Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed ** data scientists or more increased from ** percent to almost ** percent. On average, the number of data scientists employed in a organization grew from ** to **.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

The global big data analytics market size was valued at $307.52 billion in 2023 & is projected to grow from $348.21 billion in 2024 to $961.89 billion by 2032

In 2024, data scientists worldwide demonstrated varying levels of proficiency across different skills according to DevSkiller assessments. CSV handling emerged as the most proficient skill, reaching an advanced-level score of **. This high proficiency in CSV manipulation highlights the continued importance of working with structured data in various formats. Data analysis and data structures followed closely behind, with scores of ** and **, respectively, indicating strong foundational skills among data scientists. Nonetheless, several skills fell just above the intermediate threshold, including data selection, ETL fundamentals, and classification algorithms.

In 2022, over 18 thousand data science job positions were available in the BFSI sector in India. An increase in the availability of data science jobs was seen over the years from 2019. E-commerce and internet followed suite with roughly 13 thousand jobs during the same time period.

In 2022, over *** thousand of the data science job positions were available in multi-national corporation IT and KPO service provider companies in the south Asian country of India. An increase in the availability of the data science jobs was seen over the years from 2019.

United States Number of Researchers: Total data was reported at 1,889,780.000 Person in 2021. This records an increase from the previous number of 1,823,522.000 Person for 2020. United States Number of Researchers: Total data is updated yearly, averaging 1,856,651.000 Person from Dec 2020 (Median) to 2021, with 2 observations. The data reached an all-time high of 1,889,780.000 Person in 2021 and a record low of 1,823,522.000 Person in 2020. United States Number of Researchers: Total 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 United States – Table US.OECD.MSTI: Number of Researchers and Personnel on Research and Development: OECD Member: Annual.

For the United States, from 2021 onwards, changes to the US BERD survey questionnaire allowed for more exhaustive identification of acquisition costs for ‘identifiable intangible assets’ used for R&D. This has resulted in a substantial increase in reported R&D capital expenditure within BERD. In the business sector, the funds from the rest of the world previously included in the business-financed BERD, are available separately from 2008. From 2006 onwards, GOVERD includes state government intramural performance (most of which being financed by the federal government and state government own funds). From 2016 onwards, PNPERD data are based on a new R&D performer survey. In the higher education sector all fields of SSH are included from 2003 onwards.

Following a survey of federally-funded research and development centers (FFRDCs) in 2005, it was concluded that FFRDC R&D belongs in the government sector - rather than the sector of the FFRDC administrator, as had been reported in the past. R&D expenditures by FFRDCs were reclassified from the other three R&D performing sectors to the Government sector; previously published data were revised accordingly. Between 2003 and 2004, the method used to classify data by industry has been revised. This particularly affects the ISIC category “wholesale trade” and consequently the BERD for total services.

U.S. R&D data are generally comparable, but there are some areas of underestimation:

1. i) Up to 2008, Government sector R&D performance covers only federal government activities. That by State and local government establishments is excluded;
2. ii) Except for the Government and the Business Enterprise sectors, the R&D data exclude most capital expenditures. For the Business Enterprise sector, depreciation is reported in place of gross capital expenditures up to 2014. Higher education (and national total) data were revised back to 1998 due to an improved methodology that corrects for double-counting of R&D funds passed between institutions.

Breakdown by type of R&D (basic research, applied research, etc.) was also revised back to 1998 in the business enterprise and higher education sectors due to improved estimation procedures.

The methodology for estimating researchers was changed as of 1985. In the Government, Higher Education and PNP sectors the data since then refer to employed doctoral scientists and engineers who report their primary work activity as research, development or the management of R&D, plus, for the Higher Education sector, the number of full-time equivalent graduate students with research assistantships averaging an estimated 50 % of their time engaged in R&D activities. As of 1985 researchers in the Government sector exclude military personnel. As of 1987, Higher education R&D personnel also include those who report their primary work activity as design.

Due to lack of official data for the different employment sectors, the total researchers figure is an OECD estimate up to 2019. Comprehensive reporting of R&D personnel statistics by the United States has resumed with records available since 2020, reflecting the addition of official figures for the number of researchers and total R&D personnel for the higher education sector and the Private non-profit sector; as well as the number of researchers for the government sector. The new data revise downwards previous OECD estimates as the OECD extrapolation methods drawing on historical US data, required to produce a consistent OECD aggregate, appear to have previously overestimated the growth in the number of researchers in the higher education sector.

Pre-production development is excluded from Defence GBARD (in accordance with the Frascati Manual) as of 2000. 2009 GBARD data also includes the one time incremental R&D funding legislated in the American Recovery and Reinvestment Act of 2009. Beginning with the 2000 GBARD data, budgets for capital expenditure – “R&D plant” in national terminology - are included. GBARD data for earlier years relate to budgets for current costs only.

Global Data Analytics market size is expected to reach $257.96 billion by 2029 at 28.4%, segmented as by big data analytics, predictive analytics, prescriptive analytics, descriptive analytics

BackgroundData science approaches have been pivotal in addressing public health challenges. However, there has been limited focus on identifying essential data science skills for health researchers, gaps in capacity building provision, barriers to access, and potential solutions.ObjectivesThis review aims to identify essential data science skills for health researchers and key stakeholders in Africa, Asia, and Latin America and the Caribbean (LAC), as well as to explore gaps and barriers in data science capacity building and share potential solutions, including any regional variations.MethodsAn online survey was conducted in English, French, Spanish and Portuguese, gathering both quantitative and qualitative responses. Descriptive analysis was performed in R V4.3, and a thematic workshop approach facilitated qualitative analysis.FindingsFrom 262 responses from individuals across 54 low- and middle-income countries (LMICs), representing various institutions and roles, we summarised essential data science skills globally and by region. Thematic analysis revealed key gaps and barriers in capacity building, including limited training resources, lack of mentoring, challenges with data quality, infrastructure and privacy issues, and the absence of a conducive research environment.Conclusion and future directionsRespondents’ consensus on essential data science skills suggests the need for a standardised framework for capacity building, adaptable to regional contexts. Greater investment, coupled with expanded collaboration and networking, would help address gaps and barriers, fostering a robust data science ecosystem and advancing insights into global health challenges.

Snapshot img

Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

Please cite the following paper when using this dataset: N. Thakur, “MonkeyPox2022Tweets: The first public Twitter dataset on the 2022 MonkeyPox outbreak,” Preprints, 2022, DOI: 10.20944/preprints202206.0172.v2

Abstract The world is currently facing an outbreak of the monkeypox virus, and confirmed cases have been reported from 28 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.

Data Description The dataset consists of a total of 255,363 Tweet IDs of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 23rd July 2022 (the most recent date at the time of dataset upload). The Tweet IDs are presented in 6 different .txt files based on the timelines of the associated tweets. The following provides the details of these dataset files. • Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the Tweet IDs: May 7, 2022 to May 21, 2022) • Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the Tweet IDs: May 21, 2022 to May 27, 2022) • Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the Tweet IDs: May 27, 2022 to June 5, 2022) • Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the Tweet IDs: June 5, 2022 to June 11, 2022) • Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 47718, Date Range of the Tweet IDs: June 12, 2022 to June 30, 2022) • Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the Tweet IDs: July 1, 2022 to July 23, 2022)

The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used.

This dataset contains posts and comments extracted from the r/datascience subreddit, a highly active discussion forum on Reddit with over 600,000 contributors. It offers valuable insights into the conversations and trends within the data science community, providing raw material for various analytical endeavours. The content is directly generated by the subreddit's contributors, reflecting authentic community engagement.

Columns

• title: The textual title of a Reddit post.
• score: The score or upvote count for a post or comment, indicating its popularity or agreement.
• id: A unique identifier assigned to each post or comment.
• url: The web address for the Reddit post or an associated external link.
• comms_num: The total number of comments associated with a specific post.
• created: The Unix timestamp indicating when the post or comment was created.
• body: The main textual content of a Reddit post or comment.
• timestamp: Another timestamp field, likely similar to 'created', marking the time of creation.

Distribution

The dataset is typically provided in a CSV format. * Score Distribution: Scores vary significantly, ranging from -91 to 2952. A large proportion of entries, specifically 20,526, fall within the -91.00 to 61.15 score range. Another view indicates 20,762 entries are in the 0.00 to 31.75 score range. There are 21,095 unique score values. * Time Coverage Distribution: The data covers a period from December 9, 2021, to April 22, 2022. There are 20,573 unique timestamp values. Activity peaks in late March 2022, with up to 2,830 entries in a single week.

Usage

This dataset is ideal for: * Analysing discussion topics prevalent within the r/datascience subreddit. * Understanding the tone of conversations among data science professionals and enthusiasts. * Identifying the dominant sentiment expressed in posts and comments. * Exploring the lexical particularities unique to the data science community's discussions. * Tracking trends and shifts in popular topics and opinions over time.

Coverage

The dataset offers global coverage regarding the community discussions. It spans a distinct time range from December 9, 2021, to April 22, 2022. The content reflects the diverse perspectives of over 600,000 contributors to the r/datascience subreddit, providing a wide demographic scope of individuals interested in data science.

License

CC0

Who Can Use It

• Data scientists and machine learning engineers for natural language processing (NLP) tasks such as topic modeling, sentiment analysis, or text classification.
• Social media analysts and researchers studying online community behaviour, trends, and user engagement patterns.
• Linguists and computational linguists examining the specific language usage within professional online forums.
• Academic researchers interested in the evolution of discussions within the data science field.

Dataset Name Suggestions

• Reddit Data Science Community Conversations
• r/datascience Subreddit Activity Log
• Data Science Forum Discussions Archive
• Reddit Data Science Posts and Comments

Attributes

Original Data Source: Data Science on Reddit

People HR Analytics Software Market Outlook

The global People HR Analytics Software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a robust CAGR of 17.5% during the forecast period. This impressive growth can be attributed to several factors, including the increasing adoption of data-driven decision-making processes within human resource departments, the integration of advanced analytics technologies, and the rising need for efficient workforce management solutions.

Key growth drivers of the People HR Analytics Software market include the escalating demand for data analytics in human resource operations, which enables organizations to effectively manage their workforce and optimize HR outcomes. The adoption of advanced analytics helps organizations to gain deeper insights into employee performance, engagement, and retention, which in turn leads to improved productivity and reduced turnover rates. Additionally, the growing emphasis on employee experience and well-being is compelling organizations to invest in sophisticated HR analytics tools that can provide actionable insights for enhancing employee satisfaction and engagement.

Another significant growth factor is the increasing prevalence of remote and hybrid work models, which has amplified the need for HR analytics solutions that can monitor and manage dispersed workforces. The COVID-19 pandemic has accelerated the adoption of remote working, highlighting the importance of digital tools for workforce management. HR analytics software provides organizations with the capabilities to track employee performance, engagement levels, and productivity, irrespective of their physical location. This shift towards remote working is expected to sustain the demand for HR analytics solutions in the long run.

Moreover, the integration of artificial intelligence (AI) and machine learning (ML) technologies into HR analytics software is driving market growth. These advanced technologies enable predictive analytics, which assists HR professionals in forecasting workforce trends, identifying potential issues, and making proactive decisions. For instance, AI can help in identifying patterns related to employee attrition, allowing organizations to take preemptive actions to retain top talent. Similarly, ML algorithms can analyze large volumes of HR data to uncover insights that can inform talent acquisition, workforce planning, and employee development strategies.

From a regional perspective, North America holds a significant share of the People HR Analytics Software market, driven by the high adoption rate of advanced technologies and the presence of major market players in the region. The region's robust economic environment and emphasis on workforce optimization further contribute to its market dominance. However, Asia Pacific is expected to emerge as the fastest-growing region during the forecast period, fueled by the increasing digital transformation initiatives and the rising adoption of HR analytics solutions by enterprises of all sizes in countries like China, India, and Japan.

Component Analysis

The People HR Analytics Software market is segmented by component into software and services. The software segment dominates the market, driven by the increasing demand for comprehensive HR analytics solutions that offer various functionalities such as talent management, performance tracking, and employee engagement. These software solutions are designed to integrate with existing HR systems, providing a seamless experience for HR professionals to manage and analyze employee data. The continuous advancements in software capabilities, such as the incorporation of AI and ML, are further enhancing the value proposition of HR analytics software.

On the other hand, the services segment, which includes implementation, consulting, and support services, is also witnessing substantial growth. As organizations adopt HR analytics software, there is a growing need for professional services to ensure successful implementation and integration with existing systems. Consulting services are particularly in demand as organizations seek expert guidance on leveraging HR analytics to achieve strategic business objectives. Support services are equally important, providing ongoing assistance to ensure the smooth operation of HR analytics solutions and addressing any technical issues that may arise.

Another critical aspect of the component analysis is the role of cloud-based solutions within the software segment. Cloud-based HR an

Context

This data set is as part of Data Science for Good : Kiva Crowdfunding

Content

The data set consists of HDI, GDI, population, Education, Health related data. New data set consists of countries boundary in Geojson format helpful for Geo spatial analysis.

Acknowledgements

The data set is downloaded from United nation development program site. Source
Geojson Sourse

Inspiration

Some interesting analysis which could be done using the dataset are

• Visual representation of different indicators at different countries of the world
• How Kiva loans are distributed based on the economic / social / other indicators.?

Artificial intelligence to help enhance payments was significantly more an option for younger respondents than it was for their older counterparts in 2024. This is according to a survey held in 14 different countries across North America, Europe, and Latin America. The source observed in 2023 already that most respondents - regardless of age - were not yet comfortable with the idea of AI in digital payments. This revealed itself, especially, in the reply from 10 percent of the respondents that they would perhaps use artificial intelligence in two years' time when it had become more established. In 2024, the source did not ask how many people actively used AI during their payments journey. Examples of AI in day-to-day digital payments for consumers The source lists three specific use cases of artificial intelligence in consumer-driven payments: Smart wallets, AI-powered checkouts, and chatbots. One example includes Amazon's Just Walk Out (JWO) in its Amazon Go shops in the United States. The technology uses machine learning to identify what customers picked off the shelves and then bill them automatically. This solution aims at the innovation consumers hope to see most in shopping, especially online: A seamless payments experience. Payment providers had a similar impression, in that they observed a demand among their clients for real-time payments. More so than for lower payment processing costs or cross-border payment solutions. The source adds certain payment solutions might already be using AI in the background, but that consumers are simply not aware of them. AI pros and cons for financial services The finance industry is expected to make heavy use of artificial intelligence's capabilities for years to come. AI's ability to monitor trends and improve data analytics, especially, is popular among financial service providers. Another popular use is that AI can help process large quantities of data. This is especially useful for larger investment-style banks. There are concerns, though. Data issues and growing concerns about keeping talent on board to help out with issues or data sciences ranked as the top AI concerns in 2024.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

This dataset aims to address the critical issue of misinformation, which significantly impacts public perception and decision-making. It contains approximately 10,000 news articles and associated metadata, primarily scraped from the Politifact website. The dataset is designed to help data scientists analyse the spread of fake news and develop models to classify news articles as either false or true, contributing to efforts to combat the propagation of misleading information. It provides a valuable resource for understanding the characteristics of fact-checked news content.

Columns

• News_Headline: This column contains the textual content of the news information that requires analysis.
• Link_Of_News: Provides the URL linking to the original news article.
• Source: Identifies the authors or entities who posted the news information on various social media platforms, such as Facebook, Instagram, or Twitter.
• Stated_On: Indicates the date when the news information was initially posted by the source on social media.
• Date: Specifies the date when the Politifact fact-checking team verified and categorised the news information.
• Label: Contains the classification assigned to each news item. This column includes five distinct labels: True, Mostly-True, Half-True, Barely-True, False, and Pants on Fire. Users can choose to perform multi-class classification or convert these labels for binary classification (e.g., True or False).

Distribution

This dataset comprises approximately 10,000 individual news articles and their associated metadata, structured with six primary attributes. The data is typically provided in a CSV file format. The unique values for key attributes such as News_Headline, Link_Of_News, Stated_On, Date, and Label are consistently around 9,947 to 9,960 records, while the 'Source' column has 1,028 unique values.

Usage

This dataset is ideal for a range of analytical and machine learning applications. It can be used to gain insights into how to halt the spread of misinformation and to determine which approaches offer superior accuracy in combating fake news. Specific use cases include developing and training machine learning models for news classification, performing multi-class classification to distinguish between different degrees of truthfulness, or converting labels for binary classification (Fake vs. Real). It is particularly well-suited for projects involving Natural Language Processing (NLP) and Data-Mining concepts.

Coverage

The dataset's content is global in its relevance, as fake news is a worldwide concern. The information covers a time range from 20 June 2013 to 19 June 2020. The collection of data on different dates includes: * 20 June 2013 - 02 March 2014: 839 records * 02 March 2014 - 13 November 2014: 975 records * 13 November 2014 - 26 July 2015: 857 records * 26 July 2015 - 07 April 2016: 981 records * 07 April 2016 - 19 December 2016: 1,286 records * 19 December 2016 - 31 August 2017: 881 records * 31 August 2017 - 14 May 2018: 873 records * 14 May 2018 - 24 January 2019: 982 records * 24 January 2019 - 07 October 2019: 956 records * 07 October 2019 - 19 June 2020: 1,330 records The dataset includes news information posted by various sources on social media platforms and fact-checked by the Politifact.com team.

License

CC BY-SA

Who Can Use It

This dataset is primarily intended for data scientists who are interested in tackling the problem of misinformation. Users can leverage this data to train their machine learning models to identify and classify fake news, contributing to the broader effort to improve information accuracy. It supports research and development in areas such as natural language processing, data mining, and automated fact-checking.

Dataset Name Suggestions

• Fake-Real News Dataset
• Politifact Fact-Checked News
• Misinformation Detection Corpus
• Social Media News Verification Dataset
• News Authenticity Classifier Data

Attributes

Original Data Source: Fake-Real News

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

This project utilised mixed methods to examine the significance of different sectors of the gig economy within local labour markets, and includes the study of co-operative alternatives to more dominant forms of platform capitalism. Participants included drivers from a cooperative taxi company and cycle couriers employed by digital platforms, all working in Cardiff, South Wales. Interviews explored the participant’s experiences of job quality, patterns and routines in their area of employment. This research was conducted during the COVID-19 pandemic, and the interviews also provide a snapshot of working in this sector during this period. In addition to the interviews, ethnographic analysis of cycle couriers’ daily routines was undertaken by accompanying them by bicycle. These journeys were recorded using GPS devices.

WISERD celebrates its 10th anniversary this year. Over time it has grown into an international research institute that develops the next generation of research leaders. Our research brings together different disciplines (geographers, economists, sociologists, data scientists, political scientists) to address important issues for civil society at national and international levels. Our social science core provides a strong foundation for working with other disciplines including environmental science, engineering and medicine to transform our understanding and approaches to key areas of public concern. Our aim is to provide evidence that informs and changes policy and practice. This Centre will build on all previous WISERD research activities to undertake an ambitious new research programme. Our focus will be on the concept of civic stratification. This is a way of looking at divisions in society by focusing on the rights and obligations and practices of citizens and the role of civil society organisations in addressing inequalities in those rights and obligations. We will examine and analyse instances where people do not have the same rights as others (for example people who are migrants or refugees). We will also look at examples of people and groups working together within civil society to win new rights; this is referred to as civic expansion. Examples might include campaigns for animal rights or concerns about robots and Artificial Intelligence. We will investigate situations where people have the same rights but experience differences in their ability to access those rights; sometimes referred to as civic gain and civic loss (for example some people are better able to access legal services than others). Lastly, we will explore how individuals and groups come together to overcome deficits in their rights and citizenship; sometimes referred to as forms of civil repair. This might include ways in which people are looking at alternative forms of economic organisation, at local sustainability and at using new technologies (platforms and software) to organise and campaign for their rights. Our centre will deliver across four key areas of activity. First our research programme will focus on themes that address the different aspects of civic stratification. We will examine trends in polarization of economic, political and social rights, looking at how campaigns for rights are changing and undertaking case studies of attempts to repair the fabric of civil life. Second, we will extend and deepen our international and civil society research partnerships and networks and by doing so strengthen our foundations for developing further joint research in the future. Third, we will implement an exciting and accessible 'knowledge exchange' programme to enable our research and evidence to reach, involve and influence as many people as possible. Fourth, we will expand the capacity of social science research and nurture future research leaders. All our research projects will be jointly undertaken with key partners including civil society organisations, such as charities, and local communities. The research programme is broad and will include the collection of new data, the exploitation of existing data sources and linking existing sets of data. The data will range from local detailed studies to large cross-national comparisons. We will make the most of our skills and abilities to work with major RCUK research investments. We have an outstanding track record in maximising research impact, in applying a wide range of research methods to real world problems. This exciting and challenging research programme is based on a unique, long standing and supportive relationship between five core universities in Wales and our partnerships with universities and research institutes in the UK and internationally. It addresses priority areas identified by the ESRC and by governments and is informed by our continued close links with civil society organisations.