Altosight | AI Custom Web Scraping Data

✦ Altosight provides global web scraping data services with AI-powered technology that bypasses CAPTCHAs, blocking mechanisms, and handles dynamic content.

We extract data from marketplaces like Amazon, aggregators, e-commerce, and real estate websites, ensuring comprehensive and accurate results.

✦ Our solution offers free unlimited data points across any project, with no additional setup costs.

We deliver data through flexible methods such as API, CSV, JSON, and FTP, all at no extra charge.

― Key Use Cases ―

➤ Price Monitoring & Repricing Solutions

🔹 Automatic repricing, AI-driven repricing, and custom repricing rules 🔹 Receive price suggestions via API or CSV to stay competitive 🔹 Track competitors in real-time or at scheduled intervals

➤ E-commerce Optimization

🔹 Extract product prices, reviews, ratings, images, and trends 🔹 Identify trending products and enhance your e-commerce strategy 🔹 Build dropshipping tools or marketplace optimization platforms with our data

➤ Product Assortment Analysis

🔹 Extract the entire product catalog from competitor websites 🔹 Analyze product assortment to refine your own offerings and identify gaps 🔹 Understand competitor strategies and optimize your product lineup

➤ Marketplaces & Aggregators

🔹 Crawl entire product categories and track best-sellers 🔹 Monitor position changes across categories 🔹 Identify which eRetailers sell specific brands and which SKUs for better market analysis

➤ Business Website Data

🔹 Extract detailed company profiles, including financial statements, key personnel, industry reports, and market trends, enabling in-depth competitor and market analysis

🔹 Collect customer reviews and ratings from business websites to analyze brand sentiment and product performance, helping businesses refine their strategies

➤ Domain Name Data

🔹 Access comprehensive data, including domain registration details, ownership information, expiration dates, and contact information. Ideal for market research, brand monitoring, lead generation, and cybersecurity efforts

➤ Real Estate Data

🔹 Access property listings, prices, and availability 🔹 Analyze trends and opportunities for investment or sales strategies

― Data Collection & Quality ―

► Publicly Sourced Data: Altosight collects web scraping data from publicly available websites, online platforms, and industry-specific aggregators

► AI-Powered Scraping: Our technology handles dynamic content, JavaScript-heavy sites, and pagination, ensuring complete data extraction

► High Data Quality: We clean and structure unstructured data, ensuring it is reliable, accurate, and delivered in formats such as API, CSV, JSON, and more

► Industry Coverage: We serve industries including e-commerce, real estate, travel, finance, and more. Our solution supports use cases like market research, competitive analysis, and business intelligence

► Bulk Data Extraction: We support large-scale data extraction from multiple websites, allowing you to gather millions of data points across industries in a single project

► Scalable Infrastructure: Our platform is built to scale with your needs, allowing seamless extraction for projects of any size, from small pilot projects to ongoing, large-scale data extraction

― Why Choose Altosight? ―

✔ Unlimited Data Points: Altosight offers unlimited free attributes, meaning you can extract as many data points from a page as you need without extra charges

✔ Proprietary Anti-Blocking Technology: Altosight utilizes proprietary techniques to bypass blocking mechanisms, including CAPTCHAs, Cloudflare, and other obstacles. This ensures uninterrupted access to data, no matter how complex the target websites are

✔ Flexible Across Industries: Our crawlers easily adapt across industries, including e-commerce, real estate, finance, and more. We offer customized data solutions tailored to specific needs

✔ GDPR & CCPA Compliance: Your data is handled securely and ethically, ensuring compliance with GDPR, CCPA and other regulations

✔ No Setup or Infrastructure Costs: Start scraping without worrying about additional costs. We provide a hassle-free experience with fast project deployment

✔ Free Data Delivery Methods: Receive your data via API, CSV, JSON, or FTP at no extra charge. We ensure seamless integration with your systems

✔ Fast Support: Our team is always available via phone and email, resolving over 90% of support tickets within the same day

― Custom Projects & Real-Time Data ―

✦ Tailored Solutions: Every business has unique needs, which is why Altosight offers custom data projects. Contact us for a feasibility analysis, and we’ll design a solution that fits your goals

✦ Real-Time Data: Whether you need real-time data delivery or scheduled updates, we provide the flexibility to receive data when you need it. Track price changes, monitor product trends, or gather...

The no-code web scraping tool market is experiencing robust growth, driven by the increasing demand for automated data extraction across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the rise of e-commerce and the need for competitive pricing intelligence necessitates efficient data collection. Secondly, the travel and hospitality industries leverage web scraping for dynamic pricing and competitor analysis. Thirdly, academic research, finance, and human resources departments utilize these tools for large-scale data analysis and trend identification. The ease of use offered by no-code platforms democratizes web scraping, eliminating the need for coding expertise, and significantly accelerating the data acquisition process. This accessibility attracts a wider user base, contributing to market expansion. The market is segmented by application (e-commerce, travel & hospitality, academic research, finance, human resources, and others) and type (text-based, cloud-based, and API-based web scrapers). While the market is competitive, with numerous players offering varying functionalities and pricing models, the continued growth in data-driven decision-making across industries assures continued expansion. Cloud-based solutions are expected to dominate due to scalability and ease of access. Future growth hinges on the development of more sophisticated no-code platforms offering enhanced features such as AI-powered data cleaning and intelligent data analysis capabilities. Geographic regions like North America and Europe currently hold significant market share, but Asia-Pacific is poised for substantial growth due to increasing digital adoption and expanding e-commerce markets. The historical period (2019-2024) likely witnessed a moderate growth rate, setting the stage for the accelerated expansion projected for the forecast period (2025-2033). Assuming a conservative CAGR of 15% for the historical period, resulting in a 2024 market size of approximately $500 million, and applying a slightly higher CAGR of 20% for the forecast period, reflects the increasing adoption and sophistication of these tools. Factors such as stringent data privacy regulations and the increasing sophistication of anti-scraping measures present potential restraints, but innovative solutions are emerging to address these challenges, including ethical data sourcing and advanced proxy management features. The ongoing integration of AI and machine learning capabilities into no-code platforms is also expected to propel market growth, enabling more sophisticated data extraction and analysis with minimal user input.

This dataset contains information on the prices and fees charged by for-hire fishing operations in the Southeastern US.

Data Scraping Tools Market Outlook

The global data scraping tools market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 5.6 billion by 2032, growing at a robust CAGR of 15.6% during the forecast period. The market's growth is driven by the increasing adoption of big data analytics across various industries and the need for automated data extraction solutions.

One of the primary growth factors for the data scraping tools market is the exponential increase in data generation. With the ongoing digital transformation, businesses are generating enormous volumes of data that need to be analyzed to gain actionable insights. Data scraping tools offer an efficient way to extract, process, and analyze this data, making them invaluable for strategic decision-making. Additionally, advancements in artificial intelligence and machine learning have enhanced the capabilities of these tools, allowing them to handle complex scraping tasks more efficiently.

Another significant driver is the rising demand for competitive intelligence. Companies are increasingly relying on data scraping tools to gather information about competitors, market trends, and customer preferences. This data-driven approach helps businesses stay ahead of the competition by enabling them to make informed decisions based on real-time data. Furthermore, the integration of data scraping tools with other analytical and business intelligence platforms has streamlined the process of data collection and analysis, contributing to market growth.

The adoption of data scraping tools is also fueled by the increasing focus on customer experience. Businesses are leveraging these tools to gather data from various online platforms, including social media, e-commerce websites, and customer reviews, to understand customer behavior and preferences. This information is crucial for developing personalized marketing strategies and improving customer engagement. Additionally, the growing trend of hyper-personalization in marketing is expected to further boost the demand for data scraping tools.

The integration of Information Extraction IE Technology into data scraping tools is revolutionizing the way businesses handle unstructured data. By leveraging IE Technology, these tools can automatically identify and extract pertinent information from vast datasets, enhancing the accuracy and efficiency of data processing. This capability is particularly beneficial for industries that rely heavily on unstructured data sources, such as social media, customer reviews, and news articles. As businesses strive to gain deeper insights from their data, the incorporation of IE Technology into data scraping solutions is becoming increasingly essential. This advancement not only improves the quality of extracted data but also reduces the time and resources required for data analysis, thereby driving the overall growth of the data scraping tools market.

Regionally, North America holds the largest market share due to the early adoption of advanced technologies and the presence of major data scraping tool vendors. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by the rapid digitalization and industrialization in countries like China and India. Europe and Latin America are also expected to experience significant growth, owing to the increasing adoption of data analytics across various sectors.

Type Analysis

The data scraping tools market is segmented by type into web scraping, screen scraping, data extraction software, and others. Web scraping tools dominate the market due to their versatility and widespread application. They are primarily used to extract data from websites, which can then be analyzed and utilized for various purposes, including market research, competitive analysis, and customer insights. The robust demand for web scraping tools is driven by the increasing need for real-time data acquisition and the continuous growth of the online ecosystem.

Screen scraping tools, although less popular than web scraping tools, still hold a significant market share. These tools are used to capture data displayed on the screen, often from legacy systems that do not support modern API integrations. The demand for screen scraping tools is particularly high in industries with a large number of legacy applications, such as banking and financial services. The ability of

The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The data asset provides a link to all Direct Deposit Applications filed via the Internet datasets. Each dataset provides monthly volumes at the national level from federal fiscal year 2008 on for Internet Direct Deposit applications. The dataset includes only Internet Direct Deposit transactions. It should be noted that, in addition to using our online Direct Deposit application, the public might also call our 800 number, visit a field office, or request a change of direct deposit by mail. This data set pertains only to the online alternative.

The dataset comprises phishing and legitimate web pages, which have been used for experiments on early phishing detection.

Detailed information on the dataset and data collection is available at

Bram van Dooremaal, Pavlo Burda, Luca Allodi, and Nicola Zannone. 2021.Combining Text and Visual Features to Improve the Identification of Cloned Webpages for Early Phishing Detection. In ARES '21: Proceedings of the 16th International Conference on Availability, Reliability and Security. ACM.

The Antarctic Site Inventory Project has collected biological data and site-descriptive information in the Antarctic Peninsula region since 1994. This research effort has provided data on those sites which are visited by tourists on shipboard expeditions in the region. The aim is to obtain data on the population status of several key species of Antarctic seabirds, which might be affected by the cumulative impact resulting from visits to the sites. This project will continue the effort by focusing on two heavily-visited Antarctic Peninsula sites: Paulet Island, in the northwestern Weddell Sea and Petermann Island, in the Lemaire Channel near Anvers Island. These sites were selected because both rank among the ten most visited sites in Antarctica each year in terms of numbers of visitors and zodiac landings; both are diverse in species composition, and both are sensitive to potential environmental disruptions from visitors. These data collected focus on two important biological parameters for penguins and blue-eyed shags: (1) breeding population size (number of occupied nests) and (2) breeding success (number of chicks per occupied nests). A long-term data program will be supported, with studies at the two sites over a five-year period. The main focus will be at Petermann Island, selected for intensive study due to its visitor status and location in the region near Palmer Station. This will allow for comparative data with the Palmer Long Term Ecological Research program. Demographic data will be collected in accordance with Standard Methods established by the Convention for the Conservation of Antarctic Marine Living Resources Ecosystem Monitoring Program and thus will be comparable with similar data sets being collected by other international Antarctic Treaty nation research programs. While separating human-induced change from change resulting from a combination of environmental factors will be difficult, this work will provide a first step to identify potential impacts. These long-term data sets will contribute to a better understanding of biological processes in the entire region and will contribute valuable information to be used by the Antarctic Treaty Parties as they address issues in environmental stewardship in Antarctica.

The dataset collection in question is comprised of a series of related tables, which are organized in a systematic manner with rows and columns for the ease of data interpretation. These tables are part of a larger dataset collection that is primarily sourced from the website of Lantmäteriet (The Land Survey of Sweden), located in Sweden. Each table within this collection contains a variety of information and data points, providing a comprehensive overview of the subject matter at hand. The dataset collection as a whole serves as a valuable resource for comprehensive data analysis and interpretation.

Noise of Web (NoW) is a challenging noisy correspondence learning (NCL) benchmark for robust image-text matching/retrieval models. It contains 100K image-text pairs consisting of website pages and multilingual website meta-descriptions (98,000 pairs for training, 1,000 for validation, and 1,000 for testing). NoW has two main characteristics: without human annotations and the noisy pairs are naturally captured. The source image data of NoW is obtained by taking screenshots when accessing web pages on mobile user interface (MUI) with 720 $\times$ 1280 resolution, and we parse the meta-description field in the HTML source code as the captions. In NCR (predecessor of NCL), each image in all datasets were preprocessed using Faster-RCNN detector provided by Bottom-up Attention Model to generate 36 region proposals, and each proposal was encoded as a 2048-dimensional feature. Thus, following NCR, we release our the features instead of raw images for fair comparison. However, we can not just use detection methods like Faster-RCNN to extract image features since it is trained on real-world animals and objects on MS-COCO. To tackle this, we adapt APT as the detection model since it is trained on MUI data. Then, we capture the 768-dimensional features of top 36 objects for one image. Due to the automated and non-human curated data collection process, the noise in NoW is highly authentic and intrinsic. The estimated noise ratio of this dataset is nearly 70%.

Atmospheric data collection stations layer created from water management information system (WMIS) sites data. This service is for the Open Data Download application for the Southwest Florida Water Management District.

DISCOVERAQ_Maryland_Ground_Edgewood_Data contains data collected at the Edgewood ground site during the Maryland (Baltimore-Washington) deployment of NASA's DISCOVER-AQ field study. This data product contains data for only the Maryland deployment and data collection is complete.Understanding the factors that contribute to near surface pollution is difficult using only satellite-based observations. The incorporation of surface-level measurements from aircraft and ground-based platforms provides the crucial information necessary to validate and expand upon the use of satellites in understanding near surface pollution. Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) was a four-year campaign conducted in collaboration between NASA Langley Research Center, NASA Goddard Space Flight Center, NASA Ames Research Center, and multiple universities to improve the use of satellites to monitor air quality for public health and environmental benefit. Through targeted airborne and ground-based observations, DISCOVER-AQ enabled more effective use of current and future satellites to diagnose ground level conditions influencing air quality.DISCOVER-AQ employed two NASA aircraft, the P-3B and King Air, with the P-3B completing in-situ spiral profiling of the atmosphere (aerosol properties, meteorological variables, and trace gas species). The King Air conducted both passive and active remote sensing of the atmospheric column extending below the aircraft to the surface. Data from an existing network of surface air quality monitors, AERONET sun photometers, Pandora UV/vis spectrometers and model simulations were also collected. Further, DISCOVER-AQ employed many surface monitoring sites, with measurements being made on the ground, in conjunction with the aircraft. The B200 and P-3B conducted flights in Baltimore-Washington, D.C. in 2011, Houston, TX in 2013, San Joaquin Valley, CA in 2013, and Denver, CO in 2014. These regions were targeted due to being in violation of the National Ambient Air Quality Standards (NAAQS).The first objective of DISCOVER-AQ was to determine and investigate correlations between surface measurements and satellite column observations for the trace gases ozone (O3), nitrogen dioxide (NO2), and formaldehyde (CH2O) to understand how satellite column observations can diagnose surface conditions. DISCOVER-AQ also had the objective of using surface-level measurements to understand how satellites measure diurnal variability and to understand what factors control diurnal variability. Lastly, DISCOVER-AQ aimed to explore horizontal scales of variability, such as regions with steep gradients and urban plumes.

This corpus has been collected from free or free for research sources at the Internet:

A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages. A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available. A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis. the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages.

Data set consisting of data joined for analyzing the SBIR/STTR program. Data consists of individual awards and agency-level observations. The R and python code required for pulling, cleaning, and creating useful data sets has been included. Allard_Get and Clean Data.R This file provides the code for getting, cleaning, and joining the numerous data sets that this project combined. This code is written in the R language and can be used in any R environment running R 3.5.1 or higher. If the other files in this Dataverse are downloaded to the working directory, then this Rcode will be able to replicate the original study without needing the user to update any file paths. Allard SBIR STTR WebScraper.py This is the code I deployed to multiple Amazon EC2 instances to scrape data o each individual award in my data set, including the contact info and DUNS data. Allard_Analysis_APPAM SBIR project Forthcoming Allard_Spatial Analysis Forthcoming Awards_SBIR_df.Rdata This unique data set consists of 89,330 observations spanning the years 1983 - 2018 and accounting for all eleven SBIR/STTR agencies. This data set consists of data collected from the Small Business Administration's Awards API and also unique data collected through web scraping by the author. Budget_SBIR_df.Rdata 246 observations for 20 agencies across 25 years of their budget-performance in the SBIR/STTR program. Data was collected from the Small Business Administration using the Annual Reports Dashboard, the Awards API, and an author-designed web crawler of the websites of awards. Solicit_SBIR-df.Rdata This data consists of observations of solicitations published by agencies for the SBIR program. This data was collected from the SBA Solicitations API. Primary Sources Small Business Administration. “Annual Reports Dashboard,” 2018. https://www.sbir.gov/awards/annual-reports. Small Business Administration. “SBIR Awards Data,” 2018. https://www.sbir.gov/api. Small Business Administration. “SBIR Solicit Data,” 2018. https://www.sbir.gov/api.

Discharge and suspended sediment data were collected from October 2016 to Febuary 2017 at the NR0 site. Data was collected immediately down stream of Old Pacific Hwy SE bridge during a bridge measurement and approximately 100 meters below bridge for a boat measurement. Data collection from the bridge has been ongoing since 1968 but data collection from a boat was first attempted October 21, 2016 during this data collection series. Suspended sediment sample and discrete discharge data at this site are available at: https://waterdata.usgs.gov/wa/nwis/inventory/?site_no=12090240&agency_cd=USGS&. A summary of suspended-sediment sample data are provided with this data release in the file NR0_SSC_summary.csv.

Introducing E-Commerce Product Datasets!

Unlock the full potential of your product strategy with E-Commerce Product Datasets. Gain invaluable insights to optimize your product offerings and pricing, analyze top-selling strategies, and assess customer sentiment.

Our E-Commerce Datasets Source:

1. Amazon: Access accurate product data from Amazon, including categories, pricing, reviews, and more.

2. Walmart: Receive comprehensive product information from Walmart, covering pricing, sellers, ratings, availability, and more.

E-Commerce Product Datasets provide structured and actionable data, empowering you to understand customer needs and enhance product strategies. We deliver fresh and precise public e-commerce data, including product names, brands, prices, number of sellers, review counts, ratings, and availability.

You have the flexibility to tailor data delivery to your specific needs:

• Receive datasets in various formats, including JSON and CSV.
• Choose delivery via SFTP or directly to your cloud storage (e.g., AWS S3, Google Cloud Storage).
• Select from one-time, monthly, quarterly, or bi-annual data delivery frequencies.

Why Choose Oxylabs E-Commerce Datasets:

1. Fresh and accurate data: Access clean and structured public e-commerce data collected by our leading web scraping professionals.

2. Time and resource savings: Let our experts handle data extraction at an affordable cost, allowing you to focus on your core business objectives.

3. Customizable solutions: Share your unique business needs, and our team will craft customized dataset solutions tailored to your requirements.

4. Legal compliance: Partner with a trusted leader in ethical data collection, endorsed by Fortune 500 companies and fully compliant with GDPR and CCPA regulations.

Pricing Options:

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the potential of your e-commerce strategy with E-Commerce Product Datasets!

Full code and dataset for the NBP 2202 map website. Data were collected during Jan-Feb 2022 in the Amundsen sea from the Nathaniel B. Palmer. This is a Python-flask app which displays data in a javascript leaflet map. The contents of this dataset should be all you need to host the website yourself, for local viewing or to make publicly available

This upload is a copy of the GitHub repo taken on 24/03/22 with additional satellite data that was too large for git.

The github repo can be found here https://github.com/callumrollo/itgc-2022-map/

The website is currently maintained at https://nbp2202map.com/

All data are publicly available. Locations and information displayed in the map are for convenience purposes only and are not authoritative. Contact the PIS of the International Thwaites Glacier Collaboration (ITGC) for full datasets. This website is the author's personal work and does not reflect the views of the ITGC group. The author has no official affiliation with ITGC.

OSU_SnowCourse Summary: Manual snow course observations were collected over WY 2012-2014 from four paired forest-open sites chosen to span a broad elevation range. Study sites were located in the upper McKenzie (McK) River watershed, approximately 100 km east of Corvallis, Oregon, on the western slope of the Cascade Range and in the Middle Fork Willamette (MFW) watershed, located to the south of the McKenzie. The sites were designated based on elevation, with a range of 1110-1480 m. Distributed snow depth and snow water equivalent (SWE) observations were collected via monthly manual snow courses from 1 November through 1 April and bi-weekly thereafter. Snow courses spanned 500 m of forested terrain and 500 m of adjacent open terrain. Snow depth observations were collected approximately every 10 m and SWE was measured every 100 m along the snow courses with a federal snow sampler. These data are raw observations and have not been quality controlled in any way. Distance along the transect was estimated in the field. OSU_SnowDepth Summary: 10-minute snow depth observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meterological stations were located in the approximate center of each forest or open snow course transect. These data have undergone basic quality control. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN flags for missing data to NA, and added site attributes such as site name and cover. We replaced positive values with NA, since snow depth values in raw data are negative (i.e., flipped, with some correction to use the height of the sensor as zero). Thus, positive snow depth values in the raw data equal negative snow depth values. Second, the sign of the data was switched to make them positive. Then, the smooth.m (MATLAB) function was used to roughly smooth the data, with a moving window of 50 points. Third, outliers were removed. All values higher than the smoothed values +10, were replaced with NA. In some cases, further single point outliers were removed. OSU_Met Summary: Raw, 10-minute meteorological observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meteorological stations were located in the approximate center of each forest or open snow course transect. These stations were deployed to collect numerous meteorological variables, of which snow depth and wind speed are included here. These data are raw datalogger output and have not been quality controlled in any way. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN and 7999 flags for missing data to NA, and added site attributes such as site name and cover. OSU_Location Summary: Location Metadata for manual snow course observations and meteorological sensors. These data are compiled from GPS data for which the horizontal accuracy is unknown, and from processed hemispherical photographs. They have not been quality controlled in any way.

This dataset collection comprises multiple related data tables sourced from the web service interface (WFS) of the 'Tilastokeskus' (Statistics Finland) website in Finland. The data tables are organized in columns and rows, offering a structured format for the data. The information contained within this dataset collection primarily focuses on road traffic data for the year 2013. The data is comprehensive and could serve as a valuable resource for research and analysis related to road traffic patterns and statistics in Finland for the specified year. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).