dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the index "Meditation" provided by the Emotiv Control Panel as the control signal. Since it has not been made public by Emotiv, we do not have the exact formula used to compute this index out of the raw EEG signal. However, it is known to be positively correlated with the alpha rhythm and relaxation. Emotiv indexes result from a statistical analysis based on a large normative database collected of many subjects and are therefore already normalized. However, a calibration procedure is still used in order to adapt the system to subject's specific statistics. In our case, we used the standard deviation and the mean value of the "Meditation" index over the calibration period as a reference value to tune the feedback set-up. During the first minute of calibration we asked the subject to keep his eyes open and during the second minute we ask him to keep his eyes closed.
EEG Real Time Topography Application. Specific software for this application can be seen in Figure  3. During the second part of the performance, participants observe EEG raw brainwaves, followed by their representation as topography or how EEG brainwaves are distributed on the surfaces of the scalp. These EEG topographies may be likened to topographies used in elevation maps for hiking. Instead of representing the terrain elevation on the Earth surface, colors represent the strength of a specific brainwave at different locations on the head surface. In our case, we focused on brainwaves in a frequency band ranging from 8 to 12 Hz called the alpha band. Alpha brainwave amplitudes vary quickly in time and space and this dynamic may be rendered as animated colored maps on the dome. The topography is represented using either classic 2D spherical projection or an interactive 3D head model from OpenVibe software  [29].
A questionnaire was filled in by participants after the performance to collect their feelings and how their experience in the Brainarium compares to traditional conferences and lectures they attended. This questionnaire allowed us to collect demographic data about participants, on four closed questions with Likert scales, and an open text field where subject could give us their feedback freely. The first question asked the participant if he or she feels this type of demonstration promotes learning and memory compared to a conventional conference. Answer was given on a 5-point Likert scale ranging from 1 ("not at all") to 5 ("a lot"). The second question asked the participant whether it was more entertaining than a traditional conference or course. Answer was given on a 5-point Likert scale ranging from 1 ("less entertaining") to 5 ("more entertaining").
Question three addressed whether participant was more or less absorbed by the presentation on the 3D dome compared to a presentation on a conventional rectangular screen. Answer was given on a 5-point Likert scale ranging from 1 ("less absorbed") to 5 ("more absorbed"). Finally, the fourth question asked if the participant felt discomfort (i. e. , if he felt dizzy) due to the presentation on the 3D dome. Answer was given on a 5-point Likert scale ranging from 1 ("not at all") to 5 ("a lot"). We collected data on a total of 134 participants in two distinct performance places, during four different days. 52 participants were men and 82 were woman with an average age of 30. 4 ± 17. 3 years old across all participants (minimum age was 7; maximum age was 80). Results from the questionnaire are shown in Figure  5.
Discussion Planetarium domes have previously been used to display various contents. However, to our knowledge, this is the first time that real time EEG data is being shown in such an environment. Our demonstration appeared to arouse some level of popular success and seemed to provide participant with a new type of interactive experience. Thus, we have made all the tools we developed available in the public domain for anyone interested in reproducing our demonstration. In the following sections, we will focus on four domains of application in which the Brainarium may potentially be used and further developed: education applications, entertainment applications, art applications, and immersive neurofeedback applications.
Acknowledgments The authors wish to thank the  Art and Science Association "Les Chemins Buissonniers " that collaborated on this project, the association " La Ligue  de l'Enseignement" that lent the dome for the demos, the association "InCOGnu" who provided the Emotiv Epoc headset, and  Isabelle Cirla , the professional musician who played during the live exhibits. This project was supported by a grant in neuroinformatics from the  FRM Foundation  and a grant from the  French Ministry of Research  on brain computer interfaces.
Spurred by the need to analyze a particularly complex fibrinogen sample, a dedicated HPLC-SAXS (high-performance liquid chromatography coupled with SAXS) module was developed by  Brookes et al. (2013)  as a part of the small-angle scattering (SAS) section of the data analysis and simulation open source platform UltraScan solution modeler (US-SOMO; http://somo. uthscsa. edu/; Brookes, Demeler & Rocco, 2010; Brookes, Demeler et al. , 2010; Rocco & Brookes, 2014). In contrast with simpler programs that deal mainly with the automation of the repetitive tasks involved in analyzing the single different frames coming from a SEC-SAXS experiment (e. g. Shkumatov & Strelkov, 2015) , the US-SOMO SAS and HPLC-SAXS modules were developed from their inception with the aim of providing advanced tools to deal with all aspects involved, from primary data treatment to the decomposition of unresolved components, and the comparison with model curves derived from high-resolution data.
This last step is currently based on the embedded well known programs Crysol  (Svergun et al. , 1995)  and Foxs  (Schneidman-Duhovny et al. , 2013). The first key step in the HPLC-SAXS US-SOMO module is the conversion of the ensemble of n time frames (t), each containing a scattering intensity I t (q) as a function of the momentum transfer q [q = 4 sin()/, with 2 the scattering angle and the incident radiation wavelength] yielded directly by the SEC-SAXS experiment, into a series of m I q (t) versus t 'chromatograms' for each q value, where m is the number of different q values. Without any further analysis, this conversion already allows an immediate visual inspection of data quality such as capillary fouling issues when a few or several I q (t) chromatograms show intensity data not returning to the pre-peak elution ('baseline') value, a phenomenon especially evident in the low-q range (see Fig.
1 ). Furthermore, owing to the higher scattering intensity of higher molecular weight species, issues such as non-baseline-resolved peak separation can be better exposed in the I q (t) chromatograms than in the concentration profile usually associated with a SEC-SAXS experiment. Some utilities for solving or at least alleviating the abovementioned problems have been present since the first release of the US-SOMO HPLC-SAXS module  (Brookes et al. , 2013). A linear baseline tool offers a possible correction of all I q (t) chromatograms. For non-baseline-resolved peaks, single-value decomposition (SVD) analysis of the data set can inform the choice of the minimal number of components (i. e. species) necessary to account for the data. Each species can then be associated with a function describing its elution profile.
Since this profile in chromatography in general, and in SEC in The main GUI of the US-SOMO HPLC-SAXS module. Shown are the I q (t) versus t chromatograms resulting from the conversion of a series of experimental I t (q) versus q frames collected on a lysozyme solution eluting from a SEC column (see the experimental procedures). The data were automatically trimmed to remove chromatograms containing only noise (see xS1 in the supporting information). The three lowermost q values were also manually discarded as they were judged to be unreliable, being too close to the instrument beam stop. At the program level, the black-and red-labeled buttons determine the operations allowed or not allowed at each stage, respectively. particular, should in principle be well described by a series of symmetrical Gaussian functions  (Delley, 1986) , this was our initial choice  (Brookes et al.
, 2013). The Gaussian decomposition of the ensemble of I q (t) chromatograms into peak components is made possible through dedicated tools. The concentration signal (either UV-Vis or refractive index monitors are supported) can likewise be decomposed. Finally, from the I q (t) Gaussian-decomposed chromatograms, a series of I t (q) time frames for each baseline-corrected peak can be back-generated, which can then be further processed by the US-SOMO main SAS module. If the concentration signal is also available and has been processed, the relative concentration associated with each back-generated (decomposed) I t (q) frame, as well as the partial specific volume and the extinction coefficient (or dn/dc) of each species, can be carried over automatically. When not already done at the level of the beamline data-acquisition software, the data can then be put on an absolute scale using the reference I 0 value of a standard scatterer, making it straightforward to derive the molecular weight (or mass/length or mass/area ratios) associated with each peak species.
We describe here important developments that have been made in the last US-SOMO HPLC-SAXS module release. Recognizing that the linear baseline-correction tool was not appropriate to account for capillary fouling but only for simpler cases such as drifting problems, a much more sound integral baseline-correction procedure has been implemented under the assumption that fouling accumulates proportionally with the intensity scattered by the sample while in the X-ray beam. Gaussian decomposition is no longer limited to symmetrical Gaussians; instead three types of skewed Gaussian functions are now present (see Di  Marco & Bombi, 2001). This capability is similar to what is offered in commercial packages which, however, only operate on a single chromatogram at a time (e. g. PeakFit, Systat Software, San Jose, California, USA; http://www.
A general restyling of the graphical user interface (GUI) has been carried out, thus simplifying operations, and additional utilities (e. g. the possibility of exporting data present in graphs to. csv type files) are available in the latest release of the SAS and HPLC-SAXS modules of US-SOMO. The efficacy and usefulness of these new tools are first demonstrated with the adequate correction for capillary fouling in the SEC-SAXS analysis of a lysozyme sample. The tools are then applied to the extraction of pure individual I t (q) frames for higher-order complexes in the SEC-SAXS analysis of an aldolase sample, and their subsequent comparison with theoretical curves derived from high-resolution model structures.
com). After conversion to I q (t) chromatograms and data processing in the US-SOMO HPLC-SAXS module, the back-generated I t (q) frames were put on an absolute scale when necessary using the scattering by water and then converted to units of g mol À1 within the US-SOMO SAS module. The aldolase extinction coefficient (E 280 = 0. 877 ml mg À1 cm À1 ) was calculated from the composition by PROMOLP  (Spotorno et al. , 1997a,b) ; its partial specific volume (v 2 = 0. 736 ml g À1 ) and the molecular weight of the tetramer (157 131 Da) were calculated from the crystallographic structure file by US-SOMO. Automated docking with SAXS profile restraints was performed with the ClusPro 2. 0 server  (Comeau et al. , 2004; Kozakov et al. , 2006 Kozakov et al. , , 2013;;  http://cluspro. bu. edu/login. php).
Software implementation and general program features The technical specifications of US-SOMO have been described previously  (Brookes, Demeler & Rocco, 2010; Brookes, Demeler et al. , 2010; Brookes et al. , 2013). The software is primarily distributed as a binary GUI application for Linux, Mac OSX and Windows. Source code, written in C++ utilizing Qt (http://qt-project. org/), is freely available in subversion repositories as described on the US-SOMO wiki page (http://wiki. bcf2. uthscsa. edu/somo/). Registrations overlap with the parent UltraScan package, which currently has over 1500 registered individual researchers and 53 registered laboratories worldwide. Irrespective of the operating system used, the main US-SOMO program needs to be launched to access the HPLC-SAXS module. A full description of all the commands in this module can be found in the associated manual which is accessible by pressing the 'Help' button in the left-hand corner of the lower commands row of each window when running the program, and can be found directly at the URL http:// somo.
Physically, this represents the fact that no deposit due to irradiation is present at t 0 , since no sample exposure has occurred yet. It does not mean that the measured intensity at t 0 is zero. Testing of the integral baseline algorithm was done with multiple simulated Gaussian data sets. For each Gaussian data set, a simulated experimental data set was created by adding multiplied by the intensity to simulate deposits and, additionally, random noise. The simulated experimental data were processed through the algorithm and correctly recovered the simulated Gaussian data. 2. 3. 2. Non-symmetrical Gaussian functions. In addition to the classical symmetrical Gaussian function y ¼ a 0 ð2Þ 1=2 a 2 exp À 1 2 x À a 1 a 2 2 " # ; ð8Þ where a 0 , a 1 and a 2 are the area, center and width of the peak, respectively, the following non-symmetrical Gaussian functions (see Di  Marco & Bombi, 2001)  have also been implemented.
The similarity test is performed without the Holm-Bonferroni (HB) adjustment, using stringent conditions to avoid considering as identical curves exhibiting genuine differences. In this case, the lack of a multiple testing adjustment is not an issue, since conclusions are drawn from the comparison of two identical analyses. Regardless of the method used, we present the results in a synthetic way by plotting a square matrix in which the dot (i, j) contains the respective pairwise P value represented using a three-color code [the same as used in the CorMap implementation within the PrimusQt software (Atsas package version 2. 7. 1)]: green for P ! 0. 05, yellow for 0. 01 P < 0. 05 and red for P < 0. 01 when no multiple testing correction is applied. The same color code is utilized using the adjusted acceptance threshold when applying the HB procedure (see supporting information for the definition of the threshold).
Non-symmetrical Gaussian decomposition of overlapping peaks The second major improvement present in the current release of the US-SOMO HPLC-SAXS module is the possibility of using non-symmetrical Gaussian functions to decompose non-baseline-resolved peaks. We have implemented exponentially modified Gaussian (EMG) and half-Gaussian modified Gaussian (GMG) functions and a combination of the two (EMG + GMG) (see Theory, x2. 3. 2). Additional statistical tools have also been implemented to aid in judging the quality of the decompositions. This is particularly relevant since these are processes that are difficult to automate fully, and thus require direct user interaction at several steps. In Fig. 6  (top-left panel), a single low-q I q (t) chromatogram from a rabbit muscle aldolase SE-HPLC-SAXS data set (see Materials and methods, x2.
Importantly, only a minimal variation in the Gaussian centers optimized on the SAXS data set is allowed (2% from initial values by default), leaving only widths, distortions and amplitudes to be optimized. However, widths and distortions are coupled parameters. From our recent experience, it appears to be more efficient at this step to keep the widths fixed (default) and let only distortions vary. The results of this procedure on a 280 nm UV trace collected on a diode-array detector (DAD) placed before the SAXS detector for the aldolase SE-HPLC-SAXS data examined here are shown in supplementary Fig. S10  (since most concentration-related data sets do not carry associated s. d. data, the residuals are shown on an absolute scale). Although the fitting could probably be improved by releasing some constraints, it is important to stress that a correspondence with the SAXS-optimized data was the primary goal.
When exported to the main US-SOMO SAS module and processed with the 'Guinier' utility, estimates of the molecular masses are thus derived directly from the intercepts of the linear regressions. With the reshaping option, a better correspondence between the concentrations and the SAXS intensities should be obtained. However, this procedure effectively results in molecular mass estimates whose variation along a given decomposed Gaussian peak just reflects the small departures from the Gaussian fit present in the SAXS data. The molecular mass thus can be thought of as artificially 'constant'. The results of utilizing the non-reshaped and reshaped concentration Gaussians can be seen in supplementary Figs. S11  and S12 , respectively, where a number of frames under each EMG + GMG peak have first been normalized by their associated concentrations and then plotted (an average curve of the normalized frames is also plotted).
Since each rabbit muscle aldolase subunit has eight cysteines but no intrachain disulfide bridges  (Lai et al. , 1974; Blom & Sygusch, 1997) , the formation of inter-chain S-S bridges was considered. However, both SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) analyses under nonreducing conditions and SE-HPLC runs in the presence of up to 20 mM dithiothreitol failed to support this hypothesis (data not shown). While fully determining the binding/bonding nature of these higher-order aldolase complexes was beyond the scope of this work, we nevertheless attempted to model their mutual arrangement. We have thus resorted to a docking program (ClusPro 2. 0; see Materials and methods, x2. 1), which allows for generating and then screening putative complexes also on the basis of the agreement of their internally calculated scattering patterns with an input SAXS curve.
This procedure generated 30 'balanced' (i. e. with comparable contributions from electrostatic, hydrophobic and van der Waals binding forces) models for the octamer (dimer of tetramers), for each of which the I(q) curve was then recomputed. Searches for the single best fitting curve, and for a combination of curves giving the best fit, were conducted against the average curve for frames 97-110 of the aldolase mG-Pk3, using the non-negative least-squares (NNLS) routine in the US-SOMO SAS module. As can be seen in Fig. 9  (panels C and D), remarkably good fits were obtained, with model No. 17 (Fig. 9 , panel H) being the single best fitting curve, and a combination of model Nos. 8, 14 and 25 (Fig. 9 , panels I-K) giving a slightly improved score [normalized multiplied by r ; equation (12), see  Theory, x2.
Advanced statistical tools are now available to validate operations/results and to guide the user's choices at each step. The ability of our approach to retrieve structural information from a SEC-SAXS data set comes at the price of extra complexity. For the time being, it is far from being automated and cannot be considered as a highthroughput tool, although we contemplate automating several steps in a future release. The major improvement in sample quality offered by SEC-SAXS explains its availability at a growing number of synchrotron radiation facilities worldwide and the correlated computer programs  In all panels, each aldolase monomer is colored differently, while to make a comparison easier the orientation of the starting tetramer (G) is the same in all higher-order complexes produced.
developments of specific software. For instance, DATASW  (Shkumatov & Strelkov, 2015)  provides a user-friendly interface for identical frame averaging and publication-quality figure preparation, but does not venture much further. Furthermore, a recent report describes an automated pipeline for the SEC-SAXS setup available at the EMBL-P12 beamline at PETRA 3, Hamburg  (Graewert et al. , 2015). The major original feature of the setup is that, thanks to a micro-splitter valve, it allows the parallel monitoring of the eluted solution by SAXS and by a triple detector array (UV-absorption, light scattering and refractive index), a very interesting approach. However, no attempt is presented to decompose the elution profile into the various contributions from the eluting species. Finally, a recent article presents novel methods for the analysis of SEC-SAXS data  (Malaby et al.
, 2015). These methods are based on SVD and so-called Guinier-optimized linear combination to facilitate data analysis and reconstruction of protein scattering directly from peak regions. While the use of SVD for a refined buffer subtraction is of great interest, the reconstruction aspect is more limited and does not lead to a complete decomposition of the SEC-SAXS data sets into individual species contributions. As already mentioned in the introduction, the HPLC-SAXS module within US-SOMO also offers SVD analysis, used to determine in an unbiased way the minimum number of species accounting for the entire data set. This guides the subsequent choice of (modified) Gaussians [(m)G-Pk(i)] used in the decomposition process. However, we also wondered if SVD could be put to more efficient use. Indeed, once the choice of the number of species N sp is determined, all other singular values (and associated vectors) represent noise in the data.
It is thus legitimate to use, instead of the noisy original SAXS frames, their projection into the subspace of the first N sp singular vectors, thereby filtering out much of the experimental noise of individual frames. We performed a parallel analysis of the aldolase data set using the projection of experimental frames onto the first four singular vectors, in the hope of being able to extend our data to higher q values and improve the consistency of the reconstructed curves. Although the projected patterns were much less noisy, the corresponding gain for the reconstructed curves was much smaller. Further work is required before drawing a definitive conclusion on the interest of a preliminary SVD filter. However, we reasoned a posteriori that our (modified) Gaussians were determined through a global fit and that this operation implicitly performed a filtering function similar to that carried out by SVD.
Furthermore, the scattering pattern calculated from the complete aldolase crystal structure is very similar to the curve of the tetramer peak (see Fig. 9 , panels A and B). Finally, using the program ClusPro2. 0 with SAXS restraints we could build dimers and trimers of tetramers, the scattering patterns of which were already close to the corresponding peak curves, their combination providing even better fits. The reconstructed curves for both peaks mG-Pk2 and mG-Pk3 are thus perfectly compatible with bona fide oligomers of the tetramer. Our protocol therefore appears capable of recovering from a data set of essentially mixtures of oligomers the scattering patterns of isolated components. It is also worth noting that the consistency of both protocols can be checked internally simply by comparing scaled curves from a single peak of baseline-corrected data.
Acknowledgements This work was supported by  NIH  grant No. K25GM090154  and  NSF  grant No. CHE-1265817  to EB, and partially supported by  Italian Ministry of Health  '5 per mille 2011' funds to MR. We gratefully acknowledge the fundamental help of  Bing Xia, Boston University, Boston, Massachusetts, USA , and of  Jochen Hub ,  Georg-August University, Go ¨ttingen, Germany , in running ClusPro 2. 0 and WAXSiS, respectively, with very large structures. Finally, we wish to express our most sincere thanks to the anonymous reviewer who, by requesting a statistically valid evaluation of frame identity and related issues, prompted us to introduce new tools to our procedure that very significantly improved the program and the manuscript.