Environmental Data from the paper 'Combining Disparate Data Sources for Improved Poverty Prediction and Mapping' (Pokhriyal and Jacques, 2017, www.pnas.org/cgi/doi/10.1073/pnas.1700319114).For data sources, see Table S1 in the supplementray information provided with the paper.LEGEND

  LC11
  Post-flooding or irrigated croplands (or aquatic)


  LC14
  Rainfed croplands


  LC20
  Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)


  LC30
  Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%) 


  LC40
  Closed to open (>15%) broadleaved evergreen or semi-deciduous forest (>5m)


  LC50
  Closed (>40%) broadleaved deciduous forest (>5m)


  LC60
  Open (15-40%) broadleaved deciduous forest/woodland (>5m)


  LC70
  Closed (>40%) needleleaved evergreen forest (>5m)


  LC90
  Open (15-40%) needleleaved deciduous or evergreen forest (>5m)


  LC100
  Closed to open (>15%) mixed broadleaved and needleleaved forest (>5m)


  LC110
  Mosaic forest or shrubland (50-70%) / grassland (20-50%)


  LC120
  Mosaic grassland (50-70%) / forest or shrubland (20-50%) 


  LC130
  Closed to open (>15%) (broadleaved or needleleaved, evergreen or deciduous) shrubland (15%) herbaceous vegetation (grassland, savannas or lichens/mosses)


  LC150
  Sparse (15%) broadleaved forest regularly flooded (semi-permanently or temporarily) - Fresh or brackish water


  LC170
  Closed (>40%) broadleaved forest or shrubland permanently flooded - Saline or brackish water


  LC180
  Closed to open (>15%) grassland or woody vegetation on regularly flooded or waterlogged soil - Fresh, brackish or saline water


  LC190
  Artificial surfaces and associated areas (Urban areas >50%)


  LC200
  Bare areas


  LC210
  Water bodies


  LC220
  Permanent snow and ice


  LC230
  No data (burnt areas, clouds,…)






  Bio_10
  Mean Temperature of Warmest Quarter


  Bio_11
  Mean Temperature of Coldest Quarter


  Bio_12
  Annual Precipitation


  Bio_13
  Precipitation of Wettest Month


  Bio_14
  Precipitation of Driest Month


  Bio_15
  Precipitation Seasonality (Coefficient of Variation)


  Bio_16
  Precipitation of Wettest Quarter


  Bio_17
  Precipitation of Driest Quarter


  Bio_18
  Precipitation of Warmest Quarter


  Bio_19
  Precipitation of Coldest Quarter


  Bio_1
  Annual Mean Temperature


  Bio_2
  Mean Diurnal Range (Mean of monthly (max temp - min temp))


  Bio_3
  Isothermality (BIO2/BIO7) (* 100)


  Bio_5
  Max Temperature of Warmest Month


  Bio_6
  Min Temperature of Coldest Month


  Bio_7
  Temperature Annual Range (BIO5-BIO6)


  Bio_8
  Mean Temperature of Wettest Quarter


  Bio_9
  Mean Temperature of Driest Quarter

Data Integration Tool Market Outlook

In 2023, the global market size for Data Integration Tools is estimated to be around USD 9.3 billion, with a projected CAGR of 11.2% from 2024 to 2032, reaching approximately USD 22.6 billion by the end of the forecast period. The market is primarily driven by the increasing need for businesses to manage and utilize vast amounts of data efficiently. Factors such as the growing adoption of cloud computing, big data analytics, and the rising complexity of data sources are contributing to the robust growth of this market.

One of the primary growth factors for the Data Integration Tool market is the exponential increase in data generation across various industries. With the proliferation of digital technologies, IoT devices, and advanced analytics, organizations are generating more data than ever before. This surge in data necessitates robust integration tools to consolidate and make sense of disparate data sources. Moreover, the increasing emphasis on data-driven decision-making processes means that companies are investing heavily in integration solutions to ensure data accuracy, consistency, and accessibility.

The rise of cloud computing is another significant driver, as more organizations migrate their data and applications to cloud platforms. Cloud-based data integration tools offer scalability, flexibility, and cost-efficiency, making them highly attractive to businesses of all sizes. The ability to integrate data seamlessly across hybrid and multi-cloud environments is crucial for maintaining competitive advantage. Additionally, the rise of Software as a Service (SaaS) applications has further fueled the demand for integration tools that can bridge on-premises and cloud-based data.

Furthermore, regulatory compliances and data privacy concerns are encouraging organizations to invest in advanced data integration solutions. With stringent regulations like GDPR, CCPA, and others, businesses must ensure that their data management practices are compliant. Data integration tools play a pivotal role in achieving this by enabling organizations to have a unified view of their data, ensuring data governance and compliance. The need for real-time data processing and analytics also propels the market forward, as businesses strive to gain timely insights and maintain agility in a dynamic market landscape.

From a regional perspective, North America holds a significant share of the Data Integration Tool market, driven by the presence of major tech giants and a highly digitized business environment. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid digital transformation, increasing internet penetration, and growing investments in IT infrastructure. Europe also shows substantial potential, with many organizations in the region focusing on data governance and compliance due to stringent regulatory requirements.

Cloud Data Integration Software is becoming increasingly vital as businesses transition to cloud environments. These software solutions facilitate seamless data integration across various cloud platforms, ensuring that data is accessible and consistent regardless of where it is stored. As organizations adopt multi-cloud strategies, the need for robust cloud data integration tools becomes even more pronounced. These tools enable businesses to manage data across different cloud services, providing a unified view that is crucial for strategic decision-making. Furthermore, cloud data integration software often includes advanced features such as real-time data processing and automated workflows, which enhance operational efficiency and data governance. As a result, businesses can leverage their cloud investments more effectively, driving innovation and competitiveness in the market.

Component Analysis

The Data Integration Tool market is segmented into software and services. The software segment dominates the market, driven by the need for advanced tools that support complex data integration tasks. These tools offer functionalities such as data mapping, transformation, and real-time data integration, which are essential for modern businesses. The software solutions are continuously evolving, with vendors integrating AI and machine learning to enhance data integration capabilities, making them more intuitive and efficient.

Services, on the other hand, play a critical role in the successful implementation and maintenance of data i

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.

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. Here we present Medical Information Mart for Intensive Care (MIMIC)-IV, a large deidentified dataset of patients admitted to the emergency department or an intensive care unit at the Beth Israel Deaconess Medical Center in Boston, MA. MIMIC-IV contains data for over 65,000 patients admitted to an ICU and over 200,000 patients admitted to the emergency department. MIMIC-IV incorporates contemporary data and adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

The size of the Integration Platform-as-a-Service Industry market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 35.23% during the forecast period.iPaaS stands for Integration Platform-as-a-Service. This refers to cloud-based software solutions for easy integration between the organization and numerous applications or sources of data. Nowadays, any organization may utilize hundreds of different applications that relate to their customer relationship management, enterprise resource planning, or even marketing automation needs. However, these applications will not talk to each other easily and cannot exchange data smoothly. This would mean data silos and inefficiency. iPAAS will serve as a centralized hub connecting the disparate systems that help businesses automate workflows, real-time synchronization of data, and the unified view of the operations of their business. Through avoiding the tedium of manually entering data and reducing integration complexity, iPaaS empowers businesses to improve decision-making, enhance customer experiences, and deliver better overall operational efficiency. Recent developments include: December 2022 - Internet Initiative Japan Inc., one of the providers of leading Internet access and comprehensive network solutions providers in Japan, declared that it would start delivering the IIJ Cloud Data Platform Service, a data integration service that facilitates data utilization using the cloud. By aggregating data flowing between on-premise procedures and cloud services on this service platform, required data can be extracted without influencing existing systems, and data can be well integrated with cloud services., October 2022 - Virtuoso Partners (VP) was pleased to announce that it extended its capabilities into iPaaS by partnering with Workato, The collaboration would support its partners and their customers better as they look to integrate their cloud applications and on-prem systems and automate workflows across them.. Key drivers for this market are: Convergence of IoT and AI Technologies, Increasing Demand From Organizations to Streamline Business Processes. Potential restraints include: Increased Competition in the Market. Notable trends are: Retail & E-commerce to Witness Significant Growth.

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.

The spatial distribution of animals has consequences for nutrition, predator-prey dynamics, spread of diseases, and population dynamics in general. Animals must establish a home range to secure adequate resources to fuel their energetic needs. Home ranges, therefore, are temporally and spatially dynamic given the changing requirements of an animal and the availability of resources on the landscape. We used data from two populations of bighorn sheep with contrasting population dynamics following pneumonia epizootics and different habitat quality on their summer range to test the hypothesis that the distribution and size of home ranges are influenced by environmental conditions and reproductive status. We used a combination of data from 768 vegetation transects and remotely sensed metrics to index forage quality of consecutive biweekly home ranges for 27 bighorn sheep, June–August 2019–2021. There were population differences in space use that were consistent with resource limitations in t..., We used data from two populations of Rocky Mountain bighorn sheep within the Greater Yellowstone Ecosystem in northwest Wyoming, USA, 2019–2021 (Figure 1). The Whiskey Mountain population experienced a pneumonia epizootic in 1991 (Ryder et al. 1992) and has since exhibited population decline via low juvenile recruitment (22 juveniles per 100 adult females in winter on average 2019–2021; Wyoming Game and Fish Department 2021a), leaving the population at ~20% of its former population size (upwards of 1,500 animals; Wyoming Game and Fish Department, unpublished data). The Jackson population experienced pneumonia epizootics in 2001 and 2012 but has been able to recover to previous population size (~ 400 animals) and maintain higher juvenile recruitment (38.6 juveniles per 100 adult females; Wyoming Game and Fish Department 2021b). Study animals were seasonal elevational migrants. Summer ranges were high-elevation (~3,000m) alpine habitats with alpine meadows, talus fields, and rocky outcrop..., , # Disparate home range dynamics reflect nutritional inadequacies on summer range for a large herbivore

https://doi.org/10.5061/dryad.44j0zpcmc

Description of the data and file structure

PCA_data This file contains all the meterics that went into the PCA with varimax reduction. For more detail on exactly how these metrics were quantified see Wagler et al. 2023. Implications of forage quality for population recovery of bighorn sheep following a pneumonia epizootic. Journal of Wildlife Management 87:e22452. Each metric reflects the mean of that variable for the line point intercept transect. DMD_rx = mean dry matter digestibility (%) for each plant along the transect. this metric accounts for for inorganic hits (inorganic hits accounted for in the mean with a 0) CP_rx = mean crude protien (%) for each plant along the transect. this metric accounts for inorganic hits (inorganic hits accounted for in the mean with a 0) Biomass_kgha_tr...

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...

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.

This dataset contains a common standard template for representing the metadata of stable isotope results environmental samples (e.g., soils, rocks, water, gases) and a CSIRO-specific vocabulary for use across CSIRO research activities. The templates includes core properties of stable isotope results, analytical methods, and uncertainty of analyses, as well as associated metadata such as such as their name, identifier, type, and location. The templates enables users with disparate data to find common ground regardless of differences within the data itself i.e. sample types, collections. The standardized templates can prevent duplicate sample metadata entry and lower metadata redundancy, thereby improving the stable isotope data curation and discovery. They have been developed iteratively, revised, and improved based on feedback from researchers and lab technicians. Use of this template and vocabularies will facilitate interoperable and machine-readable platform-ready data collections.

Lineage: CSIRO, in partnership with the Australian Nuclear Science and Technology Organisation (ANSTO), Geoscience Australia, and the National Measurement Institute, has developed a common metadata template for reporting stable isotope results. The common template was designed to provide a shared language for stable isotope data so that the data can be unified for reuse. Using a simplified data structure, the common template allows for the supply of data from different organisations with different corporate goals, data infrastructure, operating models and different specialist skills. The common ontology describes the different concepts present in the data, giving meaning to the stable isotope observations or measurements of (isotopic) properties of physical samples of the environment. It coordinates this description of samples with standardised metadata and vocabularies, which facilitate machine-readability and semantic cross-linking of resources for interoperability between multiple domains and systems. This is to assist in reducing the need for human data manipulation which can be prone to errors, to provide a machine-readable format for new and emerging technology use-cases, and to also help stable isotope data align with Australia public data FAIR. In addition to the common template, the partners have developed a platform for making unified stable isotope data available for reuse, co- funded by the Australian Research Data Commons (ARDC). The aim of IsotopesAU is to repurpose existing publicly available environmental stable isotope data into a federated data platform, allowing single point access to the data collections. The IsotopesAU platform currently harmonises and federates stable isotopes data from the partner agencies' existing public collections, translating metadata templates to the common template.

The templates have been developed iteratively, revised, and improved based on feedback from project participants, researchers, and lab technicians.

Genotypes of Asellus aquaticus on 8 microsatellite lociDataset contains genotypes of surface and subterranean ecomorph pairs of Asellus aquaticus from Slovenia and Romania. 324 individuals were genotyped on 8 microsatellite loci. Columns in the table: sampling site – site code on Figure 1 in the article; Voucher ID – sample name from the SubBioDatabase; alleles at each locus are recorded in separate columns. Missing data is recorded as "?".Genotypes ecomorphs Asellus aquaticus Slo_Rom.xlsxMorphometric data ecomorphs Asellus aquaticusMorphometric data: List of 60 traits used in morphometric analysis of surface and subterranean Asellus aquaticus ecomorphs from Slovenia and Romania. Means, standard deviations (SD) and standard errors (SE) of 60 morphometric traits (listed in Table A) in the surface and subterranean ecomorph pairs of Asellus aquaticus from Slovenia (PP/PR*) and Romania (MD/AW*). Asterisk (*) denotes the subterranean ecomorph relative to its ancestral surface form.Population...

Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report up and down. This paper examines disaggregate or firm-level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms' categorical responses to official data using ordered discrete-choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators.

The need of a common ontology for describing orthology information in biological research communities has led to the creation of the Orthology Ontology (ORTH). ORTH ontology is designed to describe sequence homology data available in multiple orthology databases on the Web (e.g.: OMA, OrthoDB, HieranoiDB, and etc.). By sequence homology data, we mostly mean gene region, gene and protein centric orthology, paralogy, and xenology information. Depending on the database, the homology information is structured in different ways. ORTH ontology accommodates these disparate data structures namely Hierarchical Orthologous Group (HOG), cluster of homologous sequences and homologous-pairwise relations between sequences. In addition to the specific ORTH terms, this specification includes terms of the imported ontologies (e.g. Semanticscience Integrated Ontology, SIO) which are pertinents to represent the information from various orthology databases in a homogeneous way.

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.

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

Long-term quantification of temporal species trends is fundamental to the assignment of conservation status, which in turn is critical for planning and targeting management interventions. However, monitoring effort and methodologies can change over the assessment period, resulting in heterogeneous data that are difficult to interpret. Here, we develop a hierarchical, random effects Bayesian model to estimate site level trends in density of African elephants from geographically disparate survey data. The approach treats the density trend per site as a random effect and estimates a parametric distribution of these trends for each partitioning of the data. Data were available from 475 sites, in 37 countries, between 1964 and 2016 (a total of 1,325 surveys). We implemented the model separately and in combination for the African forest (Loxodonta cyclotis) and savannah (Loxodonta africana) elephant species, as well as by region. Inference from these distributions indicates a mean site-level decline for each species over the study period, with the average forest elephant decline estimated to be more than 90% compared to 70% for the savannah elephant. In combination, there has been a mean 77% decline across all sites; but in all models, substantial heterogeneity in trends was found, with stable to increasing trends more common in southern Africa. This work provides the most comprehensive assessment undertaken on the two African elephant species, illustrating the variability in their status across populations. Methods This submission contains the code for the analysis of the survey data, which are included in the submission. Due to differences in resources for monitoring across sites and the development of new techniques over decades of data collection, surveys varied widely with respect to method, effort, and frequency. More specifically, the methodology and temporal range of data differed between sites; and at any one site, surveys may have used different methods, with different associated levels of observation error, and with different survey area sizes that may or may not have included the complete elephant population. We further lacked information on intrinsic demographic rates of growth or carrying capacity, which change across the continent due to environmental conditions. These limited and inconsistent data constrained our analytical approach in three important ways. First, we modeled elephant density rather than numbers since the survey area size was not constant over time for most survey sites. Second, we were able to fit only the simplest exponential population model: A logistic model of density-dependent growth did not converge. Third, we lacked overlapping, comparative data across sites that would allow us to calculate an overall measure of population change directly from estimated site-specific trends.

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 monthly quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution).

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.

B2B Gateway Software Market Outlook

The B2B Gateway Software Market is poised for significant expansion, with a projected market size of USD 4.5 billion in 2023, anticipated to escalate to USD 10.6 billion by 2032, reflecting a robust CAGR of 9.8%. This thriving growth is primarily driven by the escalating demand for seamless data exchange between businesses, spurred by globalization and digital transformation across various sectors. The increasing reliance on cloud platforms and the need for efficient data integration solutions are central growth factors propelling the market forward. Moreover, the adoption of B2B gateway software is becoming essential for companies looking to maintain competitive advantage through optimized supply chain processes and enhanced partner collaboration.

A significant growth factor in the B2B Gateway Software Market is the universal trend towards digital commerce and globalization. Companies across the globe are seeking ways to enhance operational efficiency and reduce costs through digital means. B2B gateway software solutions offer a seamless way to integrate disparate systems and facilitate the seamless exchange of information between business units and external partners. This capability is essential as businesses expand their operations across borders, necessitating a need for robust and reliable data transmission systems. As more industries recognize the value of these solutions, investment in B2B gateway software continues to grow, signaling a strong upward trend in market demand.

The increasing adoption of cloud-based solutions is another pivotal factor contributing to the market's growth. Cloud deployment offers businesses flexibility, scalability, and cost-effectiveness that are unparalleled compared to traditional on-premises solutions. This shift is particularly beneficial for small and medium enterprises (SMEs), which may not have the resources to invest in extensive IT infrastructure. Cloud solutions allow SMEs to leverage cutting-edge technology without prohibitive upfront costs, fostering innovation and competitiveness in the marketplace. As a result, the cloud deployment mode is expected to see substantial growth, further fueling the overall expansion of the B2B Gateway Software Market.

Innovations in technology also play a crucial role in advancing the B2B Gateway Software Market. Emerging technologies, such as artificial intelligence (AI), machine learning, and blockchain, are increasingly being integrated into B2B gateway solutions, providing enhanced features such as predictive analytics, automated workflows, and fortified security protocols. These innovations help businesses not only streamline their operations but also drive strategic decision-making and risk management. As technological advancements continue to evolve, the capabilities of B2B gateway software solutions are expected to expand, offering new functionalities that cater to the complex needs of modern businesses.

Business-to-Business Middleware plays a pivotal role in the seamless integration of various systems within and across organizations. As companies expand their digital ecosystems, the need for middleware solutions that can efficiently manage and facilitate communication between different software applications becomes increasingly critical. This middleware acts as a bridge, enabling disparate systems to work together harmoniously, thus enhancing operational efficiency and reducing the complexity of IT infrastructures. By providing a unified platform for data exchange, Business-to-Business Middleware supports the automation of business processes, leading to improved productivity and faster response times. As businesses continue to adopt more sophisticated technologies, the demand for robust middleware solutions is expected to grow, driving further innovation in this sector.

Regionally, the B2B Gateway Software Market exhibits varied growth patterns, with North America leading due to its advanced technological infrastructure and high adoption rates of digital solutions. The Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization and increased digitalization initiatives in countries like China and India. Europe also presents significant opportunities, particularly in sectors such as manufacturing and retail, where the demand for efficient data integration is high. Latin America and the Middle East & Africa are gradually adopting these solutions, albeit at a slower pace, as businesses in these regions start to recognize the benefits of digital integration

The development of models of marine ecosystems in the Southern Ocean is becoming increasingly important as a means of understanding and managing impacts such as exploitation and climate change. Collating data from disparate sources, and understanding biases or uncertainties inherent in those data, are important first steps for improving ecosystem models. This review focuses on seals that breed in ice habitats of the Southern Ocean (i.e. the crabeater seal, Lobodon carcinophaga; Ross seal, Ommatophoca rossii; leopard seal, Hydrurga leptonyx; and Weddell seal, Leptonychotes weddellii). Data on populations (abundance and trends in abundance), distribution and habitat use (movement, key habitat and environmental features) and foraging (diet) are summarised, and potential biases and uncertainties inherent in those data are identified and discussed. Spatial and temporal gaps in knowledge of the populations, habitats and diet of each species are also identified.