dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of performance analysis (Dataset1: data control) To evaluate ECuADOR, we compiled 161 plastome sequences from a total of 51 major angiosperm groups, as previously used in  Ruhfel et al. (2014). ECuADOR ran fluently for each plastome, with regions identified in almost all sequences (Table  S1 ). It provided basic information regarding the location and inverted repeat lengths, reordering of the main plastome regions (LSC-IRb-SSC-IRa) as well as the repositioning for all the protein coding genes (CDS) in gff3 format file for all plastomes analyzed. Furthermore, it substantially eases the post-processing analyses of plastomes reconstructed from NGS data. Results obtained with ECuADOR and Geneious were very similar, validating the performance and accuracy of our approach (Table  S1 ).
Retrieved annotations were identical for 150 sequences out of 161. This number increased to 160 after manually setting the first position of the LSC as the start of the plastome (contrary to ECuADOR, in which this is done automatically). IR annotations were not retrieved accurately for one draft plastome sequence (GU592211), due to very poor sequence quality. ECuADOR took 15 min to analyze this dataset using a MacBook Pro, 2. 2 GHz Core 2 Duo, 16 Gb RAM. When compared to manual individual treatment (e. g. , average of 3-5 min handling per plastome), this would have taken between 8 and 13 h. The topology of the obtained phylogenetic tree (Fig. 3 ) was similar for 49 families compared to previously identified relationships  (Ruhfel et al. , 2014). An inconsistency was found in the placement of Ranunculaceae (Ranunculus macranthus), which grouped together with Piperaceae, Dioscoreaceae and Chloranthaceae.
Performance of the introduced variation simulation (Dataset2: data testing) The introduction of mismatches between both repeats is based on the loss of information, thus reducing the identity of the IRs and altering the final reorganization of the plastome. This analysis allowed us to understand how the introduced error for the different simulation sets affects the recovery of the original positions of the inverted repeats and therefore the ability of the algorithm to retrieve the reordinated sequence completely. Thus, for each mismatch level, we scored and investigated cases where ECuADOR failed to retrieve the original IRs locations (due to excessive variation in the base pair numbers within the inverted repeats), using as a model the A. thaliana chloroplast genome (Fig. 4 ). ECuADOR was able to recover and reorder the main regions of the plastomes (LSC-IRb-SSC-IRa) for each altered dataset.
As expected, the mismatching percentage affecting the true annotation increased as the alteration for each data set increased. ECuADOR showed an accuracy above 90% with 22 or fewer alterations (Table  S2 ). Such a high level of mismatch between the two IRs, likely represents major misassembled positions and we recommend such low-quality draft plastomes should first be carefully checked to assess the origin of such mismatches. Moreover, the user can easily modify the stringency of the detection process by specifying the sliding window fragment size to improve search precision. This should however only be done for noisy datasets which are known to contain high levels of mismatching or highly similar fragments throughout the plastome.
Reference data set (Dataset3: mass data evaluation) To evaluate the potential and flexibility of ECuADOR, we analyzed a total of 4,541 angiosperm plastomes using default parameters with fasta format as input. The main regions were successfully detected in 4,446 sequences (97. 90%, File S1) whereas identification and organization of the plastome structure failed in 95 sequences (2. 09%, File S2). To further assess the causes of these failed instances, the 95 sequences were evaluated in Geneious  (Ripma, Simpson & Hasenstab-Lehman, 2014)  and we found manual curation was needed to identify the main regions in these plastomes. Indeed, these plastomes had missing regions or poorly formatted annotations in GenBank, or were of extremely poor quality. Finally, several plastomes were characterized by an absence of inverted repeats (e.
g. , gymnosperm plastomes and several species in parasitic plants  (Wicke et al. , 2013 (Wicke et al. , , 2016)) ). ECuADOR requires a correct match between both repeats to be able to recover the newly generated regions (LSC-IRb-SSC-IRa) and score the analysis as successful. The script will not identify IRs in sequences of extremely poor quality. Nevertheless, it has several detection error and correction mechanisms-such as a wide detection of more than one inverted repeat region and recognition of ambiguous characters in the plastome.
DISCUSSION ECuADOR is a novel algorithm designed for the identification, reorganization and reordering of homologous regions (LSC, SSC and IRs) on large-scale plant datasets using the specific location of the inverted repeats in any point of the circular genome as a starting point. Other software aimed at helping with circular genomes exist, but none handles the post-assembly curation in a standardized manner. For example, Circlator  (Hunt et al. , 2015)  was designed to use with long sequencing reads (e. g. , PacBio data, Menlo Park, CA, USA) in bacterial chromosomes and plasmids and the plastid and mitochondrial genomes of eukaryotes to reconstruct circular genomes. However, Circlator is an assembler and thus works with the filtered, long sequencing reads (FASTQ files) to generate a circular genome.
In that way, it is not different from the many assemblers or scripts specifically designed to generate organelles genomes (e. g. , ORG. asm, NovoPlasty, GetOrganelle) that can be used before ECuADOR. As most phylogenomic studies combine both new sequences and plastomes mined from GenBank, original raw data can be missing for a significant part of the sampling. Although long sequencing reads are quickly emerging as a powerful tool in genomics, the vast majority of generated data available are short reads (<300 bp) from Illumina platforms, prohibiting the use of a long-readspecific solution. As Circlator could be involved in the assembly process, GenomeRing  (Herbig et al. , 2012)  is a visualization tool that allows an overview of several plastomes in the same coordinate system. In that sense, it could be considered as a good complement to ECuADOR, to visualize the results after alignment.
But given that an alignment is a requirement for GenomeRing and the program does not generate any files for immediate downstream analyses, we cannot consider GenomeRing as an alternative for ECuADOR. The closest algorithm to ECuADOR would be MARS  (Maurer et al. , 2005) , as it can homogeneize the starting point of a set of sequences from a circular genome, a function ECuADOR also performs as a side-effect of the reorganization of the plastome. However, ECuADOR is able to not only move the starting point, but also to keep order and orientation of the main regions of the plastome. In addition, MARS outputs a FASTA file, while ECuADOR can keep track of all annotations previously included in the input files. Fast-Plast  (McKain & Wilson, 2017) , despite being a "all-in-one" pipeline including assembly and ordering of the plastome, is not able to handle GenBank sequences, limiting its use to newly generated draft plastomes.
In addition, its structure implies to work with each plastome separately, while ECuADOR is able to homogeneize the output order and direction for a complete set of sequences, making downstream analyses easier. A possible explanation for the incongruence observed in dataset1 could be that  Ruhfel et al. (2014)  used only protein coding data of 78 genes from 360 taxa to build their phylogenetic tree. Gene conflicts in plastome-based phylogenies have recently been highlighted as a major cause of incongruence among studies  (Gonçalves et al. , 2019; Walker et al. , 2019) , and differences in methodology between our study and the study by  Ruhfel et al. (2014)  could explain the observed incongruence. The analyses performed here were based on complete plastome data and include non-coding, fast evolving regions.
Despite that these regions have been proven useful at smaller taxonomic scales, they are expected to saturate at very large scales, resulting in nonspecific phylogenetic signals in deeper parts of the tree and causing incongruence with signals inherent in codingregions. It is beyond the scope of our study to provide a detailed analysis of the problems inherent in the reconstruction of the phylogeny of angiosperms but worthy to note that the level of saturation effects in the plastome dataset seems low, with only one incongruence compared to the analysis using coding-regions only. The introduction of ECuADOR has a major step forward in our ability to quickly identify extract the main regions in a coordinated, standardized arrangement. This new reference system not only will to define a global reorganization for the main plastome regions but can also be employed to generate a chain of re-repositioning for all the remaining GenBank annotations in the sequence.
This last condition applies to all plastomes in GenBank in which the string starts randomly along the circular sequence. This allows to recast the previous reference annotation into a new coordinate system for all the available annotations and print this out in a GFF3 output file. In addition, the generation of reorganized and reoriented datasets, thus already formatted for downstream analyses (e. g. , alignment) will greatly improve the utility of plastomes, either newly sequenced or mined from GenBank. Finally, the new coordinate system can be used to implement exploratory methods for a more accurate and faster analysis of phylogenetic comparative data, either using complete regions or concrete molecular markers in case of using GenBank files. This, turn, will allow us to advance the development of more accurate hypotheses in the reconstruction of the evolutionary history of extant plant groups.
CONCLUSIONS Curating draft plastomes and formatting them for downstream phylogenetic analyses is laborious, time-consuming and error-prone. We developed ECuADOR for the robust extraction and mass reorganization of plastome regions. The proposed algorithm is based on sliding windows and dynamic suffix array approaches to track inverted repeat locations, followed by extraction and repositioning of the main chloroplast regions. In addition, the user can generate datasets in which all sequences are similarly oriented, allowing a direct inference of the homology through sequence alignment. This facilitates post-processing analyses of extra-nuclear genomes from NGS data, optimizing handling times and reducing error. We demonstrated its accuracy, especially in handling poorly reconstructed plastomes, when repeats are interrupted by misassembled positions (resulting in fragments poorly positioned throughout the sequence), preventing recovery of IRs.
This method significantly reduces handling time and complexity in the analysis of large plastome datasets and allows for error free processing of high quantities of data. Our study not only underscores the importance of developing new tools for detecting and characterizing inverted repeated sequences, but also provides a new approach to systematically identify complete regions within plastomes. ECuADOR will be maintained and regularly improved to add new features, according to the emergence of new needs with the development of innovative approaches in NGS. For example, future scheduled improvements include the extraction of the homologous genes and non-coding regions (i. e. , intergenic spacers, introns and ribosomal RNA) to generate locus-specific alignments, as currently widely used to avoid the laborious steps involved in manual curation.
Funding This work was supported by grants from  Guangxi University (Nanning, PR China) , the  State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources  ( GXU, Nanning, PR China ) and the  Bagui Scholarship team funding  under Grant No. C33600992001  to  Joeri S. Strijk , as well as a  Guangxi University grant  ( XDZ120929 ) and  China Postdoctoral Science Foundation  Grants (Nos. 2015M582481  and  2016T90822 ) to  Damien D. Hinsinger. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Before performing chemical stimulation in the Soultz geothermal system, a modelling approach is considered to discuss different scenarios in terms of the choice of acid and the amount and duration of acid injection. The approach used in this work is based on the geochemical code KIRMAT, which enables us to represent the geothermal reservoir using single-porosity and double-porosity models. The code evaluates the changes in the porosity and permeability of the Soultz reservoir based on the evolution of all primary and secondary minerals as a function of time in the geothermal system. The modelling results showed that chemical stimulation could significantly increase porosity and permeability; however, for both single and double-porosity models, the improved zones are very limited and are only present within a few meters of the reinjection well.
Before performing chemical stimulation in the Soultz geothermal system, a modelling approach is considered in order to discuss different scenarios in terms of the choice of acid, and the amount and duration of acid injection. The approach used in this work is based on the geochemical numerical code KIRMAT, which enables us to represent the geothermal reservoir using single and double porosity models and account for a wide range of mineralogy. The model evaluates changes in porosity and permeability in the geothermal reservoir based on changes in the amount of primary and secondary minerals as a function of time in the geothermal system.
Governing equations of reactive mass transport The hydrochemical model developed in this study is based on the numerical code KIRMAT (Kinetic Reaction and Mass Transport)  (Gérard, 1996) , which combines geochemical reactions and 1D mass transport equations. The mass balance equation of reactive transport in a one-dimensional porous medium is written as  (Lichtner, 1988; Gérard et al. , 1998) : ∑ ⎜ ⎟ ∂ ∂ = ⎛ ⎝ ∂ ∂ ⎞ ⎠ - ∂ ∂ - ∂ ∂ ∼ = … = - t (ϕΨ) ϕD Ψ x U Ψ x α t (ϕ V )(j 1,. ,N) j 2 j 2 j r 1 M jr r r 1 (1) and ∂ ∂ ∼ = = … - t (ϕ V ) v (r 1,. ,M) r r 1 r (2) where Eq. (1) refers to N aqueous primary species and Eq. (  2 ) refers to M primary species of reacting minerals. In these equations, Ψ j denotes the generalized (or total) concentrations (in moles per water mass or volume) of primary species, ϕ denotes the porosity of the porous medium, ϕ r and α jr denote the volume fraction and the stoichiometric reaction coefficients, respectively, of the r th mineral with molar volume ∼ V r , v r represents the reaction rates of the irreversible reaction of mi- nerals and fluids equivalent to the rate of precipitation or dissolution of reacting minerals r per unit of the rock and fluid system (by convention, v r is positive for precipitation and negative for dissolution reactions), D denotes the hydrodynamic dispersion coefficient, and U denotes the Darcy velocity.
The KIRMAT code can describe the feedback effect of the chemical and mineralogical evolution of porosity and permeability at any node of the mesh due to dissolution and precipitation reactions. In Eq. (  7 ) the intrinsic permeability k (m 2 ) is updated after each time step as follows  (Gérard, 1996) : ⎜ ⎟ = ⎡ ⎣ ⎢ ⎛ ⎝ - ⎞ ⎠ ⎤ ⎦ ⎥ - S k C ϕ ϕ (1 ϕ) 0 C 1 3 2 2 2 (7) where C 0, ϕ, C and S denote an experimental constant, the porosity of the porous medium, the cementation factor, and the grid cell surface in contact with the adjacent cell (m 2 ), respectively. In Eq. (  8 ) the relationship of the effective diffusion coefficient (D diff ) to the porosity is expressed as: = - D D ϕ diff 0 C 1 (8) where D 0 is the diffusion coefficient of the solute in free water, ϕ is the porosity, and C the cementation factor.
Modelling approach In the framework of the single porosity model, a reference case was used for the input data presented in Tables  1 2 3 4 5. These data were collected from a literature review. Additionally, HCl concentration was 0. 2 % (pH = 0. 74), and injection duration was 2 days with a Darcy velocity of 1 m. h -1. Three sensitivity cases were tested. It is important to highlight that the input data are the same for all three cases except for the tested sensitivity parameters. The numerical calculations performed in this way are able to distinguish the effects of the tested parameters on the evolution tendency of the system. For the modelling tests using the double porosity model, the matrix and fracture fractions are presented separately by their own mineralogical compositions and physical properties such as porosity and permeability.
g. ,  Ledésert et al. , 1999; Bartier et al. , 2008; Hébert et al. , 2010). A literature review reveals that the main secondary minerals in the Soultz reservoir are carbonates and illite/smectite. Depending on the degree of the fluid-rock interaction, the volume fraction of these newly formed minerals in the altered granite and vein alteration can reach approximately 50 %. As noted previously, the GPK-4 injection well was selected to apply the acid stimulation. The literature review shows that the fracture mineralogy of this well may have similar characteristics to the fracture zones of the upper and intermediate reservoir in terms of secondary fracture mineralization of quartz, calcite, illite, chlorite, sulfides, barite and haematite  (Genter and Traineau, 1992; Dezayes et al. , 2005). The dataset presented in the thesis of Jacquot  (Jacquot, 2000)  was chosen to describe the minerals that were initially present in the Soultz geothermal reservoir.
Additionally, to estimate the volume fraction of each mineral, we assumed that the fractions of fresh and altered granites are 90 % and 10 %, respectively. Tables  1  and 2  present the composition and corresponding volume fraction of primary minerals for the singleporosity and double porosity models, respectively. To fit with the Thermoddem database  (Blanc et al. , 2012) , several groups of minerals are represented by the most typical minerals in their group: plagioclase was replaced by albite and anorthite end-members; K-muscovite, annite, and phlogopite were used for mica; chlorite was represented by clinochlore and chamosite end-members; and illites are represented using three different compounds, known as Fe-illite, Mg-illite, and Alillite. The study of  Surma and Géraud (2003)  that is based on rock samples taken in bore hole EPS1 located in the neighborhood of GPK4 showed that the porosity can vary from 0.
Input data Thermodynamic data for hydrolysis reactions at 65 °C of 15 primary minerals and 7 potential secondary minerals are presented in Table  4. These data are obtained from the Thermoddem database  (Blanc et al. , 2012). The choice of potential secondary minerals may have significant impacts on the dissolution of primary minerals  (Gaucher and Blanc, 2006). The secondary minerals were chosen based on the literature review studies focused on fluid-rock interactions in the context of enhanced geothermal systems. The accuracy of thermodynamic data has an important impact on the output predictions  (Savage et al. , 2007; Zhu and Lu, 2009; Ngo et al. , 2016). It is well known that kinetic data for minerals are much less available than thermodynamic data, especially for precipitation of mineral phases.
There are important physical oceanographic sensors that are at a high TRL, such as CTD sensors, active acoustic sensors, and several optical sensing techniques for chemical compounds. The optical sensors increasingly use integrated light-emitting diodes (LEDs), lasers, and optical spectrometry technologies. Conversely, most sensors for biogeochemistry and biological compounds are at a lower TRL. Biogeochemical sensors (BGC) are used on some autonomous platforms such as gliders and profiling floats, and there is a new BGC component to the Argo program  (Johnson and Claustre, 2016). More sophisticated BGC sensors for nutrients, such as Lab-on-Chip, are not ready for routine operations. Biological sensors are less mature than BGCs, but they offer opportunities for significant advancement.
, 2014) , sea-surface wind and precipitation in remote regions  (Nystuen et al. , 2000) , and, most notably, monitoring violations of the Comprehensive Nuclear-Test-Ban Treaty  (Hanson et al. , 2001). Sound is also used to diagnose engineering issues  (Boyd and Varley, 2001; Jirarungsatian and Prateepasen, 2010). Since passive acoustic systems do not emit sound, they are easier to deploy in protected areas and in other situations where this feature is valuable. Commonly, acoustic data are collected using cabled or moored underwater recording equipment that can be deployed for extended periods. Data are also collected in situ using equipment onboard ships or by surface drifters and AUVs. The collected acoustic data are analyzed onsite or offsite, by manual or automatic methods, to detect sounds of interest.
Analysis of acoustic data often employs a priori knowledge of sound-to-source associations obtained from visual or other means. Human interpretation is usually needed to make inferences from the recorded events. Automation is a continuing challenge. Passive Acoustic Monitoring benefits directly from the continued independent advancements in sensor technology, smaller and lower-power electronics, and the transition to highcapacity solid-state data storage media. Current autonomous recorders have low power consumption, reduced internal noise, greater durability, and faster data-transfer between system components. Lower-cost devices are being designed to integrate with ocean observing systems  (Toma et al. , 2015; Pearlman J. et al. , 2017). Developing pattern recognition algorithms has evolved from employing purely deterministic methods to using statistical learning methods that offer more generalized results.
FUTURE VISION Existing autonomous observation systems have shown that there are many complex levels of dynamics in the ocean from global to meso and local, each coupled in non-linear ways. To meet the need for a better, more integrated Ocean Observing System, we need advances at the sensor, platform, and system levels, including data interoperability. Compact, low-power sensors that are calibrated and stable, will enable more and better observations for more EOVs. Robust strategies for cross-calibration of sensors will enable reliable quantitative interpretation of data from large numbers of autonomous sensors. We need autonomous platforms that are more affordable, more modular, more capable, and easier to operate. In situ power generation 20 https://www. cmre. nato. int/news-room/news-room/847-cmre-successfullydemonstrates-systems-for-persistent-autonomous-and-real-time-maritimesurveillance will also extend endurance for some types of autonomous platforms, including standardized docking stations that will also enable AUV communications and recharging.
The future technical vision includes expansion of current capabilities [such as more bio-geo-chemical (BGC) Argo 21  profilers or adopting routine plankton monitoring on GO-SHIP 22  lines], significant technical updates to existing systems (such as advanced batteries in gliders), and paradigm shifts from new capabilities (such as sensor breakthroughs to enable widespread adoption autonomous platforms). There are several factors that are common to all of these. One factor is the need to deal with large quantities of data. Eventually, all these data will need to be processed efficiently. For applications such as human health warnings, tsunamis forecasts and weather modeling, the data have the most value when they are available in near real time. Large datasets need an integrated approach to communication and then processes for translation of data to knowledge, which supports societal impactful decisions by end users  (Buck et al.