Market Analysis of Cloud-based Big Data The cloud-based big data market is projected to experience substantial growth over the forecast period from 2025 to 2033, driven by factors such as the increasing adoption of cloud computing, the growing need for data analytics, and the proliferation of IoT devices. The market size is estimated to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. Key players in the market include Teradata, Microsoft, IBM, Oracle, SAS Institute, Google, Adobe, Talend, and TIBCO Software. Market Dynamics and Trends The growth of the cloud-based big data market is being fueled by a number of factors, including the increasing volume and variety of data being generated, the need for real-time insights, and the desire to reduce costs. Cloud-based big data solutions offer a number of advantages over on-premises solutions, including scalability, flexibility, and cost-effectiveness. However, the market also faces a number of challenges, such as data security and privacy concerns, the lack of skilled professionals, and the complexity of integrating cloud-based big data solutions with existing systems. Despite these challenges, the cloud-based big data market is expected to continue to grow rapidly in the coming years, as organizations seek to gain insights from their data and improve their decision-making processes.

Map showing the Maine DEP Biomonitoring Programs wetland and stream sample stations. This map is met to be the replacement for Maine DEP Biomonitoring Programs Google Earth mapping project.

This dataset contains categorizations, readability scores calculated using readable.com, PEMAT scores by two independent raters, and compatibility with established clinical practice guidelines (by two independent raters) for Hashimoto's Thyroiditis online patient resources. All data was publically accessible from Google Search.

This dataset contains the British foods that are protected under EU law. The EU Protected Food Name scheme highlights regional and traditional foods whose authenticity and origin can be guaranteed. The product is awarded one of three marks: Protected Designation of Origin (PDO); Protected Geographical Indication (PGI); and Traditional Speciality Guaranteed (TSG). Under this system a named food or drink registered at a European level will be given legal protection against imitation throughout the EU. Producers who register their products for protection benefit from having a raised awareness of their product throughout Europe. This may in turn help them take advantage of consumers' increasing awareness of the importance of regional and speciality foods. data is provided as a KML(WGS84) and SHP(OSGB36). the data was found on this site: https://maps.google.com/maps/ms?msid=214801935067985382336.0004ef734d0081481e7be&msa=0&ll=52.173932,-1.340332&spn=5.661049,9.876709 and seems to have been sourced from here: https://www.gov.uk/protected-food-names-guidance-for-producers A licence is not visible, but as the source is a .gov.uk website it is added to sharegeo under a OGL license. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-01-16 and migrated to Edinburgh DataShare on 2017-02-22.

The dataset contains a refined version of data originally extracted from the 2019 Spanish Google Books corpus. It is related to the upcoming publication Guerrero Montero J. and Blythe R. A (in submission). 'Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model.'

This spreadsheet contains the anonymised data collected as part of a survey of UK researchers in their use of research software. We asked people specifically about 'research software' which we defined as: 'Software that is used to generate, process or analyse results that you intend to appear in a publication (either in a journal, conference paper, monograph, book or thesis). Research software can be anything from a few lines of code written by yourself, to a professionally developed software package. Software that does not generate, process or analyse results - such as word processing software, or the use of a web search - does not count as 'research software' for the purposes of this survey.' We contacted 1,000 randomly selected researchers at each of 15 Russell Group universities. From the 15,000 invitations to complete the survey, we received 417 responses - a rate of 3% which is fairly normal for a blind survey. We used Google Forms to collect responses. The responses have good representation from across the disciplines, seniorities and genders. This is a statistically significant number of responses that can be used to represent the views of people in research-intensive universities in the UK. An overview of the data is available on the worksheet 'Summary data'. Responses to questions are ordered by unique respondent ID. Please read the 'README' worksheet for additional information about the collection and processing of this data. This survey data is licensed under a Creative Commons by Attribution licence. Copyright resides with The University of Edinburgh on behalf of the Software Sustainability Institute.

This dataset describes the congestion charging zones for London. The zip contails the current zone and the Western Extension which was active between 2007-2011. It also shows small zones outside of the congestion zone where residents qualify for a 90% discount. More information on the history of the congestion charge and it's zones can be found here: http://en.wikipedia.org/wiki/London_congestion_charge. extracted from https://maps.google.co.uk/maps/ms?gl=uk&ptab=2&ie=UTF8&oe=UTF8&msa=0&msid=205941419246931266022.000497ed27dc9a2a1b0fb&dg=feature and then modified in QGIS to separate out the kmz file and break it up into the central and western extension zones. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-05-08 and migrated to Edinburgh DataShare on 2017-02-22.