A Comprehensive List of Open Data Portals from Around the World

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Public Domain Dedication and License (PDDL) PREAMBLE The Open Data Commons – Public Domain Dedication and Licence is a document intended to allow you to freely share, modify, and use this work for any purpose and without any restrictions. This licence is intended for use on databases or their contents (“data”), either together or individually.

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This document can cover either or both of the database and its contents (the data). Because databases can have a wide variety of content – not just factual data – rightsholders should use the Open Data Commons – Public Domain Dedication & Licence for an entire database and its contents only if everything can be placed under the terms of this document. Because even factual data can sometimes have intellectual property rights, rightsholders should use this licence to cover b...

CN: Industrial Enterprise: Share Holding Ltd: Total Asset data was reported at 16,708,517.000 RMB mn in 2017. This records an increase from the previous number of 15,395,257.000 RMB mn for 2016. CN: Industrial Enterprise: Share Holding Ltd: Total Asset data is updated yearly, averaging 4,413,357.000 RMB mn from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 16,708,517.000 RMB mn in 2017 and a record low of 196,994.000 RMB mn in 1998. CN: Industrial Enterprise: Share Holding Ltd: Total Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Share Holding Limited.

This is the dataset presented in the following manuscript: The Surface Water Chemistry (SWatCh) database: A standardized global database of water chemistry to facilitate large-sample hydrological research, which is currently under review at Earth System Science Data.

Openly accessible global scale surface water chemistry datasets are urgently needed to detect widespread trends and problems, to help identify their possible solutions, and determine critical spatial data gaps where more monitoring is required. Existing datasets are limited in availability, sample size/sampling frequency, and geographic scope. These limitations inhibit the answering of emerging transboundary water chemistry questions, for example, the detection and understanding of delayed recovery from freshwater acidification. Here, we begin to address these limitations by compiling the global surface water chemistry (SWatCh) database. We collect, clean, standardize, and aggregate open access data provided by six national and international agencies to compile a database containing information on sites, methods, and samples, and a GIS shapefile of site locations. We remove poor quality data (for example, values flagged as “suspect” or “rejected”), standardize variable naming conventions and units, and perform other data cleaning steps required for statistical analysis. The database contains water chemistry data for streams, rivers, canals, ponds, lakes, and reservoirs across seven continents, 24 variables, 33,722 sites, and over 5 million samples collected between 1960 and 2022. Similar to prior research, we identify critical spatial data gaps on the African and Asian continents, highlighting the need for more data collection and sharing initiatives in these areas, especially considering freshwater ecosystems in these environs are predicted to be among the most heavily impacted by climate change. We identify the main challenges associated with compiling global databases – limited data availability, dissimilar sample collection and analysis methodology, and reporting ambiguity – and provide recommended solutions. By addressing these challenges and consolidating data from various sources into one standardized, openly available, high quality, and trans-boundary database, SWatCh allows users to conduct powerful and robust statistical analyses of global surface water chemistry.

Researchers seeking to share their data with coordinating centers such as the National Database for Autism Research (NDAR), face numerous barriers to establishing new connections and maintaining existing ones. We sought to dramatically reduce the time and money required to establish and maintain the interoperability of data between research centers, by establishing a process where manual recoding of data is replaced by data sharing instructions in the form of extraction and transformation scripts. Over the course of seven typical (20-60 subjects, 400-1000 fields each) data submissions to NDAR, the need for duplication, retranscription, or restructuring of the source data was fully eliminated. Separating the extraction and transformation scripts from data files also eradicated the impact of additional data collection on the time required to repeat successful transmissions. Revision controlled management of these scripts also provided a new benefit: traceability of the transformation process itself. Now, point-in-time retrieval of extraction scripts and explanations for modifications to the data sharing interface are possible. This method has proven to be successful and efficient for interfacing research data with NDAR. It presents little-to-no impact to transmitting investigators’ data, ensures high data integrity, trivializes the complexities of repeatedly modifying a growing dataset over time, and introduces traceability to the collaborative process of integrating two collections of data with one another.

DNA Database Software Market Outlook

According to our latest research, the global DNA Database Software market size is valued at USD 1.28 billion in 2024, with a robust growth trajectory expected over the next decade. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 3.99 billion by 2033. This growth is primarily attributed to the increasing adoption of advanced genomic technologies, the rising demand for forensic and clinical applications, and the proliferation of cloud-based solutions. As per our analysis, the integration of artificial intelligence and machine learning within DNA database software is also acting as a significant catalyst, enabling more accurate, efficient, and scalable data management and analysis across a range of industries.

One of the primary growth drivers for the DNA Database Software market is the escalating need for sophisticated forensic analysis tools among law enforcement agencies worldwide. The surge in criminal activities and the necessity for accurate and rapid identification of suspects have compelled agencies to invest in advanced DNA database software. These solutions facilitate the storage, retrieval, and comparison of vast amounts of genetic data, significantly enhancing the efficiency of forensic investigations. Furthermore, the increasing digitization of criminal records and the growing emphasis on cross-border crime-solving collaborations are propelling the adoption of DNA database software, particularly in developed regions such as North America and Europe. The ability to integrate these databases with other biometric systems is further strengthening their utility and market demand.

Another significant factor fueling market growth is the expanding application of DNA database software in healthcare and research sectors. Healthcare providers and research institutes are leveraging these platforms for clinical diagnostics, personalized medicine, and large-scale genetic research projects. The rise of precision medicine and the need for comprehensive data analysis to identify genetic markers associated with various diseases have underscored the importance of robust DNA database solutions. Additionally, the increasing prevalence of genetic disorders and the growing interest in ancestry and genealogy services are broadening the market’s scope. The integration of DNA database software with electronic health records (EHRs) and laboratory information management systems (LIMS) is further streamlining workflows and improving patient outcomes, making these solutions indispensable in modern healthcare settings.

Technological advancements and the shift towards cloud-based deployment models are also accelerating market expansion. Cloud-based DNA database software offers scalability, cost-effectiveness, and remote accessibility, making it an attractive option for organizations with limited IT infrastructure. These solutions enable real-time data sharing and collaboration among geographically dispersed teams, thereby enhancing research productivity and forensic investigation capabilities. Moreover, the incorporation of advanced security features, such as encryption and multi-factor authentication, is addressing data privacy concerns and fostering greater trust among end-users. The ongoing evolution of artificial intelligence and big data analytics is expected to further revolutionize the DNA database software landscape, enabling more sophisticated pattern recognition and predictive modeling capabilities.

From a regional perspective, North America currently dominates the DNA Database Software market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading software vendors, robust healthcare infrastructure, and strong government support for forensic and biomedical research are key factors underpinning North America’s leadership position. Europe is witnessing significant growth, driven by stringent regulatory frameworks and increasing investments in public safety and healthcare innovation. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rising awareness, expanding healthcare expenditure, and the rapid adoption of digital technologies. Countries such as China, India, and Japan are at the forefront of this growth, supported by government initiatives and a burgeoning biotechnology sector.

CN: Industrial Enterprise: Share Holding Ltd: Total Liability: Long Term data was reported at 327,328.000 RMB mn in 2004. This records an increase from the previous number of 302,152.000 RMB mn for 2003. CN: Industrial Enterprise: Share Holding Ltd: Total Liability: Long Term data is updated yearly, averaging 283,864.000 RMB mn from Dec 1998 (Median) to 2004, with 7 observations. The data reached an all-time high of 327,328.000 RMB mn in 2004 and a record low of 23,114.000 RMB mn in 1998. CN: Industrial Enterprise: Share Holding Ltd: Total Liability: Long Term data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Share Holding Limited.

Community portal for researchers and content management system for data and databases. Intended to provide common source of data to research community and data about Research Resource Identifiers (RRIDs), which can be used in scientific publications. Central service where RRIDs can be searched and created. Designed to help communities of researchers create their own portals to provide access to resources, databases and tools of relevance to their research areas. Adds value to existing scientific resources by increasing their discoverability, accessibility, visibility, utility and interoperability, regardless of their current design or capabilities and without need for extensive redesign of their components or information models. Resources can be searched and discovered at multiple levels of integration, from superficial discovery based on limited description of resource at SciCrunch Registry, to deep content query at SciCrunch Data Federation.

The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a BEnchmark database Towards BCI Application (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA are in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database can be downloaded from the following link http://bci.med.tsinghua.edu.cn/download.html.

We developed spatial summary (GIS) layers for a study of factors influencing the distribution of cave and karst associated fauna within the Appalachian Landscape Conservation Cooperative region, one of 22 public-private partnerships established by the United States Fish and Wildlife Service to aid in developing landscape scale solutions to conservation problems (https://lccnetwork.org/lcc/appalachian). We gathered occurrence data on cave-limited terrestrial and aquatic troglobiotic species from a variety of sources within the Appalachian LCC region covering portions of 15 states. Occurrence records were developed from the scientific literature, existing biodiversity databases, personal records of the authors, museum accessions, state Natural Heritage programs, and The Nature Conservancy (for Tennessee). Occurrence records were identified by location and translated into a GIS database. Although the precise locations cannot be made public due the sensitivity of the information, data sharing agreements, and restrictions under the Federal Cave Resources Protection Act of 1988, we summarized the data spatially using a coarse 20x20km vector grid. We used these occurence records, summarized at the 20x20km grid resolution in statistical modeling to examine physical factors predictive of cave dwelling fauna. Spatial summaries were developed for all cave dwelling species in our database where we had location coordinates for nine faunal groups (five terrestrial and four aquatic) that are common components of terrestrial and aquatic cave communities: ground beetles (Carabidae), millipedes, pseudoscorpions, spiders, and springtails for terrestrial species groups, and amphipods (Crangonyctidae and Gammaridae), isopods (Asellidae), crayfishes (Cambaridae), and fishes (Amblyopsidae) for aquatic species groups.

Multi-Agency Ground Plot (MAGPlot) database (DB) is a pan-Canadian forest ground-plot data repository. The database synthesize forest ground plot data from various agencies, including the National Forest Inventory (NFI) and 12 Canadian jurisdictions: Alberta (AB), British Columbia (BC), Manitoba (MB), New Brunswick (NB), Newfoundland and Labrador (NL), Nova Scotia (NS), Northwest Territories (NT), Ontario (ON), Prince Edward Island (PE), Quebec (QC), Saskatchewan (SK), and Yukon Territory (YT), contributed in their original format. These datasets underwent data cleaning and quality assessment using the set of rules and standards set by the contributors and associated documentations, and were standardized, harmonized, and integrated into a single, centralized, and analysis-ready database. The primary objective of the MAGPlot project is to collate and harmonize forest ground plot data and to present the data in a findable, accessible, interoperable, and reusable (FAIR) format for pan-Canadian forest research. The current version includes both historical and contemporary forest ground plot data provided by data contributors. The standardized and harmonized dataset includes eight data tables (five site related and three tree measurement tables) in a relational database schema. Site-related tables contain information on geographical locations, treatments (e.g. stand tending, regeneration, and cutting), and disturbances caused by abiotic factors (e.g., weather, wildfires) or biotic factors (e.g., disease, insects, animals). Tree-related tables, on the other hand, focus on measured tree attributes, including biophysical and growth parameters (e.g., DBH, height, crown class), species, status, stem conditions (e.g., broken or dead tops), and health conditions. While most contributors provided large and small tree plot measurements, only NFI, AB, MB, and SK contributed datasets reported at regeneration plot level (e.g., stem count, regeneration species). Future versions are expected to include updated and/or new measurement records as well as additional tables and measured and compiled (e.g., tree volume and biomass) attributes. MAGPlot is hosted through Canada’s National Forest Information System (https://nfi.nfis.org/en/maps). --------------------------------------------------- LATEST SITE TREATMENTS LAYER: --------------------------------------------------- Shows the most recently applied treatment class for each MAGPlot site. These treatment classes are broad categories, with more specific treatment details available in the full dataset. ----------- NOTES: ----------- The MAGPlot release (v1.0 and v1.1) does not include NL and SK datasets due to pending Data Sharing Agreements, ongoing data processing, or restrictions on third-party sharing. These datasets will be included in future releases. While certain jurisdictions permit open or public data sharing, given that requestor signs and adheres the Data Use agreement, there are some jurisdictions that require a jurisdiction-specific request form to be signed in addition to the Data Use Agreement form. For the MAGPlot Data Dictionary, other metadata, datasets available for open sharing (with approximate locations), data requests (for other datasets or exact coordinates), and available data visualization products, please check all the folders in the “Data and Resources” section below. Coordinates in web services have been randomized within 5km of true location to preserve site integrity Access the WMS (Web Map Service) layers from the “Data and Resources” section below. A data request must be submitted to access historical datasets, datasets restricted by data-use agreements, or exact plot coordinates using the link below. NFI Data Request Form: https://nfi.nfis.org/en/datarequestform --------------------------------- ACKNOWLEDGEMENT: --------------------------------- We acknowledge and recognize the following agencies that have contributed data to the MAGPlot database: Government of Alberta - Ministry of Agriculture, Forestry, and Rural Economic Development - Forest Stewardship and Trade Branch Government of British Columbia - Ministry of Forests - Forest Analysis and Inventory Branch Government of Manitoba - Ministry of Economic, Development, Investment, Trade, and Natural Resources - Forestry and Peatlands Branch Government of New Brunswick - Ministry of Natural Resources and Energy Development - Forestry Division, Forest Planning and Stewardship Branch Government of Newfoundland & Labrador - Department of Fisheries, Forestry and Agriculture - Forestry Branch Government of Nova Scotia - Ministry of Natural Resources and Renewables - Department of Natural Resources and Renewables Government of Northwest Territories - Department of Environment & Climate Change - Forest Management Division Government of Ontario - Ministry of Natural Resources and Forestry - Science and Research Branch, Forest Resources Inventory Unit Government of Prince Edward Island - Department of Environment, Energy, and Climate Action - Forests, Fish, and Wildlife Division Government of Quebec - Ministry of Natural Resources and Forests - Forestry Sector Government of Saskatchewan - Ministry of Environment - Forest Service Branch Government of Yukon - Ministry of Energy, Mines, and Resources - Forest Management Branch Government of Canada - Natural Resources Canada - Canadian Forest Service - National Forest Inventory Projects Office

Journal of Big Data FAQ - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

Journal of Big Data Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material. All articles published by the Journal of Big Data are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. As authors of articles published in the Journal of Big Data you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement. For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines.

In-Memory Database as a Service Market Outlook

According to our latest research, the Global In-Memory Database as a Service market size was valued at $2.7 billion in 2024 and is projected to reach $13.4 billion by 2033, expanding at an impressive CAGR of 19.8% during the forecast period of 2025–2033. This robust growth is primarily driven by the accelerating digital transformation across industries, which has heightened the demand for real-time analytics, ultra-low latency data processing, and scalable cloud-native database solutions. Organizations are increasingly prioritizing agility and responsiveness in their IT operations, leading to a surge in adoption of in-memory database as a service (IMDBaaS) platforms that deliver instant data access and seamless scalability, thereby fueling market expansion on a global scale.

Regional Outlook

North America currently commands the largest share of the global In-Memory Database as a Service market, accounting for over 38% of total revenue in 2024. This dominance is attributed to the region’s mature enterprise IT ecosystem, widespread adoption of cloud infrastructure, and the presence of leading technology giants and cloud service providers. The United States, in particular, is a hub for innovation, with early adoption of advanced analytics, artificial intelligence, and IoT applications that require real-time data processing capabilities. Favorable regulatory frameworks, strong investment in R&D, and a highly skilled workforce further reinforce North America’s leadership in the IMDBaaS landscape. The region’s enterprises, especially in BFSI, healthcare, and IT sectors, are leveraging these platforms to drive digital transformation and gain competitive advantages.

The Asia Pacific region is poised to be the fastest-growing market for In-Memory Database as a Service, with a projected CAGR exceeding 22.5% through 2033. Rapid digitization, expanding cloud adoption, and burgeoning e-commerce and fintech sectors in countries like China, India, Japan, and South Korea are key growth drivers. Governments in the region are investing heavily in smart city projects, digital infrastructure, and data-driven governance, creating robust demand for real-time analytics and high-performance database solutions. Furthermore, the proliferation of mobile devices, IoT deployments, and a growing base of tech-savvy SMEs are accelerating the shift to IMDBaaS platforms. Strategic partnerships between global cloud providers and local enterprises are also catalyzing market penetration and innovation in the region.

Emerging economies in Latin America, Middle East, and Africa are experiencing a gradual but steady uptake of In-Memory Database as a Service solutions. While these regions collectively hold a smaller share of the global market, their growth trajectory is promising, driven by increasing cloud adoption, digital banking initiatives, and modernization of legacy IT infrastructures. However, challenges such as limited access to advanced cloud infrastructure, data sovereignty concerns, and regulatory uncertainties can impede rapid adoption. Localized demand, language barriers, and the need for region-specific compliance also impact the pace of IMDBaaS deployment. Nevertheless, as cloud service providers expand their footprint and governments introduce supportive digital policies, these regions are expected to contribute significantly to the market’s long-term growth.

Report Scope

CN: Industrial Enterprise: Share Holding Ltd: SE: Paidup Capital data was reported at 3,042,344.000 RMB mn in 2017. This records an increase from the previous number of 2,932,587.000 RMB mn for 2016. CN: Industrial Enterprise: Share Holding Ltd: SE: Paidup Capital data is updated yearly, averaging 996,750.500 RMB mn from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 3,042,344.000 RMB mn in 2017 and a record low of 45,580.000 RMB mn in 1998. CN: Industrial Enterprise: Share Holding Ltd: SE: Paidup Capital data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Share Holding Limited.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022).

Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. 10.13140/RG.2.2.31759.41123. Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html.

TERMS:

Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation.

DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Context : We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session. It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms. Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France. Duration : Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline. Documents Instructions: checklist read by experimenters during the experiments. Questionnaires: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version Performance: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires Scenarios/scripts : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI Database : raw signals Dataset A : N=60 participants Dataset B : N=21 participants Dataset C : N=6 participants

Sensitive Data Discovery for Databases Market Outlook

According to our latest research, the Global Sensitive Data Discovery for Databases market size was valued at $2.3 billion in 2024 and is projected to reach $8.2 billion by 2033, expanding at a CAGR of 14.7% during 2024–2033. One of the major factors driving the growth of this market globally is the increasing regulatory scrutiny around data privacy and security, compelling organizations across industries to proactively identify, classify, and protect sensitive information stored within their databases. With the proliferation of digital transformation initiatives and the explosion of data volumes, enterprises are recognizing the importance of robust sensitive data discovery solutions to mitigate risks, ensure compliance, and safeguard customer trust.

Regional Outlook

North America continues to dominate the Sensitive Data Discovery for Databases market, capturing the largest share of the global revenue in 2024. This region benefits from a mature IT infrastructure, early adoption of advanced cybersecurity solutions, and stringent regulatory frameworks such as the CCPA and HIPAA in the United States. The presence of leading technology providers and a high concentration of data-driven enterprises further fuel market growth in this region. North America accounted for over 38% of the global market value in 2024, and its leadership is underpinned by a robust ecosystem of managed service providers, cloud vendors, and compliance consultants. The region’s enterprises are investing heavily in automation and AI-driven data discovery tools to address mounting privacy concerns and evolving cyber threats.

The Asia Pacific region is emerging as the fastest-growing market for Sensitive Data Discovery for Databases, with a projected CAGR exceeding 18.2% between 2024 and 2033. Countries like China, India, Japan, and Australia are experiencing rapid digitalization across BFSI, healthcare, and government sectors, driving the demand for sophisticated data governance and security solutions. The surge in cloud adoption, growing awareness about data privacy, and increasing investments in IT modernization projects are key growth accelerators in this region. Moreover, new data protection laws and cross-border data flow regulations are prompting organizations to invest in advanced data discovery and classification technologies to ensure compliance and avoid hefty penalties.

In emerging economies across Latin America and the Middle East & Africa, the adoption of Sensitive Data Discovery for Databases solutions is steadily gaining momentum, albeit at a slower pace compared to mature markets. Challenges such as limited IT budgets, skill shortages, and fragmented regulatory landscapes are impeding widespread adoption. However, localized demand is rising as governments roll out new data protection mandates and multinational corporations expand their operations in these regions. Tailored solutions that address region-specific compliance requirements and language localization are gradually bridging the adoption gap, paving the way for future growth.

Report Scope

The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a \textbf{BE}nchmark database \textbf{T}owards BCI \textbf{A}pplication (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA is in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend to use the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database has an alternative download website link of http://bci.med.tsinghua.edu.cn/download.html.

This collection contains catalog records from the University of Michigan Museum of Zoology’s Insect Division’s specimen collection. Many specimen records include a specimen and label data image. The database currently contains about 400,000 specimen records out of the estimated 3 million estimated specimens in the collection. Some records contain complete collection, preparation, and taxonomic detail, while others only have a specimen data image and limited taxonomic detail. Records include specimen information from the early 1800’s through the present and are of global distribution. Most of our databased or digitized records currently are of Orthoptera (grasshoppers, katydids, & crickets), Odonata (dragonflies & damselfies), and Hymenoptera (bees, wasps & ants)

Bird Strike Database Integration Market Outlook

According to our latest research, the Global Bird Strike Database Integration market size was valued at $254 million in 2024 and is projected to reach $682 million by 2033, expanding at a robust CAGR of 11.4% during the forecast period from 2025 to 2033. The primary factor fueling the growth of this market globally is the increasing emphasis on aviation safety and regulatory compliance, which has prompted airports, airlines, and aviation authorities to adopt advanced data integration solutions for the efficient management and analysis of bird strike incidents. As the aviation industry continues to expand, the need for real-time data sharing and predictive analytics to minimize operational disruptions and enhance risk mitigation strategies is becoming more pronounced, thereby driving the widespread adoption of bird strike database integration technologies.

Regional Outlook

North America currently holds the largest share of the global Bird Strike Database Integration market, accounting for approximately 38% of the total market value in 2024. This dominance can be attributed to the region's mature aviation infrastructure, stringent regulatory frameworks, and early adoption of technology-driven safety protocols by both commercial and military aviation sectors. The Federal Aviation Administration (FAA) and other regulatory bodies have set rigorous standards for bird strike reporting and data management, compelling airports and airlines to invest heavily in integrated database solutions. Furthermore, the presence of leading technology providers and the continuous push for innovation in aviation safety systems have solidified North America's position as the primary revenue generator in this market. The region's focus on leveraging big data, artificial intelligence, and machine learning for predictive analytics further strengthens its leadership in bird strike database integration.

The Asia Pacific region is projected to be the fastest-growing market, with a forecasted CAGR of 14.7% from 2025 to 2033. This rapid growth is driven by substantial investments in airport infrastructure, the expansion of regional airline networks, and rising air traffic, particularly in countries such as China, India, and Southeast Asian nations. Governments across the Asia Pacific are increasingly recognizing the importance of wildlife hazard management and are implementing policies that mandate the integration of advanced database solutions for bird strike prevention. Additionally, the proliferation of cloud-based deployment models and the availability of cost-effective software-as-a-service (SaaS) platforms are lowering entry barriers for small and medium-sized airports, further accelerating market growth in the region. The surge in international collaborations and technology transfer agreements is also contributing to the swift adoption of bird strike database integration solutions across Asia Pacific.

In contrast, emerging economies in Latin America and the Middle East & Africa are witnessing a gradual increase in the adoption of bird strike database integration solutions, albeit at a slower pace compared to developed regions. These markets face unique challenges such as limited funding for aviation safety initiatives, a shortage of skilled IT professionals, and inconsistent regulatory enforcement. However, localized demand is on the rise due to increasing air travel, the expansion of commercial aviation fleets, and growing awareness about the economic and safety impacts of bird strike incidents. Policy reforms and international partnerships are gradually improving the adoption landscape, but the pace of implementation remains uneven, highlighting the need for tailored solutions and capacity-building efforts to overcome regional barriers.

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