In fiscal year 2024, the Indian government spent more than 107 billion Indian rupees on the space sector. It was a slight increase in comparison with the previous year. The planned budget for 2025 would reach 117 billion rupees. Indian Space Research Organization, the premier institute for the space program in India, was planning its first manned flight in 2026.

In 2014/15, the Canadian Space Agency (CSA) contracted with the firm Euroconsult, to complete a Comprehensive Socio-Economic Impact Assessment of the Canadian Space Sector. The study provides a detailed assessment of the global and Canadian space sectors, as well as the economic footprint and strategic and social value of Canadian space activities.

Forecast: Gross Value Added of Air and Space Industry in Australia 2024 - 2028 Discover more data with ReportLinker!

This dataset provides a detailed comparative analysis of technological advancements in China and Japan, covering key sectors such as Artificial Intelligence, Robotics, Telecommunications, and Clean Energy. It is a valuable resource for researchers, students, analysts, and tech enthusiasts looking to explore the technological trajectories of these two global economies.

📌 Key Features: 🔍 Technological Indicators: 📊 R&D Spending (Billion USD): Annual expenditure on research and development in both countries. 🔬 Number of Patents Filed: Total patents granted per year, showcasing innovation trends. 🌐 Internet Penetration Rate (%): Percentage of the population with internet access over time. 🤖 AI & Robotics Investments (Billion USD): Funding dedicated to artificial intelligence and robotic technologies. 🔌 Clean Energy Production (GW): Renewable energy generation capacity, including solar, wind, and hydro. 📱 5G Network Coverage (%): Percentage of the country covered by 5G infrastructure. 🏭 Industrial Automation Index: Measures the extent of automation in manufacturing and industry. 🚀 Space Exploration Milestones: Notable achievements in space technology and exploration efforts. 📡 Supercomputer Rankings: Presence in the global rankings of the fastest supercomputers. 📈 Use Cases & Applications: 🔹 Comparing China and Japan's technological growth over the decades 🔹 Analyzing global tech trends and industrial strategies 🔹 Visualizing innovation dominance across various sectors 🔹 Building predictive models for future advancements in technology 🔹 Understanding how AI, robotics, and telecom industries shape economic power

⚠️ Important Note: This dataset is designed for educational and research purposes. It is structured for easy analysis, visualization, and machine learning applications.

The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.

This dataset contains the Version 6.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset.

This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).

This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present. This is a Level 4 product, with gaps between available daily observations filled by statistical means.

The SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate.

The dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.

Data from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS).

This CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:

• Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)

• Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)

• Improved retrieval with respect to desert-dust aerosols

• Addition of dual-view SLSTR data from 2016 onwards

• Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s

• Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)

• Inclusion of L2P passive microwave AMSR data

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper:

Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). Project website: https://climate.esa.int/en/projects/soil-moisture/

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Abstract

The MODELFREE product of the ESA CCI SM v9.1 science data suite provides - similar to the COMBINED product - global, harmonized daily satellite soil moisture measurements from both radar and radiometer observations. This product contains soil moisture estimates at 0.25-degree spatial resolution, and covers the period from 2002-2023. Soil moisture is derived from observations of 13 different active and passive satellites operating across various frequency bands (K, C, X, and L-band). Unlike the COMBINED product, for which soil moisture fields from the GLDAS Noah model dataset are used to harmonize individual satellite sensor measurements, the MODELFREE product utilizes a satellite-only scaling reference dataset (Madelon et al., 2022). This reference incorporates gap-filled soil moisture derived from AMSR-E (2002-2010) and from intercalibrated SMAP/SMOS brightness temperature data (2010-2023). The merging algorithm employed is consistent with that of the v9.1 COMBINED product. The new scaling reference leads to significantly different absolute soil moisture values, especially in latitudes above 60 °N. Data from the SMMR, SSMI and ERS missions are not included in this product.

This product is in its early development stage and should be used with caution, as it may contain incomplete or unvalidated data.

Summary

• First version of a model-independent version of the ESA CCI SM COMBINED product

• 2002-2023, global, 0.25 deg. resolution

• GLDAS Noah (model) is replaced with a purely satellite-based scaling reference

• Different absolute value range compared to the COMBINED product is expected due to the different scaling reference used

Known issues

• A temporal inconsistency is observed between the AMSR-E and SMOS period (at 01-2010). This can affect long-term trends in the data

• In the period from 01-2002 to 06-2002 no data are available above 37 °N and below 37 °S respectively (all measurements in this period are from the TRMM Microwave Imager)

Technical Details

The dataset provides global daily estimates for the 2002-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_MODELFREE-YYYYMMDD000000-fv09.1.nc

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of satellite observations. Derived using triple collocation analysis.
• dn_flag: (int) Indicator for satellite orbit(s) used in the retrieval (day/nighttime). 1=day, 2=night, 3=both
• flag: (int) Indicator for data quality / missing data indicator. For more details, see netcdf attributes.
• freqbandID: (int) Indicator for frequency band(s) used in the retrieval. For more details, see netcdf attributes.
• mode: (int) Indicator for satellite orbit(s) used in the retrieval (ascending, descending)
• sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.
• t0: (float) Representative time stamp, based on overpass times of all merged satellites.

Additional information for each variable is given in the netCDF attributes.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

R. Madelon et al., “Toward the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an L-Band Scaling Reference," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 831-848, 2022, doi: 10.1109/JSTARS.2021.3137008.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

The primary objective of the study "Socioeconomic Benefits of Space Utilization" is to quantify and qualify the socio-economic value of space and related terrestrial activities to Canadians across the major application domains of imagery (EO), satellite communications (satcom) and satellite navigation (satnav). It takes specific examples based on topic areas relevant to Canadian stakeholder needs and quantifies/qualifies as to how space utilization has provided benefits, and how these may evolve.

This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on the native geometry of observation, and spanning 1980 to 2021.

The SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total.

The dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.

This CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:

• Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)

• Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)

• Improved retrieval with respect to desert-dust aerosols

• Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s

• Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)

Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

When citing this dataset please also cite the associated data paper:

Embury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w

With the phone book era far in the past, database and directory publishers have been forced to transform their business approach, focusing on their digital presence. Despite many publishers rapidly moving away from print services, they are experiencing immovable competition from online search engines and social media platforms within the digital space, negatively affecting revenue growth potential. Industry revenue has been eroding at a CAGR of 4.4% over the past five years and in 2024, a 3.9% drop has led to the industry revenue totaling $4.4 billion. Profit continues to drop in line with revenue, accounting for 4.7% of revenue as publishers invest more in their digital platforms. Interest in printed directories has disappeared as institutional clients and consumers have continued their shift to convenient online resources. Declining demand for print advertising has curbed revenue growth and online revenue has only slightly mitigated this downturn. Though many traditional publishers, such as Yellow Pages, now operate under parent companies with digital resources, directory publishers remain low on the list of options businesses have to choose from in digital advertising. Due to the convenience and connectivity that Facebook and Google services offer, traditional directory publishers have a limited ability to compete. Many providers have rebranded and tailored their services toward client needs, though these efforts have only had a marginal impact on revenue growth. The industry is forecast to decline at an accelerated CAGR of 5.2% over the next five years, reaching an estimated $3.4 billion in 2029, as businesses and consumers continually turn to digital alternatives for information and advertising opportunities. As AI and digital technology innovation expands, social media company products will likely improve at a faster rate than the digital offerings that directory publishers can provide. Though these companies will seek external partnerships to cut costs, they face an uphill battle to boost their visibility and reverse consumer habit trends.

The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.

This dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset.

This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)

Please note, this dataset has been superseded. Later versions of the data are now available.

In 2014/15, the Canadian Space Agency (CSA) contracted with the firm Euroconsult, to complete a Comprehensive Socio-Economic Impact Assessment of the Canadian Space Sector. The study provides a detailed assessment of the global and Canadian space sectors, as well as the economic footprint and strategic and social value of Canadian space activities.

The Data Visualization Platform market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from ever-expanding datasets. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of big data across various industries necessitates efficient tools for analysis and interpretation. Secondly, the rising adoption of cloud-based solutions and advanced analytics techniques, such as artificial intelligence and machine learning, is further boosting market growth. The Smart City Systems and Ultimate Digital Materialization Space applications are significant drivers, demanding sophisticated visualization capabilities for managing complex data streams and optimizing resource allocation. While data security concerns and the need for skilled professionals represent potential restraints, the overall market outlook remains positive, with significant opportunities for both established players and emerging market entrants. The market segmentation reveals a diverse landscape. Within application segments, Smart City Systems and Ultimate Digital Materialization Space lead the way, reflecting the growing importance of data-driven decision-making in urban planning and digital transformation initiatives. In terms of types, Flow Analysis and Mixed Data Analysis are currently dominant, however, Database Analysis is expected to experience significant growth due to the increasing volume and complexity of structured data. North America currently holds the largest market share, followed by Europe and Asia-Pacific. However, rapid technological advancements and increasing digitalization in emerging economies are expected to drive significant growth in Asia-Pacific and other regions over the forecast period. Key players, including Zoomdata, Tableau, JOS, Sisense, Periscope Data, Looker, Domo, and Microsoft, are constantly innovating to enhance their offerings and maintain a competitive edge in this rapidly evolving market. The competitive landscape is characterized by both intense competition and strategic partnerships, further fueling innovation and market expansion.

The SeasFire Cube is a scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.

It has been created in the context of the SeasFire project, which deals with "Earth System Deep Learning for Seasonal Fire Forecasting" and is funded by the European Space Agency (ESA) in the context of ESA Future EO-1 Science for Society Call.

It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.

Datacube properties

Feature

Value

Spatial Coverage

Global

Temporal Coverage

2001 to 2021

Spatial Resolution

0.25 deg x 0.25 deg

Temporal Resolution

8 days

Number of Variables

54

Tutorial Link

https://github.com/SeasFire/seasfire-datacube

    Full name
    DataArray name
    Unit
    Contact *




    Dataset: ERA5 Meteo Reanalysis Data





    Mean sea level pressure
    mslp
    Pa
    NOA


    Total precipitation
    tp
    m
    MPI


    Relative humidity
    rel_hum
    %
    MPI


    Vapor Pressure Deficit
    vpd
    hPa
    MPI


    Sea Surface Temperature
    sst
    K
    MPI


    Skin temperature
    skt
    K
    MPI


    Wind speed at 10 meters
    ws10
    m*s-2
    MPI


    Temperature at 2 meters - Mean
    t2m_mean
    K
    MPI


    Temperature at 2 meters - Min
    t2m_min
    K
    MPI


    Temperature at 2 meters - Max
    t2m_max
    K
    MPI


    Surface net solar radiation
    ssr
    MJ m-2
    MPI


    Surface solar radiation downwards
    ssrd
    MJ m-2
    MPI


    Volumetric soil water level 1
    swvl1
    m3/m3
    MPI







          Volumetric soil water level 2




    swvl2
    m3/m3
    MPI


    Volumetric soil water level 3
    swvl3
    m3/m3
    MPI


    Volumetric soil water level 4
    swvl4
    m3/m3
    MPI


    Land-Sea mask
    lsm
    0-1
    NOA


    Dataset: Copernicus

CEMS

    Drought Code Maximum
    drought_code_max
    unitless
    NOA


    Drought Code Average
    drought_code_mean
    unitless
    NOA


    Fire Weather Index Maximum
    fwi_max
    unitless
    NOA


    Fire Weather Index Average
    fwi_mean
    unitless
    NOA


    Dataset: CAMS: Global Fire Assimilation System (GFAS)





    Carbon dioxide emissions from wildfires
    cams_co2fire
    kg/m²
    NOA


    Fire radiative power
    cams_frpfire
    W/m²
    NOA


    Dataset: FireCCI - European Space Agency’s Climate Change Initiative





    Burned Areas from Fire Climate Change Initiative (FCCI)
    fcci_ba
    ha
    NOA


    Valid mask of FCCI burned areas
    fcci_ba_valid_mask
    0-1
    NOA



    Fraction of burnable area
    fcci_fraction_of_burnable_area
    %
    NOA


    Number of patches
    fcci_number_of_patches
    N
    NOA


    Fraction of observed area
    fcci_fraction_of_observed_area
    %
    NOA


    Dataset: Nasa MODIS MOD11C1, MOD13C1, MCD15A2





    Land Surface temperature at day
    lst_day
    K
    MPI


    Leaf Area Index
    lai
    m²/m²
    MPI


    Normalized Difference Vegetation Index
    ndvi
    unitless
    MPI


    Dataset: Nasa SEDAC Gridded Population of the World (GPW), v4





    Population density
    pop_dens
    persons per square kilometers
    NOA


    Dataset: Global Fire Emissions Database (GFED)





    Burned Areas from GFED (large fires only)
    gfed_ba
    hectares (ha)
    MPI


    Valid mask of GFED burned areas
    gfed_ba_valid_mask
    0-1
    NOA


    GFED basis regions
    gfed_region
    N
    NOA


    Dataset: Global Wildfire Information System (GWIS)





    Burned Areas from GWIS
    gwis_ba
    ha
    NOA


    Valid mask of GWIS burned areas
    gwis_ba_valid_mask
    0-1
    NOA


    Dataset: NOAA Climate Indices





    Arctic Oscillation Index
    oci_ao
    unitless
    NOA


    Western Pacific Index
    oci_wp
    unitless
    NOA


    Pacific North American Index
    oci_pna
    unitless
    NOA


    North Atlantic Oscillation
    oci_nao
    unitless
    NOA


    Southern Oscillation Index
    oci_soi
    unitless
    NOA


    Global Mean Land/Ocean Temperature
    oci_gmsst
    unitless
    NOA


    Pacific Decadal Oscillation
    oci_pdo
    unitless
    NOA


    Eastern Asia/Western Russia
    oci_ea
    unitless
    NOA


    East Pacific/North Pacific Oscillation
    oci_epo
    unitless
    NOA


    Nino 3.4 Anomaly
    oci_nino_34_anom
    unitless
    NOA


    Bivariate ENSO Timeseries
    oci_censo
    unitless
    NOA


    Dataset: ESA CCI





    Land Cover Class 0 - No data
    lccs_class_0
    %
    NOA


    Land Cover Class 1 - Agriculture
    lccs_class_1
    %
    NOA


    Land Cover Class 2 - Forest
    lccs_class_2
    %
    NOA


    Land Cover Class 3 - Grassland
    lccs_class_3
    %
    NOA


    Land Cover Class 4 - Wetlands
    lccs_class_4
    %
    NOA


    Land Cover Class 5 - Settlement
    lccs_class_5
    %
    NOA


    Land Cover Class 6 - Shrubland
    lccs_class_6
    %
    NOA


    Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice
    lccs_class_7
    %
    NOA


    Land Cover Class 8 - Water Bodies
    lccs_class_8
    %
    NOA


    Dataset: Biomes





    Dataset: Calculated





    Grid Area in square meters
    area
    m²
    NOA

*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante noa.gr) has led the efforts to collect and process them.

We will construct SciSpark, a scalable system for interactive model evaluation and for the rapid development of climate metrics and analyses. SciSpark directly leverages the Apache Spark technology and its notion of Resilient Distributed Datasets (RDDs). RDDs represent an immutable data set that can be reused across multi-stage operations, partitioned across multiple machines and automatically reconstructed if a partition is lost. The RDD notion directly enables the reuse of array data across multi-stage operations and it ensures data can be replicated, distributed and easily reconstructed in different storage tiers, e.g., memory for fast interactivity, SSDs for near real time availability and I/O oriented spinning disk for later operations. RDDs also allow Spark's performance to degrade gracefully when there is not sufficient memory available to the system. It may seem surprising to consider an in-memory solution for massive datasets, however a recent study found that at Facebook 96% of active jobs could have their entire data inputs in memory at the same time. In addition, it is worth noting that Spark has shown to be 100x faster in memory and 10x faster on disk than Apache Hadoop, the de facto industry platform for Big Data. Hadoop scales well and there are emerging examples of its use in NASA climate projects (e.g., Teng et al. and Schnase et al.) but as is being discovered in these projects, Hadoop is most suited for batch processing and long running operations. SciSpark contributes a Scientific RDD that corresponds to a multi-dimensional array representing a scientific measurement subset by space, or by time. Scientific RDDs can be created in a handful of ways by: (1) directly loading HDF and NetCDF data into Hadoop Distributed File System (HDFS); (2) creating a partition or split function that divides up a multi-dimensional array by space or time; (3) taking the results of a regridding operation or a climate metrics computation; or (4) telling SciSpark to cache an existing Scientific RDD (sRDD), keeping it cached in memory for data reuse between stages. Scientific RDDs will form the basis for a variety of advanced and interactive climate analyses, starting by default in memory, and then being cached and replicated to disk when not directly needed. SciSpark will also use the Shark interactive SQL technology that allows structured query language (SQL) to be used to store/retrieve RDDs; and will use Apache Mesos to be a good tenant in cloud environments interoperating with other data system frameworks (e.g., HDFS, iRODS, SciDB, etc.).

One of the key components of SciSpark is interactive sRDD visualizations and to accomplish this SciSpark delivers a user interface built around the Data Driven Documents (D3) framework. D3 is an immersive, javascript based technology that exploits the underlying Document Object Model (DOM) structure of the web to create histograms, cartographic displays and inspections of climate variables and statistics.

SciSpark is evaluated using several topical iterative scientific algorithms inspired by the NASA RCMES project including machine-learning (ML) based clustering of temperature PDFs and other quantities over North America, and graph-based algorithms for searching for Mesocale Convective Complexes in West Africa.

The transition from printed databases and directories to online formats has left the Canadian Database and Directory Publishing industry reeling, with revenue decreasing over the five years to 2023 because of this transition and COVID-19. From the advertisers' perspective, marketing costs are allocated to the media channels that most accurately reflect consumer behaviour. As more consumers shift to digital directory and database substitutes, demand for print advertisements, previously the industry's largest revenue source and profit indicator, has declined. Over the five years to 2023, the number of consumers with smartphones, which come with online directory capabilities via their ability to connect to the internet, has risen alongside internet connectivity. This has coincided with declining demand for industry products. Consequently, industry revenue has been declining an annualized 10.2% over the past five years, and is expected to reach $1.4 billion in 2023. This includes a decrease of 3.6% in 2023 alone.The industry has historically been dominated by several players, mainly telephone companies with access to consumer and business contact information. As the industry has contracted, companies have spun off their directory divisions. This was exemplified by the industry-defining event of Bell Canada handing off what would become Yellow Media Limited to KKR & Co. Inc. and the Ontario Teachers' Pension Plan Board. Over the past five years, this trend has continued, with companies selling off their failing segments to larger companies. The purchasing companies have used merger and acquisition activities to diversify their service and product offerings, entering various third-party fields, including market research, data processing and analytics, and database management.Over the five years to 2028, the industry will likely continue its downward spiral. During this period, total advertising expenditure is expected to rise. However, total print advertisement expenditure will likely decline as a share of total spending. The use of print advertisements will likely continue to become obsolete over the next five years. The most significant contributing factor to this decline is expected to be the growing use of digital advertisements. Consequently, IBISWorld forecasts industry revenue will decrease an annualized 3.4% to $1.2 billion over the five years to 2028.

The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part …

The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band). This collection includes the S1 Ground Range Detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected product. The collection is updated daily. New assets are ingested within two days after they become available. This collection contains all of the GRD scenes. Each scene has one of 3 resolutions (10, 25 or 40 meters), 4 band combinations (corresponding to scene polarization) and 3 instrument modes. Use of the collection in a mosaic context will likely require filtering down to a homogeneous set of bands and parameters. See this article for details of collection use and preprocessing. Each scene contains either 1 or 2 out of 4 possible polarization bands, depending on the instrument's polarization settings. The possible combinations are single band VV, single band HH, dual band VV+VH, and dual band HH+HV: VV: single co-polarization, vertical transmit/vertical receive HH: single co-polarization, horizontal transmit/horizontal receive VV + VH: dual-band cross-polarization, vertical transmit/horizontal receive HH + HV: dual-band cross-polarization, horizontal transmit/vertical receive Each scene also includes an additional 'angle' band that contains the approximate incidence angle from ellipsoid in degrees at every point. This band is generated by interpolating the 'incidenceAngle' property of the 'geolocationGridPoint' gridded field provided with each asset. Each scene was pre-processed with Sentinel-1 Toolbox using the following steps: Thermal noise removal Radiometric calibration Terrain correction using SRTM 30 or ASTER DEM for areas greater than 60 degrees latitude, where SRTM is not available. The final terrain-corrected values are converted to decibels via log scaling (10*log10(x)). For more information about these pre-processing steps, please refer to the Sentinel-1 Pre-processing article. For further advice on working with Sentinel-1 imagery, see Guido Lemoine's tutorial on SAR basics and Mort Canty's tutorial on SAR change detection. This collection is computed on-the-fly. If you want to use the underlying collection with raw power values (which is updated faster), see COPERNICUS/S1_GRD_FLOAT.

TOPography EXperiment (TOPEX) for ocean circulation (otherwise known as Poseidon) was launched on August 10, 1992 and was a joint satellite mission between NASA, the U.S. space agency, and CNES, the French space agency, to map ocean surface topography. The first major oceanographic research vessel to sail into space, TOPEX/Poseidon helped revolutionise oceanography by proving the value of satellite ocean observations. This dataset contains monthly means on a 1x1 latitude/longitude grid for 12 years (1993-2004). The data contains the following parameters: wind speed, squared wind speed, cubed wind speed, wind speed * significant wave height, significant wave height, 1/sigma0(Ku) and gas transfer velocity. TOPEX/Poseidon was a joint mission from the National Aeronautics and Space Administration (NASA), the U.S. space agency and the French space agency. The dataset was produced by Fangohr, S. and D.K. Woolf of SOCS, as part of the NERC programme's Centre for observation of Air-Sea Interactions and FluXes (CASIX) and National Centre for Earth Observation (NCEO).

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …