dataesr/scientific-paragraphs-categorization huggingface dataset:
0% (52/102) when using the VMS (p < 0. 05). Moreover, 'identification criteria not met' occurs less frequently when using the MSI software (5. 9% (6/102) vs. 27. 5% (28/102), p < 0. 05). Only one isolate not present in the MSI database (A. mangaliensis) was correctly identified at the complex level as A. iizukae. The database-related results allowed us to compare the algorithm and the architecture of the database used. The MLT database has been built on 1158 references corresponding to 336 species (yeasts and moulds) ranging from one to 31 strains per species. Only one reference was acquired for each strain. Whereas the MSI and VMS databases were built using multiple spectra per strain, giving far better results. VMS included only 79 species but its architecture is robust since it was constructed using two to 22 strains per species (75% represented by four or more isolates (D.
Pincus, personal communication)), resulting in over 20 reference spectra per species. MSI included 11 851 spectra corresponding to 1913 strains (938 species) ranging from two to eight spectra per strain. This might explain the better performances of these two systems. Obviously, this study has some limitations, particularly its small size. Thus notable pathogens are missing and larger studies are required to confirm these results. However, the relatively low number of isolates included in this study was large enough to significantly highlight the flaws of commercial MALDI-TOF MS systems in the routine mycology laboratory. Our results show that original algorithms and an enhanced database available online could compensate for the flaws of commercial databases and thus allow better identification of clinically relevant moulds.
Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis' sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al).
Introduction Metabolomics data mining describes the application of strategic data analysis methods incorporating artificial intelligence, machine learning, statistics and database operations to extract meaningful and useful information from high-dimensional and high-volume metabolomics datasets. This is a complex, time-consuming process including many steps with many possible options at each step. Further complexity is encountered in metabolomics investigations due to the fact that data analysis is often based on open source tools, each having their own parameter dependencies, also metabolomics datasets typically contain missing values and the handling of these can greatly influence the result of downstream analysis  [1, 2]. For detailed discussion and reviews of the data analysis step in metabolomics and its complexities, the reader is referred to the following publications  [3] [4] [5] [6] [7].
Despite the obvious complexity and importance of the data mining step in the overall pipeline of any metabolomics study, this section of the workflow is often given scant attention in the write up of scientific research articles. Metabolomics data analysis sections have been found to be plagued by inconsistent reporting-specifically with regards to structure, details reported, and performance metrics used  [3]. No standard method for how to analyse metabolomics data exists and therefore data analysis is in constant evolution, with new methods frequently being proposed in the literature. To discover the best methods, to build upon existing approaches and to conduct meta-analysis, the data analysis write up in metabolomics studies needs to be understandable and imitable, at a minimum. Furthermore, for those new to metabolomics data analysis, the starting point to construct a data analysis plan would most likely involve examining previously published research in the same field, with a view to reusing or adapting the various approaches.
For these purposes, the current standard of reporting in the data analysis sections of metabolomics' publications is woefully insufficient. The immediate improvement of reporting of the data analysis step is therefore vital to advance understanding and to promote reuses of data analysis protocols and eventually move closer to the ideal of reproducibility  [8, 9]. The metabolomics standards initiative (MSI)  [10]  was conceived in 2005, as an initiative of the Metabolomics Society. Under the MSI 5 working groups (WG) were established for each aspect of the metabolomic pipeline: biological context metadata WG, chemical analysis WG, data processing WG, ontology WG and data exchange WG; and a series of papers were published in 2007 with minimum reporting guidelines covering all areas of the metabolomics experiment  [11] [12] [13] [14] [15].
equator-network. org/toolkits/developing-a-reporting-guideline/ disseminating-your-reporting-guideline/. ) This would of course be in addition to previous recommendations of a supplementary audit, open code and script sharing  [18]. In recognition of the complexity of data analysis in metabolomics, we propose that distinct reporting guidelines be drawn up for separate sections of the data analysis pipeline (design of experiment, data reduction and deconvolution, data pre-processing, data annotation and identification, data pre-treatment and data analysis) as their various steps are often carried out at different times, (sometimes years pass between different steps), by different individuals or groups of various skill sets, or even often outsourced to different locations. Adherence to the guidelines is more likely to be achieved by discretising each section of the pipeline into succinct guideline sets (modules), which can be adopted by the relevant analyst(s) at the time of manuscript writing and incorporated into the report.
In other established omics reporting guidelines', namely MIAME  [19]  for microarrays and MIAPE  [20]  for proteomics, instructions do not exist for the data analysis part of the pipeline. The Equator (Enhancing the QUAlity and Transparency Of health Research) Network  [17]  is an international initiative aimed at promoting transparent and accurate reporting of health research studies to enhance the value and reliability of medical research literature. The Equator Network does not contain any guidelines for the reporting of multivariate data analysis/high dimensional data analysis/omics data analysis/supervised data analysis. The biosharing website MIBBI (Minimum Information for Biological and Biomedical Investigations)  [21]  does not contain any standards for data analysis reporting, but it does reference a standard called CIMR-Core Information for Metabolomics Reporting (CIMR)  [22]  which refers to the original proposed guidelines  [11].
However, since these initial proposed guidelines in 2007, no further work has been published on the development or dissemination of metabolomics data analysis reporting guidelines. Since the Metabolomics Standards Initiative (MSI)  [10] , significant developments in data reporting standards in metabolomics have been made through many initiatives including COSMOS  [23] , MetaboLights  [24]  and FAIR  [25] , which endeavour to ensure the consistency of metadata between datasets, and facilitate data reuse and data mergers across studies  [26]. However, with regards to the reporting of the data analysis of metabolomics studies, since the original guidelines  [11]  there have been no further advancements. There has recently been a proliferation of reporting guidelines in biomedical research  [27] , there are currently 407 reporting guidelines on the Equator Network  [17] , many containing extensions and different versions.
Data Analysis Reporting Using R Markdown With this information in mind, it is suggested that adherence to guidelines could be facilitated, if reporting guideline modules were contained in authoring tools such as the one presented in this study using R Markdown. R Markdown is a free and open source authoring framework  [31]. R Markdown documents are fully reproducible and support dozens of static and dynamic output formats. A single R Markdown file can be used to both save and execute code and generate high quality reports that can be shared with an audience. R Markdown starts with a plain text file that is edited by the user that has the extension ". Rmd". This plain text file then generates a new file that contains user selected text, code, and results from the. Rmd file. The new file can be a finished web page, PDF, MS Word document, slideshow, notebook, handout, book, dashboard, package vignette or other format.
Besides R Markdown, there are other so called "digital notebooks"  [32]  that, in the vein of literate programming, support the connection of text to underlying code and analysis results  [33]. For example Jupyter for R, Python and Julia (https://jupyter. org/) and matlabweb for MATLAB (https://ctan. org/pkg/matlabweb). As efforts towards computational reproducibility continue, an authoring tool in R Markdown such as the one presented here has the advantage that it can simultaneously achieve the aims of both ensuring reporting standards are adhered to, while also having the potential function as a digital notebook. Such an authoring tool could therefore ultimately provide an uninterrupted and transparent workflow from the initial stage of data in to the final output of an analysis report. The example of an authoring tool presented here, in this instance, is solely focused on reporting the statistical data analysis step of the pipeline, incorporating the data pre-treatment step and the actual data analysis step.
Checklist of Minimum Information For Reporting Data Analysis In Metabolomics The development of a reporting guideline checklist, specifically for the data pre-treatment and data analysis sections of the metabolomics pipeline, with a view to general applicability to other omic domains. This checklist was compiled based on the complete information required to construct a workflow diagram and to repeat the analysis using the reader's own version of code. The main areas of omissions, which we found lead to confusion and ambiguity when conducting our review  [3] , helped to inform this checklist. Existing guidelines which helped to shape this guideline list, included the TRIPOD  [34]  statement, GRIPS  [35]  statement and REMARK guidelines  [36]. Of course, the main document informing these guidelines is the original proposed guidelines for data analysis reporting in metabolomics by Goodacre et al.
An Authoring Tool For Reporting Statistical Analysis Of Predictive Omics The development of an authoring tool using R Markdown. This reporting guideline checklist is presented as a questionnaire in an R markdown file that guides the production of text and workflow diagrams based on user input. These reporting guidelines are intended to form a neutral and malleable framework and have general applicability and interoperability across various omics domains. These can be extended as needed by different domains or studies, but would represent a minimum set of information to be supplied whenever predictive data mining in metabolomics is carried out. We purposely do not develop a "user friendly" web interface, as the goal is for users to operate within the R Markdown environment.
Minimum Information About A Data Analysis (MIDAS) Checklist (Guidelines Checklist Specifically for the Data Analysis Step) Guidelines of two types generally exist: guidelines for reporting and guidelines for protocols. Since the area of data mining for metabolomics is still in rapid evolution, we suggest that guidelines or limitations on methodology at this point would be premature, as the optimal methods for extracting clinically useful biomarkers has clearly not been established. Therefore, these guidelines pertain only to reporting.
• Provide details on the package or program used for this phase of the analysis • If in house code is used, provide it or a link to it and also the language the code is written in. Another example of a facet of the data analysis report that could be developed by community participation and added to the existing template via the GitHub repository is the area of error analysis. The importance of error analysis in metabolomics and in particular the reporting of such error analysis has been advised by Moseley, who called for the reporting of "a detailed list of all known or potential biases and assumptions, along with results of any analysis and testing of these bias and assumptions; and results with adequate measures of uncertainty and confidence or at least a good explanation for why uncertainty and confidence measures are not provided"  [37].
Moseley suggested to augment existing standards, in particular for reporting known and potential sources of bias, by borrowing from clinical standards like STARD  [38]  and CONSORT  [39, 40]. For other data analysis methods currently not covered here (for example, cluster analysis and other classification and feature selection methods), similar templates can be generated with their required parameter reporting and added to the existing templates via adding branches to the GitHub repository. We actively invite the participation from metabolomics community users to become involved in this collaborative venture. Ideally, standards should be revisited on a regular schedule-preferably annually, to reflect the evolving knowledge and current best practices in the field. We suggest uploading the R Markdown report document along with other raw and processed data as well the experimental metadata data during the submission of experimental metabolomics results to the relevant repository such as MetaboLights  [41]  or Metabolomics Workbench  [42].
Link to GitHub Repository Containing Markdown Template https://github. com/MSI-Metabolomics-Standards-Initiative/MIDAS This R Markdown file containing the authoring template is contained in a GitHub repository. Having started as a code developer's collaborative platform, GitHub  [43]  is now the largest online storage space of collaborative works that exists in the world, which makes it the ideal platform to share this R Markdown template file. The beauty of R Markdown as stated above is that it can embed and execute code and this code can then be hidden or displayed in the final document. Even in the most basic of report writing templates, such as the one presented here, this aspect is very useful, as DiagrammeR  [44] , a flexible and powerful R package for generating graph and flowchart diagrams, can be used.
Participation by the wider metabolomics community in updating and optimizing these guidelines could open the discussion on defining optimal performing data analysis workflows for specific research goals, depending on an individual study's dataset limitations and the desired outcomes of the particular study. We purposely do not provide a web-based user interface for accessing this R Markdown template, as we believe that data analysts need to become comfortable in environments such as R Markdown if the production of reproducible research papers is to become a reality. Furthermore, we feel that anyone who is capable of data analysis is more than capable of using R Markdown, without the need of a "user friendly" web interface. A modularised system of authoring tools would encourage the uptake of guidelines on two fronts: The modularised facet will ensure that each researcher at each stage of the pipeline will be responsible for following the appropriate guidelines pertaining to their own area of expertise.
For this reason, this R Markdown file is not presented here as an end result, but is proposed as a starting point to encourage the data analysis section of metabolomics papers to have a more logical and stepwise presentation and to contain enough information to be understandable. So, even though this R Markdown file only attends to the authoring and not the analysis of metabolomics data, we hope that it will coax data analysts into the environment of R Markdown (and GitHub), and therefore be a nudge along the road towards readable, and ultimately, reproducible, metabolomics research. Here are the instructions to use this R Markdown authoring template.
Acknowledgments: We wish to acknowledge and thank the authors of the original guidelines for data analysis reporting in Metabolomics. ( Royston Goodacre ,  David Broadhurst ,  Age K. Smild ,  Bruce S. Kristal ,  J. David Baker ,  Richard Beger ,  Conrad Bessant ,  Susan Connor ,  Giorgio Capuani ,  Andrew Craig ,  Tim Ebbels ,  Douglas B. Kell ,  Cesare Manetti ,  Jack Newton ,  Giovanni Paternostro ,  Ray Somorjai ,  Michael Sjöström ,  Johan Trygg  and  Florian Wulfer ).
• If a list of potential biomarkers is produced at this point, please state this explicitly. Multivariate Analysis: Unsupervised analysis • What are the dimensions of the dataset entering this phase of the analysis? • Are unsupervised methods employed for visualisation and/ or data reduction and/or correlation analysis? • If yes, describe the algorithm used. • Is outlier detection and removal addressed at this point? If yes please describe and specify the outliers removed. • Are unsupervised analysis methods used for clustering? • If yes, describe and provide distance metric.
Purpose The technique 3D polarized light imaging (3D-PLI) allows to reconstruct nerve fiber orientations of postmortem brains with ultra-high resolution. To better understand the physical principles behind 3D-PLI and improve the accuracy and reliability of the reconstructed fiber orientations, numerical simulations are employed which use synthetic nerve fiber models as input. As the generation of fiber models can be challenging and very time-consuming, we have developed the open source FAConstructor tool which enables a fast and efficient generation of synthetic fiber models for 3D-PLI simulations. Methods The program was developed as an interactive tool, allowing the user to define fiber pathways with interpolation methods or parametric functions and providing visual feedback. Results Performance tests showed that most processes scale almost linearly with the amount of fiber points in FAConstructor.
Both types of simulation require numerical fiber phantoms as input, describing anatomical and optical properties and mimicking brain tissue to a certain degree of real-ism. In the present study, the focus was on white matter phantoms defined by numerous fiber models with specified fiber diameter, myelin thickness, spatial course, refractive indices, etc. The generation of such fiber models and resultant phantoms, however, is challenging and time-consuming, especially when targeting complex and large-scale models. We introduce the easy-to-use open source software tool Fiber Architecture Constructor (FAConstructor) enabling fast and efficient generation of white matter phantoms. It allows to define fiber objects (e. g. individual fibers, tracts, bundles) which can be rotated, (re)moved, duplicated, and spatially integrated into complex large-scale phantoms.
The fiber architecture constructor (FAConstructor) The FAConstructor is a simple, effective, and interactive tool to generate and visualize fiber objects as input for simulations or similar purposes. An open source version of the code and a detailed description of compilation and usage are provided through the following GitHub repository: https://github. com/ 3d-pli/FAConstructor. The tool allows an exact definition of curves using parametric functions or interpolation methods. Each curve contains a number of data points consisting of coordinates in three-dimensional space and radii for each data point. By this means, individual fibers or entire fiber bundles will be described in the following and referred to as fiber objects.
The fibers are arranged on a triangular grid (see Fig. 2b ), and the radius of every fiber and the minimum distance between neighbored fibers are defined by the user. The triangular grid is rotated from one data point to the next using the tangents of each point and computing the rotation angle using the Rodrigues formula  [5]. If the radius increases along the course of the fiber bundle, the distance between the fibers increases accordingly. This guarantees that the fibers in the bundle do not intersect with each other, provided that the user-defined minimum distance does not fall below the diameter of the fibers. The generated fibers are stored as a group, allowing efficient modifications of the fiber bundle. Here, the object has been generated by the parametric functions (x(t) = 50 • sin(t • π), y(t) = 50 • cos(t • π), z(t) = 50 • t, r (t) = -|15 • t| + 15, t ∈ [-0.
5, 0. 5] ). All other options are accessible through the menu bar at the top. b Fiber object filled with fibers. With increasing radius of the fiber object, the distance between the fibers increases accordingly Coordinates and radii of the fiber objects are defined as single-precision floating-point values, which can be saved either in a hierarchical file system-like data format (HDF5, https://www. hdfgroup. org) or, alternatively, in a plain text file format. HDF5 is advantageous, because it allows to handle metadata, it has no limitation on the number or size of data objects in the collection, giving great flexibility for big data, and it provides high-performance I/O with a rich set of integrated performance features.
Rendering As the FAConstructor allows to generate fiber objects interactively, an efficient rendering of the data is crucial. Therefore, a new frame is only rendered when the user interacts with the fiber objects by rotating or translating them, allowing fluent interaction. Rendering is realized with the OpenGL 3. 3 framework of Qt5  [4, 25]  which enables the use of widgets for applying options and generating fibers, while rendering is done in a single central widget. In addition, OpenMP  [22]  can be used optionally for several tasks granting performance improvements. Fiber objects and individual fibers are approximated by cylindrical shapes using the defined coordinates and radii. As circular objects cannot be rendered without approximation, they are represented by n-sided regular polygons.
Performance measurements To ensure that the user can work fluently with a large number of fiber points in FAConstructor, performance optimizations were implemented as mentioned in "Rendering" section and tested on the generation and rendering of fiber objects. Performance measurements were performed using a PC with an Intel Core i5-3570 processor paired with an NVIDIA GTX 1070 and 16 GB RAM running Ubuntu 18. 04. Figure  3  shows the measured performance of the program. The corresponding datasets were created with the following parameters: (A), (C) : x(t) = t, y(t) = t, z(t) = t, r (t) = t, t ∈ [0, 1,. , N ] (1) (B) : x(t) = sin(t), y(t) = cos(t), z(t) = t, r (t) = 1, t ∈ [0, 1,. , N ] (2) where x(t), y(t), z(t) are space coordinates and r (t) is the radius of the fiber object at this position.