This Excel file contains all numerical information of all data panels in S7 Fig organized in form of subfolders. The data include mean, SEM, n number, and all individual data points. (XLSX)

Sweden phone number data contains contact numbers collected from trusted sources. We define this data by ensuring that all phone numbers come from reliable and verified sources. You can even check source URLs to see where the data is collected from. Being clear makes it easy for you to trust the data. Our team is always available with 24/7 support if you need help or have questions about the data. Also, we focus on opt-in data, meaning that everyone on the list has given permission to be contacted. Sweden number data gives you access to contact information from people in Sweden. We define this data by making sure every number is accurate and useful. If you ever receive an incorrect number, we provide a replacement guarantee. We’ll make sure to fix any mistakes for you. Furthermore, we collect the data on a customer-permission basis. That means each person has agreed to share their contact details. This ensures that you are only getting numbers from people who have given permission. Moreover, we work hard to provide this data from List to Data that you can trust. By offering a replacement guarantee, we make sure that all the phone numbers you get are correct and reliable.

This data release contains six different datasets that were used in the report SIR 2018-5108. These datasets contain discharge data, discrete dissolved-solids data, quality-control discrete dissolved data, and computed mean dissolved solids data that were collected at various locations between the Hoover Dam and the Imperial Dam. Study Sites: Site 1: Colorado River below Hoover Dam Site 2: Bill Williams River near Parker Site 3: Colorado River below Parker Dam Site 4: CRIR Main Canal Site 5: Palo Verde Canal Site 6: Colorado River at Palo Verde Dam Site 7: CRIR Lower Main Drain Site 8: CRIR Upper Levee Drain Site 9: PVID Outfall Drain Site 10: Colorado River above Imperial Dam Discrete Dissolved-solids Dataset and Replicate Samples for Discrete Dissolved-solids Dataset: The Bureau of Reclamation collected discrete water-quality samples for the parameter of dissolved-solids (sum of constituents). Dissolved-solids, measured in milligrams per liter, are the sum of the following constituents: bicarbonate, calcium, carbonate, chloride, fluoride, magnesium, nitrate, potassium, silicon dioxide, sodium, and sulfate. These samples were collected on a monthly to bimonthly basis at various time periods between 1990 and 2016 at Sites 1-5 and Sites 7-10. No data were collected for Site 6: Colorado River at Palo Verde Dam. The Bureau of Reclamation and the USGS collected discrete quality-control replicate samples for the parameter of dissolved-solids, sum of constituents measured in milligrams per liter. The USGS collected discrete quality-control replicate samples in 2002 and 2003 and the Bureau of Reclamation collected discrete quality-control replicate samples in 2016 and 2017. Listed below are the sites where these samples were collected at and which agency collected the samples. Site 3: Colorado River below Parker Dam: USGS and Reclamation Site 4: CRIR Main Canal: Reclamation Site 5: Palo Verde Canal: Reclamation Site 7: CRIR Lower Main Drain: Reclamation Site 8: CRIR Upper Levee Drain: Reclamation Site 9: PVID Outfall Drain: Reclamation Site 10: Colorado River above Imperial Dam: USGS and Reclamation Monthly Mean Datasets and Mean Monthly Datasets: Monthly mean discharge data (cfs), flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1990 to 2016 were computed using raw data from the USGS and the Bureau of Reclamation. This data were computed for all 10 sites. Flow weighted monthly mean dissolved-solids concentration and monthly mean dissolved-solids load were not computed for Site 2: Bill Williams River near Parker. The monthly mean datasets that were calculated for each month for the period between 1990 and 2016 were used to compute the mean monthly discharge and the mean monthly dissolved-solids load for each of the 12 months within a year. Each monthly mean was weighted by how many days were in the month and then averaged for each of the twelve months. This was computed for all 10 sites except mean monthly dissolved-solids load were not computed at Site 2: Bill Williams River near Parker. Site 8a: Colorado River between Parker and Palo Verde Valleys was computed by summing the data from sites 6, 7 and 8. Bill Williams Daily Mean Discharge, Instantaneous Dissolved-solids Concentration, and Daily Means Dissolved-solids Load Dataset: Daily mean discharge (cfs), instantaneous solids concentration (mg/L), and daily mean dissolved solids load were calculated using raw data collected by the USGS and the Bureau of Reclamation. This data were calculated for Site 2: Bill Williams River near Parker for the period of January 1990 to February 2016. Palo Verde Irrigation District Outfall Drain Mean Daily Discharge Dataset: The Bureau of Reclamation collected mean daily discharge data for the period of 01/01/2005 to 09/30/2016 at the Palo Verde Irrigation District (PVID) outfall drain using a stage-discharge relationship.

Haptotactic Assays. Numerical Data: Mean % Cross-Over (SD, N).

Zip file containing all data and analysis files for Experiment 1 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors

Saudi Arabia phone number data is another important collection of phone numbers. These numbers come from trusted sources. We carefully check every number. This means you only get real numbers from reliable places. Furthermore, this data includes source URLs. You can use these URLs to find out where the numbers came from. This adds transparency to the data. If you have questions, you can get help anytime. Support is available 24/7. Moreover, the phone data has an opt-in feature. With customer support always on hand to help, you can feel confident using this data.Saudi Arabia number data is a special collection of phone numbers. Besides, this list includes numbers from people living in Saudi Arabia. Each number in this database has verification for accuracy. If you ever find a number that does not work, there is a replacement guarantee. This means any invalid number gets replaced with a valid one at no extra cost. The data comes from people who have given permission. Thus, this respect for privacy makes it a great tool for businesses. At List to Data, we help you find important phone numbers easily and quickly.

Monthly mean sunspot number since 1749 (monthly update): sunspots reflect sun activity, which has a 11 year cycle.We can observe Dalton Minimum: a period of low solar activity, named after the English meteorologist John Dalton, lasting from 1790 to 1830, which coincided with a period of lower-than-average global temperatures.Nombre moyen mensuel des taches solaires depuis 1749 (mis à jour mensuellement): les taches solaires reflètent l'activité solaire, qui suit un cycle de 11 ans.On peut observer le Minimum de Dalton: une période de faible activité solaire, nommée d'après le météorologiste anglais John Dalton, qui s'est étalée des années 1790 à 1830, et qui coïncide avec une période froide. File Source: Sunspot data from the World Data Center SILSO, Royal Observatory of Belgium, Brussels"Monthly mean total sunspot number obtained by taking a simple arithmetic mean of the daily total sunspot number over all days of each calendar month. Monthly means are available only since 1749 because the original observations compiled by Rudolph Wolf were too sparse before that year. (Only yearly means are available back to 1700)" http://www.sidc.be/silso/infosnmtot This article (in French) about data and sound start from these sunspot data Cet article "Comment nous avons fait chanter les jeux de données" a ce jeu de données pour point de départ

Our objective was to model mean annual number of zero-flow days (days per year) for small streams in the Upper Colorado River Basin under historic hydrologic conditions on small, ungaged streams in the Upper Colorado River Basin. Modeling streamflows is an important tool for understanding landscape-scale drivers of flow and estimating flows where there are no gaged records. We focused our study in the Upper Colorado River Basin, a region that is not only critical for water resources but also projected to experience large future climate shifts toward a drier climate. We used a random forest modeling approach to model the relation between zero-flow days per year on gaged streams (115 gages) and environmental variables. We then projected zero-flow days per year to ungaged reaches in the Upper Colorad River Basin using environmental variables for each raster stream cell in the basin. This data layer shows modeled values for zero-flow days per year of each stream cell.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

BUSINESS PROBLEM-1 BACKGROUND: The Lending Club is a peer-to-peer lending site where members make loans to each other. The site makes anonymized data on loans and borrowers publicly available. BUSINESS PROBLEM: Using lending club loans data, the team would like to test below hypothesis on how different factors effecing each other (Hint: You may leverage hypothesis testing using statistical tests) a. Intrest rate is varied for different loan amounts (Less intrest charged for high loan amounts) b. Loan length is directly effecting intrest rate. c. Inrest rate varies for different purpose of loans d. There is relationship between FICO scores and Home Ownership. It means that, People with owning home will have high FICO scores. DATA AVAILABLE:  LoansData.csv The data have the following variables (with data type and explanation of meaning)  Amount.Requested - numeric. The amount (in dollars) requested in the loan application.  Amount.Funded.By.Investors - numeric. The amount (in dollars) loaned to the individual.  Interest.rate – character. The lending interest rate charged to the borrower.  Loan.length - character. The length of time (in months) of the loan.  Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.  Debt.to.Income.Ratio – character. The % of consumer’s gross income going toward paying debts.  State - character. The abbreviation for the U.S. state of residence of the loan applicant.  Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.  Monthly.income - categorical. The monthly income of the applicant (in dollars).  FICO.range – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score.  Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.  Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  Employment.Length - character. Length of time employed at current job.

BUSINESS PROBLEM - 2 BACKGROUND: When an order is placed by a customer of a small manufacturing company, a price quote must be developed for that order. Because each order is unique, quotes must be established on an order-by-order basis by a pricing expert. The price quote process is laborintensive, as prices depend on many factors such as the part number, customer, geographic location, market, and order volume. The sales department manager is concerned that the pricing process is too complex, and that there might be too much variability in the quoted prices. An improvement team is tasked with studying and improving the pricing process. After interviewing experts to develop a better understanding of the current process, the team designed a study to determine if there is variability between pricing experts. That is, do different pricing experts provide different price quotes? Two randomly selected pricing experts, Mary and Barry, were asked to independently provide prices for twelve randomly selected orders. Each expert provided one price for each of the twelve orders. BUSINESS PROBLEM: We would like to assess if there is any difference in the average price quotes provided by Mary and Barry. DATA AVAILABLE:  Price_Quotes.csv The data set contains the order number, 1 through 12, and the price quotes by Mary and Barry for each order. Each row in the data set is the same order. Thus, Mary and Barry produced quotes for the same orders. BUSINESS PROBLEM-3: BACKGROUND: The New Life Residential Treatment Facility is a NGO that treatsteenagers who have shown signs of mental illness. It provides housing and supervision of teenagers who are making the transition from psychiatric hospitals back into the community. Because many of the teenagers were severely abused as children and have been involved with the juvenile justice system, behavioral problems are common at New Life. Employee pay is low and staff turnover (attrition) is high. A reengineering program wasinstituted at New Life with the goals of lowering behavioral problems of the kids and decreasing employee turnover rates. As a part of this effort, the following changes were made:  Employee shifts were shortened from 10 hours to 8 hours each day.  Employees were motivated to become more involved in patient treatments. This included encouraging staff to run varioustherapeutic treatment sessions and allowing staff to have more say in program changes.  The activities budget wasincreased.  A facility-wide performance evaluation system was putinto place that rewarded staff participation andinnovation.  Management and staff instituted a program designed to raise expectations about appropriate behavior from the kids. Thisincluded strict compliance with reporting of behavioral violations, insistence o...

We propose a new form of integral which arises from infinite partitions. We use upper and lower series instead of upper and lower Darboux finite sums. We show that every Riemann integrable function, both proper and improper, is integrable in the sense proposed here and both integrals have the same value. We show that the Riemann integral and our integral are equivalent for bounded functions in bounded intervals.

Trusted Research Environments (TREs) enable analysis of sensitive data under strict security assertions that protect the data with technical organizational and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks & their slight technical variations. To shine light on these problems, we give an overview of existing, publicly described TREs and a bibliography linking to the system description. We further analyze their technical characteristics, especially in their commonalities & variations and provide insight on their data type characteristics and availability. Our literature study shows that 47 TREs worldwide provide access to sensitive data of which two-thirds provide data themselves, predominantly via secure remote access. Statistical offices make available a majority of available sensitive data records included in this study.

Methodology

We performed a literature study covering 47 TREs worldwide using scholarly databases (Scopus, Web of Science, IEEE Xplore, Science Direct), a computer science library (dblp.org), Google and grey literature focusing on retrieving the following source material:

• Peer-reviewed articles where available,
• TRE websites,
• TRE metadata catalogs.

The goal for this literature study is to discover existing TREs, analyze their characteristics and data availability to give an overview on available infrastructure for sensitive data research as many European initiatives have been emerging in recent months.

Technical details

This dataset consists of five comma-separated values (.csv) files describing our inventory:

• countries.csv: Table of countries with columns id (number), name (text) and code (text, in ISO 3166-A3 encoding, optional)
• tres.csv: Table of TREs with columns id (number), name (text), countryid (number, refering to column id of table countries), structureddata (bool, optional), datalevel (one of [1=de-identified, 2=pseudonomized, 3=anonymized], optional), outputcontrol (bool, optional), inceptionyear (date, optional), records (number, optional), datatype (one of [1=claims, 2=linked records]), optional), statistics_office (bool), size (number, optional), source (text, optional), comment (text, optional)
• access.csv: Table of access modes of TREs with columns id (number), suf (bool, optional), physical_visit (bool, optional), external_physical_visit (bool, optional), remote_visit (bool, optional)
• inclusion.csv: Table of included TREs into the literature study with columns id (number), included (bool), exclusion reason (one of [peer review, environment, duplicate], optional), comment (text, optional)
• major_fields.csv: Table of data categorization into the major research fields with columns id (number), life_sciences (bool, optional), physical_sciences (bool, optional), arts_and_humanities (bool, optional), social_sciences (bool, optional).

Additionally, a MariaDB (10.5 or higher) schema definition .sql file is needed, properly modelling the schema for databases:

• schema.sql: Schema definition file to create the tables and views used in the analysis.

The analysis was done through Jupyter Notebook which can be found in our source code repository: https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb

ABSTRACT: This research project focuses upon the wake behind a two-dimensional blunt-trailing-edged body. Data are obtained numerically by means of a Direct Numerical Simulation code. The body has an elliptical nose followed by a straight section that ends in a blunt base. The present paper is dedicated to the analysis of the onset of the shedding process. The effort is certainly worthwhile, because, in contrast to the case of the circular cylinder, the boundary layers's separation points are defined and fixed. This allows a better assessment of the vital influence of the boundary layers upon the wake, in a controlled way. This is not the case for the circular cylinder, because, in this instance, the separation points oscillate in relation to a mean position. In the present analysis, the relationship between the onset of shedding Reynolds number, RehK , and the aspect ratio, AR, is obtained. To this end, a wide range of aspect ratios, between 3 and 25, was investigated. The result represented by this relationship is a novelty in the literature. Values of RehK are strongly influenced by the aspect ratio for the case of the short cylinders - for which AR is low. After AR about 9, the curve flattens and the influence of the aspect ratio upon the shedding Reynolds number is very mild. Besides, the paper discusses another very important aspect; the overall stability of the pre-shedding laminar bubble at the base of the body. It is important to stress that the latter study relies on the fact that the boundary layers separation points are fixed.

The nature of the mapping process that imbues number symbols with their numerical meaning—known as the “symbolgrounding process”—remains poorly understood and the topic of much debate. The aim of this study was to enhance insight into how the nonsymbolic–symbolic number mapping process and its neurocognitive correlates might differ between small (1–4; subitizing range) and larger (6–9) numerical ranges. Hereto, 22 young adults performed a learning task in which novel symbols acquired numerical meaning by mapping them onto nonsymbolic magnitudes presented as dot arrays (range 1–9). Learning-dependent changes in accuracy and RT provided evidence for successful novel symbol quantity mapping in the subitizing (1–4) range only. Corroborating these behavioral results, the number processing related P2p component was only modulated by the learning/ mapping of symbols representing small numbers 1–4. The symbolic N1 amplitude increased with learning independent of symbolic numerical range but dependent on the set size of the preceding dot array; it only occurred when mapping on one to four item dot arrays that allow for quick retrieval of a numeric value, on the basis of which, with learning, one could predict the upcoming symbol causing perceptual expectancy violation when observing a different symbol. These combined results suggest that exact nonsymbolic–symbolic mapping is only successful for small quantities 1–4 from which one can readily extract cardinality.Furthermore, we suggest that the P2p reflects the processing stage of first access to or retrieval of numeric codes and might in future studies be used as a neural correlate of nonsymbolic–symbolic mapping/symbol learning.

Abstract: This paper presents some results of a study that valued manipulative conditions in the process of knowledge construction by handling a sixteenth-century mathematical instrument. The study was based on a problem-situation elaborated by epistemological and mathematical questions, which emerged from an interface built between the history of mathematics and teaching. The handling of this instrument triggered a series of actions that led teachers to reflect and discuss the very notion of magnitude, number and measurement. The results of the study suggest an epistemological gap between the observer who measures, the instrument that mediates the measuring, and the measured object. This gap compromises the proper understanding of measuring and the relationship between number and magnitude in measurement process.

🔍 Dataset Overview: 🐟 Species: Name of the fish species (e.g., Anabas testudineus)

📏 Length: Length of the fish (in centimeters)

⚖️ Weight: Weight of the fish (in grams)

🧮 W/L Ratio: Weight-to-length ratio of the fish

🧠 Steps to Build the Prediction Model: 📋 Data Preprocessing: 1 - Handle Missing Values: Check for and handle any missing values appropriately using methods like:

Imputation (mean/median for numeric data)

Row or column removal (if data is too sparse)

2 - Convert Data Types: Ensure numerical columns (Length, Weight, W/L Ratio) are in the correct numeric format.

3 - Handle Categorical Variables: Convert the Species column into numerical format using:

One-Hot Encoding

Label Encoding

🎯 Feature Selection: 1 - Correlation Analysis: Use correlation heatmaps or statistical tests to identify features most related to the target variable (e.g., Weight).

2 - Feature Importance: Use tree-based models (like Random Forest) to determine which features are most predictive.

🔍 Model Selection: 1 - Algorithm Choice: Choose suitable machine learning algorithms such as:

Linear Regression

Decision Tree Regressor

Random Forest Regressor

Gradient Boosting Regressor

2 - Model Comparison: Evaluate each model using metrics like:

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

R-squared (R²)

🚀 Model Training and Evaluation: 1 - Train the Model: Split the dataset into training and testing sets (e.g., 80/20 split). Train the selected model(s) on the training set.

2 - Evaluate the Model: Use the test set to assess model performance and fine-tune as necessary using grid search or cross-validation.

This dataset and workflow are useful for exploring biometric relationships in fish and building regression models to predict weight based on length or species. Great for marine biology, aquaculture analytics, and educational projects.

🐠 Happy modeling! 👍 Please upvote if you found this helpful!

https://www.kaggle.com/code/abdelrahman16/fish-clustering-diverse-techniques

Gulf Stream paths (daily, monthly, and annual) from 1993-01-01 to 2023-12-31 are identified via the longest 25-cm sea surface height contour in the Northwest Atlantic (75°W–55°W; 33°N–43°N) from the daily 1/8° resolution maps of absolute dynamic topography from the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing, following the methodology of Andres (2016). The daily sea surface height fields are averaged to monthly and annual fields to identify the corresponding monthly and annual Gulf Stream paths.

Additionally, an updated Gulf Stream destabilization point time series (1993–2023), which builds upon the work of Andres (2016), was generated using the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing (1/8°). Similar to Andres (2016), the monthly Gulf Stream path is identified as the 25-cm SSH contour from absolute dynamic topography maps. The 12 monthly mean paths are divided yearly into 0.5° longitude bins (from 75°W to 55°W). In some months, the Gulf Stream can take a meandering path and contort over itself in an “S” curve. In these cases, the northernmost latitude is used in the variance calculation to resolve the issue of multiple latitudes for a single longitude. The variance of the Gulf Stream position (latitude) is then calculated for each year using the 12 monthly mean paths. The destabilization point is defined as the first downstream distance (longitude) at which the variance of the Gulf Stream position exceeds 0.4(°)2, which differs from the original threshold value of 0.5(°)2 in Andres (2016). The threshold value of 0.4(°)2 is the 70th percentile of variance for all years, which marks the transition from a relatively stable jet to an unstable, meandering current in the new higher-resolution (1/8°) maps of absolute dynamic topography.

Thanks to improvements in processing and combining satellite altimeter data (Taburet et al., 2019), in recent years the maps of absolute dynamic topography are different than the maps used by Andres (2016), which had 1/4° resolution. To account for the differences in the resolution of the data and corrections to the processing standards of altimeter data, a new threshold value was chosen that is consistent with the methods of Andres (2016), i.e., the threshold still signifies the transition between a stable and unstable Gulf Stream. However, a lower threshold value is necessary in the new absolute dynamic topography maps since finer-resolution data can separate distinct local maxima in variance, which could be smoothed together in coarser data, and may cause the destabilization point to be identified further downstream if the threshold were not adjusted. The 70th percentile of variance (0.4(°)2) for all years (1993–2023) was chosen as the threshold because the distribution of variance is right-skewed with a long tail and the 70th percentile separate lower variance associated with meridional shifts in the Gulf Stream path from the extreme, vigorous meadnering that occurs downstream of the "destabilization point".

The daily, monthly, annual Gulf Stream paths, and the updated destabilization point time series were generated using the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing (https://doi.org/10.48670/moi-00148).

The Numeric Display market is an essential segment of the broader electronics and display industry, characterized by the visual representation of numerical data through various technological means. This market includes a range of products such as digital counters, LED and LCD displays that are widely utilized across

Entrainment and disturbance waves are simulated by VOF method. A systematic processing method is developed to accurately and efficiently calculate film thickness, which is the basis of calculation of wave parameter.

Calculated film thickness is shown in two ways. One is z-t representation, which shows the result for each polar angle. These correspond to folders "z_t_B_0(10)_filtered(raw)", where 0 means there is no damping term, 10 means a turbulence damping term is considered, filtered means a three-point median filtering is used to remove fast droplets, raw means raw data without filtering.

The other way is theta-z representation where instantaneous film thickness distribution on the tube wall is shown for each saved time. Two videos are made based on these figures, "B_0.mp4" is for non-damping case, while "B_10.mp4" is for the simulation with turbulence damping term.

All the numerical data underlying graphs and summary statistics. Data from individual figure panels are presented on separate tabs. Listed are the numerical data, the means, and the standard deviation. (XLSX)