dataesr/scientific-paragraphs-categorization huggingface dataset:
With that in mind, using the slope angle layer (Figure ??f) together with the PIC layer, which reflects the lithology permeability, a potential infiltration map (PIM) was drawn (Figure ??g). The calculation is based on the reasoning that where the PIC is higher and the slope angle is lower, the infiltration values will be higher. This approach was developed using the ratio between PIC and slope angle (normalized values). The procedure was also applied before in studies of groundwater [? ? ]. ArcGIS environment served as the software to analyze and manage the data assignation of aquifers' features in the attribute table.
Land Cover Data and Pollution Load Index The land cover has a two-fold significance for water resources and environmental risk mapping. First, the quantitative aspect is related to the evapotranspiration, which determines the final water availability amount. Second, the qualitative aspect includes the pollution load because of phosphorus transfers, corresponding to each type of land cover. For these two aspects, the spatial distribution of crop coefficient (Kc) and pollution load index (PLI) [? ] in different periods over the Grand Est region, were determined by using the CORINE Land Cover database and Hercules projections. The CORINE Land Cover database data used were from 1990 to 2012, the source being Copernicus Land Monitoring Services [? ]. The Hercules projections indicate four land cover models related to 2040.
Normalization of Data In order to obtain a homogeneous database, all datasets were normalized between 0 and 1. Thus, the effective precipitation (EF, for 1990s, 2020s, and four scenarios for 2050s), the pollution load index (PLI) (CLC1990, CLC2012, and HERCULES Projections 2040), the potential infiltration coefficient (PIC), the hydrogeological vulnerability factor (HVF), and the potential infiltration map (PIM) were normalized and processed in a unique scale. Data normalization was done in conformity with the standard formula (Equation (  5 )). In this way, the datasets are ready for risk mapping analysis and other various spatial interference. Xnorm = (X -Xmin)/(Xmax -Xmin) (5) where, X, the values in series Xnorm, the value after normalization Xmax, the maximum value sin the series Xmin, the minim value in the series The data presented in this work were validated through publications [? ? ] regarding the climate change effect on groundwater resources in the Grand Est region, but also through spatial analysis in ArcGIS regarding the groundwater vulnerability [? ].
Normalized Layers Figure ?? shows the normalized maps of effective precipitation, PLI, PIC, HVF, and PIM in the Grand Est region, France. These maps represent a comprehensive gridded dataset that serves for the climate and pollution risk evaluation in the study area with respect to the water resources. The data grids are homogenous layers ready for different kind of analyses, such as spatial analysis by weights, inference matrix, fuzzy logic, or any other approach that is focused on the quality and quantity assessment of nowadays and future factors. In addition, our database includes the future scenarios for the 2020s and 2050s, that could help for the environmental planning and delineation of protection zones. for the 2020s and 2050s, that could help for the environmental planning and delineation of protection zones.
Discussion on the Usage Notes The comprehensive gridded dataset based on GIS technology and complex approaches was set-up for the Grand Est region, France. The gridded dataset is associated with the climate models, geology, terrain morphology, and land cover and it represents a useful cartographical support for water resources analysis and environmental risk evaluation in the Grand Est region of France. The uniform representation of climate data, geological and aquifers data, terrain and potential infiltration map data, effective precipitation, land cover data and pollution load index, as well as annual crop evapotranspiration and annual actual crop evapotranspiration were combined and described using a regular grid cell of 1 km 2. The results indicate a negative impact of climate change on the water resources for the future periods.
The PIC, HVF, and PIM are essential layers with respect to the substrate data and infiltration process. Thus, these three layers are useful for the aquifers recharge and groundwater vulnerability calculation. The effective precipitation layer illustrates directly the areas with high amounts of water that infiltrate in the fractured and porous media. Considering that the geological formations do not change in a temporal scale of 100 years, the decline of the effective precipitation in the Grand Est region is due to climate change. The originality of this study consists in providing the first regional high-resolution datasets on various patterns of distribution of the variables studied, over the Grand Est region. The work and layers prepared here are essential for water resources management and future predictions, but could be also useful for different environmental hazards, such as flooding, landslides, or droughts.
These layers are appropriate files for development of the groundwater vulnerability map using spatial analyst tools in ArcGIS, on one hand, but also for the flow accumulation and flood areas determination, on other hand. This comprehensive dataset of the Grand Est region can be combined with additional datasets (e. g. , groundwater table, soil properties) and used as a driver for hazard maps in the region. In this sense, TRIGRS and Scoops 3D are highly used for slope stability because of the large GIS data reading and results at spatial scale. Thus, the work presented here completes the previous studies of the Grand Est region, offering a much more complex homogenous dataset. An example may be provided by the spatial analysis, using GIS technology for groundwater vulnerability mapping, over the Grand Est region, performed by Ref.
Conclusions In this work, the main layers of temperature, precipitation, geology, terrain configuration, and land cover of the Grand Est region were used to analyze the annual ETc, AETc, water availability, PIC, HVF, PIM, and effective precipitation. In order to serve as a comprehensive gridded dataset for the climate change and water resources risk assessment, all these layers were processed at 1 km 2 spatial resolution and normalized. The strength of comprehensive gridded dataset processed in this study lies in the future datasets, which are of high interest for both the scientific community and the administrative authorities of the region. This region represents a study case of the relationship between climate change and agriculture. Our comprehensive gridded dataset provides the "stand alone" maps and derived maps, as well as the normalization of all layers.
Thus, the environmental planning for the near future and mid-century time period could consider the original maps that we have developed in this work. Moreover, the comprehensive gridded dataset contributes to environmental risk evaluation and to the calibration of various models (e. g. , groundwater flow models, slope stability analyses). The technical validation of the presented data and methodology is supported by the previous publications [? ? ], while future work may develop a more extensive dataset including hydrological models based on runoff and groundwater flow, spring discharge monitoring, and field research data including soil sampling and geotechnical investigations.
More details on sampling are available in Taquet  [45]. All samples were collected under the ethical approval code DA 2014-03 and project authorization (09-14-05/SG/DRCTCV), provided by La Réunion Environment, Planning and Housing Direction (DEAL) from the Ecological and Solidarity Transition French Ministry (MTES). CITES import permit numbers by country are: Juan de Nova FR0597400138-I, Europa FR0597400137-I, Mohéli FR0597400133-I, Mayotte FR0597400132-I, west Madagascar FR0597400134-I and FR0597400135-I, Tromelin FR0597400140-I, Glorieuses FR0597400139-I, Seychelles FR1297400259-I and Kenya 2371-I-165067. 2. 1. 2. DNA Extraction, Sequencing and Regional Data Compilation Total genomic DNA was extracted from tissue samples using a salting-out method, and a 385 base pair portion of the mtDNA control region was amplified by polymerase chain reaction (PCR), using both the primers TCR-5 (5-TGTACATTACTTATTTACCAC-3) and TRC-6 (5-GTACGTACAAGTAAAATACCGTATGCC-3).
Data Analysis Haplotype nomenclature was standardized according to Southwest Fisheries Science Center (http://swfsc. noaa. gov/prd-turtles. aspx) for the 386 bp fragment, with Pacific and Indian Ocean haplotypes being assigned a CmP prefix  [48] , while haplotypes belonging to the Atlantic clades had been assigned the CM-A prefix. The genetic diversity of turtles at each developmental site was assessed by calculating the haplotype (Hd) and nucleotide diversity (π)  [49]  using DnaSP v6  [50]. A parsimony network of haplotype relationships was constructed using the software TCS 1. 13  [51, 52]. To determine the stock proportions of the turtles sampled at each developmental area, we applied a Bayesian mixed stock analysis (MSA) using two different approaches. The first involved the software BAYES  [24]  (extensively used in sea turtle studies); and the second used the "many-to-many" mixstock package in R  [25].
To assess the accuracy of the MSA in estimating contributions from the south Atlantic, we performed two simple simulation experiments. The first included Atlantic rookeries, as well as the three SWIO stocks and Cocos (Keeling) Islands. A simulated foraging sample of (n = 50) was made up of 75% South Area, 20% Central Area and 5% North Area, with 0% contribution from the Atlantic and Cocos (Keeling) Islands. The results from the MSA of the simulated dataset were then compared to the true (simulated) proportions. The purpose of this exercise was to see if the MSA would overestimate the contributions from the Atlantic. As expected, several Atlantic rookeries in this scenario were significantly overestimated. To make sure that these confounding issues were not affecting the MSA results within the SWIO, we performed a second simulation restricted to the three SWIO stocks and Cocos (Keeling) Islands.
Passive Drift Simulations The passive dispersal of hatchlings and then juveniles was simulated for seven nesting beaches: two in the North Area (Seychelles granitic and Amirantes groups), four in the Central Area (Glorieuses, Mayotte, Mohéli and Tromelin) and one in the South Area (Europa). The technical setup of the simulations is identical to that of Gaspar et al. [27]. Individual trajectories are computed using the ARIANE Lagrangian trajectory simulation software (www. univ-brest. fr/lpo/ariane), fed with surface currents taken from the daily outputs of the GLORYS-1 (G1) reanalysis of the World Ocean circulation  [42]. This reanalysis, performed with the NEMO numerical ocean model (www. nemo-ocean. eu), is provided by Mercator-Ocean (www. mercator-ocean. fr). The G1 model has an eddy-permitting horizontal resolution of 0.
Limitations and Future Directions The SWIO region shares haplotypes with both the Atlantic and the eastern Indian Ocean and the Western Pacific. This provides challenges for accurately estimating the stock origin, and complicates a confident assessment of contributions from these areas based on the global mtDNA genetic data alone. When considering all rookeries that share at least one haplotype with the foraging samples, the MSA included 38 genetic stocks spanning three ocean basins. This problem is caused by common and widespread haplotypes, such as CmP49, which is common in both the SWIO and throughout Asia-Pacific, as well as haplotypes CM-A08, which is common in the SWIO and throughout the South Atlantic. When included, stocks in these distant regions result in considerable uncertainty surrounding the estimates for these stocks.
Haplotype frequencies for the four green turtle (Chelonia mydas) breeding stocks (and individual rookeries), and eight foraging sites used in the mixed stock analysis. Also shown are estimated population sizes (annual number of nesting females), three letter country code, latitude and longitude and references used for the baseline data; Table  S2. Sampling information. Table showing the location of eight green turtle developmental areas sampled in this study, as well as samples size, sampling year and the mean curved carapace length (CCL), with standard deviation (SD); Table  S3. Developmental area centric MSA. The proportional contribution of four green turtle genetic stocks to eight different developmental areas in the Southwest Indian Ocean using mixed stock analysis. The results show a comparison of two different methods: a "many to one" method using the software Bayes, and a "many to many" method using the R-package mixstock.
Funding: This research was funded by  IFREMER  ( R511-02-DF-01 ),  European Union FSE  and  FEDER  ( GENETIQUE TORTUE  project),  La Réunion Regional Council  (PhD funding),  La Réunion DEAL  ( EGETOMER  project, grant number  2013/28 ),  Total Foundation  ( MODIOT  Project  100630-SC ) and Kelonia (internal funding) and the  United Nations Development Program, Small Grants Program (UNDO-SGP)  Acknowledgments: The analyses were carried out within the framework of the  NEXT  project with the support of  IFREMER  and  Institut Océanographique de Monaco ,  Fondation Albert I er  , the  Prince of Monaco, CEDTM and KELONIA. For the Eparses Islands , we are grateful to the  French Southern and Antarctic Lands (TAAF) ,  West Indian Ocean Army Force (FAZOOI)  and  Météo France Staff  for the logistical support and permits to conduct the fieldwork.
, 2018). In this context, we have synthesized a cesium strontium FCN to study its efficiency in immobilizing both 90 Sr and 137 Cs, two radionuclides that are in most cases found together in radioactive water streams. Caesium strontium FCNs appear to be poorly described in the ICDD 2020 PDF4+ powder diffraction database  (Gates-Rector & Blanton, 2019). Since the synthesized phase Cs 2 Sr[Fe(CN) 6 ] did not match with existing entries, we decided to characterize the structure and we report the results herein. ISSN 2056-9890.
Special details Refinement. The Platon test reports 21 Alerts level C. They could be gathered in three groups for explanation : i)17 C-Alerts (out of 21) are "missing esd on x,y,z coordinates of N and C atoms. This is normal since these positions were not refined. Hence no esd was calculated by Jana2006. ii)3 C-Alerts (out of 21) are due to a slighlty larger Fourier difference density than allowed by CheckCIF. I can not enhance the quality of the data so no reduction of this value can be done. iii) The last C-Alert is about a "calc. and reported Sum Formula which differ. I did not find the origin of the Alert. Fractional atomic coordinates and isotropic or equivalent isotropic displacement parameters (Å.
Data collection: Data Collector  (Panalytical, 2011) ; cell refinement: JANA2006 (Petříček et al. , 2014); data reduction: JANA2006 (Petříček et al. , 2014); program(s) used to solve structure: coordinates from isotypic compound; program(s) used to refine structure: JANA2006 (Petříček et al. , 2014); molecular graphics: VESTA (Momma & Izumi, 2011); software used to prepare material for publication: publCIF (Westrip, 2010).
Statistical analysis Collected data were analysed using the Minitab 17 software (Minitab, LLC, company, United States). The 5% threshold comparison test was used to compare the averages (student´s test). The thresholds of significance considered are: (* Significant: P <0. 05; ** Very significant: P <0. 01; *** Highly significant; P <0. 001; NS: Not significant). The results obtained by the chemical properties evaluation show that the organic matter content of burnt soils (7. 73%) was significantly lower compared to the control zone (16. 08%) (P <0. 001). However, the statistical tests of the other properties do not indicate a significant difference, only a slight decrease was noticed for total limestone in the burnt soil in contrast to control zone. The average values of pH H 2 O and pH KCl approach each other in both zones.
Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed methods for wheat head detection from high-resolution RGB imagery. They are based on computer vision and machine learning and are generally calibrated and validated on limited datasets. However, variability in observational conditions, genotypic differences, development stages, head orientation represents a challenge in computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse and well-labelled dataset, the Global Wheat Head detection (GWHD) dataset.
It contains 4,700 high-resolution RGB images and 190,000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD is publicly available at http://www. global-wheat. com/ and aimed at developing and benchmarking methods for wheat head detection.
Even though high throughput phenotypic data acquisition is already a reality, developing efficient and robust models to extract traits from raw data remains a significant challenge. Among all traits, wheat head number per unit ground area is a major yield component and is still manually evaluated in breeding trials, which is labour intensive and leads to measurement errors around 10%  [8] ,  [9]. Thus developing a sensing-based methods to increase the throughput and accuracy of wheat heads counting in the field is highly desired to help breeders manipulating the balance between yield components (plant number, head density, grains per head, grain weight). Thanks to increases in GPU performances and the emergence of large scale datasets  [10] ,  [11]  , deep learning has become the state of the art for computer vision for object detection  [12] , instance segmentation  [13] , semantic segmentation  [14]  and image regression  [15] ,  [16].
Recently, several authors have proposed deep learning methods tailored to image-based plant phenotyping  [17] -  [19]. Several methods have been proposed for wheat head quantification from RGB high resolution images. In  [8] ,  [9] , the authors demonstrated the potential to detect wheat heads with a Faster-RCNN object detection network. They estimated in  [8]  a relative counting error of around 10% for such methods when the image resolution is controlled. In  [20] , the authors developed an encoderdecoder CNN model for semantic segmentation of wheat heads which outperformed traditional handcrafted computer vision techniques. [21]  developed a wheat head detection and probabilistic tracking model to characterize the motion of wheat plants grown in the field. While previous studies have tested wheat head detection methods on individual datasets, in practice these deep learning models are difficult to scale to real-life phenotyping platforms, since they are trained on limited datasets, with expected difficulties when extrapolating to new situations  [8] ,  [22] ,  [23].
Most training datasets are limited in terms of genotypes, geographic areas and observational conditions. Wheat head morphology may significantly differ between genotypes with notable variation in head morphology, including size, inclination, color and the presence of awns. The appearance of heads and the background canopy also change significantly from emergence to maturation due to ripening and senescence  [24]. Further, planting densities and patterns vary globally across different cropping systems and environments, with possible overlap between heads for the higher head densities. A common strategy for handling limited dataset is to train a CNN model on part of a phenotyping trial field and test it on the remaining fraction  [25]. This is a fundamental flaw of empirical approaches against causal models: there is no theoretical guarantee that a CNN is robust on new acquisitions.
In addition, comparison between methods from different authors require large datasets. Unfortunately, such large and diverse phenotyping head counting datasets do not exist today because they are mainly acquired independently by single institutions, limiting the number of genotypes, the environmental and the observational conditions used to train and test the models. Further, because the labelling process is burdensome and tedious, only a small fraction of the acquired images is processed. Finally, labelling protocols may be different between institutions, which will limit model performances when trained over shared labelled datasets. To fill the need for a large and diverse wheat head dataset with consistent labelling, we developed the Global Wheat Head Detection (GWHD) dataset that can be used to benchmark methods proposed in the computer vision community.
Image acquisition The GWHD dataset contains RGB images captured with a wide range of ground-based phenotyping platforms and cameras (Table  2 ). The height of the image acquisition ranges between 1. 8 m and 3 m above the ground. The camera focal length varies from 10 to 50 mm with a range of sensor sizes. The differences in camera setup lead to a range of Ground Sampling Distance (GSD) ranging from 0. 10 to 0. 62 mm with the half field of view along the image diagonal varying from 10° to 46°. Assuming that wheat heads are 1. 5 cm diameter, the acquired GSDs are high enough to visually detect heads and even awns. Although all images were acquired at nadir-viewing direction, some geometric distortions may be observed for few sub-datasets due to the different lens characteristics of the cameras used, excerpts of the acquired images have different.
Datasets UTokyo_1 and ETHZ_1 are particularly affected by this issue. Each institution acquired images from different platforms, including handheld, cart, mini-vehicle and gantry systems. The diversity of camera sensors and acquisition configurations resulted in a wide range of image properties that will help training deep learning models that can better generalize across the diversity of image acquisition conditions. * The field of view is measured diagonally. The reported measure is the half-angle. † Original images were cropped, and a sub-image of size 800x800 was extracted from the central area § The camera was positioned perpendicular to the ground and automatically adjusts to ensure a 2. 2 m distance was maintained between the camera and canopy.
Data harmonization A selection of images was first conducted to ensure that they could be well interpreted. Images acquired at too early stages were removed when heads were not clearly visible (Figure  2, (d) ). Images were also mostly acquired before the appearance of head senescence since heads tend to overlap when they start to bend at this stage. Object scale, i. e. the size of the object in pixels, is important to design the detection methods  [8]. Object scale depends on the size (mm) of the object and on the resolution of the image. Wheat head dimensions may vary across genotypes and growth conditions, with however around 1. 5 cm diameter and 10 cm length. However, the actual image resolution at the head level was varying significantly between sub-datasets: the GSD varies by a factor of 5 (Table  1 ) while the actual resolution at the head level depends also on canopy height and the panoramic effect of the camera.