dataesr/scientific-paragraphs-categorization huggingface dataset:
'In' = (W Self Link +W DispersedLinks +W AcquiredLinks ) As depicted in Figure  5  majority of the news medias in Twitter have shown the largest 'In' compared to their Facebook users. We can deduce from Figure  5  that news medias are more active in Twitter than in Facebook in terms of total interactions performed with others, and that a majority of them have shared self-originated content on Facebook having a limited number of interactions with others in Facebook or in Twitter. This is reasonable, since the largest number of posts were shared by the news medias in Twitter (see Section III). Among all the news medias in our dataset, highest 'In' values, more than 18K, were performed by Reuters and Cnn in Twitter. The AcquiredLinks of these two news medias compared with those of others are negligible.
6% respectively. These findings add substantially to our conclusion that Ap is a strong content consumer, as it has a high weight for its AcquiredLinks. The precise representation of this discussion is also presented in Figure  5  where Ap in Twitter is shown with all the links as incoming links. Apart from that, even though Usatoday has posted considerable amount of tweets (2587), only 6. 3% were allocated for its SelfLink and DispersedLinks, and remainder has assigned for AcquiredLinks. More interestingly, the news media with the highest number of posts in our dataset(10. 6K), Indianexpress, has contained a SelfLink value of 11%, while all its remaining links were AcquiredLinks with an insignificant number of DispersedLinks. This finding is also made visible in Figure  5. There are no significant results for strong content consumers in the news medias on Facebook.
To summarize, we identified top 10 strong content consumers in the news media community in social media as Ap, Chron, Hollywoodreporter, Indianexpress, Timesofindia, Nytimes, Theatlantic, Thedailybeast, Usatoday, and Washingtonpost. The content published by these news medias have the highest number of replicas that were previously posted by the remaining news medias in our dataset.
Therefore, we can consider that, two major attributes that are positively correlated with reader reactions are number of posts and 'In'. Nest we explore whether a regression model can be implemented using different properties mentioned above in order to predict news consumer reactions? We decided to apply regression analysis for the dataset belongs to the identified attributes. Regression analysis is used in this analyses as it can (i) predict a target variable based on a set of values, (ii) screen variables to identify which ones are more important than others to explain the response variable. To identify such a model, we examine three major attributes: number of posts, number of interactions ('In') and number of followers to predict future reader reactions on Twitter news medias as they have shown a positive correlation with the listed attributes.
Given the role of technology and social media during the COVID-19 pandemic, the aim of this paper is to conduct a social network analysis of four COVID-19 conspiracy theories that were spread during the pandemic between March to June 2020. Specifically, the paper examines the 5G, Film Your Hospital, Expose Bill Gates, and the Plandemic conspiracy theories. Identifying disinformation campaigns on social media and studying their tactics and composition is an essential step toward counteracting such campaigns. The current study draws upon data from the Twitter Search API and uses social network analysis to examine patterns of disinformation that may be shared across social networks with sabotaging ramifications. The findings are used to generate the Framework of Disinformation Seeding and Information Diffusion for understanding disinformation and the ideological nature of conspiracy networks that can support and inform future pandemic preparedness and counteracting disinformation.
Data Retrieval Table  1  below provides an overview of datasets built using Twitter data alongside the keywords used to retrieve the data, the time periods examined, the number of unique Twitter users, and the number of tweets within the dataset. NodeXL was used to retrieve data from Twitter. NodeXL can connect directly to Twitter's Search Application Programming Interface (API). This API is further described in section 3. 2. The data collection period for each conspiracy theory falls after March 11, 2020, when the World Health Organisation (WHO) declared COVID-19 a Pandemic  [89]  and our data collection period ended on June 17 2020. We can capture the initial conspiracies being shared during the first few months of the pandemic. The conspiracies listed in Table  1  all had a high frequency of hashtag use, and our keywords were designed with this in mind.
Despite increasing interest for the carbon footprint of higher education institutions, little is known about the carbon footprint associated to research activities. Air travel and attendance to conferences concentrate recent data and debates. Here we develop a hybrid method to estimate the greenhouse gas emissions (GHG) associated to research purchases. To do so, we combine macroeconomic databases, research-centered companies footprints and life-cycle analysis to construct a public database of monetary emission factors (EF) for research purchases. We apply such EFs to estimate the purchases emissions of hundreds of research laboratories in France gathering more than 20000 staff, from all disciplines. We find that purchases dominate laboratory.
Within this boundary we first propose a method to estimate the carbon footprint of all the goods and services purchased in the laboratory. We construct a public listing of monetary emission factors (EFs) associated to 1431 categories of scientific purchases and 61 physical emission factors associated to 8 labware categories using different databases and complementary methods to assess the robustness of our approach. These EFs can be used as is or through the web interface GES 1point5  28  to calculate the GHG emissions of laboratory purchases. We then compare the different emission sources from 167 carbon footprints associated to 108 distinct French laboratories from all disciplines and show that purchases represent 50% of median emissions. Emissions in general and purchases emissions in particular are very heterogeneous between laboratories and research domains.
Results and discussion Environmentally extended input-output (EEIO) methods associate environmental impacts to macroeconomic monetary flows between production and consumption sectors in a given economy or territory. 29  They have proven useful to estimate the carbon footprint of purchases in large organizations. 30  However, they should be used with caution when applied to niche products which are abundant in research laboratories. We therefore used a hybrid approach: for purchase categories most specific to research labs (scientific instruments and consumables), we completed the EEIO method by our meso and micro approaches. In a first step, each of the 1431 NACRES categories identifying goods and services was attributed one or several EFs from each one of three EEIO databases: the two American CEDA  31  and USEEIO  32, 33  databases, and the French ADEME  34  database, the first two pro-5 viding 430 EFs and the last one 38.
This constituted three databases of NACRES monetary EFs, called in the following CEDA, USEEIO and ADEME, respectively. In a second step, the GES1P5 macro database was constructed by averaging, for each NACRES category, the EFs from the three other databases. Fig. 2  and Tab. 1 show the properties of the distribution of EFs associated to the different NACRES categories for the four macro databases.
Construction of the emission factor database Lower EFs are more frequent in the USEEIO database, then comes the CEDA and then the ADEME database with respectively medians of 0. 19, 0. 27 and 0. 40 kg CO 2 e/e. The GES1P5 macro database displays a mean EF that is indeed the average of the means of the other three, with a distribution very similar to the CEDA one although without the very high values (Fig. S2 ). Fig. 2B  shows the correlation between micro/meso EFs and macro ones. For a given category, on average, EF meso are of the same order of magnitude than EF macro , but globally 2-fold lower. The difference is even more important for companies producing chemicals and animals for research, whose sector of activity was not represented in the EEIO databases. For categories corresponding to single-use plastics, with a single exception, EF micro were close to EF macro (less than a 2-fold difference).
However, EF micro were much lower than EF macro for chemicals, laboratory glassware and especially gas cylinders. This most probably reflects the small packaging of gases for laboratories compared to industries, resulting in much higher prices per kg of gas. With some exceptions (see methods), these micro and meso EFs were then incorporated into the GES1P5 macro database to constitute the GES1P5 final database. 9 % of EFs were changed (7% with meso EFs and 2% with micro EFs), which accounted for a mean of 12% of lab purchases (in e), with high disparity from one lab to another (from 0 to 53% of all purchases). Despite this small number of changes (Fig. S3 ), the use of the GES1P5 final database resulted in a 17% decrease of the average carbon intensities within all submissions compared with emissions calculated with the GES1P5 macro database (Tab.
The distribution of carbon intensities in the laboratory research economy To gather financial purchase data from French laboratories to estimate their purchase emission footprint we relied on the GES 1point5 web application. 37, 38  We created a purchases module that allowed volunteer laboratories to upload their expenses associated to NACRES categories. Interestingly, GES 1point5 allows laboratories to estimate other emission sources such as scope 1 (owned vehicles, cooling gases), scope 2 (electricity and heating) and scope  which yields Ī = 0. 28 kg CO2e/e, and the later displaying three significant peaks at 0. 4, 0. 7 and 1. 6 kg CO2e/e, associated with a higher mean carbon intensity ( Ī = 0. 43 kg CO2e/e). These results highlight the interest of using different NACRES-FE databases to estimate purchases emissions as we can evaluate, at least partially, the incertitudes of the results.
Discussion and conlusion Purchases emissions are almost systematically neglected  15, 18, 25  when calculating the carbon footprint of higher education institutions, except in few seminal studies. 16, 17, 48   kg CO2e/e 2019). Interestingly, however, Larsen et al did not find significant differences in the carbon intensities between research domains (Tab. S7), in particular with HSS, in contrast to the current work. We thus hypothesize that the distinction between research and teaching activities is important because the heterogeneity of purchases emissions found in our data suggest that mitigation strategies will need to be adapted to each laboratory. However, the results obtained for HSS laboratories need to be considered with caution because only 10 footprints from 8 distinct laboratories were available in the GES 1point5 laboratory emission database.
In addition, available data of purchases footprints in universities rely on either non-public EF  16  or general-economy EEIO EF databases such as EXIOBASE,  49  thus not offering a general method for research laboratories. Our results indicate that the NACRES-EF database allows to calculate laboratory purchases emissions with a 20% precision, although further work needs to be done to refine emissions associated to laboratory instruments. In addition, previous works do not show the great heterogeneity of emissions among research laboratories, both between different emission sources and within purchases alone. Importantly, our data suggest that laboratory budget is the main driver of purchases emissions, in a similar way as income determines the carbon footprint of households. 47  The strong linearity observed between purchases emissions and budget in Fig.
The difficulty of these mitigation strategies is that they require precise determination of the carbon footprints of one type of product from different manufacturers (or of different models of the same supplier). Few data exist for convenience goods that are part of lab purchases such as computers or printer toners. But for most laboratory equipment an additional difficulty is that they are made up of components manufactured in very small series, and LCA databases contain only data on mass-produced products that have high production costs relative to overhead. In consequence, precise process-based carbon footprints are so far inexistent for laboratory equipments or specific consumables, limiting the possibility to evaluate mitigation strategies based on supplier specific processes for labs.
Classification of goods and approach Services and goods purchased in a laboratory are classified according to the French NACRES nomenclature, used in the accountability of the majority of research institutions in France. 50  Each type of good or service is identified by a code composed of two letters and two numbers. The first letter provides the general category of the purchase, the second letter designs the domain, the first number the sub-domain and the last number the type. There are 1431 defined types split into 24 large categories (Tab. S1). In this work, each NACRES code is given an EF covering GHG emissions associated to all stages of its production (cradle-to-gate perimeter). Each NACRES code is given an EF using the macro method (see below), and certain types of goods were also attributed a meso or a micro EF (see below), that were used to construct a final hybrid database.
The meso approach To consolidate macro NACRES-FE database, we used a supplier-based approach, using GHG emissions and financial data of companies whose main segments of activity are to manufacture products of provide services to the research, analytical and health markets. We gathered 19 emission data from the Carbon Disclosure Project (CDP) 36 or from internal reports, and financial data from the annual reports of companies. A limitation of this approach is that, in November 2022, reasonably complete and reliable GHG emissions (including upstream scope 3) were available only for few large companies, listed in Tabs. S4 and S2. The emission categories used encompass all upstream activities involved in the production of goods or services, similarly to the cradle-to-gate perimeter of EEIO databases, but also downstream transportation as most shipment costs are included in prices for laboratory products.
The micro approach For laboratory mono-material products that represented important purchases from a panel of laboratories, we performed single impact cradle-to-gate LCA. This concerned 60 products distributed in 28 NACRES categories, such as all gases and some plasticware and glassware (Table  S3 ). LCA included raw material manufacturing, item manufacturing and transport to the local supplier. Emission factors of each step were obtained from the Ecoinvent database version 3. 8. The product monetary EFs are then computed by dividing the product carbon footprint by its price. More information about the Ecoinvent EFs and prices used is provided in the SI. The micro monetary EF are then computed as the mean of the monetary EFs of all products belonging to the same NACRES category (1 to 6 products by NACRES category).
2 and S4-S2). Similarly to corporate emissions in other industrial sectors, companies' EF meso most heavily depend on the emissions related to purchased goods and services, that represent 41 to 80% of their total emissions (Tab. S2). These 14 EF meso were attributed to 102 NACRES categories (Tab. S1), with a median of 0. 2 kg CO 2 e/e, which is close to the median EF of the USEEIO database. Micro EFs were computed using cradle- 6.
Median laboratory emissions are dominated by purchases with 56% of the share and a median of 2. 5 t CO 2 e/pers. Travels, heating and commuting to work are far weaker with 12-13% and a median of 0. 5-0. 6 t CO 2 e/pers. Electricity (6%, 0. 3 t CO 2 e/pers. ) comes next, with electricity being particularly low in our dataset due to the low carbon emissions of the French electricity system (60 g CO 2 e/kWh 39 ). Emissions associated to lab-owned vehicles and cooling systems are negligible on average. Laboratory emissions are however very heterogeneous and the distributions of per capita emissions per source are wide, as shown in Fig. 4Bfor purchases, with quartiles (1. 5, 3. 8) t CO 2 e/pers and extreme values of 0. 09 -29 t CO 2 e/pers. A 10 B (t CO Lab emissions per capita 2 e/pers. ) 2 4 6 8 Number of submissions 10 20 30 40 50 p u r c h a s e s t r a v e l s ( w.
Note that the y axis is truncated (see Fig. S8 and panel B ). 203 distinct laboratories. B) Distribution of purchases emissions per capita. Purchases emissions calculated with the GES1p5 final NACRES-FE database. n = 167 GHG submissions, 108 distinct laboratories, years 2018-2022. We now have a robust method to estimate laboratory purchases emissions and in the following we will use solely GES1P5 final FEs to calculate them. An important question is the relative importance of each emission source as this conditions where the efforts of reduction need to be concentrated. Fig. 4A and Tab. S6 display the distribution of emissions for the eight types of emission sources in the GES 1point5 lab emission database. Importantly, this perimeter includes all upstream and in-house laboratory emissions except those due to heavy investments (such as construction and large scientific infrastructures) and staff meals.
This database contains more than 300 GHG emission inventories from more than 200 laboratories employing more that 40000 staff, except for purchases for which more than 160 inventories from more than 100 different laboratories and employing more than 23000 staff were available 10 (Tab. S5). However, to compare these data internationally we need to correct by the carbon intensity of the electricity mix used by the laboratory. The average carbon intensity of the world electricity mix is 7. 9-fold higher (475 g CO 2 e/kWh 40 ), while the highest electricity intensities can be up to 11. 7-fold higher (700 g CO 2 e/kWh 41 ). In these cases the median of electricity emissions either equals purchases emissions per capita (2. 4 t CO 2 e/pers) or becomes preponderant (3. 5 t CO 2 e/pers).
To associate EFs with each NACRES code while having an uncertainty estimate, we used three different EEIO databases of monetary emission factors: the French Ratios Monétaires database published by the Agence De l'Environnement et de la Maîtrise de l'Energie (ADEME)in 2016; the U. S. CEDA 31 database provided by Vitalmetrics (version 4. 8 released in 2014);and the U. S. USEEIO 32, 33  compiled by the US Environmental protection agency (EPA, published in 2018). Both American databases contain approximately the same 430 categories, while the French ADEME database provides monetary factors for only 38 categories. 34  As the NACRES types cannot always be associated to a single category of the EEIO databases, we associated up to 2 ADEME EFs and up to 6 CEDA/USEEIO EFs to each NACRES category (Tab.
S1). We proceeded heuristically by attempting to assign all the EEIO cate- gories of commodities that have similarities (in terms of composition and/or manufacturing process) with the products comprised in each NACRES type. To provide a single EF for each NACRES we averaged the allocated EFs, first within each database, and then between databases. For each EF we calculated uncertainties using two methods. First, attribution uncertainties were computed as the standard deviation of the averaging within databases and across databases. Second, a uniform relative uncertainty of 80% was attributed to all EF. For calculating the footprint of a single laboratory we recommend to use the 80% un- certainty. However, for the results displayed in this work, EF uncertainties did not play any role.
Falk and Weiskopf  [29]  consider the problem of rendering 3D vector fields using a texturebased representation. The computational cost of this problem is naturally cubic with the output image resolution. The proposed algorithm uses an image-oriented sampling approach and only computes the parts of the dataset that are represented on the final image. Consequently, this algorithm is mostly independent of the dataset size and predominantly governed by the output resolution and the number of samples. Performance measurements of a GPU implementation show that the rendering times are almost constant as the dataset size increases and scale linearly with the output image and the number of samples.
This scenario seems to be the most typical among visualization papers, at least it is the most commonly found in our survey (see  Section 5). Example: Structure-aware Fisheye Views for Efficient Large Graph Exploration  [32]  Expression: sample points (example datasets) Meaning: better performance f number of edges number of nodes computing time node overlap Wang et al. [32]  tackle the visual clutter problem of large graph drawings using a focus+context interactive view. The problem size is the graph size. The main effort variables dependent on problem size are the number of overlapped node pairs which evaluates clutter, and the computing time which evaluates interactivity. The quality of the drawing as evaluated by two metrics (edge orientation offset and shape preservation) and task completion rate are also measured but not in relation to the graph size, i.
Here, the problem size is made of the number of images and the number of objects of interest, which can both be large and users of the system are technically the resources available to complete the annotation task for a dataset. The effort variable is the number of human queries, which directly correlates with the financial cost of completing the annotation task for a dataset. Similarly, systems using non-conventional display sizes (mobile displays or tiled displays) sometimes take interest in scalability with the number of composing screens/projectors or the overall display size. The dependent variables describing effort can be related to rendering characteristics like pixel error and compute time (in the form of frame rate) or user task completion time for testing human perceptual or cognitive limits in such configuration (e.
Literature Sample The selection of papers included in our study is based on the vispubdata dataset  [44] , containing 3394 papers from the IEEE Visualization (VIS) conferences from 1990 to 2020 (conference and journal articles as well as panel or poster papers). This initial set of papers was filtered automatically to select papers including the prefix scalabat least once in their abstract, title, or keywords in order to capture papers for which scalability was a main concern. This process generated a set of 157 relevant papers. After removing 30 papers because they mentioned scalability but did not discuss it, we were left with a corpus of 127 papers: 35 conference, 87 journal, and 5 others articles, among which 6 were of length 5 pages or fewer (full list available at https://osf. io/xrvu7/).
In these papers, the scalability claim is often demonstrated by showing few pictures of the final tiled display (i. e. , Few Samples). The typical paper in the VISU scenario introduced a new visualization technique, or a novel visual analytics tool that aims at solving specific analysis tasks for targeted domain users. As shown by the average coding for VISU papers in Figure  8 , the typical coded input and output are Data Size and Clutter/Readability, respectively. The typical scalability claim is demonstrated by a Case Study combined with Didactic/Argumentative discussion based on few and sometimes only one dataset and has the meaning of Extended Domain. About half of these papers address the scalability problem by employing summarization, aggregation, or sampling strategies on the data layer, the presentation layer, or both and often in combination with interaction techniques (27 papers).