dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The ArchaeoGLOBE dataset highlights broad patterns and consistencies in archaeological data while also identifying exceptions and knowledge gaps. Our data show geographical variability in total number of respondents, expertise level, and data quality, suggesting that the breadth of archaeological knowledge differs greatly from one region to another. Potential causes of geographical inconsistencies in archaeological knowledge include the varying conditions under which archaeologists work, the cumulative legacy and positive feedback of early research interests, and the physical accessibility (both real and perceived) of archaeological sites (see also  ( 38 )  ). Although we made rigorous efforts to recruit archaeological knowledge contributions as widely as possible, biases in the dataset also derive from the anglophone orientation of key project investigators, as well as the limitations of their professional networks.
For instance, archaeobotanical investigations of the cultivation and domestication of indigenous cereals in Sub-Saharan Africa  ( 41 -43 )  are beginning to shed light on earlier and more extensive forms of agriculture. Similar less-investigated indigenous agricultural practices likely characterize parts of Southeast Asia and northern India during the mid-Holocene (e. g. ,  ( 44 -46 )  ). Hence, the ArchaeoGLOBE project can help archaeologists prioritize future collection of empirical data and local capacity building to improve the reliability of global perspectives.
Acknowledgments: Funding: This material is based upon work supported by the  National Science Foundation  under Grant  CNS 115210  awarded to E. C. Ellis in 2011. Author contributions: a complete list of contributions to the paper is contained in the Full Author List in a separate file. Competing interests: authors declare no competing interests. Data and materials availability: all project data are in the public domain ( CC-0 ) and available online on the project's Dataverse page (https://dataverse. harvard. edu/dataverse/ArchaeoGLOBE,specifically https://doi. org/10. 7910/DVN/CNCANQ,https://doi. org/10. 7910/DVN/CQWUBI). We include the entire R code used for all the analysis and visualizations contained in this paper in our repository at https://doi. org/10. 7910/DVN/6ZXAGT.
The present study sample comprised MCCS participants selected for inclusion in one of seven previously conducted nested-case control studies of DNA methylation of colorectal, gastric, kidney, lung, and prostate cancer, B-cell lymphoma, and urothelial cell carcinoma (UCC). 26-31 Controls were matched to incident cases of prostate, colorectal, gastric, lung or kidney cancer, UCC or mature Bcell neoplasms on sex, year of birth, country of birth, baseline sample type and smoking status (the latter for the lung cancer study only). For the cross-sectional analyses, we excluded participants whose blood sample was taken at follow-up (303 samples from the UCC study) because their questionnaire data and storage time were different. We also excluded cases from the lung and UCC studies to avoid bias due to the strong association between smoking and these cancers.
Genome-wide association study of DNA methylation (EWAS) We assessed cross-sectional associations (baseline data) for methylation at each individual CpG by regressing DNA methylation M-values on smoking status using linear mixed-effects regression models, using the function lmer from the R package lme4. 39 Models were adjusted by fitting fixed effects for baseline values of age (continuous), country of birth (Australia/New-Zealand, Italy, Greece, United Kingdom/Malta), sex, alcohol drinking in the previous week (continuous, in grams/day), BMI (≤25 kg/m2, >25 to ≤30, >30 to ≤35, >35), sample type (peripheral blood mononuclear cells, dried blood spots, buffy coats) and estimated white blood cell composition (percentage of CD4+ T cells, CD8+ T cells, B cells, NK cells, monocytes and granulocytes, estimated using the Houseman algorithm 21,  22  ), and random effects for study, plate, and chip.
Acknowledgements and funding: This work was supported by the  Australian National Health and Medical Research Council (NHMRC)  [grant  1088405 ]. MCCS cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further supported by Australian  NHMRC  grants  209057 ,  251553  and  504711  and by infrastructure provided by  Cancer Council Victoria. Cases were ascertained through the  Victorian Cancer Registry (VCR)  and the  Australian Cancer Database (Australian Institute of Health and Welfare). The nested case-control methylation studies were supported by the  NHMRC  grants  1011618 ,  1026892 ,  1027505 ,  1050198 ,  1043616  and  1074383. M. C. S. is an  NHMRC Senior Research Fellow  ( 1155163 ).
Background: The public's understanding of science can be influential in a wide range of areas related to public health, including policy making and self-care. Through the digital and social media ecosystem, health scientists play a growing role in public science communication (SC). Objective: This review aimed to (1) synthesize the literature on SC initiated by health scientists targeting the public in the digital and social media ecosystem and (2) describe the SC strategies and communication channels used. Methods: This scoping review was based on the Joanna Briggs Institute Methodological Framework. A systematic search was performed in 6 databases (January 2000 to April 2018). Title and abstract screening, full-text review, data charting, and critical appraisal were performed independently by two review authors.
Information Sources and Search We drafted the search strategy with an experienced librarian. We first developed the search strategy for PubMed (see Multimedia Appendix 1) and then translated it for other databases. The search strategy used a combination of 3 concepts: (1) scientists and the public; (2) health SC; and (3) disintermediation, which refers to the communication channels in the digital and social media ecosystem. We searched 6 bibliographical databases from January 2000 up until April 2018: Cumulative Index to Nursing and Allied Health Literature, via EBSCOhost; Excerpta Medical Database, via Ovid; International Bibliography of the Social Sciences, via ProQuest; PubMed, via the National Center for Biotechnology Information; Sociological Abstracts, via ProQuest; and Web of Science-Science Citation Index and Social Sciences Citation Index, via the Institute for Scientific Information-Thomson Scientific.
Data Items We extracted the following data items: • Article characteristics (eg, year of publication, first author's academic discipline, country of origin, publication type, and aim) • SC-related items (eg, SC definition, SC type, SC theoretical framework, SC goal, SC context, SC strategies used or described, SC communication channels used or described, SC plan development process, and SC delivery) • Study methods, if applicable (eg, study design, population, sample size, data collection and analysis method, and article limits) • Key results related to SC.
Critical Appraisal of Individual Sources of Evidence Although critical appraisal of the included publications was not originally planned  [12] , review authors consensually decided to add this step to further qualify the literature. We critically appraised empirical works, which are original research articles and literature reviews, based on general guidance (eg, coherence between the problem described and the methods retained, adequate sample in terms of participants or the literature selected, and rigor of the data collection or extraction process). To critically appraise nonempirical types of articles (eg, editorials and viewpoints), we retained the Joanna Briggs Institute Critical Appraisal Checklist for Text and Opinion Papers  [18].
Synthesis of Results We first synthesized data regarding the nature and the extent of the literature regarding SC initiated by health scientists in the digital and social media ecosystem in a table format. Then, we synthesized narratively data regarding SC-related data items (eg, communication channels). To develop a typology of SC strategies, we used a qualitative and inductive method of data synthesis, with a constant comparison approach  [19]. This method of data synthesis allowed the types of SC strategies to emerge from the available data and not from prespecified categories. First, we listed in a single file all SC strategies identified in included publications. Second, 2 reviewers (GF and MAMC) consensually attached a provisional label to each SC strategy to identify its type or, in other words, its focus.
Acknowledgments GF was supported by the  Vanier Canada Graduate Scholarship (Canadian Institutes of Health Research) , a  doctoral fellowship  from the  Fonds de recherche du Québec-Santé , multiple scholarships from the  Canadian Nurses Foundation , a doctoral scholarship from the  Montreal Heart Institute Foundation , a doctoral scholarship from  Quebec's Ministry of Higher Education , and multiple scholarships from the  Faculty of Nursing at the University of Montreal. MAMC was supported by a doctoral fellowship from  Fonds de recherche du Québec-Santé , a doctoral scholarship from the  Montreal Heart Institute Foundation , a doctoral scholarship from  Quebec's Ministry of Higher Education , and multiple scholarships from the  Faculty of Nursing at the University of Montreal. AL was supported by a  doctoral fellowship  from the  Fonds de recherche du Québec-Santé , doctoral scholarships from the  CHU Sainte-Justine  and  TD Bank , and a doctoral scholarship from  Quebec's Ministry of Higher Education.
| Data sources To obtain the numbers of new HIV diagnoses, over time, stratified by clinical stage at diagnosis, two data sources were combined: the national HIV registry database, containing exhaustive reports of the newly diagnosed HIV cases by the seven Belgian AIDS Reference Laboratories (data available from 1994 to 2015), and the Belgian HIV Cohort  [9] , collecting data from all the AIDS Reference Laboratories and HIV Reference Centres (data available from 2006 to 2015), both managed by Sciensano, which is the Belgian Scientific Institute of Public Health, legally entitled for surveillance activities, including the infectious diseases surveillance. From these data sources, we obtained for each newly diagnosed HIV case, its sex, year of birth and date of HIV diagnosis, and the following variables at the time of diagnosis: HIV exposure group (heterosexual, MSM, injecting drug user), nationality, geographic area of residence and CD4 cell count.
| Estimating numbers and rates of undiagnosed infections Using surveillance data on newly diagnosed HIV cases and the back-calculation model, we produced estimates of the number of undiagnosed HIV infections by sex, HIV exposure group and nationality, at national and subnational levels; estimates were obtained independently for each population and geographic area. We considered four geographic areas, based on the administrative division of Belgium. Belgium consists of three regions: Flanders, Brussels-Capital Region and Wallonia. Flanders and Wallonia are further divided into provinces. We produced estimates by population at the regional level for Brussels-Capital Region and Wallonia. For Flanders, we produced estimates for two sub-regional areas: the province of Antwerp and the other provinces, because Antwerp is the second largest city of Belgium after Brussels.
Rates of undiagnosed infections were calculated by dividing the numbers of undiagnosed infections by the corresponding population size. Population sizes at national and subnational levels were determined from three data sources: Statbel, the Belgian Statistical Office, for the numbers of inhabitants aged 18 to 64 by areas, sex, and nationality, and two surveys on sexual behaviour  [14]  and drug use  [15] , for the proportions of MSM (defined as men homosexually active  [14] ) and the number of persons injecting drugs (defined as individuals who injected drugs at least once over their lifetime). Finally, using our estimates of the total number of undiagnosed infections and an estimate of the number of individuals living with diagnosed HIV obtained previously  [9] , we calculated the overall number of individuals living with HIV and the percentage of individuals unaware of their positive HIV status.
| Statistical analysis At the time of analysis, surveillance data up to 2015 were available. Multiple imputation by chained equation  [16]  was used to impute missing values. We used a two-step procedure to impute data to account for the fact that some variables (e. g. clinical stage) had more missing values before 2006. We first imputed missing data for the period 2006 to 2015 and generated ten imputed databases. Then each of the ten imputed databases was merged with the raw data for the period 1994 to 2005. Next, we imputed missing data over the 1994 to 2005 period and generated a total of 50 imputed databases, that is, five new imputed databases for each merged database. We then generated a total of 1500 new datasets, by creating several bootstrap datasets from each of the 50 imputed databases, and we ran the model on each of the 1500 datasets.
LM, DVB, CO, JD and AS declare no conflicts of interest. A U T H O R S ' C O N T R I B U T I O N S LM and VS designed the research. LM performed the research. LM and VS drafted the manuscript. All authors analysed the data and critically revised the manuscript for important intellectual content. A C K N O W L E D G E M E N T F U N D I N G This work was supported by the ANRS (France) and by IWT (Belgium) and through the framework of HIVERA JTC 2014. The sponsor had no role in the study. LM is grateful for the financial support of the ANRS, in the form of a postdoctoral research fellowship.
Questionnaire The target population for the questionnaire was French citizens aged 18 years and over. In order to maximize the response rate, the questionnaire was translated into French and also made available in English. Convenience sampling was used for the questionnaire. It was structured as follows: First, a brief description of the project was provided and participants were informed of their right to withdraw from the project at any time, as well as how all data would be handled during the project. For the purposes of this questionnaire, respondents were presented with the following definition of a disaster: "an event which has catastrophic consequences and significantly affects the quality, quantity, or availability of the service provided by the infrastructure. " Second, a Likert scale was used to measure participants' expectations.
Participants were asked two questions regarding information provision. The first asked, "During and immediately after a disaster, I expect critical infrastructure operators to provide me with information…" and presented four scenarios: via calling their telephone number, on their website, on their social media site and through traditional media e. g. interviews with television networks or the radio, press releases. The second proposition asked, "During and immediately after a disaster, I expect critical infrastructure operators to respond to my questions and comments on their social media sites e. g. Twitter. " The questionnaire also asked about the participants' demographics and social media habits. Data from the questionnaire was collected between 28 March 2016 and 30 April 2016. The questionnaires were translated back into English at the data entry stage.
Interview The findings were then compared to the current practices of the French A31 Highway. Two in-person, group interviews took place with actors from the Living Lab, one comprising of actors from DIR Est and another from DDT57. These included representatives from the office dealing with the intersection of the A31 between Nancy and Luxembourg, the office in charge of real time management for crisis interventions, the crisis management and alternative route designation office, and the urbanism and risk prevention office. Interviews were held in French and then data was translated into English for analysis. Data from the interview was collected on 25-26 May 2016. 5 Questionnaire results.
No triage or acuity assessments are made on site, however a first screening can occasionally be conducted by phone by a specialized nurse to redirect non-urgent situations (e. g. , information request). The average length of duration for the visit is around 1-3 h and no bed is specifically dedicated for an extended period of observation. After the visit, the clinician in charge of the patient enters clinical information in a specific database. Data from the electronic medical record of all patients admitted in the emergency unit at CAPD-PSH between 1st of December 2016 and 1st of December 2017 were extracted. Data were compared with information previously obtained from subjects referred to the emergency unit in 1981  (18) , in 1992 and in 2002  (19). Data were systematically collected by the psychiatrist involved using a standardized questionnaire to document demographic features (age, gender), nature of the referral (source and main reason for referral), the discharge psychiatric diagnosis, and the final care decision.
The questionnaire has been computerized since 2012. The psychiatric diagnosis was coded by using ICD-10-CM and then grouped into 11 categories based on a modification of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition classification  (20). To facilitate the comparison with data previously extracted, we selected for each case in 2017 the main reason for referral. When subjects received two reasons for referral, we considered only the one which had the greater influence on the clinical decision. For example, if a child presented both "suicidal thought" and "school failure, " the first was considered as the main reason for referral. To keep in line with the method used by Blondon et al. (19) , when two or more visits were recorded for a patient only admissions that occurred at least 3 months after the initial visit were taken into account.
First, patients above 15 who need psychiatric assessment after 7 p. m. and/or the weekend were admitted in the adult ED of the Pitié-Salpêtrière Hospital. However, during daily hours on the week and Saturday morning patients above 15 who need psychiatric assessment were addressed to the emergency unit at the CAPD-PSH. This repartition between the CAPD and adult ED at the PSH did not change during the study period. Second, patients transferred from another hospital directly to an inpatient unit of the CAPD-PSH were not assessed to the emergency consultation unit. We used descriptive statistics to summarize our results. Continuous variables such as age are presented with means ± standard deviation. Proportions such as rate of admission are presented with percentages. Data were statistically analyzed using R software.
Summary of the Main Results We used naturalist clinician-reported data collected in 1981, 1992, 2002, and 2017 to examine the evolution of the types of youth mental health problems observed in an emergency unit in a child and adolescent psychiatric department over the last three decades. In line with our first hypothesis, we found that since 1981 the number of patients presenting to the emergency service rose steadily, with an increase of 3. 85-fold between 1981 and 2017. This is in line with the observations made in pediatric EDs in the US  (5, 6, 14, 15) , in Canada  (4, 12) , and in Spain  (13). As mentioned in the introduction, in the context of the French national mental health system this finding supports the view that financial reasons are not a determining factor in the increase in the use of emergency services in children and adolescents.
The mechanisms leading to megafauna (>44 kg) extinctions in Late Pleistocene (126,000-12,000 years ago) Australia are highly contested because standard chronological analyses rely on scarce data of varying quality and ignore spatial complexity. Relevant archaeological and palaeontological records are most often also biased by differential preservation resulting in under-representated older events. Chronological analyses have attributed megafaunal extinctions to climate change, humans, or a combination of the two, but rarely consider spatial variation in extinction patterns, initial human appearance trajectories, and palaeoclimate change together. Here we develop a statistical approach to infer spatio-temporal trajectories of megafauna extirpations (local extinctions) and initial human appearance in south-eastern Australia.
Statistical methods that attempt to overcome these chronological biases (reviewed in ref. 14  ) are either not spatially explicit and disregard spatial variation in extinction patterns, initial human appearance trajectories, and palaeoclimate change  7, 15  , or they generate new spatial biases via arbitrary geographic binning  [8] [9] [10]  or spatially continuous estimation  16  that do not account for the uncertainty arising from sampling and taphonomic biases  17  , inherent dating errors  18  , or spatial biases generated when interpolating a linear chronology from unevenly spaced age estimates. We overcome these methodological limitations by developing and applying a statistical approach to the available data describing megafauna extirpations and patterns of human appearance in south-eastern Australia over the last 120,000 years to test three explicit hypotheses: (1) megafauna extirpations followed changing climate conditions, (2) megafauna extirpations followed the arrival of Aboriginal populations in the region, or (3) megafauna extirpations followed a combination of both climate change and human arrival.
Results Regional patterns of megafauna extirpation and human spread. We first identified the regional locations where and periods during which humans had putatively coexisted with megafauna species within south-eastern Australia by comparing their respective regional chronologies (i. e. , extirpation and initial arrival) and their spatial bearings. To estimate an accurate regional chronology for these patterns, we applied a spatiotemporal statistical method (see details in Methods and Supplementary Table  1 ) to high-quality (i. e. , suitable dates; see quality-rating approach in Methods) megafauna fossil records and to the archaeology of initial human appearance of Sahul (Fig. 1a , Supplementary Table  1  and Supplementary Data 1). Our method returns for each 1 × 1°terrestrial grid cell in southeastern Australia a mean estimate of both a timing of megafauna extirpation and initial human arrival along with their respective standard deviations (see details in Methods and Supplementary Note 1).
We obtained the corresponding climate data from a transient LOVECLIM  19  Earth-system model experiment, which uses time-varying orbital forcings, ice-sheet volume, and greenhouse gases as a forcing covering the past 784,000 years. We calculated the anomalies relative to the period from 50 to 30 ka (see details in Methods). None of the generalised least-squares models we tested explained any variation in the timing alone of megafauna extirpation in areas of human-megafauna coexistence (i. e. , ~0% of variance explained). However, the top-ranked model explained variation in the spatial pattern of timing of extirpation (bearing = Ext b ; Table  1 ), such as the bearing of the timing of human arrival, the bearing of mean annual precipitation, and water availability (>81. 2% of variance explained; Table  1 ).