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610 results for “Static”
Plasma-Prescribed Active Region Static Extrapolation Dataset
<p>A repository of extrapolations using RBF-FD Magnetohydrostatic techniques with imposed plasmas, based on the SHARP solar image photospheric magnetic field library.</p>
Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries
<p># About this artifact<br> This artifact contains the implementation and the results of the evaluation of a<br> static slicer for WebAssembly described in the ICSE 2022 paper titled "Static<br> Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries".</p> <p>The artifact contains a docker image (`wassail-eval.tar.xz`) that contains<br> everything necessary to reproduce our evaluation, and the actual data resulting<br> from our evaluation:<br> 1. The implementation of our slicer (presented in Section 4.1) is included in<br> the docker machine, and is available publicly here:<br> https://github.com/acieroid/wassail/tree/icse2022<br> 2. Test cases used for our evaluation of RQ1 are included in the docker machine<br> and in the `rq1.tar.xz` archive.<br> 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine.<br> 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the<br> docker machine.<br> 5. The scripts used to generate the statistics and graphs that are included in<br> the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the<br> `*.py` files in this artifact.<br> 6. The data of RQ5 that has been used in our manual investigation is included in<br> the docker machine and in the `rq5.tar.xz` archive, along with<br> `rq5-manual.txt` detailing our manual analysis findings.</p> <p># How to obtain it<br> Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007</p> <p># Setting up the Docker image<br> ## Downloading The Artifact<br> The artifact is available at the following URL: https://zenodo.org/record/5821007</p> <p>## Loading The Docker Image<br> Once the artifact is downloaded in the file `icse2022slicing.tar.xz`, it can be extracted and loaded into Docker as follows (this takes a few minutes):<br> ```<br> docker import icse2022slicing.tar.xz<br> ```<br> To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the `docker import` command resulted in the hash `54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2`, you can run:<br> ```<br> docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval<br> ```</p> <p>Once the Docker image has been loaded, you can run the following commands to<br> obtain a shell in the appropriate environment:<br> ```<br> docker volume create result<br> docker run -it -v result:/tmp/out/ wassail-eval bash<br> su - opam<br> ```</p> <p># Reproducing results of RQ1<br> Our manual translations of the "classical" examples are included in the `rq1/`<br> directory (available in the docker image and in `rq1.tar.xz`). We<br> include the slices computed by our implementation in the `rq1/out/` directory.</p> <p>A slice can be produced for each example in the docker image as follows, where<br> the first argument is the name of the program being sliced, the second the<br> function index being sliced, the third the slicing criterion (indicated as the<br> instruction index, where instructions start at 1), and the last argument is the<br> output file for the slice:</p> <p>```<br> cd rq1/<br> wassail slice scam-mug.wat 5 8 scam-mug-slice.wat<br> wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat<br> wassail slice word-count.wat 1 41 word-count-slice1.wat<br> wassail slice word-count.wat 1 43 word-count-slice2.wat<br> wassail slice word-count.wat 1 39 word-count-slice3.wat<br> wassail slice word-count.wat 1 45 word-count-slice4.wat<br> wassail slice word-count.wat 1 37 word-count-slice5.wat<br> wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat<br> wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat<br> ```</p> <p>The slice results can then be inspected manually, and compared with the original<br> version of the .wat program to see which instructions have been removed, or with<br> the expected solutions in the `out/` directory, e.g. by running:<br> ```<br> diff word-count-slice1.wat out/word-count-slice1.wat<br> ```<br> (No output is expected if the slice is correct)</p> <p># Reproducing results of RQ2, RQ3, and RQ4<br> For these RQ, we include the data resulting from our evaluation, but we also<br> allow reviewers to rerun the full evaluation if needed. However, such an<br> evaluation requires a heavy machine and takes quite some time (4-5 days to run<br> to completion with a 4 hours timeout). In our case, we used a machine with 256<br> GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to<br> run 128 slicing jobs in parallel.</p> <p>## Runnig the Evaluation<br> We explain how to run the full evaluation, or only a partial evaluation below.<br> One can directly skip to the next section and reuse our raw evaluation results,<br> provided alongside this artifact.</p> <p>### Running the Full Evaluation<br> In order to reproduce our evaluation, you can run the following commands in the<br> docker image. It is recommended to run them in a tmux session if one wants to<br> inspect other elements in parallel (tmux is installed in the docker image). The<br> timeout (set to 4 hours per binary, like in the paper) can be decreased by<br> editing the `evaluate.sh` script (vim is installed in the docker image).</p> <p>This is expected to take 2-3 days of time, on a machine with 128 cores.<br> In order to produce only partial results, see the next section.</p> <p>```<br> cd filtered<br> cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The results are outputted in the `/tmp/out/` directory.</p> <p>### Running a Partial Evaluation<br> If one does not have access to a high-end machine with 128 cores nor the time to<br> run the full evaluation, it is possible to produce partial results. To do so,<br> the following commands can be run. This will run the evaluation on the full<br> dataset in a random order, which can be stopped early to represent a partial<br> view of our evaluation, on a random subset of the data. In order to gather more<br> datapoints, it is also advised to decrease the timeout in the `evaluate.sh`<br> file, for example to 20 minutes by setting `TIMEOUT=20m` with `nano<br> evaluate.sh`. The number of slicing jobs running in parallel can also be<br> decreased to match the number of processors on the machine running the<br> experiments (the `-j 128` argument in the following command runs 128 parallel<br> jobs)</p> <p>```<br> sudo chown opam:opam /tmp/out/<br> cd filtered<br> shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The evaluation results will be stored in the `/tmp/out/` directory.</p> <p>### Skipping the Evaluation Run<br> Instead of rerunning the evaluation, one can rely on our full results included<br> in the `data.txt.xz` and `error.txt.xz` archives. These can simply be downloaded<br> from within the Docker machine and extracted in `/tmp/out/`:</p> <p>```<br> cd /tmp/out/<br> wget https://zenodo.org/record/5821007/files/data.txt.xz<br> wget https://zenodo.org/record/5821007/files/error.txt.xz<br> unxz data.txt.7z<br> unxz error.txt.7z<br> ```</p> <p>## Processing the data</p> <p>In order to process this data, we included multiple python script.<br> These require around 100GB of RAM to load the full dataset in memory.<br> The scripts should be run with Python 3.<br> When running this in the docker image, first run `cd /tmp/out/ && cp /home/opam/*.py ./`<br> - To count the number of functions sliced, run `cut -d, -f 1,2 data.txt | sort<br> -u | wc -l`. This takes around 6 minutes to run on the full dataset.<br> - To count the total number of slices encountered, run `wc -l data.txt<br> error.txt`. This takes around 15 seconds to run.<br> - To count the number of errors encountered, run `wc -l error.txt`. This takes<br> around 1 second to run.<br> - To produce data and graphs regarding the sizes and timing, run `python3<br> statistics-and-plots.py`. This will output the statistics presented in the<br> paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This<br> script takes around 35 minutes to run.<br> - To find the executable slices that are larger than the original programs, run<br> `python3 larger-slices.py > larger.txt`. This script takes around 2h30 to<br> run. It will list the slice using the notation `filename function-sliced<br> slicing-criterion` in the larger.txt file, from which the slice can be<br> recomputed by running `wassail slice function-sliced slicing-criterion<br> output.wat` in the docker image. It will also output statistics regarding<br> these slices, which you can easily inspect by running `tail larger.txt`.<br> - To investigate slices that could not be computed, run:<br> ```<br> sed -i error.txt -e 's/annotation,/annotation./'<br> python3 errors.py<br> ```<br> This will take a few seconds to run and will print a summary of the errors<br> encountered during the slicing process, and requires some manual sorting to map<br> to the categories we discuss in the paper. Here is a summary of the errors<br> encountered and their root cause:</p> <p>### Root Cause: Unsupported Usage of br_table<br> Error: (Failure"Invalid vstack when popping 2 values")<br> Error: (Failure"Spec_inference.drop: not enough elements in stack")<br> Error: (Failure"Spec_inference.take: not enough element in var list")<br> Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)")<br> ### Root Cause: Unreachable Code<br> Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.")<br> Error: (Failure"bottom annotation")<br> Error: (Failure"bottom annotation. this an unreachable instruction")</p> <p># RQ5: Comparison to Slicing C Programs<br> For this RQ, we include the following data in the `rq5.7z` archive, and in the `rq5/` directory in the docker image:<br> - The slicing subjects in their C and textual wasm form in `rq5/subjects/`<br> - The CodeSurfer slices in their C and textual wasm form in `rq5/codesurfer/`<br> - Our slices in their wasm form in `rq5/wasm-slices/`</p> <p>As this RQ requires heavy manual comparison, we do not expect the reviewers to<br> reproduce all of our results. We include a summary of our manual investigation<br> in `rq5-manual.txt`. In order to validate these manual findings, one can for<br> example inspect a specific slice. For example, the following line in<br> `rq5-manual.txt`:</p> <p>```<br> adpcm_apl1_565_expr.c.wat INTERPROCEDURAL<br> ```</p> <p>can be validated as follows:<br> ```<br> cd ~/<br> # This generates a trimmed down version of the CodeSurfer slice, only containing the function of interest<br> wassail count-in-slice rq5/codesurfer/adpcm_slices/adpcm_apl1_565_expr.c.wat slice.wat<br> # This compares the CodeSurfer slice with our slice<br> diff --side-by-side slice.wat rq5/adpcm_apl1_565_expr.c.wat<br> ```</p> <p>In this case, most extraneous instructions are present in the CodeSurfer slices,<br> at the end of the function. This indicates that these are present in order to<br> preserve interprocedural behavior, which corresponds to the `INTERPROCEDURAL`<br> tag in the `rq5-manual.txt`</p> <p> </p>
PIBE project- Experimental characterization of stall noise in static and dynamic regimes using a NACA 63(3)418 airfoil
<p>Dynamic stall noise is one of the potential sources of amplitude modulations associated with wind turbine noise. This phenomenon is related to the periodic separation and reattachment of the boundary layer on the wind turbine blade suction side during its rotation. Within the framework of the PIBE project (Predicting the Impact of Wind Turbine Noise - <a href="https://www.anr-pibe.com/en">https://www.anr-pibe.com/en</a>), experiments were conducted in the anechoic wind tunnel of the École Centrale de Lyon in order to characterize stall noise on a pitching airfoil in both static and dynamic conditions.</p> <p>In version 1.0.0 of the database, <span>data from the second campaign using an instrumented NACA63(3)418 airfoil in static and dynamic conditions are provided. The static data can be found in the file static_data_NACA63418.h5 that contains:</span></p> <ol> <li>static wall pressure data : lift and pressure coefficients;</li> <li>dynamic wall pressure data : Power Spectral Density (PSD) of fluctuating wall pressure;</li> <li>far-field acoustic data : Power Spectral Density (PSD) of acoustic pressure.</li> </ol> <p><span>The structure of the file is described in Tree_structure_static_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_static_Matlab.zip can be used.</span></p> <p><span>The dynamic data can be found in the file dynamic_data_NACA63418.h5 that contains:</span></p> <ol> <li><span>static wall pressure data : phase-averaged lift coefficients;</span></li> <li><span>dynamic wall pressure data : phase-averaged spectrograms of fluctuating wall pressure;</span></li> <li><span>far-field acoustic data : phase-averaged spectrograms of acoustic pressure.</span></li> </ol> <p><span>The structure of the file is described in Tree_structure_dynamic_data.pdf. To read the HDF5 file, the Matlab scripts given in read_HDF5_NACA63418_dynamic_Matlab.zip can be used. Only the results for a mean angle of attack of 15° and an amplitude of 15° are provided in this file.</span></p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
UC2017 Static and Dynamic Hand Gestures
<p>We introduce the UC2017 static and dynamic gesture dataset. Most researchers use vision-based systems such as the Microsoft Kinect to acquire and classify hand gesture data. Despite that, we believe that we can achieve more reliable results and allow the use of more complex gestures with wearable systems. There are not many datasets with wearable systems due to the plethora of data gloves in the market and their relative high cost. For these reasons, we opted by creating a new dataset to present and evaluate our gesture recognition framework. The objectives of the dataset are: (1) provide a superset of hand gestures for HRI, (2) have user variability, (3) to be representative of the actual gestures performed in a real-world interaction.</p> <p>We divide the dataset in two types of gestures: SG and DG. SG are described by a single timestep of data, therefore representing a single hand pose and orientation. DGs are variable-length timeseries of poses and orientations with particular meanings. Some of the gestures of the dataset are correlated with a certain meaning in the context of HRI, while others are arbitrary, to enrich the dataset and add complexity to the classification problem.</p> <p>The library is composed of 24 SG classes and 10 DG. The dataset includes SG data from eight subjects with a total of 100 repetitions for each of the 24 classes (2400 samples in total). The DG samples were obtained from six subjects and has cumulatively 131 repetitions of each class (1310 samples in total). All of the subjects are right-handed and performed the gestures with their left hand.</p> <p>We used a data glove (CyberGlove II) and a magnetic tracker (Polhemus Liberty) to capture the hand shape, position and orientation over time. The glove provides digital signals that are proportional to the bending angle of each one of the 22 sensors which are elastically attached to a subset of the hand's joints. In this way we have an approximation of the hand's shape. The tracker's sensor is rigidly attached to the glove on the wrist and measures its position and orientation in respect to a ground-fixed frame. The orientation is the rotation between the fixed frame and the frame of the sensor, given a quaternion (WXYZ). We fuse the sensor data together online since the sensors have slightly different acquisition rates -- 100Hz for the glove and 120Hz for the tracker. The tracker data are under-sampled by gathering only the closest tracker frame in time.</p> <p>The files are in the h5df format. The dimensions are (sample, time, variables).</p>
quetzal_germany_input_static
<p>This datapackage contains various input data for generation of transport models within the region of Germany. The open source transport model quetzal_germany requires these files for its modelling steps and validation. The whole model can be found on github: <a href="https://github.com/marlinarnz/quetzal_germany">https://github.com/marlinarnz/quetzal_germany</a></p>
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Github data for static site generators (SSG) popularity
<p>Number of Github stars, forks, open issues, create and last modified dates for 30 open source static site generators (SSG), including Hugo, Jekyll and Gatsby.</p>
1st_dataset-Minimal radius of magnetic skyrmions: statics and dynamics
<p>In a broad range of applied magnetic fields and material parameters isolated magnetic skyrmions<br> condense into skyrmion lattices. While the geometry of isolated skyrmions and their lattice<br> counterparts strongly depend on field and Dzyaloshinski–Moriya interaction, this issue has not been<br> adequately addressed in previous studies. Meanwhile, this information is extremely important for<br> applications, because the skyrmion size and the interskyrmion distance have to be tuned for skyrmion<br> based memory and logic devices. In this investigation we elucidate the size and density-dependent<br> phase diagram showing traditional phases in field versus material parameters space by means of<br> Monte-Carlo simulations on a discrete lattice. The obtained diagram permits us to establish that, in<br> contrast to the continuum limit, skyrmions on a discrete lattice cannot be smaller than some critical<br> size and have a very specific shape. These minimal skyrmions correspond to the micromagnetic<br> configuration at the energy barrier between the ferromagnetic and the skyrmionic states.<br> Furthermore, we use atomistic Landau–Lifshitz–Gilbert simulations to study dynamics of the<br> skyrmion annihilation. It is shown that this procees consists of two stages: the continuous skyrmion<br> contraction and its discontinuous annihilation. The detailed analysis of this dynamical process is<br> given.</p>
Dataset for Quieting the Static: A Study of Static Analysis Alert Suppressions
<h2><strong>Dataset for Quieting the Static: A Study of Static Analysis Alert Suppressions</strong></h2><p>This is the dataset for our empirical study on the practices of software bug suppression in open source projects.</p><h3><strong>Directory Structure</strong></h3><ul><li>./categorization: Contains the categorization spreadsheet data of sampled suppressions in csv format, as well as the raw JSON sample.</li><li>./categorization/html_files: Contains the annotated code fragments of the sampling process in HTML format.</li><li>./data: Contains the datasets of canonicalized configuration and annotation warning suppressions in JSON format.</li></ul><p><br> </p>
A simple, static and stage mounted direct electron detector based electron backscatter diffraction system
<h3><strong>Data set for </strong><i><strong>A simple, static and stage mounted direct electron detector based electron backscatter diffraction system</strong></i></h3><p>T.Zhang, T. B. Britton</p><p> </p><h3><strong>Contents</strong></h3><p><strong>- New in v2.0.0: CAD drawings of the stage</strong></p><p> </p><p>- Single Si(100) diffraction patterns at 4 camera lengths, and at 4 corners of the sample</p><p>- Horizontal and vertical line scan on Si(100) with 20 grid points</p><p>- 20x20 mapping scan on a polycrystalline Cu sample</p><p>Scan parameters for the line scans and map are included in logfiles within each subfolder.</p><p> </p><p>All pattern files are provided in .h5 format and .tif format. Analyses of the patterns were performed with AstroEBSD and MTEX.</p>
Insights in the structural hierarchy of statically crystallized palm oil
<p>This dataset contains all data obtained on palm oil samples and used in the publication "Insights in the structural hierarchy of statically crystallized palm oil". See paper for more information on the methods for obtaining the data.</p> <p> </p> <p>Abbreviations used:</p> <p>PO = palm oil</p> <p>PPP = tripalmitin</p> <p>I = intensity (in X-ray scattering)</p> <p>q = scattering vector (in X-ray scattering)</p> <p>SEM = Scanning Electron Microscopy</p> <p>DSC = Differential Scanning Calorimetry</p> <p>WAXS = Wide Angle X-ray Scattering</p> <p>SAXS = Small Angle X-ray Scattering</p> <p>USAXS = Ultra Small Angle X-ray Scattering</p> <p>PLM = Polarized Light Microscopy</p> <p>FC = fast cooling = 20°C/min</p> <p>SC = slow cooling = 1°C/min</p> <p>TAG = triglyceride</p> <p>FA = fatty acid</p>
Full Results from Simulations for Static and Dynamic Workloads Using BSC Slurm Simulator
<p>This dataset contains two CSV files. One is for the static runs, done by emulating LUMI's supercomputer environment, and dynamic, using CEA-Curie workload taken from Feitelson's repository.</p>
SLF4Web - MPEG-DASH datasets of static light fields
<p>MPEG-DASH datasets for the SLF4Web research project. SLF4Web is a Web-based implementation of a static light field consumption system; it allows SLF datasets to be adaptively streamed over the network (via MPEG-DASH) and then to be visualized in a vanilla Web browser. The datasets are encoded using the H.264/AVC video codec. A subset of the datasets are available in multiple qualities to allow for adaptive network streaming.</p> <p>The SLF4Web source code is available on GitHub (<a href="https://github.com/EDM-Research/SLF4Web">https://github.com/EDM-Research/SLF4Web</a>) and as a bundle at <a href="https://zenodo.org/badge/latestdoi/432214902">https://zenodo.org/badge/latestdoi/432214902</a>.</p>
Data from: Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi2Sr2CaCu2O8+δ
<p>This database contains all the necessary data of the manuscript "Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub>". Compared to the previous version (https://doi.org/10.5281/zenodo.5558592), the data of Figure S13B and Figure S13D are exchanges.</p> <p><strong>Abstract of the manuscript</strong></p> <p>In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub> (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.</p>
3D-Scere static files and result table
<p>Static files, result table and graphical representations associated with the 3D-Scere project (dashboard and publication).</p> <p> </p> <p>3D_distances.parquet.gzip: three-column parquet file with 3D distances between <em>Saccharomyces cerevisiae </em>chromosomal features.</p> <p>SCERE.db: SQLite database of <em>Saccharomyces cerevisiae </em>features build from the <a href="https://www.yeastgenome.org/">SGD</a>.</p> <p>Table1.tsv: tab-separated values file with Kolmogorov Smirnov test results for <em>Saccharomyces cerevisiae </em>transcription factors.</p> <p>supplementary-data-file-S4.zip: ZIP file with graphical representations associated to each transcriptional module.</p>
Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"
<p>Artifact for the paper "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"</p> <p>The package contains:</p> <ul> <li>example files for all static and dynamic fault tree models</li> <li>installation instructions for the three tools</li> <li>scripts to perform the benchmarking</li> <li>detailed result tables</li> </ul>
Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection
<p>This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".</p>
N2O raw data from static greenhouse gas chamber measurements
<p>This dataset contains N<sub>2</sub>O concentration measurements of a 2 years measurement campaign for greenhouse gas fluxes from agricultural soils.</p> <p>The format of the data is ready to be fed into the gasfluxes R package on CRAN to calculate fluxes for each individual chamber measurement (identical IDs are referred to one single measurement, the ID contains the measurement day, treatment and replicate).</p> <p>The data is originally published in Krauss et al. 2017 and further used for improvements of the flux calculation procedure in Hüppi et a. 2018 (see references)</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</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.