Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"

Data and code associated with "The Observed Availability of Data and Code in Earth Science  and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern. Instructions: To reproduce figures, download all associated Python and CSV files and place               in a single directory.               Run BAMS_plot.py as you would run Python code on your system. Code: BAMS_plot.py: Python code for categorizing data availability statements based on given data               documented below and creating figures 1-3.                Code was originally developed for Python 3.11.7 and run in the Spyder                (version 5.4.3) IDE.                              Libraries utilized:                 numpy        (version 1.26.4)                  pandas       (version 2.1.4)                 matplotlib  (version 3.8.0)                              For additional documentation, please see code file. Data: ASDC_AIES.csv:      CSV file containing relevant availability statement data for Artificial                      Intelligence for the Earth Systems (AIES) ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial                      Intelligence in Geosciences (AI in Geo.) ASDC_AIJ.csv:       CSV file containing relevant availability statement data for Artificial                      Intelligence (AIJ) ASDC_MWR.csv:       CSV file containing relevant availability statement data for Monthly                      Weather Review (MWR) Data documentation: All CSV files contain the same format of information for each journal. The CSV files above are  needed for the BAMS_plot.py code attached. Records were analyzed based on the criteria below.   Records:     1) Title of paper         The title of the examined journal article.     2) Article DOI (or URL)         A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is          generally given. For AIJ, the URL is given.     3) Journal name         The name of the journal where the examined article is published. Either a full         journal name (e.g., Monthly Weather Review), or the acronym used in the          associated paper (e.g., AIES) is used.     4) Year of publication         The year the article was posted online/in print.     5) Is there an ASDC?         If the article contains an availability statement in any form, "yes" is          recorded. Otherwise, "no" is recorded.     6) Justification for non-open data?         If an availability statement contains some justification for why data is not          openly available, the justification is summarized and recorded as one of the          following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be          obtained from other entities, 4) Sensitive information, 5) Available at later          date. If the statement indicates any data is not openly available and no          justification is provided, or if no statement is provided is provided "None"          is recorded. If the statement indicates openly available data or no data          produced, "N/A" is recorded.     7) All data available         If there is an availability statement and data is produced, "y" is recorded          if means to access data associated with the article are given and there is no          indication that any data is not openly available; "n" is recorded if no means          to access data are given or there is some indication that some or all data is          not openly available. If there is no availability statement or no data is          produced, the record is left blank.     8) At least some data available         If there is an availability statement and data is produced, "y" is recorded          if any means to access data associated with the article are given; "n" is          recorded if no means to access data are given. If there is no availability          statement or no data is produced, the record is left blank.     9) All code available         If there is an availability statement and data is produced, "y" is recorded          if means to access code associated with the article are given and there is no          indication that any code is not openly available; "n" is recorded if no means          to access code are given or there is some indication that some or all code is          not openly available. If there is no availability statement or no data is          produced, the record is left blank.     10) At least some code available         If there is an availability statement and data is produced, "y" is recorded          if any means to access code associated with the article are given; "n" is          recorded if no means to access code are given. If there is no  availability          statement or no data is produced, the record is left blank.     11) All data available upon request         If there is an availability statement indicating data is produced and no data          is openly available, "y" is recorded if any data is available upon request to          the authors of the examined journal article (not a request to any other          entity); "n" is recorded if no data is available upon request to the authors          of the examined journal article. If there is no availability statement, any          data is openly available, or no data is produced, the record is left blank.     12) At least some data available upon request         If there is an availability statement indicating data is produced and not all          data is openly available, "y" is recorded if all data is available upon          request to the authors of the examined journal article (not a request to any          other entity); "n" is recorded if not all data is available upon request to          the authors of the examined journal article. If there is no availability          statement, all data is openly available, or no data is produced, the record         is left blank.     13) no data produced         If there is an availability statement that indicates that no data was         produced for the examined journal article, "y" is recorded. Otherwise, the         record is left blank.     14) links work         If the availability statement contains one or more links to a data or code          repository, "y" is recorded if all links work; "n" is recorded if one or more          links do not work. If there is no availability statement or the statement          does not contain any links to a data or code repository, the record is left          blank.

opencc-by-4.0May 2022View details →

Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022

This data set contains four parts: 1) compressed folder with input parameters and results for the hydrodynamic model 2) compressed folder with input parameters and results for the particle tracking 3) compressed folder with satellite data  4) code used in the article for hydrodynamic model, particle tracking and image processing Each folder contains a readme file,

opencc-by-4.0Sep 2022View details →

ecochange: An R-package to derive ecosystem change indicators from freely available earth observation products

This release includes the R code necessary to reproduce Figures 2-4 in the Application paper entitled: "ecochange: An R-package to derive ecosystem change indicators from freely available Earth Observation products."

opencc-by-4.0Mar 2023View details →

Supporting data for "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Chan et al.

Model code, simulation outputs, digitized observation-summary tables, and Python scripts for reproducing the analysis results/ figures presented in "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.

opencc-by-4.0Mar 2023View details →

EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction

This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover. This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture. The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data. Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.

opencc-by-4.0Jan 2023View details →

A dataset of Earth Observation Data for Lithological Mapping using Machine Learning

Dataset Information Machine Learning (ML) algorithms had successfully contributed in the creation of automated methods of recognizing patterns in high-dimensional data. Remote sensing data  covers  wide  geographical areas and could be used to solve the problem of the demand of various  in-situ data.  Lithologicall mapping using remotely sensed data  is one of the most challenging  applications of ML algorithms. In the framework of the “AI for Geoapplications” project , ML and especially Deep Learning (DL) methodologies are investigated  for  the identification and characterization of the lithology based on remote sensing data in various  pilot areas  in Greece.  In order to train and test the various ML algorithms, a dataset consisting of  30 ROIs selected  mainly  from low -vegetated areas,  that cover 2% of the total  area of Greece was created Dataset Preprocessing Dataset preprocessing was executed using a combination of SNAP, QGIS and ENVI tools. Preprocessing steps: Defining areas with the following properties: <ul> <li> Zero cloud and snow coverage </li> <li> No water bodies </li> <li> Minimum vegetation </li> </ul> For the Aster Images: <ul> <li> Subset on defined areas </li> <li> Mosaic images when needed </li> <li> Digitising clouds </li> </ul> For the Labels: <ul> <li> We got the Soil map from YPEN (<a href="https://ypen.gov.gr/">https://ypen.gov.gr/</a>) </li> <li> Subset on defined areas </li> <li> All categories are represented with good analogies </li> <li> Clip label files with digitised clouds </li> <li> Rasterize </li> </ul>   For the Labels we have eighteen categories for the twenty-eight areas that we collected data. We use the following coding for the Labels of our Dataset: <table> <tbody> <tr> <td> Alluvial deposits </td> <td> 0 </td> </tr> <tr> <td> Limestone colluvial deposits </td> <td> 1 </td> </tr> <tr> <td> Limestones </td> <td> 2 </td> </tr> <tr> <td> Schists </td> <td> 3 </td> </tr> <tr> <td> Quaternary sediments </td> <td> 4 </td> </tr> <tr> <td> Gneiss </td> <td> 5 </td> </tr> <tr> <td> Slope fan debris </td> <td> 6 </td> </tr> <tr> <td> Mixed flysch </td> <td> 7 </td> </tr> <tr> <td> Flysch shale and cherts </td> <td> 8 </td> </tr> <tr> <td> Dolomites </td> <td> 9 </td> </tr> <tr> <td> Granite </td> <td> 10 </td> </tr> <tr> <td> Sandstone flysch </td> <td> 11 </td> </tr> <tr> <td> Flysch colluvial deposits </td> <td> 12 </td> </tr> <tr> <td> Peridotite and Gabbro </td> <td> 13 </td> </tr> <tr> <td> River bed deposits </td> <td> 14 </td> </tr> <tr> <td> Gneiss colluvial deposits </td> <td> 15 </td> </tr> <tr> <td> Not available </td> <td> -100 </td> </tr> <tr> <td> cloud coverage </td> <td> -999 </td> </tr> </tbody> </table> The following table lists the available areas and the categories that each contains: <a href="https://docs.google.com/spreadsheets/d/17q0L5Ltz7V4uBY9i6DhULJsJCtf7BOY1nbblB-hf3Pw/edit?usp=share_link">Lithology_Dataset</a>   For the Sentinel-2 images, we made the following process: <ul> <li> Resampling 10m </li> <li> Subset on defined areas </li> </ul> The Sentinel-2 map contains: Sentinel 2 false colour composite 11/8/4 with OSM background The Final step is the collocation of the previous into a datacube i.e a multidimensional array with 25 bands (datacube dimensions differentiate for every area) using the Aster image as base (15m spatial resolution).  <ul> <li> Bands 1-14: Aster </li> <li> Bands 15-24: S2 </li> <li> Band 25: Label </li> </ul> The code for preprocessing the dataset in order to be used for machine learning algorithms can be found in the following link:   <a href="https://github.com/georgegiannop/Lithology">https://github.com/georgegiannop/Lithology</a> Citation If you use this dataset in your work, please cite our paper: Vernikos, I., Giannopoulos, G., Christopoulou, A., Begaj, A., Stefouli, M., Bratsolis, E., and Charou, E.: A dataset of Earth Observation Data for Lithological Mapping using Machine Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17570, <a href="https://doi.org/10.5194/egusphere-egu23-17570">https://doi.org/10.5194/egusphere-egu23-17570</a>, 2023.

opencc-byOct 2024View details →

Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.

Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the land surface temperature (heatmaps) of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets. There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health.  The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows. -    2013-2017 -    2014-2018 -    2015-2019 -    2016-2020 -    2017-2021 -    2018-2022 -    2019-2023 The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>. The format of this dataset is organized in two ZIP format files: -    LANDSAT_8_L2SP_000000-milan_LST_peak.zip -    LANDSAT_8_L2SP_000000-logrono_LST_peak.zip -    LANDSAT_8_L2SP_000000-athens_LST_peak.zip Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST. The values of the images are in degree Celsius and nodata value is -9999.

opencc-by-4.0May 2024View details →

AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible. In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.

opencc-by-4.0Oct 2021View details →

Supplementary Table for Earth observation data-driven cropland soil monitoring: A review

Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.

dryad40/100

Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results

The Great Oxidation Event was a period during which Earth's atmospheric oxygen (O2) concentrations increased from ~10−5 times its present atmospheric level (PAL) to near modern levels, marking the start of the Proterozoic geological eon 2.4 billion years ago. Using WACCM6, an Earth System Model, we simulate the atmosphere of Earth-analogue exoplanets with O2 mixing ratios between 0.1% and 150% PAL. Using these simulations, we calculate the reflection/emission spectra over multiple orbits using the Planetary Spectrum Generator. We highlight how observer angle, albedo, chemistry, and clouds affect the simulated observations. We show that inter-annual climate variations, as well as short-term variations due to clouds, can be observed in our simulated atmospheres with a telescope concept such as LUVOIR or HabEx. Annual variability and seasonal variability can change the planet's reflected flux (including the reflected flux of key spectral features such as O2 and H2O) by up to factors of 5 and 20, respectively, for the same planetary phase. This variability is best observed with a high-throughput coronagraph. For example, HabEx (4 m) with a starshade performs up to a factor of two times better than a LUVOIR B (6 m) style telescope. The variability and signal-to-noise ratio of some spectral features depends non-linearly on atmospheric O2 concentration. This is caused by temperature and chemical column depth variations, as well as generally increased liquid and ice cloud content for atmospheres with O2 concentrations of <1% PAL.

opencc-zeroSep 2022View details →

opencc-by-4.0Oct 2018View details →

EOMORES earth observation and in situ data of water quality in lakes and coastal areas - year 1

EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters. The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted. The following is included: - Estonia lakes and coast: in situ data 2017 - Finland: links to repositories of EO data - Italy Trasimeno: sample of EO data 2017 - Lithuania Curonian Lagoon: sample of EO data 2017 - Netherlands Lake Markermeer: sample of EO data 2017 - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017 - UK Scotland: in situ data Loch Leven and Loch Lomond 2017 - UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017 http://eomores-h2020.eu

opencc-by-4.0Jul 2024View details →

High-Resolution Pan-European Forest Structure Maps: An Integration of Earth Observation and National Forest Inventory Data

We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m2 for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data. For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40<a> European countries, </a>forming a continuous coverage of the western part of the European continent. A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to <a>73 % </a>the South-Eastern area. The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care. The author list is sorted by last name except for the first and last authors who also serve as corresponding authors. Corresponding authors: <a href="mailto:Jukka.Miettinen@vtt.fi">Jukka.Miettinen@vtt.fi</a>, <a href="mailto:Johannes.Breidenbach@nibio.no">Johannes.Breidenbach@nibio.no</a>

opencc-by-4.0Jul 2024View details →

Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database

The database of organizational actors, "Actors and Satellites in the African Earth Observations Sector: Insights from the 2021 Radiant Earth ML for EO Market Map and the Union of Concerned Scientists Database" analyzed in "Whose Priorities? Examining Inequities in Earth Observation Advancements Across Africa" this study, is available on Zenodo, an open-access repository developed under the European OpenAIRE program. The dataset comprises information on 310 space-centric earth observation organizations, including headquarters locations. For the 31 organizations in our sample, we provide additional details including the African countries where their projects are active, the type of initiative or program, other focus areas, organizational classification (commercial, government, or nongovernmental), funding source (public or private), organizational type (research, startup, or established industry), capabilities (data analysis, data storage, image labeling, competition platforms), involvement in early warning systems, data accessibility, availability of global products, and whether they build commercial satellites. This open sharing of the compiled organizational data aims to promote transparency, reproducibility, and additional investigations into the evolving landscape of earth observation activities globally and across Africa. Analyses of this dataset's relationships, funding flows, and priorities can provide further insights to guide equitable advancement of earth observation capabilities.

opencc-by-4.0Jul 2021View details →

Datasets for Earth Observation Using Python: A Practical Programming Guide

These are the datasets used in the <a href="https://agupubs.onlinelibrary.wiley.com/doi/book/10.1002/9781119606925">2021 Edition</a> of "Earth Observation Using Python: A Practical Programming Guide." These contain example text, csv, netCDF, and GRIB files that are used in code exercises within the book. Datasets are original files and modified public domain files from large Earth satellite data providers, such as NASA, NOAA, EUMETSAT, and ESA. These files may be used freely, but if not heavilly modified, please attribute credit to the original author for collecting the examples.

opencc-by-4.0Jun 2021View details →

Earth-observation based products from the CoastObs project

The Earth-observation based products are in the CoastObs portal: https://coastobs.lizard.net. Login details to the portal will be provided after finalising the CoastObs training materials: https://coastobs.eu/e-training Products in the portal include demonstration products of the following parameters: Basic products: <ul> <li>Chl-a</li> <li>Suspended matter</li> <li>Turbidity</li> <li>Sea surface temperature</li> </ul> Innovative and higher level products: <ul> <li>Sea grass percentage coverage</li> <li>Phytoplankton size classes</li> <li>Harmful algae bloom (HAB) indicators for Pseudonitzschia and Alexandrium</li> <li>(Mean) food quality for shell fish</li> <li>Mussel growth potential</li> <li>Relative growth rate (of mussels)</li> </ul> There are products for the following areas: <ul> <li>Loire Estuary (France)</li> <li>Bourgneuf Bay (France)</li> <li>Eastern Scheldt + Voordelta (Netherlands)</li> <li>Wadden Sea (Netherlands)</li> <li>Venice Lagoon (Italy)</li> <li>Galician coast (Spain)</li> <li>Rias Baixas (Spain)</li> </ul> In the portfolio are more products, such as: <ul> <li>Primary production</li> <li>WFD indicators</li> <li>HAB forecasts</li> <li>Plume morphology</li> <li>Phytoplankton bloom morphology</li> </ul>

dryad40/100

Variability due to climate and chemistry in observations of oxygenated Earth-analogue exoplanets: Simulations and results

Open the record for dataset details and reuse information.

publicSep 2022View details →

opencc-by-4.0Sep 2020View details →

Supplementary Material for "Time-domain modelling of 3-D Earth's and planetary electromagnetic induction effect in ground and satellite observations"

1. Magnetic field residuals from Observatory and Swarm data. Details about data origin and pre-processing are given in the main paper. 2. Time series of external Spherical Harmonic coefficients estimated from observatory and satellite data as described in the main paper.

opencc-by-4.0Oct 2020View details →

Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - Simuation results and observed data

This data set contains the simulation results and observed data at NDBC buoy locations. <ul> <li>wave_data.pickle <ul> <li>File containing python data objects which store: station ID data, observed data, model data, and model output dates. Requires python 3.8.</li> </ul> </li> <li>data_access.py <ul> <li>Example python script which reads in a prints the data from wave_data.pickle. It also demonstrates how to access data from the objects stored in the pickle file.</li> </ul> </li> </ul>

opencc-by-4.0Dec 2022View details →

Dataset from paper: "Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency"

This repository contains the processed data used in the publication of Heinrich et al., 2023 (Mind the Gap: Reconciling tropical forest carbon flux estimates from Earth Observation and National Reporting requires transparency). Carbon Balance and Management: https://cbmjournal.biomedcentral.com/articles/10.1186/s13021-023-00240-2 When using this data, full reference to the raw data publications and the publication Heinrich et al., 2023 must be made.The raw datasets used in this study are all available from their original sources.