dataesr/scientific-paragraphs-categorization huggingface dataset:
Database architecture The IRHUM (Isotopic Reconstruction of Human Migration) database is a web platform to explore and share strontium isotope data sets and data products. It is built upon a highly flexible, open source software stack (Opengeosuite, Geonode) maintained by Boundless (http://boundlessgeo. com) and follows common web standards. Its current functionality allows the user to explore our data sets, upload their own data, and create basic isotope maps. It is also possible to connect to an external WMS server to load background data such as geological and soil maps. In addition to the spatial isotope data the IRHUM website stores metadata and allows the user to upload documents to describe their project and methods. This will enable others to assess the suitability of specific data for their study.
Survey Methodology Data were extracted from the National French Health Barometer survey conducted in 2010 by the French Institute for Prevention and Health Education (INPES) in consultation with the French Ministry of Health  [20]. This survey was designed to measure the evolution of key indicators regarding health-related behaviors, attitudes, and opinions in the general population. Using a computer-assisted telephone interviewing (CATI) system, 27,653 people were interviewed from October 22, 2009 to July 3, 2010. Interviewers from a private survey firm were trained by the INPES to administrate this health-related survey. We used a 2-stage random sampling design: (1) selection of households using random digit dialing covering all metropolitan French regions, and (2) random selection of one member of the household, using the method proposed by Kish  [21].
If a household or individual refused to participate or could not be reached, they were not replaced in the study. Thus, specific efforts were made to successfully reach households and increase the response rate: a formal request to participate explaining the goals of the study was sent by mail before the first call (addresses were located from the landline phone numbers when available); unsuccessful calls were repeated after 30 and 90 minutes, on different days, and at different times to a maximum of 40 attempts for each generated phone number. Individuals who refused to participate were contacted a second time by specially trained interviewers. The overall refusal rate was 39%. All collected data were anonymous and self-reported. The mean duration of an interview was approximately 32 minutes for landline phones and 34 minutes for mobile phones.
This population-based survey procedure was approved by the French data protection authority (Commission Nationale de l'Informatique et des Libertés; CNIL), an independent administrative body that operates in accordance with the national data protection legislation, amended in 2004 specifically to protect citizens' identities and privacy and ensure access to their own personal data. From the initial nationally representative sample of 27,653 people aged 15-85 years, a random sample of 1052 young adults aged 15-30 years answered a set of specific questions on their use of the Internet as an information tool for health-related issues. In this paper, we will analyze this subsample (referred to as young adults or aged 15-30 years interchangeably). Data were weighted by the number of telephone lines and eligible persons in the household.
Characteristics of Online Health Seekers According to the national Health Barometer 2010 survey data based on a random and representative sample of the French population, 45% of young adults aged 15-30 years used the Internet in the past 12 months to seek health information. This result is in-line with a study from the French National Institution of Statistics and Economical Studies (INSEE) in 2010 examining the same question, among others, but over a period of 3 months  [17]. At the international level, this proportion is lower than in other countries. A survey performed in 7 European countries (Norway, Denmark, Germany, Greece, Poland, Portugal, and Latvia) in 2005 found that, on average, 63% of individuals aged 18-29 years were online health seekers  [27]. The replication of this study in 2007 showed that this behavior was growing in all age groups  [28].
Acknowledgments I am grateful to  Parveen Akhtar ,  Lena Näre , two anonymous referees and  Zhenya Toumanova  for their helpful comments and editorial work. I would like to thank  Brunella Casalini ,  Solange Chavel ,  Lisa Eckenwiler ,  Lena Näre , and  Tiina Vaittinen  for the careful reading of the different versions of the paper, as well as  Etienne Balibar ,  Ryoa Chung ,  Anca Gheaus ,  Alex Jaunait ,  Pascale Laborier ,  Sandra Laugier ,  Olivier Nalin ,  Patrick Pharo ,  Camille Schmoll  for their stimulating discussions.
This method is based on the concepts of (i) Visibility Analysis  (Manchado et al. , 2013)  and Visual Magnitude  (Chamberlain and Meitner, 2013) ,  (ii)  human eye perception and its field of view  (Costella, 1992; Spector, 1990)  and (iii) descriptive geometry  (De Rubertis, 1979). Quantitative analysis of visual impact is performed by (i) computing the field of view of an observer at a specific distance, (ii) evaluating the object shape distortion perceived by a human eye, (iii) analysing the mutual relation between object, observer and earth morphology. The tool is developed as an add-on module for GRASS GIS, an Open Source GIS software  (Neteler and Mitasova, 2008). As the code is completely available, users can freely read, verify, redistribute and modify the code, meaning that the tool is both flexible and that the reproducibility of results is guaranteed  (Ince et al.
Material and methods The tool developed is named "r. wind. sun". It is coded in the Python programming language (Van Rossum and Drake, 2001) as an add-on module to GRASS GIS, an Open Source GIS software  (Neteler and Mitasova, 2008). The tool builds upon the existing GRASS GIS tool "r. viewshed"  (Toma et al. , 2010)  which is based on the concept of Line of Sight (LOS); the straight line between the observer and object (e. g. ,  Molina-Ruiz, 2011). In the r. wind. sun tool, visual impact is quantified by the proportion of the field of view that is obstructed by the wind turbine or photovoltaic panel. Rodrigues et al. (2010)  that measures visual impact as the size of the observed object and half of the full solid angle multiplied by the square of the distance between the object and the observer.
Calculation r. wind. sun is a Python script for GRASS GIS which evaluates the visual impact index (NI-index) for each cell of a raster map in the area surrounding one or more wind turbines or photovoltaic panels. Required inputs are: • a Digital Elevation Model (DEM); • a vector point map giving the location of centroids of the wind turbines or photovoltaic panels; • dimensions and inclination of the facilities; • distance threshold parameters to define the zone of analysis.
For example, on a using a computer with 4Gb of RAM and a 2. 0GHz processor running a Linux Operating System, applying the code to a study region with a maximum distance of 10 km from the facilities and a resolution of 10 m (more than 3 million cells) the analysis is completed in approximately 10 minutes. Moreover, the code could be parallelized in a way that GRASS GIS can process each wind turbine or panel in a different mapset.
Other outputs of the tool are: • two-dimensional vector map of the wind turbines; • three-dimensional vector map of the wind turbines. These can be useful to produce cartography or the three-dimensional rendering of the analysis results. The wind direction does not affect the analysis and is only used for descriptive purpose. The 2D and 3D outputs (vector layers), show the wind turbine rotor oriented in that direction. The 3D vector map can be exported from GRASS GIS in different formats and imported in many of the available 3D rendering softwares (as an example to KML format to visualize the object in GoogleEarth). The specific inputs for the photovoltaic analysis in the "photovoltaic" tab are: • A point vector layer containing the position of the centroids of the panels. • Height and width of the panel.
Background: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. Method and Findings: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems' performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems.
Definitions Events. Events were defined as a verified infectious disease occurrence resulting from the EI process (i. e. including verification and analysis). Events were defined by the disease, the type of cases (human or animal), the country, the province or state, and the month of occurrence regardless of the number of cases concerned. All events are considered to be independent. Signals. Signals were defined as unverified raw infectious disease information (in relation to an event included in the goldstandard data set) collected from biosurveillance systems (informal sources). Biosurveillance systems are therefore a source of signal, but are not the source of the events included in the BHI (i. e. goldstandard data set). Biosurveillance systems are used only for the detection of signals.
Once selected signals are fully processed (i. e. systematically verified, analysed, characterised, etc. ), signals can be classified as an ''event'' or ''discarded''. If biosurveillance systems are the main sources of signals, in the EI process they are not the ''source of the event''. Gold-Standard. Ideally, infectious disease signals identified by Internet biosurveillance methods should be compared to official event reports (gold-standard) during the same time period. The EI performed at InVS, for which necessary information (rational, selection and validation processes, etc. ) was readily accessible, was chosen as the best source of gold-standard data for this study. Infectious disease events (human cases and zoonosis epizootics) reported in the BHI in 2010 were included in the gold-standard data set.
Data. Signals relating to events included in the gold-standard data set were retrospectively searched on all six biosurveillance systems through ad hoc queries using keywords or a series of keywords. Searches were performed by two InVS epidemiologists (i. e. independent from the six biosurveillance systems). Discordant pairs were reviewed and the most relevant signal was kept in the final database. Rates. The crude detection rate (C-DR) was defined as the ability of a system to detect an infectious disease event, the intrinsic detection rate (I-DR) was defined as the ability of a system to detect outbreaks independently from other systems. The crude sensitivity rate (C-Se) was defined as the ability of a system to detect outbreaks prior to their publication in the BHI. The intrinsic sensitivity rate (I-Se) was defined as the ability of a system to detect outbreaks independently from other systems and before their publication in the BHI (see table 2 for details).
Gold-Standard In 2010, 132 events were reported in the BHI. 46 (35%) were excluded (26 global overviews or long-lasting events, 12 follow-ups of previously reported events and 8 non-infectious health occurrences). The 86 events included in the gold-standard data set occurred in 46 countries; 23 (27%) in the Americas, 22 (25%) in Africa, 17 (20%) in Europe, 14 (16%) in Asia and 10 (12%) in Near-East and North-Africa (Figure  1 ). Twenty-two disease events were identified. West Nile virus (WNV) infection and dengue infections represented 21% (n = 18) and 14% (n = 12) of the events, respectively. Important variations were observed according to the region of occurrence, e. g. WNV infection represented 76% of events reported in Europe, 50% in the North-Africa and Near-East, but none of the events reported in America or in Asia (Table  3 ).
Signals Detected A total of 472 raw signals relating to events included in the goldstandard data set were retrieved from the six biosurveillance systems. The language of the initial source was English for 53% (n = 249), Spanish for 15% (n = 72), French for 11% (n = 53) and other languages for 20% (n = 94). The remaining 4 events were detected through several sources in different languages and information was insufficient to rank them. Language sources varied according to the systems (p = 0. 063) and to the region of occurrence of the event (p,0. 001). All 86 events included in the gold-standard data set were detected by at least one of the systems and 57 (66%) were detected by all six systems. When early detection was considered, 84 (98%) events were detected before their publication in the BHI, 30 (35%) were detected by the six systems, 26 (30%) by five systems, 10 (12%) by four systems, 7 (8%) by three systems, 6 (7%) by two systems, 5 (6%) by a single system and 2 events (2%) were not detected prior to their publication in the BHI.
Discussion The systems' characteristics (type of moderation, sources accessed, diseases, languages, and regions covered) were found to significantly influence disease detection performance. This highlights the differences in conceptual design used to develop the biosurveillance systems, and the importance of taking advantage of synergies through combining systems' data for infectious diseases detection. C-DR was used to evaluate the global detection rate independently from the EI quality and type. With C-DR values ranging from 83% to 95%, all systems were found to have a similar ability to detect infectious events, findings consistent with other studies  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]. However, C-DR is a very crude indicator that does not take into consideration the main EI objective of early detection.
Hence, our study illustrates the significant added value resulting from the input of human analysts and their ability to balance the limits inherent to a fully automated detection. Yet, I-Se should be considered as a lower limit, because for each system only the first detected signal was considered. Therefore, it cannot be excluded that some signals primarily collected via a fellow system may have been later detected through another source, but still earlier than the goldstandard. All systems integrate a de-duplication module, aiming at reducing the volume of redundant information; de-duplication performance varies according to systems. A consistent collection of the second detected signals was not possible across all systems, and the weight of this potential bias cannot be estimated. At the time of the study, none of the systems were able to detect early all events included in the gold-standard data set, substantiating the necessity for end-users to use several systems in parallel.
For instance, 2010 was a year marked by unprecedented WNV circulation in the Mediterranean area  [21]  and all 18 WNV infection events reported in 2010 occurred in Mediterranean countries not previously considered as endemic. Hence, other EI stakeholders, with different objectives or disease distribution, would certainly select different events resulting in another gold-standard data set. The performance of the EI process might also affect the results. Unfortunately, in the absence of a recognised international goldstandard, the sensitivity of the chosen gold-standard could not be assessed. This assessment should be carried out, but was beyond the current studies objectives. Although, these results are not fully representative, France's large geographic distribution (spread over four continents) and systematic and stable approach to EI suggest that the results from this study provide a larger overview that can be transposed to other contexts.
Physical activity intentions Two items assessed participants' willingness and intention to engage in the recommended amount of PA over the next week during free time (e. g. , over the next seven days, I intend to participate in at least 30 minutes of moderate-to vigorous-intensity physical activity, 5 times a week, during my free time) using a 7-point Likert scale (15strongly disagree, 75strongly agree). These items correlated strongly (r5. 87) and were combined into a single score. Impulsive approach-avoidance tendencies towards PA and SB IAPA and IASB were measured using the same manikin task as in the Cheval et al. study  [14]. Using Eprime software, participants were asked to move a manikini. e. , a schematic image of a human figure -upwards or downwards by repeatedly pressing the ''8'' or ''2'' keys respectively.
for illustration). Before analyzing the data we excluded incorrect responses (4. 6%) as well as responses below 150 ms (,0. 001%) and above 1,500 ms (0. 08%) as recommended by  [16]. Results are presented with the median RT. Participants' impulsive approach tendencies toward PA and SB were calculated by subtracting the median avoidance RT from the median approach RT toward PA and SB images respectively. A positive score always indicates a tendency to approach rather than avoid the behavior. Past studies  [15, 16, 18]  showed the validity and reliability of manikin tasks to assess impulsive approach-avoidance tendencies. In this study, reliability was good (a 5. 75 and. 83 for PA and SB, respectively).
Data Collection Data were collected through 16 individual semi-structured faceto-face interviews. The interviews were audio-recorded and subsequently transcribed verbatim, with all nuances of the participants' expression recorded. An interview topic guide (Table  2 ) was developed in advance and included 8 open-ended questions and several prompts. The logic underpinning the construction of the interview guide was to elicit in-depth and detailed accounts of the subjects' feelings before the suicide attempt and afterwards, as well as the expectations and meanings that they connected to this action. Our overall objective in using this qualitative method was to put ourselves in the lived world of each participant and explore the meaning of the experience to each of them. Fourteen interviews took place at the adolescents' treatment facility, one at the adolescent's home, and one at the residential facility where the adolescent was living.
Data Analysis Qualitative analysis was performed according to IPA methodology. The aim of this method is to understand how people make sense of their major life experiences by adopting an ''insider perspective''  [23]. Three epistemological points underpin IPA: first, it is a phenomenological method that seeks to explore the informants' views of the world. As Husserl pointed out  [24] , the objective of phenomenology is to understand how a phenomenon appears in the individual's conscious experience. Hence, experience is conceived as uniquely perspectival, embodied, and situated  [21]. Second, IPA is based on hermeneutics: interpretative activity, as defined by Smith & Osborn  [22] , is a dual process in which the ''researcher is trying to make sense of the participant trying to make sense of what is happening to them''.
Study In 2007 and 2010, Gabon recorded simultaneous outbreaks of CHIKV (genus Alphavirus) and DENV (genus Flavivirus) infections. The 2007 outbreaks primarily affected Libreville, the capital of Gabon, and subsequently extended northwards to several other towns  [17] , while the 2010 outbreaks occurred in the south-eastern provinces  [18]. To detect other circulating arboviruses, we conducted a retrospective study based on molecular screening of 4312 sera from symptomatic patients presenting to healthcare centers; 24. 7% of the samples were obtained during the 2007 outbreaks, 9. 7% during the inter-epidemic period, and 65. 5% during the 2010 outbreaks (data not shown). We also analyzed a collection of 4665 mosquitoes captured during the same period and split into 247 pools according to the species, date and sampling site (Table  1 , see  [18]  and  [19]  for the details of the methodology used for mosquito trapping).
Phylogenetic analysis The E and NS3 sequences obtained from one Gabonese patient were concatenated and analyzed using a set of previously published ZIKV sequences. Phylogenetic relationships were reconstructed with the maximum likelihood algorithm implemented in PhyML  [21]  (available at http://www. atgc-montpellier. fr/ phyml/) with best of NNI (Nearest Neighbor Interchange) and SPR (Subtree Pruning and Regrafting) criteria for tree topology searching, and the GTR model of nucleotide substitutions. The Gamma distribution of rate heterogeneity was set to 4 categories, with a proportion of invariable sites and an alpha parameter estimated from the dataset. Branch support was assessed from 100 bootstrap replicates. Tree reconstructions were also performed by Bayesian inference with MrBayes v3.
2  [22]  under the GTR+I+G model of nucleotide substitutions, and with the distance neighborjoining method  [23]  implemented in MEGA5  [24]  with confidence levels estimated for 1000 replicates. To test for phylogenetic discrepancies, tree reconstructions were also performed independently from the envelope dataset and the NS3 dataset with PhyML according to the parameters described above. The resulting trees were visualized with the FigTree software (Available at: http://tree. bio. ed. ac. uk/software/figtree/), and rooted on midpoint for clarity. The Genbank accession numbers for the Gabonese ZIKV strain are KF270886 (envelope) and KF270887 (NS3).
This work was supported by  CIRMF , which is funded by the  Gabonese Government, Total Gabon  and the  French Foreign Ministry. It was also partially financed by grants from  Fondation Christophe et Rodolphe Merieux, Institut de France ,  Institut de recherches pour le developpement (IRD) ,and  Metabiota. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The USB Controller allowed 100 Hz sampling rate. After software calibration, the touch screen resolution was set to 1280 3 1024 pixels, resulting in a 0. 3 mm spatial accuracy. The device was inserted into a rigid PVC panel and the controller was embedded in a shielded box. To ensure that no artifacts were detectable on the EPI images, the tablet was pretested prior to the experiment, first with phantoms and then with two pilot participants, The (x,y) coordinates of the pen tip were recorded as a function of time, and displayed online to the participant through a data video projector, a rear projection screen, and a mirror positioned in front of the participant's eyes. The tablet was positioned on top of a cushion over the participant's hips, and could be oriented in the scanner so that the writing posture was comfortable.
Analysis of Kinematic Data Kinematic data were analyzed with homemade software providing a segmentation of the trace on the basis of the writing pressure. A segment was defined as a portion of the trajectory between two pen lifts (Fig. 1b ). A pen lift was defined as a portion of a trajectory in air between the first character end and the onset of the following character. For each trial, we extracted the following parameters From the recorded (x,y) trajectories (Fig. 1b ): Overall writing duration: Time between the first nonzero pressure point and last nonzero pressure point. Mean length of the written trajectory: length of the path of all the segments where pressure was nonzero. Mean writing velocity: ratio between the length of the written trajectory and the time spent in contact with the tablet.