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License information was derived automatically
Lead fell to 2,028.48 USD/T on July 11, 2025, down 0.69% from the previous day. Over the past month, Lead's price has risen 1.60%, but it is still 8.21% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lead - values, historical data, forecasts and news - updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Lead population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Lead across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of Lead was 3,035, a 0.60% increase year-by-year from 2022. Previously, in 2022, Lead population was 3,017, an increase of 1.58% compared to a population of 2,970 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Lead increased by 11. In this period, the peak population was 3,130 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Lead Population by Year. You can refer the same here
This dataset provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.
Lead service lines (LSLs), when present, are the largest source of lead in drinking water, and their removal is necessary to reduce public exposure to lead from drinking water. Unfortunately, the composition of many service lines (SLs) is uncertain. The town of Bennington, Vermont, for example, has unreliable SL records, making it challenging to build an inventory and conduct an LSL replacement program. In 2017, Bennington commenced a project to identify SL materials and replace all LSLs. 159 control homes, consisting of 99 LSL and 60 non-LSL sites, were chosen for record reviews, visual SL observations, fully flushed (FF) and sequential profile water sampling, and test excavations to evaluate method accuracies. Of the 159 control homes, records for 90 % of the 99 known LSL homes were accurate. Whereas 3 % of the 60 non-lead SL homes’ records accurately identified SL material. Fully flushed and sequential profile samples (SPSs) were 73 % and 95 % accurate for identifying LSLs and 95 % and 83 % accurate for identifying non-LSLs, respectively. Results were 100 % accurate when visual observations, FF samples, and test excavation were used in a stepwise approach. A stepwise approach consisting of visual SL observations, FF samples, and SPSs achieved a 98 % accuracy at identifying LSLs and a 67 % cost reduction compared to performing test excavations at each home. Findings from this control group study are critical for state, tribal, and local officials to inform their decisions about the selected approach to identify unknown SLs. This dataset is not publicly accessible because: EPA was not the lead in this work. The data is owned and maintained by the lead author Patrick Smart and their organization MSK Engineers. It can be accessed through the following means: Data will be made available on request. Contact the corresponding author from MSK Engineers, PE, MSK Engineers 150 Depot Street, Bennington, VT, United States, Patrick Smart (psmart@mskeng.com). Format: Data consists of the records review from 159 control homes, consisting of 99 lead service lines and 60 non-LSL sites. Data summary from visual service line observations, fully flushed and sequential profile water sampling, and test excavations to evaluate method accuracies were compiled.
This dataset is associated with the following publication: Smart, P., L. MacRae, C. Formal, and D. Lytle. Development Optimization of a Systematic Approach to Identifying Lead Service Lines - One Community’s Success. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 246: 120725, (2023).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Lead Hill population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Lead Hill across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.
Key observations
In 2023, the population of Lead Hill was 284, a 0.35% increase year-by-year from 2022. Previously, in 2022, Lead Hill population was 283, an increase of 0.71% compared to a population of 281 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Lead Hill decreased by 5. In this period, the peak population was 309 in the year 2008. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).
When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).
Data Coverage:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Lead Hill Population by Year. You can refer the same here
An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.
Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone.
There are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating, etc. ) in order to get a higher lead conversion.
X Education wants to select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score h have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.
Variables Description
* Prospect ID - A unique ID with which the customer is identified.
* Lead Number - A lead number assigned to each lead procured.
* Lead Origin - The origin identifier with which the customer was identified to be a lead. Includes API, Landing Page Submission, etc.
* Lead Source - The source of the lead. Includes Google, Organic Search, Olark Chat, etc.
* Do Not Email -An indicator variable selected by the customer wherein they select whether of not they want to be emailed about the course or not.
* Do Not Call - An indicator variable selected by the customer wherein they select whether of not they want to be called about the course or not.
* Converted - The target variable. Indicates whether a lead has been successfully converted or not.
* TotalVisits - The total number of visits made by the customer on the website.
* Total Time Spent on Website - The total time spent by the customer on the website.
* Page Views Per Visit - Average number of pages on the website viewed during the visits.
* Last Activity - Last activity performed by the customer. Includes Email Opened, Olark Chat Conversation, etc.
* Country - The country of the customer.
* Specialization - The industry domain in which the customer worked before. Includes the level 'Select Specialization' which means the customer had not selected this option while filling the form.
* How did you hear about X Education - The source from which the customer heard about X Education.
* What is your current occupation - Indicates whether the customer is a student, umemployed or employed.
* What matters most to you in choosing this course An option selected by the customer - indicating what is their main motto behind doing this course.
* Search - Indicating whether the customer had seen the ad in any of the listed items.
* Magazine
* Newspaper Article
* X Education Forums
* Newspaper
* Digital Advertisement
* Through Recommendations - Indicates whether the customer came in through recommendations.
* Receive More Updates About Our Courses - Indicates whether the customer chose to receive more updates about the courses.
* Tags - Tags assigned to customers indicating the current status of the lead.
* Lead Quality - Indicates the quality of lead based on the data and intuition the employee who has been assigned to the lead.
* Update me on Supply Chain Content - Indicates whether the customer wants updates on the Supply Chain Content.
* Get updates on DM Content - Indicates whether the customer wants updates on the DM Content.
* Lead Profile - A lead level assigned to each customer based on their profile.
* City - The city of the customer.
* Asymmetric Activity Index - An index and score assigned to each customer based on their activity and their profile
* Asymmetric Profile Index
* Asymmetric Activity Score
* Asymmetric Profile Score
* I agree to pay the amount through cheque - Indicates whether the customer has agreed to pay the amount through cheque or not.
* a free copy of Mastering The Interview - Indicates whether the customer wants a free copy of 'Mastering the Interview' or not.
* Last Notable Activity - The last notable activity performed by the student.
UpGrad Case Study
Your data will be in front of the world's largest data science community. What questions do you want to see answered?
https://www.icpsr.umich.edu/web/ICPSR/studies/38303/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38303/terms
The National Database of Childcare Prices (NDCP) provides childcare prices at the county level in the United States. The NDCP is a new data source, and the most comprehensive federal source of childcare prices at the county level in the United States. The NDCP was developed to fill a need for local-level childcare price data, standardized across U.S. states. Most existing sources of childcare price data provide prices at the state level, yet parents must choose childcare providers that are in close proximity to their homes or workplaces. Therefore, state averages are unlikely to be good estimates of the prices parents encounter in the market. State average prices do not reflect the substantial variation in prices from one locale to the next within a state and underestimate prices in urban areas. The NDCP provides data on the price of childcare by children's age groups and care setting (home-based or center-based) at the median and 75th percentile over an 11-year period (2008-2018, inclusive) at the county level. The data were obtained from state Lead Agencies responsible for conducting market rate surveys (MRS) according to Child Care and Development Fund regulations. A MRS is the collection and analysis of prices charged by childcare providers for services in the priced market. All state Lead Agencies must conduct a survey and develop a report on local childcare prices in their state every three years. The Women's Bureau contracted with ICF to obtain reports and data from previously conducted surveys to develop the NDCP. The NDCP standardizes and harmonizes data across years and geographies for about 200 previously-conducted MRS. The NDCP also provides county-level demographic and economic data from the American Community Survey. The accompanying User Guide (U.S. Department of Labor, Women's Bureau National Database of Childcare Prices: Final Report) provides detailed information about the data sources, data collection strategy, standardization and imputation of the data, and data limitations to inform and assist researchers who may be interested in using the data for future analyses. The following items are provided in the User Guide as appendices. Appendix A: Data Collection Protocol and Decisions Made During Data Entry Process, Including State Nuances Appendix B: List of Imputations Performed for Each State and Year Appendix C: Initial Price Modes per States' MRS Reports Appendix D: Data Dictionary and Additional Imputation Methodology Appendix E: Making the Database Accessible
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Register on the platform: app.success.ai See our prices: https://www.success.ai/pricing Book a demo: https://calendly.com/d/cmh7-chj-pcz/success-ai-demo-session?
https://brightdata.com/licensehttps://brightdata.com/license
Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features
Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.
Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases
Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.
Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
This live list has been running stong for several years... its scrubbed weekly and sent fresh content alnmost daily...open rates are at about 20% for third parties offers and services it sports less than a 1% bounce rate.
We are selling you the bulk list at low up front, because what you really want is the new subscriptions additions of fresh leads from that week...
Right now the newsletter is pulling in 1500-2000 new subscribers a week - emails and sms mobile numbers...and if you want access to that fresh lovely data...the only costs $0.20 a lead u to 100 - $0.15 per lead up to 500, and $0.10 after that.
This is a unique and fresh list that has been very responsive to third party ads and content...(orders have to be in by 5pm PST each Friday, first come first serve. There are other lists too that we would consider doing a similar arrangement with here at Datasons if we get some heavy buyers...
Markets
Cryptocurrency,Crypto Email List,Crypto Buyers,cryptocoin,Crypto
36714
$50.00
https://cubig.ai/store/terms-of-servicehttps://cubig.ai/store/terms-of-service
1) Data Introduction • The Supply Chain DataSet includes a variety of information across the supply chain, including production, inventory, ordering, shipping, sales, suppliers, and transportation of products, which can be used to analyze supply chains and research efficiency in the manufacturing and distribution industries.
2) Data Utilization (1) Supply Chain DataSet has characteristics that: • This dataset includes a variety of characteristics needed to operate the supply chain, including product type, SKU, price, inventory level, sales, sales, lead time, manufacturing and transportation costs, defect rates, and customer characteristics. (2) Supply Chain DataSet can be used to: • Inventory and Demand Forecast: Data such as inventory, sales volume, and lead time can be used to forecast demand and develop inventory optimization models. • Supply chain efficiency analysis: By analyzing various indicators such as sales, cost, defect rate, and transportation by product, it can be used to improve supply chain operational efficiency and strategize.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Lead is a neurotoxin commonly found in our daily lives. While lead has been eliminated from gasoline, household paint, and solder, you can still be exposed to lead from many different sources including dust containing lead from pre-1978 lead paint, paint chips, contaminated soils, water, ceramic plates, bowls, and glasses, and imported candy, toys, cosmetics, and jewelry
Lead can cause serious health problems, especially for pregnant women and young children. The US Centers for Disease Control and Prevention (CDC) has indicated that no safe blood lead level in children has been identified. Even low levels of lead in blood have been shown to affect IQ, ability to pay attention, academic achievement, and other behavioral issues.
As of January 1, 2018, Allegheny County requires every child under age six to be tested for lead exposure. The first of two tests will be conducted when a child is approximately 9-12 months old, and the second test will take place around the child’s second birthday. According to the Allegheny County Health Department, 53% of County children born in 2016 were tested for lead between the ages of nine to 12 months. This share has risen from 30% of County children born in 2009.
Children are initially tested with a capillary, or “finger prick” blood test. If an elevated level of lead is found, a venous blood test will be administered to confirm the result. For more information on the testing methods, please see the Allegheny County Health Department’s Lead Exposure in Allegheny County report, released in September, 2018. The Allegheny County Health Department currently treats confirmed blood lead level tests with 5 µg/dL or more of lead as elevated. This measurement is based on the CDC’s reference level for public health action, established in May 2012.
If a child under age 6 tests with a confirmed blood lead level of 5 µg/dl and above, ACHD offers a free home inspection. The goal of this inspection, along with XRF readings, sampling of dust, soil, and water, is to help identify any sources of lead exposure in the home. The inspection includes identifying possible alternative sources of lead exposure from jewelry, toys, cosmetics, parent occupations and/or hobbies. Inspectors also educate the family about how good nutrition can mitigate absorption of lead and immediate steps the family can take to reduce lead exposure in the home. ACHD also offers free lead testing for the uninsured or underinsured at its Immunization clinic, and at WIC offices in McKeesport and Wilkinsburg.
The Allegheny Lead Safe Homes Program currently provides free home repairs to keep families safe from lead paint. This program will test for lead-based paint in the home and will aid with repairs and prevention education to Allegheny County homeowners or renters who meet income requirements and whose home is built before 1978. All work is done in a lead-safe manner. Eligible residents must either have a child under 6 years or a pregnant woman in the household.
For additional information about how to use this data accurately and responsibly, please refer to the County's data guide
Information appearing in this description was drawn from the following sources:
Lead Exposure in Allegheny County (September 2018 pdf report)
Allegheny County Health Department’s Lead Exposure Prevention (Website)
Allegheny County Health Department’s Lead Testing (Website)
Data about lead in Allegheny County (Website)
Allegheny County Health Department’s Approach to Lead (Website)
Allegheny County Lead Safe Homes program information (Website)
Allegheny County’s Article XXIII Blood Lead Testing Regulation (pdf document)
Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.
Transform Your Business with Our Comprehensive B2B Marketing Data Our B2B Marketing Data is designed to be a cornerstone for data-driven professionals looking to optimize their business strategies. With an unwavering commitment to data integrity and quality, our dataset empowers you to make informed decisions, enhance your outreach efforts, and drive business growth.
Why Choose Our B2B Marketing Data? Unmatched Data Integrity and Quality Our data is meticulously sourced and validated through rigorous processes to ensure its accuracy, relevance, and reliability. This commitment to excellence guarantees that you are equipped with the most up-to-date information, empowering your business to thrive in a competitive landscape.
Versatile and Strategic Applications This versatile dataset caters to a wide range of business needs, including:
Lead Generation: Identify and connect with potential clients who align with your business goals. Market Segmentation: Tailor your marketing efforts by segmenting your audience based on industry, company size, or geographical location. Personalized Marketing Campaigns: Craft personalized outreach strategies that resonate with your target audience, increasing engagement and conversion rates. B2B Communication Strategies: Enhance your communication efforts with direct access to decision-makers, ensuring your message reaches the right people. Comprehensive Data Attributes Our B2B Marketing Data offers more than just basic contact information. With over 20+ attributes, you gain in-depth insights into:
Decision-Maker Roles: Understand the responsibilities and influence of key figures within an organization, such as CEOs, executives, and other senior management. Industry Affiliations: Analyze industry-specific data to tailor your approach to the unique dynamics of each sector. Contact Information: Direct email addresses and phone numbers streamline communication, enabling you to engage with your audience effectively and efficiently. Expansive Global Coverage Our dataset spans a wide array of countries, providing a truly global perspective for your business initiatives. Whether you're looking to expand into new markets or strengthen your presence in existing ones, our data ensures comprehensive coverage across the following regions:
North America: United States, Canada, Mexico Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more South America: Brazil, Argentina, Chile, Colombia, and more Africa: South Africa, Nigeria, Kenya, Egypt, and more Australia and Oceania: Australia, New Zealand Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more Industry-Wide Reach Our B2B Marketing Data covers an extensive range of industries, ensuring that no matter your focus, you have access to the insights you need:
Finance and Banking Technology Healthcare Manufacturing Retail Education Energy Real Estate Telecommunications Hospitality Transportation and Logistics Government and Public Sector Non-Profit Organizations And many more… Comprehensive Employee and Revenue Size Information Our dataset includes detailed records on company size and revenue, offering you the ability to:
Employee Size: From small businesses with a handful of employees to large multinational corporations, we provide data across all scales. Revenue Size: Analyze companies based on their revenue brackets, allowing for precise market segmentation and targeted marketing efforts. Seamless Integration with Broader Data Offerings Our B2B Marketing Data is not just a standalone product; it integrates seamlessly with our broader suite of premium datasets. This integration enables you to create a holistic and customized approach to your data-driven initiatives, ensuring that every aspect of your business strategy is informed by the most accurate and comprehensive data available.
Elevate Your Business with Data-Driven Precision Optimize your marketing strategies with our high-quality, reliable, and scalable B2B Marketing Data. Identify new opportunities, understand market dynamics, and connect with key decision-makers to drive your business forward. With our dataset, you’ll stay ahead of the competition and foster meaningful business relationships that lead to sustained growth.
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Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML’s capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.
Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.
A. Usecase/Applications possible with the data:
Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data
Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.
Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.
Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.
Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.
Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.
Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.
How does it work?
X Education requires us to build a model that assigns a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The conversion rate in the input dataset is 30% and the ballpark of the target lead conversion rate needs to be around 80%. The CEO will have the team focus on the leads that the model results as promising to make the “lead to customer” process more efficient.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
This dataset is about: (Table 1B) Lead and strontium concentrations and mass accumulation rates for selected samples from DSDP Sites 92-597 to 92-601. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.789978 for more information.
This dataset was curated for the digital humanities portion of the project "500 Years of Black History in South Florida" by Synatra Smith, Luling Huang, and Portia Hopkins.
Data was curated at the U.S. Census Tract level for four counties in South Florida: Broward, Miami-Dade, Monroe, and Palm Beach.
There are two tables in this dataset:
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The sociodemographic data come from the American Community Survey (2020 5-year estimates). The variables include fraction of black population, median income, unemployment rate, and four education level variables for population 25 years or above: fraction of population below high school, fraction of population who had high school diploma only, fraction of population who had a college degree or equivalent only, and fraction of population who had a graduate degree. Here are the table numbers and relevant columns from the U.S. Census data portal:
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The energy burden data come from the U.S. Department of Energy's Low-Income Energy Affordability Data (LEAD) tool. The air quality (PM2.5 concentration) data come from the U.S. Centers for Disease Control and Prevention's Daily Census Tract-Level PM2.5 Concentrations, 2016.
This project is conducted on behalf of the Association for the Study of African American Life and History and the National Park Service with additional funding from the Council on Library and Information Resources.
References
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This dataset curates from data existing in the public domain and can be used for other purposes freely with attribution.
Comprehensive dataset of 42 Cost accounting services in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Interactive data set on lead exposure (Blood Lead Concentrations greater than or equal to 25 micrograms per deciliter) of adults in the United States. The data comes from laboratory-reported elevated blood lead levels. Recent research has led to increased concerns about the toxicity of lead at low doses. Reflecting this increased concern, the ABLES program updated its case definition for an elevated BLL to a blood lead concentration greater than or equal to 10 micrograms per deciliter in 2009. This new case definition has also been: (1) recommended by the Council of State and Territorial Epidemiologists in 2009; (2) included in CDC''s list of nationally notifiable conditions in 2010; and (3) adopted as the Healthy People 2020 Occupational Safety and Health Objective 7. Given this new case definition, NIOSH will update the ABLES Charts and Interactive Database to include lead exposures to blood lead level greater than or equal to 10 micrograms per deciliter in the near future.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Lead fell to 2,028.48 USD/T on July 11, 2025, down 0.69% from the previous day. Over the past month, Lead's price has risen 1.60%, but it is still 8.21% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lead - values, historical data, forecasts and news - updated on July of 2025.