Spawning aggregations are an important event in the life-history of many coral reef fish species. During short time periods (typically during full moons), fish will aggregate to spawn at discrete locations along a coral reef tract. Locations of historical spawning aggregations in the Florida Keys Reef Tract were collected through interviews with fishermen and review of the scientific literature. Field assessments are conducted during predicted spawning times, typically during full moon periods in the winter, spring and summer.

This data set contains field notes related to on-water acoustic and diver surveys for reef fish spawning aggregations in the FL Keys, 2009-2014

This dataset represents the locations of licenced and permitted pits and quarries regulated by the Ministry of Natural Resources and Forestry under the Aggregate Resources Act, R.S.O. 1990. Aggregate site data has been divided into active and inactive sites. Active sites may be further subdivided into partial surrenders. In partial surrenders, defined areas of a site are inactive while the rest of the site remains active. The data includes: * site location and size * licensee name * approval type (licence or permit) * operation type (pit or quarry) * maximum annual tonnage limit * the MNRF district responsible for the site Use our interactive pits and quarries map to find active sites. This data does not include aggregate sites regulated by the Ministry of Transportation.

Python code to reproduce results presented in the paper "Privacy-Preserving Data Aggregation with Public Verifiability Against Internal Adversaries". Specifically, to run an implementation of the mPVAS family of protocols and measures its runtime.

The source code was published by the paper's authors some time after the paper was published.

Usage

Minimal usage instructions: On a system running Debian 12, with GNU Make installed, run make install test run plot.

See README.md inside the git repository for detailed usage instructions.

Code

The source code is available as a git repository. The relevant code is stored in the src directory.

Expose health and benefit data received from various sources based on business need.

Points depict aggregate sources that have been tested by the WSDOT State Materials Laboratory and are created from data in the Aggregate Sources Approval database (ASA). The Aggregate Sources Approval database identifies aggregate sources that have been tested by WSDOT, and have been assigned a county letter code with a sequential number for that county. It should be noted that there are sources that have been tested, but not classified with a county code, and are therefore not included in the database. Also, there are sources in the database that are not currently approved for use as materials on construction projects.

MATLAB code to reproduce results presented in the paper "Privacy-Preserving Data Aggregation with Probabilistic Range Validation".

The source code is available as a git repository.

The source code was published by the paper's authors several years after the paper was published.

Git repository

Relevant code is stored in the src directory.

Measures and visualises various metrics shown in the paper. The settings in the various scripts correspond exactly to those used to achieve the results in the paper. The code is fully deterministic and gives the exact same results each time.

Unfortunately, Figure 6 in the paper was generated with a version of this code in which the seed for the random number generator was not configured correctly, and as a result Figure 6 cannot be recreated exactly. However, the outputs of the scripts in the repository are not significantly different from the published Figure 6, and do not undermine or alter the conclusions in any significant way.

See ARTIFACT-EVALUATION.md in the root folder for detailed end-user instructions.

Additional file 3. Machine learning workflow.

We are no longer updating this data. It is best suited for historical research and analysis.

This spatial dataset represents the locations of aggregate pits used to build roads for forestry purposes that may also provide recreational access to forests in Ontario.

Additional Documentation

  Aggregate category 14 site - Data Description (PDF)
  Aggregate category 14 site - Documentation (Word)

Status

Obsolete: data is no longer relevant

Maintenance and Update Frequency

Not planned: there are no plans to update the data

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

This dataset is subject to licensing and approvals. Approval may be requested by contacting ryan.lenethen@ontario.ca. To request access to restricted use data, email the dataset contact or Information Access Analyst at stephanie.whyley@ontario.ca.

The data referenced here is licensed Electronic Intellectual Property of the Ontario Ministry of Natural Resources and Forestry and is provided for professional, non-commercial use only.

DATAANT provides the ability to extract travel data from public sources like: - Hotel websites - Flight aggregators - Homestay marketplaces - Experience marketplaces - Online Travel Agencies (OTA) and any open travel industry website you need.

Forecast travel trends with Booking.com, Airbnb, and travel aggregators data.

We support providing both raw and structured data with various delivery methods.

Get the competitive advantage of hospitality and travel Intelligence by scheduled data extractions and receive your data right to your inbox.

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Hamilton R.J., Matawai M., Potuku T., Kama W., Lahui P., Warku J., Smith A.J. 2005. Applying local knowledge and science to the management of grouper aggregation sites in Melanesia. SPC Live Reef Fish Information Bulletin 14:7-19.

This pie chart displays sites per site using the aggregation count in Mexico. The data is about sites.

This dataset provides details on the location of MTO aggregate pits. Aggregate pits provide the material necessary to build roadways in the province. Official LIO title: Aggregate Sites MTO *[MTO]: Ministry of Transportation

This spatial dataset represents the locations of abandoned aggregate sites (pits and quarries) that have not been rehabilitated for various reasons, including:

the site predates legislation that requires rehabilitation the site was revoked, and no rehabilitation has been completed

Information about active aggregate sites is available in related data classes and online using the interactive Pits and Quarries map.

Additional Documentation

  Aggregate Site Unrehabilitated - Data Description (PDF)
  Aggregate Site Unrehabilitated - Documentation (Word)

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Ryan Lenethen, Integration Branch, ryan.lenethen@ontario.ca

Understanding how assemblages of species responded to past climate change is a central goal of comparative phylogeography and comparative population genomics, and an endeavor that has increasing potential to integrate with community ecology. New sequencing technology now provides the potential to gain complex demographic inference at unprecedented resolution across assemblages of non-model species. To this end, we introduce the aggregate site frequency spectrum (aSFS), an expansion of the site frequency spectrum to use single nucleotide polymorphism (SNP) datasets collected from multiple, co-distributed species for assemblage-level demographic inference. We describe how the aSFS is constructed over an arbitrary number of independent population samples and then demonstrate how the aSFS can differentiate various multi-species demographic histories under a wide range of sampling configurations while allowing effective population sizes and expansion magnitudes to vary independently. We subsequently couple the aSFS with a hierarchical approximate Bayesian computation (hABC) framework to estimate degree of temporal synchronicity in expansion times across taxa, including an empirical demonstration with a dataset consisting of five populations of the threespine stickleback (Gasterosteus aculeatus). Corroborating what is generally understood about the recent post-glacial origins of these populations, the joint aSFS/hABC analysis strongly suggests that the stickleback data are most consistent with synchronous expansion after the Last Glacial Maximum (posterior probability = 0.99). The aSFS will have general application for multi-level statistical frameworks to test models involving assemblages and/or communities and as large-scale SNP data from non-model species become routine, the aSFS expands the potential for powerful next-generation comparative population genomic inference.

This dataset contains interpolated and aggregated soil and climate data of the region of North Rhine-Westphalia (Germany). The data is provided for grids of 1, 10, 25, 50 and 100 km resolutions. These data grids represent spatial aggregations of the climate of approximately 1 km resolution and soil data of approximately 300 m resolution raster. The purpose of this data is the use as input for crop models. It thus contains the key relevant soil and climate variables for running crop models. Additionally, the data is specifically designed to analyze effects of scale and resolution in crop models, e.g. data aggregation effects. It has been used for several studies on spatial scales with regard to different scaling approaches, crops, crop models, model output variables, production situations and crop management among others.

The dataset on aggregate extractions in the European seas was created in 2014 by AZTI for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across Europe. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points representing aggregate extraction sites, by year (although some data are indicated by a period of years), in the following countries: Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Lithuania, Poland, Portugal, Spain, Sweden, The Netherlands and United Kingdom. Where available, each point has the following attributes: Id (Identifier), Position Info (e.g.: Estimated, Original, Polygon centroid of dredging area, Estimated polygon centroid of dredging area), Country, Sea basin, Sea, Name of the extraction area, Area of activity (km2), Year (the year when the extraction took place; when a time period is available, the first year of the period is indicated), Permitted Amount (m3) (permitted amount of material to be extracted, in m3), Permitted Amount (t) (permitted amount of material to be extracted, in tonnes), Requested Amount (m3) (requested amount of material to be extracted, in m3), Requested Amount (t) (requested amount of material to be extracted, in tonnes), Extracted Amount (m3) (extracted amount of material, in m3), Extracted Amount (t) (extracted amount of material, in tonnes), Extraction Type (Marine sediment extraction), Purpose (e.g.: Commercial, Others, N/A), End Use (e.g.: Beach nourishment, Construction, Reclamation fill, N/A), Material type (e.g.: sand, gravel, maerl), Notes, Link to Web Sources. In 2018, a feature on areas for aggregate extractions was included. It contains polygons representing areas of seabed licensed for exploration or extraction of aggregates, in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Italy, Lithuania, Poland, Portugal, Russia, Spain, Sweden, The Netherlands and United Kingdom. Where available, each polygon has the following attributes: Id (Identifier), Area code, Area name, Country, Sea basin, Sea, Starting year (the year when the license starts), End year (the year when the license ends), Site Type (exploration area, extraction area, extraction area (in use)), License status (Active, not active, expired, unknown), Material type (e.g.: sand, gravel, maerl), Notes, Distance to coast (in metres), Link to Web Sources. In the 2024 update, extraction data until 2023 and new areas have been included.

Searchable Index of Metadata Aggregators is a database that stores general information of metadata aggregators. This database is accompanied with the “A WDS guide to Metadata Aggregators for Repository Managers”. The Searchable Index of Metadata Aggregators is an up-to-date catalogue of Dataset Metadata Aggregators (DMAs), implemented as an access database. It was designed to fill in a gap found by the Harvestable Metadata Services Working Group (HMetS-WG) members of the World Data System’s International Technology Office (WDS-ITO). These include up-to-date resources giving an overview of current infrastructures used to syndicate dataset metadata. The database contains information on DMA's supported metadata standards and software interfaces, as well as documentation on how to be aggregated by each.

The WDS Guide to Metadata Aggregators is a guidance document for the associated Searchable Index of Metadata Aggregators. We have defined DMAs as federated service infrastructures that foster the findability and accessibility of data products by enabling access to multiple, distributed metadata records via a single search interface. This guide gives a description of this catalogue and general guidance on how to use it. In the sections that follow, we give a short background to the Harvestable Metadata Services-Working Group project. Then, we outline the project's research methodology and the properties of the searchable index. Finally, we discuss this project's limitations, as well as its future development. Providing metadata to aggregators can significantly improve the findability of research data products.

Together, this guidance document and dataset package are designed to provide research data repository managers with options for participation in federated research data systems, and support institutional repositories' harvestable metadata service implementation strategies. In addition, as developers in the global research data management community seek to create pathways and workflows across data, software and compute resources, we anticipate that they're likely to prioritize connecting sites, organizations and services that have already done a lot of work harmonizing content from disparate providers. In this context, this resource will be helpful for creating roadmaps and implementation plans for integration across science clouds.

This bar chart displays sites by category using the aggregation count in the United States. The data is about sites.