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610 results for “Static”
Wrapper Impact Workloads and BSC Slurm Simulator Output of Static Traces based on Data from LUMI Supercomputer
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the static trace utilizing LUMI fitted data carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per experiment, which is a different randomly generated workload file. Within each experiment we find a folder per fair share inidicating the target platform, the workflow it was based on, and the characteristics of the tracked job: number of cores and runtime. The results folder follows the same scheme but with a file extension of ".trace".</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (Zn(II)-methionine) at 200 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.mrc</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (biotin, Cu(II)-serine, Zn(II)-histidine) at 200 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.mrc</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (Co(II) meso-tetraphenyl porphyrine at high fluence, ~100 electrons per square Angstrom) at 200 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.mrc</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (Co(II) meso-tetraphenyl porphyrin) at 200 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.mrc</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals on extra thick carbon support films (biotin, Zn(II)-methionine, Zn(II)-histidine) at 300 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets in .tvips 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.tvips</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (Zn(II)-histidine, Co(II) meso-tetraphenyl porphyrin, AVAAGA) at 300 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets in .tvips 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.tvips</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (AVAAGA, thiostrepton, proteinase K) at 200 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.mrc</p> <p>AVAAGA datasets are additionally designated "AVAAGA-dry" or "AVAAGA-vitrified", identifying diffraction from crystals dry-mounted on grids and diffraction from crystals embedded in vitreous ice, respectively.</p>
BIR-MicroED: selected area electron diffraction datasets from static microcrystals (thiostrepton) at 300 keV
<p>This deposition contains a series zip files each containing electron diffraction datasets in .tvips 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>_<em>AcceleratingVoltage</em>_<em>Temperature</em>.zip"</p> <p>Diffraction datasets within each folder are named according to the format: <em>CompoundName</em>_static_diffraction_<em>AcceleratingVoltage</em>_<em>Temperature</em>_series#.tvips</p>
Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"
<p>This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy". </p>
Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems
<p><strong>Replication Package for ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems</strong></p> <p>This is the replication package for the paper, ROSDiscover: Statically Detecting Run-Time Architecture Misconfigurations in Robotics Systems, which has been accepted at the International Conference on Software Architecture (ICSA), 2021. A preprint of the paper is included in this replication package (paper.pdf).</p> <p>This artifact is archived on Zenodo with the following DOI: <a href="https://doi.org/10.5281/zenodo.5834633">https://doi.org/10.5281/zenodo.5834633</a></p> <p>The study associated with this artifact was carried out by the following investigators:</p> <ul> <li><a href="http://christimperley.co.uk">Christopher S. Timperley</a> (Carnegie Mellon University)</li> <li><a href="https://tobiasduerschmid.github.io">Tobias Dürschmid</a> (Carnegie Mellon University)</li> <li><a href="https://www.cs.cmu.edu/~schmerl">Bradley Schmerl</a> (Carnegie Mellon University)</li> <li><a href="http://www.cs.cmu.edu/~garlan">David Garlan</a> (Carnegie Mellon University)</li> <li><a href="https://clairelegoues.com">Claire Le Goues</a> (Carnegie Mellon University)</li> </ul> <p>If you have any questions regarding the research or the replication package, you should contact Christopher, Tobias, or Bradley.</p> <p><strong>Abstract</strong></p> <p>Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.</p> <p>In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.</p> <p>We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.</p>
Combining dynamic and static analysis for automated grading SQL statements
<p><strong>Introduction</strong></p> <p>Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten.</p> <p>Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release.</p> <p>All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.</p> <p> </p> <p><strong>Datatable description</strong></p> <p> </p> <p><em><strong>exercises_result</strong></em></p> <p>This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories.</p> <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> <p>After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system.</p> <p>Take exercise 1 as an example, the task is to answer the following questions using SQL statements.</p> <p>Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s).</p> <p>The reference statement to this exercise is:</p> <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> <p>By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted.</p> <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> <p>The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'.</p> <p>Table 1 Description of exercises_result table.</p> <table> <thead> <tr> <th> <p><strong>field</strong></p> </th> <th> <p><strong>desc</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>submitted_answer</p> </td> <td> <p>statement submitted by student</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>submission_time</p> </td> <td> <p>Submission time</p> </td> <td> <p>NUM</p> </td> </tr> <tr> <td> <p>exercise_id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>is_correct</p> </td> <td> <p>Mark whether the statement is correct</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>student_id</p> </td> <td> <p>Student ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>category</p> </td> <td> <p>categories of statement</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_benchmark</strong></em></p> <p>This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement.</p> <p>Table 2 Description of exercises_benchmark table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>grade</p> </td> <td> <p>grade provided by tutor</p> </td> <td> <p>REAL</p> </td> </tr> <tr> <td> <p>tutor</p> </td> <td> <p>tutor</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_exercise</strong></em></p> <p>This datatable stores the exercises provided by tutor.</p> <p>Table 3 Description of exercises_exercise table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>title</p> </td> <td> <p>Title of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>preamble</p> </td> <td> <p>Description of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>difficulty</p> </td> <td> <p>Coefficient of difficulty</p> </td> <td> <p>integer</p> </td> </tr> <tr> <td> <p>ref</p> </td> <td> <p>Reference statement</p> </td> <td> <p>integer</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>database schema</strong></em></p> <p>Please refer to db_schema.pdf for the database schema used in the experiment.</p> <p> </p> <p><strong>BibTex</strong></p> <p>if you want to cite our paper:</p> <p> </p> <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>
Application of X‑ray Microcomputed Tomography for the Static and Dynamic Characterization of the Microstructure of Oleofoams
<p>Raw, greyscale image stacks collected during a X-Ray tomography analysis on cocoa butter-based oleofoams. The dataset is divided into three subsets: aeration, storage and heating, which contain samples that have been aerated for different amounts of time, samples that have been stored for 3 and 15 months at 20 °C, and finally samples that have been heated to the melting point of the stabilizing crystals, respectively. The dataset contains instructions and the scripts for ImageJ and MATLAB (as text files) to process and measure the bubble size distribution, and the thickness of the continous phase.</p>
Nicaragua Upper-Plate Earthquake Inversion and Static Stress Change
<p>This data set contains GPS time series and displacements (in tabular form) for the April 10 2014 and Sept 15 & 28 2016 M>5 upper-plate earthquakes in Nicaragua. The data set also contains configuration files for GBIS code, used in determining fault kinematics and Coulomb 3.3, used for calculating static stress change following the earthquakes.</p> <p>A README.txt file is in each folder and details what configuration files do and format of data.</p>
Database of Static Buckling Experiments with Cylindrical Composite Shells under Axial Compression
<p>This document lists all freely available data on thin-walled axially loaded composite cylindrical shells. The list includes the material used, the laminate lay-up, the wall thickness, the radius, the length, the determined material parameters, the boundary conditions, the buckling load, the manufacturer and manufacturing process as well as the test rig used.</p> <p>If you have new test data you want to add in this database feel free to contact us via tobias.hartwich@tuhh.de or stefan.panek@tuhh.de</p>
AnoVox_Static_Legacy
<p>This is the legacy version of AnoVox; a preliminary release prior to the full release of the remaining dataset within this Zenodo Community. We advise against using it.</p> <p>---------------</p> <p>The presented <strong>AnoVox </strong>dataset is a dataset of abnormal scenarios with unknown objects. The benchmark relies on the CARLA 0.9.14 simulator. This dataset includes a collection of road scenarios that feature abnormal objects. These scenarios were developed using the CARLA Simulator. </p> <p>The <strong>"Anovox"</strong> directory comprises ten distinct scenario folders, each assigned a unique identifier. These folders encapsulate a comprehensive array of data components crucial for characterizing and comprehending abnormal traffic situations. Each scenario presents a specific traffic context and covers a time span of 18.5 seconds, equivalent to 185 ticks.</p> <p>Within each individual scenario folder, the following subfolders and files are present:</p> <p>1. <strong>"ACTION"</strong>: This folder contains the action state values attributed to the ego vehicle for every tick throughout the scenario.</p> <p>2. <strong>"ANOMALY"</strong>: This repository houses anomalous objects or occurrences that take place within the scenario. Additionally, this folder has the potential to incorporate irregular driver and pedestrian actions in future iterations.</p> <p>3. <strong>"DEPTH_IMG"</strong>: This section comprises depth images that encode the spatial depth of each pixel through the utilization of RGB channels (for more information, refer to the CARLA docs: https://carla.readthedocs.io/en/latest/ref_sensors/#depth-camera).</p> <p>4. <strong>"PCD"</strong>: This section encompasses point cloud data derived from lidar scans, effectively representing the semantic segmentation of the simulated environment.</p> <p>5. <strong>"RGB_IMG"</strong>: This section hosts RGB images corresponding to each frame of the scenario.</p> <p>6. <strong>"SEMANTIC_IMG"</strong>: Here, you will find images that offer ground truth information via semantic segmentation.</p> <p>7. <strong>"SEMANTIC_PCD"</strong>: This section contains point cloud representations with embedded semantic segmentation details, further enriching the ground truth information.</p> <p>8. <strong>"VOXEL_GRID"</strong>: This section provides ground truth via a voxel representation of the surroundings.</p> <p>Additionally, the dataset is augmented by a separate directory titled <strong>"Scenario_Configuration_Files."</strong> This directory comprises eight JSON files tailored for different urban environments, distinguished by their names ("Town01," "Town02," "Town03," "Town04," "Town05," "Town06," "Town07," "Town10HD"). These JSON files play a crucial role in ensuring the reproducibility of scenarios. They encompass critical details, such as the spawn points of anomalies and the ego vehicle, alongside weather presets.</p> <p>The color palette used to represent the semantic segmentation is based on the Cityscapes color palette. A detailed description of this color palette can be found in the document <strong>"color_palette.txt"</strong>.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Maven 99 most popular library statical usages
<p>A SQL database containing the static usages of API elements of any version of the 99 most used maven artifact, by any of it client on maven central.</p>
Are Static Analysis Violations Really Fixed? A Closer Look at Realistic Usage of SonarQube. Dataset for OSS organizations
<p>Dataset containing all rules, files and issues mined for Apache Software Foundation and Eclipse Foundation.</p>
SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features
<p>Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable</p> <p>for more information visit <a href="https://github.com/sldeep/SLDeep">https://github.com/sldeep/SLDeep</a></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.