dataesr/scientific-paragraphs-categorization huggingface dataset:
These experiments aimed at observing the long-term field performance and DED tolerance of a broad array of field elm clones belonging to the national collection. In addition, the hedgerow-type plantations were evaluated for the capacity of native elms to thrive in hedge restoration projects with other trees and shrubs. These objectives were met, as the results presented here were extracted from data based on more than 700 elm trees from 38 native field elm clones, after nine years (or more) of experimentation in 16 tests in three different regions of France and in different environments. Similar information about native European elm clones has rarely been published. Girard (2007) reports on 11 plantations of the Institut pour le Développement Forestier (IDF), comparing five elm cultivars and four native elm clones of the French national collection but only four years after plantation, i.
The current height (i. e. , height of the highest living leaf) of all trees was measured in this period. The wilt percentage of diseased trees was estimated. From the situation of the  symptoms in a part of the tree crown and by comparison with neighbouring trees, the cause of wilting could clearly be attributed to O. novo-ulmi and not to water stress. In doubtful cases with symptoms restricted to a single branchlet, early stage wilt was scored 1% but combined with 0% scores for data analysis because these trees may still be perfectly "healthy" if symptoms had a cause other than DED (e. g. , a twig-mining larva). In case of a new infection causing dieback, the height of the tree before infection was measured. From 2016, a "loss of crown size" percentage was also estimated to quantify the dieback of the branch architecture of the tree.
Acknowledgments The elm clone testing programme of IRSTEA has received the financial support of the  French Ministry of Agriculture ; the plantings of tests #21 and 31 were respectively carried out in collaboration with the  Departmental Council of Calvados  and  J. Rieffel Agriculture School  ( F44800   Saint-Herblain ). The cooperation of  Monique Guibert  and  Aurore Desgroux  is acknowledged in arranging T. Pozzi's assistance with data analysis after E. Collin's retirement. The authors thank  Luc Harvengt  and  Aurore Desgroux  for their improvements to the manuscript, and  Andrew Brookes ,  Timothy  and  Peter Stafford  for their kind linguistic assistance. Deep gratitude is expressed to the three anonymous reviewers for their very helpful comments and suggestions, and to the diverse people, too many to mention by name, who have assisted with the testing presented in this work.
three case studies repeating previously published national scale analyses with data from a larger spatial extent. The case studies, originally done in France, Germany and the UK, explore various aspects of weed ecology (community composition, management and environmental effects and within-field distributions) and use a range of statistical techniques (canonical correspondence analysis, redundancy analysis and generalised linear mixed models) to demonstrate the utility and versatility of the AWME database. We demonstrate that (i) the standardisation of abundance data to a common measure, before the analysis of the combined dataset, has little impact on the outcome of the analyses, (ii) the increased extent of environmental or management gradients allows for greater confidence in conclusions and (iii) the main conclusions of analyses done at different spatial scales remain consistent.
These case studies demonstrate the utility of a Europe-wide weed survey database, for clarifying or extending results obtained from studies at smaller scales. This Europe-wide data collection offers many more opportunities for analysis that could not be addressed in smaller datasets; including questions about the effects of climate change, macro-ecological and biogeographical issues related to weed diversity as well as the dominance or rarity of specific weeds in Europe. K E Y W O R D S abundance measures, arable plants, cover estimates, data collection, management, nomenclature, plot size, sampling bias, weed community, weeds.
| INTRODUCTION Weed vegetation surveys are commonly used in weed science to assess the impacts of agricultural practices on weed flora (e. g. ,  Pinke et al. , 2012) , determine causes of yield losses (e. g. ,  Adeux et al. , 2019) , monitor conservation efforts (e. g. ,  Kolářová et al. , 2013)  and map species distributions (e. g. ,  Hanzlik & Gerowitt, 2012). Many surveys are designed for a particular purpose and so the collected data, and associated metadata, are specific to the particular research question. As such, the resulting local databases can contain different methodologies and data types  (Bürger et al. , 2022). Despite such discrepancies, there is potential for added benefits when surveys are analysed in combination, as demonstrated by the success of several plot-vegetation databases (e.
g. , European Vegetation Archive [EVA]  [Chytrý et al. , 2016] , European Weed Vegetation Database  [Küzmič et al. , 2020] ). The Arable Weeds and Management in Europe (AWME) database  (Bürger et al. , 2020)  provides a new resource for combining arable weed survey data and complementary management information from across Europe. The value of combining data or findings from multiple studies is clear, as illustrated by several meta-analyses aiming at improving our understanding of the response of weed communities to drivers of change (e. g. ,  Gu et al. , 2021; Richner et al. , 2015). Meta-analyses use previously published results to understand the net effect of a specific driver on weed community response. However, the utility of this approach is limited by the accessibility of published statistical results and appropriate measures of confidence in published articles.
There is, therefore, scope to provide more robust analyses by returning to the raw data, as demonstrated by the success of similar data collections in adjacent scientific disciplines (e. g. , CESTES database  [Jeliazkov et al. , 2020] , which is dedicated to analyses at the metacommunity level including species traits). However, the key challenges facing analysts using the AWME database (Table  1 ) has not been widely explored. Here, we will explore the utility of the AWME data collection using three case studies. In each case study, we will focus on an analysis previously published using data from a national scale weed survey (each of which is a component dataset within the AWME database). Each of the original studies posed a different question about weed communities and their ecology. Using a three-stage process we will determine whether (a) the data contained within AWME allow this question to be addressed at the European scale, (b) it is possible to overcome key challenges associated with analysing data gathered for different purposes and (c) the conclusions of the original publication and our reanalysis remain consistent across scales.
| The AWME database In 2019, the working group Weeds and Biodiversity of the European Weed Research Society set out to form a data collection of primary arable weed vegetation records. The resulting AWME  (Bürger et al. , 2020)  database currently comprises 36 surveys of arable weed vegetation conducted between 1996 and 2018 across 12 countries (Tables  S1  and S2 ) and contains >40 000 observations of weed vegetation. The unifying feature of these records is that each consists of a list of species found on a plot of a specific size from an arable field or its margin. Observations are complemented by metadata including the survey date and geographic coordinates. The distinguishing feature of the AWME database (from other collections such as EVA) is that observations are supplemented with information on the agricultural management of the survey site, making the data more useful to agronomists and weed scientists seeking to understand the impact of agricultural practices on weed communities.
These agricultural T A B L E 1 Key challenges associated with the analysis of data coming from multiple weed vegetation surveys and potential solutions that are incorporated within the Arable Weeds and Management database and our analyses. (vi) Disparity in plot size between surveys Plot sizes vary according to the purpose of the survey, and available resources. Observations on larger plots will likely have higher species richness, and the co-occurrence of species may depend on plot size  (Chytrý & Otýpková, 2003). Information about survey methodology and plot size are included within AWME so that the user may make an informed decision as to which approach best suits their needs. Retained all plot sizes in analysis (FR and DE) as previous work has shown that analysing data from varying plot sizes does not introduce strong bias  (Peterka et al.
, 2020 ) Accounted for differences between surveys (not just plot size) by including the survey as a random effect (UK). management data include information on crop, previous crops and rotations, tillage or weed control treatments. The 36 included weed surveys were conducted using different combinations of sampling design and methodology with varying levels of complexity. Whilst some studies had a narrow focus on a single crop, study region, or growing season, other studies were designed at a national scale sampling many crops and/or growing seasons. Different surveys focussed on distinct parts of the field, for example, to study edge effects or to compare plots with and without herbicide treatments. Most of the surveys in AWME were done just before crop harvest, when most weed species were mature, however others focussed on earlier crop growth stages concentrating on significant time points for weed management decision-making.
Each of the groups were formed by different explanatory variables. For the variation partitioning, we used Hellinger transformation to avoid horseshoe effects and to reduce the weight of rare species  (Legendre & Gallagher, 2001). We used 500 permutations of the analysis to test for significance. In the data from AWME, there were some correlations between environmental variables. However, to keep the analysis as similar as possible to the original study we opted to retain all variables in our analyses. We used the vegan package in R  (Oksanen et al. , 2022)  to calculate the RDA and the variance partitioning respectively. In contrast to the multivariate analyses in the FR and DE case studies, we used generalised linear mixed effects models (GLMMs) in the UK case study to investigate the effect of crop, herbicide treatment (presence/absence) and position in field (core/edge) on weed species richness and abundance.
| Step 1: Reframing the scope To address some of the challenges associated with combining disparate datasets several of the variables used in the original studies have been consolidated or simplified within AWME (Table  1 ) as such it was necessary to redefine the scope of each of our case studies and repeat the analysis from the original publications using only the original data in the form as it is contained within AWME. This gave us a baseline result within the original spatial extent and before any additional data transformations. In Step 1 of case study FR, we redefined the scope of the analysis to include only 11 of the original 14 explanatory variables analysed by from WorldClim data version 2. 1  [Fick & Hijmans, 2017] ), and three soil properties (phosphorus and nitrogen content, and pH from LUCAS grids  [Orgiazzi et al.
, 2018] ). This gave 10 variables for use in the RDA compared to the original work by de Mol et al. (  2015 ) which used 14 variables in their analysis of the weeds of maize crops in Germany. In their original study, which is the focus of our UK case study,  Metcalfe et al. (2019)  did repeated analyses on datasets from the same fields before and after herbicide application. However, datasets of this kind are rare in AWME and so we redefined the scope of the analysis to instead include herbicide treatment (presence/absence) as an explanatory variable. We also included crop type and field part (core/ edge; in the original publication this was a continuous variable representing transects with samples at various distances from the edge).
Fixed effect Step 1. UK (counts) N = 24 432 Step 2. UK (rescaled to zero mean and unit variance) N = 24 432 Step consolidation of several environmental and management variables and an associated reduction in the resolution of the data, this has little impact on the ability to answer important ecological questions using the data stored within AWME. In all three case studies, we were able to alter the scope of the question slightly to allow the inclusion of additional data sets from across Europe. We identified several key challenges associated with combining data from multiple surveys. Some of these challenges are addressed within the AWME database itself, but for others it was necessary to address them in our analysis (Table  1 ). Through our three case studies, we highlighted some exemplary solutions to the challenges of combining disparate datasets.
This may be explained by the fact that the original datasets from France, Germany and the UK, were collected on a national scale representing a long gradient of 'heterogeneous' data with almost unique species composition in each plot. In this case, presence-absence data is sufficient to describe between-site variation. Whereas, in the case of more homogeneous data at the scale of a small region where many species are distributed in most sites, species abundance becomes the main source of between-site variation and switching to presence-absence data will have a more serious impact  (Austin & Greig-Smith, 1968 ). These findings suggest that future monitoring approaches aiming at analysis of species composition could focus on achieving a large sample size and reduce the sampling effort on each plot by estimating species abundance rather than counting individuals.
This could allow a broader range of environments and/or management practices to be considered for the same sampling effort. It is interesting to note that whilst the trend in the results observed for the UK dataset and the European dataset were similar in our UK case study, the magnitude of the abundance and species richness metrics diverged between the datasets. For both metrics, the absolute values of predictions for herbicide-treated and untreated plots in the UK were very similar whilst at the European scale values were high in the untreated plots and much lower in the plots which had received herbicide. This exemplifies some of the key challenges described in Table  1. For the UK data, the herbicide-treated and untreated data come from the same plots which were sampled before and after herbicide treatment and so we would expect the weed communities to remain similar with a loss of some individuals and species following treatment.
However, in the rest of the AWME database the treated and untreated plots may come from vastly different surveys with different plot sizes and or different methodologies. It is also likely that the choice of the data collector to conduct a survey with or without herbicide treatment reflects the typical agronomy of the field, farm or region being surveyed. As such, survey data from sites with low or no herbicide use could be expected to have higher weed species richness and abundance than those where herbicide use is common  (Hyvönen & Salonen, 2002). The choice to rescale the data within each dataset led to information loss on the absolute size of effect, however, it allowed us to consider all datasets with information on our variables of interest and the step-wise analysis confirmed that this technique was effective in allowing us to understand the relative effects of our explanatory variables.
In all three case studies, we found some consistency in the conclusions that could be drawn at the national scale and at the European scale, particularly in terms of the role of management practices. In fact in all three cases, we saw an increase in the certainty of predictions or in the explanatory power of the fitted model. This is primarily due to the additional data incorporated into the analysis, but it also indicates that the patterns observed at the national scale are largely supported by the additional datasets as any contradictory data would likely weaken the strength of the results. Where we observed differences in the results between the national scale and the continental scale was largely in the role of the environment. In the FR case study, crop management variables were the key drivers of weed community composition at the national scale with soil pH and precipitation being the most important environmental drivers.
We have demonstrated the utility of gathering weed survey datasets into a European-scale database and explored the challenges and opportunities that such a database presents. We demonstrated that the European scale allows us to confirm and enrich previous works. Our case study results should encourage us to use existing datasets to tackle more ambitious issues which require the perspective of a larger geographical area or a range of spatial scales as these can be combined with minimal loss of information despite different methodologies. The AWME database is a growing collection, and we welcome new data contributions and requests for data for analysis.
The utility of the 'Arable Weeds and Management in Europe' database: Challenges and opportunities of combining weed survey data at a European scale Helen Metcalfe, Jana Bürger, Christoph Von Redwitz, Alicia Cirujeda, Silvia Fogliatto, Denise F Dostatny, Bärbel Gerowitt, Michael Glemnitz, José L González-andújar, Eva Hernández Plaza, et al. To cite this version: Helen Metcalfe, Jana Bürger, Christoph Von Redwitz, Alicia Cirujeda, Silvia Fogliatto, et al. The utility of the 'Arable Weeds and Management in Europe' database: Challenges and opportunities of combining weed survey data at a European scale. Weed Research, 2022, 63 (1), pp. 1 -11. 10. 1111/wre. 12562. hal-04028758.
ACKNOWLEDGEMENTS This work was made possible via the Weeds and Biodiversity working group of the  European Weed Research Society. This project was funded under a  Rothamsted Research  and  BBSRC  grant number  BB/ W510543/1. Helen Metcalfe  was partly supported by the  Natural Environment Research Council (NERC)  and the  Biotechnology and Biological Sciences Research Council (BBSRC)  research programme NE/N018125/ 1 LTS-M (ASSIST). Jana Bürger 's work on this project was supported by  Deutsche Forschungsgemeinschaft DFG  (grant number  BU 3097/1-1 ).
DATA AVAILABILITY STATEMENT The data within AWME are available on the submission of a proposal to the coordination group. Data from individual datasets will be released according to their corresponding availability regime with the majority of data accessible for all research needs. Further information is available at https://ewrs. org/en/info/Blog/93/Arable-Weeds-and-Management-in-Europe-AWME-A-database-of-weed-survey-datafrom-across-Europe.
Note: A separate model was fitted to each dataset for each response variable. N = The number of data points included in each dataset. Term included in model but not significant, *p ≤ 0. 05; **p ≤ 0. 01; ***p ≤ 0. 001, blank cells represent terms that were dropped from the model during the backwards selection process. 3. Europe (rescaled to zero mean and unit variance) N = 58 150 Species richness Abundance Species richness Abundance Species richness Abundance Position in field (P) <0. 001*** <0. 001*** <0. 001*** <0. 001*** <0. 001*** <0. 001*** Crop (C) <0. 001*** <0. 001*** <0. 001*** <0. 001*** <0. 001*** <0. 001*** Herbicide treatment (H) 0. 903 a 0. 949 a 0. 994 a 1. 000 a <0. 001*** <0. 001*** P Â C 0. 004** <0. 001*** <0. 001*** P Â H 0. 072 a 0. 038* <0. 001*** 0. 006** C Â H <0. 001*** <0.
2009 ). The Pseudomonas WB MAG04 harboured several auxiliary activities (AA2: hydroperoxidase; AA3: FAD-dependent oxidoreductase; AA6: NAD(P)H-dependent oxidoreductase; AA7: FAD/FMN-containing dehydrogenase) which were not detected in the P. extremaustralis strain 14-3 and in other Pseudomonas genomes available on the CAZy database (Table  S1 ). The CAZy content of the Pseudomonas WB MAG04 was dominated by glycoside hydrolases (53%) compared to the other families. The Pseudomonas genus  (Kim and Mackey 2008)  can act as a plant pathogen because of its wide variety of plant pathogenic strains (Espinosa-Urgel 2004). Pseudomonas WB MAG04 harboured genes encoding enzymes involved in the protocatechuate degradation III (para-cleavage pathway) such as 4-oxalocrotonate isomerase. Pseudomonas WB MAG04 had also the complete benzoic acid degradation pathway.
Sequencing was performed from the microbial communities at the beginning of the experiment (WB and WS; from the washed cells) and at the end (WB_48h and WS_48h) at MR DNA (Shallowater) on an Ion Torrent PGM following the manufacturer's guidelines. Sequences were depleted of barcodes and primers, then sequences <150 bp were removed, sequences with ambiguous base calls and with homopolymer runs exceeding 6 bp were also removed. The remained were then denoised, aligned against the SILVA database and OTUs were generated and defined by clustering at 3% divergence using QIIME software  (Caporaso et al. 2010). Final OTUs were taxonomically classified using BLASTn against a database derived from RDP (http://rdp. cme. msu. edu). Total richness and alpha diversity indices were calculated using MOTHUR software  (Schloss et al.
MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression involved in countless biological processes and are widely studied across metazoans. Although miRNA research continues to grow, the large community of fish miRNA researchers lacks exhaustive resources consistent among species. To fill this gap, we developed FishmiRNA, an evolutionarily supported miRNA annotation and expression database for ray-finned fishes: www. fishmirna. org. The selfexplanatory database contains detailed, manually curated miRNA annotations with orthology relationships rigorously established by sequence similarity and conserved syntenies, and expression data provided for each detected mature miRNA. In just few clicks, users can download the annotation and expression database in several convenient formats either in its entirety or a subset.
2011; Li and Zhang 2013; Quah et al. 2015). Although several databases and bioinformatic tools facilitate the study of miRNAs in cellular, developmental, physiological, and pathological contexts, they cover mostly humans and main laboratory model organisms, so researchers studying other species often lack appropriate resources to accurately facilitate miRNA studies in their systems. This situation is exacerbated in ray-finned fishes, actinopterygians. Although several valuable miRNA annotation databases already exist, miRNA annotations in fish species are often unavailable, incomplete, or inconsistent across fish phylogeny, inhibiting the study of miRNAs in half of all vertebrate species. The legacy database miRBase was created in 2006 but has not been updated since 2018  (Kozomara et al. 2019).
miRBase contains partial miRNA annotations for several fish species, but none are exhaustive or curated and many new or improved annotations have not been incorporated. In 2015, MirGeneDB was created and covers a breadth of metazoan species, but this database provides miRNA annotation for only one fish species, zebrafish  (Fromm et al. 2020). miRNAs, however, are of great interest in fish research with studies ranging from genomic evolution  (Xiong et al. 2019; Desvignes et al. 2021) , development  (Giraldez et al. 2006; Kasper et al. 2017; Gay et al. 2018) , medical models  (Hsu et al. 2017) , aquaculture  (Herkenhoff et al. 2018; Blo ¨dorn et al. 2021; Cardona et al. 2021) , pathology  (Andreassen and Høyheim 2017; Wang et al. 2018) , toxicology  (Goodale et al. 2019; Ahkin Chin Tai and Freeman 2020) , to adaptation, and speciation  (Franchini et al.