dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019). However, these data are generally not available to validate global maps or train new classification algorithms, as they are often the responsibility of national sovereignty. At a local scale, datasets on crop types have been acquired, and are still acquired, across multiple world regions within the context of the JECAM (Joint Experiment for Crop Assessment and Monitoring; available online: http: //www. jecam. org/, last access: 10 February 2020) international network. The JECAM initiative was first developed under the GEO (Group of Earth Observations) umbrella and then became the research and development component of GEOGLAM (GEO Global Agricultural Monitoring), to enable the global agricultural monitoring community to carry out cross-site experiments and compare results based on disparate sources of data, using various methods, over a variety of local or regional cropping systems.
Data are acquired following a given protocol and nomenclature (see  Defourny et al. , 2014). The experiment has been operating since 2013, and some in situ datasets produced at the field scale have been used in different benchmarking mapping studies  (Waldner et al. , 2016; Inglada et al. , 2015). However, only a part of the collected ground data was used in these studies, and the databases are not publicly shared. To make agricultural land use data publicly available to the remote sensing community, for classification algorithm benchmarking or LULC product validation, for example, an important work of harmonization of in situ JECAM and JECAM-like agricultural land use datasets was undertaken for nine sites located in the tropical belt. The acquisition protocol was adapted from  Defourny et al.
(2014)  to take into account the characteristics of tropical agriculture (e. g. , small field size, accessibility). At each site, information on crop type and cropping practices was collected locally, at the field level, with a detailed nomenclature. The acquisition period was between 2013 and 2020, and the number of monitoring years per site was between 1 and 7. In this paper, we describe in detail the study sites, the data collection protocol and the structure of the final database. We then discuss how the harmonization of the dataset and the diversity of the studied agrosystems, including smallholder farming, make our dataset unique and valuable for applications in emerging/developing countries in the tropics.
Data collection The acquisition protocol is based on the JECAM guidelines  (Defourny et al. , 2014)  with adaptations to consider some characteristics of tropical agriculture (mainly small field size and accessibility). Field surveys were conducted at least once in each study zone, with several sites revisited over multiple consecutive years (up to 7 years for the Burkina Faso site). Campaigns took place either around the growing peak of the cropping season, for the sites with a main growing season linked to the rainy season such as Burkina Faso, or seasonally, for the sites with multiple cropping (e. g. , São Paulo site). Except for Senegal where a stratified sampling plan for field surveys was used  (Ndao et al. , 2021) , the GPS waypoints were gathered following an opportunistic sampling approach (called the "windshield survey") along the roads or tracks according to their accessibility (which can be difficult during the rainy season, leading to fewer surveys on secondary roads or tracks in some study areas) while ensuring the best representativity of the existing cropping systems in place  (Defourny et al.
, 2014; Waldner et al. , 2019). GPS waypoints were also recorded on different types of noncrop classes (e. g. , natural vegetation, settlement areas, water bodies) to allow for differentiating crop and noncrop classes. Waypoints were only recorded for homogenous fields/entities of at least 20 m × 20 m (against a minimum sampling unit of 0. 25 ha with a minimum width of 30 m in JECAM guidelines). To facilitate the location of sampling areas and the remote acquisition of waypoints, field operators were equipped with GPS tablets (Trimble Yuma2 or Handheld Algiz 10X) providing access to a QGIS project with very high spatial resolution (VHSR) images (orthorectified Pleiades or SPOT 6/7 images ordered just before the surveys, or PlanetScope images). This equipment allowed for the in situ recording of attributes relative to each waypoint on data entry forms (with the automatic filling of IDs or dates and scrollable lists for other attributes to avoid data entry errors (Fig.
All these attributes are described in Table  1. In the specific case of the Burkina Faso, Niakhar (Senegal) and São Paulo (Brazil) sites, the same fields were revisited each year to study crop rotations and fallow practices in the region. For the South African site, some points were collected by helicopter using the Producer Independent Crop Estimates System (PICES;  Fourie, 2009)  method developed by the National Crop Statistics Consortium. Flights were per-formed at an average altitude of 500 feet and a low flying speed, allowing us to record GPS points and to determine land use using a GPS tablet associated with a GIS interface and a recent VHRS image. Only clearly identifiable land covers were kept in the database. During a field mission, the team is composed of an agronomist with geoprocessing skills, accompanied by a national researcher or technician with expertise in the local farming systems and a local driver.
Postprocessing Once the waypoints were acquired (Fig. 3b ), the boundaries of each field or noncrop entity were digitized on the VHSR images in the QGIS software, and the class labels (and other attributes; see Table  1 ) were attached to the polygon database (Fig. 3b ). Additional noncrop polygons were added by CAPI (computer-assisted photointerpretation) of the VHSR images for the built-up areas, water bodies, wetlands, mineral surfaces, and natural forest classes (land covers clearly identifiable on images). To avoid digitizing errors, this step was performed by the same operator as the one who performed the field surveys. Despite this, if there was doubt on the delineation of a given entity (e. g. , fuzzy boundaries, high heterogeneity), the given entity was removed from the database. Finally, the topology of each entity was controlled externally.
Data records This database, which contains 27 197 records, is a geographic layer in shapefile format. Each record corresponds to a polygon with 16 attributes (Table  1 ). Because of the dispersion of study sites on the globe, the layer is in a geographic coordinates system with datum WGS84. The distribution of the different records over the study sites is reported in Table 2, along with information on the temporal (corresponding years) and spatial coverage (source, number and average size of digitized polygons). Twenty different land cover types and 102 different crop types were observed. More than three-quarters of the observations are agricultural land, and the most represented crop types are maize, rice and sugarcane. The distributions of the main land cover and crop types are represented in Figs.
Quality checking Due to the nature of the dataset (in situ observation), validation is not possible. However, quality control was performed throughout the data chain, from acquisition to postprocess- ing, to ensure the quality of the datasets and their homogeneity throughout the sampled years and locations. First, the acquisition protocol was described in a technical guide provided to the field teams so that nothing was forgotten during the campaigns. The dropdown list in the data entry form reduced input and postprocessing errors. Second, during the postprocessing step, the orthorectification of the VHSR images used to digitize the fields was checked from one year to the next, for multiyear sites, and corrected if necessary by taking homologous points. The fields were then manually digitized on the VHSR images, and the photographs taken in situ were used whenever necessary.
Representativeness of datasets Because of their small size, these sites cannot be considered representative of the entire country in which they are located; however, they are claimed to be representative of an area that encompasses more than the JECAM site. To specify the extent of this representative area, we referred to existing zoning maps. We used the two reference maps available for southern countries: the FEWS-NET livelihood zones map (https://fews. net/fews-data/335, last access: 15 December 2021) and the FAO farming systems map (http://www. fao. org/farmingsystems/mapstheme_01_en. htm, last access: 15 December 2021). The livelihood zones are produced at the national scale and are available for 38 developing coun-tries. The zones are defined as geographical areas within which people broadly share the same patterns of livelihood (i.
e. , broadly the same production system, the same income earning opportunities and patterns of trade) (see  Grillo and Holt, 2009 , for more details). Farming system maps are available for the Global South (covering 130 countries). The classes are defined as a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints  (Dixon et al. , 2001; Auricht et al. , 2014). Although these two maps were not produced for the same purposes, they were derived using similar criteria (agroclimatology, elevation, landscape, dominant pattern of farm activities, etc. ) that are closely related to agricultural land use, as recorded in the database. For both maps, the type and extent of the zones corresponding to our JECAM study sites are given in Table  3.
Unfortunately, livelihood maps are available only for four of the JECAM countries presented here. With a mean size of the zone being approximately 20 000 km 2 (Table  3 ), we are confident that our JECAM sites are representative of the livelihood zone to which they belong. The datasets presented here can thus be used to train or validate land cover maps of the corresponding zones. The farming system zones are much larger (between 300 000 km 2 and 2 000 000 km 2 ) and include a larger diversity of environmental and farming conditions; in these conditions it is not possible to argue that the JECAM sites are representative of such large areas; thus, the JECAM datasets need to be completed with other datasets belonging to the same farming system class before being used for training land cover classification algorithms.
Dataset application study cases The in situ JECAM dataset and its derived land-use/landcover products have been used in a wide spectrum of studies covering several aspects linked to agricultural monitoring, attesting to the good quality of the dataset and good spatial representativeness of tropical country farming systems. First, specific site studies have been conducted to test several methodological aspects. For instance, land use maps combining a supervised object-based approach with multisource high spatial resolution time series were developed in Madagascar  (Lebourgeois et al. , 2017) and in Brazil (de Oliveira Santos et al. , 2019). The Brazilian site (São Paulo) was also included in a broader study presenting an inter-  comparison of several cropland mapping methodologies over five contrasting JECAM sites (Brazil, Ukraine, Russia, Argentina and China) in terms of growing conditions, characteristics and cropping practices  (Waldner et al.
, 2016). Very recently, following the rapid dissemination of up-to-date artificial intelligence approaches,  Gbodjo et al. (2020) and Ienco et al. (2020)  proposed testing the potential of deep-learning architectures for land cover mapping in Senegal (Niakhar) and Burkina Faso, respectively. Second, in situ data coming from the Burkina Faso site and the Madagascar site were included as test sites in the Sen2-Agri system. The Sen2-Agri system is an operational processing system that provides several agricultural products from Sentinel-2 and Landsat 8 time series during the cropping season. The two sites have been included in preliminary studies preparing the Sen2-Agri system processing chain  (Bontemps et al. , 2015; Valero et al. , 2016) , while the Madagascar site was considered later in the demonstration phase of the system at the local scale (http://www.
Conclusion and perspectives The accurate mapping of cropland and associated cropping practices in smallholder farming systems of tropical countries is crucial for the improvement of agricultural monitoring systems at local and/or global scales. The essential prerequisite to reach such objectives is to have available in situ datasets representative of the diverse agricultural practices in tropical countries. This paper presented a harmonized in situ crop type dataset acquired between 2013 and 2020 over nine sites spread over seven tropical countries. This dataset collected in the framework of the JECAM initiative is unique and very valuable, because it is produced at the field scale, based on in situ observations and quality-controlled, and contains standardized observations for various tropical cropping systems, including smallholder farming systems.
These characteristics allow this dataset to be used as a benchmark to assess the performances and robustness of newly developed classification algorithms for cropland and crop type or crop practice for mapping in diverse and documented agricultural conditions. In addition, this dataset can also be used to validate the cropland class of existing global or national LULC products, in particular those recently produced with Sentinel or Landsat image time series, as well as some crop type A. Jolivot et al. :  Harmonized in situ agricultural land use datasets and practice (fallow, double cropping) classes. In the end, it should be part of publicly online datasets and algorithm sharing platforms as promoted by the JECAM network and  Long et al. (2020) , who encourage the sharing of datasets for remote sensing applications, and more broadly to the scientific community, land use planners and agricultural monitoring agencies.
Thanks to ongoing projects and funded initiatives in which our team is involved, we will provide updates to the presented dataset on a regular basis. To date, several field campaigns are already planned on some of the presented sites, and projects are being built which will lead to the inclusion of multiple new ones. Moreover, since the paper also proposes a set of technical guidelines to integrate the database, opening up to external contributors may lead to a significant extension of the geographic coverage of the database and hence to its representativity with respect to the diversity of tropical agrosystems. As future work, we intend to carry out a study about the development of a technical solution aimed at facilitating such external contributions (e. g. , a compliant data collection tool and workflow).
, Centre de Suivi Ecologique (Senegal), Agricultural Research Council (South Africa), Pasteur Institut (Cambodia), FOFIFA/DP SPAD (Madagascar), DP ASAP (Burkina Faso), ICRAF (World Agroforestry Center, Kenya), NIPE and FEAGRI (Brazil), and Eder Araujo da Silva (Floragro Apoio, Brazil) for their technical support. This research was mainly supported by CIRAD (scientist funding) and by various projects (CNES APR TOSCA projects, SIGMA FP7 grant no. 603719; ESA Sen2Agri grant no. ESRIN 400109979/14/I-AM; CNPq grant nos. 454292/2014-7 and 307560/2016-3; FAPESP-Microsoft Research grant no. 2014/50715-9, 25 -the SERENA project funded by the CIRAD-INRA metaprogram GloFoodS). The SPOT and Pleiades images were acquired through the GEOSUD program EQPX-20, funded by French National Research Agency. The PlanetScope images were acquired through the Planet's Science Ambassador Program.
Negative Staining and Transmission Electron Microscopy (TEM) Phage preparations (2 mL) were pelleted by a mere centrifugation step at 20,800× g, 4 • C for one hour. Pelleted phages were washed and pelleted again three times with 2 mL of TEM-buffer (0. 22 µm filtered 0. 1 M NH4-acetate (pH 7)) and concentrated in a final volume of 50 µL TEM-buffer. Then, five µL drops of diluted phage suspensions (~2 × 10 8 PFU•mL -1 ) were spotted on glow discharged carbon-coated grids (EMS) and let stand for 3 min. The grids were then washed with two drops of 2% aqueous uranyl acetate and stained with a third drop for 2 min. Grids were dried on filter paper, and the samples were analyzed using a Tecnai 200 KV electron microscope (FEI). Digital acquisitions were made with a numeric camera (Eagle, FEI). Electron micrograph images were analyzed using the ImageJ software  [35].
Phage Genome Sequencing and Genetic Analysis Total phage DNA was extracted using the classical phenol/chloroform extraction method, then sequenced using the MiSeq Illumina ® technology. The phage DNA libraries were prepared using NexteraTM enzymatic fragmentation technology, a technology suitable for double-stranded DNA (dsDNA). Paired-end reads were assembled with SOAPdenovo, version 2. 04  [36]  and VELVET, version 1. 2. 10 [36,37]. Whole se-quences were compared to the NCBI BLASTn database. Protein coding sequences, ge-nomic size and GC content were predicted by the RAST ® tool  [38]. Predicted CDS were compared with the NCBI BLASTp database, NCBI conserved domain database and HHpred (PDB_mmCIF73_22_May database). Genomic comparisons were performed with shared k-mer analysis using the KI-S protocol and the similarity matrix was represented using the KI-S circle packing tool  [39]  available at the CIRM-CFBP Galaxy platform (https://iris.
angers. inra. fr/galaxypub-cfbp/, accessed on 28 March 2019). Phylogenetic analyses were generated by the VICTOR online tool  [40]  using the complete genomic sequences of the Caudovirales bacteriophages infecting Xanthomonadales present in Genbank (Supplemental Table  S1 ). All pairwise comparisons of the amino acid sequences were conducted using the Genome-BLAST Distance Phylogeny (GBDP) and trees were inferred under settings recommended for prokaryotic viruses  [40]. Taxon boundaries at the species, genus and family level were estimated with the OPTSIL program  [41].
Phage Nomenclature and Nucleotide Sequence Accession Numbers Phages isolated in this study were named according to the classification guide proposed by Adriaenssens and Brister  [42]. Note that the names of the newly isolated phages originate from neighborhood names in the city of Bogota, Colombia. The assembled genome sequences, as well as raw reads, were deposited at the European Nucleotide Archive (ENA) and the National Center for Biotechnology Information (NCBI) under the accession numbers listed in Table  2.
Genomic Characterization To compare our isolated phage genomes with the diversity available in the Genebank database, we performed a nucleotide BLASTn megablast search, optimized to retrieve highly similar sequences using the complete genomic sequences of phages FC03-Usme, FC12-Bacata, FC23-Cota, FC28-Sopo, FC30-Tabio, FC39-Tenjo, FC41-Suba and FC44-Bosa. S2.
Genomic Characterization To compare our isolated phage genomes with the diversity available in the Genebank database, we performed a nucleotide BLASTn megablast search, optimized to retrieve highly similar sequences using the complete genomic sequences of phages FC03-Usme, FC12-Bacata, FC23-Cota, FC28-Sopo, FC30-Tabio, FC39-Tenjo, FC41-Suba and FC44-Bosa. FC03-Usme, FC12-Bacata and FC44-Bosa shared homology with sequences available in the Genbank database. FC03-Usme displayed high identity with the genome of phage CP2 of Xanthomonas axonopodis pv. citri  [47]  (92% identity over 83% coverage), as did FC44-Bosa with phage DLP4 of Stenotrophomonas maltophilia  [48]  (97. 4% identity over 99% coverage), and FC12-Bacata with the Xff -infecting phage Salvo  [28]  (96. 3% identity over 89% coverage).
Two groups represented by phages FC23-Cota and FC41-Suba did not get a hit in any of the phage sequences in the Genbank database, indicating they were indeed totally new. The general characteristics of the 14 sequenced phage genomes are summarized in Table  4. The genomic organization and the main functions encoded were common to each of the PCR groups defined earlier and are described in Figure  S1. FC03-Usme, FC28-Sopo, FC30-Tabio and FC39-Tenjo encoded 58 predicted CDS, with a genome size close to 43 kb and a GC content around 67%. This group shared homology with the CP2 phage genome but to different extents (Figure  S2 ). FC03-Usme shared 92% identity over 83% of the sequence length with the CP2 phage genomic sequence. At the DNA level, FC28-Sopo, FC30-Tabio, and FC39-Tenjo phage genomes showed more than 8% DNA sequence divergence compared to FC03-Usme, but less than 6% compared to the CP2 genome sequence (Figure  S2 ).
citri phage Xc10 genome  [15]. This 2% shared DNA region corresponded to region coding for a DNA polymerase A (ASZ72017. 1) and a DNA-dependent RNA polymerase (ASZ72029. 1). Finally, the FC41-Suba genome was 45 kb long and 71 CDS were predicted, with the lowest GC content of 52%. As for FC23-Cota, the FC41-Suba genome sequence did not show any homology at the DNA level within the current Genebank database, sharing only a partial sequence of 253 nucleotides with the A. xylosoxidans phage phiAxp-1 that corresponds to a region encoding for the Terminase large subunit (YP_009220341. 1). The FC23-Cota and FC41-Suba phages were thus totally new and uncharacterized phages.
7 nm, 65. 4 nm, 63. 6 nm and 60. 6 nm, respectively. FC23-Cota, FC24 and FC34-Cajica also exhibit a podophage morphology, with an isometric capsid measuring 64. 1 nm, 65. 3 nm and 64. 4 nm, respectively. Tails and tail fibers were observed on the micrographs, but given their small sizes, the resolution was not sufficient to measure them (Table  5 ). RNA polymerase (ASZ72029. 1). Finally, the FC41-Suba genome was 45 kb long and 71 CDS were predicted, with the lowest GC content of 52%. As for FC23-Cota, the FC41-Suba genome sequence did not show any homology at the DNA level within the current Genebank database, sharing only a partial sequence of 253 nucleotides with the A. xylosoxidans phage phiAxp-1 that corresponds to a region encoding for the Terminase large subunit (YP_009220341. 1). The FC23-Cota and FC41-Suba phages were thus totally new and uncharacterized phages.
Phylogenetic Analysis of the Sequenced Phage Genomes Among our PCR groups, we distinguished various degrees of homology with known phage DNA sequences. Phages FC03-Usme, FC12-Bacata and FC44-Bosa could thus be related to phage genomes present in the NCBIdatabase. In contrast, FC23-Cota and FC41-Suba genomes had no substantial sequence similarity throughout the NCBIdatabase (accessed on January 2021). To analyze the phylogenetic relationships of the phages isolated in this study with other phages with lytic activity on Xanthomonas spp. , Stenotrophomonas spp. , and X. fastidiosa, a phylogenetic tree was generated using the VICTOR online tool  [40]  using complete genomic sequences of the Caudovirales bacteriophages infecting Xanthomonadales (Figure  4 ). 4  shows the relationships between amino acid sequences for the whole phage genomes obtained in this study and the isolated Caudovirales phages active on various species of the genera Xanthomonas, Xylella and Stenotrophomonas.
Finally, Autographiviridae encode their own RNA polymerase, which is the case for Cota (Genbank protein ID CAB1282939. 1). A genome-wide phylogenetic analysis based on protein-clustering indicates that Xylella phage Cota is related to the Pradovirus genus (Figure  4 ) but classified by OPTSIL as an "Unclassified Autographiviridae". A BLASTn analysis revealed that the two Pradoviruses Xylella phage Prado (type species of the Pradovirus genus) and Xylella phage Paz are almost as distantly related from the DNA point of view (73. 08% identity, 27% cover) than Xylella phage Prado and Xylella phage Cota (68. 15% identity and 35% cover). Taking also into account the low pseudo-bootstrap support values, we chose to classify Xylella phage Cota within the Pradovirus genus (Autographiviridae family) and not in a separated new genus as suggested by the phylogenetic tree in Figure  4.