dataesr/scientific-paragraphs-categorization huggingface dataset:
III. DATA COLLECTION CAMPAIGN A. CRAWLER ARCHITECTURE In order to collect the Instagram public content, we develop a crawler that is able to handle various tasks simultaneously. This crawler connects to Instagram via multiple channels, downloads public data content concurrently, performs some NLP based pre-processing steps, and finally stores them in a NoSQL format database. In Instagram, a reaction to a post can be active (comment) or passive (like). The crawler relies on the official Instagram APIs described in  [31]. To get the public content that is tagged with a specific hashtag or keyword, we use the Instagram Hashtag Engine which is available in  [32]. This API returns public posts that have been tagged with particular hashtags. Our crawler runs on several virtual machines in parallel 24/7.
Note, that we do not manually filter any posts and therefore we gather all posts containing the hashtags, regardless of the specific topics discussed within. Figure  1  shows the complete architecture design of our crawler, which contains four different major parts to handle data crawling: (i) API Connection Layer, (ii) Proxy Layer, (iii) Main Body, and (iv) Database Layer. The process of receiving data is as follows: 1) The API Connection Layer (Block 1 in Figure  1 ) connects to the official Instagram platform  [31] , which is currently using the Graph API. The crawler is registered as an application to be able to perform user authentication  [33]. Note, there are certain rate limitations for requesting information per hour  [31]. 2) Between the Connection Layer and the Main Module, the Proxy Layer (Block 2 in Figure  1 ) is responsible for handling multiple proxy IP addresses and creating multiple connection layers.
This helps us to receive data at a faster rate from various IP addresses. Thus, these layers are working concurrently. 3) The main body of the crawler (Block 3 in Figure  1 ) contains several inner modules such as Post, Reaction, Profile, Social Connection, and Story or Live modules. These are responsible for getting parts that are associated with their names. For example, the Post module is programmed to get Instagram posts and metadata. These modules are directly connected to a scheduler that handles time management. For example, checking daily stories, updating reactions, looking for new posts, checking highlights, and revising new social connections. Last, the Pre-Processing layer is used to perform some basic pre-processing steps such as text cleaning, data management, language extraction, etc.
C. CHALLENGES AND LIMITATIONS Note that as it is infeasible to collect all reactions. Hence, we define a limitation of 500 comments and 500 likes per post. We monitor reactions for up to two weeks to reach this limitation. In line with Instagram's Terms, Conditions, and Policies  [42]  as well as user privacy, we only gather publicly available data that is obtainable from Instagram. We also only rely on Instagram Posts and Reactions. We do not collect other data types such as Stories or Highlights. D. DATA SUMMARY Figure  2. a presents the death rate in different locations as reported by the World Health Organization (WHO). We compare that with the trends of total 'posts', 'comments', and 'likes' in Figure  2. b. As reported by the WHO, the COVID-19 outbreak began several months before January 2020 in Wuhan (China), but at the time, it was not considered a global crisis yet.
ACCESS TO DATASET This dataset is accessible through: https://github. com/ kooshazarei/COVID-19-InstaPostIDs. We publish our dataset in agreement with Instagram's Terms & Conditions  [42]. Thus, as it is not permissible to release the post content and reactions, we share the post IDs (known as shortcodes). Researchers can then use tools such as Instaloader  [43]  to dehydrate the dataset. For any further question, please contact Koosha Zarei (koosha. zarei@telecom-sudparis. eu).
1) OVERVIEW OF PUBLISHER CATEGORIES Overall, we identify approximately 13. 3K unique publishers. We observe a range of account characteristics. For example, some accounts have a high number of followers, and some represent well-known figures such as celebrities or brands. We categorize publishers into the following groups, as summarized in Table  6 : a: NEWS AGENCIES To identify News agencies, we make a list of English speaking agencies on Instagram using two sources  [46] ,  [47]. Then, we filter and verify more than twenty News media accounts in our dataset. While all these accounts are already verified and categorized as 'media/news companies' by Instagram, they usually have millions of followers. We list all existing News agencies in Table  5. We find that 12. 2% of posts, 0. 7% of unique publishers, and 26% of total reactions belong to News agencies.
b: CELEBRITIES We also witness the existence of posts from popular singers, actors, artists, sports players, and other figures. We compile a list of popular celebrities using  [48] ,  [49]  and then search for them in our dataset. We find that these celebrity accounts tend to be verified public profiles, usually with millions of followers (avg. 80M) yet few followees (avg. 230). Some of the top figures that we see are '@ladygaga', '@arianagrande', '@jlo', '@oprah', '@leonardodicaprio', '@christiano', '@leomessi', '@serenawilliams', '@davidbeckham', '@eltonjohn', '@jenniferaniston', '@theellenshow', '@kimkardashian', '@beyonce', etc. The number of celebrity accounts is not as large as other groups, and they usually publish more Instagram stories or live broadcasts rather than posts. However, they obtain a large number of reactions, especially comments, which make them a valuable source (see Figure  5 ).
4K retweets. The Business category also exhibits unique trends. Due to the national lockdowns (largely introduced in April 2020), many businesses released information via Instagram and shifted activities online. Thus, we see noticeable growth in posts during this period. We also find that Influencers, ranging from 'nano' to 'mega'  [40] , increase their number of posts during this period. Similar trends can be seen amongst Celebrity accounts. Note that most in our dataset are American English-speaking figures. Therefore, there is almost no content until the start of the pandemic in Europe (March 2020). The authors of this study  [61]  also reported the same trends in Twitter and Instagram posts associated with COVID-19 content.
A. DISTRIBUTION A considerable part of the content (62% of posts) is tagged with '#coronavirus'. So, we consider this hashtag as the main hashtag. The '#coronavirus' tag is used by nearly 11K unique accounts and receives more than 7M reactions (comments and likes). We plot the hashtag graph of the most used keywords in this dataset in Figure  6. Note, the process of generating graphs is presented in Section III-B. In order to observe the usage behaviour of hashtags in posts, we randomly select 5K posts (20% of data) that contain all categories. We manually check captions, images, hashtags, and publisher profiles. Among main hashtags (Table  2 ), we witness some important hashtags that are connected in numerous posts: (1) We observe many geographical hashtags associated with '#quarantine, such as '#spain', '#italy', '#usa', '#china', '#colombia', '#nyc', '#trip', and '#travel' in 25.
The '#Coronavirus' hashtag is the most used, as we crawl our dataset based on this keyword (14K). The second most used one is the '#covid_19' (11. 2K) which is a more academic naming version of this virus. '#lockdown' (1. 4K) and '#pandemic' (1K) hashtags follow nearly the same pattern from the very beginning days. Both tend to be tagged with posts that are talking about situations around lockdowns, the economical consequences, inviting people to work and study from home, and how to slow down the contamination rate. '#facemask' is another important hashtag that is used to encourage people to wear face masks in 9% of posts. This trend begins to grow from April 2020 when the first outbreak started in Europe and then in the USA. We can contrast these hashtags with the death and case rate. We note that these peak on two dates, in the middle of February 2020 (China, Iran, and Italy) and in April 2020 (Europe, USA, UK, Middle east).
VII. CONCLUSION & FUTURE WORK In this study, we have targeted the first wave of the COVID-19 pandemic that occurred between 5 January 2020 and 30 May 2020. We present a multilingual Instagram dataset containing 25. 7K posts, 829K comments, and 3. 2M likes. We summarize our key findings as follows: • The majority of bots in this study publish off-topic content (with regards to COVID-19). They exploit the COVID-19 hashtags to spread their content (4. 9% of posts). That is why we see many isolated islands in the graph of hashtags (Section V-B). This behaviour is also reported in  [26] ,  [39] ,  [63]  • The number of reactions to trusted publishers (Celebrities, News Agencies, and Business Pages) is 110x times higher than unreliable publishers. This highlights the importance of trustworthy accounts in critical moments (Section IV).
Gh ar-e Boof is a Paleolithic cave site in Iran well known for its rich early Upper Paleolithic Rostamian assemblages. The site is located on the edge of the Dasht-e Rostam plain in the southern Zagros. Recent excavations by the members of the Tübingen-Iranian Stone Age Research Project at Gh ar-e Boof also recovered well-stratified Middle Paleolithic assemblages. Here, we provide the first detailed luminescence chronology for the Middle and Upper Paleolithic of the site. More generally, our work is the first luminescence chronology for a Middle and Upper Paleolithic site in the Zagros Mountains region in Iran. The luminescence ages for the Upper Paleolithic of Gh ar-e Boof agree with published 14 C dates. We applied Bayesian models specifically designed for luminescence dating using the R package 'BayLum' to incorporate the well-established stratigraphic constraints, as well as the published 14 C ages with our optically stimulated luminescence (OSL) ages to improve the precision of the chronological framework.
We present the first luminescence-based chronology for the Middle Paleolithic period in the south of the Zagros Mountains. Moreover, our study goes beyond a simple Middle and Upper Paleolithic luminescence chronology for the Zagros Mountains: we also deployed Bayesian modeling as implemented in the R (R Core Team, 2019) package 'BayLum'  (Comb es et al. , 2015; Comb es and Philippe, 2017; Philippe et al. , 2018)  to incorporate all available chronological data (optically stimulated luminescence [OSL] and 14 C ages) for the site of Gh ar-e Boof, to render a precise chronology covering both the Middle and the Upper Paleolithic periods. The advantages of such incorporation in Bayesian School will be discussed.
We compared one sample from the site (GB10) with the calibration quartz (Risø, batch 90;  Hansen et al. , 2015) , which is known to be dominated by the fast component, using blue CW-OSL, and the corresponding luminescence signals were measured in the UV. Both signals were then transformed into the pseudolinearly modulated-OSL, following  Bos and Wallinga (2012)  d for results see Section 3. 1. The OSL signal was measured at 125 C for 40 s following a preheat for 10 s. The test dose was measured following a cut-heat. We employed the Analyst software v. 4. 53  (Duller, 2015)  for determining the equivalent doses (D e S). Regenerative dose points were set at  22, 44, 88, 176, and 352  Gy following a test dose of 22 Gy for regeneration dose 1 to 5 (R1 to R5). We calculated the signal intensity based on the initial 0.
Combining quartz ages with the radiocarbon ages in 'BayLum' The R (R Core Team, 2019) package 'BayLum' provides an environment for Bayesian data analysis in luminescence dating  (Philippe et al. , 2018; Christoph et al. , 2019 ; for an application see;  Heydari et al. , 2020). However, 'BayLum' is also capable of combining available chronological information, in particular, stratigraphic constraints and 14 C dates with OSL ages to improve luminescence chronologies. This may be considered as the most important advantage of performing data analysis in a Bayesian framework (although gains in precision and accuracy have already been demonstrated when using the models implemented in 'Bay-Lum';  Heydari and Gu erin, 2018 ) since it had the potential to lead to more precise chronologies (e. g. ,  Rhodes et al.
The Bayesian approach toward more precise ages In Section 3. 5, we combined the OSL ages with the available 14 C ages  (Conard and Ghasidian, 2011; Baines et al. , 2014; Becerra-Valdivia et al. , 2017)  to build up the Bayesian chronology using the R package 'BayLum'. The correlation between OSL samples due to shared systematic error mainly produced by calibrating the instruments, was addressed by creating a theta matrix. For all OSL dating measurements (both the equivalent dose and the dose rate), the same instruments were used except for estimating the gammadose rate. We noticed that the gamma-dose rate of samples GB10 and GB11 (0. 43 ± 0. 02 and 0. 39 ± 0. 01 Gy ka À1 , respectively) deduced from laboratory measurements agreed with the in situ gamma-dose rate of sample GB9 (0. 40 ± 0. 03 Gy ka À1 ), which is in close location to samples GB10 and GB11 in the profile with in situ gamma-dose rate available (Fig.
Conclusions We have presented a luminescence chronology based on quartz and the polymineral for the Middle and Upper Paleolithic sequences of the site of Gh ar-e Boof. Our investigation showed that the pIRIR 290 ages for the polymineral fractions are in good agreement with the blue-OSL ages of the quartz grains. In addition, previously published 14 C ages for the Upper Paleolithic part of the cave agree with the luminescence ages. We then applied Bayesian modeling, specifically designed for luminescence dating on blue-OSL quartz signals using the R package 'BayLum', which enabled us to incorporate published 14 C ages and the well-established stratigraphic constraints to further improve the precision of the chronology. The obtained Bayesian ages resulted in a significantly improved precision by reducing the age uncertainties.
Acknowledgments This work is part of the Ph. D. Dissertation of M. H. financed by the  Laboratoire d'Excellence Sciences arch eologiques de Bordeaux (LaScArBx) , a programme supported by the  Agence Nationale de la Rerchere (ANR)  -n  ANR-10-LABX-52. We are grateful to  David Alba , associate editor, and three anonymous reviewers for providing constructive feedback that improved our manuscript. Sebastian Kreutzer  is thanked for creating the map and running the.
Biotic threats for 23 major non-native tree species in Europe Elisabeth Pötzelsberger et al. # For non-native tree species with an origin outside of Europe a detailed compilation of enemy species including the severity of their attack is lacking up to now. We collected information on native and non-native species attacking non-native trees, i. e. type, extent and time of first observation of damage for 23 important non-native trees in 27 European countries. Our database includes about 2300 synthesised attack records (synthesised per biotic threat, tree and country) from over 800 species. Insects (49%) and fungi (45%) are the main observed biotic threats, but also arachnids, bacteria including phytoplasmas, mammals, nematodes, plants and viruses have been recorded. This information will be valuable to identify patterns and drivers of attacks, and trees with a lower current health risk to be considered for planting.
Moreover, due to their limited distribution, the forest health situation and dynamics are different to those of native tree species. According to the enemy release hypothesis  3  non-native trees may be more vital and productive because their natural enemies are absent and thus give them a competitive advantage. However, a lack of adaptation of non-native trees to European pests and pathogens could pose a high risk of attack for non-native trees. In the course of the NNEXT project, we compiled country specific information on observed biotic threats for non-native trees and their impacts for 27 countries (Fig. 1 ). The data sources utilised by the entomologists and pathologists of each country encompassed international scientific and grey literature, national forest health databases and national and regional reports.
Besides, we also allowed for expert knowledge/observations to be integrated in the assessment of the biotic threats. The information compiled in this database has a high practical and scientific value. Practitioners intending to test new non-native trees may wish to learn about European or invasive insect pests, pathogens or other organisms, which have been observed to impact a specific non-native tree in other European countries and the extent and type of damage encountered. On the scientific side, a lot remains to be learned about the dynamics of pest and pathogen impacts on non-native trees. Previous studies have shown that at the continental scale the main drivers of insect and pathogen attacks on non-native trees are the abundance of a non-native tree, the presence of congeneric species and the time since introduction  4, 5.
O EN spatial resolution (continental scale) or small study extent and the limited consideration of the extent of damage caused by insects and pathogens. With the data presented here it will be possible to investigate whether drivers of and mechanisms underlying the variability in the level of damage might differ at a finer spatial resolution. By providing important baseline information the data will also serve to compare future data with and thus allow to analyse temporal trends of impacts of pest species on non-native trees. Not least this database allows to identify trees with a lower current health risk to be considered for planting, while we acknowledge that the plant health situation is not static and new or more severe attacks may occur for different reasons, e. g. , through imported or naturally arriving non-native pests or pathogens, or caused by climate change (e.
In total, we requested biotic threats information for 24 non-native trees. One subjected non-native tree, Acacia melanoxylon with only one entry from Spain, was discarded due to limited data. We approached forest damage experts in all 36 European member countries of NNEXT. Country representatives in NNEXT were either themselves forest protection experts or they contacted experts at universities or national forest research stations/institutes. Twenty-nine countries responded to our request to fill the biotic threats database, but only 27 countries eventually filled the database in the requested manner. The information on the year of 1 st observation turned out to be difficult to verify and thus we received many NAs or ambiguous information. Several improvement steps were taken. If "<" than a specified year had been entered, we changed it to the specified year (e.
We thus decided to homogenize this information across countries based on current knowledge on species autecology and considering the information provided by the country experts. The damage information was also restructured. Instead of primary and secondary type of damage, we introduced eight columns for the eight possible types of damages, where the observation of a type of damage was given as 0 (not observed), 1 (observed) or 2 (observed and filled as primary type of damage by data providers, see above). This was done by the three database managers (one forest entomologist, one forest pathologist and one general forest ecologist). Although one particular pest or pathogen species could cause damage to different parts of the same tree in different countries (e. g. an insect that attacks different tree parts during larval and adult stage), we decided to use a common classification of all eight types of damage across countries because of the above-mentioned inconsistent data provided by country experts, but still allowed for a weighting of the primary damage in each country.
While this approach will not allow for testing fine scale differences in damage types caused on a particular tree species across Europe, it will still allow for coarse differences in main impacted tissue among countries and identifying traits of species that might increase the probability of attack. Furthermore, the LEVEL and MAX_AREA of damage provided by our database still varies among countries, allowing for tests on the main drivers of damage variability across Europe, e. g. depending on county-specific differences in the number of congeneric tree species, Table  1. Selection options (bold) and supplementary explanations (italic) for the columns PRIM_DAMAGE, SEC_DAMAGE, LEVEL, MAX_AREA, AGE_CLASS. Please mind that the columns PRIM_DAMAGE and SEC_DAMAGE were dropped in the final table and replaced with single columns for all eight damage types where 0 means not observed, 1 observed and 2 observed and originally filled as primary type of damage.
time since introduction of the non-native tree and area of the non-native tree, which have been shown to be main drivers at the European scale  4, 5. The database managers added two additional columns. A column SPECIALISATION was introduced to differentiate the host plant niche breadth of species based on the main higher plant lineages  6  following the approach published by Gossner et al. 7. An organism was assigned 'monophagous' if it attacks species of one genus, 'oligophagous' if it attacks species of one higher plant lineage (i. e. , bryophytes, ferns, gymnosperms, angiosperms: monocots, angiosperms: basal eudicots, angiosperms: eurosids, angiosperms: euasterids) and 'polyphagous' if it attacks species from more than one higher plant lineage. The column RELATIVE_ORIGIN categorises the biotic threats into species native to Europe but not at the origin of the non-native tree ('Europe'), species from same origin as the tree species and not native in Europe ('origin'), species native in both, Europe and the home range of the non-native tree ('both') and species from another region, neither Europe, nor the origin of the non-native tree ('third').
technical Validation All country tables were checked by the database managers for formal correctness of the information provided. For example, entries for fungi occurring only on dead plant material or in association with ectomycorrhiza were removed, because they are not the topic of this data collection. The database managers checked all scientific names, families and orders and changed them where necessary to the current accepted name. Fungal nomenclature is particularly complex due to sexual (teleomorph) and asexual (anamorph) states having different names. The new codex rules adopted at the International Botanical Congress in 2011 advocated the abandoning of dual naming system for pleomorphic fungi ("one fungus, one name" convention)  9. Therefore, we took the currently valid name from the online databases for fungal nomenclature, Indexfungorum.
org and MycoBank. org, matched Table  4. Data quality assessment by the data providers concerning a potential bias in insect pests and pathogens, and steps taken thereafter (contacting of new experts and completing the database). Every data provider agreed to check for new records and fill NAs in existing entries. The bias is evaluated according to the following scheme: 1 -'The data well reflect the situation of the pest/pathogen impact. There is no bias due to prioritization of certain tree species and/or lack of experts'; 2 -'The data on pest/pathogen impact have some bias. The bias due to prioritization of certain tree species and/or lack of experts is, however, minor'; 3 -'The data on pest/pathogen impact have major bias. Due to prioritization of certain tree species and/or lack of experts the data does not reflect the complete situation in the country and thus should not be used in a cross-country analysis'; n.