dataesr/scientific-paragraphs-categorization huggingface dataset:
Other tables are automatically updated during the Pixel Web App data analysis life cycle (see Fig. 1C ). An enlarged version of this picture together with full documentation is available online https://github. com/Candihub/pixel/blob/master/docs/pixel-db. pdf. Full-size DOI: 10. 7717/peerj. 6623/fig- 3  which the researcher is working on: (i) the studied organism and its genetic background (Species and Strain, e. g. , Candida glabrata and ATCC2001), (ii) the type of monitored cellular components (Omics Unit Type, e. g. , mRNA, protein) and (iii) the nature of the experimental HT technology (Omics Area, e. g. , RNA sequencing, mass spectrometry). All Omics Units must be declared in the Pixel Web App before new Pixel Set submission. They must be defined with a short description and a link to a reference database.
Storage of Pixel Sets in the database Import of new Pixel Sets in the Pixel Web App requires the user to follow a workflow for data submission. It corresponds to six successive steps that are explained below (Fig. 4A ). 1. The ''Download'' step consists in downloading a template Excel file from the Pixel Web App (see Fig. 4B ). In this file, multiple-choice selections are proposed for ''Species'', ''Strain'', ''Omics Unit Type'' and ''Omics Area'' fields. These choices reflect what is currently available in the database and can be easily expanded. User must fill other annotation fields related to the ''Experiment'', ''Analysis'' and ''Pixeler'' information. The Excel file is next bundled into a ZIP archive with the secondary data file (in tab-separated values format), the user notebook (R markdown (https://rmarkdown.
rstudio. com/) or Jupyter notebook (http://jupyter. org/) for instance) that contains the code used to produce the Pixel Sets from the secondary data file. 2. The ''Upload'' step consists in uploading the ZIP file in the Pixel Web App. 3. The step ''Meta'' consists in running an automatic check of the imported file integrity (md5sum checks are performed, Excel file version is verified, etc. ). Note that no information is imported in the database at this stage, but a careful inspection of all Omics Units listed in the submitted Pixel Sets is done. This is why Omics Units need to be pre-registered in the Pixel Web App (see previous section). 4. In ''Annotation'' step, the annotations of Pixel Sets found in the Excel file (see Fig. 4C ) are controlled and validated by the user. 5. Next, the ''Tags'' step is optional.
It gives the opportunity to the user to add tags to the new Pixel Sets (see Fig. 4C ), that could be helpful for further Pixel Set explorations (see next section). 6. The final step ''Import archive'' consists in importing all Pixel Sets in the database, together with annotations and tags. Note that the procedure of importing metadata as an Excel file has been inspired from the import procedure widely used in GEO  (Clough & Barrett, 2016).
Exploration of Pixel Sets The Pixel Web App aims to help researchers to mine and integrate multiple Pixel Sets stored in the system. We developed a dedicated web interface to explore all the Pixel Sets stored in a particular Pixel instance (see Fig. 5 ). The upper part named ''Selection'' lists a group of Pixel Sets selected by the user for further explorations (Fig. 5A ). The middle part named ''Filters'' lists the Pixel database contents regarding the Species, Omics Unit Types, Omics Areas and Tags annotation fields. The user can select information (Candida glabrata and modified pH here), search and filter the Pixel Sets stored in the database (Fig. 5B ). The lower part is a more flexible search field in which keywords can be type. These keywords are searched in the Analysis and Experiment detailed description fields as illustrated here with LIMMA.
, 2009; Pfaller et al. , 2012). The genome of Candida glabrata has been published in 2004  (Dujon et al. , 2004). Its size is 12. 3 Mb with 13 chromosomes and is composed of ∼5,200 coding regions. Our research team is familiar with functional genomic studies in C. glabrata. In collaboration with experimental biologists, we published in the past ten years half dozen of articles, in which HT technologies were used  (Lelandais et al. , 2008; Goudot et al. , 2011; Merhej et al. , 2015; Merhej et al. , 2016; Thiébaut et al. , 2017). In our lab, the Pixel Web App is installed locally and store all the necessary genomics annotations to manage any multi-omics datasets in this species. As a case study, we decided to present how the Pixel Web App can be helpful to answer a specific biological question with only a few mouse clicks.
glabrata in several growth conditions, including pH shift experiments. The primary dataset (RNAseq fastq files) is available in the Gene Expression Omnibus  (Clough & Barrett, 2016)  under accession number GSE61606. The secondary dataset (log2 Fold Change values) is available in Table  S1  on the journal website (https://academic. oup. com/nar/article/43/3/1392/2411170). A first Pixel Set (labelled A) was created from this secondary dataset, annotated and imported into our Pixel Web App instance, following the procedure previously described. The associated ZIP archive is provided as Supplemental Information, along with all the details related to the experiment set up and the analysis. The Pixel Set A thus illustrates how publicly available data can be managed with the Pixel Web App. In our laboratory, we performed mass spectrometry experiments that also include pH shift (ZIP archive of the data is provided as Supplemental Information).
Secondary dataset issued from these experiments leads to the Pixel Set B. Pixel Sets A and B comprise 5,253 Pixels and 1,879 Pixels (Fig. 6 ). Transcriptomics (Pixel Set A) and proteomics (Pixel Set B) are interesting complementary multi-omics information that can be easily associated and compared with the Pixel Web App. In that respect, tags allowed to rapidly retrieve them using the web interface, applying the keywords ''Candida glabrata'' and ''alkaline pH'' (Fig. 6 , Step 1). As we wanted to limit the analysis to the C. glabrata genes potentially involved in the yeast pathogenesis, a filter could be used to only retain the Omics Units for which the keyword ''pathogenicity'' is written in their description field (see Fig. 6 , Step 2). As a result, a few numbers of Pixels were thus selected, respectively 17 in Pixel Set A and 6.
DISCUSSION In this article, we introduced the principle and the main functionalities of the Pixel Web App. With this application, our aim was to develop a tool to support on a daily basis, the biological data mining in our multi-omics research projects. It is our experience that research studies in which HT experimental strategies are applied, require much more time to analyse and interpret the data, than to experimentally generate the data. Testing multiple bioinformatics tools and statistical approaches is a critical step to fully understand the meaning of a biological dataset and in this context, the annotation, the storage and the ability to easily explore all results obtained in a laboratory can be the decisive steps to the success of the entire multi-omics project. The data modelling around which the Pixel Web App was developed has been conceived to find a compromise between a too detailed and precise description of the data (which could discourage the researchers from systematically using the application after each of their analyses) and a too short and approximate description of the data (which could prevent the perfect reproduction of the results by anyone).
(Skrzypek et al. , 2017)ive Omics Unit information as described in the Candida Genome Database(Skrzypek et al. , 2017). All the description fields comprise the keyword ''pathogenesis'' (in bold). LogFC values measured in transcriptomic (Pixel Set A) and proteomic (Pixel Set B) experiments are shown in the third and fourth columns. Quality scores (QS) are following logFC values. They are p-values coming from the differential analysis of logFC replicates. The entire table of multipixel sets is available in Supplementary Data. The last step consists in combining the mRNA and protein information (see Fig. 6, Step 3). For that a table comprising the multi-pixel sets can be automatically generated and easily exported. We present Table1: five genes for which logFC values were obtained both at the mRNA and the protein levels, and for which statistical p-values were significant (<0.
05). Notably two genes (CAGL0I02970g and CAGL0L08448g, lines 3 and 5 in Table1) exhibited opposite logFC values, i. e. , induction was observed at the mRNA level whereas repression was observed at the protein levels. Such observations can arise from post-translational regulation processes or from possible experimental noise, which could explain approximative mRNA or protein quantifications. In both cases, further experimental investigations are required. The three other genes (CAGL0F04807g, CAGL0F06457g and CAGL0I10516g, underlined in grey in Table1) exhibited multi-omics coherent results and significant inductions were observed at the mRNA and protein levels. Again, further experimental investigations are required to fully validated these observations. Still, it is worth noting that the gene CAGL0F04807g, is described as ''uncharacterized'' in the Candida Genome Database (http://www.
Data Availability The following information was supplied regarding data availability: Source code can be freely downloaded from the GitHub repository of the project: https://github. com/Candihub/pixel. The present version of Pixel (4. 0. 4) is also archived at Zenodo: Durand, William, Maupetit, Julien, Denecker, Thomas, Hébert, Charles, Poulain, Pierre, & Lelandais, Gaëlle. (2018, September 24). Pixel (v4. 0. 4): Integration of smart 'omics' data (Version 4. 0. 4). Zenodo. http://doi. org/10. 5281/zenodo. 1434316.
Again, further experimental investigations are required to fully validated these observations. Still, it is worth noting that the gene CAGL0F04807g, is described as ''uncharacterized'' in the Candida Genome Database (http://www. candidagenome. org/cgibin/locus. pl?locus=CAGL0F04807g&organism=C_glabrata_CBS138). Considering that logFC values for this gene are particularly high (>1), such an observation represents a good starting point to refine the functional annotation of this gene, clearly supporting the hypothesis that it has a role in the ability of C. glabrata to deal with varying pH situations. Omics unit Description A B A (QS) B (QS) 1. CAGL0F04807g Ortholog(s) have role in pathogenesis and cell surface, 1,09 1,81 2,23E-19 7,31E-05 hyphal cell wall, integral component of mitochondrial outer membrane, plasma membrane localization 2.
We mainly based these tools on interaction data analysis in order to provide the competence identification and recommendations, as well as the evaluation process. In this work, we are mainly interested the following scientific aspects expressed through these two research questions: How to design an automated system, based on interaction data, for competences recommendation and evaluation? The automated task here relates to collecting and processing data. This task is out of scope of this work. Our study is based on the prior existence of both competences repository and user information database. The proposed system is rather a decision-making tool that assists the user by helping them to cope with either the scarcity or the overload of information. We aimed to conceive and develop tools in order to provide competences recommendations, and to assess their relevance by means of interaction data such as user feedback.
Definition of competence and related models There are various definitions of "competence" in literature. In  [2] , a detailed summary of those definitions is provided. For our research needs, we choose the definition stated by Le Boterf, which defines a competent person as: "a person who knows how to act appropriately in a particular context, by choosing and mobilizing a double equipment: personal resources (knowledge, qualities, culture, emotions. ) and network resources (databases, documents, expertise networks,. ) "  [3]. [4] examined the competence concept and proposed a cognitive model for defining competence. This model aims to simplify its representation and to clarify the link between knowledge and competence. Later,  [5]  proposed a method based on fuzzy logic for competence characterization related to a work situation.
The platform is based on the PROXYMA (PROXY for Multiple Analyses) approach  [13]. In perspective, the designed approach aspire to propose, via Web data analysis, the recommendation of resources, such as documents or skills, and the appraisal of such recommendations through evaluation methods. Several studies have tackled the issue of using interaction data for social network analysis. In  [14] , a research was made on face-to-face communication network. The authors used sociometrics to measure offline interactions at the workplace in order to assess work performance and therefore improve group structuring. [15]  presented a research study based on email data analysis aiming to understand how social networks impact information worker productivity.
Proposed methods Most of competence recommendation systems mainly rely on text analysis (summary documents, databases), web browsing history analysis, or e-mail. Other types of activity traces can also be considered to identify experts within an organization such as activity logs (a tool/system use frequency indicator), or user interactions with professional social networks. These types of traces would allow user profile to be dynamically updated and associated competences identification to be improved. A survey was carried out among a list of companies to identify the tools usually used by DHR or managers to find competent people for leading projects or carrying out collaborative activities. The survey also aimed to find the gaps in the used tools and determine the needs of recruiters/ managers.
The competences repository is a database which differs from one company to another. This database is an input to our decision-making tool proposed for the automatic competence identification and their recommendation. When designing the platform for competences recommendation and evaluation, we chose a graphical representation for system recommendations. In fact, recommended competences are displayed as networks, which are "human-centered". All the nodes represent people, and the connections represent the characterized relationships (trust degree, collaboration degree), with a focus on one node called "ego". As a perspective, other representations could be considered depending on the intended utilization. For example, a "product-centric" representation could incorporate other elements into the graph which is centered on one or more products, such as the corresponding manufacturing processes, that are associated with competences profiles, themselves linked to people.
It will therefore update the competences repository used by the system. The second objective is to identify social links between users in order to generate community networks. For example, people interacting a lot with each other are likely to share a strong relationship. The third objective is to evaluate the use of the proposed tool, through user interactions, regarding its interest and relevance. The proposed tool is user-centered as shown in Fig. 1 , and has mainly two goals: providing the user with competence profile recommendations on specific request, and assessing the relevance of those recommendations. The usefulness or the addedvalue of such system is appraised through an evaluation process. The user-profile database consists of a set of information about each user such as user name, contact information, and list of skills.
Fig. 2  shows the main scenario: a user with a need for a given profile sends a request to the decision-making system. The system extract potential candidates via the user profile database. Former users' interactions and requesters' feedbacks are used to build this database. Our system is centered on a user-profile database. We retrieve information from the Web in order to feed and update this database. In order to estimate the use frequency of social networks among companies and identify the most used networks to retrieve information from them, we carried out field investigations [6] for this purpose. According to the results, the use rate of social networks is around 83% and LinkedIn is the most popular one. We classified extracted competences through web scraping process into two categories: user-declared and system-computed.
Model and experimental platform We have designed a platform which consists of different components (Fig. 3 ): a human-machine interface for the communication between users and the system, a web scraping tool, a competence repository and the graphical analysis tool Gephi. Human-computer interface By the means of an input/output window, the user interacts with the system through either a request or a feedback. The recommendation process, implemented in python, has a read-only access to the users' profiles database (SQL DB server) and produces two output text files. The firs one, named output filter file, is used to filter the community network in order to let the user visualize system recommendations instead of the whole network. The second file named [6] A questionnaire had been proposed to business incubators: the business incubator of Savoie Technolac and the incubator "Le Mixeur" in Saint-Etienne.
This investigation aimed to study how members of such organizations search for competences and by which means. output nodes file, enables the spatial apportionment of the recommended network nodes based on their social connectivity with the requester. A current study is focusing on developing the evaluation process, along with the recommendation process. This study would focus on user feedbacks analysis and metrics measurement by means of a set of tools. The user should visualize interaction data-based indicators obtained from this process via the same input/output interface. Web scraping of social networks Further information retrieved from the web such as social networks can help in updating the users' profiles database. LinkedIn have been targeted for this study. A python parser was developed to this purpose, in order to get users' LinkedIn profiles and collect useful information like declared skills or achieved projects.
Competences repository The proposed system was designed to be generic. To generate the associated social network with skills in the studied field, the system should be able to process any data set describing users and field-related competences. For a first use of the system, such a data set should be stored in a database beforehand. Gephi analysis tool For a better spatial apportionment of the network nodes, we applied spatial layout algorithms. In order to offer to the user a more harmonious display in a Flat Design mode, but also the ability of interacting with the system and looking over the network, the graphical visualization of computed recommendations delivered by the system was therefore exported in html version. The two files output filter and output nodes are used as input data for Gephi.
Dataset and experimental context We have suggested using interaction data analysis to identify competences and to combine it with social network data to generate profile's recommendations. We carried out two different experiments aiming to illustrate and test this proposal with real data. In  [25] , we presented and described an experiment implemented with students of the Internet and multimedia department at the university. In this paper, we describe and discuss the results obtained with an experiment carried out at SYMME [8] laboratory. The main idea was to automatically collect all the scientific publications of each member of the laboratory from Google Scholar. The aim of this experiment was to analyze publications in order to characterize the relationships between researchers and to create the laboratory's network.
This social network was built with collaboration degrees between members. The collaboration degree is the number of common publications between two members. An algorithm has been developed to automatically calculate this degree from the publications database. An extract of this database is shown in Fig. 5. Fig. 6  exposes a graphical visualization of the studied social network in Gephi analysis tool. Gephi [9] is a free open source interactive software for networks visualization based on graphical elements such as nodes, edges or hubs. This exploratory data analysis software is used in visual analytic research field. Gephi expects CSV files as input, where data represent network's matrix structure (list of edges with their weight).
, 2016; Markram et al. , 2015; Potjans and Diesmann, 2014; Traub et al. , 2005)  that enable investigation of the mechanisms underlying circuit function. Biologically detailed circuit models are necessarily complex and typically have a large number of parameters. Experimental measurements from connectomics  (Helmstaedter et al. , 2013; Kasthuri et al. , 2015) , functional activity mapping  (Ahrens et al. , 2013) , and multi-cell and automated patch clamping  (Annecchino et al. , 2017; Guzman et al. , 2016) , together with datasets from large-scale brain initiatives  (Amunts et al. , 2016; Hawrylycz et al. , 2016; Insel et al. , 2013; Kandel et al. , 2013) , are providing an increasingly wide range of data to constrain such models, thereby improving their accuracy. But confidence in the predictions from biologically detailed models is currently limited by their complexity and their perceived lack of constraint.
Constructing well-constrained models of neurons and circuits from a raw dataset takes a considerable amount of time and skill despite well-established simulation tools  (Carnevale and Hines, 2006; Gewaltig and Diesmann, 2007; Goodman and Brette, 2008; Ray and Bhalla, 2008). Once built, the complexity of detailed models makes them difficult to modify for new scientific questions. Moreover, running large-scale circuit models often requires high-performance computing facilities, which may not be accessible to many end users and brings an additional layer of difficulty to setting up simulations and managing the resultant datasets. These technical barriers hinder access to the structural and functional properties of biologically detailed models, limiting scientific scrutiny and adoption of these powerful tools by the wider community.
Ensuring biologically detailed models are robust and error free is challenging given the length and complexity of their software implementations. Common errors include typos in equation definitions and parameter values, unit conversions, inconsistent use of temperature dependencies, and incorrect translation of reconstructed neuronal morphologies. In industry, open source software development is increasingly being used to create well-tested, modular software components and applications, which can be shared publicly using code development and collaboration platforms, such as GitHub  (Perkel, 2016). GitHub records changes in the code and allows multiple users to manage and recombine different versions, track issues, and flag stable versions of the code. Errors can be minimized by regularly testing each modular component with automated routines and then assembling them into larger structures.