dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics and statistics For 16S sequences, the base quality of the reads 1 and reads 2 was controlled using the functions fastx_qual-ity_stats and fastq_quality_boxplot_graph of the FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_ toolkit/). PANDAseq  (Masella et al. 2012 ) was used to assemble the read 1 and the read 2 using the RDP algorithm, a minimum and maximum length of the resulting sequence of 410 bp and 500 bp, respectively, a minimum and maximum overlap length of 20 bp and 100 bp, respectively. The resulting sequences were stripped out from the primers and annotated at the genus or family level by RDP Classifier  (Wang et al. 2007 ) using the RDP 16S rRNA database and an assignment confidence cutoff of 0. 6. This signifies that 60% of K-mers will match at the genus level. Singletons were removed.
For ITS sequences, forward and reverse reads were merged using vsearch  (Rognes et al. 2016). Sequences were quality filtered and assembled in QIIME pipeline  (Caporaso et al. 2010). Chimeras were removed using UCHIME  (Edgar et al. 2011)  with closed reference and de-novo approach. OTUs (operational taxonomic units) were assembled at 97% similarity using vsearch clustering algorithm at default settings  (Rognes et al. 2016 ) and blasted against UNITE 7. 2 database  (Ko ˜ljalg et al. 2013). Singletons were removed. Fungal OTUs were merged on genus level (i. e. , 603 genera) for all analyses apart from alpha diversity, to account for species length polymorphisms in fungal ITS regions below genus level  (Gomes et al. 2002). Negative control bacterial genera resp. fungal OTUs from the kit and the sampling liquid were subtracted for all samples (see online supplementary material Table  S10  and Fig.
S1  for sequence statistics and rarefaction curves). Statistical analyses were done in R (R Core Team 2015) using the phyloseq (McMurdie and Holmes 2013), vegan  (Oksanen et al. 2018 ) and ggplot  (Wickham 2009)  packages. The alpha diversity Chao1 richness index S 1 was calculated as follows: S 1 ¼ S obs þ F 2 1 2F 2 where S obs is the number of species in the sample, F 1 is the number of singletons, and F 2 is the number of doubletons. The alpha diversity Shannon evenness-Index H 0 was calculated as follows: H 0 ¼ X i p i Ã ln p i mit p i ¼ n i N with p i being the relative abundance of a certain species i of the total number of individuals N and n i being the absolute number of individuals belonging to one species. The data set was run both rarefied and untreated, which did not show differences in statistical analyses, but removed rare genera.
To include otherwise removed genera and to present the data characteristics appropriately, relative abundance analysis was carried out on non-filtered data  (Weiss et al. 2017 ). The fungal dataset was third-root transformed. Bray-Curtis distances for bacteria and fungi were calculated on datasets normalized to relative abundance  (Weiss et al. 2017 ) and ordinated with non-metric multidimensional scaling (NMDS). The samples May_T12, May_5, May_T6 and May_T12 had to be excluded from the ITS analyses, as they had too few reads to calculate NMDS. Pairwise PERMANOVA was conducted using the package ''pairwiseAdonis'' (Martinez Arbizu 2017); Bonferroni p value correction was applied as default for multiple corrections. Statistical parameters of ANOSIM (999 permutations), ADONIS (999 permutations), Kruskal-Wallis, Dunns PostHoc and PERMANOVA analysis are reported in the online supplementary data (Tables  S5-S9 ).
SIMPER (Similarity Percentage), describing the species contribution of Bray-Curtis distance between two groups, was calculated in vegan (supplementary Tables  S12-S18 ). Obtained air and precipitation samples were blasted (blast version 2. 7. 1) against a nucleotide database of organisms containing the ice nucleation protein (search for ''ice nucleation protein''), created from ncbi (www. ncbi. nlm. nih. gov), with a threshold of 97% similarity. The obtained reads were filtered for matches longer than 400 base pairs and merged on genus level.
Meteorology Meteorological and aerosol data were obtained from the Sonnblick Observatory repository. An aerosol backscatter profile from VAISALA CL51 ceilometer located at Kolm-Saigurn (the base of Mount Sonnblick at 1500 m asl. ), and PBL heights were obtained from ZAMG (Zentralanstalt fu ¨r Meteorologie und Geodynamik, Vienna). The mixing layer heights were calculated according to  Lotteraner and Piringer (2016)  (see online supplementary information Figs. S3-S6 ). The lagrangian model FLEXPART  (Stohl et al. 2002)  was used in backward mode to detect the most probable source regions for microorganisms which may have been transported with longrange air flows. The model output fields depict the regions with high source-receptor sensitivity due to the prevailing three-dimensional flow fields predicted by the ECMWF model for 120-h backcalculation (see online supplementary material Fig.
Alpha diversity Bacterial and fungal read counts per sample ranged from 13478 to 41882 and 568 to 34638, respectively, after quality filtering. The whole dataset comprised 1590 bacterial genera and 2573 fungal OTUs, which accounted for 963 bacterial genera and 1912 fungal OTUs (resp. 603 fungal genera) after removal of blanks. The overall median and mean of bacterial Chao1 (describing richness, Fig. 2b ) were 48, with the lowest median in August (40. 75) and the highest in November (70. 06), and the lowest value in August (19. 33) and the highest in February (160. 00). November differed significantly from May (p = 0. 023) and August (p = 0. 0368), whereas in February significant differences to August were detected (p = 0. 0368, Kruskal-Wallis p = 0. 03). The highest variation was found in May (0.
In the total dataset, rain and snow showed the biggest difference (PERMAOVA p non-adj. = 0. 025). Snow and cloud in cold seasons (November and February) appear similar in bacterial composition, with Acidobacteria (3. 2-6. 2%), Actinobacteria (18. 6-40. 7%), Bacteriodetes (5. 5-22. 0%) and Cyanobacteria (13. 4-20. 8%) being abundant, whereas air is dominated by Firmicutes (48. 1%-78. 4%). Proteobacteria make up a comparable share in air, cloud and snow in winter (11. 2-27. 2%). In May, air and cloud are more similar with high abundances in Proteobacteria (54. 3% in air, 51. 1% in cloud), while May snow features high abundances of Actinobacteria (53. 8%) and Firmicutes (19. 0%), while cloud and snow in May feature Cyanobacteria (7. 8% in snow, 8. 6% in cloud) that are very low in May air (0.
9%, Fig. 6 ). Air in August features also high numbers of Proteobacteria (49. 4%) that are even higher in hail (88. 8%). August snow is dominated by Bacteriodetes (47. 4%), Proteobacteria (27. 1%) and Actinobacteria (20. 1%), with Firmicutes lacking, and the lowest abundances of Planctomycetes (0. 6%), Chloroflexi (0. 01%) and Cyanobacteria (2. 7%) compared to snow from the other seasons. Fungi show a similar pattern, with rain and snow being most different in the overall dataset (PERMA-NOVA p. adj. = 0. 04) and precipitation never significantly differing within season comparison. Highest similarity occurred between air and snow in May (PERMANOVA p adj. = 1. 0), but also all precipitations in August and did not differ significantly to air (PERMANOVA snow p adj. = 0. 102, rain p adj. = 0. 066, hail p adj.
Ice nucleation-active bacteria In total, there were 53 genera containing the ice nucleation protein (INP) detected in the dataset. In air samples, the relative abundance of INP reads of the total reads per sample was lowest in November (1. 56%) and highest in August (5. 28%), but with high variation. In clouds, relative abundance of IN varied strongly between seasons displaying the highest value in the dataset (15. 82%), whereas in May accounted for only 2. 25% and in February 4. 16%. In snow the mean IN abundance varied between 5. 22% in May and 11. 51% in August. Hail ranged at 7. 04% and rain between 0 and 11% (Fig. 7a ). The composition relative abundance of the most abundant INP-containing bacteria showed a similar pattern than the composition of most abundant phyla (discussed in Chapter 3.
3, Fig. 6 ), with snow and cloud being similar in February and November, and more similarity of air and cloud in May, and high variability in August. (Fig. 7b ) NMDS (Fig. 8a ) reveals that cloud and air have a higher overlap, than snow and air, with cloud at the interface between air and snow. The two rain samples are different in IN-composition. Hail clusters with snow and cloud. Most of the 53 identified genera containing the ice nucleation protein, present in the dataset, were associated with snow. The occurrence of ice nucleation-active reads is more similar in the frozen water samples (hail, snow) to the clouds, than air. However, the Venn diagram (Fig. 8b ) reveals, that the most genera are shared between all of the samples or air, snow and cloud, but by NMDS, indicating, that they are more abundant in snow and cloud.
Comparison of community structure and abundance in air and precipitation Microorganisms precipitated with snow, hail or rain or being transported by clouds differ in their species composition from free tropospheric air masses and do not mirror the air community structure. They are distinct in composition, 16S:18S ratio, abundance and diversity from free-floating PBA. Clouds and snow generally had the highest diversity in both bacterial and fungal datasets, with atmosphere and rain being the least diverse. Cloud bacterial communities were dominated by Proteobacteria, Actinobacteria and Bacteriodetes, which has also been observed in several cloud water studies from China, France, southern Africa and the USA  (Jiaxian et al. 2019; Amato et al. 2017; Kourtev et al. 2011; Zhu et al. 2018; Evans et al.
In air as compared to snow, cloud, hail and rain especially differences in Bray-Curtis distance were driven on genus level by Geobacillus (Firmicutes) and Aminobacter (Alphaproteobacteria), an organism found in various terrestrial environments  (McDonald et al. 2005 ) that supports legume nodulation  (Maynaud et al. 2012)  and is capable of herbicide degradation  (Schultz-Jensen et al. 2014). A further driving organism was Bosea (Alphaproteobacteria) an amoebaresisting bacteria which is associated with hospital water supply and intensive care ventilator-acquired pneumonia  (Khamis et al. 2003; La Scola et al. 2003)  and rhizosphere  (Ahern et al. 2007 ). On phylum level, air was especially enhanced in Firmicutes. Hail was the only precipitation form not yielding significant differences to air at the respective season in unadjusted testing for bacteria and fungi in our dataset.
2017). Further, the median of 16S to 18S ratio was lowest in clouds, suggesting a higher capacity of cloud doplets to hold fungi in clouds in comparison with air, snow or rain. The result of lowest ratio of bacteria to fungi in clouds is in accordance with  Bauer et al. (2002) , whose counts result in a bacteria-to-fungi ratio of 4. 4 for clouds, 5 for snow and 16. 4 for air. Bacteria could provide a surface area for the condensation of water vapor on the surface of aerosolized particulate matter  (Jiaxian et al. 2019). In rain, 16S:18S ratio was highest, suggesting a separation mechanism for fungal spores before rain forms. Rain was high in 16S rRNA gene abundance but low in 18S abundance and in bacterial and fungal diversity. Air and rain were not significantly different in total bacterial air dataset, but in unadjusted testing in August dataset, fungi were significantly different in the total dataset.
Rain was the only precipitation form that yielded a significant difference when compared with another precipitation form in the total dataset, but it was not significant within the August dataset. However, the snow here was likely more influenced by melting, aging and aerial deposition. Rain was different to bacterial diversity in snow (unadjusted) and more significantly to fungi in snow. Hail had the lowest 16S: 18S ratio, which might be due to the formation from cloud droplets that have also low 16S:18S ratio, where spores might possibly get stuck to ice surface during sudden freezing. 4. 5 Seasonal effects related to temperature A significant separation of air masses and precipitation samples was observed, and differences varied between seasons. In the winter seasons (November and February), the composition was similar in cloud and snow, but different to air, whereas in May bacterial composition of cloud and air was more similar, but pronouncedly different from snow.
Erwinia and Xanthomonas were unique in air in this dataset, though they are usually abundant in the environment, but also absent in other water-based IN-surveys  (Du et al. 2017). Zweifel et al. (2012)  found Erwinia and Pantoea in low frequencies, but recurringly in free tropospheric air samples. The microbes present in clouds, snow and rain survived long-range transport, ice nucleation and cold cloud processes. Thus, it is likely that wet or snow deposited microbes will outperform their dry deposited counterparts in numbers and adaptions. As clouds often form far from the place where they precipitate and biological nucleators are frequently involved, their microbial composition is different from the present surface air microbiome. 4. 8 Precipitation is no proxy for free troposphere air communities Caliz et al.
(  2018 ), who recently used snow as a proxy for air mass composition in the free troposphere, identified ''airborne'' Chytridiomycota as abundant in spring snow. The overall relative abundance of Chytridiomycota in all air samples was 0. 01%, compared to 2. 49% in snow and 6. 15% in rain but only 0. 13% in hail and 0. 22% in clouds in our dataset, indicating scavenging snow and rain precipitation. Bacteriodetes, a phylum identified by Caliz et al. (2018) as abundant in snow, was identified as the one of three phyla driving most differences between air and snow (5. 67% air, 18. 40% snow). While there is no doubt that microorganisms are aerosolized, incorporated in snow and then deposited  (Harding et al. 2011) , based on our dataset, selection at the phylum level occurs. Cyanobacteria were high in snow and cloud compared to air.
Acknowledgements Open access funding provided by  University of Innsbruck and Medical University of Innsbruck. We thank for great support of three students:  Christina Schweighofer ,  Karoline Wieser ,  Tamara Schober ; the whole team of the  Laboratoire Ampere of the E ´cole Centrale de Lyon ,  Elke Ludewig ,  Christian Meier ,  Alexander Hieden ,  Christoph Lotteraner  and the whole team of the Sonnblick Observatory of the ZAMG and most important Klaus Unterberger. Funding Open access funding provided by  University of Innsbruck and Medical University of Innsbruck. This work received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  No. 675546.
Acknowledgements Open access funding provided by  Max Planck Society. We wish to thank four anonymous referees, as well as helpful comments given by  Malinda Carpenter ,  Katherina Helming ,  Andreas Falck ,  Richard Moore ,  Philip Pettit ,  Barbora Siposova ,  Brent Strickland , and audiences in Leipzig ( Max Planck Institute for Evolutionary Anthropology ,  2016 ),  St. Andrews  ( ESPP 2016 ),  Prague  ( Czech Academy of Sciences 2016 ), Croatia (U. Rijeka 2016),  London  ( JAM 2017 ) and  Berlin (Berlin School of Mind and Brain 2017 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution,.
To the best of our knowledge, two other sources can be used to check the validity of our data: one from the Comparative Manifesto Project (CMP), the other from the POLCON project (www. eui. eu/Projects/POLCON), which is based on a content analysis of newspaper reports during the electoral campaign. While all of these data sources focus on the same period, there are important differences in timing, in the type of coded actors, and in the list of issues used to code actors' political statements. For the CMP dataset, the programmes of three presidential candidates have been coded, whereas we have to resort to their parties' programmes for the subsequent legislative elections in the case of the Parti Socialiste and Les Républicains. In the case of the POLCON project, the coding is at the level of parties or party groups (such as extreme-left or extreme-right parties).
One unique copy of this edition of the De Unius Legis Veritate (1522) is kept in the Mazarine Library (accession number 487). Colophon: Petri Montii de Unius Legis Veritate: Et sectarum falsitate: opus excellentissimum: Perfectum fuit non parvo labore: Summaque cura revisum: & per solertem calcographum. Io. Angelum Scinzenzeler Impressum Mediolani. Anno verbi incarnati. M. cccccxxii, Pridie Kalendas decembris. De via ad exercitia militaria, which had already disappeared in the nineteenth century. 7.
4 One unique copy of this edition of the De Unius Legis Veritate (1522) is kept in the Mazarine Library (accession number 487). Colophon: Petri Montii de Unius Legis Veritate: Et sectarum falsitate: opus excellentissimum: Perfectum fuit non parvo labore: Summaque cura revisum: & per solertem calcographum. Io. Angelum Scinzenzeler Impressum Mediolani. Anno verbi incarnati. M. cccccxxii, Pridie Kalendas decembris. De via ad exercitia militaria, which had already disappeared in the nineteenth century. 7 5 Solmi, Scritti Vinciani,Petri Montij exercitiorum atque artis militaris collectanea In tris libros distincta. Colophon/souscription [f61(g7)] Impressum Mediolani per Joannem Angelum Scinzenzeler anno domini. M. ccccc. viiij die xxvij. Julii. [ 117(g2)] PETRUS MONTJUS COMPONEBAT. […] Impressum Mediolani.
Data brokers such as Acxiom and Experian are in the business of collecting and selling data on people; the data they sell is commonly used to feed marketing as well as political campaigns. Despite the ongoing privacy debate, there is still very limited visibility into data collection by data brokers. Recently, however, online advertising services such as Facebook have begun to partner with data brokersto add additional targeting features to their platform-providing avenues to gain insight into data broker information. In this paper, we leverage the Facebook advertising system-and their partnership with six data brokers across seven countries-in order to gain insight into the extent and accuracy of data collection by data brokers today. We find that a surprisingly large percentage of Facebook accounts (e.
In parallel, online services such as Facebook and Google have been collecting information about people's online activities. Their business model is to build advertising platforms that use this data to provide advertisers with fine-grained targeting features  [29, 31]. Recently, data brokers and online services have begun partnering together, allowing for the data collected about users online to be linked against data collected offline. This enables online services to provide advertisers with targeting features that concern users' offline information (e. g. , advertisers can then target users based on their net worth, purchase behavior, and so forth  [4, 34] ). While this linking of offline and online data may seem to only further stoke the privacy debate, it does have one significant benefit: it offers a unique opportunity to gain visibility into the data broker ecosystem.
Specifically, advertising platforms often provide advertisers with statistics about any audience an advertiser can target, including those created using data broker-provided attributes. This provides the first opportunity to gain insight into the extent of data collection by offline data brokers. In this paper, we use the Facebook targeted advertising platform to study the coverage and accuracy of data collected by four offline data brokers (Acxiom, Epsilon, Experian, and Oracle Data Cloud -formerly Datalogix) across seven countries (U. S. , U. K, Australia, Germany, France, Japan, and Brazil). 1  To examine the coverage of data brokers (i. e. , the fraction of the population they have data on), we collect statistics from the Facebook advertising interface on how many Facebook users possess each of more than 600 data broker-provided attributes.
Our results demonstrate that a large percentage of Facebook accounts (e. g. , ranging from over 48% of users in Japan to over 90% of users in the U. S. ) are indeed linked to some data broker information, thus demonstrating for the first time the extent of offline-online data linkage, in addition to demonstrating the coverage of offline data brokers. Furthermore, we devise a methodology to study the coverage at a fine granularity based on the targeting mechanisms and statistics commonly provided by online advertising platforms in Section 3. We find that data broker coverage generally increases with the age of users, and can be significantly lower in U. S. counties with higher poverty levels. We also find that data brokers have high levels of coverage for potentially sensitive user attributes such as income (76.
To study the accuracy of attributes sourced from data brokers, we run Treads to a set of crowdworkers who installed a browser extension that collects the Treads seen by the worker, and surveys them about the accuracy of the inferred broker information. We use the results from 183 workers-corresponding to 1,432 individual inferred data broker attributes-to analyze the overall accuracy of broker and Facebook information, the per-user accuracy of broker and Facebook information, and the accuracy of broker information across various information categories. We find that the wide coverage of data broker information comes at the cost of mediocre accuracy, with around 40% of attributes being reported as "Not at all accurate" by workers. We also find that inaccuracy does not occur in an all-or-none fashion (i.
Facebook's advertising platform Facebook's advertising platform is one of the largest and most mature online advertising platforms. It leverages Facebook's user data to allow advertisers to target audiences of users (i. e. , sets of users with particular attributes) with ads. Targeting parameters When specifying an audience, advertisers must specify the location (e. g. , countries, states, or postal codes), age range (between 13 and 65+), and gender of users to target. In addition, advertisers can choose to include or exclude users who have certain attributes, choosing from a list containing 1,121 attributes; of these, 614 attributes come from Facebook and are present in all countries, while the others come from data brokers and can vary across countries  [2]. In addition, advertisers are able to create targeting formulas of boolean expressions over attributes  [11].
Size estimates Once the advertiser specifies the particular attributes of users to target, Facebook then gives the advertiser a size estimate (called the potential reach) that represents the number of monthly active Facebook users who meet the targeting criteria. 2 rior work found that the potential reach was calculated by rounding the number of matching users to two significant digits  [52]. Partner categories Facebook refers to the offline information obtained by linking its profiles with data brokers (also called Facebook Marketing Partners) as partner categories  [4]. Facebook reports that partner categories are "available to people targeting audiences located in the United States, Brazil, France, Germany, the United Kingdom, Australia, and Japan"; this includes data from Acxiom, Acxiom Japan, CCC Marketing (for Japan), Epsilon, Experian, Oracle Data Cloud, and Quantium  [4].