dataesr/scientific-paragraphs-categorization huggingface dataset:
When mesosaurs are excluded from the analysis, eureptiles are united by six synapomorphies (character number in parentheses): postorbital/supratemporal contact absent (12); posterolateral corner of skull table formed by parietal and small supratemporal (15); supratemporal small (17); squamosal and posttemporal fenestra in contact (25); quadrate anterior process short (35); and arcuate flange of pterygoid absent (42). The fact that mesosaurs share half of these characters  (12, 35, and 42)  weakens support for Eureptilia. This topology is new to our knowledge, although  Lee (2013)  had found, in some of his 12 analyses (eight of a diapsid-focused dataset, and four of a parareptile-focused dataset), that the parareptile Eunotosaurus, which has been claimed to be a close relative of turtles  (Lyson et al.
, 2009; Tsuji et al. , 2010; Bever et al. , 2015) , and temporal morphology may be influenced by several factors  (Werneburg, 2012) , which complicates interpretation of observed morphology. Thus, the question of diapsid monophyly (aside from the inclusion of turtles) might be worth investigating further. To sum up, our study suggests that mesosaurs are the basalmost sauropsids; this result appears to be fairly well-supported, at least with our dataset. Moreover, our results raise several problems about the phylogeny of early amniotes, some of which might be worth reinvestigating with an increased sample of taxa and characters, and a fresh look at various specimens. In addition to suggesting yet another hypothesis about the origin of turtles, our results highlight the importance of including mesosaurs in phylogenetic analyses of amniotes, because they can potentially change the topology near the base of Amniota and weaken support for Eureptilia and Diapsida.
We first discuss how an existing algorithm to extract long-distance trips from mobile phone data can be supplemented with man-made heuristics to arrive at plausible domestic tourism trips for thousands of users. In a second phase, we investigate the temporal, spatial and social characteristics of mobile phone use during the obtained domestic tourism trips. Given that no ground-truth is available, our analysis, interpretation and discussion offer an approval of our methodology to extract plausible domestic tourism trips. Our explorations are experimental, meaning that they do not offer fully developed applications. As such, we refrain from validation or comparison with literature. Rather we sketch possible directions to study different aspects of domestic tourism with mobile phone data, while carefully documenting the different constraints.
Passive mobile phone tracking and tourism research In this paper, we focus on ICT-based tracking by mobile phone, more specifically on CDR (Call Detail Record) data. CDR data have been used in (tourism) research for a decade now. Deployment of CDR data has been successful in distinguishing tourism destinations  (Raun et al. , 2016) , deriving seasonality of foreign tourists' space consumption  (Ahas et al. , 2007) , evaluating travel distances of events visitors and regular visitors  (Nilbe et al. , 2014)  and in segmenting repeat visitors  (Kuusik, 2011). Although findings based on mobile phone data are collected at a much larger scale and treated in a more quantitative framework, they tend to accord well with findings from surveys and questionnaires. One interesting observation is that, typically, CDR data shows higher numbers in terms of call activities, present population, or undertaken trips than can be found in official statistics.
Smaller observation periods, less collected data, less validation possibilities and a far higher heterogeneity of human spacetime paths all pose challenges that are not easily to overcome. Specifically for the case of detecting destinations for domestic tourism, an extra difficulty lies in distinguishing domestic tourism trips from other activities based on CDR data only (as almost no cross-over with other datasets is allowed due to privacy regulations). This compared to the study of foreign tourists CDR users with a foreign sim card can be assumed to be on a tourism trip. To the best of our knowledge, no works in literature exists on how to extract, let alone validate, domestic tourism trips from CDR datasets, nor has there been work investigating the possibilities to detect destinations from such domestic tourism trips.
, 2016; Phithakkitnukoon et al. , 2012; Toole et al. , 2015) , no work has yet been undertaken to transfer and elaborate these insights to the case of (domestic) tourism. Probably because of the difficulties to i) extract long-distance trips from CDR data, ii) define tourism trips from these long-distance trips iii) uncover a single (or multiple) destinations for these tourism trips, and iv) most importantly, the difficulty to properly validate the previous three steps at a large scale. The first of these difficulties has recently been addressed by  Janzen et al. (2016a) , the second and the third form the main body of this paper, whereas the overarching last problem is left unsolved. The latter, as mentioned before, makes our work mainly experimental with as goal to describe the barriers and show the potential to apply mobile phone data to the study of domestic tourism.
Active mobile positioning is "mobile tracing in which the location of the mobile phone is determined (asked) with a special query using a radio wave. In order to ask the location of certain phones, a special environment and permit from the phone holder is required"  (Ahas et al. , 2008, p. 470). On the other hand, passive mobile positioning data is automatically stored in log files of mobile operators, mostly for billing purposes. Such logs, which are often in the format of a Call Detail Record (CDR), are only created for actions initiated by the user at the operator's cell tower  (Ahas et al. , 2008; Kuusik, 2011). In other words, A CDR record is a trace created when a mobile phone user makes a call (or text) and typically consists of an anonymous (but consistent) ID of the caller and called person, the duration of the call, the time of the call event, the identifiers of the cell tower to which the user was connected (which can be linked to the exact geographical location of the cell tower) and the type of activity (call or text message).
(Dis)advantages of CDR data Using CDR data to reconstruct individual mobility pattern offers several advantages compared to other ICT tracking methods, surveys or questionnaires. For starters, mobile phone data is available over long time periods and covers large study areas with rather high spatial resolution. In addition, penetration rates of mobile phones are high (estimated 86% for 2007 (ARCEP, 2008)), rendering really large samples of populations. As a consequence, work on various time and spatial scales can be done for large shares of the population, opening up unseen applications in many areas  (Deville et al. , 2014; Järv et al. , 2014; Nielsen, 2011; World Bank, 2013). For example, aggregating data over extended periods of time enables complex analysis and diminishes the influence of singular events and/or non-routine behaviour, which offers opportunities to investigate specific activities compared to baseline behaviour  (Vanhoof et al.
, n. d. ). Compared to accommodation surveys and questionnaires, CDR data is less biased as surveys and questionnaires can lead to respondent fatigue. In addition, gathering CDR data for large samples is much more cost-effective, among others because the CDR data storage happens automatically  (Axhausen et al. , 2007; Golob and Meurs, 1986; Järv et al. , 2014). Compared to other mobile sensing applications, like GPS tracking, CDR data has the advantage of not requiring active participation of users for the activation of tracking, or by consenting to sharing their exact location data, which for many means a burden or raises privacy concerns  (Chen et al. , 2010). The most important characteristic of CDR data, however, is that, in addition to movement data, it also stores logs of interactions between people (who calls whom).
This way, direct links between human interaction and human movement can be investigated and exploited, even though this is not yet common practice in literature  (Toole et al. , 2015). The biggest disadvantages of CDR data are the temporal scarcity in observations (in 2007, on average, around 4 records per user per day were collected but the actual numbers differ between users depending on the individual usage) and the spatially inhomogeneous distributions of covered areas, which results from a demand-driven, non-uniform distribution of cell tower locations (more cell towers in densely populated areas). Both disadvantages pose substantial challenges for the automated (spatial) analysis of CDR data and makes inferring information at the individual level much more complicated compared to other sources like, for instance, GPS data  (Vanhoof et al.
The French CDR dataset In this paper, we use access to an anonymized CDR dataset recorded by Orange. The data covers mobile phone usage of ~18 million subscribers on the Orange network in France during a period of 154 consecutive days in 2007  (May 13, 2007 to October 14, 2007). Mobile phone penetration being estimated at 86% (ARCEP, 2008) at that time and given a population of 63,945 million inhabitants during the observed period 1  , that is a rough 32% of all French mobile phone carriers and 28% of the total population. The Orange France 2007 CDR dataset is one of the largest CDR datasets available worldwide in terms of population-wide coverage and has been extensively studied  (Deville et al. , 2014; Grauwin et al. , 2015; Sobolevsky et al. , 2013)  One can define domestic tourism as: "tourism of inhabitants of a certain country that only takes place within the borders of that country, but outside their usual environment" (UNWTO, 2008), and oppose it to abroad or international tourism, which is tourism where "the destination is located outside the country of residence"  (UNWTO, 2008).
Constructing long distance trips Following the definition, domestic trips can be defined as movements outside user's usual environments though still inside the national borders. In our case, the latter restriction is automatically fulfilled thanks to our national dataset. Concerning the former restriction,  Janzen et al. (2016a)  elaborate a methodology to extract long-distance trips from CDR data. They use the homeanchor model by  Ahas et al. (2010)  to define a cell tower that represents home for each user. The usual environment of a user then is constructed as an 80 km radius from that cell tower, as proposed by the French National Travel Survey (ENTD 2  ). A long-distance trip, hence, consists of a series of CDR observations in which an individual user leaves his usual environment for an undefined time period before returning to it.
5% for July and only 5. 6% for August) compared to the other months so we decided on working with these months only. Additionally, as can be observed in Table  1 , limiting the duration of the trip also impacts the share of trips with professional purpose. As the source data for Table  1  did not offer a crossover of duration and purpose for all trips longer than 4 days, we assumed a duration of 8-15 days to be a safe choice given that, this way, the inclusion of a full weekend in the trip was ensured. It should be noted, however, that in contemporary tourism practices professional and personal (touristic) activities are increasingly overlapping, making a differentiation increasingly difficult (Den Hoed & Russo, 2017;  Larsen et al. , 2007). Based on the reasons mentioned before, we decide to define domestic tourism trips in our analysis as: • long--distance trips • with a duration of 8 to 15 days • occurring only during July and August 2007 in order to i) ensure that at least one full week(end) took place within the trip, ii) avoid inference with longer stays as they might influence the monthly-based home detection algorithms to extract long-distance trips (see  Janzen et al.
EXTRACTING DOMESTIC TOURISM TRIPS FOR FRANCE 5. 1. Study area and user sample Given the high time cost of our analysis, we randomly sample users with a supposed home location in one of the 33 most populated cities in France. We decided to omit Strasbourg from this list because of its closeness to the German border, often resulting in incomplete CDR for users in this area. We also omited users that do not have a consistent home location when applying the methodology by  Ahas et al. (2010)  for each month in the dataset. Afterwards, we selected 2000 users per city, except for Paris, where we selected 5000. Our sample selection resulted in a subset of CDR data with activities of 69 000 randomly chosen users.
Notice that the 14 th of July, the French national holiday, took place on a Saturday in 2007 and hence goes rather unnoticed. Remarkably, as indicated by the 7-day moving average in Figure  3 , the number of long-distance trips goes down during July and August. This might seem counterintuitive (given that they are the main holiday months) but is actually logical when one remembers that the detection of long-distance trips bases on leaving the usual environment. Therefore, long-distance trips with short durations are very well presented in the dataset (as can also be observed in Figure  4 ). As a consequence, when more long duration trips are performed, the occurrence of short duration trips drops, resulting in lower total amount of long distance trips observed in July and August.
Limiting to July and August: Domestic Tourism Remember that we defined domestic tourism trips to be long-distance trips with duration between 8 and 15 days and occurring in July or August. We added the July and August restriction because of the lower share of professional trips made during this period according to the ENTD survey as well as according to Eurostat numbers  (Eurostat, 2017). Applying both restrictions, our dataset limits to a total of 18 380 trips performed by 16 024 distinct users which we call domestic tourism trips and on which we will focus on in the rest of this paper. By means of recapitulation, Table  4  provides a short overview of the different pre-processing steps we undertook to arrive at the dataset of domestic tourism trips. Figure  6  frames the total amount of CDR data (with a unit being one CDR) available for all users that are not in a trip, on a long-distance trip, or on a domestic tourism trip.
RESULTS Possessing a CDR dataset of 18 380 presumable domestic tourism trips we can investigate different properties of the digital traces left by mobile phone use during these domestic tourism trips. In the next paragraphs, our analysis will explore temporal, spatial and social aspects of the available CDR data. As such we try to provide both an indirect validation of the proposed logic to derive domestic tourism trips (given that no ground-truth data is available) and inspiration for future, more in-depth work A first question we ask ourselves is whether patterns of mobile phone use change, or not, during domestic tourism trips. As such, we investigate whether people during domestic tourism trips i) call/text more, ii) have a different weekly time pattern, and iii) have a different hourly time pattern.
Spatial patterns of domestic tourism trips. A second question to explore is whether we can estimate the destination of a domestic tourism trip based one the available CDR data. To tackle this question, we present the Duration and Calls Ratio (DCR) algorithm. In the next sections, we discuss its construction, its sensitivity to parameter choice, and its performance based on expert validation. This way, we try to assess the feasibility of detecting destination of domestic tourism trips from CDR data. The application of DCR algorithm to our database results in an empirical estimation of the domestic tourism flows in France departing from 32 cities during the summer of 2007.
Duration and Call's Ratio (DCR) algorithm To construct a destination detection algorithm operating on CDR data of domestic tourism trips, we tested several heuristics including, but not exclusively: furthest distance from home location, highest amount of calls, and the highest amount of calls during restricted periods (like nights). Ultimately, we decided to use the combination of amount of calls and the amount of distinct days to detect a destination from CDR data. Specifically, the Duration and Call's Ratio (DCR) algorithm defines the destination of a domestic tourism trip to be the cell tower with the highest ratio of mobile phone activity and the highest ratio of amount of active distinct days during the entire period of the domestic tourism trip. As an example, imagine the pattern of one domestic tourism trip as shown in Figure  10.
Social networks during domestic tourism trips A third and last question that can be explored is how activation of the social network changes when user perform domestic tourism trips. Because CDR data store metadata on both identifiers of the contacting and contacted persons, it is possible to investigate such changes at the level of individual, egocentric networks. Given that we already know mobile phone communication to lower during domestic trips (see Figure  7 ) the question now becomes whether social networks are activated differently, or not, during domestic tourism trips.
Extracting egocentric networks from CDR data Extracting social networks from CDR data has been widely deployed to study properties of human interactions (e. g. Phithakkitnukoon et al. , 2012)  even though they have only sparsely been linked with mobility characteristics  (Toole et al. , 2015). The most simple and microscopic social network that can be extracted from CDR data is an egocentric network, which summarizes the interaction between one user and its peers. In egocentric networks, nodes are thus the persons that have been contacted by a user, whereas the edges denote the amount of contact between two users (in this case, the amount of calls and texts).
FUTURE RESEARCH Based on the presented results and our own experiences, we'd like to suggest three different lines for future research. Firstly, and above all, the need for ground truth data is fundamental for the further development of similar empirical research. The magnitude of digital traces that are rapidly becoming available for large populations will remain of limited use when no efforts for ground-truth data collection are made. Specifically, for CDR data, ground truth data on both the usage of mobile phones and the actual mobility patterns of individuals are paramount but lacking, hereby obstructing the further development of research. In absence of good validation data becoming available, the assessment of methodologies using CDR data leans upon a general willingness to either deploy high-level comparison with census data (e.
Concerning the latter, the analysis of the egocentric network of one user offered an exciting start to investigate the largely unexplored relations between activation of the social network and (domestic tourism) mobility. Conditions to scale up such analysis are challenging, with parts of the analysis being computationally expensive and their interpretation far from generic (e. g. Figure  13 ). In the future, however, this should not obstruct research to derive insights from combining the main dimensions of information in CDR: social activity and human mobility. Thirdly, we suggest future research to address the sensitivity, and thus sustainability, of methodologies deploying CDR data for changes in time periods and/or study area. Until now, the question remains on transferability of findings with the driver of uncertainty being the rapidly changing uses of ICT in general and mobile phones in particular, and thus the changing nature of the data collected.
Longitudinal studies as well as research that compares between regions should, ultimately, form the cornerstone on which transferability of methodologies and insights can be build. In our case, the use of simple parameters to tune heuristics is both advantageous and disadvantageous in this perspective. Advantageous, in the sense that easy adaptability and a close feedback loop to the input data allow for flexibility in coping with the changing nature of digital traces or in transferring methodologies to other datasets. Disadvantageous because parameter choice remains subjective, and as such might not always render correct results (which again is difficult to assess when little to no validation data exists). Even though expert validation and sensitivity analysis offer guidance, it remains a challenging research question to see empirical research using heuristics to, for example, determine destination of domestic tourism trips, can be better objectified and still be applicable to different datasets at different times and in different regions.
Information on the destination of trips based on CDR data possibly augmented with insights on historical call patterns, for instance, could allow for a better understanding of the important 'visiting friends and family' share of domestic tourism. Another example could be insights on the use of mobile phones and the activation of the social network during different mobilities phase that allow to better understand the (diminishing) division between leisure and work during tourism trips. A third and final example could be CDR data enabling a better, long-term understanding of the relation between the development of urban areas, second-homes and the overall effects on rural tourism. Ultimately, these ideas might be a bit to envisioned and their eventual developments will definitely have their flaws, but the main idea stands that there is an opportunity to explore new aspects with regards to domestic tourism in mobile phone data, and that such an empirical approach is worth elaborating as it can help to both advance and anchor theoretical ideas.
CONCLUSION Concluding, we showed how a data-driven logic can be applied to mobile phone data in order to study different aspects of one specific type of mobility: domestic tourism trips. In total, we derived 18,380 domestic tourism trips from CDR data of 69,000 users sampled in 32 of the biggest cities of France. Our results showed how temporal aspects of domestic tourism trips differ from baseline day-to-day behaviour in ways that seem plausible and logical. Subsequently, we proposed the DCR algorithm to detect destinations of domestic tourism trips. Performing both a sensitivity analysis and expert validation led to an informed parameter choice, resulting in the construction of a spatial pattern of domestic tourism flows in France for a sample size that questionnaires and surveys cannot easily obtain.
The home location (H) is defined by the anchor model from Ahas et al. (2010)  and forms the center of a usual environment that is within a radius of 80 km. The different numbers represent the sequence of CDR observations collected in the trip with the first and the last point of the trip being situated in the usual environment. Applying their methodology to the same French CDR dataset as this paper and after comparing results to the ENTD-survey, Janzen et al. (2016b)  conclude that national surveys heavily underestimate the amount of persons that perform long distance trips, as well as the amount of long distance trips per person. The analysis of 1. 4 million French mobile phone users during a one month period in June 2007, showed 58,7% of French users to perform long-distance trips (compared to 25.
It is the latest CDR dataset available for France that allows for a long term, continuous tracking of individual users in France. More recent datasets are limited by The French Data Protection Agency (CNIL) in anticipation of the EU General Data Protection Regulation and do not allow to collect individual traces during longer time periods as they are considered risky even if personal identification information is irreversibly recoded. 4. DETECTING DOMESTIC TOURISM TRIPS FROM CDR DATA4. 1. Defining Domestic Tourism.