dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic reconstruction Every DNA BUSCO locus was aligned with MAFFT v7. 427  (Katoh et al. , 2002)  using the L-INS-i method (Supplementary Data). We removed sites that had fewer than three non-gap characters from the resulting multiple sequence alignments (MSAs). These trimmed MSAs were concatenated to form a supermatrix. We inferred a maximum likelihood (ML) phylogenetic tree (Supplementary Data) from the supermatrix (a. k. a. concatenated alignment) using IQ-TREE v1. 6. 5  (Nguyen et al. , 2015) , and treated the supermatrix as a single partition. IQ-TREE was run under GTR+I+G substitution model, as inference under any other substitution model will not necessarily lead to better accuracy of tree topology estimation  (Abadi et al. , 2019). To estimate the support for each node in this tree, we used three different reliability measures.
Fossil Dating We implemented the Bayesian algorithm of MCMCTREE  (Yang, 2007)  with approximate likelihood computation to estimate divergence times within Drosophila using several calibration schemes (SI Appendix, Table  S1 ). First, we estimated branch length by ML and then the gradient and Hessian matrix around these ML estimates in MCMCTREE using the DNA supermatrix. Because large amounts of sequence data are not essential for accurate fossil calibration  (Anisimova, 2012) , we performed dating analysis using a random sample of 1,000 MSA loci (out of 2,791) for the sake of computational efficiency. Thus, for this analysis the supermatrix was generated by concatenating 1,000 randomly selected gene-specific MSAs. Using fewer loci (10 and 100) for fossil calibration did not drastically affect nodal age estimation (SI Appendix, Fig.
S15 ; Supplementary Data). We removed sites that had less than 80 non-gap characters from all these supermatrices. Second, we used the gradient and Hessian matrix, which constructs an approximate likelihood function by Taylor expansion (dos  Reis and Yang, 2011) , to perform fossil calibration in MCMC framework. For this step we specified a GTR+G substitution model with four gamma categories; birth, death and sampling parameters of 1, 1 and 0. 1, respectively. To ensure convergence, the analysis was run for 7 × 10 6 generations (first 2 × 10 6 generations were discarded as burn-in), logging every 1,000 generations. We used the R package MCMCtreeR  (Puttick, 2019)  to visualize the calibrated tree.
Using these subsampled clade topologies, we formed all possible network topologies having a single reticulation node (with the exception of networks having reticulation nodes connecting sister taxa) (Supplementary Data). Because PhyloNet takes gene trees as input, for each clade we subsampled each gene tree to include only the subset of 10 species selected for the PhyloNet analysis (or all species in the case of clade 3); any gene trees missing at least one of these species were omitted from the analysis. Finally, we used the GalGTProb program  (Yu et al. , 2012)  of the PhyloNet suite to obtain a likelihood score for each network topology for each clade. We report networks with the highest likelihood scores (Supplementary Data).
Acknowledgements: We thank  M. Hahn ,  M. Turelli ,  A. Yassin , and  M. Matschiner  for helpful feedback on previous drafts. AS was supported by the  NIH  under award no. R00HG008696  and DRS was supported by the  NIH  under award nos. R00HG008696  and  R35GM138286. BYK was supported by the  NIH  under award no. F32GM135998. JW was supported by the  NIH  under award no. K01DK119582. DP, ERRD, DRM and AAC were supported by  NSF Dimensions of Biodiversity  award  1737752  and  NIH  award  R01GM121750. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We then added LiveRamp CMP  [33] , already studied by Hils et al. because of its novelty, which is linked to a major data broker  [34]. Finally, we interviewed with a Data Protection Officier (DPO) who works for EU and US companies: they pointed us to Cookie Script CMP  [35] , which is particularly popular among Small and Medium Enterprises (SME). We preselected both free and paid services, including "premium" ones (see Table  1 ). Installation of preselected CMPs. We contacted six of the identified companies -Quantcast, OneTrust, TrustArc, Cookiebot, Crownpeak, LiveRamp -via their websites using contact forms or provided emails, and received different types of responses. Quantcast replied that their paid CMP was discontinued, and that all the functionalities were now integrated into the free one  [36].
During a presentation call, LiveRamp said they would come back to us to say if it was possible to test their service that was "only for publishers", but they failed to do so after six weeks. Cookiebot and Cookie Script CMP services were directly available on the website and we installed their consent pop-ups directly. We studied both the free and paid versions of Cookiebot. For Cookie Script, we studied three services: the free version, plus the cheapest (Lite) and most expensive (Plus) paid versions. Summary of selected CMPs. After removing the CMPs that refused installation for research purposes or did not respond in one month, we obtained ten different services provided by five CMPs: Quantcast, CookiePro, Cookiebot, Cookie Script, and Crownpeak (Table  2 ). For our analysis of the registration process (further described in Section 4.
1), we also used results from some of the preselected CMPs to highlight their manipulative strategies during the registration. Configuration used for experiments. For our tests, we used a dedicated version of Mozilla Firefox (v84. 0) with an independent profile  [37] , running on GNU/Linux Ubuntu 18. 04. 5 LTS. To avoid interpretation errors resulting from different browser versions, we blocked automated updates of the used browser. We have also enabled all third-party cookies and disabled the "Enhanced Tracking Protection" of Firefox to avoid interference with our experiments. We install the exten-sion Ernie  [38]  on this browser, that is able to detect 6 categories of cross-site tracking via cookies, including several types of cookie synchronizations. This extension, that implements cookie-based tracking detection proposed by Fouad et al.
[39]. We then repeat the experiment, (1) giving a full consent by clicking "Accept all" on the consent pop-up, and (2) refusing to give any consent by clicking "Reject all" on the pop-up (when available). When no "Reject all" button or similar option was provided on the first page of the consent pop-up, we decline consent for all categories in the customization interface. We record our observations, and try to explain them with the help of the documents provided by the companies such as Privacy Policies and commercial documentation. We keep a record of these documents at the date of the consultation. We also search in the code of the consent pop-ups to find possible unnecessary data sent to third-parties. Additionally, we take the name of each studied company as recorded in the CMP list  [45]  provided by the IAB Europe, and search for it in the Global Vendor List (GVL)  [46]  for a possible matching to identify companies that operates both as vendor (advertiser) and CMP.
Ethical considerations Our study was conducted on an empty website hosted by our French institution, and involved real companies registered as CMPs in the IAB Europe TCF Framework. Our study did not involve real users, but instead took the role and simulated the user journey of website publishers when installing services proposed by CMPs. We installed a consent pop-up directly via a website of a CMP, whenever possible (which is the case of CookiePro Free CMP builder, "Free" account, and "Standard" account, Cookiebot free, Crownpeak Business, and Cookie Script Free, Lite and Plus). However, for TrustArc, and LiveRamp, we had to contact the companies via contact forms or emails. We intentionally shared our main purpose of the study, which is academic research, in order to provide transparency as to the purpose of the usage of selected CMPs.
The complete process is designed to make the inclusion of vendors easy (large number of vendors included by default, addition of Google vendors in one click) while blocking vendors needs to be done manually. In the "Free CMP Builder" service offered by Cook-iePro, it is not even possible to customize the list of vendors at all. In the "logged" version of CookiePro free (used with an account), the CMP built by default a consent pop-up with an "Accept" button and no "Reject" on the first page, and an "Allow all" button on the top of the second page. Both lead to a bulk consent and close the pop-up. On the opposite, Crownpeak Business (a "Premium" service), Cookiebot, CookiePro Standard, and Cookie Script include only the vendors that were found on the website when scanning it, or that were added manually by the publisher.
You may click to consent to our and our partners' processing as described above. Alternatively you may click to refuse to consent or access more detailed information and change your preferences before consenting. Please note that some processing of your personal data may not require your consent, but you have a right to object to such processing. " This text does not indicate explicitly that a user who clicks on the "Disagree" button is only refusing to give an explicit consent to the processing based on it, but is not objecting to other processing, based on the legal basis of legitimate interest. To completely refuse any processing of her data, the user who wants to object should instead select More options, Legitimate Interest, and then object to several or all vendors. Therefore, users do not have information by default on how to object to legitimate interest-based processing in the Quantcast consent pop-up.
The categorization is made by matching the cookies found with the Cookiepedia database (also owned by OneTrust). In order to perform an analysis, the publisher needs to fill a form with a name, an email address, and the URL of the website to scan. CookiePro scanner displays an overview of its finding in the browser, and sends the report by email as a PDF file. The first overview contains four sections: (1) a summary of the website, with the number of pages and the number of cookies found, (2) a Privacy checklist which indicates if the scanner was able to find a consent pop-up, cookie policy, and pri- a detailed list of the cookies found (when applicable). The CookiePro's detailed PDF report contains the number and list of cookies, tags, forms, and webpages found, analyzed and sorted by categories such as first/third party, session/persistent, web object types, and cookie purposes.
However, nothing indicates that it has any tracking role. After scanning a website, the tool redirects to a report page prompting to "Add cookie compliance" with an exclamation mark, even if no cookie was detected on our empty website. Since our website does not include trackers, the usage of a consent pop-up is not mandatory and such solicitation, like in case of CookiePro, is not needed. Cookie Script also adds a link to the report in the consent pop-up, introducing confusion to the website visitors who might believe that the visited (empty) website contains trackers. Regarding the detection process, we can therefore conclude that CookiePro and Cookie Script are not mentioning the presence of any cookie on our empty website, while Cookiebot mention its own consent cookie, assuming that we will install their banner.
CMP abusing its position to collect data for its own use In this section we monitor the resulting empty web site (one per CMP service, as described in Table  2 ) that includes the CMP consent pop-up. Our goal is to assess if this integrated consent pop-up content could be leveraged to collect data for the CMP own use, and whether some CMPs exploit their privileged position to actually collect data for their own use. We therefore aim at answering the following research question: -RQ5. Are CMPs abusing their central and privileged position and their presence on a large number of websites to collect data for their own use? Case of Quantcast Choice. When a page including Quantcast universal tag, the Javascript code provided by Quantcast to provide Quantcast Choice CMP, is loaded by a browser, it sends 10 distinct thirdparty requests, aiming to 6 different subdomains.
This cookie have an expiry time of one year. Notice that this tracking pixel was integrated by default in all Quantcast Choice banners even before the user makes a decision regarding acceptance or refusal of consent in the pop-up interface! This finding confirms the behaviour reported by Santos et al. [6]. The requests to these Quantcast servers are also flagged as tracking by tracking filter lists such as Disconnect 1. 1 https://github. com/disconnectme/disconnect-trackingprotection/blob/21134d05e7a407739d7db0b695cbbf359afffdd2/ services. json#L5646 After an update on March 5th, 2021, the behaviour of the choice. js script has changed: the request for 1x1 invisible pixel to pixel. quantcount. com does not set a tracking cookie anymore unless the user gives a positive consent, If the user does not consent, no mc cookie is set in the browser.
" To conclude, even if it is possible that the mc cookie attached to the request made to pixel. quantserve. com is a part of Quantcast measure service, the CMP does not disclose any other information about this cookie in its privacy policy  [55]. Case of Cookie Script Free. With Cookie Script free, the banner itself does not include any tracking request. However, it does include a link to the page of the last scan report ran on the website, previously described in Section 4. 4. The tracker scan page includes a Google Analytics service, as well as social sharing buttons, all of which generating a total of 41 third-party requests and the deposit of 6 cookies without the user's prior consent according to Firefox developer tools. 16 of these requests are flagged as related to "known trackers" in the Disconnect list.
More precisely, Quantcast and CookiePro tend to propose to the publishers by default the entire Global Vendors List provided by the IAB Europe, which contained 751 companies end of May 2021, potentially complemented with the Google Ad Tech Providers (ATP) list that is almost the same size. In their turn, the publishers, in particular when they rely on default settings, present the same list to the users, with several hundreds of companies. So if a user agrees, she explicitly accepts her personal data to be exchanged among hundreds of companies, instead of being limited to those present on the visited website. Then a user cannot comprehend such a long list, and Veale and Borgesius  [56]  have demonstrated that the "informed choice" requirement cannot be fully met in these circumstances, which theoretically voids the user consent.
On the opposite, by default, the services of Cookiebot, Crownpeak, and Cookie Script do not include the whole list of vendors but instead customize it automatically by using the results of a scan, which clearly benefits to the end user. In addition, many CMPs present themselves as being able to help publishers increase the consent rate of their web site visitors, which raises questions about the very function of consent pop-ups. Indeed, it implicitly validates that the purpose of these pop-ups is ultimately more about "extracting" positive consent than letting users make a free and informed choice. On the other hand, CMPs consent pop-ups can create additional privacy and security issues. Our methodology involves creating an empty website that does not include any tracking tool. However, by adding a consent pop-up, we found analytics tools -presented as meant to provide statistics on consent rates -in Quantcast, and in a scan report page made available by Cookie Script -presented as meant to monitor views of the scan report page.
IP-based tracking  [63]  and fingerprinting scripts  [64]  can also be used with first-party cookies to target non registered users who block or delete cookies. In their February 2021 report  [59] , the IAB Europe explores "Identity solutions" such as email-based Customer Relationship Management. Such data is then aggregated in large databases, in a pseudonymized manner, often after hashing the user email address  [59, 65]. Of course, this pseudonymization approach enables persistent, cross-device, and cross-site tracking, to the benefit of data brokers such as LiveRamp and their partners  [65]. From a privacy viewpoint, in the long run, end users may lose visibility and have less control with this evolution, because an increasing part of the tracking process will happen directly on server side, and it is no longer a matter of storing or removing a cookie in the user's web browser, which is easily viewable.
Acknowledgments This work was supported by the  ANR   JCJC  project  Pri-vaWeb  ( ANR-18-CE39-0008 ) and the  H2020 SPARTA Cybersecurity Competence Network  project. We would like to thank the reviewers for their helpful comments,  Vera Wesselkamp  for her assistance with the ERNIE extension,  Jean-François Scariot  for his technical support,  Cristiana Santos  for providing useful legal resources, and  Midas Nouwens  for his help regarding the analysis of CMPs' plans.
Data Sources and Measurement The questionnaires included 17 questions in 2 parts. Part 1 comprised 13 closed-ended questions on participants' sociodemographic characteristics, such as age, gender, outpatient department attendance, practice setting, qualification, and years of experience in TB diagnosis and treatment. Part 2 comprised the following 4 open-ended questions to assess TB diagnostic practice, treatment monitoring practice, treatment duration, and TB regimen prescription practice. Mention the combination of anti-TB drugs used to treat drug-susceptible pulmonary tuberculosis in your hospital. For how many months do you treat drugsusceptible pulmonary tuberculosis in your hospital? What diagnostic test do you use to diagnose TB in your hospital? What test do you use to follow up drug-susceptible TB patients on treatment in your hospital? We pretested the questionnaires across 10 selected non-NTP facilities that were not part of the study.
Data Collection and Analysis Trained independent data entry clerks first entered the data into Microsoft Excel. Data collected were then categorized, coded, and cleaned before being imported into SPSS version 22 for additional statistical analyses. In addressing the research objectives, we applied descriptive statistics to summarize the sociodemographic status and described the proportion of participants with different TB diagnostic and treatment practice responses. Incorrect and correct practices were presented using frequency and percentages based on provision of the NTP guidelines.
A survey of the empirical evidence from the recent literature Early analyses hypothesized an increase in technological unemployment  (Keynes, 1930; Leontief, 1952; Lucas & Prescott, 1974) , however they lacked empirical support. A next generation of studies were able to confront theoretical models with data. Their analyses have been primarily conducted at the national or industry level and have mostly conveyed the idea of automation having a negative impact on aggregate employment and aggregate wages: automation is primarily reducing labor demand. Yet these analyses fall short of describing the process that goes on within firms. It is only over the past few years, thanks to the increasing availability of new firm-level datasets, that analyses of the effects of automation on employment could be performed at a more disaggregated level.
For instance, positive demand shocks may induce firms to automate and simultaneously raise employment and wages. Similarly, firms may adopt robots to increase productivity after some negative labor market shock. This implies that the OLS estimates of the effects of automation on labor market outcomes could be biased, either upward or downward. To account for the potential endogeneity of ΔRobots ct , we build on  Autor, Dorn and Hanson (2013)  and construct an instrument that is meant to isolate the variation in ΔRobots ct induced by supply shocks in robot exporting countries, rather than by shocks occurring in individual CZs. To construct the instrument, we source (from UN Comtrade) data on robot exports from non-US countries to eleven European economies over 1989-2018. 12 To apportion the countrylevel robot imports to individual industries, we use the share of each industry in total machinery imports into a given country, as extracted from countryspecific Import Matrices available in the World Input-Output Database  (Timmer et al.
We also classify occupations depending on how easy it is to relocate their tasks to foreign countries. Our main index of occupational offshorability is sourced from  Autor and Dorn (2013). The authors use the simple average of two variables constructed by  Firpo, Fortin and Lemieux (2011) , who employ data from the O*Net database to measure the degree to which workers require face-to-face interaction and physical presence on the job. The index is reversed, so higher levels indicate higher offshorability. We standardize the index to have mean 0 and standard deviation 1 across occupations, and define as offshorable all occupations whose index is above the median. The two occupational characteristics are available for 331 US Census occupations. We match these characteristics to the US Censuses and the ACS using information on each worker's occupation of employment provided in the two data sources.
(  12 ). The eleven European countries are Austria, Denmark, Finland, France, Germany, Italy, Netherlands, Spain, Sweden, Switzerland and the UK. In the UN Comtrade database, trade in industrial robots is recorded under code 847950 of the Harmonized System classification. Wright (2014)  proposes a plausibly exogenous measure of offshoring, derived using variation in US offshoring to China. In case an index is missing for an occupation, we use information for the corresponding broader occupational group. See, among others,  Acemoglu and Restrepo (2019)  for additional evidence on the growth in the usage of industrial robots in the US based on data from the IFR. 16  The change in offshoring in a CZ is constructed as ΔB_Offsh ct = ∑ j λ cjt. ΔB_Offsh jt , where ΔB_Offsh jt is the change in offshoring in industry j over period t.
(  12 ) with ten-year equivalent log changes in expenditure on software and databases, ICT, machinery and other types of capital and machinery. 19  The proxy for robot exposure based on changes in the stock of robot imports over a given period is constructed as ΔRobots_Stk ct = ∑ j λ cjt ∑ τ E t ln(1+ Rob_M jτ ), where τ denotes individual years within time period t. The other two proxies are constructed analogously to eq. (  12 ). 20 We implement the correction for spatial clustering using the approach presented in  Conley (1999). We define the spatial cluster of a CZ as including all other CZs within a range of 660 km or 768 km. These distances ensure that the spatial cluster of the most remote CZ consists of at least 5 or 10 CZs, respectively. The resulting clusters can overlap with each other and can span different states.
Mukherjee et al. (2019)  stress the role of contract negotiation skills, the ability to monitor and evaluate the performance of suppliers, or the knowledge of alternative supplier arrangements and their cost structure. Whether the "paradox of automation" is of any practical relevance, and whether something similar applies also to offshoring is, first of all, an empirical issue. We look for traces of the paradox at the sector-occupation level. 6 We focus on 92 occupations at the 3-digit ISCO-88 level and 16 (out of 21) sectors according to the NACE Rev. 2 classification. To make sure that our results are not driven by specific countries or institutional contexts, our dataset covers 13 European countries in the period 1995-2010. We analyze the impact of automation and offshoring on "selectivity" as measured by skill concentration, unemployment duration and educational mismatch.