The dataset contains statistical information on the number of persons with a specific combination of personal names and personal names (multiple names) included in the Register of Natural Persons (until 06.28.2021). Population Register). It should be noted that the Register of Natural Persons also includes personal names of foreigners in the Latin alphabet transliteration according to the travel document issued by the foreign state (for example, Nicola, Alex), which does not comply with the norms of the Latvian literary language.
As of 2023.10.01, the dataset contains information on gender (male, female) of combinations of names and personal names of persons registered in the Register of Natural Persons.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This users dataset is a preview of a much bigger dataset, with lots of related data (product listings of sellers, comments on listed products, etc...).
My Telegram bot will answer your queries and allow you to contact me.
There are a lot of unknowns when running an E-commerce store, even when you have analytics to guide your decisions.
Users are an important factor in an e-commerce business. This is especially true in a C2C-oriented store, since they are both the suppliers (by uploading their products) AND the customers (by purchasing other user's articles).
This dataset aims to serve as a benchmark for an e-commerce fashion store. Using this dataset, you may want to try and understand what you can expect of your users and determine in advance how your grows may be.
If you think this kind of dataset may be useful or if you liked it, don't forget to show your support or appreciation with an upvote/comment. You may even include how you think this dataset might be of use to you. This way, I will be more aware of specific needs and be able to adapt my datasets to suits more your needs.
This dataset is part of a preview of a much larger dataset. Please contact me for more.
The data was scraped from a successful online C2C fashion store with over 10M registered users. The store was first launched in Europe around 2009 then expanded worldwide.
Visitors vs Users: Visitors do not appear in this dataset. Only registered users are included. "Visitors" cannot purchase an article but can view the catalog.
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.
Questions you might want to answer using this dataset:
Example works:
For other licensing options, contact me.
FOI-02559 confirmed 445 claims to the Vaccine Damage Payment Scheme had met the criteria for causation but failed to satisfy the condition of 60% disability so not considered disabled enough to receive an award. Please can you tell me of the 445 claims: 1. How many were assessed as having at least 20% disability? 2. How many were assessed as having at least 50% disability? Our response I can confirm that the NHS Business Services Authority (NHSBSA) holds the information you have requested. The information is enclosed in this response. All data as of 31 January 2025. All data relates to claims received by the NHSBSA and those transferred from the Department for Work and Pensions (DWP) on 1 November 2021. All figures provided relate to COVID-19 vaccines only. Claimants are provided with a clinical assessment report, explaining how the medical assessor reached their decision. In the medical assessment report, level of disablement is sometimes reported as a range. Where the medical assessor has reported the vaccinated person’s disability as a range, we've used the upper number reported to categorise the claim. This data must therefore be interpreted as a guide only, because it is not possible to determine the accurate number of claims in each category. Question 1 - How many were assessed as having at least 20% disability? Of the 445 claims which met the criteria for causation, 180 were assessed as having a level of disablement between 20% to 49%. Question 2 - How many were assessed as having at least 50% disability? Of the 445 claims which met the criteria for causation, 10 were assessed as having a level of disablement above 50%.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The main goal of this model is to help me create an app that count How much money does a picture has.
Descriptions of each class type
I don't seperate country base money and don't seperate front and back
EUR-1-cent dasdasd
EUR-2-cent
EUR-5-cent
EUR-10-cent
EUR-20-cent
EUR-50-cent
EUR-1-euro
EUR-2-euro
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
The dataset was retrieved from the European Centre for Disease Prevention and Control (downloaded on 2020-June-18). My prio interest is an infection risc for gender and age groups. I have searched and found full info in this dataset, however restricted by Europe only.
The data includes 53308 entries for time periond from 1 Jan 2020 to June 2020, and contains information about age groups, gender, outcomes (alive vs fatal), treatment (if hospitalization or intensive care were applyed), source of infection (if detected), reporting country and reporting week.
I am indebted the European Centre for Disease Prevention and Control for letting the data in open access.
For me first of all was interesting the following: - Time peak of the pandemy. And if the peak is shifted in different european countries. - General statistics, showing infection risc for different age groups. - Frequency of infection cases between age and gender groups. - How much effective was ICU(intensive care unit) during pandemy? The dataset is promising for models, predicting fatal (death) cases on several factors.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
United States 45th President Donald Trump has used Twitter as no one else. He primarily ran his government from a twitter firehose. Twitter has officially banned his account on January 8th 2021 after a deadly riot at Capitol on January 6th 2021. Twitter cites its World Leaders on Twitter: Principles and Approach as a guide to adhere to for public leaders.
Trump tweets and policies have far reaching effects that one can realize or he would accept to realize himself. Since, twitter is suspended there is no public way to read his past tweets and analyze it for public policy outcome or link it with global issues.
Here we are presenting the complete treasure trove of President Trump's tweet, all 56,572 for the public, data scientists and researchers.
The dataset contains 56,572 tweets, tweet IDs, Tweet Date, How many liked and retweeted it.
I like to acknowledge Twitter and Trump's Tweet Archives on the Internet that have helped me create this dataset
I’d like to call the attention of my fellow Kagglers and Data Scientists to use Machine Learning and Data Sciences to help me explore these ideas:
• How many times Trump discussed a particular country in his tweets and if we can label the sentiments? (North Korea, India, Pakistan, Mexico?) • How many times Trump talks about immigrants and border wall? • How many times and ways he has insulted? • Can you find a link between his tweets and stock market prices? • How many times he has downplayed Corona/Covid? • How many times he has called the election fraud? • How many tweets about Hillary Clinton, Obama or Joe Biden? • Anything else you can find that surprises us?
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘WHO national life expectancy ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mmattson/who-national-life-expectancy on 28 January 2022.
--- Dataset description provided by original source is as follows ---
I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.
This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.
A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.
Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.
There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.
--- Original source retains full ownership of the source dataset ---
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset got a lot of love from the community and I saw many people asking for an updated version, so I have uploaded the latest scraped and processed data ( as of 21/03/2021). Now it's super easy for anyone to get the latest dataset (Just use a single command), so in case you need bleeding-edge data, or you want to see the code, you can look here. Hope this solves all problems! If there are any issues with the data, please forgive me and write about it in the comments or raise an issue on github. I will pick it up 👍 Thank you everyone for the emails and messages. As usual, have fun! ❤️ 😁
This is a list of every UFC fight in the history of the organisation. Every row contains information about both fighters, fight details and the winner. The data was scraped from ufcstats website. After fightmetric ceased to exist, this came into picture. I saw that there was a lot of information on the website about every fight and every event and there were no existing ways of capturing all this. I used beautifulsoup to scrape the data and pandas to process it. It was a long and arduous process, please forgive any mistakes. I have provided the raw files incase anybody wants to process it differently. This is my first time creating a dataset, any suggestions and corrections are welcome! Incase anyone wants to check out the work, I have all uploaded all the code files, including the scraping module here
Have fun!
Each row is a compilation of both fighter stats. Fighters are represented by 'red' and 'blue' (for red and blue corner). So for instance, red fighter has the complied average stats of all the fights except the current one. The stats include damage done by the red fighter on the opponent and the damage done by the opponent on the fighter (represented by 'opp' in the columns) in all the fights this particular red fighter has had, except this one as it has not occured yet (in the data). Same information exists for blue fighter. The target variable is 'Winner' which is the only column that tells you what happened. Here are some column definitions:
R_
and B_
prefix signifies red and blue corner fighter stats respectively_opp_
containing columns is the average of damage done by the opponent on the fighterKD
is number of knockdownsSIG_STR
is no. of significant strikes 'landed of attempted'SIG_STR_pct
is significant strikes percentageTOTAL_STR
is total strikes 'landed of attempted'TD
is no. of takedownsTD_pct
is takedown percentagesSUB_ATT
is no. of submission attemptsPASS
is no. times the guard was passed?REV
is the no. of Reversals landedHEAD
is no. of significant strinks to the head 'landed of attempted'BODY
is no. of significant strikes to the body 'landed of attempted'CLINCH
is no. of significant strikes in the clinch 'landed of attempted'GROUND
is no. of significant strikes on the ground 'landed of attempted'win_by
is method of winlast_round
is last round of the fight (ex. if it was a KO in 1st, then this will be 1)last_round_time
is when the fight ended in the last roundFormat
is the format of the fight (3 rounds, 5 rounds etc.)Referee
is the name of the Refdate
is the date of the fightlocation
is the location in which the event took placeFight_type
is which weight class and whether it's a title bout or notWinner
is the winner of the fightStance
is the stance of the fighter (orthodox, southpaw, etc.)Height_cms
is the height in centimeterReach_cms
is the reach of the fighter (arm span) in centimeterWeight_lbs
is the weight of the fighter in pounds (lbs)age
is the age of the fightertitle_bout
Boolean value of whether it is title fight or notweight_class
is which weight class the fight is in (Bantamweight, heavyweight, Women's flyweight, etc.)no_of_rounds
is the number of rounds the fight was scheduled forcurrent_lose_streak
is the count of current concurrent losses of the fightercurrent_win_streak
is the count of current concurrent wins of the fighterdraw
is the number of draws in the fighter's ufc careerwins
is the number of wins in the fighter's ufc careerlosses
is the number of losses in the fighter's ufc careertotal_rounds_fought
is the average of total rounds fought by the fightertotal_time_fought(seconds)
is the count of total time spent fighting in secondstotal_title_bouts
is the total number of title bouts taken part in by the fighterwin_by_Decision_Majority
is the number of wins by majority judges decision in the fighter's ufc careerwin_by_Decision_Split
is the number of wins by split judges decision in the fighter's ufc careerwin_by_Decision_Unanimous
is the number of wins by unanimous judges decision in the fighter's ufc careerwin_by_KO/TKO
is the number of wins by knockout in the fighter's ufc careerwin_by_Submission
is the number of wins by submission in the fighter's ufc careerwin_by_TKO_Doctor_Stoppage
is the number of wins by doctor stoppage in the fighter's ufc careerInspiration: https://github.com/Hitkul/UFC_Fight_Prediction Provided ideas on how to store per fight data. Unfortunately, the entire UFC website and fightmetric website changed so couldn't reuse any of the code.
Print Progress Bar: https://gist.github.com/aubricus/f91fb55dc6ba5557fbab06119420dd6a To display progress of how much download is complete in the terminal
You can check out who I am and what I do here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 26 cities in the Waldo County, ME by Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 29 cities in the Kennebec County, ME by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 28 cities in the York County, ME by Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 28 cities in the Cumberland County, ME by Non-Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 28 cities in the York County, ME by Hispanic Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 29 cities in the Kennebec County, ME by Non-Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 14 cities in the Androscoggin County, ME by Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
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License information was derived automatically
Context
This list ranks the 36 cities in the Oxford County, ME by Multi-Racial Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 26 cities in the Waldo County, ME by Hispanic American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 36 cities in the Oxford County, ME by American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 36 cities in the Oxford County, ME by Non-Hispanic Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
This list ranks the 14 cities in the Androscoggin County, ME by Hispanic American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
The dataset contains statistical information on the number of persons with a specific combination of personal names and personal names (multiple names) included in the Register of Natural Persons (until 06.28.2021). Population Register). It should be noted that the Register of Natural Persons also includes personal names of foreigners in the Latin alphabet transliteration according to the travel document issued by the foreign state (for example, Nicola, Alex), which does not comply with the norms of the Latvian literary language.
As of 2023.10.01, the dataset contains information on gender (male, female) of combinations of names and personal names of persons registered in the Register of Natural Persons.