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.

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

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.

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).

This dataset provides supporting information for the species distribution data used in the associated manuscript. Collections of five non-native fish species were made by a number of institutions, and several capture techniques were used. This dataset also includes number of individuals of each species captured at each locality.

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).

This dataset contains the historical Unidata Internet Data Distribution (IDD) Global Observational Data that are derived from real-time Global Telecommunications System (GTS) reports distributed via the Unidata Internet Data Distribution System (IDD). Reports include surface station (SYNOP) reports at 3-hour intervals, upper air (RAOB) reports at 3-hour intervals, surface station (METAR) reports at 1-hour intervals, and marine surface (BUOY) reports at 1-hour intervals. Select variables found in all report types include pressure, temperature, wind speed, and wind direction. Data may be available at mandatory or significant levels from 1000 millibars to 1 millibar, and at surface levels. Online archives are populated daily with reports generated two days prior to the current date.

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.

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.

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.

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 contains a collection of known point locations of Hawaiian monk seals identified via automated satellite tracking of tagged organisms. This can be useful for assessing species abundance, population structure, habitat use, and behavior. This collection is aggregated from multiple tagged organisms and survey periods. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. NOAA's Pacific Islands Fisheries Science Center (PIFSC) deploys satellite tags on Hawaiian monk seals to track their movements around the main Hawaiian Islands with the intent of improving our understanding and assisting in the recovery of this critically endangered species. For further information, please see: http://www.pifsc.noaa.gov/hawaiian_monk_seal/

Data integration improves species distribution forecasts under novel ocean conditions

This repository contains model outputs to help recreate visualizations and interpret results. Specifically, the repo contains:

1. Model performance outputs (i.e., AUC, MAE, Boyce, & Novelty metrics) from retrospective monthly forecast analysis
2. Each model's spatial forecast prediction for an example month (September 2015)
3. Response curves (i.e., marginal effects) from each model

Note: The data provided in this repository do not contain the raw data, but only model-derived outputs, due to restrictions associated with both datasets used in the study.

Specifically, logbook data for the U.S. albacore troll and pole-and-line fishery are confidential U.S. government data and are not publicly available. The raw data cannot be made public under the Magnuson–Stevens Fishery Conservation and Management Reauthorization Act of 2006, section 402(b), 16 U.S.C. 1881a. To request access to U.S. Highly Mi...

This dataset contains a collection of 13 known point locations of green sea turtles identified through direct human observation via aerial surveys between March and April of 1995. Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for turtles and cetaceans in Hawaiian waters from 1993-2003.

Projected changes in habitat suitability for 33 marine species on the Northeast US shelf. Changes in habitat suitability are calculated based on species distribution models fit to bottom trawl survey data from the NOAA Northeast Fisheries Science Center. Positive values indicate an increase in habitat suitability by 2040-2050 relative to historical (1963-2005). The spatial resolution of projections is 0.25 x 0.25 degrees. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=The following methods are excerpted from Rogers et al. (in press):
Bottom trawl data from the NOAA Northeast Fisheries Science Center (NEFSC) fall (1963-2014) surveys were used to characterize the realized thermal niches of species. At each survey station, fish of each species were counted and weighed, and surface and bottom temperature measurements were taken. Correction factors were applied to standardize catch rates for changes in vessel and gear type. A total of 33 species were selected based on their near continuous presence in the survey as well as relative importance to commercial fisheries. For 4 species, data from 1972 onwards were used because observations were irregular prior to that year.

Generalized Additive Models were used to estimate the realized thermal niches of species. We restricted k (number of knots) to 4 or 6 for each of our covariates to ensure biologically meaningful responses. Our response variable was probability of occurrence in a trawl haul, and we used a binomial response with logit transform:

p(occur\u1d67,\u2c7c) ~ logit-1 (s(ST\u1d67,\u2c7c)+s(BT\u1d67,\u2c7c)+s(meanbiomass\u1d67)+s(rugosity\u2c7c))

where ST\u1d67,\u2c7c and BT\u1d67,\u2c7c are sea surface temperature and bottom temperature measured at each haul location j in year y, and meanbiomass\u1d67 is the average annual catch across all hauls to account for interannual changes in abundance due to, e.g., fishing. Rugosity\u1d67 is a measure of benthic habitat roughness, measured as the Terrain Ruggedness Index, using the GEBCO 2014 30-arcsecond bathymetry data (downloaded 4 Feb 2015 from http://www.gebco.net/). The resulting estimated smooth functions describing the relationship between probability of occurrence and temperature can be interpreted as realized thermal niches.

For each species, the change in predicted probability of occurrence under future (2040-2050) projected climate conditions was compared to historical (1963-2005) conditions for each cell within a 0.25\u00b0x0.25\u00b0 spatial grid. Because the modeled probability of occurrence included a component of catchability, values for each species were scaled by dividing by the maximum observed or predicted probability of occurrence across the study area. Positive values for a grid square indicated a projected increase in probability of occurrence, whereas negative values indicated a projected decrease in probability of occurrence.

See related dataset for\u00a0NEFSC bottom trawl data:\u00a0"%5C%22https://www.bco-%0Admo.org/dataset/753142%5C%22">https://www.bco- dmo.org/dataset/753142\u00a0(doi:\u00a010.1575/1912/bco-dmo.753142.1) awards_0_award_nid=559955 awards_0_award_number=OCE-1426891 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Projected changes in habitat suitability PIs: Lauren Rogers & Malin Pinsky Version date: 22-April-2019 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.765386.1 Easternmost_Easting=-64.875 geospatial_lat_max=44.875 geospatial_lat_min=33.625 geospatial_lat_units=degrees_north geospatial_lon_max=-64.875 geospatial_lon_min=-76.875 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/765386 institution=BCO-DMO keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/765386 Northernmost_Northing=44.875 param_mapping={'765386': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/765386/parameters people_0_affiliation=Rutgers University people_0_person_name=Malin Pinsky people_0_person_nid=554708 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Stanford University people_1_person_name=Lauren Rogers people_1_person_nid=765425 people_1_role=Principal Investigator people_1_role_type=originator people_2_affiliation=Stanford University people_2_person_name=Robert Griffin people_2_person_nid=768380 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Rutgers University people_3_person_name=Kevin St. Martin people_3_person_nid=559961 people_3_role=Co-Principal Investigator people_3_role_type=originator people_4_affiliation=Princeton University people_4_person_name=Emma Fuller people_4_person_nid=748888 people_4_role=Scientist people_4_role_type=originator people_5_affiliation=Rutgers University people_5_person_name=Talia Young people_5_person_nid=752628 people_5_role=Scientist people_5_role_type=originator people_6_affiliation=National Oceanic and Atmospheric Administration - Alaska Fisheries Science Center people_6_affiliation_acronym=NOAA-AFSC people_6_person_name=Lauren Rogers people_6_person_nid=765425 people_6_role=Contact people_6_role_type=related people_7_affiliation=Woods Hole Oceanographic Institution people_7_affiliation_acronym=WHOI BCO-DMO people_7_person_name=Shannon Rauch people_7_person_nid=51498 people_7_role=BCO-DMO Data Manager people_7_role_type=related project=CC Fishery Adaptations projects_0_acronym=CC Fishery Adaptations projects_0_description=Description from NSF award abstract: Climate change presents a profound challenge to the sustainability of coastal systems. Most research has overlooked the important coupling between human responses to climate effects and the cumulative impacts of these responses on ecosystems. Fisheries are a prime example of this feedback: climate changes cause shifts in species distributions and abundances, and fisheries adapt to these shifts. However, changes in the location and intensity of fishing also have major ecosystem impacts. This project's goal is to understand how climate and fishing interact to affect the long-term sustainability of marine populations and the ecosystem services they support. In addition, the project will explore how to design fisheries management and other institutions that are robust to climate-driven shifts in species distributions. The project focuses on fisheries for summer flounder and hake on the northeast U.S. continental shelf, which target some of the most rapidly shifting species in North America. By focusing on factors affecting the adaptation of fish, fisheries, fishing communities, and management institutions to the impacts of climate change, this project will have direct application to coastal sustainability. The project involves close collaboration with the National Oceanic and Atmospheric Administration, and researchers will conduct regular presentations for and maintain frequent dialogue with the Mid-Atlantic and New England Fisheries Management Councils in charge of the summer flounder and hake fisheries. To enhance undergraduate education, project participants will design a new online laboratory investigation to explore the impacts of climate change on fisheries, complete with visualization tools that allow students to explore inquiry-driven problems and that highlight the benefits of teaching with authentic data. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES. The project will address three questions: 1) How do the interacting impacts of fishing and climate change affect the persistence, abundance, and distribution of marine fishes? 2) How do fishers and fishing communities adapt to species range shifts and related changes in abundance? and 3) Which institutions create incentives that sustain or maximize the value of natural capital and comprehensive social wealth in the face of rapid climate change? An interdisciplinary team of scientists will use dynamic range and statistical models with four decades of geo-referenced data on fisheries catch and fish biogeography to determine how fish populations are affected by the cumulative impacts of fishing, climate, and changing species interactions. The group will then use comprehensive information on changes in fisher behavior to understand how fishers respond to changes in species distribution and abundance. Interviews will explore the social, regulatory, and economic factors that shape these strategies. Finally, a bioeconomic model for summer flounder and hake fisheries will examine how spatial distribution of regulatory authority, social feedbacks within human communities, and uncertainty affect society's ability to maintain natural and social capital. projects_0_end_date=2018-08 projects_0_geolocation=Northeast US Continental Shelf Large Marine Ecosystem projects_0_name=Adaptations of fish and fishing communities to rapid climate change projects_0_project_nid=559948 projects_0_start_date=2014-09 sourceUrl=(local files) Southernmost_Northing=33.625 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-76.875 xml_source=osprey2erddap.update_xml() v1.3

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020). Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological Invasions Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

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 >

These data consist of (inferred) presence-absence records for 23 dead-wood inhabiting bryophytes in Sweden and the associated environmental variables at each data point at the 100 m grid cell resolution. The data were applied to fit single species distribution models, and multi-species predictive fourth-corner models.

Full details of data compilation, sources and application can be found in: Löbel, S., Mair, L., Lönnell, N., Schröder, B., & Snäll, T. (2018). Biological traits explain bryophyte species distributions and responses to forest fragmentation and climatic variation. Journal of Ecology, 106(4), 1700-1713. doi:10.1111/1365-2745.12930

This dataset contains a collection of two known point locations of fin whales identified through direct human observation via shipborne and aerial surveys in 2012 and 1998, respectively. This collection is aggregated from multiple data sources and survey periods listed below. Each data point contains attributes for further information about the time and source of the observation. This dataset was compiled by the Pacific Islands Ocean Observing System (PacIOOS) and may be updated in the future if additional data sources are acquired. Cascadia Research Collective (CRC) has been undertaking shipborne surveys for cetaceans in Hawaiian waters since 2000. In addition, Dr. Joseph Mobley of the Marine Mammal Research Consultants (MMRC) led aerial surveys for cetaceans in Hawaiian waters from 1993-2003.