dataesr/scientific-paragraphs-categorization huggingface dataset:
This will in subsequent versions allow the user to save the output of different tools for each specimen, and combine these results for further analysis. The tab-delimited format also allows easy editing of the data tables in spreadsheet editors. This specimen-based architecture needed for alphataxonomic programs remains valid whether specimens are represented by physical vouchers, images, or in the future maybe by full genome sequences. Simplicity and user-friendliness are at the core of the toolkit we are developing. Because the majority of taxonomists is not familiar with programming languages, such as Python, all our tools are accessible via graphical user interfaces (GUI) -analyses can therefore be carried out with a few intuitive mouse clicks, under default or custom settings, without the need to enter commands in a command line.
We also have added autocorrect routines to avoid the loss of time associated with the search for small misspellings or incorrect characters in input files that cause programs to fail. Furthermore, we will provide detailed manuals and wikis with screenshots, along with example files. We chose Python as the main programming language for our package, because it is characterized by its good readability and simple-to-learn syntax, and we documented newly written code extensively, to allow its re-use by other programmers. This comes at the cost of speed that would have been achieved by using the C programming language, but our toolkit in this early phase is not designed to cope with huge genomic datasets or analyses with tens of thousands of specimens. Currently iTaxoTools is designed to provide support for the most common taxonomic research projects that discover and name a limited number of species only  (Miralles et al.
2020 ), but will be extended to large-scale projects in the future. Considering that powerful programs exist for phylogenetic, phylogenomic and DNA metabarcoding analyses, we did not attempt to include such functionalities in our toolkit. Similarly, we also did not focus on dedicated multiple sequence alignment programs or genome assemblers because (i) these bioinformatic tasks are more efficiently carried out by programs written in C language, (ii) GUI-driven programs and pipelines already exist for alignment and phylogeny (e. g. , PAUP, MEGA, PAUP, BEAST: Swofford 2003;  Kumar et al. 2018; Bouckaert et al. 2019), genomics, and DNA metabarcoding (e. g. , Anslan et al. 2017 ) and (iii) there is an active community both of commercial companies and academic research teams constantly extending these kinds of programs.
We are, however, adding graphical user interfaces and new functionalities to other existing tools that are important for analyses in the context of systematics and that are not yet optimized with user-friendly GUIs. For instance, we have updated the code of Partitionfinder  (Lanfear et al. 2016 ) from Python v. 2 to v. 3, and aim to add a GUI also to the sequence alignment program MAFFT  (Katoh & Standley 2013). These developments will be added successively to iTaxoTools. Functionalities implemented in iTaxoTools 0. 1 Our work on iTaxoTools is ongoing and will be intensified in the period 2021-2023 thanks to support by the DFG SPP 1991 TaxonOmics priority funding program. The current version, published along with the present article, already includes a series of functional tools that we predict will be useful in different steps of the alpha-taxonomic workflow, Data Preparation (mainly Conversion), Analysis, Delimitation, and Diagnosis.
Data Preparation Several tools convert among data formats, with the major modules being dnaconvert for converting among common DNA sequence formats, latlonconverter for converting among geographic coordinate systems (elaborated upon below), and pyr8s for converting nonultrametric trees to ultrametric. A collection of simpler tools includes fastmerge and fastsplit for splitting and merging large fasta and fastq files, including advanced filtering options by sequence name or sequence motif; specimentablemerger and specimentablepruner for splitting and merging tab-delimited text files by specimen identifiers; linebreaker for converting among Linux and Windows line-break styles (often necessary when processing input files from other bioinformatic tools); nodenamecorrector for replacing all non-standard ASCII characters from Newick-format trees; and unitconverter for distance, time, volume, molarity, and other units.
From these spreadsheets, it is then easy to copy-paste the sequence, specimen-voucher, species and locality columns into dnaconvert and obtain a sequence file for analysis, e. g. in fasta format, with all respective information concatenated in the sequence name. The program also supports a format in which these metadata are bracketed as required for uploading the sequence data along with metadata to the NCBI Genbank repository (i. e. , via Submission Portal or BankIt). Lastly, the program also converts Genbank flatfiles into a tabular format, allowing the user to immediately have all relevant metadata associated with the Genbank record in separate columns in a spreadsheet. latlonconverter allows batch conversion of geographic coordinates from a large number of different formats into standard decimal format as required by most geographical information system (GIS) programs.
By performing a series of autocorrections of possible typos and then using a heuristic approach, latlonconverter is able to recognize and transform many idiosyncratic formats of geographical coordinates as they are commonly found in specimen databases containing geographical information taken by different researcher. With spartmapper, geographical coordinates in combination with a species partition file (spart;  Miralles et al. submitted)  can be previewed on a map, and then transformed in a kml file that plots all localities on Google Earth and visualizes the geographical distribution of respective species hypothesis (Fig. 4 ). pyr8s is one of our flagship modules (Fig. 5 ). For many evolutionary analyses, but also for species delimitation (e. g. GMYC), ultrametric phylogenies are required where non-ultrametric trees are available.
This conversion is rather complex and can be time-intensive. While numerous programs exist to calculate time trees (e. g. , MCMCtree, BEAST, MEGAX:  Yang & Rannala 2006; Bouckaert et al. 2019; Kumar et al. 2018) , they usually require DNA sequence information in addition to a previously inferred phylogenetic tree. For iTaxoTools, we opted to recuperate a vintage approach, non-parametric rate smoothing (NPRS), initially developed by  Sanderson (1997)  and later implemented as part of the program r8s  (Sanderson 2003). This method only requires a phylogenetic tree as input, with the option to add one or more time calibration points. NPRS has previously been implemented in the R package ape  (Paradis et al. 2004 ), but was removed from the latter and from the newest releases of r8s due to licensing issues.
Specifically, the original version of r8s relied on a modified implementation of Powell's conjugate direction method which was incompatible with open-source licensing  (Powell 1964; Gill et al. 1981; Press et al. 1992 ). In the GUI-driven tool pyr8s, the NPRS algorithm has been newly coded, making use of the open-source libraries DendroPy  (Sukumaran & Holder 2010)  and SciPy  (Virtanen et al. 2020) , thus resolving the previous licensing issues. This new version provides a GUI for userfriendly setting of time constraints, exposes a Python interface for lower-level analysis and maintains support for r8s-formatted input files. Analysis We include several data analysis modules: TaxI2 for calculation of pairwise distances among individuals, and morphometricanalyzer for basic morphometric analyses (elaborated upon below).
For convenience, we also include simplestatscalculator, a utility for quick, basic statistical analyses of manually entered or pasted data. TaxI2 is a tool for pairwise sequence comparison. To analyze DNA barcoding data sets,  Steinke et al. (2005)  proposed the program TaxI, which performs pairwise alignments between sequences and calculates pairwise distances based on these alignments. Compared to a multiple sequence alignment (MSA) the authors argued that these distance calculations may be more accurate in the case of highly divergent markers including multiple insertions and deletions, such as stretches of mitochondrial ribosomal RNA genes. The pure-Python tool TaxI2 performs similar calculations, with numerous added functionalities such as support for pre-MSA aligned data sets. The tool has two main analysis modes: First, following the original TaxI approach, it 5 can compare a set of sequences against a reference database, via pairwise alignments, identifies for each query the closest reference sequence, and calculates various genetic distances among the two.
Second, it also can perform all-against all comparisons of a set of sequences. In this latter approach, sequences can be added in tab-delimited table format along with species name, and from these data the program calculates within-species, between-species, and between-genus distances. Various metrics and graphs defining the barcode gap in a given data set are also included in the output. The program furthermore performs a simple threshold-based clustering of DNA sequences into OTUs, following the approach previously implemented in TaxonDNA (http://taxondna. sourceforge. net/; Meier et al. 2006) , and outputs the resulting species partition as SPART file  (Miralles et al. 2021 ) morphometricanalyzer is our tool for exploratory analysis of morphometric datasets. Integrative taxonomists do not only use molecular data.
Finally, the program also outputs pre-formulated taxonomic diagnoses, with full-text sentences specifying by which morphometric value or ratio a species/population differs from other species/populations with statistical significance, or without value overlap. It would also be possible to explore nonmorphological (e. g. bioacoustic) data with this tool, although it is primarily developed for morphometrics. Delimitation A special emphasis in the first development phase of iTaxoTools is species delimitation, a burgeoning field in systematics. The available species delimitation algorithms mostly use DNA sequence data and tend to overestimate the number of species in a data set (e. g. ,  Miralles et al. 2013) ; indeed, they may delimit populations rather than species  (Sukumaran & Knowles 2017 ).
Yet, such automated delimitation may play a role in formulating initial species hypotheses that can then be tested in an integrative taxonomy pipeline. In the first version of iTaxoTools, we have focused on tools already available in Python programming language. For these tools, we added user-friendly GUIs and slightly extended the functionality, for example by enabling them to output species partition information in the standardized "spart" format proposed by  Miralles et al. (2021). The current version of iTaxoTools includes GUI-enhanced versions of PTP  (Zhang et al. 2013 ) (Fig. 6 ) and GMYC  (Pons et al. 2006; Fujisawa & Barraclough 2013 ; Python version J. Zhang) which delimit species from single-locus trees; tr2  (Fujisawa et al. 2016)  and DELINEATE  (Sukumaran et al. 2020 ) that use coalescence-based approaches on multiple gene trees; and ABGD  (Puillandre et al.
2012 ) (Fig. 7 ) and ASAP  (Puillandre et al. 2020 ) that are alignment-based and rely on calculations of genetic distances. For some of these tools (PTP, GMYC, tr2, DELINEATE) the current pre-release GUI versions are still basic and only run under default settings; options to change and refine parameters will be added to the first complete release. iTaxoTools also includes LIMES 2. 0, a program to handle and compare species partitions obtained by these various approaches  (Ducasse et al. 2020 , Miralles et al. 2021 ).
Diagnosis The diagnosis of new species -rather than its lengthy description -represents the most important part of the alpha-taxonomic process, and in all Nomenclatural Codes, diagnosis can be based on molecular, as well as morphological characters  (Renner, 2016). Several software tools have been proposed to extract diagnostic nucleotide positions of clades and species, either phylogeny-based (caos;  Sarkar et al. 2008)  or primarily alignment-based (MolD, Fastachar, DeSignate:  Fedosov et al. 2019; Merckelbach & Borges 2020; Hütter et al. 2020). In order to facilitate the use of such DNA characters in differential diagnoses of new species, we implemented a crucial new tool for DNA taxonomy named dnadiagnoser. Compared to other tools, dnadiagnoser has various functionalities to improve the use of DNA characters in species diagnosis.
In addition, we have also programmed a GUI for MolD  (Fedosov et al. 2019) , a program that is tailored for recovering DNA-based diagnoses in large DNA dataset, and is capable of identifying diagnostic combinations of nucleotides (DNCs) in addition to single (pure) diagnostic sites. The crucial and unique functionality of MolD allows assembling DNA diagnoses that fulfil predefined criteria of reliability, which is achieved by repeatedly scoring diagnostic nucleotide combinations against datasets of in-silico mutated sequences.
For more sophisticated analyses, connecting iTaxoTools (via data formats such as SPART) with dedicated toolboxes for analysis of spatial biodiversity data such as SDMToolbox  (Brown et al. 2017 ) could allow e. g. for comparative niche modelling of alternative species partitions. (ii) Extraction of diagnostic traits from specimen data: Besides molecular diagnosis with MolD and dnadiagnoser, we plan to develop a tool that automatically outputs diagnoses based on (specimen-based) categorical data sets of morphological characters. (iii) Connection to other programs: We also plan to explore options to connect iTaxoTools to the DELTA (DEscription Language for TAxonomy) software package  (Coleman et al. 2010). DELTA is a format for coding descriptive taxonomic information that however is primarily species-based (not specimen-based as iTaxoTools), and a series of programs have been developed on this basis, spearheaded by M.
Dallwitz at CSIRO (Canberra, Australia)  (Dallwitz 1974 (Dallwitz , 1980)). The new Free DELTA platform launched in 2000 (http://freedelta. sourceforge. net/) includes options for editing and maintenance of data sets in DELTA formats, as well as utilities for data conversion, interactive identification of taxa, automated generation of diagnostic keys, and descriptions. Especially, information on speciesspecific molecular and morphological characters identified in iTaxoTools could be seamlessly coded in DELTA, making use of pydelta (http://freedelta. sourceforge. net/pydelta/). The biggest gap in taxonomy software so far is the integrative aspect in the sense of  Dayrat's (2005)  concept of integrative taxonomy (see also  Padial et al. 2010). That is, the many available species delimitation programs all output a species hypothesis based on one analytical approachusually based on only a molecular data set, with a few exceptions such as iBPP, which can integrate morphometric and molecular data  (Solís-Lemus et al.
2015). Approaches that combine information from different lines of evidence into species delimitation are exceedingly scarce. As an example, DELINEATE  (Sukumaran et al. 2020)  allows the user to fix a series of species hypotheses (i. e. , firmly assign a series of specimens to species) while letting the other specimens "float" freely in the analysis and assign them to either one of the previously defined species, or to a new species. Such an option of "prior" species delimitation should be universally available to users as a manual option (e. g. , if evidence comes from field or experimental data on hybridization, genomic information, or other data that are yet difficult to code or implement in species delimitation software), similar to what is implemented in DELINEATE. But ideally, automated proposals of firm a priori evidence for two specimens to either belong to two species, or to the same species, could also be elaborated by the software -for instance, using evidence such as sympatric geographical occurrence without gene flow, full concordance between genetic and morphological characters, or exceedingly high genetic distances.
We plan to develop concepts for such analysis priors, and start implementing them in a iTaxoTools webserver pipeline, in the next years. Importantly, our project is open for other developers to join, and for the taxonomic community as a whole to provide suggestions. We especially welcome proposals of additional tools that could help to streamline and accelerate the whole process of delimiting and naming species (whether it concerns the initial step of data acquisition, their treatment, their analyses, or their final submission to a dedicated repository). Only practicing taxonomists know which parts of the alpha-taxonomic workflow for their group of taxa is particularly time-consuming, and where time and effort is lost with repetitive, manual tasks that could be as well automatically performed by a computer program -and thereby formulate requirements for such dedicated programs.
Perspectives for iTaxoTools The different taxonomic tools made available here are performing analyses offline on a local computer (and in the future will also be available on a webserver), but without linking to external resources. True next-generation taxonomy will require linking specimen-based taxonomy 8 software with online resources and databases, and scaling the analyses to data of many thousands of specimens. On the one hand, this involves archiving newly acquired data in dedicated repositories  (Miralles et al. 2020). But on the other hand, it means aggregating for each specimen identifier DNA sequences, morphological characters, images, and increasingly -omics data (e. g. ,  Lendemer et al. 2020) , and then entering these large-scale cyberspecimen data into species delimitation, diagnosis and naming pipelines.
The process could be coupled with machinelearning programs to automatically extract diagnostic traits e. g. from images, with data aggregators such as GBIF (gbif. org) and online tools such as Map of Life (mol. org), or Timetree of Life (timetree. org) to obtain geographical and temporal context, and distribution models for alternative species hypotheses. These bioinformatic opportunities may gain power under a view of species as probabilistic hypotheses that may allow defining probability thresholds of integrative taxonomic analysis above which lineages can be confidently named as species by semi-automated pipelines. While the current version of iTaxoTools is far from this vision, it may represent a seed for developing the necessary environment, and a sandbox to test software tools with the potential to significantly accelerate the inventory of life.
Repositories of the code of the tools included in the 0. 1 version of iTaxoTools. The table also lists the main programmers involved in the development of each tool or its graphical user interface (GUI), and informs whether a tool was newly programmed for this project, adjusted from existing code (by adding a GUI plus sometimes additional functionalities), or included as original code and GUI without modification. FIGURE 3. Screenshot of one of the newly programmed quick conversion tools, DNAconvert, which implements numerous autocorrect options to avoid sequence output files generating errors in downstream programs. DNAconvert also supports tab-delimited table input and its conversion to common sequence formats such as FASTA, NEXUS, or PHYLIP, to facilitate storage and management of sequences and sequence metadata in spreadsheet editors such as Microsoft Excel.
-Reads GenBank flat files and converts from and to tab-delimited files to manage sequence in spreadheet editors. -Single-file conversion, batch conversion and conversion of copy-pasted files -Parses a large variety of formats of WGS84 geographical coordinates -Batch-conversion of coordinates in tables or copy-pasted lists which can contain coordinates in different formats (recognized by heuristic approach) -Main output in decimal degree format -Can merge large /definition?/ files that usually cannot be opened in editors. -Works for any text file but includes additional features when processing fasta and fastq. -Allows for filtering sequences and sequence names with certain motifs and include/exclude them in the merged file -Can split large files /definition?/ that usually cannot be opened in editors and split them into a series of equally sized smaller files -Designed for fasta and fastq, but works for any text file.
This process identified 70 different crop rotations implemented on the fields of the seven farms. Two farmers (AF1 and LF1) used an irrigation system for silage maize at the recommended rate of four applications of 300 m 3 /ha each year. Climate data were obtained from the SAFRAN dataset (8 km × 8 km) of Météo France  (Vidal et al. , 2010). General soil data were obtained from the European Soil Database v2 at the 1:1,000,000 scale  (European Soil Data Centre, 2019; Panagos et al. , 2012). When available, soil data were improved using soil analyses provided by the farmers. Spatially intersecting data layers for field boundaries from the LPIS with soil and climate data provided a consistent description of the soil and climate characteristics of each field  (Murgue et al. , 2016). Economic information (crop prices, premiums and input costs) was provided by agricultural advisers from CRAPL for 2015, 2016 and 2017.
Yet, we recommend that this case study be replicated with different social contexts. The IAM methodology has a relevant generic and reproducible character and can be applied and expanded to other and larger territories. For Europe, key data can be obtained from national or European databases. Maps of crop rotations can be obtained from the LPIS  (Levavasseur et al. , 2016; Zimmermann et al. , 2016) , climate data can be extracted from the E-OBS dataset (Copernicus ECMWF, 2019), and soil data can be obtained from the European Soil Data Centre (European Soil Data Centre, 2019). For larger territories (e. g. administrative departments), crop (and livestock) management-decision rules will need to be generalised (e. g. for each crop within a specific cropping system) and not consider specific farmer characteristics  (Murgue et al.
Spatial Λ-Fleming-Viot process (ΛFV) The ΛFV can be used to model migration and evolution of individuals within a population distributed across an area. The geographical area A under consideration is usually a torus (as in the simulator discsim  [26] ), or a rectangle (as in the phylogeographic inference software PhyREX  [16] ). Migration is only allowed from and into A, potentially representing, for example, the case of an island or a continental mass. Individuals of the population are assumed to be spread over A with uniform density ρ. Migration and reproduction of individuals are modeled through reproduction-extinction events (from now on, just "events") which happen at rate λ over time. Each event e i happening at time t i is centered at a location c i taken at random uniformly from A.
Can the ΛFV correct the sampling bias in BMP? As mentioned before, the ΛFV has a number of differences from the BMP. One of these differences is that the model does not assume that the sampled locations are representative of the range of the outbreak, but instead the model assumes uniform density of cases over a considered, limited space. For this reason, the ΛFV should not be affected by sampling bias (see "Models" Section). We performed inference using the software PhyREX within the package PhyML v3. 3. 20190909 [16]  downloaded on 4th of January 2020 from https://github. com/ stephaneguindon/phyml. git. PhyREX implements the ΛFV model on a rectangular space. We fixed the tree to the simulated true one to greatly reduce the parameter space to be explored and to consider the case in which tree uncertainty is negligible (for example due to abundant genetic data).