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Version consistent with revision at the end of round 2

Version consistent with revision at the end of round 2

Your Maps Your Way (YMYW) Background YMYW is an interactive Google Earth Engine application. It is designed to allow users with only basic knowledge of satellite imagery and supervised learning techniques to create detailed habitat/land cover maps anywhere in the world with a thematic structure chosen by the user (Your Maps, Your Way). The current version of YMYW allows the classification of optical Sentinel-2 and Landsat imagery, but advanced users will find it straightforward to adapt the code to other available image collections. The development of YMYW began during the COVID-19 pandemic (2020-2021). It allowed us to engage and collaborate with local ecologists in South America (Argentina, Brazil, and Chile) to create customised habitat maps for pollination modelling and analysis. Throughout the development of YMYW, our goal was to provide an intuitive and user-friendly interface to facilitate collaboration, support local experts and promote knowledge exchange between groups. When using YMYW, users can harness the computing power of Google's Earth Engine and integrate essential local knowledge to create high-quality land cover maps. How to cite this material Morton, R.D., & Schmucki, R. (2023). YMYW - Your Maps Your Way with Google Earth Engine (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.7624622 In a Nutshell - Before you start with YMYW, you must Sign Up and get access to Google's Earth Engine (GEE). - In GEE, you can paste the JavaScript of YMYW into a new file in the GEE editor. Your Maps Your Way in 10 steps Draw the Area of Interest (AOI) and press the "Run" button. Select the collection of satellite images to be classified (Sentinel-2 or Landsat). Define the year and the time periods (dates and months) that capture the land cover changes and phenology in the region of interest. Press the "Show composites" button to visualise the composite images; adjust cloud tolerance and time period as needed. Digitise training objects for specific land cover classes, using composite layers, the Google satellite layer, or other sources of information. To improve cross-validation, aim to draw many small training objects distributed across the AOI. Press the "Classify" button to run a random forest classifier to classify each pixel of the Area of Interest (AOI). Press the "Validate classification" button to cross-Validate the classification and evaluate its accuracy. Digitise additional training objects for misclassified land covers and areas. Repeat steps 6 to 8 until a satisfactory classification is achieved (go back to step 3 if necessary). Press the "Export classification and more" button to export the results: the land cover map, the training dataset, and the validation dataset. Export appears under the "Task" tab. Press "RUN" and fill in the export details to initiate the specific export. See the sequence of screenshots. Classification with YMYW is a heuristic, iterative process. At each iteration, training objects can be added and/or removed until the classification converges to an optimal result. The training objects are digitised using online image collections as base maps and the user's local knowledge of the area of interest. YMYW uses a supervised machine learning algorithm (Random Forests) that "learns and improves" mainly when its supervisor identifies where it makes mistakes (misclassification). In most cases, and with some practice, YMYW will produce a high-quality land cover/habitat map in a few iterations. YMYW allows the user to select and define the thematic structure of the classification used for land cover mapping. While this gives freedom to the user, we would also like to emphasise the importance of adopting a consistent and systematic approach when defining land cover classes (e.g., FAO Land Cover Classification System (LCCS). Examples To illustrate the use of YMYW, we provide two examples of digitised training data to produce a land cover map for 1) an area of interest near Zurich (Switzerland) and 2) an area of interest around the Leven Estuary near Ulverston (UK). Example 1. Zurich - This example (link above) contains the geometries (multipolygons) and parameters used to classify the Sentinel-2 images and create a land cover map for the region (AOI) around Zurich (Switzerland) using 10 land cover classes. Example 2. Ulverston & Leven Estuary - This example (link above) contains the geometries (multipolygons) and parameters used to classify the Sentinel-2 images and create a land cover map for the region (AOI) around the Leven Estuary near Ulverston (UK) using 13 land cover classes. Funding The development of the YMYW tool was funded by the UKRI Natural Environment Research Council (NERC) through the Latin American Biodiversity Programme [Grant No. NE /S011870/2]. The project SURPASS2 was concerned with the safeguarding of pollinators and pollination services in Latin America (Argentina, Brazil and Chile). In this context, we developed YMYW to enable local ecologists to produce bespoke habitat maps for pollination models and analyses. YMWY receives ongoing support from the SABIOMA project and UKRI’s National Capability funding. Credits Daniel Morton (DM) developed the first iteration of YMWY and Reto Schmucki (RS) implemented revisions and enhancements. DM and RS will continue to develop and improve and extend the functionality of the YMYW distribution. YMYW incorporates the collective ideas and knowledge of past and present members of the UKCEH Land Cover Team, in particular Daniel Morton, Clare Rowland, Chris Marston and Luis Carassco. To cite YMYW, use: Morton, R. D., & Schmucki, R. (2023). YMYW - Your Maps Your Way with Google Earth Engine (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.7624622

An agent-based simulation was created using simulation software AnyLogic Version 8.8.1 and grounded in social impact theory to investigate possible impacts of in-person school closures due to COVID-19 on the prevalence of non medical prescription opioid use among youth.

This STATA dofile provides the syntax for the main analysis conducted in the article "Deaths during the first year of the COVID‑19 pandemic: insights from regional patterns in Germany and Poland" by Myck, Oczkowska, Garten, Krol, Brandt in BMC Public Health (DOI: 10.1186/s12889-022-14909-9). More details in the text file 'readme.txt'.

3D-MolGNNRL, couples reinforcement learning (RL) to a deep generative model based on 3D-Scaffold to generate target candidates specific to a protein pocket building up atom by atom from the core scaffold. 3D-MolGNNRL provides an efficient way to optimize key features within a protein pocket using a parallel graph neural network model. The agent learns to build molecules in 3D space while optimizing the binding affinity, potency, and synthetic accessibility of the candidates generated for the SARS-CoV-2 Main protease

VADR is a suite of tools for classifying and analyzing sequences homologous to a set of reference models of viral genomes or gene families. It has been mainly tested for analysis of Norovirus, Dengue, and SARS-CoV-2 virus sequences in preparation for submission to the GenBank database. This research was supported by the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health.

The Miss America 2023 Competition will crown Miss America for 2023 on Thursday night. The event will take place live from the Mohegan Sun Arena in Connecticut— and Parade has a look at all 51 of the Miss America candidates who are vying for more than $500,000 in scholarship money. ♠√Click Here To Miss America 2023 Live Watch the Miss America competition, which, in 2023, is only available to stream at pageantslive.com and on the Pageants Live app on various streaming platforms on Thursday, Dec. 15 at 8p.m. ET. Miss America 2022 Emma Broyles of Alaska will crown her successor at the end of the event. Who will host Miss America 2023? The final night of competition will be hosted by Laura Rutledge, host of ESPN’s NFL Live and SEC Network’s SEC Nation, Miss Florida 2012 and top 15 at Miss America 2013. Who are the judges for Miss America 2023? The judges include TV personality Aparna Shewakramani; former professional NBA player Trevor Booker; Trish Regan, an award-winning journalist and host of ‘The Trish Regan Show’ podcast and former Miss New Hampshire; Home Improvement actress Debbe Dunning; and Mirai Nagasu, American figure skater and two-time Winter Olympic athlete. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd 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:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa 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 621,235 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:gww 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.sff 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.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

The Miss America 2023 Competition will crown Miss America for 2023 on Thursday night. The event will take place live from the Mohegan Sun Arena in Connecticut— and Parade has a look at all 51 of the Miss America candidates who are vying for more than $500,000 in scholarship money. ♠√Click Here To Miss America 2023 Live Watch the Miss America competition, which, in 2023, is only available to stream at pageantslive.com and on the Pageants Live app on various streaming platforms on Thursday, Dec. 15 at 8p.m. ET. Miss America 2022 Emma Broyles of Alaska will crown her successor at the end of the event. Who will host Miss America 2023? The final night of competition will be hosted by Laura Rutledge, host of ESPN’s NFL Live and SEC Network’s SEC Nation, Miss Florida 2012 and top 15 at Miss America 2013. Who are the judges for Miss America 2023? The judges include TV personality Aparna Shewakramani; former professional NBA player Trevor Booker; Trish Regan, an award-winning journalist and host of ‘The Trish Regan Show’ podcast and former Miss New Hampshire; Home Improvement actress Debbe Dunning; and Mirai Nagasu, American figure skater and two-time Winter Olympic athlete. 2021-09-09: Version 6.0.0 was created. Now includes data for the North Sea Link (NSL) interconnector from Great Britain to Norway (https://www.northsealink.com). The previous version (5.0.4) should not be used - as there was an error with interconnector data having a static value over the summer 2021.dd Version 144 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.gd 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:gs Τhe CORD-19 dataset released by the team of Semantic Scholar1 and Τhe curated data provided by the LitCovid hub2.sa 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 621,235 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:gww 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.sff 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.ggwsw Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.dfgg 2021-05-05: Version 5.0.0 was created. Datetimes now in ISO 8601 format (with capital letter 'T' between the date and time) rather than previously with a space (to RFC 3339 format) and with an offset to identify both UTC and localtime. MW values now all saved as integers rather than floats. Elexon data as always from www.elexonportal.co.uk/fuelhh, National Grid data from https://data.nationalgrideso.com/demand/historic-demand-data Raw data now added again for comparison of pre and post cleaning - to allow for training of additional cleaning methods. If using Microsoft Excel, the T between the date and time can be removed using the =SUBSTITUTE() command - and substitute "T" for a space " "gww 2021-03-02: Version 4.0.0 was created. Due to a new interconnecter (IFA2 - https://en.wikipedia.org/wiki/IFA-2) being commissioned in Q1 2021, there is an additional column with data from National Grid - this is called 'POWER_NGEM_IFA2_FLOW_MW' in the espeni dataset. In addition, National Grid has dropped the column name 'FRENCH_FLOW' that used to provide the value for the column 'POWER_NGEM_FRENCH_FLOW_MW' in previous espeni versions. However, this has been changed to 'IFA_FLOW' in National Grid's original data, which is now called 'POWER_NGEM_IFA_FLOW_MW' in the espeni dataset. Lastly, the IO14 columns have all been dropped by National Grid - and potentially unlikely to appear again in future.fgwf 2020-12-02: Version 3.0.0 was created. There was a problem with earlier versions local time format - where the +01:00 value was not carried through into the data properly. Now addressed - therefore - local time now has the format e.g. 2020-03-31 20:00:00+01:00 when in British Summer Time.stuu 2020-10-03: Version 2.0.0 was created as it looks like National Grid has had a significant change to the methodology underpinning the embedded wind calculations. The wind profile seems similar to previous values, but with an increasing value in comparison to the value published in earlier the greater the embedded value is. The 'new' values are from https://data.nationalgrideso.com/demand/daily-demand-update from 2013.fgew Previously: raw and cleaned datasets for Great Britain's publicly available electrical data from Elexon (www.elexonportal.co.uk) and National Grid (https://demandforecast.nationalgrid.com/efs_demand_forecast/faces/DataExplorer). Updated versions with more recent data will be uploaded with a differing version number and doidf All data is released in accordance with Elexon's disclaimer and reservation of rights.derrr This disclaimer is also felt to cover the data from National Grid, and the parsed data from the Energy Informatics Group at the University of Birmingham.

UWW World Wrestling Freestyle World Cup 2022 Live It's a great day for a premier international wrestling event and a great day for live blogging, and so we will do both here in the Xtreme Arena in Coralville, Iowa as we follow the men's and women's freestyle wrestling World Cup in real-time. WATCH LIVE MATCHES ONLINE HERE The American men and women have two duals each today on Day 1. Tomorrow is third and first place matches for both men's and women's freestyle. The duals today determine what, if any, placement Team USA will wrestle for. The men's quad takes on Mongolia at 11:00 AM eastern, and Georgia later at 7:00 PM eastern. The women wrestle China at 12:30 PM eastern and then wrap up the day with the All-World team at 8:30 PM. The full schedule can be found here. The list of rosters is here. All the archives can be found here (search for a wrestler or team to pull up the specific match you're looking for). Box scores will be at the top of the article, followed by the live blog. VOLUNTEERS The Race Organizing Committee is seeking volunteers to fill various positions on December 09 & 10, 2022. More information here. Τhe curated data provided by the LitCovid hub2.gdgdgdffdh 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:dfhgf 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/zdhPaperRanking) library4.dgfdhfd The washers dataset features 70 defective parts. The gears and screws datasets feature 35 defective, 35 intact and several hundred unannotated parts. Some defects, such as notches and holes, are visible in most images (illuminations) with intensity and texture variations among them, while others, such as scratches, are only visible in a few.fghgj We split the datasets into train and test sets. The train sets contain 32 samples, and the test set 38 samples. Each sample comprises 108 images (each captured under a different illumination angle), an automatically extracted foreground segmentation mask, and a hand-labeled defect segmentation mask.fghgfj This dataset is challenging mainly because: each raw sample consists of 108 gray-scale images of resolution 512×512 and therefore takes 27MB of space; the metallic surfaces produce many specular reflections that sometimes saturate the camera sensors; the annotations are not very precise because the exact extent of defect contours is always subjective; the defects are very sparse also in the spatial dimensions: they cover only about 0.2% of the total image area in gears, 0.8% in screws, and 1.4% in washers; this creates an unbalanced dataset with a highly skewed class representation. gfhj The dataset is organized as follows: each sample resides in the Test, Train, or Unannotated directory; each sample has its own directory which contains the individual images, the foreground, and defect segmentation masks; each image is stored in 8-bit greyscale png format and has a resolution of 512 x 512 pixels; Image file names are formatted using three string fields separated with the underscore character: prefix_sampleNr_illuminationNr.png, where the prefix is e.g. washer, the sampleNr might be a three-digit number 001, and the illuminationNr is formed of 3 digits, first corresponding to the elevation index (1 - highest angle, 9 - lowest angle), and the additional two corresponding to the azimuth index (01-12). Each dataset contains light_vectors.csv, which contains the illumination angles (in lexicographic order of the illuminationNr), and light_intensities.csv that contains the numbers corresponding to the light intensity on the scale from 0 to 127. Please, be aware, that the azimuth angles were not calibrated and might be a few degrees misaligned.fdhfgj 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:sdgfdfhfggh 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/diwifss/PaperRanking) library4.sddfghfggd 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.sddggf safs 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.asdfujsgdg Colour Science for Python Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. sdg It is freely available under the New BSD License terms.uiuol Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States Draft Release Notes The draft release notes of the develop branch are available at this url.uiu Sponsors We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.uiu Features Colour features a rich dataset and collection of objects, please see the features in the documentation for more information.iu User Guid Installation Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:oluip $ pip install --user colour-science The detailed installation procedure for the secondary dependencies is described in the Installation Guide. Colour is also available for Anaconda from Continuum Analytics via conda-forge:oiup $ conda install -c conda-forge colour-science Tutorial The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.oui How-To The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases. Contributing If you would like to contribute to Colour, please refer to the following Contributing guide.oi Colour by Colour Developers Copyright 2013 Colour Developers – colour-developers@colour-science.org This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause https://github.com/colour-science/colourolip sf 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.sfbsdf 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.ftgujyol 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, PdfMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).yjytik Rich offline experience, periodic background sync, push notification functionality, network requests control, improved performance via requests caching are only a few of the functionalities provided by the Service Worker (SW ) API. This new technology, supported by all major browsers, can significantly improve users’ experience by providing the publisher with the technical foundations that would normally require a native application. Albeit the capabilities of this new technique and its important role in the ecosystem of Progressive Web Apps (PWAs), it is still unclear what is their actual purpose on the web, and how publishers leverage the provided functionality in their web applications. In this study, we shed light in the real world deployment of SWs, by conducting the first large scale analysis of the prevalence of SWs in the wild.huiuop We see that SWs are becoming more and more popular, with the adoption increased by 26% only within the last 5 months. Surprisingly, besides their fruitful capabilities, we see that SWs are being mostly used for In-Page Push Advertising, in 65.08% of the SWs that connect with 3rd parties. We highlight that this is a relatively new way for advertisers to bypass ad-blockers and render ads on the user’s displays natively.ik