Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only available for mammals, to birds. In addition, Frugivoria adds newly described species not included in other databases and harmonizes species classifications among databases. Frugivoria and its workflow enable researchers to quantify relationships between traits and the environment, as well as spatial trends in functional diversity, contributing to basic knowledge and applied conservation of frugivores in this region. By harmonizing trait information from disparate sources and providing code to access species occurrence data, this open-access database fills a major knowledge gap and enables more comprehensive trait-based studies of species exhibiting frugivory in this ecologically important region.

Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NE LIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix “iws”) and HUC 4 (variable prefix “hu4”) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NE GEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.

The Veterans Health Administration (VHA) is increasingly dependent upon data. Most of its employees generate and use vast amounts of data on a daily basis. To improve our capacity for data analysis while providing the most efficient and the highest quality health care to our Veteran patients, VHA, working with the VA Office of Information and Technology, implemented a health data warehouse. Central to this plan is consolidating data from disparate sources into a coherent single logical data model. The Corporate Data Warehouse (CDW) is the physical implementation of this logical data model at the enterprise level for VHA. Although the CDW initially began to store data as early as 2006, a renewed effort began in 2010 to accelerate CDW's content by including more subject areas from Veterans Health Information Systems and Technology Architecture (VistA) and content from other existing national data systems. CDW supports fully developed subject areas in its production environment as well as supporting rapid prototyping by extracting data directly from source systems with very minor data transformations. The Regional Data Warehouses and the Veterans Integrated Service Network (VISN) Data Warehouses share content from CDW and allow for greater reporting flexibility at the local level throughout the VHA organization.

Analysis of ‘Coho Distribution [ds326]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b1cc7bc9-0960-4008-a7e6-ffbae224a88e on 27 January 2022.

--- Dataset description provided by original source is as follows ---

June 2016 VersionThis dataset represents the "Observed Distribution" for coho salmon in California by using observations made only between 1990 and the present. It was developed for the express purpose of assisting with species recovery planning efforts. The process for developing this dataset was to collect as many observations of the species as possible and derive the stream-based geographic distribution for the species based solely on these positive observations.For the purpose of this dataset an observation is defined as a report of a sighting or other evidence of the presence of the species at a given place and time. As such, observations are modeled by year observed as point locations in the GIS. All such observations were collected with information regarding who reported the observation, their agency/organization/affiliation, the date that they observed the species, who compiled the information, etc. This information is maintained in the developers file geodatabase (©Environmental Science Research Institute (ESRI) 2016).To develop this distribution dataset, the species observations were applied to California Streams, a CDFW derivative of USGS National Hydrography Dataset (NHD) High Resolution hydrography. For each observation, a path was traced down the hydrography from the point of observation to the ocean, thereby deriving the shortest migration route from the point of observation to the sea. By appending all of these migration paths together, the "Observed Distribution" for the species is developed.It is important to note that this layer does not attempt to model the entire possible distribution of the species. Rather, it only represents the known distribution based on where the species has been observed and reported. While some observations indeed represent the upstream extent of the species (e.g., an observation made at a hard barrier), the majority of observations only indicate where the species was sampled for or otherwise observed. Because of this, this dataset likely underestimates the absolute geographic distribution of the species.It is also important to note that the species may not be found on an annual basis in all indicated reaches due to natural variations in run size, water conditions, and other environmental factors. As such, the information in this dataset should not be used to verify that the species are currently present in a given stream. Conversely, the absence of distribution linework for a given stream does not necessarily indicate that the species does not occur in that stream. The observation data were compiled from a variety of disparate sources including but not limited to CDFW, USFS, NMFS, timber companies, and the public. Forms of documentation include CDFW administrative reports, personal communications with biologists, observation reports, and literature reviews. The source of each feature (to the best available knowledge) is included in the data attributes for the observations in the geodatabase, but not for the resulting linework. The spatial data has been referenced to California Streams, a CDFW derivative of USGS National Hydrography Dataset (NHD) High Resolution hydrography.Usage of this dataset:Examples of appropriate uses include:- species recovery planning- Evaluation of future survey sites for the species- Validating species distribution modelsExamples of inappropriate uses include:- Assuming absence of a line feature means that the species are not present in that stream.- Using this data to make parcel or ground level land use management decisions.- Using this dataset to prove or support non-existence of the species at any spatial scale.- Assuming that the line feature represents the maximum possible extent of species distribution.All users of this data should seek the assistance of qualified professionals such as surveyors, hydrologists, or fishery biologists as needed to ensure that such users possess complete, precise, and up to date information on species distribution and water body location.Any copy of this dataset is considered to be a snapshot of the species distribution at the time of release. It is impingent upon the user to ensure that they have the most recent version prior to making management or planning decisions.Please refer to "Use Constraints" section below.

--- Original source retains full ownership of the source dataset ---

According to Cognitive Market Research, the global semantic knowledge graphing market size is USD 1512.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 14.80% from 2024 to 2031.

North America held the major market of around 40% of the global revenue with a market size of USD 604.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.0% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 453.66 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 347.81 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.8% from 2024 to 2031.
Latin America market of around 5% of the global revenue with a market size of USD 75.61 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.2% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 30.24 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.5% from 2024 to 2031.
The natural language processing knowledge graphing held the highest growth rate in semantic knowledge graphing market in 2024.

Market Dynamics of Semantic Knowledge Graphing Market

Key Drivers of Semantic Knowledge Graphing Market

Growing Volumes of Structured, Semi-structured, and Unstructured Data to Increase the Global Demand

The global demand for semantic knowledge graphing is escalating in response to the exponential growth of structured, semi-structured, and unstructured data. Enterprises are inundated with vast amounts of data from diverse sources such as social media, IoT devices, and enterprise applications. Structured data from databases, semi-structured data like XML and JSON, and unstructured data from documents, emails, and multimedia files present significant challenges in terms of organization, analysis, and deriving actionable insights. Semantic knowledge graphing addresses these challenges by providing a unified framework for representing, integrating, and analyzing disparate data types. By leveraging semantic technologies, businesses can unlock the value hidden within their data, enabling advanced analytics, natural language processing, and knowledge discovery. As organizations increasingly recognize the importance of harnessing data for strategic decision-making, the demand for semantic knowledge graphing solutions continues to surge globally.

Demand for Contextual Insights to Propel the Growth

The burgeoning demand for contextual insights is propelling the growth of semantic knowledge graphing solutions. In today's data-driven landscape, businesses are striving to extract deeper contextual meaning from their vast datasets to gain a competitive edge. Semantic knowledge graphing enables organizations to connect disparate data points, understand relationships, and derive valuable insights within the appropriate context. This contextual understanding is crucial for various applications such as personalized recommendations, predictive analytics, and targeted marketing campaigns. By leveraging semantic technologies, companies can not only enhance decision-making processes but also improve customer experiences and operational efficiency. As industries across sectors increasingly recognize the importance of contextual insights in driving innovation and business success, the adoption of semantic knowledge graphing solutions is poised to witness significant growth. This trend underscores the pivotal role of semantic technologies in unlocking the true potential of data for strategic advantage in today's dynamic marketplace.

Restraint Factors Of Semantic Knowledge Graphing Market

Stringent Data Privacy Regulations to Hinder the Market Growth

Stringent data privacy regulations present a significant hurdle to the growth of the Semantic Knowledge Graphing market. Regulations such as GDPR (General Data Protection Regulation) in Europe and CCPA (California Consumer Privacy Act) in the United States impose strict requirements on how organizations collect, store, process, and share personal data. Compliance with these regulations necessitates robust data protection measures, including anonymization, encryption, and access controls, which can complicate the implementation of semantic knowledge graphing systems. Moreover, concerns about data breach...

The local law enforcement locations feature class/ shapefile contains point location and tabular information pertaining to a wide range of law enforcement entities in the United States. Law Enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Census of State and Local Law Enforcement Agencies (CSLLEA). Unlike the previous version of this dataset, published in 2009, federal level law enforcement agencies are excluded from this effort. Data fusion techniques are utilized to synchronize overlapping yet disparate source data. The primary sources for this effort are the DOJ-BJS CSLLEA from 2008 and the previously mentioned 2009 feature class from Homeland Security Infrastructure Foundation-Level Data (HIFLD). This feature class contains data for agencies across all 50 U.S. states, Washington D.C. and Puerto Rico.

The Modernising Energy Data Access (MEDA) competition was set up by Innovate UK and the Modernising Energy Data group to help develop the concept of a Common Data Architecture (CDA) for the Energy Sector. One of the main goals of the Common Data Architecture is to improve data sharing across the energy sector and make data more interoperable across organisations. In order to effectively understand and combine data from disparate sources, it is necessary for the data provider and data user to have a common understanding of the key concepts and terms used in the data. However, the lack of authoritative definitions or standards in the energy sector means that this is often not the case. In addition, the National Digital Twin Programme has identified that it will be important to extend this common understanding across the wider economy to ensure that data is interoperable and understandable across traditional sector boundaries. Within this page we will document existing Glossaries and Vocabularies from across the energy sector, analyse these to identify overlaps, disagreements and gap and begin to reconcile issues for a number of key use cases that have been identified in the MEDA competition.