Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data were collected on November 8th, 2006 on an arterial segment on Peachtree Street located in Atlanta, Georgia. The data represents 30 minutes total, segmented into two periods (12:45 p.m. to 1:00 p.m. and 4:00 p.m. to 4:15 p.m.). The dataset includes files for both raw and processed video data from each of the eight cameras for the two time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (8). The raw video files give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data were collected on June 16th, 2005 on an arterial segment on Lankershim Boulevard located in Los Angeles, California. The data represents 30 minutes total, segmented into two periods (8:30 a.m. to 8:45 a.m. and 8:45 a.m. to 9:00 a.m.). The dataset includes files for both raw and processed video data from each of the five cameras for the two time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (5). The raw videos give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

ITS DataHub has partnered with the Federal Highway Administration's (FHWA's) Next Generation SIMulation (NGSIM) program to make available detailed vehicle trajectory data and supporting data files along with the raw and processed video files from the NGSIM data collection efforts. Researchers for the NGSIM program collected the specified data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, GA.

This article provides a brief overview of the NGSIM program data collection as well as what types of data are available on ITS DataHub. Some examples of possible uses for the data and information on how to cite the various NGSIM datasets are also included.

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data was collected on a freeway segment of US 101 (Hollywood Freeway) located in Los Angeles, California on June 15th, 2005. A total of 45 minutes of transcribed data are included in this full data set, segmented into three 15 minute periods representing: 1) 7:50 a.m. to 8:05 a.m., 2) 8:05 a.m. to 8:20 a.m., and 3) 8:20 a.m. to 8:35 a.m. on June 15th, 2005. The dataset includes files for both raw and processed video data from each of the eight cameras for the three time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (8). The raw video files give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

As part of the Federal Highway Administration’s (FHWA) Next Generation Simulation (NGSIM) project, video data was collected on a segment of Interstate 80 located in Emeryville, California on April 13, 2005. A total of 45 minutes of video data are available, segmented into three 15 minute periods: 1) 4:00 p.m. to 4:15 p.m.; 2) 5:00 p.m. to 5:15 p.m.; and 3) 5:15 p.m. to 5:30 p.m. The dataset includes files for both raw and processed video data from each of the seven cameras for the three time periods available for download. Camera numbering is in order of southern-most (1) to northern-most (7). The raw videos give the original vehicle movement data and offer users a view of how the section was observed. The processed video files provide videos of the vehicles along with a superimposition of the vehicle identification numbers. These videos can be used alone or can be used for cross referencing of the textual vehicle trajectory data provided in the NGSIM trajectory data with the corresponding video.

For related datasets please see the following: - NGSIM Vehicle Trajectories and Supporting Data: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras.NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset.

Principal component score coefficient matrix.

nextGEMS is a collaborative European project. Funded by the EU’s Horizon 2020 programme, it will tap expertise from fourteen European Nations to develop two next generation (storm-resolving) Earth-system Models. Through breakthroughs in simulation realism, these models will allow us to understand and reliably quantify how the climate will change on a global and regional scale, and how the weather, including its extreme events, will look like in the future. See further details at https://nextgems-h2020.eu/ and https://cordis.europa.eu/project/id/101003470.

Finite Element Bird Strike Simulation Software Market Outlook

According to our latest research, the global finite element bird strike simulation software market size reached USD 375 million in 2024, demonstrating robust growth driven by increased safety requirements in the aerospace and automotive industries. The market is expected to expand at a CAGR of 11.2% from 2025 to 2033, reaching a projected value of USD 1,027 million by 2033. This surge is primarily fueled by stringent regulatory standards, the proliferation of advanced simulation technology, and the growing emphasis on virtual testing for structural integrity and passenger safety.

A significant growth factor for the finite element bird strike simulation software market is the escalating regulatory scrutiny and compliance requirements imposed by international aviation authorities and transportation safety bodies. Organizations such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA) have established rigorous certification criteria for aircraft and automotive components to withstand bird strike impacts. This regulatory landscape compels OEMs and Tier 1 suppliers to invest in sophisticated simulation tools that can accurately predict structural responses to bird strikes. By adopting finite element analysis (FEA) software, manufacturers can optimize designs, minimize physical prototyping costs, and accelerate time-to-market while ensuring compliance with safety mandates.

Another pivotal driver is the rapid advancement in computational power and simulation algorithms, which has substantially enhanced the accuracy, speed, and scalability of bird strike simulations. The integration of high-performance computing (HPC) resources, cloud-based deployment models, and advanced material modeling capabilities has enabled engineers to perform complex simulations with higher fidelity and reduced turnaround times. This technological evolution is particularly beneficial in industries such as aerospace, defense, and automotive, where lightweight materials and innovative structural designs are increasingly adopted. The ability to virtually test and iterate designs under realistic bird strike scenarios fosters innovation, reduces development costs, and mitigates the risk of catastrophic failures.

The rising adoption of digital transformation initiatives across research institutes, academic organizations, and industrial R&D centers further fuels the marketÂ’s expansion. As simulation-driven design becomes central to engineering workflows, educational institutions and research bodies are leveraging finite element bird strike simulation software to train the next generation of engineers and conduct cutting-edge research. This trend not only broadens the user base but also stimulates continuous product innovation, as software vendors collaborate with academia to refine algorithms, improve user interfaces, and expand application domains. The synergy between industry and academia is expected to sustain long-term growth and foster the development of next-generation simulation solutions.

Finite Element Analysis Software plays a crucial role in the development and optimization of bird strike simulation tools. By leveraging advanced FEA capabilities, engineers can model complex interactions between bird strikes and aircraft structures with high precision. This allows for the detailed analysis of stress distribution, deformation, and potential failure points within the structure. The integration of FEA software into simulation workflows not only enhances the accuracy of predictions but also facilitates the exploration of various design alternatives. As a result, manufacturers can achieve more resilient and efficient designs, ultimately improving safety and performance in real-world scenarios.

From a regional perspective, North America and Europe continue to dominate the finite element bird strike simulation software market, owing to their advanced aerospace and automotive sectors, well-established regulatory frameworks, and substantial investments in R&D. However, the Asia Pacific region is witnessing the fastest growth, driven by burgeoning air traffic, expanding defense budgets, and the emergence of indigenous aircraft and automotive manufacturers. Latin America and the Middle East & Africa are also gradually increasing t

Federal contract opportunity data for NATO Business Opportunity: Next Generation of Modelling and Simulation (NexGen M&S) Program Agile Procurement, posted by COMMERCE, DEPARTMENT OF in the Other Computer Related Services sector. Sourced from SAM.gov federal procurement database.

Passenger Boarding Simulation Software Market Outlook

According to our latest research, the global Passenger Boarding Simulation Software market size reached USD 1.24 billion in 2024, reflecting a robust demand for advanced simulation solutions across transportation hubs worldwide. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 3.71 billion by 2033. This growth is primarily driven by the increasing need for operational efficiency, passenger experience optimization, and infrastructure planning in the aviation, railway, and maritime sectors, as well as the growing adoption of digital transformation initiatives by commercial and government entities.

A key growth factor for the Passenger Boarding Simulation Software market is the rising complexity of passenger flows in modern transportation environments. With airports, train stations, and seaports experiencing unprecedented passenger volumes, operators are under pressure to optimize boarding, reduce bottlenecks, and enhance overall throughput. Simulation software empowers stakeholders to model various scenarios, test boarding strategies, and predict outcomes without disrupting real-world operations. This capability is particularly valuable as transportation providers seek to meet stringent regulatory requirements, maintain safety standards, and deliver a seamless passenger journey. The integration of artificial intelligence, machine learning, and real-time data analytics further augments the capabilities of these platforms, enabling dynamic adjustments and continuous improvement in passenger handling processes.

Another significant driver of market expansion is the ongoing digital transformation within the transportation sector. Both commercial enterprises and government agencies are increasingly investing in advanced simulation tools to support infrastructure development, resource allocation, and contingency planning. The adoption of cloud-based deployment models is accelerating this trend, as they offer scalability, cost-effectiveness, and remote accessibility. Additionally, the growing emphasis on sustainability and energy efficiency is prompting operators to leverage simulation software for optimizing passenger flows and minimizing idle times, thereby reducing energy consumption and operational costs. These factors collectively contribute to the strong momentum observed in the Passenger Boarding Simulation Software market.

Technological advancements and the proliferation of smart transportation initiatives are also catalyzing market growth. The integration of Internet of Things (IoT) devices, real-time passenger tracking, and predictive analytics is enabling more granular and accurate simulation models. This not only enhances the precision of boarding simulations but also supports proactive decision-making and rapid response to disruptions. Furthermore, the increasing collaboration between software providers, transportation authorities, and academic institutions is fostering innovation, leading to the development of more user-friendly, customizable, and interoperable solutions. As a result, the market is witnessing the emergence of next-generation simulation platforms that cater to the evolving needs of diverse end-users, from airports and airlines to railways and maritime operators.

Regionally, the Asia Pacific market is expected to witness the fastest growth, driven by large-scale infrastructure investments, rapid urbanization, and the expansion of air and rail networks. North America and Europe continue to dominate in terms of market share, owing to their mature transportation ecosystems, high technology adoption rates, and stringent regulatory frameworks. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by government initiatives to modernize transportation infrastructure and enhance passenger experience. The global landscape is thus characterized by a dynamic interplay of technological innovation, regulatory compliance, and shifting passenger expectations, all of which are shaping the future trajectory of the Passenger Boarding Simulation Software market.

Aircraft Simulation Market Outlook

According to the latest research conducted in 2025, the global aircraft simulation market size is valued at USD 7.8 billion in 2024, reflecting robust expansion fueled by technological advancements and increased pilot training requirements. The market is expected to grow at a CAGR of 6.4% from 2025 to 2033, reaching a projected value of USD 13.6 billion by 2033. This impressive growth trajectory is primarily driven by the rising demand for pilot training across both commercial and military aviation sectors, as well as the ongoing modernization of simulation technologies worldwide.

One of the primary growth factors for the aircraft simulation market is the escalating need for highly trained pilots, prompted by the increasing global air traffic and the expansion of airline fleets. Airlines and training institutes are investing heavily in advanced simulation systems to ensure compliance with stringent regulatory standards and to enhance the safety and efficiency of pilot training. The adoption of next-generation simulation technologies, such as virtual reality (VR) and artificial intelligence (AI), has further contributed to the marketÂ’s expansion by offering more realistic and immersive training environments, effectively reducing operational risks and costs associated with live training flights.

Another significant driver is the rapid technological innovation in simulation hardware and software. The integration of high-fidelity graphics, real-time data analytics, and cloud-based simulation platforms has revolutionized the training landscape, allowing for more flexible, scalable, and cost-effective solutions. This technological evolution is particularly evident in the development of full flight simulators (FFS) and flight training devices (FTD), which now offer enhanced motion systems, improved visual displays, and more accurate flight dynamics modeling. Such advancements are not only boosting the efficiency of pilot training but are also extending the application of simulation technologies to aircraft maintenance, research and development, and mission rehearsal for military operations.

The aircraft simulation market is also benefiting from the growing focus on safety and regulatory compliance in aviation. Regulatory authorities, such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA), are mandating regular simulator-based training for pilots and crew members to mitigate in-flight risks and enhance operational preparedness. This regulatory emphasis is compelling both commercial airlines and military organizations to invest in state-of-the-art simulation systems, thereby fueling market growth. Additionally, the rising trend of unmanned aerial vehicles (UAVs) in both civilian and defense applications is creating new opportunities for simulator manufacturers to develop specialized training solutions tailored to the unique requirements of UAV operators.

The role of Military Simulation and Training in the aircraft simulation market cannot be overstated. As military operations become increasingly complex, the demand for sophisticated simulation technologies that can replicate diverse combat scenarios is on the rise. Military organizations are leveraging these technologies to enhance the preparedness and effectiveness of their personnel, ensuring that they are equipped to handle a wide range of operational challenges. The integration of advanced simulation systems in military training programs not only improves tactical decision-making but also reduces the risks and costs associated with live training exercises. This trend is driving significant investments in simulation infrastructure, particularly in countries with robust defense budgets and strategic military interests.

Regionally, North America continues to dominate the aircraft simulation market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The strong presence of leading simulator manufacturers, coupled with substantial investments in aviation infrastructure and training, underpins North AmericaÂ’s leadership. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid fleet expansion, increasing air travel demand, and government initiatives to bolster aviation safety and training standards. Europe remains a key mark

There is a lack of consensus on how next-generation sequence data should be considered for phylogenetic and phylogeographic estimates, with some studies excluding loci with missing data, while others include them, even when sequences are missing from a large number of individuals. Here we use simulations, focusing specifically on RAD sequences, to highlight some of the unforeseen consequence of excluding missing data from next-generation sequencing. Specifically, we show that in addition to the obvious effects associated with reducing the amount of data used to make historical inferences, the decisions we make about missing data (such as the minimum number of individuals with a sequence for a locus to be included in the study) also impact the types of loci sampled for a study. In particular, as the tolerance for missing data becomes more stringent, the mutational spectrum represented in the sampled loci becomes truncated such that loci with the highest mutation rates are disproportionately excluded. This effect is exacerbated further by factors involved in the preparation of the genomic library (i.e., the use of reduced representation libraries, as well as the coverage) and the taxonomic diversity represented in the library (i.e., the level of divergence among the individuals). We demonstrate that the intuitive appeals about being conservative by removing loci may be misguided.

Quantum-Assisted Traffic Simulation Market Outlook

According to our latest research, the global Quantum-Assisted Traffic Simulation market size is valued at USD 412.5 million in 2024 and is expected to reach USD 3.12 billion by 2033, expanding at a robust CAGR of 24.8% over the forecast period. The rapid growth of this market is primarily driven by the increasing complexity of urban transportation networks and the urgent need for advanced simulation tools capable of handling massive datasets and delivering real-time, actionable insights. As cities and transportation systems become more interconnected and data-driven, quantum-assisted technologies are emerging as a transformative force in traffic simulation, enabling unprecedented levels of modeling accuracy and operational efficiency.

One of the primary growth factors propelling the Quantum-Assisted Traffic Simulation market is the escalating demand for smarter urban mobility solutions. Urbanization continues to accelerate, with more than 55% of the global population now residing in cities, leading to heightened congestion, pollution, and logistical challenges. Traditional simulation tools often struggle to process the vast, dynamic datasets generated by modern transportation networks. Quantum computing, with its ability to solve complex optimization problems exponentially faster than classical systems, is revolutionizing traffic simulation by enabling real-time scenario analysis, predictive modeling, and adaptive traffic control. This technological leap is empowering city planners, transportation authorities, and mobility service providers to optimize traffic flows, reduce congestion, and enhance commuter safety in ways previously unattainable.

Another critical driver is the growing integration of autonomous vehicles and connected transportation infrastructure. The proliferation of self-driving cars, intelligent traffic lights, and vehicle-to-everything (V2X) communication networks is creating an intricate web of interactions that demand sophisticated simulation environments. Quantum-assisted solutions can model these multidimensional systems with high fidelity, accounting for variables such as vehicle behavior, environmental factors, and human-machine interactions. This capability is essential for validating autonomous vehicle algorithms, stress-testing smart infrastructure, and ensuring the safety and reliability of next-generation mobility solutions. As a result, automotive companies, research institutes, and government agencies are increasingly investing in quantum-powered simulation platforms to accelerate the development and deployment of advanced transportation technologies.

Furthermore, the adoption of cloud-based deployment models and the rise of simulation-as-a-service offerings are making quantum-assisted traffic simulation more accessible to a broader range of stakeholders. Cloud infrastructure enables organizations to leverage quantum computing resources without the need for significant capital investment in specialized hardware. This democratization of access is fostering innovation across the transportation ecosystem, from small municipal agencies to large automotive manufacturers. Additionally, the integration of artificial intelligence and machine learning algorithms with quantum simulation engines is enhancing the accuracy and scalability of traffic models, enabling continuous improvement and adaptation to evolving urban landscapes. Such synergies are expected to further accelerate market growth throughout the forecast period.

Regionally, North America and Europe are leading the adoption of quantum-assisted traffic simulation technologies, driven by substantial investments in smart city initiatives, advanced transportation infrastructure, and research and development. The Asia Pacific region, however, is poised for the fastest growth, fueled by rapid urbanization, expanding metropolitan areas, and increasing government focus on sustainable mobility solutions. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in metropolitan centers facing acute congestion and infrastructure challenges. As global cities strive to become more resilient, efficient, and sustainable, the Quantum-Assisted Traffic Simulation market is set to play a pivotal role in shaping the future of urban mobility.

Component Analysis

The Quantum-Assisted Traffic Simulation market is segmented by component into software, hardware, and services, ea

This dataset contains python scripts to analyse outputs from the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) and accompanies the paper “Needham, J.F., Chambers, J., Fisher, R., Knox, R., and Koven, C. D., Forest responses to simulated elevated CO2 under alternate hypotheses of size- and age-dependent mortality, 2020, Global Change Biology”. These scripts process FATES outputs from size- and age-dependent mortality simulations which were run to test the effect of different mechanisms of mortality on forest response to elevated CO2 (eCO2). Specifically, these scripts will process single plant functional type (PFT) simulations in which mortality is either a constant background rate, size-dependent or age-dependent. In each case, a simulation with constant woody NPP is compared to a simulation in which woody NPP increases by 25% to simulate the growth response of forests to eCO2. In addition, the data package contains scripts to analyse ensemble simulations with size- and age-dependent mortality and two PFTs, that were run to test the impact of different demographic rates on coexistence and the forest response to eCO2. Finally, the dataset includes scripts for processing simulations testing the sensitivity of results to allometry and to the recruitment scheme. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: Funding for NGEE-Tropics data resources was provided by the U.S. Department of Energy Office of Science, Office of Biological and Environmental Research.

BackgroundMetagenomics can reveal the vast majority of microbes that have been missed by traditional cultivation-based methods. Due to its extremely wide range of application areas, fast metagenome sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of metagenomics analysis tools.ResultsWe present here a customizable metagenome simulation system: NeSSM (Next-generation Sequencing Simulator for Metagenomics). Combining complete genomes currently available, a community composition table, and sequencing parameters, it can simulate metagenome sequencing better than existing systems. Sequencing error models based on the explicit distribution of errors at each base and sequencing coverage bias are incorporated in the simulation. In order to improve the fidelity of simulation, tools are provided by NeSSM to estimate the sequencing error models, sequencing coverage bias and the community composition directly from existing metagenome sequencing data. Currently, NeSSM supports single-end and pair-end sequencing for both 454 and Illumina platforms. In addition, a GPU (graphics processing units) version of NeSSM is also developed to accelerate the simulation. By comparing the simulated sequencing data from NeSSM with experimental metagenome sequencing data, we have demonstrated that NeSSM performs better in many aspects than existing popular metagenome simulators, such as MetaSim, GemSIM and Grinder. The GPU version of NeSSM is more than one-order of magnitude faster than MetaSim.ConclusionsNeSSM is a fast simulation system for high-throughput metagenome sequencing. It can be helpful to develop tools and evaluate strategies for metagenomics analysis and it’s freely available for academic users at http://cbb.sjtu.edu.cn/~ccwei/pub/software/NeSSM.php.

Due to their remarkable properties, single-layer 2-D materials appear as excellent candidates to extend Moore's scaling law beyond the currently manufactured silicon FinFETs. However, the known 2-D semiconducting components, essentially transition metal dichalcogenides, are still far from delivering the expected performance. Based on a recent theoretical study that predicts the existence of more than 1800 exfoliable 2-D materials, we investigate here the 100 most promising contenders for logic applications.

The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.

As part of this dataset, the following files were provided:

• I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X.
• I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes.
• Annotation on Regions.zip, which includes images that visually map lanes (I90_94_moving_lane1.png through I90_94_moving_lane6.png) to their associated numerical lane IDs.

Next Generation Data Management of Large-Scale CFD Simulations, Phase II