This is the official website for downloading the CA sub-dataset of the LargeST benchmark dataset. There are a total of 7 files in this page. Among them, 5 files in .h5 format contain the traffic flow raw data from 2017 to 2021, 1 file in .csv format provides the metadata for sensors, and 1 file in .npy format represents the adjacency matrix constructed based on road network distances. Please refer to https://github.com/liuxu77/LargeST for more information.

With a market cap of over two trillion U.S. dollars, Amazon ranks first among the leading large cap e-commerce companies worldwide. According to March 2025 data, the e-commerce giant ranks ahead of Alibaba and Pinduoduo. During the measured period, Alibaba's market cap amounted to over 300 billion U.S. dollars. Amazon is one of the most valuable brands worldwide Ranked fourth out of the leading brands worldwide, Amazon continues to grow, with its brand value estimated at 577 billion U.S. dollars. Amazon.com was founded in 1994 and has since become one of the world’s largest e-commerce retailers selling goods like books, clothing, electronics, and even having its own subscription service, Amazon Prime. In 2024, the company achieved a net sales revenue of 638 billion U.S. dollars, showing its continued profitability. Despite its worldwide popularity, Amazon’s annual net sales revenue is remarkably higher in North America (388 billion U.S. dollars) when compared to how the company performs internationally (143 billion U.S. dollars). A closer look at Amazon's third-party sellers Most of Amazon's units (62 percent) are sold by third-party (3P) sellers, meaning independent sellers who list and sell products on the marketplace but do not sell the products to the platform itself. In 2024, Amazon made 156 billion U.S. dollars in global net revenue from 3P sales alone. However, in Amazon's biggest market, the United States, most sellers use a hybrid model (74 percent), this includes both 3P and first-party (1P) sellers, with the latter being a business or manufacturer that sells its products directly to a retailer or platform.

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China was the biggest emitter of carbon dioxide (CO₂) emissions in 2023, accounting for over ** percent of total global emissions. The world's top four largest polluters were responsible for roughly ** percent of global CO₂ emissions in 2023. Cumulative emissions Although China currently emits the highest levels of CO₂ annually, it has emitted far less than the United States over the past three centuries. Since 1750, the United States has produced more than *** billion metric tons of cumulative carbon dioxide emissions. Global CO₂ emissions have increased dramatically since the birth of the Industrial Revolution, and in 2023 reached a record high. Which countries are reducing emissions? As of 2023, seven of the 20 biggest CO₂ emitters had recorded overall emissions reductions when compared to 1990 levels. The United Kingdom, for example, slashed its carbon emissions by almost ** percent between 1990 and 2023, while Germany recorded reductions of roughly ** percent. In comparison, many developing countries saw their emissions increase massively over the same period.

When modeling rare events using logistic regression, independent samples of event occurrence (ones) and nonoccurrence (zeros) are commonly taken from large datasets in order to fit models efficiently. A deterministic offset may then be included in the model to compensate for that sampling method. We propose a more complex sampling approach using stratified sampling within the sets of ones and zeros to ensure that we may sample more zeros from strata of interest. This design may avoid situations in which a random sample of zeros fails to capture the range of a key covariate. We employ sampling weights along with stratum-specific intercepts to obtain unbiased estimates of the logistic regression coefficients (including the intercept) and their standard errors. We use simulation to show that this method provides unbiased parameter estimates comparable with those of maximum likelihood. We also illustrate an application of this method to wildland fire occurrence prediction in a study area in northwestern Ontario, Canada.

1. Image of the Alexandria database state corresponding to the paper "Improving machine-learning models in materials science through large datasets".

• Static pbe calculations for 1D, 2D, 3D compounds can be found in 1D_pbe.tar.gz, 2D_pbe.tar.gz, 3D_pbe.tar.gz in batches of 100k materials. The latter also contains a separate convex hull pickle with all compounds on the pbe convex hull (convex_hull_pbe_2023.12.29.json.bz2) and a list of prototypes in the database (prototypes.json.bz2). The systematic 3D calculations performed for the article Improving machine-learning models in materials science through large datasets (in the paper referred to as round 2 and 3) can be found by the location keyword in the data dictionary of each ComputedStructureEntry containing "cgat_comp/quaternaries" (round 2) and "cgat_comp2/" (round 3). Round 1 (10.1002/adma.202210788) can be found under "cgat_comp/ternaries", ""cgat_comp/binaries".
• Static pbesol calculations for 3D compounds can be found in 3D_ps.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the pbesol convex hull (convex_hull_ps_2023.12.29.json.bz2).
• Static scan calculations for 3D compounds can be found in 3D_scan.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the scan convex hull (convex_hull_scan_2023.12.29.json.bz2).
• Geometry relaxation curves for 1D and 2D and 3D compounds calculated with PBE can be found in geo_opt_1D.tar.gz, geo_opt_2D.tar.gz. and geo_opt_3D.tar. Each file in each folder contains a batch of up to 10k relaxation trajectories.
• PBESOL relaxation trajectories for 3D compounds can be found in geo_opt_ps.tar

2. Crystal graph attention networks to predict the volume (volume_round_3.tar.gz) and distance to the convex hull (e_above_hull_round_3.tar.gz) trained for the paper "Improving machine-learning models in materials science through large datasets".

Can be used with the code at https://github.com/hyllios/CGAT/tree/main/CGAT.
Note will predict the distance to the convex hull not normalized per atom when using the code on the github.

3. Alignn models as well as m3gnet and mace models corresponding to the publication can be found in alexandria_v2.tar.gz

4. scripts.tar.gz Some scripts used for generating CGAT input data/ performing parallel predictions and for relaxations with m3gnet/mace force fields

Access verified company data from official trade registers with 1.83 million company records in Vietnam or 380 million globally. Delivered via API, Excel, or CSV. Accurate, structured data for compliance, sales, marketing, and more.

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

collabllm/collabllm-multiturn-math-hard-large dataset hosted on Hugging Face and contributed by the HF Datasets community

Access 10K+ verified company records from official trade registers in Greenland or choose from 380M companies worldwide. Delivered via lists, API, Excel or CSV. Reliable, accurate data tailored to support your business objectives.

Bosnia and Herzegovina BA: Population in Largest City data was reported at 346,859.000 Person in 2024. This records an increase from the previous number of 345,549.000 Person for 2023. Bosnia and Herzegovina BA: Population in Largest City data is updated yearly, averaging 340,814.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 346,859.000 Person in 2024 and a record low of 169,319.000 Person in 1960. Bosnia and Herzegovina BA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

The astonishing diversity of brain sizes observed across the animal kingdom is typically explained in the context of trade-offs: the benefits of a larger brain, such as enhanced cognitive ability, are balanced against potential costs, such as increased energetic demands. Several hypotheses have been formulated in this framework, placing different emphasis on ecological, behavioural, or physiological aspects of trade-offs in brain size evolution. Within this body of work, there exists considerable taxonomic bias towards studies of birds and mammals, leaving some uncertainty about the generality of the respective arguments. Here, we test three of the most prominent such hypotheses, the ´expensive tissue´, ´social brain´, and ´cognitive buffer´ hypotheses, in a large dataset of fishes, derived from a publicly available resource (FishBase). In accordance with predictions from the ´expensive tissue´ and the ´social brain´ hypothesis, larger brains co-occur with reduced fecundity and increase...

Graph and download economic data for Nasdaq US Large Cap Financial Services Index (NASDAQNQUSL3020) from 2011-05-23 to 2025-09-15 about large cap, market cap, NASDAQ, large, financial, services, indexes, and USA.

weikaih/ai2thor-counting-large dataset hosted on Hugging Face and contributed by the HF Datasets community

AI in Pharmaceutical Market Outlook

As of 2023, the AI in pharmaceutical market size is estimated at approximately USD 1.5 billion and is projected to reach USD 8.8 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 21.3%. This significant growth is driven by the increasing adoption of AI technologies to streamline drug discovery processes, improve clinical trial efficiencies, and personalize patient care.

One of the primary growth factors in the AI in pharmaceutical market is the rising need for cost reduction and efficiency in drug development. Traditional drug discovery processes are notoriously time-consuming and expensive, often taking over a decade and billions of dollars to bring a new drug to market. AI can significantly reduce these timelines and costs by enabling virtual screening, predicting molecular behavior, and optimizing drug compounds. This technology not only accelerates the drug discovery process but also increases the likelihood of success in later stages of development.

Another key growth driver is the increasing complexity and volume of biomedical data. With the advent of next-generation sequencing and other advanced technologies, the amount of data generated in the pharmaceutical industry has grown exponentially. AI technologies, particularly machine learning and deep learning, are exceptionally well-suited to handle and analyze large datasets. These capabilities allow for more precise patient stratification, identification of novel drug targets, and improved predictive modeling, leading to more effective and targeted therapies.

The rise of personalized medicine is also fueling the adoption of AI in the pharmaceutical sector. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, requiring a deep understanding of genetic, environmental, and lifestyle factors. AI algorithms can integrate and analyze diverse data sources to provide insights into disease mechanisms and suggest personalized treatment plans. This approach not only improves patient outcomes but also reduces adverse drug reactions and healthcare costs.

Regionally, North America dominates the AI in pharmaceutical market due to the presence of major pharmaceutical companies, significant investments in R&D, and supportive regulatory policies. Europe follows closely, with strong government initiatives and collaborations between academic institutions and industry players. The Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing healthcare expenditures, a growing focus on healthcare digitization, and a rising number of clinical trials in countries like China and India.

Component Analysis

The AI in pharmaceutical market can be segmented by components into software, hardware, and services. The software segment is expected to hold the largest market share due to the widespread use of AI algorithms and platforms in drug discovery, clinical trials, and personalized medicine. AI software solutions, such as machine learning models, predictive analytics tools, and natural language processing systems, are crucial for analyzing complex biomedical data, identifying novel drug targets, and optimizing clinical trial designs.

Hardware components, including high-performance computing systems and specialized AI processors, are essential for running advanced AI algorithms and managing large datasets. While the hardware segment may not hold as large a market share as software, it is still a critical enabler of AI applications in the pharmaceutical industry. The increasing adoption of cloud-based AI solutions is expected to drive growth in this segment, as cloud infrastructure providers continue to invest in powerful computing resources and AI capabilities.

The services segment includes a range of offerings such as consulting, integration, and maintenance services. These services are vital for the successful implementation and operation of AI solutions in pharmaceutical companies. Consulting services help organizations identify the most suitable AI applications for their needs, while integration services ensure seamless deployment and interoperability with existing systems. Maintenance services are essential for keeping AI solutions up-to-date and running efficiently, ensuring continuous improvement and adaptation to evolving industry requirements.

Overall, while software remains the dominant component in the AI in pharmaceutical market, the importance of hardware and services should not b

In the first quarter of 2025, SK Hynix held a DRAM market share of ** percent, while Samsung occupied a market share of **** percent. Micron sat as the third-largest DRAM supplier with a market share of **** percent during the first quarter of 2025. DRAM market fluctuations Overall DRAM revenues amounted to ** billion U.S. dollars for the first quarter of 2025, down from the *****billion U.S. dollars observed in the fourth quarter of 2024. SK Hynix is the largest manufacturer of DRAM in terms of revenues in the latest recorded quarter. DRAM vs. SRAM The primary memory of a computer is called RAM, with the two most used forms of modern RAM being static RAM (SRAM) and dynamic RAM (DRAM). DRAM is a type of volatile memory that, unlike non-volatile flash memory, loses data quickly when cut off from a power supply. Compared to other varieties of volatile memory, DRAM is relatively structurally simple. Whereas SRAM requires four to six transistors per bit, DRAM requires only one transistor and capacitor per bit.

About this dataset

Context

This list ranks the 342 cities in the Tennessee by Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Black Population: This column displays the rank of cities in the Tennessee by their Hispanic Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Black Population: The Hispanic Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Tennessee Hispanic Black Population: This tells us how much of the entire Tennessee Hispanic Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

About this dataset

Context

This list ranks the 4 cities in the Brazos County, TX by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Brazos County, TX by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Brazos County Black Population: This tells us how much of the entire Brazos County, TX Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Niger NE: Population in Largest City: as % of Urban Population data was reported at 28.102 % in 2017. This records a decrease from the previous number of 28.626 % for 2016. Niger NE: Population in Largest City: as % of Urban Population data is updated yearly, averaging 34.017 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 37.980 % in 2001 and a record low of 28.102 % in 2017. Niger NE: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Niger – Table NE.World Bank: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

About this dataset

Context

This list ranks the 65 cities in the Oakland County, MI by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Oakland County, MI by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Oakland County Black Population: This tells us how much of the entire Oakland County, MI Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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