dataesr/scientific-paragraphs-categorization huggingface dataset:
Notification and updating of equine premises We combined the two surveys to obtain a more complete view of the premises where equines are kept. The keeper survey was essentially used to check information on premises recorded in the SIRE database, while the owner survey aimed to identify premises that had not been declared to the IFCE and to characterize these premises. Among the 1166 respondent keepers who had one or more open equine premises recorded in the SIRE database, 4. 9% (n = 57) did not still have one at the time of the survey, and 32 of the 51 respondents having premises that were closed according to SIRE records, actually had open premises at the time of the survey (Fig. 2d ). The owner survey indicated that, out of the 1865 respondents not recorded as being keepers in the SIRE database, in fact 52.
6% (n = 981) hosted equines, so these equine premises should have been recorded (Fig. 2c ). Only 37. 1% (n = 364) of them kept equines not owned by them. The majority (n = 617) kept only their own equines: of these, 21. 6% (n = 133) of them owned only the reference equine and 78. 4% (484) also owned one or more other equine(s). Finally, the global inconsistency rate concerning equine premises in the keeper survey was 7. 5% (92/1217) and the combination of the two questionnaires indicated a global inconsistency rate for notifications concerning equine premises of around 40. 7%. Our results confirm the massive under-declaration of premises to the IFCE by keepers. The reasons for non-declaration are not all known, but could include a limited knowledge of regulations, and incomprehension about when to open or close premises: some of the equine premises in the database are marked open yet have zero equines associated with them (perhaps because they do not host equines on a permanent basis), while others are opened then closed the same day, for example.
Additionally, for professionals, under-declaration could be linked to the fact that many of them already notified their professional databases (such as those of farmers or equestrian centers) and do not know that it is also mandatory (and free) to declare information to the IFCE for inclusion in the SIRE database, or consider this complementary declaration as redundant and useless. A sociological approach could shed light on the obstacles and identify the levers to be used to promote complianceespecially of non-professionals -with mandatory regulations. To sum up, the lack or poor quality of owner contact information and the under-declaration of equine premises slow down the management of health events (information, surveillance of restricted movement area, epidemiological investigation…).
Conclusion and prospects We estimated that the SIRE data are of good quality and a valuable and reliable source for epidemiological research and studies as soon as the identified biases are taken into account. The main limits were the lack of updating of deaths, the under-reporting of equine premises, and the lack of availability of a valid contact of owners and keepers as well as a contact agreement. Our work evidenced the complexity of the management of a declarative database in a complex context with i) the particular status of equines in animal regulation, ii) a majority of non-professional owners and keepers, iii) a lack of interoperability of complementary databases and iv) the opposition between the protection of personal data regulation (European regulation 2016/679) and the needs of epidemiological  surveillance.
Despite the obligation to set up a central database in each European country, a great disparity currently exists between the different European states (for example, the United Kingdom, Sweden and the Netherlands do not yet have operational centralized databases (Engelsen, 2017). Our work could help other European countries to develop or improve their mandatory database. This work also contributes to the reflections to be carried out in terms of regulation within the framework of the identification and traceability of equines in Europe.
Acknowledgments We are grateful to the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES) , to the French horse and riding institute (IFCE) and the  Fonds Eperon , for their financial support and for providing access to data. We would also like to thank the owners and keepers who answered our surveys and various people from the  IFCE and ANSES: Jean-Philippe Amat ,  Marie Delerue ,  Romain Dhollande ,  Bénédicte Ferry ,  Carole Margelidon ,  Marion Wujek Moreau ,  Julie Schneider  and  Caroline Teyssier. We would also like to thank  Delphine Libby-Claybrough , professional translator and native English speaker, for reviewing the manuscript.
Climate change is one of the threats to the dairy supply chain as it may affect the microbiological quality of raw milk. In this context, a probabilistic model was developed to quantify the concentration of Escherichia coli in raw milk and explore what may happen to France under climate change conditions. It included four modules: initial contamination, packaging, retailing, and consumer refrigeration. The model was built in R using the 2nd order Monte Carlo mc2d package to propagate the uncertainty and analysed its impact independently of the variability. The initial microbial counts were obtained from a dairy farm located in Saudi Arabia to reflect the impact of hot weather conditions. This country was taken as representative of what might happen in Europe and therefore in France in the future due to climate change.
A large dataset containing 622 data points was analysed. They were fitted by a Normal probability distribution using the fitdistrplus package. The microbial growth was determined across various scenarios of time and temperature storage reflecting the raw milk supply-chain in France. Existing growth rate data from literature and ComBase were analysed by the Ratkowsky secondary model. Results were interpreted using the nlstools package. The mean E. coli initial concentration in raw milk was estimated to be 1. 31 [1. 27; 1. 35]  log CFU/ mL and was found to increase at the end of the supply chain as a function of various time and temperature conditions. The estimations varied from 1. 73 [1. 42; 2. 28]  log CFU/mL after 12 h, 2. 11 [1. 46; 3. 22]  log CFU/mL after 36 h, and 2. 41 [1. 69;3. 86] log CFU/mL after 60 h of consumer storage.
, 2012; Grace et al. , 2008). These studies reflect two different retailing scenarios:  Giacometti et al. (2012)  have performed a risk assessment on vended raw milk while  Grace et al. (2008)  evaluated the informally marketed raw milk. Therefore, the first novelty of the study presented here lies in having built a farm-to fork probabilistic assessment model to evaluate the E. coli concentration under hot weather conditions. For this purpose, an original dataset from a large-scale farm in Kingdom of Saudi Arabia have been collected and analysed. Next, the current raw milk handling practices in France has been introduced in the model to run realistic scenario. The second novelty of this study is to present a 2nd order Monte Carlo model, separating uncertainty and variability, applied to raw milk consumption and the interpretation of its outputs by sensitivity analysis.
Module 1: Raw milk contamination level in bulk milk tanks at farm setting The initial contamination levels of E. coli in raw milk, as representative of hot weather conditions, were obtained from a set of data collected in bulk milk tank in 2019 at AlSafi-Danone, AlKharj, Kingdom of Saudi Arabia. The average temperature in Alkharj, where the farm was located, in 2019 varied between 13. 9 • C (January, the coldest month) and 36. 9 • C (August, the hottest month). In comparison, in France (average values from 30 different locations), the temperature during summer reached 20. 1 • C (June 2019), 23 • C (July 2019) and 21. 8 • C (August 2019). This average temperature included daily fluctuations; during the hottest period of the day (midday and beginning of afternoon), the temperatures fluctuated between 25 and 27 • C with several peaks above 30 • C observed in France during July 2019.
The E. coli counts in raw milk were obtained by performing the colony count method based on the norm  NF ISO 4832 (updated in 2006 ). An undiluted 1 mL of raw milk sample were transferred to Petri dishes while 10-12 mL of violet red bile agar (VRBA) (Oxoid, Ltd. , UK) (cooled into 45 ± 1 • C) was also added and solidified as the initial layer. An overlay of 3-5 mL of VRBA was then subsequently added to the original basal-sample medium. The plates were then incubated at 37 ± 1 • C for 24 h. Colonies showing purplish red color with a reddish zone of precipitated bile (≥0. 5 mm diameter) were enumerated. The E. coli counts represented 1695 data points taken from the operations for the year 2019 in different farm units. The dataset was checked and cleaned. Only the farm unit containing the most number of data (622 data points) was selected for further analysis since mixing data from the different farm units would have brought additional variability.
The data were fitted to Normal, Gamma, and Lognormal distributions using the R package fitdistrplus. The final probability distribution was selected based on its fitting in the Cullen and Frey diagram and statistical performance in terms of Akaike Information Criterion (AIC). A bootstrap procedure was subsequently performed to quantify the uncertainty and build a confidence interval around the distribution parameter estimates. In this module, the temperature in the milk tank was assumed to follow the cold chain requirements of the French standard in raw milk production, i. e. ≤ 4 • C. This assumption was confirmed by data (temperature probe in the tank). Therefore, significant microbial growth of E. coli was not considered in this module.
Modelling The exposure assessment model was implemented in R software  (R Core Team, 2019). The bootstrap procedures were carried out using the bootdistcens package of the fitdistrplus  (Delignette-Muller & Dutang, 2015). The second order Monte Carlo procedure was used to propagate uncertainty and variability separately using the mc2d package  (Pouillot & Delignette-Muller, 2010). The number of iterations performed for uncertainty was 1000 and for variability 100,000.
Uncertainty analysis A sensitivity analysis was performed to evaluate the impact of uncertainty on the main model output, i. e. the microbial concentration at the consumer level (log N3). The tornadounc function of the mc2d package was used with the Spearman rank correlation method. The results obtained from this analysis determined the influence of the input uncertainties on the uncertainty around the 95th percentile of log N3. This percentile was chosen as representative of the upper tail of the distribution of E. coli concentration.
Module 1: Initial microbial load in bulk milk tank The initial microbial concentration (namely, logN 0 ) was obtained from the one-year operation in a dairy farm in Saudi Arabia. The data were fitted by normal, log normal, and, gamma distributions and the results were compared based on the AIC value (Table  3 ). The normal distribution provided the best fit (AIC = 903). A bootstrap procedure was then performed to estimate the uncertainty around the normal distribution parameters (Fig. 1a ). This resulted in an estimated mean value of 1. 31 log CFU/mL with a confidence interval of 1. 27-1. 35, and, a standard deviation of 0. 53 with a confidence interval of 0. 50-0. 57. The probability of the milk tanks exceeding the E. coli criteria was ¤ and ¤¤ values used to build the probability distribution regarding the intercept.
039, 0. 005). The results of the 2nd order Monte Carlo simulation analysing the uncertainty and variability of the T min is presented in Fig. 3. The different strains of E. coli have a mean value of 4. 7 • C with a 95% confidence interval of [1. 8; 7. 6] • C. This large confidence interval around the mean value reflects the uncertainty in the estimation process due to lack of data and model misfit when applying the Ratkowsky secondary model. Its influence on the final output will be assessed by sensitivity analysis hereafter. Besides, T min variability is also large with variation from a 5th percentile estimated to 3. 4 • C [-0. 3; 6. 5] • C up to a 95th percentile estimated to 6. 1 • C [3. 2; 9. 8] • C.
Microbial growth during retailing period The growth parameters estimated by analysing data from both the literature and Combase were used to predict the growth rate of E. coli under specific temperature conditions and then to determine the microbial concentration during retailing (log N2). The microbial load during retailing depends on temperature but also on duration of retailing on local markets. The maximal duration was set to 12 h (i. e. maximal time between milking and selling raw milk allowable in France). The E. coli concentration (1. 53 [CI:1. 30; 2. 11] and sd 0. 55 [CI:0. 51; 0. 67] log CFU/mL) in raw milk after 12 h at 8 • C (Fig. 1b ) was greatly  Table 4  E. coli concentration in bulk milk tank and packaged raw milk: mean value, standard deviation, 95th percentile of the distribution; probability of exceeding the 2-log and 1-log limit at different stages across the dairy supply chain.
The use of hot weather conditions and E. coli as test organism in understanding the future of raw milk consumption The current probabilistic model has shown that raw milk consumption might pose microbial food quality concerns in the future under hot weather conditions brought by climate change. In order to understand the possible impact of hot weather conditions on raw milk, data from a dairy farm in Saudi Arabia was obtained. These were considered to be representative of what initial microbial counts might look in the future for countries undergoing shifts in high temperature due to climate change. The selection of this farm allowed an insight to a certain extent on what microbial quality might look like in the future under hot weather conditions. The comparison with the farms in France is possible because in the farm selected in our study, Holstein breed cows (a very common breed in France for milk production) are raised.
Also, the best practices in dairy farming such as good veterinary practices (GVP) and good hygiene practices (GHP) applied at the farm are comparable with the ones being applied elsewhere with the difference only in its location and hot weather conditions. The data used are E. coli counts from bulk milk tanks, collected and analysed as part of routine operations. These were used to assess the raw milk contamination just after the milking step. This approach supports the notion that the contamination pathway of E. coli in the dairy supplychain starts in the early stages of raw milk supply chain  (Perrin et al. , 2015). E. coli was used in this study because aside from being a microbial hazard commonly linked with raw milk consumption it is also a microorganism that is foreseen to pose a concern in the future for the raw milk produced under hot weather conditions  (Fairbrother & Nadeau, 2006 ).
Acknowledgement The authors would like to acknowledge the inputs provided by  Mrs Florence Daviaud , a French raw milk farming expert (Foix) and  Prof. Nathalie Baraille , a French veterinary expert from  BIOEPAR, Oniris (Nantes). This research has received funding from the  European Union 's  Horizon 2020 research and innovation programme   under Marie Skłodowska Curie  grant agreement No. 813329 , Project  PROTECT.
Birds RIR (brown-egg layers) and WL (white-egg layers) are the two main breeds of laying hens used for egg production in the world. This study involved two pure lines-a RIR line and a WL line-created and selected by the Novogen breeding company (Pledran, France). These two lines are specifically selected for egg number, egg quality, body weight, and behavior-related traits such as feather pecking. Data were obtained from 2017-2019 for three batches of RIR, which contained 1,430 laying hens (146 sires and 546 dams), and two batches of WL, which contained 1,008 laying hens (100 sires and 359 dams; Table  1 ). The known pedigree used to estimate genetic parameters included seven (3,433 individuals) and five (2,354 individuals) generations for RIR and WL, respectively. Hens were raised on the nucleus farm in a barn with floor pens (one pen per line) from 17-64 weeks of age.
Data recording system Floor pens of the barn were equipped on two levels (top and bottom) with individual electronic nests (20 Width × 40 Length × 27 Height cm). The density was about 5 hens for 1 nest (Table  1 ). These nests were developed and optimized by Novogen to record individual nesting behavior. The data recording system used radio-frequency identification with a specific antenna for each nest and a specific transponder for each hen tagged on one leg. A hen's transponder was automatically recorded when the hen visited a nest. For each visit, the system recorded the hen's identification, the time of entry in the nest, and the time of exit in the nest. An egg collection system connected to each nest recorded nest visits, with oviposition or not,   1 ). In the present study, only the time of entry for nest visits with oviposition was used to define and calculate traits, for two reasons.
First, oviposition time may be incorrect if the egg laid remains stuck in the nest until or after the hen leaves the nest. When this occurs, the oviposition time recorded by the egg collection system is later than the true oviposition time. These data had to be excluded for analyses, unlike the corresponding time of entry in the nest. Second, when the oviposition time was known to be correct, the time of entry in the nest correlated strongly with oviposition time for both lines (phenotypic correlation = +0. 99). To facilitate comparison of mean oviposition time (MOT) with results in the literature, we calculated phenotypes of MOT in the nests when oviposition time was known to be correct (see results in Table  1 ), which represented 90. 2% and 87. 4% of the data recorded for the RIR and WL lines, respectively.
The oviposition interval is the duration between two consecutive times of entry in the nests for visits with oviposition (i. e. for two consecutive days). Hens with less than 10 data available to calculate oviposition traits were excluded from analysis; thus, 0. 05-0. 10% of data for each period and each line were excluded. Finally, nest acceptance for laying (NAL) was defined to estimate the ability of hens to lay eggs in the nests. Hens with LRN < 50% or � 50% were assigned the value 0 or 1, respectively.
Individual electronic nests record nest-related traits In the present study, phenotypic data recorded by individual electronic nests in a cage-free system are consistent with those in the literature. Here, MOT ranged from 2 h 5 min to 3 h 44 min after turning on the lights, which is consistent with times reported in individual cages or other cage-free systems. In individual cages, 51% of RIR eggs are laid 30 min to 3 h 30 min after turning on the lights  [26]. Likewise, using electronic nests in a cage-free system, brownegg layers reached maximum daily egg production 3 h after turning on the lights  [16]. However, the white-egg layers reached the maximum daily egg production 6 h after turning on the lights  [16] , which suggests that this trait has different phenotypes. The MOI observed are also consistent with those in the literature.
| Data analyses IBM ® SPSS ® Version 26 for MacOsX was used for all statistical analyses. The Shaphiro-Wilk normality test was used in order to determine the normality of distributions within mare groups (GnRHa vs hCG and ovulating or not) for age, breed, BCS and sulpiride treatment (days). An independent sample t test or a Mann-Whitney U test was applied as appropriate. To identify variables associated with ovulation rates, univariable analysis of the data was performed with Fisher's exact test with Bonferroni correction to compare ovulation rates between agents (GnRHa vs hCG), phases of the year (early transitional period vs late transitional period) and heat expression (full heat vs incomplete heat), while Chi square test with Bonferroni correction was used to assess the influence of breed (Standardbred, Thoroughbred and other breeds) and year of the study.
Gaits Data A group of 1,477 show jumping horses aged 4 (42%) to 5 years (58%) were measured during 27 official young horse competitions in France, during the Spring of 2015 and 2016. This group of horses was composed of 50% females, 35% geldings, and 15% males. A large majority of them (83%) were Selle Français. The rest were foreign sport horses (10%), Anglo-Arabs (3%), or crossed saddle breeds (4%). The gaits were measured using an Equimetrix R accelerometer  (Barrey et al. , 1994)  after the competition, while the horses were being ridden. Recordings were taken by an accelerometer held in place by a leather pocket over the caudal part of the sternum on the girth. The riders were asked to perform a quick gait test with walk, trot, and canter in an arena that was 60 meters across in order to have a sufficiently long duration of measurement.
Longevity in Competition Data Competition data was provided by the French Equestrian Federation. These data related to horses born from 1981 onwards and that had participated in show jumping competitions between 1985 and 2018. Horses for which a career in show jumping was not the main objective were excluded from the analysis. These were horses participating in eventing or dressage events (for more than a year), horses born abroad or that had started their career with foreign riders, and ponies or draft horses. Ultimately, 179,448 horses remained for the analysis, corresponding to 918,667 cumulative years of performance. The success of a year's performance is characterized by the sum of the points earned. The points depend on the rank and difficulty of the event. All horses that enter an event earn points.
Genomic Data Some of the horses with gait data were genotyped, as were other jumping horses and stallions derived from previous studies  (Ricard et al. , 2013; Chassier et al. , 2018). A total of 3,658 jumping horses were genotyped with three different SNP density chips: the Illumina Equine SNP50 BeadChip (54 k) that includes 54,602 SNPs, the Illumina Equine SNP70 BeadChip (65 k) that includes 65,157 SNPs, and the Affymetrix Axiom Equine genotyping array (670 k) that includes 670,806 SNPs. There were 1,157 horses with gaits data that had been genotyped, and 498 horses with longevity data that had been genotyped. Of the 3,658 genotyped horses, 29% were genotyped with the Illumina Equine SNP50 BeadChip, 2% with the Illumina Equine SNP70 BeadChip, and 69% with the Affymetrix Axiom Equine genotyping array.
Imputation was performed using Fimpute 3. 0 software  (Sargolzaei et al. , 2014). Equine 3. 0 reference sequence (www. ncbi. nlm. nih. gov/assembly/GCF_002863925. 1) was used as the reference map when the SNP's position was available or Equine 2. 0  (Wade et al. , 2009)  when it was not available. First, quality control removed SNPs that did not reach the test requirements for quality. The total number of SNPs on the three chips was 675,475. After deleting the SNPs with unknown chromosome locations, 634,148 SNPs remained. We only used autosomal SNP, and as there were 27,731 SNPs on the X chromosome and 2 SNPs on the Y chromosome, 606,415 SNPs remained, which after suppression of a handful of duplicates became 606,343 SNPs. We deleted SNPs with a MAF < 5% (196,864), followed by the Hardy-Weinberg disequilibrium test with P < 10 -6 (25,596), a call rate < 90% (6,614), and then for inconsistency between the MAF of the different chips with P< 10 -5 (1,582).