dataesr/scientific-paragraphs-categorization huggingface dataset:
The following databases were used across all teams: PubMed, Embase, and Global Health for scientific literature, and Wholis and OpenGrey for grey literature. Based on the keywords list, we defined appropriate descriptors for each scientific database, as they vary from one database to another (e. g. MeSH terms for PubMed; EMTREE for Embase, etc. ). All search strategies were reviewed several times and were launched only after validation by a specialized librarian and the consortium coordinator to ensure replicability and standardization among the consortium. Because database searches may not always go as planned, a second validation of all search histories was performed by the research coordinator and the librarian. The reference lists of all included articles were also cross-checked for relevant studies.
References retrieved were saved in either Zotero (www. zotero. org) or Mendeley (www. mendeley. com) reference manager software. Initially we opted to use Zotero for all teams to facilitate coordination and problem-solving, but some teams that retrieved very large numbers of references experienced technical difficulties with that software (slowness and abrupt shutdown), so we shifted towards Mendeley. We chose these two programs to be able to share complete libraries among all contributors and the consortium coordinator, for purposes of transparency and standardization STEP 3: selecting the studies After all duplicates were removed, each team performed a pilot round of screening with 20 randomly selected references. Two contributors screened titles and abstract, labelled (included or excluded) each of the 20 citations independently, and provided an explanation for their decision.
Any new contributor to the selection process first performed a pilot screening test, validated by the team leader, to ensure a common understanding of the criteria and their application. Given that an abstract cannot entirely reflect an article's content, when a doubt persisted or information was lacking, the reference was included so that the full text would be screened. When the selection based on abstracts was completed, all references labelled "included" were kept for the subsequent step, and those labelled "excluded" were removed from the database. The same selection process was performed for the full text screening. All reasons for exclusion were detailed and then compiled in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow Diagram. Reference lists in all included articles were then manually checked for potential additional studies.
After this, no further study was included in the review Each team performed a pilot round to ensure their understanding of the grid and its application and, as before, standardization among the consortium. Five studies were selected randomly for data extraction by two independent contributors, i. e. , the research assistant (or the scoping leader) mainly involved in data extraction and the team leader (if appropriate) or the consortium coordinator. The number was decided intuitively to have enough data to fully test the grid, and, when necessary, more studies were selected to have a more diverse panel of studies. The grids were compared, and we arbitrarily settled on a disagreement threshold of 15% between the two contributors to validate the grid. To calculate the percentage of disagreement, row by row (i.
Macro-data extraction with the template for intervention description and replication The third section of the data extraction grid was based on the TIDieR checklist (Template for Intervention Description and Replication). A health-related intervention is defined by WHO as "an activity or set of activities aimed at modifying a process, course of action or sequence of events in order to change one or several of their characteristics such as performance of expected outcome"  [13]. This definition encompasses a very wide variety of studies, such as studies on medication, health services, programs related to health habits, etc. The TIDieR was developed to help researchers report health interventions appropriately, as its authors observed that, in many reports, interventions are insufficiently or poorly described.
[5]  Systematic reviews are required to assess the quality of the evidence presented  [4] , which has led to exclusion criteria being applied in numerous meta-analyses. In contrast, the matter of quality assessment is still under debate in relation to scoping reviews; it is rarely done, under the rationale that, by nature, scoping reviews include all relevant studies retrieved in databases, without exclusions based on study design or quality  [6]. In our approach, we decided to include qualitative assessment of the studies as a tool to inform readers on the availability of strong evidence. It was applied during data extraction, and as such, there was never any intention to exclude studies based on quality assessment. Because of this, we were sometimes able to highlight a lack of strong evidence despite the availability of several studies.
Third, it would have been an opportunity to provide training in bibliographic research methods. Most of the researchers and assistants thought doing literature reviews was very intuitive, as they were accustomed to navigating scientific databases every day; but in fact, performing a constructed valid search strategy is complex. Finally, the workshop would have allowed all scoping leaders to be equally involved in the first steps. Given that we had similar key concepts across the consortium, we decided to standardize keywords. However, some teams were delayed in starting their scoping reviews and paid less attention to the related communication. By the time they started their own search strategy, it was no longer possible to integrate their comments, as other teams had already completed the search strategy, and as such, the later teams may have felt excluded.
ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github. com/ASaiM/framework and documentation can be found online (http://asaim. readthedocs. io). Conclusions: Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.
Background The study of microbiota and microbial communities has been facilitated by the evolution of sequencing techniques and the development of metataxonomics, metagenomics, and metatran-scriptomics. These techniques are giving insight into taxonomic profiles and genomic components of microbial communities. However, meta'omic data exploitation is not trivial due to the large amount of data, their complexity, the incompleteness of reference databases, and the difficulty to find, configure, use, and combine the dedicated bioinformatics tools, etc. Hence, to extract useful information, a sequenced microbiota sample has to be processed by sophisticated workflows with numerous successive bioinformatics steps  [1]. Each step may require execution of several tools or software. For example, to extract taxonomic information with the widely used QIIME  [2]  or Mothur  [3] , at least 10 different tools with at least four parameters each are needed.
Tools for microbiota data analyses The tools integrated in ASaiM can be seen in Table  1. They are expertly selected for their relevance with regard to microbiota studies, such as Mothur (mothur, RRID:SCR 011947) [3], QIIME (QIIME, RRID:SCR 008249)  [2] , MetaPhlAn2 (MetaPhlAn, RRID:SC R 004915)  [45] , HUMAnN2  [46] , or tools used in existing pipelines such as EBI Metagenomics' one. We also added general tools used in sequence analysis such as quality control, mapping, or similarity search tools. An effort in development was made to integrate these tools into Conda and the Galaxy environment (>100 tools integrated) with the help and support of the Galaxy community. We also developed two new tools to search and get data from EBI Metagenomics and ENA databases (EBISearch  [20]  and ENASearch  [21] ) and a tool to group HUMAnN2 outputs into Gene Ontology Slim Terms  [47].
Visualization Export2graphlan [49], GraPhlAn  [50] , KRONA  [51]  This table presents the tools, organized in sections and subsections to help users. A more detailed table of the available tools and some documentation can be found in the online documentation (http://asaim. readthedocs. io/en/latest/tools/). other tools to explore and represent the community structure: KRONA  [51]  and GraPhlAn  [53]. Moreover, as in any Galaxy instance, other visualizations are included such as Phyloviz  [54]  for phylogenetic trees or the genome browser Trackster  [55]  for visualizing SAM/BAM, BED, GFF/GTF, WIG, bigWig, bigBed, bed-Graph, and VCF datasets.
Analysis of raw metagenomic or metatranscriptomic shotgun data The workflow quickly produces, from raw metagenomic or metatranscriptomic shotgun data, accurate and precise taxonomic assignations, wide extended functional results, and taxonomically related metabolism information (Fig. 1 ). This workflow consists of (i) processing with quality control/trimming (FastQC and Trim Galore!) and dereplication (VSearch  [11] ); (ii) taxonomic analyses with assignation (MetaPhlAn2  [45] ) and visualization (KRONA, GraPhlAn); (iii) functional analyses with metabolic assignation and pathway reconstruction (HUMAnN2  [46] ); (iv) functional and taxonomic combination with developed tools combining HUMAnN2 and MetaPhlAn2 outputs. This workflow has been tested on two mock metagenomic datasets with controlled communities (Supplementary material).
We have compared the extracted taxonomic and functional information to such information extracted with the EBI metagenomics' pipeline and to the expectations from the mock datasets to illustrate the potential of the ASaiM workflow. With ASaiM, we generate accurate and precise data for taxonomic analyses (Fig. 2 ), and we can access information at the level of the species. More functional information (e. g. , gene families, gene ontologies, pathways) are also extracted with ASaiM compared to the ones available on EBI metagenomics. With this workflow, we can go one step further and investigate which taxons are involved in a specific pathway or a gene family (e. g. , involved species and their relative involvement in different step of fatty acid biosynthesis pathways, Fig. 3 ). For the tests, ASaiM was deployed on a computer with Debian GNU/Linux System, 8 cores Intel(R) Xeon(R) at 2.
40 GHz and 32 Go of RAM. The workflow processed the 1,225,169 and 1,386,198 454 GS FLX Titanium reads of each datasets, with a stable memory usage, in 4h44 and 5h22 respectively (Supplementary material). The execution time is logarithmically linked to the input data size. With this workflow, it is then easy and quick to process raw microbiota data and extract diverse useful information.
Documentation and training A tool software is easier to use if it Hence, extensive helps the users to be familiar the tool and also prevents mis-usage. For ASaiM, we developed an extensive documentation  [15]  , mainly to explain how to use it, how to deploy it, which tools are integrated with small documentation about these tools, which workflows are available, and how to use them. In addition to this online documentation, training materials have been developed. Some Galaxy interactive tours are included inside the Galaxy instance to guide users through entire microbiota analyses in an interactive (step-by-step) way. We also developed several step-by-step tutorials to explain the concepts of microbiota analyses, the different tools and parameters, and ASaiM workflows with toy datasets. Hosted within the Galaxy Training Material  [63] , the tutorials are available online at  [64]  and also directly accessible from ASaiM and its documentation for self-training.
Conclusion ASaiM provides a powerful framework to easily and quickly analyze microbiota data in a reproducible, accessible, and transparent way. Built on a Galaxy instance wrapped in a Docker image, ASaiM can be easily deployed with its extensive set of tools and their dependencies, saving users from the hassle of installing all software. These tools are complemented with a set of predefined and tested workflows to address the main questions of microbiota research (assembly, community structure, and function). All these tools and workflows are extensively documented online  [15]  and supported by interactive tours and tutorials. With this complete infrastructure, ASaiM offers a sophisticated environment for microbiota analyses to any scientist while promoting transparency, sharing, and reproducibility.
Acknowledgements The authors would like to thank EA 4678 CIDAM, 454  INRA ,  M2iSH ,  LIMOS, AuBi, M ésocentre, and de. NBI  for their involvement in this project, as well as  Réjane Beugnot ,  Thomas Eymard ,  David Parsons , and  Bj Gr  üning for their help.
Availability of supporting source code and requirements r Project name: ASaiM r Project home page: https://github. com/ASaiM/framework r Operating system(s): Platform independent r Other requirements: Docker r License: Apache 2 r RRID:SCR 015878GTN All tools described herein are available in the Galaxy Toolshed (https://toolshed. g2. bx. psu. edu). The Dockerfile to automatically deploy ASaiM is provided in the GitHub repository (https://github. com/ASaiM/framework) and a pre-built Docker image is available at https://quay. io/repository/bebatut/asaim-f ramework.
Cattle and horse movements The number of cattle and horse movements was calculated based on the TRAde Control and Expert System (TRACES, EU database). According to TRACES, during the one-year period from July 2015 to July 2016, only 182 cattle originating from the at-risk area were introduced in France through seven batches coming from Romania and Hungary (Table  1 ). During this period, no animals were imported from at-risk countries outside the EU. All the batches of animals were introduced on French farms (= n1). No animals came from at-risk countries to be slaughtered. In order to evaluate the potential impact of vectors transported by trucks going to slaughterhouses (= n2), a scenario was developed using the same number of batches of cattle introduced on farms. Between September 2015 and September 2016, 44 horses were introduced into French facilities for breeding, from Bulgaria, Croatia, Greece and Hungary.
If cattle came to be slaughtered in France (scenario), the risk would be much lower than the risk for animals sent to farms. The risk related  to vectors that travel with horses is still lower than the risk due to the transport of animals to be slaughtered. During the period in which we obtained the data on animal movements, no horses were introduced from zones where LSD is present. Considering that in the event of an LSD outbreak, horse movements are not banned, it is important to disinsectise trucks coming from affected zones in order to minimise the risk. According to the sensitivity analysis, some interesting mitigating measures were highlighted, such as the importance of early LSD detection in the country of origin (limiting the spread of LSD), the importance of vaccine campaigns in LSD-affected countries (reducing the circulation of LSDV), and the importance of reducing the number of Stomoxys in the truck through its cleaning, disinfection and disinsectisation according to the European legislation  [42].
Acknowledgments This study was performed by an ad hoc working group of the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES). The authors thank  Galon N ,  Tuppurainen E ,  Maartens L, Antoniou SE , Klement E and Chobanov G for their critical advice provided during the risk assessment, especially on the probabilities P1 to P9. The authors declare no conflicts of interest.
g. , encroachment, selective logging), and keeping artifacts at minimum level. The monitoring approach was validated within four test sites with an overall accuracy of almost 78% using very high resolution satellite reference imagery. The methodology was implemented in a Google Earth Engine (GEE) script requiring no user interaction. A threshold is applied to the final output dataset in order to separate signal from noise. The approach, capable of detecting sub-pixel disturbance events as small as 0. 005 ha, is transparent and reproducible, and can help to increase the credibility of monitoring, reporting and verification (MRV), as required in the context of reducing emissions from deforestation and forest degradation (REDD+).
The objective of our study is the development of a robust and to a large extent topography-independent methodology for monitoring forest canopy disturbance. We use the normalized burn ratio (NBR) index, based on the near infrared (NIR) and the short-wavelength infrared 2. 1 µm (SWIR2) bands, in order to detect the signal-components of bare soil or non-photosynthetic vegetation within the canopy cover of evergreen tropical rainforests  [45] [46] [47]. Similar to the approach in  [48]  for mapping burnt areas and deriving information about the burn severity, our study applies the delta NBR to monitor the disturbance in forest canopy closure. Calculating spectral differences between two datasets stemming from different periods allows discrimination between recent canopy disturbances and naturally open crown cover, as done for CLAS and CLASlite  [3, 49].
Study Area, Satellite and Auxiliary Data In this study, we analyzed Landsat 8 imagery in order to detect crown cover disturbances within evergreen and semi-evergreen forests of Cambodia and Laos occurring in 2015 and 2016. Both countries are characterized by a tropical monsoon climate with a pronounced dry season from November to April  [52]. The major inland forest and woodland types of the region belong to different ecosystems ranging from moist tropical rain forests to dry deciduous forests, with various intermediate types of semi-evergreen and semi-deciduous forests, and the typical dry Dipterocarp forests and woodlands. A clear separation between individual forest types by means of remote sensing is often difficult  [53]. Over the last decades, these natural forest ecosystems have been under pressure by human intervention, including, for instance, large-scale conversions to cash crops and forest plantations, dam and road construction, as well as various types of logging  [54].
We used all existing top-of-atmosphere (ToA) ortho-rectified Landsat 8 data (spatial resolution of 30 m) available in the GEE archive for the period from 1 January 2014 to 31 December 2016, covering the total area of Cambodia and Laos. This translates into a total number of 1030 and 1371 Landsat scenes for Cambodia and Laos, respectively. For the validation process, some additional Landsat 8 imagery stemming from 2013 and 2017 were processed to derive crown cover disturbance maps matching the acquisition dates of the very high resolution satellite images used as reference data (see Section 2. 2. 4). For validation and accuracy assessment, we were restricted to defining six test sites determined by the availability of high (HR) and very high-resolution (VHR) satellite imagery over the target region (Figure  1a ).
During the selection of the VHR data, off-nadir angles >20 degree were rejected to avoid unnecessary interpretation problems. However, VHR data for a total area of almost 30,000 ha, and additional HR data for more than 230,000 ha were obtained, covering forest areas typical for the region and having experienced various forest disturbance processes, such as shifting cultivation or selective logging. Furthermore, they differ in terms of the predominant topography. Whilst the test sites in Laos (Figure  1b-f ) are characterized by more or less hilly and rugged terrain, the central Cambodia site (mostly belonging to the Prey Lang Wildlife Protected Area) has rather flat topography (Figure  1g ). This study focuses on tropical evergreen and semi-evergreen rain forests, hereafter referred to as 'evergreen' forests.
For the detection of changes in canopy crown cover, indicating processes of forest disturbance or potential forest degradation, we calculated the difference (∆) of a modified normalized burn ratio (rNBR) vegetation index, hereafter referred to as '∆rNBR', over consecutive periods. A GEE script was developed, aimed at operational processing and the analysis of large amounts of satellite data without any user interaction, but also performing the required steps of pre-and post-processing enabling inter-scene comparability and the temporal aggregation of the data. The processing chain can be broken down into five subsequent processing steps: 2. 2. 1. Masking of All Landsat ToA Scenes All selected Landsat 8 data are masked automatically, selecting for further processing only those pixels identified as representing evergreen forest ('evergreen forest maps'), and not affected by cloud or cloud shadow interference.
We derived these evergreen forest maps from a pan-tropical forest dataset at 30 m spatial resolution, which displays evergreen forest cover as well as change to non-forest within an observation period and which is to be published soon  (Vancutsem et al. , personal communication). This dataset was generated by an automated classification (decision tree) of a time-series of 33 years of Landsat data , examining the photosynthetic activity ('greenness') for every pixel location and every single-date image. A pixel is classified as 'undisturbed' when always detected as fully vegetated over the 33-year period. The validation, presently only available for the African continent, shows an overall accuracy of 95. 8% with 3. 6% of omission and 4. 3% of commission errors, respectively  [55]. For our study, we derived from this dataset binary evergreen forest maps for the years 2014, 2015 and 2016, grouping the original classes 'undisturbed forest' and 'old vegetation regrowth' as 'forest' class (fully vegetated at least after 2006).
Therefore, a second capping step is introduced, resetting all negative ∆rNBR values to 0 (Equation (  6 )). Ultimately, the final countrywide ∆rNBR y+1_cap products and the temporally adjusted ∆rNBR y+1_cap products for the six validation sites as well as the corresponding time stamp datasets of each of the rNBR cap_max_y intermediate results were exported and downloaded from GEE. Figure  2  provides a flow chart of all major processing steps.
Accuracy Assessment Based on VHR Reference Satellite Imagery In order to identify small-scale openings in the canopy cover, as expected to be detected by the ∆rNBR, reference datasets of higher spatial resolution are mandatory for validation. In addition, as the ∆rNBR signals refer to canopy closure change, two reference scenes from the end of both investigation periods are required. Furthermore, instead of comparing the canopy closure conditions at two points in time, the ∆rNBR approach works with temporal composites of two investigation periods that reflect the most open canopy closure condition per pixel location occurring within each investigation period. We assume that the crown cover disturbance occurring within an investigation period can still be identified on the reference image acquired at the end of that period.