This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

Analysis of ‘CENSORED WEB-SITES BY ALL COUNTRIES’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/brsdincer/censored-websites-by-all-countries on 28 January 2022.

--- Dataset description provided by original source is as follows ---

CENSORED WEB-SITES BY ALL COUNTRIES

Sites that were or are currently banned.

This data was created by each country's own users.

• Some of the sites you have seen may have been active again.

--- Original source retains full ownership of the source dataset ---

The summary from the detailed analysis of the case study in EPA (1988b) is provided in Table 3 of the manuscript, and was used as the data source for the two datasets used in this study. These include a flat and hierarchical structure of the five balancing criteria, shown in Table 4 and Table 5, respectively. Table 4 provides a comprehensive score for each balancing criterion, similar to the summary tables presented in the FS of Superfund sites (e.g., (EPA 2016b, AECOM 2019)). Table 5 uses the same information in Table 3, but in this case, each piece of information is used to define multiple sub-criteria for each balancing criterion, except the cost one. This leads to a much more elaborate information table with the four remaining balancing criteria, now characterized by 13 sub-criteria. It is important to note that the scoring provided in Table 4 and Table 5, with the exception of the cost (c_5), were derived from the author’s interpretation of the descriptive language of the detailed analysis in for the hypothetical case study in presented in Table A-7 in Appendix A of the guidance document of EPA (1988b). It should be noted that the analysis of the three remedy alternatives presented in this hypothetical case study is governed by site-specific characteristics and may not represent potential performance of these remediation alternatives for other sites . The intent of this exercise is to illustrate the flexibility and adaptability of the MCDA process to address both the main, overarching criteria, as well as sub-criteria that may have specific importance in the decision process for a particular site. Ultimately, the sub-criteria can be adapted to address specific stakeholder perspectives or technical factors that may be linked to properties unique to the contaminant or physical characteristics of the site. This dataset is associated with the following publication: Cinelli, M., M.A. Gonzalez, R. Ford, J. McKernan, S. Corrente, M. Kadziński, and R. Słowiński. Supporting contaminated sites management with Multiple Criteria Decision Analysis: Demonstration of a regulation-consistent approach. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 316: 128347, (2021).

This asset includes environmental justice-related analyses of population located within a mile of Superfund and RCRA Corrective Action sites. It characterizes demographics and socio-economic characteristics of near-site communities as compared to the average U.S. population. It also examined children of up to 17 years of age living within 1 mile of SF and RCRA CA sites where human health protective measures may not have been in place. It compared data on the health status of these children to the status of all children in the U.S. Information from this study contributed to the America's Children and the Environment (ACE) report for 2013.

Analysis of ‘COVID-19 Alternative Housing Sites’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0cb625c4-7d2f-41d4-8d8f-5bfc357e7efb on 26 January 2022.

--- Dataset description provided by original source is as follows ---

A. SUMMARY This dataset includes aggregate data on the type, status, population served, and individuals placed at each alternative housing site under contract with HSA.

B. HOW THE DATASET IS CREATED Site Type, Status, and Population The HSA DOC leadership inform the data tracker owner when the legal status, site type, or intended population to serve changes.

Daily Census and Units Available The site monitors at each site inform the data tracker owner at the HSA DOC at least once daily with the updates to the daily census.

C. UPDATE PROCESS Updated several times daily, whenever new information is shared with the data tracker owner. The data tracker owner inputs the data directly into the underlying SharePoint spreadsheet.

D. HOW TO USE THIS DATASET Use the data for aggregate data on the site type, status, and daily census of individuals placed in the sites. Do not use this spreadsheet for individual-level information. There is no personally identifying or medical information in this dataset.

--- Original source retains full ownership of the source dataset ---

Raster data providing site suitability results for the production of castor throughout Brazil. The pixel value range from 1 (currently not suitable) to 10 (highly suitable) for a suitability ranking in the given pixel location. The site suitability for castor was conducted using data associated with agronomic and disease characteristics. The various characteristics were subject to a weighted overlay analysis in conjunction with an analytical hierarchy process. The raster was the result of these analytics.

Daily streamflow discharge data from 139 streamgages located on tributaries and streams flowing to the Gulf of Mexico were used to calculate mean monthly, mean seasonal, and decile values. Streamgages used to calculate trends required a minimum of 65 years of continuous daily streamflow data. These values were used to analyze trends in streamflow using the Mann-Kendall trend test in the R package entitled “Trends” and a new methodology created by Robert M. Hirsch known as a “Quantile-Kendall” plot. Data were analyzed based on water year using the Mann-Kendall trend test and by climate year using the Quantile-Kendall methodology to: (1) identify regions which are statistically similar for estimating streamflow characteristics; (2) identify trends related to changing streamflow and streamflow alteration over time; and (3) to identify possible correlations with estuary health in the Gulf of Mexico.

In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project of the National Water-Quality Program. One of the major goals of the NAWQA project is to determine how water-quality conditions change over time. To support that goal, long-term consistent and comparable monitoring has been conducted on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water-quality conditions. Data from these multiple sources have been combined to support one of the most comprehensive assessments conducted to date of water-quality trends in the United States. In order to interpret these water-quality trends, it is important to also understand how streamflow has changed during the same period. This USGS data release contains all of the input and output files necessary to reproduce the analyses of trends in streamflow described in the U.S. Geological Survey Scientific Investigations Report. Data preparation for input to the model is also fully described in the above mentioned report.

Analysis of ‘Environmental Remediation Sites’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9322a8aa-b6b8-4b72-8adb-1b308bb23d7b on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Environmental Remediation Sites are areas being remediated under one of DEC's remedial programs, including State Superfund and Brownfield Cleanup. This database contains records of the sites which have been remediated or are being managed under by the agency. All sites listed on the "Registry of Inactive Hazardous Waste Disposal Sites in New York State" are included in this database. The Database also includes the "Registry of Institutional and Engineering Controls in New York State".

Each site record includes: Administrative information, including site name, classification, unique site code, site location, and site owner(s). Institutional and Engineering Controls implemented at the site. Wastes known or thought to be disposed at the site.

--- Original source retains full ownership of the source dataset ---

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

This Excel Spreadsheet holds the rankings and final scores of the theatres after completing the suitability analysis. Within the spreadsheet you will find pages to show the rankings and final scores of the theatres for all the walking distances (5,10,15 minutes) to make it easier to complete your analysis. There are also pages to show the ranking and score of the theatres after adjusting the criteria. Use of this spreadsheet is optional and just gives another option to present the data in a form that may be easier for you to analyse.

UNESCO World Heritage Sites Dataset

UNESCO World Heritage Sites Dataset

By Throwback Thursday [source]

About this dataset

How to use the dataset

Here are some tips on how to make the most out of this dataset:

• Data Exploration:

  • Begin by understanding the structure and contents of the dataset. Evaluate the number of rows (sites) and columns (attributes) available.
  • Check for missing values or inconsistencies in data entry that may impact your analysis.
  • Assess column descriptions to understand what information is included in each attribute.

• Geographical Analysis:

  • Leverage geographical features such as latitude and longitude coordinates provided in this dataset.
  • Plot these sites on a map using any mapping software or library like Google Maps or Folium for Python. Visualizing their distribution can provide insights into patterns based on location, climate, or cultural factors.

• Analyzing Attributes:

  • Familiarize yourself with different attributes available for analysis. Possible attributes include Name, Description, Category, Region, Country, etc.
  • Understand each attribute's format and content type (categorical, numerical) for better utilization during data analysis.

• Exploring Categories & Regions:

  • Look at unique categories mentioned in the Category column (e.g., Cultural Site, Natural Site) to explore specific interests. This could help identify clusters within particular heritage types across countries/regions worldwide.
  • Analyze regions with high concentrations of heritage sites using data visualizations like bar plots or word clouds based on frequency counts.

• Identify Trends & Patterns:

  • Discover recurring themes across various sites by analyzing descriptive text attributes such as names and descriptions.
  • Identify patterns and correlations between attributes by performing statistical analysis or utilizing machine learning techniques.

• Comparison:

  • Compare different attributes to gain a deeper understanding of the sites.
  • For example, analyze the number of heritage sites per country/region or compare the distribution between cultural and natural heritage sites.

• Additional Data Sources:

  • Use this dataset as a foundation to combine it with other datasets for in-depth analysis. There are several sources available that provide additional data on UNESCO World Heritage Sites, such as travel blogs, official tourism websites, or academic research databases.

Remember to cite this dataset appropriately if you use it in

Research Ideas

• Travel Planning: This dataset can be used to identify and plan visits to UNESCO World Heritage sites around the world. It provides information about the location, category, and date of inscription for each site, allowing users to prioritize their travel destinations based on personal interests or preferences.
• Cultural Preservation: Researchers or organizations interested in cultural preservation can use this dataset to analyze trends in UNESCO World Heritage site listings over time. By studying factors such as geographical distribution, types of sites listed, and inscription dates, they can gain insights into patterns of cultural heritage recognition and protection.
• Statistical Analysis: The dataset can be used for statistical analysis to explore various aspects related to UNESCO World Heritage sites. For example, it could be used to examine the correlation between a country's economic indicators (such as GDP per capita) and the number or type of World Heritage sites it possesses. This analysis could provide insights into the relationship between economic development and cultural preservation efforts at a global scale

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Throwback Thursday.

The navigation site market is a rapidly growing industry, with a market size of XXX million in 2025 and a projected CAGR of XX% from 2025 to 2033. The growth of the navigation site market is being driven by the increasing number of internet users, the rising popularity of mobile devices, and the growing demand for location-based services. The market is expected to be further driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in navigation sites. The navigation site market is segmented by application, type, and region. By application, the market is divided into automotive, pedestrian, and public transportation. By type, the market is divided into web-based navigation sites and mobile-based navigation apps. By region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the navigation site market throughout the forecast period, followed by the Asia Pacific region.

Analysis of ‘Metro E Sites’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/eca83545-1051-442e-b673-e587557d95aa on 27 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Latitudes and longitudes for 45 sites samples for flathead catfish population genetic analyses.

Snapshot img

Website Builder Software Market Size 2024-2028

The website builder software market size is forecast to increase by USD 612.2 million at a CAGR of 5.1% between 2023 and 2028.

The market is experiencing significant growth due to the increasing importance of online branding for businesses. Functional websites have become essential for organizations to reach a wider audience and facilitate digital transformation. Cloud-based platforms and web development tools enable the creation of mobile-responsive designs, ensuring accessibility on various devices. AI applications integrated into website builders streamline various processes, such as big data analytics, media and entertainment, retail, and e-commerce. Website templates offer affordable website solutions, making it easier for businesses to establish an online presence. 
Moreover, the advancement of technology, including the AI revolution in website building, enhances the user experience. Open source options provide flexibility and customization opportunities. Website maintenance and security are crucial aspects, with cloud-based platforms offering reliable solutions to mitigate risks. In summary, the market is thriving, driven by the need for functional and secure online branding solutions.

What will be the Size of the Website Builder Software Market During the Forecast Period?

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Website builders have emerged as essential tools In the digital evolution, empowering businesses to create engaging websites and establish a strong online presence. These solutions facilitate digital adoption by individuals and organizations, enabling them to code and develop websites without extensive programming skills. In the context of the current business landscape, the integration of Artificial Intelligence (AI) infrastructure into website builders has become a significant trend. These applications include real-time data processing, NLP, video recognition, and parallel processing. By utilizing AI infrastructure, businesses can enhance their brand identity and optimize their online presence. Versatility and Sustainability: Website builders with AI capabilities offer versatility, allowing businesses to leverage advanced technologies without requiring specialized expertise. Moreover, the integration of AI chips and inference chips In these solutions ensures energy efficiency and reduced energy costs. Deep learning models and matrix multiplications are essential components of AI infrastructure. They enable website builders to provide advanced features such as personalized user experiences, predictive analytics, and automated content generation.
These capabilities can significantly improve user engagement and conversion rates. Cloud computing and parallel processing are essential technologies that support the integration of AI infrastructure into website builders. They facilitate efficient data-intensive computing and real-time processing, ensuring that businesses can quickly respond to market trends and customer demands. The integration of AI infrastructure into website builders has a profound impact on media, entertainment, retail, and e-commerce industries. For instance, media and entertainment companies can use AI to analyze user preferences and provide personalized content recommendations. Retailers can optimize their inventory management and offer personalized product recommendations based on user behavior. E-commerce platforms can leverage AI to enhance their search functionality and provide more accurate and relevant results. Website builders with AI infrastructure represent a significant advancement in digital evolution, enabling businesses to create engaging websites, optimize their online presence, and leverage advanced technologies without requiring specialized expertise. By integrating deep learning models, matrix multiplications, cloud computing, and parallel processing, these solutions offer versatility, sustainability, and significant improvements in user engagement and conversion rates. As businesses continue to adopt digital technologies, the role of AI-enabled website builders will become increasingly essential.

How is this Website Builder Software Industry segmented and which is the largest segment?

The website builder software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


End-user

  Commercial
  Individual


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period

This file contains the recreation scores for all 49 research sites. The legal accessibility, physical accessibility and recreational infrastructures rankings for each sites were combined to form the recreation ecosystem service scores.This file also contains the cross-tabulation analysis of the recreation scores verses four area groups (one - between 1m2 and 2499m2; two - between 2500m2 and 5499m2; three - between 5500m2 and 7999m2; four - larger than or equal to 8000m2) and two type of sites (aquatic, terrestrial).The area groups cross tabulation analysis shows that two sites (33 - Primrose Primary School pond and 38 - Scott Avenue allotment green roof) having a recreation score of two, and they both are less than 2500m2. Site 33 is situated inside the school, and access to the pond is for the staff and students of the school only – public access prohibited. There is a tall fence and locked gate to prevent public access from outside of the school premise – physically restricting access. However, the recreational infrastructures (benches, viewing platform and footpaths) are well maintained. Site 38 is situated inside a council owned allotment. Access into the allotment is only for people who paid to rent out allotment plots for growing food, therefore the general public is prohibited from accessing the site. The allotment green roof requires a ladder for access, and the allotment itself has a tall fence surrounding it and a locked gate. Therefore, access is physically restricted. At the other end of the scale, 22 sites achieved the maximum score of six. Seven of the 22 sites are larger than or equal to 8000m2, and they are sites 14 - Footpath beside David Lewis Sports Ground, 18 - Heaton Park boating pond, 24 - Nutsford Vale, 32 - Platt Field pond, 44 - The Meadows, 45 - Three Sisters and 49 - Woodland walkway within Alexandra Park. These sites are all situated within either public parks or local nature reserves; hence there is no issue with legal accessibility or physical accessibility. They all have well maintained recreational facilities because of their land use purposes. Seven sites within the 21 sites that achieved the maximum score are smaller than 2500m2. They are sites 12 - Chorlton Water park pond, 19 - Heaton Park Dell Garden pond, 21 - Hullard Park pond, 35 - Range Road public garden, 36 - Salford University garden, 43 - Stevenson Square green roof and 47 - Untrimmed vegetation area inside Hulme Park. Area wise, these sites appear to be too small to possess any recreational potential. However, sites 12, 19, 21 and 47 are situated within public parks, site 43 is a public garden, site 36 is situated in the middle of a university campus, and site 43 is in the middle of a public square in the Manchester city centre. Therefore, their maximum scores are justified based on the land use of their surrounding areas.Statistical analysis was performed to find out if there is a relationship between the size of the sites and the recreation scores they can achieve. The 49 sites were further categorised into two categories (1 = sites less than or equals to 5500m2; 2 = sites more than 5500m2) for the analysis. After performing the non-parametric Kruskal-Wallis analysis, it was found that the p-value of 0.943 is larger than 0.05. This implies that there is no significant difference between site sizes compared with the recreation score each site is awarded, out of the 49 sites surveyed.The type of site cross tabulation analysis shows that 22 out of 49 sites (44.9%) achieved the highest recreational score, which is six. The 22 sites are split evenly between sites with only terrestrial characteristics and sites with aquatic characteristics. The two sites that achieved the lowest scores (sites 33 – Primrose Primary School pond, and 38 – Scott Avenue allotment green roof) are also split evenly, with site 33 being aquatic dominated and site 38 being terrestrial dominated. The recreation ecosystem service scores were statistically examined to see if there is a significant difference between aquatic and terrestrial sites. After performing the non-parametric Kruskal-Wallis analysis, it was found that the p-value of 0.181 is larger than 0.05. This implies that there is no significant influence between the type of site compared with the recreation score each site gets, out of 49 sites surveyed.

Altosight | AI Custom Web Scraping Data

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

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🔹 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

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🔹 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

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🔹 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

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

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➤ 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

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

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✔ 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

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✦ 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...

MGVB is a collection of tools for proteomics data analysis. It covers data processing from in silico digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. The toolset is developed with efficiency in mind. It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. MGVB, as it is a native application, is faster than existing proteomics tools such as MaxQuant and, at the same time, finds very similar, in some cases even larger, numbers of peptides at a chosen level of statistical significance. It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. This report describes the algorithms behind the tools, presents benchmarking data sets analysis comparing MGVB performance to MaxQuant/Andromeda, and provides step by step instructions for using it in typical analytical scenarios.

Data output for Ripley's K analysis of actual DGB sites published in the paper "Analysis of Feature Intervisibility and Cumulative Visibility Using GIS, Bayesian and Spatial Statistics: A Case Study from the Mandara Mountains, Northern Cameroon."