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100 results for “Maven”
Technical Leverage Dataset for Java Dependencies in Maven
<p>In finance, leverage is the ratio between assets borrowed from others and one's own assets. A matching situation is present in software: by using free open-source software (FOSS) libraries a developer leverages on other people's code to multiply the offered functionalities with a much smaller own codebase. In finance as in software, leverage magnifies profits when returns from borrowing exceed costs of integration, but it may also magnify losses, in particular in the presence of security vulnerabilities. We aim to understand the level of technical leverage in the FOSS ecosystem and whether it can be a potential source of security vulnerabilities. Also, we introduce two metrics change distance and change direction to capture the amount and the evolution of the dependency on third-party libraries. Our analysis published in [1] shows that small and medium libraries (less than 100KLoC) have disproportionately more leverage on FOSS dependencies in comparison to large libraries. We show that leverage pays off as leveraged libraries only add a 4% delay in the time interval between library releases while providing four times more code than their own. However, libraries with such leverage (i.e., 75% of libraries in our sample) also have 1.6 higher odds of being vulnerable in comparison to the libraries with lower leverage.</p> <p>This dataset is the original dataset used in the publication [1]. It includes 8494 distinct library versions from the FOSS Maven-based Java libraries An online demo for computing the proposed metrics for real-world software libraries is also available under the following URL: https://techleverage.eu/.</p> <p>The original publication is [1]. An executive summary of the results is avialble as the publication [2]. This work has been funded by the European Union with the project AssureMOSS (https://www.assuremoss.eu).</p> <p>[1] Massacci, F., & Pashchenko, I. (2021, May). Technical leverage in a software ecosystem: Development opportunities and security risks. In <em>2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)</em> (pp. 1386-1397). IEEE.</p> <p>[2] Massacci, F., & Pashchenko, I. (2021). Technical Leverage: Dependencies Are a Mixed Blessing. <em>IEEE Secur. Priv.</em>, <em>19</em>(3), 58-62.</p>
Goblin: Neo4J Maven Central dependency graph
<p>This repository contains a Neo4j dump of Maven Central dependency graph generated using <a href="https://github.com/Goblin-Ecosystem/goblinDependencyMiner">goblinDependencyMiner</a>.<br>To import this graph into neo4j, <strong>please use a version 4.x</strong>.</p> <p>Our dependency graph structure and metamodel are shown in images "goblin_dg_structure" and "metamodel".</p> <p>The latest available version dates from April 20, 2025, contains <span>16,939,391</span> nodes (712,509 libraries and 16,226,882 releases) and <span>152,434,085</span> edges (136,207,203 dependencies and 16,226,882 versioning edges).</p> <p>This repository contains two dump of the database:</p> <ul> <li><strong>goblin_maven_20_04_25.dump: </strong>This dataset contains the entire Maven Central dependency graph.</li> <li><strong>with_metrics_goblin_maven_20_04_25.dump</strong>: This dataset is the same as the previous one, but enriched with new “AddedValue” nodes (49,393,155 new nodes) representing the following metrics: CVE (dated may 13, 2025), freshness, popularity and speed. More information in this <a href="https://github.com/Goblin-Ecosystem/goblinTutorial">tutorial</a>.</li> </ul> <p>More details in the dedicated paper: <strong>Goblin: A Framework For Enriching And Querying the Maven Central Dependency Graph </strong>(https://doi.org/10.1145/3643991.3644879)<strong> </strong>- 21st International Conference on Mining Software Repositories (MSR'24).<br>If you use it, please <strong>cite</strong> this paper: <a href="https://dl.acm.org/doi/10.1145/3643991.3644879">https://dl.acm.org/doi/10.1145/3643991.3644879</a></p> <p>⚠️ This dataset is the subject of the <strong>Mining Challenge at the MSR 2025 conference</strong>, more information <a href="https://2025.msrconf.org/track/msr-2025-mining-challenge">here</a>.</p>
Monthly Normalized MAVEN Data (2014-2021)
<p>These data were obtained from the magnetometer (MAG), Neutral Gas and Ion Mass Spectrometer (NGIMS), and Solar Wind Ion Analyzer (SWIA) onboard the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. These two data sets were derived from these three instruments' data products and utilized in the article <strong>Influence of Magnetic Fields on Precipitating Solar Wind Hydrogen at Mars</strong>, which we plan to submit to Geophysical Research Letters.</p> <p>The backscattered and downward propagating penetrating proton data have been separated into two data files titled respectively. The contents of each file are as follows:</p> <p>1. Time [s]: epoch time (seconds since Jan. 1, 1970) for each penetrating proton measurement</p> <p>2. Orbit [#]: MAVEN's orbit number</p> <p>3. LSM [degrees]: Martian solar longitude</p> <p>4. Flux [eV/(eV s sr cm^2)]: peak angle averaged differential energy flux for each 4-s spectrum in electronvolt per electronvolt x second x steradian x square centimeters.</p> <p>5. Normalized Flux: monthly normalized peak flux (unitless)</p> <p>6. Energy [eV]: energy at which peak flux occurred for each 4-s spectrum found from Gaussian fit in electronvolts</p> <p>7. FWHM [eV]: full width at half maximum of 4-s spectrum found from Gaussian fit in electronvolts</p> <p>8. Altitude [km]: spacecraft altitude in kilometers </p> <p>9. SZA [degrees]: solar zenith angle in degrees </p> <p>10. X_MSO [km]: X position of MAVEN in the Mars-Sun-Orbit (MSO) coordinate system in kilometers</p> <p>11. Y_MSO [km]: Y position of MAVEN in the MSO coordinate system in kilometers</p> <p>12. Z_MSO [km]: Z position of MAVEN in the MSO coordinate system in kilometers</p> <p>13. Latitude [degrees]: Martian planetary latitude</p> <p>14. Longitude [degrees]: Martian planetary longitude</p> <p>15. Bx [nT]: X component of magnetic field measured by MAG in MSO coordinates in nanotesla </p> <p>16. By [nT]: Y component of magnetic field measured by MAG in MSO coordinates in nanotesla</p> <p>17. Bz [nT]: Z component of magnetic field measured by MAG in MSO coordinates in nanotesla</p> <p>18. Elevation Angle [degrees]: local magnetic field elevation angle measured relative to a vector tangent to the Martian surface during each penetrating proton measurement. 90 degree elevation angle corresponds to radial configurations while 0 degree elevation angle corresponds to horizontal configurations.</p> <p>19. Column Density [cm^-2]: CO<sub>2</sub> column density derived from NGIMS measurements in inverse square centimeters. All NaN values were used to fill for times during which NGIMS inbound verified data were not available, and the column density could not be determined.</p>
The Maven Central 2022 Dataset
<p>We present the Maven Central 2022 Dataset (MC22), based on a snapshot of Maven Central dated April<br> 4, 2022, for our replication study and future scientific work.</p> <p>The dataset contains metadata on jars in Maven Central and evolution metrics on syntactically SemVer-adhering<br> artifacts. It can be used to answer questions like: How often and when are updates of existing components<br> published? How do version tags look like and how do their syntactical forms change over time? How<br> common is the use of deprecation tags? and many more.</p> <p>The dataset contains three groups of information: Metadata on all jars in the snapshot, metrics on an-<br> alyzed upgrades and metrics on analyzed components. The metrics are calculated seperately regarding<br> three types of elements: public classes, public methods and protected methods.</p> <p> </p> <p> </p>
Maven central dependency graph
<p>The Maven dependency graph is an open dataset of Maven Central artifacts, their dependencies, as well as other relationships. Its main intent is to domesticate the wild within and around the Maven central ecosystem, in particular, and JVM-based libraries at large, making it more harnessable to both academics and industry. It is intended to answer high-level research questions concerning artifacts releases, evolution, and usage trends over time. It can also be used to assist researchers in selecting relevant datasets, among the mass of existing software artifact, for assessing particular empirical software engineering challenges. The complexity of these questions can range from simple pattern matching to advanced big data analysis and machine learning techniques.<br> <br> The accompanying paper to this dataset is has been accepted for publication in the proceedings of the International Conference on Mining Software Repositories 2019 and has received the MSR 2019 Data Showcase Award. This paper is available for download on <a href="https://arxiv.org/abs/1901.05392">arXiv</a>.</p>
Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central (Dataset)
<p>The content presented in this repository accompanies the paper "Breaking Bad? Semantic Versioning and Impact of Breaking Changes in Maven Central" authored by Lina Ochoa, Thomas Degueule, Jean-Rémy Falleri, and Jurgen Vinju. The paper was submitted and accepted in the Journal of Empirical Software Engineering (EMSE'21). This study is an external and differentiated replication study of the paper <a href="https://jstvssr.github.io/assets/pdf/semantic-versioning-maven.pdf">"Semantic Versioning and Impact of Breaking Changes in the Maven Repository"</a> presented by Steven Raemaekers, Arie van Deursen, and Joost Visser.</p> <p><strong>Content</strong></p> <ul> <li><strong>README.md: </strong>document with the main description to start exploring the bundle.</li> <li><strong>data.zip: </strong>contains the datasets used within the study. These datasets must be used to get the same results like the ones presented in the article.</li> <li><strong>maven-api-dataset.zip:</strong> contains the code used to generate the datasets and to analyse the obtained results. Check the README.md file within this bundle for more information.</li> </ul> <p><strong>Relevant Links</strong></p> <ul> <li><strong>maven-api-dataset repository:</strong> <a href="https://github.com/tdegueul/maven-api-dataset">https://github.com/tdegueul/maven-api-dataset</a></li> <li><strong>maracas repository:</strong> <a href="https://github.com/crossminer/maracas">https://github.com/crossminer/maracas</a></li> <li><strong>Companion webpage:</strong> <a href="https://crossminer.github.io/maracas/2021/08/16/emse21/">https://crossminer.github.io/maracas/2021/08/16/emse21/</a></li> </ul>
Processed EUVM and NGIMS data from the MAVEN spacecraft
<p>The .csv files contain EUVM and NGIMS data that have been interpolated onto a uniform altitude grid and time scale. For every orbit, the data are interpolated onto a uniform altitude scale. The time at for the average of these points is used to interpolate the EUVM data. the density is recorded in the 'density' variable and the EUVM data are recorded for four spectral bands: (0-7 nm = da, 17-22 nm = db, 0-45 nm = dc, 117-125 nm = dd). Auxiliary parameters of local solar time (LST), longitude (lon), latitude (lat), solar zenith angle (SZA), and the distance between maven and the sun (Rs), are also provided.</p> <p>The .p file contains the same data, but with additional fields for the relative changes in both the EUVM and NGIMS data as well as the slope and r value for a linear fit inside of a rolling 27 day bin. The relative change is defined as the 5 day running mean of the 27-day residuals. Additionally, days with high flares are removed as these data may be affected. The .p file is a pickle file, made with python. </p>
Martian BS crossing_MAVEN_V1
<p>The database is provided by the Institute of Space Weather, Nanjing University of Information Science and Technology, Nanjing, PR China.<br> If you have any questions, please contact us at suihaoyang1998@163.com.</p> <p>The dataset has 5 columns and 10,283 rows, recording the time and location of 10,283 Mars bow shock crossings. The locations of crossings are all calculated in the MSO coordinate system.<br> </p>
MAVEN Shock Crossing data
<p>This file contains the dataset compiles the data for Fruchtman et al's <em>Seasonal and Mach Number Variation of the Martian Bow Shock Structure</em>. It contains the automatically identified timestamps of shock crossings, associated timestamps for the beginning and end of representative upstream and downstream intervals to be used for measuring average values of locally characteristic quantities, the shock crossing's positions in MSO, L<sub>S</sub>, latitude, longitude, measured upstream species moments (n<sub>i</sub>,n<sub>e</sub>, T<sub>i</sub>,T<sub>e</sub>), measured upstream (both from SWIFS and SWICS) and downstream (just SWICS) ion velocities, upstream and downstream magnetic field data, calculated normal vectors, the normal vector (in cylindrical coordinates) associated with the closest point on the Trotignon (2006) conic, fit parameters used to fit the magnetic time-series shock profiles to a hyperbolic tangent function, and various derived upstream quantities. </p> <p>Note that shockdataExpandedFormatted.csv differs from shockdataExpanded.csv in that the latter contains both the field names and their respective units together in line 1, while the formatted version records the units of each field in line 2.</p> <p>The codebase used to construct this dataset is found at https://doi.org/10.5281/zenodo.8161685</p>
The Updated Maven Central Dependency Graph
<p><strong>Maven Central Dependency Graph</strong></p> <p>This is an updated version of the artifact at https://zenodo.org/record/1489120</p> <p>The Maven dependency graph is an open dataset of Maven Central artifacts, their dependencies, as well as other relationships. Its main intent is to domesticate the wild within and around the Maven central ecosystem, in particular, and JVM-based libraries at large, making it more harnessable to both academics and industry. It is intended to answer high-level research questions concerning artifacts releases, evolution, and usage trends over time. It can also be used to assist researchers in selecting relevant datasets, among the mass of existing software artifact, for assessing particular empirical software engineering challenges. The complexity of these questions can range from simple pattern matching to advanced big data analysis and machine learning techniques.</p> <p>The accompanying paper to this dataset is has been accepted for publication in the proceedings of the International Conference on Mining Software Repositories 2019 and has received the MSR 2019 Data Showcase Award. This paper is available for download on <a href="https://arxiv.org/abs/1901.05392">arXiv</a>.</p> <p><strong>What is new?</strong></p> <p>The previous version included artifacts until September 6, 2018.<br> This version includes artifacts until September 10, 2019.</p> <p>This version includes license information as well as information about associated code repository.</p> <p>This version contains 4 201 392 artifacts (version) of 308116 distinct libraries from 47481 distinct group IDs.</p> <p>Note 33 638 artifacts represents version ranges and note actual versions. They can be filtered out by excluding version containing ','.</p> <p><br> <strong>Usage</strong></p> <p>Usage:</p> <ul> <li> Download the archive from zenodo</li> <li> Decompress the archive</li> </ul> <pre><code class="language-bash"># Pull the image and start the container docker run -d --name mm-neo4j -p 7474:7474 -p 7687:7687 -v /path/to/neo4j-data:/data --env=NEO4J_dbms_memory_heap_max__size=8g lyadis/mm-neo4j:latest</code></pre> <ul> <li> Open http://127.0.0.1:7474/browser/ in your browser</li> </ul>
Transport Ratios for Plasma Waves Observed by MAVEN at Mars
<p>Transport ratios calculated for coherent plasma waves observed upstream from Mars by the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission. These values were calculated by sub-sampling the ion velocity distribution measurements made by the Solar Wind Ion Analyzer (SWIA) instrument and binning them by the wave phase as estimated from the vector magnetic field measured by the Magnetometer (MAG) instrument. </p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</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>
Neo4J Maven Central dependency graph
<p>⚠️ Updated archive here: <a href="../records/11104819">https://zenodo.org/records/11104819</a> ⚠️</p> <p>Neo4j dump of the dependency graph of Maven Central as of April 04, 2023.<br> </p>
Maven Central Analysis
<p>The structure of the Maven ecosystem provides a valuable source of data to study and analyze the distribution of Java libraries. In this study we scrutinize four different categories of information available on Maven; packaging types content of the libraries, Java aspects of builds, required space by the packages, and finally, version control reproducibility of the libraries.</p>
POM of 2326 archetypes in Maven central repository
<p>This dataset contains <em>pom.xml </em>files of 2326 archetypes in Maven central repository.</p> <p>The format of file name of each <em>pom.xml</em> is "<groupId>=<artifactId>=<version>=<orginal file name>", where "." in groupId is replace as "=".</p> <p>For example, the file name "am=ik=archetype=spring-boot-blank-archetype=0.9.0=spring-boot-blank-archetype-0.9.0.pom" represents: </p> <ul> <li>groupId: am.ik.archetype</li> <li>artifactId: spring-boot-blank-archetype</li> <li>version: 0.9.0</li> <li>pom.xml original filename: spring-boot-blank-archetype-0.9.0.pom</li> </ul> <p> </p>
An empirical model of the dayside Martian ionosphere based on the radio occultation data from MGS, MEX, and MAVEN
<p>This empirical model is developed from the radio occultation data to reconstruct the dayside Martian ionosphere. The observations include the measurements from MGS, MEX, and MAVEN. Based on PCA and optimized regression, this model well reproduces the peak electron density and altitude in observations.</p>
Thermal Structure of the Martian Upper Atmosphere from MAVEN NGIMS
<p>This repository contains data to accompany the publication of our manuscript, "Thermal Structure of the Martian Upper Atmosphere from MAVEN NGIMS" which appears in the Journal of Geophysical Research: Planets.</p> <p>The repository contains .mat files of NGIMS densities and temperatures, as well as associated ephemerides. The filenames have the following structure,</p> <p> </p> <p>mvn_ngi_s3_BIN#_YYYYMMDDtoYYYYMMDD_TID##toTID##_ORBITtoORBIT.mat.</p> <p> </p> <p>Each .mat file contains a MATLAB struct named s3. Each s3 struct contains arrays of TIDs, orbits, dates, filenames of data products used to construct the s3, and periapsis information for each orbit. Mean profiles of detector counts and count rates, Ar, CO2, and N2 densities, density corrections, temperatures derived from the densities, altitude, latitude, longitude, local time, solar zenith angle, Ls, heliocentric distance, spacecraft position and velocity, solar latitude and longitude, and NGIMS ram angle are also included. In the "pass" struct within each s3 struct, there are data for each individual orbit in the s3 file. That is, the pass structure contains the data from each orbit which has been binned to obtain the mean profiles in the s3 struct. The pass struct is accessed by commands of the form,</p> <p> </p> <pre><code>s3.pass(n).FIELD</code></pre> <p> </p> <p>where n is an integer between 1 and length(s3.orbit) indicating the orbit in s3.orbit or TID in s3.tid to be accessed and FIELD the field, such as den_Ar (Ar density) or tmp_Ar (Ar temperature), to be accessed.</p> <p> </p> <p>For more information, please contact the corresponding author of the manuscript.</p>
MAVEN 40k dependency data
<p>This is the dependency data used in the "Analyzing the Criticality of Apache Maven Packages Through a Temporal<br> Dependency Graph" paper, with the following structure:</p> <pre><code>{"pkgs":[{ "name": "react", "versions": { "1.00": { "timestamp": "06-05-2022T10:00:01", "dependencies": { "name": "^1.0.2" } } } }, ... ]}</code></pre>
IMF clock angle proxy: orbital averaged magnetic field data from MAVEN in Martian magnetosheath
<p>The By and Bz components can be used as IMF clock angle proxy (IMF direction in MSO y-z plane). See the publication by Dong et al. 2019 for details: Dong, Y., Fang, X., Brain, D. A., Hurley, D. M., Halekas, J. S., Espley, J. R., et al. (2019). Magnetic field in the Martian magnetosheath and the application as an IMF clock angle proxy. <em>Journal of Geophysical Research: Space Physics</em>, 124, 4295–4313. <a href="https://doi.org/10.1029/2019JA026522">https://doi.org/10.1029/2019JA026522</a></p> <p>Formats: IDL save file and ASCII file</p> <p>Data descirption:</p> <p>Time span: Nov 11 2014 - May 21 2024</p> <p>Time: averaged unix time of MAVEN in magnetosheath of each s/c orbit</p> <p># of data points: # of data points (time resolution: 4s) taken in magnetosheath of each s/c orbit. Suggest using only # of data points >150 (i.e. duration > 10 min)</p> <p>Bx, By, Bz in MSO coordinate system</p> <p> </p>
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