dataesr/scientific-paragraphs-categorization huggingface dataset:
Pedigree Data The pedigree data was provided by the Institut Français du cheval et de l'équitation, on behalf of the breeding organizations. The study of longevity involved sire and maternal grandsire relationships. The 179,448 horses with measured longevities were derived from 8,991 sires and 9,477 maternal grandsires. The ancestors of these sires represented 16,564 stallions included in the survival analysis to construct the relationship matrix. After the longevity analysis, the sires used to perform join analysis with the accelerometric data were restricted to those with sufficient accuracy. Hence, only 1,968 stallions were retained at the end of the longevity analysis. The ancestors of these horses, those of the 1,477 horses with gaits, and those of the 3,658 genotyped horses were reassembled over four generations, and they constituted a group of 24,446 horses.
Relationship Between Gait Data, Longevity Data, and Genomic Data Of the horses with gaits data, 1,157 (78%) were genotyped. Of the 1,968 horses measured for functional longevity, 498 (25%) were genotyped. None had both longevity and gaits data since longevity records were analyzed using a sire model. Of the 1,477 horses with gaits data, 1,035 (70%) had their sire estimated for longevity (224 sires) and 939 (64%) had their sire genotyped (263 sires), namely 782 (53%) with their sire estimated for longevity and genotyped.
Variance components and effects were estimated by maximizing the joint posterior density using 'the Survival Kit' software  (Meszaros et al. , 2013). The heritability was computed using h 2 = 4σ 2 s σ 2 s +1/p , with p the proportion of uncensored records and σ 2 s the variance of the sire effect  (Yazdi et al. , 2002).
We implemented a program (R software) that allowed: -calculation of R of the stallions by deleting information provided by the relationship between them  (Ricard et al. , 2013) , see Supplementary Material S2 for details. Cleaned reliability is R ′ i. -deduction of weights w i , from R ′ i = 1 - 1 σ 2 u ( w i σ 2 ǫ + 1 σ 2 u ) -deduction of pseudo-phenotypes y *. In order to retain only consistent pseudo-phenotypes, we calculated the pseudo-phenotypes of the stallions that still had enough reliability left after cleaning. We, therefore, calculated a pseudo-performance for longevity for the stallions with reliability still greater than or equal to 0. 40, which corresponded to 1,968 stallions. Selected threshold for reliability (0. 40) corresponded to usual mean of reliability qualified as "intermediary" on the publication of breeding values for stallions in France for jumping competition.
, 2018). This inverse was then used with ASReml software version 4. 1  (Gilmour et al. , 2014)  to estimate the variance-covariance components by restricted maximum likelihood. ASReml software allows some of these variance components to be fixed. Because genetic and residual variances for longevity were better estimated with the survival analysis, these components were fixed, while the variances for gait traits and covariances between gait traits and longevity were estimated.
GWAS of Longevity As a by-product of the single-step approach, we performed a genome-wide association study (GWAS) of longevity with back solutions of SNP effects and P values calculated as described in  Aguilar et al. (2019). The BLUPF90 family of programs package was used with single-trait analysis of longevity as pseudophenotypes with weights. The model was the same as the one above for the bi-trait analysis using all of the genotyped and phenotyped animals for longevity in a single step GBLUP. The H matrix was constructed using preGSf90 and then postGSf90 was used to obtain estimates and P values for the SNP effects.
Accuracy of the Results Because the data were obtained from official competitions, a large number of unselected horses were used to estimate the breeding values for the functional longevity of stallions on a very long timescale (33 years). This resulted in accurate estimated heritability and breeding values. A much smaller number of horses was used to record gaits. The accuracy for the estimation of variance components for the gait traits was lower due to the small number of progenies from the stallions (mean 3. 0). A large number (70%) of horses with gaits data were derived from a sire with a pseudo-phenotype for longevity, which assured accuracy for the genetic correlations, and with a higher number of progenies derived from these sires (4. 6). Moreover, the absence of horses with both traits may -to some extent-prevent the confusion between genetic and environmental correlations in case of interaction between genetic and environments effects.
DATA AVAILABILITY STATEMENT The data used in this study are the property of the Selle Français Stud-Book and the owners of the horses. There are restrictions on the availability of this data and it is not publicly available. Access to the data may be authorized by the partners of the SoGen project within the framework of which they were collected, on request from the authors. Requests to access these datasets should be directed to Manon Dugué, manon. dugue@inrae. fr.
Prepartum Urinary pH The primary purpose of measuring pH was to ensure appropriate acidification (deemed as values within the range of 6 to 7) and to serve as a covariate for other outcomes of interest. Overall mean urinary pH (UpH) of all urine samples collected after commencement of acidification until parturition was computed by cow and used as a covariate for the prepartum urinary Ca:creatinine ratio analysis (detailed below). Mean urinary pH from the last 3 samples prepartum was used as a proxy for urinary pH at day of calving (UpHdoc) and used as a covariate for postpartum data (i. e. , blood BHB and serum Ca concentrations). Statistical analyses revealed that sample number was not significant (p = 0. 48) among these last 3 samples, confirming the appropriateness of this approach.
Prepartum Urinary Ca Excretion Plots of prepartum urinary Ca excretion (UCE), expressed as Ca:creatinine ratio (mol:mol), suggested a sigmoidal increase and subsequent decrease over time (Supplemental Figure  S1. Various double sigmoidal functions, accommodating an ascending and subsequent descending phase were constructed, including an exponential function with a discrete lag, logistic function, and Gompertz function. Fits to these functions were evaluated based on information criteria, robustness, and biological interpretability of the parameters. Based on the above criteria, the final model selected was the logistic equation based on the parameterization of Zwietering et al. [25]. Model parameterization consisted of the baseline values prior to acidification (BL), the maximum increase above baseline (A), the maximal rate (days -1 ) of increase (µ 1 ) and decrease (µ 2 ) and the corresponding time (days) of maximal rate of increase (λ 1 ) and decrease (λ 2 ) in Equation (1) below: f(t) = BL + A 1 + exp 4µ 1 A (λ 1 -t) + 2 × 1 1 + exp 4µ 2 1 (t -λ 2 ) + 2 (1) To fit this function, we used a nonlinear mixed effects model (NLMEM), using the saemix package of R  [26].
Postpartum Time Course of Serum Ca and BHB Time course postpartum serum Ca and BHB data were fit to a linear mixed model using the lme function of the nlme package  [28]  to evaluate the effects of time and its interactions with covariates. Categorical covariates consisted of breed, parity group, treatment, and sampling time. Centered continuous covariates consisted of days of acidification, mean daily equivalents of Cl over the last 3 samples prepartum, UpHdoc, UCEdoc, and colostrum production (kg). Data were fit to the full model which contained the main effects and all possible interactions among categorical covariates as well as the linear and quadratic terms of each continuous covariate (interactions between categorical and continuous covariates were not accommodated by the model). The model also included individual cow as a random effect and autocorrelation was accommodated using a first order autoregressive function, corAR  (1).
Postpartum Univariate Measures: Serum Ca Concentration, UCEdoc and Colostrum Production Two univariate measures of serum Ca concentrations were evaluated, the value immediately post-calving (Ca0) and the minimum value observed over time (CaMin). These data were analyzed by generalized least squares using the gls function of the nlme package  [28]. Categorical covariates consisted of breed, parity group, and supplemental oral Ca treatment while centered continuous covariates consisted of mean daily equivalents offered obtained from the last 3 samples prior to parturition, UpHdoc, UCEdoc, and colostrum production (kg). Categorical and continuous covariates were evaluated using two separate approaches. For categorical covariates, the full statistical model consisted of all main effects and interactions among the categorical covariates and the linear and quadratic terms of each centered continuous covariate (interactions between categorical and continuous covariates were not accommodated by the model).
Results Two Jerseys (each fourth parity, group CON) were diagnosed with clinical milk fever (defined as a recumbent cow unable to stand and walk that responded to intravenous Ca gluconate) and removed for the remainder of the trial. Serum Ca concentrations in blood samples taken immediately prior to treatment were 0. 80 mmol/L at 12 h for one cow and 0. 90 mmol/L at 24 h for the other cow. These values confirmed the diagnosis of clinical milk fever for both cows. All data for these two cows obtained up to the time of their clinical milk fever, including their pre-treatment serum Ca concentrations, were included in the statistical analyses. The overall mean dose of CaCl 2 offered was 3. 38 (±0. 79 SD) equivalents Cl/cow per day and the overall mean UpH for cows during the close-up dry period was 6.
We are unaware of any previous single study reporting an evaluation of either breed or any interactions among parity, breed, and oral Ca treatment for multiparous cows. The results of the current study were broadly consistent with previously published patterns. First, nadir serum Ca concentrations were observed at 24 h. Second, serum Ca concentrations had returned at least to Ca0 levels by 48 h, and, by 72 h, they had stabilized at levels well above Ca0, which likely reflected prepartum baseline levels. These data underscore the fact that the critical window of vulnerability to milk fever is generally only within the first 48 h postpartum. Third, both cases of clinical milk fever in the current study were observed in fourth parity Jerseys; this is consistent with well-known principles that increasing parity and Jerseys vs.
Significant pre-prandial correlations relate to the relationship between the quantity of feed consumed in the meal (meal size) and the length of the interval before the meal; this is interpreted as a satiety-related intake regulation mechanism  [5]. On the other hand, post-prandial correlations relate to the relationship between the meal size and the length of the interval that follows it; this is interpreted as a hunger-related mechanism  [5, 6]. Few studies focus on prandial correlations, and they are often not standardized among themselves  [2, 4, 7]. Further, many studies are not conclusive, mainly because they are developed from small databases (few animals and/or few observations per animal). With this in mind, large databases, constructed in real-time through electronic feeding systems, are of great value in understanding the structure of the animals' feeding behavior.
Data Source The animals, facilities, and experimental procedures used in this study were previously described  [3]. In brief, information on the feed intake of 70 pigs (35 female and 35 male), with the same high-performance genotype (Fertilis 25 × G-Performer 8. 0, Genetiporc Inc. , Saint-Bernard, Quebec, Canada), housed together in a single room was obtained using 5 automatic feeding stations (Automatic and Intelligent Precision Feeder, University of Lleida, Lleida, Spain). Each feeding station consisted of a single-access device in which helical conveyors simultaneously combined volumetric quantities of up to four feeds stored in separate compartments located on the top of the feeder  [3]. Feed density was measured weekly, and the feeders were calibrated to provide the required amount of feed from feed volumes and ensure that the pigs individually received a feed blend with the desired nutrient concentration.
Diets and Feeding Two experimental feeds (named A and B) were separately formulated based on net energy and standardized ileal digestible (SID) amino acids and using the same ingredient composition database (which analyzed the gross composition of ingredients derived according to the EvaPig software, version 1. 3. 1. 4; INRA, Saint-Gilles, France), without growth promoters or any other additive. Feed A was formulated to meet the SID lysine requirements of the most demanding pig at the beginning of the growing period, and feed B was formulated to meet the SID lysine requirements of the least demanding pig at the end of the finishing period. The feeds were formulated according to standard recommendations for AA profiles  [9]  and digestible P  [10]. Feeds A and B were produced in 1 batch each and were provided in the steam-pelleted form.
Data Collection and Management The feeders were equipped with a monitoring device that continuously and automatically recorded the start and end time of each visit of each pig (day, hour, minute, and second), the number of services, and the total amount of feed served in each visit. All the information collected was used to construct an initial database, which was managed and analyzed using R Statistical Software (version 2. 14. 0; R Foundation for Statistical Computing, Vienna, Austria). In the first information filtering procedures, certain atypical observations were detected through graphical analysis and critically evaluated from a biological point of view. The data on animals with between-visit intervals longer than 720 min (12 h; 1. 4% of the original database observations) were removed.
Such episodes were probably related to atypical physiological conditions such as subclinical infectious or inflammatory processes or accidental loss of the identifying transponder. In the latter case, as soon as the loss was noticed, the animal received a new transponder and returned to the experiment. In both situations, the data were excluded from the database because they were considered without interest for this study (atypical conditions). Additionally, visits longer than 40 min and those shorter than 0. 05 min were discarded (5 observations). These data were likely generated by a technical failure of the system. However, this was considered a one-off limitation, given that a fairly limited number of observations were removed from the dataset.
Statistical Analysis After the meal criterion was defined, a new database was created with visits grouped into meals. Descriptive statistics were used to characterize the variables before and after the use of the meal criterion. The data grouped into meals were also used to calculate the meal size (minutes) and the between-meal intervals (pre-and post-prandial, minutes). A new analysis of p start was also performed to establish the probability of starting to feed according to circadian rhythm, the effect of age, the effects of the feeding programs, and the prandial correlations following the procedures described above. The use of this method allowed the dynamics of the demand for feed over time to be observed, which made it possible to predict the probability of an animal returning to the feeder to start a new feeding event.
General Description and Meal Criterion Throughout the experiment, the pigs consumed feed and gained weight according to the expected performance based on their genotype. No serious health problems were detected other than inflammatory hoof problems, which were unrelated to the treatments and identified in three castrated males during the last phase of the experiment. The animals involved were isolated from the group and received adequate veterinary treatment, and their data were not considered in the analysis. Detailed performance responses are available  [3]. In short, feed intake and feed efficiency were not influenced by the evaluated feeding programs. The first database contained information from 157,632 feeder visits (outlier data already excluded). In this dataset, the meal criterion value for the total experiment period was 37 min, and this was considered an appropriate meal criterion.
Meal Criterion Estimating feed intake by pigs is a complex task since feed ingestion depends on various intrinsic and extrinsic factors of the animal. Despite the difficulties, correctly estimating feed intake is fundamental for the implementation of more precise feeding programs. In addition, knowledge of the normal feeding behavior of pigs is fundamental when implementing automatic systems so that health disorders that are often associated with changes in feeding motivation can be identified. To understand pigs' feeding behavior, it is first necessary to carefully define meals within the database. The animals invariably take short breaks during a meal; because of this, several recorded visits to electronic feeders are commonly obtained. These events, although closely linked or separated by very short intervals of time (e.
g. , seconds or a few minutes), may be identified and recorded as more than one visit by the feeder, depending on its configuration. For proper assessment of the prandial structures, these short breaks ("intra-meal") should be distinguished from the long breaks that take place between meals. The methodology previously proposed  [14]  and used in this study is among the most accepted means of defining a specific meal criterion for the dataset. Despite the importance of this procedure, many recent studies have arbitrarily defined the meal criteria, i. e. , without using an adequate methodology to identify the minimum time interval between meals. The impact of applying a meal criterion and even of choosing the method for defining this criterion on observed behavior responses cannot be denied, although it may be higher or lower depending on the database characteristics.
The meal criterion was estimated at 37 min in this study. Based on this criterion, visits by the same animal to the feeder, separated by intervals of less than 37 min (or 2040 s), were grouped as a single meal. The descriptive statistics differed strongly between the initial database (visits) and the revised database that took the meal criterion into account. This difference between the results obtained from the same database indicates that the use of a probabilistic model to define the meal criterion is of utmost importance. Using an inadequate criterion generally divides the meals into smaller portions  [15] , thereby interfering with the interpretation of the answers obtained from that dataset  [16]. For example, when an arbitrary meal criterion was applied to rats  [17] , it was observed that food deprivation resulted in an increase in meal frequency without a change in meal size.
This may also be related to the high number of individuals and observations in the database, as well as the natural variability of behavioral characteristics among the animals in the population. The vast majority of pre-prandial correlations and post-prandial correlations identified in our study were significant. This differs from what was previously described  [5] , when only a small proportion of the variation in meal size was related to variation in the postmeal-time interval, with no effect of animal age on this response. In this study, pre-prandial correlations were higher during the night, which suggests a greater influence of the satiety mechanism during this period. The pigs with higher pre-prandial correlations may have been unable to feed freely during the peak feeding activity time (daytime period) and, therefore, may have been forced to feed at times when there was less competition for the feeder (nighttime period).
Roe deer data Roe deer of known age are captured annually as part of a long-term monitoring program. Details of the capture procedure are available in Gaillard et al. [35]. Fecal samples were collected rectally in immobilized roe deer captured between January and March in 2018 and 2019 under a protocol approved by the Director of Food, Agriculture and Forest (Prefectoral order 2009-14 from Paris) and under authority of the O ce Français de la Biodiversité. For each individual, we recorded the sex and age as well as the approximate location of capture. We also veri ed that individuals of different age and sex are equally distributed throughout the capture period and spatial localization (data not shown).
Bioinformatics All analyses were carried out using R 3. 6 (R Core team, 2020). We curated DNA sequence data in two steps (see Appendix A1). Firstly, the sequence reads subsequently passed through a DADA2-based pipeline  [37]. Speci c details are available at www. nemabiome. ca. Primers were removed with Cutadapt [38] after the removal of ambiguous bases. Then, reads were discarded based on the number of errors (> 2 and > 5 in forward and reverse reads respectively), length (< 50 bp) and quality (truncG = 2 and phiX genome). Clean forward and reverse reads were merged only if they overlapped by > 12 bp with low mismatches (i. e. , max. 3 for > 100 bp, max. 2 for > 50 bp and 1 for < 50 bp reads). Putative chimeras were automatically removed but we veri ed the effect of this ltering on the dataset post-analysis, as these programs are known to produce a lot of false positives  [39].
A conservative approach was used for taxonomic assignment of ASVs in which three different assignment methods were used (Appendix A2): (1) IDTaxa using the default conservative threshold of 60, which corresponds to the con dence at which to truncate the output taxonomic classi cation  [40] , (2) assignTaxonomy  [37, 41]  and (3) BLASTn  [42]. The nematode ITS-2 rDNA database 1. 1. 0 [43]  containing 9811 complete and non-redundant sequences, with a few additional manual corrections of species assignation corresponding to morphotypes, was used for both IDTaxa and assignTaxonomy. (3) BLASTn, where the three best hits were considered only if they reached a threshold of 90% similarity against NCBI database sequences (2021/04/13). All specieslevel assignments were ascribed a 'con dence level' (Appendix A2) : High con dence indicates that all 3 methods assigned to the same species and moderate con dence indicates that 2 out of the 3 methods agree at the species level and also the third method was in aggreement but could only assign to the genus level.
Taxonomic assignment was limited to the genus level when there was disagreement across any of the three methods at the species level (but consistency at the genus level). Raw ASV dataset was curated following a procedure developed by  Taberlet et al. (2018) , see also De  Barba et al. (2014)  for a visualization of each step expectation and Calderón-Sanou et al. (2020) for the consequence of each step of the data curation on the nal data set. Amplicon sequence variants (ASV) observed only once in the whole data set were considered spurious and were removed. [46] To delete potential contaminants  [47] , we discarded any ASV more abundant in negative controls (max 55 reads) than in other samples and reads with < 0. 13% (maximizing the removal of sequences with low abundance in controls) of the total ASV abundance in the entire dataset.