View details of Fragrance Net Buyer and Max Value Bv Supplier data to US (United States) with product description, price, date, quantity, major us ports, countries and more.

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

The ESA funded GlobSnow project produced snow water equivalent (SWE) monthly estimates for the Northern Hemisphere for the years 1979-2013.

SWE describes the amount of liquid water in the snow pack that would be formed if the snow pack was completely melted.

The monthly aggregate, a single product for each month, is calculated by determining the mean and the maximum of the weekly SWE samples. This dataset presents the monthly maximum value of SWE only.

The SWE product shall cover the Northern Hemisphere, excluding the mountainous areas, Greenland, the glaciers and snow on ice (lakes/seas/oceans).

The spatial resolution of the product is 25 km on EASE-grid projection.

Construction of the 30 years historical data set will be carried out using SMMR, SSM/I and SSMI/S data along with ground-based weather station data. The data are utilized for the different years as follows:

1979/09/11 - 1987/10/30 SMMR (Scanning Multichannel Microwave Radiometer onboard Nimbus-7 satellite) 1987/11/01 - 2008/12/31 SSM/I (Special Sensor Microwave/Imager onboard the DMSP satellite series F8/F11/F13) 2009/01/01 - present SSM/I(S) (Special Sensor Microwave/Imager (Sounder) onboard the DMSP satellite series F17/F18/)

These data may be redistributed and used without restriction.

View details of Perfumery Products Import Data of Max Value Bv Supplier from Belgium to US at New Yorknewark Area Newark Nj Port with product description, price, date, quantity and more.

Each pixel value corresponds to the actual number (count) of valid Best-quality Max-NDVI values used to calculate the mean weekly values for that pixel. Since 2020, the maximum number of possible observations used to create the Mean Best-Quality Max-NDVI for the 2000-2014 period is n=20. However, because data quality varies both temporally and geographically (e.g. cloud cover and snow cover in spring; cloud near large water bodies all year), the actual number (count) of observations used to create baselines can vary significantly for any given week and year.

Track the MAX VALUE in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 636018836, IMO: 9508299

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

📹Project Video available on YouTube - https://youtu.be/4hYOkHijtNw

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

The Weather Dataset is a time-series data set with per-hour information about the weather conditions at a particular location. It records Temperature, Dew Point Temperature, Relative Humidity, Wind Speed, Visibility, Pressure, and Conditions.

This data is available as a CSV file. We have analysed this data using the Pandas library.

Using this dataset, we answered multiple questions with Python in our Project.

Q. 1) Find all the unique 'Wind Speed' values in the data.

Q. 2) Find the number of times when the 'Weather is exactly Clear'.

Q. 3) Find the number of times when the 'Wind Speed was exactly 4 km/h'.

Q. 4) Find out all the Null Values in the data.

Q. 5) Rename the column name 'Weather' of the dataframe to 'Weather Condition'.

Q. 6) What is the mean 'Visibility' ?

Q. 7) What is the Standard Deviation of 'Pressure' in this data?

Q. 8) What is the Variance of 'Relative Humidity' in this data ?

Q. 9) Find all instances when 'Snow' was recorded.

Q. 10) Find all instances when 'Wind Speed is above 24' and 'Visibility is 25'.

Q. 11) What is the Mean value of each column against each 'Weather Condition ?

Q. 12) What is the Minimum & Maximum value of each column against each 'Weather Condition ?

Q. 13) Show all the Records where Weather Condition is Fog.

Q. 14) Find all instances when 'Weather is Clear' or 'Visibility is above 40'.

Q. 15) Find all instances when : A. 'Weather is Clear' and 'Relative Humidity is greater than 50' or B. 'Visibility is above 40'

These are the main Features/Columns available in the dataset :

• Date/Time - The timestamp when the weather observation was recorded. Format: M/D/YYYY H:MM.

• Temp_C - The air temperature in degrees Celsius at the time of observation.

• Dew Point Temp_C - The temperature at which air becomes saturated with moisture (dew point), also measured in degrees Celsius.

• Rel Hum_% - The relative humidity, expressed as a percentage (%), indicating how much moisture is in the air compared to the maximum it could hold at that temperature.

• Wind Speed_km/h - The speed of the wind at the time of observation, measured in kilometers per hour.

• Visibility_km - The distance one can clearly see, measured in kilometers. Lower values often indicate fog or precipitation.

• Press_kPa - Atmospheric pressure at the time of observation, measured in kilopascals (kPa).

• Weather - A text description of the observed weather conditions, such as "Fog", "Rain", or "Snow".

ABSTRACT: In order to solve the problem that the stem nodes are difficult to identify in the process of sugarcane seed automatic cutting, a method of identifying the stem nodes of sugarcane based on the extreme points of vertical projection function is proposed in this paper. Firstly, in order to reduce the influence of light on image processing, the RGB color image is converted to HSI color image, and the S component image of the HSI color space is extracted as a research object. Then, the S component image is binarized by the Otsu method, the hole of the binary image is filled by morphology closing algorithm, and the sugarcane and the background are initially separated by the horizontal projection map of the binary image. Finally, the position of sugarcane stem is preliminarily determined by continuously taking the derivative of the vertical projection function of the binary image, and the sum of the local pixel value of the suspicious pixel column is compared to further determine the sugarcane stem node. The experimental results showed that the recognition rate of single stem node is 100%, and the standard deviation is less than 1.1 mm. The accuracy of simultaneous identification of double stem nodes is 98%, and the standard deviation is less than 1.7 mm. The accuracy of simultaneous identification of the three stem nodes is 95%, and the standard deviation is less than 2.2 mm. Compared with the other methods introduced in this paper, the proposed method has higher recognition and accuracy.

A series of annual geochemical models were created by RockWare utilizing RockWorks20 which were interpolated based on the 1,4-dioxane levels that were measured during 1986 through 2024. In cases where the same intervals were samples on more than one occasion during a given year, the highest 1,4-dioxane values were used. The extent of each annual model were limited to polygons based on only the wells that were sampled during the associated year to eliminate interpolating in areas where data is not present. The annual geochemical models were then filtered based on lithology to eliminate any voxels within the areas deemed impermeable based on lithology. The models were further constrained by utilizing the maximum historical water level surface (MHWLS) grid model to further restrict the interpolation from areas lacking measured data. Finally, the voxel models were converted to annual grid models, in which the cell values are based on the highest value within the corresponding column of voxels. The 2024 plume presented here was created from the RockWorks project database files on May 01, 2025 (Gelman6.sqlite). The grid file titled 2024-01-01_to_2024-12-31.RwGrd was converted by The Mannik and Smith Group (MSG) to a raster file compatible in ArcGIS and converted to polygons of areas of concentrations between the following values: 3 ppb, 7.2 ppb, 85 ppb, 150 ppb, 280 ppb, 500 ppb, 1000 ppb, 1900 ppb, 3000 ppb, and 5000 ppb. The 7.2 ppb lines were created because it represents the current EGLE Part 201 generic residential cleanup criterion (GRCC). The 85 ppb lines were created to represent the Consent Judgement 3 (CJ3) drinking water criteria. The 280 ppb lines were created because that is the new EGLE groundwater-surface water interface (GSI) criterion, and 1900 ppb is the Vapor Intrusion criteria. EGLE is contouring the 3 ppb level as this is the trigger for response if detected in sentinel wells in the 4th Consent Judgment.Field NameField DescriptionDepth1The minimum depth of the well screen in feet below ground surfaceDepth2The maximum depth of the well screen in feet below ground surfaceMAX_ValueThe maximum 1,4-dioxane concentration measured as parts per billion (ppb) at this boring in the year; non-detect values are given a value of one half the detection limitBoreName associated with the boringNameThe chemical name of the analytical resultYear_txThe display text year for which this is the maximum dioxane concentration in that calendar yearYearThe year for which this is the maximum dioxane concentration in that calendar yearPOINT_XEasting in Michigan State Plane Coordinate System (South Zone - FIPS 2113), NAD83 international feetPOINT_YNorthing in Michigan State Plane Coordinate System (South Zone - FIPS 2113), NAD83 international feetTdata_YNYes/No determination if this value is used as T-Data in the Rockworks geochemical modelMAX_Value_txThe display text of the maximum 1,4-dioxane concentration measured at this boring in the year; non detect values are given as NDThis is the latest version of the Dioxane Plume data. Earlier vintages are available at: Gelman Site of 1,4-Dioxane Contamination - Dioxane Plume Map (2020 Data) and Gelman Site of 1,4-Dioxane Contamination - Dioxane Plume Map (2023 Data). This data is used in the Gelman Site of 1,4-Dioxane Contamination web map (item details). If you have questions regarding the Gelman Sciences, Inc site of contamination contact Chris Svoboda at 517-256-2849 or svobodac@michigan.gov. Report problems or data functionality suggestions to EGLE-Maps@Michigan.gov.

The data you provided seems to represent sales or some measurement (labeled "BJ sales") recorded over a period of 150 time intervals (likely days, but this is not explicitly stated). Here’s a detailed analysis of the data:

https://www.stat.auckland.ac.nz/~wild/data/Rdatasets/?utm_source=chatgpt.com

Key Characteristics:

1. Time Periods: The data spans from time period 1 to time period 150. This suggests it could represent daily or weekly sales (or measurements) for a certain product or service.

2. Sales Data (BJ sales): This column contains values that likely represent the sales or some performance metric at each time point. It fluctuates over time, showing trends that we can analyze.

General Observations:

• The values begin at 200.1 at time 1 and generally show a steady upward trend for the first 80 time periods.
• There are fluctuations, but the overall pattern seems to be gradually increasing during the first 90 intervals.
• After around time 90, the values seem to peak, reaching values around 246 in time period 96 and fluctuating around those levels, gradually increasing further around time 120.
• The data appears to stabilize after around time period 130 and remains mostly in the 257 range, with some small variations.

Trends:

1. Increasing Trend (Time 1 to Time 96):

   • Sales grow from around 200 to a peak around 247.8.
   • There is a noticeable upward slope, indicating either seasonal growth, promotions, or other factors contributing to higher sales.

2. Fluctuations Around Time 100 to Time 150:

   • After hitting the peak in the early 240s, the values seem to fluctuate between 247.6 and 262.7.
   • While there is a general high point around 260 starting from time 117 onward, there is some instability, suggesting potential changes in the market, product demand, or other factors influencing sales.

Statistical Insights:

• Maximum Value: The highest value is 263.3 (at time 146).
• Minimum Value: The lowest value is 198.6 (at time 7).
• Range: The difference between the maximum and minimum value is approximately 64.7 (263.3 - 198.6).
• Average Sales: To compute an average (mean) sales figure, we would calculate the sum of all sales divided by 150 time periods.

These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years.The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible.These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall. An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivationData pushed to ArcGIS Online on November 9, 2025 at 4:28 AM by SFGIS.Data from: https://data.sfgov.org/d/ynuv-fyniDescription of dataset columns:

 sf16_bldgid
 San Francisco Building ID using criteria of 2016-09, 6-char epoch, '.' , 7-char zero-padded AreaID or new ID in editing epochs after initial '201006.'


 area_id
 Epoch 2010.06 Shape_Area sort of 177,023 building polygons with area > ~1 sq m


 mblr
 San Francisco property key: Assessor's Map-Block-Lot of land parcel, plus Right-of-way area identifier derived from street Centerline Node Network (CNN)


 p2010_name
 Pictometry 2010 building name, if any


 p2010_zminn88ft
 Input building mass (of 2010,) minimum Z vertex elevation, NAVD 1988 ft


 p2010_zmaxn88ft
 Input building mass (of 2010,) maximum Z vertex elevation, NAVD 1988 ft


 gnd_cells50cm
 zonal statistic: LiDAR-derived ground surface grid, population of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_mincm
 zonal statistic: LiDAR-derived ground surface grid, minimum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_maxcm
 zonal statistic: LiDAR-derived ground surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_rangecm
 zonal statistic: LiDAR-derived ground surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_meancm
 zonal statistic: LiDAR-derived ground surface grid, mean value of 50cm square cells sampled in this building's zone, from integer NAVD 1988 centimeters


 gnd_stdcm
 zonal statistic: LiDAR-derived ground surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters


 gnd_varietycm
 zonal statistic: LiDAR-derived ground surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_majoritycm
 zonal statistic: LiDAR-derived ground surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_minoritycm
 zonal statistic: LiDAR-derived ground surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 gnd_mediancm
 zonal statistic: LiDAR-derived ground surface grid, median value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 cells50cm_1st
 zonal statistic: LiDAR-derived first return surface grid, population of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 mincm_1st
 zonal statistic: LiDAR-derived first return surface grid, minimum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 maxcm_1st
 zonal statistic: LiDAR-derived first return surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 rangecm_1st
 zonal statistic: LiDAR-derived first return surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 meancm_1st
 zonal statistic: LiDAR-derived first return surface grid, mean value of 50cm square cells sampled in this building's zone, from integer NAVD 1988 centimeters


 stdcm_1st
 zonal statistic: LiDAR-derived first return surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters


 varietycm_1st
 zonal statistic: LiDAR-derived first return surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 majoritycm_1st
 zonal statistic: LiDAR-derived first return surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 minoritycm_1st
 zonal statistic: LiDAR-derived first return surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 mediancm_1st
 zonal statistic: LiDAR-derived first return surface grid, median value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters


 hgt_cells50cm
 zonal statistic: LiDAR-derived height surface grid, population of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_mincm
 zonal statistic: LiDAR-derived height surface grid, minimum value of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_maxcm
 zonal statistic: LiDAR-derived height surface grid, maximum value of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_rangecm
 zonal statistic: LiDAR-derived height surface grid, maximum value of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_meancm
 zonal statistic: LiDAR-derived height surface grid, mean value of 50cm square cells sampled in this building's zone, from integer centimeters


 hgt_stdcm
 zonal statistic: LiDAR-derived height surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters


 hgt_varietycm
 zonal statistic: LiDAR-derived height surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_majoritycm
 zonal statistic: LiDAR-derived height surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_minoritycm
 zonal statistic: LiDAR-derived height surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer centimeters


 hgt_mediancm
 zonal statistic: LiDAR-derived height surface grid, median value of 50cm square cells sampled in this building's zone, integer centimeters


 gnd_min_m
 summary statistic: zonal minimum ground surface height, NAVD 1988 meters


 median_1st_m
 summary statistic: zonal median first return surface height, NAVD 1988 meters


 hgt_median_m
 summary statistic: zonal median height surface value, meters


 gnd1st_delta
 summary statistic: discrete difference of (median first return surface -- minimum bare earth surface) for the building's zone, meters


 peak_1st_m
 summary statistic: highest cell value of first return surface in the building's zone, NAVD 1988 meters


 globalid
 Global Identifier


 shape
 Multi-Polygon geography


 data_as_of
 Timestamp the data was updated in the source system


 data_loaded_at
 Timestamp the data was loaded to the open data portal

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

Introduction

This dataset contains data from 18 stations observed in Antarctica. Monthly data from 1991.01 to 2021.11 are missing from all 18 datasets to varying degrees, so dealing with missing data is a major challenge some data miss some years.

Features introduction

every xlsx in the dataset contains 30 columns, but some columns are all nan or 0, the columns are useless columns: Site number altitude longitude latitude year month Average temperature (℃) Average maximum temperature (℃) Average minimum temperature (℃) Extreme value of maximum temperature (℃) Extreme value of minimum temperature (℃) Days with average temperature ≥ 18 ℃ Days with average temperature ≥ 35 ℃ Days with average temperature ≤ 0 ℃ Average dew point temperature (℃) Precipitation (mm) Maximum daily precipitation (mm) Precipitation days Mean sea level pressure (hPa) Minimum sea level pressure (hPa) Average station air pressure (hPa) Snow depth (mm) Average visibility (km) Minimum visibility (km) Maximum visibility (km) Average wind speed (knots) Average maximum sustained wind speed (knots) Daily maximum average wind speed (knots) Average maximum instantaneous wind speed (knots) Maximum instantaneous wind speed extreme value (knots)

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

How to deal with those nan values and the columns which all are 0

These data are the tropical storm tracks calculated using the "TRACK" storm tracking algorithm. The storm tracks are from experiments run as part of HighResMIP (High Resolution Model Intercomparison Project; Haarsma, R. J. and co-authors) a component of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The raw HighResMIP data are available from the Earth System Grid Federation (ESGF), here the calculated storm tracks are available.

The storm tracks are provided as Climate Model Output Rewriter (CMOR)-like NetCDF files with one file per hemisphere for all years in the simulated period of HighResMIP experiments: 1950-2014 - highresSST-present, atmosphere-only; 2015-2050 - highresSST-future experiment, atmosphere-only; 1950-2050 – control-1950, coupled atmosphere-ocean; 1950-2014 – hist-1950, coupled atmosphere-ocean; 2015-2050 – highres-future, coupled atmosphere-ocean using SSP585 scenario. There is one tracked variable in each file with time, latitude and longitude coordinates associated at each six-hour interval.

Other variables associated with each track are also provided, e.g. the minimum or maximum value adjacent to the track of the variable of interest and these variables have their own latitude and longitude coordinate variables. If a maximum/minimum value is not found, then a missing data value is used for the respective latitude-longitude values.

Given a graph with a source and a sink node, the NP-hard maximum k-splittable s,t-flow (MkSF) problem is to find a flow of maximum value from s to t with a flow decomposition using at most k paths. The multicommodity variant of this problem is a natural generalization of disjoint paths and unsplittable flow problems. Constructing a k-splittable flow requires two interdepending decisions. One has to decide on k paths (routing) and on the flow values on these paths (packing). We give efficient algorithms for computing exact and approximate solutions by decoupling the two decisions into a first packing step and a second routing step. Our main contributions are as follows: (i) We show that for constant k a polynomial number of packing alternatives containing at least one packing used by an optimal MkSF solution can be constructed in polynomial time. If k is part of the input, we obtain a slightly weaker result. In this case we can guarantee that, for any fixed epsilon>0, the computed set of alternatives contains a packing used by a (1-epsilon)-approximate solution. The latter result is based on the observation that (1-epsilon)-approximate flows only require constantly many different flow values. We believe that this observation is of interest in its own right. (ii)Based on (i), we prove that, for constant k, the MkSF problem can be solved in polynomial time on graphs of bounded treewidth. If k is part of the input, this problem is still NP-hard and we present a polynomial time approximation scheme for it.

US Average, Maximum, and Minimum Temperatures

Analyzing Daily Temperatures Across the USA

By Matthew Winter [source]

About this dataset

This dataset features the daily temperature summaries from various weather stations across the United States. It includes information such as location, average temperature, maximum temperature, minimum temperature, state name, state code, and zip code. All the data contained in this dataset has been filtered so that any values equaling -999 were removed. With this powerful set of data you to explore how climate conditions changed throughout the year and how they varied across different regions of the country. Dive into your own research today to uncover fascinating climate trends or use it to further narrow your studies specific to a region or city

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset offers a detailed look at daily average, minimum, and maximum temperatures across the United States. It contains information from 1120 weather stations throughout the year to provide a comprehensive look at temperature trends for the year.

The data contains a variety of columns including station, station name, location (latitude and longitude), state name zip code and date. The primary focus of this dataset is on the AvgTemp, MaxTemp and MinTemp columns which provide daily average, maximum and minimum temperature records respectively in degrees Fahrenheit.

To use this dataset effectively it is useful to consider multiple views before undertaking any analysis or making conclusions:
- Plot each individual record versus time by creating a line graph with stations as labels on different lines indicating changes over time. Doing so can help identify outliers that may need further examination; much like viewing data on a scatterplot looking for confidence bands or examining variance between points that are otherwise hard to see when all points are plotted on one graph only.
- A comparison of states can be made through creating grouped bar charts where states are grouped together with Avg/Max/Min temperatures included within each chart - thereby showing any variance that may exist between states during a specific period about which it's possible to make observations about themselves (rather than comparing them). For example - you could observe if there was an abnormally high temperature increase in California during July compared with other US states since all measurements would be represented visually providing opportunity for insights quickly compared with having to manually calculate figures from raw data sets only.

With these two initial approaches there will also be further visualizations possible regarding correlations between particular geographical areas versus different climatic conditions or through population analysis such as correlating areas warmer/colder than median observances verses relative population densities etc.. providing additional opportunities for investigation particularly when combined with key metrics collected over multiple years versus one single year's results exclusively allowing wider inferences to be made depending upon what is being requested in terms of outcomes desired from those who may explore this data set further down the line beyond its original compilation starter point here today!

Research Ideas

• Using the Latitude and Longitude values, this dataset can be used to create a map of average temperatures across the USA. This would be useful for seeing which areas were consistently hotter or colder than others throughout the year.
• Using the AvgTemp and StateName columns, predictors could use regression modeling to predict what temperature an area will have in a given month based on it's average temperature.
• By using the Date column and plotting it alongside MaxTemp or MinTemp values, visualization methods such as timelines could be utilized to show how temperatures changed during different times of year across various states in the US

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2015 USA Weather Data FINAL.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Matthew Winter.

The near real-time data presented here is intended to provide a 'snapshot' of current conditions within Narragansett Bay and has been subjected to automated QC pipelines. QA of data is performed following manufacturer guidelines for calibration and servicing of each sensor. QC'd datasets that have undergone additional manual inspection by researchers is provided in a 3 month lagging interval. Following the publication of human QC'd data, automated QC'd data from the previous 3 month window will be removed. See the 'Buoy Telemetry: Manually Quality Controlled' dataset for the full quality controlled dataset.The Automated QC of measurements collected from buoy platforms is performed following guidelines established by the Ocean Observatories Initiative (https://oceanobservatories.org/quality-control/) and implemented in R. Spike Test: To identify spikes within collected measurements, data points are assessed for deviation against a 'reference' window of measurement generated in a sliding window (k=7) . If a data point exceeds the deviation threshold (N=2), the spike is replaced with the 'reference' data point, which is determined using a median smoothing approach in the R package 'oce'. Despiked data is then written into the instrument table as 'Measurement_Despike'. Global Range Test: Data points are checked against the maximum and minimum measurements using a dataset of global measurements provided by IOOC (https://github.com/oceanobservatories/qc-lookup). QC Flags from global range tests are stored in the instrument table as 'Measurement_Global_Range_QC'. QC Flags: Measurement within global threshold= 0, Below minimum global threshold =1, Above maximum global threshold =2. Local Range Test: Data point values are checked against historical seasonal ranges for each parameter, using data provided by URI GSO's Narragansett Bay Long-Term Plankton Time Series (https://web.uri.edu/gso/research/plankton/). QC Flags from local range tests are stored in the instrument table as 'Measurement_Local_Range_QC'. Local Range QC Flags: Measurement within local seasonal threshold= 0, Below minimum local seasonal threshold =1, Above maximum local seasonal threshold =2. Stuck Value Test: To identify potential stuck values from a sensor, each data point is compared to subsequent values using sliding 3 and 5 frame windows. QC Flags from stuck value tests are stored in the instrument table as 'Measurement_Stuck_Value_QC' QC Flags: No stuck value detected= 0, Suspect Stuck Sensor (3 consecutive identical values) =1, Stuck Sensor (5 consecutive identical values) =2. Instrument Range Test: Data point values for meterological measurements are checked against the manufacturer's specified measurement ranges. QC Flags: Measurement within instrument range= 0, Measurement below instrument range =1, Measurement above instrument range =2. cdm_data_type=Other Conventions=COARDS, CF-1.6, ACDD-1.3 Easternmost_Easting=-71.388 geospatial_lat_max=41.6424 geospatial_lat_min=41.57 geospatial_lat_units=degrees_north geospatial_lon_max=-71.388 geospatial_lon_min=-71.3902 geospatial_lon_units=degrees_east infoUrl=riddc.brown.edu institution=Rhode Island Data Discovery Center keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=41.6424 sourceUrl=(local files) Southernmost_Northing=41.57 standard_name_vocabulary=CF Standard Name Table v55 subsetVariables=station_name testOutOfDate=now-26days time_coverage_end=2022-04-22T16:00Z time_coverage_start=2022-01-25T10:00Z Westernmost_Easting=-71.3902

Forecast: Ventilating or Recycling Hoods Incorporating a Fan, with a Maximum Horizontal Side Less than 120 cm Market Size Value Per Capita in Germany 2023 - 2027 Discover more data with ReportLinker!

The Property Valuation Data Listing offered by BatchData delivers an extensive and detailed dataset designed to provide unparalleled insight into real estate market trends, property values, and investment opportunities. This dataset includes over 9 critical data points that offer a comprehensive view of property valuations across various geographic regions and market conditions. Below is an in-depth description of the data points and their implications for users in the real estate industry.

The Property Valuation Data Listing by BatchData is categorized into four primary sections, each offering detailed insights into different aspects of property valuation. Here’s an in-depth look at each category:

1. Current Valuation AVM Value as of Specific Date: The Automated Valuation Model (AVM) estimate of the property’s current market value, calculated as of a specified date. This value reflects the most recent assessment based on available data. Use Case: Provides an up-to-date valuation, essential for making current investment decisions, setting sale prices, or conducting market analysis. Valuation Confidence Score: A measure indicating the confidence level of the AVM value. This score reflects the reliability of the valuation based on data quality, volume, and model accuracy. Use Case: Helps users gauge the reliability of the valuation estimate. Higher confidence scores suggest more reliable values, while lower scores may indicate uncertainty or data limitations.

2. Valuation Range Price Range Minimum: The lowest estimated market value for the property within the given range. This figure represents the lower bound of the valuation spectrum. Use Case: Useful for understanding the potential minimum value of the property, helping in scenarios like setting a reserve price in auctions or evaluating downside risk. Price Range Maximum: The highest estimated market value for the property within the given range. This figure represents the upper bound of the valuation spectrum. Use Case: Provides insight into the potential maximum value, aiding in price setting, investment analysis, and comparative market assessments. AVM Value Standard Deviation: A statistical measure of the variability or dispersion of the AVM value estimates. It indicates how much the estimated values deviate from the average AVM value. Use Case: Assists in understanding the variability of the valuation and assessing the stability of the estimated value. A higher standard deviation suggests more variability and potential uncertainty.

3. LTV (Loan to Value Ratio) Current Loan to Value Ratio: The ratio of the outstanding loan balance to the current market value of the property, expressed as a percentage. This ratio helps assess the risk associated with the loan relative to the property’s value. Use Case: Crucial for lenders and investors to evaluate the financial risk of a property. A higher LTV ratio indicates higher risk, as the property value is lower compared to the loan amount.

4. Valuation Equity Calculated Total Equity: based upon estimate amortized balances for all open liens and AVM value Use Case: Provides insight into the net worth of the property for the owner. Useful for evaluating the financial health of the property, planning for refinancing, or understanding the owner’s potential gain or loss in case of sale.

This structured breakdown of data points offers a comprehensive view of property valuations, allowing users to make well-informed decisions based on current market conditions, valuation accuracy, financial risk, and equity potential.

This information can be particularly useful for: - Automated Valuation Models (AVMs) - Fuel Risk Management Solutions - Property Valuation Tools - ARV, rental data, building condition and more - Listing/offer Price Determination