dataesr/scientific-paragraphs-categorization huggingface dataset:
The longitude and latitude selected for the 10 s window were those corresponding to the mid-point recording within the window. The 10 s windows of behavior combined with the associated geographical coordinates were thereafter referred to as the geolocated behaviors. Each paddock was split into squares of 8 m × 8 m, hereafter referred to as zones. This surface was chosen to broadly encompass the GPS static position error in the open area (±1. 72 m) and to maintain a sufficiently fine description of the paddock. In this way, 576 zones and 841 zones were obtained in the PG and the TG, respectively. The geographical coordinates of each zone were recorded using the data from the French's National Geographic Institute  [19]. For every cow and every day of grazing, the longitude and latitude of the geolocated behaviors were used to identify in which 8 m × 8 m zone the cow was during the expression of the behavior, as illustrated in Figure  4a.
of behavior was thus associated with the position (longitude and latitude) of the cow while she was expressing the behavior, based on the date and time of the accelerometer and GPS records. It should be noted that the GPS data were collected every second (1 Hz), therefore there were 10 recorded positions associated to each 10 s window. The longitude and latitude selected for the 10 s window were those corresponding to the mid-point recording within the window. The 10 s windows of behavior combined with the associated geographical coordinates were thereafter referred to as the geolocated behaviors. Each paddock was split into squares of 8 m × 8 m, hereafter referred to as zones. This surface was chosen to broadly encompass the GPS static position error in the open area (±1. 72 m) and to maintain a sufficiently fine description of the paddock.
In this way, 576 zones and 841 zones were obtained in the PG and the TG, respectively. The geographical coordinates of each zone were recorded using the data from the French's National Geographic Institute  [19]. For every cow and every day of grazing, the longitude and latitude of the geolocated behaviors were used to identify in which 8 m × 8 m zone the cow was during the expression of the behavior, as illustrated in Figure  4a. This step was carried out using the R package sp  [27]. The time-budgets associated with each behavior were therefore computed in each zone of the pastures for every cow and every day of grazing. The unit of these time-budgets (hereafter referred to as tbu) was thus seconds per area of 8 m × 8 m, per cow and per day. The overall cow location in each zone for every cow and for every day of grazing was the sum of the time-budgets in the corresponding zone.
Characterization of Each Zone in the Pasture This step aimed to assign pasture characteristics to each area of each pasture. The pasture characteristics were defined by a combination of the structural characteristics, slope, soil moisture and botanical characteristics, as illustrated in Figure  4b. The characterization of zones was performed as follows using the sp package  [27] : • Structural characteristics As explained in Section 2. 1. 2, the structural characteristics were geolocated in pastures (Figure  2a ). The geographic coordinates were used to describe the structural characteristics in each zone. As illustrated in Figure  4b , each zone was described in terms of trees (T1, T2 or T3), hedges (H1,. , H10) and boundaries (F1 or PA) for the PG and in terms of hedges (H1 or H2) and boundaries (F1, F2, F3, F4 or PA) for the TG.
Botanical characteristics As explained in Section 2. 1. 2, plant species were identified in each pasture (97 measures/ha). As the number of different species identified in the pastures was very large (76 and 41 in the PG and TG, respectively), each geolocated measurement was described by a botanical class in a second stage, instead of keeping a rating for every species. For this purpose, a hierarchical ascendant classification (aggregation: Ward criterion) was applied with the R package FactoMiner  [28]. Based on the evolution of intraclass inertia over the aggregation process, 8 and 3 classes were identified in the PG and TG, respectively. The botanical classes were then described by the plant species which are the most represented in the class (F-test: v-test > 2) and by the plant species which are the least represented in the class (F-test: v-test < -2).
Consideration of the Correlations between the Pasture Characteristics The lack of independence between some pasture characteristics (e. g. , some plant species often found together in the same area) could have biased the interpretation of the linear model results. For this reason, we grouped together characteristics within structural elements, slopes or botanical classes which were highly correlated to reduce the main correlation before applying the linear models. For this, a multiple correspondence analysis was first applied followed by an agglomerative hierarchical clustering (AHC) (aggregation: Ward criterion) using the R package FactoMiner  [29]. We identified the most correlated characteristics as those being the most represented within the same cluster (Chi square test: v-test > 2). The groupings carried out are presented in Table  1.
Although there are still technical limitations, our approach constitutes a promising way to investigate animal-environment interactions in order to develop precision grazing and improve animal health and welfare. Note: the reference levels are written in italics. Some different types of pasture characteristics were correlated as H2 and MS1 (Figure  2a, c ). All the pasture characteristics concerned by such correlations were also identified using the clusters of the AHC (Section 2. 3. 1). The correlations between the different types of pasture characteristics led to a repetition of the same information within the same row of the dataset and thus to a potential misinterpretation of the effect of these pasture characteristics. As grouping these characteristics (Table  1 ) was not possible, we condensed the information on a single characteristic for the entire combination.
11. 2008 ), whereas, for example, in France, only half of the equine livestock was kept on farms in 2010  [10]. This problem is similar in other European countries (in Germany, for example)  [11]. No other database exists at this scale, so the current official figures are misleading. To counter this, the European Union required the creation of a central national database for each Member State that would identify all equines. This requirement was presented in the EU Regulation (EC) No 262/2015 3. 03. 2015, and the creation of these databases remains a work in progress in some member states today  [9]. This lack of data concerning equine numbers in Europe complicates descriptions about their importance and impacts. At present, the diversity of equine uses leads to a large variety of impacts on the environment, especially when activities are exclusive to equines, such as sports or racing.
Consequently, this study was supplemented by French-language references. From March 2019 until November 2019, we used two main databases, Web of Science and Google Scholar to search for articles and reports. To select documents, we searched for the terms "equid*", "equine*," "environment*," "horse*," and all words related to green assets, in both French and English. This literature review highlighted key arguments for five green assets: • Equine grazing: Equine grazing incidence is unique because of this animal's morphology and its physiology specificities, especially regarding ruminants (the main herbivores raised on grasslands). In particular, equine grazing is done on different patches made of lawns and high grasses. • Domestic biodiversity: Human and environmental selection has led to a rich diversity of equine breeds all over the world.
On Arable Lands On arable lands, soil compaction is known to be the most severe form of degradation in conventional agriculture  [55, 58]. There is a difference between the paths made by machines (continuous, because of their tyres, with deep soil compaction) and equines (intermittent, because of their hooves, with superficial soil compaction)  [58]. The soil porosity was higher after using donkeys or cattle compared to a motorized machine  [55]. A comparison between the use of a donkey and a motorized machine for the ploughing, fertilization, and preparation of rapeseed in the context of the high hills in northern Italy was made thanks to the life-cycle assessment (LCA) approach for which inventory data were taken from the GaBi 4. 0 database. All aspects related to the life spans of animals were considered, except the end of life: pregnancy, growing and maintenance (health care, feed, keep, and equipment), and work.
| Selection of studies for the meta-analysis Following the protocol of  Dujon and Schofield (2019)  and  Dujon (2019) , for each of the three transmissible cancer types we searched the Thomson Reuters ISI Web of Science TM database, the Scopus K E Y W O R D S data-mining, emerging diseases, meta-analysis, outbreak, quantitative review, research impact database and Google Scholar for relevant publications with specific terms in the topic field, which included the title, abstract, keywords and keywords plus (i. e. words that frequently appear in the titles of the articles cited within a publication). For DFTD, we used the following terms: "Tasmanian devil cancer," "Tasmanian devil facial tumour" and "Tasmanian devil tumour. " For CTVT, we used the following terms: "dog transmissive tumour" and "canine transmissible venereal tumour.
Until August 2019 (i. e. 31 August 2019), we identified 171 publications for DFTD (two lineages), 314 publications for CTVT (one linage) and 112 publications for DN (six lineages), for which we had access to the full text and that met the criteria of our analysis. These publications covered a time period of over 50 years, extending back to  2006, 1963 and 1969  for the three cancers, respectively. The year 1963 corresponded to the year of publication of the oldest study that was found (on CTVT), and for which we were able to access the full text. Thus, we excluded any studies published before this date from the analysis. For each publication, we determined: (1) the number of citations up to 31 August 2019 using Google Scholar (due to its broader coverage than the Web of Science and Scopus databases,  Harzing & Alakangas, 2016) , (2) the list of institution(s) involved in the study based on the affiliations of each authors (i.
| Analysis of temporal and geographical trends The citation data collected for each of the three cancer types represent a cross-sectional study. We, therefore, used linear regression models to investigate how publications accumulated citations over time, Poisson regression models to investigate trends in the number of institutions involved in a publication and logistic regression models to investigate the percentage of publication involving an international collaboration or published in a journal with more than once subject area  (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). Full details of model fitting and validation are provided in Supplementary Method 1.
| Reporting of statistical results and software All statistical analyses were performed in the Bayesian framework. Throughout, we report the estimated parameters followed by their 95% credible intervals in parentheses  (Kruschke, 2015). All Bayesian models were computed using the MCMCglmm package  (Hadfield, 2010)  in R software version 3. 3. 2. (R Development Core & Team, 2013), and the models were fitted using noninformative priors  (Hadfield, 2010). Social network metrics and simulations were performed using the Igraph R package  (Csardi & Nepusz, 2006). Geographical data were assimilated using the RWorldmap package  (South, 2011).
, 2017). A possible explanation for this is that Tasmanian devils are only found in a relatively small geographical area, making it more difficult to obtain data and to establish collaborations compared to DN and CTVT which are globally distributed. This unexploited potential might not be compatible with the rapid decline of Tasmanian devil populations  (Lachish, McCallum, & Jones, 2009; Lazenby et al. , 2018)  and with the relatively high risk of emergence of new types of tumours (two independent transmissible cancers appeared between 1996 and 2019,  Pye et al. , 2016; Stammnitz et al. , 2018). This species will likely require collaborations as efficient as possible to mitigate the effect of transmissible cancers  (Hamede et al. , 2019). There are multiple benefits in forming collaborative networks to obtain insights on transmissible cancers.
Data Processing Data processing included five steps: (1) the raw data management,  (2)  the synchronization between the marker-based and IMU motion data, (3) marker-based data processing, (4) IMU data processing, and (5) statistical analysis. First, the raw measurements of both motion-capture systems were managed. From the marker-based system, 3D coordinates for each marker named MoCap data were computed by the manufacturer's dedicated software (Nexus 2. 8. 0, Oxford Metrics Ltd, Oxford, UK). From IMUs, 3D accelerations and rotational velocities, named IMU data, were extracted from the dedicated software (InertiaStudio, Inertia Technology BV, Enschede, The Netherlands). Then MoCap and IMU data were processed by calculation software (Matlab R2019b, The MathWorks Inc. , Natick, MA, USA). Second, data from the Mocap system and IMU were homogenized at 500 Hz using a continuous derivative interpolation function.
Once the sampling frequency was standardized, a synchronization process has been performed. On the data coming from the IMU positioned on the wither and from the kinematic marker fixed on the center of this IMU, the peaks corresponding to the synchronization keystrokes made at the start and end of recording were detected. Data were cut down to keep only the data between these two peaks on each of the systems. A correlation calculation was performed from the vertical position of the wither from the kinematic data and the IMU data to ensure that the data of the two systems were well synchronized. The average correlation value for all the trials of the eight horses is 0. 84 ± 0. 09. Third, the locomotion parameters calculated from the MoCap were used to define the beginning and the end of the stance phase for each recorded stride.
Data Processing Data processing included five steps: (1) the raw data management, (2) the synchronization between the marker-based and IMU motion data, (3) marker-based data processing, (4) IMU data processing, and (5) statistical analysis. First, the raw measurements of both motion-capture systems were managed. From the marker-based system, 3D coordinates for each marker named MoCap data were computed by the manufacturer's dedicated software (Nexus 2. 8. 0, Oxford Metrics Ltd. , Oxford, UK). From IMUs, 3D accelerations and rotational velocities, named IMU data, were extracted from the dedicated software (InertiaStudio, Inertia Technology BV, Enschede, The Netherlands). Then MoCap and IMU data were processed by calculation software (Matlab R2019b, The MathWorks Inc. , Natick, MA, USA). Second, data from the Mocap system and IMU were homogenized at 500 Hz using a continuous derivative interpolation function.
These events labeled respectively MoCapFootOn(i) and MoCapFootOff(i) of i-th gait cycle, were calculated using the method validated by Merkens and Schamhardt  [22] , with i equal 1 to n and n > 25. The stride duration named MoCapStrideDuration(i) (Equation (  1 )) and the stance phase duration MoCapStanceDuration(i) (Equation (2)) of the the i-th gait cycle were computed with MoCapFootOn(i) and MoCapFootOff(i) frames. MoCapStrideDuration(i) = MoCapFootOn(i +1)-MoCapFootOn(i) (1) MoCapStanceDuration(i) = MoCapFootOff(i)-MoCapFootOn(i) (2) The fourth step was the data processing of the IMU data which included 2 substeps. The first substep was the windowing of the IMU data. From the low pass filtered signal (2nd-order lowpass Butterworth filter with a cutoff frequency of 20 Hz) of the Y-axis of the gyroscope, each maximum peak was identified (Figure  3 ).
This filter eased to identify the cycle pattern of the gait cycle as we noticed that the average stride frequency for all horses participating in the experiment at 4 m/s is less than 10 Hz. Then, a processing window was determined between two consecutive peaks. Each processing window includes unique gait cycle data. So, n windows were identified and the i-th was named ImuWindow(i). MoCapStrideDuration( )= MoCapFootOn( +1)-MoCapFootOn( ) (1) MoCapStanceDuration( )= MoCapFootOff( )-MoCapFootOn( ) (2) The fourth step was the data processing of the IMU data which included 2 substeps. The first substep was the windowing of the IMU data. From the low pass filtered signal (2nd-order lowpass Butterworth filter with a cutoff frequency of 20 Hz) of the Y-axis of the gyroscope, each maximum peak was identified (Figure  3 ).
This filter eased to identify the cycle pattern of the gait cycle as we noticed that the average stride frequency for all horses participating in the experiment at 4 m/s is less than 10 Hz. Then, a processing window was determined between two consecutive peaks. Each processing window includes unique gait cycle data. So, n windows were identified and the -th was named ImuWindow( ). The second substep was the detection of Foot on and Foot off events from IMU data by four methods. These events identified for each of the methods will then be compared with those detected on the MOCAP data in order to assess their accuracy (Figure  4a ). Method A: in ImuWindow( ), the first minimum peak on the Y-axis of the gyroscope was identified, corresponding to the Foot on event labeled ImuFootOn_A( ). Then for the Foot off labeled ImuFootOff_A( ) was identified as the peak preceding the penultimate minimum peak, detected with a threshold of 20% of the minimum value of the gyroscope signal on the Y-axis in the processing window (Figure  4b ).
The peak of interest was then sought on the reconstructed signal, which corresponds to the first minimum peak in each ImuWindow(i). Then, on the gyroscopic signal The second substep was the detection of Foot on and Foot off events from IMU data by four methods. These events identified for each of the methods will then be compared with those detected on the MOCAP data in order to assess their accuracy (Figure  4a ). Method A: in ImuWindow(i), the first minimum peak on the Y-axis of the gyroscope was identified, corresponding to the Foot on event labeled ImuFootOn_A(i). Then for the Foot off labeled ImuFootOff_A(i) was identified as the peak preceding the penultimate minimum peak, detected with a threshold of 20% of the minimum value of the gyroscope signal on the Y-axis in the processing window (Figure  4b ).
In this study, four methods of detecting Foot on and Foot off of the horse locomotion by a single IMU located on the canon bone were compared. Two methods processed gyroscopic data of the IMU, using thresholding (method A) or wavelet (method C), and had performances superior to those developed using thresholding (method B) or wavelet (method D) from accelerometric data. These methods demonstrated a bias less than 0. 6% of stride for Foot on and 0. 1% of stride for Foot off. The.
In this study, four methods of detecting Foot on and Foot off of the horse locomotion by a single IMU located on the canon bone were compared. Two methods processed gyroscopic data of the IMU, using thresholding (method A) or wavelet (method C), and had performances superior to those developed using thresholding (method B) or wavelet (method D) from accelerometric data. These methods demonstrated a bias less than 0. 6% of stride for Foot on and 0. 1% of stride for Foot off. The measurements of the Stance and Stride durations were also more precise for methods A and C. Several methods were proposed to detect locomotion events by IMU measurement. Raw data of the accelerometers and gyroscope were commonly used. Methods by threshold detection on accelerometric signals were the most common  [13, 14].
They also highlighted that a reduction in the maternal immunity coverage would lead to a decrease in the prevalence of viraemic pigs at slaughter (dropping to 12. 5%), whereas a 100% passive immunity cover would greatly increase the risk of viraemic pigs (19. 8%). Several studies have been conducted in order to describe and quantify HEV transmission between pigs. For instance,  Satou and Nishiura (2007)  built a model that took the distribution of time between infection and seroconversion into account to calculate age at infection. They then estimated the basic reproduction ratio from serological data pertaining to Japanese pig farms (R0 = 4. 02-5. 17). Backer et al. (2012)  obtained similar R0 values using a Bayesian framework to analyse the prevalence of HEV shedding according to age group from UK data.
Epidemiological parameters The two epidemiological models run simultaneously in the population (Fig. 2 ). The piglet's individual characteristics with respect to HEV dynamics vary depending on its state of health regarding the IMV (latency period, individual transmission rate, quantity of HEV shed). All the parameters involved in the infectious process are fully described in Table  2 , along with their definition and the origin of input values. HEV parameters were derived from transmission experiments and other data in the literature. The values of the IMV model parameters were consensually chosen to represent the transmission of a typical airborne virus such as PRRSV or PCV2.
Stochasticity The model has been developed in a C++ language (Visual Studio IDE). It is a discrete-time model and is implemented on a daily basis during which the individuals are subjected to two types of processes run sequentially. First, the demographic processes are considered with a biologically relevant and logical order: ageing and mortality for all individuals; reproduction processes for breeding animals along with birth of offspring, culling and replacement of sows. If time-relevant, batches are transferred into the sector and room corresponding to their physiological state, the individuals being distributed among the pens. The epidemiological process is then implemented both for the IMV and HEV. At the beginning of a simulation, the herd is composed only of sows. The initial number of sows is equal to the number of batches multiplied by the number of pens in the farrowing room.
, , 2015;; Salines et al. , 2019b). Factoring an environmental compartment into the HEV model design is also of particular importance, since the key role of viral environmental accumulation in HEV dynamics has already been demonstrated: indeed, despite frequent cleaning and disinfection procedures in pig herds, the accumulation of viral particles in the pigs' environment can explain HEV persistence on farms  (Andraud et al. , 2013). Most of the epidemiological parameter values were derived from published data when available. In particular, the model uses different parameters for HEV dynamics depending on the pig's status regarding an IMV; these parameters were obtained from several experimental trials. The IMV parameters were chosen to represent the typical behaviour of an airborne immunomodulating virus; they were not selected to specifically represent the dynamics of PRRSV and/or PCV2 but the chosen R0 was consistent with the ones reported for PRRSV and PCV2 in the literature (5.