dataesr/scientific-paragraphs-categorization huggingface dataset:
A Lack of Ancillary Data, With a Limited Accessibility Processing and analysis of satellite images required ancillary data. For instance, atmospheric information from ground station are needed to calibrate correction model during the preprocessing phase of satellite images. Besides, information related to topography, soil type, climate, or agricultural statistics are very useful for image processing and evaluation of resulting products. However, since the 1980's weather systems and agricultural statistics systems of several African countries underwent an ongoing decrease of quality, due to a lack of resources and institutional coordination  (World Bank et al. , 2010). This results in the paucity of ground databases, which is critical for developing and assessing the accuracy of remote sensing based indicators and methods  (Becker-Reshef et al.
, 2020). NDVI and precipitation products are also largely used for crop and pasture yield forecasting and estimation. Many African countries have developed their own forecasting systems based on different datasets and models and produce monthly or seasonal bulletins. In South Africa, the official crop forecast for summer and winter crops is released monthly by the Crop Estimates Committee. Remote sensing data are used for crop condition monitoring on a broad scale, but despite some progress, crop acreage assessments using remote sensing data has never reached an operational stage in South Africa, mainly due to resource constraints  (Bernardi et al. , 2016). In Senegal, the annual biomass is estimated on an operational basis to monitor fodder availability in the national pastoral rangelands.
The FAO Desert Locust Information Service (DLIS) is the key monitoring and early warning tool in preventing desert locust plagues from devastating farmers' fields in Africa and Asia. The DLIS operates an early warning system that monitors weather (rainfall and temperature derived from models, satellite images or ground stations), ecological conditions (vegetation detection and vegetation period length and density), and locust infestations in the potentially affected area on a daily basis  (FAO, 2015). The DLIS produces monthly situation summaries and forecasts for each country and sends warnings and alerts about potential invasions or other significant developments to affected countries and donors. Apart from a few regional successes, the general scarcity of information services over the African continent (as in most regions of the world) is explained by the relatively high difficulty in designing decision support products compared to that of using off-the-shelf products, and by the difficulty in implementing research products over large areas as they often include complementary data and methods adapted to the local conditions (e.
The gap between EO and science policy is reinforced in African countries where the technology resources strategy, largely driven by institutions of the Global North, aims to share data and products that only partially meet African demand. The data processing methods are often inadequate for the African agricultural systems, which are more diversified and much less documented than agricultural systems in industrialized countries. The environmental conditions, the diversity of agricultural systems, the scarcity of free images at very high spatial resolutions, the paucity of ground databases, and the challenges of partnerships are just some of the transfer and adaptation constraints. There are also other reasons that compromise the quality of the services available. Many of these challenges have been reported during the UN Information for Meeting Africa's Agricultural Transformation and Food Security Goals conference  (IMAAFS, 2014).
Another need is to update and make available "basic" geographic data such as topographic maps, land use maps and cadastral maps. Some work is often necessary to improve these maps, which are "often out of date and in analog form. Cadastral maps or databases are mostly unavailable; coherent and standardized geographic names are not available yet; and in most cases geodetic controls are not yet unified and adjusted and are not in sufficient density for information management in Africa" (UNECA, 2017). This raises the question of whether or not these services should incur a fee: do they constitute a public service or should users participate financially? In any case, the pooling of resources helps to reduce costs. Pooling of resources is also recommended for georeferenced updated field data, that are indispensable for developing sustainable services based on remote sensing.
g. ,  Montfort et al. , 2020). Today, access to data is facilitated by data repositories and many freely available datasets. Data processing capabilities can be resolved by cloud-based platform such as Google Earth Engine. The arrival of very promising tools and approaches, such as machine learning or deep learning algorithms applied to satellite imagery, in conjunction with relatively high frequency and resolution data, should boost the Earth observation market in the upcoming decade, provided that massive training databases are available, which is not yet the case for many countries in Africa and in other regions of the world. To benefit from this technological advancement and bridge the gap between technical analysts and policy makers, some key points are fundamental: capacity building, political, will and institutional commitment, public-private partnership, and proofs of concept.
Data analysis Data were analysed using R software (R Core team, Version 3. 5. 0; R Studio, Version 1. 1. 453). The panel performance for CATA questions was checked using the reproducibility index (Ri), as described by  Campo et al. (2008) , and calculated on the replicate sample to check assessor repeatability. For the CATA descriptors of selected data, the attributes on the list were ranked according to their citation frequency to identify the most relevant descriptors for tasting and to better distinguish each extract. A Correspondence Analysis (CA) was performed on the CATA frequencies table (descriptors in rows and evaluated extract in columns) using Factominer R package  (Lê et al. , 2008). CATA significant attributes were assessed with Cochran's Q test  (Varela and Ares, 2012). Since CATA was performed as a preliminary approach to underlining attributes potentially relevant for further investigation, the criteria for significance was increased to p < 0.
1 to avoid missing relevant terms. The panel performance for descriptive analysis (DA) was evaluated using PanelCheck software (version 1. 4. 2). The panel performance was tested on the subset of replicate samples by three-way ANOVA with "replicate", "sample" (extract), and "assessors" as factors and their interaction. The evaluation was considered adequate when there were no significant differences (p > 0. 05) in "assessors"*"replicates" and "assessors"*"sample" interactions. Principal component analysis (PCA) as Tucker-1 plots were performed for both attributes and assessors as described by  Tomic et al. , 2010. The assessors' projections were checked in the loading plot for bitterness, overall astringency, particulate, irritation and surface smoothness in order to assess their agreement on each attribute.
DA results Panel performance was evaluated using a PCA approach (data not shown) to understand the consensus in the attributes investigated and ANOVA for the effects of assessors, extracts and replicates and their interactions in order to assess panel repeatability. Regarding consensus, assessor projections were grouped in the loading plot for overall astringency, particulate, and surface smoothness, showing that the panel had agreed on the interpretation of these terms. For irritation, however, the assessors were spread over the loading plot, which suggests that the assessors did not interpret this attribute in the same way. In addition, for the terms involved, the ANOVA test for assessors resulted in significant differences (p < 0. 05), underlining the differences in the evaluation of these attributes on the scale.
Acknowledgments The authors will like to thank all technicians that helped in any form to bring out this findings, especially technicians at  KNUST, Kumasi. The authors will also like to declare that the research was conducted using the physical facilities of  Kwame Nkrumah University of Science and Technology, Kumasi, Twene Amanfo Senior Secondary School, Sunyani and Notre Dame Girls Senior Secondary School, Fiapre  and it was financially supported by the authors.
m. local time  (42) ] as well as by the evaporation of water droplets from leaves after rain events, which both impede photosynthesis when VPD is very low by slowing carbon diffusion  (27, 28)  and making vegetation more susceptible to fungi  (29, 30). It has been shown that during light precipitation events, canopy interception can be up to 60% of total rainfall in tropical rainforests  (43) , and evaporation from intercepted canopy water in the tropics can represent up to 15% of total evapotranspiration (ET)  (44). However, it should be noted that the evaporation of morning dew is not a major explanation for these results, as a separate analysis using contiguous SIF  (45) , a dataset based on a satellite observations at 1:30 p. m. (when VPD is higher), also reveals similar sensitivity to the morning time SIF.
The SIF data are averaged with the eight adjacent pixels surrounding the pixel of interest to smooth the dataset, which is inherently noisy. Although it is known that there has been GOME-2 SIF sensor degradation, here we assess seasonal to subseasonal scale responses, and thus, an interannual trend should not contribute to our findings. For each pixel location, two inputs are used per each of the five datasets (to equal 10 predictors total) to represent both the mean value of each variable as well as its variability (both temporally and spatially). We use (i) the temporal mean minus the spatial tropic wide mean, divided by the temporal SD, and (ii) the temporal SD minus the spatial mean of the temporal SDs, divided by the SD of the temporal SDs.
Smoothed monthly SIF  (42)  from June 2007 to May 2016 is used as a response variable. Because we are interested in the effects of soil moisture but are limited by the depth of measurement of microwave observations over the study period (a lack of remotely sensed root-zone soil moisture data), especially in dense tropical forests, precipitation data at different monthly lags are used instead. As additional predictor variables, precipitation data 2 and 4 months ahead of the other datasets were used to incorporate memory in the system of earlier rainfall. We choose 2-and 4-month lags based on Humphrey et al. (59) , where it was established that the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage has the highest correlation with precipitation data in the tropics for periods varying between 1 and 4 months.
This is also confirmed using monthly in situ soil moisture profile and precipitation data from BAN, the only Amazonian flux tower site with soil moisture data publicly available and which displays a 2-month lag (fig. S9 ). Adding the lagged precipitation data improves model performance demonstrated by increases in r values. All datasets are normalized by the cluster mean and SD before running the models. Analyses are also performed with the addition of near-surface air temperature as a predictor variable, but r values do not improve and so these data were excluded from the analysis. As an additional check, analyses are also performed using the near-surface air temperature in lieu of VPD, but this degrades model performance throughout the study area, reducing r values by an average value of 0.
PolyA+ library preparation was performed from 500 ng total RNA using NEBNext Ultra II Directional RNA Prep Kit for Illumina (New England Biolabs) according to manufacturer's instruction and sequenced on a NextSeq550 Instrument (SE 75 bp). All data treatments were carried out under a local galaxy instance  [35]. Reads quality was checked using the Fastq-X toolkit  [36]  and since all reads display a Phred score above 26 over 90% of the their length no subsequent quality filtering was done. Adpator trimming was then performed using CutAdapt  [37]. Paired-end mapping to the C. gigas reference genome (assembly version V9  [13] ) was performed using RNAstar using default parameters (Galaxy Version 2. 4. 0d-2 [38] ). The HTSeq-count was used to count the number of reads overlapping annotated genes.
The parameters used were; mode = Union, Stranded = No, Minimum alignement quality = 10, Feature type = exon; ID attribute = gene_id; all other parameters used the default value (Galaxy Version v0. 6. 1) [39]. Finally, the differential gene expression levels were analysed with the DESeq2 R package  [40]. Fold changes between each resistant and susceptible oysters were considered significant when the adjusted p-value (Padj) for multiple testing with the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR), was < 0. 05. Thus, each resistant family (6 replicates) was compared to each susceptible family (6 replicates) separately (RF21 vs. S F11 , R F21 vs. S F14 , R F21 vs. S F15 ; R F23 vs. S F11 , R F23 vs. S F14 , R F23 vs. S F15 ; R F48 vs. S F11 , R F48 vs. S F14 , R F48 vs.
Gene ontology annotation and enrichment analysis To work with current functional annotations of the C. gigas gene set, we performed a functional annotation (Additional file 6). Blastx comparison against the NR database was performed for the 28,027 genes annotated in the genome, with a maximum number of target hits of 20 and a minimum e-value of 0. 001. From these 20 hits, a percentage of mean similarity was calculated and we retained only results with mean similarity > 40%. XML blast result files were loaded onto Blast2GO  [41]  for GO mapping and annotation with the b2g_sep13 version of the B2G database. These results were used as inputs for GO enrichment analysis, which was performed using adaptive clustering and a rank-based statistical test (Mann-Whitney U-test combined with adaptive clustering).
The R and Perl scripts used  [42]  can be downloaded [https://github. com/z0on/GO_MWU]. The following parameters were used for adaptive clustering: largest = 0. 5; smallest = 5; clusterCutHeight = 0. 25. For the continuous value characterization of each gene in the dataset, we used a strategy aiming to take into account both the level of expression and the significance of the differential expression. To combine these two factors, the log2 fold change was attributed to genes that were significantly differentially expressed (adjusted p < 0. 05), while a zero was attributed to the others. A category was considered enriched with a FDR < 1%. DNA extraction and quantification of OsHV-1 and total Vibrio DNA extractions were performed from the same samples used for RNA extraction using the Nucleospin tissue kit (Macherey-Nagel) according to the manufacturer's protocol.
Acknowledgements We warmly thank the staff of the  Ifremer site of Argenton (LPI, PFOM) and Sète (LER) , and the  Comité Régional de Conchyliculture de Méditerranée (CRCM)  for technical support in the collection of the oyster genitors and reproduction. We also thank  Vangeli Geshkovski ,  Maxime Lafont ,  Eve Toulza  and  Delphine Destoumieux-Garzón  for technical support or fruitful discussions and the  Bio-Environnement platform of the University Perpignan Via Domitia  for support in NGS library preparation and sequencing. The authors are grateful also to  Philippe Clair  from the qPHD platform/Montpellier genomix for RNA quality control and useful advice. This work, through the use of the GENSEQ platform (http://www. labex-cemeb. org/fr/genomique-environnementale-2)from the  labEx CeMEB , benefited from the support of the  National Research Agency  under the " Investissements d'avenir" program  (reference  ANR-10-LABX-04-01 ).
In our analysis, we assume that the time necessary to renew the total amount of P present in a Hedley pool (MRT) can be equated to the time necessary for phosphate ions in solution to exchange with all phosphate ions located in this pool (Fig. 1a ). This allowed us to calculate MRT using the function for isotopically exchangeable P as determined in isotope exchange kinetic experiments. However, it also means that potentially important processes influencing P MRT, such as biological and solid-state P transformations, were neglected. The second objective of this work was to determine if variation in MRT among soils could be explained by soil properties and climatic variables. To meet these aims, we searched the literature for soil samples for which both P sequential extraction and kinetic isotope exchange data were available.
Dataset We found 53 soils for which both P sequential extraction and isotope exchange kinetic data were available. Twelve samples were from a Hawaiian climatic gradient  (Helfenstein et al. , 2018a) , five from a long-term field trial in Switzerland  (Keller et al. , 2012) , 11 from a study of different soils throughout New Zealand  (Chen et al. , 2003) , 14 from a forested geosequence in Germany  (Lang et al. , 2017) , seven from field experiments on highly weathered soil in Colombia  (Buehler et al. , 2003; Oberson et al. , 1999) , and four from an agricultural field trial on Fluvisols in Italy  (Borda et al. , 2014). Information on soil properties (pH, soil texture, organic C concentration), climate (mean annual temperature and mean annual precipitation), soil P pools (total P, resin-extractable P, NaHCO 3 -extractable inorganic P, NaOHextractable inorganic P, HCl-extractable P), and P exchange properties (P w , m, n, P inorg ; see Sect.
This yielded unreasonably high MRTs because of the asymptotic behavior of the E (t) equation. In the discussion, we briefly discuss how excluding these soils affected multipleregression models. Despite only encompassing 53 soils from 20 geographic sites, the dataset included samples from a wide set of climatic conditions (Supplement Fig. S1 ), and a variety of soil texture classes (Fig. S2 ). Eleven of the soil samples were from arable land use, 14 from forest, and 28 from grassland. The world reference base soil orders entailed were Acrisol (1), Andosols (17), Cambisols (19), Ferralsols (7), Fluvisols (4), and Luvisols (5)  (IUSS Working Group WRB, 2015). While some of the soils are considered to be low in available P  (Buehler et al. , 2003; Lang et al. , 2017; Oberson et al. , 1999) , the dataset also includes soils developed on P-rich parent materials  (Helfenstein et al.
, 2001; Pistocchi et al. , 2018; Weiner et al. , 2018). This holds especially true under grassland or forest and under low P availability. We consider the later explanation less likely, since microbial uptake-release tend to be negligible during the isotopic exchange kinetic experiments  (Oehl et al. , 2001) , or it is suppressed using microbial inhibitors  (Bünemann et al. , 2012). However, we cannot completely rule out such an influence as in most soil samples included in our dataset this effect was not systematically tested. The pH dependence of MRT in HCl-P is likely because the composition of the HCl pool varies strongly with pH. Under high pH, the HCl pool tends to contain apatites, calcium phosphate minerals which are highly stable  (Moir and Tiessen, 1993; Nriagu, 1976). Our results predict that under such conditions, MRT of HCl-P may be on the order www.
According to this assumption and using the data published by  Buehler et al. (2002)  from the same Ferralsols also included in our dataset, we could estimate a MRT of the NaOH pool between 7 and 14 d (soils under savanna and pasture) or longer than 2 weeks (two soils under rice). These values are similar to MRTs from our study: 1 and 5 d for the soils under savanna and pasture, respectively, and 28 and 88 d for the soils under rice. Generally, specific activity of 33 P in the HCl pool did not equilibrate during the duration of the experiment (2 weeks to 3 months, depending on the study), suggesting longer MRTs for this pool  (Buehler et al. , 2002; Bünemann et al. , 2004; Pistocchi et al. , 2018; Vu et al. , 2010). Nevertheless, for stable pools such as the HCl-P, it is questionable whether our estimates of MRTs are realistic, as the extrapolation of E values (Eq.
, 2017). resented in our study, while other important soils such as Vertisols, Podzols, or carbonate-or organic-matter-rich soil orders were not represented or underrepresented. In addition, soils with large amounts of NaOH-and HCl-P were overrepresented in our study compared to a larger global dataset (Fig. S5 ). However, our resin P values closely match resin P frequency distribution of a larger global dataset  (Hou et al. , 2018a). In terms of P exchange kinetics, our soils covered the full range of reported m and n values and can thus be considered to reflect the full range of P exchange kinetic properties observed in soils (Fig. S6 )  (Achat et al. , 2016; Helfenstein et al. , 2018b). Assumptions taken to calculate MRT of P in soil P pools required making several simplifications. Our approach only considers a simplified system of soil and water in steady-state conditions and excludes biological activity.
Acknowledgements. The authors would like to thank all primary data producers whose data was re-used in this study. We are also very grateful to  Enqing Hou ,  Lin You ,  Sebastian Doetterl ,  Peter Vitousek  and an anonymous reviewer for providing valuable comments to improve the manuscript. Also, the authors are grateful to fruitful discussions with  Janine Burren ,  Philippe Deleporte ,  Timothy McLaren , and  Federico Cantini. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. 200021_162422 ). Review statement. This paper was edited by  Edzo Veldkamp  and reviewed by  Enqing Hou  and two anonymous referees.
Acknowledgments The ideas presented in this article were developed during the workshop "The hierarchy-of-hypotheses approach: Exploring its potential for structuring and analyzing theory, research, and evidence across disciplines, " 19-21 July 2017, and refined during the workshop "Research synthesis based on the hierarchy-of-hypotheses approach, "  10-12 October 2018 , both in Hanover, Germany. We thank  William Bausman ,  Adam Clark ,  Francesco DePrentis ,  Carsten Dormann ,  Alexandra Erfmeier ,  Gordon Fox ,  Jeremy Fox ,  James Griesemer ,  Volker Grimm ,  Thierry Hanser ,  Frank Havemann ,  Yuval Itescu ,  Marie Kaiser ,  Julia Koricheva ,  Peter Kraker ,  Ingolf Kühn ,  Andrew Latimer ,  Chunlong Liu ,  Bertram Ludäscher ,  Klaus Mainzer ,  Elijah Millgram ,  Bob O'Hara ,  Masahiro Ryo ,  Raphael Scholl ,  Gerhard Schurz ,  Philip Stephens ,  Koen van Benthem  and  Meike Wittman  for participating in our lively discussions and  Alkistis Elliot-Graves  and  Birgitta König-Ries  for help with refining terminology.
Furthermore, we thank  Sam Scheiner  and five anonymous reviewers for comments that helped to improve the manuscript. The workshops were funded by  Volkswagen Foundation  ( Az 92 , 807  and  94 , 246 ). TH, CAA, ME, PG,  ADS , and JMJ received funding from  German Federal Ministry of Education and Research  within the  Collaborative Project "Bridging in Biodiversity Science " (grant no. 01LC1501A ). ME additionally received funding from the  Foundation of German Business , JMJ from the  Deutsche Forschungsgemeinschaft  (grants no. JE 288/9-1  and  JE 288/9-2 ), and IB from  German Federal Ministry of Education and Research  (grant no. FKZ 01GP1710 ). CJL was supported by a grant from The  Natural Sciences and Engineering Research Council of Canada  and in-kind synthesis support from the  US National Center for Ecological Analysis and Synthesis.