The centuries-old quest for other worlds like our Earth has been rejuvenated by the intense excitement and popular interest surrounding the discovery of hundreds of planets orbiting other stars. There is now clear evidence for substantial numbers of three types of exoplanets; gas giants, hot-super-Earths in short period orbits, and ice giants. The following websites are tracking the day-by-day increase in new discoveries and are providing information on the characteristics of the planets as well as those of the stars they orbit: The Extrasolar Planets Encyclopedia, NASA Exoplanet Archive, New Worlds Atlas, and Current Planet Count Widget. The challenge now is to find terrestrial planets (i.e., those one half to twice the size of the Earth), especially those in the habitable zone of their stars where liquid water and possibly life might exist. The Kepler Mission, NASA Discovery mission #10, is specifically designed to survey a portion of our region of the Milky Way galaxy to discover dozens of Earth-size planets in or near the habitable zone and determine how many of the billions of stars in our galaxy have such planets. Results from this mission will allow us to place our solar system within the continuum of planetary systems in the Galaxy.

Regional GAM Species Counts

Observing Animal Species Abundance and Distribution

By [source]

About this dataset

This dataset captures detailed information about the abundance and distribution of multiple animal species in different parts of the Regional GAM network. By analyzing this data, researchers gain valuable insight into species trends over time, species population growth or decline, seasonal migration patterns, and other important ecological patterns. Moreover, this dataset helps us to understand risks associated with animal populations and ecosystems; aiding decision-making related to land use for conservation and sustainability initiatives. This data provides an easily accessible resource for monitoring changes in animals' ranges and distributions across the region – enabling powerful analysis which can inform sound management decisions to promote conservation efforts. In sum, this dataset holds great promise for scientists seeking an improved understanding of wildlife dynamics; making it a powerful tool for both monitoring biodiversity in our changing world as well as informing proactive management strategies that will ultimately help keep our planet healthy into the future

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How to use the dataset

This dataset contains information about animal species and their occurrence per site, which can be used to gain insights into species abundance and distribution in the Regional GAM network. This data can help researchers analyze species trends, population growth or decline, animal migrations, and other important ecological factors.

Users of this dataset can analyze the presence or absence of a particular species in different sites across the region, as well as their abundance by counting individual sightings. Additionally, by combining datasets such as those contained in this one with other environmental factors (e.g., water levels), users can gain further insight into animals’ behavior and ecology within any given location over time.

The following steps outline how to use this dataset to analyze animal populations: - Download all necessary files from Kaggle for your analysis - Use an online tool such as Pandas or RStudio to extract desired data from each file into one unified table - Select relevant columns for your analysis (e.g., Species Name, Location/Site Name), specify date ranges if necessary and arrange them in an easily readable manner using sorting tools within the software program you’re using
- Filter entries related to a certain period of time (e.g., last 7 days), location or unique combination of both if needed 5) Choose appropriate chart or graph types depending on what kind of data you want to present visually 6) Finally plot/display your findings on a map / basis plot / 3D-model / etc…for best clarity

This dataset provides valuable insight into environmental conditions which may affect wildlife behavior. By following these simple steps researchers should be able visualize trends associated with certain areas over periods of time allowing them better understand how animal populations are affected by land-use decisions and climate change among others!

Research Ideas

• Species Conservation: This data set can be used to assess the health of a species' population in a particular region and how this varies over time. Researchers can use data trends to identify declining populations and areas of conservation needs, allowing them to create appropriate management plans focused on species protection.
• Wildlife Monitoring: Observing the species count at different sites can provide researchers with an insight into animal behavior, migration patterns and habitat usage which in turn informs wildlife management plans.
• Climate Change: By assessing population changes over time, researchers can use this dataset to explore how climate change is impacting specific animal populations and inform conservation initiatives accordingly/

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Dataset multispecies Regional GAM.csv | Column name | Description ...

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains raw stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. For more information on specific dataset, see the methods section. All data are projected in WGS 1984 Mollweide.

This dataset contains key characteristics about the data described in the Data Descriptor Population Centroids of the World Administrative Units from Nighttime Lights 1992-2013. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format 


      Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.

CONTEXT: There are many dangerous bodies in space, one of them is N.E.O. - "Nearest Earth Objects". Some such bodies really pose a danger to the planet Earth, NASA classifies them as "is_hazardous". This dataset contains ALL NASA observations of similar objects from 1910 to 2024!!! There are 338,199 records of N.E.O. in the Dataset! Try to predict "is_hazardous" as accurately as possible! (otherwise we will not be ready for an asteroid attack) SOURCES: NASA Open API: https://api.nasa.gov/… See the full description on the dataset page: https://huggingface.co/datasets/IvanSher/NASA_Nearest_Earth_Objects_1910-2024.

This raster dataset is a grid of world countries. These are the standard country boundaries. Also included is a DBF (countries.dbf) giving the country name for each country "number" in the grid and has demographic factors similar to the Admin1 table. This dataset is part of the LandScan 2012 Global Population Database.

Summary:

There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.

The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.

How to cite this dataset:

Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. Earth System Science Data, 14(9), pp.4397-4418.

Update history:

I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)

Version 1.03: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.

Version 1.02: New data summary, add Netcdf data format

Version 1: Initial dataset for peer-review, CSV format only

Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.

SPAM2010:

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.

GAEZv4:

FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021

The dataset of Ricciardi et al.'s:

Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.

The global dominant field size dataset:

Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.

GLC-Share:

Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.

CAAS-IFPRI cropland extent map:

Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. The top level of this dataset contains the raster data for the modeled impacts map, along with a high resolution jpg version. Sub-levels of this dataset include: raw stressor data, transformed stressor data (raw data is log(x+1) transformed and rescaled by dividing by maximum global value so values range from 0-1), and ecosystem data. All spatial data are projected in WGS 1984 Mollweide.

Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. Tang et al. (2023) analyze how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables.

Summary

This dataset contains bi-temporal images pairs with associated labels of change or no-change describing whether there was a change in surface features between the two images acquired over the same location at two different times. This dataset contains images from four different instruments orbiting three different planets, each of which contains four representations of the bi-temporal image pair: composite grayscale, absolute difference, signed difference, and autoencoder bottleneck representations (described in detail in [1]). We also include the grayscale image tiles these representational datasets were created from. All datasets contain 100x100 images tiles that were cropped from larger images. We describe each below.

Contents

hirise_rsl.zip : all subdirectories contain change and no-change examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after HiRISE image of recurring slope lineae on Mars. Subdirectory names are garni_XXXXXX_YYYYYY where Garni is the name of the crater on Mars shown in the images, XXXXXX is the HiRISE image ID of the before image, and YYYYYY is the HiRISE image ID of the after image. The "*_lcn" ending on some directories indicates that local contrast normalization was applied. The "*_gs_illum" and "*_gs_slope" directories contain composite grayscale representation with a third band that contains the difference between illumination (illum) and slope values at the same locations. Images with line endings _vflip, _hflip, _rot90, _rot180, and _rot270 were the result of vertical flips, horizontal flips, 90-deg rotations, 180-deg rotations, and 270-deg rotations of the image with the corresponding prefix.

ctx_impacts.zip : all subdirectories contain change and no-change examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after CTX image of meteorite impacts on Mars. The prefix in each image name corresponds to the image pair described in the Appendix in [1].

lroc_impacts.zip : all subdirectories contain change and no-change examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after LROC image of meteorite and spacecraft landing impacts on the Moon. Filenames correspond to pair names provided in the Appendix in [1].

planet_misc.zip : all subdirectories contain change and no-change examples represented as composite grayscale, absolute difference, signed difference, and autoencoder bottleneck from a before and after PlanetScope image of miscellaneous processes on Earth. Filenames correspond to pair names provided in the Appendix in [1].

*_before_after_grayscale.zip : before and after grayscale tiles used to create image representations in above directories (indicated with _before and _after suffix in filenames). Images that contain "_0_" in the filename have the label no-change and images with "_1_" in the filename have the label change.

[1] Kerner et al. (2019) Deep Learning Methods Toward Generalized Change Detection on Planetary Surfaces. In review at Journal of Selected Topics in Earth Observations and Remote Sensing.

Attribution

If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.2373798 as well as the paper below:

Kerner, H. R., Wagstaff, K. L., Bue, B. D., Gray, P. C., Bell, J. F., & Amor, H. B. (2019). Toward generalized change detection on planetary surfaces with convolutional autoencoders and transfer learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(10), 3900-3918.

Here we provide information for the PlanetScope and d Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) Derived Spectral Library of Agricultural Crops in California which was developed using PlanetScope Dove-R high spatial resolution data and DESIS hyperspectral data acquired for 2020. PlanetScope images are available through Planet Labs (2022). The DESIS images used for this dataset are available through the German Aerospace Center and Teledyne Brown (2022). The crop type data and confidence layer for 2020 can be accessed through the United States Department of Agriculture National Agricultural Statistics Service (2022). The PlanetScope and DESIS Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with PlanetScope and DESIS data provided in two separate CSV files. Related Primary Publication: Aneece, I., Foley, D., Thenkabail, P.S., Oliphant, A., and Teluguntla, P. 2022. New generation hyperspectral ...

Thermospheric density and crosswind data products derived from GOCE data. Latest baseline _0200. The GOCE+ Air Density and Wind Retrieval using GOCE Data project produced a dataset of thermospheric density and crosswind data products which were derived from ion thruster activation data from GOCE telemetry. The data was combined with the mission's accelerometer and star camera data products. The products provide data continuty and extend the accelerometer-derived thermosphere density data sets from the CHAMP and GRACE missions. The resulting density and wind observations are made available in the form of time series and grids. These data can be applied in investigations of solar-terrestrial physics, as well as for the improvement and validation of models used in space operations. Funded by ESA through the Support To Science Element (STSE) of ESA's Earth Observation Envelope Programme, supporting the science applications of ESA's Living Planet programme, the project was a partnership between TU Delft, CNES and Hypersonic Technology Göttingen. Dataset history Date Change Reason 18/04/2019 - Time series data v2.0, covering the whole mission - Updated data set user manual - New satellite geometry and aerodynamic model - New vertical wind field - New data for the deorbit phase, (GPS+ACC and GPS-only versions) Updated satellite models and additional data 14/07/2016 - Time series data v1.5, covering the whole mission - Updated data set user manual Removal of noisy data 31/07/2014 - Time series data v1.4, covering the whole mission - Gridded data, now including error estimates - Updated data set user manual; Updated validation report; Updated ATBD Full GOCE dataset available 28/09/2013 Version 1.3 density/winds timeseries and gridded data released. User manual updated to v1.3 Bug fix and other changes 04/09/2013 Version 1.2 density/winds timeseries and gridded data released, with user manual First public data release of thermospheric density/winds data

In this project I will concentrate on some important factors that will affect humanity's potential to survive on planet Earth: - Global Demographic Shifts. - Inequality. - Climate change. - Resource depletion.

I chose the following countries from the data (HNP_StatsData.csv), for the bulk of the project. However, the data contains many more countries.

Australia, Belgium, Canada, China, Denmark, France, Germany, India, Italy, Japan, Mexico, Morocco, Russian Federation, South Africa, Spain, Switzerland, United Kingdom, United States.

1. Global Demographic Shifts:

First I decided to look at the Crude Birth Rate (CBR) for the above countries, for the years 1961-2021.

The crude birth rate is the number of live births occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. It is called "crude" because it does not take into account age or gender differences within the population.

Formula: CBR = Midyear population / Number of births in a year × 1000

Visualisation of CBR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F011e9ff704f24c74ab2393cdd66e8ee2%2FScreenshot%202023-09-23%2014.50.36.png.jpg?generation=1695508413958340&alt=media" alt="">

Next I looked at CDR for the above countries, for the years 1961-2021. The crude death rate is the number of deaths occurring among the population of a given geographical area during a given year, per 1,000 of the population estimated at midyear. Like the CBR, it is called "crude" because it doesn't consider the age or gender differences within the population.

Formula: CDR = Midyear population / Number of deaths in a year × 1000

Visualisation of CDR: For the chosen countries - using Google sheets. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fe79ff544bff129ec2193471f9e6b4480%2FScreenshot%202023-09-23%2014.55.38.png.jpg?generation=1695510070529372&alt=media" alt="">

Both these rates (CBR & CDR) are basic demographic indicators that give a general overview of the demographic situation in a country or region. They do offer a broad understanding of birth and death patterns.

A high CDR in a particular year or time period can be influenced by various factors, including epidemics, famines, natural disasters, wars, and social and economic changes.

Let's look at China and Morocco during the 1960s:

• The Great Chinese Famine (1959-1961): This is arguably the main reason for the high CDR in China around 1960. This devastating famine was the result of a combination of social, political, and natural factors. The "Great Leap Forward" campaign (1958–1962), initiated by Mao Zedong, aimed to transform China from an agrarian society into an industrial socialist society. One of the policy implementations was the formation of people's communes, which resulted in the collectivisation of agriculture. The government's over-reporting of grain production and subsequent excessive grain requisition, coupled with poor weather conditions and pestilence, led to widespread food shortages. The resultant famine caused the deaths of millions of Chinese people.
• Public health issues: Prior to the significant public health reforms that would later be implemented in the 1960s and 1970s, China grappled with various diseases that could have contributed to a high CDR.
• Tuberculosis (TB): Throughout the early to mid-20th century, TB was a major public health concern in China. Malnutrition, poor living conditions, and lack of access to medical treatment contributed to the spread of this bacterial disease.
• Schistosomiasis: This parasitic disease, caused by flatworms and transmitted by freshwater snails, was and still is endemic in certain regions of China. Chronic infection can lead to liver damage, kidney failure, and other complications.
• Malaria: While major strides have been made in controlling malaria in China in recent decades, in the 1960s, the disease was still prevalent in many regions of the country.
• Other diseases: Cholera, typhoid, and dysentery were also concerns in certain areas, especially given the combination of poor sanitation, water contamination, and food shortages.

Morocco in 1960: - Colonial legacy and independence: Morocco achieved independence from France and Spain in 1956. The post-independence years were marked by political instability, which can indirectly impact public health, food security, and other factors related to the death rate. - Economic conditions: Morocco, being a primarily agrarian society during that period, was vulnerable to fluctuations in agricultural output. Poor harvests due to droughts, pests, or other factors could impact food availability and lead to higher death rates. - Public health: Like many developing nations ...

Context

Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day.

This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process.

The aim of this dataset is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

Content

The dataset consists of images extracted from Planet satellite imagery collected over the San Francisco Bay and San Pedro Bay areas of California. It includes 4000 80x80 RGB images labeled with either a "ship" or "no-ship" classification. Images were derived from PlanetScope full-frame visual scene products, which are orthorectified to a 3-meter pixel size.

Provided is a zipped directory shipsnet.zip that contains the entire dataset as .png images. Each individual image filename follows a specific format: {label} _ {scene id} _ {longitude} _ {latitude}.png

• label: Valued 1 or 0, representing the "ship" class and "no-ship" class, respectively.
• scene id: The unique identifier of the PlanetScope visual scene the image was extracted from. The scene id can be used with the Planet API to discover and download the entire scene.
• longitude_latitude: The longitude and latitude coordinates of the image center point, with values separated by a single underscore.

The dataset is also distributed as a JSON formatted text file shipsnet.json. The loaded object contains data, label, scene_ids, and location lists.

The pixel value data for each 80x80 RGB image is stored as a list of 19200 integers within the data list. The first 6400 entries contain the red channel values, the next 6400 the green, and the final 6400 the blue. The image is stored in row-major order so that the first 80 entries of the array are the red channel values of the first row of the image.

The list values at index i in labels, scene_ids, and locations each correspond to the i-th image in the data list.

Class Labels

The "ship" class includes 1000 images. Images in this class are centered on the body of a single ship. Ships of different sizes, orientations, and atmospheric collection conditions are included. Example images from this class are shown below.

https://i.imgur.com/tLsSoTz.png" alt="ship">

The "no-ship" class includes 3000 images. A third of these are a random sampling of different land cover features - water, vegetation, bare earth, buildings, etc. - that do not include any portion of a ship. The next third are "partial ships" that contain only a portion of a ship, but not enough to meet the full definition of the "ship" class. The last third are images that have previously been mislabeled by machine learning models, typically caused by bright pixels or strong linear features. Example images from this class are shown below.

https://i.imgur.com/cyG2Z54.png" alt="no-ship">

Scenes

Eight full-scene images are included in the scenes directory. Scenes can be used to visualize the performance of classification models trained on the dataset. Verify a model's accuracy by applying it across a scene and viewing where 'ship' classifications occur - the context provided by the scene helps determine positive hits from false alarms. An example scene is shown below.

https://i.imgur.com/FEttq8o.png" alt="Scene_1">

Acknowledgements

Satellite imagery used to build this dataset is made available through Planet's Open California dataset, which is openly licensed. As such, this dataset is also available under the same CC-BY-SA license. Users can sign up for a free Planet account to search, view, and download their imagery and gain access to their API.

Replication data for "An inclusive, empirically grounded inventory facilitates recognition of diverse area-based conservation of nature" Authors: Siyu Qin, Yifan He , Rachel E. Golden Kroner , Sushma Shrestha , Bruno Henriques Coutinho, Marion Karmann , Juan Carlos Ledezma , Christian Martinez, Vilisa Morón-Zambrano , Roberto Ulloa , Edgard Yerena , Curtis Bernard , Joseph W. Bull , Eddy Mendoza , Nyls de Pracontal, Katie Reytar , Peter Veit , Erik Olsson , Clara L. Matallana-Tobón , Liz Alden Wily , Michael B. Mascia Note: This repository includes all sharable datasets, including: S1. Attributes collected (Excel) S2. Data Sources for Area-based Conservation Governance Systems in Amazonia (Excel) S3. Category Assignment for Area-based Conservation Governance Systems in Amazonia (Excel) S4. Point and non-spatial data of area-based conservation governance systems in Amazonia S5. Sharable spatial data of Area-based Conservation Governance Systems in Amazonia In S5, we have included the shapefile of all datasets that we have legal permission to share publicly in Data_S5. We are unable to share part of the data via shapefile or online maps due to the sharing restrictions imposed by data owners. Most of these non-sharable data are from the WDPA, which is publically accesible via Protected Planet (https://www.protectedplanet.net/en). We have provided the data source information in Data_S2 which would enable readers to track all datasets. Replication code and pre-processed data (e.g. summary statistics) used to produce all tables and figures in the manuscript are found in Github repository: https://github.com/florayh/conservationinventory.git

Dataset Overview

The Planetary Dataset is a comprehensive collection of data detailing the physical and atmospheric properties of the planets in our solar system. This dataset provides a rich resource for analyzing and comparing the diverse characteristics of each planet, ranging from the small, rocky terrestrial planets to the massive gas and ice giants.

Context and Relevance

The planets in our solar system exhibit a remarkable variety of physical and environmental conditions. Understanding these differences is crucial for fields such as astronomy, planetary science, and space exploration. This dataset offers insights into the fundamental properties that define each planet, enabling researchers, educators, and enthusiasts to explore the following key aspects:

Planetary Classification: The dataset includes both terrestrial planets (Mercury, Venus, Earth, Mars) and giant planets (Jupiter, Saturn, Uranus, Neptune). This classification helps in studying how different types of planets form and evolve.

Physical Characteristics: By providing detailed measurements such as mass, radius, and density, the dataset allows for a better understanding of each planet's size, composition, and structure. This is essential for comparing how planets differ in terms of their physical properties.

Atmospheric Conditions: Attributes like surface gravity, escape velocity, mean temperature, and surface pressure are crucial for studying the atmospheres of the planets. These factors influence weather patterns, climate, and the potential for habitability.

Orbital and Rotational Dynamics: The dataset includes information on orbital periods, distances from the Sun, eccentricity, and inclination. These parameters are important for understanding the planets' positions in their orbits and their interactions with other celestial bodies.

Surface and Environmental Features: The dataset details surface characteristics, including the dominant color and geological activity. This information helps in studying the surface composition and potential for surface-based phenomena.

Magnetic and Ring Systems: Information about the magnetic field strength and ring systems provides insights into the planets' magnetic environments and the presence of ring structures, which are significant for understanding planetary protection and satellite interactions.

Applications

This dataset is valuable for a range of applications:

Educational Purposes: It can be used in educational settings to teach students about planetary science and comparative planetology.

Scientific Research: Researchers can use the dataset to study planetary formation, atmospheric conditions, and other fundamental questions in planetary science.

Space Exploration: Insights from this dataset can inform mission planning and exploration strategies for space agencies and space enthusiasts.

Summary

Overall, the Planetary Dataset offers a detailed and organized view of the planets in our solar system, presenting an array of attributes that facilitate a deeper understanding of planetary science. It serves as a foundational resource for anyone interested in the study of our celestial neighbors and their unique characteristics.

This dataset comprises 10,000 artificially generated student essays using GPT4, accompanied by holistic scores ranging from 1 to 6. These essays were generated based on the data from the Automated Essay Scoring 2.0 competition.

The Method

My aim was to produce essays that closely resembled those in the original AES dataset, essentially creating paraphrases while ensuring they remained distinct compositions. Equally important was maintaining scores consistent with the original holistic scoring system used in the competition. To accomplish this, I followed the process outlined below:

Prompt Template

The basic prompt template looks like this:

prompt_template = ''''
You are a {AGE} year old German student writing an English test, but you're stuck! Luckily, your neighbour is doing well and so you take a glimpse at his sheet and you could catch the following text:

=========
"{TEXT}"
=========

But you cannot simply copy it, you need to change it a bit so the teacher doesn't notice that you copied it, 
hence you copy it with the following rules:
- Paraphrase the text just a bit
- Adhere to the style and level of the original text
- Sprinkle some errors into the text, akin to the original
- Remember your age and incroporate that into the essay so it's feasible for a {AGE} year old student who writes not in his native language!

Output only the essay
'''

The produced essay woud be scored the same score as the original essay passed into the {TEXT} variable.

This prompt tries to implement a couple of ideas:

• Through the incentive of being a student who copies another student in a test situation, but doesn't want to be noticed by the teacher, the model should produce a mixture of copied, paraphrased and own text chunks. This would also allow the essay to be of similar quality (hopefully meaning similar score).
• Through the {AGE} variable, I tried to enforce the score of the original essay by prompting essays with a lower score, a lower age (minimum 11, highest 14) and thus also lowering the quality of the produced essay. The formular for the age is defined as: \(age = 15 - (4 - (originalEssayScore // 2))\)
• I tried to replicate the spelling and grammar mistakes of the original essay into the newly produced essay, but language models are really hard to get them to add random mistakes into their outputs, hence: I counted the spelling mistakes in the original essay through python's spellchecker and added as much random mistakes into the newly generated essays to again, replicate the score as best as I can.
• Since we have a class imbalance (way more 3s and 4s than 1s and 6s), I purposely made the essay pool, from which a new essay was prompted from, imbalanced in favour of those lower-quantity classes. The dataset should therefore contain more of these.

Examples

Here are some examples:

Initially, the author addresses the perception that Mars is tooy hazardous to explore. Many people are deterred by Mars' reputation as a dangerous and inhospitable planet. The author acknowledges these challenges but demonstrates how safe travel can still be achieved. By detailing Jthe plan proposed by the National Aeronautics and Space Administration (NASA) for astronauts to float above the dangerous conditions, the writer assures the audience of the safety measures in place. Specific aspects of the plan, such as Earth-like air pressure and abundant solar power, are highlighted to emphasize the feasibility of human survival. Drawing a comparison to a blimp-like vehicle, the author simplifies the concept for better understanding. By dispelling the notion of Mars being too perilous, the writer strengthens the argument for explorRing the planet.

Furthermore, the writer emphasizes the educational potential that studying Mars offers. Beyond simple facts about Mars' proximity in size and density to Earth, the author delves into the possibility of Mars once resembling Earth. Describing Mars' current environment as Earth-like with rocky surfaces, valleys, mountains, and craters, the author suggests that Mars may have supported life in the past, similar to Earth. This parallel betwveen the two planets Hcaptivates the audienc...

Properties of Stars in Our Galaxy

A Dataset for Investigating the Potential for Life on Exoplanets

About this dataset

This dataset contains information on various properties of stars. You can find things like a star's mass, radius, and distance from Earth. It also contains a star's name and other identifying factors. This dataset is perfect for anyone interested in learning more about the stars in our galaxy!

How to use the dataset

A Dataset for Investigating the Properties of Stars This dataset contains information on various properties of stars. The data was collected from observations made by the Gaia satellite, and it includes the following columns:

Star name: The name of the star Distance: The distance to the star, in parsecs Mass: The mass of the star, in solar masses Radius: The radius of the star, in solar radii

Research Ideas

Acknowledgements

License

See the dataset description for more information.

Columns

File: cleaned.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | Star_name | The name of the star. (String) | | Distance | The distance of the star from Earth in light years. (Float) | | Mass | The mass of the star in solar masses. (Float) | | Radius | The radius of the star in solar radii. (Float) |

File: final.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | Star_name | The name of the star. (String) | | Distance | The distance of the star from Earth in light years. (Float) | | Mass | The mass of the star in solar masses. (Float) |

File: final_data.csv | Column name | Description | |:--------------|:------------------------------------------------------------| | Star_name | The name of the star. (String) | | Distance | The distance of the star from Earth in light years. (Float) | | Mass | The mass of the star in solar masses. (Float) |

File: total_stars.csv | Column name | Description | |:---------------|:------------------------------------------------------------| | Star_name | The name of the star. (String) | | Distance | The distance of the star from Earth in light years. (Float) | | Mass | The mass of the star in solar masses. (Float) | | Radius | The radius of the star in solar radii. (Float) | | Luminosity | The luminosity of the star. (Float) |

The data has been extracted using NASA's publicly available API. This data comprises of the asteroids that approach earth from time to time.

An asteroid is a minor planet—an object that is neither a true planet nor an identified comet— that orbits within the inner Solar System. They are rocky, metallic, or icy bodies with no atmosphere. They can cause extreme damage to the planet.

The dataset has 10 columns, each column describing the attribute of the asteroid.

This dataset can be used to find common patterns of the asteroid to classify them as hazardous or non-hazardous.

Planets

The planets.csv file contains information about planets in our Solar System including dwarf planet Pluto. The source of data is Planetary Fact Sheet from NASA Jet Propulsion Laboratory.

Fields and units in the planetary dataset

See also official Planetary Fact Sheet Notes for more information about individual fields.

• Mass (1024kg)
• Diameter (km)
• Density (kg/m3)
• Gravity (m/s2)
• Escape Velocity (km/s)
• Rotation Period (hours)
• Length of Day (hours)
• Distance from Sun (106 km)
• Perihelion (106 km)
• Aphelion (106 km)
• Orbital Period (days)
• Orbital Velocity (km/s)
• Orbital Inclination (degrees)
• Orbital Eccentricity
• Obliquity to Orbit (degrees)
• Mean Temperature (C)
• Surface Pressure (bars)
• Number of Moons (number)
• Ring System? (Yes/No)
• Global Magnetic Field? (Yes/No)

Planetary satellites (moons)

The satellites.csv file contains information about planetary satellites (moons) of planets in our Solar System. Moons of dwarf planet Pluto are included as well. The source of data is Planetary Satellite Physical Parameters from NASA Jet Propulsion Laboratory.

Fields and units in the satellites dataset

• planet: owning planet of the satellite
• name: name of the satellite
• gm: GM (km3/sec2)
• radius: Mean radius (km)
• density: Mean density (g/cm3)
• magnitude: Magnitude V0 or R
• albedo: Geometric Albedo