dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset was compiled from monthly extractions of the databases for the two parent companies of horse racing in France: Le Trot (for trotters) and France Galop (for gallopers). For each horse in pre-training or training, each company records the SIRE identifier, the commune of the horse's location (premises where it is kept or where it is trained), and date of its entry/exit. Because these equines were accurately located, and the location of the owner(s) does not necessarily correspond to the location of the equine, due to the need for a specific (and rare) training structure, we did not apply the location algorithm to this population. However, we used the racehorse dataset to remove horses in training from the SIRE dataset and live equine sample. From these four datasets, we obtained: -A dataset of 1,068,142 equine-owner pairs corresponding to 1,068,142 equines presumed to be alive and belonging to 397,039 owners living in 32,837 communes in metropolitan France and resulting from the SIRE extraction after removing dead animals and cross-checking with the other data sources.
The details of the different steps are shown in Fig. 1. The dataset obtained is referred to as the equine dataset in the following. -A dataset of 44,768 racehorses in training (12,785 gallopers and 31,983 trotters) along with their location, resulting from the removal of dead equines then the selection of a location for each equine in metropolitan France on the date closest to April 1st, 2019 (date on which the data were extracted from the SIRE database). This dataset is referred to as the racehorse dataset in the following.
Estimation of the parameters Parameters σ and μ of the lognormal distribution of the distances in Eq. (  2 ) were estimated using the least squares adjustment method. Parameter β of Eq. (  3 ) was estimated using a Bayesian approach, using Markov chain Monte Carlo (MCMC) methods to generate a distribution for the parameter. The prior considered was a non-informative uniform distribution and the likelihood was given by: P(data | β) = ∏ Nobs i=1 P(r i /r 0i ) (4) where N obs was the number of owner-equine pairs in the sample, and r 0i and r i the respective communes of the owner and his/her equine(s) for the i th pair. We used the MCMCpack R package  (Martin et al. , 2011)  to generate a chain of 6000 iterations with a burn-in of 1000 iterations. The chain's convergence was monitored by Geweke's convergence diagnostic (geweke.
Assignment of holding commune(s) to owners and equines For each of the 1,068,142 owner-equine pairs from the equine dataset, we assigned one or more probable holding communes for equines using the previously calculated parameters and the urban coverage of the owners' commune of residence. The data (owner-equine pairs) did not allow us to quantify exactly the number of different locations for the equines of the same owner, however, the survey showed us that almost (83%) of the owners hosted their equines in one commune, 12% in two communes and the rest in 3 communes  (Farchati et al. , 2021). Thus, we tested two different assignment approaches. In the first one, we assigned each owner a single holding commune regardless of the number of equines linked with that owner, i. e. the holding commune selected was the same for all the equines paired with the same owner.
Characteristics of the samples and datasets A description of the live equine sample (n = 2353), the dead equine sample (n = 13,595), and the equine dataset (n = 1,068,142) is available in Table  1. The distribution of sex among the dead equines was almost identical to those of both the live equine sample and the equine dataset, with a slight under-representation of males and overrepresentation of females in the dead equine sample. Saddle horses were over-represented in the live equine sample, while draft horses were under-represented in both the live and dead equine samples. The age distribution differed in the dead equine sample, with an overrepresentation of old equines (over 20 years of age) and very young equines (less than two years old). The number of equines per owner differed between the three datasets, with more owners linked to more than one equine in the dead equine sample (74% vs.
59% in the live equine sample, and 36% in the equine dataset). The proportion of owners who kept their equines in their commune of residence decreased as the percentage of the commune's urban coverage increased (Fig. 2A ). In addition, the distribution of the percentage of urban coverage was the same in both samples (live and dead equines, Wilcoxon test, p > 0. 05). Most of the owners kept their equines in their commune of residence, but when they did keep them in a different commune, it was less than 30 km away (Fig. 2B ), with no difference in the distance distributions between the live and dead equine samples (Wilcoxon test, p > 0. 05).
This error was exacerbated by the fact that Method 1 located all the equines of a single owner in the same commune. For example, 10,828 equines were located in Ile-de-France (supplementary material Fig. S-4 ) probably due to the presence in the Paris of the national guard equines. This hypothesis is supported by the fact that the estimate based on the dead equine sample better factored in the urban area with both methods. The dead equine sample was more variable than the live equine sample, with a few more distances over 30 km and probably more retired equines further away from their owners. Nevertheless, this sample probably overrepresented professionals and multiple owners, as we aggregated data from several years. Statistically, owners with more equines were more likely to i) be included, ii) use the ATM system for dead equine removal, iii) provide their equine identification number and iv) update their information in the SIRE database.
This is confirmed by the difference in the number of equines kept, with only 25% of owners of dead equines having only one equine and 40% having more than six, compared with 41% of owners of live equines having only one equine and 18% having more than six. Additionally, despite close cross-checking of data and information to differentiate owners from keepers among the people requesting cadaver removal, some keepers (as opposed to owners) may have been retained in the dead equine sample, participating in the overrepresentation of multiple owners. This probably explained the very high density of equine estimates in French breeding areas (Normandy, Brittany, Bouches-du-Rhône and Pyrénées-Atlantique (supplementary material Fig. S-4 )) when using Method 1 with the dead equine sample. Finally, the quality of the equine dataset used to assign the equines may have an impact on spatial estimates of equines.
Indeed, a previous study of SIRE data quality  (Farchati et al. , 2020)  indicated that nearly one-third of owners did not update their home address when they moved, while the location assignment of equines is based on this information. However, most of the owners who moved remained in the same département  (Farchati et al. , 2021) , so the equine location error remains spatially localized (the equines distribution on a département scale (scale greater than the commune) is more robust). In addition, although we cross-checked the data, it is likely that many equines in this dataset were actually dead. It is possible that the probability of death is spatially homogeneously distributed (30-40% of owners did not report the death of their equine), in which case the overestimation of the number of equines is also likely to be evenly distributed across France.
Furthermore, the estimation of the spatial distribution of equines was very sensitive to the assignment method used, despite a first random step to select, for each owner, a holding commune from a list of all plausible holding communes. Assignment method 1 assumed that all the equines of a given owner were kept in the same commune. This approach limited the variability of holding communes, and exacerbated the effect of the bias in the samples used to estimate β. The results showed areas of very high local equine density, including the densest urban areas such as large cities. The bias in the spatial estimation was more limited when using the dead equine sample, the dataset including more variability. Assignment method 2 allowed more variability in the holding commune for owners having more than one equine.
Acknowledgments We would like to thank the  French Horse and Riding Institute (IFCE)  and the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES)  for their financial support. We are also very grateful to the data providers: IFCE,  Le Trot ,  France Galop , the French ministry for agriculture and food. We would also like to thank the owners and keepers who answered our surveys. We need to thank various people having contributed in different ways to the completion of this work:  Jean-Philippe Amat ,  Marie Delerue ,  Mathilde Dhollande ,  Romain Dhollande ,  Xavier Dornier ,  Joelle Dop ,  Arnaud Duluard ,  Bénédicte Ferry ,  Giovanni Lo Lacono ,  Carole Margelidon ,  Aurélie Merlin ,  Marion Wujek Moreau ,  Julie Schneider ,  Caroline Teyssier , and  Sonia Wittreck.
Case-control study of behavior data from automated milk feeders in healthy or diseased dairy calves Jannelle L. Morrison, 1 Charlotte B. Winder, 1 Catalina Medrano-Galarza, 1,2 Pauline Denis, 3 Derek Haley, 1 Stephen J. LeBlanc, 1 Joao Costa, 4 Michael Steele, 5 and David L. Renaud 1 * #3212). This study is reported following the STROBE-Vet reporting guidelines for observational studies  (O'Connor et al. , 2016; Sargeant et al. , 2016). The data from this study were previously collected as a part of a larger study completed in Ontario, Canada  (Medrano-Galarza et al. , 2017). Two dairy farms were selected based on their size, use of an AMF, proximity to the University of Guelph, and willingness to participate in this study. Farm 1 had a herd size of 120 milking cows and farm 2 had 85 milking cows.
Both farms housed milk cows in a freestall system and were visited 4 times, once each season between fall 2015 (September 21) and fall 2016 (September 21) to collect treatment records, birth records, and AMF data for any calf born during the trial period. Fall visits occurred between November 2 and December 1, 2015; winter visits between February 3 and  March 16, 2016; spring visits between April 19 and May 30, 2016; and  summer visits between August 3 and September 6, 2016. On farm 1, calves were introduced to the group pen between 6 and 9 d of age and had an average of 11 calves per pen; on farm 2, calves were introduced on the day of birth to a pen with 2 calves, and then moved at 4 d of age to a larger pen with an average of 9 calves per group. Both farms group housed calves with an AMF (CF1000, Förster-Technik) and used milk replacer to feed preweaning calves.
Cases could have been treated for either NCD or BRD, and day of treatment was considered to be d 0. Healthy control calves were all selected at the end of the study period from calves that did not receive any treatment for disease. Control and case calves were matched by farm and to the same number of days on the AMF. As a calf entered the AMF, a radiofrequency identification (RFID) tag in their ear was scanned and individual behavioral feeding metrics were recorded onto a computer. For each case and control calf, all information pertaining to milk feeding plan for the preweaning period was obtained from the AMF to aid in analyses. These data included type of milk fed to the calves (milk replacer), milk feeding regimen [starting milk allotment (L/d), peak milk allotment (L/d), latency to peak milk allotment and length of peak, maximum meal size, and minimum time between meals], weaning method (age at beginning of wean, length of weaning period (days)], and age when fully weaned.
Individual feeding metrics for each case and control calf were exported from the AMF Calf Manager software for the 14 d surrounding a treatment event. Feeding metrics included daily milk consumption [amount of milk consumed by each calf per 24-h period (L/d)], drinking speed [the average speed at which a calf ingested all of its milk meals in a 24-h period (mL/min)], rewarded visits (the number of times a calf entered the AMF and received a milk meal each day), unrewarded visits (the number of times a calf entered the AMF and was not provided with a milk allotment), and total visits to the feeder (number of rewarded and unrewarded visits added together). Calf treatment records and AMF data were imported into Excel (Microsoft Corp. ) and cleaned and sorted. Excel files were then imported into STATA 15 (Stata/IC Version 15.
Calf was used as a random effect to account for repeated measurement in each model. Model fit was assessed for each model using residuals. The assumptions of normality and homoscedasticity of best linear unbiased predictors (BLUPs) were assessed graphically for each model. All models except unrewarded visits met all required assumptions of model fit. Attempts were made to normalize the unrewarded visits model through logarithmic and quadratic transformations but neither improved the fit, so the data were analyzed nonparametrically using a nonparametric kernel regression model. To determine sensitivity and specificity of each feeding behavior on disease prediction, optimal cut-points were determined separately for each variable using Youden's index. A total of 28 cases and 28 controls (n = 56 calves) were enrolled in the study.
Materials and Methods Semi-directive interviews were carried out in French with stakeholders from the French Agronomical Research Institute and Environment (INRAE) in Nouzilly, France. The interviewers (J. C. and L. F. ) asked open questions to stakeholders; these questions had been pre-prepared in an interview guide, which was identical for all interviews. The interviews lasted approximately one hour. The stakeholders interviewed were involved with the research of either the infectious diseases of livestock and/or to a lesser extent, common laboratory animals, or with livestock behavior. The slaughter of any livestock at this research center, either for experimental purposes or market value, were carried out at an on-site abattoir. Semi-directive interviews were selected based on the relative merits of the methodology to gather qualitative empirical research data  [19, 20].
The stakeholders interviewed had at least four years' research experience in the field and included researchers, laboratory technicians, animal caretakers, and an abattoir worker. The characteristics of the stakeholders interviewed are presented in Table  1. The recorded interviews were transcribed into a Word text. Tropes (V8. 5) speech analysis software was first used to process the data [21] based on cognitive analysis of the interviews  [22]  and then analyzed using multivariate methods  [22, 23]  applied to the most frequently used words in the interview. We aimed to identify homogeneous groups amongst the stakeholders, as has previously been performed on horse breeders  [24]. Significant differences between homogeneous groups of stakeholders were assessed using Z score statistics for two populations; where the proportions were low (less than 4%), Fisher's exact test was applied to the number of occurrences for each word.
We would thereby expect the results to be similar across the different research centers within the same institute. Experimental research institutes within Europe follow the same directives for animal welfare; as such, our conclusions could potentially be generalized across other institutes. This may, however, be different in the field of genetic selection, particularly for mice, where it has been suggested that super-reification may occur  [25]. An increasing number of computer software programs aim to support content analysis or qualitative data analysis, with several tools dedicated specifically to text analysis and to the computer-aided interpretation of interviews  [23]. The Tropes software [21] offers semantic analysis tools, with graphs based on multivariate analyses, and it is freely available in several languages, including English.
Its efficacy, particularly in establishing homogeneous groups of interviewed farmers, has been demonstrated previously  [24]. It was again efficient here in categorizing the stakeholders according to their respective positions, including the animal caretakers involved in the research of infectious diseases, the animal caretakers involved in behavioral studies, and the researchers. Based on speech analysis, the Tropes software also highlighted in which respects these groups differed. The word "animal" was used as the center-point for the multivariate analyses, as it was the main interest of this paper and the most commonly used word in the interviews. The graphs clearly showed a difference in the relationship of the words between the stakeholders interviewed (Figure  1 ). Comparing the frequency with which the words were spoken helped to differentiate the stakeholder's relationship to the word "animal" and to words such as "team experience", "animal suffering", etc.
(Table  1 ). Interpreting interviews is traditionally based on the manual extraction of the sentences of interest. In the absence of any prior categorization of the people interviewed, it can be a delicate task to carry out. Here, by first categorizing the stakeholders into groups using the Tropes software, it was possible to identify, in detail, the views of the stakeholders. We will discuss the most relevant themes based on the information gained by this two-step method: (i) the semi-automatized analyses of the interviews to categorize them and (ii) the extraction of the most characteristic sentences from within these groups. Animal caretakers were previously reported to have experienced a lack of recognition by researchers and laboratory technicians  [12, 13]  across different research institutes, which may have consequently resulted in a degree of suffering amongst the animal caretakers  [9, [26] [27] [28].
Animals Data were recorded in 2 French commercial stud farms, one located about 60km south west of Paris, the other 150 km south of Paris, both managed by one artificial insemination cooperative during one breeding season (2019). From beginning of February to late August 2019, the study enrolled 277 sport mares over 506 estrus periods. For each mare, date of birth, breed, number of previous breedings and foalings were extracted from the national database (Infochevaux: https://infochevaux. ifce. fr/fr/info-chevaux). Mares were clustered according to age in two (≤ 10 years old vs > 10 years old) or four classes (≤ 5 years old, ]5, 10], ]10, 15], > to 15 years old). They were also clustered in 2 classes according to previous fertility (number of previous foaling/number of previous breedings <0.
Pregnancy rate was calculated as the ratio between the number of pregnant mares at the end of the breeding season and total number of mares bred (overall pregnancy rate at the end of the season) and as the ratio of number of positive diagnoses on D14 / total number of used cycles (pregnancy rate per cycle). Embryonic loss was estimated by the number of mares not pregnant on D30 after a positive pregnancy diagnosis on D14. Late embryonic loss was calculated by the number of mares not pregnant during the autumn after a positive pregnancy diagnosis on D14. Foal birth was checked the next year using the national database. Total embryonic/fetal loss was defined as the number of mares that did not foal after a positive pregnancy diagnosis on D14. Mares were only considered as non-foaling when abortion/stillbirth or new breeding without foaling were recorded in the national database in 2020.
Factors associated with pregnancy rate per cycle After univariate analysis, the following factors were associated with pregnancy rate at the 20% threshold: age in 2 classes, parity, cycle number, AI modality, month of insemination, number of observed ovulations and post-breeding inflammation. All the associated variables were introduced in multivariate models. As significant effect of number of ovulations was observed after univariate analysis, only estrous cycle with ovulations that were observed by ultrasonography were considered (i. e. , 501 estruses among the 506 recorded). Stepwise regression and backward elimination led to a model containing only 3 significant variables (p<0. 05): mare age, number of ovulations and semen conditioning (Supplementary Table  2 ). Data that significantly influenced pregnancy rates are summarized in Figure  2.
Acknowledgements The authors tanks  V. Lehuraux ,  A Jugland ,  B. Brisset  and  P. Drevillon  from the  Stud of "Charmoy" (89) and Stud of "Les Bréviaires " (78) for the availability of the data and for their hospitality during the visits of their organizations. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Kinematics Data First, the accelerations and angular velocities expressed along the three axes of the IMU reference frame were extracted. With the IMU orientation estimation, we expressed the acceleration in a global coordinate system in order to calculate the components of the acceleration along the vertical axis and in the horizontal plane. In addition, the angle of each axis of the inertial unit was calculated relative to the earth's vertical. Finally, for the sensors placed on the limbs, a swing-twist decomposition  [26, 32]  was applied. This decomposition provides the rotation angle around the limb axis (twist) and the angle around the lateral axis of the horse (swing). In total, 13 kinematic signals for the cannons and 11 for the other sensors placed on the head or the sacrum and sternum were recorded.
Stride Segmentation From the vertical acceleration of the sternum, a stride segmentation of the collected data was taken. For that, we applied a 4th order Butterworth filter to the raw signal. We chose a low-pass filter with a cut-off frequency of 3 Hz, which is a sufficient value to include stride frequency in passband for each gait  [33]. A search for consecutive peaks in the filtered signal was performed, imposing a delay greater than 75% of the period between two successive peaks. A manual check on the signal graph was conducted to ensure the correct stride segmentation.
For acceleration vector → a (t), over time segment [t 1 , t 2 ], LDLJ-A is computed as LDLJ a = -ln t 2 -t 1 a peak 2 t 2 t 1 d → a (t) dt 2 2 dt (2) where a peak is acceleration magnitude peak after removing the mean acceleration vector. All calculations were made using MATLAB 2019b software (The MathWorks Inc. , Natick, MA, USA).
Discussion The aim of the present paper was to define which kinematic indicators extracted from the IMUs' data could identify locomotion changes following a training session. These indicators could be used in the future to build an on-board system that could help to anticipate detrimental fatigue during training. For this, various locomotion indicators were considered to assess for changes in the kinematics following a training session. It was expected for the training session to induce fatigue. The riders all interpreted the instruction in the same way and worked with the same objective; however, it was well below the physiological reality. Given the heart rate and blood lactate values, the energy demand (anaerobic and aerobic) was indeed not elevated. The anaerobic threshold expected for the measurements  [34]  was then not reached, even if the horses were felt to be fatigued according to their riders.