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License information was derived automatically
Protein–ligand binding affinity reflects the equilibrium thermodynamics of the protein–ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure–kinetics relationship (QSKR) aim at predicting protein–ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein–ligand complexes with experimental dissociation rate constants (koff), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein–ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (koff) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein–ligand atom pair descriptors for predicting koff values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).
Basic information is lacking about many pastoralist areas in the world. As a result, many services, programmes and policies do not effectively address the needs of pastoralist communities. The Government Cooperative Programme (GCP) project GCP/GLO/779/IF “Pastoralists-driven Data Management System”, was based on the idea that pastoralist associations could themselves collect, manage and share data from among their communities. This information could then be used to advocate for better targeted and pastoralist-friendly policies at local, national and international level. The project aimed at strengthening the capacities of pastoral organizations in data collection, analysis and information management, in order to facilitate evidence-based policy decision-making. It was implemented in Argentina, Chad and Mongolia, managed by the Pastoralist Knowledge Hub (PKH), and supported by the Agricultural Research Centre for International Development (Centre de coopération internationale en recherche agronomique pour le développement - CIRAD).
In Chad, the project was implemented by the Billital Maroobe Network (Réseau Billital Maroobé - RBM). An innovative approach for collecting data was developed through close partnership among the stakeholders involved, and was adopted during two successive surveys. The two questionnaires for collecting data on pastoralism were discussed and adapted to the national context, through the contribution of the participants and their deep knowledge of the field. This was one of the most innovative and successful aspects of the project, i.e. the pertinence of the method, as a result of the proactive involvement of the beneficiaries. The first survey, which aimed to identify and describe the pastoralist population, gathered information on 8,938 households. The second survey, which was more in-depth and aimed to assess the pastoralist economy and its contribution to the national economies, was conducted on a sample (based on the results of the first survey) of 1,010 households. As well as demonstrating that pastoralist organizations had the potential to successfully manage data, the surveys revealed the actual contribution of pastoralism to the economy of the country. In particular, they showed that pastoralism contributed to the national economy more than studies usually indicated, as, owing to specific characteristics, such as high levels of self-consumption, pastoralists' contribution to Gross Domestic Product (GDP) was often underestimated . During the project, it emerged that pastoralism could contribute up to 27 percent to the GDP of Chad.
National coverage
Households
Pastoralist Households
Sample survey data [ssd]
The first survey, which aimed to identify and describe the pastoralist population, gathered information on 8,938 pastoralist households in Chad. The second survey, which was more in-depth and aimed to assess the pastoralist economy and its contribution to the national economy, was conducted on a sample (based on the results of the first survey) of 1,010 pastoralist households.
The target regions for the second survey were originally 15, out of a total of 23 regions. However, owing to unforeseen constraints, only 10 regions were covered.
Computer Assisted Personal Interview [capi]
The survey was conducted in 2 rounds. For the first round, a short questionnaire was submitted to a representative of each household, addressing the following topics: i) households' socio-demographic characteristics; ii) livestock numbers and ownership; iii) land tenure and access; and iv) water access and use.
For the second round, the questionnaire focussed on the economic activity of pastoralists and their contribution to the national GDP. It covers the following topics: i) household identification ii) socio-demographic characteristics iii) livestock herd composition iv) products and final destination v) agricultural production, fishing and hunting activity vi) income and sales vii) household expenses viii) shock and adaptation strategies.
Web Development Market Size 2025-2029
The web development market size is forecast to increase by USD 40.98 billion at a CAGR of 10.4% between 2024 and 2029.
The market is experiencing significant growth, driven by the increasing digital transformation across industries and the integration of artificial intelligence (AI) into web applications. This trend is fueled by the need for businesses to enhance user experience, streamline operations, and gain a competitive edge in the market. Furthermore, the rapid evolution of technologies such as Progressive Web Apps (PWAs), serverless architecture, and the Internet of Things (IoT) is creating new opportunities for innovation and expansion. However, this market is not without challenges. The ever-changing technological landscape requires web developers to continuously update their skills and knowledge. Additionally, ensuring web applications are secure and compliant with data protection regulations is becoming increasingly complex.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on building a team of skilled developers, investing in continuous learning and development, and prioritizing security and compliance in their web development projects. By staying abreast of the latest trends and technologies, and adapting quickly to market shifts, organizations can successfully navigate the dynamic the market and drive business growth.
What will be the Size of the Web Development Market during the forecast period?
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The market continues to evolve at an unprecedented pace, driven by advancements in technology and shifting consumer preferences. Key trends include the adoption of Agile methodologies, DevOps tools, and version control systems for streamlined project management. JavaScript frameworks, such as React and Angular, dominate front-end development, while Magento, Shopify, and WordPress lead in content management and e-commerce. Back-end development sees a rise in Python, PHP, and Ruby on Rails frameworks, enabling faster development and more efficient scalability. Interaction design, user-centered design, and mobile-first design prioritize user experience, while security audits, penetration testing, and disaster recovery solutions ensure website safety.
Marketing automation, email marketing platforms, and CRM systems enhance digital marketing efforts, while social media analytics and Google Analytics provide valuable insights for data-driven decision-making. Progressive enhancement, headless CMS, and cloud migration further expand the market's potential. Overall, the market remains a dynamic, innovative space, with continuous growth fueled by evolving business needs and technological advancements.
How is this Web Development Industry segmented?
The web development industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
End-user
Retail and e-commerce
BFSI
IT and telecom
Healthcare
Others
Business Segment
SMEs
Large enterprise
Service Type
Front-End Development
Back-End Development
Full-Stack Development
E-Commerce Development
Deployment Type
Cloud-Based
On-Premises
Technology Specificity
JavaScript
Python
PHP
Ruby
Geography
North America
US
Canada
Europe
France
Germany
Spain
UK
APAC
China
India
Japan
South America
Brazil
Rest of World (ROW)
By End-user Insights
The retail and e-commerce segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the digital transformation sweeping various industries. E-commerce and retail sectors lead the market, driven by the increasing preference for online shopping and improved Internet penetration. To cater to this trend, businesses demand user-engaging web applications with smooth navigation, secure payment gateways, and seamless product search and purchase features. Mobile shopping's rise necessitates mobile app development and mobile-optimized websites. Agile development, microservices architecture, and UI/UX design are essential elements in creating engaging and efficient web solutions. Furthermore, AI, machine learning, and data analytics enable data-driven decision making, customer loyalty, and business intelligence.
Web hosting, cloud computing, API integration, and growth hacking are other critical components. Ensuring web accessibility, data security, and e-commerce development is also crucial for businesses in the digital age. Online advertising, email marketing, content strategy, brand building, and data visualization are essential aspects of digital marketing. Serverless computin
The Sol Genomics Network (SGN) is a clade-oriented database dedicated to the biology of the Solanaceae family which includes a large number of closely related and many agronomically important species such as tomato, potato, tobacco, eggplant, pepper, and the ornamental Petunia hybrida. SGN is part of the International Solanaceae Initiative (SOL), which has the long-term goal of creating a network of resources and information to address key questions in plant adaptation and diversification. A key problem of the post-genomic era is the linking of the phenome to the genome, and SGN allows to track and help discover new such linkages. Data: Solanaceae and other Genomes SGN is a home for Solanaceae and closely related genomes, such as selected Rubiaceae genomes (e.g., Coffea). The tomato, potato, pepper, and eggplant genome are examples of genomes that are currently available. If you would like to include a Solanaceae genome that you sequenced in SGN, please contact us. ESTs SGN houses EST collections for tomato, potato, pepper, eggplant and petunia and corresponding unigene builds. EST sequence data and cDNA clone resources greatly facilitate cloning strategies based on sequence similarity, the study of syntenic relationships between species in comparative mapping projects, and are essential for microarray technology. Unigenes SGN assembles and publishes unigene builds from these EST sequences. For more information, see Unigene Methods. Maps and Markers SGN has genetic maps and a searchable catalog of markers for tomato, potato, pepper, and eggplant. Tools SGN makes available a wide range of web-based bioinformatics tools for use by anyone, listed here. Some of our most popular tools include BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an Alignment Analyzer and browser for phylogenetic trees. The VIGS tool can help predict the properties of VIGS (Viral Induced Gene Silencing) constructs. The data in SGN have been submitted by many different research groups around the world. A web form is available to submit data for display on SGN. SGN community-driven gene and phenotype database: Simple web interfaces have been developed for the SGN user-community to submit, annotate, and curate the Solanaceae locus and phenotype databases. The goal is to share biological information, and have the experts in their field review existing data and submit information about their favorite genes and phenotypes. Resources in this dataset:Resource Title: Website Pointer to Sol Genomics Network. File Name: Web Page, url: https://solgenomics.net/ Specialized Search interfaces are provided for: Organisms/Taxon; Genes and Loci; Genomic sequences and annotations; QTLs, Mutants & Accessions, Traits; Transcripts: Unigenes, ESTs, & Libraries; Unigene families; Markers; Genomic clones; Images; Expression: Templates, Experiments, Platforms; Traits.
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License information was derived automatically
The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.
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...
Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights
Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.
Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.
Use Case: Analyze Year Over Year Growth Rate by Region
Problem A public investor wants to understand how a company’s year-over-year growth differs by region.
Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends
Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume
Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels
Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.
Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer
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Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically
Protein–ligand binding affinity reflects the equilibrium thermodynamics of the protein–ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure–kinetics relationship (QSKR) aim at predicting protein–ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein–ligand complexes with experimental dissociation rate constants (koff), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein–ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (koff) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein–ligand atom pair descriptors for predicting koff values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).