dataesr/scientific-paragraphs-categorization huggingface dataset:
The group of more experienced engineers is considered as system S 1 and the group of young engineers is the system S 2. Technical sheets on crops from the Institute of Rural Economy and the Ministry of Agriculture have been used to collect data on cultivation techniques. The SyrAgri (system S 3 ) database has been populated by them. 2: the three system made recommandations about the cultures as Recommandation S 1 , Recommandation S 2 , Recommandation S 3. 3: The recommandations from step 2 are submitted to researchers from the Institute of Rural Economy in order to qualitatively evaluate them based on form shown in Appendix C. 4: Researchers evaluated the systems as recommandation S 1 , recommandation S 2 and recommandation S 3 evaluated. 5: The majority mention method has been used to interpret the results of researchers evaluation.
Interpretation This case study covers both a recommendation system and an assessment of the quality of recommendations in terms of good practice. In order to solve the challenges related to the lack of updated information on different crops, seasons, farming techniques, etc. We tested the system with 20 users who are divided into two categories of 10 users each and three categories of 5, 5 and 10 users respectively. These are 5 agricultural engineers with between 2 and 5 years of experience, 5 agricultural engineers with at least 35 years of experience and 10 researchers in agronomy. The 10 agricultural engineers divided into two groups of 5 provided data in response to the questions available in the question proposed in Appendix B. We considered the two sets of responses to be the recommendations of two systems S 1 and S 2.
5 g/L glucose-DMEN, 15% FCS, 0. 1 mM nonessential amino acids, 0. 1 mM beta-mercaptoethanol, 1 mM glutamine, 500 U/mL LIF, gentamicin), then passaged onto feeder-free 0. 2% gelatin-coated plates for at least two passages to remove feeder cells. For in vitro differentiation to neural precursor cells (NPCs), F1 ES cells were cultured in the same medium supplemented with 1 µM PD03259010 and 3 µM CHIR99021 ("2i" conditions) and without feeders. The cells were then cultured for six days with medium without LIF or 2i and with 10% FCS, and with 5 µM retinoic acid for the final four days, to generate embryoid bodies  (Bibel et al. , 2007)  Pre-Processing 4C Data for 4See All bait sequence (including and downstream of the primer sequence, up to but not including the GATC DpnII site) are trimmed by the demultiplexing Sabre tool (https://github.
Analysis and Plotting of 4C Data Sets by Other Methods Three 4C data sets (two replicates from ES cells; one replicate from in vitro differentiation of ES cells towards NPCs) were analyzed and plotted by 4Cseqpipe (van de Werken et al. , 2012), r3Cseq  (Thongjuea et al. , 2013) , fourSig  (Williams et al. , 2014) , FourCSeq  (Klein et al. , 2015) , 4C-ker  (Raviram et al. , 2016) , and peakC  (Geeven et al. , 2018) , using the default or recommended parameters given within the documentation accompanying the tools. 4See System Requirements 4See is a GUI written in R (version > = 3. 2), with the following packages (and their dependencies) additionally required, found on Bioconductor and/or CRAN: tcltk2, tkrplot, limma, caTools, rtracklayer. All scripts and test data are available under the terms of the GNU General Public License, version 3, on Github: https:// github.
Input Format 4See deals with a simple text format, which we term the "cis" format, entailing header with three tab-delimited fields (data set name, bait chromosome, bait coordinate) followed by a twocolumn table, denoting the coordinate of the mid-point of every restriction fragment found on the same chromosome as the bait, and its corresponding number of supporting sequence reads. The cis format is generated by a perl script, coord2frag. pl, provided with 4See, which maps the genomic coordinates of 4C sequencing results into their corresponding restriction fragments and then counts the number of reads for each fragment. The perl script accepts any non-headed text format for sequences, as long as a column for chromosome, coordinate, and strand can be specified. The restriction fragment information is provided by "frag" tables, headed four-column tables, giving a unique integer identifier, the chromosome, coordinate, and fragment length for each restriction fragment.
Annotations To put the 4C profiles into a wider biological context, 4See supports the inclusion of three different types of annotations: genes, linear epigenomic profiles (termed "tracks") and called interactions. Gene information is provided as a tab-delimited headed text file with the following fields: Name, Chr (with the prefix "chr"), Start, End, Strand (as "+" or "-"). When selected, the gene track is plotted in blue directly underneath the 4C profile. Only one gene track can be loaded at a time. Management of epigenomic profiles is more flexible. Any format supported by the import function of the rtracklayer package can be supported, but for running time efficiency we recommend loading bigWig files. As for the 4C profiles, the color and plotting level for each individual track can be altered by the user in an automatically loaded dialog box.
(A) The 4See dialog box for conditions settings automatically opens when cis files are first loaded, in this instance two ES replicates and one NPC 4C data set for the Sox2 SCR bait. The two ES replicates have been assigned different integers to be treated independently, and the NPC data set has been omitted by assigning it 0. (B) The resultant 4See plot from the conditions set in (A), whereby the two ES 4C replicates are quantile normalized and plotted separately, one in black, the other gray. The plot has normalized 4C signal as the y-axis and genomic coordinate of the interacting fragment as the x-axis. The position of the SCR bait is denoted by a black vertical line, and gene position (blue arrows) and the ES H3K27ac ChIP-seq profile (black) is shown underneath the 4C plot. The profiles are highly consistent between replicates, with a strong interaction peak centered on the Sox2 gene; note that both the gene and enhancer have a strong enrichment for H3K27ac.
(C) As for (A), but in this case the two ES replicates are given the same value to be averaged together, and the NPC data set is included as a different integer to the ES data sets. (D) As for (B), but with the settings conditions of (C), and the redundant gene and H3K27ac tracks omitted. The averaged ES 4C plot is given in black and the NPC 4C plot in red, showing a strong perturbation of the SCR-Sox2 interaction on differentiation. The main control panel of 4See, including options to set the x-axis ("start coordinate," "end coordinate," and "plot window") and y-axis ("max y plot") plot limits, to choose a bait name for the plot title ("bait name"), and to set the running mean window size ("smooth window") in number of restriction fragments. (B) 4See plots for a 4C data set in DP thymocytes with the Satb1 gene promoter as bait.
DISCUSSION Using novel and previously published data sets for demonstration, we have shown the flexibility and utility of 4See in exploring 4C data. With limited processing of sequencing results, and one line of code in the R prompt, a user-friendly windows-based interface is available for a broader community to explore chromatin interaction profiles. As a consequence, we envisage that 4See will be of great use to the chromatin field. The input cis files are not very large (~4 MB for mouse or human), so the browser can be run on most desktop computers and laptops. The major systems limitation comes from the importing of epigenomic tracks (which can be >500 MB) with the rtracklayer package, which is the slowest step and may overload some standalone computers if too many tracks are imported at once.
FUNDING This study was supported by funds from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Starting Grant  678624-CHROMTOPOLOGY ), the  ATIP-Avenir program , and the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. YBZ is supported by funds from  LabEX INRT, la Region Grand Est  and the  ERC. AP is supported by funds from  LabEX INRT , the  ERC , and the  Ligue Nationale Contre le Cancer. AM is supported by funds from  IDEX (University of Strasbourg ) and the  Institut National du Cancer. TS is supported by  INSERM.
DATA AVAILABILITY STATEMENT Raw 4C data and the processed cis files first described here are available on GEO (GSE137417); previously published 4C results  (Narendra et al. , 2015)  are available as data set GSE60240. ChIPseq tracks were obtained from GEO for mouse ES  (Shen et al. , 2012)  (CTCF and H3K27ac; GSE29218) and DP cells  (Vanhille et al. , 2015)  (H3K27ac; GSE63732).
In the information age, smart data modelling and data management can be carried out to address the wealth of data produced in scientific experiments. In this paper, we propose a semantic model for the statistical analysis of datasets by linear mixed models. We tie together disparate statistical concepts in an interdisciplinary context through the application of ontologies, in particular the Statistics Ontology (StatO), to produce FaIR data summaries. We hope to improve the general understanding of statistical modelling and thus contribute to a better description of the statistical conclusions from data analysis, allowing their efficient exploration and automated processing.
Introduction The amount of scientific data is rapidly increasing; hence, efficient approaches for processing and managing the data are required. The sharing of comprehensive data summaries that include adequate metadata is a way to knowingly address the data flood and to enable dataset discoverability and reuse. In the case of experimental data, information regarding the analytical objectives of the underlying scientific experiments and their statistical conclusions can advance the search for, preselection of and comprehension of the datasets by other researchers. Although the publication of raw scientific data is generally encouraged, there is still unexploited potential in sharing and reusing derived statistical information about scientific datasets. Typically, the conclusions from statistical analysis are not being published in any universal, computer-enabled form that allows automated processing.
We demonstrate the idea of sharing the statistical information of datasets in a FAIR (Findable, Accessible, Interoperable and Reusable)  1  way for the purpose of analysis documentation and data exploration, exemplified by statistical modelling. In the context of scientific experiments following a factorial design, in which quantitative variables are observed, one of the statistical methods frequently used to process experimental data is linear mixed model (LMM) fitting  2. The analysis executed with the use of the LMM is, in many cases, sufficient for statistical inference, as it allows us to examine the role and significance of the experimental factors and draw statistical conclusions about the variables of interest. Data analysis by the LMM approach is a way to uniformly process datasets from designed experiments so that the obtained parameters are interpretable and comparable.
The modelling outcomes constitute an informative summary of the experimental results and can be used as criteria for data discovery. Most precise information about a statistical model is conveyed by the algebraic formulations and -for the practical purposes of computation -by means of statistical programming languages used in packages such as R, SAS or Genstat. They convey direct, complete information about the model specification and allow one to access all parts of the analytical procedure and its results, as well as to rerun the analysis, provided the same computational environment is available (cf. 3  ). However, different approaches and terminologies for specifying the model within scripts, as well as various output formats from the statistical packages, make it difficult to share the model description directly between the tools and their users and to deliver the analysis conclusions to researchers.
For a more detailed description of the custom experiment and analysis models, the statistics ontology (STATO) (http://stato-ontology. org) can be used (as in, e. g. , a neuroimaging data model: http://nidm. nidash. org/specs/nidm-results). In this paper, we propose a semantic model for the results (or derived data) obtained from LMM analysis. Our hypothesis is that a structured model of the analysis can advance the exploration of experimental datasets by enabling automated processing of the modelling results. Moreover, a semantic model of the LMM, which provides a comprehensive view of the data analysis and its results, can bridge the gap between statisticians, developers, data managers, and researchers and improve their mutual understanding of the experimental data and scientific conclusions.
Methods Motivation. Currently, data management policies increasingly support open science  10. They stimulate the sharing of scientific data in an accessible and interoperable way and emphasise the need to include adequate experimental metadata  11  to make the data findable and reusable. However, the focus is usually on raw data. Little attention is paid to the publication of statistically sound, well-documented and computationally enabled data summaries. Conclusions from scientific experiments obtained from data analyses are being published in papers mainly by means of textual descriptions, tables and figures, which do not allow for their automated processing. As a result, public datasets in searchable repositories can usually only be discovered by experimental metadata and not by conclusions from their analysis.
Our approach aspires to document the statistical analysis of a dataset in a FAIR way, which enables the analytical results (such as statistical tests or summary statistics) to be suitable for automated processing and fills in the gaps in scientific data publishing (Fig. 1 ). linear mixed modelling. Linear mixed modelling is a flexible approach that can be used to analyse data where some observed variables are explained by means of other coexisting variables. Different types of LMMs and the corresponding estimation methods are used in applied sciences. Here, we consider typical use cases of LMMs for analyses of the results of scientific experiments where a trait of interest (e. g. , crop yield or body weight) observed for a number of experimental units (samples or individuals, e. g. , plants or animals) is investigated in the context of several experimental factors (e.
There exist a few terminologies that address statistical terms. The STATO is a general ontology that aggregates rich statistical vocabulary with its textual and formal definitions and allows its classification. It relies on an upper-level ontology (the Basic Formal Ontology) frequently used in many fields of research, notably in the life sciences. It is also compatible with other ontologies that are part of the Open Biological and Biomedical Ontology (OBO) Foundry. Thus, by taking advantage of the general framework of the STATO to annotate the LMM entities, we can put the modelled knowledge in the broad interdisciplinary context. The proposed model aims to provide sufficient semantics for the elements of the LMM to be used by experimental data managers and domain experts to study the assumptions and conclusions from the statistical analysis of datasets.
However, it is not supposed to capture all computational or statistical technicalities. Our approach aims at an accurate compromise between the theoretical profoundness and usability of the solution for the needs of experimental data management and dataset discovery. If needed, it should be possible to extend our approach Fig. 1  Position of statistical analysis in the life cycle of scientific data. The green path corresponds to the contribution of our work to scientific data discovery. By storing computer-readable summaries of statistical analyses, the search for data according to the corresponding statistical conclusions is enabled, as well as the potential aggregation of results. with a more detailed and accurate projection of some concepts (e. g. , by further partitioning some processes into basic statistical operations and their components).
specify concepts missing from the ontologies in a local resource to extend the STATO, resulting in the STATO-LMM, 4. formalise the semantic model representation by mapping statistical concepts to ontology terms (62 entities reused from the STATO, 38 entities declared in the STATO-LMM), 5. request to extend the STATO with the missing concepts; continuously update the model whenever a term is added to the STATO with the new identifier (22 entities accepted at the point of preparing the manuscript), 6. validate the model against the requirements formulated through the initial questions, extend the scope where necessary and update the model with ontology terms accordingly. A summary of the modelling process is given in Supplementary File 2. To facilitate access to the set of classes proposed for the semantic model of the LMM, an ontology module was extracted from the STATO (using the Ontofox tool  29  which implements the MIREOT methodology  30  ) and imported to an extended STATO-LMM ontology so that all the necessary terms could be easily found.
In later sections of the paper, we give complete examples of the models for the LMM analyses. First, we focus on the declaration of a linear mixed model (Fig. 4 ). The model itself is represented by an instance of the STATO linear mixed model class. It can be denoted by a formula to provide its corresponding mathematical or software-specific notation for simple human readability. The explicit semantics for the model structure can be declared through its components: terms and design matrix. In our basic approach, which is driven by practical applications of the standard types of the models to the experimental datasets, a model is usually declared by specifying its terms (each one being part of the model), typically at least one fixed term, at least one random term, and one error term. A term is linked to its corresponding variables via the is about relationship, with one link per variable.
An interaction term is modelled by using multiple links to variables, while the intercept term has none. This approach corresponds to declaring a model by a descriptive equation, as used in statistical software suites (e. g. , dependentVariable = independentVariable + error). The model can also be denoted by a design matrix. In this paper, we consider design matrices of the binary form, sufficient for LMMs usually used for designed experiments; thus, the term design matrix does not appear explicitly in Fig. 3 , although we indicate its role in the definition of model effects. The design matrix allows for the consideration of more general designs and thus for the declaring and annotating of more model types than can be done through model terms. The design matrix class denotes the study design (which, by definition, encapsulates the specifications and protocols of the study at the origin of the modelled dataset), which declares the roles of the involved variables as study design dependent and independent variables.
The output of the test, a p-value, which is possibly transformed into a q-value or according to a family-wise error rate, characterises the null hypothesis and enables drawing a conclusion. In Fig. 6 , the model fitting procedure is presented from the processing perspective. The process of model fitting evaluates whether a model appropriately represents a dataset and assesses the quality of fit of the model. Primary processes constituting (via the has part relationship) the LMM analysis are model parameter estimation, confidence interval calculation, and statistical hypothesis testing. The main inputs and outputs of the operations are shown in the graph and modelled via has specified input/output relationships. Semantics of the multivariate linear mixed model. A semantic description of the multivariate LMM (as described in Supplementary File 1, model (  2 )) is an extension of the schema of the univariate mixed model (Fig.
However, we maintain the identity of separate dependent variables for the sake of accurate dataset and effect descriptions to avoid blurring the actual study design and to allow us to clearly identify the separate outputs. Broader context of the model. Having described the knowledge schema directly related to the LMM, we can analyse the place of modelling from the wider perspective provided by the STATO and more abstract, underlying ontologies: Ontology for Biomedical Investigations (OBI), Information Artifact Ontology (IAO) and Basic Formal Ontology (BFO). Figure  7  illustrates a schema showing how the LMM concepts are linked to other terms. An important IAO class is a data item, defined as an information content entity that is "intended to be a truthful statement about something". Several terms in the graph are data items: dataset, defined as an "aggregate of data items that have something in common", which in our case is the part of the study design variables containing the data points themselves; linear mixed model, being a subclass of a more general statistical model; design matrix; and Fig.
Results Application of the proposed semantic model to the LMM analysis of experimental data can be done by annotating individual elements of the analysis with the corresponding ontological concepts. All necessary terms (defined in either the extended STATO-LMM ontology, the core STATO ontology, or the underlying upper-level ontologies) are available through the STATO-LMM ontology module. As a result, the concepts are linked as in the presented semantic schema to form an RDF graph. This graph, serialised to a chosen format, can be uploaded to an RDF triple store and used in search and data discovery. To facilitate the process of creating RDF graphs, we provide an R package semLMM  33  that allows us to automatically build a semantic model from the LMM analysis performed in the R statistical software (in the lme4 and nlme packages).
Exemplary analyses. Below, we briefly introduce exemplary LMM analyses of scientific datasets of different complexity to be modelled. Their detailed descriptions can be found in Supplementary File 1. The corresponding semantic models are given in Additional Examples  34  and can also be explored as RDF triples or as visual graphs in an online database (a proof-of-concept GraphDB triple store) at http://lmm. cropnet. pl. The purpose of these examples is to illustrate basic use cases typically seen in the LMM analyses of experimental datasets and thus to demonstrate the scope of semantic models to be used later in Exemplary Queries for data search and discovery. Example 1. Consider a simple dataset consisting of 6 observations obtained from an experiment performed for 3 different treatments in 2 blocks.
The analysis is done with the linear mixed model approach, denoted informally as: y Treatment Block Error = + + The block effects are assumed to be random uncorrelated variables having the same variance; likewise, the error term. Estimates of the model parameters (the effects for each treatment, the block and error variances, and the contrasts between treatments) and their significance are sought. Together with the description of the methods applied, they constitute a summary of the dataset. A graph presenting the basic elements of the semantic model of the LMM for this use case is shown in Supplementary File 3. Example 2. Consider an extension of Example 1 to a situation where the observation is repeated at four time points. Data analysis by a multivariate linear mixed model can be denoted as: =.