dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of the users' domicile Estimating our sample individuals' place of residence is the starting point for the analysis of tweets in Bangkok since it allows us to verify the quality of the dataset by comparing it with the census data. Our first hypothesis is that, for a person, the most visited Tweet place between 8pm and 8am corresponds to his home  (Luo et al. , 2016). However, in the context of Bangkok, the effervescence of the nocturnal activity in certain neighborhoods, which could skew this domicile attribution process, must be taken into account. We thus added a few conditions for the determination of the place of residence for Twitter users: • The residence is one of the 3 places where the person records the most number of days with tweets sent notably between 8pm and 8am; • The residence is located in a residential area; we thus remove the markets, railway stations, airports and parks; • The residence is the place where we recorded the most number of days with sent messages.
Finally the population in the Eastern periphery of the city with few inhabitants is overestimated. On the other hand, the southwestern inhabitants of the megacity use less Twitter than the rest of the city. These sub-districts are highly populated, more rural and less privileged, which could explain this under-representation. Nevertheless, the fact that geolocated tweets represent only a small part of the overall flow of messages -between 1. 5 to 3%  (Murdock, 2011)  -does not seem to be a limitation for our study, as we managed to collect a sample of ~25 000 persons residing in Bangkok, which seems to be, for the most part, spatially representative of the total population in the place of residence -even if it is difficult to assess its socio-economic representativity. Furthermore, our dataset is probably made up of essentially adolescents and young adults who are the main Twitter users  (Luo et al.
DISCUSSION & CONCLUSION In this paper we have presented a study of daily mobilities in Bangkok using Twitter data. We chose to look into mobility using the concept of activity space, that is, all the places visited and activities defined at an individual level in time and in space. We relied on large emerging databases for analyzing urban mobilities using geo-localized and dated messages from thousands of Twitter users as well as for characterizing land use by using points of geographic interest from Google Places database. Using geo-localized Tweets in Bangkok, we have presented different methods that allowed us to know the time when the users visit the places and establish mobility profiles according to the place of residence, number of places visited and distances covered. These results might have been different in another time period, even if the high duration of data collection may tend to mitigate these differences.
These will then provide an original cartography of the risk of infection at a scale, which has never been carried out till today. But sensitive analysis has to be performed as the landuse classification might depend on the size and shape of the spatial entity. Nevertheless, this approach could also be used for instance in a CDR activity-based analysis by classifying landuse in Voronoi polygons, even if we might end up with slightly different results. Furthermore, Google is not the only provider of Points of Interest and other sources could be used, such as Foursquare or Facebook. Comparison of these spatial datasets will be done in a future work. The question of representativity of Twitter users with respect to the population of the city is always an important issue. We've shown here that the estimated home location is correlated with the sub-district population, but we don't know if our sample is correlated with the socio-cultural and economic background of Bangkok's citizens.
However, we assume this general criticism in the specific context of the study of dengue, given that the population age groups at risk in Bangkok might correspond to the age group of the main users of virtual social networks. Furthermore, Twitter data only gives a snapshot of where and when people have been, and as far as our knowledge, there is no proper way to quantify the distortion with the reality on such a large sample. People could have over tweeted in anecdotal places according to their real activity space and under-tweeted in more important places such as their office or places they visit more often. The question of the intention beyond the creation of such digital footprint is also raised by social network studies. We should also consider that there are more and more gateways between social networks, as it's possible to post an Instagram picture or a check-in from Swarm on Twitter.
Unfortunately, we have not recorded the source of the message, so we cannot quantify the share of each sources (Twitter from Android, Instagram, Foursquare, etc. ) but this should be considered for future studies. For instance, a geolocated picture from Instagram shared publicly on Twitter does not exhibit the precise location of the user but the location of the picture, selected from a list of nearby places -but the user can choose anyplace in the database. Even if our bot detection algorithm might have deleted some of such users, some bias might still exist in our dataset, which could also explain the over-representation of some places (i. e. , touristic and commercial places downtown). Moreover, geolocated tweets shouldn't be used as a standalone dataset, and should be merged with other large and easily accessible Internet databases, such as Facebook or Foursquare check-ins, to enhance the attractivity pattern of the city.
These relatively new sources of data are full of biases, (i. e. does the numerical footprint reflect the real mobility pattern of the targeted group?) and should therefore be mixed with more "classic" mobility data, such as institutional time use survey or qualitative interviews from the field, to have a more precise view of the studied phenomena. Finally, using the Twitter data without the user being aware of what the data he produced might be used for raises the same kind of ethical problems as the use of CDR. But in the first case, the user might know that he shared publicly some information with a geographical context and is therefore widely accessible, contrary to CDR, where the users don't expect that the meta-data resulting from a phone call could be re-used for other purposes than billing and network maintenance.
We provided here a 3 stage anonymization: spatially, by merging tweets into a 180m cell, temporally by aggregating the tweets into a typical week and thematically with our land-use typology that doesn't display the precise activity on an user. This approach make it possible to perform some spatial analysis with a relatively privacy protection of the users of the dataset, as it is not possible to ensure a 100% secure anonymization for all the users in the dataset  (de Montjoye et al. , 2013).
Methods: The study population consisted of a multicentre cohort of patients with CLM (N ¼ 12 406) operated on, with intention to resect, from January 2005-June 2013 and whose data were prospectively collected in the LiverMetSurvey registry. Results: Overall, the group X10 CLM (N ¼ 529, 4. 3%) experienced a 5-year overall survival (OS) of 30%. A macroscopically complete (R0/R1) resection (72. 8% of patients) was associated with a 3-and 5-year OS of 61% and 39% vs 29% and 5% for R2/no resection patients (Po0. 0001). At multivariate analysis, R0/R1 resection emerged as the strongest favourable factor of OS (HR 0. 35 (0. 26-0. 48)). Other independent favourable factors were as follows: maximal tumour size o40 mm (HR 0. 67 (0. 49-0. 92)); age o60 years (HR 0. 66 (0. 50-0. 88)); preoperative MRI (HR 0.
Study population LiverMetSurvey registry. We used the multicentric cohort of patients operated on for CLM between January 2005 and June 2013, and whose data were prospectively registered in the LiverMetSurvey international registry. LiverMetSurvey (http:// www. livermetsurvey. org/) is an international database that prospectively collects clinical and pathological data of patients undergoing surgery for CLM  (Adam et al, 2010; Andres et al, 2012; Viganoet al, 2012). This register currently involves 485 centres across a total of 59 countries. Data are prospectively entered by using an online questionnaire, which includes demographic and pathologic variables as well as informations concerning the type, duration and effects of preoperative treatment, the surgical procedure, the timing, location and treatment of recurrence, as well as the post-operative and longterm outcome.
Data are regularly updated by each centre, and a quality control of the data is performed by a data manager who sends twice a year to each contributing centre a personalised information, concerning the items to complete or to update for each patient of the centre cohort. Selection of the study population. Our initial study population consists of all consecutive patients registered in the LiverMetSurvey during the study period with available number of CLM on imaging studies at diagnosis (CT or MRI) of the liver disease. The resulting cohort consists on a total of 12 406 patients. Of them, we specifically focused on patients exhibiting 10 CLM or more (N ¼ 529). All patients included in this study were operated on for CLM with an intention to resect on a curative intent. Some of them were not resected due to intraoperative discovery of extrahepatic disease or to more extended liver disease contraindicating radical surgery.
Statistical analysis Comparisons. Comparisons between categorical data and continuous data were done by using the w 2 -test and ANOVA test, respectively. Survival analysis. Continuous variables were transformed into categorical data by using the roughly rounded values of the mean as cut-off for more readability. The survival probabilities were calculated according to Kaplan-Meier method and survival plots were compared with the log rank test. For OS, time was calculated from the date of CLM diagnosis to the date of last news. For 'primary' DFS, time was calculated from the time of resection to the date of first relapse or death. Incomplete resection was considered as a relapse at time 0. In case of multistep procedures (i. e. , two-stage hepatic resection), the DFS was calculated from the date of last resection.
ACKNOWLEDGEMENTS We thank all centres contributing to LiverMetSurvey and  Valerie Delvart  for performing the statistical analysis of this study. There is no financial support for the study. LiverMetSurvey is supported by a grant from  Sanofi-Aventis. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4. 0 Unported License.
Since 1994, the In-service Aircraft for a Global Observing System (IAGOS) program has produced in-situ measurements of the atmospheric composition during more than 51000 commercial flights. In order to help analyzing these observations and understanding the processes driving the observed concentration distribution and variability, we developed the SOFT-IO tool to quantify source/receptor links for all measured data. Based on the FLEXPART particle dispersion model  (Stohl et al. , 2005) , SOFT-IO simulates the contributions of anthropogenic and biomass burning emissions from the ECCAD emission inventory database for all locations and times corresponding to the measured carbon monoxide mixing ratios along each IAGOS flight. Contributions are simulated from emissions occurring during the last 20 days before an observation, separating individual contributions from the different source regions.
The main goal is to supply added-value products to the IAGOS database by evincing the geographical origin and emission sources driving the CO enhancements observed in the troposphere and lower stratosphere. This requires a good match between observed and modeled CO enhancements. Indeed, SOFT-IO detects more than 95% of the observed CO anomalies over most of the regions sampled by IAGOS in the troposphere. In the majority of cases, SOFT-IO simulates CO pollution plumes with biases lower than 10-15 ppbv. Differences between the model and observations are larger for very low or very high observed CO values. The added-value products will help in the understanding of the trace-gas distribution and seasonal variability. They are available in the IAGOS data base via http://www. iagos. org. The SOFT-IO tool could also be applied to similar data sets of CO observations (e.
Introduction Tropospheric pollution is a global problem caused mainly by natural or human-triggered biomass burning, and anthropogenic emissions related to fossil fuel extraction and burning. Pollution plumes can be transported anthropogenic and biomass burning) and since CO has a lifetime of months in the troposphere  (Logan et al. , 1981; Mauzerall et al. , 1998) , it is often used as a tracer for pollution transport  (Staudt et al. 2001; Yashiro et al. , 2009; Barret et al. , 2016). It is therefore convenient to follow past examples and use simulated CO source contributions to gauge the influence of pollution sources on the measurements also with SOFT-IO. Our methodology uses the FLEXPART Lagrangian particle dispersion model  (Stohl et al. , 2005)  and emission inventories from the ECCAD emission database  (Granier et al.
, 2012)  in order to quantify the influence of emissions sources on the IAGOS CO measurements. The goal is to provide the scientific community with added value products that will help them analyzing and interpreting the large number of IAGOS measurements. The methodology has the benefit to be adaptable to multiple emission inventories without re-running FLEXPART simulations. It is also easily adaptable to analyze other datasets of trace gas measurements such as from ground based observations, sondes, aircraft campaigns or satellite observations. The methodology will be described in the next section, and then evaluated at the example of case studies of pollution plumes observed by IAGOS aircraft. Further evaluation is performed through statistical analysis. Finally we discuss the limitations of the methodology by estimating its sensitivity to different input data sets (emission inventories, meteorological analyses).
In-situ observations database: MOZAIC and IAGOS programs The MOZAIC program  (Marenco et al. , 1998)  was initiated in 1993 by European scientists, aircraft manufacturers and airlines to better understand the natural variability of the chemical composition of the atmosphere and how it is changing under the influence of human activity, with particular interest in the impact of aircraft exhaust. Between August 1994 and November 2014, MOZAIC performed airborne in-situ measurements of ozone, water vapor, carbon monoxide, and total nitrogen oxides. The measurements are geolocated (latitude, longitude and pressure) and come along with meteorological observations (wind direction and speed, temperature). Data acquisition is performed automatically during round-trip international flights (ascent, descent and cruise phases) from Europe to America, Africa, Middle East, and Asia (Fig.
1 ). Based on the technical expertise of MOZAIC, the IAGOS program  (Petzold et al. , 2015, and references therein)  has taken over and provides observations since July 2011. The IAGOS data set still includes ozone, water vapor, carbon monoxide, meteorological observations, and measurements of cloud droplets (number and size) are also performed. Depending on optional additional instrumentation, measurements of nitrogen oxides, total nitrogen oxides or, in the near-future, greenhouse gases (CO 2 and CH 4 ), or aerosols, will also be made. Since 1994, the IAGOS-MOZAIC observations have created a big data set that is stored in a single database holding data from more than 51000 flights. The data set can be used by the entire scientific community, allowing studies of chemical and physical processes in the atmosphere, or validation of global chemistry transport models and satellite retrievals.
Estimation of carbon monoxide source regions: methodology To establish systematic source-receptor relationships for IAGOS observations of CO, the Lagrangian dispersion model FLEXPART  (Stohl et al. , 1998 (Stohl et al. , , 2005;; Stohl and Thomson, 1999)  is run over the entire database. Lagrangian dispersion models usually represent the differential advection better than global Eulerian models (which do not well resolve intercontinental pollution transport;  Eastham et al. , 2017) , at a significantly lower computational cost. In particular, small-scale structures in the atmospheric composition can often be reconstructed from large-scale global meteorological data, which makes model results comparable to highresolution in situ observations  (Pisso et al. , 2010). In the past, many studies  (Nédélec et al.
Emission inventories from the ECCAD project The main goal of the Emissions of atmospheric Compounds & Compilation of Ancillary Data (ECCAD) project  (Granier et al. , 2012)  is to provide scientific and policy users with datasets of surface emissions of atmospheric For the current study, we selected five CO emission inventories. Four of them are available at global scale (MACCity and EDGAR v4. 2 for anthropogenic; GFED 4 and GFAS v1. 2 -GFAS v1. 0 for 2002-for fires) from the ECCAD database and cover most of the IAGOS CO database presented here  (2002 -2013). The global scale inventories have a 0. 1° × 0. 1° to 0. 5° × 0. 5° horizontal resolution. They are provided with daily, monthly or yearly time resolution. They are listed in Table  2  along with the references describing them. The four global inventories are used to study the model's performance and sensitivity in Sect.
5. To further test the sensitivity to the emission inventories, we also used one regional inventory, which is expected to provide a better representation of emissions in its region of interest than generic global inventories. For biomass burning, the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) campaign's North American emissions inventory developed by  Turquety et al. (2007)  for the summer of 2004 and provided at 1° × 1° horizontal resolution was tested. It combines daily area burned data from forest services with the satellite data used by global inventories, and uses a specific vegetation database, including burning of peat lands which represent a significant contribution to the total emissions.
Automatic detection of CO anomalies For individual measurement cases, plumes of pollution can most of the time be identified by the human eye using the observed CO mixing ratio time series or the CO vertical profiles. However, this is not feasible for a database of tens of thousands of observation flights. In order to create statistics of the model's performance, we need to systematically identify observed pollution plumes in the IAGOS database. The methodology to do this is based on what has been previously done for the detection of layers in the MOZAIC database  (Newell et al. , 1999; Thouret et al. , 2000) , along with more recent calculations of the CO background and CO percentiles define for different regions along the IAGOS data set  (Gressent et al. , 2014). An example demonstrating the procedure, which is described below, is shown in Fig.
Selected case studies to evaluate CO emission inventories and SOFT-IO's performance As described in Sect. 2, a number of case studies documented in the literature were selected from the IAGOS database in order to get a first impression of the model's performance. These case studies have been chosen to represent the different pollution situations that are often encountered in the troposphere in terms of emissions (anthropogenic or biomass burning) and transport (at regional or synoptic scale, pyro-convection, deep convection, frontal systems). Systematic evaluation of the model performance against emission inventories will be presented in Sect. 5.
The dashed blue line represents the residual CO C R UT. Observations indicate that the aircraft encountered several polluted air masses with CO mixing ratios above 110 to 120 ppb, which are the seasonal median CO values in the two regions visited by the aircraft, obtained from the IAGOS database (see  Gressent et al. , 2014). Three pollution plumes are measured: • around 100°W (around +10 ppb of CO enhancement on average): plume 1 • between 80°W and 50°W (+30 ppb of CO enhancement on average): plume 2 • between 0° and 10°E (+40 ppb of CO enhancement on average): plume 3. These polluted air masses are surrounded by stratospheric air masses with CO values lower than 80-90 ppb. As polluted air masses were sampled at an altitude of around 10 km, they are expected to be due to long-range transport of pollutants.
Biomass burning emission inventories In order to evaluate and choose biomass burning emission inventories, we have selected eleven case studies with fire-induced plumes (Table  1 ). Seven of them focused on North-American biomass burning plumes observed in the free troposphere above Europe (flights on 30 th of June, 22 nd and 23 rd of July 2004) and in the upper troposphere/lower stratosphere above the North Atlantic (29 th of June 2004) (e. g. et al. , 2009). Two are related to the fires over Western Europe during the 2003 heat wave  (Tressol et al. 2008). The two last ones, on the 30 th and 31 st of July 2008, focused on biomass burning plumes observed in the ITCZ region above Africa as described in a previous study  (Sauvage et al. , 2007a). The three datasets selected to represent biomass burning emissions are based on different approaches: GFAS v1.
(2010). Outside of the plume, the CO concentration decreases with altitude, from around 150 ppb near the ground, to 100 ppb background in the upper free troposphere. This last value corresponds to the median CO seasonal value deduced from the IAGOS database  (Gressent at al. , 2014). CO is not considered as an anomaly near the ground as C R UT < C R UT_season (Q3). SOFT-IO simulations were performed for this case using MACCity to represent anthropogenic emissions, and GFAS v1. 2 (green line), GFED 4 (yellow line), or the ICARTT campaign inventory (red line). Fire vertical injection is realized using the MIXED approach for the three biomass burning inventories, in order to only evaluate the impact of choosing different emission inventories. In the three simulations, contributions show two peaks, one near the ground that is half due to local anthropogenic emissions and half due to contributions from North American biomass burning and thus not considered in this discussion.
For that reason we aim to provide simulations with different global and regional inventories in for the IAGOS data set. As the ICARTT campaign inventory was created using local observations in addition to satellite products, the large difference in the simulated CO compared to the other inventories may in part be due to different quantification of the total area burned (for GFED, GFAS using the FRP as constraint). Turquety et al. (2007)  also discussed the importance of peat land burning during that summer. They estimated that they contributed more than a third of total CO emissions (11 Tg of the 30 Tg emitted during summer 2004). deduced from seasonal IAGOS mixing ratios over this region. Such CO enhancements have been attributed to regional fires injected through ITCZ convection  (Sauvage et al.