dataesr/scientific-paragraphs-categorization huggingface dataset:
The file Validation_points_GlobalOilPalmLayer_2019. shp includes the 13 495 points that were used to validate the product. Each point includes the attribute "Class", which is the labeled class assigned by visual interpretation, and the attribute "predClass", which reflects the predicted class by the convolutional neural network. The "Class" and "predClass" values are the same as the raster files: (1) industrial closed-canopy oil palm plantations, (2) smallholder closed-canopy oil palm plantations, and (3) other land covers/uses that are not closed-canopy oil palm. The data can be visualized online at the BIOPAMA application portal: https://apps. biopama. org/oilpalm/ (last access: 18 March 2021). The BIOPAMA application portal also includes the probability layer, which shows the probability (from 0 to 100) that a pixel is a closedcanopy oil palm plantation.
The data can also be visualized on the Google Earth Engine (GEE) experimental app: https://adriadescals. users. earthengine. Earth Engine Data Catalog, 2017, respectively). Despite the fact that the data are hosted by GEE, these satellite data are the same as those accessed via the official portal (Copernicus Open Access Hub: https://scihub. copernicus. eu/, last access: 18 March 2021); data ingested and hosted in GEE are always maintained in their original projection, resolution, and bit depth  (Gorelick et al. , 2017). The WorldClim bioclimatic variables (WorldClim V1 Bioclim)  (Hijmans et al. , 2005)  were also accessed through GEE: https://developers. google. com/earth-engine/ datasets/catalog/WORLDCLIM_V1_BIO (last access: 18 March 2021). The data can be accessed in the official portal at https://www.
worldclim. org/data/v1. 4/worldclim14. html (last access: 18 March 2021). The IUCN industrial oil palm layer  (Meijaard et al. , 2018 ) can be found at https://doi. org/10. 5061/dryad. ghx3ffbn9 (Meijaard and Gaveau, 2021). The oil palm layer of Indonesia and Malaysia for the year 2016  (Xu et al. , 2020)  can be found at https://doi. org/10. 5281/zenodo. 3467071 (Xu et al. , 2019). The oil palm layer of Sumatra for the year 2019, developed with the same methodology as in  Descals et al. (2019)  for Riau province (Indonesia), is hosted as a GEE asset at https: //code. earthengine. google. com/?asset=users/adriadescals/ shared/Sumatra_oilPalm_L2_Descals_et_al_2019 (last access: 18 March 2021). Very high-resolution images (spatial resolution <1 m) from DigitalGlobe can be visualized in the GEE code editor or Google Maps (i.
e. , https://www. google. com/maps/ @-3. 969372,105. 048514,782m/data=!3m1!1e3, last access: 18 March 2021; Google, 2021). The country-wide harvested area of oil palm was extracted from the FAOSTAT database (accessed on 10 Jun 2020): http: //www. fao. org/faostat/en/ (FAO, 2020).
Conclusions This study presents the first global map of oil palm plantations for the year 2019 derived from remotely sensed data with a spatial resolution of 10 m. We classified Sentinel-1 and Sentinel-2 data onto a map that discriminates between smallholders and industrial oil palm plantations. We obtained high accuracies with user's and consumer's accuracy generally above 80 % thanks to the use of cutting-edge deeplearning algorithms. The method is deployable and can generate yearly maps for oil palm monitoring in a cloud processing environment and based on freely available satellite imagery. Our global oil palm map makes an important contribution to the palm oil debate. It will be useful to solve or at least clarify a range of social and environmental debates. We know that oil palm plantations are a major cause of deforestation in Indonesia and Malaysia  (Austin et al.
Such insights are essential for the health of people and the economy  (Wardeh et al. , 2020). 2020 ) for 2016. In order to compare both methodologies, we applied the CNN to Sentinel-1 and Sentinel-2 composites of the second half of 2016, which corresponds to the last year of the multi-annual analysis in the dataset of  Xu et al. (2020). We used 5199 points randomly distributed in the study area, which covers Malaysia, Sumatra, and Borneo. The accuracy metrics are reported with a confidence interval (95 % confidence level). Global OP year 2016 (SE Asia)  Xu et al. (2020).
app/view/ global-oil-palm-map-2019 (last access: 18 March 2021). The global oil palm map is hosted in GEE as an Image Collection: https://code. earthengine. google. com/?asset= users/adriadescals/shared/OP/global_oil_palm_map_v1 (last access: 18 March 2021). The Sentinel-1 SAR GRD and Sentinel-2 Level-2A used in this study (scenes taken in the second half of 2019 in the tropics and second half of 2016 in Sumatra) are available at https://scihub. copernicus. eu/ (last access: 18 March 2021) and can be retrieved in GEE. When using GEE, the Sentinel-1 and Sentinel-2 data are hosted and accessed in the Earth Engine data catalog (the links to the data are https://developers. google. com/ earth-engine/datasets/catalog/COPERNICUS_S1_GRD, last access: 18 March 2021, Earth Engine Data Catalog, 2014, and https://developers.
Acknowledgements. The authors would like to thank the  BIOPAMA (European Commission, Joint Research Centre ) technical developer team members' effort with the online visualization tool ( Luca Battistella ,  Martino Boni ,  James Davy ). The authors also thank  Aaron McKinnon  ( Copernicus Emergency Management Service and Global Land Service -Communication Manager ) for proofreading the paper. Financial support. This study was supported by the  Biodiversity and Protected Areas Management (BIOPAMA) program , an initiative of the  Organization of African, Caribbean, and Pacific Group of States  financed by the 10th and 11th  European Development Funds of the European Union  and co-managed by the  European Commission Joint Research Centre  and the  International Union for Conservation of Nature.
Ferroverdin A Quantification by High Pressure Liquid Chromatography (HPLC) Ferroverdin extraction with ethyl acetate, drying, and resuspension in acetonitrile were performed as described previously  [19]. Samples were analyzed by HPLC and Ferroverdin A was detected at 440 nm and quantified by peak area integration, as described previously  [19]. Data were analyzed using Empower 3 (Waters, Milford, MA, USA). The HPLC-based protocol for ferroverdin A semi-quantitative analysis is detailed in Appendix A.
Molecular Network Construction MS and MS/MS raw data obtained via the Excalibur Software (Thermo) were converted to a 32-bit mzXML file using MSconvert software (Proteo Wizard toolkit  [20] ). mzXML files were subjected to the Global Natural Product Social Molecular Networking (GNPS) site (https://gnps. ucsd. edu) as described in  [21]. For the parent MS, the mass tolerance was set to 0. 01 Da and for the MS/MS fragment ions, the mass tolerance was set to 0. 5 Da, while the minimum cosine score was set to 0. 7. The data were clustered using MSCluster with a minimum cluster size of four spectra. The spectra in the network were also searched against GNPS spectral libraries. A minimum score of 0. 5 was set for spectral library search, with at least two fragment peaks matching. Cytoscape 3. 7.
Phylogeny Analyses The Multi Locus Sequence Analysis (MLSA) based on housekeeping genes atpD, gyrB, recA, rpoB, and trpB of all S. lunaelactis strains (Figure  1 ) was performed as follows. The five genes were identified in the S. lunaelactis genomes based on sequence homology, with the loci available from the Streptomyces MLST website (https://pubmlst. org/strteptomyces/). For each strain, the 100 closest relative strains (amongst all the available genomes of actinobacteria in NCBI GenBank as of September 2019) were retrieved based on the best cumulative blastp scores of the five housekeeping gene products. From the resulting unique closest relatives, only those with an assembly status of "complete genome" or those tagged as "representative genome" were retained. A protein multiple sequence alignment was performed for each gene (MAFFT v7.
453  [24] , options-maxiterate 1000-localpair;) as a guide for subsequent nucleotide alignment (PAL2NAL v14  [25] ). The five full-length nucleotide sequence alignments were then merged and trimmed (trimAl v1. 2rev59  [26] , method-automated1). Phylogenetic inference was deduced with the maximum likelihood method, as implemented in RAxML (v8. 1. 17 [27] , rapid bootstrapping mode with 1000 replicates, GTR+I+G evolutionary model), using S. coelicolor as the outgroup. The phylogeny analysis based on the full-length bag/fev cluster of all S. lunaelactis strains was performed likewise with the fev cluster of Streptomyces sp. WK-5344 (1-18563 nt of GenBank accession AB689797. 1) as the outgroup. Data for the average nucleotide identity (ANI) of the 18 S. lunaelactis strains genomes were generated with the default "compare" workflow of dRep  [28]  applied to the 18 genomes, implementing the fast MASH [29] and accurate ANIm algorithms  [30].
Genome Sequencing The genome sequences of all S. lunaelactis strains were obtained as described previously  [14, 15, 31]. Genomic DNA was extracted with the GenElute Bacterial Genomic DNA Kit (Sigma-Aldrich, St. Louis, MO, USA). Genomic libraries were constructed using the Nextera XT kit (Illumina, Inc. , San Diego, CA, USA). Sequencing was carried out on an Illumina MiSeq platform with 2 × 300-bp read configuration. Complete genomes were assembled de novo from raw sequence data with SPAdes v3. 6. 2  [32] , and the quality of the assemblies was subsequently assessed with QUAST v2. 3  [33]. All assemblies are available under NCBI BioProject accession PRJNA30192.
The gene fevT encoding a LuxR-family transcriptional regulator is the open reading frame that contains the highest number of mutations (eleven) compared to the sequence originally found in MM109 T (GenBank: AUG90788. 1). At the opposite, genes fevV (tyrosine ammonia-lyase) and fevF (o-aminophenol oxidase) present identical amino acid sequences in the eighteen S. lunaelactis strains. MM103 is the S. lunaelactis strain closest to MM109 T with only two mutations both in fevL involved in the decarboxylation of the trans-coumaric acid in p-vinylphenol (Figure  4 ). The highlighted sequence heterogeneity suggests that these different strains, despite that they all taxonomically belong to the species S. lunaelactis, could indeed present important differences in production levels of both bagremycins and ferroverdins.
The gene fevT encoding a LuxR-family transcriptional regulator is the open reading frame that contains the highest number of mutations (eleven) compared to the sequence originally found in MM109 T (GenBank: AUG90788. 1). At the opposite, genes fevV (tyrosine ammonia-lyase) and fevF (oaminophenol oxidase) present identical amino acid sequences in the eighteen S. lunaelactis strains. MM103 is the S. lunaelactis strain closest to MM109 T with only two mutations both in fevL involved in the decarboxylation of the trans-coumaric acid in p-vinylphenol (Figure  4 ). The highlighted sequence heterogeneity suggests that these different strains, despite that they all taxonomically belong to the species S. lunaelactis, could indeed present important differences in production levels of both bagremycins and ferroverdins.
These results demonstrate that a strain prioritization strategy based on genome mining and phylogeny would have prevented the exploitation of the best naturally engineered producers of specific forms of known bagremycins. Next to the evaluation of the production levels of known bagremycins, we assessed if the different S. lunaelactis strains were able to produce new variants of bagremycins. MS/MS fragmentation analysis of UPLC-HRMS data allowed us to identify three "tag fragments" (Supplementary Figure  S2 ) associated with the currently known bagremycins. The first ion "tag fragment" of m/z 121. 06 (C 8 H 8 O + ) corresponds to the p-vinylphenol ionized part of all the "group 1" bagremycins (bagremycins A, B, E and G). The sulfur-containing bagremycins (bagremycins C and F, group 2) present another "tag fragment" of m/z 255.
lunaelactis strains contains 977 nodes clustered in 150 constellations (Figure  6 ). Biomolecules 2020, 10, x FOR PEER REVIEW 9 of 18 Next to the evaluation of the production levels of known bagremycins, we assessed if the different S. lunaelactis strains were able to produce new variants of bagremycins. MS/MS fragmentation analysis of UPLC-HRMS data allowed us to identify three "tag fragments" (supplementary Figure  S2 ) associated with the currently known bagremycins. The first ion "tag fragment" of m/z 121. 06 (C8H8O + ) corresponds to the p-vinylphenol ionized part of all the "group 1" bagremycins (bagremycins A, B, E and G). The sulfur-containing bagremycins (bagremycins C and F, group 2) present another "tag fragment" of m/z 255. 04 (C10H11N2O4S + ). Finally, bagrelactone-like compounds (group 3) are characterized by the "tag fragment" of m/z 178.
lunaelactis strains is much more important than currently known (only seven bagremycins and one bagrelactone identified so far). A combination of MS/MS fragmentation data with HRMS exact mass and the isotopic distribution of each compound allowed us to propose a list of predicted molecular formulas (Table  1 ) and to propose the structure for some of the m/z ions identified by the GNPS software (Supplementary Figures  S3  and S4 ). Next to the identification of the five already bagremycins (compounds 1 to 5 in Table  1 ) and the bagrelactone (compound 12, Table  1 ), Figures  S2  and S3  present, respectively, the MS/MS spectra and molecular fragmentation pathways associated to the UPLC-HRMS/MS data that allowed us to propose a possible structure for six new bagremycins (compounds 6 to 11, Table  1 ), and four new bagrelactones (compounds 13 to 16, Table  1 ).
Lines in grey refers to compounds previously known. For each compound, the best producing strain is highlighted in bold. Abbreviations: mass delta (∆m) in parts per million (ppm) calculated based on the theoretical and the experimental masses and using the Xcalibur software v2. 2 software (Thermo Fisher Scientific, San Jose, CA, USA); experimental (exp); neutral loss indicates an alternative non-charged fragmentation of the predicted tag fragment (NL); group not defined (ND); * indicates the presence of a fragment of 176. 0706 corresponding to an unsaturated form of the predicted fragment tag of Group 3.
The five most intense ions were then selected to perform MS/MS experiments by Higher Energy Collision Dissociation (HCD) fragmentations using stepped normalized collision energy (NCE; 21,2; 25; 28) within 2 amu isolation windows (resolution 17500, 1 × 10 5 ions accumulation target). A dynamic exclusion was enabled for 10 s. Data were analyzed using MassLynx V4. 1 (Waters, Milford, MA, USA) and Xcalibur v2. 2 (Thermo Fisher Scientific, San Jose, CA, USA).
e. , 5-aminosalicylates and thiopurines) and biologics. Adherence to medications was assessed using a 5-point Likert scale asking how often did patients forget to take their medications (never-rarely-sometimes-often-always). A secondary outcome was to identify factors independently associated with low adherence. The proportion of patients with inadequate adherence to treatment-the primary endpoint-was defined as the number of patients who answered the options "rarely" or "sometimes" or "often" or "always" to the question "how often do you miss medication intake?" This definition was used to define inadequate adherence to 5-aminosalicylates and thiopurines and biologics separately. 2. 2. Statistical Analysis. Statistical analysis was carried out using the Stata program (v11. 1, StataCorp, College Station, Texas, USA).
In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others.
Following a herd immunity approach, similar to the one initially adopted by the UK government, the Swedish government did not introduce strict bans but formulated non-binding recommendations only (https:// www. folkhalsomyndigheten. se/nyheter-och-press/). Predictive models, however, suggest that such a strategy might ultimately overwhelm the healthcare system  10. Poor control policies have potentially dramatic repercussions on public health. Although the need for data on country-based responses to COVID-19 was urgent and is still crucial, there is a limited opportunity to capture this information. Started in mid-March 2020, our project aims to generate a comprehensive structured dataset on government responses to COVID-19, including the respective time schedules of their implementation. During the COVID-19 crisis, several data collection efforts related to NPIs have emerged (https://lukaslehner.
github. io/covid19policytrackers/). Some of them focus on a specific type of interventions, e. g. the closure of educational institutions (https://en. unesco. org/covid19/educationresponse), travel restrictions (https://www. iata. org/ en/programs/safety/health/diseases/), trade-related measures (https://www. wto. org/english/tratop_e/covid19_e/ trade_related_goods_measure_e. htm), or measures to ensure continuity of supply of personal protective equipment and critical medical products (http://www. wcoomd. org/en/topics/facilitation/activities-and-programmes/ natural-disaster/list-of-countries-coronavirus. aspx), whereas others encompass a larger range of NPIs. In this paper, we also show how we distinguish our work from other concomitant initiatives. In the context of the current COVID-19 health crisis, open knowledge  11  and data sharing are crucial to understand and help to mitigate the pandemic.
In this article, we document and share the methodologies, tools and approaches used to produce the Complexity Science Hub COVID-19 Control Strategies List (CCCSL) dataset following the principles of open science. We provide a detailed description of the dataset and present examples of how it can provide insights into the global government response to COVID-19. The dataset is readily usable for modelling and machine learning analyses and exhibits a great analytical flexibility  12. In particular, researchers have leveraged on the hierarchical structure and the granularity of the data to disentangle the individual impacts of the NPIs on the reduction of the effective reproduction number through a top-down approach (from theme to code). Results show that social distancing measures, travel restrictions, but also active risk communication, play a major role in containing the epidemic.
The study further distinguishes the impact of different levels of implementation of some specific measures, e. g. those related to face covering  12. Considering the imperative necessity for data on government interventions, we released version 1 of the dataset on 2 April 2020. Version 2, displaying a consolidated coding scheme, is available since 7 May 2020. We also provide user-friendly documentation and materials (codes, visualisation interface, and library of sources) along with the dataset, which allow a maximum understanding of the data and promote its use among non-experts. The dataset is not complete and we continuously update it with new available records. Depending on resources, updates are planned until the end of December 2020.
Discrepancies in code assignments were discussed within the coding team and were resolved by consensus. The objective of this hierarchical coding scheme for NPIs was to standardize the data collection and obtain a structured dataset that uses a consistent taxonomy, and therefore, promotes common understanding. On 19 March 2020, we set up a platform for students, researchers, and volunteers to collect data on the NPIs implemented by the governments for preventing and limiting the spread of COVID-19, including the time schedules for the implementation. Data collectors received clear instructions on the objective of the project and indications on how to proceed for data collection. Data collectors were asked to use the template of a priori themes, categories, subcategories, and codes or to refer to the data curators if a measure could not be coded using this a priori coding system.
Therefore, throughout the data collection process, new categories, subcategories, and codes emerged, derived directly from the text data sources. The emergent (inductive) categories and subcategories were openly coded by the data collectors or by the data curators. In a second step, inductive categories and subcategories were compared together and in relation to the entire dataset to detect co-occurrences (codes that partially or completely overlap) and redundancies. Codes with the same meaning were aggregated  16. The categories and subcategories were tightened up to the point that maximized mutual exclusivity and exhaustiveness  15. This resulted in a Master List of Codes (a list of all the codes that were developed and used in the study), including the curated a priori and inductive coding categories.
The Master List replaced the a priori template for categorisation of the measures during data collection. It was shared with the data collectors via a Google spreadsheet and updated daily. Different public sources were used to populate, update and curate the dataset, including official government sources, peer-reviewed and non-peer-reviewed scientific papers, webpages of public health institutions (World Health Organization, Centers for Disease Control and Prevention, and European Centre for Disease Prevention and Control), press releases, newspaper articles, and government communication through social media. We collected data on the following: (i) country, (ii) state/region (when measures were implemented at subnational-level), (iii) date of implementation of the measure, (iv) implemented measure coded following the four-level classification scheme described above (theme, category, subcategory and code), and (v) source.
For each country, data were preferentially collected in the language of the country by native data collectors (i. e. Austria, Belgium, Bosnia and Herzegovina, Brazil, Canada, Croatia, Czech Republic, Ecuador, El Salvador, France, Germany, Ghana, Honduras, Hong Kong, India, Italy, Kazakhstan, Kosovo, Kuwait, Mauritius, Mexico, Montenegro, North Macedonia, New Zealand, Poland, Portugal, Ireland, Romania, Senegal, Serbia, Spain, Syria, Taiwan, and United Kingdom). If this was not possible, Google Translate was used to translate documents  17. All records were hand-coded.
Data Records A static copy of the dataset has been archived in figshare  18  , including all NPIs recorded as of time of submission (17 July 2020), spanning the period 31 December 2019 to 15 July 2020. A dynamic version of the dataset, which is planned to be continually updated, can be accessed via GitHub: https://github. com/amel-github/ covid19-interventionmeasures or from Google Drive: https://drive. google. com/open?id=1041U8iWPDS-GI6KHIn9Dg7THkXIo3-gui, in CSV format. Each of the rows represents a single individual NPI and is identified by a unique ID. The Master List of Codes is also available (an additional Master List file displays the hierarchical relationship between each pair of parent/child codes, i. e. L1-L2, L2-L3, and L3-L4, and the number of times each pair occurs in the dataset).