dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study is part of a Ph. D. financed by the  Region Bourgogne-Franche-Comté  and the  SensoStat Company. The authors would like to thank  Barry Callebaut©  for providing the chocolate samples. Calculations in the Appendix were performed using HPC resources from  PSIUN CCUB (Centre de Calcul de l'Université de Bourgogne, France). The  Institut de Mathématiques de Bourgogne  is supported by the  EIPHI Graduate School  (contract  ANR-17-EURE-0002 ).
Trial conditions and experimental design The eld trial was established in 2014 at the INRAE unit in Estrées-Mons (49° 72' N, 3° 00' E) in northern France, relying on the frame of the Biomass For the Future (BFF) project. It was repeated twice in 2014 and in 2015 using a staggered-start design  [21]. The eld soil type was characterized as deep loam (Ortic luvisol, FAO, classification). The trial received no nitrogen input and weeds were regularly manually removed. Meteorological data were recorded daily at a short distance from the trial (about 1 km) and downloaded from the CLIMATIC database (INRAE AGROCLIM, 2020).
82 genotypes were common to both groups and established twice in 2014 and 2015 to allow the partition of the year effect into age and climate effects. The staggered-start design comprised a total of 1155 plants. Each established group comprised 5 blocks of 90 individuals each (6 rows of 15 plants each). The two parents were grown in each block while the G1 remaining genotypes were randomly spread into four blocks on average and the G2 genotypes into at least three blocks. The two groups of genotypes, which were adjacent in the eld, were separated using 75 plants (15 x 5) and surrounded by 180 border plants, to guarantee similar competition effects. Individuals were randomly planted using the Excel package « BICA » wh,ich was based on VBA programming language and developed for the randomization of incomplete blocks (JC Bastien, personal communication).
In addition, one variable was calculated: the interval between heading and owering (IHF) that was de ned as the difference between the heading and owering dates observed on the rst panicle that emerged and owered. To reduce bias and increase accuracy in the data To guarantee data accuracy, phenotypic observations of the traits were made three times a week, starting with the rst plant that showed panicle emergence. The rst panicle to appear on the plant was marked and used for further determination of the date of anther appearance to facilitate determination and avoid sampling bias. In addition, other traits were observed in the population: the emergence date (ED), the senescence date (SD) that corresponded to the date on which the rst stem cohort had become completely senescent, and the date of the rst winter frost (FD).
Statistical analyses Two different analyses for unbalanced datasets were performed, according to the "age" effect and the "climate" effect models (Fig. 1 ). 159 genotypes were included in each of these analyses, with 82 in common between the G1 and G2 genotype groups. An initial round of analyses of Linear Mixed Models consisted of a full statistical model which took into account block and spatial effects using the "breedR" package implemented in the R software  [28]. It accounted for the environment heterogeneity within each trial among the blocks and diagnosed spatially distributed patterns of remaining residual variations using variograms. All variance components of the progeny were estimated by using the remlf90 function of the package, based on restricted maximum likelihood estimations.
This modeling was carried out by using two subsets of the data (rectangles colored in blue in Fig. 1 ). The year 2018 was considered for G1 and G2 and the age effect was taken into account by 4-year old plants with G1 and 3-year old with G2. Plants of different ages grew in the same climate during a same year (2018), which makes it possible to take into account the age effect in the same climate conditions. The age effect was also analyzed in 2019 in the same manner when the genotypes were one year older. The performance of the full model including the interaction term was compared with the corresponding sub-model with no interaction by using the Akaike information criterion. Based on the AIC, the analysis showed that a model that included interaction effect was always more performant than the sub-model for all traits.
It allowed a more precise comparison of the climate and age interaction effects, thus avoiding biases due to non-common genotypes between the comparisons. Genetic and phenotypic correlations were then assessed for each trait, considering a given age or a given year. Genetic correlations were estimated as described by Howe et al  [32]. To calculate phenotypic correlations, Pearson correlation coe cients were computed using R package "stats" and visualized using R package "corrplot"  [33]. A Principal Component Analysis (PCA) was performed on the weather indicators determined in Table 2 using the R "FactoMineR" package. This PCA was followed by a clustering that was applied to the coordinates of the individuals in the two rst components of the PCA. These individuals corresponded to combinations between the periods and the two years.
The genetic and environmental variance traits were calculated for the owering-time related traits using the two models that were distinguished by the age and climate effects, and by the corresponding interaction effects with the genotype (models 1 and 2). These traits included SPE, AA, and MPE, CPE, and IHF (Fig. 2 ). Considering the climate effect model, an initial dataset was used, which corresponded to G1 tested in 2018 and G2 in 2019 (Fig. 2a ): all these plants presented the same plant age of 4 years old, but grew in different years, i. e. different climatic conditions, which made it possible to test the climate effect on the variance components. A second dataset was also used as it corresponded to the previous one which was restricted to the common genotypes between G1 and G2 (Fig. 2b ).
The results showed that genetic variance most in uenced each owering trait (Fig. 2a, b ). The variance corresponding to the interaction between genotype and climate was substantial, while the residual variance was lower than the two previous ones. Therefore, the broad-sense heritability estimations for all owering-time related traits were moderate and ranged from 0. 55 to 0. 62, whereas the progeny-mean heritability estimations were higher than expected, and ranged from 0. 77 to 0. 82. The second dataset, which was restricted to the common genotypes between G1 and G2, provided similar trends. Therefore, estimates of heritability were relatively similar for balanced designs using these common genotypes compared to the unbalanced dataset (i. e. the rst dataset): broad-sense heritability estimations for all owering-time related traits were moderate and ranged from 0.
53 to 0. 61 while the progeny-mean heritability estimations were high and varied from 0. 79 to 0. 85. Regarding the age effect model, two datasets were observed: G1 (established in 2014) and G2 (established in 2015) that were observed either in 2018 (dataset corresponding to Fig. 2c ) or in 2019 (dataset corresponding to Fig. 2e ). G1 and G2 corresponded to the plant groups at different ages but grew during the same year. In 2018, when the plant age was 4 for G1 and 3 for G2, all owering-time related traits showed large genetic variances but displayed small variances for the interaction of the genotype with age, as well as for the spatial effect (Fig. 2c ). Due to these small environmental component variances, the broad-sense heritability estimations for all owering-time related traits were higher than those observed with the climate effect model and ranged from 0.
In contrast, the spatial and residual variances were rather stable between the climate effect model and the age effect modelled in 2018. To avoid biases in these comparisons, the datasets were restricted to the common genotypes between G1 and G2 (Fig. 2b, d ) and provided very similar results to those obtained from the whole datasets by comparing Fig. 2a  to Fig. 2c. When comparing the climate effect model to the age effect modelled in 2019 (Fig. 2a, e ), the trend was the same, although the total variance of the age effect modelled in 2019 was lower and the increase due to the climate was of a lesser extent: nevertheless, the interaction variance between the genotype and the climate for all owering-time related traits was still higher than the interaction variance between the genotype and the age for all owering-time related traits modelled in 2019, but increased from two to fourfold.
In this case, plants of the same age grew in two different climate conditions, related to each year considered. Figure  2  Estimations of genetic and environmental variances and heritability for owering-time related traits considering age effect (model 1) or climate effect (model 2) models. The owering-time related traits included the start of panicle emergence (SPE), anther appearance (AA), mid-point panicle emergence (MPE), complete panicle emergence (CPE), and the interval between anther appearance and the start of panicle emergence (IHF). 5 datasets were modelled: the climate effect model using the whole dataset (on a) or common genotypes between G1 and G2 (b), the age effect model observed in 2018 using the whole dataset (c) or common genotypes between G1 and G2 (d) and nally, the age effect model observed in 2019 using the whole dataset (e).
In this paper, we compared the crop yields of CA and CT systems under current and future climate conditions based on a new, global database of paired yield observations of NT (including CA and NT without crop rotation and/or residue retention) vs. CT. We compiled the datasets collected from the previous meta-analyses  [5] [6] [7]  , and supplemented them by the latest literature references on this topic and the inclusion of a broader set of climatic variables from external databases, such as precipitation (P)  9  , minimum air temperature (Tmin)  10  , average air temperature (Tave) 9 , maximum air temperature (Tmax)  10  and potential evapotranspiration (E)  11, 12  over the crop growing seasons  13  considered in each experiment. As an indicator of water availability for crops, the precipitation balance (PB) was defined as (P -E) over the growing season  5.
Discussion Compared to previous studies on the productivity of CA  [5] [6] [7] 18  , this is the first time that the probabilities of yield gain with CA systems have been mapped in current and future climate scenarios. Th s, our results offer meaningful and new information for policymakers, agricultural extension services, and farmers. Relying on a global experimental dataset, we were able to identify favorable and unfavorable climatic conditions and geographical regions for the implementation of CA for eight major staple crops under current and future climate conditions. Some of the most promising geographical regions in our analysis had also been identified in previous studies  18  , but we were able to report information on yield gains as probabilities instead of the simple categories of yield increase or decrease.
Data collection The literature search was done in February 2020 using the following keywords 'Conservation agriculture / No-till / No tillage / Zero tillage' & 'Yield / Yield change' in the websites 'ScienceDirect' and 'Science Citation Index (web of science)'. A total of 1012 potentially relevant papers were identified by reviewing the title and abstract, these papers were then screened according to the procedure summarized in S7. In the end, 422 papers left (published between 1983 to 2020), and 4071 paired yield observations from NT (including CA and NT without crop rotation or/and residue retention) and CT were collected from those papers, including 8 major crop species (369 observations for barley, 94 observations for cotton, 1580 observations for maize, 169 observation for rice, 145 observations for sorghum, 552 observations for soybean, 60 observations for sunflower, 1122 observations for wheat) in 51 countries (S8) from the experiment year 1980 to 2017.
We also retrieved from the papers the information of crop type, year and location of the experiments, and agricultural management activities including crop irrigation (crop irrigated vs. rainfed), the application of fertilizers (field fertilized vs. unfertilized), the management of weed and pest (controlled vs. non-controlled), crop rotation (rotated vs. monoculture) and the management of crop residues (retained vs. removed). Additional data were extracted from several external databases, pertaining to crop growing season  13  , soil texture  46  and climate factors such as precipitation (P) 9 , minimum temperature (Tmin)  10  , average temperature (Tave) 9 , maximum temperature (Tmax)  10  and potential evapotranspiration (E)  11, 12  in the growing season  13  in a particular year, and the precipitation balance (PB) was defined as precipitation minus total evapotranspiration, which indicated the water availability in the growing season.
Global projection The fitted random forest model was used to estimate the probability of yield gain from CA (NT with crop rotation and residue retention) for each grid cell located in cropping regions under current (2011-2020) and future (2051-2060) climate scenarios. The monthly-average values of the climatic variables (PB, Tmin/Tave/Tmax) were calculated in each grid cell over the two time periods considered, and then these data were used to calculated the climatic variables during the growing season based on the crop calendar database  13  (assume no change in current and future scenario). All the climatic data in both current and future scenarios were obtained from four climate models: Gfdl-esm2m, Hadgem2-es, Ipsl-cm5a-lr, and Miroc5, and 4 RCP scenarios: RCP 2. 6, RCP 4. 5, RCP 6. 0, and RCP 8.
5. This results in 32 combinations (4 climate models x 4 RCP scenarios x 2 periods). We mainly focused on the Ipsl-cm5a-lr model and RCP 4. 5 scenario in the baseline simulations, because of their importance and similar role in the protocol of ISIMIP2b project  47. However, results from all combinations were analyzed and shown in S6. All the climatic data can be downloaded through the website of Lawrence Livermore National Laboratory 48. We did not change the categorical inputs describing cropping practices between current and future scenarios. The global soil texture was set based on HWSD dataset  46. To estimate the performance of CA vs. CT, we set the agricultural management activities as crop rotated, crop residue retained. As for other agricultural management practices, the field was set to be fertilized, and with the control of pest and weed.
49. When more than 50% of the area in a grid cell were under rainfed conditions for a given crop in the MIRCA2000 database, then this cell was considered as non-irrigated for this crop, and vice versa. See S1 for the details of model settings. The model outputs were mapped at a spatial resolution of 0. 5°  × 0. 5°  based on the MIRCA2000 crop mask database  49. Accumulated area fractions under different levels of yield gain probability and different levels of probability change between current and future scenarios were computed at the global scale and in different climate regions.
This article introduces a dataset of high-resolution colour images of grapevines. It contains 99 images acquired in the vineyard from a cruising tractor. Each image includes the full foliage of a grapevine plant. These images display a diverse range of symptoms caused by the grapevine downy mildew ( Plasmopara viticola ), a major fungal disease. The dataset also includes various confounding factors, i. e. anomalies that are not related to the disease. These anomalies are the natural and common phenomena affecting vineyards such as results of mechanical wounds, necroses, chemical burns or yellowing and discolorations due to nutritional or hydric deficiencies. Images were acquired in-situ on "Le Domaine de la Grande Ferrade" a public experimental facility of INRAE, in the area of Bordeaux. Acquisitions took place at early fruiting stages (BBCH 75-79) corresponding to the main sanitary pressure during growth.
The acquisition device, embedded on a vine tractor, is composed of an industrial colour camera synchronised with powerful flashes. The purpose of this device is to produce a "day for night" effect that mitigates the variation of sunlight. It enables to homogenise images acquired during different weathers and time of the day and to ensure that the foreground (containing foliage) displays appropriate brightness, with minimum shadows while the background is darker. The images of the dataset were an-notated manually by photo-interpretation with a careful review of expertise regarding phytopathology and physiological disorders. The annotation process consists in associating pixels with a class that defines its membership to a type of organ and its physiological state. Pixels from healthy, symptomatic or abnormal grapevine tissues were labelled into seven classes: "Limbus", "Leaf edges", "Berries", "Stems", "Foliar mildew", "Berries mildew" and "Anomalies".
Subject Agricultural engineering Specific subject area Grapevine disease detection by proximal imagery and machine learning Type of data Raw RGB images and associated annotation images. How data were acquired Industrial Basler Ace (acA2500-14gc GigE) 5 Megapixels RGB camera Neewer speedlite 750ii flash Data format Raw Analysed Filtered Annotated ( i. e. groundtruthing) Parameters for data collection The dataset is composed of 99 high-resolution RGB images (2592 × 2048 pixels, around 4px/mm) in JPEG format. Ninety-five (95) of them are accompanied by annotated ( i. e. labelled) image versions that provide membership classes for a significant number of pixels (PNG format). Among the 95 annotations, 23 are complete annotations while the other 72 cover only mildew symptoms.
Description of data collection The image dataset was collected in-field, at day-light during varying sunlight. Images represent the trellising plane of plants and were acquired orthogonally from the middle of the row, at 70 cm above ground and at 50 cm from the target. Annotated images were obtained by photo-interpretation with the GIMP2 software. 95 images were randomly selected in order to represent the variability among the plots, in terms of morphology and physiology. In total, more than 5 × 10 6 pixels are annotated.
Value of the Data • This dataset is a collection of annotated and standardised images of grapevines acquired insitu in vineyards affected by downy mildew and various abiotic stresses. These data are essential for the study of health status, whether it is used to calibrate, validate or compare models. These data were used for to develop an application of real-time and in-situ detection of downy mildew foliar symptoms in  [2]. • The proposed dataset can be exploited by research or technical institutes whose activities are related with the monitoring of phytopathology but also with other precision viticulture applications. • These data can be used to calibrate models / image processing pipelines, compare results on standardised samples or enrich other image databases. In addition, it provides basic guidelines for the acquisition and annotation of images acquired in-situ.
Data Description This dataset contains 99 images of grapevine plants, which are stocked in the main folder. Some of these images have been annotated and these annotations are stored in two folders. The first one is "Annotation_complete" (23 images), where the annotations concerned healthy and contaminated vine organs (leaf, grape and vine shoot). The second folder is "mildew_annotation" (72 images) where only leaf symptoms have been annotated. Here are the different classes and their RGB values (0-255): • 0,0,0: downy mildew foliar symptoms Fig. 1  shows a typical image of the dataset representing a "Merlot Noir" plant at an early fruiting phenological stage (~BBCH 75). The image exhibits various symptoms of downy mildew such as "textbook case" oilspots. Some examples of symptoms are detailed in Fig.
A second PCR was then performed using dual barcoded primers with Illumina adapters (2. 5 μl of 50 × diluted first PCR products template and 0. 1 μM of each primer). The conditions were 98 °C for 60 s, followed by 12 cycles at 98 °C for 10 s, 63 °C for 30 s, 72 °C for 30 s, and then 72 °C for 5 min. PCR products were run on a 1. 5 % agarose gel to confirm successful PCR amplification. In total, 80 samples were then pooled into a single library and subjected to a gel extraction using a QIAquick Gel Extraction Kit (Qiagen). The library was quantified with quantitative PCR (Kapa Library Quantification Kits; Kapa Biosystems, Wilmington, MA, USA) and sequenced on the Illumina MiSeq platform (Illumina Inc. ) using V3 chemistry with 300 cycles on both paired ends. The raw sequences are available at the European Nucleotide Archive under the accession number PRJEB43301.
O R I G I N A L U N E D I T E D M A N U S C R I P T 200 bp removed. All reads were subsequently quality-filtered allowing for a maximum expected error of 0. 5. Operational taxonomic units (OTUs) with 97 % similarity were clustered using UPARSE  (Edgar, 2013)  after dereplication. One sample from control plots in February was discarded due to few reads, and the other samples were randomly rarified to the same number of reads (11,981 reads per sample) for all downstream analyses. The taxonomy assignment was performed with the RDP database  (Cole et al. , 2014)  using the SINTAX algorithm  (Edgar, 2016). All statistical analyses were conducted in R software v. 3. 6. 1  (Team, 2013). Alpha diversity index in terms of Richness, Shannon-Wiener, Inverse Simpson and Pielou's evenness were calculated using the diversity function (in "vegan" package)  (Oksanen, 2013).
Initial exploration of the data showed a very strong block effect on community composition. Therefore, alpha diversity was analyzed using linear mixed effects models (lme function in "nlme" package)  (Pinheiro et al. , 2017)  with treatments and months as fixed factors, where block was a random factor. The analysis was done separately during the rain-exclusion period and after roof removal, respectively. The normality of residuals and homoscedasticity of the data were evaluated using the qqnorm and leveneTest function in "nlme"  (Pinheiro et al. , 2017)  and "car" package  (Fox et al. , 2007). PERMANOVA was performed using the adonis function in "vegan" package on the data in different periods to evaluate the rain-exclusion, month and block effect, based on Bray-Curtis distances. Because block had a significant impact on microbial communities (Figure  S1 ), we then further used partial distance-based redundancy analysis in the "vegan" package when evaluating the influence of prolonged drought on community composition, where block effect was partialled out.
The statistical significance of axes and explanatory variables of partial dbRDA were tested with the anova function. For a visual representation of the treatment effect, the partial dbRDA axis 1 and O R I G I N A L U N E D I T E D M A N U S C R I P T axis 2 scores were used as proxies for community composition to evaluate the microbial community dissimilarity between rain-exclusion and control plots using Euclidean distance. Additionally, indicator species analysis was performed to identify potential indicator OTUs for rain-exclusion using multipatt function (IndVal metric) in the "indicspecies" package (De  Caceres et al. , 2016)  where indicator value index was calculated by the 9,999-permutation test (De  Cáceres et al. , 2010). Only OTUs with a p value < 0. 001 were selected as indicators.