dataesr/scientific-paragraphs-categorization huggingface dataset:
She also thanks the computer resources provided by BSC/RES (Spain) and the Centro de Computación Cientifíca (UAM, Spain). BF acknowledges support from the Gordon and Betty Moore Foundation and the STFC grants ST/H002774/1 and ST/K0090X/1. JH acknowledges a fellowship from the European Commissions Framework Programme 7, through the Marie Curie Initial Training Network CosmoComp (PITN-GA-2009-238356), too. SIM acknowledges the support of the STFC Studentship Enhancement Programme (STEP). SP also acknowledges a fellowship from the European Commission's Framework Programme 7, through the Marie Curie Initial Training Network CosmoComp (PITN-GA-2009-238356) as well as from the PRIN-INAF09 project "Towards an Italian Network for Computational Cosmology". SP and VQ thank partial financial support from Spanish MINECO (grant AYA2010-21322-C03-01).
Data For instance, HBT, a halo tracking finder, needs to analyse multiple outputs to produce results (even though only the time for the z = 0 analysis is reported here) and VOBOZ timings are dependent on the size of the surrounding region chosen. This included the entire simulation in the A-5 case, but subregions were analyzed in other cases. MENDIETA also returned timing information, but only after the workshop (i. e. not run on the same machine as the other codes) stating that A-5 (A-4) took 328 (25844) seconds on a Xeon E5520 cpu using only one core. Aquarius A-4 contains roughly 8 times more particles than Aquarius A-5, and all the codes returning data appear to scale relatively well. The take home message is simple: for a wide variety of theoretical approaches it is possible to extract a good subhalo catalogue from the Aquarius A-5 dataset in a few minutes, whereas for the A-4 dataset this process takes of order an hour even on a single processor.
C2 ADAPTAHOP The code ADAPTAHOP is described in Appendix A of  Aubert et al. (2004). The first step is to compute an SPH density for each particle from the 20 closest neighbours. Isolated haloes are then described as groups of particles above a density threshold ρt, where this pa- rameter is set to 80, which closely matches results of a FOF group finder with parameter b = 0. 2. To identify subhaloes within those groups, local density maxima and saddle points are detected. Then, by increasing the density threshold, it is a simple matter to decompose haloes into nodes that are either density maxima, or groups of particles whose density is between two values of saddle points. A node structure tree is then created to detail the whole structure of the halo itself. Each leaf of this tree is a local density maximum and can be interpreted as a subhalo.
However, further post-processing is needed to define the halo structure tree, describing the host halo itself, its subhaloes and subhaloes within subhaloes. This part of the code is detailed in  Tweed et al. (2009) ; the halo structure tree is constructed so that the halo itself contains the most massive local maximum (Most massive Sub maxima Method: MSM). This method gives the best result for isolated snapshots, as used in this paper. In more detail, ADAPTAHOP needs a set of seven parameters. The first parameter is the number of neighbours nnei used with a kD-tree scheme in order to estimate the SPH density. Among these nnei neighbours, the n hop closest are used to sweep through the density field and detect both density maxima and saddle points. As previously mentioned, the parameter ρt sets the halo boundary.
05, n members = 20). It is important to understand that all nodes are treated as leaves and must comply with aforementioned criteria before being further decomposed into separate structures. As for defining haloes and subhaloes themselves, this is done by grouping linked lists of particles corresponding to different nodes and leaves from the node structure tree. Further, the halo and subhalo centres are defined as the position of the particle with the highest density and the velocity is the centre-of-mass velocity. The halo edge corresponds to the ρt density threshold, whereas the saddle points define the subhalo edge. Please note that ADAPTAHOP is a mere topological code that does not feature an unbinding procedure. For substructures (whose boundaries are chosen from the saddle point value) this may impact on the estimate of the mass as well as lead to contamination by host particles.
Once the refinement levels are set up, the algorithm applies the SO method independently at each of those levels. The parameters needed by the code are the following: i) the cosmological parameters when analysing cosmological simulations (given by the simulation itself), ii) the size of the coarse cells (determined by the ratio between the box size and the number of cells in the coarse level of refinement), the maximum number of refinement levels (N levels ), and the maximum number of patches (N patch ) for all levels in order to build up the AMR hierarchy of nested grids (these parameters, which only represent maximum values in order to avoid memory problems, are usually taken as N levels = 10 -20 and N patch = 10 5 -10 6 ), iii) the number of particles per cell in order to choose the cells to be refined (usually set to 3-4 particles per cell), and iv) the minimum number of particles in a halo (usually set to 10-20).
After this first step, the code naturally produces a tentative list of haloes of different sizes and masses. Moreover, a complete description of the substructure (haloes within haloes) is obtained by applying the same procedure on the different levels of refinement. A second step, not using the cells but the particles within each halo, makes a more accurate study of each of the previously identified haloes. These prospective haloes (subhaloes) may include particles which are not physically bound. In order to remove unbound particles, the local escape velocity is obtained at the position of each particle. To compute this velocity we integrate Poisson equation assuming spherical symmetry. If the velocity of a particle is higher than the escape velocity, the particle is assumed to be unbound and is therefore removed from the halo (subhalo) being considered.
C5 BDM The Bound Density Maxima (BDM) halo finder originally described in  Klypin & Holtzman (1997)  uses a spherical 3D overdensity algorithm to identify haloes and subhaloes. It starts by finding the local density at each individual particle position. This density is defined using a top-hat filter with a constant number of particles N filter , which typically is N filter = 20. The code finds all maxima of density, and for each maximum it finds a sphere containing a given overdensity mass M∆ = (4π/3)∆ρcrR 3 ∆ , where ρcr is the critical density and ∆ is the specified overdensity. For the identification of distinct haloes, the code uses the density maxima as halo centres; amongst overlapping sphere the code finds the one that has the deepest gravitational potential. Haloes are ranked by their (preliminary) size and their final radius and mass are derived by a procedure that guarantees smooth transition of properties of small haloes when they fall into a larger host) halo becoming subhaloes: this procedure either assigns R∆ or R dist as the radius for a currently infalling halo as its radius depending on the environmental conditions, where R dist measures the distance of the infalling halo to the surface of the soon-to-be host halo.
For the stellar object, the jump in the stellar mass profile is used as a first satellite detection (i. e. , location and velocity), that is later on refined by searching for maxima in 6-dimensional phase-space within an allowance region about that first center, returning the object stellar sizes rstar as well. The jumps in the gas profile are then matched to the stellar objects and gas particles inside a spherical region defined by the radial extend of the gas jump (rgas) are then associated to the stellar object. Note that for the detection of the jumps only cold gas is considered. Please note that the approach of JUMP-D is substantially different to halo finders in general. The code only locates a baryonic object ('galaxy') without considering the dark matter. To this extent JUMP-D cannot be subjected to the common post-processing pipeline when it comes to subhaloes as that pipeline heavily relies on the embedding of satellite galaxies within dark matter subhaloes.
The physical code parameters are the linking length for the FOF haloes, and overdensity parameter ∆ for SO haloes, which have been chosen as 0. 2 and 200 for the present study, respectively. Technical parameters are the overload size and the minimum number of particles in a halo. The LANL halo finder is included in the standard distributions of PARAVIEW 13 package, enabling researchers to combine analysis and visualization of their simulations.
C16 PFOF Parallel FOF (PFOF) is a MPI-based parallel Friends-of-Friends halo finder which is used within the DEUS Consortium 15 at LUTH (Laboratory Universe and Theories). It has been parallelized by Roy and was used for several studies involving large N -body simulations such as  Courtin et al. (2011); Rasera et al. (2010). The principle is the following: first, particles are distributed in cubic subvolumes of the simulation and each processor deals with one 'cube', and runs Friends-of-Friends locally. Then, if a structure is located close to the edge of a cube, PFOF checks if there are particles belonging to the same halo in the neighbouring cube. This process is done iteratively until all haloes extending across multiple cubes have been merged. Finally, particles are sorted on a per halo basis, and the code writes two kinds of output: particles sorted per region, particles sorted per halo.
C17 PSO The parallel spherical overdensity (PSO) halo finder is a fast, highly scalable MPI-parallelized tool directly integrated into the FLASH simulation code that is designed to provide on-the-fly halo finding for use in subgrid modeling, merger tree analysis, and adaptive refinement schemes  (Sutter & Ricker 2010). The PSO algorithm identifies haloes by growing SO spheres. There are four adjustable parameters, controlling the desired overdensity criteria for centre detection and halo size, the minimum allowed halo size, and the resolution of the halo radii relative to the grid resolution. For the cases here the overdensity thresholds were both chosen to be 200 × ρcrit, the minimum halo size the the spacing of the grid, and the radius resolution half of the grid spacing with the grid resolution chosen to be of order the force resolution of the simulation.
The algorithm discovers halo centres by mapping dark matter particles onto the simulation mesh and selecting cell centres where 15 www. deus-consortium. org the cell density is greater than the given overdensity criterion. The algorithm then determines the halo edge using the SO radius by collecting particles using the FLASH AMR tree hierarchy. The algorithm determines the halo centre, bulk velocity, mass, and velocity dispersion from all enclosed particles without additional postprocessing. PSO is provided as both an API for use in-code and as a stand-alone halo finder.
This process is repeated at all levels of the hierarchy until all particles in the base FOF group have been assigned to haloes. Unbinding is performed using the full particle potentials (calculated using a modified Barnes & Hut method,  Barnes & Hut (1986) ); halo centres are defined by averaging particle positions at the FOF hierarchy level which yields the minimum estimated Poisson error-which in practice amounts to averaging positions in a small region close to the phase-space density peak. For further details about the unbinding process and for details about accurate calculation of halo properties, please see  Behroozi et al. in prep. ROCKSTAR is a massively parallel code (hybrid OpenMP/MPI style); it can already run on up to 10 5 CPUs and on the very largest simulations (> 10 10 particles).
C20 STF The STructure Finder (STF a. k. a VELOCIraptor,  Elahi et al. 2011 ) is a hybrid OpenMP+MPI code that identifies objects by utilizing the fact that dynamically distinct substructures in a halo will have a local velocity distribution that differs significantly from the mean, i. e. smooth background of the halo. This method consists of two main steps, identifying particles that appear dynamically distinct and linking this outlier population using a Friends-of-Friends-like approach. Specifically, outlier particles are identified by estimating the mean velocity distribution function using a coarse grain approach and comparing the predicted distribution to that of a particle's local velocity distribution, which is calculated using a nearneighbour kernel technique. Specifically, the local velocity density is estimated using 32 nearest neighbours in velocity space drawn from a larger sample of 256 nearest neighbours in physical space.
GeneProf Data (http://www. geneprof. org) is an open web resource for analysed functional genomics experiments. We have built up a large collection of completely processed RNA-seq and ChIP-seq studies by carefully and transparently reanalysing and annotating high-profile public data sets. GeneProf makes these data instantly accessible in an easily interpretable, searchable and reusable manner and thus opens up the path to the advantages and insights gained from genome-scale experiments to a broader scientific audience. Moreover, GeneProf supports programmatic access to these data via web services to further facilitate the reuse of experimental data across tools and laboratories.
INTRODUCTION High-throughput profiling technologies such as microarrays and, more recently, next-generation sequencing (NGS) have become invaluable tools for biomedical research. Their popularity is reflected by the everincreasing growth of the associated primary data archives, most prominently ArrayExpress  (1) , GEO  (2)  and the databases of the International Nucleotide Sequence Database Collaboration  (3) (4) (5). Simply archiving the data, however, is not sufficient to make it immediately accessible to the scientific community. The sheer amount and complexity of the data make it challenging to process, analyse and interpret. We and others have therefore developed user-friendly software facilitating streamlined analysis of large quantities of high-throughput data  (6) (7) (8) , but nevertheless much time is spent analysing the same data sets in different laboratories.
To reduce further replication of efforts and to make state-of-the-art insights from genome-wide experiments instantly interpretable to scientists, we have started building up a database of high-profile functional genomics data sets as a resource for biomedical research. To this end, we have used the web-based GeneProf data analysis system  (6)  to carefully reanalyse and curate a large number of public data sets and to bring them all together under one common framework. We have paid special attention to make this resource useful for experimental and computational biologists alike: the data can either be browsed, searched and visualized via the website or retrieved programmatically using a collection of web services. Importantly, thanks to its integration into the broader GeneProf data analysis suite, each result in the database is connected with the full analysis workflow that was used to generate it and all data can immediately be reused in new projects and integrated with the users' own data to enrich their results.
Data sources, types and extent In its current form, the GeneProf database hosts gene expression data from RNA-seq experiments as well as regulatory data from ChIP-seq, such as transcription factor binding sites and their putative targets or genes with enriched histone marks (Figure  1a ). GeneProf covers data from a range of different organisms, but the vast majority of data comes from human and mouse (Figure  1b ). GeneProf is actively being used by our group and others and the database is constantly growing as new data is being made available. At the time of writing, GeneProf's collection of highthroughput data comprises >76 billion sequence reads from 2617 sequencing experiments, or a total of 7 TB of public data (30 July 2013, Supplementary Table  S1 ). The data have been manually selected by the authors from 139 publications and a number of large-scale projects [e.
g. ENCODE  (9, 10)  or the Epigenomics Roadmap  (11) ] with the aim to cover a wide range of diverse data sets of interest to the community. Further data sets are constantly being added by our team, and users may even contribute their own data. All data in the system has been reanalysed, curated and annotated by the authors and other contributors (Figure  1c ). Where possible we have imported available annotations from the source databases, but all experiments have additionally been curated by hand to complete missing annotations and to make all annotations more consistent across experiments. Analysis results and data sets are further supplemented with plots and visualizations to support data interpretation.
As such, we have made use of tried-and-tested popular algorithms such as TopHat  (12)  and DEseq  (13)  for RNA-seq analysis and Bowtie  (14)  and MACS  (15)  for ChIP-seq data. Unfortunately, issues with the reproducibility of bioinformatic analysis of high-throughput data sets are pervasive  (16). To address part of these problems, the GeneProf database therefore couples all results with the corresponding analysis workflows and quality control measures, so it is possible to recapitulate each step of the analysis and trace the origin of every single piece of data in the system (Figure  2 ). This may help to increase the long-term impact of research projects  (17). Data sets in the GeneProf database are organized in terms of 'experiments', where each experiment typically corresponds to a publication or another logically linked set of sequencing experiments.
Experiment overview pages To make it straightforward for researchers to find all information linked to a particular experiment, we provide a one-stop summary page for each experiment in the database (Figure  2 ). Along with a name, description and keywords, this page records the references to associated articles and external websites, the owner of the experiment (the person who uploaded the data and carried out the analysis) and to the analysis history of the experiment (a complete log of each analysis step carried out in the past). The page further contains a listing of all input data sets along with sample annotations for each input data set, detailing experimental factors and other parameters, for example, organism, tissue and cell type of origin of each sample, where applicable. The next section of the page lists selected main analysis results of the experimental workflow, as chosen by the creator of the project.
Data set pages Each experiment consists of many different input, intermediate and output data sets. Each of these data sets comes with a linked data set page. Most records in the GeneProf database are of a tabular nature and can be browsed, sorted and filtered dynamically via the website (Supplementary Figure  S1 ). For example, a table may be filtered by P-value to look for differentially expressed genes in an expression data set. Alternatively, all data sets can be exported in a variety of popular file formats (e. g. FASTA/Q, BED, WIG, CSV, XLS, XML or Rdata), so users can transfer the data into their favourite applications. Additionally, integrated plotting tools make it possible to create publication-quality scatter plots, histograms, heatmaps, pie charts, box plots and Venn diagrams directly from within the application.
Gene-centric summary reports Many scientists are interested in obtaining information concerning a particular gene, but lack the time and expertise to exploit the wealth of knowledge buried in highthroughput data sets. GeneProf summarizes all the functional data contained in its databases on a gene-centric level, which makes it easy to instantly benefit from the results of genomics experiments (Figure  3 ). Each gene summary page first repeats general information about the gene, such as names and identifiers, genomic location, protein structure, known protein interactions and functional annotation. This information is mined from the Ensembl  (18) , PDB  (19) , BioGRID  (20)  and Gene Ontology  (21)  databases. The remainder of the page displays GeneProf-specific data, starting with gene expression (based on RNA-seq experiments in the database): A simple bar chart shows the average expression level of the chosen gene across different categories of samples, where the user can choose the categorization criterion, e.
Integrated Genome Browser Genome browsers are powerful and popular tools to visually explore large-scale genomics data sets. We have built in a simple genome browser directly into GeneProf (Supplementary Figure  S3 ), which is based on GenomeGraphs  (22). Using this tool, it is straightforward to quickly visualize genomic data sets in the context of ongoing experiments or other public data. In its current release, the GeneProf database hosts 2049 genome browser tracks (30 July 2013). If desired, genomic data sets can still be exported in WIG and BED format, so that they can be opened in more powerful external applications, e. g. the UCSC genome browser  (23)  or IGV  (24). In addition to these main feature pages, GeneProf hosts utility pages for user management, gene identifier conversion and genome information ('Genome Trivia'; Supplementary Figure  S4 ).
Web services As an alternative to the standard graphical access to the GeneProf database, we have implemented a range of web services that enable programmatic access to the data (Supplementary Table  S2 ). As such, the web services allow computational biologists and software developers to use data from outside the GeneProf application itself and to wire the data directly into external analysis pipelines and tools. GeneProf web services may be broadly subdivided into five categories:. Search: Lookup of matching database records. Metadata: General information about experiments and data sets. Gene expression: Gene-centric summaries of all available expression data. Gene regulation: Overview of the targets of DBPs. Data retrieval: Retrieval of individual data sets in genomic, sequence or text data formats.
Gathering data about genes of interest The transcription factor Nanog is a central component of the transcriptional network of embryonic stem cells (ESCs)  (26). Using the GeneProf search page (Supplementary Figure  S2 ), the user can easily find the records corresponding to the Nanog gene in all supported organisms by just entering 'nanog' into the search box and ticking the boxes for the organisms of interest (example: 'Mus musculus' and 'Homo sapiens'). By clicking on the name 'Nanog' in the search results, the gene summary page will open. Browsing through the information on the page, a researcher can quickly find out that, for example, Nanog is most highly expressed in ESCs and that its expression is turned on as early in development as the four-cell stage blastomere. Closely correlated genes include the functionally related transcription factor Esrrb  (27).
The GeneProf database currently contains three ChIP-seq data sets profiling Nanog in mouse ESCs. By clicking the 'Browse Target Datasets' button on the gene page and filtering the dynamic table on the next page for genes positively bound in all three data sets, we can identify 2930 putative target genes of Nanog. Returning to the gene page, we find a large number of regulatory elements in the proximity of the Nanog promoter; for instance, the list contains the aforementioned gene Esrrb as well as Nanog itself (with three independent data sets). By ticking the checkboxes for the four data sets concerned and clicking 'Browse Nanog Locus', the user can access the genomic neighbourhood of Nanog in the genome browser, from which at least two overlapping regulatory regions for Nanog and Esrrb (upstream of the Nanog promoter) are visually apparent (Supplementary Figure  S3 ).