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NFL · Zenodo (CERN European Organization for Nuclear Research) · 2022
New England Patriots vs Minnesota Vikings FREE LIVE, also known as NFL Thanksgiving FOOTBALL 2022, is a professional football tournament in World for men. There are overall 32 teams that typically compete in a period between November and December \tCLICK HERE TO WATCH LIVE FREE New England Patriots vs Minnesota Vikings: \tWhere: U.S. Bank Stadium in Minneapolis, Minnesota \tWhen: Thursday, November 24 \tStart Time: 8:20 p.m. ET NFL Thanksgiving Football Games Live Online, kick-off Time ,TV Channel From Anywhere This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on: Currently, Switzerland rank 4th, while Cameroon hold 2nd position. Looking to compare the best-rated player on both teams? Sofascore's rating system assigns each player a specific rating based on numerous data factors. On Sofascore livescore you can find all previous Switzerland vs Cameroon results sorted by their H2H matches. Sofascore also provides the best way to follow the live score of this game with various sports features. Therefore, you can: Through the theory collected, it can be understood how the construction of neo-constitutionalism affects different areas and generates a strong current with which various theories arise that seek to reach the same answer that is nothing more than the political order being governed by the clarity and transparency of the various bodies and above all people who are responsible for carrying out their raison d'être. This research seeks to describe how neoconstitutionalism goes through different stages in which it is involved and the changes it has undergone throughout its current understanding, which is why it is seen with a critical look from a literary analysis. Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2. These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset. Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article. Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).