" Our USA Consumer Phone Number Database is a helpful contact tool for promoting your business across all sectors. It is an easy-to-acquire and highly valuable dataset that serves as a powerful tool for marketers and a strategic asset to boost your revenue. With this targeted list, you can execute your marketing campaigns efficiently and reach potential customers directly. Most importantly, it offers the potential for a strong return on investment (ROI), helping you recoup your spending and generate profits. Expand your market reach through well-planned marketing efforts with B2C Databases.

Format: Excel / CSV (CRM-Ready) Fields: Name · Phone · Email · Location · Lead Info Coverage: USA (B2B + B2C) Best For: Telemarketing · SMS Campaigns · Direct Outreach

USA Consumer Call List | Easy & Profitable Marketing

The USA Call List makes marketing both simple and profitable. With authentic B2B and B2C contacts, businesses can reach the right customers faster. Our website offers all types of marketing databases, making us the best partner for your growth. Every contact is verified with a high accuracy rate and kept fresh with active users. Moreover, our database prices are affordable, ensuring the best return on investment (ROI).

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Find Best Quality USA Leads | Verified Contacts for Marketing

Discover the best quality USA leads that directly deliver your marketing to the right audience. Each database contains thousands of active and authentic contacts ready for promotional campaigns. Collected from trusted open sources, e-commerce sites, and opt-in providers, these lists ensure your marketing messages and sales pitches reach the intended audience effectively.

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Genuine Source of USA Dataset | Buy USA Cell Phone Numbers

B2C Databases provides a reliable source of both B2B and B2C contacts across multiple categories. We offer phone numbers, as well as WhatsApp and Telegram contacts. Our lists are neatly organized by industry, job function, and user categories, making it easy to target the audience you need. From crypto users to e-commerce buyers to C-level executives — pick the exact segment you require.

Categories: Industry · Job Function · C-Level · Niche Markets Contacts Available: Phone · WhatsApp · Telegram Easy Segmentation & Targeting Reliable Source · CRM-Compatible Data

Our USA Cell Phone Data contains the best contacts to direct your marketing toward the right audience. Save valuable time, stay on track, and maximize benefits with our affordable and high data hygiene lists. With B2C Databases, your business will stay ahead of the competition in the US market.

"

BatchData's Deed Dataset - Real Estate Transaction Data + Property Transaction Data

Unlock a wealth of historical real estate insights with BatchData's Deed Dataset. This premium offering provides detailed real estate transaction data, including comprehensive property transaction records with over 15 critical data points. Whether you're analyzing market trends, assessing investment opportunities, or conducting in-depth property research, this dataset delivers the granular information you need.

Why Choose BatchData?

At BatchData, we are committed to delivering the most accurate and comprehensive datasets in the industry. Our Deed Dataset exemplifies our dedication to quality and precision:

• Comprehensive Datasets: As a single-vendor provider, we offer an extensive array of data including property, homeowner, mortgage, listing, valuation, permit, demographic, foreclosure, and contact information. All this is available from one reliable source, streamlining your data acquisition process.

• Technical Excellence: Our dataset comes with clear documentation, purpose-built APIs, and extensive developer resources. Our technical teams are supported by robust engineering resources to ensure seamless integration and utilization.

• Tailor-Fit Pricing and Packaging: We understand that different businesses have different needs. That’s why we offer flexible pricing models and practical API metering. You only pay for the data you need, making our solutions scalable and aligned with your business objectives.

• Unmatched Contact Information Accuracy: We lead the industry with superior right-party contact rates, ensuring you get multiple accurate contact points, including highly reliable phone numbers.

Choose BatchData for your real estate data needs and experience unparalleled accuracy and flexibility in data solutions.

This dataset shows the count of Deck structures per county in the United States. the count is divided into safety ratings for each county, with values from 0-9. '0' Failed '1' Imminent '2' Critical '3' Serious '4' Poor '5' Fair '6' Satisfactory '7' Good '8' Very good '9' Excellent 'Unk' Unknown 'N' Not applicable Also Dangerous is summation of 0,1,2 and 3. Risky is summation of 4, 5 and 6. Safe is summation of 7, 8 and 9. Data Source: http://www.fhwa.dot.gov/bridge/britab.htm

This dataset has ratings for superstructures by county. each county counts the number of each superstructures with a particular rating. values range from 0-9. '0' Failed '1' Imminent '2' Critical '3' Serious '4' Poor '5' Fair '6' Satisfactory '7' Good '8' Very good '9' Excellent 'Unk' Unknown 'N' Not applicable Also Dangerous is summation of 0,1,2 and 3. Risky is summation of 4, 5 and 6. Safe is summation of 7, 8 and 9. Data source: http://www.fhwa.dot.gov/bridge/britab.htm

Welcome to BatchData, your trusted source for comprehensive US homeowner data, contact information, and demographic data, all designed to empower political campaigns. In the fast-paced world of politics, staying ahead and targeting the right audience is crucial for success.

At BatchData, we understand the importance of having the most accurate, up-to-date, and relevant data to help you make informed decisions and connect with your constituents effectively. With our robust data offerings, political campaign agencies can easily reach both homeowners and renters, using direct contact information such as cell phone numbers, emails, and mailing addresses.

The Power of Data in Political Campaigns In the digital age, political campaigns are increasingly reliant on data-driven strategies. Precise targeting, tailored messaging, and efficient outreach have become the cornerstones of successful political campaigning. BatchData equips political campaign agencies with the tools they need to harness the power of data in their campaigns, enabling them to make the most of every interaction. Harness the power of voter data and campaign & election data to effectively run political campaigns.

Key Features of BatchData 1. US Homeowner Data At BatchData, we understand that having access to accurate and comprehensive homeowner data is the bedrock of a successful political campaign. Our vast database includes information on homeowners across the United States, allowing you to precisely target this key demographic. With our homeowner data, you can segment your campaign and craft messages that resonate with this audience. Whether you're running a local, state, or national campaign, our homeowner data is an invaluable asset.

1. Contact Information 258M Phone Numbers (US Phone Number Data) BatchData doesn't just stop at providing you with demographic data; we go a step further by giving you direct contact information. We offer cell phone numbers, email addresses, and mailing addresses, ensuring that you can connect with your audience on multiple fronts. This multifaceted approach allows you to engage with potential voters in a way that suits their preferences and lifestyles. Whether you want to send targeted emails, reach out through phone calls, or even send physical mailers, BatchData has you covered with both the data and the tools. (See BatchDialer for more Info).

2. Demographic Data In addition to homeowner data and contact information, BatchData provides a treasure trove of demographic data. You can refine your campaign strategy by tailoring your messages to specific demographics, including age, gender, income, religious preferences, and more. Our demographic data helps you understand your audience better, allowing you to craft compelling messages that resonate with their values and interests.

3. Targeting Both Homeowners and Renters We understand that not all political campaigns are exclusively focused on homeowners. That's why BatchData caters to a diverse range of campaign needs. Whether your campaign is directed at homeowners or renters, our data sets have you covered. You can effectively target a broader spectrum of the population, ensuring that your message reaches the right people, regardless of their housing status.

Flexible Data Delivery Methods BatchData understands that political campaigns are time-sensitive, and efficiency is paramount. That's why we offer a variety of data delivery methods to suit your specific needs.

1. API Integration For real-time access to data, our API integration is your go-to solution. Easily integrate BatchData's data into your campaign management systems, ensuring that you always have the latest information at your fingertips.

2. Bulk File Delivery When you require a large volume of data in one go, our bulk file delivery option is ideal. We'll deliver the data to you in a format that's easy to import into your campaign databases, allowing you to work with a comprehensive dataset on your terms.

3. S3 Data Storage If you prefer to host your data in an S3 bucket, BatchData can seamlessly deliver your datasets to the cloud storage location of your choice. This option ensures that your data is readily available whenever you need it.

4. Self-Service List Building Our self-service list building tool empowers you to create custom lists based on your specific criteria. You have the flexibility to choose the data elements you need, ensuring that your campaign efforts are tailored to your goals.

5. File Exporting Need to download data for offline use or share it with your team? Our file exporting feature lets you export data in a user-friendly format, making it easy to work with.

6. On-Demand Concierge Services For those campaigns that require a more personalized touch, BatchData offers on-demand concierge services. Our experienced team is here to assist you in building lists, refining your targeting, and providing support as needed. This high-touch service ensures that you have t...

According to a November 2019 survey regarding U.S. media polarization, it was found that ** percent of consumers who had heard of ABC, trusted it as a news source, whereas the lowest ranking source for political news was MSNBC with a ** percent trust rating.

" USA cell phone number lead gives you access to thousands of verified contacts for marketing. With this dataset, you can choose from multiple contact packages suited for businessmen, marketers, bankers, C-level executives, and more. Each mobile number is real and active, making it a smart choice for businesses looking to expand in USA. Sourced from trusted data providers, B2C Databases ensures that more than 95% of the contacts are authentic and accurate, making it a reliable partner for direct marketing success.

This cell phone number lead is one of the most effective tools for business growth in USA. It helps you boost sales, connect with the right audience, and secure a high return on investment (ROI) in a short time. After purchase, the dataset is delivered in Excel or CSV format, ready to be integrated with any CRM software. With our affordable pricing, you can easily achieve sales targets and maximize ROI.

USA Marketing Number Data

USA marketing number data provides you with suitable contacts for SMS marketing, telemarketing, and brand promotions. Every dataset complies with GDPR guidelines, ensuring safe and legal use. This contact list helps businesses connect with both B2B and B2C clients, supporting brand growth through cold calls and direct outreach. Many traders have reported earning profits within days of using this resource.

In short, this phone number library assists in rapid business expansion by helping you engage with clients nationwide. Our website is known for trust and quality, so you can rely on us completely. As a result, your business growth can accelerate significantly within months when you use this data strategically.

USA Verified Mobile Number List

The USA verified mobile number list delivers the most effective results for marketing campaigns. Whether through cold calls or SMS, reaching customers directly is a powerful and time-saving strategy. B2C Databases is your go-to source for verified phone numbers that target the right customers, helping to boost company productivity and speed up deal-making.

A genuine mobile number database allows you to connect with real people effortlessly. This dataset is suitable for small and large businesses alike. It ensures higher returns than your initial investment, provided you use it effectively. By purchasing this data, you’ll have the perfect resource to strengthen your telemarketing and grow your business faster.

"

According to a survey held among adults in the United States in February 2022, ABC and CBS were considered to be the most credible news sources in the country, with 61 percent of respondents believing the organizations to be very or somewhat credible. Sources which fared less well were MSNBC, Fox News, National Public Radio, and HuffPost, with less than 50 percent of adults agreeing that they found these to be reliable news outlets. The credibility of all the news sources in the ranking was higher in 2022 than in the previous year, though the figures in 2021 were particularly low.

Trust and bias in news Finding trustworthy, impartial news sources can be difficult for audiences in a world where fake news is in constant circulation and bias in news is a growing concern. More than 50 percent of total respondents to a survey held in early 2020 believed that there was a fair amount or great deal of bias in the news sources they used most often. The same study found that close to 70 percent of respondents were more concerned with bias in news that other people may consume than with their own news source.

A report exploring trust in news found that radio, network news, and newspapers were the most trusted news sources in the United States, whereas social media was not considered reliable in this regard. The lack of trust in news on social media has yet to affect consumption – social networks are the most used source of news among many consumers, particularly younger generations. In fact, some news consumers are moving away from official news platforms altogether and getting their updates from influencers rather than journalists.

This dataset shows the count of substructures per county in the United States. the count is divided into safety ratings for each county, with values from 0-9. '0' Failed '1' Imminent '2' Critical '3' Serious '4' Poor '5' Fair '6' Satisfactory '7' Good '8' Very good '9' Excellent 'Unk' Unknown 'N' Not applicable Also Dangerous is summation of 0,1,2 and 3. Risky is summation of 4, 5 and 6. Safe is summation of 7, 8 and 9. Data Source: http://www.fhwa.dot.gov/bridge/britab.htm

This data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset is meant to depict the surface owner or manager of the land parcels. In the vast majority of land parcels, they will be one and the same. However, there are instances where the owner and manager of the land surface are not the same. When this occurs, the manager of the land is usually indicated. BLM's Master Title Plats are the official land records of the federal government and serve as the primary data source for depiction of all federal lands. Information from State of New Mexico is the primary source for the depiction of all state lands. Auxilliary source are referenced, as well, for the depiction of all lands. Collection of this dataset began in the 1980's using the BLM's ADS software to digitize information at the 1:24,000 scale. In the mid to late 1990's the data was converted from ADS to ArcInfo software and merged into tiles of one degree of longitude by one half degree of latitude. These tiles were regularly updated. The tiles were merged into a statewide coverage. The source geodatabase for this shapefile was created by loading the merged ArcInfo coverage into a personal geodatabase. The geodatabase data were snapped to a more accurate GCDB derived land network, where available. In areas where GCDB was not available the data were snapped to digitized PLSS. In 2006, the personal geodatabase was loaded into an enterprise geodatabase (SDE). This shapefile has been created by exporting the feature class from SDE.

Unlock access to residential listing data, with a subset of high value fields designed to fulfill a wide range of business use cases.

KEY FEATURES: - Residential Data Type: Access to high-quality residential data to enhance your business analysis

• Core Fields: A subset of high-value fields (220+) to support a variety of use cases - covering key property features, characteristics, etc.

• Fast & Fresh: Updated daily with data sourced directly from MLSs

The sample data includes access to the property and media resources, with additional data samples available for rooms detail and listing history. Contact sales for more information.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Peru were selected based on the following criterion: (a) smallholder potato growers Location: Huara, Barranca, Cañete (Canta Gallo),Huanuco Med Tech Adoption: -productivity 20T/Ha -CP usage -traditional growers: minimum tillage, use a mix of generic and CP quality products

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

B. Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a "quota based random sampling" procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from France were selected based on the following criterion: (a) Grain (or silage corn if the weather doesn't allow for grain corn) growers in Allier, Calvados, Côte-d'Or, Côtes-d'Armor, Eure-et-Loir, Finistère, Isère, Meurthe-et-Moselle, Pas-de-Calais, Haut-Rhin - Grain or silage corn growers (grain is the objective, but it could turn into silage corn if weather conditions don't allow for grain corn production) - Growers with a relatively good technology level / professionalism - Hybrid corn

(b) Sunflower growers in Charente, Charente-Maritime, Cher, Haute-Garonne, Gers, Indre - Sunflower growers - Growers with a relatively good technology level / professionalism - Growers who are rotating their crops (implicating that we are measuring data on similar but different plots every year)
- Un tournesoliculteur (volontaire ayant un savoir-faire et historique culture -> the farmer must be experienced ( ie having a good know how in farming in general). He is used to manage sunflower)
- Hybrid sunflower

In departments: Poitou-Charentes: 16 and 17 Sud Ouest: 31 and 32 Centre: 18 or 36

(c) Grapes growers in Champagne - Vine grapes (for vine processing, so NOT table grapes)
- In Champagne
- Farmer is also a processor or belongs to processor with a strict contract (guideline on quality)
- Background: don't go to big chateaux (they are already high level, probably don't want to share information because of competitive advantage)

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

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Understanding the effects of climate change on the phenological structure of plant communities will require measuring variation in sensitivity among thousands of co-occurring species across regions. Herbarium collections provide vast resources with which to do this, but may also exhibit biases as sources of phenological data. Despite general recognition of these caveats, validation of herbarium-based estimates of phenological sensitivity against estimates obtained using field observations remain rare and limited in scope. Here, we leveraged extensive datasets of herbarium specimens and of field observations from the USA National Phenology Network for 21 species in the United States and, for each species, compared herbarium- and field-based standardized estimates of peak flowering dates and of sensitivity of peak flowering time to geographic and interannual variation in mean spring minimum temperatures (TMIN). We found strong agreement between herbarium- and field-based estimates for standardized peak flowering time (r=0.91, p<0.001) and for the direction and magnitude of sensitivity to both geographic TMIN variation (r=0.88, p <0.001) and interannual TMIN variation (r=0.82, p<0.001). This agreement was robust to substantial differences between datasets in 1) the long-term TMIN conditions observed among collection and phenological monitoring sites and 2) the interannual TMIN conditions observed in the time periods encompassed by both datasets for most species. Our results show that herbarium-based sensitivity estimates are reliable among species spanning a wide diversity of life histories and biomes, demonstrating their utility in a broad range of ecological contexts, and underscoring the potential of herbarium collections to enable phenoclimatic analysis at taxonomic and spatiotemporal scales not yet captured by observational data.

Methods Phenological data The dataset of field observations consisted of all records of flowering onset and termination available in the USA National Phenology Network database (NPNdb), representing an initial 1,105,764 phenological observations. To ensure the quality of the observational data, we retained only observations for which we could determine that the dates of onset and termination of flowering had an arbitrary maximum error of 14 days. To do this, we filtered the data to include only records for which the date on which the first open flower on an individual was observed was preceded by an observation of the same individual without flowers no more than 14 days prior, and for which the date on which the last flower was recorded was followed by an observation of the same individual without flowers no more than 14 days later. After filtering, field observations in our data had an average maximum error of 6.4 days for the onset of flowering, and of 6.6 days for the termination of flowering. The herbarium dataset was constructed using an initial 894,392 digital herbarium specimen records archived by 72 herbaria across North America. We excluded from analysis all specimens not explicitly recorded as being in flower, or for which GPS coordinates or dates of collection were not available. We further filtered both datasets by only retaining species that were found in both datasets and that were represented by observations at a minimum of 15 unique sites in the NPN dataset. For each species, and to more closely match the geographic ranges covered by each dataset, we filtered the herbarium dataset to include only specimens within the range of latitudes and longitudes represented by the field observations in the NPN data. Finally, we retained only species represented by 70 or more herbarium specimens to ensure sufficient sample sizes for phenoclimatic modeling. This procedure identified a final set of 21 native species represented in 3,243 field observations across 1,406 unique site-year combinations, and a final sample of 5,405 herbarium specimens across 4,906 unique site-year combinations. For the herbarium dataset, sample sizes ranged from 69 unique sites and 74 specimens for Prosopis velutina, to 1,323 unique sites containing 1,368 specimens for Achillea millefolium. Sample sizes in the NPN dataset ranged from 15 unique sites with 74 observations for Impatiens capensis, 108 unique sites with 321 observations for Cornus florida. These 21 species represented 15 families and 17 genera, spanning a diverse range of life-history strategies and growth forms, including evergreen and deciduous shrubs and trees (e.g., Quercus agrifolia and Tilia americana, respectively), as well as herbaceous perennials (e.g., Achillea millefolium) and annuals (e.g., Impatiens capensis). Our focal species covered a wide variety of biomes and regions including Western deserts (e.g., Fouquieria splendens), Mediterranean shrublands and oak woodlands (e.g., Baccharis pilularis, Quercus agrifolia), and Eastern deciduous forests (e.g., Quercus rubra, Tilia Americana). To estimate flowering dates in the herbarium dataset, we employed the day of year of collection (henceforth ‘DOY’) of each specimen collected while in flower as a proxy. Herbarium specimens in flower could have been collected at any point between the onset and termination of their flowering period and botanists may preferentially collect individuals in their flowering peak for many species. Therefore, herbarium specimen collection dates are more likely to reflect peak flowering dates than flowering onset dates. To maximize the phenological equivalence of the field and herbarium datasets, we used the median date between onset and termination of flowering for each individual in each year in the NPN data as a proxy for peak flowering time. Due to the maximum error of 14 days for flowering onset and termination dates in the NPN dataset, median flowering dates also had a maximum error of 14 days, with an average maximum error among observations of 6.5 days. To account for the artificial DOY discontinuity between December 31st (DOY = 365 or 366 in a leap year) to January 1st (DOY = 1), we converted DOY in both datasets into a circular variable using an Azimuthal correction. Climate data Daily minimum temperatures mediate key developmental processes including the break of dormancy, floral induction, and anthesis. Therefore, we used minimum surface temperatures averaged over the three months leading up to (and including) the mean flowering month for each species (hereafter ‘TMIN’) as the climatic correlate of flowering time in this study; consequently, the specific months over which temperatures were averaged varied among species. Using TMIN calculated over different time periods instead (e.g., during spring for all species) did not qualitatively affect our results. Then, we partitioned variation among sites into spatial and temporal components, characterizing TMIN for each observation by the long-term mean TMIN at its site of collection (henceforth ‘TMIN normals’), and by the deviation between its TMIN in the year of collection (for the three-month window of interest) and its long-term mean TMIN (henceforth ‘TMIN anomalies’). For each site, we obtained a monthly time series of TMIN from January, 1901, and December, 2016, using ClimateNA v6.30, a software package that interpolates 4km2 resolution climate data from PRISM Climate Group from Oregon State University, (http://prism.oregonstate.edu) to generate elevation-adjusted climate estimates. To calculate TMIN normals, we averaged observed TMIN for the three months leading up to the mean flowering date of each species across all years between 1901 and 2016 for each site. TMIN anomalies relative to long-term conditions were calculated by subtracting TMIN normals from observed TMIN conditions in the year of collection. Therefore, positive and negative values of the anomalies respectively reflect warmer-than-average and colder-than-average conditions in a given year. Analysis We also provide R code to reproduce all results presented in the main text and the supplemental materials of our study. This code includes 1) all steps necessary to merge herbarium and field data into a single dataset ready for analysis, 2) the formulation and specification of the varying-intercepts and varying-slopes Bayesian model used to generate herbarium- vs. field-based estimates of phenology and its sensitivity to TMINsp, 3) the steps required to process the output of the Bayesian model and to obtain all metrics required for the analyses in the paper, and 4) the code used to generate each figure. Contributing Herbaria Data used in this study was contributed by the Yale Peabody Museum of Natural History, the George Safford Torrey Herbarium at the University of Connecticut, the Acadia University Herbarium, the Chrysler Herbarium at Rutgers University, the University of Montreal Herbarium, the Harvard University Herbarium, the Albion Hodgdon Herbarium at the University of New Hampshire, the Academy of Natural Sciences of Drexel University, the Jepson Herbarium at the University of California-Berkeley, the University of California-Berkeley Sagehen Creek Field Station Herbarium, the California Polytechnic State University Herbarium, the University of Santa Cruz Herbarium, the Black Hills State University Herbarium, the Luther College Herbarium, the Minot State University Herbarium, the Tarleton State University Herbarium, the South Dakota State University Herbarium, the Pittsburg State University Herbarium, the Montana State University-Billings Herbarium, the Sul Ross University Herbarium, the Fort Hays State University Herbarium, the Utah State University Herbarium, the Brigham Young University Herbarium, the Eastern Nevada Landscape Coalition Herbarium, the University of Nevada Herbarium, the Natural History Museum of Utah, the Western Illinois University Herbarium, the Eastern Illinois University Herbarium, the Northern Illinois University Herbarium, the Morton Arboretum Herbarium, the Chicago Botanic Garden

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This data set provides information about the Air Quality Index (AQI). The AQI is an index for reporting how clean or polluted the air is and the associated health risks. Data in this set are given in number of days that the AQI fell within a certain range (good - hazardous). This data also provides information on which pollutants were in the air (O3, CO, SO2, etc). All data is given on the county level for the United States from 2001 to 2006. Data can be seen for earlier years and all of this data can be found at the EPAs website . This dataset can be used to see which areas have the poorest or best air quality, which can be useful for a number of industries, from real estate to health care and insurance.