CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. This gridded CLIGEN parameterization complements existing coverage for South America and Africa by adding new coverage for Central America, the Caribbean, the Middle East, South Asia, Southeast Asia, Australia, New Zealand, and various islands. This parameterization used the methodology and trained machine learning models discussed in a dataset article by Fullhart et al. (2022), https://doi.org/10.1080/20964471.2022.2136610. The primary dataset for South America and Africa may also be found in Ag Data Commons at https://doi.org/10.15482/USDA.ADC/1524754.The data are formatted as CLIGEN .par files, which are the only required input for CLIGEN. The files are contained in the "Grid Files" download with n=37105 files. The files are labeled according to grid point lat/lon coordinates (WGS84) in decimal degrees. The labeling convention uses 'N' and 'E' (north, east) to represent coordinates with a positive sign and 'S' and 'W' (south, west) to represent coordinates with a negative sign.Resources in this dataset:Resource Title: Grid Files.File Name: Grid Files.zipResource Description: CLIGEN input files (.par)Resource Title: Summary Table.File Name: SummaryTable.docxResource Description: Summary table that lists CLIGEN parameters and basic dataset characteristics of the gridded parameterization.Resource Title: Map Layer.File Name: Map Layer.kmzResource Description: Map layer showing point locations of the CLIGEN grid.

This dataset lists the global monthly-spatially aggregated catch of tuna and tuna-like species (i.e. billfish,bonitos,and mackerel) from 1950-01-01 to 2019-12-31.

This dataset was computed using public domain georeferenced catch-and-effort datasets released by the five tuna Regional Fisheries Management Organizations:the Commission for the Conservation of Southern Bluefin Tuna (CCSBT),the Inter-American Tropical Tuna Commission (IATTC),the International Commission for the Conservation of Atlantic Tunas (ICCAT),the Indian Ocean Tuna Commission (IOTC) and the Western and Central Pacific Fisheries Commission (WCPFC). Species-specific catches expressed in weight or number are stratified by year,month,reporting / fishing fleet,fishing gear,fishing mode (i.e. type of school association) and area (statistical squares of 1° or 5° longitude and latitude). "FIRMS level 0" identifies the processes applied to the primary datasets by the FIsheries and Resources Monitoring System (FIRMS) to generate the dataset. t-RFMO specific descriptions of the original input data sets can be found at the following links:

- CCSBT:https://www.ccsbt.org/en/content/sbt-data

- IATTC:https://www.iattc.org/PublicDomainData/IATTC-Catch-by-species1.htm

- ICCAT:https://www.iccat.int/en/accesingdb.html

- IOTC:https://iotc.org/data/datasets/latest/CEAll

- WCPFC:https://www.wcpfc.int/public-domain

The processes applied to produce this FIRMS level 0 dataset at global scale consist of a series of steps:Original catch-and-effort data are disseminated in such a way that redundancy may exist between the various datasets released,or that dimensions may be split over the datasets for some strata. To cope with these issues and collate a unique and (possibly) complete value of catch per stratum (i.e. with all the available dimensions),the original datasets had to be merged and post-processed by removing the duplicated strata or reassembling those strata with all available dimensions split over multiple datasets;Removal of WCPFC data within the overlapping zone between the IATTC and the WCPFC areas of competence,as information on reporting / fishing fleet is available from IATTC only;Removal of all Southern Bluefin Tuna data provided by t-RFMOs other than the Commission for the Conservation of Southern Bluefin Tuna (CCSBT),which is considered the only authoritative source of information for the species;Mapping of the original code lists (t-RFMO specific) to standard FAO / CWP code lists (e.g.,for gears and species) or to ad-hoc classifications as in the case of reporting / fishing fleets. These mappings have been done in collaboration with the t-RFMOs Secretariats and might be subject to future revisions.More details on the processes are provided in the lineage section.

Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to "virtual" Earth exploration. Currently, the mission's homepage "http://www.jpl.nasa.gov/srtm" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at "http://www2.jpl.nasa.gov/srtm/dataprod.htm" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, "http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated: Radar Image Radar Image with Color as Height Radar Image with Color Wrapped Fringes -Shaded Relief Perspective View with B/W Radar Image Overlaid Perspective View with Radar Image Overlaid, Color as Height Perspective View of Shaded Relief Perspective View with Landsat or other Image Overlaid Contour Map - B/W with Contour Lines Stereo Pair Anaglypgh The SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.

Days-of-Sales-Outstanding Time Series for American Express Company. American Express Company, together with its subsidiaries, operates as integrated payments company in the United States, Europe, the Middle East and Africa, the Asia Pacific, Australia, New Zealand, Latin America, Canada, the Caribbean, and Internationally. It operates through four segments: U.S. Consumer Services, Commercial Services, International Card Services, and Global Merchant and Network Services. The company's products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in 1850 and is based in New York, New York.

Macrostrat.org is a live database project collecting geochemical, geospatial, and geochronological data associated with specific rock units within stratigraphic columns with a geographic footprint. The data in Macrostrat is an aggregate of previously reported measurements in the literature. The database is constantly updated, expanded, and improved. Hence, data are archived (5-2-23) in this file for posterity in support of the manuscript titled "Evolution of the crustal phosphorus reservoir" (Walton et al., 2023). In all measurements in the data file: ages are in millions of years (Ma) and elemental compositions are in wt%. Further details of all definitions and standards in Macrostrat data reporting are permanently available at https://macrostrat.org/api/defs. Macrostrat data are useful for weighting geochemical data by the relative areal and volume abundance of the rock units from which they derive, helping to address questions of (over/under) sampling-induced bias. Data in these files represent direct exports from Macrostrat.org via the API root, supplemented with data from Reinhard et al (2017). Unit areal extents for data from Reinhard et al 2017) are approximated with the relevant Eon average from Macrostrat.org. Macrostrat.org is maintained by Shanan Dr Peters, Dr Daven Quinn, and the hard work of many others (https://macrostrat.org/#people).

Net-Profit-Margin Time Series for American Express Company. American Express Company, together with its subsidiaries, operates as integrated payments company in the United States, Europe, the Middle East and Africa, the Asia Pacific, Australia, New Zealand, Latin America, Canada, the Caribbean, and Internationally. It operates through four segments: U.S. Consumer Services, Commercial Services, International Card Services, and Global Merchant and Network Services. The company's products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in 1850 and is based in New York, New York.

This dataset contains information on:

· Estimated resident population (ERP) at 30 June 1996, 2001, 2006, 2013, and 2018 for total population

· ERP at 30 June 2018 by ethnic groups (European or Other (including New Zealander), Māori, Pacific, Asian, and Middle Eastern/Latin American/African) – estimates and percentage

· Sex ratio – number of males per 100 females

· ERP at 30 June 2018 by broad age groups and median age

· Geographies available are regional council areas, territorial authority and Auckland local board areas, Statistical Area 2, and urban rural.

Note: The geography corresponds to 2020 boundaries

Note: -999 indicates data are not available.

​

About the estimated resident population

The estimated resident population at 30 June in the census year is based on the census usually resident population count, with updates for:

· net census undercount (as measured by a post-enumeration survey)

· residents temporarily overseas on census night

· births, deaths and net migration between census night and 30 June

· reconciliation with demographic estimates at the youngest ages.

The estimated resident population is not directly comparable with the census usually resident population count because of these adjustments.

For more detailed information about the methods used to calculate each base population, see DataInfo+ Demographic estimates.

​

Ethnic groups

It is important to note that these ethnic groups are not mutually exclusive because people can and do identify with more than one ethnicity. People who identify with more than one ethnicity have been included in each ethnic group.

The 'Māori', 'Pacific', 'Asian' and 'Middle Eastern/Latin American/African' ethnic groups are defined in level 1 of the Ethnicity New Zealand Standard Classification 2005. The estimates for the 'European or Other (including New Zealander)' group include people who belong to the 'European' or 'Other ethnicity' groups defined in level 1 of the standard classification. If a person belongs to both the 'European' and 'Other ethnicity' groups they have only been counted once. Almost all people in the 'Other ethnicity' group belong to the 'New Zealander' sub-group.

​

Time series

This time series is irregular. Because the 2011 Census was cancelled after the Canterbury earthquake on 22 February 2011, the gap between the 2006-base and 2013-base estimated resident population is seven years. The change in data between 2006 and 2013 may be greater than in the usual five-year gap between censuses. Be careful when comparing trends.

​

Rounding

Individual figures may not sum to stated totals due to rounding.

​

More information

See Estimated resident population (2018-base): At 30 June 2018 for commentary about the 2018 ERP.

Subnational population estimates concepts – DataInfo+ provides definitions of terms used in the map.

Access more population estimates data in NZ.Stat:

Theme: Population estimates.

Aim The Hemiptera is the fifth-largest insect order but comprises more established non-native insect species than any other insect order. This over-representation may result from high propagule pressure or from high species invasiveness. Here, we assess the reasons for over-representation in this group by analyzing geographical, temporal and taxonomic variation in numbers of historical invasions. Location Global Method We assembled lists of historical Hemiptera invasions in 12 world regions, countries or islands (Australia, Chile, Europe, New Zealand, North America, South Africa, South Korea, Japan, and the Galapagos, Hawaiian, Okinawa, and Ogasawara Islands) and border interception data from 9 countries (Australia, Canada, European Union, United Kingdom, Hawaii, Japan, New Zealand, South Korea, USA mainland, South Africa). Using these data, we identified hemipteran superfamilies that are historically over-represented among established non-native species, and superfamilies that are over-represented among arrivals (proxied by interceptions). We also compared temporal patterns of establishments among hemipteran suborders and among regions. Results Across all regions, patterns of over- and under-representation were similar. The Aphidoidea, Coccoidea, Aleyrodoidea, Cimicoidea and Phylloxeroida were over-represented among non-native species. These same superfamilies were not consistently over-represented among intercepted species indicating that propagule pressure does not completely explain the tendency of some Hemiptera to be over-represented among invasions. Asexual reproduction is common in nearly all over-represented superfamilies and this trait may be key to explaining the exceptional invasion success of these superfamilies. Geographical and temporal patterns of historical numbers of species established per decade mirror trends of naturalization of non-native plants. Conclusions We conclude that both propagule pressure and species invasiveness traits are drivers of the exceptional invasion success of the Sternorrhyncha suborder and Hemiptera in general. Most Hemiptera are plant-feeding; we conclude that non-native plant invasions provide ecological niches for non-native Hemiptera and play a role in driving their invasions worldwide. Methods These data list individual non-native Hemiptera species established in 12 regions around the globe: Australia, Chile, Europe (including its major islands and the European part of Russia), the Galapagos Archipelago, the Hawaiian Archipelago, Japan (excluding outlying islands), New Zealand, Okinawa (Nansei Islands), North America (Canada, continental USA), the Ogasawara Islands (also known as Bonin Islands, Japan), South Africa, and South Korea. Family, superfamily and the year of initial discovery in each region where it is present are included for records when these data are available. This dataset was assembled from various sources by an interdisciplinary scientific working group. Most of the records here are also included in Turner, R., Blake, R., & Liebhold, A. M. (2021). International Non-native Insect Establishment Data (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5245302. Data have been cleaned of most typographic and taxonomic errors using the code in the R package insectcleanr: Initial release (DOI: 10.5281/zenodo.4555787), which is based on the Global Biodiversity Information Facility (GBIF) taxonomic backbone (GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2022-02-09, i.e. the https://doi.org/10.15468/43g7-9874 backbone).

The New Zealand American Submarine Ring of Fire 2005 (NZASRoF'05) Expedition explored active submarine volcanoes in the Kermadec Arc, located north of New Zealand, with a pair of manned submersibles. This is a subduction zone where tectonic plates converge and a chain of restless volcanoes is formed along the boundary. The dive sites chosen were at volcanoes that showed evidence of having vigorous seafloor hot springs. This evidence comes from previous New Zealand / American expeditions to the area that mapped the seafloor and surveyed the ocean above each volcano for signs of hydrothermal plumes. Seafloor hot springs are dynamic environments where heat and chemicals from inside volcanoes are vented into the ocean and support unique biological communities. Most of the dive sites have never been visited before and so the potential for exciting discoveries was high.

The Hydrologic Derivatives for Modeling and Analysis (HDMA) database provides comprehensive and consistent global coverage of raster and vector topographically derived layers. The HDMA includes five raster layers: digital elevation model (DEM) data, flow direction, flow accumulation, slope, and compound topographic index (CTI); and three vector layers: streams, catchment boundaries, and processing units. The coverage of the data is global (-180º, 180º, -90º, 90º) with the underlying DEM being a hybrid of three datasets: HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) and the Shuttle Radar Topography Mission (SRTM). For most of the globe south of 60º North, the raster resolution of the data is 3-arc-seconds, corresponding to the resolution of the SRTM. For the areas North of 60º, the resolution is 7.5-arc-seconds (the smallest resolution of the GMTED2010 dataset) except for Greenland, where the resolution is 30-arc-seconds. The streams and catchments are attributed with Pfafstetter codes, based on a hierarchical numbering system, that carry important topological information.

Free-Cash-Flow-To-Equity Time Series for American Express Company. American Express Company, together with its subsidiaries, operates as integrated payments company in the United States, Europe, the Middle East and Africa, the Asia Pacific, Australia, New Zealand, Latin America, Canada, the Caribbean, and Internationally. It operates through four segments: U.S. Consumer Services, Commercial Services, International Card Services, and Global Merchant and Network Services. The company's products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in 1850 and is based in New York, New York.

The New Zealand American Ring of Fire 2007 expedition has been an outstanding success for all partners. The automous benthic explorer (ABE) team leader, Dana Yoerger, showed us the final map of the caldera (depressions) of Brothers Volcano. Flying into the volcano, you pass over the two cones: the large smooth-sided cone with its small crater that is the site of most of the recent volcanic activity, and the smaller cone that is partially eroded but still has intense hydrothermal activity at its summit. As you approach the wall of the caldera, the rugged topography (surface features) created by the degradation of the rocks from older eruptions is apparent. The comprehensive, co-located topographic, magnetic, and water-column mapping of Brothers Volcano is a new tool for exploring this "type" submarine volcano, and we are seeing it in greater detail than ever before.

This dataset contains information on:

· Estimated resident population (ERP) at 30 June 1996, 2001, 2006, 2013, and 2018 for total population

· ERP at 30 June 2018 by ethnic groups (European or Other (including New Zealander), Māori, Pacific, Asian, and Middle Eastern/Latin American/African) – estimates and percentage

· Sex ratio – number of males per 100 females

· ERP at 30 June 2018 by broad age groups and median age

· Geographies available are regional council areas, territorial authority and Auckland local board areas, Statistical Area 2, and urban rural.

Note: The geography corresponds to 2020 boundaries

Note: -999 indicates data are not available.

​

About the estimated resident population

The estimated resident population at 30 June in the census year is based on the census usually resident population count, with updates for:

· net census undercount (as measured by a post-enumeration survey)

· residents temporarily overseas on census night

· births, deaths and net migration between census night and 30 June

· reconciliation with demographic estimates at the youngest ages.

The estimated resident population is not directly comparable with the census usually resident population count because of these adjustments.

For more detailed information about the methods used to calculate each base population, see DataInfo+ Demographic estimates.

​

Ethnic groups

It is important to note that these ethnic groups are not mutually exclusive because people can and do identify with more than one ethnicity. People who identify with more than one ethnicity have been included in each ethnic group.

The 'Māori', 'Pacific', 'Asian' and 'Middle Eastern/Latin American/African' ethnic groups are defined in level 1 of the Ethnicity New Zealand Standard Classification 2005. The estimates for the 'European or Other (including New Zealander)' group include people who belong to the 'European' or 'Other ethnicity' groups defined in level 1 of the standard classification. If a person belongs to both the 'European' and 'Other ethnicity' groups they have only been counted once. Almost all people in the 'Other ethnicity' group belong to the 'New Zealander' sub-group.

​

Time series

This time series is irregular. Because the 2011 Census was cancelled after the Canterbury earthquake on 22 February 2011, the gap between the 2006-base and 2013-base estimated resident population is seven years. The change in data between 2006 and 2013 may be greater than in the usual five-year gap between censuses. Be careful when comparing trends.

​

Rounding

Individual figures may not sum to stated totals due to rounding.

​

More information

See Estimated resident population (2018-base): At 30 June 2018 for commentary about the 2018 ERP.

Subnational population estimates concepts – DataInfo+ provides definitions of terms used in the map.

Access more population estimates data in NZ.Stat:

Theme: Population estimates.

Ebit-Interest-Coverage Time Series for American Express Company. American Express Company, together with its subsidiaries, operates as integrated payments company in the United States, Europe, the Middle East and Africa, the Asia Pacific, Australia, New Zealand, Latin America, Canada, the Caribbean, and Internationally. It operates through four segments: U.S. Consumer Services, Commercial Services, International Card Services, and Global Merchant and Network Services. The company's products and services include credit card, charge card, banking, and other payment and financing products; network services; expense management products and services; and travel and lifestyle services. It also provides merchant acquisition and processing, servicing and settlement, point-of-sale marketing, and information products and services for merchants; and fraud prevention services, as well as the design and operation of customer loyalty programs. In addition, the company leases and operates lounges at airports. Further, it designs and develops a software to manage company expenses. The company sells its products and services to consumers, small businesses, mid-sized companies, and large corporations through mobile and online applications, affiliate marketing, customer referral programs, third-party service providers and business partners, direct mail, telephone, in-house sales teams, telephone, and direct response advertising. American Express Company was founded in 1850 and is based in New York, New York.

Transform Your Business with Our Comprehensive B2B Marketing Data Our B2B Marketing Data is designed to be a cornerstone for data-driven professionals looking to optimize their business strategies. With an unwavering commitment to data integrity and quality, our dataset empowers you to make informed decisions, enhance your outreach efforts, and drive business growth.

Why Choose Our B2B Marketing Data? Unmatched Data Integrity and Quality Our data is meticulously sourced and validated through rigorous processes to ensure its accuracy, relevance, and reliability. This commitment to excellence guarantees that you are equipped with the most up-to-date information, empowering your business to thrive in a competitive landscape.

Versatile and Strategic Applications This versatile dataset caters to a wide range of business needs, including:

Lead Generation: Identify and connect with potential clients who align with your business goals. Market Segmentation: Tailor your marketing efforts by segmenting your audience based on industry, company size, or geographical location. Personalized Marketing Campaigns: Craft personalized outreach strategies that resonate with your target audience, increasing engagement and conversion rates. B2B Communication Strategies: Enhance your communication efforts with direct access to decision-makers, ensuring your message reaches the right people. Comprehensive Data Attributes Our B2B Marketing Data offers more than just basic contact information. With over 20+ attributes, you gain in-depth insights into:

Decision-Maker Roles: Understand the responsibilities and influence of key figures within an organization, such as CEOs, executives, and other senior management. Industry Affiliations: Analyze industry-specific data to tailor your approach to the unique dynamics of each sector. Contact Information: Direct email addresses and phone numbers streamline communication, enabling you to engage with your audience effectively and efficiently. Expansive Global Coverage Our dataset spans a wide array of countries, providing a truly global perspective for your business initiatives. Whether you're looking to expand into new markets or strengthen your presence in existing ones, our data ensures comprehensive coverage across the following regions:

North America: United States, Canada, Mexico Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more South America: Brazil, Argentina, Chile, Colombia, and more Africa: South Africa, Nigeria, Kenya, Egypt, and more Australia and Oceania: Australia, New Zealand Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more Industry-Wide Reach Our B2B Marketing Data covers an extensive range of industries, ensuring that no matter your focus, you have access to the insights you need:

Finance and Banking Technology Healthcare Manufacturing Retail Education Energy Real Estate Telecommunications Hospitality Transportation and Logistics Government and Public Sector Non-Profit Organizations And many more… Comprehensive Employee and Revenue Size Information Our dataset includes detailed records on company size and revenue, offering you the ability to:

Employee Size: From small businesses with a handful of employees to large multinational corporations, we provide data across all scales. Revenue Size: Analyze companies based on their revenue brackets, allowing for precise market segmentation and targeted marketing efforts. Seamless Integration with Broader Data Offerings Our B2B Marketing Data is not just a standalone product; it integrates seamlessly with our broader suite of premium datasets. This integration enables you to create a holistic and customized approach to your data-driven initiatives, ensuring that every aspect of your business strategy is informed by the most accurate and comprehensive data available.

Elevate Your Business with Data-Driven Precision Optimize your marketing strategies with our high-quality, reliable, and scalable B2B Marketing Data. Identify new opportunities, understand market dynamics, and connect with key decision-makers to drive your business forward. With our dataset, you’ll stay ahead of the competition and foster meaningful business relationships that lead to sustained growth.

Unlock the full potential of your business with our B2B Marketing Data – the ultimate resource for growth, reliability, and scalability.

This dataset contains the share of employment in manufacturing for 126 countries from the 1950s to 2022. The data provides a truly global and historical perspective on the importance of manufacturing and potential deindustrialisation trends. Data is combined from the ILOSTAT database and the GGDC-10 sector database, 2014 release. The database contains a total of 126 countries; 4,810 observations, with an average of 38 observations per country, a minimum of 5 and a maximum of 75 observations. In a broad sense, the data covers 9 main regions. 30% of the dataset consists of European economies, 20% of Asia, 20% of Latin America and 15% of Africa, with the remainder being China, North America, Australia and New Zealand, island economies and other economies.

Statistical data from a world-wide wind energy resource assessment is provided for a selection of stations in Central and South America, Africa, Asia, Australia, New Zealand, and the Pacific Islands. The input data span the period from 1902 to 1978, but the input period of record for each station is highly variable. The statistics include average wind speed and wind power density by season and annually.

Wind speed frequencies, covariances of wind speed and direction, and wind power densities are provided for 22 Wyoming stations as part of a separate wind resource assessment. The input period for this study is approximately 1938-1978.

1. Specifications Format : 44.1 kHz/48 kHz, 16bit/24bit, uncompressed wav, mono channel.

Recording environment : professional recording studio.

Recording content : general narrative sentences, interrogative sentences, etc.

Speaker : native speaker

Annotation Feature : word transcription, part-of-speech, phoneme boundary, four-level accents, four-level prosodic boundary.

Device : Microphone

Language : American English, British English, Japanese, French, Dutch, Catonese, Canadian French,Australian English, Italian, New Zealand English, Spanish, Mexican Spanish

Application scenarios : speech synthesis

Accuracy rate: Word transcription: the sentences accuracy rate is not less than 99%. Part-of-speech annotation: the sentences accuracy rate is not less than 98%. Phoneme annotation: the sentences accuracy rate is not less than 98% (the error rate of voiced and swallowed phonemes is not included, because the labelling is more subjective). Accent annotation: the word accuracy rate is not less than 95%. Prosodic boundary annotation: the sentences accuracy rate is not less than 97% Phoneme boundary annotation: the phoneme accuracy rate is not less than 95% (the error range of boundary is within 5%)

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go AI & ML Training Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/tts?source=Datarade

Measuring the completeness of survey inventories created by citizen-science initiatives can identify the strengths and shortfalls in our knowledge of where species occur geographically. Here, we use occurrence information from eBird to measure the survey completeness of the world's birds in this database at three temporal resolutions and four spatial resolutions across the annual cycle during the period 2002 to 2018. Approximately 84% of the earth's terrestrial surface contained bird occurrence information with the greatest concentrations occurring in North America, Europe, India, Australia, and New Zealand. The largest regions with low levels of survey completeness were located in central South America, northern and central Africa, and northern Asia. Across spatial and temporal resolutions, survey completeness in regions with occurrence information was 55–74% on average, with the highest values occurring at coarser temporal and coarser spatial resolutions and during spring migration within temperate and boreal regions. Across spatial and temporal resolutions, survey completeness exceeded 90% within ca. 4–14% of the earth's terrestrial surface. Survey completeness increased globally from 2002 to 2018 across all months of the year at a rate of ca. 3% per year. The slowest gains occurred in Africa and in montane regions, and the most rapid gains occurred in India and in tropical forests after 2012. Thus, occurrence information from a global citizen-science program for a charismatic and well-studied taxon was geographically broad but contained heterogeneous patterns of survey completeness that were strongly influenced by temporal and especially spatial resolution. Our results identify regions where the application of additional effort would address current knowledge shortfalls, and regions where the maintenance of existing effort would benefit long-term monitoring efforts. Our findings highlight the potential of citizen science initiatives to further our knowledge of where species occur across space and time, information whose applications under global change will likely increase.

Operating-Return-On-Assets Time Series for Corporate Travel Management Ltd. Corporate Travel Management Limited, a travel management solutions company, manages the procurement and delivery of travel services in Australia and New Zealand, North America, Asia, and Europe. . The company provides corporate travels, meetings and event travel management, resources travel, sports travel, leisure travel, loyalty travel, and wholesale travel services, as well as accommodation agency services. Corporate Travel Management Limited was founded in 1994 and is based in Brisbane, Australia.