dataesr/scientific-paragraphs-categorization huggingface dataset:
Alternative PSI approaches can also be found in  (Hooper et al. 2004 (Hooper et al. , 2007)) , where a phase coherence analysis is used in order to refine the initial PS selection, and  (Ferretti et al. 2011)  where joint analysis of PS and CP is addressed. A reference book in this area is  (Kampes 2006). The first application of polarimetry to urban PSI was proposed in  (Perissin and Ferretti 2007)  which made use of ENVISAT incoherent dual-pol data to recognize target physical features and to classify PS. Another PS classification strategy supported by polarimetry was also presented in (Dheenathayalan and Hansen 2011) in the context of building-versus-ground relative movement estimation, using TerraSAR-X coherent dual-pol data. To further exploit polarimetric diversity at initial PSI processing stages, an algorithm aiming to increase the quality and number of pixels pre-selected for PSI processing was introduced in (Pipia et  al.
We have adapted this formulation to the dual-pol case, as the TerraSAR-X data at our disposal. For dual-pol data, the target vector is given by: k ¼ 1 ffiffi ffi 2 p HH þ VV HH À VV ! ð5: 21Þ and the projection vector can be parameterized as: ω ¼ cos α ð Þ sin α ð Þe iψ ! , 0 α π 2 Àπ ψ < π ( ð5 :22Þ so in this case the search is reduced to just two real parameters, α and ψ. The optimization approach is tested here for the two most commonly used criteria of selection: the average interferometric coherence and the amplitude dispersion index. In the context of polarimetric interferometry, the average coherence γ j j can be expressed as: γ j j ¼ 1 K X K k¼1 γ k j j, with γ k ω ð Þ ¼ ωΩ ij ω ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ωT ii ω ffiffiffiffiffiffiffiffiffiffiffiffiffi ωT jj ω p q ð5: 23Þ where subscript k denotes the k-th interferogram obtained by combining images i and j.
Experimental Results with Dual-Polarimetric Data and Comparison with Single-Polarimetric Data A set of 41 dual-pol, Single-look Slant-range Complex (SSC) images acquired by TerraSAR-X from February 19, 2009, to May 27, 2010, over the urban area of Murcia (Spain) has been used to test the proposed algorithms. All images have been acquired using StripMap mode (SM) at HH and VV channels, along ascending passes, with a mean incidence angle of 37. 8 degrees. Azimuth and slant range resolutions are 6. 6 m and 1. 17 m, whereas pixel dimensions are 2. 44 m and 0. 91 m, respectively. Therefore, the resulting oversampling factors are 2. 7 and 1. 28 in azimuth and range. The processing has been applied over a part of the image with 2000Â2000 pixels. For comparison purposes, we have also computed deformation maps using a set of 41 single-pol SSC TerraSAR-X images, acquired at VV channel using SM mode, along descending passes, with a mean incidence angle of  35.
3 degrees, from February 1, 2009 , to May 20, 2010 (i. e. , during the same period as the dual-pol images). These images have azimuth and slant-range resolutions of 3. 0 m and 1. 17 m, with a pixel spacing of 1. 89 m and 0. 91 m, respectively. We have selected a crop of 2582Â2000 pixels corresponding approximately to the same area considered for the dual-pol images. The test site and corresponding radar and validation data sets selected for the generation of this showcase on urban ground deformation monitoring are presented in Table  5. 12. Despite the advantage of fully polarimetric data over dual-pol for this application, as stated later in the text, this test site was preferred due to the availability of reference ground data for validation and the granted access to the TerraSAR-X images for this purpose.
In this section, we present results obtained for dual-pol and single-pol data, using PSI based on both PS (selection by amplitude dispersion index) and CP (selection by average interferometric coherence). Note that for average coherence computation, a 7Â7 multi-look scheme has been considered for both dual-pol and single-pol sets. Taking into account the oversampling factors presented before, this corresponds to an equivalent number of looks (ENL) of approximately 22 for single-pol images and 14 for dual-pol images. These ENLs are sufficient to ensure a negligible bias in the coherence estimation for coherence values over 0. 6 for both data sets  (Touzi et al. 1999). In order to minimize decorrelation issues and to keep a reasonable stack size, a 100 m limit for the perpendicular baseline and 100 days for the temporal baseline have been defined, resulting in the generation of 166 interferograms from the dual-pol images and 140 interferograms from the single-pol images.
6). For more demanding thresholds (0. 8, 0. 9), the increment becomes more noticeable, up to a 60% increase in comparison with HH. In this case, the simple algorithm consisting in choosing the best from a reduced set of channels (denoted as Union) achieves a suboptimal solution at a considerably lower computational cost. Also note that, in this urban scenario, the HH-VV channel, generally associated with double-bounce reflections like the ones originated by building façade-ground dihedrals, is the best choice from the set of copolar and Pauli channels. The increase in the number of selected pixels is more spectacular for PS selection since in this case the optimization is applied to single-look data (i. e. , not spatially averaged), which are more sensitive to the geometrical and polarimetric features of dominant scatterers inside the resolution cell.
Consequently, improvement ranges in this case from 130% (more than 2 times more pixels selected) for the least restrictive threshold (0. 3) to 170% (2. 7 times more pixels) for the most restrictive threshold (0. 15), when comparing the optimum channel to the reference single-pol channel HH. Notice that in this case, the Union approach is far from providing the same improvement, yielding results at midway between single-pol and optimized ones. For comparison, Table  5. 14 shows results obtained for the single-pol data set, at VV channel. Given the different acquisition geometry (descending vs ascending), the selected area of study is not exactly the same as for the dual-pol set, but results are still quite similar to those obtained for the dual-pol set at VV channel in terms of selected pixels density.
At a more southern site, mammal sedaDNA has not been detected even in the presence of abundant coprophilous fungal spores  [63]. Nevertheless, sedaDNA has the advantage of allowing identification at the species level, which is not possible using pollen, Sporormiella spores or specific bacteroidales. While currently no mammalian DNA and faecal DNA datasets have been compared, this is, nevertheless, a promising complementary approach. Indeed, ratios between different stanols and bile acids can be used to distinguish between omnivore and ruminant species and between humans and pigs  [131, [133] [134] [135]. Though limited, evidence from archaeological sites suggests that shotgun metagenomic reads assigned to a range of different animal taxa, mirror their respective biomass estimated using classical analyses of bone remains  [136].
Nevertheless, some work remains to establish whether this quantitative relationship for sedaDNA might hold across different types of archives. DNA from aquatic biota compared to lipids, pigments and subfossils: The sedaDNA signal of past aquatic biota has mainly been compared to lipid, pigment, or subfossil (such as diatom frustules) records. The current view is that the DNA information in sediments degrades faster than pigments or lipids, although significant positive correlations have been observed between these different proxies  [18, 72, 77, [137] [138] [139] [140] [141]. Studies comparing diatom diversity retrieved from sediment using morphological and genetic approaches consistently show that beta-diversity values obtained from both methods were highly comparable (i. e. , the turnover is similar), whereas alpha-diversity values and taxonomic assignment datasets were not  [45, [142] [143] [144] [145] [146] [147].
This discrepancy in the reconstructions is at least in part attributable to very sparsely populated genetic databases available for diatoms, which can be tested in the near-future thanks to the exponential growth of genomic databases and initiatives such as the Earth Biogenome Project  [148]. Heinecke et al. [149]  demonstrated that the detection of sedaDNA identified as Potamogetonaceae was consistent with the recovery of subfossil remains from a species within this family of hydrophytes, whereas Clarke et al. [26]  recently detected Callitriche and Sparganium in both pollen and sedaDNA, whereas each of the proxies detected an additional two and five taxa of aquatic macrophytes.
g. , plankton, vegetation, fish)  [167]. This method is based on the barcoding principle, which consists of sequencing standardised markers that are conserved enough to be specific to the target higher taxonomic group but variable enough to contain enough information to discriminate lower taxonomic groups, such as species or genera  [198]. Available reference sequences are compiled into databases to which the metabarcode sequences are compared for taxonomic assignment. Metabarcoding is a robust and powerful tool that has been widely applied in sedaDNA studies (Table  S1 )  [11, 13, 74, 181, 199]. The power, but also limitation, of metabarcoding is the fact that it is PCR-based. This makes it possible to amplify minute quantities of template molecules, but also introduces PCR and amplification biases (e.
g. ,  [200] ). Because the targeted, reference databases can often be relatively complete for specific biological groups, bioinformatics become easier to handle. This is achieved by adding unique combinations of forward and reverse primers that include short unique tags of 6-15 nucleotides, which enables pooling of a large number of PCR products that can be sequenced on the same run. After sequencing, the DNA reads obtained can be demultiplexed by subsetting the DNA reads based on the tag associated with each primer. Metabarcoding requires the template to be present in the sample, and, if the fragments in the sample are too degraded, then an intact template, including the primer binding sites, might no longer be present. Thus, generally short barcodes are targeted, which may have lower taxonomic resolution compared to longer ones  [201].
When sufficiently comprehensive genomic reference databases are available, the detection of species may be dramatically enhanced  [29]. With the growth of these databases the potential for identifying species using the shotgun metagenomics approach will greatly increase. One such promising initiative is the recently launched Earth Biogenome Project setting out to provide a high-quality genome inventory of eukaryotic life on earth  [148]. Currently, there is a strong bias towards mammals, agricultural and medicinal plants, and pathogenic microbes in available databases. However, this will change as an increasing number of large-scale genome skims data-i. e. , shallow shotgun sequencing of genomes to reconstruct multicopy markers and organellar genomes-become available  [204, 205]. Thus, we expect metagenomics-based approaches to be increasingly used in the near future.
We note that the performance of metagenomics will almost certainly improve further as genomic reference databases become more complete. Differences in taxonomic composition can be observed between metabarcoding and metagenomics datasets, especially if the DNA is very degraded  [189, 212]. In all cases, comparisons will be context-dependent, relying on parameters such as DNA degradation, sequencing depth, primer selection, detection thresholds, available reference libraries, and appropriate use of controls.
DNA Markers and Reference Databases Used in Current sedaDNA Research sedaDNA from lake sediments is degraded into short fragments. For instance, shotgun libraries from Holocene lake sediments consisted mainly of DNA reads between 30 and 100 bp  [33, 62] , although fragments up to 560 bp have been found in 10,000-year-old sediments in the Black Sea (  [11]. Thus, targeting long barcodes (typically >150 bp;  [213] ), which generally have higher taxonomic resolution  [198, 214, 215]  is not a viable option for sedaDNA approaches, even if shorter barcodes generally have lower taxonomic resolution  [201, 216, 217 ]. An alternative is to use multiple barcodes and compare the results  [48]. If shotgun metagenomics is used, then a representative sample of all DNA fragments present in an extract is sequenced, independent of the length.
Nevertheless, as mentioned in Section 3. 8, there will be a bias in the data related to representation in the reference databases  [29]. This bias increases the risk of random matches to model organisms that are well covered in reference libraries  [81] , although fortunately, methods have recently been developed to address this issue (e. g. ,  [218] ). For any of the methods, there is a real risk of false positive assignment due to incomplete reference libraries or inappropriate filtering (see Section 3. 10). This risk is inversely related to the taxonomic resolution of the marker, as there is a higher chance of obtaining incorrect assignments for a conserved sequence than a highly specific one. An error in the reference library will furthermore have a greater effect the more conserved the marker is.
For cyanobacterial assemblages, Monchamp et al. [73, 74, 84 ] used a 16S primer set (400 bp) specifically designed for cyanobacteria by Nübel et al. [237]. To track past changes in the diversity and composition of microbial eukaryotic communities (including phytoplankton and fungi), the 18S rRNA gene V7 region marker used in Capo et al. [76]  offers a good tradeoff between taxonomic resolution and fragment length (260 bp). It has been proven to capture past modifications related to environmental change  [34, 38, 76, 141]. Coolen et al. [11]  targeted the V1-V3 region of 18S rRNA genes while Kisand et al. [78]  and More et al. [79]  targeted 18S V4 and V9 region, respectively. Specific databases, such as PR2  [238]  and SILVA  [239] , can be used to identify microbial eukaryotes from any of the 18S genetic regions.
In addition, the ITS region has been proposed as a DNA marker for fungal specific barcoding from environmental samples  [240]. Such DNA markers have been for instance used by Ortega-Arulú et al. [241]  targeting ITS 1 and Tõnno et al. [242]  ITS 2 to identify fungal taxonomy against the UNITE database  [243]. The detection of diatom communities in environmental DNA can be traced by targeting the chloroplast rbcL  [105, 144, 147, 244]  and the nuclear 18S V4 region  [245]  because both markers are well represented in current reference databases, which facilitates the assignment of sequences to lower taxonomic levels. The short rbcL metabarcode (67-76 bp)  [105]  has been shown to facilitate specific amplification of diatoms from tropical  [105]  and arctic lake sediments  [143, 146, 147] , as well as marine deposits  [246].
Although COI has a much greater reference database coverage, due to being the universal metazoan barcode  [198] , many reads are assigned to non-target groups making it difficult to obtain reproducible signals for fish taxa, which will be especially problematic given the low amounts of fish DNA compared to bacterial or planktonic groups in sediments. The MiFish-U 12S primers, in combination with a high annealing temperature, have been used to successfully amplify a hypervariable 163-185 bp fragment of fish DNA in surface sediments  [221] , although applications to older sediments remain uncertain.
Regardless of the procedure applied, "true" presences can be removed (type II error), and "false" presences retained (type I error)  [82, 183]. To limit these errors, sedaDNA data can be cross-validated by comparing it with independent proxies  [22, 24, 39, 82]. When no cross-validation is possible, one can set the threshold where the majority of retained taxa are biogeographically likely to be true positives whereas the taxa filtered out are not expected in the sample  [27, 47]. However, such an approach has the potential to be overly conservative and bias the taxonomic inventory toward the present. The detection of a given suspect taxon in the same sample as taxa with similar ecological preferences can also be used to validate the presence of this suspect taxon  [39]. For metagenomic datasets, taxa with very low read counts (e.
g. , n = ~3-5 per sample or 1 count in each of 3-5 samples) are generally excluded as spurious and controls thoroughly screened  [62]. In metagenomic data sets, it is also imperative that a strict minimum fragment length cutoff (e. g. , 35 bp) is applied to reduce spurious alignments  [257]. Where doubt remains, it is important that the filtering procedure does not impact the conclusions of the study  [25]. A major issue with the lack of standard bioinformatic pipelines to analyze DNA metabarcoding or shotgun metagenomics data is that slightly different procedures may lead to contrasting results  [167, [258] [259] [260]. Many sedaDNA works use OBITOOLs  [261]  to analyze DNA metabarcoding data (e. g. ,  [16, 31, 35, 199] ), using mainly the EMBL nucleotide database or a custom reference database depending on the target taxonomic group or geographic region.
This package, along with similar packages such as the Anacapa toolkit  [262] , contains various functions allowing for a comprehensive control of different sections of the data handling pipeline from read alignments, cleaning and filtering (i. e. , removal of potential sequence variants generated by sequencing errors) to taxonomic classifications with simple user-customizable parameters. For shotgun metagenomics data obtained from sedaDNA, an example emerging procedure is the Holi pipeline  [62]. By mapping shotgun reads to the full EMBL genomic and/or nucleotide database, which encompasses all realms of life, and subsequently assigning those matches to the lowest common ancestor taxa, Holi greatly diminishes the risk of misassignments  [62]. In addition to the cleaning, merging, mapping, and annotation steps provided by the tools compiled in this pipeline, tools such as pmdtools  [263]  and mapDamage  [264]  can be used to identify ancient DNA present in alignments to ultimately differentiate between modern and ancient DNA molecules ensuring, therefore, the reliability of the sedaDNA signal obtained as per described in Section 2.
3. The choice of reference library used to annotate DNA sequences is a key component for obtaining reliable ecological information. Indeed, a major source of false positives may also be due to errors in reference libraries or sequence sharing combined with local species lacking in the reference library. Hence, the choice between the use of curated and non-curated reference databases (using more liberal alignment parameters) and the use of regionally local reference databases (like that made for the Norway/Polar regions and the European Alps) is of utmost importance to avoid misidentifications. Details on statistical data analysis for processed sedaDNA data (univariate and multivariate) are beyond the scope of the present review. We instead direct readers to some key publications providing relevant recommendations about this step of the workflow  [188, [265] [266] [267].
Include and sequence negative controls from sampling, extraction, and downstream steps, as a minimum. Compare sequences/taxa that appear in both the controls and samples-these should, at a minimum, be treated with caution or removed entirely. Whenever possible, perform a multi-proxy approach with diagnostic macroscopic, microscopic, cellular, and/or DNA markers for specific taxa to cross validate the sedaDNA approach, especially if results are unexpected or ground-breaking. Use analytical methods that remove putative contaminants from the dataset. Choice of DNA extraction protocol: In most cases, the following protocols should efficiently extract DNA from sediments: Powersoil (Pro) kit for small amounts of sediment (approx 0. 25 g), PowerMax or FastDNA kit for larger sample sizes (up to 10 g), although the sediment type (organic-, clay-, carbonate-rich, etc.
Supplementary Materials: The following are available online at https://www. mdpi. com/2571-550 X/4/1/6/s1. Funding: This work was supported by funding from the Knut and Alice Wallenberg Foundation (grant 2016. 0083), the Swedish Research Council for Sustainable Development Formas (grant FR-2016/0005), the Norwegian Research Council (grant 250963/F20, "ECOGEN"), the Deutsche Forschungsgemeinschaft (grants OR 417/1-1 granted to W. O. and VU 94/1-1 granted to A. V), the German Research Foundation (grant EP98/2-1 to L. S. E,) and the Australian Research Council Discovery (grant DP160102587), the RTG 164 (PhD scholarship to LH). The computations and data handling of case studies A1 and A4 were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX partially funded by the Swedish Research Council through grant agreement no.
Data Availability Statement: For case studies A1, A3, A4, A5, A7, raw data are available at https: //doi. org/10. 6084/m9. figshare. 13007279. v1 (accessed on 1 February 2021). For case study 1, the raw amplicon sequencing reads are not yet published but might be shared on request. Contact Kevin Nota (kevin. nota@ebc. uu. se) or Laura Parducci (laura. parducci@uniroma1. it). For case study A2, The 16S data are available in SRA BioSample accessions SAMN13324854 to SAMN13324920 and from the European Nucleotide Archive (http://www. ebi. ac. uk/ena (accessed on 1 February 2021)) under study accession number PRJEB14484. For Case study A6, raw data are available within amplicon libraries JIE4 and AOHL-3-8 under project accession PRJEB39329 on the European Nucleotide Archive (ENA). Tag sequences and the bioinformatic filtering scripts are freely available on Github at https: //github.
com/pheintzman/metabarcoding (accessed on 1 February 2021) and https://github. com/ Y-Lammers/MergeAndFilter (accessed on 1 February 2021), respectively. A table from case study A6 is available at https://doi. org/10. 6084/m9. figshare. 13007279. v1 (accessed on 1 February 2021). Acknowledgments: For case studies A1 and A4, we thank the Swedish Phytogeographical Society (SVS) for funding this work through the B. Lundman's fund for botanical studies scholarship. For case study A3, we thank Cécile Chardon and Louis Jacas (UMR CARRTEL INRAE, France) for the laboratory work of case study A3. For case study A5, we thank Claudia Havel for assistance in the lab and Paolo Ballota, Zafar Mahmoudov and Rasmus Thiede for assistance during fieldwork. For case study A6, we thank Francisco Javier Ancin Murguzur for assistance in the field, Iva Pitelkova for conducting LOI, Enrique Tejero Caballo and Karina Monsen for support with XRF scanning and highresolution imagery, and Youri Lammers for assistance with bioinformatics.
Candidate sequences were checked for secondary structures using the IDT oligo analyzer (https://eu. idtdna. com/calc/analyzer (accessed on 1 February 2021)). Sequences that did not show any secondary structures were blasted against all sequences present in GenBank. We selected and further used only sequences with no matches in GenBank. The primer binding sites were manually edited to create the most "optimal" primer pair (Table  A2 ). The 85 oligonucleotide "Inh_Nota85" nucleotide oligos were synthesized as primers, diluted to 0. 0001 pM and used as the template in qPCR. Inhibition testing with qPCR amplification-All qPCR reactions were run on the same qPCR machine (CFX96, BioRad). qPCR amplification was performed in 10-µL reaction volumes containing 1X TATAA SYBR ® GrandMaster Min, 0. 5 µM forward primer, 0.