dataesr/scientific-paragraphs-categorization huggingface dataset:
Morphological analysis. The paraffin block was cut into 4 µm thick sections and then fixed to slides. The sections were hydrated using a xylene and alcohol series, then stained with hematoxylin and eosin, and examined at 10 × using a Leica DM2500 microscope with a Leica DFC7000 T camera (Leica Microsystems, Wetzlar, Germany). Measurements of each anatomical district were taken in a casually selected area using LAS X software (Leica Microsystems, Wetzlar, Germany). For the evaluation of each measurement (length of the villi, width of the villi, depth of the crypts and number of the goblet cells), three villi and three crypts were chosen randomly for each section, making sure that the villi used were in perfect morphological condition, without distortion, absence of epithelium or folds in the tissue  60.
Statistical analysis Data were analysed using a two-way type III analysis of variance, including treatment and trial effect using the R (4. 0. 2) software (R core Team, 2014). Observations were weighed according to the number of birds they were based on. Two databases were created because some treatments were not represented in all trials. The All Comparisons (AC) database compared the four treatments and used the following three trials: A, C, and E. The Treatment Effects (TE) database compared only IUC and ITT and included all trials: A, B, C, D, and E. Posthoc analyses were performed using emmeans (1. 5. 4 version) and multcomp packages (1. 4-16 version).
Results Prechallenge period (P0-P1 0-14 Days) Before the challenge, there was no significant difference in ADFI and ADG between the treatment groups, regardless of the database considered (P > 0. 75, Fig. 1 ). However, in the TE database, a significant improvement in FCR was observed in the ITT group compared to the IUC group (1. 104 ± 0. 0055 vs 1. 124 ± 0. 0055 g/d, P < 0. 05). Postchallenge period (P1-P2 14-21 Days) Coccidiosis altered bird performance in the first week following the challenge, as shown by the significant decrease in ADG and an increase in FCR in the IUC group compared with the UUC in the AC database (P < 0. 001, Fig. 2 ). Contrarily, ADFI was not significantly different across treatments (P > 0. 05). The negative effect of the challenge was fully reversed in the ITC group.
Overall period (P0-P3 0-35 Days) Comparisons over the entire trial period (from P0 to P3) in the AC database showed that the challenge significantly decreased ADG without altering ADFI, which translated into a higher FCR in the IUC group compared to UUC group (P < 0. 001, Fig. 3 ). The supplementation of the anticoccidial drug or the supplement fully reversed the negative effect of the challenge on performance. Consistent observations could be done in the TE database showing that the supplementation of functional amino acids and polyphenols improved both ADG and FCR compared to the IUC group (P < 0. 001, Fig. 3 ). Final body weight (FBW) followed the same pattern as ADG (Fig. 4 ): the coccidiosis challenge significantly decreased FBW which was reversed fully by the supplementation of the anticoccidial drug or the supplement.
We collected data on the behaviour of dairy cows in barns, clinical signs of diseases as well as events that may stress or agitate the cows. A Real-Time Locating System gives the position of individual cows every second. The position of the cow is determined by triangulation based on radio waves emitted by a tag fixed on each cow neck collar and captured by antennas in the barn. The cow's activity is inferred from its position: 'eating' if the cow is positioned at the feeding table, 'resting' if the cow is in a resting area (typically cubicles), else 'in alleys'. We aggregated this information to get the time spent in each activity per hour. We also calculated the activity level of the cow for each hour of the day by attributing a weight to the time spent in each activity. For each cow and day, we collected information on health events or other events that may affect behaviour.
There were 11 types of events. Six events were linked to health: lameness; mastitis; LPS (i. e. administration of lipopolysaccharide (LPS) in the mammary gland, an experimental treatment to induce udder inflammation); subacute ruminal acidosis; other diseases (such as colic, diarrhoea, ketosis, milk fever or other infectious diseases); and accidents (such as retained placenta or vaginal laceration). Two events were linked to reproduction: oestrus and calving. Three events were stress events: animal mixing, disturbance (i. e. mild intervention on animals such as late feeding, alarm test) and marginal management changes (ration changes, fill bed). In addition, a Boolean sums up whether this hour was considered as normal or not. Data contain four datasets. It consists of univariate time series.
Each time series corresponds to the hourly activity level of a cow. Datasets 1 and 2 are from the INRAE Herbipôle experimental farm and include data from experiments; datasets 3 and 4 are from commercial farms. They contain data on respectively 28, 28, 30 and 300 cows monitored for 6 months, 2 months, 40 days and one year. The data can be used to study the links between health, reproduction events and stress on the one hand and cow behaviour on the other hand. More specifically, it can be used to build and test tools for an earlier detection of health and disturbances, with a view to inform caretakers so that corrective actions can be rapidly put in place.
Type of data Table How data were acquired We collected data from the CowView system (GEA Farm Technology, Bönen, Germany), which is a Real-Time Locating System (RTLS) that gives the position of each cow in a barn every second. The cow's activity is inferred from its position: 'eating' if the cow is positioned at the feeding table, 'resting' if the cow is in a resting area (typically cubicles), else 'in alleys'. We also calculated the level of activity of the cow for each hour of the day. For each cow and day, we collected information on health events or other events that may affect behaviour. Event data were collected from logbooks or from sanitary information system (SICPA sanitaire, https://doi. org/10. 15454/ A1UV8Q). The caretakers logged any event as soon as it was observed (oestrus Even though data on activity can be obtained by different sensors, the results obtained are likely to be valid on datasets of animal activity obtained by other means (other sensors or observation)  (Buller et al.
Data description Data are composed of four datasets and one table  (Table 1 ). The datasets are structured as follows: cow working id (3 or 5 digits); No cow belongs to several datasets; therefore, there are 386 unique cows. date; hour (integer). The time spent in each activity is aggregated at hourly scale because lower scales contain too much variability; the hourly scale allows to study variations during the day and results in datasets which can be processed easily by softwares such as R. The agreement between INRAE and GEA Farm Technologies was thus established on the basis of activity data collected per hour; the time(s) spent 'walking' during one hour (the cow is positioned 'in alleys'); the time(s) spent 'resting' during one hour (the cow is in a resting area (typically cubicles)); the time(s) spent 'eating' during one hour (the cow is positioned at the feeding table ); the activity level (unitless), which is the weighted sum of the time spent in each activity (with the following weights: À0.
23 for resting, +0. 16 for in alleys, and +0. 42 for eating,  (Wagner et al. , 2021) ). Due to the weights, the hourly activity level can range from À828 (i. e. À0. 23 Â 3 600) to 1512 (i. e. 0. 42 Â 3 600). To attribute a weight to each activity, a Factorial Analysis of Correspondence was run with observations being the hours of the day and variables being the time spent in each activity  (Veissier et al. , 2017). The weights attributed to each activity to calculate the activity level are those obtained on the first component which expressed at most the variability between hours of the day and is thus likely to reveal the circadian cycle. This analysis was performed on each dataset. Although the datasets were obtained in farms managed under varying conditions (e. g. conventional vs. automatic milking, mixed diet vs.
roughage and concentrate distributed separately), the weights obtained showed good closeness (mean absolute variation of coefficient was 6. 7% ±. 11. 0%). We thus calculated averaged weights across the four datasets, that we think generic enough to be applied on any farm even outside of the present datasets; Finally, for each of the 11 types of events, a Boolean is provided, for the question ''is there this type of event on this hour ?" (i. e. 1 means this type of event was reported for this hour; 0 means that this type of event was not reported for this hour). Note that in fact daily events are reported here at hourly scale. There are 11 types of events: -oestrus [ATOL_0005321]; -calving [ATOL_0000877]; -lameness [AHOL_0003029] includes all types of lameness and issues on claw or leg; -mastitis [AHOL_0003088] includes all types of mastitis; -Event LPS is specific to experimentation as it is for administered lipopolysaccharide (LPS) in the mammary gland on one day to induce inflammation  [AHOL_0003064].
This event is reported in the dataset, but is not considered to influence the animal behaviour; Table  2  gives a summary of the number of events per type and per dataset. A final Boolean sums up the information on whether this hour is considered as normal (i. e. if all the Booleans (without considering the management changes one) are equal to 0, then this hour can be considered as normal, otherwise (at least one event) then the Boolean is set to 1). Dataset 1 -This dataset contains data for 28 cows monitored for 6 months (5 124 cow Â day, i. e. the number of times where data are present for a specific cow on a specific day). Due to some missing data, the dataset contains only 107 665 cow Â hour observations (i. e. the number of times where data are present for a specific cow on a specific hour, 12.
3% of missing data). Table  1  is linked to dataset 1. For each cow, it indicates whether the cow received a non-steroidian anti-inflammatory drug (Ketofen Ò 10%, 3 mg/kg; CEVA Santé Animale, Libourne, France) (NSAID), or a physiological serum (0. 9% NaCl, 3 mL/100 kg, BIOLUZ, St Jean de Luz, France) (CONTROL). Cow 6664 is present in dataset 1, but it did not receive lipopolysaccharide (LPS) because it was lame at the time of the LPS challenge, it is then noted as 'NONE' in the column 'Treatment' of the table.
Experimental design, material and methods All the data were obtained on dairy cows. Raw data from sensors were processed by the CowView system (GEA Farm Technology, Bönen, Germany): position changes were kept only if the position change was higher than 15 cm or if the cow kept this position for more than 60 s). Then using the building map, we extracted raw information on cow positions (i. e. in alleys, in cubicles or in feeding table) at hourly scale. Incomplete hours were not kept. Event data were collected from logbooks or from the sanitary information system (SICPA sanitaire, https://doi. org/10. 15454/ A1UV8Q). The caretakers logged any event as soon as it was observed (oestrus, calving, lameness, clinical mastitis, clinical signs of other diseases, accident-related health problems, disturbances such as handling for vaccination, change of pen, mixing of animals) in a logbook (or the information system), together with the treatment applied to the animal.
Dataset 1 comes from INRAE Herbipôle experimental farm. The cows were milked at fixed times twice a day. The food was delivered in the morning then pushed back close to the feeding gates three times in the afternoon. The cows were administered 25 lg ultra-pure LPS from Escherichia Coli O111 (tlrl-3pelps, InVivogen, United States) in the mammary gland on one day to induce inflammation. The acute inflammation lasted 2 days. In addition, the challenged cows received an intramuscular injection of either saline solution (0. 9% NaCl, 3 mL/100 kg, BIOLUZ, St Jean de Luz, France) (LPS group) or Ketoprofen (Ketofen Ò 10%, 3 mg/kg; CEVA Santé Animale, Libourne, France) (LPS + NSAID group) on average 30 min (minimum-maximum: 26-31 min) after been challenged. Volumes of saline solution injected in LPS group were equivalent to the volumes of Ketoprofen injected.
Cow body temperature was monitored to check that they reacted to LPS. This dataset contains events of oestrus, calving, lameness, mastitis, LPS, other diseases, disturbance and mixing, but no occurrences of accidents or marginal management changes. Acidosis was not measured. Dataset 2 comes from INRAE Herbipôle experimental farm. The cows were milked at fixed times twice a day, and fed twice a day. Half of the cows received a high-starch diet for 1 month to induce subacute ruminal acidosis. Ruminal pH was monitored using a sensor (eCow bolus, Exeter, UK). According to the method proposed by  Villot et al. (2018) , we normalised the ruminal pH values of each cow to take into account inter-individual variability, sensor drift and sensor noise. Then, we considered that a cow was under subacute ruminal acidosis (SARA) when the normalised ruminal pH (NpH) decreased by at least 0.
3 for more than 50 min/d and the daily standard deviation in NpH was above 0. 2 or the daily NpH range was above 0. 8. This dataset contains events of oestrus, lameness, mastitis, acidosis, other diseases, disturbance and marginal management changes, but no occurrences of calving, accidents or mixing. LPS was never administered on these cows. Dataset 3 comes from three commercial farms. On two farms, the cows were milked at fixed times twice or three times a day, and food was delivered twice a day or only once in the morning then pushed back after each milking. The third farm was equipped with an automatic milking system, so the cows had no fixed milking times. Food was delivered in the morning and regularly pushed back by a robot. Contrary to datasets 1, 2 and 4 where oestrus was detected by caretaker observation, in dataset 3, progesterone was assayed in the milk daily, and oestrus was detected when progesterone concentration dropped dramatically for at least three consecutive days (e.
g. from 20 down to 5 ng/mL; Ovucheck Ò Milk test, Biovet Ò , Saint-Hyacinthe QC, Canada). This dataset contains only events of oestrus and no other types of events (LPS was not administered and acidosis was not measured). Dataset 4 comes from a commercial farm with 300 cows monitored for 12 months. The farm was equipped with an automatic milking system. The food was delivered in the morning and regularly pushed back by a robot. This dataset contains events of oestrus, calving, lameness, mastitis, other disease, accidents, disturbance and marginal management changes, but no occurrences of or mixing. LPS was not administered, and acidosis was not measured.
Financial support statement This collaborative work was made possible by funding from the  French government research agency ''Agence Nationale de la Recherche  through the  Investissements d'Avenir" programme  ( 16-IDEX-0001 CAP 20-25 ,  2017 ). Nicolas Wagner PhD grant was provided by  INRAE  ( Phase division ) and  Université Clermont Auvergne  ( SPI doctorate school ). The experiment on LPS injections (in dataset 1) was funded by the  INRAE GISA programme  ( Long-Health Project, coord. Pierre Germon ) and  CEVA Santé Animale. The experiment on subacute acidosis (in dataset 2) was part of the # 311825  EU- PLF  project (Animal and farmcentric approach to precision livestock farming in Europe) co-financed by the  European Commission. Quentin Ruin work was supported by the # 730924   SmartCow  project also co-financed by the  European Commission.
2018) , opening the door to the implementation of future integrated systems. Including fodder production in the assessment of DPS typologies is presented as a necessary step to estimate the specific needs and specificities of each region, apply adapted measures, optimize resource use, and reduce negative environmental impacts. Thus, the main objective of this work is to identify and describe representative DPS typologies and account their connection with selected fodder crop production systems at the European NUTS2 scale. In addition, this work evaluates the limitations of current databases for the characterization of different dairy and fodder crop production typologies across European regions. The proposed typology analysis will facilitate informed decisions when selecting mitigation and sustainability measures through a better understanding of the sector's diversity at the regional scale.
Dairy and fodder production indicators Indicators related to physical characteristics, economic performance, and emissions have been commonly used for the determination of farm typologies  (Gonzalez-Mejia et al. 2018; Bánkuti et al. 2020; Kihoro et al. 2021). Therefore, a framework of indicators was built for the identification of the existing DPS typologies based on their structural, land use, socio-economic, and emission intensity characteristics. The boundaries of the analysis were the farm itself, discarding all possible indicators describing off-farm impacts or characteristics. Consequently, a set of 11 indicators was selected for this analysis (Table  1 ). The results of the Farm Structure Survey (FSS) were used as data source for populating the indicators (EUROSTAT 2013a). Specific data for DPS was obtained by selecting the "FT45-specialist dairying" farm category.
Intensity of total GHG emissions was estimated by means of the 2013 National Inventory Reports (NIR) (European Environmental Agency 2022). The following most representative direct farmlevel GHG emission categories from DPS were assessed: (i) CH 4 emissions from enteric fermentation, (ii) CH 4 emissions from manure management, and (iii) direct N 2 O emissions from manure management. Due to the lack of specific data at the European NUTS2 scale, a three-fold approach was followed for their estimation: (i) total national emissions were determined for each GHG category through the NIR, (ii) the share of livestock units (LU) for "specialist dairying" category in the region over the total national population was used to calculate regional emissions, and (iii) the raw milk production per NUTS2 was used for the estimation of emission intensity per region for each GHG.
Data for the year 2013 was used for populating this indicator. The following equation was used (Eq. 2): E reg ¼ GHG totalÂ POP reg À Á Milk ð2Þ where E reg is the emission intensity per unit of product for each one of the GHG at a NUTS2 scale (kgCO 2eq kg milk -1 ), GHG total are the total national emissions for dairy cattle for each GHG category (kgCO 2eq ), POP reg is the share of livestock units (LU) for the "specialist dairying" category in the region over the total national dairy cattle population, and the Milk is the total regional raw milk production (kg of raw milk). Total regional GHG emissions were obtained by adding all individual emissions of each of the gases estimated (Eq. 3): ∑GHG ¼ E CH4 ent þ E CH4 man þ E N 20 man ð3Þ where ∑GHG is the total GHG emission intensity of milk production (kgCO 2eq kg -1 ), E CH4 ent are the CH 4 emissions from enteric fermentation (kgCO 2eq kg -1 ), E CH4 man are the CH 4 emissions from manure management (kgCO 2eq kg -1 ) and E N20 man are the direct N 2 O emissions from manure management (kgCO 2eq kg -1 ).
Individual GHG emissions for CH 4 and N 2 O were converted to CO 2eq using the Global Warming Potential (GWP100) for the year 2021  (Arias et al. 2021). GWP values of 27. 2 and 273 were used for the CH 4 and N 2 O respectively. In order to estimate the intensity of NH 3 emissions from manure management, national emissions were retrieved from the data reported on the 2013 Informative Inventory Reports (IIR) in the context of the Convention on Long Range Transboundary Air Pollution (CLRTAP) (European Environmental Agency 2022). Share of livestock units (LU) for "specialist dairying" category in the region over the total national dairy cattle population and raw milk production per NUTS2 were used for the estimation of emission intensity per region. Data for the year 2013 was used for populating this indicator.
Permanent crops are associated with the same land for more than 5 years. In this regard, the EU statistics considers fodder roots, brassicas, temporary grasslands, green maize, and legumes as temporary fodder crops, and permanent meadows and grasslands as permanent fodder crops (EUROSTAT 2013b). In order to analyze the different patterns of fodder crop production at the European regional level, a database with the areas occupied by selected fodder crop categories (temporary grasslands, leguminous crops, green maize, and permanent grasslands) for each of the NUTS2 regions was created (Supplementary material 1). The FSS for the year 2013 was used as the data source for populating all the 4 indicators selected (Table  1 ). The ratio of each crop over the total UAA of the region was calculated to determine the predominance of one or another crop category in the region.
Data analysis Identification of existing DPS clusters was carried out following a three-step multivariate statistical approach: (i) principal component analysis (PCA), (ii) K-means clustering, and (iii) cluster description and comparison. For the identification of existing fodder crop production clusters, a two-fold approach was applied: (i) K-means clustering, and (ii) cluster description and comparison. PCA analysis was not applied in this second clustering process due to the lower dimensionality of the data. Similar multivariate approaches have been described as a useful procedures for identifying farm typologies  (Madry et al. 2013; Robert et al. 2017; Sinha et al. 2021). NUTS2 regions with incomplete data were excluded from the DPS typology analysis and subsequently from the fodder crops database.
Then, the data was standardized. Of the 283 regions initially included in the analysis, 32 were excluded (11. 3%) based on the criteria of data completeness. The data was analyzed using the R statistical software (R Core Team 2021). Identified DPS and fodder crop production clusters were spatially represented using geographic information systems by means of the QGIS software (version 3. 16) (QGIS Development Team 2021).
Principal component analysis In order to analyze the existing interrelationships between DPS indicators, and thus reduce the number of variables used in successive steps, a principal component analysis (PCA) analysis was carried out. New linear combinations were calculated from existing indicators, cumulating the variability of the data in a reduced number of principal components (PC). This analysis also enables to assess the contribution of each of the original indicator to the obtained PC. Before performing the PCA, a correlation matrix of all DPS indicators was computed, in order to identify the level of correlation between the indicators in the dataset. Of those indicators that were highly correlated (r < -0. 85 or r > 0. 85), only one of each pair was retained. The "Corrplot" package of R was used to visualize the correlation matrix  (Wei and Simko 2017).
The suitability of the sample size for this statistical procedure was determined using the Kaiser-Meyer-Olkin (KMO) measure. In addition, Bartlett's test of sphericity  (Bartlett 1951 ) was applied to check if the correlation matrix was an identity matrix. Both functions are included in the R "Psych" package (Revelle 2020). The "prcomp" function was used to build the PC. A number of PC whose cumulative variance was over 70%  (Rea and Rea 2016)  of the total variance was retained. Rotation of the eigenvectors of the respective PC was computed with the objective of analyzing the contribution of each indicator to each PC (< -0. 4 and > 0. 4). The "Factoextra" (Kassambara and Mundt 2020) package was used to visualize the results of the analysis.