The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

In 2020, the human machine interface (HMI) market worldwide was valued at **** billion U.S. dollars, with forecasts predicting this will grow to **** billion U.S. dollars by 2026. As suggested by the source, the market is estimated to grow at a compound annual growth rate (CAGR) of **** percent.

Data on petroleum production, imports, inputs, stocks, exports, and prices. Weekly, monthly, and annual data available. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal Register (USDA & USDI, 2001) and Radeloff et al. (2005). According to these definitions, two classes of WUI were identified: 1) the intermix, where there is at least 50% vegetation cover surrounding buildings, and 2) the interface, where buildings are within 2.4 km ...

The dataset collection in question is a compilation of statistical area data. It includes one or more tables of interconnected data, structured in the form of rows and columns. The data in the collection is sourced from the 'Statistics Centre' (Tilastokeskus), a recognized institution in Finland. The description provided by the data source, translated to English, is 'Statistical Centre's Service Interface (WFS)'. This suggests that the dataset collection is likely a representation of statistical data provided through a web feature service by the Statistics Centre. The dataset collection might include various statistical area details, possibly related to the greater area of 1000 square kilometers, as suggested by the year 2015, which may indicate the time period the data covers. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Network Interface Module Market Outlook

The global Network Interface Module market size was estimated at USD 5.2 billion in 2023 and is projected to reach USD 9.8 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of approximately 7.4% during the forecast period. This growth is driven by the increasing demand for high-speed internet connectivity and the proliferation of IoT devices, which necessitate robust and efficient network interfaces for seamless communication.

One of the primary growth factors is the rapid advancement in communication technologies, which has led to enhanced network infrastructure globally. The transition from traditional wired networks to more sophisticated wireless networks has spurred the demand for advanced network interface modules. This transition is crucial for supporting the ever-increasing data traffic generated by various applications, including industrial automation and consumer electronics. Furthermore, the advent of 5G technology is expected to offer unprecedented opportunities, further driving market growth.

Another significant driver is the escalating implementation of IoT devices across various sectors. From smart homes to industrial automation, IoT devices require efficient network interface modules to ensure reliable and high-speed data transfer. The increasing adoption of smart technologies in manufacturing, healthcare, and energy sectors is contributing significantly to market expansion. As industries strive to enhance operational efficiencies and reduce downtime, the demand for reliable and high-performance network interface modules is expected to grow substantially.

Moreover, the data center boom is another critical factor propelling the market. With the exponential growth of cloud computing and data storage needs, data centers are increasingly being established and expanded. Network interface modules play a crucial role in these data centers by managing and optimizing data flow, ensuring minimal latency and high-speed data transfer. The continuous investment in data center infrastructure, particularly in emerging economies, is expected to drive the demand for network interface modules over the forecast period.

From a regional perspective, Asia Pacific is anticipated to witness significant growth in the network interface module market. The region's rapid industrialization, coupled with the increasing proliferation of smart devices and the expansion of data centers, is expected to fuel market growth. North America and Europe are also forecasted to experience substantial growth, driven by advancements in network technologies and the increasing adoption of IoT and smart home devices. The growing focus on enhancing telecommunications infrastructure in these regions will further contribute to market expansion.

Product Type Analysis

Ethernet Interface Modules Analysis

Ethernet interface modules represent a significant segment in the network interface module market, owing to their widespread adoption across various industries. These modules are integral to establishing reliable wired connections, offering high-speed data transfer and robust network performance. The demand for Ethernet interface modules is particularly high in industrial automation, where they are used to connect various industrial equipment and devices to the central control system. The continuous advancements in Ethernet technology, such as the development of Gigabit and 10 Gigabit Ethernet, are expected to drive the growth of this segment.

Moreover, the increasing adoption of Ethernet interface modules in data centers is another key driver. Data centers require high-performance network interfaces to manage and optimize data flow, ensuring minimal latency and high-speed data transfer. Ethernet modules provide the necessary reliability and speed, making them essential components in data center infrastructure. With the ongoing expansion of data centers globally, the demand for Ethernet interface modules is projected to grow significantly.

In the telecommunications sector, Ethernet interface modules are used extensively to support high-speed internet services. As telecom operators strive to enhance their network infrastructure to meet the growing demand for high-speed connectivity, the adoption of Ethernet interface modules is expected to increase. The transition towards 5G networks

This API provides international data on metallurgical coke production. Data organized by country. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Financial overview and grant giving statistics of System Management Interface Forum Inc

The dataset collection in question is a comprehensive assembly of related data tables sourced from Statistics Finland (Tilastokeskus). This collection includes several tables that contain related data, structured in a format that utilizes columns and rows for organization. The data within these tables is derived from the Statistics Finland's service interface (WFS). This collection provides a wealth of statistical information, potentially spanning various years, as suggested by the inclusion of 2013 and 2015 in some of the table names. Given the source, this dataset collection is likely to contain a wealth of valuable statistical data pertinent to Finland. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

The brain machine interface market was valued at USD 2.65 billion in 2024 and is projected to grow at a CAGR of 18.0% from 2025 to 2034.

The dataset collection at hand comprises a set of related data tables, sourced from the 'Tilastokeskus' (Statistics Finland) website in Finland. The contents of these tables are derived from the Statistical Service Interface (WFS), as described by the data source. As a collection, these tables form a comprehensive and interconnected group of data, providing a detailed and extensive statistical overview. The data is arranged in a tabular format, with rows and columns for easy interpretation and analysis. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Downloads and additional Metadata. A tiled map service depicting wildland urban interface data for 2010. The wildland-urban interface (WUI) is the area where houses meet or intermingle with undeveloped wildland vegetation. This makes the WUI a focal area for human-environment conflicts such as wildland fires, habitat fragmentation, invasive species, and biodiversity decline. Using geographic information systems (GIS), we integrated U.S. Census and USGS National Land Cover Data, to map the Federal Register definition of WUI (Federal Register 66:751, 2001) for the conterminous United States for 2010. These data are useful within a GIS for mapping and analysis at national, state, and local levels. Data are available as a feature class and include information such as housing and population densities for 2010; wildland vegetation percentages for 2011; as well as WUI class in 2010. This WUI feature class is separate from the WUI datasets maintained by individual forest units, and it is not the authoritative source data of WUI for forest units. This map service shows the WUI data for 2010 only.

This dataset contains the experimentally collected data used for the paper: Shin H, Suma D and He B (2022) Closed-loop motor imagery EEG simulation for brain-computer interfaces. Front. Hum. Neurosci. 16:951591. doi:10.3389/fnhum.2022.951591

The data was recorded from 10 unique healthy human subjects (assigned subject numbers S01 to S10). All data is organized into LIVE and SIMULATED directories, followed by BW, NT and CV subdirectories according to which experimental condition the data came from. For details on the live vs. simulated parallel experiment design and the implementation of BW, NT and CV parameters, please refer to the paper. All identifying information have been removed.

Please cite the above paper if you use any data included in this dataset. Code related to this study are also available from https://github.com/mcvain/bci-simulator.

The Port Statistical Areas dataset was updated on June 05, 2025 from the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). USACE works with port authorities from across the United States to develop the statistical port boundaries through an iterative and collaborative process. Port boundary information is prepared by USACE to increase transparency on public waterborne commerce statistic reporting, as well as to modernize how the data type is stored, analyzed, and reported. A Port Statistical Area (PSA) is a region with formally justified shared economic interests and collective reliance on infrastructure related to waterborne movements of commodities that is formally recognized by legislative enactments of state, county, or city governments. PSAs generally contain groups of county legislation for the sole purpose of statistical reporting. Through GIS mapping, legislative boundaries, and stakeholder collaboration, PSAs often serve as the primary unit for aggregating and reporting commerce statistics for broader geographical areas. Per Engineering Regulation 1130-2-520, the U.S. Army Corps of Engineers' Navigation Data Center is responsible to collect, compile, publish, and disseminate waterborne commerce statistics. This task has subsequently been charged to the Waterborne Commerce Statistics Center to perform. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922. Included in this work is the definition of a port area. A port area is defined in Engineering Pamphlet 1130-2-520 as: (1) Port limits defined by legislative enactments of state, county, or city governments. (2) The corporate limits of a municipality. The USACE enterprise-wide port and port statistical area feature classes per EP 1130-2-520 are organized in SDSFIE 4.0.2 format. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/2ngc-4984

This collection of datasets originates from the Statistics Center's service interface, known as Tilastokeskus (Statistics Finland), in Finland. The collection is composed of related data tables, with each table presenting a variety of related data in a structured format of columns and rows. The data in this collection is highly detailed and organized, providing a valuable resource for those seeking to understand specific statistical areas. The datasets in this collection are current as of 2024. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Benchmark for End-User Structured Data User Interfaces (BESDUI) based on the Berlin SPARQL Benchmark (BSBM) but intended for benchmarking the user experience while exploring a structured dataset, not the performance of the query engine. BSBM is just used to provide the data to be explored. This is a cheap User Interface benchmark as it does not involve users but experts, who measure how many interaction steps are required to complete each of the benchmark tasks, if possible. This also facilitates comparing different tools without the bias that different end-user profiles might introduce. The way to measure this interaction steps and convert them to an estimate of the required time to complete a task is based on the Keystroke-Level Model (KLM)

Financial overview and grant giving statistics of Interface Ministries Inc.

10946 Global import shipment records of Human Machine Interface with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.