dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR products were purified from contaminants using the MiniElute PCR purification kit (Qiagen, Hamburg, Germany). Sequencing was performed using the BigDye Ò Terminator v3. 1 Cycle sequencing kit (Applied Biosystems, Carlsbad, USA). Products from the sequencing reactions were purified using the Agencourt CleanSEQ kit (Beckman Coulter Genomics, Danvers, USA). Sequences were retrieved using a 31309 Genetic Analyzer (Applied Biosystems, Carlsbad, USA). Sequence data were processed using the CLC main workbench software version 6. 0. 2 (CLCbio, Aarhus, Denmark). Ambiguous bases in all sequences were resolved manually. Obtained sequences were aligned and analyzed using the ARB software version 071207  (Ludwig et al. 2004 ) and the official SILVA database (http://www. arb-silva. de) for small subunit RNA sequences (SSUR-ef_NR99_115_SILVA_20_07_13_opt.
arb) (Pruesse et al. 2007). Complete cyanobacterial 16S rRNA gene sequences available from GenBank were imported and aligned to the sequences in the ARB database. To evaluate the consistency of computed tree topologies, subsets of data were analyzed using various algorithms as follows. A variety of single and multiple out-group sequences representing phylogenetically diverse organisms were included in the analysis. To assess the influence of the most variable nucleotide positions, they were excluded from some calculations by applying filters based on character frequency (ARB manual). The obtained cyanobacterial forward and the reverse complementary of the reverse sequences obtained were aligned against each other in order to obtain consensus sequences. These sequences were then aligned with the sequences in the ARB database using the alignment ARB tool.
The alignment was corrected manually. Phylogenetic trees were calculated by applying the three different methods integrated in the ARB software namely maximum likelihood (ML) method, maximum parsimony and neighbor joining, based on long 16S rRNA gene sequences ([ 1300 bp). The ML tree was finally selected for presentation because of its stability. Partial sequences obtained in this study were inserted into the tree using the parsimony ARB tool, while maintaining the overall tree topology without changes. The 16S rRNA gene sequence of E. coli was used as out-group.
The bottles were incubated in a flow-through aquarium. This aquarium was connected to a water pump that provided continuous water flow to ensure a similar temperature in the aquarium as on the reef (27-29 °C). Light intensity and temperature during the incubations were recorded with a UA-002-08 data logger (Onset Computer Corporation, Pocasset, USA). Light intensities (lux) were converted to the availability of photosynthetically active radiation (PAR) using the equation: 1 mmol quanta (400-700 nm) m s -1 = 51. 2 lx (sensu  Valiela 1984). The average light intensities (± SEM) in the laboratory around noon (1150-1210 h; sampling intervals 1 min; averaged over all incubations) were 201. 6 ± 27. 9 lmol photons m -2 s -1 for the incubated cyanobacterial mats originating from 7 m depth and 90. 9 ± 4.
Statistical analyses Differences in species abundance in mat samples were formally tested using a one-way permutation-based analysis of variance (PERMANOVA, Anderson 2001) based on unrestricted permutations of the raw untransformed abundance score data pooled across all site 9 depth combinations, and mat type as a fixed factor. Given the low number of possible permutations in the individual pairwise tests, we used the Monte Carlo asymptotic p values. Principal coordinates analysis (PCO) was used to visualize the data in two dimensions. Species contributing to differences between mat types were identified based on the strength of their Spearman correlations with the PCO axes. Analyses were performed in PRIMER 6 and PERMANOVA ? statistical package  (Anderson et al. 2008). Frequencies of morphotypes in mat samples were then averaged for each depth 9 site combination with frequency taken as the midpoint of the respective semiquantitative scale.
The Shannon Index was used to determine whether the diversity of the cyanobacterial species was comparable between all studied sites. We used an analysis of covariance (ANCOVA) with BCM site abundance (i. e. , high/low) as a fixed factor and depth as a covariate to determine whether species richness differed between sites with low and high mat cover, and whether species richness within the mats was affected by depth. Since only one patch from each of the six mats was assessed for nitrogen fixation and the pieces of 1 cm 2 of mat surface sampled from each patch constitute ''pseudoreplicates'' (sensu  Hurlbert 1984) , nitrogen fixation data were not amenable to statistical analysis.
Acknowledgements Open access funding provided by  Max Planck Society. This research was funded by the  European Union Seventh Framework Programme  ( P7/2007-2013 ) under Grant Agreement No. 244161  ( Future of Reefs in a Changing Environment ). Financial support for genetic analyzes was provided by the  PACES research program  of the  Alfred-Wegener-Institute Helmholtz-Zentrum  fu ¨r Polar-und Meeresforschung. MMN acknowledges support from the  CNRS Chaire d'Excellence. We wish to thank the CARMABI foundation and the friendly staff for support during fieldwork, especially  M. Vermeij ,  P. Stokkermans  and  C. Winterdaal. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://crea tivecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Terrestrial and airborne laser scanning, photogrammetry and more generally 3D recording techniques are used in a wide range of applications. After recording several individual 3D datasets known in local systems, one of the first crucial processing steps is the registration of these data into a common reference frame. To perform such a 3D transformation, commercial and open source software as well as programs from the academic community are available. Due to some lacks in terms of computation transparency and quality assessment in these solutions, it has been decided to develop an open source algorithm which is presented in this paper. It is dedicated to the simultaneous registration of multiple point clouds as well as their georeferencing. The idea is to use this algorithm as a start point for further implementations, involving the possibility of combining 3D data from different sources.
INTRODUCTION Terrestrial laser scanning and photogrammetry are two techniques that have proved their efficiency not only for documentation purposes, but also for the monitoring of natural, industrial or man-made sites. Whatever the final use of the produced 3D datasets, the raw products arising from these techniques are most of the time point clouds. While considering raw data that have not been directly georeferenced in a global reference frame, each individual point cloud acquired is expressed in its own local system. To build a consistent global point cloud, all the datasets first need to be grouped in a unique reference frame during the step known as registration. Due to the large amount of sensors dedicated to the acquisition of 3D data, it becomes common practice in more and more projects to combine datasets from various sensors within a same model.
This makes it possible to complete missing parts of a project later on, to work at different scales and levels of detail, or to reach particular areas that may be hardly recorded if using a unique acquisition method. As regards the existing software solutions dedicated to the registration of 3D datasets, two main lacks have been observed: first, even if various data formats may be imported within the program, the heterogeneous properties of datasets acquired using different sensors are not clearly managed. Differences in terms of point density or a priori precision for example are probably not considered during registration process, and this traceability gets lost in the finally delivered model. The second point is related to the way quality criteria are computed and precision is assessed.
Even when working with a unique dataset, this may vary from one tool to another, which makes the result comparison between two methods tricky. Moreover, these criteria and their computations are most of the time not clearly explained. This 'black-box' effect can lead to misunderstandings or bad analysis of the result quality, which may be problematic for surveyors or people who have to ensure reliable results. In order to find a solution to manage potential data heterogeneity, but also to overcome every lack of transparency encountered either during processing or quality assessment, it has been decided to develop an open-source global registration algorithm using the Matlab (MathWorks) language. The final aim of these developments is to simultaneously register multiple point clouds that may come from different sources.
The aim of this paper is thus to validate the first step of the algorithm, which deals with the global registration of several point clouds acquired with a unique instrument. After a quick introduction to registration basic principle and a review of related work as well as existing solutions in Section 2, the proposed methodology is developed in Section 3. The problem formulation and the required data inputs are discussed in this section, and the computed residuals and quality criteria are explained. Section 4 is finally dedicated to the analysis of first results achieved for the simultaneous registration of ten laser scanning point clouds. Based on the same input dataset and criteria, a thorough comparison of these results with the ones obtained using two existing software solutions is finally carried out for the validation of the current version of the algorithm.
, 2012)  has been considered, as well as the use of 2D information coming from range or intensity images in the work of  (Weinmann et al. , 2011). If the registration of point clouds using images is based on the detection of 2D keypoints in these images, more recent researches focus on the use of 3D keypoints directly detected in the 3D datasets, as proposed by  (Theiler et al. , 2014). At this stage, the proposed method is based on the use of artificial or natural common points as described in Section 3. 2. If promising results are achieved, then the introduction of a 3D keypoint detection algorithm will be considered in the future, in order to increase the level of automation.
Software and tools Mainly based on the principles mentioned before, software solutions are available to deal with the registration of laser scanning point clouds. When developed by the device manufacturers, such as FARO Scene or Trimble RealWorks among other companies, these software enable the automatic registration of the acquired datasets and provide visually good results. Unfortunately their algorithms and especially the way the accuracy is assessed are not clearly outlined in these commercial solutions. Moreover, they are initially not designed to automatically allow the combination with exterior datasets, which can be point clouds captured using image-based techniques or low-cost sensors. To consider the registration of point clouds acquired using different techniques, open-source solutions such as CloudCompare  (Girardeau-Montaut, 2018)  and also software from the academic domain such as 3DVEM  (Fabado et al.
, 2013 ) can be found. In the latter case, even if the registration methodology implemented is not clearly detailed, the quality criteria applied are easier to interpret by the user than those provided by commercial solutions. To confront the results delivered by the algorithm proposed in this paper with reference results, two software solutions have been selected to reprocess the same test dataset presented in Section 4. 1. Among the commercial solutions, FARO Scene has been chosen since the considered dataset has been acquired using a FARO terrestrial laser scanner. As regards tools taken from the academic domain, 3DVEM has been preferred to CloudCompare which is limited to pairwise registration, because the aim of this paper is to assess a global registration methodology. The two selected tools are quickly described in the next subsections.
FARO Scene is largely used to handle the 3D point clouds produced with the terrestrial laser scanners of the FARO Focus series, from data import to export through the registration and georeferencing of point clouds. Several methods are offered for the registration, based either on natural or artificial targets placed by the user in the environment or based on top views into the 3D data, which is especially suited for indoor environments. A cloud to cloud refinement method is also available. The detection and matching of artificial targets that are spheres or flat targets is automatic, unless the user wants to manually add some specific points or assign the correspondences. The version of FARO Scene used in this study is the currently most recent, namely Scene 7. 1. 1. 81.
Basic principle and formulation While considering a global registration approach, all the relationships between the point clouds being registered should be considered and expressed in a mathematical form. The sets of parameters that will be simultaneously estimated during adjustment computation are the transformation parameters that link each moving point cloud to the reference system. For a dataset composed of n point clouds, n-1 point clouds are moving point clouds and one cloud is taken as reference. Thus there will be as many individual sets of six or seven parameters (depending on the presence of a scale factor) to determine as there are moving clouds. Even if the unknown parameters to determine connect moving point clouds to the reference cloud, the links between adjacent moving data have to be taken into account in order to achieve a global approach.
Potential indirect georeferencing Once all point clouds have been registered into a same reference system, a second transformation of these grouped data may be necessary depending on the project. Georeferencing consists in the transformation of a dataset known in a local system into a global system, such as a national framework for example. One speaks about indirect georeferencing when the point clouds have first been registered, before the registered dataset is georeferenced. Indirect georeferencing can be carried out using the developed algorithm, if necessary. This step requires to have determined beforehand the three-dimensional coordinates of representative points in the global system, thanks to tacheometric observations for example. These points may be artificial targets if some have been placed in the scene, or natural points that are selected due to their representativeness.
If all the reference points have been used during the georeferencing process, they can no more be considered as really independent to the computation. In this case, a RMS error can still be calculated but it will rather define the internal precision reached by the computation. To be able to give an absolute precision (or accuracy), residuals have to be computed based on reference points that have not been included in the georeferencing process. These can thus be used as external checkpoints. Both approaches are possible within the proposed algorithm, depending on the availability or not of an external reference dataset. Regardless of the approach, in the case of indirect georeferencing, the residual values are computed for the reference points. Thus reference points presenting high deviations can be excluded from the georeferencing.
Dataset and results The dataset used in this context has been acquired in the interior part of the Saint-Pierre-le-Jeune Catholic Church in Strasbourg (France). Ten laser scanning stations have been set up using a FARO Focus X330 terrestrial laser scanner. Spheres have been evenly placed in the digitized environment for the automatic registration of the data using FARO Scene software. Among the 12 spheres that have been placed in the recorded environment, the centers of 11 spheres have been accurately measured with a total station for the later georeferencing of the project. The distribution of scanning stations and of the 12 spheres is depicted in Figure  3. Since the implemented algorithm as well as 3DVEM software require common point coordinates as input data, spheres have been automatically detected using FARO Scene and their local coordinates have been listed before performing the registration.
These coordinates have been reused as input data with MORPHεD and 3DVEM, thus the three methods can be compared based on the same inputs. As regards the results delivered by MORPHεD, Figure  4a  shows a top view of the ten unorganized point clouds before the registration, whereas Figure  4b  gives an overview of the registered data. The considered dataset mainly covers the transept of the church between the nave and the chancel, even if the scanned points cover almost the whole building due to the scanning range of the device, as it can be seen in Figure  5. Based on a simple visual inspection, it appears in Figure  4b  that all the point clouds have been correctly placed in the reference system. This qualitative analysis will be completed with a quantitative analysis in the next section.
Methods of comparison The quantitative analysis of the proposed algorithm is based on a comparison of the obtained results with results achieved using FARO Scene and 3DVEM software. The comparison can be undertaken at different levels: for example, the finally adjusted transformation parameters as well as the available quality criteria can be compared to this end. The risk while comparing the adjusted transformation parameters lies in the way the rotation matrix is expressed, because this may differ between the three approaches. This would result in different rotation angles that are not directly comparable. Moreover, a difference of some hundredth of radians for the computed rotation angles does not give a clear idea of the difference in terms of precision between the different registration solutions.
Residuals in 3DVEM: As regards the 3DVEM software, considering the report which is generated after registration, it seems that the coordinates of tie points available in the reference point cloud are taken as references to compute the residuals. As a matter of facts, residuals and 3D deviations related to the reference cloud are zero. For the remaining tie points that do not appear in the reference point cloud, coordinates are taken as references from one of the moving point clouds where they appear in a way is unfortunately not described. This assumption is made based on the fact that residuals are zero for each of the remaining tie points in one or several moving clouds. Thus the coordinates chosen as reference to compute residuals are not selected uniformly. Using these residuals, 3D deviations are computed for each tie point of each scan, but no unique value is synthetized per scan.
Comparisons and assessment of the method 4. 4. 1 Registration: Assessing the registration is possible thanks to the residuals that have been computed for each tie point of each station. These residuals are not exactly the same for the three registration tools, varying from some millimeters but with the same order of magnitude. This is due to the fact that different transformation parameters and thus different transformed coordinates of tie points have been estimated in each case. delivered by the three methods. Having a look at this graph, one can assume that the developed computation methodology is closer to the one applied in 3DVEM than in Scene because of very close standard deviations. However the processing of further datasets is required to confirm such an assumption. Indeed, the small variations are quite insignificant and may be due to rounding errors during the computation process.
Georeferencing accuracy: To finally assess the global accuracy reached for this dataset after georeferencing using Geo-MORPHεD, external checkpoints unused during the transformation estimation but whose coordinates are known in the global system are necessary. To this end, georeferencing has been reprocessed for the considered dataset with the proposed algorithm only. Among the 11 targets that have been measured using a total station, six targets have been used to achieve this second georeferencing. The five remaining spheres can thus be defined as checkpoints, since they are not involved in the computation. 3D deviations with respect to the reference values are then calculated for these checkpoints, and a RMS error of 2 mm can be derived from these residuals listed in Table  8. Table  8. Georeferencing residuals of five checkpoints The reached georeferencing accuracy of 2 mm is a satisfactory result regarding the a priori precision of the laser scanner used and the overall dimensions of the building.
This is another indicator of the efficiency of the algorithm presented in this paper. Note that the identifiers of the spheres and stations are visible in Figure  3. Nevertheless, no detailed analysis of the individual results is made here, since the real accuracy assessment of the test dataset is not the topic of this paper. To go further in the analyses and complete the quality criteria offered in the presented algorithm, one could imagine determining and plotting error ellipsoids for the stations or for the tie points (registration), but also for reference points (georeferencing). This would help for the visual inspection of reliable and non-reliable points, and will be considered in future work.
CONCLUSION AND FUTURE WORK In this paper, an open source algorithm dedicated to the global registration of multiple point clouds has been presented. The mathematical model used to that end has been inspired by the aerotriangulation principle in photogrammetry domain. The global registration of the 3D data is based on the use of common points such as targets or manually selected representative points, which may be automatically and robustly matched if the initial correspondences are unknown. In this paper, the algorithm has been tested for the global registration of ten laser scanning point clouds representing the interior part of a church. To validate the results obtained using MORPHεD introduced in this paper, the same dataset composed of raw point clouds and their associated common point files has been processed with one commercial and one academic software.
To compare the results delivered by the three solutions, a thorough analysis of the quality criteria and a reflexion about the meaning of these values have been carried out. To achieve a rigorous comparison, the residuals and statistic criteria computed based on the common points have been reprocessed in the same way. The high similarity between the resulting values enabled to conclude about the efficiency of the proposed algorithm, which builds a strong basis for future developments. Besides, the advantage of using MORPHεD is the transparency of the quality assessment. As regards the supported input 3D data, for the moment the algorithm is dedicated to the registration of point clouds from different data formats. A first way to improve the methodology will be the solution proposed to handle the heterogeneity of datasets acquired with various sensors.