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This guide will introduce the open data resources available in the CA Nature website and familiarize you with key features and capabilities of the site.CA Nature is an online Geographic Information System (or GIS), that collects a suite of publicly accessible interactive digital mapping tools and data.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.
User personas are a human-centered design tool that help open data program administrators design programs offerings for the full community open data users for maximum reach and impact. User personas help keep real people in mind when designing program offerings and can identify user segments in the open data community that have the potential to use open data to help solve problems. The Metropolitan Transportation Authority (MTA) is excited to share our open data user personas which were designed in collaboration with our existing open data community through multiple stakeholder workshops.
As of the early of 2020, around ** percent of surveyed respondents in China were awared that many online shopping and e-commerce mobile apps overused user permissions. Social media and messenger apps were the second app category with a low user trust in data security.
As of March 2021, YouTube was the video and streaming app found to collect the largest amount of data from global iOS users. The app collected a total of ** data points from each of the examined data types, respectively. The mobile app of video streaming service Amazon Prime Video followed, with ** data points collected across all the examined data types.
Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.
Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico
The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.
As of January 2025, around 13.7 percent of paid iOS apps admitted collecting data from users engaging with their mobile products. In comparison, approximately 53 percent of free-to-download iOS apps reported they collect private data from users worldwide, while approximately 86 percent of paid apps have not declared whether they collect users' privacy data.
Find User Profiles Data with LinkedIn profiles for nonprofit and NGO executives, managers, and administrators worldwide. Includes verified contact details, organizational affiliations, and professional histories. Best price guaranteed.
Success.ai’s User Profiles Data for Nonprofit and NGO Leaders provides businesses, organizations, and researchers with comprehensive access to global leaders in the nonprofit and NGO sectors. With data sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and contact details for executives, program managers, administrators, and decision-makers. Whether your goal is to partner with nonprofits, support global causes, or conduct research into social impact, Success.ai ensures your outreach is backed by accurate, enriched, and continuously updated data.
Why Choose Success.ai’s User Profiles Data for Nonprofit and NGO Leaders? Comprehensive Professional Profiles
Access verified LinkedIn profiles of nonprofit leaders, NGO managers, program directors, grant writers, and administrative executives. AI-driven validation ensures 99% accuracy for efficient communication and minimized bounce rates. Global Coverage Across Nonprofit Sectors
Includes profiles from nonprofits, humanitarian organizations, environmental groups, social enterprises, and advocacy organizations. Covers key markets across North America, Europe, APAC, South America, and Africa for global reach. Continuously Updated Dataset
Reflects real-time professional updates, organizational changes, and emerging trends in the nonprofit landscape to keep your targeting relevant and effective. Tailored for Nonprofit Insights
Enriched profiles include work histories, organizational affiliations, areas of expertise, and social impact projects for deeper engagement opportunities. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of nonprofit and NGO professionals worldwide. 100M+ Work Emails: Direct communication with executives, managers, and decision-makers in the nonprofit sector. Enriched Organizational Data: Gain insights into leadership structures, mission focuses, and operational scales. Industry-Specific Segmentation: Target nonprofits focused on healthcare, education, environmental sustainability, human rights, and more. Key Features of the Dataset: Nonprofit and NGO Leader Profiles
Identify and connect with executives, program managers, fundraisers, and policy directors in global nonprofit and NGO sectors. Engage with individuals who drive decision-making and operational strategies for impactful organizations. Detailed Organizational Insights
Leverage firmographic data, including organizational size, mission, regional activity, and funding sources, to align with specific nonprofit goals. Advanced Filters for Precision Targeting
Refine searches by region, mission type, role, or organizational focus for tailored outreach. Customize campaigns based on social impact priorities, such as climate action, gender equality, or economic development. AI-Driven Enrichment
Enhanced datasets provide actionable insights into professional accomplishments, partnerships, and leadership achievements for targeted engagement. Strategic Use Cases: Partnership Development and Outreach
Identify nonprofits and NGOs for collaboration on social impact projects, sponsorships, or grant distribution. Build relationships with decision-makers driving advocacy, fundraising, and community initiatives. Donor Engagement and Fundraising
Target nonprofit leaders responsible for managing fundraising campaigns and donor relationships. Tailor outreach efforts to align with specific causes and funding priorities. Research and Analysis
Analyze leadership trends, mission focuses, and regional nonprofit activities to inform program design and funding strategies. Use insights to evaluate the effectiveness of social impact initiatives and partnerships. Recruitment and Talent Acquisition
Target HR professionals and administrators seeking qualified staff, consultants, or volunteers for nonprofits and NGOs. Offer talent solutions for specialized roles in program management, advocacy, and administration. Why Choose Success.ai? Best Price Guarantee
Access industry-leading, verified User Profiles Data at unmatched pricing to ensure your campaigns are cost-effective and impactful. Seamless Integration
Easily integrate verified nonprofit data into your CRM or marketing platforms with APIs or downloadable formats. AI-Validated Accuracy
Rely on 99% accuracy to minimize wasted outreach efforts and maximize engagement outcomes. Customizable Solutions
Tailor datasets to focus on specific nonprofit types, geographical regions, or areas of social impact to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API
Update your internal records with verified nonprofit leader profiles to enhance targeting and engagement. Lead Generation API
Automate lead generation for a consistent pipeline of nonprofit and NGO professionals, scaling your outreach efforts efficiently. Success.ai’s User Profiles Data for Nonprofit and NGO Leader...
This page pulls together resources for various types of data.wa.gov users, including developers, publishers and data users.
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Benchmark for End-User Structured Data User Interfaces (BESDUI) based on the Berlin SPARQL Benchmark (BSBM) but intended for benchmarking the user experience while exploring a structured dataset, not the performance of the query engine. BSBM is just used to provide the data to be explored. This is a cheap User Interface benchmark as it does not involve users but experts, who measure how many interaction steps are required to complete each of the benchmark tasks, if possible. This also facilitates comparing different tools without the bias that different end-user profiles might introduce. The way to measure this interaction steps and convert them to an estimate of the required time to complete a task is based on the Keystroke-Level Model (KLM)
Johnyquest7/OctoTools-Gradio-Demo-User-Data dataset hosted on Hugging Face and contributed by the HF Datasets community
National Center for Health Statistics (NCHS) population health survey data have been linked to VA administrative data containing information on military service history and VA benefit program utilization. The linked data can provide information on the health status and access to health care for VA program beneficiaries. In addition, researchers can compare the health of Veterans within and outside the VA health care system and compare Veterans to non-Veterans in the civilian non-institutionalized U.S. population. Due to confidentiality requirements, the Restricted-use NCHS-VA Linked Data Files are accessible only through the NCHS Research Data Center (RDC) Network. All interested researchers must submit a research proposal to the RDC. Please see the NCHS RDC website (https://www.cdc.gov/rdc/index.htm) for instructions on submitting a proposal.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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About Dataset:
Domain : Marketing Project: User Profiling and Segmentation Datasets: user_profile_for_ads Dataset Type: Excel Data Dataset Size: 16k+ record
KPI's: 1. Distribution of Key Demographic Variables like: a. Count of Age b. Count of Gender c. Count of Education Level d. Count of Income Level e. Count of Device Usage
Understanding Online Behavior like: a. Count of Time Spent Online (hrs/Weekday) b. Count of Time Spent Online (hrs/Weekend)
Ad Interaction Metrics: a. Count of likes and Reactions b. Count of click through rates (CTR) c. Count of Conversion Rate d. Count of Ad Interaction Time (secs) e. Count of Ad Interaction Time by Top Interests
Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results
This data contains bar chart, horizontal bars, circle, treemap, area chart, square, line chart, dashboard, slicers, navigation button.
This dataset comprises two resources. The first resource contains a list of random people with their date and place of birth. This can be used for demographics and hypothetical scenario testing. The second resource includes user behavior data on various device models, detailing app usage, screen time, and other metrics, which is beneficial for analyzing mobile usage patterns.
Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.
We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.
What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.
Products: API Suite Web UI Full and Custom Data Feeds
Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.
The dataset represents a compilation of user interaction data generated by users who participated in the project's pilot activities in Patras, Greece. Data was generated by users in the SMARTBUY app and includes information about users, stores, product categories, professions, and events.
The dataset comprises the following data: - users: user account data for the Patras pilot users - occupation: all possible occupations that the pilot users could choose from - stores: stores which participated in the Patras pilot - sel_products_cat: products uploaded to the SMARTBUY platform by retailers - events: geo-stamped and time-stamped descriptions of a user interaction event (for instance, "user_id 67 rated product_id 722 with rating 4 at location x1 at datetime y1", or "user_id 91 denoted product_id 78 as favorite at location x2 at datetime y2") - event_types: all possible event types captured by the SMARTBUY platform ('Product searches', 'Product views', 'Featured product', 'Products near you views', 'Product photos browsed', 'Product ratings', 'Clicks on Read More button to read product reviews', 'Clicks on Open map button', 'Clicks on Send this info by email button', 'Products denoted as Favorite')
Privacy-sensitive information such as user names, retailer owner names and store names and keywords searched are anonymized.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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🇬🇧 영국
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques"
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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AIT Log Data Sets
This repository contains synthetic log data suitable for evaluation of intrusion detection systems. The logs were collected from four independent testbeds that were built at the Austrian Institute of Technology (AIT) following the approach by Landauer et al. (2020) [1]. Please refer to the paper for more detailed information on automatic testbed generation and cite it if the data is used for academic publications. In brief, each testbed simulates user accesses to a webserver that runs Horde Webmail and OkayCMS. The duration of the simulation is six days. On the fifth day (2020-03-04) two attacks are launched against each web server.
The archive AIT-LDS-v1_0.zip contains the directories "data" and "labels".
The data directory is structured as follows. Each directory mail..com contains the logs of one web server. Each directory user- contains the logs of one user host machine, where one or more users are simulated. Each file log.log in the user- directories contains the activity logs of one particular user.
Setup details of the web servers:
OS: Debian Stretch 9.11.6
Services:
Apache2
PHP7
Exim 4.89
Horde 5.2.22
OkayCMS 2.3.4
Suricata
ClamAV
MariaDB
Setup details of user machines:
OS: Ubuntu Bionic
Services:
Chromium
Firefox
User host machines are assigned to web servers in the following way:
mail.cup.com is accessed by users from host machines user-{0, 1, 2, 6}
mail.spiral.com is accessed by users from host machines user-{3, 5, 8}
mail.insect.com is accessed by users from host machines user-{4, 9}
mail.onion.com is accessed by users from host machines user-{7, 10}
The following attacks are launched against the web servers (different starting times for each web server, please check the labels for exact attack times):
Attack 1: multi-step attack with sequential execution of the following attacks:
nmap scan
nikto scan
smtp-user-enum tool for account enumeration
hydra brute force login
webshell upload through Horde exploit (CVE-2019-9858)
privilege escalation through Exim exploit (CVE-2019-10149)
Attack 2: webshell injection through malicious cookie (CVE-2019-16885)
Attacks are launched from the following user host machines. In each of the corresponding directories user-, logs of the attack execution are found in the file attackLog.txt:
user-6 attacks mail.cup.com
user-5 attacks mail.spiral.com
user-4 attacks mail.insect.com
user-7 attacks mail.onion.com
The log data collected from the web servers includes
Apache access and error logs
syscall logs collected with the Linux audit daemon
suricata logs
exim logs
auth logs
daemon logs
mail logs
syslogs
user logs
Note that due to their large size, the audit/audit.log files of each server were compressed in a .zip-archive. In case that these logs are needed for analysis, they must first be unzipped.
Labels are organized in the same directory structure as logs. Each file contains two labels for each log line separated by a comma, the first one based on the occurrence time, the second one based on similarity and ordering. Note that this does not guarantee correct labeling for all lines and that no manual corrections were conducted.
Version history and related data sets:
AIT-LDS-v1.0: Four datasets, logs from single host, fine-granular audit logs, mail/CMS.
AIT-LDS-v1.1: Removed carriage return of line endings in audit.log files.
AIT-LDS-v2.0: Eight datasets, logs from all hosts, system logs and network traffic, mail/CMS/cloud/web.
Acknowledgements: Partially funded by the FFG projects INDICAETING (868306) and DECEPT (873980), and the EU project GUARD (833456).
If you use the dataset, please cite the following publication:
[1] M. Landauer, F. Skopik, M. Wurzenberger, W. Hotwagner and A. Rauber, "Have it Your Way: Generating Customized Log Datasets With a Model-Driven Simulation Testbed," in IEEE Transactions on Reliability, vol. 70, no. 1, pp. 402-415, March 2021, doi: 10.1109/TR.2020.3031317. [PDF]
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This guide will introduce the open data resources available in the CA Nature website and familiarize you with key features and capabilities of the site.CA Nature is an online Geographic Information System (or GIS), that collects a suite of publicly accessible interactive digital mapping tools and data.