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.

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.

Not only does sea ice provide an irreplaceable habitat for many polar species, but it also is essential for the proper functioning of Earth’s climate system. Reductions of sea ice extent are accelerating warming along with exposing otherwise protected areas to resource exploitation. Track the status of sea ice in the Arctic and Antarctic using the latest information from NOAA at the National Snow and Ice Data Center available in Esri’s Living Atlas and learn more about changing conditions in the polar regions.

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.

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.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

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.

Open and free data for assessing the human presence on the planet.

The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources.

The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

As an inescapable concomitant with the traditional route of economic development, Pakistan has been facing natural resource degradation and pollution problems. The unsavory spectacle of air pollution, water contamination and other macro environmental impacts such as water logging, land degradation and desertification, are on rise. All this, in conjunction with rapid growth in population, have been instrumental to the expanding tentacles of poverty. In order to assess the environmental problems as a prelude to arrest the pace of degeneration and provide for sustainable course of economic development, the availability of adequate data is imperative. This publication is an attempt to provide relevant statistics compiled through secondary sources collected from different departments. The task of environmental data collection does not consist just in determining the frame and approaching the selected sources of information because environmental statistics per se do not exist as a ready-to-compile/pick category as generally perceived about data and statistics. The information on environment has generated through deliberate scientific observations and measurements in a consistent way, under the aegis of specialized agencies. Since it is skill and resource intensive pursuit and generally undertaken in public sector, the overall budgetary/financial constraints do take the toll of the canvas and continuity of environmental data generation down the time lane. Consequently, availability of the statistics falls short of desired level. Further, the studies pertaining to normal over a period of time are repeated after long time intervals, which may not conform with the quinquennial periodicity of this document. Similarly, many variables antecedental, associated with and, consequential to, environment are derived from population census, which is yet to be carried out even though the stipulated decennial time frame has long been overstepped. Nevertheless, the latest update of the compendium is a good attempt to mirror quite a few environmental factors as a means to raise awareness and help stay focus on the pivotality of environmental concerns for instituting sustainable development paradigm-the only way forward to ensuring the continuity of human race on the face of planet earth.

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.

Future Asteroids

All known future asteroids poised to pass near Earth, some dangerous (PHO)

By Mark Di Marco [source]

About this dataset

This fascinating dataset visualizes the ever-changing and dynamic world of Near Earth Asteroids (NEAs) that are either on their way to us or have recently came by! This real-time data offers an insight into our universe, helping you get a grasp of just how often asteroids fly by our planet and how close they can get. With this dataset containing information on those NEAs, you'll be able to get up close and personal with the cosmic travelers that grace the hood of our galaxy. We've included data like their known names, dates & times of their close approaches, distances in both astronomical units & Lunar Distances from Earth, velocities relative to us & the sun as well as other essential properties that will help paint a humanistic picture of these celestial objects. So join us on this exploration and take a journey through time into our cosmos with these asteroids!

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How to use the dataset

Introduction This dataset provides information about Near Earth Asteroids that will make a close approach to Earth within the next 12 months, or have made a close approach within the last 12 months. The columns of data include characteristics such as distance from Earth and relative velocity, among others. To gain more insight on Near Earth Asteroids, follow these steps below:

Download the Dataset Download this Investigate Near-Earth Asteroid – Track Close Approaches to Earth! dataset from Kaggle. With this download you’ll receive two CSV files: future.csv and all.csv. The first file (future) covers asteroids making a close approach in the next 12 months and ones that have made one in last 12 months; while all covers asteroids making a close approaches at later times (further than twelve months away). Analyze, Interpret & Vizualize Once you’ve downloaded your data files onto your machine, open them up with Microsoft Excel or Google Sheets to begin analyzing your collected asteroid dataset! Utilize organizational tools available in each spreadsheet program to sort through each column of data observing its classification as well as minimum distances etc… for any correlations/conclusions one can draw about these objects as they pertain our current space environment . After exploring patterns found among the contents it’s time for data visualization ! Using programs such as Tableau or looker assist in creating interactive charts and graphs visually depicting collected asteroid knowledge based upon attributes like distances traveled and composition classifications observed throughout researching available entries across both csv sheets! Begin to compile stories generated through gathered info presented using said aforementioned charting platforms leading readers/viewers deeper into their own analysis of various NEA boundaries; showcasing understanding found through digging passed tabular datasets utilizing more impressive display visuals suitable for broader consumption beyond personal analysis !

Find Trends & Patterns The Future spreadsheet outlines all known asteroids categorized by their Distance Nominal(LD), Composition Classifications (GK), minimum (relative) speed VRelative(km/s)through space , size Vinfinity(km/s), standard deviation N Sigma of orbital path pertaining to earth; forming meaningful comparisons understandable almost anyone regardless their background knowledge when viewing provided visualizations created earlier during workflow joining together interpreted values researched throughout 3–4 emphasizing significance each metric holds when attempting assess risk posed our society at given moment presence current yearly trends collated applicable datasheets analyzed beforehand helping

Research Ideas

• Use the data to build an accurate 3D-printed model of a NEA at different scales, depending on the size and shape it describes
• Build a computer simulation which simulates close approaches of NEAs and the risk they pose to Earth
• Develop an interactive map which displays current positions of NEAs and radar detection for confirmed threats

Acknowledgements

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

License

License: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) - You are free to: - Share - copy and redistribute the material in any mediu...

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. Global data for marine ecosystems are largely non-existent; here we used available data for several ecosystems, modeled the distribution of many other ecosystems, and assumed a uniform distribution for several intertidal ecosystems for which no data exist. We recognize that differences exist in how people classify ecosystems; for example, estuaries are often considered an ecosystem, but here we focus on the ecosystems (also often labeled ‘habitats’) that occur within estuaries (salt marsh, intertidal mud, beach, soft sediment, mangroves, etc.). All ecosystem data were represented at 1 km2 resolution. This dataset contains maps for 20 distinct marine ecosystems used in the impacts model. More information on data sources can be found in the methods section.

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 ...

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

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 ---

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.

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...

The SUCCESS_UTAH_PDL data set contains ground-based measurements made by the University of Utah Polarization Diversity LIDAR at the CART site during the April-May 1996 SUCCESS Mission.SUbsonic aircraft: Contrail & Clouds Effects Special Study (SUCCESS) is a NASA field program using scientifically instrumented aircraft and ground based measurements to investigate the effects of subsonic aircraft on contrails, cirrus clouds and atmospheric chemistry. The experiment is cosponsored by NASA's Subsonic Assessment Program and the Radiation Sciences Program which are part of the overall Aeronautics and Mission to Planet Earth Programs, respectively. SUCCESS has well over a hundred direct participants from several NASA Centers, other agencies, universities and private research companies.

Copernicus is the European Union's Earth Observation Programme, looking at our planet and its environment for the ultimate benefit of all European citizens. It offers information services based on satellite Earth Observation and in situ (non-space) data. The vast majority of data and information delivered by the Copernicus space infrastructure and the Copernicus services are made freely available and accessible to any citizen and any organisation around the world. Copernicus services transform satellite and in situ data into value-added information by processing and analysing the data. Datasets stretching back for years and decades are comparable and searchable. Maps are created from imagery, features and anomalies are identified and statistical information is extracted. These value-adding activities are streamlined through the six thematic streams of Copernicus services: atmosphere, marine, land, climate change, security, and emergency.

Machine Readable Tables containing the time series photometry and radial velocity data used in Burt et al. 2024 paper 'TOI-1685 b is a Hot Rocky Super-Earth: Updates to the Stellar and Planet Parameters of a Popular JWST Cycle 2 Target'

Notes (also in ReadMe Document)

TOI1685_Full_TimeSeries_Photometry_DataSet.dat : the juliet light curve input file for the transit fit presented in the paper.

The nine columns in the file correspond to, Time (BJD_TDB), Normalized flux, Normalized flux error, Dataset label, Airmass, Centroid X offset, Centroid Y offset, PSF full-width half-maximum (FWHM) and PSF peak brightness. As these are not used to model the TESS light curves, these values are blank for the TESS data. For the MuSCAT2 data, entropy (a proxy for the PSF FWHM) is used in place of PSF FWHM, and PSF peak brightness is not used at set to 0. For the OMM data, total background across the target and comparison stars is used in place of PSF peak brightness. All values in the final five columns are normalized between -1 and 1 for any given dataset.

~~~~~~~~~~~~~

TOI1685_Full_RV_DataSet.csv : The full data sets from the MAROON-X data presented in this paper, as well as the CARMENES data from Bluhm et al. 2021 and the IRD data from Hirano et al. 2021.

The fit presented in the paper only makes use of the RV time series from each instrument, but the SERVAL results for MAROON-X and CARMENES also provide a variety of activity indicators which we include as additional columns here for completeness.