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1,916 results for “software”
INTERPNT Software for Mapping Trees Using Distance Measurements
The INTERPNT method can be used to produce accurate maps of trees based solely on tree diameter and tree-to-tree distance measurements. For additional details on the technique please see the published paper (Boose, E. R., E. F. Boose and A. L. Lezberg. 1998. A practical method for mapping trees using distance measurements. Ecology 79: 819-827). Additional information is contained in the documentation that accompanies the program. The Abstract from the paper is reproduced below. "Accurate maps of the locations of trees are useful for many ecological studies but are often difficult to obtain with traditional surveying methods because the trees hinder line of sight measurements. An alternative method, inspired by earlier work of F. Rohlf and J. Archie, is presented. This "Interpoint method" is based solely on tree diameter and tree-to-tree distance measurements. A computer performs the necessary triangulation and detects gross errors. The Interpoint method was used to map trees in seven long-term study plots at the Harvard Forest, ranging from 0.25 ha (200 trees) to 0.80 ha (889 trees). The question of accumulation of error was addressed though a computer simulation designed to model field conditions as closely as possible. The simulation showed that the technique is highly accurate and that errors accumulate quite slowly if measurements are made with reasonable care (e.g., average predicted location errors after 1,000 trees and after 10,000 trees were 9 cm and 15 cm, respectively, for measurement errors comparable to field conditions; similar values were obtained in an independent survey of one of the field plots). The technique requires only measuring tapes, a computer, and two or three field personnel. Previous field experience is not required. The Interpoint method is a good choice for mapping trees where a high level of accuracy is desired, especially where expensive surveying equipment and trained personnel are not available."
P-S waves 3D velocity model of Los Humeros area from earthquake based travel-time tomography using CAT3D software (OGS)
<p>The dataset contains the 3D velocity model (VP (m/s), VS (m/s) and VP/VS) obtained from the tomographic inversion of seismological data in the area of Los Humeros (Mexico). The model was performed in the frame of the GEMex project (Mexico‐Europe Cooperation for research of enhanced geothermal systems and super-hot geothermal systems, WP5 ‘Detection of deep structures’, Jousset et al., D5.3, 2019).</p> <p>The inversion used 2661 P arrivals and 2272 S arrivals associated to 395 earthquakes recorded by 37 stations. The picking data was provided by Toledo et al., 2019.</p> <p>The inversion was performed by CAT3D software, a tomographic tool developed by OGS, which uses the SIRT method (Simultaneous Iterative Reconstruction Technique, Stewart, 1993) as inversion algorithm and the ray tracing procedure based on minimum time principle (Böhm et al., 1999). The velocities used as initial model for tomography were provided by the interpolated values obtained from the velocity analysis of four 2D seismic lines acquired inside the same investigated area by the tomographic inversion (See GEMex deliverable D5.3).</p> <p>The 3D velocity model is defined by a 3D grid of 61 nodes in X, 69 nodes in Y and 29 nodes in Z, equally spaced by 250 m in all directions. The total dimensions of the model is 15x17x7 km and the borders positions are (m) (WGS 84/UTM ZONE 14N):</p> <p>Xmin = 655000, Xmax = 670000</p> <p>Ymin = 2168000, Ymax = 2185000</p> <p>Zmin = -3000, Zmax = 4000</p>
Data and software: Stress and heat flux via automatic differentiation
<h4><strong>glp-archive</strong></h4><h2><strong>Code and Data for "Stress and heat flux with automatic differentiation"</strong></h2><p>This repository contains data, code, and related artefacts supporting the following publication (<a href="https://arxiv.org/abs/2305.01401">preprint</a>):</p><p>Stress and heat flux via automatic differentiation</p><p>by Marcel F. Langer, J. Thorben Frank, and Florian Knoop</p><p><i>J. Chem. Phys.</i> 159, 174105 (2023) <a href="https://doi.org/10.1063/5.0155760">doi:10.1063/5.0155760</a></p><p>This repository is available at <a href="https://github.com/sirmarcel/glp-archive">https://github.com/sirmarcel/glp-archive</a>. Selected versions are archived on Zenodo, under <a href="https://doi.org/10.5281/zenodo.7852529">doi:10.5281/zenodo.7852529</a>.</p><h2><strong>Overview</strong></h2><p>Each subfolder in this repository contains a README.md with additional information. The subfolders are:</p><ul><li>results/: Data and code that produced the figures in the manuscript</li><li>work/: Computational workflows, models, etc.</li><li>infra/: Project-specific infrastructure code</li><li>meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.</li></ul><h2><strong>Related external code</strong></h2><p>The work in this repository relies on a few tools that the authors maintain separately:</p><ul><li><a href="https://github.com/sirmarcel/glp">glp</a> implements the quantities discussed in the manuscript</li><li><a href="http://github.com/thorben-frank/mlff">mlff</a> implements the so3krates model</li><li><a href="https://github.com/flokno/tools.mlff">tools.mlff</a> provides tools for the equation of state experiments</li></ul><p>These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:</p><ul><li>glp @ v0.1.0 (tag)</li><li>mlff @ v1.0 (branch)</li><li>mlff.tools @ v0.0.1</li></ul><p>We additionally note that the GK-MD functionality has been factored out into <a href="https://github.com/sirmarcel/gkx">gkx</a>.</p><h2><strong>Versions</strong></h2><ul><li>v1.1: published version, archived at <a href="https://doi.org/10.5281/zenodo.8406532">doi:10.5281/zenodo.8406532</a></li><li>v1.0: arXiv submission v1, archived at <a href="https://doi.org/10.5281/zenodo.7852530">doi:10.5281/zenodo.7852530</a></li></ul>
A collection of datasets for software vulnerability detection
<p>This is a collection of datasets that are used for AI-based software vulnerability detection. All the datasets are in the .csv format and each row represents a sample. Each dataset includes a set of functions written in C and the target of each function is either 0 (non-vulnerable) or 1 (vulnerable).</p> <ol> <li><strong>data_C_Lin2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://dl.acm.org/doi/10.1145/3133956.3138840">Vulnerability Discovery with Function Representation Learning from Unlabeled Projects</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/DanielLin1986/function_representation_learning">https://github.com/DanielLin1986/function_representation_learning</a></li> <li>This dataset includes 44 vulnerable and 577 non-vulnerable functions from the LibPNG project.</li> </ul> </li> <li><strong>data_C_LineVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://ieeexplore.ieee.org/document/9796256">LineVul: A Transformer-based Line-Level Vulnerability Prediction</a>, 2022.</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset">https://huggingface.co/datasets/Partha117/LineVul_Test_Dataset</a></li> <li>This dataset includes 1055 vulnerable and 17809 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_PrimeVul_test.csv:</strong> <ul> <li>Reference paper: <a href="https://arxiv.org/abs/2403.18624">Vulnerability Detection with Code Language</a><br><a href="https://arxiv.org/abs/2403.18624">Models: How Far Are We?</a> 2024.</li> <li>Data source on GitHub: <a href="https://github.com/DLVulDet/PrimeVul">https://github.com/DLVulDet/PrimeVul</a></li> <li>From the data source, the primevul_test.jsonl was used to created this dataset.</li> <li>This dataset includes 695 vulnerable and 25213 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Choi2017_test.csv:</strong> <ul> <li>Reference paper: <a href="https://www.ijcai.org/proceedings/2017/0214.pdf">End-to-End Prediction of Buffer Overruns from Raw Source Code</a><br><a href="https://www.ijcai.org/proceedings/2017/0214.pdf">via Neural Memory Networks</a>, 2017.</li> <li>Data source on GitHub: <a href="https://github.com/mjc92/buffer_overrun_memory_networks">https://github.com/mjc92/buffer_overrun_memory_networks</a></li> <li>From GitHub, all the data in trainnig_100.txt, test_1_100.txt, test_2_100.txt,test_3_100.txt,test_4_100.txt, and corresponding _labels.txt files are combined to create this dataset.</li> <li>This dataset includes 7054 vulnerable and 6946 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Devign_test.csv:</strong> <ul> <li>Reference paper: <a href="https://proceedings.neurips.cc/paper_files/paper/2019/file/49265d2447bc3bbfe9e76306ce40a31f-Paper.pdf">Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks</a>, 2019</li> <li>Data source on Hugging Face: <a href="https://huggingface.co/datasets/claudios/code_x_glue_devign">https://huggingface.co/datasets/claudios/code_x_glue_devign</a></li> <li>From Hugging Face, all the data in train, validation, and test are combined to create this dataset.</li> <li>This dataset includes 12460 vulnerable and 14858 non-vulnerable functions.</li> </ul> </li> <li><strong>data_C_Ours_{train,test}.csv:</strong> <ul> <li>This dataset is manually collected from projects on GitHub that have registered CVEs into NVD from 2002 to 2023. The 6,766 non-vulnerable code functions are extracted from the <a href="https://dl.acm.org/doi/10.1145/3607199.3607242">DiverseVul dataset</a> to increase the code diversity. </li> <li>This training set includes 5413 vulnerable and 5413 non-vulnerable functions.</li> <li>The test set includes 1353 vulnerable and 1353 non-vulnerable functions.</li> </ul> </li> </ol>
Software and suspect database for: "A large scale multi-laboratory suspect screening of pesticide metabolites in human biomonitoring: From tentative annotations to verified occurrences"
<p>This upload contains the pesticide suspect list aggregated among the laboratories of work package 16 of the HBM4EU (https://www.hbm4eu.eu) project for a large-scale pesticide suspect screening and the resolving search templates for each pesticide. Additionally, we provide the used software version of MetAlign applied in this screening.</p>
An Empirical Evaluation of the Relationship between Technical Debt and Software Security
<p>This dataset contains the static analysis results of 50 open source software applications retrieved from Github. The results were produced by using SonarQube, PMD, CKJM Extended and Findbugs tools. Further details can be found in the relevant publication: </p> <ul> <li>Siavvas, M., Tsoukalas, D., Janković, M., Kehagias, D., Chatzigeorgiou, A., Tzovaras, D., Aničić, N., Gelenbe, E. <em>An Empirical Evaluation of the Relationship between Technical Debt and Software Security</em>. In: Konjović, Z., Zdravković, M., Trajanović, M. (Eds.) ICIST 2019 Proceedings Vol.1, pp.199-203, 2019</li> </ul>
Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction [dataset]
<p>This dataset contains the extension of a publicly available dataset that was published initially by Ferenc et al. in their paper:</p> <p><em>“Ferenc, R.; Hegedus, P.; Gyimesi, P.; Antal, G.; Bán, D.; Gyimóthy, T. Challenging machine learning algorithms in predicting vulnerable javascript functions. 2019 IEEE/ACM 7th InternationalWorkshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). IEEE, 2019, pp. 8–14.”</em></p> <p>The dataset contained software metrics for source code functions written in JavaScript (JS) programming language. Each function was labeled as vulnerable or clean. The authors gathered vulnerabilities from publicly available vulnerability databases.</p> <p>In our paper entitled: “<strong>Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction</strong>” and cited as:</p> <p><em>“Kalouptsoglou I, Siavvas M, Kehagias D, Chatzigeorgiou A, Ampatzoglou A. Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction. Entropy. 2022; 24(5):651. <a href="https://doi.org/10.3390/e24050651">https://doi.org/10.3390/e24050651</a>”</em></p> <p>, we presented an extended version of the dataset by extracting textual features for the labeled JS functions. In particular, we got the dataset provided by Ferenc et al. in CSV format and then we gathered all the GitHub URLs of the dataset's functions (i.e., methods). Using these URLs, we collected the source code of the corresponding JS files from GitHub. Subsequently, by utilizing the start and end line information for every function, we cut off the code of the functions. Each function was then tokenized to construct a list of tokens per function.</p> <p>To extract text features, we used a text mining technique called sequences of tokens. As a result, we created a repository with all methods' source code, the token sequences of each method, and their labels. To boost the generalizability of type-specific tokens, all comments were eliminated, as well as all integers and strings, which were replaced with two unique IDs.</p> <p>The dataset contains 12,106 JavaScript functions, from which 1,493 are considered vulnerable.</p> <p>This dataset was created and utilized during the Vulnerability Prediction Task of the Horizon2020 IoTAC Project as training and evaluation data for the construction of vulnerability prediction models. The dataset is provided in the csv format. Each row of the csv file has the following parts:</p> <ul> <li>Label: Flag with values ‘1’ for vulnerable and ‘0’ for non-vulnerable methods</li> <li>Name: The name of the JavaScript method</li> <li>Longname: The longname of the JavaScript method</li> <li>Path: The path of the file of the method in the repository</li> <li>Full_repo_path: The GitHub URL of the file of the method</li> <li>TokenX: Each next row corresponds to each token included in the method</li> </ul>
Software Tools to Collect and Use Provenance in R
The software tools that scientists use to process and analyze data are typically optimized for performance and ease of use. Few if any such tools are designed to capture and record the details of what happens as the tool performs its task. This detailed information, and more generally the history of an item of data from its creation to its present state, is known as provenance. Provenance has the potential to make science more transparent, reliable, and reproducible. This project focused on collecting and using provenance for scripts written in the R statistical language, which is widely used by ecologists and environmental scientists for data analysis and visualization. Our tools include a provenance collector (rdtLite), which collects provenance as an R script executes (or during a console session), as well as other tools that use the collected provenance to document and visualize the execution or to support activites such as script debugging. The R packages included here are also available on CRAN. For more details, see the project website on GitHub (https://end-to-end-provenance.github.io).
Software Developer Expertise GitHub and Stack Overflow data sets
<p>Cross-Platform Software Developer Expertise Learning by Norbert Eke</p> <p>This data set is part of my Master's thesis project on developer expertise learning by mining Stack Overflow (SOTorrent) and Github (GHTorrent) data. Check out my portfolio website at norberte.github.io</p>
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
Ascon SCA software and hardware databases
<h2><strong>Software and hardware side-channel analysis databases for attack on Ascon AEAD</strong><br> </h2><p>This repository contains datasets collected for side-channel analysis attack on Ascon AEAD.</p><p>The repository is divided into two folders, one for software side-channel analysis and one for hardware side-channel analysis:</p><p> </p><p>- cw.zip</p><p> - ascon_collect.ipynb : Jupyter Notebook used to collect the traces for the unprotected Ascon implementation</p><p> - ascon_protected_collect.ipynb : Jupyter Notebook used to collect the traces for the protected Ascon implementation</p><p> - simpleserial-ascon : Ascon firmwares used to collect the traces</p><p>- hw.zip</p><p> - ascon_g_protected</p><p> - test_ascon.py : Script to test the protected Ascon implementation</p><p> - collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope</p><p> - rtl_src : RTL source files for the protected Ascon implementation</p><p> - ascon_g_unprotected</p><p> - test_ascon.py : Script to test the unprotected Ascon implementation</p><p> - collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope</p><p> - rtl_src : RTL source files for the unprotected Ascon implementation</p><p>- helpers.zip</p><p> - ASCON.py : Python implementation of Ascon</p><p> - SASEBO.py : Helper functions to communicate with the SAKURA-G board</p><p> - lecroy3.py : Helper functions for Lecroy oscilloscope</p><p> - ascon_helper.py : Helper functions for Ascon</p><p> - convert_trs_to_h5.py : Script to convert Trsfile traceset to HDF5 database</p><p> </p><p>ascon_cw_protected.h5 : Side-channel database for software protected Ascon implementation</p><p>ascon_cw_unprotected.h5 : Side-channel database for software unprotected Ascon implementation</p><p>ascon_hw_protected.h5 : Side-channel database for hardware protected Ascon implementation</p><p>ascon_hw_unprotected.h5 : Side-channel database for hardware unprotected Ascon implementation</p><p> </p><p>ascon_cw_protected.trs : Traces for software protected Ascon implementation</p><p>ascon_cw_unprotected.trs : Traces for software unprotected Ascon implementation</p><p>ascon_hw_protected.trs : Traces for hardware protected Ascon implementation</p><p>ascon_hw_unprotected.trs : Traces for hardware unprotected Ascon implementation</p><p> </p><h3><strong>Ascon authenticated encryption attack on a Chipwhisperer STM32F4</strong> </h3><p>The dataset was used for side-channel attack on Ascon initialization phase attack of the authenticated encryption mode on a ChipWhisperer STM32F4 target board.</p><p>The power traces are collected with the ChipWhisperer-Lite oscilloscope at a sampling rate of 4x the target clock frequency, and captures the first call of the round function of the Chi function of Ascon permutation.</p><p>The code used to collect the traces is also available in this repository, and the trace collection can be replicated with a ChipWhisperer-Lite and a STM32F4 target board.</p><p> </p><h3><strong>Ascon authenticated encryption attack on a SAKURA-G FPGA</strong></h3><p>The hardware designs of the unprotected and protected Ascon implementations are available in the `hw` folder.</p><p>Both implementations are written in VHDL/Verilog and can be synthesized for Spartan6 (XC6SLX75) with Xilinx ISE.</p><p>Traces are collected with a Lecroy WaveRunner 610Zi oscilloscope at a sampling rate of 500 MS/s.</p><p> </p><h3><strong>Databases description:</strong></h3><p>Database | Ntraces | Traces (samples) | Label* (bytes) | Metadata (bytes) </p><p> | Nf | Nr | | | Key | Nonce | Plaintext | Associated data | Ciphertext | Tag</p><p>ascon_cw_unprotected.h5 | 100,000 | 100,000 | 772 | 64 | 16 | 16 | 4 | 4 | 4 | 16</p><p>ascon_cw_protected.h5 | 500,000 | 500,000 | 1408 | 64 | 32 | 32 | 16 | 16 | 16 | 32</p><p>ascon_hw_unprotected.h5 | 100,000 | 100,000 | 6,000 | 64 | 16 | 16 | 4 | 4 | 4 | 16</p><p>ascon_hw_protected.h5 | 500,000 | 500,000 | 10,000 | 64 | 32 | 32 | 8 | 8 | 8 | 32</p><p> </p><p>*Label computed with the intermediate_value leakage model described in `ascon_helper.py`</p>
Experimental data and software for: Defaults: a double-edged sword in governing common resources
<p>Experimental data and software for the paper: <strong>Defaults: a double-edged sword in governing common resources</strong></p> <p>The experiment consisted in three treatments of the Common Pool Resource Dilemma, where three default interventions were applied: pro-social, self-serving and no default. Plus, the participants had to complete an SVO task and a Risk assessment task.</p> <h4>Description of the data and file structure</h4> <p>In the file called <code>all_participants.csv</code> is the full dataset of all participants that took part of the experiment. This includes participants who will end up excluded and dropouts.</p> <p>The experimental data files come in two formats: wide and long. The wide version, called <code>data_wide_format.csv</code> contains one row per participant and a column for all the fields, including rounds from 1 to 10 of the CPR task. Also, this file includes all demographic information of the participants, times and payments. The ID shown is generated internally and has no relationship with the participants' Prolific ID.</p> <p>The long version, called <code>data_long_format.csv</code>, contains 10 rows per participant, and columns for the extraction and other variables necessary for analysis. This version contains the necessary data to reproduce all the figures and statistics detailed in the main manuscript.</p> <p>In both of the previous files, the participants taken into account were the ones who completed the whole experiment. Those who did not complete the comprehension test, dropped out or did not sign the Informed Consent Form were excluded from the experimental data used. More details in the Methods below.</p> <p>In the file <code>default_opinions.csv</code>, we manually classified the responses by participants to whether they were influenced by the default presented.</p> <p>The file "<code>Instructions of the experiment.pdf</code>" contains the instructions of the experiment as shown to participants, also screenshots of the platform.</p>
Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software
<p>Support material for the research paper "Visualization and quantification of coral reef soundscapes using CoralSoundExplorer software"</p>
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
Librecast: IoT Software Updates over IPv6 Multicast Archive of all experimental data
<p>The librecast project states that ``Multicast is, by definition, the most efficient way for multiple nodes to communicate''. This experiment is designed to provide evidence of this efficiency by comparing multicast and unicast methods of sending the same data to a large number of nodes, as would for example happen when a software update is released.<br> <br> The data set covers the experimental runs on the Virtual Wall 1 at IMEC as part of the Fed4Fire+ "SME and NGI Cascaded Experiments" https://www.fed4fire.eu/demo-stories/cc/librecasttesting/</p> <p>This directory contains raw experiment results as produced by the "run-experiment" script. File names containing ".test." are experiment runs using code changes which we decided not to keep, and are excluded from processing and summarising. File names containing ".partial." are experiment runs which were interrupted for some reason (usually when some nodes in a testbed stopped responding, and we could not get a complete set). These are also excluded from processing and summarising.</p> <p>Summaries:</p> <p>Results collated by testbed:</p> <table> <tbody> <tr> <th>Testbed</th> <th>Booted</th> <th>Clients</th> <th>Routers</th> <th>Runs</th> </tr> <tr> <td>S1L20B</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>1</td> </tr> <tr> <td>S1L20C</td> <td>2022-01-19 10:15:08 UTC</td> <td>20</td> <td>0</td> <td>30</td> </tr> <tr> <td>S1L40</td> <td>2022-02-16 12:17:41 UTC</td> <td>40</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L48A</td> <td>2022-02-18 21:27:33 UTC</td> <td>48</td> <td>0</td> <td>6</td> </tr> <tr> <td>S1L49F</td> <td>2022-02-25 19:03:03 UTC</td> <td>49</td> <td>0</td> <td>11</td> </tr> <tr> <td>S1L50</td> <td>2022-02-17 21:02:31 UTC</td> <td>50</td> <td>0</td> <td>2</td> </tr> <tr> <td>S1L51G</td> <td>2022-02-28 19:23:06 UTC</td> <td>51</td> <td>0</td> <td>14</td> </tr> <tr> <td>S1R1L19C</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>1</td> </tr> <tr> <td>S1R1L19D</td> <td>2022-01-28 08:41:43 UTC</td> <td>19</td> <td>1</td> <td>8</td> </tr> <tr> <td>S1R1L20A</td> <td>2022-02-11 17:14:31 UTC</td> <td>20</td> <td>1</td> <td>13</td> </tr> <tr> <td>S1R3L10H</td> <td>2022-03-09 20:46:54 UTC</td> <td>40</td> <td>7</td> <td>4</td> </tr> <tr> <td>S1R3L5B</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>1</td> </tr> <tr> <td>S1R3L5C</td> <td>2022-01-24 19:09:44 UTC</td> <td>20</td> <td>7</td> <td>7</td> </tr> </tbody> </table> <p> </p> <p>Results collated by number of clients:</p> <table> <tbody> <tr> <th>Collection</th> <th>Clients</th> <th>Runs</th> </tr> <tr> <td>Multiple LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>Two LANs</td> <td>19</td> <td>9</td> </tr> <tr> <td>All testbeds</td> <td>19</td> <td>9</td> </tr> <tr> <td>Single LAN</td> <td>20</td> <td>31</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>21</td> </tr> <tr> <td>Two LANs</td> <td>20</td> <td>13</td> </tr> <tr> <td>Multiple LANs</td> <td>20</td> <td>8</td> </tr> <tr> <td>All testbeds</td> <td>20</td> <td>52</td> </tr> <tr> <td>Single LAN</td> <td>40</td> <td>6</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>Multiple LANs</td> <td>40</td> <td>4</td> </tr> <tr> <td>All testbeds</td> <td>40</td> <td>10</td> </tr> <tr> <td>Single LAN</td> <td>48</td> <td>6</td> </tr> <tr> <td>All testbeds</td> <td>48</td> <td>6</td> </tr> <tr> <td>Single LAN</td> <td>49</td> <td>11</td> </tr> <tr> <td>All testbeds</td> <td>49</td> <td>11</td> </tr> <tr> <td>Single LAN</td> <td>50</td> <td>2</td> </tr> <tr> <td>All testbeds</td> <td>50</td> <td>2</td> </tr> <tr> <td>Single LAN</td> <td>51</td> <td>14</td> </tr> <tr> <td>All testbeds</td> <td>51</td> <td>14</td> </tr> </tbody> </table> <p> </p> <p>All results together:</p> <table> <tbody> <tr> <th>File size</th> <th>Runs</th> </tr> <tr> <td>32</td> <td>104</td> </tr> <tr> <td>128</td> <td>104</td> </tr> <tr> <td>512</td> <td>104</td> </tr> <tr> <td>2048</td> <td>104</td> </tr> <tr> <td>All</td> <td>416</td> </tr> </tbody> </table> <p>All results together, immediate, size 2048:</p> <table> <tbody> <tr> <th>Update</th> <th>Runs</th> </tr> <tr> <td>multicast</td> <td>104</td> </tr> <tr> <td>scp</td> <td>104</td> </tr> <tr> <td>tcp</td> <td>104</td> </tr> <tr> <td>udp</td> <td>104</td> </tr> </tbody> </table> <p>Router results for selected multicast runs and routers</p> <table> <tbody> <tr> <th>Testbed</th> <th>Run</th> </tr> <tr> <td>S1R3L10H</td> <td>20220309222056</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310074238</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310172944</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311033431</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128192622</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129114604</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129215606</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130060831</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130144549</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130224025</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131063956</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131164414</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310001954</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310093547</td> </tr> <tr> <td>S1R3L10H</td> <td>20220310193037</td> </tr> <tr> <td>S1R3L10H</td> <td>20220311052100</td> </tr> <tr> <td>S1R3L5B</td> <td>20220128210828</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129132315</td> </tr> <tr> <td>S1R3L5C</td> <td>20220129234328</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130075126</td> </tr> <tr> <td>S1R3L5C</td> <td>20220130162044</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131002021</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131083908</td> </tr> <tr> <td>S1R3L5C</td> <td>20220131181549</td> </tr> </tbody> </table> <p> </p> <p> </p>
Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol
<p><strong>Image 1.tif is a Brain slice</strong>. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.</p>
Heliostat and receiver efficiency calculated with DLR's software HFLCAL
<p>The heliostat field and the receiver design efficiency was estimated for the design point by optimizing for lowest LCOE.</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
Heliostat field positions after optimization with DLR's software HFLCAL
<p>The dataset provides the design of the heliostat field layout, in terms of number and positions. The filed layout is optimized by minimizing the estimated LCOE of the system, using the DLR tool Visual HFLCAL software</p> <p>The datasets could help other people design a heliostat field.</p> <p>For detailed analysis, please refer to Deliverable 1.2 (Process Parameters of Solar Particle Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf">https://www.compassco2.eu/wp-content/uploads/2022/03/Deliverable1.2_COMPASsCO2_V2.pdf</a></p>
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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)
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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.