The AmeriList RV Owners Database is a powerful, up-to-date mailing list comprising over 10.7 million RV owners across the United States. This specialized consumer dataset is built to fuel targeted direct marketing campaigns via postal mail, email, and telemarketing, helping brands, service providers, and marketers reach RV enthusiasts with precision. Whether you’re in outdoor gear, insurance, travel, campground services, RV parts & accessories, or hospitality, this database unlocks access to high-value prospects who live for the open road.

Key Features & Data Quality

• Extensive Universe: 10,774,530 RV owners in the U.S. matched for multiple channels (postal, email, telemarketing).
• Rich segmentation options: You can target by RV type and class (Motorhome Class A, B, C, travel trailers, camper types), RV year; demographics such as age, household income, net worth; geography (state, zip, city, radius etc.); interests, hobbies; presence of children, ethnicity, and more.
• High data integrity: The list is compiled from multiple sources: registration data, point-of‐sale & service, membership & subscription records, surveys and online activity. It is standardized & verified via major data hygiene tools and postal certifications including USPS certified procedures, CASS, LACSLink, NCOALink, and DPV verification.
• Update frequency: Data is refreshed monthly to keep contacts current. Address changes are managed with NCOA (National Change of Address).
• Flexibility in order size & use: Minimum order, usage levels etc., designed to accommodate both large and smaller campaigns.

Typical Profile & Behavior - The average RV owner in the U.S. is about 48 years old and likely to travel multiple times per year in their vehicle. - They tend to seek comfort, quality, adventure, and gear, making them especially responsive to offers for travel services, camping supplies, insurance, outdoor lifestyle brands, RV accessories, maintenance & repair providers.

Ideal Use Cases / Campaign Fit This dataset is especially well suited for marketers and businesses in: - Outdoor recreation & camping gear & supplies - RV parks, campgrounds & travel accommodations - Insurance & extended warranty providers for RVs - Automotive service, RV repair, parts & accessories - Travel brands, restaurateurs, fuel stations along travel corridors - Financial services, lifestyle brands targeting affluent / adventure-minded customers

By combining detailed demographic and RV usage / ownership segmentations, campaigns can be highly tailored, improving response rates, reducing waste, and driving higher ROI.

Technical & Operational Details - Channels delivered: Postal mail, email, telemarketing. - Certifications & Accuracy tools: USPS-certified address and mailing standards; CASS; LACSLink; DPV; NCOALink for address update; regular monthly refreshes. - Minimum order thresholds & pricing: Minimum orders start at 5,000 records. Base rates vary depending on campaign channel, refinement, order size, and segment selections.

Data delivery format & options: Lists can be delivered electronically (e.g. Excel, comma-delimited text), and via postal mailing list services. Suppression, hygiene, de-duplication, and other enhancements are generally available.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

RVS Stock Forecast: A Sell For The Next 1 Year

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

DNA data underlying the paper 'From the Surface Ocean to the Seafloor: Linking Modern and Paleo-genetics at the Sabrina Coast, East Antarctica (IN2017_V01)' by Armbrecht et al. (https://doi.org/10.1029/2022JG007252). In this study, a modern and a paleo-genomics approach to investigate vertical profiles of marine organisms (bacteria and eukaryotes) through the water column and underlying sediments were conducted at three sampling stations off the Sabrina Coast, East Antarctica. Water and sediment samples were collected during the “Sabrina Seafloor Survey” (IN2017_V01) and represent the surface waters, chlorophyll maximum depth, bottom waters, and several depths below the seafloor. Conductivity–temperature–depth (CTD) data, seawater samples, and sediment cores (Kasten Cores [KCs]) were collected during the RV Investigator voyage IN2017_V01 (“Sabrina Seafloor Survey”) between January and March 2017. A combination of 16S and 18S rRNA amplicon sequencing (modern DNA) and shotgun metagenomics (sedimentary ancient DNA, sedaDNA) was used.

Data were extracted from supporting information file (https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1029%2F2022JG007252&file=2022JG007252-sup-0001-Supporting+Information+SI-S01.pdf) provided with the research paper (https://doi.org/10.1029/2022JG007252).

DNA dataset were downloaded from Armbrecht, L. & Focardi, A. (2022), Totten Glacier ocean & sediment DNA (IN2017_V01). University of Tasmania Research Data Portal [Dataset], https://dx.doi.org/10.25959/hwk8-cc81. All read counts, sequence id for Bacteria, Archaea and Eukaryota identified using sedaDNA at KC02, KC06, KC14 are extracted at the lowest identified taxonomic level from MEGAN (CE v.6.24.23).

Voyage details (metadata, projects, other datasets either online or as downloads, publications and reports, events, maps etc) can be accessed at https://www.marine.csiro.au/data/trawler/survey_details.cfm?survey=IN2017_V01 If this data has been used in any products, please acknowledge with the following: We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353) in undertaking this research.

Hot Jupiters generally do not have nearby planet companions, as they may have cleared out other planets during their inward migration from more distant orbits. This gives evidence that hot Jupiters more often migrate inward via high-eccentricity migration due to dynamical interactions between planets rather than more dynamically cool migration mechanisms through the protoplanetary disk. Here we further refine the unique system of WASP-132 by characterizing the mass of the recently validated 1.0-day period super-Earth WASP-132c (TOI-822.02) interior to the 7.1-day period hot Jupiter WASP-132b. Additionally, we announce the discovery of a giant planet at a 5-year period (2.7 AU). We also detect a long-term trend in the radial velocity data indicative of another outer companion. Using over nine years of CORALIE RVs and over two months of highly-sampled HARPS RVs, we determine the masses of the planets from smallest to largest orbital period to be Mc=6.26^+1.84^-1.83_M_Earth, Mb=0.428^+0.015^-0.015_M_Jup, and Mdsini=5.16^+0.52^-0.52_M_Jup, respectively. Using TESS and CHEOPS photometry data we measure the radii of the two inner transiting planets to be Rc=1.841^+0.094^-0.093_R_Earth and Rd=0.901^+0.038^-0.038_R_Jup. We find a bulk density of rho_c_=5.47^+1.96^_-1.71_g/cm^3^ for WASP-132c, which is slightly above the Earth-like composition line on the mass-radius diagram. WASP-132 is a unique multi-planetary system in that both an inner rocky planet and an outer giant planet are in a system with a hot Jupiter. This suggests it migrated via a more rare dynamically cool mechanism and helps to further our understanding of how hot Jupiter systems may form and evolve.

We consider WIYN/Hydra spectra of 329 photometric candidate members of the 420Myr old open cluster M48 and report lithium detections or upper limits for 234 members and likely members. The 171 single members define a number of notable Li-mass trends, some delineated even more clearly than in Hyades/Praesepe: the giants are consistent with subgiant Li dilution and prior MS Li depletion due to rotational mixing. A dwarfs (8600-7700K) have upper limits higher than the presumed initial cluster Li abundance. Two of five late A dwarfs (7700-7200K) are Li-rich, possibly due to diffusion, planetesimal accretion, and/or engulfment of hydrogen-poor planets. Early F dwarfs already show evidence of Li depletion seen in older clusters. The Li-Teff trends of the Li Dip (6675-6200K), Li Plateau (6200-6000K), and G and K dwarfs (6000-4000K) are very clearly delineated and are intermediate to those of the 120Myr old Pleiades and 650Myr old Hyades/Praesepe, which suggests a sequence of Li depletion with age. The cool side of the Li Dip is especially well defined with little scatter. The Li-Teff trend is very tight in the Li Plateau and early G dwarfs, but scatter increases gradually for cooler dwarfs. These patterns support and constrain models of the universally dominant Li depletion mechanism for FGK dwarfs, namely rotational mixing due to angular momentum loss; we discuss how diffusion and gravity-wave-driven mixing may also play roles. For late G/K dwarfs, faster rotators show higher Li than slower rotators, and we discuss possible connections between angular momentum loss and Li depletion.