Dataset

This dataset was created by Parag Zode

Contents

I found this Interesting Dataset on Maven Analytics about Space Missions and decided to work on it. The Dataset comes with the Data of Space Missions from 1957 to 2022. It consist of Date, Location, Rocket Name, Rocket Status, Mission Name, Mission Status, and the Company Launch the Mission. 🚀

Firstly, I ensure Data quality by meticulously Cleaning and Preparing it for Analysis. Then, I create Pivot Tables to Summarize and Analyze the Data from different angles. Next, I dive into Visualization, leveraging Tools to Transform complex Datasets into Clear, Actionable Insights. After Creating the Visuals, I Delve Deeper to Uncover Valuable Trends and Patterns, Empowering informed Decision-Making Insights. Every step, from Cleaning the Data to Visualization to Extracting Insights, is essential in Unlocking the True Power of Data-Driven Strategies. 📊 📈

ACTIONABLE DATA-DRIVEN INSIGHTS FROM THIS DASHBOARD:

1. THE NUMBER OF SPACE MISSIONS BY YEAR IS INCREASING. This suggests that there is a Growing Interest in Space Exploration. Businesses and Organizations Involved in Space Exploration could take Advantage of this Trend by Developing New Products and Services.
2. THE OVERALL SUCCESS RATE OF SPACE MISSIONS IS INCREASING. This could be due to a Number of Factors, such as Improvements in Technology and Engineering. Companies Involved in Space Exploration can Leverage this Information to Market their Services to Potential Customers.
3. (RVSN USSR) IS THE COMPANY WITH THE MOST TOTAL MISSIONS. As of 2022, they have Launched 1777 Missions. This suggests that they are a Leader in the Space Exploration Industry. Other Companies Looking to Enter the Space Exploration Industry may want to Study (RVSN USSR)'s Business Model.
4. ARIANESPACE HAS THE HIGHEST SUCCESS RATE OF ANY COMPANY LISTED ON THE DATASET AT 96.25%. This suggests that they are a Reliable Provider of Space Launch Services. Companies Looking to Launch Satellites or other Spacecraft into Orbit may want to consider Using Arianespace's Services.
5. THE MAJORITY OF SPACE MISSIONS (4162) HAVE BEEN SUCCESSFUL. This is a Positive Sign for the Future of Space Exploration. It suggests that Space Missions are Becoming more Routine and Less Risky. This could lead to an Increase in the Number of Private Companies and Organizations Involved in Space Exploration.

Overall, the Data in this Dashboard suggests that Space Exploration is a Growing Industry with a Bright Future. Companies and Organizations that are Involved in Space Exploration can take Advantage of this Trend by Developing New Products and Services. 🚀 📊

TOOL USED: Microsoft Excel

DataAnalytics #DataScience #DataAnalyst #DataVisualization #BusinessIntelligence #DataAnalysis #DataStorytelling #DataDrivenDecisions #DataDriven

The Maven Dependency Dataset contains the data as described in the paper "Mining Metrics, Changes and Dependencies from the Maven Dependency Dataset". NOTE: See the README.TXT file for more information on the data in this dataset. The dataset consists of multiple parts: A snapshot of the Maven repository dated July 30, 2011 (maven.tar.gz), a MySQL database (complete.tar.gz) containing information on individual methods, classes and packages of different library versions, a Berkeley DB database (berkeley.tar.gz) containing metrics on all methods, classes and packages in the repository, a Neo4j graph database (graphdb.tar.gz) containing a call graph of the entire repository, scripts and analysis files (scriptsAndData.tar.gz), Source code and a binary package of the analysis software (fullmaven.jar and fullmaven-sources.jar), and text dumps of data in these databases (graphdump.tar.gz, processed.tar.gz, calls.tar.gz and units.tar.gz).

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 examine the required space by the packages. Main dataset can be found here: https://doi.org/10.5281/zenodo.8077125

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.

Dataset

This dataset was created by tanvir25

Released under Apache 2.0

Contents

The freshness score is a critical metric in dependency management. It reflects how recently a dependency has been updated relative to its latest available version. We try to focus on two research questions: i) Do projects with a large number of dependencies tend to have a higher “outdated time” or missed releases compared to those with fewer dependencies? In other words, is there any relationship or pattern between dependency counts and freshness?ii) To what extent are the dependencies in the latest software releases outdated?The files related to the first research question are inside the RQ1 folder, and those related to the second are inside the RQ2 folder.

This dataset, sourced from Maven Analytics, provides a historical account of global CO2 emissions with a focus on various contributing factors and metrics. The data spans from the year 1850 onwards and includes a comprehensive range of variables associated with CO2 emissions. This dataset is valuable for analyzing historical trends in CO2 emissions, understanding the impact of different sources of emissions, and evaluating the effectiveness of policies aimed at reducing carbon footprints globally.

This dataset is extracted from the Maven central dependency graph database. Query was performed on the database to get the information from the Artifacts, Nodes, Dependency edges, and dependency-Release to finally get the desired data.

In early 2019, the Mars Atmosphere and Volatile Evolution (MAVEN) mission underwent an ~2-month aerobraking campaign, during which time the spacecraft periapsis altitude was lowered from its nominal altitude range of 140-160 km to as low as ~123 km. Excluding spacecraft walk-in/out maneuvers, accelerometer measurements were made along 272 orbits with coverage spanning Ls 340-3°, latitudes ~5-54°S, longitudes 0-360°, and Local Solar Time (LST) ~22-17 hours. In this study, we perform a diagnostic analysis of the full aerobraking data set by fitting 4-harmonic waves to mass densities. We then study the variations of these waves as a function of latitude with an emphasis on those observed previously in Mars’ thermosphere by MAVEN and other missions. Additionally, we utilize data collected during the same time period from the Mars Reconnaissance Orbiter’s Mars Climate Sounder to study the vertical propagation of waves originating from the middle atmosphere. Key results indicate that normalized wave amplitudes decrease with latitude, and this is consistent with the latitudinal structure of a diurnal Kelvin mode. We also observe that waves imprinted from the middle atmosphere show normalized amplitude growth with increasing altitude. A complete summary of data sets, analysis methodology, and scientific results is given. The purpose of this study is to add to the body of knowledge surrounding Martian atmospheric wave features and to provide further constraints for future numerical modeling and subsequent tidal mode identification.