Abstract: This data shows the model nodes, indicating water level only and/or flow and water levels along the centre-line of rivers that have been modelled to generate the CFRAM flood maps. The nodes estimate maximum design event flood flows and maximum flood levels. Flood event probabilities are referred to in terms of a percentage Annual Exceedance Probability, or ‘AEP’. This represents the probability of an event of this, or greater, severity occurring in any given year. These probabilities may also be expressed as odds (e.g. 100 to 1) of the event occurring in any given year. They are also commonly referred to in terms of a return period (e.g. the 100-year flood), although this period is not the length of time that will elapse between two such events occurring, as, although unlikely, two very severe events may occur within a short space of time. The following sets out a range of flood event probabilities for which fluvial and coastal flood maps are typically developed, expressed in terms of Annual Exceedance Probability (AEP), and identifies their parallels under other forms of expression: 10% (High Probability) Annual Exceedance Probability which can also be expressed as the 10 Year Return Period and as a 10:1 odds of occurrence in any given year. 1% (Medium Probability - Fluvail/River Flood Maps) Annual Exceedance Probability which can also be expressed as the 100 Year Return Period and as 100:1 odds of occurrence in any given year. 0.5% (Medium Probability - Coastal Flood Maps) Annual Exceedance Probability which can also be expressed as the 200 Year Return Period and as 200:1 odds of occurrence in any given year. 0.1% (Low Probability) Annual Exceedance Probability which can also be expressed as the 1000 Year Return Period and as 1000:1 odds of occurrence in any given year. The Mid-Range Future Scenario extents where generated taking in in the potential effects of climate change using an increase in rainfall of 20% and sea level rise of 500mm (20 inches). Data has been produced for the 'Areas of Further Assessment' (AFAs), as required by the EU 'Floods' Directive [2007/60/EC] and designated under the Preliminary Flood Risk Assessment, and also for other reaches between the AFAs and down to the sea that are referred to as 'Medium Priority Watercourses' (MPWs). River reaches that have been modelled are indicated by the CFRAM Modelled River Centrelines dataset. Flooding from other reaches of river may occur, but has not been mapped, and so areas that are not shown as being within a flood extent may therefore be at risk of flooding from unmodelled rivers (as well as from other sources). The purpose of the Flood Maps is not to designate individual properties at risk of flooding. They are community-based maps. Lineage: Fluvial and coastal flood map data is developed using hydrodynamic modelling, based on calculated design river flows and extreme sea levels, surveyed channel cross-sections, in-bank / bank-side / coastal structures, Digital Terrain Models, and other relevant datasets (e.g. land use, data on past floods for model calibration, etc.). The process may vary for particular areas or maps. Technical Hydrology and Hydraulics Reports set out full technical details on the derivation of the flood maps. For fluvial flood levels, calibration and verification of the models make use of the best available data, including hydrometric records, photographs, videos, press articles and anecdotal information. Subject to the availability of suitable calibration data, models are verified in so far as possible to target vertical water level accuracies of approximately +/-0.2m for areas within the AFAs, and approximately +/-0.4m along the MPWs. For coastal flood levels, the accuracy of the predicted annual exceedance probability (AEP) of combined tide and surge levels depends on the accuracy of the various components used in deriving these levels i.e. accuracy of the tidal and surge model, the accuracy of the statistical data and the accuracy for the conversion from marine datum to land levelling datum. The output of the water level modelling, combined with the extreme value analysis undertaken as detailed above is generally within +/-0.2m for confidence limits of 95% at the 0.1% AEP. Higher probability (lower return period) events are expected to have tighter confidence limits. v101 (March 2025) The section of map near Oranmore Galway updated following a map review process see https://www.floodinfo.ie/map-review/ for further information, Map Review Code: MR019. v102 (July 2025) The section of map near Claregalway updated following a map review process see https://www.floodinfo.ie/map-review/ for further information, Map Review Code: MR057. Purpose: The data has been developed to comply with the requirements of the European Communities (Assessment and Management of Flood Risks) Regulations 2010 to 2015 (the “Regulations”) (implementing Directive 2007/60/EC) for the purposes of establishing a framework for the assessment and management of flood risks, aiming at the reduction of adverse consequences for human health, the environment, cultural heritage and economic activity associated with floods. .hidden { display: none }

ABSTRACT In order to search for an ideal test for multiple comparison procedures, this study aimed to develop two tests, similar to the Tukey and SNK tests, based on the distribution of the externally studentized amplitude. The test names are Tukey Midrange (TM) and SNK Midrange (SNKM). The tests were evaluated based on the experimentwise error rate and power, using Monte Carlo simulation. The results showed that the TM test could be an alternative to the Tukey test, since it presented superior performances in some simulated scenarios. On the other hand, the SNKM test performed less than the SNK test.

Performance of k-means, according to ARIfnc and AMImax, on various scaled versions of the data sets in Table 1.

In cluster analysis, a common first step is to scale the data aiming to better partition them into clusters. Even though many different techniques have throughout many years been introduced to this end, it is probably fair to say that the workhorse in this preprocessing phase has been to divide the data by the standard deviation along each dimension. Like division by the standard deviation, the great majority of scaling techniques can be said to have roots in some sort of statistical take on the data. Here we explore the use of multidimensional shapes of data, aiming to obtain scaling factors for use prior to clustering by some method, like k-means, that makes explicit use of distances between samples. We borrow from the field of cosmology and related areas the recently introduced notion of shape complexity, which in the variant we use is a relatively simple, data-dependent nonlinear function that we show can be used to help with the determination of appropriate scaling factors. Focusing on what might be called “midrange” distances, we formulate a constrained nonlinear programming problem and use it to produce candidate scaling-factor sets that can be sifted on the basis of further considerations of the data, say via expert knowledge. We give results on some iconic data sets, highlighting the strengths and potential weaknesses of the new approach. These results are generally positive across all the data sets used.

Abstract: This data indicates the maximum estimated depth of river flooding (fluvial flooding) in meters (m) at a given location, for a flood event of a particular probability. The flood depths are calculated by subtracting the ground levels from the predicted water level. The flood depths are mapped as constant depths over grid squares of 5x5m, whereas in reality depths may vary within a given square.

Abstract: This data shows the modelled extent of land that might be flooded by rivers (fluvial flooding) during a theoretical or ‘design’ flood event with an estimated probability of occurrence, rather than information for actual floods that have occurred in the past.

Using ETWatch model with the system complete the heihe river basin scale 1 km resolution 2014 surface evaporation data with middle oasis 30 meters resolution on scale data set, the surface evaporation raster image data of the data sets, it is the time resolution of scale from month to month, the spatial resolution of 1 km scale (covering the whole basin) and 30 meters scale (middle oasis area), the unit is mm.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter.

File naming rules are as follows: 1) 1 km resolution remote sensing data set Monthly cumulative ET value file name: heihe-1km_2014m01_eta.tif Heihe refers to heihe river basin, 1km means the resolution is 1km, 2014 means the year of 2014, m01 means the month of January, eta means the actual evapotranspiration data, and tif means the data is tif format. Name of quarterly cumulative ET value file: heihe-1km_2014s01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, s01 represents the period from January to march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-1km_2014y_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format. 2) remote sensing data set with a resolution of 30 meters Monthly cumulative ET value file name: heihe-midoasa-30m_2014m01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, m01 represents January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-midoasa-30m_2014s01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, s01 represents january-march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-midoasa-30m_2014y_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.

Flipkart Mobiles Dataset

This dataset containing specs of various Mobile brands in India has been scraped from an ecommerce website 'Flipkart'. This dataset has 3114 samples with 8 attributes. There are some missing values as well.

Attributes- 1. Brand- Name of the Mobile Manufacturer 2. Model- Model number of the Mobile Phone 3. Color- Color of the model. 4. Memory - RAM of the model (4GB,6GB,8GB, etc.) 5. Storage- ROM of the model (32GB,64GB,128GB,256GB, etc.) 6. Rating- Rating of the model based on reviews (out of 5). Missing or Null values indicate there are no ratings present for the model. 7. Selling Price- Selling Price/Discounted Price of the model in INR when this data was scraped. Ideally price indicates the discounted price of the model 8. Original Price- Actual price of the model in INR. Missing values or null values would indicate that the product is being sold at the actual price available in the 'Price' column.

Inspiration- You can use this dataset to answer some interesting questions like- * Different Price range segments for mobiles in India * Brand with most product offerings for the Indian Market * Brand catering to all different segments (low range, mid range, premium - *an additional data column would be required to sort the data in the above segments) * Most common specs offered by various brands (eg. if 4 GB memory and 64GB storage models are more commonly offered by all brands) * Compare premium offerings by top brands * Most commonly offered colors by all Brands * Compare Two Brands based on specs * Are higher rated mobiles always premium or expensive? * Does a brand have better than 4 ratings for all its products? * and so on...

This dataset comprises 19 subsets. Each subset measures a different parameter or is produced by a different sensor provider. The measurement period for this dataset was from October 11, 2024, to October 31, 2024, and the measurement interval depends on the type of parameter being measured, ranging from 1 second to 15 minutes. The dataset includes six indoor low-cost sensor providers with their respective measuring sensors. Three of these providers had only one sensor at the location, while one had 16 sensors, and the other two had 4 and 2 sensors, respectively. Human presence was monitored using a camera and a motion detection sensor. Window and door opening and closing were monitored using Xiaomi Door/Window sensors. In addition to the indoor low-cost sensors, the location was equipped with reference sensing units that were calibrated to the measuring station. Furthermore, outdoor low-cost sensors were also used. Specifically, one was a low-cost sensor, and the other was a mid-range sensor in terms of pricing. This dataset also includes black carbon data and CPC data. A camera was set up on the balcony to monitor the road in front of the house, so traffic data is also included in the dataset. Additionally, on-site measuring data from the Croatian Meteorological and Hydrological Service was made available in this dataset, sourced from the two nearest locations to the measuring site, as well as satellite data from the Climate Data Store. Every single parameter is detailed in the Data.xlsx file, which is integrated into the data.zip archive.

The αk’s are the ones leading to the highest values of ARIfnc in the intervals on the rightmost column of Table 2.

Comprehensive YouTube channel statistics for Midrange MartinezTV, featuring 194,000 subscribers and 43,787,160 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category. Track 385 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The dataset originates from 10-year mid-range values (2013-2022) of sea temperature in the surface, the water masses (intervals for 5 m, 15 m, 30 m, 50 m, 100 m, 150 m, 200 m and 250 m), as well as at the seabed. The distance from the seabed goes with the current model layout from a few cm on shallow water and up to 1.5 m when the total depth is 100 m or more. Temperatures are given in degrees celcius. The data set is available as WMS and WCS services, as well as for download via the Institute of Marine Research’s Geoserver https://kart.hi.no/data – select Layer preview and search for the data set for multiple download options. The coastal model Norkyst (version 3) is a calculation model that simulates e.g. current, salinity and temperature with 800 meters spatial resolution, in several vertical levels and with high resolution in time for the entire Norwegian coast, based on the model system ROMS (Regional Ocean Modeling System, http://myroms.org). NorKyst is being developed by the Institute of Marine Research in collaboration with the Norwegian Meteorological Institute. https://imr.brage.unit.no/imr-xmlui/handle/11250/116053

This dataset contains information about the cost of living in almost 5000 cities across the world. The data were gathered by scraping Numbeo's website (https://www.numbeo.com).

Data dictionary

Column	Description
city	Name of the city
country	Name of the country
x1	Meal, Inexpensive Restaurant (USD)
x2	Meal for 2 People, Mid-range Restaurant, Three-course (USD)
x3	McMeal at McDonalds (or Equivalent Combo Meal) (USD)
x4	Domestic Beer (0.5 liter draught, in restaurants) (USD)
x5	Imported Beer (0.33 liter bottle, in restaurants) (USD)
x6	Cappuccino (regular, in restaurants) (USD)
x7	Coke/Pepsi (0.33 liter bottle, in restaurants) (USD)
x8	Water (0.33 liter bottle, in restaurants) (USD)
x9	Milk (regular), (1 liter) (USD)
x10	Loaf of Fresh White Bread (500g) (USD)
x11	Rice (white), (1kg) (USD)
x12	Eggs (regular) (12) (USD)
x13	Local Cheese (1kg) (USD)
x14	Chicken Fillets (1kg) (USD)
x15	Beef Round (1kg) (or Equivalent Back Leg Red Meat) (USD)
x16	Apples (1kg) (USD)
x17	Banana (1kg) (USD)
x18	Oranges (1kg) (USD)
x19	Tomato (1kg) (USD)
x20	Potato (1kg) (USD)
x21	Onion (1kg) (USD)
x22	Lettuce (1 head) (USD)
x23	Water (1.5 liter bottle, at the market) (USD)
x24	Bottle of Wine (Mid-Range, at the market) (USD)
x25	Domestic Beer (0.5 liter bottle, at the market) (USD)
x26	Imported Beer (0.33 liter bottle, at the market) (USD)
x27	Cigarettes 20 Pack (Marlboro) (USD)
x28	One-way Ticket (Local Transport) (USD)
x29	Monthly Pass (Regular Price) (USD)
x30	Taxi Start (Normal Tariff) (USD)
x31	Taxi 1km (Normal Tariff) (USD)
x32	Taxi 1hour Waiting (Normal Tariff) (USD)
x33	Gasoline (1 liter) (USD)
x34	Volkswagen Golf 1.4 90 KW Trendline (Or Equivalent New Car) (USD)
x35	Toyota Corolla Sedan 1.6l 97kW Comfort (Or Equivalent New Car) (USD)
x36	Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment (USD)
x37	1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans) (USD)
x38	Internet (60 Mbps or More, Unlimited Data, Cable/ADSL) (USD)
x39	Fitness Club, Monthly Fee for 1 Adult (USD)
x40	Tennis Court Rent (1 Hour on Weekend) (USD)
x41	Cinema, International Release, 1 Seat (USD)
x42	Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child (USD)
x43	International Primary School, Yearly for 1 Child (USD)
x44	1 Pair of Jeans (Levis 501 Or Similar) (USD)
x45	1 Summer Dress in a Chain Store (Zara, H&M, ...) (USD)
x46	1 Pair of Nike Running Shoes (Mid-Range) (USD)
x47	1 Pair of Men Leather Business Shoes (USD)
x48	Apartment (1 bedroom) in City Centre (USD)
x49	Apartment (1 bedroom) Outside of Centre (USD)
x50	Apartment (3 bedrooms) in City Centre (USD)
x51	Apartment (3 bedrooms) Outside of Centre (USD)
x52	Price per Square Meter to Buy Apartment in City Centre (USD)
x53	Price per Square Meter to Buy Apartment Outside of Centre (USD)
x54	Average Monthly Net Salary (After Tax) (USD)
x55	Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate
data_quality	0 if Numbeo considers that more contributors are needed to increase data quality, else 1