This dataset provides country code, postal code, latitude, longitude, as well as names of state, county/province, community etc. for all countries where the data is available.

COMMERCIAL LICENSE

For subscribing to a commercial license for John Snow Labs Data Library which includes all datasets curated and maintained by John Snow Labs please visit https://www.johnsnowlabs.com/marketplace.

This dataset is a .csv file containing central latitude and longitude points for all countries around the globe

List of countries of the world with their ISO codes

Retrieve our dataset regarding Countries, consisting of 194 rows and 3 columns, based on data from SIPRI, World Bank and Reporters Without Borders.

Country codes: ISO 2ISO 3UNLANGLABEL (EN, FR, SP)

In the world we live in, natural resources are not distributed equally in all countries. This issue becomes the first factor that facilities are not the same in countries. Of course, having natural resources is a necessary condition for having an advanced country, otherwise there are countries like Japan that are among the top countries in all areas without having natural resources. However, to help with issues such as education and health and other issues in weaker countries, there are organizations that help the people of those countries. HELP International is one of those organizations. Suppose this organization has given you an amount as a budget, and you, as a data scientist, want to divide this budget among the countries using the data you have.

Context

I needed this dataset to map some countries in the analysis: Advanced Global Warming Analysis with Plotly. Feel free to use this mapping for whatever cool analysis you're doing. :)

Content

• name - Country name in english
• alpha-2 - ISO code formed of 2 letters
• alpha-2 - ISO code formed of 3 letters (use this in your plotly maps ;) )
• country code - unique
• region - the continent of provenience
• sub-region - subcontinent
• intermediate region
• codes for region/ subregion/ intermediate region

Acknowledgements

Dataset was taken from lukes on GitHub: https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes/blob/master/all/all.csv. I made only some small changes to the country names to mach my needs in the dataset (eg. United States of America transformed in United States).

Context

I needed all dataset to cards some countries inches the analyses: Advanced Global Warming Analysis with Plotly. Feel liberate to use this mapping for whenever crystal analytics you're what. :)

Content

• name - Country get in english
• alpha-2 - INVENTORY code formed of 2 letters
• alpha-2 - ISO code formed of 3 letters (use this for your plotly maps ;) )
• country code - unique
• locality - the continent of provenience
• sub-region - subcontinent
• intermediate region
• codes for region/ subregion/ mittelfristig region

Acknowledgements

Dataset was taken for lukes on GitHub: https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes/blob/master/all/all.csv. I constructed alone some small changes to the country names to mach mine needs in the dataset (eg. United Status starting America transformation in United States).

Luxembourgish country border expressed as a CSV list of 5000 coordinates: First list entry contains northmost coordinates. Last list entry (row 5001) is identical to first entry. List sequence follows border in a clockwise way. All coordinates have a precision of seven decimal digits. Data was manually derived from Apple Maps, thus not representing legal/official border data.

Dataset with the number of UNESCO World Heritage sites across countries and nation variables: GDP, population and area.

Analysis of ‘Population by Country - 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tanuprabhu/population-by-country-2020 on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Context

I always wanted to access a data set that was related to the world’s population (Country wise). But I could not find a properly documented data set. Rather, I just created one manually.

Content

Now I knew I wanted to create a dataset but I did not know how to do so. So, I started to search for the content (Population of countries) on the internet. Obviously, Wikipedia was my first search. But I don't know why the results were not acceptable. And also there were only I think 190 or more countries. So then I surfed the internet for quite some time until then I stumbled upon a great website. I think you probably have heard about this. The name of the website is Worldometer. This is exactly the website I was looking for. This website had more details than Wikipedia. Also, this website had more rows I mean more countries with their population.

Once I got the data, now my next hard task was to download it. Of course, I could not get the raw form of data. I did not mail them regarding the data. Now I learned a new skill which is very important for a data scientist. I read somewhere that to obtain the data from websites you need to use this technique. Any guesses, keep reading you will come to know in the next paragraph.

https://fiverr-res.cloudinary.com/images/t_main1,q_auto,f_auto/gigs/119580480/original/68088c5f588ec32a6b3a3a67ec0d1b5a8a70648d/do-web-scraping-and-data-mining-with-python.png" alt="alt text">

You are right its, Web Scraping. Now I learned this so that I could convert the data into a CSV format. Now I will give you the scraper code that I wrote and also I somehow found a way to directly convert the pandas data frame to a CSV(Comma-separated fo format) and store it on my computer. Now just go through my code and you will know what I'm talking about.

Below is the code that I used to scrape the code from the website

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3200273%2Fe814c2739b99d221de328c72a0b2571e%2FCapture.PNG?generation=1581314967227445&alt=media" alt="">

Acknowledgements

Now I couldn't have got the data without Worldometer. So special thanks to the website. It is because of them I was able to get the data.

Inspiration

As far as I know, I don't have any questions to ask. You guys can let me know by finding your ways to use the data and let me know via kernel if you find something interesting

--- Original source retains full ownership of the source dataset ---

This is a high-detail wold map layer inspired by the printed atlas. Overseas and unincorporated territories are digitized but included as part of the country they are dependent on. Many enclaves are included in the same manner. Many Independent Non-UN-Member-States are included. The polygons for nations of chains of islands (also American Samoa and French Polynesia) are defined as the boundary of their territorial waters. Use a world base map to reference the location of the islands.Use the 3-letter country code (Abbreviation) to join the layer to statistic and index data. It may be necessary to use the Excel 'Vlookup' function to assign the abbreviations to data and then save the file to be joined in .csv format before adding the table to the layer. See the 'Credit (Attribution)' data of the layer information page for sourcing on most of the attribute values. The map includes World Bank 'Income Group', 'Regions'; Freedom House's Freedom in the World 2019 is the basis for 'FreedomLevel'; 'NukeStatus' is ArmsControl.org's nuclear weapons inventories, etc. The intended use of this map is academic.Changes to 'GovernmentType', 'Dependent', and Pop-ups (in the app) are all coming.

The database contains index measures of linguistic similarity both domestically and internationally. The domestic measures capture linguistic similarities present among populations within a single country while the international indexes capture language similarities between two different countries. The indexes reflect three aspects of language: common official languages, common native languages, and linguistic proximity across languages.

Surgo Ventures' Africa CCVI ranks 756 regions across 48 African countries on their vulnerability—or their ability to mitigate, treat, and delay transmission of the coronavirus. Vulnerability is assessed based on many factors grouped into seven themes: socioeconomic status, population density, access to transportation and housing; epidemiological factors; health system factors; fragility; and age. The index reflects risk factors for COVID-19, both in terms of clinical outcomes and socioeconomic impact.

The Africa CCVI is the only index to measure vulnerability to COVID-19 within most countries in Africa at this level of detail. The index is modular to reflect the reality that vulnerability is a multi-dimensional construct, and two regions could be vulnerable for very different reasons. This allows stakeholders to customize pandemic responses informed by vulnerability on each dimension. For example, policymakers can identify areas for scaling up COVID-19 testing that are more vulnerable on theme two - population density - or direct community health workers or mobile health units to areas that are vulnerable due to weak health systems infrastructure. The modularity of the Africa CCVI can help governments design lean and precise responses for subnational regions during each phase of the pandemic.

Data files:

1. Africa_CCVI_subnational_zenodo.csv: Africa CCVI and seven themes' scores for 756 administrative level-1 regions across 48 countries
2. Africa_CCVI_country_zenodo.csv: Africa CCVI and seven themes scores across 36 countries (12 countries excluded as country-specific data sources were used for them)
3. DHS_raw_indicators_Zenodo.csv: this CSV contains indicator data for 36 countries, data was primarily sourced from Demographic and Health Surveys (DHS) in addition to other sources (listed in accvi-data-sources.xlsx)
4. non_DHS_raw_indicators_Zenodo.csv: 12 countries that did not have a recent DHS, so we used country-specific surveys, MICS UNICEF, and other sources (listed in accvi-data-sources.xlsx)
5. accvi-data-sources.xlsx: data sources used for ACCVI indicators
6. zenodo_data_dictionary.csv: names and definitions of variables used in data files

Results descriptions, indicators and program descriptions for MCC's programs. All fields are also included in MCC's programmatic data in .xml.

Any work using this dataset should cite the following paper:Nirmalya Thakur, Saumick Pradhan, and Chia Y. Han, “Investigating the impact of COVID-19 on Online Learning-based Web Behavior”, Proceedings of the 7th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications (IHIET-AI 2022), Lausanne, Switzerland, April 21-23, 2022, DOI: http://dx.doi.org/10.54941/ahfe100850AbstractCOVID-19, a pandemic that the world has not seen in decades, has resulted in presenting a multitude of unprecedented challenges for student learning and education across the globe. The global surge in COVID-19 cases resulted in several schools, colleges, and universities closing in 2020 in almost all parts of the world and switching to online or remote learning, which has impacted student learning in different ways. This has resulted in both educators and students spending more time on the internet than ever before, which may be broadly summarized as both these groups investigating, learning, and familiarizing themselves with information, tools, applications, and frameworks to adapt to online or remote learning. Studying such web behavior, in the form of Big Data mining and analysis, originating from different countries of the world provides the scope for identifying, investigating, and quantifying the needs, interests, and challenges related to online learning in different countries of the world on account of COVID-19. Therefore, this work presents an open-access dataset that consists of the web behavior related to online learning that originated from different countries of the world on a monthly basis from 2004-2021. For the development of this dataset, the web behavior data in the form of search interests related to online learning, recorded from Google Searches, was mined using Google Trends, as Google is the most popular search engine across the world. Even though the first case of COVID-19 in humans was recorded in 2019, the dataset presents the web behavior data related to online learning starting from 2004, so that the degrees to which web behavior related to online learning changed and the trends in these changes in different countries of the world can be quantified and interpreted easily. At this point, the dataset consists of the web behavior data related to online learning for the 20 countries which were worst affected by COVID-19 at the time of development of this dataset. Future work on this dataset would involve incorporating more countries into the study and expanding the dataset. Data Description The dataset consists of one .csv file named – “Online_Learning_Data.csv”. The data was collected by using Google Trends on October 7th, 2021. This dataset has 21 attributes. The first attribute, “Month,” stands for the month from January 2004 to October 2021, as the data was collected on a monthly basis in this range. The remaining 20 attributes stand for each of the 20 countries - USA, India, Brazil, UK, Russia, France, Turkey, Iran, Argentina, Colombia, Spain, Italy, Indonesia, Germany, Mexico, Poland, South Africa, Philippines, Ukraine, and Peru, that were a part of this research study. Each of these attributes that are named after one of these countries represents the search interest related to online learning from that specific country on a monthly basis in this time range. The minimum value of this search interest is 0, and the maximum value is 100. These minimum and maximum values of search interests are as per the scaling factor used by Google Trends for all Google Search data. Details on the methodology and procedure that were followed for the development of this dataset are included in the above-mentioned paper. For any questions related to this dataset or the paper, please contact Nirmalya Thakur at thakurna@mail.uc.edu

About this Dataset

This is a very simple list of countries, file names, and URLs to the flag images in tabular format from Wikipedia. This way you can include flags in any data visualization report that uses countries as a dimension for easy exploration and storytelling.

Objectives

This Dataset will help you get a graphical representation of countries in your report depending on which data visualization tool you're using.

Background

• As we created some Power BI reports that included country metrics, the functionality of the tool allowed us to include images as visual components and interactive objects in reports, but we couldn't find a friendly Dataset that would list all assets in a way that you could plug right into the report.
• Who's involved? The Power BI team.
• How it was it built? We used Power BI Desktop to connect to the Wikipedia page that lists all flags and then cleaned up the page script to extract country list, files names and URLs.

Get involved

How can others contribute? Any ideas on how to make this Dataset richer or more useful is welcome!

External resources

• Wikipedia Source Page
• Microsoft Power BI

Data Preparation: 1. The poverty data was downloaded as a CSV file from: United

Nations Millennium Development Goals Indicators web site. 2. Numeric field names

(1990, 1991, and so forth) were renamed to begin with a letter: Y1990, Y1991, etc...;

footnotes were removed, and the CSV file was brought into ArcMap. 3. The Copy Rows

tool was used to create a table that was joined to country level geometry. 4. The

first and last years that each country had a data value, the change between those two

values, and the mean value for all years with data was then computed using Calculate

Field. Data Analyses: 1. A Hot Spot Analysis was performed on the change in poverty

to see regions of improvement and regions where poverty is becoming more prevalent.

1. A Cluster and Outlier Analysis was performed on the change in poverty to see

countries that are performing much better or much worse than surrounding countries.

1. A graph of the countries with the biggest change (both increases and decreases in

poverty percentages) was created. 3a. The best and worst performers were selected and

Copy Features was used to create a new dataset with only those records. 3b. A

horizontal line graph was created for those subset features.

All the contries with their ISO codes related with their regions and Flag URLs