dataesr/scientific-paragraphs-categorization huggingface dataset:
Control assays, where HA hydrolysis occurred normally in absence of plant extracts, were performed and stopped using the same protocol: 35 μL of incubation buffer solution was preincubated with an appropriate volume of HA (at 4 mg mL -1 ) at 37 °C for 10 min, then 5 μL of enzyme was added into the resulting mixture. All reactions were performed in triplicate. A PA800+ CE apparatus equipped with a photodiode array detector and Beckman 32 Karat software (Sciex, Redwood City, CA, USA) was used for the analysis of the reaction mixtures and the aqueous raw extracts. Fused silica capillaries (from Polymicro Technologies, Phoenix, AZ, USA) were used with a total length of 57 cm (47 cm effective length) and 50 μm inner diameter. Detection wavelength was set to 200 nm. Between runs, the capillary was flushed with NaOH (for 5 min), water (for 0.
Statistical analysis The Shapiro-Wilk test showed that not all data were normally distributed. Therefore, they were assessed by Kruskal-Wallis non-parametric test  (Kruskal & Wallis, 1952 ) using R statistical software (R Core Team, 2018). Here, the H value is the test statistic that is used to calculate the P values. Differences were considered to be significant at P < 0. 05. Standard deviations were calculated for each series of replicated measurement using the sum of individual variances weighted by the individual degrees of freedom  (Box, Hunter, & Hunter, 1978). Principal Component Analysis (PCA) was performed using the package "FactoMineR"  (Lê, Josse, & Husson, 2008)  for R statistical software.
Composition of cell wall polysaccharides Table  2  summarizes the yields and compositions of the AIS of raw apples (FR), pulp (PL) and serum (SE). Puree results are not shown given that Le Bourvellec et al. (  2011 ) described only minor modifications in CW polysaccharides from raw apples into puree during processing. Post-harvest storage was identified as the factor inducing the most significant changes in CW polysaccharides compared to raw material or refining. Consequently, for legibility, Table  2  only shows the evolution of cell wall polysaccharides and starch during post-harvest storage for not refined samples, averaged for GS, GD1 and GD2. The whole dataset can be found in Supplementary Table  S1. AIS composition of raw apples (Table  2 ) is consistent with previous studies  (Fischer & Amado, 1994; Le Bourvellec et al.
Acknowledgments: The authors are grateful to  Jean-Marie Guionnet  and  Angélique Moro  for animal care and to  Peter Biggins  for editing the English. Thanks to  Vectopole Sud  for funding the insectary where Ornithodoros moubata were raised and to the  Direction Générale de l'Alimentation  ( Framework Convention SPA-17 n • 0079-E)  and the  National Institute of Food and Agriculture in the United-States  (grant n •  2019-67015-28981 ) for the financial support to CIRAD to conduct scientific activities on ASF. We are thankful to CIRAD and  ANSES  for funding the PhD grant of  Rémi Pereira De Oliveira.
With no legal entity, an UMT is composed of at least one qualified technical institute and one public research organization or higher education establishment. UMTs are issued from calls for proposals. The projects must be in line with the scientific priorities of the two partner institutions. The expected outputs are the development of models, decision support tools, exploitation and management of databases, possible patent applications, co-authored publications in recognized scientific or technical journals, and deliveries to companies. In 2019, there were 17 UMTs for the agricultural sector (about half in the plant sector) with a strong contribution of INRA in most of them (https:// agriculture. gouv. fr/developpement-agricole-et-rural-re seaux-et-unites-mixtes-technologiques).
g. Sunrise project) and/or for advisers (UMT project) (Fig. 2 ). Multidisciplinarity and the pooling of resources are two strengths of this research system. Indeed, the work carried out is pluri-and even interdisciplinary: agronomy, ecophysiology, pathology, genetics, genomics, applied mathematics, technology, economics. In the same way, research resources are shared between the partners: networks of farmer's fields, field experiments, greenhouses, growth chambers, high-throughput phenotyping platforms, laboratories, databases, models. The UMT's research program has led to the emergence of collaborations with the RECORD platform of the INRA Centre in Toulouse  (Bergez et al. , 2013) , but also with seed companies, agricultural cooperatives, ACTA, GEVES, SRAL Midi-Pyrénées, the Midi-Pyrénées Regional Chamber of Agriculture, RMT Modélisation, UMT "Water" and UMT CAPTE.
During these years, international collaborations were also reinforced in agronomy, especially with Argentina (Unidad Integrada de Balcarce) which federates similarly to our cluster several organizations such as Conicet (research), INTA (extension) and the University of Mar del Plata (education). Research work on sunflower agronomy would not have been possible without a variety of financial supports from the Ministry in charge of agriculture (via the DGER for the UMT basic support and CASDAR funds for 5 research projects), INRA (Agriculture Scientific Direction, Environment and Agronomy Scientific Division), Terres Inovia (including the co-funding of 5 theses), the Midi-Pyrénées Region (infrastructures, support for 3 theses and projects), the ANR (via the SUNRISE Investment Program for Future, the MicMac Design project or the PROMISES project of Carnot Plant2Pro, for example), the PROMOSOL association (SUNFLO-Maladies, SUNFLOWER-Pest, DEMELER projects), and the GIS GCHP2E (internship grants).
Building together an agronomic model that can be used by Terres Inovia for diagnosis, advice and varietal assessment Several experiments had been attempted as early as the 1990s to import the agronomic modeling of sunflower within the routine activity of a technical institute such as Terres Inovia (e. g. Heol software based on EPIC À Texier, 1992; STICS Sunflower,  Brisson and Levrault, 2010). These attempts had not led to the operational integration of a crop model into the practice of engineers, whether for studies (e. g. optimal irrigation strategies), end of season analysis or collective advices. The complexity of the functioning of the crop growth models, their "black box" side and the impossibility of easily parameterizing them from current experiments limited their practical interest and consequently their appropriation by Terres Inovia  (Flénet et al.
One of the original features of SUNFLO is that it describes the intraspecific variability of phenotypes by means of about ten so-called "varietal" parameters, all of which being measured according to a standardized protocol  (Debaeke et al. , 2010). A large number of parameters are directly accessible from several platforms set up by Terres Inovia for the post-registration evaluation of varieties  (Casadebaig et al. , 2016a). Parameters of response to water stress require the use of an outdoor pot platform (Heliaphen) developed by INRA in Auzeville but open to Terres Inovia for the evaluation of new varieties  (Blanchet et al. , 2016 (Blanchet et al. , , 2018)). In addition, the soil module, which is limited to water and nitrogen availability, uses a small number of variables accessible to users either by direct measurement or via soil maps or databases from the technical institutes (e.
In addition, on these basis, we have co-constructed a prototype tool for varietal evaluation (VARIETO) with the network managers of GEVES and Terres Inovia  (Debaeke et al. , 2012b). The main applications for this tool were the agronomic diagnosis of actual trials (e. g. identification of water stress periods experienced by varieties)  (Debaeke et al. , 2012c) , pooling of trials by soil type or stress profiles, analysis of the representativeness of networks with respect to actual production conditions  (Debaeke et al. , 2012a) , and comparison of the performance of varieties of different release generations. Some VARIETO functionalities have been tested by GEVES in particular. This prefigures the prototyping of a decision support tool better connected to environmental databases and variety characteristics in order to inform varietal choice by production situation.
Developing new crop management systems In order to develop new crop management systems for sunflower, several approaches were carried out by coupling (i) classical multi-factorial experimentation (e. g. a range of plant densities applied to different varieties), (ii) processbased modelling with SUNFLO to simulate different "yieldfactor level" response curves, (iii) agronomic diagnosis in numerous farmers' plots and (iv) "cropping system" trials in a limited number of experimental stations from INRA and Terres Inovia. With regard to sunflower population density, an original approach was undertaken, coupling the analysis of real data from 38 Terres Inovia experiments and the analysis of virtual data from simulations with the SUNFLO cropping model. As a whole, more than 140 000 combinations between 16 varieties, 4 sites, 35 climatic years, 3 soil water reserve levels, 3 sowing dates and 7 stand densities were generated, providing access to a virtual database that was not possible to generate with field experiments and is very powerful for interaction analysis  (Casadebaig et al.
Similarly, during the season, early knowledge of the expected quality of harvest would facilitate the silo management for the cooperative. However, this prediction comes up against the diversity of agronomic, genetic and environmental factors that interact  (Andrianasolo et al. , 2016a). As a result, in 2011 there was no oil concentration forecasting model for sunflower encompassing all this complexity. It is with this objective in mind that the thesis of F. Andrianasolo (2014)  was decided in order to better understand and model the development of the oil (and protein) concentration of sunflower seeds (achenes). As we did not have satisfactory models for this prediction, we conducted a dual approach based on both statistical and dynamic modelling. Based on a multi-annual and multi-local database (400 cropping situations), resulting from the pooling of INRA and Terres Inovia data, several statistical models were constructed and compared  (Andrianasolo et al.
, 2018; Champolivier et al. , 2019; Attia et al. , 2020)  (Fig. 9 ). We also explored the intra-plot variability of leaf area index values and its dynamic evolution for the purposes of growth path typology and diagnosis of limiting factors (poor emergence, end-of-cycle diseases, drought). The PROMISES project supported by Carnot Plant2Pro consolidates these methodological bases with a view to developing a decision support tool for irrigation and nitrogen fertilization and providing quantity and quality collection forecasts. The originality of the approach is based on this combination of models and data observed by satellite to make a prediction not only at the scale of the agricultural plot, but also at the scale of the farm and the collection basin. This tool should be connectable to the technical-economic databases of cooperatives to benefit from the information provided by farmers.
Acknowledgements. Research on sunflower agronomy within the UMT would not have been possible without a variety of financial supports from the Ministry in charge of agriculture (via DGER for the UMT basic support and CASDAR funds for projects),  INRA (Agriculture Scientific Direction, Environment and Agronomy Scientific Division) ,  Terres Inovia , the  Midi-Pyrénées Region , the  National Research Agency, PROMOSOL  and the  GIS   GCHP2E. The  Agri Sud-Ouest Innovation  and  Competitiveness Cluster  has also labelled several projects. We sincerely thanks all these actors for supporting and trusting our research during these 10 years.
Akhani (Halophytes and C 4 Plants Research Laboratory, School of Biology, University of Tehran), the Royal Botanic Garden Edinburgh Herbarium (E), and the Herbarium of Russian Academy of Sciences-V. L. Komarov Botanical Institute (LE). Finally, we received some unpublished data such as Cyperaceae of the Arabian Peninsula, kindly provided by Dr. David A. Simpson through personal communications (Royal Botanical Gardens Kew). The published literature has been screened for all C 4 species in the area, their respective habitat, distribution, life form, choro-, morpho-, and ecotypes. Taxonomic treatment and nomenclature of the C 4 species were mainly based on global databases such as IPNI (2019) and  POWO (2019). The naming of families followed the Angiosperm Phylogeny Group classification (APG IV, 2016) with the exception of the family Chenopodiaceae, which is treated as a separate family and not as a part of Amaranthaceae following  Hernandez-Ledesma et al.
Climate Data and Statistical Analysis For the correlation of C 4 taxonomic group distributions within the study area and climatic variables, bioclimatic data were extracted from the Worldwide Bioclimatic Classification System  (Rivas-Martinez and Rivas-Saenz, 1996-2019; Djamali et al. , 2011). For a few stations we obtained climatic data of the Iranian Meteorological Organization (IRIMO) and  Scholte and De Geest (2010)   1 ). Phytogeographic boundaries after  White and Leónard (1991). We considered 29 weather stations for the evaluation of distribution of main C 4 taxonomic groups in relation to climate. The two extreme climatic conditions showing winter rainfall regime (Tehran) and summer rainfall regime (Lahore) are shown. All other climatic diagrams are depicted in Supplementary Figure  1.
, 2010; Uddin et al. , 2014). Suaeda aegyptiaca is cultivated in southern Iran and Amaranthus tricolor in Pakistan, India, and E Asia for food  (Akhani, 2006). Soda inermis, growing on saline soils throughout Armenia, Iran, Turkey, and Turkmenistan, is cultivated and highly prized as a leaf vegetable (agretti) in the Mediterranean region  (Centofanti and Bañuelos, 2015). Another chenopod, the common ruderal and ornamental Bassia scoparia, is cultivated for the production of a caviar substitute in E Asia ("Useful Temperate Plants Database";  Han et al. , 2006; Nedelcheva et al. , 2007). Recent efforts have also been done to introduce locally collected leaf vegetables (Cleome gynandra, Boerhavia sp. and Sesuvium sesuvioides) and medicinal plants [Blepharis sp. (Figure  10F ) and Tribulus terrestris] into cultivation  (Hanelt, 2001; Cheikhyoussef et al.
, 2011; Mahesh et al. , 2012; Boteva et al. , 2014; Kripa and Vijayalakshmi, 2016; Salamon et al. , 2016; 'PROTA4U web database', 2018). Furthermore, tuber bearing sedges, like Cyperus esculentus are interesting candidates as food crops, already cultivated throughout the world  (Pascual et al. , 2000; Arafat et al. , 2009). Several C 4 Eudicots [e. g. , Gisekia pharnacoides, Hypertelis cerviana, Suaeda spp. , Tecticornia indica, Atriplex spp. , and Soda and Salosla spp. ], are used locally as famine foods or collected as local leaf vegetables and may be interesting for further investigations as crop plants  (Hanelt, 2001 ; "PROTA4U web database", 2018; "Useful Temperate Plants Database"). Some C 4 plants are cultivated worldwide for their essential oils, such as Cymbopogon spp. (citronella oil, palmarosa oil, etc.
ACKNOWLEDGMENTS The authors thank the curators of the  Royal Botanic Garden Edinburgh Herbarium (E)  and the  Herbarium of Russian Academy of Sciences -V. L. Komarov Botanical Institute (LE)  for the provided plant samples, the  Iranian National Science Foundation  for a travel support to  St. Petersburg Herbarium  (AR),  Sibald Trust  for a herbarium visit to  Edinburgh Botanical Garden (HA)  and  LIA HAOMA CNRS  Project for funding the isotopic analyses. We would like to thank  Mr. Denis Fiorillo  at the  SSMIM/MNHN  for the isotopic measurements and  Dr Marie Balasse  who authorised us to use the facilities of this Laboratory. We also appreciate the help of  Dr. Claudia Deigele  ( Munich ) for providing us the unpublished carbon isotope database of late  Prof. Hubert Ziegler. Finally , we would like to thank  Dr.
This is more usual when dealing with flows from the background system, i. e. , from life cycle inventory databases (e. g. , Ecoinvent). For instance, beneficial effects B I,UAS , such as biogenic carbon dioxide sequestration, are accounted for and counter-balanced with adverse effects A I,UAS , such as fossil carbon dioxide emissions, generating the carbon footprint of the product, which would be the N I,UAS (in this case, focused on climate change). Therefore, it only makes sense to call this "carbon footprint" as Net Indirect Handprint, if the former has a negative sign (e. g. , carbon footprint ¼ À5 kg CO 2 eq), which means a net beneficial result. A few examples of footprint-consistent Indirect Handprint (Net and/or Partial), with the specific impact category between brackets, are: The absorption of NO x when using TiO 2 as coating material in buildings (generating a benefit for terrestrial acidification)  (Pini et al.
Normally, the net values (e. g. , N P,IU ) may be calculated by simple arithmetic's, in case the beneficial and adverse effects are expressed in the same units. However, more complex calculation procedures may be required, including the use of ISO 14040's optional steps for LCIA, i. e. , normalization, grouping and weighting. Moreover, the delta values are only possible to be calculated if the absolute values of the compared products were calculated beforehand. Therefore, the delta values come at a second stage on the procedure. It is important to mention that this approach is in line (and in a complementary perspective) with the Footprint concept and LCA framework. For instance, in the goal and scope definition, it is defined which handprint types may be included in the study, in the inventory analysis the necessary data will be collected (depending on the impact assessment method).
Acknowledgements The authors would like to thank the following organizations and research projects for the (partial) financial support to the researchers, which enabled their participation in this article:  EIT Raw Materials ;  REPAiR project (ID 688920 ) under  H2020-EU. 3. 5. 4. dEnabling the transition towards a green economy and society through eco-innovation,WASTE-6b-2015dEco-innovative strategies;  INTERREG FWVL program  for the  ALPO  project (2016e2020; http://www. alpo-interreg. eu/); Province East Flanders (Belgium) ;  MATTER project (Mechanical and Thermochemical Recycling of Plastic Waste ), a  Catalisti-ICON  project, supported by  Flanders Innovation and Entrepreneurship (VLAIO).
| Computerized data files PAG recorded into JMP © data files each population from the transcribed laboratory book noting the variables Bateman (1948) listed as "distinctive features" of each population. The primary data set we constructed summarizes observations AJB reported by hand in his laboratory notes as a set of 65 tables, each representing a unique population. The observations included the observable phenotypes of 20,417 adult offspring from 65 populations, representing 1,300 parental nametag combinations in the reported adult offspring from 65 populations. We devised unique names for each population using the "distinctive characteristics of each population" as described in  Bateman (1948).
DATA AVA I L A B I L I T Y S TAT E M E N T The data set we constructed summarizes observations AJB reported by hand in his laboratory notes as a set of 65 tables, each representing a unique population. The observations included the observable phenotypes of 20,417 adult offspring from 65 populations, representing 1,300 parental nametag combinations in the reported adult offspring from 65 populations. We devised unique names for each population using the "distinctive characteristics of each population" as described in  Bateman (1948). The original handwritten laboratory note data were in the form of 65 separate tables each describing the adult female and male nametag phenotypes in a particular population along with the inherited nametag phenotypes in offspring. Each table catalogues the number of offspring inheriting nametag phenotypes from each adult in a population.
Geoinformation to Support Agricultural Policies Thanks to its capacity to observe the Earth at local, regional, and global scales and with different thematic focuses, remote sensing is an essential technology for providing geoinformation in support to land and agricultural policies. The use of remote sensing for mapping land characteristics has been studied for ∼40 years, resulting in the use of a large number of land surface and climatic variables and products for application demonstrations. The main EO variables and products identified in Table  1  to support agricultural public policies can be categorized as follows: -The baseline maps, to ensure spatial consistency within heterogeneous datasets; -The land use and land cover maps produced at high spatial resolutions and updated on a regular basis, to monitor the land changes (evaluation, prospective), support the statistical sampling protocols (agricultural census), or manage the agricultural risks and opportunities; -The biogeophysical maps of crop (phenology, biomass, growth anomalies, etc.
Overview of the EO Products Available for Agricultural Monitoring Baseline Maps Metric and sub-metric resolution images are used to produce baseline maps that provide geographically localized information and offer a shared vision for a territory. To date, there are no baseline maps at the global scale, but projects to produce these maps at national scales are multiplying throughout the world. In West Africa, Benin 9 has recently joined the circle of those countries (Senegal, Mali and Burkina Faso 10 with an up-to-date and homogeneous national topographic database and the tools to exploit them. The maps were produced at a scale between 9 https://www. geobenin. bj/. 10 https://www. ignfi. fr/en/portfolio-item/cartographie-burkina-faso/). 1:50 000 and 1:200 000, ranging from mosaics of satellite high resolution orthoimages (e.
Waldner et al. (2015)  evaluated the available cropland datasets at the global scale and concluded that Africa (mainly West African and South African countries) is one of the highest priority areas for the improvement of cropland mapping. At the national scale, land cover maps have been produced in several African countries, mainly in the context of North-South partnership projects or national land cover programs. For instance, in the framework of the Africover and the Global Land Cover Network projects overseen by the Food and Agriculture Organization (FAO), more than fifteen countries, mainly in East Africa, have been mapped through visual interpretation of Landsat images  (Latham et al. , 2014). Recently, the Sen2-Agri project has developed a platform to produce monthly dynamic cropland masks and cultivated crop type maps at a 10 m resolution twice during the agricultural season based on Sentinel-2 and Landsat-8 data  (Defourny et al.
, 2019). Finally, at the subnational scale, many isolated land cover projects were conducted based on the need for research projects or thematic expertise (e. g. , the AfricaRISING program in southern Mali 12 However, these maps are generally not accurate enough for the operational requirements of a large-scale project, such as land use plans and the computation of agricultural statistics. Land use maps at a relatively high spatial resolution can be produced on request for large areas, often as a part of a project, such as the production of the Burkina Faso land cover database (BDOT 13 at a 1/100 000 scale developed by the Geographic Institute of Burkina Faso (IGB).