer, dimer and larger species are found following FLAG-affinity chromatography as determined by size-exclusion chromatography (SEC) and SEC-multi-angle light scattering (SEC-MALS). This experiment aimed to begin to investigate how HTT self-associates and aggregates over time in a range of different conditions. </p>
Replication Package for "How Developers Engage with Static Analysis Tools in Different Contexts"
<p>This is the replication package for the paper "How Developers Engage with Static Analysis Tools in Different Contexts".</p> <p>We include all the artifacts necessary to replicate the results obtained in our paper. Specifically, we provide (i) the survey questions together with all the valid answers we received including the demographics of our respondents, (ii) the most relevant statements that we extracted from the interviews including the demographics of our interviewees, (iii) the results of the card sorting performed on the development activities where our participants adopt Static Analysis Tools, (iv) all the data related to Krippendorff’s Alpha calculation for the performed card sorting, and (v) mapping of ASATs to the "rules" categories defined by Novak et al. (2010) and script for calculating occurrence, definition, and enforcement of the different ASAT types together with input and output data. Furthermore, we include the list of links to Reddit posts and inspected open-source projects together with their inspection data and the scripts for computing the inter-rater agreement during the inspection. Finally, we provide the Github features computed for each project and script for generating the sets of projects.</p>
Research data supporting "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models"
<p>Research data supporting the publication: Aengenheister, L. et al., 2019, "Investigating the accumulation and translocation of titanium dioxide nanoparticles with different surface modifications in static and dynamic human placental transfer models", Eur J Pharm Biopharm. https://doi.org/10.1016/j.ejpb.2019.07.018</p>
BIR-MicroED: TEM image series revealing bend contour motion in static microcrystals (biotin, Zn(II)-methionine, Co(II)-porphyrin, AVAAGA) and diffraction patterns acquired from the same crystals at 200 kV
<p>This deposition contains a series zip files each containing TEM image series and electron diffraction images in .mrc file format. Each folder collects data acquired from crystals of a particular compound under the same conditions (electron energy, temperature). Zip files are named according to the format: <em>"CompoundName</em>_bendcontour_imageseries_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Data is further divided into sub-directories according to the particular crystal studied (crystal1, crystal2, crystal3), each containing a TEM image series (name format: "<em>CompoundName</em>_static_imageseries_<em>AcceleratingVoltage</em>_<em>Temperature</em>_crystal#.mrc") and 10 diffraction snapshots (2 frames each, each convering 1 second of electron beam exposure) acquired at equally spaced time intervals throughout the image series. These are named according to the format:</p> <p>"CompoundName_bendcontour_crystal#_diffraction_snap#.mrc"</p>
In vitro protein and starch digestion kinetics of individual chickpea cells: from static to more complex in vitro digestion approaches
<p>Attention has been given to more (semi-)dynamic <em>in vitro</em> digestion approaches ascertaining the consequences of dynamic <em>in vivo </em>aspects on <em>in vitro </em>digestion kinetics. As these often come with time and economical constraints, evaluating the consequence of stepwise increasing the complexity of static <em>in vitro </em>approaches using easy-to-handle digestion set-ups has been the center of our interest.</p> <p>Starting from the INFOGEST static <em>in vitro </em>protocol, we studied the influence of static gastric pH <em>versus </em>gradual gastric pH change (pH 6.3 to pH 2.5 in 2 h) on macronutrient digestion in individual cotyledon cells derived from chickpeas. Little effect on small intestinal proteolysis was observed comparing the applied digestion conditions. Contrary, the implementation of a gradual gastric pH change, with and without the addition of salivary α-amylase, altered starch digestion kinetics rates, and extents by 25%. The evaluation of starch and protein digestion, being co-embedded in cotyledon cells, did not only confirm but accounted for the interdependent digestion behavior. The insights generated in this study demonstrate the possibility of using a hypothesis-based approach to introduce dynamic factors to <em>in vitro</em> models while sticking to simple and cost-efficient set-ups.</p> <p> </p> <p>The data used for the graphs in the paper: K. Pälchen, D. Michels, D. Duijsens, S. T. Gwala, A. K. Pallares Pallares, M. Hendrickx, A. Van Loey and T. Grauwet, Food Funct., 2021, DOI: 10.1039/D1FO01123E</p>
TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021
<p>This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.</p>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
Dataset for the paper Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification
<p><strong>Short Description: </strong>This is the dataset for the paper "Exploring the Use of Static and Dynamic Analysis to Improve the Performance of the Mining Sandbox Approach for Android Malware Identification", accepted for publication in the Journal of Systems and Software. </p> <p><strong>Link to this repository: </strong><a href="https://github.com/droidxp/paper-replication-package">https://github.com/droidxp/paper-replication-package</a></p> <p><strong>Authors of the Paper</strong></p> <ul> <li>Francisco Handrick da Costa</li> <li>Ismael Medeiros</li> <li>Thales Menezes</li> <li>João Victor da Silva</li> <li>Ingrid Lorraine da Silva</li> <li>Rodrigo Bonifácio</li> <li>Krishna Narasimhanb</li> <li>Márcio Ribeiro</li> </ul> <p><strong>Abstract</strong></p> <p>The popularization of the Android platform and the growing number of Android applications (apps) that manage sensitive data turned the Android ecosystem into an attractive target for malicious software. For this reason, researchers and practitioners have investigated new approaches to address Android's security issues, including techniques that leverage dynamic analysis to mine Android sandboxes. The mining sandbox approach consists in running dynamic analysis tools on a benign version of an Android app. This exploratory phase records all calls to sensitive APIs. Later, we can use this information to (a) prevent calls to other sensitive APIs (those not recorded in the exploratory phase) or (b) run the dynamic analysis tools again in a different version of the app. During this second execution of the fuzzing tools, a warning of possible malicious behavior is raised whenever the new version of the app calls a sensitive API not recorded in the exploratory phase.</p> <p>The use of a mining sandbox approach is an effective technique for Android malware analysis, as previous research works revealed. Particularly, existing reports present an accuracy of almost 70% in the identification of malicious behavior using dynamic analysis tools to mine android sandboxes. However, although the use of dynamic analysis for mining Android sandboxes has been investigated before, little is known about the potential benefits of combining static analysis with a mining sandbox approach for identifying malicious behavior. Accordingly, in this paper we present the results of two studies that investigate the impact of using static analysis to complement the performance of existing dynamic analysis tools tailored for mining Android sandboxes, in the task of identifying malicious behavior.</p> <p>In the first study we conduct a non-exact replication of a previous study (hereafter BLL-Study) that compares the performance of test case generation tools for mining Android sandboxes. Differently from the original work, here we isolate the effect of an independent static analysis component (DroidFax) they used to instrument the Android apps in their experiments. This decision was motivated by the fact that DroidFax could have influenced the efficacy of the dynamic analyses tools positively---through the execution of specific static analysis algorithms DroidFax also implements. In our second study, we carried out a new experiment to investigate the efficacy of taint analysis algorithms to complement the mining sandbox approach previously used to identify malicious behavior. To this end, we executed the FlowDroid tool to mine the source-sink flows from benign/malign pairs of Android apps used in previous research work.</p> <p>Our study brings several findings. For instance, the first study reveals that DroidFax alone (static analysis) can detect 43.75% of the malwares in the BLL-Study dataset, contributing substantially in the performance of the dynamic analysis tools in the BLL-Study. The results of the second study show that taint analysis is also practical to complement the mining sandboxes approach, with a performance similar to that reached by dynamic analysis tools.</p> <p> </p>
Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components
<p>The provided dataset contains the data used by "Analyzing Static Analysis Metric Trends towards Early Identification of Non-Maintainable Software Components", in order to evaluate the maintainability degree of a software class and identify software components that will eventually become non-maintainable.</p>
Static visual predator recognition in jumping spiders
<p>Visually detecting, recognizing, and responding appropriately to predators increases survival. Failure to detect a predator or long decision times carry high and potentially fatal costs. Consequently, many animals show general anti-predatory responses toward threatening stimuli, e.g., looming objects. However, in the context of lurking or stalking ambush predators, visual recognition is based on static visual cues, making this task computationally demanding.</p> <p>Jumping spiders (Salticidae) have superb vision and are excellent ambush predators but they can equally fall prey to other jumping spiders. In a hierarchical decision-making setup, we tested whether the common zebra jumping spider (<em>Salticus scenicus</em>) can visually recognize stationary predators. We measured the spiders’ behavioural responses towards predator (naturally co-occurring, non-co-occurring and artificial) and non-predator objects as well as towards objects with modified features.</p> <p>Our experiments show that salticids demonstrate a robust, fast, and repeatable “freeze and retreat” behaviour when presented with stationary predators, but not similarly sized non-predator objects. Anti-predator responses were triggered by co-occurring and non-co-occurring salticid predators, as well as by 3D-printed salticid models (based on micro-CT scans), suggesting a generalized predator detection/classification. Using modified 3D-printed models, we found evidence that eyes act as an important cue. However, eyes alone did not explain the responses, suggesting that underlying processes rely on multiple rather than single features.</p> <p>To address the role of learning and memory, we tested newly emerged spiderlings and found the same behavioural responses towards predator objects suggesting an innate response. The ability of jumping spiders to innately recognize a non-moving threat is surprising in terms of underlying cognitive processes and the evolution thereof.</p> <p>Escaping from a predator before an attack has been launched likely carries sufficient selective benefits. From a cognitive perspective, the overlap of static visual characteristics between salticid predators, prey, and conspecifics invites further questions considering the mechanisms of such nuanced visual discrimination and categorization in animals with complex vision but relatively small nervous systems.</p>
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.