dataesr/scientific-paragraphs-categorization huggingface dataset:
Added value of this study A major impediment to large-scale epidemiological studies analysing the short-term health effects of ambient temperatures is the restricted access to daily health records in many countries, regions and cities. A possible solution to circumvent this problem would be the use of weekly or monthly time-series of health outcomes, which are more readily available in open access due to the lower level of temporal disaggregation. We here temporally aggregate a daily temperature and mortality database from 147 contiguous regions in 16 European countries, and analyse and contrast the estimates obtained from daily, weekly, 2-weekly and monthly data models. We estimated temperature-lagmortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal data aggregation.
database from Eurostat has been recently used to develop data-driven indicators tracking progress on health and climate change,  11  or to quantify the heatrelated mortality burden of the record-breaking temperatures registered during the summer of 2022 in Europe. 1  Nonetheless, it remains to be determined if, when and to which extent it is possible to use temporally aggregated data to generate accurate temperature-related mortality estimates. Therefore, the aim of the present work is to temporally aggregate a daily temperature and mortality database from 147 contiguous regions in 16 European countries, and to analyse and contrast the estimates obtained from daily, weekly, 2-weekly and 4weekly data models. To address this aim, we estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily time-series, and compared the results with those obtained with different degrees of temporal data aggregation.
Data sources We used the spatiotemporally homogeneous mortality database originally used in Robine et al. 19  Further details are provided in subsequent studies using the same or similar data. [20] [21] [22] [23]  The dataset included 27,444,314 daily allcause mortality counts for the period 1998-2004 in 147 contiguous regions from 16 European countries, representing their whole population (i. e. rural and urban) of over 400 million people. These countries are Austria (9 regions), Belgium (11), Croatia (2), Czech Republic (8), Denmark (1), France (22), Germany (16), Italy (21), Luxembourg (1), the Netherlands (1), Poland (16), Portugal (5), Slovenia (1), Spain (16), Switzerland (7) and the United Kingdom (10 regions in England and Wales only). There were no missing values in the dataset. We transformed the daily gridded (0.
e. attributable number, AN) and fractions (i. e. attributable fraction, AF), following the methodology described in Gasparrini and Leone. 28  Heat-(cold-) related mortality was calculated for the days, weeks, 2weeks and 4-weeks with average temperatures above (below) the location-specific MMT in the corresponding data model. 29  Regional temperature-related mortality was aggregated to obtain the national and European burdens. 3, 4  Similarly, we computed 1000 Monte Carlo simulations of the regional temperaturerelated mortality, and separately aggregated them in each simulation to calculate the 95% empirical CI at the national and continental levels. 7, 8, 26  Role of the funding source The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Discussion To our knowledge, this is the first study to analyse the effect of temporal data aggregation in time-series epidemiological studies of temperature and mortality. We analysed differences in the temperature-related mortality estimates by temporally averaging (aggregating) the daily temperature (mortality) time-series into weekly, 2-weekly and 4-weekly data values. We separately estimated temperature-lag-mortality relationships by using state-of-the-art time-series quasi-Poisson regression models adapted to each of the data aggregations. Results revealed a progressive increase in differences with the degree of temporal data aggregation, with generally increasing underestimations of the MMT, the RR and the attributable mortality. Regardless of the magnitude of the differences, we generally found a good qualitative agreement between the daily, weekly and 2weekly data models, with comparable results in terms of exposure-lag-response association and the weekly, monthly, seasonal and annual variability of the total, cold-and heat-related mortality.
Given the asymmetric Vshaped relation between temperature and mortality, with the MMT displaced towards warm percentiles (Fig. 1a ), the relative contribution of these misclassified aggregated periods is expected to be higher in the case of heat-related mortality. We analysed the sensitivity of the weekly, monthly, seasonal and annual variability of the attributable mortality to the degree of temporal aggregation of the data. Although the weekly and 2-weekly data models systematically underestimated the lowest AF values, a similar systematic underestimation was not found for the highest AF values (Fig. 2 ). This asymmetry suggests that the magnitude of differences is smaller during extreme cold and heat conditions, likely explaining why the underestimation of the heat-related mortality was the smallest during the record-breaking hot summer of 2003,  19  i.
e. an underestimation of 4. 62% in the weekly data model (Table  S3 ). Recently, Ballester et al. 1  used a large weekly temperature and mortality database for 35 European countries to estimate a heat-related mortality burden of 62,862 (95% CI 37,935-88,780) deaths during the year of 2022. Authors compared their estimates based on weekly data models with those of a previous study using daily temperature and mortality data in Spain only,  30  and found that the summer heat-related mortality in the country was underestimated by 6. 06% (i. e. 11,324 vs. 12,054). This underestimation is aligned with the analysis shown in Fig. 5c , f and Figure  S4c  and f , given that the weekly data model used in Ballester et al. 1  estimated a heat AF of 0. 03014 (0. 02066-0. 04031) during the year 2022 in Spain, which would correspond to an underestimation of 4.
Acknowledgements JB gratefully acknowledge funding from the  European Union 's  Horizon 2020 and Horizon Europe research and innovation programmes  under grant agreement No  865564  ( European Research Council   Consolidator  Grant EARLY-ADAPT, https://www. early-adapt. eu/), 101069213 (European Research Council Proof-of- Concept HHS-EWS ) and  101123382  (European Research Council Proof-of- Concept FORECAST-AIR ). JB, KvD, JMA, CT, JS and RL acknowledge funding from the  European Union 's  Horizon Europe research and innovation programme  under grant agreement No  101057131  ( Horizon Europe  project  CATALYSE , https://catalysehorizon. eu/) and  101057554  ( Horizon Europe  project IDAlert, https://idalertproject. eu). CATALYSE and IDAlert are part of the EU climate change and health cluster (https://climate-health.
eu). JB and XB acknowledge funding from the  Ministry of Research and Universities of the Government of Catalonia  ( 2021-SGR-01563 ). JB also acknowledges funding from the  Swedish Research Council (FORMAS)  under grant agreement No  2022-01845  (project  ADATES ), and from the  Spanish Ministry of Science and Innovation  under grant agreement No  RYC2018-025446-I  (programme Ramón y Cajal). ZC acknowledges support from the grant  PRE2020-091985  funded by  MCIN/AEI/ 10. 13039/501100011033  and by  European Social Fund  invests in your future. HA acknowledges funding from the  European Union 's  Horizon Europe research and innovation programme  under grant agreement No  101065876  ( MSCA Postdoctoral Fellowship  TEMP-MOMO). RL was supported by a  Royal Society Dorothy Hodgkin Fellowship. ISGlobal authors acknowledge support from the grant  CEX2018-000806-S  funded by  MCIN/AEI/10.
97) shows that Bolsonaro enjoyed sound, enduring grassroots support over time. Yet quite significant variations also appear to be at work. We therefore renew our estimates taking the results of the 2022 election. Econometrically speaking, it is not clear whether this dataset provides a better way to estimate the 'Bolsonaro effect'. On the one hand, it has the advantage of better reflecting the state of current opinion, as there is no guarantee that the 2018 Bolsonarist municipalities were still such at the start of the pandemic in early 2020. Furthermore, the 2018 election was marked by very strong rejection of the PT, which drove part of the electorate (from the right and centre in particular) to vote for Bolsonaro in the first round. This was much less the case in 2022. On the other hand, the estimates are directly subject to the risk of reverse causality bias: President Bolsonaro's handling of the pandemic may have impacted the results of the 2022 election.
Research Overview We tested our predictions across three studies. In Study 1, using email data collected over five months, we examined the effects of tertius separans brokering relative to tertius iungens brokering on burnout and abusive behavior for 1,536 employees of a South American university. In Study 2, we replicated Study 1 findings, using a multiwave survey of 242 full-time employees of U. S. organizations who reported their daily brokering behaviors. In Study 3, an experiment conducted with 273 full-time employees of U. S. organizations, we manipulated the two types of brokering behaviors to examine the causal claim that tertius separans brokering leads to an increase in burnout, which then triggers individuals to engage in abusive behavior. The details of data, code, and online supplement are available through open science framework (OSF) at the following link: https://osf.
Data and Procedure We collected data from the employees of a large private South American university. The main campus of the university was located in the second largest city in the country; the university had three secondary campuses in smaller cities. The university also operated a university hospital and a school. The data consisted of three components: email network data constructed from employees' email exchange records, burnout and abusive behavior data from employee surveys, and demographic data from the university's human resources (HR) department. This data collection occurred as part of a larger process, required by law, that measured employees' psychosocial working conditions. Participants received no compensation for their participation. We monitored internal email communications between all employees in the organization to collect information about senders, recipients, and timing, but we did not access message content.
We monitored emails over a five-month period, after which we measured employees' burnout and abusive behavior using online surveys. Detailed information about the collection and cleaning of the email data are available in the online supplement. Our complete data set contained information about email communications, burnout, and abusive behavior from 1,536 users (59% female; M age � 45. 25, SD age � 9. 83; 77% with a bachelor's degree or higher), out of 2,367 active email users (a 65% response rate). 3 Among them, 56% had a permanent contract and 44% had fixed-term contracts. The organization was organized hierarchically into five levels, 4% of employees were executives, 10% were directors, 45% were professionals,.
" We created a composite measure of abusive behavior by averaging the scores of the five items (α � 0. 84). Brokerage Position. We controlled for employees' brokerage positions to verify whether tertius separans brokering behavior affected outcomes above and beyond their structural positions. To measure employees' brokerage position, we calculated constraint, the extent to which the alters of an employee are connected to each other  (Burt 1992). First, we aggregated email communications between employees in the organization during the full observation period, resulting in a 2,367 by 2,367 matrix in which each cell (i, j) represents the number of emails that i sent to j. We then calculated constraint on this valued network using the igraph package  (Csardi and Nepusz 2006)  in R  (R Core Team 2019).
General Discussion The three studies presented in this paper provide substantial empirical support, across different methods, measurement techniques, and empirical contexts, for the idea that employees who engage in tertius separans brokering, relative to tertius iungens brokering, experience higher levels of burnout, with a subsequent increase in abusive behavior. In Study 1, our dynamic analyses of five months of email data showed that the more employees at a South American university engaged in tertius separans brokering behavior (relative to tertius iungens brokering), the more they suffered from burnout at the end of the five-month period and the more they displayed abusive behavior. In Study 2, we used timeseparated data to test the relationships between the two types of self-reported brokering behaviors, burnout, and subsequent abusive behavior toward coworkers.
The results confirm our finding that engaging in tertius separans brokering behaviors is significantly associated with employees' state burnout and subsequent behaviors, above and beyond the effects of their chronic burnout on those outcomes. 13 The mean score of abusive behavior was between 2. 02 and 2. 41. We speculate that social desirability response bias (Van de Mortel 2008) might have played some role as we used self-reported measures of abusive behavior across studies. Abusive behavior is considered a low base-rate phenomenon  (Tepper 2007) , and the mean scores of abusive behavior in our data are consistent with prior research (see  Mackey et al. 2017  for a meta-analysis of the scale). 14 Individuals from Western, educated, industrialized, rich, democratic backgrounds  (Henrich et al.
Acknowledgements Funding has been provided by the  European Union's Joint Programming Initiative on Cultural Heritage  project  PHE  (The Past Has Ears, phe. pasthasears. eu), the  French  project  PHEND  (The  Past Has Ears at Notre-Dame ,  Grant  no. ANR-20-CE38-0014 , phend. pasthasears. eu), and the  Chantier Scientifique CNRS/ MC Notre-Dame. The authors would like to thank  Brian F. G. Katz  for his thoughtful comments on this paper.
Microscopy imaging in microfluidic chambers We used an automated inverted epifluorescence microscope Olympus IX83 equipped with a Motorised stage (Prior Pro scan III), a Zyla 4. 2 sCMOS Andor camera with an Olympus 20X Plan (N. A. 0. 4) objective, a DMD (MOSAIC3 Andor) and a pE-4000 CoolLed as a light source to achieve spatially resolved optogenetic activation. For optogenetic activation, we used a 460 nm LED at 20% intensity through a filter cube (EX) 470 nm/40; (EM) 525 nm/50 (49002) with a 200 ms exposure time every 6 min. Brightfield images were also captured every 6 min. Microscopy experiments were carried out in a thermostat chamber set to 30 °C. Liquid perfusion was delivered using an Ismatec IPC (ISM932D) peristaltic pump at 50 µL/min. Particle image velocimetry (PIV) analysis was performed using the open-source JPIV software (https://github.
Data collection and analysis The empirical data for this study was gathered between 2018 and 2021. We paid two fieldwork visits to each city and used three methods to gather data from various respondents and sources. First, we analyzed official water policy documents, reports, laws, and water supply regulations, and examined various academic literature relating to Accra and Dar es Salaam in order to obtain a broad understanding of the policy and institutional contexts of water supply and donor funding schemes across the two cities. Second, we conducted semi-structured interviews focusing on how donor funding schemes operate in the water sectors of the two countries. Two different interview guides were used, depending on the interviewees. When interviewing government officials, international donors/development partners, NGOs, and local research institutions, we explored issues related to urban water supply, infrastructure financing, key actors and their responsibilities, specific donor funding schemes, institutional conditionalities, and the technologies donors fund in the two cities.
Results Below, we first present an overview of the urban water sectors of Ghana and Tanzania, before going on to demonstrate how the politics of tied water aid in the two cities affect maintenance and repair through 1 Before commencing this study, we obtained formal approval from both water companies and also obtained informed consent from all research participants before they were interviewed. Personal data on the research participants was also anonymized in the interviews and analyses to ensure confidentiality. conditionalities, transferred technologies, and transferred knowledge.
Acknowledgements This research was supported by the  German Academic Exchange Service (DAAD)  under funding number RF:  91688752  within the  Research Training Group Critical Infrastructures : Construction ,  functional crises and Protection in Cities (KRITIS ) at the  Technical University of Darmstadt, Germany  as well as by the  Department of Human Geography and Spatial Planning at Utrecht University. Therefore, we would like to express our gratitude to the three organizations. Also, we thank the numerous respondents, especially the donors, development partners and officials of the  Ghana Water Company Limited (GWLC, Accra)  and at the  Dar es Salaam Water and Sewerage Authority (DAWASA, Dar Es Salaam)  for their collaboration and for providing helpful insights during the fieldwork. We are also grateful to  Ton Markus  ( Utrecht University ) for drawing the maps and to  Joy Burrough  for her language editing.
Research methods The research framework is developed based on computationally intensive big data-driven methods, as highlighted in editorial directions  (Berente et al. , 2019; Kar and Dwivedi, 2020; Miranda et al. , 2022). It has highlighted a four-step process including (i) sampling and data collection, (ii) synchronic analysis, (iii) lexical framing, and (iv) Diachronic analysis. In our study, the data collected are news articles from multiple sources for sampling and data collection. Second, we merged synchronic analysis and lexical framing, a part of our exploratory study for categorization, identifying the raw association of concepts and developing lexical frameworks to analyze data used to understand the raw association between constructs. Lexical farming influences what patterns we identify and how we express and interpret them.
Data collection We downloaded the news articles using a scraping tool developed in Python. The articles collected are from the United States of America, Europe, and India. European countries include Belgium, Bosnia and Herzegovina, Croatia, Czech Republic, Denmark, Finland, France, Georgia, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Netherlands, North Macedonia, Poland, Portugal, Serbia, Spain, Sweden, Ukraine, and the United Kingdom. We ensured that the data collected were in English from various sources: India Today articles, NDTV, Reuters U. K. , The Hindu, The New York Times, etc. Few official news websites allow access to historical news articles using public API (application programming interface). We utilized these APIs as well to build our sample. The data collection exercise was done from February '21 to May '21.
Classification and intercoder reliability The news articles were classified manually, i. e. , by supervised data, by the Coronavirus Infodemic Database by the International Fact-Checking Network (IFCN) Poynter Institute 2020. At the time of the study, i. e. , until March 2023, the Poynter website included 7100 fact checks from more than 70 nations in over 40 languages. IFCN is a global body that collaborates with numerous organizations. Hence, the collected data is considered valid and reliable for measuring the propensity of fake news. To fact-check a news article and segment the same as Fake or Genuine, the analysts of the Poynter Institute started with a keyword search of the new article. The goal is to start small and expand to a wider internet community search, involving single or multiple keywords depending on the article the team tries to verify.
To evaluate SC disruption, we compared the number of out-of-stock comments/average number of out-of-stock comments. The criterion for estimating demand variation leading to SC disruption is motivated by one assumption: the users read news articles on some topics, influencing the consumers' perception and resulting in irrational buying behavior. Ideally, sales data are considered for accessing demand variation and SC disruption, but generally, sales data are not open to the public and are unavailable in our study. Hence, we assumed, based on past literature, that panic buying had made the products stock out, leading to SC disruption. The proxy for measuring this disruption was based on mining out-of-stock comments in user reviews. We argue that when customers read the news articles, panicked, and purchased the products in higher volumes than needed, it made products go out of stock.
Demand variation = No. of ratings t+14 Average ratings per week t-28 The analysis of the study includes sentiment analysis to understand how emotions change throughout the crisis. SentiStrength is used to determine sentiment strengths. SentiStrength assesses the resilience of both happy and negative emotions  (Thelwall et al. , 2010). When measuring sentiment strength, we examine each online review as a separate unit before averaging the identical product strength scores for all reviews. SentiStrength assessed the strength of the text based on the most important emotional term. Multiple reviews are combined, resulting in the average score being predominated by the strongest emotional terms in the reviews. SentiStrength determines the strength of each word and the review valence using the Linguistic Inquiry and Word Count (LIWC) vocabulary using natural language processing.
Unverified information sharing (UIS) The topics identified in the corpus have helped develop a variable of UIS amongst users. Two social sharing websites, sharedcount. com and sharesscores. com, were determined to understand the sharing pattern of news articles across the internet (Table  5 ). Python code was developed to access the propagation of news articles across the internet. Each news article was checked on social sharing websites to validate the results, and the social sharing scores across counters were averaged. UIS was measured based on social shares on Twitter, Facebook, and Instagram posts. The t-test results highlighted a significant mean difference between sharing fake and genuine news articles, i. e. , Table  6. The results highlighted that genuine news articles were shared less on SM than on fake news articles.
Research methods We assigned all news articles a coefficient based on a word score using topic modeling analysis, which was then employed in regression analysis to develop and test a hypothesis. Finally, each document (news line) was assigned to the closest themes and rated using a Likert scale. In content analysis methodologies, multiple regression analysis is adequate for empirical studies and is free of low multicollinearity impacts  (Kar, 2021). We initially kept an out-of-sample portion to evaluate the model on an unknown dataset. To assess the robustness of the proposed model results, we used techniques such as k-fold cross-validation. We divided the analysis sample into multiple cross-validation folds, which were utilized to check the stability and robustness of the result (Appendix A1).