Data format: .csv Image size:

The global household computer penetration in was forecast to continuously increase between 2024 and 2029 by in total 2.4 percentage points. After the eleventh consecutive increasing year, the computer penetration rate is estimated to reach 52.78 percent and therefore a new peak in 2029. Depicted is the estimated share of households owning at least one computer.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the household computer penetration in countries like Australia & Oceania and Caribbean.

Data format: .csv Dataset size:

This data release includes two Wikipedia datasets related to the readership of the project as it relates to the early COVID-19 pandemic period. The first dataset is COVID-19 article page views by country, the second dataset is one hop navigation where one of the two pages are COVID-19 related. The data covers roughly the first six months of the pandemic, more specifically from January 1st 2020 to June 30th 2020. For more background on the pandemic in those months, see English Wikipedia's Timeline of the COVID-19 pandemic.Wikipedia articles are considered COVID-19 related according the methodology described here, the list of COVID-19 articles used for the released datasets is available in covid_articles.tsv. For simplicity and transparency, the same list of articles from 20 April 2020 was used for the entire dataset though in practice new COVID-19-relevant articles were constantly being created as the pandemic evolved.Privacy considerationsWhile this data is considered valuable for the insight that it can provide about information-seeking behaviors around the pandemic in its early months across diverse geographies, care must be taken to not inadvertently reveal information about the behavior of individual Wikipedia readers. We put in place a number of filters to release as much data as we can while minimizing the risk to readers.The Wikimedia foundation started to release most viewed articles by country from Jan 2021. At the beginning of the COVID-19 an exemption was made to store reader data about the pandemic with additional privacy protections:- exclude the page views from users engaged in an edit session- exclude reader data from specific countries (with a few exceptions)- the aggregated statistics are based on 50% of reader sessions that involve a pageview to a COVID-19-related article (see covid_pages.tsv). As a control, a 1% random sample of reader sessions that have no pageviews to COVID-19-related articles was kept. In aggregate, we make sure this 1% non-COVID-19 sample and 50% COVID-19 sample represents less than 10% of pageviews for a country for that day. The randomization and filters occurs on a daily cadence with all timestamps in UTC.- exclude power users - i.e. userhashes with greater than 500 pageviews in a day. This doubles as another form of likely bot removal, protects very heavy users of the project, and also in theory would help reduce the chance of a single user heavily skewing the data.- exclude readership from users of the iOS and Android Wikipedia apps. In effect, the view counts in this dataset represent comparable trends rather than the total amount of traffic from a given country. For more background on readership data per country data, and the COVID-19 privacy protections in particular, see this phabricator.To further minimize privacy risks, a k-anonymity threshold of 100 was applied to the aggregated counts. For example, a page needs to be viewed at least 100 times in a given country and week in order to be included in the dataset. In addition, the view counts are floored to a multiple of 100.DatasetsThe datasets published in this release are derived from a reader session dataset generated by the code in this notebook with the filtering described above. The raw reader session data itself will not be publicly available due to privacy considerations. The datasets described below are similar to the pageviews and clickstream data that the Wikimedia foundation publishes already, with the addition of the country specific counts.COVID-19 pageviewsThe file covid_pageviews.tsv contains:- pageview counts for COVID-19 related pages, aggregated by week and country- k-anonymity threshold of 100- example: In the 13th week of 2020 (23 March - 29 March 2020), the page 'Pandémie_de_Covid-19_en_Italie' on French Wikipedia was visited 11700 times from readers in Belgium- as a control bucket, we include pageview counts to all pages aggregated by week and country. Due to privacy considerations during the collection of the data, the control bucket was sampled at ~1% of all view traffic. The view counts for the control title are thus proportional to the total number of pageviews to all pages.The file is ~8 MB and contains ~134000 data points across the 27 weeks, 108 countries, and 168 projects.Covid reader session bigramsThe file covid_session_bigrams.tsv contains:- number of occurrences of visits to pages A -> B, where either A or B is a COVID-19 related article. Note that the bigrams are tuples (from, to) of articles viewed in succession, the underlying mechanism can be clicking on a link in an article, but it may also have been a new search or reading both articles based on links from third source articles. In contrast, the clickstream data is based on referral information only- aggregated by month and country- k-anonymity threshold of 100- example: In March of 2020, there were a 1000 occurences of readers accessing the page es.wikipedia/SARS-CoV-2 followed by es.wikipedia/Orthocoronavirinae from ChileThe file is ~10 MB and contains ~90000 bigrams across the 6 months, 96 countries, and 56 projects.ContactPlease reach out to research-feedback@wikimedia.org for any questions.

This table contains 2124 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (30 items: Austria; Belgium (Flemish speaking); Belgium; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age group (3 items: 11 years;13 years;15 years ...), Activity (2 items: Watch VCR movies; Play computer games ...), Time spent (6 items: Not at all;1 to 3 hours; Less than 1 hour;4 to 6 hours ...).

Taro Leaf Blight, caused by the pathogen Phytophthora colocasiae, manifests as distinct necrotic spots with white sporangia bands and orange droplets on the leaves. Thriving in temperatures between 25°C to 30°C, the disease spreads rapidly through rain splash and wind-blown spray. This blight not only reduces yields but also affects the income of smallholder farmers who depend heavily on taro cultivation. In recent years, taro yields in Africa have declined due to this disease, combined with other factors such as limited input utilization and cultivation on less fertile lands.

Early detection of Taro Leaf Blight is essential for effective management and prevention. Technologies such as smartphone-based apps, handheld spectrometers, drone-mounted sensors, and biosensors are being explored to enable real-time disease identification. These methods empower farmers to implement timely measures, thus minimizing yield losses and preserving crop quality. However, challenges like the financial barriers of advanced technologies and the need for technical knowledge pose limitations, especially for smallholder farmers in developing countries.

A critical part of combating TLB is building robust datasets for training deep-learning models for disease detection. To this end, a meticulous data collection effort was undertaken by teams in Nigeria and Ghana. This initiative focused on capturing images of taro plants at various stages of TLB infection—Taro Early Blight, Taro Mid Blight, Taro Late Blight, and Taro Healthy. The result of the initiative is the Taro Leaf Blight Disease Image Dataset.

The dataset consists of 18,248 images. The breakdown of each class is as follows: Taro-Late: 1,270 Taro-Mid: 3,370 Taro-Early: 4,864 Taro-Healthy: 8,744 Taro-Not-Early: 4,640 (Combination of Taro-Late & Taro-Mid) Each image is a JPG (RGB) of size 500x500.

Support for implementation of project activities was made possible by the Research Grant (109705-001/002) by the Responsible Artificial Intelligence Network for Climate Action in Africa (RAINCA) consortium made up of WASCAL, RUFORUM and AKADEMIYA2063 provided by IDRC.

This dataset contains all of the programs and the unrestricted data used for our research:Reversing the U: new evidence on the Internet and democracy. The relationship between the internet and democratic developments has long been a controversial topic, hampered in part by the lack of empirical evidence. This study is undertaken to investigate the effects of Internet penetration on democratization based on the panel data of 125 countries gathered from 1993 to 2014. The authors apply machine learning method (i.e. random forest) to effectively screen the variables that are more closely related to democracy. The results of different estimation models reveal an inverted U-shaped relationship between Internet penetration and democratization, and also distinguish the impacts of the Internet on advanced and less advanced democracies. Then, we arrive at the conclusion that Internet penetration brings a late-starting advantage in the development of democracy for less advanced democracies. These conclusions are further confirmed by robust test.

This dataset contains the salaries of Data Science Professionals for year 2020 and 2021.

About Dataset :

work_year : The year during which the salary was paid. There are two types of work year values: 2020 Year with a definitive amount from the past 2021e Year with an estimated amount (e.g. current year)

experience_level : The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type : The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title : The role worked in during the year. salary The total gross salary amount paid.

salary_currency : The currency of the salary paid as an ISO 4217 currency code.

salaryinusd : The salary in USD (FX rate divided by avg. USD rate for the respective year via fxdata.foorilla.com).

employee_residence : Employee's primary country of residence in during the work year as an ISO 3166 country code.

remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote 100 Fully remote (more than 80%)

company_location : The country of the employer's main office or contracting branch as an ISO 3166 country code.

company_size : The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Dataset Source - ai-jobs.net Salaries

Code and more details: https://github.com/geohci/wiki-region-groundtruthThere are two files:* region_groundtruth_2020_11_23.tsv.bz2: for each item, whether it had coordinates, whether a region was identified based on the coordinates, and what regions were found with what properties. NOTE: for speed, coordinates were only geolocated if no other properties were linked to a region.* region_groundtruth_2020_11_23_aggregated.json.bz2: this is just the aggregated version of the first file for those who do not care which properties lead to which regions.This 1st version of the data was generated from the 23 November 2020 Wikidata JSON dumps and contains 11,065,563 items that had a sitelink to at least one Wikipedia article and was geolocated to at least one region.

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 0.000 EUR mn in 2022. This stayed constant from the previous number of 0.000 EUR mn for 2020. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2022, with 12 observations. The data reached an all-time high of 0.190 EUR mn in 2015 and a record low of -0.490 EUR mn in 2018. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

This table contains 2124 series, with data for years 1990 - 1998 (not all combinations necessarily have data for all years), and was last released on 2007-01-29. This table contains data described by the following dimensions (Not all combinations are available): Geography (30 items: Austria; Belgium (Flemish speaking); Belgium; Belgium (French speaking) ...), Sex (2 items: Males; Females ...), Age group (3 items: 11 years;13 years;15 years ...), Activity (2 items: Watch VCR movies; Play computer games ...), Time spent (6 items: Not at all;1 to 3 hours; Less than 1 hour;4 to 6 hours ...).

Context

Cricket is the second most popular sport watched in the world (Wiki). The sport is loaded with a lot of emotion and drama till the last ball of the match. And, there are cricketers who had proved again and again, that they are true masters of this game, changed the equation of losing a match to winning, and brought many victories to their countries with their magic spells during the game. Now, it's time for us, as a fan of Cricket, to use our deep learning skills to have more fun with this dataset and to detect/predict the greatest cricketers of all time.

Data Preparation

In 2019, BBC asked viewers to vote for The Greatest cricketer of All Time and by the end, they released the 30 great cricketers of all-time list, based on max votes received. This dataset is created with images extracted from Google Images of these 30 cricketers.

The players' list is as shown below:

1. Sir Don Bradman (Australia)
2. Sachin Tendulkar (India)
3. Sir Garfield Sobers (West Indies)
4. Imran Khan (Pakistan)
5. Sir Ian Botham (England)
6. Shane Warne (Australia)
7. Sir Viv Richards (West Indies)
8. Brian Lara (West Indies)
9. Jaques Kallis (South Africa)
10. MS Dhoni (India)
11. Wasim Akram (Pakistan)
12. Virat Kohli (India)
13. James Anderson (England)
14. Sir Alastair Cook (England)
15. Muttiah Muralitharan (Sri Lanka)
16. Kumar Sangakkara (Sri Lanka)
17. Kapil Dev (India)
18. Sir Richard Hadlee (New Zealand)
19. Adam Gilchrist (Australia)
20. Chris Gayle (West Indies)
21. Glenn McGrath (Australia)
22. Ricky Ponting (Australia)
23. Steve Waugh (Australia)
24. Rahul Dravid (India)
25. Sunil Gavaskar (India)
26. Shoaib Akhtar (Pakistan)
27. Sir Curtly Ambrose (West Indies)
28. Mahela Jayawardene (Sri Lanka)
29. Dale Steyn (South Africa)
30. Allan Donald (South Africa)

Reference : BBC - the-greatest-cricketer-of-all-time-your-votes-revealed

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computers and Peripheral Equipment data was reported at 0.000 EUR mn in 2023. This stayed constant from the previous number of 0.000 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computers and Peripheral Equipment data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 0.000 EUR mn in 2023 and a record low of 0.000 EUR mn in 2023. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computers and Peripheral Equipment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 0.022 USD mn in 2023. This records an increase from the previous number of -0.307 USD mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.022 USD mn from Dec 2005 (Median) to 2023, with 17 observations. The data reached an all-time high of 1.732 USD mn in 2006 and a record low of -1.181 USD mn in 2013. Lithuania LT: Foreign Direct Investment Income: Inward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Financial Flows: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at -3.400 EUR mn in 2023. This records a decrease from the previous number of 0.000 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Financial Flows: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.150 EUR mn from Dec 2010 (Median) to 2023, with 7 observations. The data reached an all-time high of 1.850 EUR mn in 2010 and a record low of -3.400 EUR mn in 2023. Lithuania LT: Foreign Direct Investment Financial Flows: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Financial Flows: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 193.000 EUR mn in 2023. This records a decrease from the previous number of 233.000 EUR mn for 2022. France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 73.500 EUR mn from Dec 2013 (Median) to 2023, with 10 observations. The data reached an all-time high of 233.000 EUR mn in 2022 and a record low of -37.000 EUR mn in 2018. France Foreign Direct Investment Position: Outward: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s France – Table FR.OECD.FDI: Foreign Direct Investment Position: by Industry: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) has never been observed or is very negligible. It would be treated as portfolio investment in theory. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Direct Influence/Indirect Control (DIIC) method. Debt between fellow enterprises are completely covered. Collective investment institutions not covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the resident direct investor. Statistical unit: Enterprise.

Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data was reported at 1.775 USD mn in 2023. This records an increase from the previous number of 1.756 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data is updated yearly, averaging 0.102 USD mn from Dec 2013 (Median) to 2023, with 10 observations. The data reached an all-time high of 2.949 USD mn in 2020 and a record low of -10.106 USD mn in 2013. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Repair of Computers, Personal and Household Goods: Other Personal Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computer, Electronic and Optical Products data was reported at 3.140 EUR mn in 2023. This records an increase from the previous number of 1.370 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computer, Electronic and Optical Products data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2023, with 16 observations. The data reached an all-time high of 3.140 EUR mn in 2023 and a record low of -0.180 EUR mn in 2017. Lithuania LT: Foreign Direct Investment Income: Outward: Total: Manufacture of Computer, Electronic and Optical Products data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Computers and Peripheral Equipment data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Computers and Peripheral Equipment data is updated yearly, averaging 0.000 USD mn from Dec 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: Manufacture of Computers and Peripheral Equipment data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Position: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

The household computer penetration in the Philippines was forecast to continuously increase between 2024 and 2029 by in total 0.9 percentage points. After the ninth consecutive increasing year, the computer penetration rate is estimated to reach 25.71 percent and therefore a new peak in 2029. Depicted is the estimated share of households owning at least one computer.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the household computer penetration in countries like Thailand and Cambodia.