dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of tasks expected from peer reviewers We retrieved 2 117 citations from our search of articles related to tasks for peer reviewers; 98 full-text articles were selected and 90 were evaluated. Overall, among the 90 articles selected and evaluated, 48 were secondarily excluded because they did not report any recommendation or tasks dedicated to peer reviewers, 34 reported specific recommendations and tasks, and 8 referred to other sources (such as online training resources, website, etc. ) from which further data were extracted (Fig. 1 ). The selected articles are in Additional file 5. Overall, we selected 171 journals. All journals had a website. In total, 95 (56 %) journals did not have recommendations for peer reviewers available on their website and publisher interface; 34 (20 %) had recommendations only on their website, 32 (18 %) only on the publisher interface, and 10 (6 %) on both.
Discussion This study involved the identification and sorting of all tasks expected from peer reviewers when evaluating the report of an RCT. We performed a large systematic review of the literature and editors' guidelines and surveyed peer reviewers from various backgrounds who evaluated peer reviewer tasks by the Q-sort method. Overall, we identified 221 different tasks that could be expected from peer reviewers when evaluating an RCT report. These tasks involved different levels of expertise. For example, assessing the adequacy of statistical analysis involves methodological and statistical expertise, whereas assessing the adequacy of the selection of participants and clinical setting involves content expertise. The tasks rated as most important were related to the methodology (evaluating the risk of bias of the trial, whether the control group is appropriate, and the reliability and validity of the outcome measures), statistics (evaluating the adequacy of statistical analyses), and results (determining whether the manuscript conclusions are consistent with the results and whether all outcomes are adequately reported, and searching for any attempt to distort the presentation or interpretation of results).
Further, they may misunderstand what they are expected to do. Moreover, there may be ambiguities regarding who should perform a specific task. Some tasks, such as checking the quality of reporting or checking trial registration, can be considered by editors as a reviewer task and by reviewers as an editorial task. Editors could try to achieve consensus on what they expect from peer reviewers when evaluating the report of an RCT. Furthermore, important simple tasks that could easily increase a paper's quality were poorly rated by peer reviewers and rarely requested by editors. In fact, the task dedicated to trial registration (i. e. , the item to compare information recorded on a clinical trial register such as ClinicalTrials. gov and reported in the manuscript) was classified 32nd among the 36 condensed items and never clearly requested by editors.
However, this survey focuses on qualitative input and it was mainly important to have a variety of expertise. Second, our sample included only 171 journals, with a response rate of journal editors of 45 %. Third, only one author extracted data on the tasks expected of peer reviewers (AC). However, two reviewers achieved consensus on the classification of these tasks. Fourth, our study was a Fig. 2  Representation for each task of the proportion of participants rating the task in the first tertile (i. e. , ≥2 on the scale from -5 to +5 for each task) and the proportion of editors requesting the task in their recommendations to authors. The tasks are sorted according to the mean ranking of participants snapshot of editors' recommendations, and some journals may have since updated their websites and their instructions to peer reviewers  (30 July, 2014).
Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called ''SOAX'', which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values.
N etwork structures made of filamentous biopolymers are ubiquitous among biological systems. Biophysicists and cell biologists routinely use static and time-lapse confocal fluorescence microscopy to image intracellular networks of actin filaments 1,2 and microtubules 3,4 as well as extracellular polymers such as fibrin  5, 6  , both in vitro and in live cells. To gain insight in the structural, dynamical, and mechanical properties of these networks and to understand the mechanisms of their formation requires image analysis methods for automated quantification of massive image datasets. However, user-friendly, flexible, and transparent 7 software tools to reliably quantify the geometry and topology of these (often dense) networks and to localize network junctions in 3D are scarce. Previous methods for extracting biopolymer network structures include morphological thinning of a binary segmentation  [8] [9] [10] [11]  or a computed tubularity map  12, 13  , Radon transform 14 and template matching  15, 16.
However, most of these methods extract disconnected points (i. e. pixels) on centerlines without inferring network topology and they have not been implemented as part of a software platform. One available software tool is ''Network Extractor'' (http://cismm. cs. unc. edu/), which finds one-pixel wide 3D network centerlines by thresholding and thinning a tubularity map. Thresholding results, however, can suffer from inhomogeneous signal-to-noise ratio (SNR). Other software for extracting curvilinear network structure are designed for neuronal structures  [17] [18] [19] [20]. Vaa3D-Neuron 19 (http://www. vaa3d. org/) is a semi-automatic neuron reconstruction and quantification tool which requires the user to pinpoint the end points of a neuronal tree so that a minimal path algorithm can reconstruct the structure.
The Farsight Toolkit (http://farsight-toolkit. org/) also contains 3D neuron tracing and reconstruction software command-line modules  21, 22. To fill this gap in available software, here we provide an open source program, SOAX, designed to extract the centerlines and junctions of biopolymer networks such as those of actin filaments, microtubules, and fibrin, in the presence of image noise and unrelated structures such as those that appear in images of live cells. SOAX provides quantification and visualization functions in an easy-to-use user interface. The underlying method of SOAX is the multiple Stretching Open Active Contours (SOACs) method that was proposed to extract the 3D meshwork of actin filaments imaged by confocal microscopy 23. Here we implement this method in SOAX and apply it generally to different types of biopolymer networks.
choose from, without relying on prior knowledge of ground truth. The selected optimal extraction result can be subsequently used for quantitative analysis of biopolymer filaments, such as their spatial distribution, orientation and curvature. Time lapse movies can be conveniently analyzed by reusing the selected parameters from one image for other images drawn from the same dataset. We demonstrate SOAX's potential to help provide quantitative results to answer key questions in cell biology and biophysics from a quantitative viewpoint.
Results Description of SOAX software. SOAX extracts network structures in three stages: SOAC initialization, SOAC evolution, and junction configuration (Fig. 1a , Supplementary Note 1, Supplementary Movie 1)  23. A SOAC is a parametric curve that ''evolves'': it is attracted towards the centerline of a filament, stretches by elongation, and stops stretching when its end reaches a filament tip. Figure  1b  and 1c  show examples of the extraction process for synthetic images. In the initialization stage (second column in Fig. 1 ), multiple short SOACs are automatically placed along intensity ridges of the image, which correspond to centerlines of filaments in 3D or 2D, depending on the dimensionality of the image. A ridge threshold parameter (t) specifies the minimal intensity steepness for a ridge to host an initial SOAC.
Examining how the entire network is distributed and how it is reorganized over time can provide insight in the mechanisms that cells use to change shape and move or divide. The SOAX program can extract the microtubules inside the entire cell (Fig. 5b ). To better show the effectiveness of SOAX, Figure  5c  shows an enlarged region corresponding to the blue window in Figure  5a , in which most microtubules are oriented along the northeast direction. As before, we picked a good extraction result from a set of 27 optimal candidates selected from 220 combinations of ridge threshold t and stretch factor k str (distributed in the range 0. 002 , t , 0. 022 and 0. 1 , k str , 2, similar to Fig. 3 ) using the F-function (Fig. 5d ). As a quantitative validation that most microtubules in Figure  5c  point northeast, on average, we computed the orientation histogram of azimuthal angle Q.
Discussion We showed how SOAX provides a powerful and user-friendly platform for extracting and quantifying biopolymer networks imaged by confocal microscopy in 3D. The accompanying parameter optimization program helps the user select the best extraction results among candidates for subsequent analysis. Once a good parameter set is found for one image, it can be re-used for a set of images obtained under the same conditions, with care to use consistent image offset background subtraction and rescaling (the latter can be adjusted in SOAX but unless otherwise specified the program rescales the image so that the maximum intensity is 1). The user can also manually edit the best result to further improve it. The SOAX software and its parameter optimization program are open source and available for download at http://www.
Acknowledgments This work was supported by  NIH  grants  R01GM098430  (to DV and XH) and  R01GM086546  (to JQW). The work of FCT and GK was supported by a  VIDI grant  from the  Netherlands Organization for Scientific Research (NWO)  and by the  Foundation for Fundamental Research on Matter (FOM) , which is part of  NWO. EY acknowledges support from the  Fulbright Visiting Scholar Program  and  The World Academy of Sciences (TWAS).
The complexity of expressions grows fast, but it is important to stress that in principle one can explicitly recover all Tand Y-functions in terms of P 1 , P 2 , P 3 , P 4 , µ 12 and their analytic continuations through the cuts. In the appendix A. 1 we wrote a simple Mathematica code which allows to automatize this procedure. So far our parameterization ensures all the nice properties of the right and of the left bands of T-hook, but can we guarantee the correct analytic properties of the upper band? Namely, we have to ensure that the analyticity strips of T are such as dictated by  (3. 5). In particular, it must be that T 2,1 has no cut on the real axis or, in other words, that the analytic continuation around the point u = 2g is trivial T 2,1 -T2,1 = 0. This can be written from  (3. 15)  as P [-2] 1 P [+2] 2 -P [+2] 1 P [-2] 2 µ 12 -μ12 -P1 P 2 + P2 P 1 = 0.
Acknowledgements We thank  M. Alfimov ,  J. Balog ,  A. Cavaglià ,  D. Fioravanti ,  I. Kostov ,  F. Levkovich-Maslyuk ,  V. Pestun ,  A. Sever ,  G. Sizov ,  R. Tateo  and  Z. Tsuboi  for discussions. The work of V. K. is supported by the  ANR  grant  StrongInt  ( BLANC-SIMI-4-2011 ) and by the  ESF  grant  HOLOGRAV-09-RNP-092. The research of N. G. , V. K. , and D. V. leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme   FP7/2007-2013/ under REA Grant Agreement No 317089. The research of V. K. leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement no. 320769 ). V. K. is also very grateful to  Yukawa Institute for Theoretical Physics (Kyoto)  for hospitality, where a part of this work was done.
It is similar to threshold the resulting image with a ThicknessMin threshold. Figure  7  shows a despeckle of document figure  6  with ThicknessMin=2. We choose to set HeightMin and WidthMin to the value 3 in order to keep and thin characters like 'I' or '1'. For printing documents, we set WidthMax and HeightMax to value 64 for 300dpi images, because characters cannot exceed this size. For ligatured manuscripts we set WidthMax and HeightMax to value 512 and 128 respectively, in order to shift correctly handwritten words in the image TextsImage. We keep the graphical information into a binary image for further analysis to localise frames and detect tables in future work. Tables or frames bordered by dot lines or dashed lines cannot be separated from the text image because the geodesic width or height are similar to the size of characters.
Global Performances We first illustrate the performances of the color automatic segmentation on a test image provided by the private company Janich&KlassComputertechnik GmbH which has developed the software DpuScan and its Advanced Color Document Processing (ACDP) tool, which is widely used in the industry and distributed worldwide. DpuScan is well-known to be the best tool to separate text colors in business documents. But it is achieved manually by selecting each color background and text color. Figure  11  shows that AColDPS find text color automatically and achieve a good segmentation of this image. We display all color layers of text in the same image with an artificial white background to save place. Figure  12  and 13 show the pertinence to use color information to segment text from complex pre-printed backgrounds.
Evaluation on the Database We have tested the proposed system on 529 color images of various invoices and forms in real situation. Among 529 images we manually found 4 images with some problems of segmentation. Among these errors, we found 2 images that present a change of text color, detected by the system, because of the ink bleed trough of the color background to the characters of the foreground (Figure15). 1 images show handwritten texts totally illegible because of the ink fading. 1 error is due to the printing of black characters crossing a large black frame. Most of errors can be explained by the quality of the document itself. We have achieved 99. 25 % of correctly segmented document.
These data were submitted to public archives, collected at The Roslin Institute and then passed on to the Ensembl team who used the information to help annotate the latest chicken genome assembly, Galgal4 as part of Ensembl release 71 (April 2013) (Table  3 ). This new annotation includes 15,495 protein coding genes, 1,049 micro RNAs, 456 non-codingRNAs and 42 pseudo genes. This gene_build is primarily concerned with coding genes, but there are many more non-coding genes which remain unannotated. Consortium members have analysed the RNAseq data for long non-coding RNAs (lncRNAs) [manuscript in preparation], snoRNAs  (Gardner et al. , 2014)  and other features of interest. Around 14,000 potential long non-coding RNA genes have thus far been identified from the RNAseq data. Ensembl release 71 marked a significant update in the annotation of the chicken genome with gene models based on experimental data.
com/ products/smrt-technology/smrt-sequencing-advantage/]. We currently have brain transcriptomic PacBio data generated from a female Brown Leghorn J-line chicken  (Blyth and Sang 1960 ). This will be analyzed alongside stranded RNAseq data that has been generated from 21 different tissues. The advantage of using strand-specific sequence information is that it provides an insight into antisense transcripts and their potential role in regulation and strand information of non-coding RNAs as well as aiding in accurately quantifying overlapping transcripts. It is particularly useful for finding unannotated genes and ncRNAs. This strategy should allow us to obtain full-length transcript sequences, identify novel transcripts and low-level transcripts, map transcription start and stop sites and confirm further ncRNAs.
Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract highdimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data.
We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain. org) and via github (https://github. com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www. nsgportal. org) through a convenient web-interface.
Introduction In biology, the notion that structure predicts function is widespread. In human neurosciences, different modalities image different structural aspects making their integration imperative to predict function  (Sporns, 2013). The Virtual Brain (TVB, thevirtualbrain. org) uses empirical structural and functional data to build full brain models of individual primates-consisting of interacting dynamic local models-that predict individual whole-brain activity on different scales  (Ritter et al. , 2013; Sanz-Leon et al. , 2013; Roy et al. , 2014; Woodman et al. , 2014). The interactions between neuronal populations in a full brain model are constrained by the anatomical fiber skeleton, i. e. , the structural connectome, obtained from diffusion-weighted magnetic resonance imaging (dwMRI) using tractography techniques.
g. ,  (Biswal et al. , 2010; Van Essen et al. , 2012). The Neuroscience Information Framework (http:// neuinfo. org/) lists over 2500 different databases with relevance for neuroscience. This high number of heterogeneous resources requires standardized and efficient processing routines in order to (i) extract interpretable and relevant information and to (ii) organize and integrate it in a systematic and unifying structure: "Perhaps the single biggest roadblock to higher order data mining is the lack of standardized frameworks for organizing neuroscience data"  (Akil et al. , 2011). We propose to go one step further: In order to get from pure data gathering to knowledge inference we need to connect functional and structural data by means of model-based integration  (Jirsa et al. , 2002 (Jirsa et al.
Up to now we pre-processed 50 full data sets using this pipeline. All data sets were stored in the TVB XNAT  (Marcus et al. , 2007)  database in Toronto where they are made available to the TVB consortium. Along with the processing steps, we illustrate the challenges posed when working with multimodal imaging data and integrating them in a single framework such as provided by TVB. These challenges range from storage requirements due to large amounts of data, interoperability and interfacing between different toolboxes and coordinate systems, fallacies of dwMRI tractography to outcome validation. Each of the imaging modalities serves different purposes during model generation and optimization within TVB: (i) High resolution T1-weighted MRI scans are used to obtain parcellations of cortical and subcortical white and gray matter (WM, GM) into subregions of interest based on anatomical landmarks and to construct anatomically constrained dipole source models for forward modeling and inverse source reconstruction of EEG and magnetoencephalography (MEG).
Resulting lead-field matrices and inversion kernels are used to map cortical activity to scalp locations of (M)EEG sensors and vice versa. Furthermore, high-resolution scalp/head, skull and cortex-surface triangulations are used for highly resolved surface simulations and output visualization. (ii) fMRI volumes are parcellated according to the high-resolution atlases derived from T1-weighted data yielding region-wise aggregated BOLD time-series and FC matrices generated from these are used to fit model output by means of parameter tuning. (iii) Diffusion-weighted MRI (dwMRI) data are parcellated according to the high-resolution atlases derived from T1-weighted data yielding estimated white matter fiber tracts and SC matrices. The parcellations are used for defining seed-and stop-locations during tractography.
54, p b. 01, 95% CI [0. 53, 0. 82])  (Zimmermann et al. , 2014) ). The software can be installed from online repositories (https://github. com/BrainModes/TVB-empirical-data-pipeline or thevirtualbrain. org) or directly used with HPC resources on the Neuroscience Gateway Portal (http://www. nsgportal. org) through a convenient web-interface  (Sivagnanam et al. , 2013).
Multimodal data acquisition In this study we analyze 49 resting state simultaneous EEG-fMRI data sets acquired at Berlin Center for Advanced Imaging, Charité University Medicine, Berlin, Germany (age ranged from 18 to 80 years, mean 41. 55 ± 18. 44; 30 females and 19 males) and one simultaneous EEG-fMRI data set under task conditions acquired at Baycrest Centre, Toronto, Canada (54 year old female). After EEG-fMRI acquisition, anatomical and diffusion weighted images of subjects were acquired. In order to characterize the test-retest robustness of the connectome estimation, an additional dataset of three subjects was acquired in Berlin consisting of anatomical and diffusion weighted scans. In this case, each subject was scanned three times; the first two scans were acquired in sequence without a break in between.
Pipeline workflow In the following, we demonstrate the individual steps of the processing pipeline on one example data set (Fig. 1  and supplementary Movie M1) and evaluate its outputs. The results provide insights on the meaning and interpretability of various measures used in previous studies, e. g. , metrics of structural or functional connectivity, and shall assist future users of the pipeline for selecting suitable outputs in the context of their study. A detailed overview of the pipeline workflow and the interaction of different software modules are shown in Supplementary Fig. 1. The average amount of processed and stored data generated by the pipeline is 89. 9 ± 15. 12 GB per subject, but depends on the properties of the data set (e. g. , resolution of dwMRI data) and used pipeline settings (e.