dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank  Denis Geraads , the anonymous reviewer and the associate editor,  Lorenzo Rook , for the corrections and comments which greatly improved the quality of the manuscript. We thank  Bernhard Zipfel  from  University  of the Witwatersrand as well as  Stephany Potze ,  Mirriam Tawane ,  Heidi Fourie  and  Lazarus Kgasi  from the  Ditsong Museum, Pretoria , for access to collections. CMS acknowledges the funding contribution of the  Centre of Excellence in Palaeosciences (CoE-Palaeo ) and the  Palaeontological Scientific Trust (PAST). RH would like to acknowledge the  ATM MNHN "Paranthropus Diet. Régime alimentaire et comportement de subsistance des Paranthropes sud-africains " (resp. S. Prat) and the  UMR 7194/MNHN  for the funding support.
Acknowledgements The authors would like to thank the financial support of the Occitanie region (ECODREDGE MED 2 READYNOV 20023364 and by the  LabEx DRIIHM ,  French Programme "Investissements d'Avenir"  ( ANR-11-LABX-0010 ) which is managed by the  French ANR , under the  Human-Environment Observatory "Mediterranean coast" (OHMLittoralMéditerranéen) OHM LM. The authors also thank the staff of the four ports for their logistical support during the sampling campaigns.
The MANGO original study The MANGO study design has been described in detail elsewhere  (Kangas et al. , 2019; Nikièma, Kangas, et al. , 2021). The study was conducted in 10 health centers in East Burkina Faso, from December 2016 to August 2018. Before enrolment, all parents of eligible SAM children received information on the trial objectives and processes, and the possibility to withdraw at any time, and provided written consent to participate into the trial. SAM treatment followed the Burkina national CMAM guidelines (Ministèrede la santé, 2014) except for the RUTF dose: from the third treatment week onwards, 402 children were randomized to the intervention arm and received either 1 or 2 sachets per day if they weighed <7 kg or ≥7 kg respectively (Table  1 )  (Kangas et al. , 2019). At enrollment, data were collected about the sociodemographic characteristics, anthropometry, 2-week retrospective morbidity of the child, and a clinical examination was performed.
Data collection Data were collected in a longitudinal study, by structured individual in-depth interviews with caregivers one month (at week 4) and 2 months (at week 8) after the start of treatment. Additional data were collected at week 6 of treatment. At weeks 4 and 8, caregivers were asked about their perception of whether the amount of RUTF prescribed for the child's treatment was sufficient or not, availability of RUTF throughout the week and consumption of RUTF by the child. Variables of RUTF's consumption that were assessed were the total amount of RUTF consumed during the week, the amount of RUTF remaining at the end of the week, the frequency of RUTF daily's consumption, and the children's appreciation of RUTF. Additionally, a questionnaire related to the use of RUTF within the household, including sharing and knowledge about the RUTF, forms of consumption, side effects following the consumption of RUTF during the week, practices of conservation and access to RUTF by other members of the household were administered at week 6 of treatment.
Data analysis The statistical software package Stata version 15. 0 (Stata Corp) was used to perform the statistical analyses. Descriptive analysis was summarized as proportion and means (±Standard Deviations: SDs) in order to present the general profile of children at baseline and at time of the recall concerning their socio-demographic, maternal, and household characteristics. Differences between intervention and control arms were assessed using Standard Poisson regression model for RUTF weekly quantity prescribed and consumed, RUTF total prescribed and consumed during treatment, mixed logistic regression models for binomial variables (RUTF availability during week, Proportion of caregivers who perceived that "RUTF sufficient", side effect of RUTF consumption), and ordered logistic regression model for multinomial ordered variables (frequency of daily RUTF consumption, modalities of perception on RUTF quantities received, form of RUTF consumption; types of sides effects, conservation of RUTF in household, sharing of RUTF).
e. , how often the neurologist gets it wrong). In this analogy, DL can be thought of as replicating this process through experience. In DL terms, this may mean more data analysis, larger datasets, greater computational power, better models, more training, which is the analogy to sophisticated human learning referred to in terms of doctors improving their accuracy-the accuracy increases for what it is optimized for. However, this analogy only works on the surface. We have described both the doctor as improving and the DL as improving in terms of accuracy. We note that the accuracy of a prediction is the ratio between all correct predictions and all possible cases. It is appropriate in a large sense for describing how the doctors improve their skills, which has been described in broad terms (as sophisticated cognitive phenomena) and is very much an open question; i.
DL architectures have excellent abilities, but, despite the successes in image classification, sequences prediction, and language translation, they should be used with extreme diligence to avoid known vulnerabilities during operation, such as adversarial attacks,  27  or during learning, such as catastrophic forgetting. 28 n both cases, the ultimate decision stays with the human operator, who decides among different options like, for example, model retraining or more advanced learning methods (e. g. , self-supervised learning or active learning). 29 Curing by retraining is a cost that implies a more complex infrastructure for better ground-truth datasets and computational resources. Using more sophisticated learning methods does not ensure better results. The unfortunate reality is that AI works only because a plethora of human beings is taking care, controlling, and intervening to mitigate issues and critical problems, to reduce external costs and side effects.
An AI-driven hammer will be able to sculpt a replica after proper training with massive datasets. An AI system is made of different subsystems or tools (e. g. , face recognition tools, language translation tools, object detection tools) each with a very specific functionality. It is indeed possible to compare hammer tool with face recognition tool from a functional perspective. The difference is that the hammer is a physical extender of a hand to strike a nail, while a face recognition tool is animated by the intellectual labor used for learning the model from data. In this respect, the current form of AI is still a technological extender for transforming huge masses of data into actionable items that can be consumed by humans. In this sense, an AI system is not so different from an automatic controller that produces the same set of outputs given a certain set of inputs using a transfer function computational algorithm for mapping inputs to outputs.
The difference is that the AI system learns the model directly from data under certain contexts during the training phase and relies on certain assumptions, leading to misoperation when the training assumptions are not met. This is what we refer to as sui generis value capture. For example, a dataset used for training a cybersecurity system for malware detection is only valid at a given time and in a given possible world, after which it is too unsafe to be useful and requires retraining with trustworthy data. As such, the value captured is very much time dependent (which we may also state in terms of human know-how being time dependent). Finally, it is of critical importance to state that, when the assertion that AI is a capture of value is made, it is to be understood within the framework of relational ontology; it is not the AI as the expression of meaningful information in and of itself.
Procedure and sample We collected the data from India through a combination of online and paper surveys. A total of 2348 corporate executives received email invitations to an online form, followed by two reminders. We administered paper questionnaires across different executive programs conducted at the Indian Institute of Management in Indore and Mumbai campuses from December 2016 to May 2017. Participation was voluntary and we informed everyone that there were no right or wrong answers. Additionally, we guaranteed anonymity and confidentiality to the respondents. Moreover, we did not include questions that asked individuals to identify the religion they identified with because of the different degrees of how people internalize or externalize religions  (Allport & Ross, 1967). While the response rate for online survey was less than 1%, the paper format had a response rate of about 55%.
Psychometric assessments Scale reliabilities were assessed using SPSS 21. All measures had Cronbach's alphas of above 0. 70, indicating adequate reliabilities  (Cronbach, 1951; Nunnally, 1978). The confirmatory factor structures were assessed using AMOS 21. Several models for the three vignettes were examined. First, we tested the first-order factor with intrinsic and extrinsic religiosities, five ethical principles, social desirability, ethical recognition, and ethical intention. Next, we tested an alternative model with a second-order factor for intrinsic and extrinsic religiosities as well as a second-order factor for the five ethical principles. Finally, we conducted the common latent factor test to detect the presence of common method variance  (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).
Recruitment ultimately aimed to engage a sufficient number of participants to satisfy validity criteria for cultural consensus analysis. Adequate sample size ultimately depends on the level of agreement found in a group, and the accuracy with which one hopes to estimate group-level answers to questions  (Weller, 2007) : At low levels of agreement, about thirty people per group is needed to estimate group-level answers with confidence; while in cases of extremely high consensus, as few as five people may be sufficient. We therefore aimed to recruit around thirty people per group. No personally identifiable information was collected and all procedures were performed in compliance with General Data Protection Regulation guidelines. The research was exempt from ethical clearance as per Wageningen University and Research regulations, and informed consent was obtained at the beginning of the survey.
There was only weak agreement (55 %) that most users need special training to be able to try new products. In terms of trust-generating factors related to design, intuitive layout, color schemes and other features that promote user-friendliness (compatible format, customizable zoom and scroll features) are important considerations, in addition to the use of consistent terminology. There was only weak agreement (55 % users, 54 % producers) that value-added products (such as 'traffic light' or other merged visualizations of diverse datasets) are trusted more than products that depict raw data. Finally, five propositions (propositions 8, 23, 24, 25, 26) probed a baseline understanding of issues around existing user needs. Responses confirm that there is a need for more sea ice information as currently available products do not offer adequate spatial or dynamic coverage of conditions to support Arctic maritime operations.
Acknowledgments The research underlying this paper is part of the  SALIENSEAS  project, which is part of the  ERA4CS  programme, is co-funded by the  Netherlands Organisation for Scientific Research , by the  Innovation Fund Denmark  and by the  Horizon 2020 Framework Programme  of the  European Union  (Grant  690462 ). SALIENSEAS is part of the  Year of Polar Prediction (YOPP) , a flagship activity of the Polar Prediction Project (PPP), initiated by the  World Weather Research Programme (WWRP)  of the  World Meteorological Organisation (WMO).
Students were endowed with the following information: production available, marginal revenue, consumers' demand and the theoretical optimal solutions. We also ask students to deliver a written report, including relevant information about the procedures they followed to achieve agreements and the split of profits. Together with the data collected, we also compiled information about the bargaining processes and the variety of game concepts involved in them. Hence, interactive qualitative and quantitative analyses are used to analyze the behavior followed by a student-subject pool, focusing on the extent to which they want to cooperate with each other in order to maximize profits. We identify equilibrium terms of the transportation game where both fairness and efficiency concerns were relevant.
Funding:  Carlos Gutiérrez-Hita  and  Joaquín Sánchez-Soriano  acknowledge financial support from the  Spanish Government  through grant  PGC2018-097965-B-I00  funded by  MCIN/AEI/10. 13039/501100011033and  by  ERDF "A way of making Europe"/EU , and from  Generalitat Valenciana  under project  PROM-ETEO/2021/063. Nikolaos Georgantzis  acknowledges support by the  Spanish Ministerio de Ciencia ,.
Study design and data source This was a historical cohort study conducted within the French General Sample of Beneficiaries (EGB), which is a 1/97e random sample of the French health insurance reimbursement database. The EGB records individual anonymous information from primary and secondary care (data from PMSI, the French Diagnosis Related Group-based medical information system), and it currently covers more than 80% of the French population. It contains: (a) characteristics (gender, month and year of birth, month and year of death if applicable); (b) all nonhospital reimbursed healthcare expenditures with date and code (visits and medical procedures, laboratory tests, drugs, and medical devices, but not the corresponding medical indication or results); (c) hospital discharge summaries (ICD-10 diagnoses codes for all medical, obstetric, and surgery hospitalizations with the date and duration of hospitalization, medical procedures, hospital department, and cost coding system)  (8, 9).
Study population We enrolled all women who gave birth and men who took paternity leave in 2016 or 2017, with enough historical data, i. e. , available data over the following periods: (i) In the 10 years before (5 years for women 25 yearsold or under) and in the 6 months after childbirth for women, (ii) In the 10 years before (5 years before for men 25 yearsold or under) and in the 5 months after the start of paternity leave for men. We used these time periods to track back the immunization status of the enrolled population. This is in line with the current recommendations for pertussis vaccination within the cocooning strategy  (10). As it is not possible to directly find fathers corresponding to each childbirth in the EGB, we identified men with a newborn using the start date of their paternity leave.
Variables For each person, we identified the following variables within the database: age at childbirth, characteristics of the HCP who prescribed the pertussis vaccine (private or hospital setting and prescriber specialty in private practice), and pertussis vaccination status. In women, the pertussis Vaccination Coverage Rate (VCR) was estimated as the ratio of women having received a shot of vaccine containing pertussis valence (DTaP-IPV or Tdap-IPV), over the tracking periods defined before, to the number of women enrolled, i. e. , the number of women having given birth in 2016 or 2017. Similarly, in men, the VCR was estimated as the ratio of men having received a shot of vaccine containing pertussis valence (DTaP-IPV or Tdap-IPV), over the tracking periods defined before, to the number of men enrolled, i.
Person's characteristics Of the 743,814 persons in the EGB database, we included 3,170 and 3,234 women who gave birth to a live child and 1,868 and 1,835 men who took paternity leave in 2016 and 2017, respectively, with enough historical data in the EGB (Figure  1 ). The mean age of selected women who were vaccinated was 31 years old, slightly higher than those who were not vaccinated (29 years old). The mean age of selected men was similar in vaccinated and in unvaccinated men, i. e. , 34 years old. Mean ages were similar in 2016 and in 2017 for both women and men (Table  1 ).
Specialty of the healthcare professional (HCP) who prescribed the pertussis vaccine in private practice during the "parental project" in 2016 and 2017. between 2016 and 2017, the percentage is still low (around 13%). However, this can be explained by the fact that most midwives work in hospitals with no possibility in the database to track the specialty of the prescribers in a hospital setting  (27, 28). In contrast, gynecologists work mainly in private practice  (29) , thus the percentages of prescription coming from them during the "parental project" (15. 4% in 2016 and 10. 1% in 2017) observed in this study are lower than expected.
Moreover, we selected men who took and declared a paternity leave, thus who are not representative of all fathers-to-be. Indeed, the selected fathers may be more involved in the care of the child and therefore more likely to be vaccinated. In order to assess the vaccination coverage, we selected only the population with enough historical data. In people over 25 years old, only those affiliated to the general health insurance scheme could have 10 years of historical data and be included, due to variations in the population of the EGB. People under 25 years, for whom only 5 years of historical data were needed, were not included if they were affiliated to a student health insurance over this period because data from this health insurance scheme are available only since 2015. This may lead to a selection bias and an overestimation of the vaccination coverage.
It has been indeed demonstrated that higher income and education level or a better occupational category are associated with higher vaccination coverage rates  (30). Finally, in some health centers, individuals' health care consumptions are not recorded and are therefore not available in SNDS database. As a result, some vaccinations may have been missed in this study. This could have led to an underestimation of the vaccination coverage. In addition, the SNDS database does not make it possible to link the mother and the father of one child. It could happen that one of the two parents is vaccinated while the other is not, while the main principle of the cocooning strategy is to create a cocoon against pertussis around the infant. It is important that both parents are vaccinated to offer the optimal protection to a newborn in its first months of life.
Conclusion This study provides recent and valid data on pertussis vaccination coverage using a medico-administrative database, in a country with near-universal healthcare coverage and a unified national healthcare data system. To optimize the protection in infants, which is the main objective of the cocooning strategy, pertussis immunization coverage of adults and seniors needs to be improved. Moreover, cocooning vaccination linked to a parental project needs to be performed earlier, during pregnancy (for those around the mother) or as soon as possible in immediate postpartum, for instance during the maternity stay. A series of different and coordinated interventions need to be considered to protect newborns from pertussis. Indeed, GPs have a key role in the vaccination against pertussis, but a better sensibilization of other healthcare professionals, specifically those dedicated to pregnancy follow-up, is needed to develop the cocooning strategy, before and during the pregnancy, as well as in postpartum.
Related articles The literature review process informs the theoretical agenda right before and during the pandemic of COVID19 on (1) FDI academic literature in Central and West Africa; (2) policymaking analysis; and (3) sustainability characteristics in both policy and academic literature from January 2019 till March 2022. 1 The article built upon Newman and Gough's  [33]  literature review findings and observed best practice  [27]  to support the methodological validation and repeatability of the literature reviews: The seven steps of critical literature review proposed by Juntunen and Lehenkari  [24]  were implemented: (1) selecting research questions, (2) selecting bibliographic or paper databases; (3) selecting search criteria; (4) applying practical screening criteria; (5) applying methodological screening criteria; (6) conducting the review; and (7) synthesizing the results (Additional file 1).
In this paper we assess the transit quality of service (QoS) from a user's standpoint, using smart data. A number of bus lines with different characteristics, operating in the Metropolitan Area of Athens, were chosen as a case study. The data used were gathered by an Automatic Passenger Counting (APC) system. APC technologies provide exact temporalized passenger counting along the line for each service, thus assisting to better understand causalities of delays and avoid operational problems. By employing archived APC data from buses running on crosstown routes between 15 January 2019 and 15 April 2019 we conducted a statistical analysis to explore occupancies and assess QoS, including under a social distancing scenario. The passenger distribution along the stops, the bus's occupancy level, the stops that are maximum occupancy points and their rate of occurrence and, lastly, the passenger's average trip length during the day and the week are examined.
Literature Review APC data are becoming more available since APC devices have been increasingly installed in vehicles and data accessibility is easier. The use of APC data can greatly impact both passengers and agencies and improve the quality of Public Transport services  [14]. Hammerle et al. [15]  proved how useful APC data can be in evaluating Public Transport services. There are plenty of technologies that are used to collect data depending on the need and purpose of their usage, such as passive thermal, digital cameras with threedimensional vision, etc. [16]. APC systems prevail from manual traffic data collectors, as they capture passengers both at the entry and exit level  [17]. APC data are used in inferring the transit route level  [18]  but also in developing an interactive data analytics platform in order to assess service quality and determine service problems  [19].
To better understand bus bunching in different spatial and temporal levels, Feng and Figliozzi  [20] , with the use of APC data, were able to aid agencies in developing efficient strategies in order to improve their level of service. Last but not least, APC data were used in creating a systematic evaluation framework to quantify the impacts of combined Public Transport services  [21]. The use of APC data in dynamic models for the estimation of up-to-minute bus service information has been proved to be a significant tool. Patnaik et al. [22]  created a regression model that estimates bus travel times and arrival information for passengers. Another dynamic model was developed to not only better inform passengers, but also to examine the variability on bus travel times for more accurate scheduling provision  [23].
Chen and Chen  [24]  simulated passenger demand and bus operation for fixed routes, using APC data, to predict and prevent irregularities on the bus routes. APC dynamic data were deployed to develop a bus travel time model in order to cope with poor scheduling and misleading information of bus arrival and departure times to passengers  [25]. Similarly, Nuzzolo et al. [26]  showed how to use APC data in a mesoscopic model to upgrade Origin-Destination (OD) matrices. Ji et al. [27]  used APC data to derive OD flow matrices to estimate transit route passenger OD flow matrices. More recent studies employ the use of APC data to examine different aspects of public transport usage. Jenelius  [28]  used real-time and historic APC data to extract personalized predictive public transport crowding information in the public transport network in Stockholm.