dataesr/scientific-paragraphs-categorization huggingface dataset:
Model The coupled land-atmospheric model used is the IPSLCM version 4  (Dufresne et al. 2013) , which is involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5)  (Taylor et al. 2012). IPSLCM contains the atmospheric component of the Laboratoire de Météorologie Dynamique zoom model (LMDZ)  (Hourdin et al. 2006; Li 1999 ) and the land surface component of the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE)  (Krinner et al. 2005). In detail, ORCHIDEE contains a hydrological module SECHIBA  (Ducoudré et al. 1993) , a dynamic global vegetation model LPJ  (Sitch et al. 2003) , and a carbon module STOMATE consisting of terrestrial carbon dynamics and phenology  (Krinner et al. 2005). Here only the SECHIBA is activated to calculate the energy and water exchange between the atmosphere and land and the soil water budget  (Krinner et al.
Experiment design The simulations are performed following the Atmospheric Model Intercomparison Project (AMIP) framework by coupling the land and atmosphere components with the prescribed ocean. Prior to simulation, the model was spun up from a cold start and ran for 60 years to allow all diagnostic simulations to start with the same reasonable initial states. We use the LAI3g global LAI dataset to extract the evolution of greening patterns over time and space  (Zhu et al. 2013). To integrate the satellite-derived LAI into the land component ORCHI-DEE, we first interpolate the original LAI3g values (1/12 degree; 15 days) to match the 1. 5° × 3. 0° resolution of other model inputs. According to the increasing trend of satellite-derived LAI during 1982-2011 (Additional file 1: Fig. S1 ), we then replaced the static LAI values with monthly satellite-derived LAI for two periods of  1982-1986 (1980s) and 2007-2011 (2010s)  to represent low and high LAI conditions (Table  1 ).
Validation To validate the model results, we compared the modeled precipitation and ET with observation-based datasets. To validate the precipitation, we use the gridded (0. 5° × 0. 5°) Climate Research Unit (CRU) Time-series (TS) version 4. 05 dataset, provided by the University of East Anglia Climate Research Unit  (CRU et al. 2021). To validate the ET, we used a global ET dataset with monthly values at 0. 5° resolution provided by  Jung et al. (2010). The ET data are derived by integrating the in situ measurements at the FLUXNET sites, satellite images of the fraction of photosynthetically active radiation absorbed by the canopy (FAPAR), and surface meteorological data  (Jung et al. 2010 ). To compare with the model results, the observational-based precipitation and ET datasets are interpolated linearly to 1.
Satellite-derived and reconstructed observational data To discuss the climate variabilities in Australia from 1982 to 2011, we use observation-based datasets of LAI, precipitation, and SST. LAI is again constructed from the LAI3g dataset (see above). For monthly Australian mean precipitation data, we use the gridded (0. 5° × 0. 5°) CRU TS version 4. 05 dataset  (CRU et al. 2021). The precipitation data required were interpolated to 1. 5° × 3. 0° grids to match the model, and annual means were computed for land-dominated grid cells of the Australian continent. For SST, we use the 2° × 2° gridded monthly NOAA Extended Reconstructed Sea Surface Temperature V3b dataset  (Smith et al. 2008) , which again was interpolated to match the model grid spacing. We also use this SST product to compute the Niño 3.
Availability of data and materials The monthly maps of sea surface temperature and sea ice data to drive the model can be found in the Atmospheric Model Intercomparison Project (AMIP; https:// pcmdi. llnl. gov/ mips/ amip/ amip. html). The NOAA Extended Reconstructed Sea Surface Temperature V3b dataset is obtained from https:// psl. noaa. gov/ data/ gridd ed/ data. noaa. ersst. v3. html. The CRU TS version 4. 05 is available at https:// catal ogue. ceda. ac. uk/ uuid/ edf8f ebfda ad48a bb2cb af7d7 e846a 86.
Methods We conducted a literature review and synthesis in several steps. First, we updated and summarized the available knowledge on the population status and empirical evidence for main drivers of population dynamics in red kites. This was based on summarizing literature found through searches in the Web of Knowledge (www. webofknowledge. com) database, Google Scholar (scholar. google. com) database, along with unpublished reports in our own bibliographic databases. When searching literature databases, we used various combinations of the keywords "red kite", "Milvus milvus", in addition to the common name used in other languages (e. g. , "Rotmilan" in German). This search resulted in 263 literature sources. Second, we used this information to examine management actions that have been implemented to conserve this migratory species.
Acknowledgements The 2018 Red Kite Symposium was organized by GREFA and IREC at  CENEAM  ( Centro Nacional de Educacio´n Ambiental ) facilities kindly handed over by  MITECO. Financial and logistic support were provided by  GREFA, IREC ,  UCLM  ( Universidad de Castilla-La Mancha ),  CSIC  and  MITECO. We appreciate the kind help and support of personnel at CENEAM, with special thanks to  Lourdes del Horno  for organizing traveling arrangements for the attendees and  Salvador Luna  for helping with transport and logistics. Aaron Nebreda  prepared information about red kites and protected areas in Spain. We also thank  J. Havens  and  D. Semmens  for support with developing Fig. 3. Unless defined above, acronyms for organizations are defined in Table S. 1. We thank the two anonymous reviewers for their helpful comments on an earlier version of this manuscript.
a b s t r a c t Hereby presented data consists of a land use / land cover map of an 84 km 2 part of the Vavatenina district, in the Analanjirofo Region of the east coast of Madagascar, where the landscape is dominated by woody vegetation. This map was obtained by processing very high spatial resolution multispectral images acquired by the Pleiades satellite sensor in 2018. Pleiades data were produced by Airbus defence & Space (France), and distributed by the French National Space Agency (CNES, France). An object-based approach was chosen to exploit the advantages of such images, allowing the use of various texture indices derived from the image to discriminate between different surfaces sharing similar radiometric properties while being structurally different. The object-based image analysis consists of iterative segmentations of the image into homogeneous regions, called objects, which can afterwards be classified using radiometric and textural variables.
The reference database, computed out of field knowledge and used to train the mapping methodology, is also described here. It includes 2105 georeferenced points labelled with one of the 9 following classes: Built up/road/bare areas, Annual crops/pasture/short vegetation, Clove dominated park, Clove monoculture, Diversified agroforest, Diversified park, Plantation of woody species, Shrubby fallow, Woody fallow. It is formatted as a geographical information system, accessible for any other purpose like further methodology development. These ground-truth data also helped to the definition of the 13 thematic land use / land cover types to be distinguished in the area. Multiple segmentation levels were thus necessary to gradually derive these classes, with different classification algorithms depending on the level.
The final map provides an exhaustive description of the area with a good overall accuracy, reaching 80%. It is openly available as an ESRI shapefile to allow quantitative spatial analysis in key applications such as agriculture, environment, or food security. © GPS. An infrared false-colour composition of the Pleiades image was loaded as the base-map into the GPS to support the exploration and land-use class definition. The interpretation of the land use / land cover type was directly made in the field, along with additional observations that could be useful for the image processing, but are not included in the database. Additional photo-interpreted reference points (1683) were selected in the satellite image multiband compositions based on expert knowledge reinforced by field initial survey.
The object-based image analysis (OBIA) was performed under Trimble's eCognition® software, and consisted in iterative segmentations followed by hierarchical classifications of the features derived from the satellite image. Classification algorithms were chosen depending on the level, including expertized thresholding and nearest neighbour classifications. The classification accuracy assessment, and the production of the two final databases as shapefiles were performed using ESRI's ArcGIS®, and QGIS platforms.
Data Description The data presented in this article are of two kinds: (1) a reference database, and (2) a landuse/land-cover map. Both products concern an area covering 84 km 2 , located in the Vavatenina district, Analanjirofo Region, on the north-eastern coast of Madagascar, and both are provided as a geographical information system (GIS) in ESRI's shapefile format, in the standard UTM local projection: WGS 84 UTM 39 South, EPSG code 32,739. Both datasets are referenced in the CIRAD Dataverse and freely available for download [dataset] [1] [dataset] [2]. (1) The reference database GIS includes 2105 points, representative of the diversity of lands in the area. A first column of the attribute table of this shapefile indicates literally, for each point, his class label corresponding to one of the 9 types of land-use or land-cover present in the area, and listed in Table  1.
These quite precise classes correspond to deeper interpretation of the 9 initial survey classes (cf. Table  1  ),  based on observations made during the field work and not included into the database. The first attribute of the shapefile stores the label of the class recognized for this piece of land, listed in the Table  2  , while the second attribute indicates its corresponding area expressed in hectares. The overall precision of this classification reaches 80%. This GIS can easily be displayed in any dedicated software to produce a map, thus giving an exhaustive spatial representation of the lands in the studied area, at the spatial resolution of 0. 5 m/pixel (cf. Fig. 2  ).
It results in a multi-level algorithm with successive hierarchical segmentation/classification combinations. We carried out the whole processing using Trimble's eCognition® software. Three segmentation levels were then required, with a specific set of parameters for each one: the "shape", the "compactness" and the "scale" factors, that have to be tuned to provide the best segmentation performance in relation with the considered level. To define the proper value for each parameter at each level, a first arbitrary value was set: 0. 5 for the shape factor to give equal weight to radiometry and shape, 0. 5 for the compactness factor to let the object shape loose (compact/linear, indented/smooth, …), and 400 for the scale factor because we knew by experience that this is quite close to the optimum value for small agricultural plots in VHRS imagery.
Assessment of the best algorithm was achieved by comparing confusion matrices and visual observations. NN classifier thus proved to be the most reliable for our application and its results were kept for further processing. Resulting objects were also merged whenever contiguous and belonging to the same class. In parallel, we classified the non-vegetated objects into bare soil, built up areas, rice fields, and open water surfaces using NN supervised by the samples belonging to the ground-truth database. Finally, additional photointerpretation of rice fields, guided by field references and expert knowledge, and based on several color compositions of the stacked layers, consolidated and completed this step.
Acknowledgment We would like to thank  Juliette Mariel  and  Vanesse Labeyrie , from the  UMR SENS, CIRAD research unit , for their participation in the creation of the learning database, contributing to the definition of the land-cover and land-use classes to reach an accurate typology representing the Vavatenina region biosphere. We also thank the collaborators of the  "Forests and Biodiversity in Madagascar" Platform in Partnership for Research and Training (DP CIRAD/FOFIFA/University of Antananarivo ), for their logistic support in site. We are particularly grateful to the farmers who guided H. Herimandimby during the field survey. This work was supported by a financial contribution from the  Agropolis Fundation, Montpellier, France , through the  SoLDivA Young Scientists Project  [Grant number  1702-022  ].
Workshop coordinators invited scientists with CAP expertise from various disciplines (ecology, agronomy, economics, and social sciences). In some MS, experts from stakeholder groups participated as well. Workshop protocols and individual expert replies were compiled into a predetermined template, where references supporting their statements were provided. We used MAXQDA software to code and extract relevant quotes throughout the questionnaires. We then organized the experts' insights, distilled important guiding principles, and synthesized key findings for this study (for detailed methods, see Supporting Information 2). Overall, we received inputs from over 300 participants in 23 MS (Figure  2 ) (further details in  Pe'er et al. , 2021).
The new delivery model requires tight monitoring of these indicators to enable rapid performance evaluation, especially of HNVF, landscape features and grassland management. MS should employ improved and harmonized standards in recording and archiving relevant biodiversity, landscapes, and socioeconomic data and improve access to databases, especially the Integrated Administration and Control System (IACS), for researchers and the public. Otherwise, existing barriers to data access will continue impeding evidence-based evaluation of the CAP's performance. Mapping efforts can be readily expanded by increasing the comprehensiveness of Land Parcel Identification Systems (LPIS) to generate a broader picture of the state of European farmlands.
Site description and sampling of P. Turgidum Roots of P. turgidum were collected in May 2015 from an oasis at the gates of the Sahara Desert, Ksar Ghilane, Tunisia (GPS coordinates N32°59. 557 0 , E9°36. 941 0 , 221. 9 m above sea level). The map showing the sampling location was generated using the software ArcGIS (https://www. arcgis. com/home/index. html) (Fig. 1 ). Samples were then either irradiated (R) or not (C), and both were stored at 4 °C until use. Roots of P. turgidum were homogenized and prepared for irradiation without drying and washing of soil particles. They were exposed immediately to gamma rays after collection without any treatment.
Results are always shown with the corresponding controls unexposed to IR, which were normalized to 100%  (Morini et al. , 2017). The absorbance in each well was measured, including blanks, at 570 nm using a microtiter plate reader (Shimadzu, UV-1800). Numerical values of D 10 (kGy) were determined using the RadioP1 database available at www. radiop. org. tn (Benhamda et al. , 2015). The classification of an isolate as belonging to IRRB was done based on a previously proposed criterion  (Sghaier et al. , 2008 ) -D 10 greater than 1 kGy. All manipulations were done in triplicates (3). 2. 3. Metataxonomic approach 2. 3. 1. DNA extraction from roots of P. Turgidum Non-irradiated and irradiated -10 kGy -roots of P. turgidum were ground to a fine powder with a porcelain mortar and pestle. Extraction of genomic DNA was then carried out using Qiagen DNeasy Kit (Qiagen, Valencia, CA), quantified and checked for purity at a ratio 260/280 nm using a Nanodrop spectrophotometer (Implen NP80, Thermo Fisher Scientific, USA) and stored at À 20 °C.
NGS via an Illumina MiSeq platform Genomic DNA was sent to Inqaba Biotechnical Industries, a commercial NGS service provider, for sequencing. Briefly, genomic DNA samples were PCR amplified using a universal primer pair (341F and 785R targeting V3 and V4 of the 16S rDNA gene). The resulting amplicons were gel purified, end-repaired and Illumina specific adapter sequences were ligated to extremities. Following quantification, samples were individually indexed and a bead based purification step (AmpureXP) was performed. Amplicons were then sequenced on an Illumina TM MiSeq platform (San Diego, Ca) using a MiSeq v3 (600 cycle) kit. Fifty Mb of data (2 Â 300 bp long paired end reads) were produced for each sample.
Metataxonomic post-NGS in silico analyses Reads were merged using the software FLASH  (Magoc ˇand Salzberg, 2011). Microbial diversity associated with nonirradiated and irradiated roots of P. turgidum, was quantified with One Codex (https://www. onecodex. com/). Taxonomic Units (OTUs) were determined at different levels; and taxa with relative abundance higher than 1% were retained for statistical and comparative analyses. The SILVAngs pipeline (SILVAngs 1. 4, https://ngs. arbsilva. de/silvangs/) was used to create rarefaction curves based on rDNA amplicon reads from high-throughput sequencing  (Wang et al. , 2007; Quast et al. , 2013). Functional inferences and comparisons, based on 16S rDNA datasets, were generated using the multi-modular web-platform iVikodak via global and local mapper modules (https://web.
Arid and desert environments were reported to host an abundance of diversified xerotolerant microorganisms belonging to the phylum Actinobacteria  (Yandigeri et al. , 2012; Yu et al. , 2015; Mohammadipanah and Wink, 2016). These strains demonstrated interesting capacities to survive under high temperatures, large radiation doses, elevated salt concentrations and drought conditions  (Kurapova et al. , 2012; Shivlata and Satyanarayana, 2015). Furthermore, the functional potentials of microbial communities associated with roots of P. turgidum were predicted based on 16S rDNA gene sequencing datasets. The functional profiles were characterized by the presence of several pathways associated with environmental adaptations, such as polysaccharides and carotenoids biosynthesis, carbon fixation and xenobioticbiodegradation pathways (Fig.
Acknowledgements This work was supported by grants from the  Tunisian Ministry of Higher Education and Scientific Research ; without this generous and strong assistance, this study could not have been undertaken. It was performed under the auspices of the  Tunisian National Center for Nuclear Sciences and Technologies (CNSTN)  and  Institut Pasteur (Tunisia)  in collaboration with the  Université de Lyon (France). We thank  Betty Bigai ,  Corinne Sannaire  and platform  Genomique Environnementale PGE ,  UMR CNRS 5557  for technical support.
Generation of simple 2D and 3D images We first performed a simple experiment using 2D ellipse and 3D ellipsoid shapes to verify that the proposed architecture is useful. An ellipse can be defined by the lengths of its horizontal (x) and vertical (y) axes, plus a third axis z for 3D shapes. We drew 2D ellipses with cv2. ellipse() function from OpenCV python package  (Bradski 2000)  randomly varying x and y axis lengths, that is, ellipses differed in shape, size, and orientation. Images were black and white of size 64 x 64 pixels. The decoder network (described below) was trained using an input vector containing x/y ratio and ellipse size, i. e. , the two 'causative loci', and 100 random uniformly distributed variables. The 100 random numbers were aimed at representing noise from DNA information that is unrelated to the 'phenotype', i.
e. , the image containing the ellipse. We generated 3D ellipsoids as three-dimensional binary arrays using pymrt package  (Metere and Möller 2017) , array size was 32 x 32 x 32. As in the previous example, images were predicted from x, y, and z axes lengths plus 100 random uncorrelated variables. For representation of the 3D shapes, ellipsoid projections were drawn using the marching cubes algorithm as implemented in skimage (van der Walt et al. 2014) and the plot_trisurf package. However, since these 3D plots were not too accurate, we also plotted the ellipsoid sections across the x, y, and z axes. Both observed and predicted shapes were plotted.
Cucurbit shapes C. pepo fruits can adopt an enormous diversity of shapes (Figure  1A ). This variability appeared only after domestication, since all wild fruits are small and round  (Paris 1986). According to  (Paris 1989 (Paris , 2001)) , C. melo shapes may have followed several evolutive pathways. One pathway would be wild gourd (akin to pumpkin shape)  scallop  acorn; a second pathway would be wild gourd  marrow  straightneck  zucchini  cocozelle (Figure  1B ). See also Figure  17  in  (Paris 1989 ). We extracted contours from the OpenCV library  (Bradski 2000). Contours were centered and 500 pseudo-landmarks were obtained with the algorithm in  Zingaretti et al. (2021). Next, contours were aligned with a generalized procrustes algorithm implemented in python package 'procrustes'  (Meng et al.
2022 ) and images were resized to 64 x 64 pixels. To generate C. pepo shapes along the putative evolutive gradient, we first sampled a random number from a uniform distribution s ~ U(-1, 1), where s = -1 defines an 'acorn' form; 0, a 'pumpkin', and 1 corresponds to 'cocozelle' (Figure  1B ). Using the sampled s value, the two closest basic shapes were identified, and we defined a function that drew an intermediate shape between the nearest basic shapes, weighted by the proximity to each of the bounding contour (Figure  1C , see code in GitHub https://github. com/miguelperezenciso/dna2image/blob/main/dna2img. cucurbita. ipynb). The fruit corresponding to shape s was drawn in a 64 x 64 pixel image and noise was added to mimic rugosity of naturally observed fruits. This was done by adding an autoregressive noise to the contour (see code in GitHub).
Shape prediction We used a simple decoder architecture made-up of a first fully connected layer, followed by a reshaping layer and by three transposed convolutional layers (Figure  2 ). Code was implemented in keras and tensorflow (https://keras. io/, Abadi et al. 2015; Chollet 2015)  and is inspired in autoencoder architectures  (Brownlee 2019; Chollet 2021 ). The same decoder architecture was used for ellipse, cucurbita or tomato shape prediction, except that layer dimensions were adjusted according to image size (Figure  2 ). For ellipsoid 3D predictions, 3D transposed convolution layers were used instead of 2D transposed convolutions, but architecture was otherwise identical (see code in https://github. com/miguelperezenciso/dna2image).
-Epistasis: the offspring ellipse is drawn by swapping the x and y coordinates of an ellipse intermediate between parents' coordinates. That is, the epistatic offspring ellipse is a transposed additive ellipse. We generated ~ 1,000 ellipse trios for each inheritance pattern to train the network. We trained the network for each inheritance pattern separately. In the second, more realistic scenario, we used all combinations of male, female and offspring tomato images in a given cross from the previously described dataset. This resulted in a dataset of 2,325 tomato image trios. We utilized the same autoencoder architecture in both ellipse and tomato scenarios. Input consisted of two images that fed two separate CNN layers, one for each parental image, that were next concatenated (Figure  3 ).