Dataset

This dataset was created by Cole Dieckhaus

Contents

• Name: name of the exoplanet.
• Mass (MJ): mass of the exoplanet.
• Radius (RJ): radius of the exoplanet.
• Period (days): the period the exoplanet takes to make a full orbit around it's sun.
• Semi-major axis (AU): is half of the longest diameter of an ellipse.
• Temp: Temperature of the exoplanet.
• Discovery method: method used to discover the exoplanet.
• Disc. Year: Discovery year.
• Distance (ly): Distance from earth in light years.
• Host star mass (M☉): the star the exoplanet orbits around. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11500118%2Feb625962226193956d508475219dceb2%2Ffile-20220104-15-14cv9v7.webp?generation=1691678674158628&alt=media" alt="">

Dataset

This dataset was created by Satakshi Krishna

Contents

Analysis of ‘🚀 Kepler Confirmed Exoplanets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/kepler-confirmed-planetse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Kepler Telescope

Updates!
Over 100 confirmed exoplanets were found during Kepler's K2 mission.

Check out the new planets here, and read the K2 Mission result announcement.

The scientific objective of the Kepler Mission is to explore the structure and diversity of planetary systems. This is achieved by surveying a large sample of stars to:

1. Determine the abundance of terrestrial and larger planets in or near the habitable zone of a wide variety of stars;
2. Determine the distribution of sizes and shapes of the orbits of these planets;
3. Estimate how many planets there are in multiple-star systems;
4. Determine the variety of orbit sizes and planet reflectivities, sizes, masses and densities of short-period giant planets;
5. Identify additional members of each discovered planetary system using other techniques; and
6. Determine the properties of those stars that harbor planetary systems.

The Kepler Mission also supports the objectives of future NASA Origins theme missions Space Interferometry Mission (SIM) and Terrestrial Planet Finder (TPF),

1. By identifying the common stellar characteristics of host stars for future planet searches,
2. By defining the volume of space needed for the search and
3. By allowing SIM to target systems already known to have terrestrial planets.

(Source)

Helpful links

http://kepler.nasa.gov/Mission/QuickGuide/

http://exoplanetarchive.ipac.caltech.edu/index.html

http://kepler.nasa.gov/

http://www.nytimes.com/2015/07/24/science/space/kepler-data-reveals-what-might-be-best-goldilocks-planet-yet.html

Schema

This file was produced by the NASA Exoplanet Archive http://exoplanetarchive.ipac.caltech.edu

COLUMN pl_hostname:  Host Name
COLUMN pl_letter:   Planet Letter
COLUMN pl_discmethod: Discovery Method
COLUMN pl_pnum:    Number of Planets in System
COLUMN pl_orbper:   Orbital Period [days]
COLUMN pl_orbpererr1: Orbital Period Upper Unc. [days]
COLUMN pl_orbpererr2: Orbital Period Lower Unc. [days]
COLUMN pl_orbperlim:  Orbital Period Limit Flag
COLUMN pl_orbsmax:   Orbit Semi-Major Axis [AU]
COLUMN pl_orbsmaxerr1: Orbit Semi-Major Axis Upper Unc. [AU]
COLUMN pl_orbsmaxerr2: Orbit Semi-Major Axis Lower Unc. [AU]
COLUMN pl_orbsmaxlim: Orbit Semi-Major Axis Limit Flag
COLUMN pl_orbeccen:  Eccentricity
COLUMN pl_orbeccenerr1: Eccentricity Upper Unc.
COLUMN pl_orbeccenerr2: Eccentricity Lower Unc.
COLUMN pl_orbeccenlim: Eccentricity Limit Flag
COLUMN pl_orbincl:   Inclination [deg]
COLUMN pl_orbinclerr1: Inclination Upper Unc. [deg]
COLUMN pl_orbinclerr2: Inclination Lower Unc. [deg]
COLUMN pl_orbincllim: Inclination Limit Flag
COLUMN pl_bmassj:   Planet Mass or M*sin(i)[Jupiter mass]
COLUMN pl_bmassjerr1: Planet Mass or M*sin(i)Upper Unc. [Jupiter mass]
COLUMN pl_bmassjerr2: Planet Mass or M*sin(i)Lower Unc. [Jupiter mass]
COLUMN pl_bmassjlim:  Planet Mass or M*sin(i)Limit Flag
COLUMN pl_bmassprov:  Planet Mass or M*sin(i) Provenance
COLUMN pl_radj:    Planet Radius [Jupiter radii]
COLUMN pl_radjerr1:  Planet Radius Upper Unc. [Jupiter radii]
COLUMN pl_radjerr2:  Planet Radius Lower Unc. [Jupiter radii]
COLUMN pl_radjlim:   Planet Radius Limit Flag
COLUMN pl_dens:    Planet Density [g/cm**3]
COLUMN pl_denserr1:  Planet Density Upper Unc. [g/cm**3]
COLUMN pl_denserr2:  Planet Density Lower Unc. [g/cm**3]
COLUMN pl_denslim:   Planet Density Limit Flag
COLUMN pl_ttvflag:   TTV Flag
COLUMN pl_kepflag:   Kepler Field Flag
COLUMN pl_k2flag:   K2 Mission Flag
COLUMN pl_nnotes:   Number of Notes
COLUMN ra_str:     RA [sexagesimal]
COLUMN ra:       RA [sexagesimal]
COLUMN dec_str:    Dec [sexagesimal]
COLUMN dec:      Dec [sexagesimal]
COLUMN st_dist:    Distance [pc]
COLUMN st_disterr1:  Distance Upper Unc. [pc]
COLUMN st_disterr2:  Distance Lower Unc. [pc]
COLUMN st_distlim:   Distance Limit Flag
COLUMN st_optmag:   Optical Magnitude [mag]
COLUMN st_optmagerr:  Optical Magnitude Unc. [mag]
COLUMN st_optmaglim:  Optical Magnitude Limit Flag
COLUMN st_optmagblend: Optical Magnitude Blend Flag
COLUMN st_optband:   Optical Magnitude Band
COLUMN st_teff:    Effective Temperature [K]
COLUMN st_tefferr1:  Effective Temperature Upper Unc. [K]
COLUMN st_tefferr2:  Effective Temperature Lower Unc. [K]
COLUMN st_tefflim:   Effective Temperature Limit Flag
COLUMN st_teffblend:  Effective Temperature Blend Flag
COLUMN st_mass:    Stellar Mass [Solar mass]
COLUMN st_masserr1:  Stellar Mass Upper Unc. [Solar mass]
COLUMN st_masserr2:  Stellar Mass Lower Unc. [Solar mass]
COLUMN st_masslim:   Stellar Mass Limit Flag
COLUMN st_massblend:  Stellar Mass Blend Flag
COLUMN st_rad:     Stellar Radius [Solar radii]
COLUMN st_raderr1:   Stellar Radius Upper Unc. [Solar radii]
COLUMN st_raderr2:   Stellar Radius Lower Unc. [Solar radii]
COLUMN st_radlim:   Stellar Radius Limit Flag
COLUMN st_radblend:  Stellar Radius Blend Flag
COLUMN rowupdate:   Date of Last Update  

This dataset was created by Mark Di Marco and contains around 3000 samples along with Pl Orbperlim, Pl Orbsmaxerr2, technical information and other features such as: - Pl Orbpererr1 - Pl Orbeccenerr2 - and more.

How to use this dataset

• Analyze St Optband in relation to Pl Radj
• Study the influence of Pl Denserr1 on St Masslim
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Mark Di Marco

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Jessica Rippman

Contents

Description:

👉 Download the dataset here

SpaceNet is a hierarchically structured and high-quality astronomical image dataset, created using a novel double-stage augmentation process. This dataset, comprising approximately 12,900 images, is designed for both fine-grained and macro classification tasks. SpaceNet incorporates a range of resolutions from lower (LR) to higher resolution (HR) images, using standard augmentations and a diffusion approach for generating synthetic samples. This allows for superior generalization across various recognition tasks such as classification. The dataset also includes diverse celestial objects, making it a valuable resource for both academic research and practical applications in astronomy and astrophysics.

Download Dataset

Dataset Structure:

Fine-Grained Classes: The dataset includes 8 distinct classes: planets, galaxies, asteroids, nebulae, comets, black holes, stars, and constellations.

Dataset Composition:

Total Samples: Approximately 12,900 images

Fine-Grained Class Distribution:

Asteroid: 283 images

Black Hole: 656 images

Comet: 416 images

Constellation: 1,552 images

Galaxy: 3,984 images

Nebula: 1,192 images

Planet: 1,472 images

Star: 3,269 images

Usage: SpaceNet is ideal for:

Training and evaluating machine learning models on fine-grained and macro astronomical classification tasks.

Conducting research on hierarchical classification methods within the astronomy field.

Developing robust models that demonstrate excellent generalization across both in-domain and out-of-domain datasets.

This dataset is sourced from Kaggle.

Dataset

This dataset was created by Santosh kumar

Contents

It contains the following files:

Analysis of ‘Near earth objects observed by NASA(1900-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ramjasmaurya/near-earth-objects-observed-by-nasa on 13 February 2022.

--- Dataset description provided by original source is as follows ---

https://upload.wikimedia.org/wikipedia/commons/thumb/c/ce/Asteroids-KnownNearEarthObjects-Animation-UpTo20180101.gif/600px-Asteroids-KnownNearEarthObjects-Animation-UpTo20180101.gif">

A near-Earth object is an asteroid or comet which passes close to the Earth's orbit. In technical terms, a NEO is considered to have a trajectory that brings it within 1.3 astronomical units of the Sun and hence within 0.3 astronomical units, or approximately 45 million kilometers, of the Earth's orbit. NEOS represent potentially catastrophic threats to our planet. The International Asteroid Warning Network (IAWN) and the Space Mission Planning Advisory Group (SMPAG) are two entities established in 2014 as a result of United Nations-endorsed recommendations, and represent important mechanisms at the global level for strengthening coordination in the area of planetary defense.TThe scientific interest in comets and asteroids is due largely to their status as the relatively unchanged remnant debris from the solar system formation process some 4.6 billion years ago. The giant outer planets (Jupiter, Saturn, Uranus, and Neptune) formed from an agglomeration of billions of comets, and the leftover bits and pieces from this formation process are the comets we see today. Likewise, today’s asteroids are the bits and pieces left ove from the initial agglomeration of the inner planets that include Mercury, Venus, Earth, and Mars.

https://image.slidesharecdn.com/cometsasteroids-and-meteors-171013071324/95/comets-asteroids-and-meteors-2-638.jpg?cb=1581516590">

As the primitive, leftover building blocks of the solar system formation process, comets and asteroids offer clues to the chemical mixture from which the planets formed some 4.6 billion years ago. If we wish to know the composition of the primordial mixture from which the planets formed, then we must determine the chemical constituents of the leftover debris from this formation process - the comets and asteroids.

--- Original source retains full ownership of the source dataset ---

This dataset contains comprehensive data for planetary systems in NASA's Exoplanet Archive. More specifically, this is from the Planetary Systems Composite Parameters Table, which combines data from multiple sources for each planet so that each row contains a unique planet.

A detailed data dictionary can be found here. Here are simple descriptions of the columns for reference:

# COLUMN pl_name:    Planet Name
# COLUMN hostname:    Host Name
# COLUMN sy_snum:    Number of Stars
# COLUMN sy_pnum:    Number of Planets
# COLUMN discoverymethod: Discovery Method
# COLUMN disc_year:   Discovery Year
# COLUMN disc_facility: Discovery Facility
# COLUMN pl_controv_flag: Controversial Flag
# COLUMN pl_orbper:   Orbital Period [days]
# COLUMN pl_orbpererr1: Orbital Period Upper Unc. [days]
# COLUMN pl_orbpererr2: Orbital Period Lower Unc. [days]
# COLUMN pl_orbperlim:  Orbital Period Limit Flag
# COLUMN pl_orbsmax:   Orbit Semi-Major Axis [au])
# COLUMN pl_orbsmaxerr1: Orbit Semi-Major Axis Upper Unc. [au]
# COLUMN pl_orbsmaxerr2: Orbit Semi-Major Axis Lower Unc. [au]
# COLUMN pl_orbsmaxlim: Orbit Semi-Major Axis Limit Flag
# COLUMN pl_rade:    Planet Radius [Earth Radius]
# COLUMN pl_radeerr1:  Planet Radius Upper Unc. [Earth Radius]
# COLUMN pl_radeerr2:  Planet Radius Lower Unc. [Earth Radius]
# COLUMN pl_radelim:   Planet Radius Limit Flag
# COLUMN pl_radj:    Planet Radius [Jupiter Radius]
# COLUMN pl_radjerr1:  Planet Radius Upper Unc. [Jupiter Radius]
# COLUMN pl_radjerr2:  Planet Radius Lower Unc. [Jupiter Radius]
# COLUMN pl_radjlim:   Planet Radius Limit Flag
# COLUMN pl_bmasse:   Planet Mass or Mass*sin(i) [Earth Mass]
# COLUMN pl_bmasseerr1: Planet Mass or Mass*sin(i) [Earth Mass] Upper Unc.
# COLUMN pl_bmasseerr2: Planet Mass or Mass*sin(i) [Earth Mass] Lower Unc.
# COLUMN pl_bmasselim:  Planet Mass or Mass*sin(i) [Earth Mass] Limit Flag
# COLUMN pl_bmassj:   Planet Mass or Mass*sin(i) [Jupiter Mass]
# COLUMN pl_bmassjerr1: Planet Mass or Mass*sin(i) [Jupiter Mass] Upper Unc.
# COLUMN pl_bmassjerr2: Planet Mass or Mass*sin(i) [Jupiter Mass] Lower Unc.
# COLUMN pl_bmassjlim:  Planet Mass or Mass*sin(i) [Jupiter Mass] Limit Flag
# COLUMN pl_bmassprov:  Planet Mass or Mass*sin(i) Provenance
# COLUMN pl_orbeccen:  Eccentricity
# COLUMN pl_orbeccenerr1: Eccentricity Upper Unc.
# COLUMN pl_orbeccenerr2: Eccentricity Lower Unc.
# COLUMN pl_orbeccenlim: Eccentricity Limit Flag
# COLUMN pl_insol:    Insolation Flux [Earth Flux]
# COLUMN pl_insolerr1:  Insolation Flux Upper Unc. [Earth Flux]
# COLUMN pl_insolerr2:  Insolation Flux Lower Unc. [Earth Flux]
# COLUMN pl_insollim:  Insolation Flux Limit Flag
# COLUMN pl_eqt:     Equilibrium Temperature [K]
# COLUMN pl_eqterr1:   Equilibrium Temperature Upper Unc. [K]
# COLUMN pl_eqterr2:   Equilibrium Temperature Lower Unc. [K]
# COLUMN pl_eqtlim:   Equilibrium Temperature Limit Flag
# COLUMN ttv_flag:    Data show Transit Timing Variations
# COLUMN st_spectype:  Spectral Type
# COLUMN st_teff:    Stellar Effective Temperature [K]
# COLUMN st_tefferr1:  Stellar Effective Temperature Upper Unc. [K]
# COLUMN st_tefferr2:  Stellar Effective Temperature Lower Unc. [K]
# COLUMN st_tefflim:   Stellar Effective Temperature Limit Flag
# COLUMN st_rad:     Stellar Radius [Solar Radius]
# COLUMN st_raderr1:   Stellar Radius Upper Unc. [Solar Radius]
# COLUMN st_raderr2:   Stellar Radius Lower Unc. [Solar Radius]
# COLUMN st_radlim:   Stellar Radius Limit Flag
# COLUMN st_mass:    Stellar Mass [Solar mass]
# COLUMN st_masserr1:  Stellar Mass Upper Unc. [Solar mass]
# COLUMN st_masserr2:  Stellar Mass Lower Unc. [Solar mass]
# COLUMN st_masslim:   Stellar Mass Limit Flag
# COLUMN st_met:     Stellar Metallicity [dex]
# COLUMN st_meterr1:   Stellar Metallicity Upper Unc. [dex]
# COLUMN st_meterr2:   Stellar Metallicity Lower Unc. [dex]
# COLUMN st_metlim:   Stellar Metallicity Limit Flag
# COLUMN st_metratio:  Stellar Metallicity Ratio
# COLUMN st_logg:    Stellar Surface Gravity [log10(cm/s**2)]
# COLUMN st_loggerr1:  Stellar Surface Gravity Upper Unc. [log10(cm/s**2)]
# COLUMN st_loggerr2:  Stellar Surface Gravity Lower Unc. [log10(cm/s**2)]
# COLUMN st_logglim:   Stellar Surface Gravity Limit Flag
# COLUMN rastr:     RA [sexagesimal]
# COLUMN ra:       RA [deg]
# COLUMN decstr:     Dec [sexagesimal]
# COLUMN dec:      Dec [deg]
# COLUMN sy_dist:    Distance [pc]
# COLUMN sy_disterr1:  Distance [pc] Upper Unc
# COLUMN sy_disterr2:  Distance [pc] Lower Unc
# COLUMN sy_vmag:    V (Johnson) Magnitude
# COLUMN sy_vmagerr1:  V (Johnson) Magnitude Upper Unc
# COLUMN sy_vmagerr2:  V (Johnson) Magnitude Lower Unc
# COLUMN sy_kmag:    Ks (...

Analysis of ‘All Tsunamis between 1950-2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ankanhore545/tsunami-19502000 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Tsunamis are considered to be one of the most destructive natural calamity on our planet. Therefore, its necessary to analyze this hazard.

Please find all the relevant data on Tsunamis for the past seventy years on the planet. The dataset would surely help all of us to analyse the cause, geographies and patterns behind the repeated hazards that is an imminent danger to our planet.

The data was collected from the site: https://www.ngdc.noaa.gov/

We wouldn't be here without the help of others. Please cite DOI:10.7289/V5PN93H7 Your data will be in front of the world's largest data science community.

For any further queries: haz.info@noaa.gov

--- Original source retains full ownership of the source dataset ---

Context

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

To aid researchers, data scientists, and analysts in the effort to combat COVID-19, Google is making a hosted repository of public datasets including OpenStreetMap data, free to access. To facilitate the Kaggle community to access the BigQuery dataset, it is onboarded to Kaggle platform which allows querying it without a linked GCP account. Please note that due to the large size of the dataset, Kaggle applies a quota of 5 TB of data scanned per user per 30-days.

Content

This is the OpenStreetMap (OSM) planet-wide dataset loaded to BigQuery.

Tables: - history_* tables: full history of OSM objects. - planet_* tables: snapshot of current OSM objects as of Nov 2019.

The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing.

Resources

You can read more about OSM elements on the OSM Wiki. This dataset uses BigQuery GEOGRAPHY datatype which supports a set of functions that can be used to analyze geographical data, determine spatial relationships between geographical features, and construct or manipulate GEOGRAPHYs.

Star Wars Datasets

A Galaxy Awaits

The galaxy is vast and full of knowledge. If you're seeking to explore the lives of heroes and villains, uncover the mysteries of unknown species, or dream of piloting starships, you've found the right place. Arm yourself with the finest weaponry, align with powerful organizations, and venture where few have gone before. This is the way.

Choose Your Data Format

Our knowledge comes in four formats. If you prefer simplicity, choose CSV. For deeper understanding, use SQLite or DuckDB. Parquet is swift and efficient for high-performance queries.

Begin Your Journey

These starter notebooks are ready for you. Take the first step:

• CSV: Star Wars Starter for CSV
• Parquet: Star Wars Starter for Parquet
• SQLite: Coming soon.
• DuckDB: Patience is key.

Discover the Data

There's much to learn:

• battles: Detailed accounts of significant events.
• characters: Stories of all who walk this path.
• cities: Knowledge awaits in bustling hubs and remote outposts.
• droids: Tireless workers, each with a purpose.
• events: Understand moments that changed the galaxy.
• films: Revisit the tales that started it all.
• music: Listen carefully to the soundtracks of the Force.
• organizations: Choose your allies wisely.
• planets: Many worlds to explore.
• quotes: Words of wisdom echo across the stars.
• shows: The saga continues with new adventures.
• species: The galaxy is rich with diversity.
• starships: These vessels will take you to the stars.
• timeline: Follow a map through time.
• vehicles: Transport awaits on land or air.
• weapons: Learn the tools of the trade well.

Trusted Sources

• Star Wars Movies: These tales are canonical.
• Star Wars TV Shows: They reveal new stories.
• Ultimate Star Wars: A comprehensive guide.
• Wookieepedia: A vast repository of knowledge.

This is the way.

For this project, I cleaned data on a data sheet that had some errors within the data. After cleaning this data, I created 2 pivot tables to summarize the number of products for the top suppliers.

The Search for New Earths

GitHub

The data describe the change in flux (light intensity) of several thousand stars. Each star has a binary label of 2 or 1. 2 indicated that that the star is confirmed to have at least one exoplanet in orbit; some observations are in fact multi-planet systems.

As you can imagine, planets themselves do not emit light, but the stars that they orbit do. If said star is watched over several months or years, there may be a regular 'dimming' of the flux (the light intensity). This is evidence that there may be an orbiting body around the star; such a star could be considered to be a 'candidate' system. Further study of our candidate system, for example by a satellite that captures light at a different wavelength, could solidify the belief that the candidate can in fact be 'confirmed'.

https://cdn.pbrd.co/images/5g0jyccQF.png" alt="Flux Diagram">

In the above diagram, a star is orbited by a blue planet. At t = 1, the starlight intensity drops because it is partially obscured by the planet, given our position. The starlight rises back to its original value at t = 2. The graph in each box shows the measured flux (light intensity) at each time interval.

Description

Trainset:

• 5087 rows or observations.
• 3198 columns or features.
• Column 1 is the label vector. Columns 2 - 3198 are the flux values over time.
• 37 confirmed exoplanet-stars and 5050 non-exoplanet-stars.

Testset:

• 570 rows or observations.
• 3198 columns or features.
• Column 1 is the label vector. Columns 2 - 3198 are the flux values over time.
• 5 confirmed exoplanet-stars and 565 non-exoplanet-stars.

Acknowledgements

The data presented here are cleaned and are derived from observations made by the NASA Kepler space telescope. The Mission is ongoing - for instance data from Campaign 12 was released on 8th March 2017. Over 99% of this dataset originates from Campaign 3. To boost the number of exoplanet-stars in the dataset, confirmed exoplanets from other campaigns were also included.

To be clear, all observations from Campaign 3 are included. And in addition to this, confirmed exoplanet-stars from other campaigns are also included.

The datasets were prepared late-summer 2016.

Campaign 3 was used because 'it was felt' that this Campaign is unlikely to contain any undiscovered (i.e. wrongly labelled) exoplanets.

NASA open-sources the original Kepler Mission data and it is hosted at the Mikulski Archive. After being beamed down to Earth, NASA applies de-noising algorithms to remove artefacts generated by the telescope. The data - in the .fits format - is stored online. And with the help of a seasoned astrophysicist, anyone with an internet connection can embark on a search to find and retrieve the datafiles from the Archive.

The cover image is copyright © 2011 by Dan Lessmann

This dataset that was used for a project I was doing in college. The data started out pretty basic and was added to over time. Some of the data was retrieved from papers and some of it was calculated.

Contents

eName - string - the name of the object isPlanet - boolean - is the object a planet (this includes the five dwarf planets) semimajorAxis - integer - mean orbital distance in km perihelion - integer - closest distance from the Sun during its orbit aphelion - integer - furthest distance from the Sun during its orbit eccentricity - double - ratio of perihelion to aphelion inclination - double - difference in angle between body's orbit and the ecliptic density - double - average density of the body gravity - double - surface gravity, measures in m/s^2 escape - integer - escape velocity at surface level meanRadius - double - average total radius equaRadius - double - average equatorial radius polarRadius - double - average polar radius flattening - double - ratio of equatorial radius to polar radius dimension - string - approximate spatial dimensions of irregular shaped objects sideralOrbit - double - orbital period in Earth days sideralRotation - double - rotational period in hours discoveryDate - date - date of discovery, this is left blank for any objects that were known since antiquity mass_kg - integer - total estimated mass of object in kg volume - integer - approximate volume in km^3 orbit_type - class - either primary; orbites the Sun, or secondary; orbits a planet orbits - class - the planet that the body orbits. If it does not orbit a planet then it is NA bondAlbedo - double - Bond albedo, portion of light/energy that get reflected by the surface geomAlbedo - double - Geometric albedo, modified reflective metric for spherical objects which, because of opposition effects, can be greater than 1 RV_abs - double - radial velocity of Sun due to object's gravitational pull p_transit - double - probability that a transit will be observable transit_visibility - double - angle from the ecliptic that a transit will be visible transit_depth - double - portion of Sun's energy that is blocked during transit massj - integer - mass compared to Jupiter semimajorAxis_AU - integer - orbital radius in Astronomical Units grav_int - gravitational interaction with the Sun

This dataset contains the index, from global design firm Arcadis and the Centre for Economics and Business Research, ranks cities’ success based on social, environmental, and economic factors.

Arcadis used 32 indicators and a cross section of the world’s urban areas, so not all capitals or large cities are necessarily represented. A city is scored on each of the three sustainability factors; its overall score is the average of those.

Population of the world by Countries From 1960 to 2021

Currently the population of our planet is around 7 billion and is increasing rapidly. The dataset given below is from data.worldbank.org and contains every nation's population from 1960 to 2021.

Keep in Mind

It is important to mention that this dataset may not be suitable for all audiences, as it contains reviews that may include harsh language, offensive or toxic content, and ASCII art of inappropriate body parts. This might not be suitable for all users. We want to make it clear that we do not endorse or condone any of the content within the dataset. This information is presented solely as a means of providing an unfiltered and authentic view of how players experience CS:GO. Most of the time it's just trolling and shouldn't be taken too seriously, however, it is essential to acknowledge that the reviews included have not been censored in any way, shape or form - this is precisely how they were presented on the Steam website.

About the Dataset

This dataset contains a wealth of reviews for the highly acclaimed first-person shooter, CS:GO, or Counter Strike: Global Offensive.. Developed by Valve and Hidden Path Entertainment, the game's impressive longevity and continued player engagement is evident in the wide range of reviews included within this dataset. Featuring opinions on gameplay mechanics, graphics, overall game experience, and more, the dataset offers a vast array of perspectives from players across the board. The diverse mix of reviews lends itself to the possibility of a variety of use cases, including sentiment analysis, natural language processing, and machine learning. The inclusion of both positive and negative reviews ensures that the dataset is comprehensive, providing an accurate and detailed view of the sentiment surrounding the game. As such, this dataset offers valuable insights into the perception of CS:GO by its players and serves as an excellent resource for further research and analysis of the game's popularity, player satisfaction and overall experience.

Artwork source: https://www.artstation.com/artwork/vJyaZO

This dataset provides comprehensive information about animals from Animalia.bio, covering a wide range of species, habitats, behaviors, and conservation statuses. It aims to support research, education, and data exploration related to wildlife and biodiversity.

Potential Uses: - Education: Develop learning materials or visualizations for wildlife awareness. - Research: Analyze species diversity, geographical distribution, or conservation trends. - Data Science Projects: Apply machine learning for habitat prediction, conservation priority ranking, or behavioral clustering.

Tsunamis are considered to be one of the most destructive natural calamity on our planet. Therefore, its necessary to analyze this hazard.

Please find all the relevant data on Tsunamis for the past seventy years on the planet. The dataset would surely help all of us to analyse the cause, geographies and patterns behind the repeated hazards that is an imminent danger to our planet.

The data was collected from the site: https://www.ngdc.noaa.gov/

We wouldn't be here without the help of others. Please cite DOI:10.7289/V5PN93H7 Your data will be in front of the world's largest data science community.

For any further queries: haz.info@noaa.gov