dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The present study is a retrospective simulation in which we analysed animal performance of young fattening crossbred Holstein bulls in order to investigate whether adopting a multiphase diet strategy could improve both the economic and environmental impact of cattle production, similar to what has been demonstrated in pig production  (Pomar et al. , 2014; Andretta et al. , 2016). This retrospective study included data from two in vivo trials involving crossbred Holstein bulls fed balanced diets (straw and concentrates A or B). Individual trajectories of NE and MP requirements were modelled, and the overall relationship between these requirements and the BW was established. By undertaking this comprehensive modeling, we were able to estimate the difference between the requirements (NE and MP) and the observed intake,  1 Minerals and vitamin mix: 5% P, 25% Ca, 8% Mg, 0.
Acknowledgements The author  M. Benaouda  would thank the  ''region Bourgogne-Franche-Comté"  for the financial support. The authors  M. Devant ,  S. Marti , and  L. Llonch  belong to the  Consolidated Research Group of Sustainable Animal Husbandry , funded by the  AGAUR (Generalitat de Catalunya ; ref. 2021 SGR 01552 ). IRTA  also thanks the support of the  Generalitat de Catalunya  through the  CERCA Program.
Data Analysis The documented wild plant ingredients were organized in MS Excel. We utilized emic food categories as much as possible. Raw snacks referred to (parts of) plants eaten on the spot without any processing. According to this definition, salads are not considered raw snacks, as they are eaten after some preparation and during meals. The data were categorized into four datasets: one containing all identified taxa, the second one comprised of the identified most frequently quoted taxa (reported by more than 40% of the informants), the third one containing wild food plant taxa, and the fourth one comprised medicinal and veterinary taxa. These four datasets, which were generated for each of the three considered groups, were compared through proportional Venn diagrams and the Jaccard Similarity Index (for each pair of datasets) following the application designed for the use of this ecological index in the ethnobotanical domain  [40].
Nevertheless, the turkeys were occasionally evaluated dorsally at the exit of the WB and during bleeding rather than ventrally (in SH-2, SH-3 and SH-5) or before but not during bleeding (SH-6) due to variability in the design and layout of the SHs. When it occurred, the assessment of breathing by direct observation of abdominal muscles around the cloaca was impaired. Data were recorded as binomial as 0 if the outcome of unconsciousness was observed and 1 when an outcome of consciousness was observed. The presence of at least one outcome of consciousness indicates the risk of the bird being conscious or regaining consciousness after WBS and therefore of an ineffective stunning or a long stun-to-stick interval (i. e. , time from stunning to the start of bleeding). Bird shows arched and stiff neck (i.
Inter-Observer Repeatability of Animal-Based Indicators The overall level of agreement between observers for each ABI were determined and expressed by two values: the crude proportion of agreement (PoA) and the Fleiss' kappa (κ). Both were computed using the "irr" package of R software  [19]. The PoA can be misleading, as it does not take into account the scores that the observers assign due to chance. This flaw is solved by computing the κ since it expresses the extent to which the observed concordance between observers exceeds the proportions that would be expected if all observers scored completely at random. κ ranges from -1 to +1, with -1 representing perfect disagreement between observers, 0 indicating the amount of agreement one can expect given a random chance and 1 representing perfect agreement between observers  [20].
Correlation among Animal-Based Indicators The chi-squared % defective test was used to determine if there were statistical differences (divergence) among observers between the expected and the observed frequencies of every outcome of consciousness of the evaluated indicators. If one observer differed statistically from the others at evaluating the ABIs, the mean of the proportion of the closest evaluations or the in between value when scoring were not consistent among them were recorded. Since data did not follow normal distribution, associations between observed ABIs of consciousness was performed using Spearman's rank correlation test. Correlation results were presented as heat map. Proportions among combinations of ABIs were performed as a Venn diagram considering all turkeys assessed in the present study using the "eulerr" package  [22].
Breathing Birds with presence of breathing during bleeding were observed in all SHs assessed except in SH-1. There was no divergence on scoring presence of breathing among observers in any SH assessed (p > 0. 05) and highest prevalence occurred in SH-6 (8. 8%), followed by SH-4 (6. 7%), SH-2 (5. 0%), SH-5 (1. 9%), SH-7 (1. 1%), SH-3 (0. 4%) and SH-8 (0. 4%) as shown in Table  6. The PoA among observers was above 95% in all SHs assessed (Table  7 ). κ could not be computed in SH-1 as no bird assessed breathed during bleeding, but this outcome of consciousness offered κ interpreted from "fair to good" to "excellent" (Table  7 ). Considering the data from all SH, the mean prevalence was 2. 3% and the detection of breathing did not differ statistically among evaluators (p > 0. 05; Table  6 ). Moreover, the average PoA was 98.
More specifically, the data paper displays all the data used to obtain environmental impacts (calculated by using life cycle assessment (LCA)) of the production of Feta, from cradle to consumer. It includes the -sheep and goat -milk productions, the transformation into cheese, the packaging and the transport to wholesalers, then stores and then consumers. The raw data have mostly been obtained through interviews and surveys with the cheese and milk producers and complemented by literature. Data were used to build a life cycle inventory (LCI). For the milk production, the LCI was modeled using MEANS InOut software. For the whole LCI, Agribalyse 3. 0 and Ecoinvent 3. 8 were used as background databases, with modifications to reflect Greek context. The dataset also compiles the life cycle impact assessment (LCIA).
The characterization method used is method EF3. 0. This dataset participates in filling two gaps: (1) providing data to represent the variability between Feta cheese production systems and (2) providing data linking impacts of farm, transformation, retail and transport in a value chain perspective. This is done by (1) enlarging the perimeter when most studies found in literature focus on one stage (e. g. the production of milk) and (  2 ) applying LCA to data specific to a regional production (Stymfalia in Greece.
Value of the Data • The article presents a unique set of LCI and LCIA data for on the production of Feta in Stymfalia region, Greece. • Data from different farms are used and variability between farms is presented. • The LCI for sheep and goat milks were calculated using MEANS InOut software. • The article can be used to identify hotspots for Feta production as well as for sheep and goat milk productions on a regional scale.
Objective Literature shows a high variability in environmental impacts between existing systems, as for example between cheeses  [1]  and the necessity of more case studies to validate statements  [2]. In this context, this data paper provides some data related to Feta production in Greece, based on 8 farms of a cooperative (7 sheep livestock and one goat livestock). This work was conducted in the framework of EU FAIRCHAIN project Feta cheese is PDO (Protected Designation of Origin), composed solely of goat's milk and sheep's milk under specific percentages (at least 70% sheep). More specifically, the data paper displays all the data used to obtain environmental impacts (calculated by using life cycle assessment (LCA)) of the production of Feta, from cradle to consumer. This dataset has the objective to participate in filling two gaps: (1) providing data to represent the variability between Feta cheese production systems and (2) providing data linking impacts of farm, transformation, retail and transport in a value chain perspective.
Data Description The dataset associated with this article contains eight files with information on raw data, inventory data and the LCIA: 1. (FAIRCHAIN)CS-Gre_raw_data_milk_production: it describes the raw data collected with the dairy to build the inventories and completed by literature for the milk productions. Type of data (collected, literature, calculated) is specified as well as hypothesis. 2. (FAIRCHAIN)CS-GRE_raw_data_other_stages: it describes the raw data collected with the dairy to build the inventories and completed by literature for other stages (processing, packaging, distribution). Type of data (collected, literature, calculated) is specified as well as hypothesis. 3. (FAIRCHAIN)CS-Gre_LCI_milk_production: it describes the life cycle inventories calculated with MEANS InOut and adapted with Simapro.
The values of input/output flows are specified as well as the database and processes used to model them. 4. (FAIRCHAIN)CS-Gre_LCI_one_year_production: it describes the life cycle inventory for oneyear of production of the cheese factory. The values of input/output flows are specified as well as the database and processes used to model them. 5. (FAIRCHAIN)CS-Gre_LCI_packaging: it gives details on the simplified inventories used to model the packaging solutions. 6. (FAIRCHAIN)CS-Gre_LCIA_milk_production_contributions: it presents LCIA at farm for the production of all milks involved as well as for the weighted average for sheep milk. 7. (FAIRCHAIN)CS-Gre_LCIA_one_year_value_chain: it presents LCIA for the whole value chain for one-year production. 8. Bibliography: it details the additional bibliographical sources cited in the different data files.
Goal and scope In the framework of FAIRCHAIN, the LCA is a first step analysis towards the implementation of innovations. The objectives are to collect data to build a LCI that is representative of the current situation. The calculation of the environmental impacts (LCIA) is carried out with the main objective to constitute the frame of reference that will allow to evaluate the environmental impacts of the innovation in the future. The innovation should only have a marginal effect on markets. Following these objectives, an attributional approach is chosen.
Functional unit The functional units are defined as "the annual production of Feta at the cheese factory". However, LCIA is also calculated and provided at farms level with functional units defined as "1 kg of milk produced in farm [X]". The Greek CS concerns sheep and goat milk cheeses produced in Stymfalia region, in Peloponnese. Specific data were provided by the dairy cooperative for 7 sheep farms and 1 goat farm. These data were used to model and calculate LCI for each farm using MEANS-InOut software ( https://www6. inrae. fr/means ). The Agribalyse 2022 model set was selected to calculate the emissions. The models associated are named in dataset_models_MEANS. The LCI generated were exported in Simapro software then adapted to the context. Firstly, the electricity mix was modified from French (data set by default in MEANS-InOut) to Greek.
Transformation. The dataset at the cheese factory represents one-year production, involving more milk than produced by the 8 farms for which specific data were available ((FAIRCHAIN)CS-Gre_LCI_one_year_production). Following, the judgement of the dairy representative about the representativity of the sample, the goat's milk is taken as proxy for goat's milk and an average sheep milk has been considered as input for the cheese factory, based on a weighted average of the 7 sheep's milks. I n v entory 1 kg a v erage sheep milk = i I n v entory 1 kg of sheep milk f rom f arm i * Q i Q T Where Q i is the quantity of milk produced at farm i and Q T is the total quantity of sheep milk produced in the 7 studied farms. The collection and transportation of the milk from farms to the cheese factory is modelled in the same way, based on the weighted average distance from farms to the cheese factory.
Packaging, distribution and consumption. As this part of the inventory was not part of the perimeter under FAIRCHAIN project, it should be only considered as a first approach aiming at representing being included in the whole value chain. However, it provides valuable inputs from the cheese factory and has been judged relevant to be included in the dataset. Four types of packaging and their respective shares in the whole cheese packaging were modelled based on specific data provided by the cheese factory: two tin containers, one wood barrel and one plastic container((FAIRCHAIN)CS-Gre_LCI_packaging. tab). The repartition between those packaging is given in (FAIRCHAIN)CS-Gre_LCI_one_year_production. In the absence of specific data, the packages were considered managed following generic mixes available in Ecoinvent database (FAIRCHAIN)CS-Gre_LCI_packaging.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  1010 0 0723. This document reflects only the authors' view. The European Commission is not responsible for any use that may be made of the information it contains. The authors thank the members and colleagues of  FAIRCHAIN  for great discussions. The authors also thank the members of AgorACV, the  French regional Western network on LCA. The authors are most grateful to  MEANS-INRAE-CIRAD  platform for its contribution in achieving the here-presented work. This research was funded, in whole or in part, by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 1010 0 0723. A CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission, in accordance with the grant's open access conditions.
Data Analysis The experiment design consisted of two repetitions of the enzymatic hydrolysis treatment, with laboratory tests in duplicate. The MIR spectra distribution was analyzed according to the principal component analysis (PCA)  [18]. PCA was calculated with Origin 9. 0, with the MIR specters. In simplified terms, single value decomposition was used in order to transform the wavenumber bands from the spectral data into two main variables (principal component 1 and 2), that contain the variance of the whole dataset.
In simple terms, the PCA is a dimensionality reduction technique that allows the visualization and analysis of multidimensional data (such as in infrared spectral data, in which each wavenumber is a variable or dimension) in a planar or bidimensional graph. The first 2 PCs (shown in both axis of Figure  3 ) are new variables calculated from the whole spectra that contain dataset information and correlations between samples, as shown in the explained variance of PC1 (64. 00%) and PC2 (19. 00%). Therefore, the samples' proximity, shown in the PCA figure, demonstrates the similarity between samples. Figure  3  allows us to consider that among the evaluated modifications, the sample of sour cassava starch was closer to etherified starches (0. 40%), followed by oxidized starches (0. 30%). The sample of acid-modified starch that is an important part of the modification process explained only 0.
07% of the data. However, what most influenced the distribution of explanation percentages was the high acetylation cassava starch sample, with 2% explanation. More discussion about the relationship between samples shown in the PCA analysis was included in the Discussion section. In simple terms, the PCA is a dimensionality reduction technique that allows the visualization and analysis of multidimensional data (such as in infrared spectral data, in which each wavenumber is a variable or dimension) in a planar or bidimensional graph. The first 2 PCs (shown in both axis of Figure  3 ) are new variables calculated from the whole spectra that contain dataset information and correlations between samples, as shown in the explained variance of PC1 (64. 00%) and PC2 (19. 00%). Therefore, the samples' proximity, shown in the PCA figure, demonstrates the similarity between samples.
In planta Grapevine plants of cv. Tempranillo plants were grown in greenhouse (Neiker-Tecnalia, Arkaute). Plants were treated three times with water, copper or R. okamurae aqueous extract (RU2), and after, plants were infected with P. viticola by foliar application. Leaves samples were collected prior and after fungal inoculation to examine gene expression (using the NeoViGen96 chip), hormone and polyphenols induction, changes on the photosynthetic pigments, oxidative enzymes activity and soil microbiological characterization. In addition, disease incidence/severity was monitored for 2 weeks, visually and by microscopy (Fig. 5 ). The processing of these samples is ongoing, but the data obtained so far validated our previous results regarding the biostimulant capacity of RU2. A significant induction of stilbenes (trans-piceid, trans-resveratrol, viniferin and -viniferin) was observed in leaves after receiving 3 applications of RU2 (prior to the pathogen inoculation).
Statistical analysis The statistical analyses were accomplished using SPSS Software (version21). The normality of distribution was verified with Shapiro-Wilk test. Two-way anova was applied in order to test significant differences among treatments (p<0. 05). Moreover, principal component analysis (PCA) was performed by using ADE4TkGUI  (Thioulouse and Dray, 2007). The results p-values related to the two-way ANOVA results are represented in supplementary material table  S1.
21, 25 [28] [29]  Despite its potential, disparity has seldom been applied to insects. Insect mouthparts distribution revealed that the last three major crises (Permian-Triassic, Triassic-Jurassic, and Cretaceous-Cenozoic) had no depletion effect on disparity. 30, 31 Instead, the number of mouthparts classes steadily increased from the Devonian onwards. However, even though mouthparts bear crucial information on insect feeding strategies, their documentation in the pre-amber fossil record rests on rare, well-exposed fossils. Compared to mouthparts, wings are far more abundant as fossils and easier to document. Moreover, they are a privileged item to compute landmark-based morphometrics analyses  [32] [33] [34] [35] [36]  and to elaborate a dataset exploitable with a focus on disparity. Although a general pattern of insect wing venation homologies is widely accepted,  37  consensus is still lacking on how it applies on some insect groups.
Data and code availability d Landmark dataset and vector-line drawings have been deposited at Mendeley and are publicly available as of the date of publication. The DOI is listed in the key resources table. d All original code has been deposited at Mendeley and is publicly available as of the date of publication. The DOI is listed in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Species selection A list of fossil Odonata species, with their stratigraphic record, was extracted from the Paleobiology Database  44  from the Cisuralian (early Permian) to the Upper Jurassic. It was double-checked and complemented if necessary. A pattern of vein homologies that could be consistently applied to the entire selection of Odonata was then adopted (Figure  S4 ). Because this pattern can only be applied on (sub-)complete, undeformed wings, only a subset of the know specimens could be used. The list of these specimens is provided in Data S1. As for the Permian, 16 Cisuralian (lower Permian) species, over 61 known, were considered; 9 over 23 for the Guadalupian (middle Permian). As for the Lopingian (upper Permian), the wing venation pattern could be applied to a single species only (over the two known); this epoch was therefore ignored, as computation of disparity metrics requires several observations, yet the corresponding species contributed the total morphospace.
Specimen retro-deformation and reconstruction To complement our dataset, and in particular for the Triassic, several specimens from the Madygen locality were retro-deformed. This approach best addresses pitfalls inherent in this material. The options are to either (i) remove deformed specimens from the dataset, (ii) include them uncorrected, or (iii) include them once retro-deformed. The pitfall of option (i) is that shapes expected to occupy critical areas of the morphospace will not be considered. This can have critical implications when comparing time bins. Conversely, adopting the option (ii) could introduce undue gain in morphospace volume by adding genuinely false shapes. As for option (iii), the potential pitfall is that the distinctiveness of particular taxa at a particular time bin could be under-estimated (if shapes from another time bin were used in the retro-deformation process).
Disparity metrics Disparity is the quantification of the diversity of morphologies. 16, 19 The multidimensional space derived from a Principal Component Analysis is assumed to represent an empirical morphospace of wing shape diversity. Species coordinates on PC axes are used as raw variables to compute disparity measures. 7] [18]  The size approximates the amount of space occupied, higher values indicating the occurrence of more extreme shape combinations. Dispersion metrics tend to become higher when observations are sparsely distributed, and lower when observations are densely distributed. The position metrics informs on changes in the location of the occupied areas within the morphospace. Size of morphospace occupation was estimated by computing the sum of ranges of the PC scores; dispersion as the sum of variances of the PC scores; and position as the sum of distances between centroids of data subsets.
ACKNOWLEDGMENTS We are very grateful to three anonymous reviewers for their constructive comments. We thank Roy Beckmeyer, Dmitry V. Vasilenko, and Anastasia S. Felker for providing photographs of fossil material, Aimie Doriath-Do ¨hler for producing vector drawings for some of the Jurassic taxa, and Anastasia S. Felker and Vladimir Silantiev for providing stratigraphic data. This work is part of the first author's PhD project funded by 'Sorbonne Universite' and of a post-doctoral grant to the DIM-MAP REFORM project.
Attempts to reconstruct past land cover have been made to model land-use and landcover change (LULCC) over Holocene time scales (e. g. , HYDE 3. 2  [19]  and KK10  [20] ). Such LULCC scenarios have been used in combination with dynamic vegetation models to understand interactions between different components of the Earth system in the past (Earth system modeling (ESM),  [21] ). However, there can be considerable disagreement between different LULCC scenarios, and this highlights the need to use independent and empirical datasets of land use and land cover  [22]. Pollen archives remain the best empirical data to address differences between LULCC scenarios, as they provide a direct proxy for vegetation cover  [22] [23] [24] [25]. Efforts to improve our understanding of past vegetation using pollen have led to the development of models that correct the non-linear pollen-vegetation relationship and can compensate for plant taxon-specific differences in pollen production, dispersal, and deposition  [26, 27].