dataesr/scientific-paragraphs-categorization huggingface dataset:
The reference line chosen to build profiles was the banktop line of the first acquisition. Vertical profiles were automatically extracted each 10 m using 3DReshaper software with a section thickness of 0. 1 m and a maximal edge distance of 0. 5 m. Profiles were extracted from 0 to 540 m which corresponds to the overlapping area between all surveys. The profiles length of 15 m is constant and corresponds to the observed maximal bank retreat. Extracted profiles were manually cleaned in Autodesk in order to remove points corresponding to vegetation and smooth the bank profiles. Each cleaned 2D lines was saved as DXF AutoCAD file and a Matlab program was developed in order to automatically open these files and superimpose them (figure  6 ). The stratigraphical limit between the fine and coarse sediment layers was visually defined using point cloud textured 3D model of the first survey, because the image brightness allowed to identify both layers.
The elevation of this limit was supposed to be constant during the surveying period. Submerged data was however not exploitable in the sense that it was highly noisy, due to the reflection of water and the change of medium. Therefore, to be consistent, each profile was cut off at the elevation corresponding to the highest water level being observed during all of the surveys. In fact, water levels of the first and last surveys were relatively low and similar, but the water level of the third survey was 60 to 70 cm higher. Thus, the water level measured during this campaign was used as lowest reference. This shows the importance to collect data during low flow in order to dispose of the most complete bank profile. But, as indicated in figure  7 , available survey windows were restricted over the year and especially during the spring and the winter.
DIACHRONIC PROFILE ANALYZES Areas under each bank profile (figure  6 ) were calculated in Matlab and the eroded surfaces were deduced by subtracting values obtained between each survey. As the bank height varied along the meander bend, each bank profile was normalized, in order to be able to compare the surface eroded between profiles extracted at different locations. Therefore, for each profile, the banktop elevation was considered as 1 m, while the bank toe elevation was considered as 0 m. Thus, if between two surveys, the bank retreated of 1 m and the shape of the both bank profiles was constant, the calculated eroded surface equals 1 m². For each profile, eroded areas were cumulated and represented in figure  8. There are no values at 400 m because of the presence of a tree, which felled into the channel during the study.
Finally, in order to compare the observed eroded areas to the observed retreats of the banktop, a Matlab program was developed to automatically identify banktop positions along the profiles. Banktop retreats of the first studied flood were not represented because the banktop positions were nearly identical. the bank profile is due to the presence of a paleochannel that was filled by fine sediments over the time. Another paleochannel was also visible at 270 m, 350 m and 410 m, but with a reduced width (5-10 m) and depth (0. 6-0. 8 m). However, the erodibility of the fine sediment layer is lower than this of the gravel layer, due to cohesion forces. Thus, the presence of former channels into the concave bank controls the bank retreat rate, and more generally the current morphodynamics of the meander  (Eschbach et al.
INTEGRATION OF PHOTOGRAMETRIC DATA INTO GIS AND COUPLING WITH BATHYMETRIC DATA The point cloud extracted from the last photogrammetric campaign was combined with a bathymetric survey in order to study the development of the meander bend. Bathymetric data were collected using an acoustic Doppler current profiler (ADCP) coupled with an accurate differential GPS (DGPS). Recorded water depths were associated to DGPS coordinates during the post-processing via timecode according to a time step of 1 second. Field measurements were carried out after the photogrammetric campaign at higher water levels to have an overlay between both datasets and facilitate point clouds merging. The boat trajectory followed a zigzag path from one side to the other side of the river to optimized the river bed mapping and simplify interpolation between each transects.
Some longitudinal profiles were also carried over the thalweg and along both river banks to complete this dataset and characterize the maximum lateral extension of the river bend. However, longitudinal extension of the bathymetric survey was reduced to the central part of the meander compared to photogrammetric surveys due to riffles located at each bend inflection which prevents taking measurements from a boat. Moreover, as the water levels were low during the survey, acoustic beams were concentrated in a small area and lateral beams did not improve the river bed mapping. Therefore, only the vertical beam was used to map the meander bend bottom. These bathymetry points were interpolated using a Triangular irregular networks (TIN) available in ArcGIS software. Edges of the TIN were manually adjusted in order to connect nodes between transects according to flow paths.
So, the bank retreat of the basal layer will not be investigated here. Furthermore, before merging photogrammetric and bathymetric point clouds, underwater points were deleted from the imagebased dataset. Indeed, elevation accuracy of underwater points was difficult to estimate without underwater ground control points at the time of the survey. However, the influence of refraction on underwater three-dimensional reconstruction may be estimated, but this may also be affected by the water turbidity. Considering that turbidity varied from one campaign to the other, integration of underwater points did not seem relevant as a quick and easy way to study bank retreat. In fact, most surveys were carried out with a water level below the bank toe. So, all the bank profiles were characterized satisfactorily.
CONCLUSION This project located on a particularly active meander of the Moselle River is part of a larger project that extends over the "Wild Moselle" nature reserve. The accurate 3D information acquired over several periods allows to follow the evolution of the banks in a particularly accurate way. The implementation of the drone, coded targets and GPS allows the acquisition of data for the monitoring of the banks in only a few hours. Data processing needs the same time laps, and the process put in place as well as Matlab workflow now makes possible to generate profiles and calculate volumes of bank erosion automatically. It is thus that such a project makes it possible to meet specialists from different fields, including topography and cartography with those of fluvial geomorphology.
De novo 97% OTUs (Operational Taxonomic Units) were clustered and classified through a custom VSEARCH-QIIME 1. 9. 1 pipeline (Greengenes 13. 8 taxonomy;  Caporaso et al. , 2010; McDonald et al. , 2012; Rognes et al. , 2016) , after which OTU tables were created from averaging 100 rarefactions at 6000 reads per sample, and UniFrac dissimilarity matrixes were computed  (Lozupone et al. , 2011). More details are provided in Supplementary information 1, raw sequence data is deposited at EBI (project number PRJEB31437) and the bioinformatics script is available as a Jupyter notebook (https://bitbucket. org/smonteux/mesocosms_methods). For measuring microbial biomass and abiotic soil properties, a column of soil (l=5 cm, w=5 cm) from the top of the permafrost (10 cm deep) was sampled, and the overlying thin crust layer (0.
Environmental data To monitor microclimate inside and outside the mesocosms without disturbing the ecosystem processes in the experimental mesocosms, we established an additional mesocosm in exactly the same way as the treatment with 2 mm mesh in six of the experimental blocks. We recorded soil temperature and volumetric water content (5 cm depth, 5TM sensor, Decagon, USA) in active layer compartment in the mesocosms, and outside each of these mesocosms hourly on Decagon EM50 loggers over the duration of the experiment. We recorded air temperature hourly close to the ground   3 ). We did not analyze the effects of mesocosm for the permafrost compartment (2 mm mesh) due to the lack of a natural nearby thermokarst site with disturbance of similar age as in our experiment.
Then, we tested whether this effect was greater than zero, since active layer soil fauna and plant roots were expected to spread into permafrost. The effects of possible lateral movement of microorganisms on DNA-and RNA-based bacterial community composition in the permafrost compartment (0. 5 µm vs 20 µm) were visualized using non-metric multidimensional scaling on weighted UniFrac dissimilarity (Fig. 3b , Suppl. information 2). The overall effects were tested with the anova. manyglm function (in the R package mvabund,  Wang et al. , 2012)  using treatment, nucleic acid, their interaction and block as fixed factors, and 999 PIT-trap resampling permutations. The differences between the effects of microarthropod inoculum and the unhindered lateral movement (20 µm mesh vs 20 µm mesh with inoculation and 2 mm mesh) were tested with anova.
manyglm function using treatment and block as fixed factors, and 999 PIT-trap resampling permutations. We analyzed the effects of 'mesocosm' (active layer compartment, 2 mm mesh) vs surrounding peatland on air temperature during the growing seasons 2015 (July 21 -September 18) and 2016 (June 1 -September 18) and for soil temperature and moisture year-round. We calculated the effect size of the mesocosm-effect as '(temperature or moisture inside mesocosm) -(ambient temperature or moisture)' for each block. We then analyzed the deviation from zero of the effect-size of the mesocosms separately for the two summers and for winter (September 19, 2015 -May 31, 2016), using one-sample t-tests. All data were visually checked for normality by quantilequantile plots and by plotting residuals against fitted values.
Raw forward and reverse reads were merged with VSEARCH 2. 7. 1  (Rognes et al. , 2016)  allowing 15 mismatches over the overlapping region. VSEARCH fastq_filter command was used to trim primers from the reads and apply quality filtering (discarding reads with more than one expected error). Sequences were then dereplicated and clustered in 97% similarity operational taxonomic units (OTUs) using VSEARCH cluster_size algorithm. The OTUs were checked for putative chimeras using the UCHIME  (Edgar et al. , 2011)  algorithm in VSEARCH against the GOLD database  (Haas et al. , 2011). Quality-filtered reads were mapped to OTUs using the usearch_global algorithm, and the resulting OTU table was imported in QIIME 1. 9. 1  (Caporaso et al. , 2010a)  for further processing. Representative sequences for each OTU were aligned using pyNAST  (Caporaso et al.
a global database of soil nematode abundance and functional group composition Johan Hoogen et al. # as the most abundant animals on earth, nematodes are a dominant component of the soil community. they play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns.
Background & Summary To generate a global and quantitative understanding of the biogeography of soil organisms, critical players in global biogeochemistry, large and comprehensive datasets are needed. Due to methodological challenges and the labor-intensiveness of characterizing soil biota, many previous studies have focused on a relatively limited number of spatially distinct sampling sites. Whilst these studies are valuable to dissect local and regional scale patterns, they may not hold the depth of information that is needed to feed global-scale models 1. Soil nematodes are present in all trophic levels in the soil food web, play central roles in regulating carbon and nutrient dynamics, control soil microorganism populations 2-4 and, consequently, are good indicators of biological activity in soils 5.
Here, we present a dataset of 6,825 spatially distinct soil nematode samples from all terrestrial biomes and continents, an updated version of the dataset that was originally used to create a global map of soil nematode abundance and community composition 6. The original version contained 6,759 samples; the updated version that we present here contains 66 additional samples located in Ireland. This dataset can prove useful to disentangle the effects of environmental drivers of soil nematode abundance and community composition across broad spatial scales. The original version of this dataset was used to create a high-resolution map of soil nematode abundance, which revealed that nematodes are present in higher densities in sub-Arctic regions compared to tropical and temperate regions 6. Soil properties are the primary drivers of soil nematode abundance, whereas climatic conditions have an indirect effect by altering soil conditions 6.
The overall latitudinal gradient, with decreasing abundance towards the equator, is the inverse of patterns often observed in aboveground organisms, but is in line with what has been shown for other belowground biota 7,8. Besides data on the total number of nematodes per sample, the dataset contains quantification of the abundance of individuals in different functional groups of soil nematodes classified according to five feeding guilds 9 : bacterivores, fungivores, herbivores, omnivores, predators. For geospatial mapping, these sampling data were aggregated into 1,933 unique 30 Arc-seconds pixels (~1 km 2 at the equator) and combined with 73 global covariate layers including information on soil physiochemical properties, and vegetation, climate, and topographic, anthropogenic, and spectral reflectance information.
the top 15 cm of soils, including 66 additional samples compared to the dataset used in our related work  6. Across all samples, 67. 2% originate from natural sites and 32. 8% from agricultural or managed sites. Nematodes were extracted from soil using standard elutriation methods, including the Baermann funnel method  10  , sugar-floatation/centrifugation  11, 12  , decanting and sieving  13  , Oostenbrink elutriation 14 , Whitehead tray  15  and Seinhorst elutriation  16. These methods may include variations of the original methods. Most samples present in the dataset were obtained using the Baermann funnel method, followed by Oostenbrink elutriation and sugar-flotation (Jenkins/ Freckman) (Fig. 1 ). Per-sample method descriptions, sampling depth, and data provider information are available via figshare  17.
Environmental metadata: soil, climate, topography, vegetation, anthropogenic characteristics. For all sampling locations we provide paired environmental metadata, which can be used to provide insight into the environmental drivers of soil nematode abundance and community composition across spatial scales. To do so, we first prepared a covariate stack of 73 layers, for which we downloaded the covariate layers as geotiff files. Fig. 2  Data processing approach. 6,825 georeferenced samples are included in the raw dataset. These sampling locations represent 1,933 unique 30 arc-seconds pixels (~1 km at the equator), or 1,895 pixels excluding locations falling off the covariate grid. To gain mechanistic insights and discern the major environmental drivers of nematode abundance, these pixels were sampled across 73 global covariate layers.
Next, all layers were resampled and reprojected to a unified pixel grid in EPSG:4326 (WGS84) at 30 arc-seconds resolution. Layers with a higher original pixel resolution were downsampled using a mean aggregation method; layers with a lower original resolution were resampled using simple upsampling (i. e. without interpolation) to align with the higher resolution grid. Next, all layers were converted into a multiband image, i. e. the covariate stack, that was used for pixel sampling. To prepare the dataset for this purpose, we first need to match the resolution of the dataset to that of the global covariate layer stack that contains the environmental metadata: 30 arc-seconds, which corresponds to approximately 1-km 2 at the equator. In this step, we aggregate all data points falling within the same pixel by taking the mean value, resulting in 1,933 unique pixels.
We stress that the covariate layer stack has no coverage in Antarctica and therefore the 503 samples located in this region were dropped at the pixel aggregation step. Next, pixel values across the 73 layers were retrieved and stored as a csv file. This dataset is available via figshare  17. We stress that, as some covariate layers were reprocessed since the publication of the nematode mapping study 6 , there are some slight differences in the sampled covariate data in this updated version. The approach is visualized in Fig. 2. Full metadata, including descriptions, units, and source information of all global covariate layers are available via figshare  17. In short, information about soil texture and physiochemical properties was obtained from SoilGrids  18  , limited to the upper soil layer (top 15 cm).
Climate information was obtained from WorldClim 19 (version 2), which includes climate data averaged across 1970-2000 (http://www. worldclim. org/). Plant productivity data (i. e. EVI, NDVI, Gpp, Npp) and spectral reflectance data were obtained from Google Earth Engine (https://developers. google. com/earth-engine/datasets/). Aridity index and potential evapotranspiration layers were obtained from CGIAR  20  (version 1) (http://www. cgiar-csi. org/data/global-aridity-and-pet-database). Anthropogenic information (i. e. human development, population density) was obtained from WCS  21.
technical Validation Soil nematode abundances are highly variable within and across terrestrial biomes 6. On average, the number of nematodes per 100 g dry soil is in the few hundred -few thousand range (median = 859, mean = 2,671), although the highest recorded abundances exceed 20,000 nematodes per 100 g dry soil. Across biomes, bacterivores are the most abundant trophic group and predatory nematodes the least abundant ( Percentage of pixels within sampled range 75% 100% Fig. 4  Environmental representativeness of the dataset. The sampled locations represent a wide range of environmental conditions. For illustrative purposes, ten environmental variables were chosen from the full set of 73. (median = 44) soils (Fig. 3 , Table  2 ). We stress that these numbers slightly differ from the values reported in our accompanying paper  6  , where we reported the aggregated pixel median values.
As with any global ecological dataset, combining data from many researchers across the world, there is inherent variation in the data. Also, the different nematode extraction methods may vary in their efficiencies  25, 26. This underscores the need for large datasets for global scale analyses of ecological patterns. When a sufficiently large sample size allows to detect strong patterns through this statistical noise, we can be confident that a biological pattern exists 6. As a consequence, there may be limitations to the use of the dataset at finer scales. Yet, by subsetting the dataset by extraction method or region, for example, it can serve as a starting point for local scale studies.
Environmental representativeness of the dataset. To evaluate the comprehensiveness of the dataset, we explored the environmental conditions that the sampling locations represent. Across individual environmental variables, the samples represent a wide range of environmental conditions (Fig. 4 ). To gain spatial insight into the environmental representativeness of the dataset, information that is important when comparing observations across spatial scales, we evaluated how the multidimensional environmental space covered by the dataset compares to the global environmental space. To do so, we used a similar approach as in our previous work  6. First, we set out to reduce the computational load, as exploring the full stack of 73 global environmental covariate layers across ~210 million terrestrial pixels would require exorbitantly large computing power.
To this end, we transformed the set of global environmental covariate layers into Principal Component (PC) space. We reduced the number of selected PCs to 17, collectively explaining more than 90% of variation. Next, we assessed the proportion of the world's terrestrial pixels falling within convex hulls of the 136 bivariate combinations of the 17 PCs. The resulting map provides a spatially-explicit depiction of the representativeness of the dataset, showing that the majority of the terrestrial pixels fall within these convex hulls, with most of the outliers existing in arid regions such as the Sahara and Arabian Deserts, and in sub-arctic regions such as the far north of Canada and Russia (Fig. 5 ).
Acknowledgements We are thankful to  Walter S. Andriuzzi  and  Louise E. Jackson  for help in the initial phases of this study and for providing data. This research was supported by a grant from  DOB Ecology  to T. W. C. , a grant from the  Netherlands Organization for Scientific Research  (grant  016. Veni. 181. 078 ) to S. G. , grants from  NSF  ( OPP 1115245 ,  1341736 ,  0840979 ) to B. J. A. , by a  Ramon y Cajal fellow award  ( RYC-2016-19939 ) to R. C. H. , a grant from  UNEP & Global Environment Facility  to J. E. C. , grants from  NERC 's  Soil Security Programme  to  R. D. B. ( NE/M017028/1) T. C. (NE/M017036/1 ), a grant from  FAPEMIG/FAPESP/VALE S. A. (CRA-RDP-00136-10 ) to L. B. C. , through the strategic programme  UID/BIA/04050/2013  ( POCI-01-0145-FEDER-007569 ) awarded to S.
05-2017. 330 )to T. A. D. N. , by an  ARC   Discovery project  ( DP150104199 ) to U. N. N. , by the  National Key Research and Development Program of China  ( 2016YFC0502101 ) and the  National Natural Science Foundation of China  ( 31370632 ) to  K. Pan , a  ERC Research Council Advanced  grant ( ERC-Adv 323020 SPECIALS)  to W. H. v. d. P, a grant from the  Natural Environment Research Council (NERC)  to D. G. W. , a grant from  BAPHIQ  ( 106AS-9. 5. 1-BQ-B3 ) to J. -i. Y. , a grant from the  Russian Foundation for Basic Research  ( 18-29-05076 ) to A. V. T. The  James Hutton Institute  receives financial support from the  Scottish Government Rural and Environment Science and Analytical Services (RESAS) division. Investigations in Northwest Russia were carried out under state order for IB KarRC RAS and are partially supported by the  Russian Foundation for Basic Research  ( 18-34-00849 ).
Data records All data are available via figshare  17. Raw nematode abundance data (6,825 samples) are available as a csv file: "nematode_full_dataset_wBiome. csv". Sample IDs 20001-20066 are samples not present in our related work 6. Abundance data aggregated into 30 Arc-seconds pixels (1,933 unique locations), combined with environmental covariate data are available as a csv file: "nematode_abundance_aggregated_wCovar. csv". Full metadata, including descriptions, units, and source information, of all global covariate layers are available as a csv file: "metadata. csv".