dataesr/scientific-paragraphs-categorization huggingface dataset:
The heterogeneity in Java Runtime Environment (JRE) versions is solved by OpenMOLE shipping with its own JRE (OpenJDK 1. 8. 0) to execute on remote machines. It has been built against a 2. 6. 32 Linux kernel in order to ensure it re-executes successfully on the largest possible range of Linux platforms. The execution time is only reported here as a marker of successful re-execution on the given platform. Multiple parameters can explain the variability from one environment to another, the most obvious being the different availability of the required resources. Table  4  reports the prediction scores resulting from running the pipeline on the first subject of the dataset. The prediction scores obtained are similar to those obtained in the tutorial for equivalent parameters (ex: C = l1, p = 50), down to the second decimal.
Several processing steps are required in order to recover the white matter tracts and consequently parcellate the brain from dMRI volumes. In  Parisot et al. (2015) , the data is processed using FSL's bedpostX and probtrackX  (Behrens et al. , 2007; Jbabdi et al. , 2012) , which estimate the fibres orientations at each voxel and perform probabilistic tractography respectively. Both methods are very time consuming. On high quality data such as the HCP database 18  , BedpostX takes approximately a week on CPU and 3 h on GPU, while ProbtrackX runs for approximately 30 h. In order to process a large group of subjects for group-wise analysis in a reasonable amount of time, it is necessary to use BedpostX's GPU-enabled version  (Hernández et al. , 2013)  and process the subjects in parallel. This workflow benefits from OpenMOLE's capacity of delegating different tasks of the pipeline to different computing environments.
Advanced Parameter Tuning Methods This third workflow performs parameter optimization for cortical surface registration. In this example, cortical surface 20 https://github. com/MIRTK/DrawEM/blob/c98022a5b78ee99bef5d329fc23f57f9 c15b1a5f/pipelines/neonatal-pipeline-v1. 1. sh. alignment is performed using the Multimodal Surface Matching tool (MSM)  (Robinson et al. , 2013) ; developed as part of the HCP to enable between subject alignment of multiple different types of cortical surface features (for example functional activations and cortical folding). Registration is optimized to maximize the ratio of feature similarity relative to surface warp distortions. Here, we study a simplified version of the parameter optimization. The workflow consists in optimizing the value of nine parameters of the MSM tool for a fixed pair of subjects.
CONCLUSION In this paper, we have shown the ability of the OpenMOLE scientific workflow engine to provide reproducible pipelines that can be shared and distributed on any Linux based environment. We have seen that OpenMOLE DSL provided a highlevel description of experiments that can be shared and reused by scientists on any platform with a JVM. The newly added CARETask offers a solution to ensure Linux-based application can be packaged and re-executed seamlessly on another Linux host without the need to obtain administrator privileges. This criterion was necessary to target HPC environments, a de-facto choice to distribute experiments in the scientific world. Extensions to the OpenMOLE DSL led to a fine integration of CARE in the framework. Archives only contain binaries and their dependencies, leaving the data to process to be injected in the archive's pseudo-filesystem at runtime from the dataflow.
Draw-EM 19 (Developing brain Region Annotation With Expectation-Maximization) is an open-source software for neonatal segmentation based on the algorithm proposed in Makropoulos et al. (2014). The algorithm performs atlas-based segmentation to divide the neonatal brain MRI into 87 regions. The different parts of the workflow are: • Data pre-processing. The original MRI is brain-extracted to remove non-brain tissue and corrected for intensity inhomogeneity. • Initial tissue segmentation. A spatio-temporal tissue atlas is registered to the brain MRI. The MRI is segmented into the different tissue types with an Expectation-Maximization scheme that combines an intensity model of the image with the tissue priors of the atlas. 19 https://github. com/MIRTK/DrawEM. • Structural atlas registration.
Design and Content ULTIR consists of images generated from experiments conducted in a controlled underwater setting. This section describes the contents of the repository, the experimental setup used to generate the imagery, and the web-interface where the imagery can be accessed. A schematic of the design and contents of the repository is shown in Figure  1. This figure presents the web-page that users will first encounter. From here they can find links to the imagery and metadata in the repository. The imagery is organized into three damage categories related to: cracks, surface damage, and 3D shape information. There is metadata associated with each image. It includes EXIF (Exchangeable Image File Format) information that reveals camera settings such as the shutter speed and ISO, along with concise contextual information about the specimens under consideration such as their size, shape, material, and damage description, where appropriate.
Application of Image based Techniques This section compares some of the main algorithmic approaches related to each category of ULTIR, namely; crack detection, surface damage detection, and 3D shape recovery. The purpose of this section is to characterize the performance of image algorithms under a given set of environmental conditions and to identify state-of-the-art methods. A number of algorithms are applied to the representative datasets shown in Figure  5 -7. The performance of submitted techniques are evaluated and ranked using Receiver Operating Characteristic (ROC) curves. The ROC curves offer a convenient way of characterising the performance of NDT methods under various environmental conditions  (Rouhan and Schoefs, 2003)  and have been expanded to image detection  (Pakrashi et al.
As this paper does not deal with risk analysis where the shape to the ROC acts as a key factor, only the delta, δ, parameter is required as a measure of performance. A low value for δ is indicative of a strong performance. While there has been recent spurts of interest around image processing and structural monitoring  (Jahanshahi et al. , 2009; Jahanshahi et al. , 2011; Zaurin and Catbas, 2011; Le Blanc et al. , 2013; Kohut et al. , 2013; Kim et al, 2013) , comprehensive databases have not been developed for such purposes and comprehensive studies have been seldom carried out despite the use of such techniques for a significantly longer period of time  (Patsias and Staszewskiy, 2002).
If a contextual relationship is an important factor for segmenting a particular image, than noncontextual techniques will have limited success compared to contextual techniques. These techniques are applied to the imagery in Figure  6  and the results are shown in Figure  13 , the performance levels are quantified in Table  3. ROC curves for each technique under the varying lighting and turbidity conditions are shown in Figure  14. These ROC curves are generated by applying the techniques to all of the specimens in the surface damage section of ULTIR multiple times, each time adjusting the considered technique's critical parameters, and comparing the resulting detected damaged region with the corresponding ground truth data in order to obtain a set of sensitivity and specificity pairs. The ROC curves for all of the specimens under a certain lighting and turbidity condition are then averaged to produce a curve representative of the overall performance of a particular technique at that lighting and turbidity condition.
The disparity relates how far each point is from the camera, i. e. its depth in the scene. The goal is to find a piecewise smooth horizontal disparity map consistent with the observed data which minimizes total energy. This method promotes a smooth disparity map as it penalizes cases where neighbouring pixels have different disparity values. The second method is based on the well-known Birchfield and Tomasi (1998) matching cost that is robust to image sampling. This method compares each pixel in the reference  Results show a marked variation across the range of specimens in ULTIR. Specimens, such as the rough concrete cube (specimen no. 1) and the aged metallic case (specimen no. 8), facilitate more reliable stereo matching due to the richly textured nature of these surfaces This is in contrast with specimens that have more uniform and homogeneous surfaces, such as the plastic cylinder (specimen no 15), whereby the lack of unique features is seen to inhibit the performance of the stereo algorithms.
Otsu's method performs the best in low turbidity conditions, while it is amongst the worst performers in high turbidity conditions. REMPS, on the other hand, remains a consistently good performer, with only minor performance deterioration with increasing turbidity. Additionally, the results reveal that in many cases high turbidity can be partially mitigated by having bright light source. Image based methods undoubtedly have value as a quantitative inspection tool, however, the performance of these methods are often hampered by the poor visibility and complex underwater light field. This large and well-annotated dataset paves the way for researchers to develop data-driven algorithms, such as deep learning techniques, that may be more robust to the underwater conditions. Such algorithms have already attracted significant interest in other fields owing to the high performances that they can achieve; however, they have not had an.
Driving measures To capture subtle changes in driving behavior between the three age groups, driving performance was evaluated using 18 specific driving measures (see Table  1 ). Driving is an inherently multifactorial task and variations in driving ability cannot necessarily be well understood based on any single measure or combination of different measures  [41]. Therefore, rather than exclude potentially interesting measures based on subjective criteria, we developed a methodology aimed at better capturing the nuanced driving behavior of our subjects while also reducing our dataset to the most pertinent measures. Thus, all the measures recorded by the driving simulator were initially involved in our analyses. As a preliminary step, we performed a bivariate correlation on these 18 measures aggregated across the three scenarios.
Mean speed km/h Average speed of all driving. For each data point, speed inferior to 10 km/h or recorded 300 m before and 100 m after an event were discarded from the averaging. 12 Hazard rating log (m/s/m) Log of "Speed at closest" divided by the minimum distance between the participant's vehicle and the object. If there is a crash, this is computed from the last data point prior to the crash.
A decrease in these abilities has been previously linked to crash risk (see Anstey et. al. [61]  for an in-depth review). While past research has focused on more isolated perceptual-cognitive factors (i. e. selective and divided attention, processing speed, useful field of view, etc. ), to date comparatively little research has made use of more integrative and dynamic tests like the NeuroTracker. If decreased perceptual-cognitive ability is indeed related to driving performance, then we should expect performance on the NeuroTracker to be associated with of our driving measures. We subsequently performed a multiple linear regression analysis on these data to model the extent to which these driving measures could be predicted by Neurotracker speed thresholds, age and mean driving speed. These latter two measures were included within the model as additional predictors to clarify the relative value of each component.
This research was supported by the  Societede l'assurance automobile Quebec/Fonds de recherche Societeet culture  ( 2012-OU-146156 ) (https://saaq. gouv. qc. ca/en/ & http://www. frqsc. gouv. qc. ca/en/), with continued work funded by  Natural Sciences and Engineering Research Council of Canada/Essilor  (http://www. nserc-crsng. gc. ca/index_eng. asp& www. essilor. ca)Industrial Research Chair awarded to JF ( IRCPJ305729-13 ). The funders provided support in the form of salaries for authors DB, GG and PH, but did not have any additional role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
It also provides insightful graphical outputs to improve interpretation and inspect each study during the integration process. We validated MINT in a subset of the MAQC project, which was carefully designed to enable assessment of unwanted systematic variation. We then combined microarray and RNA-seq experiments to classify samples from three human cell types (human Fibroblasts (Fib), human Embryonic Stem Cells (hESC) and human induced Pluripotent Stem Cells (hiPSC)) and from four classes of breast cancer (subtype Basal, HER2, Luminal A and Luminal B). We use these datasets to demonstrate the reproducibility of gene signatures identified by MINT.
mgPLS Multi-group PLS is an extension of the PLS framework we recently proposed to model grouped data  [22, 23] , which is relevant for our particular case where the groups represent independent studies. In mgPLS, the PLS-components of each group are constraint to be built based on the same loading vectors in X and Y. These global loading vectors thus allow the samples from each group or study to be projected in the same common space spanned by the PLS-components. We extended the original unsupervised approach to a supervised approach by using a dummy matrix Y as in PLS-DA to classify samples while modelling the group structure. For each dimension h = 1,. , H mgPLS-DA seeks to maximize max ||a h || 2 =||b h || 2 =1 M m=1 n m cov X (m) h a h , Y (m) h b h , ( 2 ) where.
Case studies We demonstrate the ability of MINT to identify the true positive genes on the MAQC project, then highlight the strong properties of our method to combine independent data sets in order to identify reproducible and predictive gene signatures on two other biological studies. The MicroArray quality control (MAQC) project. The extensive MAQC project focused on assessing microarray technologies reproducibility in a controlled environment  [5]. Two reference samples, RNA samples Universal Human Reference (UHR) and Human Brain Reference (HBR) and two mixtures of the original samples were considered. Technical replicates were obtained from three different array platforms -Illumina, AffyHuGene and AffyPrime-for each of the four biological samples A (100% UHR), B (100% HBR), C (75% UHR, 25% HBR) and D (25% UHR and 75% HBR).
Stem cells. We integrated 15 transcriptomics microarray datasets to classify three types of human cells: human Fibroblasts (Fib), human Embryonic Stem Cells (hESC) and human induced Pluripotent Stem Cells (hiPSC). As there exists a biological hierarchy among these three cell types, two sub-classification problems are of interest in our analysis, which we will address simultaneously with MINT. On the one hand, differences between pluripotent (hiPSC and hESC) and non-pluripotent cells (Fib) are well-characterised and are expected to contribute to the main biological variation. Our first level of analysis will therefore benchmark MINT against the gold standard in the field. On the other hand, hiPSC are genetically reprogrammed to behave like hESC and both cell types are commonly assumed to be alike.
However, differences have been reported in the literature  [26] [27] [28] , justifying the second and more challenging level of classification analysis between hiPSC and hESC. We used the cell type annotations of the 342 samples as provided by the authors of the 15 studies. The stem cell dataset provides an excellent showcase study to benchmark MINT against existing statistical methods to solve a rather ambitious classification problem. Each of the 15 studies was assigned to either a training or test set. Platforms uniquely represented were assigned to the training set and studies with only one sample in one class were assigned to the test set. Remaining studies were randomly assigned to training or test set. Eventually, the training set included eight datasets (210 samples) derived on five commercial platforms and the independent test set included the remaining seven datasets (132 samples) derived on three platforms (Table  1 ).
The pre-processed files were downloaded from the http://www. stemformatics. org collaborative platform  [29]. Each dataset was background corrected, log2 transformed, YuGene normalized and mapped from probes ID to Ensembl ID as previously described in  [11] , resulting in 13 313 unique Ensembl gene identifiers. In the case where datasets contained multiple probes for the same Ensembl ID gene, the highest expressed probe was chosen as the representative of that gene in that dataset. The choice of YuGene normalisation was motivated by the need to normalise each sample independently rather than as a part of a whole study (e. g. existing methods ComBat  [7] , quantile normalisation (RMA  [30] )), to effectively limit over-fitting during the CV evaluation process. Breast cancer. We combined whole-genome geneexpression data from two cohorts from the Molecular Taxonomy of Breast Cancer International Consortium project (METABRIC,  [31]  and of two cohorts from the Cancer Genome Atlas (TCGA,  [32] ) to classify the intrinsic subtypes Basal, HER2, Luminal A and Luminal B, as defined by the PAM50 signature  [20].
The METABRIC cohorts data were made available upon request, and were processed by  [31]. TCGA cohorts are gene-expression data from RNA-seq and microarray platforms. RNA-seq data were normalised using Expectation Maximisation (RSEM) and percentile-ranked gene-level transcription estimates. The microarray data were processed as described in  [32]. The training set consisted in three cohorts (TCGA RNA-seq and both METABRIC microarray studies), including the expression levels of 15 803 genes on 2 814 samples; the test set included the TCGA microarray cohort with 254 samples (Table  2 ). Two analyses were conducted, which either included or discarded the PAM50 genes from the data. The first analysis aimed at recovering the PAM50 genes used to classify the samples. The second analysis was performed on 15,755 genes and aimed at identifying an alternative signature to the PAM50.
2 , BER of 9. 8 and 7. 1 vs. 20. 8 and 11. 9), respectively. Finally, MINT was largely superior in terms of computational efficiency. The training step on the stem cell data which includes 210 samples and 13,313 was run in 1 s, compared to 8 s with the second best performing method ComBat+sPLS-DA (2013 MacNook Pro 2. 6 Ghz, 16 Gb memory). The popular method ComBat took 7. 1s to run, and sPLS-DA 0. 9s. The training step on the breast cancer data that includes 2817 samples and 15,755 genes was run in 37s for MINT and 71. 5s for ComBat(30. 8s)+sPLS-DA(40. 6s).
MINT seeks to identify a common projection space for all studies that is defined on a small subset of discriminative variables and that display an analogous discrimination of the samples across studies. Therefore, MINT provides sample plot and classification performance specific to each study (Fig. 3 ). Among the compared methods, MINT was found to be the fastest and most accurate method to integrate and classify data from different microarray and RNA-seq platforms. Integrative approaches such as MINT are essential when combining multiple studies of complex data to limit spurious conclusions from any downstream analysis. Current methods showed a high proportion of false positives (44% on MAQC data) and exhibited very poor prediction accuracy (PLS-DA, sPLS-DA and RF, Fig. 2 ). For instance, RF was ranked second only to MINT on the breast cancer learning set, but it was ranked as the worst method on the test set.
This reflects the absence of controlling for batch effects in these methods and supports the argument that assessing the presence of batch effects is a key preliminary step. Failure to do so, as shown in our study, can result in poor reproducibility of results in subsequent studies, and this would not be detected without an independent test set. We assessed the ability of MINT to identify relevant gene signatures that are reproducible and platformagnostic. MINT successfully integrated data from the MAQC project by selecting true positives genes that were also differentially expressed in each experiment. We also assessed MINT's capabilities analysing stem cells and breast cancer data. In these studies, MINT displayed the highest classification accuracy in the training sets and the highest prediction accuracy in the testing sets, when compared to sixteen sequential procedures (Fig.
The expertise and exhaustive screening required to homogeneously annotate samples hinders data integration, and because it is a process upstream to the statistical analysis, data integration approaches, including MINT, can not address it. A second issue in the general process of integrating datasets from different sources is data access and normalisation. As raw data are often not available, this results in integration of data sets that have each been normalised differently, as was the case with the breast cancer data in our study. Despite this limitation, MINT produced satisfactory results in that study. We were also able to overcome this issue in the stem cells data by using the stemformatics resource  [29]  where we had direct access to homogeneously pre-processed data (background correction, log2-and YuGene-transformed  [11] ).
Funding This project was partly funded by the  ARC   Discovery grant  project  DP130100777  and the  Australian Cancer Research Foundation for the Diamantina Individualised Oncology Care Centre at the University of Queensland Diamantina Institute (FR) , and the  National Health and Medical Research Council (NHMRC) Career Development fellowship APP1087415 (KALC). The funding bodies did not play a role in the design of the study and collection, analysis, and interpretation of data.
Availability of data and materials The MicroArray Quality Control (MAQC) project data are available from the Gene Expression Omnibus (GEO) -GSE56457. The stem cell raw data are available from GEO and the pre-processed data is available from the (http://www. stemformatics. org) platform. The breast cancer data were obtained from the Molecular Taxonomy of Breast Cancer International Consortium project (METABRIC,  [31] , upon request) and from the Cancer Genome Atlas (TCGA,  [32] ). The MINT R scripts and functions are publicly available in the mixOmics R package (https://cran. r-project. org/ package=mixOmics), with tutorials on http://www. mixOmics. org/mixMINT.
Apparatus The experiments were conducted on iMac computers, using a mouse on mouse pad. The program was developed using custom-built scripts running on Python. White Xs and Os were presented on a gray background. Each trial started with 10 stimuli of each type (targets vs. distractors). In unmasked trials, the background was gray (115/255 RGB scale). In masked trials, the noise mask consisted of a Gaussian-filtered patch of randomly distributed grayscale intensities. This was applied on top of the main game screen, except for the half gray horizontal bar and white box (i. e. , the mouse cursor), which were drawn on top of the mask. This enabled masking of the stimuli based on which participants decided what to do, while allowing participants to track their movements equally well in both masked and unmasked conditions.