FSL's Evaluation Example Data Suite from Dr. Steve Smith and the FMRIB Analysis Group at the Univeristy of Oxford.

LATAM Data Suite provides high-quality datasets in Spanish, Portuguese, and American English. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.

Discover our expertly curated language datasets in the LATAM Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes:

• Monolingual and Bilingual Dictionary Data Featuring headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata.

• Sentences Curated examples of real-world usage with contextual annotations.

• Synonyms & Antonyms Lexical relations to support semantic search, paraphrasing, and language understanding.

• Audio Data Native speaker recordings for TTS and pronunciation modeling.

• Word Lists Frequency-ranked and thematically grouped lists.

Learn more about the datasets included in the data suite:

1. Portuguese Monolingual Dictionary Data
2. Portuguese Bilingual Dictionary Data
3. Spanish Monolingual Dictionary Data
4. Spanish Bilingual Dictionary Data
5. Spanish Sentences Data
6. Spanish Synonyms and Antonyms Data
7. Spanish Audio Data
8. Spanish Word List Data
9. American English Monolingual Dictionary Data
10. American English Synonyms and Antonyms Data
11. American English Pronunciations with Audio

Key Features (approximate numbers):

1. Portuguese Monolingual Dictionary Data

Our Portuguese monolingual covers both European and Latin American varieties, featuring clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Portuguese language.

• Words: 143,600
• Senses: 285,500
• Example sentences: 69,300
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Portuguese Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Portuguese and from Portuguese to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span both European and Latin American Portuguese varieties.

• Translations: 300,000
• Senses: 158,000
• Example translations: 117,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Monolingual Dictionary Data

Our Spanish monolingual reliably offers clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Spanish language.

• Words: 73,000
• Senses: 123,000
• Example sentences: 104,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Spanish and from Spanish to English. It is annually reviewed and updated by our in-house team of language experts. Offers significant coverage of the language, providing a large volume of translated words of excellent quality.

• Translations: 221,300
• Senses: 103,500
• Example sentences: 74,500
• Example translations: 83,800
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. Spanish Sentences Data

Spanish sentences retrieved from corpus are ideal for NLP model training, presenting approximately 20 million words. The sentences provide a great coverage of Spanish-speaking countries and are accordingly tagged to a particular country or dialect.

• Sentences volume: 1,840,000
• Format: XML and JSON formats
• Delivery: Email (link-based file sharing) and REST API

1. Spanish Synonyms and Antonyms Data

This Spanish language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for building linguistically aware AI systems and language technologies.

• Synonyms: 127,700
• Antonyms: 9,500
• Format: XML format
• Delivery: Email (link-based file sharing)
• Updated frequency: annually

1. Spanish Audio Data (word-level)

Curated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language.

• Audio files: 20,900
• Format: XLSX (for index), MP3 and WAV (audio files)

1. Spanish Word List Data

This language data contains a carefully curated and comprehensive list of 450,000 Spanish words.

• Wordforms: 450,000
• Format: CSV and TXT formats
• Delivery: Email (link-based file sharing)

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost au...

Summary:The Fire Event Data Suite, or FEDS, algorithm uses high resolution VIIRS observations to map fire perimeters, identify the active portion of fire fronts, and track the progression and attributes of individual fires every 12 hours. For individual fire events, FEDS contains information on the latest active fire detections, as well as the total fire event history in 12 hour increments.Suggested Usage:Perimeter data are helpful for understanding the time series progression of a fire event, as observed via the VIIRS sensor. Given the source data, the same considerations that must be taken for FIRMS active fire data are applicable here. All data are experimental and should always be verified with supplementary sources of information when available.Date of Next Image:Updates available at approximate 12-hour intervals.Satellite/Sensor:Suomi NPP and NOAA-20 satellites carrying the VIIRS sensor. The FEDS algorithm uses the locations and sizes of each pixel to derive perimeter information and track individual fire events.Resolution:375m at nadirCredits:NASA Earth Information System (EIS)Doug Morton, Melanie Follette-Cook, Elijah Orland, Tempest McCabe (all GSFC), Yang Chen (UC Irvine)Scientific PaperEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/chile_wildfires_202402/EIS_FEDS/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2024/chile_wildfires_202402/ChileNRT/

APAC Data Suite offers high-quality language datasets. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.

Discover our expertly curated language datasets in the APAC Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes:

• Monolingual and Bilingual Dictionary Data
  Featuring headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata.

• Semi-bilingual Dictionary Data Each entry features a headword with definitions and/or usage examples in Language 1, followed by a translation of the headword and/or definition in Language 2, enabling efficient cross-lingual mapping.

• Sentence Corpora
  Curated examples of real-world usage with contextual annotations for training and evaluation.

• Synonyms & Antonyms
  Lexical relations to support semantic search, paraphrasing, and language understanding.

• Audio Data
  Native speaker recordings for speech recognition, TTS, and pronunciation modeling.

• Word Lists
  Frequency-ranked and thematically grouped lists for vocabulary training and NLP tasks. The word list data can cover one language or two, such as Tamil words with English translations.

Each language may contain one or more types of language data. Depending on the dataset, we can provide these in formats such as XML, JSON, TXT, XLSX, CSV, WAV, MP3, and more. Delivery is currently available via email (link-based sharing) or REST API.

If you require more information about a specific dataset, please contact us Growth.OL@oup.com.

Below are the different types of datasets available for each language, along with their key features and approximate metrics. If you have any questions or require additional assistance, please don't hesitate to contact us.

1. Assamese Semi-bilingual Dictionary Data: 72,200 words | 83,700 senses | 83,800 translations.

2. Bengali Bilingual Dictionary Data: 161,400 translations | 71,600 senses.

3. Bengali Semi-bilingual Dictionary Data: 28,300 words | 37,700 senses | 62,300 translations.

4. British English Monolingual Dictionary Data: 146,000 words | 230,000 senses | 149,000 example sentences.

5. British English Synonyms and Antonyms Data: 600,000 synonyms | 22,000 antonyms.

6. British English Pronunciations with Audio: 250,000 transcriptions (IPA) | 180,000 audio files.

7. French Monolingual Dictionary Data: 42,000 words | 56,000 senses | 43,000 example sentences.

8. French Bilingual Dictionary Data: 380,000 translations | 199,000 senses | 146,000 example translations.

9. Gujarati Monolingual Dictionary Data: 91,800 words | 131,500 senses.

10. Gujarati Bilingual Dictionary Data: 171,800 translations | 158,200 senses.

11. Hindi Monolingual Dictionary Data: 46,200 words | 112,700 senses.

12. Hindi Bilingual Dictionary Data: 263,400 translations | 208,100 senses | 18,600 example translations.

13. Hindi Synonyms and Antonyms Dictionary Data: 478,100 synonyms | 18,800 antonyms.

14. Hindi Sentence Data: 216,000 sentences.

15. Hindi Audio data: 68,000 audio files.

16. Indonesian Bilingual Dictionary Data: 36,000 translations | 23,700 senses | 12,700 example translations.

17. Indonesian Monolingual Dictionary Data: 120,000 words | 140,000 senses | 30,000 example sentences.

    1. Korean Monolingual Dictionary Data: 596,100 words | 386,600 senses | 91,700 example sentences.

18. Korean Bilingual Dictionary Data: 952,500 translations | 449,700 senses | 227,800 example translations.

19. Mandarin Chinese (simplified) Monolingual Dictionary Data: 81,300 words | 162,400 senses | 80,700 example sentences.

20. Mandarin Chinese (traditional) Monolingual Dictionary Data: 60,100 words | 144,700 senses | 29,900 example sentences.

21. Mandarin Chinese (simplified) Bilingual Dictionary Data: 367,600 translations | 204,500 senses | 150,900 example translations.

22. Mandarin Chinese (traditional) Bilingual Dictionary Data: 215,600 translations | 202,800 senses | 149,700 example translations.

23. Mandarin Chinese (simplified) Synonyms and Antonyms Data: 3,800 synonyms | 3,180 antonyms.

24. Malay Bilingual Dictionary Data: 106,100 translations | 53,500 senses.

25. Malay Monolingual Dictionary Data: 39,800 words | 40,600 senses | 21,100 example sentences.

26. Malayalam Monolingual Dictionary Data: 91,300 words | 159,200 senses.

27. Malayalam Bilingual Word List Data: 76,200 translation pairs.

28. Marathi Bilingual Dictionary Data: 45,400 translations | 32,800 senses | 3,600 example translations.

29. Nepali Bilingual Dictionary Data: 350,000 translations | 264,200 senses | 1,300 example translations.

30. New Zealand English Monolingual Dictionary Data: 100,000 words

31. Odia Semi-bilingual Dictionary Data: 30,700 words | 69,300 senses | 69,200 translations.

32. Punjabi ...

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The GOES-Observed Fire Event Representation (GOFER) algorithm uses geostationary satellite observations of active fires from GOES-East and GOES-West to map the hourly progression of large wildfires (over 50,000 acres or 202 sq. km). GOES observes North and South America with a spatial resolution of 2 km at the equator and at a frequency of 10-15 minutes for the full disk view. Along with the fire perimeter, we derive the active fire lines and fire spread rates. We tested the GOFER algorithm on a set of 28 wildfires in California from 2019-2021 and produced three versions of the product: GOFER-Combined, GOFER-East, and GOFER-West. GOFER-Combined uses both GOES-East and GOES-West observations, while GOFER-East and GOFER-West use only GOES-East and only GOES-West observations, respectively. We find that GOFER performs reasonably well compared to final perimeters from California's Fire and Resource Assessment Program (FRAP) and 12-hourly perimeters from the Fire Event Data Suite (FEDS), derived from 375-m active fire observations. See our GOFER Visualization app on Earth Engine Apps for an overview of the dataset, alongside other datasets, such as FEDS and FRAP perimeters and 30-m burn severity from Monitoring Trends in Burn Severity (MTBS). Please refer to the corresponding GitHub repository for the code and detailed dataset description.

Summary: The Fire Event Data Suite, or FEDS, algorithm uses high resolution VIIRS observations to map fire perimeters, identify the active portion of fire fronts, and track the progression and attributes of individual fires every 12 hours. For individual fire events, FEDS contains information on the latest active fire detections, as well as the total fire event history in 12 hour increments. Suggested Usage: Perimeter data are helpful for understanding the time series progression of a fire event, as observed via the VIIRS sensor. Given the source data, the same considerations that must be taken for FIRMS active fire data are applicable here. All data are experimental and should always be verified with supplementary sources of information when available. Date of Next Image:Updates available at approximate 12-hour intervals.Satellite/Sensor:Suomi NPP and NOAA-20 satellites carrying the VIIRS sensor. The FEDS algorithm uses the locations and sizes of each pixel to derive perimeter information and track individual fire events. Resolution:375m at nadirCredits: NASA Earth Information System (EIS) Doug Morton, Melanie Follette-Cook, Elijah Orland, Tempest McCabe (all GSFC), Yang Chen (UC Irvine) Scientific PaperEsri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/hawaii_wildfires_august2023/EIS_FEDS_Observations/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2023/hawaii_wildfires_202308/EIS_FEDS_Observations/

EMEA Data Suite offers 43 high-quality language datasets covering 23 languages spoken in the region. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.

Discover our expertly curated language datasets in the EMEA Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes:

• Monolingual and Bilingual Dictionary Data Featuring headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata.

• Sentence Corpora Curated examples of real-world usage with contextual annotations for training and evaluation.

• Synonyms & Antonyms Lexical relations to support semantic search, paraphrasing, and language understanding.

• Audio Data Native speaker recordings for speech recognition, TTS, and pronunciation modeling.

• Word Lists Frequency-ranked and thematically grouped lists for vocabulary training and NLP tasks.

Each language may contain one or more types of language data. Depending on the dataset, we can provide these in formats such as XML, JSON, TXT, XLSX, CSV, WAV, MP3, and more. Delivery is currently available via email (link-based sharing) or REST API.

If you require more information about a specific dataset, please contact us Growth.OL@oup.com.

Below are the different types of datasets available for each language, along with their key features and approximate metrics. If you have any questions or require additional assistance, please don't hesitate to contact us.

1. Arabic Monolingual Dictionary Data: 66,500 words | 98,700 senses | 70,000 example sentences.

2. Arabic Bilingual Dictionary Data: 116,600 translations | 88,300 senses | 74,700 example translations.

3. Arabic Synonyms and Antonyms Data: 55,100 synonyms.

4. British English Monolingual Dictionary Data: 146,000 words | 230,000 senses | 149,000 example sentences.

5. British English Synonyms and Antonyms Data: 600,000 synonyms | 22,000 antonyms

6. British English Pronunciations with Audio: 250,000 transcriptions (IPA) |180,000 audio files.

7. Catalan Monolingual Dictionary Data: 29,800 words | 47,400 senses | 25,600 example sentences.

8. Catalan Bilingual Dictionary Data: 76,800 translations | 109,350 senses | 26,900 example translations.

9. Croatian Monolingual Dictionary Data: 129,600 words | 164,760 senses | 34,630 example sentences.

10. Croatian Bilingual Dictionary Data: 100,700 translations | 91,600 senses | 10,180 example translations.

11. Czech Bilingual Dictionary Data: 426,473 translations | 199,800 senses | 95,000 example translations.

12. Danish Bilingual Dictionary Data: 129,000 translations | 91,500 senses | 23,000 example translations.

13. French Monolingual Dictionary Data: 42,000 words | 56,000 senses | 43,000 example sentences.

14. French Bilingual Dictionary Data: 380,000 translations | 199,000 senses | 146,000 example translations.

15. German Monolingual Dictionary Data: 85,500 words | 78,000 senses | 55,000 example sentences.

16. German Bilingual Dictionary Data: 393,000 translations | 207,500 senses | 129,500 example translations.

17. German Word List Data: 338,000 wordforms.

18. Greek Monolingual Dictionary Data: 47,800 translations | 46,309 senses | 2,388 example sentences.

19. Hebrew Monolingual Dictionary Data: 85,600 words | 104,100 senses | 94,000 example sentences.

20. Hebrew Bilingual Dictionary Data: 67,000 translations | 49,000 senses | 19,500 example translations.

21. Hungarian Monolingual Dictionary Data: 90,500 words | 155,300 senses | 42,500 example sentences.

22. Italian Monolingual Dictionary Data: 102,500 words | 231,580 senses | 48,200 example sentences.

23. Italian Bilingual Dictionary Data: 492,000 translations | 251,600 senses | 157,100 example translations.

24. Italian Synonyms and Antonyms Data: 197,000 synonyms | 62,000 antonyms.

25. Latvian Monolingual Dictionary Data: 36,000 words | 43,600 senses | 73,600 example sentences.

26. Polish Bilingual Dictionary Data: 287,400 translations | 216,900 senses | 19,800 example translations.

27. Portuguese Monolingual Dictionary Data: 143,600 words | 285,500 senses | 69,300 example sentences.

28. Portuguese Bilingual Dictionary Data: 300,000 translations | 158,000 senses | 117,800 example translations.

29. Portuguese Synonyms and Antonyms Data: 196,000 synonyms | 90,000 antonyms.

30. Romanian Monolingual Dictionary Data: 66,900 words | 113,500 senses | 2,700 example sentences.

31. Romanian Bilingual Dictionary Data: 77,500 translations | 63,870 senses | 33,730 example translations.

32. Russian Monolingual Dictionary Data: 65,950 words | 57,500 senses | 51,900 example sentences.

33. Russian Bilingual Dictionary Data: 230,100 translations | 122,200 senses | 69,600 example translations.

34. Slovak Bilingual Dictionary Dat...

Here is a dataset containing informations relative to the EURUSD pair (date/time/open/high/low/close/tick volume) on the 15m timeframe. The dataframe begins at the year 2003 until 2024 and it has been extracted by using the software Tick data suite.

This tabular data set contains information on historic and projected land-use/land-cover, compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for select regions of the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2.1 data suite by the unique identifier COMID. The source data is from the Modeled historic and projected land use and land cover for the conterminous United States produced by Terry Sohl and others (2014, 2018). The data provided here contains information for the years, 1980 through 2100, compiled as described above. The units are in percentages. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values were computed using the xstrm python software package (Wieferich and others, 2021).

The digital '1:2 500 000 geological map of Western Australia' comprises four layers: the primary 1:2 500 000 State interpreted bedrock geology, the 1:2 500 000 State Cenozoic geology, the 1:2 500 000 State interpreted dyke suites, and the 1:2 500 000 State interpreted bedrock geology structural lines. The 1:2 500 000 interpreted geology contains geoscience data covering themes such as geology and tectonic boundaries that have been digitally rolled up from 1:500 000 data sources for optimal display at 1:2 500 000 scale. The Cenozoic geology layer can be superimposed over the bedrock layer to allow the extension of older bedrock geology under Cenozoic cover where geological and geophysical information permits, and to depict Cenozoic paleovalleys and additional significant Cenozoic cover. The dyke suites layer is primarily interpreted from aeromagnetic data, using mapped dyke extents as a guide. Individual dykes are assigned to named dyke suites wherever possible. The attribution of linear structures and dykes as concealed or exposed takes into account both the interpreted bedrock and the Cenozoic geology layers and does not reflect regolith cover. Detailed fault attribution has been simplified from the 1:500 000 scale, except in the case of major fault systems or tectonic boundaries. The nomenclature and hierarchy of lithostratigraphic units is based on the GSWA weekly updates from the Explanatory Notes System (ENS). In order to provide the most up-to-date geological information for the units in question, attribution from the original source is modified to remove superseded stratigraphic units, display correct stratigraphic relationships and include more recent geochronology. Stratigraphic units are mostly displayed at greater than or equal to Group or Suite level; Subgroups and Formations are only displayed where areal extent warrants it and the unit does not have a higher order parent.

The Saskatchewan Research Council (SRC) collected surface meteorological and radiation data from December, 1993 until Decemb er 1996. The data set is comprised of the Suite A (meteorological and energy balance measurements) and Suite B (diffuse sol ar and longwave measurements) components. Suite A measurements were taken at each of ten sites and suite B measurements were made at five of the suite A sites. These data cover an area of roughly 1000 km by 1000 km (a large portion of northern Man itoba and northern Saskatchewan). The measurement network was designed to provide researchers with a sufficient record of n ear-surface meteorological and radiation measurements.

The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data... The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data transformed from single element values (obtained from whole rock ppm/ppb conversion) normalised to times average crustal abundance. The maximum silver value from each drill hole has then been selected and displayed on SARIG.

This dataset consists of historical estimates and future projections of land use and climate data summarized within the 1:100,000 National Hydrography Dataset Version 2 (NHDPlusV2) framework for catchments and upstream accumulated watersheds. Historical land use data are for the year 2005 and future land use projections are for the years 2030, 2060, and 2090. The projections offer a unique combination of thematic detail (17 land-use and land-cover classes). Historical climate estimates are averaged over the time period 1980-1999 and future climate projections are averaged over 20-year periods centered around the years 2030, 2060, and 2090. Climate data include seasonal measures of average air temperature (℃) and total precipitation (mm). The data set also includes bioregions as defined within the Chesapeake basin-wide index of biotic integrity for stream macroinvertebrates (Chessie BIBI). The COMID field (analogous to FEATUREID) can be used to link these data to the NHDPlus data suite. These data were used to develop models forecasting future stream conditions within the Chesapeake Bay Watershed under an array of potential future land use and climate scenarios. Results from this work can be used by researchers and managers to identify areas most suitable for conservation, mitigation, or restoration efforts while considering the non-static nature of the environment.

This data set represents monthly precipitation (in millimeters), and minimum and maximum temperature (in degrees Fahrenheit) over the period 1980-2023 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusV2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlusV2 by the unique identifier COMID. Reach catchment information characterizes data at the local scale. Reach catchments are accumulated upstream through the river network using a modified routing database (Schwarz and Wieczorek, 2018) to navigate the NHDPlusV2 reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. The variables included are precipitation (gridMet_CAT_pr and gridMet_TOT_pr), minimum temperature (gridMet_CAT_tmmn and gridMet_TOT_tmmn), and maximum temperature (gridMet_CAT_tmmx and gridMet_TOT_tmmx) summarized from gridMET (Abatzoglou, 2011). Summaries are provided for five regions corresponding to NHDPlusV2 vector processing units (VPUs): VPU 02, VPU 03w, VPU 04, VPU 14, and VPU 17.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Use classes for each year. See additional information about Land Use in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS.

Dataset

This dataset was created by imene mana

Contents

The market for Database Management Suite is expected to expand significantly, reaching a market size of XXX million by 2033, growing at a CAGR of XX%. The market is primarily driven by the increasing data generation and the need for efficient data management and analytics. The adoption of cloud-based database management solutions is a key trend shaping the market, as it offers cost-effective and scalable solutions for businesses of all sizes. The market is segmented into types, applications, and regions. By type, the market is divided into relational databases and nonrelational databases. Relational databases are traditional databases that store data in tables, while nonrelational databases (such as NoSQL databases) store data in a more flexible format. By application, the market is segmented into small and medium-sized enterprises (SMEs) and large enterprises. SMEs are increasingly adopting database management solutions to improve their data management capabilities, while large enterprises continue to be major users of database management systems. Geographically, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is the largest market, followed by Europe and Asia Pacific.

Journal Article Tag Suite (JATS) is an application of NISO Z39.96.2019, which defines a set of XML elements and attributes for describing the textual and graphical content of journal articles and describes three article models.

Explore the dynamic Database Management Suite market, projected to reach $75 billion by 2025 with an 11.5% CAGR. Discover key drivers, emerging trends, and regional insights for data management solutions.