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License information was derived automatically
From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:
Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.
Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.
Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.
Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.
This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:
A diagram of the MySQL database schema.
A SQL dump of the MySQL database schema, excluding the data.
A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
Raw data from each database table in CSV format.
A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
A text file containing the definitions of the size categories used in the collection_unit table.
The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:
Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.
Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.
Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.
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According to Cognitive Market Research, the global Business Intelligence market size is USD 16.9 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.50% from 2023 to 2030.
The demand for Business Intelligence s is rising due to the increasing data complexity and rising focus on data-driven decision-making.
Demand for adults remains higher in the Business Intelligence market.
The Business intelligence platform category held the highest Business intelligence market revenue share in 2023.
North American Business Intelligence will continue to lead, whereas the Asia-Pacific Business Intelligence market will experience the most substantial growth until 2030.
Growing Emphasis on Data-Driven Decision-Making to Provide Viable Market Output
In the Business Intelligence Tools market, the increasing recognition of the strategic importance of data-driven decision-making serves as a primary driver. Organizations across various industries are realizing the transformative power of insights derived from BI tools. As the volume of data generated continues to soar, businesses seek sophisticated tools that can efficiently analyze and interpret this information. The ability of BI tools to convert raw data into actionable insights empowers decision-makers to formulate informed strategies, enhance operational efficiency, and gain a competitive edge in a data-centric business landscape.
In June 2020, SAS and Microsoft established a comprehensive technology and go-to-market strategic alliance. As part of the collaboration, SAS's industry solutions and analytical products will be moved to Microsoft Azure, SAS Cloud's preferred cloud provider.
Source-news.microsoft.com/2020/06/15/sas-and-microsoft-partner-to-further-shape-the-future-of-analytics-and-ai/#:~:text=and%20SAS%20today%20announced%20an,from%20their%20digital%20transformation%20initiatives.
Rise in Adoption of Advanced Analytics and Artificial Intelligence to Propel Market Growth
Another significant driver in the Business Intelligence Tools market is the escalating adoption of advanced analytics and artificial intelligence (AI) capabilities. Modern BI tools are incorporating AI-driven functionalities such as machine learning algorithms, natural language processing, and predictive analytics. These technologies enable users to uncover deeper insights, identify patterns, and predict future trends. The integration of AI not only enhances the analytical capabilities of BI tools but also automates processes, reducing manual efforts and improving the overall efficiency of data analysis. This trend aligns with the industry's pursuit of more intelligent and automated BI solutions to derive maximum value from data assets.
In March 2020, IBM created a new, dynamic global dashboard to display the global spread of COVID-19 with the assistance of IBM Cognos Analytics. The World Health Organization (WHO) and state and municipal governments provide the COVID-19 data displayed in this dashboard.
Source-www.ibm.com/blog/creating-trusted-covid-19-data-for-communities/
Market Dynamics of the Business Intelligence tool Market
Data Security and Privacy Concerns to Restrict Market Growth
One of the key restraints in the Business Intelligence Tools market revolves around persistent concerns regarding data security and privacy. As organizations increasingly rely on BI tools to process and analyze sensitive business information, the risk of data breaches and unauthorized access becomes a prominent challenge. Heightened awareness of regulatory requirements, such as GDPR, has intensified the focus on protecting sensitive data. Businesses face the challenge of implementing robust security measures within BI tools to ensure compliance with regulations and safeguard against potential data vulnerabilities, thereby slowing down the adoption pace.
Impact of COVID-19 on the Business Intelligence market
The COVID-19 pandemic has had a profound impact on the Business Intelligence (BI) market. As organizations grappled with unprecedented disruptions, the need for timely and accurate insights became paramount. The pandemic accelerated the adoption of BI tools as businesses sought to navigate uncertainties and make data-driven decisions. Remote work became a norm, prompting increased demand for BI solutions that support virtual collaboration and enable users to access analytics from anywhere. Moreover, there w...
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The Big Data Analytics in Power Sector Market Report is Segmented Based On Power Industry (Power Generation, and Power Transmission and Distribution), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.
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He Power-To-X Market Report is Segmented by Dashboard (Power-To-H2, Power-To-NH3, Power-To-Methane, Power-To-Methanol, Other Dashboard), by End-User (Transportation, Agriculture, Manufacturing, Residential, Other End-Users), by Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:
Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.
Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.
Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.
Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.
This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:
A diagram of the MySQL database schema.
A SQL dump of the MySQL database schema, excluding the data.
A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
Raw data from each database table in CSV format.
A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
A text file containing the definitions of the size categories used in the collection_unit table.
The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:
Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.
Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.
Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.