Data sets and related data products and services provided by SEDAC managed by the NASA Earth Science Data and Information System (ESDIS) project. SEDAC is one of the Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), part of the ESDIS project.

About SEDAC. http://sedac.ciesin.columbia.edu/about, Retrieved 27 Oct 2020.

The SWOT Level 2 Lake Single-Pass Vector Prior Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of lakes identified in the PLD.

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SOLVE1_Satellite_Data is the supplementary satellite data for the SAGE III Ozone Loss and Validation Experiment (SOLVE). Data were collected by instruments such as the Halogen Occultation Experiment (HALOE), the Polar Ozone and Aerosol Measurement III (POAM III) satellite, and the Global Ozone Monitoring Experiment (GOME). Data collection for this product is complete.The SOLVE campaign was a NASA multi-program effort of the Upper Atmosphere Research Program (UARP), Atmospheric Effects of Aviation Project (AEAP), Atmospheric Chemistry Modeling and Analysis Program (ACMAP) and Earth Observing System (EOS) of NASA’s Earth Science Enterprise (ESE). SOLVE’s primary objective was for calibrating and validating the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements, while examining the processes that controlled ozone levels at a mid- to high-latitude range. The major goal of SAGE III was to quantitatively assess ozone loss at high latitudes. SOLVE was a two-phase experiment, the first phase, SOLVE, occurred during the fall of 1999 through the spring of 2000. The second phase, SOLVE II, occurred during the winter of 2003.SOLVE took place in the Arctic high-latitude region during the winter. The polar ozone depletion processes cause by human-produced chlorine and bromine are most active in mid-to-late winter and early spring in the high Arctic. In order to conduct this validation experiment, NASA deployed the NASA ER-2 aircraft and NASA DC-8 aircraft. The ER-2 measured a variety of atmospheric data, including ozone (O3), H2O, CO2, ClONO2, HCl, ClO/BrO, and Cl2O2. The DC-8 aircraft measured ozone, ClO/BrO, and aerosol, among other atmospheric data. SOLVE also utilized balloon platforms, ground-based instruments, and collaborations with the German Aerospace Center’s (DLR) FALCON aircraft equipped with the OLEX Lidar to achieve the mission objectives. Overall, the campaign had 28 flights, with SOLVE featuring 17 total flights among the different aircrafts and SOLVE II featuring 11 flights.

IDEA Section 618 Data Products: Static Tables Part B Discipline Table 3 Number of children and students ages 3 through 21 served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state by type of disability.

This is a data product to support state indicators that are based from groundfish biological data, derived using primary data from surveys undertaken in the Northeast Atlantic between 1983 and 2020. Catch records by taxonomic group and by length category in terms of biomass and numbers of fish standardised to duration (per hour) or to the area swept by the haul. Data are available from multiple surveys using data downloaded from the ICES database of trawl surveys (DATRAS) once quality-controlled and standardised following procedures detailed in Greenstreet and Moriarty 2017. Data file names reflect the OSPAR region sampled, country conducting the sampling, fishing gear and time of years of sampling (as defined by Greenstreet and Moriarty 2017), e.g.: BBICFraBT4 refers to Bay of Biscay and Iberian Coast data from France by a Beam Trawl survey in quarter 4 of the year and GNSIntOT3 refers to Greater North Sea data from International (multiple countries) sampling by an Otter Trawl survey in quarter 3 of the year etc. Greenstreet, S.P.R. and Moriarty, M. (2017) OSPAR Interim Assessment 2107 Fish Indicator Data Manual (Relating to Version 2 of the Groundfish Survey Monitoring and Assessment Data Product). Scottish Marine and Freshwater Science Vol 8 No 17, 83pp. DOI: 10.7489/1985-1 Scientific survey data collected by multiple countries and made available through ICES DATRAS (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx). Swept-area estimates were generated by ICES 2021ab (ICES. 2021a. Workshop on the production of swept-area estimates for all hauls in DATRAS for biodiversity assessments (WKSAE-DATRAS). ICES Scientific Reports. 3:74. https://doi.org/10.17895/ices.pub.8232; ICES. 2021b. Workshop on the production of abundance estimates for sensitive species (WKABSENS); ICES Scientific Reports. 3:96. https://doi.org/10.17895/ices.pub.8299). ICES Data Centre host the database of trawl surveys (DATRAS) for groundfish and beam trawl data. DATRAS has an integrated quality check utility. All data, before entering the database, have to pass an extensive quality check. Despite this errors and missing data arise, which are subsequently dealt with by the data submitters from the contributing countries as required. However, this screening process was implemented in 2009 for data from 2004 onwards. Since some survey time-series extend back to the 1960s, historic data (unless re-evaluated and re-submitted by contributing countries) may not have been subject to the same level of quality control as these more recent data. Furthermore, the type of information collected, the level of detail and resolution in the data, has gradually evolved over time. In order to derive a single format, quality assured monitoring programme data product covering the entire Northeast Atlantic region inconsistencies in the datasets required resolution. These corrections are detailed in ICES 2021a,b: Biological data for trawl surveys are downloaded directly from DATRAS in raw exchange format (known as “HL data”). Ancillary data were processed by ICES 2021a,b to create the “SweptAreaAssessmentOutput” (which replaces the “HH data”) and these were downloaded from the same location: https://datras.ices.dk/Data _ products/Download/Download _ Data _ public.aspx Data are processed to create a standalone data product to be used for indicator assessments of fish and elasmobranchs. Initially, hauls are subset to determine the Standard Monitoring Programme (i.e. excluding invalid hauls including those of duration shorter than 13 minutes or longer than 66 minutes, following Greenstreet and Moriarty 2017) and these hauls are used to define the Standard Survey Area by excluding areas sampled infrequently over time). Biological data were accepted with ICES SpecVal of 1, 4, 7, 10 (see http://vocab.ices.dk/ for further information on SpecVal categories). Additional QA/QC is made at this step to determine if species identification issues are present in the raw biological data and these were discussed and agreed with the Chief Scientist for each survey.

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

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.

KORUSAQ_Model_Data features ancillary model data products for the KORUS-AQ field campaign. This product features output from the WRF model, CAM-chem, model inter-comparisons, and GEOS-chem models. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.

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.

This document is adapted from the iUTAH Research Data Policy (Horsburgh and Jones, 2017), which was adopted by a large group of collaborative researchers in Utah working on an NSF-funded research project. It is offered here for potential general use in defining research products and data sharing workflows. This document is intended to be used as an example from which specific data policies, timing, and best practices for data sharing can be defined and adopted for research projects like the Critical Zone Collaborative Network Thematic Cluster projects. Revised by Clara Cogswell, Shannon Syrstad, Jeff Horsburgh 2/17/2022

Satellite-derived data products for the Coral Triangle Region (funded through USAID).

148MM+ highly curated US B2B Contact Data records and associated company information ( based on where the contact works now).

Fields Include:

• Contact Name (First & Last)
• Contact Work Address
• Contact Work Email
• Contact Personal Email
• Contact Mobile Phone
• Contact Direct-Dial Work Phone
• Contact Job Title
• Contact Job Function
• Contact Job Level
• Contact LinkedIn URL
• Company Name
• Company Domain
• Company Email Domain
• Company HQ Address
• Company Employee Size
• Company Revenue Size
• Industry
• NAICS and SIC Codes + Descriptions

Leverage this B2B Contact data for Sales Prospecting, Lead Generation, Marketing, Recruiting, Identity Resolution, Analytics, Research, Etc.

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• Purchase targeted lists of contacts based on your specific criteria
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• License our entire database ( or subset of contacts) if you are planning to leverage this data within your products / platform / service offerings.

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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.

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.

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 RSS SSM/I Oceean Product Grids 3-Day Average from DMSP F8 netCDF dataset is part of the collection of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products produced as part of NASA's MEaSUREs Program. Remote Sensing Systems generates SSM/I and SSMIS binary data products using a unified, physically based algorithm to simultaneously retrieve ocean wind speed, water vapor, cloud water, and rain rate. The SSMIS data have been carefullyintercalibrated to the brightness temperature level of the previous SSM/I and therefore extend this important time series of ocean winds, vapor, cloud and rain values. This algorithm is a product of 20 years of refinements, improvements, and verifications. The Global Hydrology Resource Center has reformatted the binary data into a netCDF data product for each temporal group for each satellite. The netCDF SSMI/SSMIS collection will be available for F8 for 3-day average.

US B2B Contact Database | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON Elevate your sales and marketing efforts with America's most comprehensive B2B contact data, featuring over 200M+ verified records of decision-makers, from CEOs to managers, across all industries. Powered by AI and refreshed bi-weekly, this dataset ensures you have access to the freshest, most accurate contact details available for effective outreach and engagement.

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US Business Contacts | B2B Email Database | Sales Leads | CRM Enrichment | Verified Phone Numbers | ABM Data | CEO Contact Data | US B2B Leads | US prospects data

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.

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.