dataesr/scientific-paragraphs-categorization huggingface dataset:
This reduces the bias caused by attrition to some extent, as the cross-sectional database is less affected by attrition. However, due to its integrated design, not only the longitudinal but also the cross-sectional databases are affected by attrition in EU-SILC. Attrition is potentially linked to fertility, as starting or enlarging a family might incite parents to move, which increases the risk of not being followed up in EU-SILC. This risk is even higher in case of household splits. If a member of the household leaves the household to live elsewhere, the chances are high that he/she is no longer followed up, especially if the person is not the principal respondent. Adult children who leave the parental home are thus potential candidates for having relatively low follow-up rates. These are in general likely to have a first child, which might explain why EU-SILC especially underestimates childbirth of order one for young women below the age 30.
Following this logic, first childbirth will be particularly underestimated for those adult children who leave the parental home because they start their own families. While in some European countries it is relatively common to live alone or to share a flat or live with a partner for a certain time before having children (Nordic countries, Continental countries), in other countries, especially Mediterranean ones, it is usual to leave the parental household only just before having one's own children  (Eurostat 2015 , Prioux 2006 , Macura and Beets 2002 , Kiernan 2002). These childbirths are particularly at risk of not being observed in the longitudinal EU-SILC database. Another group of individuals that might be particularly affected by attrition is people who divorce and separate  (Iacovou, Kaminska, and Levy 2012).
Births for those who found a new family shortly after separation also risk being under-reported in EU-SILC. In order to gain more insight into the question of who is particularly concerned by attrition, we now analyze follow-up rates for women aged 15 to 55 who entered the EU-SILC in 2009 and who are supposed to be followed up for four years until wave 2012 (women are in the same rotation group, in each country). EU-SILC does not provide longitudinal data for Ireland, Switzerland, Germany, and Croatia. Sweden, Slovakia, and Romania are missing in Figure  2 , as for these three countries longitudinal data was only available until 2011 at the time we conducted the analysis. On average, for the 23 European countries 61% of women are followed up for four years. Figure  7  illustrates that the follow-up rates are very heterogeneous among European countries in EU-SILC.
This might also be linked to the fact that the in the Nordic countries (as in the Netherlands and Slovenia), data collection is based on http://www. demographic-research. org 535 administrative registers (they use registers to collect several variables) and other information is obtained via interviews with a 'representative' person in the household  (Iacovou, Kaminska, and Levy 2012). 6  Those countries presented in Figure  2  that have the highest follow-up rates are not necessarily those countries with the lowest bias in TFR. Lithuania, Bulgaria, Estonia, and France, for example, have relatively high follow-up rates but also high downward biases in TFR. Thus, a good follow-up does not necessarily lead to less-biased periodic fertility measures. Even if 80% or more individuals of a population are followed up, fertility will be downward-biased due to attrition if the individuals who are most 'at risk' of childbirth are the ones that are most likely to drop out of the database.
Applying a retrospective approach Our TFR measure presented in Figure  1  is based on a cross-sectional database, using individual cross-sectional weights. The fact that we identify a downward bias in total fertility rates suggests that the weighting process in the cross-sectional database is not efficient enough to compensate for the underrepresentation of childbirths/newborn children. Following our argument that the downward bias in TFR in the cross-sectional database is linked to attrition, children should appear in the survey sometime after their birth. Once parents have moved and are settled with their children they potentially reenter the survey in a nationally representative probability sample. Under these circumstances, measures of periodic fertility will be underestimated in EU-SILC for most recent childbirths, but should be less biased for childbirths that occurred slightly longer ago.
To test this hypothesis, we calculate total fertility rates of certain years by using different waves of the cross-sectional EU-SILC database. The total fertility rate of a certain year is the sum of age-specific fertility rates of this year, while we observe childbirths of all birth orders. The cross-sectional database is used because of its larger sample size. Figure  5  illustrates the logic of our retrospective approach with a Lexisdiagram. For example, we use the cross-sectional wave of 2012 to calculate total fertility rates for  2011, 2010, 2009, and 2008. For the TFR of 2011 we consider all children whose reported year of birth is 2011 by women aged 16 to 50 years old in 2012 (ages calculated by deducting the year of their birth from the survey year). For the TFR of 2010 we consider all children whose reported year of birth is 2010 by women aged 17 to 51 years old in 2012, and so on.
The event "childbirth" is thus under-represented in EU-SILC, and children enter the survey during their first years. Thus, to limit the downward bias in periodic fertility measures, it seems reasonable to observe childbirth in a retrospective way; i. e. , to allow for a certain time delay between the childbirth year and the survey year. Figure  7  illustrates that applying a retrospective approach limits the downward bias in TFR to a significant extent in all countries. The EU-SILC measures of total fertility rates presented in Figure  12  allow for a time delay of more than one year between the time of the survey and the birth event. We use the cross-sectional database of 2012 to observe births in the years  2008, 2009, and 2010. Grouping together three http://www. demographic-research. org 543 years obtains a sufficiently large sample size for each country, which also serves for the latter analysis, decomposing TFR by age and birth order.
To further reduce random variations we aggregate age-specific fertility rates in five-year age groups. With this procedure we observe a minimum of 30 births for each age group and birth order. We compare these TFR measures to the unbiased measures of total fertility rates for the year 2009, which are calculated by using age-specific fertility rates from the Human Fertility Database. We also use the HFD measures for decomposition analysis by age and birth order, which is presented later in this article. Data on age-specific fertility rates by birth order is only available for 16 countries in the HFD. To ensure comparability, the HFD measures are also aggregated in five-year age groups. Figure  7  illustrates that with the retrospective approach, the EU-SILC measures of TFR are not significantly different from the HFD for 14 out of 16 countries.
With this retrospective approach, childbirths of birth order 2 are very clearly observable in EU-SILC for the majority of European countries, independent of woman's age. The blue and red lines are almost identical for most countries. For children of birth order 3 there are no longer significant differences for any countries and any ages. Note, however, that from birth order 3 on, in many countries the confidence intervals become very large. Nevertheless, almost parallel red and blue lines can be found in the high-fertility countries like Norway, Sweden, Finland, and Iceland, as well as in the Czech Republic, Austria, and Slovenia. Unfortunately, the HFD does not provide data by birth order for France. Note that these results were obtained by measuring fertility in EU-SILC with a retrospective approach, which limits the bias caused by attrition.
Differentiation by rotational groups In the cross-sectional database, around a quarter of individuals are observed for the first time, around a quarter for the second time, around a quarter for the third time, and around a quarter for the fourth time (as illustrated in the schematic in the introduction). The cross-sectional dataset is thus composed of individuals/households of four different rotation groups. Following the logic that childbirth causes attrition, total fertility rates should be lower for those rotation groups in the cross-sectional SEU-ILC sample that contain individuals who have been observed for more than one wave. To test this hypothesis, we calculate total fertility rates (retrospectively for the period 2008 to 2010, as in the previous section) based on the cross-section database of 2012, and differentiated by rotation group.
Conclusion The European Union Statistics on Income and Living Condition (EU-SILC) are increasingly used not only in economic but also in demographic analysis. Researchers benefit from the large country coverage and the availability of several harmonized socioeconomic measures. These variables are observed on an individual basis, and household members can be merged. The fact that women's and their partners' characteristics can be observed simultaneously represents a major advantage in comparison to other more demographic surveys such as the GGS. However, as a nondemographic survey, birth events risk being underreported in EU-SILC. This paper sheds light on the quality of periodic fertility measures in EU-SILC. We find that attrition in the longitudinal database is linked to fertility behavior.
Childbirth and residential mobility are correlated events. Consequently, individuals who are likely to have children have a higher probability of not being followed up. This particularly concerns childless women in their twenties. However, follow-up probabilities do not differ significantly between socioeconomic groups in EU-SILC. Thus, attrition is not a general problem in obtaining consistent estimates when analyzing socioeconomic determinants and consequences of childbirth. Yet childbirths, and in particular first childbirths of women under 30, are underrepresented in EU-SILC. Longitudinal weights reduce the downward bias caused by attrition, as EU-SILC provides longitudinal weights such that the longitudinal population represents the crosssectional population. However, childbirths are also underrepresented in the crosssectional sample, as the annual cross-sectional data are produced from the longitudinal panel (integrated design).
EU-SILC is a unique database which has several advantages: it covers a large set of countries and a relatively long time period, it includes not only cross-sectional but also longitudinal data, and, most importantly, it provides comparable socioeconomic information for all adult household members. EU-SILC thus has a strong potential for enriching not only economic but also demographic analyses. In order to improve the quality of measures of periodic fertility behavior, and to allow EU-SILC users to infer information about national populations, a possible direction for future research is to derive weights that can be used to correct estimates of fertility. http://www. demographic-research. org 553.
Control for attrition in EU-SILC by longitudinal weighting Longitudinal weights provided in EU-SILC are supposed to take into account the phenomenon of non-response. To identify how far these longitudinal weights control for attrition linked to fertility within the EU-SILC databases, we compare fertility measures obtained with the cross-sectional and the longitudinal databases, with and without the respective weights. More precisely, we calculate age-specific fertility rates by birth order for women aged 15 to 50 in 2009. First, in the longitudinal database, we concentrate on women who are observed for the full period of four years, from 2009 to 2012, and we observe their childbirths during this period. Second, we use the cross-sectional database of 2012 to observe childbirths for the years 2009 to 2012.
In each dataset we group together all countries that are available in the two datasets. We expect fertility to be lower in the longitudinal database, as here we selected only women who are observed for the entire four years, while the cross-sectional database contains four rotation groups. Figure A-2 illustrates our findings for first childbirth. Without taking into account the cross-sectional and longitudinal weights, we find that the phenomenon of first childbirth is underrepresented in the longitudinal database compared to the cross-sectional database, while the difference is significant for ages 20 to 30 (left panel). For second and third childbirths there is no significant difference between the two datasets (results are available on request). This again indicates that attrition is linked to first childbirth of young women.
The right panel in shows that once we focus on partnered women, the difference disappears. In the crosssectional database we do not know if women were already partnered in 2009 (beginning of the observed period in the longitudinal database). In order to obtain the best comparability between the two datasets we select in both the longitudinal and the crosssectional dataset women who were partnered in 2012. In line with the previous section, this suggests that being partnered is a good proxy for having founded one's own household, which increases the chance of being followed up in EU-SILC, even when the household as a whole moves. It seems that women who do not live with their parents until shortly before or after first childbirth are particularly affected by attrition. A country-by-country analysis (available on request) shows that the difference in first childbirth for ages 20 to 30 between the cross-sectional and the longitudinal database is particularly pronounced in Mediterranean countries, where late cohabitation with parents is quite common, while the differences are not significant in the Nordic countries, France, and other Continental countries, even for young ages.
Finally, taking into account the cross-sectional and longitudinal weights leads to the fact that the differences between the two datasets are no longer significant for all ages (all women, not only the partnered ones), as  It seems thus that in EU-SILC, the cross-sectional database, which is less affected by attrition, serves to create longitudinal weights that reduce the fertility bias caused by attrition in the longitudinal database. However, attrition also affects the cross-sectional.
This was done by checking whether the author instructions mentioned the domain or a closely related aspect, e. g. , a reference to a publication of a reporting guideline, in the author instructions. Data were extracted independently in a random sample of 10% of the retrieved journals by a second researcher. Discrepancies were noted in only 2. 3% of the entries corresponding to an almost perfect agreement (κ = 1. 00 (p>0. 001)) [44]. Based on the wording of the author instructions, we determined whether (i) a journal required adherence to a specific domain, for example, by stating that trial registration is a requirement for publication and wording that implies that the paper would not be published without trial registration, (ii) recommended its use, for example, by stating that authors should/ or are recommended to pay attention to a certain reporting guideline, or (iii) did not mention it at all.
For conflict of interest statements we distinguished between journals that (i) did not mention whether authors were asked to disclose conflicts of interest, (ii) "would (likely)" publish a statement with the article or whether it (iii) "would (likely) not" publish a statement with the article. Journal characteristics were extracted from the Journal Citation Report or, in case of unavailability, from the National Library of Medicine Catalog of journals referenced in the National Center for Biotechnology Information Databases (http://www. ncbi. nlm. nih. gov/nlmcatalog/journals). Information extracted included start year, language and geographical region of publication, publisher, and journal impact factor. For the longitudinal study part, author instructions were retrieved from the journals' websites during three time periods i.
1. 2 [46]. For all mixed models with binary outcome (endorsement yes/ no), we used function "glmer" from R package lme4. For the longitudinal analysis investigating potential effects on change in impact factor (continuous outcome), we used function "lmer" from R package lme4. Statistical significance was set at p-value < 0. 05. All original data are available in the supporting information files (S2 File Extracted Data 2010, S3 File Extracted Data 2012, S4 File Extracted Data 2015).
6%) also indicated a suitable trial registry such as the ones listed in the WHO International Clinical Trials Registry Platform (http://www. who. int/ictrp/en/), ClinicalTrials. gov (www. clinicaltrials. gov) by the U. S. National Institutes of Health and others. According to their website, most journals (211; 91. 3%) had a policy for disclosure of conflicts of interests of authors. We judged based on provided information in the author instructions that 146 (63. 2% of the journals with conflict of interest policy) would publish or consider publishing a conflict of interest statement together with a manuscript.
Digital knowledge gave birth to massive communication spaces, new access paths and new cleavages. Our experiment deals with the challenging issue of accessing this knowledge on the Internet. Computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is partly automatized. Using a real-life dataset, our goal is to simulate the iterative behavior shift produced by most used recommender engines. On this basis, we show that in the context of recommendation, existing evaluation metrics are driven by prediction testing methods and we argue that ambiguity has to be raised between prediction and recommendation. Secondly, we propose alternative evaluation metrics for recommendation systems, targeting mitigating the bias problem of information cascades and confirmation biases.
This feedback enriches the initial dataset. As it was explained by  Adomavicius et al. ,  this model produced a feedback loop, where users feed the engine, which in turn feeds the users  (Adomavicius et al. , 2013). Here is how the model is defined : in addition to its natural cognitive functions, the user relies on artificial cognition functions (the engine), built with structured items and AI algorithms. These artificial functions are composed with a form of the user self-cognition aggregated with a form of all other users cognitions. Natural biases tend to be impacted in this context. Here we focus on information cascades and confirmation bias. The first one relies on collective behavior and the second one is a self-directed bias. One of our goal is to demonstrate empirically, that in the presence of a recommender, these biases might be emphasized, producing behavior shifts.
Coverage Data-mining techniques implies a preprocessing step where data are extracted, cleaned and organized. The final step of this overall process is the actual running of the engine through a well-prepared and clean matrix. But, for many reasons this complicated operation might make some part of the dataset unavailable. Distributed dataset, hugeness, real-time execution or difficulty to qualify data might be the reason. Coverage measures the ability to cover the overall set of items or users  (Ge et al. , 2010).
Our Experimental Measures In this article, we want to challenge accuracy, precision and recall. These measures might be abusively used. That observation seems to be shared with others, who think that there is something beyond accuracy  (Ge et al. , 2010). Here we unveil our three experimental measures with a new evaluation workflow. We discover for each item which is the most similar item. So we obtain two sets: the initial dataset and the recommendation. These elements can be represented as an associated array, with a (key, value) mechanism, where the key is an item and the value is the corresponding most similar item.
Real-life Dataset Our application context is Web browsing. The reallife dataset is composed with users, items (web pages), timestamps and items categories. Other information that we did not used were available. It has been downloaded on Kaggle. com 1 , a web shared platform for datasets and relative applications. This dataset is provided by Outbrain, a global company that develops recommender engines. The dataset was made available in the context of a competition, featuring 1302 competitors and a $25. 000 award. The dataset was huge: several billions of user, item interactions and a CSV file up to 100GO. Our experiment focuses on 2% of this dataset.
Recommendation Distribution Widening is between 0. 15 and 0. 36. The ideal coefficient is 1, which means that 100% of the items are recommended once. Despite some divergences, we observe that no method is able to open its recommendations to a wide portion of the initial dataset. Some methods are not wide but have a high level of novelty. For example, the CB engine recommends 35% of the items and has a neutral popularity influence. It means that the relatively small portion of recommendations are chosen from the entire data set. Besides, AA engine has a 0. 23 widening coefficient oriented toward the initial head at 83%. In figure  2  we observe that all recommendation distributions are fitting a power law. Some items tend to be more recommended than other. It is not a surprise, because widening coefficient are low.
Limits and Further Work We are aware that our experiment is subject to limitations. Some limitations may be pushed away with further work. • Multiplying empirical experiments with a similar methodology, relying on large, reliable and various datasets. Moreover, meta information that we have used in confirmation measure, is difficult to obtain. Other data might be used. In addition, we have chosen to connect successively visited items. It's a common way to structure users activities. But, some other models of interactions might be experimented. • Formalizing mathematical validations: reproducing empirical demonstrations should not be enough to fully validate our purpose. Mathematical demonstrations should be given. • Experimenting in real-time: working with terminated user activities makes a key factor invisible: whether or not the user has been influenced by the suggestion.
We aggregate the climate projections of five models (under one climate pathway of global future development (RCP 6. 0)) from the open-source database Climate Change Health Impact Profiles project (ClimateCHIP, www. climatechip. org). The ensemble approaches of aggregation differ in the way they score the different models with respect to the monthly Mean Absolute Error (MAE), which is computed (for each climate model) by assessing the difference between the recorded and computed air temperature recorded (from 1981 to 2005). The following weighting strategies are considered for the aggregation  [15; 16; 21] : i) weight proportional to the inverse of the MAE; ii) weight proportional to the logarithm of the inverse of the MAE; iii) weight proportional to a Borda count-based ranking  [15]. We consider the Passive Containment Cooling System (PCCs) of AP1000 NPPs as case study  [22]  because: i) nuclear power is an energy option considered to reduce greenhouse gas emissions  [23; 24] ; ii) it has been shown that the safety and reliability of NPPs are significantly influenced by changes of air temperature, precipitation, river flows, sea level, shoreline erosion, coastal storms, floods, heat waves, etc.