dataesr/scientific-paragraphs-categorization huggingface dataset:
Network-based approaches These approaches consider the whole connection patterns in the network. Historical approaches include classical clustering algorithms. The adjacency matrix can be used as a similarity one, or topological similarity between each couple of nodes can also be computed. Spectral clustering approaches  [59]  and hierarchical clustering approaches can then be used  [70]. Usually the number of clusters to be found should be provided as an input for the algorithm. Another drawback of spectral clustering is its high computation complexity which might be cubic on the size of the input dataset. Some distributed implementations of these approaches are proposed to provide efficient implementations  [85]. More popular network-based approaches are those based on optimizing a quality metric of graph partition.
Nodes represent dolphins and edges represent frequent associations between dolphin pairs occurring more often than expected by chance. Analysis of the data revealed two main groups. American college football dataset This dataset contains the network of American football games  [23]. The 115 nodes represent teams and the edges represent games between 2 teams. The teams are divided into 12 groups containing around 8-12 teams each and games are more frequent between members of the same group. Also teams that are geographically close but belong to different groups are more likely to play one another than teams separated by a large distance. Therefore, in this dataset groups can be considered as known communities. American political books This is a political books copurchasing network. Nodes represent books about US politics sold by the online bookseller Amazon.
The configuration adopted for LICOD is the following: Centrality metric is betweenness centrality, Voting method is local Kemeny, σ = δ = 0. 9, and = 0. Table  2  gives obtained results on the four datasets. These results show that LICOD performs better than the other algorithms for both Zachary and US Politics networks. It also gives competitive results in the other two networks. This could be explained by the absence of leaders in these two networks, which makes the communities detection task more difficult. These results show also that the modularity metric does not correspond to the best decomposition into communities as measured by both NMI and ARI. For instance, the Louvain method obtains always the best modularity (even better than the modularity of the ground-truth decomposition), however, it is ranked not first according to NMI.
Data clustering-driven evaluation We propose here to use the task of data clustering to apply a task-driven evaluation of community detection algorithms. The basic idea is to transform a data clustering problem into a community detection one. Some earlier work has already applied community detection algorithms to the clustering task  [17]. Figure  6  illustrates the overall approach. First, a relative neighborhood graph (RNG), as defined in  [84] , is constructed over the set of items to cluster. The choice of RNG graph is motivated by the topological characteristics of these graphs that are connexe and sparse. To build an RNG graph, we first compute a similarity matrix between couple of items in the dataset (Fig. 7 ). This results in a symmetric square matrix of size n × n where n is the number of items in the dataset.
A RNG graph is defined by the following simple construction rule: two points x i and x j are connected by an edge if they satisfy the following property: d(x i , x j ) ≤ max l {d(x i , x l ), d(x j , x l )}, ∀l = i, j (8) where d(x i , x j ) is the distance function. A community detection algorithm is applied on the obtained graph to cluster the distance dist euc (x, y) = n i=1 |x i -y i | 2 Cosine similarity dist cos (x, y) = 1 -x. y |x||y| Chebyshev distance d cheb (x, y) = max i (x i -y i ) given examples. Clustering evaluation criteria a-can then be used to compare different algorithms. We have tested our approach on five classical datasets publicly available from UCI website. 1 The selected datasets are briefly described in Table  3. We have constructed the different RNG graphs on these datasets using the following classical distance cited in Table  4.
We present composite spectra constructed from a sample of 242,150 Lyman-α (Lyα) forest absorbers at redshifts 2. 4 < z < 3. 1 identified in quasar spectra from the Baryon Oscillation Spectroscopic Survey (BOSS) as part of Data Release 9 of the Sloan Digital Sky Survey III. We select forest absorbers by their flux in bins 138 km s -1 wide (approximately the size of the BOSS resolution element). We split these absorbers into five samples spanning the range of flux -0. 05 F < 0. 45. Tests on a smaller set of high-resolution spectra show that our three strongest absorption samples would probe circumgalactic regions (projected separation < 300 proper kpc and |∆v| < 300 km s -1 ) in about 60% of cases for very high signal-to-noise ratio. Within this subset, weakening Lyα absorption is associated with decreasing purity of circumgalactic selection once BOSS noise is included.
g. Aguirre et al. 2001; Oppenheimer & Davé 2006; Schaye et al. 2010; Barai et al. 2013). The pattern of elemental abundances present may also reveal the stellar populations that give rise to them. The abundance of individual ionization species of these elements allows investigation of the extragalactic UV background (due to the impact of photoionization), and the density and temperature of the gas (due to collisional ionization and recombination). A large number of metal species are required to measure all of these effects and break degeneracies between them  (Aguirre et al. 2008; Pieri et al. 2010b). In this paper, we apply a novel analysis technique; we stack quasar spectra on the rest-frame locations of Lyα absorbers  (Pieri et al. 2010b, P10 hereafter)  in the enormous spectral database of the Baryon Oscillation Spectroscopic Survey (BOSS,  Dawson et al.
DATA The Baryon Oscillation Spectroscopic Survey is one of four spectroscopic surveys that make up SDSS-III  (Eisenstein et al. 2011 ), all conducted from the 2. 5-meter Sloan telescope  (Gunn et al. 2006)  at Apache Point Observatory. The BOSS instrument c x RAS, MNRAS 000, 1-19 Probing the Circumgalactic Medium at High-Redshift 3  (Smee et al. 2013 ) consists of two double spectrographs each with a blue channel and a red channel. We use the SDSS-III Data Release 9  (Ahn et al. 2012)  high-redshift quasar sample, selected using the techniques and data outlined in  Ross et al. (2012)  (see also  Bovy et al. 2011). The sample of high-redshift quasars was compiled by visual inspection of quasar candidates as outlined in  Pâris et al. (2012). The resolution of these spectra varies over R ≈ 1650 to R ≈ 2500 and is broadly lower (higher) at the low (high) wavelength end of both the blue and red channel of each BOSS spectrograph.
Production of simulated spectra The simulated Lyα spectra we use to study issues related to absorber selection are generated by computing the transmitted flux fraction F along random sightlines through an N-body+smoothed particle hydrodynamic (SPH) simulation in a 100 h -1 Mpc box with 2 × 576 3 particles (with equal numbers of gas and dark matter particles). The simulation was run with a version of the code GADGET-2  (Springel 2005)  with several modifications described by  Oppenheimer & Davé (2008) , including modeling of galactic outflows with a momentum-driven wind model. The cosmological parameters assumed for the simulation are consistent with results from the Five-Year Wilkinson Microwave Anisotropy Probe (WMAP5,  Hinshaw et al. 2009). For the analysis presented here, we use only the z = 2.
Simulation motivated selection Figure  2  provides an illustration of the typical spectral characteristics that form part of our selection. The bottom panel presents a portion of spectrum with simulation resolution (reproducing data with fully resolved lines) and no noise. The middle panel shows the same portion of simulated spectrum with BOSS resolution, our 2-pixel rebinning, and no noise. Comparison of these two panels confirms that low flux at high-resolution is not sufficient to produce low flux at BOSS resolution; a degree of blending is also required. The top panel displays the same spectrum as the middle panel with the addition of typical noise in our sample. Note that we treat each wavelength bin that falls into our target flux range as an absorber. As is clear in this figure, adjacent strongly absorbed wavelength bins arise from extended structure in velocity space, and not thermal wings of lines.
To address this expectation more directly in the context of our dataset, we have examined data from the VLT LBG survey  (Bielby et al. 2011; Crighton et al. 2011; Bielby et al. 2013 ) and a forthcoming VLT/FORS2 survey (N. Crighton et al. , in preparation) , which measure spectroscopic redshifts of BX-selected  (Adelberger et al. 2004 ) and LBG-selected galaxies in the fields of 13 bright quasars with high-resolution echelle spectra. We also include the 10 closest pairs in the  Rudie et al. (2012)  sample using high-resolution QSO spectra from the Keck archive. These bright quasars were selected for their high incidence of local LBGs. We convolve these high-resolution, high S/N quasar spectra with a Gaussian broadening profile to match BOSS resolution, and rebin them to a wavelength scale with bin width 128 km s -1.
Line list modelling We have compared our selection function to hydrodynamic simulations and observed sight-lines near LBGs. Here we complete the picture by exploring the range of column densities of systems selected. We achieve this by reconstructing model spectra of five quasars from the line list of  Kim et al. (2002) , which was compiled in order to investigate evolution in the line number density. This was achieved by generating a Voigt profile for each line using the quoted column density, b-parameter and redshift using the ESO MIDAS data reduction package. We produced high resolution mock spectrum of each quasar, smoothed them to reproduce BOSS resolution and rebinned them to our 138 km/s wavelength bins. This allowed us to reproduce our absorber selection function and explore c x RAS, MNRAS 000, 1-19 the HI column densities of lines that contributed to these absorbers, again in essentially noiseless spectra.
1, this is done in order to bound the range of viable integration paths lengths for each line in the composite spectrum. The central bin provides no absorption profile to fit, so we must assume a b-parameter. Since the central bin H I analysis provides plausible single line b-parameters, we take these values (25 km s -1 for -0. 05 F < 0. 05 and 15 km s -1 for all other absorption samples), which corresponds to the minimum integration scale that we can plausibly investigate in the context of metal lines that show clear clustering and/or complexes. For these central bin measurements, we construct a suite of models and take our column densities and their upper and lower error bars from the central bin fluxes and error estimates. We perform our full profile fits and construct our central bin models using both VPFIT and the ESO MIDAS data reduction package and find excellent agreement between them.
ACKNOWLEDGMENTS We would like to offer special thanks to the anonymous referee, whose constructive criticism led to significant improvement of this manuscript. We also thank  Romeel Davé ,  Neal Katz ,  Benjamin Oppenheimer  and  Mark Fardal  for the use of their SPH simulations and  Benjamin Oppenheimer  for the use of his CLOUDY look up tables. Also we thank  Todd Tripp  and  Joop Schaye  for their insightful comments on this work. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n [PIIF-GA-2011-301665]. MM was partially supported by  NASA  grant  NNX12AG71G  and  CCAPP at Ohio State. DW acknowledges support of  NSF  Grant  AST-1009505  and  NASA ATP  grant  NNX10AJ95G   Funding for SDSS-III  has been provided by the  Alfred P.
Background: Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. Results: jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams.
Venn diagrams with up to four lists are easy to read and understand but Venn diagrams with more than four lists, are much harder to interpret. To solve this problem, the Edwards-Venn  [2]  representation introduces new shapes providing a clearer view, shown in the example of Figure  1. Many Venn diagram software packages are already available. The first six lines of Table  1  present the main packages with their main features (maximum number of input lists, input data formats, Venn diagram layouts, application types and output formats). The table gives insight on several aspects of the Venn diagram production and highlights that, up to now, no web application handled up to six lists. VENNTURE  [3]  is the only application able to produce such diagrams but it only implements Edwards layout and runs only under MS-Windows OS, producing static MS-PowerPoint and MS-Excel files.
Proportional Venn diagrams can only display a very limited number of lists, three at maximum. The only feature available in other software which is not in jvenn is the proportional diagram. This is justified by the fact that jvenn was designed to display up to six lists and that proportional diagram is not suited to visualize more than three lists. Hereafter we introduce jvenn, a JavaScript library, developed as a jQuery plug-in  [9] , including many features easing diagram production and enhancing their readability. In particular, jvenn can handle up to 6 lists, is a dynamic tool and implements both proportional and Edwards layouts. The library has already been used and cited in two scientific publications  [10, 11]. It is already embedded in  different web applications such as nG6  [12] , RNAbrowse  [13]  and WallProtDB  [14].
Web application jvenn can also be directly used as a web application, which is available at http://bioinfo. genotoul. fr/jvenn/example. html (Figure  3 ). jvenn's web application performances depend on the client browser. Using the current version on a standard Linux computer (one cpu, four GB of RAM), it displays a six lists diagram of 10,000 identifiers in two seconds. [18]  have compared seven methods for normalization and search of differentially expressed genes in RNASeq data. This study is designed to provide a set of best practices to help biologists with their data processing. Table  2  of the cited article is the contingency table of the differentially expressed genes obtained from the seven methods, where counts in the table correspond to the intersection of two lists obtained from two different methods.
M. A. Dillies and colleagues The same analysis was performed with VENNTURE, the only other tool able to generate a six list Edwards Venn diagram. First, the software package was installed on a computer running under MS-Windows OS. The six gene lists were loaded in an MS-Excel spreadsheet and VENNTURE was run using the spreadsheet as input generating a static MS-PowerPoint file containing the diagram and a MS-Excel file with all the intersection contents.
Conclusion jvenn enables to compare up to six lists and updates the diagram automatically when modifying the lists content. Compared to VENNTURE it does not need any local installation of a new program and it gives access to a dynamic diagram providing simple tools to extract gene lists and perform searches. jvenn's statistics charts give a simple and quick overview of the sizes of the different lists and of their overlaps. It permits to compare different Venn diagrams. These features are not available in the VENNTURE software package. The running version of jvenn accessible at http://bioinfo. genotoul. fr/jvenn/example. html. This one allows the user to set all jvenn main features. The layout can be changed from classical to Edwards, a search box is accessible, the switch button panel and the exporting button are available and the statistical charts are displayed.
On the right of the page, each of the six textareas can be filled with a list of elements (one per line). If the same item is given multiple times, this one will be considered as unique. The list labels can also be customized using the text field on the top of each textarea. For biologists using different techniques in their experiment or in their statistical analysis, jvenn enables to quickly extract the shared identifiers. When comparing different methods applied to extract differentially expressed genes, these features ease the analysis. Thanks to its numerous features, dynamic behavior and graphical layout quality, jvenn can be efficiently used in many cases to compare different sets of results and easily extract shared elements. Being a simple JavaScript plug-in allows developers to embed it in any web environment.
© 2014Bardou et  al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
The rise of personalized medicine and the availability of high-throughput molecular analyses in the context of clinical care have increased the need for adequate tools for translational researchers to manage and explore these data. We reviewed the biomedical literature for translational platforms allowing the management and exploration of clinical and omics data, and identified seven platforms: BRISK, caTRIP, cBio Cancer Portal, G-DOC, iCOD, iDASH and tranSMART. We analyzed these platforms along seven major axes. (  1 ) The community axis regrouped information regarding initiators and funders of the project, as well as availability status and references. (  2 ) We regrouped under the information content axis the nature of the clinical and omics data handled by each system. (3) The privacy management environment axis encompassed functionalities allowing control over data privacy.
Integrative genomics and systems biology, driven by this new knowledge and technologies, have greatly advanced our understanding of human diseases  [9]. For example, there has been new evidence regarding the metastatic colon cancer that mutations activating the KRAS gene abrogate the therapeutic effect of anti-Epidermal Growth Factor Receptor therapies (like Cetuximab-ERBITUX Õ , or Panitumumab-VECTIBIX)  [10] [11] [12]. A considerable and growing amount of omics data is generated by these high-throughput technologies, covering a broad spectrum of domains. These omics data need to be considered in the context of the cellular processes to achieve their full potential. With >1500 different biomolecular-related databases listed in the latest Nucleic Acids Research database issue  [13] , researchers need tools to find the relevant information in the maze of biological data available.
Among them, translational research platforms, able to integrate large data sets of clinical information with omics data, are now actively being developed. Such translational research platforms should be able to blend in researchers' workflow for an optimal use. Therefore, they should provide (i) the storage and integration of clinical and omics data; (ii) an analysis framework, enabling scientists to explore their data and generate hypotheses; and (iii) additional information cross-referenced from external databases (e. g. link to a specific gene description in published literature or public databases). In this review, we focused on the main storage, integration and analysis platforms for translational research. Our goal is to provide translational researchers with background knowledge to approach the main translational research platforms currently available.
MATERIALS AND METHODS We used PubMed Õ to explore the scientific literature and subsequently identified 2359 articles potentially describing translational platforms (PubMed Õ queries are available in Appendix 1). We manually reviewed the articles to identify systems (i) enabling the integration of private clinical and omics data and (ii) providing researchers with data analysis functionalities. Analysis of the accessed publications was completed by Google Õ search and analysis of the paper references to search for other possible candidate solutions. Seven of the main translational research platforms were included in the review: BRISK  [23] , caTRIP  [24] , cBio Cancer Genomics Portal for Cancer Genomics  [25] , Georgetown Database of Cancer (G-DOC)  [26] , integrated clinical omics database (iCOD)  [27] , integrating data for analysis, anonymization and sharing (iDASH)  [28]  and tranSMART  [29].
Overview of translational platforms BRISK: Biology-Related Information Storage Kit (2011) The Biology-Related Information Storage Kit (BRISK) is a package of three open-source webbased applications providing a cohesive data integration and management platform. It was initially developed to provide a data-sharing solution for researchers in the AllerGen (The Allergy, Genes and Environment Network) consortium (http://www. allergen-nce. ca). BRISK can handle clinical phenotype description and somatic mutation (singlenucleotide polymorphisms) information. It provides researchers with genome-wide association studies (GWAS) analysis capabilities. This solution also includes a laboratory-oriented application managing physical sample, subject and container data.
caTRIP (2006) The caTRIP platform was developed as a component of the caBIG project in the early 2000s to allow users to query across the caBIG grid. The caBIG was a U. S. National Cancer Institute program. Its goal was to develop an open-source network across the United States for secure exchanges on cancer research. The goals of caTRIP include allowing physicians to find patients with similar profiles, analyze their outcomes and find information about successful treatments across the caBIG data grid. The system interoperates with several caBIG applications, including the Tumor Registry, a clinical system used to collect data; the cancer Text Information Extraction System, a natural language processing (NLP) tool designed to extract clinical knowledge from surgical pathology free-text report using controlled terminologies; the caTissue CORE, a tissue bank repository; the Cancer Annotation Engine and the caIntegrator, a tool for storing, querying and analyzing data.