In 2024, the Calorie Counter app had the largest number of collected data points possibly linked to the user identity. Out of the total 22 collected data types, 20 were linked to the users' identity, while seven data points could potentially be used to track users. Calorie counting app Eato did not display any of the collected data types that could potentially be used to track users. The iOS mobile app for the Weight Watchers Program collected seven different data points that were not linked to users.

BatchData's Deed Dataset - Real Estate Transaction Data + Property Transaction Data

Unlock a wealth of historical real estate insights with BatchData's Deed Dataset. This premium offering provides detailed real estate transaction data, including comprehensive property transaction records with over 15 critical data points. Whether you're analyzing market trends, assessing investment opportunities, or conducting in-depth property research, this dataset delivers the granular information you need.

Why Choose BatchData?

At BatchData, we are committed to delivering the most accurate and comprehensive datasets in the industry. Our Deed Dataset exemplifies our dedication to quality and precision:

• Comprehensive Datasets: As a single-vendor provider, we offer an extensive array of data including property, homeowner, mortgage, listing, valuation, permit, demographic, foreclosure, and contact information. All this is available from one reliable source, streamlining your data acquisition process.

• Technical Excellence: Our dataset comes with clear documentation, purpose-built APIs, and extensive developer resources. Our technical teams are supported by robust engineering resources to ensure seamless integration and utilization.

• Tailor-Fit Pricing and Packaging: We understand that different businesses have different needs. That’s why we offer flexible pricing models and practical API metering. You only pay for the data you need, making our solutions scalable and aligned with your business objectives.

• Unmatched Contact Information Accuracy: We lead the industry with superior right-party contact rates, ensuring you get multiple accurate contact points, including highly reliable phone numbers.

Choose BatchData for your real estate data needs and experience unparalleled accuracy and flexibility in data solutions.

The accurate prediction of physical properties is critical for the successful application of both conventional and novel chemicals across various industries. This work focuses on predictive modeling for the density and viscosity of ternary mixtures of ionic liquids (ILs) using a combination of the group contribution (GC) method and three machine learning algorithms: artificial neural network (ANN), XGBoost, and LightGBM. Initially, a comprehensive collection of reliable open-source data is compiled, comprising 10,553 data points from densities for 28 classes of ILs and 33 classes of organic solvents (os) and 3581 data points from viscosity for 15 classes of ILs and 17 classes of os. The modeling results demonstrate that all three machine learning algorithms yield reliable predictions. Notably, the ANN-based model showed the best performance in both density and viscosity property predictions, with a density fit of more than 0.99 and a viscosity fit of more than 0.98. To gain a deeper understanding of the influencing factors, the study employed the Shapley Additive Interpretation (SHAP) technique. This study provides valuable insights into accurately predicting two important properties of IL–organic solvent–water ternary mixtures. By enabling more efficient screening of IL–os–water mixed solvents in industrial design, these findings contribute to the advancement and optimization of IL-based processes across various applications.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

Points of sale (PoS) and other points of interest (POI) are now the most frequently used point data: Hardly any map display on the Internet can do without them. PTV GmbH also offers a large number of other point data for example location files, kindergartens and schools, house coordinates and others.

A 2D design space with two parameters were created using different sampling methods: grid, Latin hypercube sampling (LHS), random, and antithetic version of the last two. The number of sample points to cover the study space are: 100, 225, 625, 1225, and 2500. The lower values for both parameters equal to 0.2 and upper values equal to 1. The design space is based on the geometry characterised by non-linear equations, and non-convexity. The synthetic tabular datasets contain two parameters and consider a binary classification problem, where points are “Good” denoted with “1” if they are in the interior of the design space and “Bad” denoted with “0” if they are not. The datasets were used to extract two extra datasets to train, evaluate, and compare classification models coupled with active learning strategies. The two extra datasets extracted from the datasets containing the values of parameters and the target associated are: (i) the indexes of the initial labelled samples and (ii) the indexes of the initial training samples.

Altosight | AI-Powered Amazon Data, eBay Data & More | Global Marketplace Insights

✦ Altosight offers robust, AI-powered Amazon Data services that provide deep insights into product listings, reviews, prices, and sales trends.

✦ Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data are also covered, giving businesses the tools they need to make data-driven decisions across the world’s largest marketplaces.

Our Amazon Data encompasses a broad range of publicly available information from Amazon’s marketplace, which can be used to improve customer experience, personalize recommendations, optimize operations, and drive business success.

With unlimited free data points, fast delivery, and no setup costs, Altosight provides unparalleled flexibility and efficiency.

➤ We offer multiple data delivery options including API, CSV, JSON, and FTP, ensuring seamless integration into your business processes at no additional charge.

― Key Use Cases ―

➤ Marketplace Expansion & Product Assortment Optimization

🔹 Identify gaps in your product offerings by comparing competitor inventories with Alibaba Data, Amazon Data, and eBay Data.

🔹 Expand your product catalog by analyzing trends in best-sellers, emerging products, and market demand.

🔹 Use Digital Shelf Data to track product placements, best-seller rankings, and availability across major marketplaces to optimize your digital shelf space.

➤ Customer Sentiment & Product Review Analysis

🔹 Leverage Amazon Reviews Data to understand customer feedback, identify common complaints, and highlight product strengths.

🔹 Analyze AliExpress Data to track seller ratings and customer reviews, providing insights into consumer sentiment across different marketplaces.

🔹 Use these insights to refine product offerings, improve customer satisfaction, and enhance your brand’s reputation.

➤ Competitive Price Monitoring & Dynamic Repricing

🔹 Track product prices across Amazon, eBay, Alibaba, and AliExpress to ensure you remain competitive in the marketplace.

🔹 Use Amazon Data and eBay Data for real-time insights into competitor pricing and discounts.

🔹 Implement dynamic repricing strategies to react to price changes in real-time, ensuring your products always stay competitively priced.

➤ Product Sourcing & Wholesaler Opportunities

🔹 Use Alibaba Data and AliExpress Data to uncover new product opportunities and identify potential wholesalers.

🔹 Discover trending products to source for your business and form partnerships with reliable suppliers, streamlining your supply chain and business growth.

➤ Market Trend Identification & Forecasting

🔹 Use Alibaba Data and AliExpress Data to identify emerging trends in consumer behavior, product categories, and price fluctuations.

🔹 Conduct comprehensive market research to forecast product demand and industry trends based on historical data from Amazon and other marketplaces.

🔹 Stay ahead of market changes by leveraging real-time data for strategic decision-making, product launches, and marketing initiatives.

➤ Retailer & Brand Performance Tracking

🔹 Track the performance of specific retailers or brands across Amazon, eBay, Alibaba, and AliExpress using detailed sales and review data.

🔹 Monitor how frequently products move up or down in rankings, providing valuable insights for brand positioning and marketing effectiveness.

🔹 Analyze which retailers sell particular brands and products, helping businesses identify new partnerships or distribution opportunities.

― Data Collection & Quality ―

✔ Publicly Sourced Data: Altosight collects Amazon Data, Amazon Reviews Data, eBay Data, Alibaba Data, and AliExpress Data from publicly available sources. This includes product information, transaction data, reviews, and other valuable data points that are essential for making informed business decisions.

✔ AI-Powered Scraping: Our AI-driven technology handles CAPTCHAs, dynamic content, and JavaScript-heavy websites to ensure continuous and accurate data collection. We extract and structure Amazon Reviews Data, Digital Shelf Data, and other relevant marketplace data for easy integration into your existing systems.

✔ High-Quality Data: Altosight ensures all data is cleaned, structured, and ready for use, with high accuracy and reliability. Our solutions are ideal for market research, competitor analysis, and operational optimization.

― Why Choose Altosight? ―

✔ Unlimited Data Points: Altosight offers unlimited free data points, allowing you to extract as many product attributes or sales data as needed without additional charges. This ensures cost-effectiveness while maintaining access to all the insights you require.

✔ Proprietary Anti-Blocking Technology: Our proprietary scraping technology ensures continuous access to Amazon Data, eBay Data, Alibaba Data, and AliExpress Data by bypassing CAPTCHAs, Cloudflare, and other blocking mechanisms.

✔ Custom & R...

Our POI Data connects people's movements to over 14M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world. Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated based on Factori’s Mobility & People Graph data aggregated from multiple data sources globally. To achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 40 data records possible for one POI based on the combination of these attributes. Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly). Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Data Attributes: Location ID n_visitors day_of_week distance_from_home do_date month part_of_day travelled_countries Visitor_country_origin Visitor_home_origin Visitor_work_origin year

Identity Resolution Market Outlook

The global identity resolution market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach a valuation of around USD 5.8 billion by 2032, growing at a CAGR of 16.2% during the forecast period from 2024 to 2032. This remarkable growth is primarily driven by the increasing need for organizations to accurately identify and understand their customers, thereby enhancing marketing efficiency and reducing fraud.

One of the significant growth factors for the identity resolution market is the exponential increase in digital interactions. With the proliferation of digital channels such as social media, e-commerce platforms, and mobile applications, organizations face the challenge of integrating diverse data points to create a unified customer profile. Identity resolution technology enables businesses to overcome this challenge by linking disparate data sources, thereby providing a holistic view of the customer. This capability is particularly crucial in enhancing targeted marketing campaigns, improving customer engagement, and boosting overall business performance.

Another critical driver is the rising incidences of fraud and cyber threats. As digital transactions surge, the risk of identity theft and fraud also escalates. Businesses are increasingly adopting identity resolution solutions to detect and prevent fraudulent activities in real-time. These solutions employ advanced algorithms and machine learning techniques to analyze data patterns and identify anomalies. By doing so, businesses can protect themselves and their customers from potential fraud, thereby safeguarding their reputation and financial stability.

The push for regulatory compliance also fuels the demand for identity resolution solutions. Various regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), mandate organizations to manage and protect personal data effectively. Identity resolution tools help organizations comply with these regulations by ensuring data accuracy and consistency across different systems and applications. This not only mitigates the risk of non-compliance penalties but also enhances customer trust and loyalty.

Regionally, North America is expected to hold the largest market share in the identity resolution market during the forecast period. This dominance is attributed to the early adoption of advanced technologies, a high concentration of market players, and stringent data privacy regulations. Moreover, the growing focus on customer experience management and security concerns are driving the adoption of identity resolution solutions in the region. The Asia Pacific region is also anticipated to witness significant growth, driven by the rapid digital transformation, increasing internet penetration, and rising awareness about data privacy and security.

Component Analysis

The identity resolution market is segmented by component into software and services. The software segment encompasses various tools and platforms designed to integrate and reconcile different data points to form a unified customer identity. This segment is expected to hold a significant share of the market due to the increasing reliance on advanced analytics and machine learning algorithms to process large volumes of data. These software solutions are pivotal in ensuring data accuracy and consistency, thereby enabling businesses to derive actionable insights from their data.

Within the software segment, various types of solutions are available, including customer data platforms (CDPs), data management platforms (DMPs), and identity graph technologies. CDPs and DMPs are particularly popular due to their ability to aggregate data from multiple sources, allowing for real-time customer identity resolution. Identity graph technologies, on the other hand, focus on mapping relationships between different data points, thereby enhancing the accuracy of customer profiles. The continuous innovation in these software solutions is expected to drive the growth of the software segment.

The services segment includes consulting, implementation, and support services offered by various vendors to help organizations deploy and maintain identity resolution solutions. Consulting services are vital in assessing an organization's current data landscape and identifying the best strategies for implementing identity resolution technologies. Implementation services ensure the seamless integration of these solutions into existing systems, while support services provide ongoing m

This data are corn point clouds collected by Terrestrial laser scanning. They contain a total of 48 individual corn point clouds in three different growing seasons. The data format is pcd, and each point cloud data contains the (x,y,z) coordinates of the points.

This dataset contains all the different types of stratigraphic data points archived at the Ohio Department of Natural Resources, Division of Geological Survey.

GPS wear time and average daily valid GPS points by different types of data completion.

This data set contains surface and subsurface information used in the construction of the bedrock-topography maps for the state of Ohio. This data set was created as part of a project to create the new state bedrock-geology map for Ohio. The data in the dataset come from a number of different sources, which include water wells, ODOT bridge borings, Ohio EPA waste-disposal site borings, oil and gas wells, and information from published and unpublished reports, some of which are on file at the ODGS. Location information is plotted on 7.5-minute quadrangles.

Geolocet's POI Data spans the entire European continent, offering a wealth of information about Points of Interest in all countries. The extensive database covers a wide spectrum of sectors, providing valuable insights into the retail landscape, healthcare facilities, educational institutions, and much more. Whether seeking insights into markets, healthcare services, or educational access, Geolocet's POI data offers access to comprehensive information.

🔍 Uncover the Essence of Localities

Geolocet's POI Data allows exploration into the unique characteristics of various localities. With information available for more than 2,500 types of Points of Interest (POIs), including businesses, services, and amenities within specific regions, Geolocet provides valuable aggregated insights. Alternatively, for those seeking precise locations, Geolocet can provide the exact coordinates of individual POIs. This granularity offers the flexibility to craft insightful profiles of local communities or pinpoint specific POIs, aiding in tailored strategies and decisions for specific areas.

🌍 Customizable Data Solutions

At Geolocet, we recognize the significance of tailored solutions, which is why our POI Data is entirely customizable to meet your specific requirements. Whether you need data for a single region, or multiple countries, Geolocet's flexible data solutions empower you to select and acquire precisely the information you need.

Tailored Selection: Our platform allows users to choose the sectors and geographic regions that align most closely with their objectives.

Preferred Formats: Data can be received in your preferred formats, whether it's Shapefile, GeoJSON, or any other compatible format.

Moreover, we provide two distinct lists of available attributes to cater to your diverse data needs:

For Customers Requiring Points Data: - ID - Name - Category - Location Latitude - Location Longitude - Address (available for 50% of records) - Phone Number - Email Address - Website - Opening Hours - Brand - Operator - Wheelchair Accessibility - Uber Grid Cell IDs

Please note that data availability within the above list of attributes may vary depending on the POI category.

For Customers Needing Aggregated Data:

• ID of the Area
• Name of the Area
• Number of Clothing Stores
• Number of Restaurants
• Number of Cafes
• Number of Bars
• Number of Cinemas
• Number of Schools
• Number of Universities
• Number of Hospitals

It's important to emphasize that the attributes listed for the Aggregated datasets serve as examples. Geolocet offers complete flexibility, allowing you to customize attributes to suit your specific needs.

Reach out to Geolocet today to explore how our POI Data can enhance your decision-making processes and provide invaluable insights for your success.

🔄 Regular Data Updates

To maintain current and relevant insights, Geolocet's POI data undergoes regular updates. Our subscription models provide access to the latest information, enabling users to stay ahead in analyses and strategies. Recognizing the importance of up-to-date data in today's fast-paced world, Geolocet supports ongoing data needs.

🌐 Integration Potential

Geolocet's POI Data seamlessly integrates with other data offerings, including Administrative Boundaries Spatial Data and Demographic Data. This integration enriches insights and provides a holistic understanding of regions. Combining POI data with administrative boundaries and demographic information empowers data-driven decisions that consider the broader context.

🔍 Craft Informed Strategies

Geolocet's POI Data goes beyond numbers, uncovering the essence of each locality and understanding its unique characteristics. Whether in retail, healthcare, education, or any other sector, the data equips users with the insights needed to craft informed strategies, optimize resource allocation, and make decisions that resonate with the target audiences.

🔍 Customized Data Solutions with DaaS

Geolocet's Data as a Service (DaaS) offers flexibility tailored to needs. The transparent pricing model ensures cost-efficiency, allowing payment solely for the required data. Whether a startup is exploring a local market or a multinational corporation is analyzing multiple regions, Geolocet provides solutions that align with those objectives.

Contact Geolocet today to explore how the POI Data can elevate decision-making processes and provide valuable insights for success in those endeavors.

Between 2004 and January 2024, internet users in Canada have seen a high number of breaches of different types of data. Passwords were most likely to be among the breached data. Usernames were the second-most breached data type, followed by password hash.

The dataset comprises 4 fire experiments (repeated 3 times) and 3 nuisance experiments (Ethanol: repeated 3 times, Deodorant: repeated 2 times, Hairspray: repeated 1 time), with various background sequences interspersed between the conducted experiments. All exeriments were caried out in random order to reduce the influence of prehistory. It consists of a total of 305,304 rows and 16 columns, structured as a continuous multivariate time series. Each row represents the sensor measurements (CO2, CO, H2, humidity, particulate matter of different sizes, air temperature, and UV) from a unique sensor node position in the EN54 test room at a specific timestamp. The columns correspond to the sensor measurements and include additional labels: a scenario-specific label ("scenario_label"), a binary label ("anomaly_label") distinguishing between "Normal" (background) and "Anomaly" (fire or nuisance scenario), a ternary label ("ternary_label") categorizing the data as "Nuisance," "Fire," or "Background," and a progress label ("progress_label") that allows for dividing the event sequences into sub-sequences based on ongoing physical sub-processes. The dataset comprises 82.98% background data points and 17.02% anomaly data points, which can be further divided into 12.50% fire anomaly data points and 4.52% nuisance anomaly data points. The "Sensor_ID" column can be utilized to access data from different sensor node positions.

This digital data release presents subsurface data from multiple geologic units that were part of a previous study of the regional subsurface structural configuration of the Powder River Basin in Wyoming and Montana. The original data within this geodatabase is sourced from an unpublished doctoral dissertation by Jessie Melick at Montana State University (Melick, 2013). Data contained in this release were generated from elevation grids developed by Jessie Melick using 28,000 wells and geophysical well logs penetrating Paleozoic to Mesozoic strata over a 70,000 square-kilometer area designated by the Department of Energy as a realistic locality for geologic carbon sequestration (Melick, 2013). Information included in this release represents a small component of the larger geomodel, which includes rock-property details such as facies analysis, porosity calculations, and net to gross thickness, among others. Well locations, well identification numbers, geophysical logs, and any other non-public data or information used in the creation of this dataset has been explicitly omitted. Data in this release includes elevation point features on the stratigraphic tops of the Mesaverde Group, Frontier Formation, Lakota Formation, Tensleep Formation, Madison Group, and Precambrian basement that were exported from the original horizon grids as points on a 500x500 m grid spacing. This release additionally contains structure contour maps of the tops of these same units; the contours were digitally generated from the point arrays using automated contouring methods within a geographic information system. Characterizing these units in the subsurface is of value, as they have been identified as potential reservoirs for the geologic sequestration of carbon, units of interest for geothermal energy production, may serve as regional groundwater aquifers, and are currently considered productive hydrocarbon reservoirs (Melick, 2013). Formation top points and structure contours were formatted and attributed as GIS data sets for use in digital form as part of U.S. Geological Survey’s ongoing effort to inventory, catalog, and release subsurface geologic data in geospatial form. This effort is part of a broad directive to develop 2D and 3D geologic information at detailed, national, and continental scales. This data approximates, but does not strictly follow the USGS NCGMP GeMS data structure schema for geologic maps.Structure contour lines for each formation are stored within separate “IsoValueLine” feature classes, while formation tops for each formation are stored as point data in separate “MapUnitPoints” feature classes. These are distributed within a geographic information system geodatabase and are also saved as shapefiles. Contour and point data are provided in both feet and meters to maintain consistency with the original publication and for ease of use. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units referenced herein. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and accompanying nonspatial tables.

This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

Our identity dataset allows businesses to submit their customer IDs, which our platform matches to identities across various platforms and devices. This process opens up new communication channels by using multiple data points to determine or probabilistically match users to their corresponding identities.

Identity Data Reach

Our dataset links device data to hashed email data from first-party data owners. Leveraging our identity graph, we connect IP addresses, device IDs, and other platform identities, enabling more comprehensive communication channels.

• Record Count: 500 Million+
• Updated: Monthly
• Historical Data: Past 6 Months

Data Export Methodology

We dynamically collect and update data, providing the latest insights through Data Clean Rooms. This method ensures privacy compliance while enriching your data according to your specific requirements.

Use Cases

Our identity dataset is crucial for identity resolution and data enrichment, empowering businesses to enhance their customer data and expand their reach across multiple platforms and devices.

This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard). The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM. The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV). Usage of the data is restricted to the citation of the article mentioned below. Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.