The "Random Stochastic Distributions" dataset is a collection of random numbers generated from various common stochastic distributions. The dataset was created by sampling random values from distributions such as Normal, Uniform, Exponential, Gamma, Poisson, Binomial, Geometric, Lognormal, Beta, and Negative Binomial. Each distribution has its own set of parameters, providing a diverse range of data patterns.

This Notebook shows how the data was generated, and also includes an EDA.

Note: It's important to mention that the dataset was generated for educational and exploratory purposes, and while it provides representative samples from the specified distributions, it does not cover the entire parameter space or represent real-world data distributions in all contexts.

These data are species distribution information assembled for assessing the impacts of land-use barriers, facilitative interactions with other species, and loss of long-distance animal dispersal on predicted species range patterns for four common species in pinyon-juniper woodlands in the western United States. The layers in the data release are initial distribution records of two kinds: point occurrence records and a raster layer for the general vegetation types where the species is a co-dominant, compiled from other sources. Both types of data are the baseline information in species distribution models for the associated publication(see Larger Work Citation).

Global Data Distribution Frames Market is segmented by Application (Telecommunications_Data Centers_Enterprises_Defense_Energy), Type (Fiber Distribution Frames_Copper Distribution Frames_Modular Frames_Wall-Mount Frames_Floor-Standing Frames), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

In the first half of 2024, healthcare providers reported *** data breaches in the U.S. healthcare sector, becoming the entity with the highest number of reported breach incidents. As of the time of the reporting, business associates ranked second with the number of reported data breaches.

The Remote Sensing Coastal Change Simple Data Service provides timely and long-term access to emergency, provisional, and approved photogrammetric imagery, derivatives, and ancillary data through a web service via HyperText Transfer Protocol to a folder/file structure organized by data collection platform and survey (collection effort) with metadata sufficient to facilitate both human and machine access. Data are acquired, processed, and published using standardized workflows. Each data type added to the service has a peer-reviewed metadata and data review of sample data generated with standardized methods to ensure compliance with U.S. Geological Survey (USGS) Fundamental Science Practices (FSP).

Between May 2023 and April 2024, more than 81 percent of the reports about personal data security breaches were about unintentional incidents. Malicious activities and access abuse ranked second, with around four percent of the reported incidents, followed by eavesdropping or interception.

Market Data Distribution Platforms Market Outlook

According to our latest research, the market size of the global Market Data Distribution Platforms Market reached USD 8.7 billion in 2024, with a robust growth trajectory supported by a CAGR of 9.1% projected for the period 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 19.1 billion. This remarkable growth is primarily driven by the increasing demand for real-time data analytics and the rising adoption of cloud-based distribution solutions across financial institutions, telecommunications, and other data-intensive sectors. As per our latest research, the proliferation of algorithmic trading, regulatory mandates for transparency, and digital transformation initiatives are further propelling the adoption of advanced market data distribution platforms globally.

One of the most significant growth factors for the Market Data Distribution Platforms Market is the exponential rise in data volumes generated by financial markets and other industries. The surge in electronic trading, high-frequency trading, and the adoption of algorithmic strategies have necessitated the need for platforms that can distribute large volumes of market data with minimal latency and maximum reliability. Financial institutions, in particular, require real-time access to market data to make informed trading decisions and to comply with stringent regulatory requirements. The increasing complexity of financial instruments and the globalization of trading activities have made efficient data distribution a critical component of the financial services infrastructure. Furthermore, the growing integration of alternative data sources, such as social media sentiment and geospatial data, is pushing market data distribution platforms to evolve, ensuring they can handle diverse data types while maintaining speed and accuracy.

Another key driver is the widespread adoption of cloud technology and the shift towards hybrid IT environments. Organizations across sectors are recognizing the benefits of cloud-based market data distribution platforms, including scalability, flexibility, and cost efficiency. Cloud deployment allows enterprises to manage and distribute data seamlessly across geographically dispersed teams and trading desks, supporting business continuity and operational agility. Additionally, cloud platforms offer enhanced security features, disaster recovery capabilities, and the ability to integrate with advanced analytics and artificial intelligence tools. These advantages are particularly appealing to small and medium enterprises (SMEs), which may lack the resources to maintain extensive on-premises infrastructure but still require robust market data solutions to remain competitive.

The increasing regulatory scrutiny and the need for transparency in financial transactions are also fueling the demand for advanced market data distribution platforms. Regulatory bodies worldwide are enforcing rules that mandate accurate and timely dissemination of market data to ensure fair trading practices and to protect investors. Market participants must adhere to regulations such as MiFID II in Europe and the Dodd-Frank Act in the United States, which impose strict requirements on data reporting, order execution, and market surveillance. Compliance with these regulations necessitates the deployment of sophisticated data distribution systems capable of supporting real-time monitoring, audit trails, and secure data sharing. This regulatory landscape is compelling financial institutions and other end-users to upgrade their existing platforms or invest in new solutions that offer enhanced compliance features and reporting capabilities.

From a regional perspective, North America continues to hold the largest share of the Market Data Distribution Platforms Market, driven by the presence of major financial hubs, advanced IT infrastructure, and early adoption of innovative technologies. The United States, in particular, is home to leading financial institutions, trading firms, and exchanges that rely heavily on real-time data distribution solutions. Europe follows closely, with significant demand stemming from regulatory reforms and the expansion of electronic trading. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitalization of financial services, increasing investments in fintech, and the proliferation of stock exchanges in countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by o

This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.

Landings are defined as the part of the catch that is retained (not discarded) and landed. This dataset shows the distribution of landings by Irish vessels measured as average weight or value of landing per kilometre square, per year. Data from years 2014 to 2018 was used to produce this data for the Marine Institute publication the Atlas of Commercial Fisheries around Ireland, third edition (https://oar.marine.ie/handle/10793/1432). This dataset is derived from the following 2 primary data types - data on vessel positioning and data on landings and gear types used: Vessel Monitoring Systems (VMS) is supplied by the Irish Naval Service. VMS data provided geographical position and speed of vessel at intervals of two hours or less (Commission Regulation (EC) No. 2244/2003). VMS do not record whether a vessel is fishing, steaming or inactive. Logbooks collected by the Sea-Fisheries Protection Authority and supplied by the Department of Agriculture, Food & the Marine were the primary data source for information on landings and gear types used by Irish vessels. The fishing gear data was classified into eight main groups: demersal otter trawls; beam trawls; demersal seines; gill and trammel nets; longlines; dredges; pots and pelagic trawls. The VMS data was analysed using the approach described by Gerritsen and Lordan (IJMS 68(1)). The VMS points are filtered for fishing activity using speed criteria, vessels were assumed to be actively fishing if their speed fell within a certain range (depending on the fishing gear used). The recorded landings are averaged according to the number of active fishing points and assigned to the VMS positions where the vessel was actively fishing. The points are then aggregated into a spatial grid to produce a raster dataset showing landings (by weight (kg) or value (€)) per kilometre square per year for each gear type group. .hidden { display: none }

This dataset provides detailed information on the distribution of blood types (ABO and Rh) across various countries and regions worldwide. Blood types, also known as blood groups, are classifications based on the presence or absence of specific antibodies and antigens on the surface of red blood cells (RBCs). The blood group system includes types A, B, AB, and O, and each can be Rh-positive (+) or Rh-negative (-). These distributions vary significantly across populations, making this dataset a valuable resource for demographic, medical, and health-related studies.

The data consists of the following key components:

Country/Dependency: The country or region where the blood type distribution is recorded. Population: The population of the country or dependency. Blood Group Distribution: The percentage distribution of each blood group (O+, A+, B+, AB+, O-, A-, B-, AB-). Source: The data has been sourced from Wikipedia's article on Blood Type Distribution by Country: https://en.wikipedia.org/wiki/Blood_type_distribution_by_country#cite_note-42

Blood Type Distribution by Country (Wikipedia) The dataset was compiled by aggregating and cleaning the information provided on this Wikipedia page, ensuring that it includes the most relevant and up-to-date information available from various public sources.

Missing Values: Several countries in this dataset have missing blood group values. These countries either have incomplete data or missing reports on certain blood groups. Below are the countries with missing values in the dataset, along with the specific blood group(s) missing:

Egypt: Missing blood groups: O-, A-, B-, AB- Mongolia: Missing blood group: AB+ How to Use the Dataset: This dataset can be used in various fields such as:

Medical Research: For understanding regional and global trends in blood type distribution, which can assist in improving blood donation strategies and health care planning. Epidemiology: To study how blood group distributions correlate with genetic and environmental factors, including disease susceptibility. Healthcare Planning: Governments and healthcare institutions can use this data for better planning of blood donation drives, ensuring an adequate supply of specific blood types in different regions. Educational Purposes: To teach students and researchers about the distribution of blood types and its implications on health and genetics. Target Audience: Researchers: In the fields of genetics, epidemiology, and public health who wish to analyze global or regional blood type distributions and their effects. Health Organizations: Organizations like the World Health Organization (WHO), national health services, or NGOs working on global healthcare improvements and emergency planning. Government Agencies: Public health departments and policy-making bodies that require blood group data for emergency management and blood bank planning. Educational Institutions: Schools, colleges, and universities studying genetics, epidemiology, or human biology. Medical Professionals and Hospitals: Blood banks, hospitals, and clinics working on blood donation and transfusion services.

Shuvo Kumar Basak. (2023). Scenarios_BloodGroup256x256 [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/7054946

This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.

The eight main blood types are A+, A-, B+, B-, O+, O-, AB+, and AB-. The most common blood type in the United States is O-positive, with around 38 percent of the population having this type of blood. However, blood type O-positive is more common in Latino-Americans than other ethnicities, with around 53 percent of Latino-Americans with this blood type, compared to 47 percent of African Americans and 37 percent of Caucasians. Blood donation The American Red Cross estimates that every two seconds someone in the United States needs blood or platelets, highlighting the importance of blood donation. It was estimated that in 2021, around 6.5 million people in the U.S. donated blood, with around 1.7 million of these people donating for the first time. Those with blood type O-negative are universal blood donors, meaning their blood can be transfused for any blood type. Therefore, this blood type is the most requested by hospitals. However, only about seven percent of the U.S. population has this blood type. Blood transfusion Blood transfusion is a routine procedure that involves adding donated blood to a patient’s body. There are many reasons why a patient may need a blood transfusion, including surgery, cancer treatment, severe injury, or chronic illness. In 2021, there were around 10.76 million blood transfusions in the United States. Most blood transfusions in the United States occur in an inpatient medicine setting, while critical care accounts for the second highest number of transfusions.

Secondary (distribution) transformer phasing data, inferred from Transformer Reference Type and/or GIS line data, inclusive of Local Authority. We have developed the Distribution Transformer Phasing dataset to assist customers, including but not limited to Local Authorities and consultants acting on their behalf, in making more informed applications (e.g. when applying for LEVI funding). The dataset provides indicative information only and cannot be relied upon to assess the suitability of specific premises for connection or funding applications. Transformer phasing data is inferred from information held on record and has not been verified through on-site checks. Where phasing cannot reasonably be inferred from asset records, it is derived from GIS data. Distribution Transformer Phasing is a substation-level dataset. Where multiple transformers are present at a single substation, and those transformers have different phasing; the phasing associated with the transformer with the largest number of phases has been used. The value published may not, therefore, be representative of all transformers within a substation. NGED does not guarantee the completeness of this dataset. Some substations have been omitted deliberately, for data privacy reasons or where internal records are deemed inaccurate or incomplete. Whilst we use reasonable endeavours to ensure that this dataset and related information is accurate, we do not warrant, and do not accept any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on the data therein.

Spain - Distribution of population by household types: Single person was 11.30% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Spain - Distribution of population by household types: Single person - last updated from the EUROSTAT on October of 2025. Historically, Spain - Distribution of population by household types: Single person reached a record high of 11.30% in December of 2024 and a record low of 8.40% in December of 2009.

Traditional differential expression genes (DEGs) identification models have limitations in small sample size datasets because they require meeting distribution assumptions, otherwise resulting high false positive/negative rates due to sample variation. In contrast, tabular data model based on deep learning (DL) frameworks do not need to consider the data distribution types and sample variation. However, applying DL to RNA-Seq data is still a challenge due to the lack of proper labeling and the small sample size compared to the number of genes. Data augmentation (DA) extracts data features using different methods and procedures, which can significantly increase complementary pseudo-values from limited data without significant additional cost. Based on this, we combine DA and DL framework-based tabular data model, propose a model TabDEG, to predict DEGs and their up-regulation/down-regulation directions from gene expression data obtained from the Cancer Genome Atlas database. Compared to five counterpart methods, TabDEG has high sensitivity and low misclassification rates. Experiment shows that TabDEG is robust and effective in enhancing data features to facilitate classification of high-dimensional small sample size datasets and validates that TabDEG-predicted DEGs are mapped to important gene ontology terms and pathways associated with cancer.

Finland - Distribution of population by household types: Single person was 25.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by household types: Single person - last updated from the EUROSTAT on November of 2025. Historically, Finland - Distribution of population by household types: Single person reached a record high of 25.80% in December of 2024 and a record low of 19.00% in December of 2010.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. Methods Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

Aim: Many countries lack informative and high‐resolution, wall‐to‐wall vegetation or land‐cover maps. Such maps are useful for land‐use and nature management, and for input to regional climate and hydrological models. Land‐cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: mainland Norway, covering ca. 324 000 km2. Methods: We used presence‐absence data for 31 different VTs, mapped wall‐to‐wall in an area‐frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 117 explanatory variables, recorded in 100×100‐m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that, rare VTs are better predicted than common ones, and coastal VTs are better predicted than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

Species distribution model (SDM) is a crucial tool for forecasting ranges of species and mirroring habitat references and quality. Different types of species distribution data have been commonly used in SDMs regarding different purposes and availability, whereas, the influences of data types on model performances have not been well understood. This study considered three data types characterized by different levels of organism information and cost in data acquisitions, namely presence/absence (P/A), ordinal data, and abundance data. We developed a range of distribution models for nine demersal species in the coastal waters of Shandong Peninsula, China, using two modeling algorithms [the Generalized Additive Model (GAM) and Random Forest]. Firstly, we evaluated the performances of all models on predicting species occurrence (i.e., habitat suitability or range boundaries), and then compared the models built with ordinal data and abundance data on projecting ordinal predictions (i.e., relative density or habitat quality). Their predictive abilities were assessed through cross-validation tests with diverse performance measurements. Overall, no data type is superior in all situations, but combined with two algorithms, the abundance data slightly outperformed the ordinal data and P/A data unexpectedly exerted reliable performances. Specifically, the effectiveness of data type for two application purposes of SDMs substantially varied with modeling algorithms, revealing that GAMs always benefit most from ordinal data and the opposite was true for Random Forest. For some small resident organisms with moderate prevalence, rough distribution data might be adopted for providing reliable projections. Our findings highlight the importance of clarifying the objectives of SDMs when choosing data types for species distribution modeling.

Estonia - Distribution of population by household types: Single person was 22.30% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Estonia - Distribution of population by household types: Single person - last updated from the EUROSTAT on December of 2025. Historically, Estonia - Distribution of population by household types: Single person reached a record high of 22.30% in December of 2024 and a record low of 15.00% in December of 2009.