This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Inflation Rate in the United States increased to 2.90 percent in August from 2.70 percent in July of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This data is intended for reporting purposes only. Please visit SummerFood.org or text FOOD to 304-304 to find an open summer meal sites near you. Help us provide the most useful data by completing our ODP User Feedback Survey for Summer Meal Programs Data About the Dataset This dataset contains site-level meal counts from approved TDA claims for Seamless Summer Option (SSO) and Summer Food Service Program (SFSP) in 2025. Summer meal programs typically operate mid-May through August. Any meals reported in non-summer months have been approved due to extenuating circumstances that have disrupted normal school meal service. Participants have 60 days to submit claims to TDA. Meal count information for individual summer meal programs can be found as filtered views of this dataset on our TDA Data Overview - Summer Meals Programs page. Meal reimbursement data is collected at the sponsor/CE level and is reported in the "Summer Meal Programs - Seamless Summer Option (SSO) - Meal Reimbursements" and “Summer Meal Programs – Summer Food Service Program (SFSP) – Meal Reimbursements” datasets found on the Summer Meal Program Data Overview page. An overview of all Summer Meal Program data available on the Texas Open Data Portal can be found at our TDA Data Overview - Summer Meals Programs page. An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page. About Dataset Updates TDA aims to post new program year data by July 15 of the active program period. Participants have 60 days to submit claims. Data updates will occur daily and end 90 days after the close of the program year. Any data posted during the active program year is subject to change. About the Agency The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs, the Child and Adult Care Food Programs (CACFP), and summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles. For more information on these programs, please visit our website.

About this dataset

Context

The dataset tabulates the population of John Day by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for John Day. The dataset can be utilized to understand the population distribution of John Day by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in John Day. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for John Day.

Key observations

Largest age group (population): Male # 60-64 years (85) | Female # 10-14 years (102). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the John Day population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the John Day is shown in the following column.
• Population (Female): The female population in the John Day is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in John Day for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for John Day Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of John Day by race. It includes the population of John Day across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of John Day across relevant racial categories.

Key observations

The percent distribution of John Day population by race (across all racial categories recognized by the U.S. Census Bureau): 87.49% are white, 0.39% are Black or African American, 2.98% are Asian and 9.14% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the John Day
• Population: The population of the racial category (excluding ethnicity) in the John Day is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of John Day total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for John Day Population by Race & Ethnicity. You can refer the same here

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

About this dataset

Context

The dataset tabulates the population of Day town by race. It includes the population of Day town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Day town across relevant racial categories.

Key observations

The percent distribution of Day town population by race (across all racial categories recognized by the U.S. Census Bureau): 98.12% are white, 0.08% are Black or African American and 1.80% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Day town
• Population: The population of the racial category (excluding ethnicity) in the Day town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Day town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Day town Population by Race & Ethnicity. You can refer the same here

Help us provide the most useful data by completing our ODP User Feedback Survey for School Nutrition Data About the Dataset This dataset contains claims information for paid or approved to pay meal reimbursement for sites participating in school nutrition programs for program year 2024-2025. An overview of all SNP data available on the Texas Open Data Portal can be found at our TDA Data Overview - School Nutrition Programs page. An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page. More information about accessing and working with TDA data on the Texas Open Data Portal can be found on the SquareMeals.org website on the TDA Food and Nutrition Open Data page. About Dataset Updates TDA aims to post new program year data by September 1 of the active program year. Updates will occur daily during the active program year and end 90 days after the close of the program year. Any data posted during the active program year is subject to change. About the Agency The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs, the Child and Adult Care Food Program (CACFP), and summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles. For more information on these programs, please visit us at SquareMeals.org.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. In March 2015, new Alaska data was included in the nClimDiv dataset. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS. It includes maximum temperature, minimum temperature, average temperature and precipitation. In January 2025, the National Centers for Environmental Information (NCEI) began summarizing the State of the Climate for Hawaii. This was made possible through a collaboration between NCEI and the University of Hawaii/Hawaii Climate Data Portal and completes a long-standing gap in NCEI's ability to characterize the State of the Climate for all 50 states. NCEI maintains monthly statewide, divisional, and gridded average temperature, maximum temperatures (highs), minimum temperature (lows) and precipitation data for Hawaii over the period 1991-2025.

Tourist Arrivals in the United States increased to 6275257 in July from 5278944 in June of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data is intended for reporting purposes only. Please visit SummerFood.org to find an open summer meal site near you or dial 2-1-1 to speak to an operator regarding food assistance. Help us provide the most useful data by completing our ODP User Feedback Survey for Summer Meal Programs Data About the Dataset This dataset contains contact and program participation information for summer meal sites approved by TDA to operate summer 2025. Changes in participant information can occur through August. Unless otherwise noted, summer meal programs operate mid-May through August. Under approved circumstances, summer meal programs may be used during the school year to prevent interruption in school meal service. An overview of all summer meal program data available on the Texas Open Data Portal can be found at our TDA Data Overview - Summer Meal Programs page. An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page. More information about accessing and working with TDA data on the Texas Open Data Portal can be found on the SquareMeals.org website on the TDA Food and Nutrition Open Data page. About Dataset Updates TDA aims to post new program participation data by May 1 of the active program year. Data updates will occur daily and end 60 days after then close of the program year. Any data posted during the active program year is subject to change. About the Agency The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs the Child and Adult Care Food Program (CACFP), and summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles. For more information on these programs, please visit us at SquareMeals.org.

During a 2024 survey, 77 percent of respondents from Nigeria stated that they used social media as a source of news. In comparison, just 23 percent of Japanese respondents said the same. Large portions of social media users around the world admit that they do not trust social platforms either as media sources or as a way to get news, and yet they continue to access such networks on a daily basis.

              Social media: trust and consumption

              Despite the majority of adults surveyed in each country reporting that they used social networks to keep up to date with news and current affairs, a 2018 study showed that social media is the least trusted news source in the world. Less than 35 percent of adults in Europe considered social networks to be trustworthy in this respect, yet more than 50 percent of adults in Portugal, Poland, Romania, Hungary, Bulgaria, Slovakia and Croatia said that they got their news on social media.

              What is clear is that we live in an era where social media is such an enormous part of daily life that consumers will still use it in spite of their doubts or reservations. Concerns about fake news and propaganda on social media have not stopped billions of users accessing their favorite networks on a daily basis.
              Most Millennials in the United States use social media for news every day, and younger consumers in European countries are much more likely to use social networks for national political news than their older peers.
              Like it or not, reading news on social is fast becoming the norm for younger generations, and this form of news consumption will likely increase further regardless of whether consumers fully trust their chosen network or not.

Help us provide the most useful data by completing our ODP User Feedback Survey for Child and Adult Care Food Program (CACFP) Data About the Dataset This data set contains claims information for meal reimbursement for CACFP participating as Adult Day Care centers for program year 2024 - 2025. The CACFP program year begins October 1 and ends September 30. This dataset only includes claims submitted by CACFP sites participating as Adult Day Care centers. Sites can participate in multiple CACFP sub-programs. For reimbursement data on CACFP participants operating as Day Care Homes, Child Care Centers, At-Risk Child Care Centers, Head Start Centers, emergency shelters, or centers providing care for students outside school hours, please refer to the corresponding “Child and Adult Care Food Programs (CACFP) – Meal Reimbursement” dataset for that sub-program available on the State of Texas Open Data Portal. An overview of all CACFP data available on the Texas Open Data Portal can be found at our TDA Data Overview - Child and Adult Care Food Programs page. An overview of all TDA Food and Nutrition data available on the Texas Open Data Portal can be found at our TDA Data Overview - Food and Nutrition Open Data page. More information about accessing and working with TDA data on the Texas Open Data Portal can be found on the SquareMeals.org website on the TDA Food and Nutrition Open Data page. About Dataset Updates TDA aims to post new program year data by December 15 of the active program year. Participants have 60 days to file monthly reimbursement claims. Dataset updates will occur daily until 90 days after the close of the program year. After 90 days from the close of the program year, the dataset will be updated at six months and one year from the close of program year before becoming archived. Archived datasets will remain published but will not be updated. Any data posted during the active program year is subject to change. About the Agency The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs, the Child and Adult Care Food Program (CACFP), and summer meal programs. TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — Feeding the Hungry and Promoting Healthy Lifestyles. For more information on these programs, please visit our website.

Daily Level-2 Aqua MODIS ocean products (Ocean Color, Inherent Optical Properties, and Sea Surface Temperature) collected over the Gulf of America on 27 May 2025. Each granule is provided in NetCDF format at 1 km spatial resolution. Data are processed by NASA’s Ocean Biology Processing Group (OBPG) and distributed via the Ocean Biology Distributed Active Archive Center (OB.DAAC). These swath-level products support research on ocean color, optical properties, and surface temperature variability in the region.