dataesr/scientific-paragraphs-categorization huggingface dataset:
If we are to move forward with the application of AI and CV to the identification of taxa, we must have a common reference standard. Our proposed database aims to meet this need through future development that will enable the database to interface with image annotation software, such as Squidle  [120]  and BIIGLE 2. 0  [121, 122]. These annotation softwares enable users to mark the x,y position of organisms within an image and attribute this point / polygon with a taxon identification. This process of image annotation is the means by which ecologists extract semantic data from an image in order to then apply numerical and statistical analysis to these data and answer ecological questions. This annotated dataset is also the base data needed in the development of AI and CV algorithms. These algorithms require large numbers of images to "learn" the features that distinguish the different OTUs to which they have been exposed, and which of these features are characteristic of each OTU  [123, 124].
If researchers are able to use a common reference standard, thus extracting the same information from an image regardless of who is annotating it, then collated datasets from various origins could reach the size needed to train and test CV algorithms (acknowledging challenges of observer bias). Their use within the field of deep-sea benthic ecology, will then increase exponentially through accumulation of data, skill and experience. This can only serve to facilitate the development of CV and bring us closer to automation of image annotation and data extraction. Ultimately, standardisation of tools and methods is central to long-term monitoring and assessments of ocean health. Woodall et al. [125]  recognised this and produced GOSSIP (General Ocean Survey and Sampling Iterative Protocol), which outlines a framework of 20 biological, chemical, physical, and socioeconomic parameters that allow marine scientists to generate comparable data on the function, health and resilience of the ocean.
Conclusions We have developed a database structure (and data transfer format) to facilitate the standardisation of morphospecies image catalogues between individuals, research groups, and nations. We have also proposed a framework for coordination of international efforts to develop reference guides for the identification of deep-sea species from images. We have highlighted the potential gains to be made through the use of this database structure by the deep-sea community in: increasing the quality and quantity of data available to researchers, improvement of overall understanding of the deep-sea ecosystem, more effective management and monitoring by statutory bodies and industry alike, and realising the potential benefits of emerging AI and CV approaches. To make these gains it is critical there is now uptake of this database format by the community, and additional funding is found to contribute to stage two development.
/ 25 the  Marine Institute  https://www. marine. ie/Home/home under the Investigators Programme cofunded under the  European Regional Development Fund   2014-2020 , https://ec. europa. eu/regional_policy/en/funding/erdf/. AB-H was supported by the  Oceanic Observatory of Madeira  project ( M1420-01-0145-FEDER-000001 -Observatorio Oceanico da Madeira-OOM) co-financed by the  Madeira Regional Operational Programme  (Madeira  14-20 ) under the Portugal 2020 strategy through the  European Regional Development Fund  https://ec. europa. eu/regional_policy/en/funding/erdf/,and the  Portuguese Foundation for Science and Technology (FCT , Portugal) https://www. fct. pt/,through the strategic project  UID/MAR/04292/ 2013  granted to MARE. JV is supported by  Oil  and Gas UK https://oilandgasuk. co. uk/ and the  ATLAS  project funded by the  European Commission 's H2020 Scheme https://ec.
fct. pt/, PO2020   MapGes  ( Acores-01-0145-FEDER-000056 ) http://www. azores. gov. pt/Portal/en/principal/, and  H2020 ATLAS  (grant agreement no. 678760 ) https://ec. europa. eu/programmes/horizon2020/en. RV was funded by the  Fundacão para a Ciência e a Tecnologia  ( FCT/ SFRH/BD/84030/2012 ) https://www. fct. pt/, with additional support provided by  Cefas  through the  Science Futures  programme https://www. cefas. co. uk/. JRX research is funded by the  H2020 EU Framework Programme for Research and Innovation  through the  SponGES  project (grant agreement No. 679849 ) https://ec. europa. eu/programmes/horizon2020/en and partially supported by the  Strategic Funding   UID/Multi/ 04423/2019  through national funds provided by the  Foundation for Science and Technology (FCT ) https://www.
Funding: KH, NP, RR, NF was supported by the  Natural Environment Research Council  funded  DeepLinks  project  NE/K011855/1 , https://nerc. ukri. org/. The workshop was funded by the  Deep Sea Biology Society 's Lounsbery Workshop Award https://dsbsoc. org/. ALA and CLM are supported by Grant Number  SFI/15/IA/3100  to ALA from  Science Foundation  Ireland http://www. sfi. ie/ and.
mnhn. fr/fr/collections/ mer Mediterranee patrimoines-naturels/guide-de-la-faune- profonde-de-la-mer-mediterranee Deep-sea sponges of the MED Sponges Poster and FAO; [81] http://www. fao. org/3/a-i6945e. pdf Mediterranean Sea digital file Deep-sea corals of the MED Corals Poster and FAO; [82] http://www. fao. org/3/a-i7256e. pdf Mediterranean Sea digital file (Continued ) [85,86,87,5,86,87, 88]), and, thus, is the internationally agreed standard upon which further database development should be based. Darwin Core Archives (DwC.
Location locationID required http://www. marineregions. org/gazetteer. php?p=search Location locationRemarks optional Free text field for more detailed location data Location decimalLatitude optional In decimal degrees N Location decimalLongitude optional In decimal degrees E Location minimumDepthInMeters required Value in meters of the depth the image was taken at. Use positive Location maximumDepthInMeters required values. If exact depth known please put same value in both fields Location institutionID required An identifier for the institution having custody of the object(s) or Record-level information referred to in the record. collectionID optional Identifies the collection or dataset within that institute This could Record-level identify a specific catalogue e. g. Howell & Davies 2010.
bibliographicCitation optional Citation for the original image database e. g. Howell & Davies, Record-level 2010. modified autopopulate The most recent date-time on which the resource was changed. It Record-level is required to use the ISO 8601:2004(E) standard dcterms:license required A legal document giving official permission to do something with Record-level the resource. dcterms:rightsHolder required A person or organization owning or managing rights over the Record-level resource. dcterms:accessRights required Information about who can access the resource or an indication Record-level of its security status. Access Rights may include information regarding access or restrictions based on privacy, security, or other policies. previousIdentifications optional This field is intended to capture changes in opinion on the OTU Organism number of the animal in the image.
Size optional Approximate size of animal in cm n/a SubstrateMethod optional e. g. Folk, Wenthworth, EUNIS, Other. n/a ProjectName optional e. g. DeepLinks, CoralFish, SponGES. n/a Link to external database optional For example link to another non merged online species guide n/a https://doi. org/10. 1371/journal. pone. 0218904. t003.
2014; Li 2014; Pabinger et al. 2014; Sandmann et al. 2017; Rajaby and Sung 2018) , we compared the performance of calling small variants, with both SAMtools and FreeBayes, from competitive and standard mappings as a function of the coverage. As expected, using standard mapping for complete homozygous diploids, the F 1 score increased with coverage showing saturation at 50Â (fig. 2C ). On the contrary, calling small variants from heterozygous diploid data mapped The phylogenetic tree is reproduced from previous work  (Yue et al. 2017 ). (B) Selected parental strains are combined into the diploid ancestor. The genetic crossing eludes the problems in assembling and phasing diploid genomes. (C) Hybrids are evolved and accumulate different types of de novo variants. (D) Genomes from evolved hybrids are sequenced at high coverage by short-read sequencing.
2E ). Thus, we included in MuLoYDH a module that automatically calculates the boundaries of regions characterized by reads with low mapping quality (i. e. , MAPQ 5). These regions are investigated through standard mapping. Although this prevents direct variant phasing, it allows for testing the presence of small variants in the whole accessible regions of the genome. We also investigated the efficiency of the small variants detection strategy in genomic regions which are present only in one parent and which are not reported in the S. cerevisiae S288C reference genome. Using DBVPG6765/YPS128 (WE/ NA in short, from the Wine/European and North American oak clades, respectively) hybrid data and the corresponding annotated assemblies, we calculated the F 1 score considering only the variants lying within a unique region of the WE strain on chromosome XV, derived from a 65-kb horizontal gene transfer (HGT) from Torulaspora microellipsoides, previously described  (Marsit et al.
Simulations of Short Reads for Heterozygous Hybrids Simulated paired-end short reads were generated using the DWGSIM package  (Escalona et al. 2016). In order to produce simulated short-read data from genome assemblies, two input reference assemblies were concatenated to produce a single multi-FASTA, which was sampled to build simulated paired-end (150 bp, insert size 500 bp) Illumina experiments with different coverage levels (10Â, 50Â, 100Â, and 150Â). The mutation rate was set to 10 À5 with the purpose of balancing a relevant number of small variants ($240 per genome) with the storage and the computational resources required for data processing. All the simulations were performed using the following parameters: 0. 01 error rate for both forward and reverse read (according to estimations from Illumina data), and 0.
Experimental Data Samples from data sets 1 and 2 were chosen from large sequencing data sets to benchmark MuLoYDH against genomes harboring a large number of both small variants and LOHs (data set 1), and complex CNVs (data set 2). Sample IDs are reported in supplementary material, Supplementary Material online. Data set 1 comprises the sequencing data from a mutation accumulation experiment using a mutator SK1/BY hybrid evolved for 25 single-cell bottlenecks (MATa/MATa; ARG4/ arg4-nsp, bgl; his3D1/HIS3; leu2D0/leu2; met15D0/MET15; ura3D0/ura3; tsa1::KanMX/tsa1::KanMX) generated as previously described  (Serero et al. 2014) , and the corresponding ancestor. This data set was analyzed using the SK1 and S288C assemblies included in MuLoYDH. Data set 2 consists of the short reads from the UWOPS03-461.
Parental Assemblies All the parental assemblies reported in this work were downloaded from the "Population-level Yeast Reference Genomes" website (https://yjx1217. github. io/Yeast_PacBio_2016/welcome/; last accessed August 07, 2019). The assembly of S. paradoxus strain N17 was obtained correcting the genome sequence of its close relative CBS432, for which a complete assembly is available  (Liti et al. 2009; Yue et al. 2017). The correction was performed using Pilon  (Walker et al. 2014 ) with short-read data from Illumina sequencing of a diploid homozygous N17 sample. The command line is reported in supplementary material, Supplementary Material online.
All the scripts described in the following sections are embedded in MuLoYDH. Accurate Tracking of the Mutational Landscape of Diploid Hybrid Genomes. doi:10. 1093/molbev/msz177 MBE Quality Check, Mapping, Mapping Refinement, and Coverage Calculation Data quality is assessed by FastQC version 0. 11. 4. Competitive and standard mappings of Illumina reads are performed with BWA version 0. 7. 12-r1039 using the MEM algorithm  (Li and Durbin 2009). Assemblies can be downloaded from the "Population-level Yeast Reference Genomes" website (https://yjx1217. github. io/Yeast_PacBio_2016/welcome/; last accessed August 07, 2019). Duplicates are removed by SAMtools 1. 3. 1 (using HTSlib 1. 3. 1). Depth of coverage is calculated with SAMtools (depth) and awk scripts (supplementary table  S38 , Supplementary Material online).
Thus, in order to balance performance (in terms of F 1 score) and both the required computational resources and running time, two general-purpose small variants callers are implemented in MuLoYDH. De novo SNVs and indels are called with 1) SAMtools (mpileup) [-u -min-MQ5 -E] and BCFtools (call) [-c -Oz] and 2) FreeBayes  (Li 2011a; Garrison and Marth 2012). Only variants called by both are retained. Both callers are exploited using competitive and standard mappings as described above. Regions characterized by reads with MAPQ 5 in competitive mappings are determined by custom R scripts, bash scripts and bedtools  (Quinlan and Hall 2010). Parental and control hybrid variation is subtracted from hybrids data using custom bash scripts, VCFtools  (Danecek et al. 2011 ) and tabix  (Li 2011b). The resulting variants are quality-filtered masking those characterized by quality < ðl À rÞ, where l is the sample markers mean quality value and r is the corresponding standard deviation.
Analysis of Homozygous Diploids In order to analyze data from homozygous diploids, we set up a dedicated pipeline (mirroring MuLoYDH) which is described in the following section. Reads from homozygous diploids were mapped against the proper assembly with BWA version 0. 7. 12-r1039 (MEM algorithm). Assemblies were downloaded from the "Population-level Yeast Reference Genomes" website. Duplicates were removed by means of SAMtools 1. 3. 1 (using HTSlib 1. 3. 1). Depth of coverage was calculated with SAMtools (depth) and awk scripts. Following duplicates removal, small variants were called with SAMtools and FreeBayes. The intersection of their outputs was retained and variants reported in control samples were removed. Small variants were annotated by means of SnpEff. SnpEff database was built exploiting the annotation data downloaded from the "Population-level Yeast Reference Genomes" website.
Acknowledgments This work was supported by  1 )  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-13-BSV6-0006-01 ,  ANR-16-CE12-0019 ,  ANR-16-JPEC-0005 ,  ANR-18-CE12-0004 , and  ANR-10-EQPX-03 Investissement  d'Avenir to the IC NGS platform) and  Fondation ARC pour la Recherche sur le Cancer  ( PJA20151203273 ) and 2) French government, through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. S. M. is funded by the convention CIFRE 2016/0582 between Meiogenix and  ANRT. We thank  Matteo De Chiara  for discussions,  Olivier Croce  for technical support,  Agnes Llored  for experimental help,  Gilles Fischer  for critical reading of the manuscript, and the  NGS  platform of the  Institut Curie  for NGS sequencing.
The presence of CNVs was assessed by means of Control-FREEC with no matched normal samples. RC data were normalized by GC-content and mappability, whereas the latter was calculated by means of GEM- mappability. Results were annotated with P values calculated with both Kolmogorov-Smirnov and Wilcoxon Rank-Sum tests. The pipeline is available at https://bitbucket. org/lt11/ muloydhom; last accessed August 07, 2019.
Even the breakup time is complex to predict and requires the resolution of hypersonic flows on complex geometries coupled with finite element simulations to estimate the thermal and mechanic response of the US. Such accurate modeling is rarely available, and space object reentry predictors use further simplifying assumptions permitting to obtain predictions within the specified time constraint. There are two major classes of space object reentry predictors developed in the literature. i) The Object-oriented models use predefined fragments collected inside a parent object such that, at a given altitude chosen by the user, the child fragments are released, and their trajectories are computed. For each debris, an ablation module calculates the mass loss rate. Examples of such pieces of software are for instance DEBRISK developed at Centre d'Etude Spatiale (CNES)  [2]  coupled with a risk estimation tool  [3].
Similarly, ORSAT  [4]  is an object-oriented spacecraft that has been used to predict the reentry of an Upper Atmosphere Research Satellite UARS spacecraft  [5]. Similar approaches are implemented in DRAMA  [6]  from the European Space Agency (ESA) and DRAPS  [7]  that uses a Monte Carlo approach to account for the uncertainties in the breakup prediction and the debris survivability. In all those solvers, the breakup time has to be chosen a priori by the user and is necessarily deterministic. The object-oriented predictors have a limited computational cost but having a fixed, a priori breakup altitude introduces a significant bias in the ground footprint estimate. ii) More complex models are implemented in vehicle oriented software such as SCARAB  [8] , assuming a thermally driven breakup.
Such approaches do not model fragmentation as a single catastrophic event but as a sequence of fragment releases where the shape of the fragments are estimated based on the flight conditions and the object structure. Additionally, SCARAB uses an extensive material library allowing to model many materials used for aerospace applications  [8]. The software has been successfully applied to predict ATV and Ariane 5 cryogenic main stage (EPC) reentries  [8, 9]  or the demisabibility of propellant tanks  [10]. Recently, Onera developed a spacecraft oriented software  [11] , called MUSIC/FAST, that include the modeling of debris aerodynamic interaction  [12]. Despite significant efforts from the community to improve breakup models, it is acknowledged that those simplifying models include mismodeling errors  [13]  and should include a quantification of the uncertainties to increase the robustness of the predictions.
Thermal module ADRYANS  [32]  is a thermal model used along a trajectory to compute the heat flux q aero (used in Eq. 4). The chosen model assumes uniform object temperature and neglects chemical reactions at the surface of the object such as metal oxidation or molecular recombination. The uniform temperature assumption has been widely used in object-orientated software [? ] and is acceptable for random tumbling objects but also depends on the internal architecture of the upper stage and the location of heat sinks that affects the distribution of the object. These assumptions have consequences on the breakup conditions and are accounted for in the breakup model parameters presented in Section 2. 3 and 4. 2. Hence, q aero is the sum of two contributions: q aero = q conv + q rad , (5) where q conv is the convective heat flux and q rad is the radiated heat flux.
( 18 ) with k A (x) = (k(x, x 1 ) • • • k(x, x p )) T and K A i,j = k(x i , x j ). The choice of covariance functions is critical and is extensively discussed in  [41]. In our problem, the covariance functions are sums of uni-variate squared exponential covariance functions and a joint non-isotropic squared exponential covariance function. The covariance function hyper-parameters are a priori unknown and are fitted by maximizing the GP likelihood with respect to A  [28]. Practical implementation is based on the python software GPy  [42]  3. 1. 2. Predictions with a multiple GPs SoGPs are obtained by substituting the actual solvers in the SoS by GP models. The composition of Gaussian Processes has been used for machine learning application in  [43]. In general, the resulting predictive distribution is not Gaussian distributed and it has no simple analytical expression except for very particular kernels  [44].
We observed clear elevation bimodality in our wettest sites, indicating microsite divergence into two states: elevated hummocks and low elevation hollows. We coupled the TLS dataset to a 3-year water level record and soil-depth measurements, and showed that hummock height (mean = 0. 31±0. 06 m) variability is largely predicted by mean water level depth (R 2 = 0. 8 at the site scale, R 2 = 0. 12-0. 56 at the hummock scale), with little influence of subsurface microtopography on surface microtopography. Hummocks at wetter sites exhibited regular spatial patterning (i. e. , regular spacing of ca. 1. 5 m, 25 %-30 % further apart than expected by chance) in contrast to the more random spatial arrangements of hummocks at drier sites. Hummock size distributions (perimeters, areas, and volumes) were lognormal, with a characteristic patch area of approximately 1 m 2 across sites.
In this study, we evaluated wetland soil elevations, hummock spacing, and hummock sizes and their associations with hydrologic regimes in black ash (Fraxinus nigra Marshall) forested wetlands in northern Minnesota, USA. To do so, we characterized microtopography with a 1 cm spatial resolution dataset from a terrestrial laser scanning (TLS) campaign. We also evaluated subsurface mineral layer topography and daily water levels to determine the extent to which these variables influenced observed surface microtopography. Specifically, we tested the following predictions: 1. elevation will exhibit a bimodal distribution, but the degree of bimodality and the overall variability in elevation will be greater in wetter sites than drier sites; 2. surface topography will not reflect subsurface mineral topography, but will instead be representative of selforganizing processes at the soil surface; 3.
Surface model processing and validation For each site, we first filtered the site-level point-clouds in the CloudCompare software  (Othmani et al. , 2011)  and created an initial surface model with the absolute minima in a moving 0. 5 cm grid. We removed tree trunks from this initial surface model using a slope analysis and implemented a final outlier removal filter to ensure all points above ground level were excluded. Our final site-level surface models meshed the remaining slope-filtered point cloud using a local minima approach at a 1 cm resolution. We validated this final 1 cm surface model using the 60 validation spheres per site. Before we analyzed surface models from each site, we first detrended sites that exhibited site-scale elevation gradients (e. g. , 0. 02 cm m -1 ). These gradients may obscure analysis of site-level relative elevation distributions  (Planchon et al.
To validate the hummock delineation, we compared manually delineated and automatically delineated hummock size distributions at one depression site (D2) and one transition site (T1), both with clearly defined hummock features. We omitted using a lowland site for validation because none of these sites had obvious hummock features that we could manually delineate with confidence. We manually delineated hummocks for the D2 and T1 sites with a qualitative visual analysis of raw TLS scans using the clipping tool in CloudCompare (2018). Stovall et al. (2019)  found no significant differences between the manual and automatically segmented hummock distributions, and feature geometry had an RMSE of less than approximately 20 %. After the automatic delineation procedure and subsequent validation, we performed a data cleaning procedure by manually inspecting outputs in the CloudCompare software.