dataesr/scientific-paragraphs-categorization huggingface dataset:
Facebook recently announced that it would soon be terminating these partnerships and removing these categories  [49]  in the wake of the Cambridge Analytica scandal, and ultimately did so in October 2018. Linking partner data One key concern with partner data is the linking process: essentially, identities in the partner's database need to be correctly linked with Facebook's user database to enable accurate targeting. While the exact linking methodology is not public, Facebook has stated that it is based on users' unique identifiers  [34] : For each Facebook user, the company computes a hash code of the person's phone number, email address and other major identifiers and transmit those to Acxiom, Oracle, Epsilon and the other data brokers it works with and requests that they return all available marketing segments they offer for that user.
Another small-scale study  [45]  surveyed 8 graduate students about their information collected by Oracle BlueKai as revealed by its transparency feature, with all participants finding some inaccuracies in their profiles. These two studies focus on a non-representative sample of users, on one data broker, and (in the case of the former study) on a small set of attributes provided by the broker; in comparison, our methodology allows us to study a more general sample of users, covering multiple data brokers, avoiding the use of a sanitized version of the data, and focusing on all 507 data broker attributes that Facebook makes available through its advertising platform. Online-offline linkage Prior work  [36]  has explored the potential for data brokers to link online data (Facebook profiles) and offline data (voter registration records) in target cities in the U.
Data collection In order to decode these Treads and to infer a user's data broker attributes, we built a browser extension for Google Chrome. The extension scans for our ads on Facebook as the user normally browses, and locally stores only the numeric identifiers in the Treads that are seen (for privacy reasons). We then use our extension to survey users about the accuracy of their data broker attributes. As a baseline, we also survey users about the accuracy of information about them collected internally by Facebook. To do so, we gather the user's Facebook ad preferences page  [28]  just prior to the survey; we call these Facebook attributes. When surveying participants, we randomly shuffle the data broker attributes and Facebook attributes, without revealing which is which to users. In order to avoid user fatigue, we survey them on a random sample of at most 25 data broker attributes and at most 30 attributes overall.
For each user attribute, we show the user the attribute, and ask them questions about its accuracy. Specifically, we ask users whether the inferred attribute is "Not at all accurate", "Somewhat accurate", "Mostly accurate", or "Completely accurate. " For data broker attributes, we also show Facebook's description of the attribute to the user. 6 For Facebook attributes, this description is not available; hence, we only show the attribute. Finally, we ask about their demographic information. Users are asked to answer only those questions they feel comfortable answering. Changed targeting attributes Early-on during our experiments in this section (in August 2018), we found Facebook stopped supporting 120 out of the 507 original targeting attributes. 7 By freshly obtaining the set of targeting attributes from the API (in the same way as in Section 3.
User recruitment Our user study was reviewed and approved by Northeastern University's Institutional Review Board (IRB). We recruited users from Prolific [44], a crowdsourcing platform geared towards researchers; the quality of data obtained from Prolific users has been found to be comparable to that of the dominant crowdsourcing platform, Amazon Mechanical Turk  [41]. We used Prolific's filters to selectively target our study at users aged 18 and above, residing in the U. S. , and who use Facebook on a regular basis (at least once a month). We also followed Prolific's guidelines to selectively obtain high-quality participants  [18] , only considering users who had at least 10 prior submissions on Prolific, and an approval rating of at least 95 out of 100. This gave us a pool of over 4,000 potential participants, from which we recruited 300 users.
Participants were invited to participate in a research study about their reactions to their Facebook information (we did not mention data brokers until the end of the study so as not to bias participants). Participants were first asked to install our extension, for which they were paid $2. Participants were also asked to browse Facebook as they normally would in the subsequent days, and were informed how our extension works. After a period of a month, we followed up with the 231 users that still had our extension correctly installed, and requested them to take the survey (via the extension). We compensated participants $8 for completing the survey. 183 users completed the survey; this attrition rate of 39% (compared to the initial 300 users recruited) is well within the range of attrition rates observed in longitudinal studies on Prolific  [18].
8 igure  3  shows the distribution of the number of data broker and Facebook attributes for these users who took the survey. 9 While around 61% of these users had at least one data broker attribute, 41% of the users had 10 or more data broker attributes, with the median user having four attributes. The median participant had 196 Facebook attributes; given our limits on the number of attributes surveyed, the median number that we surveyed them on was 22. Participant demographics To quantify the bias in the set of surveyed participants, we compare their demographics with those of the overall U. S. population  [3]. For age/gender, we compare against the population of targetable Facebook identities (see Section 3). Table  2  compares the age and gender distribution of participants. We find 56.
12. 6% of our participants reported themselves as "Hispanic or Latino", as opposed to 18. 1% of the overall population. Finally, our partipants are spread across 39 U. S. states. Limitations Inaccuracy in data broker information revealed via Facebook's advertising platform can arise from two sources: (i) inaccuracy in the information collected by data brokers, and (ii) inaccuracy in linking data broker information to Facebook accounts. Thus, our methodology measures the cumulative effect of these two sources of inaccuracy, providing an upper bound on the inaccuracy of information collected by data brokers. However, as discussed in Section 2, Facebook only uses unique identifiers such as phone numbers and email addresses to match data broker information  [34] , thus indicating that the inaccuracy introduced due to the linking process is likely limited.
Thus, our upper bounds are likely tight. Additionally, the information exposed by Facebook via its advertising platform may only cover a subset of information held by data brokers. However, recent reports have stated that Facebook receives "all available marketing segments offered for [each] user" from data brokers  [34]. Moreover, since our methodology relies on Treads to reveal user attributes, not all data broker attributes of each user might be successfully revealed (e. g. , if some Treads that are not delivered). Finally, our methodology relies on the accuracy of attributes as reported by participants; while each participant is the best source for evaluating the accuracy of their own data, they might misunderstand what certain attributes mean, may lie, or might be subject to fatigue (which we try to limit).
Analysis of accuracy We next analyze the responses from participants in order to understand the accuracy of information collected by data brokers. Overall accuracy of attributes We begin by analyzing the overall accuracy of the collected data broker attributes, shown in Table  4. We find a largely bimodal distribution: 40. 6% of the 1,432 surveyed broker attributes are reported "Completely accurate", while 40. 5% of them are reported "Not at all accurate. " By contrast, a higher percentage (proportion test, p < 0. 001) of Facebook attributes are reported "Completely accurate" (51. 7% of the 3,464 surveyed attributes). Also, a lower percentage (p < 0. 001) of Facebook attributes are reported "Not at all accurate" (25. 3% of the 3,464 surveyed attributes). This may be due to the significantly larger amount of data that Facebook can directly observe in users' activities, when compared to data brokers.
In contrast, the percentage of incorrect attributes from Facebook that are stale is higher (32. 3%). Thus, data broker attributes appear to suffer from many inaccuracies, only partially explained by out-of-date data. User-level accuracy Inaccuracy in a user's attributes might often be because of data brokers' inability to uniquely or correctly identify users from different source databases (e. g. , the user might have a very common name). Thus, it might be expected that users' attributes are inaccurate in an all-or-none fashion (i. e. , that either almost all a user's attributes are incorrect, or almost all a user's attributes are correct). To study whether this is the case, we show the variation in accuracy by user in Figure  4. Contrary to our expectation, we see that users typically have a subset of their broker attributes inaccurate, with the percentage of attributes marked "Not at all accurate" varying widely between 0% and 100%, with the median user having around 40% of their broker attributes marked "Not at all accurate.
Future applicability of methodology Facebook removed partner categories as of October 2018  [4] , meaning that it is no longer possible to study the coverage and accuracy of data brokers via Facebook's advertising platform. However, our methodology is still applicable on other platforms such as Twitter, which offers similar categories sourced from Acxiom and Oracle Data Cloud  [32]. In addition, our methodology could also be used to study the coverage and accuracy of information internally gathered by online advertising platforms such as Facebook, Google, and Twitter, themselves. The existing transparency mechanisms of these platforms, revealing collected user information, have been shown to reveal an incomplete view to users  [2, 17, 54]. Thus, our methodology for studying accuracy using Treads could potentially reveal the accuracy of the data that these platforms have actually collected.
CONCLUSION The extent of data collection by online sites and offline data brokers, and the extent of linkage between online and offline identities of users are both crucial inputs to ongoing debates about user privacy. This paper used Facebook's advertising platform to (a) measure for the first time the actual extent of linkage between online (Facebook) identities and offline identities (as compiled by data brokers), (b) measure for the first time the coverage of data brokers from seven countries in a fine-grained manner, and (c) measure the accuracy of information from data brokers for U. S. -based users. Taken together, our analysis and results of the fine-grained coverage of data brokers provide new insights into the workings of data brokers, that can feed into ongoing privacy debates. We hope this paper serves as a starting point for more such analyses in the future.
ACKNOWLEDGEMENTS We thank the anonymous reviewers for their helpful comments. This research was supported in part by the  Data Transparency Lab , by  NSF  grant  CNS-1616234 , by the  French National Research Agency (ANR)  through the  ANR-17-CE23-0014  grant, by the  ERC Advanced Grant "Foundations for Fair Social Computing"  (No. 789373 ), and by a  UMIACS  contract under the partnership between the  University of Maryland  and  DoD. Additionally ,  Elissa Redmiles  acknowledges support from the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE 1322106  and a  Facebook Fellowship.
In contrast, the disadvantages of sensed big datasets are that they suffer from spatial and temporal sparseness  (Lu et al. , 2017; Vanhoof et al. , 2018b) , from lack of -or an unknown degree ofrepresentativeness  (Longley et al. , 2015; Arai et al. , 2016) , and from issues regarding anonymity  (De Montjoye et al. , 2015). Within the urban sensing literature, mobile phone data play a prominent role as they form a source of passively collected information (users do not need to make an explicit action to share their locations as would be the case in, for example, location-based services or social networks), for large shares of populations (high shares of the world population now owns a mobile device of any sort), captured at a rather high spatial resolution (in general, the density of cell towers is high in urban areas).
I. 2. Mobile phone indicators One problem of the single-city focus in previous research is that it can not ensure that observations made in one city (usually a capital city of large population) remain valid for other cities. As a consequence, it is unclear whether findings can be generalized over different types of cities. The creation of mobile phone indicators avoids this problem. Since mobile phone indicators are typically calculated for large user samples covering multiple cities, aggregation of individual-level indicators in space allows to compare findings between cities. In addition, mobile phone indicators can be paired with other datasets so that multi-variate methods can support interpretation. In the case of mobile phone data, creating individual indicators is possible at a nation-wide scale (as datasets are mostly provided by national operators) but it is not a straightforward task.
II. Data This section introduces the census and mobile phone indicators we will use in our investigation. We limit the analysis of census data to three socioeconomic urban indicators chosen for their social relevance and easy interpretation. They relate to three dimensions of the economic organisation of cities: their level of poverty (or deprivation), their level of inequality (distribution of wages) and their level of segregation (spatial distribution of wages) and are introduced in II. 1. Regarding mobile phone indicators we deploy 15 mobile phone indicators derived from a mobile phone dataset in France and covering aspect of human mobility and social interaction. Their creation and properties are described in II. 2.
Wage inequality at the level of the city was computed similarly to  Cottineau et al. (2018). The inequality index is calculated using the Gini index method  Fuller (1979)  on groups of similar wage earners as described in the CLAP database holding information on French firms and establishments. The inequality index measures the overall dispersion in the distribution of wages at the city level, and varies between 0 (extreme equality) and 1 (extreme concentration of wages). Wage segregation for cities was also computed similar to  Cottineau et al. (2018) , using the Reardon (2009)'s R O index of ordinal segregation for classes of wages retrieved, again, from the CLAP database. This segregation indicator measures the spatial dispersion of the distribution of wages between communes of the city, and varies between 0 (homogeneous city) and 1 (extreme segregation by wages in the city).
Qualitative individual interviews Semistructured interviews with a total of 57 healthcare personnel were conducted from October to November 2017 during the peak caseload season. Prior to implementation, a guideline composed of open-ended questions on knowledge, attitudes and barriers to hand hygiene was developed. The guideline was tested during a 4 d pilot period before data collection, when questions and transcripts were assessed for data quality, monitored for relevance to predefined themes and, if necessary, reformulated if misunderstood. Interviews with all participants were conducted face-to-face in the local language and audio-recorded.
Data analysis Hand hygiene compliance was defined as the proportion of observed hand hygiene opportunities that resulted in 'handrubbing with ABHR solution' or 'handwashing with water and soap'. Hand hygiene compliance was calculated by caseload season (peak vs non-peak) and further stratified by personnel type, WHO hand hygiene moment and type of care provided. Individual interview audio recordings were transcribed and translated verbatim from Hausa (or local languages) into French narrative text. Textual responses generated from the openended questions underwent a manual word-by-word inductive and deductive review to identify predefined and emergent thematic areas considering importance relative to the study aims when deeming emergent themes to be salient. 14, 15 Exemplar quotations were presented in text to illustrate key findings.
Annual training for healthcare personnel to improve their understanding of the necessary moments for hand hygiene and emphasising that gloves are not a replacement for hand hygiene may support improved knowledge of when hand hygiene is necessary and how it is performed correctly. Implementation of monthly routine monitoring and performance feedback for hand hygiene, as recommended by the WHO Guidelines,  12  may also encourage healthcare personnel to adopt hand hygiene into routine behaviour. This study had a number of strengths and limitations. We triangulated data from environmental sampling, hand hygiene observations and qualitative interviews to more fully describe contamination and drivers of poor hand hygiene within the study setting; we also considered differences in hand hygiene practice by caseload seasonality.
Remerciements L'auteur remercie plusieurs enseignants-chercheurs, spécialistes du tourisme et du Bénin, pour leurs échanges scientifiques autour de cet article. Noukpo Agossou , professeur de géographie ( PTU-CAMES ),  directeur du Laboratoire d'Aménagement Régional et Développement (LARD) , directeur des  Éditions Populaires Africaines (ÉPA), directeur de l'Institut Supérieur des Métiers de l'Audiovisuel (ISMA) , a relu et corrigé les questions traitant de géographie économique et culturelle. Antoine Yves Tohozin , professeur titulaire de géographie humaine et économique (CAMES),  Département de Géographie et Aménagement du Territoire ,  Faculté des Sciences Humaines et Sociales, Université d'Abomey-Calavi , a relu et corrigé les questions traitant des populations, des groupes humains, de  la géographie et de l'histoire du Bénin.
Analysis of data produced by prominent free-market think tanks suggests that this claim is premature. Solar geoengineering is being discussed in US free-market circles and might become more prominent as our climate predicament worsens but, until now, it has not been a key objective of those institutions. It is notable, however, that most of those who did consider SRM tended to support funding for research often, though not always, as an alternative to mitigation policies. Hence the need for researchers to keep track of what is being said about SRM in the freemarket think tanks that have a close relationship with the Republican party. It could also be instructive to find out whether elected officials start taking up their recommendations in favor of SRM funding. So far, the influence of free-market think tanks on climate policy has been largely negative in that it has consisted in defeating or limiting the implementation of policy mechanisms intended for climate change mitigation, such as carbon taxes and emissions trading systems.
Acknowledgments. We would like to thank  Olivier Morin  and  Christophe Heintz  for feedback on this project. H. M. 's work is supported by an  Agence Nationale de la Recherche  grant  EUR  FrontCog  ANR-17-EURE-0017. H. M. 's work is supported by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ) and  ERC  grant agreement no. 609819  (SOMICS).
But these days the hardware for audio-and video-recording is increasingly cheap, small, and wearable, and is often combined with other types of simultaneously recorded data (e. g. , GPS, heart rate, body acceleration). Software for audio manipulation, annotation, and analysis is also improving rapidly, helping researchers to find meaningful moments in large, complex datasets without first requiring them to spend months manually annotating their data. By bringing these technologies together, we have already begun to get a glimpse into the language used in peoples' daily lives, particularly with respect to children's language environments (see Section 1. 1). With the refinement of these technologies, we can expect to see major advances in models of language processing and learning. What can we do to usher this progress along? Today's data collections become the basis for tomorrow's theories and tools.
As researchers who work on language, our current task is to carefully consider how we can collect, annotate, store, and analyze data such that we can optimally produce informative work in the present while also benefiting our future research. In the rest of this Introduction we will briefly review past research using LFSE recordings with child and adult populations, discuss what is needed in future LFSE research, and then give an outline for the remainder of the paper. 2010;  Hoff, 2006). Major research questions for child language development include: Who talks to the child, how often, and what do they say? What influence does the child's own speech have on what they hear? How do those measures change with age? Could these behaviors be used to effectively detect language delays/disorders and the effects of interventions? In order to propose and test mechanisms for language learning, researchers need to know what information is available to children in their language environments.
LENA's recording device also comes with software that automatically detects and classifies stretches of speech into noise-type categories (e. g. , target child speech, female adult speech, etc. , plus several nonspeech categories; see Ganek & Eriks-Brophy 2018a, for an overview of the system). LENA-based research has been conducted to different extents in a range of communities in North America, Europe, and East Asia;  (Bergelson & Aslin, 2017; van Alphen, Meester, & Dirks, 2017; Busch, Sangen, Vanpoucke, & Wieringen et al. , 2017; Schwarz, Botros, Lord, & Marcusson, 2017; Canault, Normand, Foudil, Loundon, & Thai-Van, 2016; Ganek & Eriks-Brophy 2018a; 2018b; Elo, 2016; Gilkerson et al. , 2015; Soderstrom & Wittebolle, 2013; VanDam, Ambrose, & Moeller, 2012; Weisleder & Fernald, 2013). By combining the LENA system's automated output with manual coding-for example, by adding information about who the speaker is talking to-researchers have been able to make several basic characterizations of children's daily speech environments.
, 2018). Though LENA's system was originally designed for use with American English, it has been recognized by researchers as a useful tool in several other language communities. In these cases, researchers have worked to validate its use for their languages and circumstances of interest (e. g. ,  Marchman, Martinez, Hurtado, Grüter, & Fernald, 2016; Canault et al. , 2016; Woynaroski et al. , 2016; Gilkerson et al. , 2015). Overall, LFSE recordings have been used very actively in the last decade to study child language. Their use to study adult language has grown somewhat more slowly, as we will see below.