dataesr/scientific-paragraphs-categorization huggingface dataset:
" The pressure was represented by the daily rate of persons hospitalized for COVID-19 per Département; ie, the number of persons hospitalized for COVID-19 (source: the COVID-19 specific national database https:// www. gouvernement. fr/info-coronavirus/carte-et-donnees) divided by the population of the Département (source: projections for 2020 of national census data https://www. insee. fr/fr/statistiques/2859843). The population density around each nursing home was calculated as the mean of population densities (number of inhabitants per km 2 ) of the nursing home location city and neighboring cities. The neighboring cities were those that share boundary points with the nursing home location city. They were determined using function poly2nb of the spdep package of R statistical software. The level of outbreaks in the population of each Département during the 2 waves was characterized by the cumulative number of people hospitalized for COVID-19 and the cumulative hospital COVID-19erelated deaths (source: https://www.
Nursing Home Data The data on COVID-19 outbreaks in the nursing homes were extracted from a specific database designed and maintained by Santé Publique France to monitor the spread of the virus in all medico-social facilities (cf. https://www. gouvernement. fr/info-coronavirus/carte-etdonnees). All nursing homes of the ARA region that reported at least 1 suspected case with symptoms or a confirmed case in residents or staff members during the first or second pandemic wave (March 1 to July 31, 2020, or August 1 to December 31, 2020) were considered to have had an outbreak during that wave. Whenever a nursing home reported several outbreaks during any wave, only the date of symptom onset in the first disease case of the first outbreak was retained. In case of missing information, the date of symptom onset was imputed using a nearest neighbor imputation algorithm.
Predicted outbreak rates and COVID-19erelated death numbers A Monte-Carlo method was used to obtain predictions of outbreak rates and death numbers with 95% prediction intervals. From the final multivariate models, 1000 samples of the parameters drawn from the multivariate normal distribution of the estimated parameters were used to obtain the distributions of the predicted outbreak rates and death numbers. The median, the 2. 5, and the 97. 5 percentiles of these distributions were retained as predicted values and used to build the 95% prediction intervals. All analyses were carried out with R statistical software, version 3. 6. 1. 14.
Acknowledgments The authors thank the health care professionals of the nursing homes in the  Auvergne-Rhône-Alpes region  who helped carrying out the survey and the agents of  Agence Régionale de Santé Auvergne-Rhône-Alpes  who were involved in the implementation of the control measures. The authors also thank the following epidemiologists involved in the surveillance system:  Delphine Casamatta ,  Philippe Pépin ,  Christine Saura ,  Garance Terpant , and  Mélanie Yvroud  from  Santé Publique France Auvergne-Rhône-Alpes  as well as  Kostas Danis ,  Scarlett Georges ,  Côme Daniau , and  Laure Fonteneau  from  Département des Maladies Infectieuses, Santé Publique France.
This work was supported by the  Agence Nationale de la Recherche  ( Flash COVID-19 program , grant  ANR-20-COVI-000 ) and the  Fondation de France  ( Engagement 105969 ). The funders had no role in the study design, data collection, data analysis, or preparation of the paper. The patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research. The study was conducted in agreement with the European General Data Protection Regulation and approved by the  institutional research ethics committee (Comite d'Éthique du Centre Hospitalier Universitaire de Lyon , N 20e81). The use of anonymized and aggregated data obviated the need for participant consent.
Social security data We use administrative data from the French family benefits social security organization, the Caisse Nationale des Allocation Familiales (CNAF). More than 98% of households affected by the reform are covered by our sample; registration is automatic, and the health costs of pregnancies are covered only if they are registered. 6 We exploit the monthly administrative files containing detailed information on family composition and the monthly benefits received. As the reform differs for single parents, we restrict our sample to families with two parents, whether married, in a civil union, or cohabiting. For confidentiality, the exact date of birth is not reported. To identify whether a household is affected by the reform, we use the child's year and month of birth. We select households where a child is born in December 2014 or January 2015, i.
e. , either one month before or after the reform. As discussed below, we also include households that had a child in December 2013 or January 2014 in our sample to control for any calendar effects unrelated to the reform. While social security benefits are reported monthly, earnings and unemployment benefits are extracted from annual tax returns. Tax returns are reported with a two-year lag because family allowances in year t depend on the taxable income in the antepenultimate year t-2. Because of this two-year lag, the tax data start two years before the child's birth and, thus, one year before the mother's pregnancy. On the other hand, while we have monthly information on family benefits until March 2022, annual tax returns are available only until fiscal year 2020, the sixth year after birth for the first cohort affected by the reform.
Administrative data on labor supply As discussed earlier, if there are no informational barriers or stigma, parents working part-time should take parental leave benefits they are entitled to after the reform. Estimating the take-up rate of parental leave benefits from part-time workers is thus interesting to assess the presence of potential barriers to the participation decision. A difficulty in estimating this take-up rate is that social security data do not report the number of hours or days worked corresponding to annual earnings. Instead, we rely on the French administrative panel 'Échantillon démographique permanent (EDP),' which contains a 4% sample of the population. 7  The data include birth certificates that allow us to identify young parents affected by the reform and administrative information from employers on earnings and the number of hours worked.
Effects of the reform on labor earnings and household income To understand the labor supply response to the reform of fathers and mothers, we examine how the reform affected the household's income and its components. As the income data are obtained from fiscal data, their frequency is annual and thus cannot be adjusted to the precise birth month. We define the 2015 calendar year as event year zero for births in both December 2014 and January 2015. By convention, the year zero is defined as the child's first year, from birth to her first anniversary. We consider in Table  5  how the reform affected total household income over the eligibility period, that is, over the first year of the child for first-time parents and up to the third anniversary for second-time parents. For first-time parents, Panel A indicates that despite the 0.
views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the  IZA Guiding Principles of Research Integrity. The  IZA Institute  based policy advice on labor market issues. Supported by the  Deutsche Post Foundation, IZA  runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. Boyer ,  Benoît Ceroux ,  Stéphane Donné  and  Florence Thibault. This research was supported by a  French  state grant to the  COMUE Université Paris-Saclay   ANR-11-IDEX-0003-02. This paper is an updated version of our previous work titled "by 𝑈 𝑐 (𝑐 * , 𝑝 𝑓 * , 𝑝 𝑚 * ) = 𝑈 𝑝 𝑓 (𝑐 * ,𝑝 𝑓 * ,𝑝 𝑚 -𝑝 𝑓 * ) 𝑈 𝑐 (𝑐 * ,𝑝 𝑓 * ,36-𝑝 𝑓 * ) -(𝑤 𝑚 -𝑏).
Automation and First Measurement Tests Now that we understand the principles of measuring axes and circles, it is important to coordinate the movements of the telescope, of its dome (to avoid masking prisms), of the total station and of the stepper motor. The MeOCenter software was developed to monitor all of them (Fig. 7 ). From measurements to processing, it provides a complete determination of the SLR reference coordinates. In the software interface, the user can choose to determine a single axis or to have a complete determination of the center (azimuth and elevation axes). The total station and the stepper motor are respectively driven by a Raspberry Pi and Arduino microcontrollers. Arduino communication is provided by serial port via the Firmata protocol whereas the MeOCenter software communicates with the telescope, the dome and the total station via Sockets through the OCA computer network.
At the end of the measurement process, the angle and distance data provided by the total station are formatted in a text file. They are sent to a Linux server where the Comp3d5 software is installed, in order to calculate the point coordinates in a local projected coordinate system. Comp3D, developed by IGN-France, is a micro-geodesy compensation software that implements a global 3D leastsquares adjustment of several topometric observation types  (Pesce 2013). The coordinates computed are sent back to the MeOCenter client by Sockets. Then the parameters of elevation and azimuth circle axes are determined by least squares circular regressions of these points. Finally, the SLR reference point is estimated by the orthogonal projection of the elevation axis onto the azimuth. Since the implementation of the automatic method on GRSM-7845 station, several test sessions have been per-formed.
However, a growing number of studies attempt to simultaneously analyse several life domains  (Fajth & Lessard-Phillips, 2022; Heath & Schneider, 2021; Lessard-Phillips, 2017; Telles & Oritz, 2009; Wallace et al. , 2022; Zhou & Xiong, 2005). The present study seeks to increase our understanding of the experiences of migrants' descendants, by analysing the outcomes of secondgeneration migrants in Sweden across three life domains. Empirically, we assess the likelihood that second-generation migrants resemble the majority group in terms of their socioeconomic, social and residential attainment. We use Swedish register data to study socioeconomic outcomes measured by educational attainment, employment and disposable income, social outcomes measured by intermarriage and the ethnic composition at the workplace, and residential outcomes measured by the ethnic composition and level of deprivation of the neighbourhood of residence, as well as homeownership.
Data and methods We use Swedish individual-level register data covering the period 1990-2015, which includes a wide range of socioeconomic, demographic and residential variables. 3 Multigenerational linkages allow us to distinguish between second-generation migrants, that is individuals born in Sweden with two foreign-born parents, and the Swedish majority group including individuals born in Sweden with two Swedish-born parents. Multigenerational linkages also enable us to account for differences in parental socioeconomic status among secondgeneration migrants and the majority group.
8 The housing tenure variable in Swedish register data indicates whether a dwelling is rented or owned. As it relates to the dwelling rather than to the individual, it cannot show whether an owned dwelling is sub-rented. 9 This approach follows the practice of comparing averages between migrant and non-migrant groups  (Alba & Foner, 2016 ; OECD/EU, 2019). It therefore differs from comparisons between children and their parents  (Bratu & Bolotnyy, 2022; Hermansen, 2016; Li & Heath, 2016) , and comparisons between migrants and different strata of the majority society  (Portes & Zhou, 1993). of origin. The second model adds controls for parental socioeconomic status. Parental characteristics include educational attainment (which differentiates among both, one or neither of the parents having only compulsory education in 1990), employment status (again differentiating among both, one or neither of the parents being employed in 1990) and the age of the mother.
Introduction: Smartphone technology can provide an effective means to bring real-life and (near-)real-time feedback from hearing aid wearers into the clinic. Ecological Momentary Assessment (EMA) encourages listeners to report on their experiences during or shortly after they take place in order to minimize recall bias, e. g. , guided by surveys in a mobile application. Allowing listeners to describe experiences in their own words, further, ensures that answers are independent of predefined jargon or of how survey questions are formulated. Through these means, one can obtain ecologically valid sets of data, for instance during a hearing aid trial, which can support clinicians to assess the needs of their clients, provide directions for fine-tuning, and counselling. At a larger scale, such datasets would facilitate training of machine learning algorithms that could help hearing technology to anticipate user needs.
Methods: In this retrospective, exploratory analysis of a clinical data set, we performed a cluster analysis on 8,793 open-text statements, which were collected through self-initiated EMAs, provided by 2,301 hearing aid wearers as part of their hearing care. Our aim was to explore how listeners describe their daily life experiences with hearing technology in (near-)real-time, in their own words, by identifying emerging themes in the reports. We also explored whether identified themes correlated with the nature of the experiences, i. e. , selfreported satisfaction ratings indicating a positive or negative experience. Results: Results showed that close to 60% of listeners' reports related to speech intelligibility in challenging situations and sound quality dimensions, and tended to be valued as positive experiences.
Large-scale, real-world data sets collected in this manner would also facilitate training of machine learning algorithms, which could foster hearing technology to anticipate user needs. Here, we describe a retrospective, exploratory analysis of clinical data, collected through EMA. The dataset consists of self-reported satisfaction ratings (indicating whether a momentary experience with hearing technology was positive or negative) and written open-text statements describing the experiences. Both were provided by real-world hearing aid wearers as part of their hearing care, by self-initiating surveys on a mobile application. Our aim was to explore how listeners describe their experiences with hearing technology, in real-time and in their own words, and identify emerging themes in the reports.
Also, we explored whether identified themes correlated with the nature of the experiences, i. e. , positive vs. negative self-reported satisfaction ratings. We hypothesized hearing aid wearers to describe perceptual experiences and experiences with hearing technology, with guidance for further fine-tuning, as well as related to practical matters, e. g. , in terms of handling of the hearing devices  (13, 14). As the EMAs were selfinitiated, we hypothesized listeners to rely on the tool mostly to request help from a clinician, report problems, or negative experiences. To the best of our knowledge, this is the first report of a large, real-world EMA dataset that was collected clinically, as part of adult hearing care. Also, to the best of our knowledge, it is the first report of a substantial amount of open-field, written text samples that were collected through self-initiated EMAs.
Study design A total sample of 8,793 self-initiated EMAs, provided by 2,301 hearing aid wearers as part of their hearing care, were analyzed retrospectively. The de-identified EMA data was collected through a smartphone mobile application compatible with commercially available hearing aids. No demographic data was collected, other than country in which the hearing aids were adjusted, as well as brand and type of the hearing aidsfrom which the technology level of the devices could be deducted. All listeners were lawfully informed that their de-identified data could be analyzed for clinical and research purposes upon acknowledging the data privacy notice of the mobile application. Ethical approval was obtained from the Institutional Review Board at Lamar University, Beaumont, Texas, United States (IRB-FY21-252) prior to analyzing the data.
Data collection The smartphone mobile application through which the data was collected, was freely available for download in the app store. The particular feature of the app through which the data was collected, was accessible for hearing aid wearers when it was activated by a clinician in the fitting software. Clinicians could choose to activate the feature whenever they believed that the listener and their hearing rehabilitation would benefit from a real-time feedback system, e. g. , to highlight situations relevant for further counselling or fine-tuning. Listeners were required to open the mobile application on their own initiative, when they decided that an event of interest took place (self-initiated EMA), and (1) indicate whether they were having a positive or negative experience (multiple choice, further referred to as self-reported satisfaction rating); (2) respond to one, sometimes two (depending on the answers provided by the listener) multiple choice questions related to the momentary listening environment and problems encountered in these environments (not considered for this report); (3) optionally, add additional feedback through an opentext field, by describing their experiences in their own words.
Data extraction and data preprocessing An initial sample of 30,127 self-initiated EMAs on real-world hearing aid experiences was collected worldwide between May 2018 and June 2021 and extracted from cloud-based data logging of the smartphone mobile application. The data was preprocessed using R statistical software  (19). As visualized in Figure  1 , only EMAs containing an optional open text statement  (18, 170  EMAs, or 60% of the original sample) and EMAs collected from hearing aids fitted in English-speaking countries (i. e. , Australia, Canada, England, Ireland, New Zealand, United States) were considered for further analysis. Text statements shorter than 20 characters were removed to eliminate entries with little content, after which a further manual data cleaning ensured that spelling mistakes were corrected, nonsense text, and a couple more non-English entries were removed.
Data analysis The final sample of 8,793 self-initiated EMAs was analyzed using R statistical software  (19)  and Iramuteq [version 0. 7, alpha 2] (20), an interface of R statistical software  (19)  for automated text analysis. To explore how listeners describe their experiences with hearing technology, a cluster analysis was performed on the open text data, in order to identify emerging themes in the reports. The cluster analysis method applied here was a Descending Hierarchical Classification using the Reinert Method  (21, 22). This method was chosen as it is an automated text analysis method that allows to process large amounts of text data in a time-efficient way, while at the same time aiming for similar goals as classical (manual) qualitative text analysis methods  (23). The method considers segments of text as textual units and is based on the principle that words that occur together are related to each other, in terms of semantic meaning (reflecting topics or themes) or context (reflective of a certain period in time, geographic location, or sociodemographic traits of the speaker or writer)  (24).
The algorithm then splits the matrix into smaller matrices, each split relying on a factorial correspondence analysis  (22) , a statistical method that summarizes a dataset along two dimensions based on the Pearson Chi-square (χ 2 ) statistic. Hereby, the χ 2 -value reflects the association between the rows and columns of a contingency table and χ 2 /n (with n the grand total of the contingency table) the association strength between two categorical variables (for a review on factorial correspondence analysis, see  (35)  note that χ 2 /n applies only to the simple use case of a two-way contingency table). For the first split, the algorithm divides the initial matrix into two sub-matrices, which are as lexically different from each other as possible. This is done by minimizing the number of word forms that the two submatrices have in common  (23) , or in terms of the factorial correspondence analysis underlying it, by maximizing χ 2 /n for the contingency table calculated by summing the two resulting sub-matrices  (22).
The result of the descending hierarchical classification is a number of lexical themes or clusters, each characterized by a list of words that have the strongest association, i. e. , the highest χ 2 values, with each respective cluster. For an example of how χ 2 is calculated for individual word forms, see Pélissier  (36, p. 35). The Descending Hierarchical Classification will further be referred to as cluster analysis. Concretely, the data set considered here was first pre-processed to match the required data format for Iramuteq [version 0. 7, alpha 2] (20), as per the available instructions  (36, 37). In the second step, textual units were defined. For the current dataset, one text statement, reflecting one written feedback from one hearing aid wearer, was considered as one textual unit.
Based on the output of the algorithm, it is the researcher's task to interpret the clusters and name them, based on a list of characteristic words and text statements that are most associated with each cluster. For this report, five researchers (i. e. , CV, IO, VM, SL, DWS) with expert domain knowledge interpreted the output of the algorithm separately to afterwards agree on the naming of the clusters. Based on the output of Iramuteq [version 0. 7, alpha 2] (20), a χ 2 test of Independence was performed using R statistical software  (19) , to explore the association between the identified clusters and selfreported satisfaction ratings, i. e. , positive vs. negative. A positive or negative hearing experience could be specified by the listener as part of the self-initiated EMAs, in the first screen of the survey.
Demographics The 8,793 self-initiated EMAs were logged through a mobile application by 2,301 individual hearing aid wearers. The data was skewed: the sample contained between 1 and 4 EMAs for 78% of listeners (median (IQR) = 2 (1-4) EMAs per listener) and between 4 and 90 EMAs for 22% of listeners (min = 1; max = 90 EMAs per listener). Most EMAs included in the sample were logged for hearing aids fitted in the United States of America (5,577; 63%) and Canada (1,349; 15%), followed by Australia (945; 11%), England (893; 10%), New Zealand (19; 0. 2%), and Ireland (10; 0. 1%). Almost half of the EMAs (4,226; 48%) were logged for hearing aids with premium technology levels (see Figure  2 ). Overall, listeners indicated 3,462 positive experiences with hearing technology (39%) and 5,331 negative experiences (61%).