dataesr/scientific-paragraphs-categorization huggingface dataset:
Results To describe the impact of P. ramosa on the photosynthetic performance of A. thaliana, CF parameters were compared: inoculated vs. non-inoculated plants of ecotypes Col-0 and Ler. CF parameters were measured or calculated at the dark-adapted state, during the light-adaptation, at the light-adapted steady-state and during the darkadaptation (Table  1 ). Finally, every measuring day, 55 images were recorded for each plant. Given our experimental design, our dataset reached 9900 image files and 9900 text files containing the histograms Plants of ecotypes Ler and Col-0 of A. thaliana non-inoculated or inoculated with P. ramosa were imaged for 13 CF parameters (55 transient measures). This table presents the symbols and the names of the measured and the calculated CF parameters ( § : n can be 25, 38, 50 or 62.
For example, F o was measured four times during the light adaptation at 25 s, 38 s, 50 s and 62 s). Significant differences between the global CF values of non-inoculated and inoculated plants of ecotype Col-0, non-inoculated and inoculated plants of ecotype Ler or non-inoculated plants of ecotype Col-0 and non-inoculated plants of ecotype Ler are indicated respectively in the fourth, fifth and sixth columns (*: Mann-Whitney U test, p-value < 0. 05, **: Mann-Whitney U test, p-value < 0. 01). associated to each image. Such a dataset is too big to be manually analyzed. Therefore, we aimed at developing a script that first, automatically sorts out the data to provide an easy overview of the data; second, facilitates the identification of CF parameters significantly impacted during infection of A.
Overview of the dataset by creating contact sheets of images and associated histograms The aim of such an overview of the data is to make the visual inspection of all the images and associated histograms easier. Therefore, the script sorts out the images and/or the histograms on three types of contact sheets (Figure  1A ). First, to control the quality of images, each image faces its associated histogram. One contact sheet per plant and per CF parameter is produced. Second, contact sheets are produced that enable the comparison of the images (or histograms) of plants having undergone different treatments. Thus, on these contact sheets, all the images (or histograms) from one treatment face those from another treatment. These sheets enable the rapid visual identification of CF parameter(s) potentially impacted by a treatment.
Most healthy control plants clustered together, whereas most inoculated plants formed a separate cluster (Additional file 4). Using this dataset as well, we could explain all the apparent mis-clustering events: inoculated leaves that clustered with non-inoculated ones corresponded to leaves for which inoculation failed. Moreover, non-inoculated leaves that clustered with inoculated ones corresponded to leaves that displayed obvious stresses or growth defects (Additional file 5). The second advantage using a bean image dataset is that we can quantify the amount of symptoms. We previously showed that the CF parameter F v /F m could actually be reliable to quantify the amounts of symptoms on bean leaves  [5]. Interestingly, in this dataset, the obtained dendrogram clustered images according to their respective amounts of symptoms (Additional file 5).
A web application available at http://www. phenoplant. org In order to make it available to the plant phenotyping community, the procedure described in the present paper was implemented in a web resource available at http://www. phenoplant. org. The web resource gathers two scripts aiming at making the analysis of fluorescence images easier. We also implemented a second script previously described aiming at the quantitative phenotyping of plant resistance to a biotic and abiotic stresses  [5]. The source files are available for download at http://www. phenoplant. org. Users may upload their datasets as zip files on the web site, and may select their preferences among the proposed options for the analysis. It was designed for images of individual leaves or plants and it accommodates images in TIFF format and histograms in text format.
Discussion We propose a web resource devoted to the image analysis of large CF image datasets. This resource was designed for CF image datasets but could probably be adapted also to other types of image dataset, if relevant. This resource requires grayscale images in TIFF format and their associated histograms. Datasets are uploaded at http://www. phenoplant. org as zip files. Users just have to follow a specific nomenclature for image files or folders that is explained in the user's guidelines. This application contains two types of image analysis procedures that answer two different questions. A previously described script aims at the quantitative phenotyping of plant resistance to biotic and abiotic stresses  [5]. Quantitative phenotyping is important in plant breeding in order, for example to screen accessions for resistance to pathogens as plant resistance to pathogens may either be qualitative or quantitative.
Thus, when a given CF parameter can be used as a descriptor of plant stress, the quantitative procedure implemented in this script enables the evaluation of the severity of the stress. In the present paper, a new script is described to provide three complementary approaches to get an overview of large image datasets, and to identify CF parameters that can be used as descriptors of plant stresses. Several other algorithms are described in the literature to perform specific image analyses such as the measuring of the area of leaves, plants or grains  [12] [13] [14] , the impact of pests and abiotic stresses  [5, 12, 13]  or the callose deposition  [15]. Free web-based applications such as Pheno-Phyte  [12]  are available to the plant phenotyping community to analyze plant images obtained by conventional imaging techniques.
However, to our knowledge, no software is providing an overview of large image datasets. Given the size expected for high-throughput phenotyping datasets, even simple procedures are problematic if not automated. In our view, a simple algorithm that sorts out the images to provide relevant contact sheets will help the user to get acquainted with his data. Therefore, the first output by the script described in the present paper produces contact sheets of images or histograms meant to visually compare plants having undergone different treatments. The second output of the script aims at the identification of transient measures that significantly discriminate between two groups of plants (i. e. Col-0 vs. Ler plants, inoculated vs. non-inoculated plants, healthy vs. stressed plants). Most CF parameter measures could significantly discriminate between ecotypes Col-0 and Ler (Table  1 ).
Here, we proposed to discriminate between treatments following the assumption that the distance between the histograms of plants under different treatments is higher than between plants under the same treatment. A similar clustering approach was recently described for the automated sorting of fluorescence microscopy images  [48]. Using this procedure, we could retrieve images of plants giving false negative or false positive results, i. e. inoculated plants for which inoculation failed or control plants that were stressed in an uncontrolled manner, respectively. Experiments conducted on the control dataset of bean images suggest that such a clustering approach may allow a grouping of images displaying similar stress intensity. In the present paper, such a classification procedure was based on images obtained from single measures of CF parameter.
Conclusions In this paper, we describe a web resource available at http://www. phenoplant. org, devoted to the image analysis of large CF image datasets. We propose two types of image analysis. A first analysis aims at the quantitative phenotyping of plant resistance to a stress, as described in  [5]. A second analysis, described in this paper, aims at providing an overview of large image datasets and at the identification of CF parameters impacted by a stress. On the one hand, CF parameters of interest are first identified based on a comparison of their global values (i. e. the mean value of the parameter over the whole imaged plant). On the other hand, CF parameters of interest may be identified using a clustering approach based on pairwise distances between images. In the latter, each pixel-value is taken into account, in an attempt to exploit maximum information in each image.
Comparison of the global values of the CF parameters For each image, the mean of the values of all the pixels is calculated to obtain the global value of the image. Then, for each treatment the mean of the global values is represented on radial plots using the R package ggplot2  [45]. Four radial plots are drawn separately to display parameters whose mean is less than 1, between 1 and 25, between 25 and 300, and superior to 300. Box plots describing the minimum and maximum values observed, the lower and upper quartile and the percentile are drawn using the R package ggplot2  [45]. A Mann-Whitney U test  [44]  is performed using the wilcox. test function implemented in R to statistically compare the global CF values observed for each treatment.
Clustering based on the histograms The script builds dendrograms in which each leaf represents a histogram. First, the distances between all the pairs of histograms were calculated from the Bhattacharyya coefficient  [45]  using the R package StatMatch  [56]. Second, a clustering step is performed according to six agglomeration methods, i. e. single linkage ("single"), complete linkage ("complete"), UPGMA ("average"), WPGMA ("mcquitty"), WPGMC ("median") and UPGMC ("centroid") using the function hclust of the R package stats  [57]. Finally, a dendrogram is calculated based on the clustering step, using the R package stats  [57]. The relevance of the dendrograms produced through the various agglomeration methods was tested in relation to either the treatment of the plants (i. e. inoculated vs.
non-inoculated), or according to their apparent health status. The apparent health had previously been estimated by experts after visual inspection of color photographs for each plant. For both treatment and apparent health, the rate of mis-clustered images was determined for each dendrogram obtained, using the various agglomeration methods. Technical limitations of the www. phenoplant. org web service are mentioned on the website, and may improve overtime. At the present date, data upload is limited to 2048 Mo (2 Go). The script is implemented on a 16Go RAM server. The dataset used in this paper gives a zip file of 114 Mo and the complete analysis run during 10 hours. The source files for the R scripts are published on the web site under CeCILL FREE SOFTWARE LICENSE AGREEMENT, and may be downloaded at the following URL: http://www.
Acknowledgements This work was supported by the project PHENOROB from the  SFR 4207 QUASAV. Céline Rousseau  was funded by the  SFR 4207 QUASAV  and the  University of Angers. We thank  Installations Expérimentales Mutualisées  ( INEM ) for plant cultures. We thank the  PHENOTIC Platform  for allowing image acquisitions. We thank  Philippe Simoneau  and  Jean-Pierre Renou  for their support to Phenoplant by the PHENOTIC platform. We also thank  C. Collomb-Boureau  for editing English.
Database search and data analysis The Mascot Daemon software (version 2. 3. 0, Matrix Science, London, UK) was used to perform database searches against Homo sapiens entries in Uniprot protein database. The mass tolerances in MS and MS/MS were set to 5 ppm and 0. 6 Da, respectively, and the instrument setting was specified as "ESI-Trap". Mascot results were parsed with the in-housedeveloped software Mascot File parsing and Quantification (MFPaQ) v4. 0. 0 software (http://mfpaq. sourceforge. net/), and protein hits were automatically validated if they satisfied one of the following criteria: identification with at least one top-ranking peptide with a Mascot score of more than 39 (P-value 0. 001) or at least two top-ranking peptides each with a Mascot score of more than 22 (P-value 0. 05).
When several proteins matched exactly the same set of peptides, only one member of the protein group was reported in the final list. To evaluate the false-positive rate in these experiments, all the initial database searches were performed using the "decoy" option of Mascot. Mascot results were parsed with the in-house-developed software MFPaQ v4. 0. 0 (http://mfpaq. sourceforge. net/), and protein hits were automatically validated.
Qualitative and quantitative reproducibility in protein harvesting Although NP-treated samples allowed a great increase in protein identification, providing access to low-abundant proteins, it was important to confirm the reproducibility of NPs for RBC protein harvesting. First, in a qualitative approach, we compared the MS spectra of each replicate and observed that the MS signal of 90%-94% of identified peptides was present in both replicates per type of NPs, thus confirming the qualitative reproducibility of NPs for protein harvesting (Figure  3A ). Then, to assess the quantitative reproducibility of NP-strategy, we used the MFPaQ software,  40  which was designed to parse and validate protein identifications from Mascot result files and quantify the identified proteins. The quantification module of MFPaQ v4.
Functional analysis of rBc proteins As NPs considerably increased the number of identified proteins in RBC samples and as the different surfaces harvested different subproteomes, we were interested in determining the functional pathways that were significantly represented by NP-harvested proteins in comparison with nontreated sample. In this aim, we submitted our data set to Ingenuity Pathway Analysis software  42  (Ingenuity Systems, Redwood City, CA, USA) to perform the functional characterization of the RBC proteome and identify the main molecular pathways and networks. This automatic annotation tool uses a knowledge database and assigns proteins to functional classes or specific canonical pathways (CPs) related to various biological processes. According to the functional analysis, the 893 proteins identified from the three different kinds of NPs had been associated to 140 different and significantly represented CPs and revealed 98 species related to hematological diseases; the 109 proteins from the crude extract only highlighted 40 different CPs and 31 proteins previously described in hematological diseases.
Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions.
Introduction As science becomes increasingly data driven, many scientists have adopted workflows as a means to specify and automate repetitive experiments that retrieve, integrate, and analyse datasets using distributed resources  [1]. Using a workflow, an experiment can be defined as a graph where the nodes represent analysis operations, which can be supplied locally or accessible remotely, and edges specify dependencies between the operations. The value of a workflow definition is not limited to its original author, or indeed to the original study for which it was created. Once specified, a workflow can be re-used or repurposed by other scientists. This reuse can be as a means of understanding an experimental process, replicating a previous experimental result, or even using the workflow as a building-block in the design of new workflow-based experiments.
Even in situations where the users were able to understand the overall analysis implemented by the workflow, it was difficult to determine what kind of data values to use as inputs to successfully execute that workflow. (iii) Volatile third-party resources. Many workflows could not be run because the third party resources they rely on were no longer available (e. g. , web services implementing their steps). For example, the SOAP web services provided by KEGG  1  to query its databases have been replaced by Rest Web Services. As a result, a large number of the workflows that use the SOAP services in myExperiment could not be run. (iv) Execution environment. In certain cases, the execution of the workflow required some specific software infrastructures to be installed locally, e. g. , the R statistical tool.
(3) High-level functional annotations to the steps of a workflow, using controlled vocabularies, in order to facilitate domain-specific level understanding about what each task aims to achieve. R 4. Changes/Evolutions of workflows, data, and environments should be trackable. According to our empirical study  [4] , volatility of third party resources accounts for 50% of the cause to workflow decay. Although changes of third party resources is not always under the author's control, mechanisms can be provided to remedy the issue. At the same time, attempting to re-run a workflow or reproduce in settings different from the original ones (like at a different time, on a different machine, using different datasets), is a common practice in scientific research. Therefore, we must provide support for users to deal with and document changes and subsequently trace through changes, so that users can (1) retrieve the original version of input data and environment setting in order to reproduce/verify the original results; (2) retrieve the different parameter configurations used to generate the different versions of outputs; and (3) identify the different changes made to the workflow specification in the process of experimenting with alternative/replacement services, different parameter settings, and etc.
The same file, database entry, or piece of data with an identifier, may be used in any number of experiments. It is the contextual information about the role it played that is required for understanding. Without this important contextual information when workflows are shipped from one lab to another we may lose the link between specific versions and configurations in the sea of trials-and-errors. Being able to share all these resources and auxiliary information about them (like provenance or annotations) as a single entity, and keep this relationship information within, is therefore fundamental. It is this need to not only aggregate content, but richly describe that aggregation that is the driving motivation for us to adopt the Research Object model for building our workflow-specific extensions.
Epigenetic alterations can be responsible for such transcriptional abnormalities. Linking changes in gene expression to epigenetic information might shed light on the disease aetiology. The team from HG-LUMC analysed HD gene expression data from three different brain regions that they integrated with publicly available epigenetic data to test for overlaps between differentially expressed genes in HD and these epigenetic datasets. The epigenetic datasets considered in this analysis were CpG islands and chromatin marks. Epigenetic changes can switch genes on and off and control which genes are transcribed. Therefore, they are suspected to be implicated in various diseases. CpG islands and the selected chromatin marks are such areas on the genome where these changes can occur. CpG islands are areas of the genome with a high concentration of the CG dinucleotides that methylation occurs and if they are located near a gene promoter can affect the expression of that particular gene.
Methylated areas on the genome are responsible for turning a gene off. Chromatin marks are also playing an important role in gene transcription by making chromatin regions accessible or repressed. The genes that overlapped with each of those epigenetic datasets were interpreted and prioritised using a text mining method called concept profile matching  [28, 29]. For interpreting the gene lists they used annotations of Biological processes to enrich them and export those annotations that describe them the best. In addition to that, the gene list was further prioritised based on its relation with Huntingdon (HTT) (Fig. 1 ). Fig. 1  sketches the two main analyses that the scientists followed for gene interpretation, namely gene annotation and gene prioritisation. The ellipses in the figure represent data artifacts, rectangles represent analyses steps, and the edges specify the dataflow dependencies.
The workflow uses a local knowledge base that is mined from literature by a text mining tool  [30]  to enrich their knowledge about a set of input genes. Therefore, this workflow takes as an input a list of comma separated entrez gene identifiers, the database name that will be used to map the gene ids to the local concept profile identifiers (in this case of entrez gene ids, the database name should be ''EG''), a cut off parameter for the number of annotations to be obtained, and an identifier for the predefined concept set id that is used in the local database (in this case ''5'' is used, which stands for Biological processes). The workflow can be found on myExperiment http://www. myexperiment. org/workflows/3921. html.
Also included is a sketch that depicts the main steps of the overall investigation, specified using a graphical drawing tool. Specifications of workflows are provided in their native language, in this case the T2 flow language of Taverna. The workflow specifications are then automatically transformed into the RDF wfdesc format, supported by the Workflow Research Object. This format  can be used, for instance, for querying the workflows and retrieving information about their constituent steps using the SPARQL query language  [32]. The scientists upload files containing example inputs that can be used to feed the execution of the uploaded workflows, and specify which files can be used as an input for each workflow. These are then followed up with files containing the traces of workflow runs, obtained by executing the uploaded workflows.
Parameter values and process runs are connected to the workflow descriptions using the properties wfprov:describedByParameter and wfprov:describedByProcess respectively. All the processors are connected to the wfprov:WorkflowRun through the property of wfprov:wasPartOfWorkflowRun, so we can navigate easily through them. The wfprov RDF representation of the above example can be found in Appendix A. 2, and the complete RDF file is available online. 9   8 For simplification purposes we illustrate the encoding of a fragment of the workflow. 9 purl. org/net/jwsWfprov. Workflow descriptions and provenance can be used to enrich the description of the data produced by the workflow, to indicate the original datasets used by the workflow to produce the results, and the transformations that were applied to the data retrieved from the original data sources.
All queries can all be seen to address requirement R 0 by being predicated on the availability of additional data or metadata. In this section we evaluate the queries against the structured data and metadata captured in our HD case study Research Object. For each we present a description of each query, their translation into a SPARQL, and the results obtained by evaluating them. Query 1 Find the creator of the Workflow Research Object. This query is useful, e. g. , for the (re)-user of the workflow to identify the person to credit. The SPARQL  [32]  query that can be used for answering this query can be formulated as follows: The results obtained by evaluating the query can be found in Appendix A. 4. Specifically, the results point out that the Workflow Research Object was created by Eleni Mina.