dataesr/scientific-paragraphs-categorization huggingface dataset:
ONLINE AUDIENCE AND THE RETURNS TO ORIGINALITY In this section, we attempt to address the following key question: given the magnitude of online copying, what are the incentives to produce original content? Using article-level variations and media-level daily audience combined with article-level social media statistics, we show that an increase in originality leads to an increase in audience, thereby mitigating the newsgathering incentive problem raised by copying. Unfortunately, our main dataset does not include article-level information on the number of visitors, but only aggregated information on web traffic at the daily level for the media outlets (all articles combined). We attempt to overcome this limitation by using alternative article-level information that we collect from Facebook and Twitter.
Furthermore, we use an additional dataset from Le Monde to relate article-level Facebook shares and article-level numbers of views. This section is organized as follows. Using the Le Monde dataset, we first document the relationship between the number of times an article is viewed and the number of times it is shared on social media (5. 1). We then provide estimates of the returns to originality using our different proxies for article-level audience (5. 2). Finally, we compute an audience-weighted measure of the importance of original content (5. 3), and provide some orders of magnitude as to the returns to originality (5. 4).
Specifically, we characterize the joint distribution of the number of Facebook shares and the number of unique visitors at the daily level, and use a rank-rank specification with 20 quantile categories. We find that the relationship between the number of views and the number of shares is almost perfectly linear. A 10-percentile-point increase in the number of Facebook shares is associated with a 7. 3-percentile-point increase in the number of views on average. Hence, for each article a published by the media n on a given date d, we can use its Facebook rank (P FBadn ) to compute its rank in the number of visitors distribution (P Vadn ). This relationship can be summarized with only two parameters: a slope and an intercept. Given that in our main dataset we only have aggregated information on the total audience at the daily level for each media outlet, the second step consists in investigating the average number of visitors in each rank of the number of visitors distribution.
An audience-weighted measure of the importance of original content Finally, we compute the audience-weighted share of original content in the dataset defined as: , where a index the articles. We do so by using our different measures of the number of views. Figure  6  presents the results. First, for the sake of comparison, we compute the share of original content in the dataset. This share is equal to 32. 5%. 53 Regardless of the methodology we use to compute article-level number of views, we find that the audience-weighted share of original content is higher than the actual share of original content in the dataset. The audience-weighted share of original content.
REVIEW OF ECONOMIC STUDIES Notes: The figure reports the audience-weighted share of original content we obtain using our different approaches to compute article-level number of views. The first bar ("In the dataset") simply reports the share of original content in the dataset (with no weight). The second bar ("Naive (media-level) approach") reports the share of original content we obtain when we attribute the same number of views to all the articles published by a media outlet on a given date. The third bar ("Weighted by annual audience") reports the share of original content we obtain when we weight the content of each article by the average annual audience of the media outlet in which it was published. To compute the fourth bar ("Weighted by Facebook"), we attribute number of views to articles assuming a linear relationship between the number of Facebook shares and the number of article views.
CONCLUSION This article documents the extent of copying online and estimates the returns to originality in online news production. It builds a unique dataset combining the online production of information of the French news media during the year 2013 with micro audience data, and develops a number of algorithms which could be of future use to other researchers studying media content. First, we investigate the speed of news dissemination and distinguish between original information production and copy-and-paste. We find that only 32. 5% of online news content is original. Even if we focus only on external copying and exclude content copied from the news agencies, we show that, on the extensive margin, nearly two-thirds of the articles in events contain at least some copying. Furthermore, the copied parts are arguably the most valuable parts of the 61.
Share Visits adn = δ +γ 1 Share Facebook adn +γ 2 Share Facebook 2 adn +γ 3 Share Facebook 3where Share Visits adn is the share of the total views received by media n on date d represented by article a, and Share Facebook adn is similarly the share of the total number of Facebook shares received by media n on date d represented by article a. We use the estimated parameters to compute in our main dataset the number of views received by each article from the number of times it has been shared on Facebook.
Acknowledgments. We are grateful to the Editor ( Nicola Gennaioli ) and to five anonymous referees for insightful comments that substantially improved the article. We gratefully acknowledge the many helpful comments and suggestions from  Charles Angelucci ,  Yasmine Bekkouche ,  Filipe Campante ,  Lucien Castex ,  Etienne Fize ,  Matthew Gentzkow ,  Sergei Guriev ,  Emeric Henry ,  Ali Hortacsu ,  Elise Huillery ,  Laurent Joyeux ,  Benjamin Marx ,  Petra Moser ,  Elisa Mougin ,  Aurélie Ouss ,  Arnaud Philippe ,  Thomas Piketty ,  Andrea Prat ,  Valeria Rueda ,  Agnès Saulnier , and  Katia Zhuravskaya. We are grateful to participants at the  Barcelona GSE Summer Forum , the  Big Data for Media Analysis Conference , the  CEPR Public Economics Annual Symposium , the  Economics of Media and Communication Conference , the  IEA World Congress , the  NBER Political Economy Meeting , the  NET Institute Conference on Network Economics , and SIOE 2017, and to seminar participants at  Banque de France , the  Paris School of Economics, Sciences Po Paris , the  Toulouse School of Economics , and the  University Carlos III of Madrid.
We thank  Jérôme Fenoglio  and  Pierre Buffet  for sharing Le Monde's data on the number of views per article,  Andrew Montalenti  for giving us access to Parsely's data, and  Richard Fletcher  for providing us the survey data from the 2013 and 2018 Reuters' Digital News Reports. Edgard Dewitte ,  Anais REVIEW OF ECONOMIC STUDIES Galdin ,  Béatrice Mazoyer ,  Lucile Rogissart  and  Jeanne Sorin  provided outstanding research assistance. This research was generously supported by the  NET Institute , the  Paris School of Economics , the  Banque de France, Sciences Po's Scientific advisory board (SAB) , and the  French National Research Agency  ( ANR-17-CE26-0004 ). Since November 2015,  Julia Cagé  has been a Board member of the Agence France Presse; this paper does not reflect the views of the AFP and responsibility for the results presented lies entirely with the authors.
Share Visits adn = δ +γ 1 Share Facebook adn +γ 2 Share Facebook 2 adn +γ 3 Share Facebook 3where Share Visits adn is the share of the total views received by media n on date d represented by article a, and Share Facebook adn is similarly the share of the total number of Facebook shares received by media n on date d represented by article a. We use the estimated parameters to compute in our main dataset the number of views received by each article from the number of times it has been shared on Facebook. adn +γ 4 Share Facebook 4 adn +γ 5 Share Facebook 5 adn +γ 6 Share Facebook 6 adn + adn ,.
Given that documents are of different lengths, we also compute the ratio of original content in the dataset out of the total content. We find that the share of original content is equal to 32. 6%. In other words, nearly 70% of online information production is copy-and-paste. This finding is consistent with the results obtained by Boczkowski (2010)  who highlights the rise of homogeneization in the production of news stories online by two Argentinean newspapers.
This approach relies on two assumptions. First, we assume that the relationship between the number of Facebook shares and the number of views we uncover for 2017 also holds in 2013. We think this assumption is relevant given the stability of the media market we discuss in Section 6. 3. Second, we assume that this relationship is constant across all the media outlets in our sample. While we cannot test this assumption, in the Supplementary Appendix Section 1. 4 we provide additional evidence of the shape of the relationship using a dataset we obtain from Parsely. This dataset covers the year 2017 and contains information on the number of clicks and on the number of shares on social media originating from the U. S. for 1,363,308 articles published in English. These data rely on articles published by a large number of different media outlets and, in line with the evidence we obtain using Le Monde data, we find that the relationship between the number of clicks and the number of shares is almost perfectly linear.
We only consider here the articles for which we have audience data, and in particular we drop the AFP and Reuters. If we were to consider all the articles, then the share of original content in the dataset is equal to 32. 6%. The difference with the average originality rate of the articles comes from the fact that articles are of different length. Downloaded from https://academic. oup. com/restud/advance-article-abstract/doi/10. 1093/restud/rdz061/5673396 by Fondation Nationale Des Sciences Politiques user on 20 January 2020.
Accessory features were also present, such as those conferring resistance to antibiotics and toxic compounds, e. g. , ␤-lactamases, arsenic, and copper. In silico analysis using the CGE server (https://cge. cbs. dtu. dk/) revealed that this isolate belonged to the European clone II, a widespread clone associated with carbapenemase dissemination. We describe here the full-genome sequence of the first OXA-72producing A. baumannii strain from Serbia. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. LKIB00000000. The version described in this paper is version LKIB01000000.
Metabarcoding surveys were performed using both mitochondrial (Cytochrome Oxidase I) and nuclear (small subunit 18S ribosomal RNA) markers, and compared to morphological inventories compiled by a long-term benthic monitoring network. A sampling strategy was defined to enhance performance and accuracy of results by preventing the dominance of larger animals, boosting statistical support through replicates, and using two genes to compensate for taxonomic biases. Molecular barcodes proved powerful by revealing a remarkable level of diversity that vastly exceeded the morphological survey, while both surveys identified congruent differentiation of the meadows. However, despite the addition of individual barcodes of common species into taxonomic reference databases, the retrieval of only 36% of these species suggest that the remaining were either not present in the molecular samples or not detected by the molecular screening.
For alpha diversity estimates, the amount of taxonomic units revealed through a single molecular snapshot largely exceeded those uncovered through the morphological survey. Furthermore, the use of multi-gene metabarcoding also provided more reliable estimates of community composition than single genes, as each molecular marker has taxonomic specific affinities. Results also disclosed congruent patterns of beta diversity estimates across molecular and morphological surveys to confirm the reliability of metabarcoding, in addition to the molecular surveys uncovering meiofauna taxa. We also identified pitfalls and suggestions for improvement of molecular inventories, including the need for multi-marker assessments to unravel the broadest possible taxonomic diversity, as well as improved reference databases.
Sequencing of REBENT taxonomically identified metazoans To determine if commonly observed and morphologically identified species could be uncovered in our molecular datasets, tissues from the 50 most common benthic metazoan species from Zostera marina seagrass communities (Table  2 ) were isolated for DNA extraction and sequencing. The 50 species were collected and identified as a part of the morphological inventory compiled by the IUEM/REBENT network  [34] , and classified as the most common due to their consistent presence and high abundance at the six seagrass meadows described in this study. A detailed protocol for the extraction and sequencing of these metazoan species can be found in the S1 Protocol in the supplemental material section. A 710bp fragment of COI was successfully amplified and sequenced in 14 of the 50 species, while a 450bp fragment of 18S was successfully amplified and sequenced for 33 of those species, each using the respective universal primer set.
Bioinformatics data processing and analyses Raw pyrosequenced reads for COI and 18S were processed, clustered and taxonomically assigned, using the Quantitative Insights into Microbial Ecology (QIIME) v. 1. 7. 0 pipeline  [40]. A workflow of QIIME scripts executed for this project can be found in S2 Protocol of the supplemental material section. During the processing stage, dataset demultiplexing and quality checks, including the removal of low quality and short sequences (< 200bp), were implemented using the split_libraries. py script. Filtered reads were clustered into de novo molecular operational taxonomic units (MOTUs,  [41] ) with the aid of UCLUST  [42] , using the script pick_otus. py in QIIME at a defined pairwise sequence identity cut off value was 97%  [5, 6, 43]. Next, a representative sequence set of the clusters was generated using the pick_rep_set.
py script. Chimeras and "quasi-singletons" (sequences appear < 3 times in the dataset) were identified and removed from the MOTU table using USEARCH v. 6. 1  [42]  implemented in QIIME. The 33 and 14 prescreened and taxonomically confirmed DNA barcode sequences for 18S and COI (accessions KJ182970-KJ183017) were added to previously compiled reference databases for taxonomic assignment. Fourteen (18S) and three (COI) of these prescreened and taxonomically confirmed sequences were already present in the GenBank public database (www. ncbi. nlm. nih. gov/genbank), based only upon their shared sequence names (see Table  2 ). For 18S, the morphologically confirmed barcodes were added to the SILVA SSU r115 EMBL eukarya database (  [44] , http://www. arb-silva. de/) to produce a reference database composed of 11,617 sequences.
The COI barcodes were added to a COI metazoan-only reference database that we complied from GenBank to produce a database composed of 48,734 sequences. These databases were used to assign taxonomic identifications to representative MOTU sequences based on the highest similarity score using the BLAST assignment method  [45]  implemented through the assign_taxonomy. py script in QIIME. Sequences that did not return significant hits at >90% identify were labeled as "no blast hit" (unassigned), and we attempted to determine their putative identity by using inferences based upon global blast search. Molecular data were analyzed on the basis of presence/absence of taxonomically assigned MOTUs in each sample, as quantitative data based on the number of sequences ascribed to a given MOTU are unlikely to provide accurate quantitative estimates due to factors other than species abundance that likely interfere with the number of times a sequence was observed (e.
g. number of biological cells present in the sediment, DNA density in tissue, primer matching, etc. ). For the most accurate comparisons of metazoan diversity across morphological and molecular datasets, analyses were performed with and without the unassigned MOTUs. While the results were congruent, those obtained excluding the unassigned MOTUs are reported here, and results including these MOTUs can be found in the supplemental information section. The decision to exclude unassigned MOTUs was particularly important for the COI dataset, which had the most unassigned (93% of all MOTUs). We determined the putative identity of the unassigned COI and 18S MOTUs with the aid of NCBI's BLAST stand-alone program  [46] , using the public nucleotide database implemented with the blastn algorithm at default parameters.
Presence/absence information obtained from the molecular datasets were compared to a quantitative morphological dataset containing 322 species collected and taxonomically identified by the IUEM/REBENT network in 2011, in order to appraise the loss or gain of information obtained with each method in terms of alpha and beta diversity estimates. Given the inherent difficulties of morphologically identifying organisms smaller than 1mm, the morphological dataset was generated from animals with body sizes 1mm, while the molecular datasets included meiofauna (animals with body sizes < 1mm  [47] ) in addition to macrofauna. Therefore, diversity analyses were performed comparing 1) morphological data (1mm) with all molecular data ( 0. 5mm) and 2) morphological data (1mm) with molecular data 1mm (excluding 0.
5mm) for consistency and to better evaluate similarities and differences across survey techniques. A list of the 322 morphologically identified species and their presence/absence in each meadow is shown in S1 Dataset. Multiple rarefactions were applied to the molecular datasets to obtain standardized estimates of alpha diversity using Chao1 measurement for species richness  [48, 49]. Chao1 values were calculated using the specpool function of the Vegan community ecology package (v. 2. 0-10), executed in the statistical software R (v. 3. 0. 2)  [50]. Analysis of Similarities (ANOSIM) testing was done to determine if significant differences exist between sampling groups (core, mesh size, meadow), while Principal Component analyses (PCA) were completed to provide spatial illustrations of community structure across meadows, and was performed on the basis of Jaccard distances to minimize the weight given to absence (0) values.
Molecular and morphological datasets The Roche 454 FLX sequencing of Zostera marina sediment produced a total of 622,468 and 190,509 raw sequences, which were quality filtered to 412,838 and 153,463 for COI and 18S, respectively (S1 Table ). The additional removal of singletons and chimeras produced 411,086 (COI) and 150,677 (18S) sequences, which were then clustered into 13,492 and 1,316 representative MOTUs for COI and 18S, respectively. Table  2  presents a list of the 50 most common metazoan species recovered and morphologically identified from the sediment of Z. marina seagrass by the IUEM for the REBENT network. The list details the presence of COI and 18S barcode sequences matching the genera and/or species available in the public sequence database GenBank (NCBI), as well as those families, genera or species that match to MOTU sequences in the environmental metadatasets.
At the present, 51% (COI) and 80% (18S) of sequence barcodes of the 50 most common and abundant metazoan species are publically available in NCBI, encompassing 35 families and 48 genera (Table  2 ). From the COI environmental metadataset, seven MOTUs blasted up to the species level (14% of all common species and 50% of the barcodes added to the COI reference database), and five to the family level. For the 18S metadataset, 14 MOTUs blasted up to the species level (28% of all common species and 42% of the barcodes added to the 18S reference database), seven to the genera and five to the family (Table  2 ).
Uncovering the identity of taxonomically unassigned MOTUs "No blast hit", or unassigned matches, recognized an inability of the BLAST method implemented through QIIME to uncover a close sequence matching the target with an identity of >90%. Of the 13,492 MOTUs uncovered for COI, 944 were assigned to metazoans, while 12,548 (93%) remained unassigned. To glean the identity of the unassigned MOTUs, we performed the blastn procedure for each unassigned sequence against NCBI's global public database with the aid of the stand-alone BLAST command line program. The overall findings identified the closest match for 1. 5% of the MOTU sequences as Archaea, 69. 9% as Bacteria, 27. 8% as Eukarya, 0. 1% as viruses and 0. 7% as still unknown/unidentified with no match in the public database (Fig. 3A , S2 Dataset).
Further analysis of these 0. 7% unknown revealed open reading frame interruptions, suggesting the amplification of pseudogenes, possibly "numts", or nuclear copies of mitochondrial derived genes that become non-functional/coding and are often seen in COI  [53, 54]  (Fig. 3A ). As the blastn algorithm can be less stringent than the BLAST program implemented in QIIME, we cannot discard the occurrence of numts among the MOTUs unassigned after QIIME BLAST but assigned after the blastn procedure. Therefore, while numts are expected to fall close to their original mitochondrial sequence, we detail only analyses with the 944 initially assigned MOTUs below. We did, however, compare diversity results including and excluding the initially unassigned 12,548 MOTUs (S2 ). The results for both datasets showed similar qualitative results for community structure, although diversity estimates are lower bounded when excluding the initially unassigned MOTUs.
Of the 1,316 MOTUs uncovered for 18S, 1,174 were identified as metazoans and 94 as nonmetazoans, while 48 (3. 5%) remained unassigned, which is a relatively low percentage compared to what has been observed previously (10%,  [55] ). To glean the putative identity of the 48 unassigned MOTUs, these sequences were also blasted against the NCBI public database, for which we found 48% of the initially unassigned MOTUs matched to non-metazoan eukaryotes, while 52% were still unknown (Fig. 3B , S3 Dataset). Given that the 52% of unknown MOTUs could contain metazoans, we compared diversity results excluding and including unassigned 18S MOTUs, but for the rest of manuscript, we discuss only the MOTUs initially assigned, similarly to what was done for the COI dataset. Both diversity analyses including and excluding the 48 initially unassigned MOTUs also show similar results (S2.
Taxonomic community composition by phyla The number and frequency of MOTUs assigned to 13 major metazoan phyla uncovered by morphologic and molecular methods are illustrated in Fig. 4. Colored bar lengths correspond to the frequency of MOTUs assigned to a specific phyla by each survey method, while the numbers above each frequency bar refer to the number of MOTUs identified. Numbers in brackets [N] above the COI bars correspond to the number of COI MOTUs that were initially unassigned, but once individually blasted to identify taxonomy, matched to one of the major phyla. Numbers without brackets above COI correspond to those MOTUs that were initially identified (n = 944). All frequency values can be found in the S4 Dataset. While Annelida, Arthropoda and Mollusca were the most frequently observed phyla with the highest number of MOTUs being assigned to these groups, each method differed in its taxa specific affinity.