dataesr/scientific-paragraphs-categorization huggingface dataset:
In sum, while the GDPR is extraterritorial in scope, it is legitimate to expect that US-based and EU-based content providers would be differentially affected by it in terms of personal data acquisition and revenue generation. We expect the magnitude of ecosystem effects on a website's downstream outcomes to be moderated both by where the website is based and by the share of each website's traffic that originates from the EU, independently of a given website's GDPR response. Ultimately, we expect websites located in the EU, and websites with a higher percentage of EU-based traffic, to be more affected than websites with a lower percentage of EU traffic.
First, faced with the compliance burden imposed by the GDPR, websites-especially those based outside the EU-have the option of exiting the EU data and advertising market that website's revenues independently of its own distinct GDPR response. altogether by blocking all traffic originating from the EU. In our analysis we refer to this response as "Blocks EU. " This response could arguably be considered the most aggressive, as cutting off EU visitors would directly curtail potential future advertising revenue. This option may be attractive for websites based outside the EU which received only a small share of traffic from the EU prior to May 2018. The exit of these websites from the EU market may lead their former visitors to visit other websites, but would likely not result in large negative ecosystem effects on the tracking ability or targeting accuracy of other websites.
Downstream Outcomes We use third-party repositories to measure the quantity of content generated by websites over time and user engagement with that content (a proxy for its quality). These metrics do not change as function of the country of the visitor and they are aggregated across different sources. We collect these metrics from April 2017 to November 2019. Content Quantity: To measure content quantity, we use the Global Database of Events, Language, and Tone, or GDELT (https://www. gdeltproject. org). GDELT gathers and provides metadata for articles from news and media websites going back to 2015 from both domestic (US) and international sources. The database provides metadata including the URL, publication date, and publisher website for each article, and has been used in studies that examined global events  (Gallea and Rohner, 2021; Ferreira et al.
By contrast, cookie banners inform users about cookies, but do not provide them 30 An ad blocker is a small piece of software or module incorporated into a user's browser (add-on) that prevents the display of banners and other advertising formats. Ad-blockers filter advertisements using community maintained lists that contain the URLs and HTML tags used by the main ad servers and advertising networks (these lists are known as blocklists). We cross-reference the data we collected using OpenWPM with these blocklists to identify advertising related cookies and content. We rely on two blocklists (last retrieved in February 2020): Adblock Plus (https://adblockplus. org/fr/subscriptions), and Disconnect (https://disconnect. me/). 31 AdBlock Easylist consists of a set of rules used by AdBlockers to detect and hide elements that correspond to advertising.
EAGER data will also be shared via the Autism Sharing Initiative (ASI), a global collaboration connecting autism data through federated mechanisms  (30). The genetic, phenotypic, and clinical data collected in EAGER can therefore be analysed with other international autism datasets such as MSSNG (31), POND  (32) , LEAP  (33) , SPARK  (34) , and Searchlight  (35) , providing the opportunity to investigate key research questions.
LG receives funding from  Horizon Europe :  Risk and Resilience in Developmental Diversity and Mental Health  ( R2D2-MH ). CPS receives funding from  Foundation of Prader-Willi Research ,  European Joint Program for Rare Diseases, Illumina ,  German Children's Cancer Foundation , and the  German Ministry of Education and Research. RB, RM, SC, BM receive funding from the  Italian Ministry of Health (Ricerca Corrente  and  5x1000  to  IRCCS Fondazione Stella Maris ). Genomic data utilised here will be sequenced in part/whole through the financial contribution of  F Hoffmann La-Roche. This paper represents independent research part-funded by the  NIHR Maudsley Biomedical Research Centre at South London  and  Maudsley NHS Foundation Trust  and  King's College London. The views expressed are those of the author(s) and not necessarily those of the  NIHR  or the  Department of Health and Social Care.
ASI is a new collaboration working to create the first federated, global network for sharing genomic and clinical data in autism research. The ASI is led by software company DNAStack and involves other collaborators such as Autism Speaks, Roche, and the Ontario Brain Institute (please see (54)  for the full list). This is an ongoing project and new collaborators may be added. Data federation allows researchers to interrogate and analyse data hosted in different locations, in compliance with data regulations and improving privacy and security. A federated data network involves connecting individual datasets, which remain in their protected local environments (i. e. , at the research institution where the data was collected), across one cloud-based network. To our knowledge, the ASI carries no additional risk to participant re-identification than traditional data sharing.
Data This study is based on the Survey of Health, Aging and Retirement in Europe (SHARE) which is a longitudinal multidisciplinary database of micro-data on health, socioeconomic status, and intergenerational transfers on individuals aged 50 or more, and conducted in 28 European countries  (Börsch-Supan et al. , 2013). We firstly use the first and second waves of the SHARE Corona survey  (Börsch-Supan et al. , 2022e and 2022f). These are special surveys conducted by phone for people who previously participated to the Survey of Health, Aging and Retirement in Europe (SHARE) to study the consequences of the coronavirus on elderly European people. The first wave of the Corona survey was conducted in June and July 2020 and collected information on unmet needs since the beginning of the pandemic, while the second wave was conducted one year later (in June and July 2021).
We estimate that there were 2983 individuals in the official alert area at the exercise time, but 11,196 individuals if we consider those located in a telecommunication cell that intersect the official alert area, i. e. , a potential overalert of 375% (3. 7 times the target population). We estimated this number based on the working population, rather than the residential population (the exercise took place at 4 p. m. ). We used the same dasymetric approach as  Bopp and Douvinet (2022) , crossing the databases of INSEE (typology of workers and inactive people by municipality) and IGN (typology of buildings). We divided the individuals according to their type of employment, in proportion to the number of buildings dedicated to this type of employment: managers, employees and craftsmen were located in the trade and service buildings, workers in the industrial buildings, farmers in the agricultural buildings and the inactive (unemployed, retired and students) in the residential buildings.
Data collection Fast geolocation surveys were conducted to maximise the number of responses within a constrained time frame (n = 375). The surveys were carried out mainly face-to-face by a team of students, PhD students and postdoctoral fellows, immediately after the first alert notification (n = 295) and then completed by telephone calls the following days (n = 80). In each case, three questions were asked: 1) "Did you receive an alert message on the main screen of your mobile phone around 4. 05p. m. ?"; 2) Is your phone an Android or iOS? 3) What is your telephone operator? In order to avoid interpretation bias, the investigators may explain how the CB worked to respondents who appeared confused. In some cases, a confirmation question was asked: "Did your phone make a continuous sound around 4.
05 p. m. , when the first notification was issued. This explains the concentration of points patterns collected in commercial areas to the detriment of more residential areas: the dispersion index ranging from 1 (maximum concentration) to >2 (high dispersion) is R = 1. 36. Questionnaires were administered along a centre-periphery gradient, starting in the official alert area and extending until 1. 1 km beyond its boundaries (visible as "study area" in Fig. 2. a). The survey teams took different trajectories in order to spread the collected responses as widely as possible in space. The responses were randomly located within the building. Efforts were made to have a homogeneous number of responses along the centre-periphery gradient. A second set of data was collected by the Cannes City Hall.
Data analysis The data analysis was carried out in three stages to meet the different research objectives. The first analysis focused on a reference area, which corresponds to the official alert area, to which we added a 25 m band. This is the area where we are certain that the message has been broadcast. The 25 m around the official area was added in order to increase the statistical weight of reference (from 79 responses to 112) and because a first modeling of the edge effect (using Voronoi polygons from the spatial data of the antennas) reveals that this area is entirely covered by telecommunication cells which intersect the official alert area. In this reference area, data from the Cannes city hall (n = 50) was discarded because it does not indicate the type of telephone nor the operator.
Acknowledgments The authors wish to thank the students mobilised in the field:  Titouan Blanchard ,  Lucie Boulat ,  Darry Mandrindra Ramaroson , and  Maé Merlu. The authors also wish to thank the  Direction de la Transition Numérique of the Ministère de l'Intérieur et des Outre-mer , the prefecture of the  Alpes-Maritimes  and the city of Cannes, and particularly  Yannick Ferrand  and  Laurent Boisseau  for the information given and the logistics. Fig. 9. Schematisation of the spatial issues of cell broadcast at a local level.
Exome sequencing (ES) generates secondary findings (SFs) in 2 % of tested individuals if one follows the American College of Medical Genetics and Genomics (ACMG) guidelines. 1, 2  However, the rate of incidental and secondary findings (ISFs) is higher in routine clinical practice because of (i) the use of trio ES instead of solo sequencing and (ii) the exclusion of the incidental findings (IFs) of medical value concerning genes in the ACMG list. Hence, it is not clear how sufficient is a restricted list of genes to detect every ISF of major clinical value; and what is the amount of additional workload for the laboratory. Using 100 trio ES datasets, we determined the proportion of pathogenic or likely-pathogenic ISFs and their clinical value (See supplementary file 1 for Materials and Methods).
We evaluated the accuracy of three lists of genes (see supplementary file 2) for detection of SFs: the 2021 ACMG v3 SFs list, a list of genes involved in treatable intellectual disabilities  3  (treat-ID list), and a list of genes involved in the 20 most frequent diseases in general populations (CS20 list). When considering 100 trio ES datasets with prenatal and postnatal indications (Fig. 1A ), we were able to establish a diagnosis in 36 cases and identified 526 P/LP variants related to SFs (Fig. 1B, C ; supplementary file 3; 335 (63. 7%) P and 191 (36. 3%) LP). The median (range) number of ISFs per trio was 5 (1e13) (Fig. 1D ). The 526 ISF variants were found in 400 genes. The level of redundancy was low (Fig. 1E ): 314 (78. 5%) genes were reported once, 64 (16. 0%) were reported twice, and 22 (5.
1G ): 23 (4. 4%) in the ACMG list, 33 (6. 3%) in the CS20 list, and 36 (6. 8%) in the treatID list. Sixteen of the 23 variants found in the ACMG list should be reported, according to the ACMG statement. Overall, just 16% of trio ES datasets contained SFs of the ACMG list. However, 81% of trio ES contained ISFs of major clinical relevance for the patient, and at least 31% for family planning. Furthermore, 15% of trio ES contained ISFs related to severe autosomal dominant disorders that would probably affect the carrier in his/her lifetime but for which no effective treatments are available; hence, these ISFs must not be reported. The efficiency of the lists of genes in detecting ISFs of major clinical value appears disappointing. Exhaustive analysis revealed 81 variants of great clinical value when only 16 of them have been detected by the list approach.
Nevertheless, the ACMG v3 list was the only list able to detect these variants when CS20 and treatID lists failed to highlight variants of great clinical value for the patient. This yield appears to be disappointingly low and we recommend considering clinical value for the patient and family in every ISF found in ES. Our results showed that at least one ISF was present in every trio ES dataset. Luckily, not all ISFs are of major clinical relevance, so not all ISFs have to be reported. Nevertheless, the discovery of ISFs means that the analysis takes longer and the results must be discussed more extensively by laboratory geneticists and clinicians. This situation is of great concern regarding clinical management, the laboratory's workload, patient information, and ethical aspects. It is important to bear in mind the intrinsic difference between IFs and SFs.
Introduction Intraspecific diversity is at the nexus of ecological and evolutionary dynamics  [1, 2]. Its study can be done at several levels of biological organization, from molecules to populations. Among them, the genomic level has received much attention. Intraspecific genomic datasets allow to determine if genome diversity is a good proxy for phenome diversity (i. e. , the diversity of phenotypes), if evolutionary patterns detected at the intraspecific levels match those described at the interspecific level (e. g. , hot-spots of mutations, gene diversifications, genome size variations), or if the reconstruction of demographic, selective or ancestry patterns are consistent across the whole genome. Besides these evolutionary considerations, associations between reliable genome assemblies/annotations and phenome descriptions at the intraspecific level are fundamental in cellular biology and medicine to ascertain the functional role of specific genomic regions in a given biological process or disease  [3].
thermophila in a few asexual generations  [31]. The evolutionary origin of this somatic genome remains unclear but a role in the domestication of transposable elements  [32] [33] [34] , as well as a role in phenotypic plasticity through somatic selection  [30] , have been proposed. Several ciliate MAC reference genomes have been published, including of species of the genera Tetrahymena  [35] , Paramecium  [29, 36] , Oxytricha  [37]  or the heterotrichean Stentor  [38]. However, as for most eukaryotic microorganisms, very few intraspecific datasets are available, except in the Paramecium genus  [29, 36]. To the best of our knowledge, comparative genomic studies in the genus Tetrahymena have only been performed at the interspecific level  [35]. The mapping of recently evolved genes on the MIC assembly of T.
thermophila revealed their concentration on pericentromeric and subtelomeric chromosome regions, suggesting these regions as genome innovation centres  [35]. While this dataset sheds light on intra-genus genome evolution, the extent and organization of genomic diversity at the intra-specific scale is warranted. Here, we analyse the polymorphism of T. thermophila MAC genome after sequencing 22 laboratory strains originating from strains collected by Professor Paul Doerder between 2002 and 2009. Since their sampling, these strains have been maintained under laboratory conditions, and have been used in experimental microcosms (e. g. ,  [11, 15] ). They differ in many phenotypic features, including gross morphology, mobility, thermal optimum, growth parameters and dispersal ability (e. g. ,  [11, 39, 40] ).
Evolutionary distances and phenetic trees Reads of our 22 strains were trimmed using Trimmomatic v0. 39  [41]  with recommended settings for Illumina paired-end reads and assembled into genome assemblies by SPAdes v3. 14. 1  [42]  using the options '--only-assembler --careful'. MAC and mitochondrial chromosomes were reconstructed by scaffolding the SPAdes contigs onto the reference SB210 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. genome using RagTag v1. 1. 0 (https://github. com/malonge/RagTag). Then, all-versus-all pairwise Jukes-Cantor distances were estimated among the 22 genome assemblies and the reference SB210 MAC genome using andi v0. 23  [43]. The resulting distances matrix was used by Ninja v1. 2. 2  [44]  to infer MAC and mitochondrial neighbour-joining trees.
These MIC-SNPs are present in 1n in DNA extracts, and should therefore be present at an expected coverage of ~1x in our 50x dataset. We also only kept SNPs and indels supported by at least one forward and one reverse alignment to limit mapping errors. Then, we chose to only consider variant calling positions that had a coverage of at least 20x (to avoid repeat regions) and below 80x (to avoid segmental duplications not present in the reference genome). Our dataset should thus mainly be composed of MIC-derived-SNPs and high frequency/fixed MAC-de novo-SNPs (Figure  1 ). However, MIC-SNPs not retained in MACs after phenotypic assortment, as well as de novo MIC mutations, could still be present at mid-high frequency in this dataset due to potential hyperdiploidy of some MIC chromosome regions in some strains.
Aneuploidy can indeed occur in T. thermophila MICs (between 1n and up to almost 6n  [49]  as a consequence of multiple vegetative reproduction cycles  [50] ). Hyperploidy should randomly affect MIC chromosome regions and strains, meaning that the origin of mid-high frequency mutations is difficult to ascertain when performing MAC sequencing. As this could influence estimations of MAC mutational and diversity parameters, we decided to only consider positions for which the nucleotide different from the reference SB210 genome had a minimum frequency of 30x when calculating nucleotide polymorphism metrics. In all 22 strains combined, they represented 95. 6% of all varying positions. We thus believe that after this last filtering step, our dataset was a reliable representative of the MAC genomic landscape.
thermophila gene was calculated as the median of all pairwise values between this gene and the genes of the eight other available Tetrahymena species. The DoS values were finally calculated for all genes having orthologs in the Tetrahymena genus (available dN and dS) and dN+dS and pN+pS values different from 0, resulting in 16,892 T. thermophila genes with DoS values. We also computed pN/pS and DoS distributions of gene clustered by their annotation using the slim Gene Ontology (GO)  [54]  to determine which functional pathways evolved especially fast or slowly in the T. thermophila species. To test for a functional enrichment among genes present around MIC centromeric regions, we created four gene datasets. The first one was composed of the MAC chromosomes corresponding to the MIC centromere position (MAC 24, 62, 96, 125 and 168).
The three others were composed of these five MAC chromosomes plus one, two or three chromosomes around them. Gene counts in these regions can be found in Supplementary Material 4. For each of these datasets, we calculated the mean DoS, pN/pS and dN/dS and tested for functional enrichment using g:profiler  [55]  among the genes having higher values than the mean regional values (i. e. , genes evolving faster compared to the others present in the considered regions). Since we observed a significant decrease in the number of genes for which orthologs were found in the other species of the Tetrahymena genus around MIC centromeres (no available DoS estimation), we also tested for functional enrichment among genes that had pN/pS estimates but no DoS estimates. These genes should correspond either to recent (duplicated) genes originating after the T.
Mapping, SNP calling and evolutionary distance When mapped on the SB210 MAC reference genome (Supplementary Material 3), the 22 Illumina datasets had an average coverage between 47. 6 and 61. 7x (Supplementary Material 5A). The percentage of the SB210 MAC genome mapped at a minimum coverage of 20x varied among strains between 91. 6 and 99. 5%. Keeping variant calling positions supported by at least three reads and at least one forward and one reverse mapping, and considering only positions that had a coverage between 20x and 80x (see Methods), this percentage varied between 88. 3 and 98. 5%. After excluding positions with more than two character-states (no more than a few hundred per strain), a total of 2,326,587 biallelic positions were found, corresponding to 2. 25% of the MAC genome. This value is similar to those obtained in the Paramecium genus  [36].
Indeed, based on between 10 and 13 isolates resequenced with comparable coverage in five Paramecium species, 0. 7% to 7. 4% of the MAC genome was found polymorphic  [36]. The number of differences compared to the SB210 reference genome varied greatly, from 48 in strain D18 to 1,129,251 in strain D12 (Supplementary Material 5B), representing ~0 to ~1. 2% of divergence with the SB210 strain. We estimated evolutionary distances between MACs of our 22 strains plus the reference SB210 by calculating Jukes-Cantor distances between all pairs of MAC genome assemblies. As shown by the Neighbour-Joining (NJ) tree inferred from these distances (Figure  2A ), our dataset was composed of seven clusters of highly similar strains: D3/D6/D12, D10, D1, D11, D5, D16 and a large cluster comprising 14 strains nearly identical to the reference SB210 strain (referred hereafter as 'SB210-like strains').
In contrast, the strains D5 and D16 appeared to have distinct sets of MAC chromosomes, either similar or distant from SB210. This suggests that D5 and D16 were the result of two independent and relatively recent crosses between SB210-like strains and divergent strains. The MAC chromosomes originating from the centromeric region of the MIC chromosomes seemed to show relative higher divergences (higher number of differences with SB210 near MIC centromeres represented by dark blue hot-spots in Figure  2B , see also higher SNP density near MIC centromeres in Supplementary Material 7). This pattern could be concordant with previous observations suggesting MIC centromeres as genome innovation centres in T. thermophila  [35]. Finally, we inferred a NJ tree from the mitochondrial genome. When mapped on the SB210 reference mitochondrial genome, the 22 Illumina datasets had an average coverage of 3,000x (Supplementary Material 8A-B), except for the extremities of this linear genome  [57]  that showed very high coverages, ranging from ~25,000x and ~40,000x (not included in analyses).
Thirdly, D18 had nearly identical MDS junctions to those present in the reference MAC genome of SB210, with only three indels detected at the 9,973 MDS junctions. Considering the previous observation of a very low number of nucleotide differences compared to SB210 (only 48 overall genome), we assume that the strain D18 is a recent descendent of SB210. In addition, we observed that D18 has secondarily lost its MIC genome (Supplementary material 10), as supported by microscope observations (data not shown). Amicronucleated lineages are frequent in the Tetrahymena genus, and can represent up to 25% of wild isolates  [60]. To further illustrate the impact of imprecise IES excisions on the distribution of indels at MDS junctions in strains issued from the same conjugation event (i. e. , sharing the same ancestral MIC genome), we analysed the dataset published in  [61].