dataesr/scientific-paragraphs-categorization huggingface dataset:
The panoramic effect will be much larger when images were acquired too close to the canopy. Images were therefore rescaled to keep more similar resolution at the head level. Bilinear interpolation was used to up-or down-sample the original images. The scaling factor applied to each subdataset is displayed in Table  3. Most deep-learning algorithms are running over individual squared patches. The size of the patches should be adjusted to limit the probability to get heads crossing the edges of the patches. Images were therefore split into 1024 x 1024 squared patches containing roughly 20 to 60 heads with only few heads crossing the edges. The number of patches per original images was varying between 1 to 6 (Table  3 ). These squared patches will be termed "images" in the following of this study.
Labelling A web-based labelling platform was set up to handle the evaluation and labelling of the shared sub-datasets using the coco annotator (https://github. com/jsbroks/coco-annotator; [26] ). The platform hosts all the tools required to label objects. In addition, it also grants simultaneous access to different users, thus allowing contributions from all institutions. Wheat heads were interactively labelled by drawing bounding boxes that contained all the pixels of the head. Labelling is difficult if heads are not clearly visible, i. e. if they are masked by leaves or other heads. We did not label partly hidden heads, unless at least one spikelet was visible. This was mostly the case for images acquired at an early stage when heads were not fully emerged. Overlap among heads was more frequently observed when the images were acquired using a camera with a wide field of view as in UTokyo_2 or ETHZ_1.
These overlaps occurred mainly towards the borders of the images with a more oblique view angle. When the bounding box was too large to include the awns, it was restricted to the head only (Figure  2  (a)). Further, heads cropped at the image edges were labelled only if more than 30% of their basal part was visible (Figure  2 , (e)). Several institutions already labelled their sub-datasets. For the datasets not labelled, we used a "weakly supervised deep learning framework"  [27]  to label images efficiently for these sub-datasets. A YoloV3 model  [28]  was trained over UTokyo_1 and Arvalis_1 sub-datasets and then applied to the un-labelled sub-datasets. Boxes with an associated confidence score larger than 0. 5 were retained and proposed to the user for correction. This semi-automatic active learning increased by a factor of four the throughput of the labelling process as compared to a fully interactive process.
Description of the dataset 3. 1. General statistics The GWHD dataset represents 4,698 squared patches extracted from the 2219 original high resolution RGB images acquired across the 11 sub-datasets (Table  3 ). It represents 188,445 labelled heads which makes on the average 40 heads per image in good agreement with the 20 to 60 targeted heads per image. However, the distribution between and within sub-datasets is relatively broad (Figure  3a ). About 100 images contain no heads while few ones contain more than 100 heads with a maximum of 120 heads. Multiple peaks corresponding to the several sub-datasets (Figure  3b ) can be observed corresponding mainly to variations in head density that depends on genotypes and environmental conditions. The size of the bounding boxes around the heads shows a slightly skewed gaussian distribution with a median typical dimension of 77 pixels (Figure  3b ).
The typical dimension is computed as the square root of the area. It corresponds well to the targeted scale, i. e. 1. 5 cm x 10 cm approximate head size with an average resolution close to 0. 4 mm/pixel which represents a typical dimension of 97 pixels per head, although the simple horizontal area projected does not corresponds exactly to the viewing geometry of the RGB cameras. The harmonization of the scale across sub-datasets can be further confirmed by the visual impression as illustrated in Figure  4. *the bounding box size is defined as the square root of the bounding box area in pixel.
Diversity of sampled genotypes, environments, and developmental stages The diversity of acquisition conditions sampled by the GWHD dataset is well illustrated by Figure  4 : illumination conditions are very variable, with a wide range of heads and background appearance. Further, we observe a variability of the ear orientation and view directions, with almost nadir directions up to oblique viewing as in the case of ETHZ_1 (Figure  4 ). A selection of bounding boxes extracted from the several subdatasets (Figure  5 ) shows variation of bounding-box area and aspect ratio, depending on the head orientation and viewing direction. A large diversity of head appearance is observed, with variation in the presence of awns and awn size, head color, as well as blurriness and few heads cut when the bounding box was crossing the edge of the image (Figure  5 ).
Comparison to other datasets Several open-source datasets have already been proposed in the plant phenotyping community. The CVPPP datasets  [29]  have been widely used for rosette leaf counting and segmentation. The KOMATSUNA dataset also includes segmented rosette leaves, but in time-lapse videos  [30]. The Nottingham ACID Wheat dataset includes wheat head images captured in a controlled environment with individual spikelets annotated  [31]. However, comparatively few open-source datasets include images from outdoor field contexts, which are critical for the practical application of phenotyping in crop breeding and farming. A few datasets have been published for weed classification  [32][33]. The GrassClover Dataset includes images of forage fields and semantic segmentation labels for grass, clover and weed vegetation types  [34].
Datasets for counting sorghum  [27] [35] and wheat heads  [36]  have also been published with dot annotations. In terms of phenotyping datasets for object detection, our GWHD dataset is currently the largest open labelled dataset freely available for object detection for field plant phenotyping. MinneApple  [37]  is the only comparable dataset in terms of diversity in the field of phenotyping but proposes fewer images and less diversity in terms of location. Other datasets like MS COCO  [38]  or Open Images V4  [39]  are much larger and sample many more object types for a wide range of other applications. The corresponding images usually contain fewer objects, typically less than ten per image (Figure  6 ). However, some specific datasets like PUCPR  [40] , CARPK  [41] , SKU-110K  [42]  aim at solving the problem of detecting objects (e.
g. , cars, products) in dense contexts. They have a much higher object density than GWHD Dataset, but with fewer images for PUCPR and CARPK, while SKU-110 contains more images than our GWHD Dataset (Figure  6 ). The high occurrence of overlapping and occluded objects is unique to the GWHD Dataset. This makes labelling and detection more challenging, especially compared to SKU-110K which does not seem to present any occlusion. Finally, wheat heads are complex objects with a wide variability of appearance as demonstrated previously, surrounded by a very variable background which would constitute a more difficult problem than detecting cars or densely packed products on store shelves.
Split between training and testing datasets In machine learning studies, it is common to randomly split a dataset into training and testing samples. However, for the GWHD competition, we specifically aim to test the performance of the method for unseen genotypes, environments, and observational conditions. Therefore, we grouped all images from Europe and North America as the training dataset, which covers enough diversity to train a generic wheat head detection model. This training dataset corresponds to 3422 images representing 73% of the whole GWHD dataset images. To evaluate model performance, including robustness against unseen images, the test data set includes all the images from Australia, Japan, and China, representing 1276 images.
Evaluation Metrics The choice of bounding boxes as labels allows leveraging the dataset for an object detection dataset. The mean average precision computed from the true and false positives is usually used to quantify detection performances. A true positive corresponds to a predicted bounding box with an intersection over union (IoU) higher than 0. 5 with the closer labelled bounding box. A false positive corresponds to a predicted bounding box with IoU<0. 5 with the closer labelled bounding box. The mean average precision noted "mAP@0. 5" is the metrics considered to evaluate the performances of the several detection methods tested within the wheat head detection challenge organized in 2020. The head detection is mandatory for characterizing the heads in terms of size, inclination, color, or sanitary state.
Baseline method To set a baseline detection accuracy for the GWHD dataset, we provide results based on a standard object detection method. We trained a two-stage detector, Faster-RCNN, with a ResNet34 as the backbone. Due to memory constraints, the input size was set to 512x512 pixels. For each image in the training dataset, we randomly sampled 10 patches of size 512 x 512 pixels. We predicted on a set of overlapping patches of size 512 x 512 pixels regularly extracted from the test images of size 1024 x 1024 pixels and then merged the results. After 20 epochs, representing 26638 steps the model yielded a mAP@0. 5 of 0. 68 and a mean RMSE of 13. 75 wheat heads per image which corresponds to The relatively poor performance of a standard object detection network on the GWHD dataset, provides an opportunity for substantial future improvement with novel methods.
Further work should investigate this important question. Finally, our experience suggests that using a sub-millimetric resolution at the top of the canopy is required for efficient head detection. However, the optimal resolution is still to be defined. Previous work  [8]  was recommending 0. 3 mm GSD, while the GWHD dataset includes GSD ranging from 0. 28 to 0. 55 mm. Further work should investigate this important aspect, particularly regarding the possibility to use UAV observations for head density estimation in large phenotyping platforms.
Minimum information associated to the sub-datasets and FAIR principles The FAIR principles (Findable, Accessible, Interoperable and Reusable  [43]  should be applied to the images that populate the GWHD dataset. To verify the FAIR principles, a minimum set of metadata should be associated to each image as proposed in  [44]. The lack of metadata was an issue for precise data harmonization and is limiting for further interpretation  [45]  and possible meta-analysis. We therefore recommend attaching a minimum set of information to each image and sub-dataset. In our case, a sub-dataset generally corresponds to an image acquisition session, i. e. a series of images acquired over the same experiment on the same date and with the same camera. the experiment metadata are all the metadata related to agronomic characteristics of the session, the acquisition metadata are all the metadata related to the vector and the sensor.
Need for GWHD expansion The innovative and unique aspect of the GWHD dataset is the significant number of contributors from around the world, resulting in a large diversity across images. However the diversity within each continent and environmental conditions is not well covered today: more than 68% of the GWHD dataset come from Europe and 43% from France. Further, some regions are currently missing, including Africa, Latin America, the Middle East. An expansion of the GWHD dataset is therefore highly desirable to get a more comprehensive dataset. We therefore invite potential contributors to complement the GWHD dataset with their sub-datasets. The proposed guidelines for image acquisition and the associated metadata should be followed to keep a high level of consistency and respect the FAIR principles.
Conclusion Object detection methods for identifying wheat heads in images is useful for head population density estimation. However, we showed that an accurate knowledge of the reference level to compute the image footprint area was mandatory. More work is needed to solve this important question. However, head detection may be also considered as a first step in the search for additional traits characterizing the heads such as the spatial distribution between rows, the presence of awns, size, inclination, color, grain filling stage, or sanitary state. These traits may be useful for the breeders, but some of these traits including the sanitary state may also serve the needs of farmers to better manage their crops. The Global Wheat Head Detection Dataset is an extensive and diverse dataset for wheat head detection and localization.
It is designed to develop, and benchmark head detection methods proposed by the community. It represents a large collaborative international effort. An important aspect gained through the compilation of diverse sub-datasets was to propose guidelines for image acquisition, minimum metadata to respect the FAIR principles, and guidelines and tools for labelling the heads. These guidelines will help practitioners to get more consistent sub-datasets that could ultimately contribute to expand the GWHD dataset. The GWHD dataset collaborative effort is expected to represent a major contribution for advancing high throughput detection of wheat heads by letting many teams to compete for finding the more accurate and robust method. The solutions proposed in the competition will be made open-source and shared with the plant phenotyping community to give access to a larger number to state-of-the-art methods.
Acknowledgments The French team received support from  ANRT  for the  CIFRE grant of Etienne David , co-funded by  Arvalis. The study was partly supported by several projects including  ANR  PHENOME,  ANR BREEDWHEAT, CASDAR LITERAL and FSOV "Plastix". Many  thanks to the people who annotated the French datasets, including  Frederic Venault ,  Xiuliang Jin ,  Mario Serouard ,  Ilias Sarbout ,  Carole Gigot ,  Eloïse Issert ,  Elise Lepage. The Japanese team received support from  JST CREST  Grant Number  JPMJCR16O3 ,  JPMJCR16O2 ,  JPMJCR1512  and  MAFF  Smart-breeding system for Innovative Agriculture ( BAC1003 ),  Japan. Many thanks to the people who annotated the Japanese dataset, including  Kozue Wada ,  Masanori Ishii ,  Ryuuichi kanzaki ,  Sayoko Ishibashi ,  Sumiko Kaneko. The Canadian team received funding from the  Plant Phenotyping and Imaging Research Center  through a grant from the  Canada First Research Excellence Fund.
Many thanks to  Steve Shirtliffe ,  Scott Noble ,  Tyrone Keep ,  Keith Halco , and  Craig Gavelin  for managing the field site and collecting images. Rothamsted Research received support from the  Biotechnology and Biological Sciences Research Council (BBSRC)  of the United Kingdom as part of the  Designing Future Wheat  ( BB/P016855/1 ) project. We are also thankful to  Prof Malcolm J Hawkesford  who leads the  DFW  project and  Dr Nicolas Virlet  for conducting the experiment at  Rothamsted Research. The Gatton, Australia dataset was collected on a field trial conducted by  CSIRO  and  UQ , with trial conduct and measurements partly funded by the  Grains Research and Development Corporation (GRDC)  in project  CSP00179. A new GRDC project involves several of the authors and supports their contribution to this paper.
The dataset collected in China was supported by the  Program for High-Level Talents Introduction of Nanjing Agricultural University  ( 440-804005 ). Many thanks to  Jie Zhou  and many volunteers from  Nanjing Agricultural University  to accomplish the annotation. The data set collection at ETHZ was supported by  Prof. Achim Walter  who leads the  Crop Science group. Many thanks to  Kevin Keller  for the initial preparation of the ETHZ dataset, and  Lara Wyser ,  Ramon Winterberg ,  Damian Käch ,  Marius Hodel  and  Mario Serouard  ( INRAE ) for annotation of the ETHZ dataset and to  Brigita Herzog  and  Hansueli Zellweger  for crop husbandry.
In the case of Arvalis_1, Arvalis_2, UQ_1, NAU_1, the experiments were designed to compare irrigated and water stressed modalities. Sub-dataset name Institution Country Lat (°) Long (°) Year Nb. of dates Targeted stages Row spacing (cm) Sowing density (seeds•m-2) Nb. of genotypes UTokyo_1 NARO & UTokyo Japan 36. 0N 140. 0E 2018 3 Post-flowering 15 186 66 UTokyo_2 NARO & UTokyo Japan 42. 8N 143. 0 2016 6 Flowering* 12. 5 200 1 Arvalis_1 Arvalis France 43. 7N 5. 8E 2017 3 Post-flowering -Ripening 17. 5 300 20 Arvalis_2 Arvalis France 43. 7N 5. 8E 2019 1 Post-flowering 17. 5 300 20 Arvalis_3 Arvalis France 49. 7N 3. 0E 2019 3 Post-flowering -Ripening 17. 5 300 4 INRAE_1 INRAE France 43. 5N 1. 5E 2019 1 Post-flowering 16 300 7 USask_1 University of Saskatchewan Canada 52. 1N 106. W 2019 1 n. a 30.
385 ETHZ_1 gantry Canon EOS 5D Mark II 35 32. 2 fixed 5616×3744 3 0. 55 NAU_1 handheld Sony RX0 24 16. 9 automatic 4800×3200 2 0. 21 UQ_1 handheld Canon 550D 55 17. 3 automatic 5184×3456 2 0. 2 Images characteristics of the sub-datasets composing the GWHD Dataset. All cameras looked vertically downward.
COSMO-RS σ-Moments as relevant molecular descriptors The COSMO-RS theory (Conductorlike Screening MOdel for Real Solvents), introduced by Klamt in 1995, is based on unimolecular quantum chemical calculations combined with statistical thermodynamics  [122]. This model, which is implemented in the COSMOtherm software, provides the necessary information for computing the chemical potential µ of a solute in a liquid phase without requiring any experimental input. This chemical potential can then be transformed into relevant physicochemical properties such as the partition coefficient of compounds between two phases  [123]  or between an aqueous phase and a micelle  [124]. Therefore, for a SOW system, COSMOtherm should be able to calculate the amount of a given oil penetrating into the interfacial film of surfactant considered here as a pseudo-phase.
| Oligo-marker approaches Oligo-marker approaches (i. e. , based on about 100 makers or less) potentially provide high-spatial resolution neutral maps of population genetic connectivity (Figure  1 ). The use of small marker datasets may be the only strategy compatible with budget limitations when it is necessary to analyze thousands of samples to quantify contemporary dispersal in parentage  (Baetscher et al. , 2019; Moore, Draheim, Etter, Winterstein, & Scribner, 2014)  or genetic assignment studies  (Johansson et al. , 2018). Provided that a sufficient level of genetic differentiation exists among populations, the description of the fine-scale genetic structure makes it possible to test for demographic uncoupling. For instance,  Nykänen et al. (2019)  showed that genetically differentiated populations of bottlenose dolphins (Tursiops truncatus) from the Northwestern Atlantic are connected by very low migration rates (<1%), a value too low to assume demographic cohesiveness  (Hastings, 1993).
To handle the large amount of information in large population genomic datasets, demographic inference methods generally use summary statistics of the data. A powerful summary statistics that captures many aspects of a species historical demography is the site frequency spectrum (SFS), which conveniently summarizes allele frequency data obtained from reduced representation genome sequencing data (such as RAD-Seq, ddRAD-Seq, or GBS) in one or multiple populations. With this type of approach, the spatial sampling resolution required for inferring longterm migration rates is much coarser (i. e. , population samples taken from a few representative locations) than for analyzing contemporary dispersal from geographic patterns of relatedness. Moreover, demographic inferences enable testing for gene flow between demographic entities that rarely exchange genes in nature, such as populations or evolutionary lineages separated by a physical barrier to dispersal or a tension zone.
, 2018; Edelman et al. , 2019; Leitwein et al. , 2019; Martin, Davey, Salazar, & Jiggins, 2019; Schumer et al. , 2018)  and the importance of selected mutations density in shaping the outcome of gene flow  (Aeschbacher, Selby, Willis, & Coop, 2017). In the future, complementary approaches based on Fisher's geometric model may help disentangle the relative contributions of the different modes and mechanisms of action of mutations in empirical population genomic datasets  (Simon, Bierne, & Welch, 2018). The integration of demographic history and contemporary gene flow studies can help to bridge the gap between macro-and micro-evolutionary scales. This is important to understand the contemporary consequences of genetic connectivity and, in the long term, to predict what might happen in the future.
Promising approaches that combine the best of both worlds are being developed  (Bradburd, Coop, & Ralph, 2018; Bradburd & Ralph, 2019; Harris, 2019). Among them, methods leveraging tree-based information from inferred ancestral recombination graphs  (Griffiths & Marjoram, 1996)  are revolutionizing the analysis of large-scale genetic variation datasets  (Kelleher et al. , 2019; Speidel, Forest, Shi, & Myers, 2019). These methods use the complete genealogical information available for each segment of DNA (i. e. , between two historical recombination F I G U R E 2 Integration of micro-and macro-evolutionary time scales. During the long-term evolutionary history of populations, ancestral genetic variation is sorted at different rates among descendant populations, possibly accelerated by linked selection or braked by gene flow.
would like to thank Nicolas Bierne, Sophie Arnaud-Haond, Rita Castilho, and the organizing committee of the Fifth International Marine Connectivity (iMarCo 2019) Conference in Aveiro, who motivated the writing of this review. I also thank Thomas Broquet and François Bonhomme for their helpful comments on the manuscript, as well as the Associate Editor Anne-Laure Ferchaud and an anonymous reviewer for their constructive comments. I am grateful to Maud Duranton and Pierre Barry for the contribution of their work to the study of speciation of Atlantic-Mediterranean marine fishes. This work was supported by the ANR grant CoGeDiv ANR-17-CE02-0006-01. CO N FLI C T O F I NTE R E S T None declared.
I would like to thank Nicolas Bierne, Sophie Arnaud-Haond, Rita Castilho, and the organizing committee of the Fifth International Marine Connectivity (iMarCo 2019) Conference in Aveiro, who motivated the writing of this review. I also thank Thomas Broquet and François Bonhomme for their helpful comments on the manuscript, as well as the Associate Editor Anne-Laure Ferchaud and an anonymous reviewer for their constructive comments. I am grateful to Maud Duranton and Pierre Barry for the contribution of their work to the study of speciation of Atlantic-Mediterranean marine fishes. This work was supported by the ANR grant CoGeDiv ANR-17-CE02-0006-01. CO N FLI C T O F I NTE R E S T None declared.
Arbuscular mycorrhizal fungi (AMF) are widespread microscopic fungi that provide mineral nutrients to most land plants by forming one of the oldest terrestrial symbioses. They have sometimes been referred to as an "evolutionary cul-de-sac" for their limited species diversity and their ecological niches restricted to plant-symbiotic life style. Here we use the largest global database of AMF to analyze their diversification dynamics in the past 500 million years (Myr) based on the small subunit (SSU) rRNA gene. We demonstrate that overall the SSU rRNA gene is variable enough to delineate AMF species and find that AMF have low diversification rates. After a diversification peak around 150 Myr ago, they experienced an important diversification slowdown in the last 100 Myr, likely related to a shrinking of their mycorrhizal niches.