This dataset contains data for last 10 seasons of Italian Serie A including current season. The data is updated on weekly basis via Travis-CI. The dataset is sourced from http://www.football-data.co.u...

Complete database of football players with most titles including Champions League, La Liga, Premier League, Serie A, Bundesliga, and international tournaments

This is a new, open, and transparent database of toxicokinetic data supporting EPA decision making. The database has already become the basis of research efforts within EPA to improve HTTK modeling using generic TK models and has facilitated the creation and validation of models for new exposure routes. Publishing the database supports open, transparent science and this database (the largest public database for this domain) will spur improvement and development of TK models by external experts in the field. Future efforts to improving the accessibility of this database (with a graphical user interface) and encouraging crowdsourcing to expand the size and scope of the database will lead to larger validation sets for our modeling efforts and likely lower uncertainties when estimating TK.

This dataset is associated with the following publication: Sayre, R., J. Wambaugh, and C. Grulke. Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals. Scientific Data. Springer Nature Group, New York, NY, 7: 122, (2020).

Time Series Database as a Service Market Outlook

As per our latest research, the global Time Series Database as a Service (TSDBaaS) market size reached USD 1.12 billion in 2024, driven by the exponential growth of big data and the increasing demand for real-time analytics across diverse industries. The market is experiencing robust expansion, registering a CAGR of 18.7% from 2025 to 2033. By the end of the forecast period in 2033, the TSDBaaS market is anticipated to attain a value of USD 6.11 billion. This remarkable growth is propelled by the rising adoption of IoT devices, the proliferation of cloud-based solutions, and the critical need for scalable and high-performance data management platforms in modern enterprises.

One of the primary growth drivers for the Time Series Database as a Service market is the surging adoption of IoT technologies across industries such as manufacturing, energy, healthcare, and smart cities. The proliferation of connected devices generates massive volumes of time-stamped data, which require specialized storage and analytics solutions. TSDBaaS platforms offer the scalability, flexibility, and real-time processing capabilities needed to manage this influx of data efficiently. Furthermore, organizations are increasingly recognizing the value of leveraging time series data for predictive analytics, anomaly detection, and operational optimization, which fuels the demand for advanced TSDBaaS solutions. The seamless integration of these platforms with existing cloud infrastructures further amplifies their appeal, making them a critical component in the digital transformation journey of enterprises.

Another significant driver is the shift toward cloud-native architectures and the growing preference for managed services among enterprises of all sizes. As organizations strive to reduce their IT overhead and focus on core business objectives, they are turning to TSDBaaS providers to handle the complexities of database management, maintenance, and scaling. This trend is particularly pronounced among small and medium enterprises (SMEs), which often lack the resources to deploy and manage on-premises time series databases. By leveraging TSDBaaS, these organizations can access enterprise-grade database capabilities without the need for significant capital investment or specialized IT expertise. The pay-as-you-go pricing models offered by most TSDBaaS vendors further enhance cost efficiency, making these solutions accessible to a broader range of businesses.

The increasing importance of real-time analytics in mission-critical applications is also playing a pivotal role in the expansion of the Time Series Database as a Service market. Industries such as financial services, energy & utilities, and healthcare are leveraging TSDBaaS platforms to monitor and analyze real-time data streams, enabling faster decision-making and improved operational agility. The ability to process and analyze high-velocity data in real time provides a competitive edge, allowing organizations to respond swiftly to market changes, optimize resource utilization, and enhance customer experiences. As data-driven decision-making becomes a cornerstone of modern business strategies, the demand for robust and scalable TSDBaaS solutions is expected to remain strong throughout the forecast period.

From a regional perspective, North America currently leads the Time Series Database as a Service market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of cloud technologies, a mature IT infrastructure, and the presence of leading TSDBaaS vendors in the region. Meanwhile, Asia Pacific is emerging as a high-growth market, driven by rapid digitalization, increasing investments in IoT, and the expanding footprint of cloud service providers. The Middle East & Africa and Latin America are also witnessing steady growth, albeit at a comparatively slower pace, as organizations in these regions gradually embrace digital transformation and cloud-based data management solutions.

Component Analysis

The component segment of the Time Series Database as a Service market is bifurcated into software and services, each playing a vital role in the overall ecosystem. The software component encompasses the core TSDBaaS platforms, which are designed to ingest, store, and analyze vast volumes of time-stamped data with h

Here we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies in the database, and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

We include here:

• SQL file of the database - biotime_v2_sql_15April25.sql
• RDS file of the query combining raw data with species names - biotime_v2_query_15April25.rds
• csv file for metadata information - biotime_v2_metadata_15April25.csv
• csv file of citations - references_biotime_v2_15April25.csv
• text file of BIB text citations - BIB_biotime_v2_15April25.csv.txt

for issue of version 2.0 of the BioTIME database

The U.S. Geological Survey (USGS) Oceanographic Time-Series Measurements Database contains oceanographic observations made as part of studies designed to increase understanding of sediment transport processes and associated ocean dynamics. This report describes the instrumentation and platforms used to make the measurements; the methods used to process and apply quality-control criteria and archive the data; and the data storage format. The report also includes instructions on how to access the data from the online database at https://stellwagen.er.usgs.gov/.

Investigator(s): Harold J. Spaeth, James L. Gibson, Michigan State University This data collection encompasses all aspects of United States Supreme Court decision-making from the beginning of the Warren Court in 1953 up to the completion of the 1995 term of the Rehnquist Court on July 1, 1996, including any decisions made afterward but before the start of the 1996 term on October 7, 1996. In this collection, distinct aspects of the court's decisions are covered by six types of variables: (1) identification variables including case citation, docket number, unit of analysis, and number of records per unit of analysis, (2) background variables offering information on origin of case, source of case, reason for granting cert, parties to the case, direction of the lower court's decision, and manner in which the Court takes jurisdiction, (3) chronological variables covering date of term of court, chief justice, and natural court, (4) substantive variables including multiple legal provisions, authority for decision, issue, issue areas, and direction of decision, (5) outcome variables supplying information on form of decision, disposition of case, winning party, declaration of unconstitutionality, and multiple memorandum decisions, and (6) voting and opinion variables pertaining to the vote in the case and to the direction of the individual justices' votes.Years Produced: Annually

This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.

Helper functions for accessing data stored in an sqlite database. There are generic functions, such as one to read a database table into a Pandas dataframe . There are also more specific functions designed to interface specifically with the data schema implemented in the database in this HydroShare resource: https://www.hydroshare.org/resource/9e1b23607ac240588ba50d6b5b9a49b5/.

The Time Series Databases (TSDB) software market, valued at $745 million in 2025, is projected to experience robust growth, driven by the escalating demand for real-time data analytics across diverse sectors. The Compound Annual Growth Rate (CAGR) of 5.5% from 2025 to 2033 indicates a substantial expansion, fueled by the increasing adoption of cloud-based solutions and the growing need for efficient processing and analysis of time-stamped data. Key drivers include the rise of IoT devices generating massive volumes of time-series data, the expanding use of AI and machine learning for predictive modeling, and the need for enhanced operational efficiency and improved decision-making across industries like finance, manufacturing, and healthcare. Large enterprises are currently leading the adoption, but the market is seeing significant growth from SMEs as cloud-based solutions become more accessible and cost-effective. While the market faces some restraints, such as the complexities involved in managing and analyzing vast datasets and the need for specialized expertise, these are being mitigated by the development of user-friendly interfaces and managed services. The competitive landscape is dynamic, with established players like InfluxData and Amazon Timestream alongside emerging competitors, fostering innovation and driving market expansion. The geographical distribution of the TSDB market shows strong presence in North America, driven by early adoption and technological advancements. However, significant growth opportunities exist in Asia Pacific, particularly in China and India, as digitalization accelerates in these rapidly developing economies. Europe also presents a substantial market, with several countries showing increasing investment in data infrastructure and analytics capabilities. The segmentation by application (Large Enterprises, SMEs) and type (Cloud-based, Web-based) allows for tailored solutions to meet specific business needs. The forecast period (2025-2033) anticipates considerable market expansion across all segments and regions, primarily driven by the ongoing digital transformation and the increasing reliance on data-driven decision making. The market will see a continued evolution in technology, with a greater emphasis on scalability, security, and ease of use.

In-Vehicle Time Series Database Market Outlook

According to our latest research, the global In-Vehicle Time Series Database market size was valued at USD 1.14 billion in 2024. The market is anticipated to grow at a robust CAGR of 16.7% during the forecast period, reaching a projected value of USD 5.18 billion by 2033. This remarkable growth is primarily driven by the increasing integration of advanced data analytics and connected technologies within the automotive sector, as well as the rising demand for real-time data processing to support next-generation mobility solutions.

One of the foremost growth factors for the In-Vehicle Time Series Database market is the rapid proliferation of connected vehicles and the evolution of the automotive industry towards digital transformation. Modern vehicles are now equipped with a multitude of sensors and telematics devices that generate vast amounts of time-stamped data. This data is crucial for applications such as fleet management, predictive maintenance, and driver behavior analysis. The need to efficiently store, retrieve, and analyze this continuous stream of data in real time has significantly increased the adoption of specialized time series databases within vehicles. Furthermore, the shift towards electric and autonomous vehicles, which require even more sophisticated data management capabilities, is further accelerating market expansion.

Another significant driver is the growing emphasis on predictive analytics and artificial intelligence within automotive operations. In-vehicle time series databases enable automotive OEMs, fleet operators, and aftermarket service providers to harness real-time insights from vehicle-generated data. This capability is critical for improving operational efficiency, reducing maintenance costs, and enhancing vehicle safety. For instance, predictive maintenance powered by time series data helps in early detection of component failures, thus minimizing downtime and ensuring optimal vehicle performance. Additionally, the integration of infotainment systems and telematics for personalized user experiences and regulatory compliance is propelling the demand for robust in-vehicle data management solutions.

The ecosystem of partnerships between automotive manufacturers, technology providers, and software vendors is also contributing to the market’s growth trajectory. As vehicles become increasingly software-defined, the collaboration between these stakeholders is essential to develop interoperable and scalable database solutions. The rise of cloud-based deployment models and edge computing further amplifies the capabilities of in-vehicle time series databases, allowing seamless data synchronization between vehicles and centralized systems. Moreover, regulatory mandates regarding vehicle data logging and cybersecurity are pushing both OEMs and fleet operators to invest in advanced data infrastructure, thus fueling the market’s upward momentum.

Regionally, the Asia Pacific market is witnessing the fastest growth, driven by the burgeoning automotive industry in countries like China, Japan, and South Korea. North America and Europe remain leading markets due to early adoption of connected vehicle technologies and strong presence of automotive OEMs and technology innovators. The Middle East & Africa and Latin America are also showing promising growth prospects as vehicle connectivity and smart mobility initiatives gain traction. Each region presents unique regulatory and consumer dynamics that influence the adoption patterns of in-vehicle time series databases, making regional strategies critical for market participants.

Component Analysis

The In-Vehicle Time Series Database market is segmented by component into Software, Hardware, and Services, each playing a crucial role in the ecosystem. Software forms the backbone of the market, providing the core functionalities for data ingestion, storage, querying, and analytics. As vehicles generate massive streams of time-stamped data, sophisticated software solutions are required to ensure data integrity, scalability, and low-latency access. The evolution of database architectures, including in-memory processing and distributed systems, has significantly enhanced the performance of in-vehicle data management. Leading software vendors are focusing on delivering lightweight, resource-efficient solutions that can operate reliably in constrained automotive

Automotive Time Series Database Market Outlook

According to our latest research, the global automotive time series database market size was valued at USD 1.42 billion in 2024. The market is anticipated to expand at a robust CAGR of 17.8% from 2025 to 2033, reaching a projected value of USD 7.12 billion by 2033. This strong growth trajectory is driven by the accelerating adoption of connected vehicles, advanced telematics, and the rising need for real-time data processing in automotive applications. The market is witnessing rapid technological advancement as automotive manufacturers and service providers increasingly integrate time series databases to manage, analyze, and derive insights from the enormous volumes of time-stamped data generated by modern vehicles.

A primary growth factor for the automotive time series database market is the proliferation of connected vehicles and the Internet of Things (IoT) within the automotive sector. Modern vehicles are now equipped with a range of sensors and telematics systems that continuously collect and transmit data related to vehicle performance, driver behavior, location, and environmental conditions. This data, often generated in real-time and at high frequency, requires robust time series database solutions for efficient storage, querying, and analysis. As automakers and fleet operators strive to enhance vehicle safety, optimize fleet operations, and deliver personalized user experiences, the demand for scalable and high-performance time series databases is surging. The ability to process and analyze real-time data streams is becoming a critical differentiator for automotive companies aiming to stay ahead in a highly competitive market.

Another significant driver is the growing emphasis on predictive maintenance and advanced analytics in the automotive industry. By leveraging time series databases, automotive companies can monitor vehicle health, predict component failures, and schedule maintenance proactively, thereby reducing downtime and operational costs. This is particularly important for commercial fleet operators and logistics companies, where unplanned vehicle breakdowns can have substantial financial implications. Time series databases enable the aggregation and analysis of historical and real-time sensor data, facilitating the development of predictive models and machine learning algorithms. These capabilities are essential for optimizing maintenance schedules, improving vehicle reliability, and extending asset lifecycles, further fueling market growth.

The rapid advancement of autonomous vehicles and smart mobility solutions is also playing a pivotal role in expanding the automotive time series database market. Autonomous vehicles generate massive volumes of time-stamped data from LIDAR, radar, cameras, and other sensors, which must be processed and analyzed in real time to ensure safe and efficient operation. Time series databases provide the necessary infrastructure to handle these data streams, supporting critical applications such as real-time decision-making, anomaly detection, and route optimization. As the automotive industry continues to move towards higher levels of automation and connectivity, the need for advanced data management solutions is expected to intensify, driving further adoption of time series databases.

From a regional perspective, North America currently leads the global automotive time series database market, supported by the presence of major automotive OEMs, advanced technology providers, and a strong ecosystem of connected vehicle initiatives. Europe follows closely, driven by stringent regulatory requirements, rapid adoption of electric vehicles, and a focus on smart mobility. The Asia Pacific region, particularly China, Japan, and South Korea, is emerging as a high-growth market, fueled by increasing vehicle production, urbanization, and government investments in intelligent transportation infrastructure. These regional dynamics are shaping the competitive landscape and influencing market strategies across the globe.

Component Analysis

The automotive time series database market is segmented by component into software, hardware, and services. The software segment holds the dominant share, accounting for more than 55% of the total market revenue in 2024. This dominance is attributed to the critical role of database management systems, analytics engines, and visualization tools in processing and interpreting vast amounts of

Time Series Databases Software Market Outlook

The global time series databases software market is experiencing significant expansion, with market size estimated at approximately USD 1.5 billion in 2023 and projected to reach USD 4.2 billion by 2032, registering a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is driven by the increasing need for real-time analytics and the management of time-stamped data across various industry verticals. The proliferation of IoT devices and the growing importance of time-stamped data in decision-making processes are key factors contributing to this upward trajectory. As businesses seek to leverage these capabilities, the demand for efficient time series databases continues to rise.

One of the major growth factors driving the time series databases software market is the burgeoning IoT ecosystem. With millions of devices generating vast amounts of data every second, there is an unprecedented demand for systems that can efficiently process, store, and analyze time-stamped data. IoT applications, such as smart cities, connected vehicles, and industrial automation, rely heavily on real-time data insights to optimize operations and improve outcomes. Consequently, organizations are investing in advanced time series databases to harness the potential of IoT-driven data streams effectively. This trend is expected to accelerate as IoT adoption continues to grow across various sectors.

Another pivotal growth factor is the increasing emphasis on predictive analytics and machine learning across industries. Time series databases play a crucial role in these areas by enabling businesses to analyze historical data patterns and predict future trends. In sectors like finance, healthcare, and energy, the ability to forecast future events accurately can lead to improved decision-making and strategic planning. For instance, financial institutions utilize time series databases for stock market analysis, while healthcare providers use them for patient monitoring and prognosis. This growing reliance on predictive analytics is expected to fuel the demand for time series database solutions in the coming years.

The need for high-performance and scalable data architectures is also contributing to market growth. Traditional relational databases are often ill-equipped to handle the unique challenges posed by time-stamped data, such as high write and query loads and the need for efficient compression and data retention strategies. Time series databases are specifically designed to address these challenges, offering features such as efficient storage, fast retrieval, and seamless integration with analytics tools. As organizations grapple with increasingly large datasets, the adoption of time series databases is anticipated to rise, driven by the demand for scalable and cost-effective solutions.

Regionally, North America holds a significant share of the time series databases software market, driven by the presence of numerous tech-savvy industries and a strong focus on digital transformation. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, the expansion of smart city initiatives, and increasing investments in IoT infrastructure. Europe also presents substantial growth prospects due to the growing adoption of advanced analytics solutions across various sectors. Meanwhile, Latin America and the Middle East & Africa are gradually embracing these technologies, albeit at a slower pace, as infrastructure and digital initiatives continue to develop. Each region's growth trajectory is influenced by local economic conditions, technology adoption rates, and regulatory frameworks.

Deployment Type Analysis

The analysis of deployment types in the time series databases software market reveals a dynamic landscape shaped by varying organizational needs and technological preferences. On-premises deployment remains a viable option for many businesses, particularly those in regulated industries where data security and control are paramount. Organizations in sectors such as finance and healthcare often prefer on-premises solutions to maintain stringent control over their data environments. These deployments offer the advantage of complete data custody and the flexibility to tailor configurations to specific organizational requirements. However, these benefits come with the trade-offs of higher upfront costs and the need for in-house technical expertise to manage and maintain the infrastructure effectively.

On the other hand, the cloud-based deployment model is witnessing

The global cloud-based time series database market is expected to reach USD 9.3 billion by 2033, growing at a CAGR of 12.8% during the forecast period. The market growth is attributed to increasing demand for real-time data analytics, growing adoption of IoT devices, and rising need for efficient and scalable storage solutions for large time-series datasets. However, high implementation cost and data security concerns may restrain market growth. The cloud-based time series database market is segmented by application into BFSI, retail, mining, chemical, automotive, manufacturing, scientific research, telecommunication, aerospace and defense, and others. The BFSI segment is expected to hold the largest market share due to increasing adoption of cloud-based solutions by financial institutions for real-time data analysis, fraud detection, and risk management. The retail segment is also anticipated to witness significant growth, as retailers are investing in cloud-based time series databases for inventory management, demand forecasting, and customer behavior analysis. Cloud-based time series databases (TSDBs) are designed to handle large volumes of timestamped data, enabling businesses to analyze and visualize data over time.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center // Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. See methodology at https://www.census.gov/programs-surveys/international-programs/about/idb.html

For environmental data measured by a variety of sensors and compiled from various sources, practitioners need tools that facilitate data access and data analysis. Data are often organized in formats that are incompatible with each other and that prevent full data integration. Furthermore, analyses of these data are hampered by the inadequate mechanisms for storage and organization. Ideally, data should be centrally housed and organized in an intuitive structure with established patterns for analyses. However, in reality, the data are often scattered in multiple files without uniform structure that must be transferred between users and called individually and manually for each analysis. This effort describes a process for compiling environmental data into a single, central database that can be accessed for analyses. We use the Logan River watershed and observed water level, discharge, specific conductance, and temperature as a test case. Of interest is analysis of flow partitioning. We formatted data files and organized them into a hierarchy, and we developed scripts that import the data to a database with structure designed for hydrologic time series data. Scripts access the populated database to determine baseflow separation, flow balance, and mass balance and visualize the results. The analyses were compiled into a package of scripts in Python, which can be modified and run by scientists and researchers to determine gains and losses in reaches of interest. To facilitate reproducibility, the database and associated scripts were shared to HydroShare as Jupyter Notebooks so that any user can access the data and perform the analyses, which facilitates standardization of these operations.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

A comprehensive database for human surface and intracranial EEG data that is suitable for a broad range of applications e.g. of time series analyses of brain activity. Currently, the EU database contains annotated EEG datasets from more than 200 patients with epilepsy, 50 of them with intracranial recordings with up to 122 channels. Each dataset provides EEG data for a continuous recording time of at least 96 hours (4 days) at a sample rate of up to 2500 Hz. Clinical patient information and MR imaging data supplement the EEG data. The total duration of EEG recordings included execeeds 30000 hours. The database is composed of different modalities: Binary files with EEG recording / MR imaging data and Relational database for supplementary meta data.

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

Investigator(s): Federal Judicial Center The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 100 court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant.Years Produced: Updated bi-annually with annual data.