dataesr/scientific-paragraphs-categorization huggingface dataset:
It follows form these notions that pE-DB will be complementary to more traditional databases, such as BMRB  (19) , which is mandated to host NMR-measurable information but not structural ensembles, and PDB  (20) , which is mandated to handle experiment-only well-defined structure coordinates. Neither PDB nor BMRB have the mandate or the capacity to handle the type of information contained in pE-DB, in which ensembles could be generated from NMR, SAXS, single-molecule fluorescence and other non-NMR techniques, integrated in model coordinate data rather than well-defined structure coordinates, which are not accepted by PDB either. One has to be aware, of course, that the ensembles are not precise or complete representations of disordered states but rather models that fit a specifically defined subset of data, and unique solutions cannot be expected owing to the extreme conformational freedom of IDPs and the limited data  (12, 49).
The more data we can incorporate into model building, however, the more realistic the ensemble will be, and the major ambition of pE-DB is to help stimulate and guide this process. It is important to have data of different types used for best results (i. e. at least some data on local or secondary structural propensities such as CSs, some data on global hydrodynamic properties such as SAXS or NMR PFG-derived Rh and some data on specific tertiary contacts such as PRE, etc. ) because ensembles calculated with data from only a certain class will have limitations (i. e. a SAXS-refined ensemble will not provide information about the secondary structural elements, also encountered in PRE-refined ensembles. Conversely, ensembles with residue-specific information (CS and RDCs) will not properly describe a PRE profiles or a SAXS curve).
These different aspects of experiment parameters are outlined in Table  2. The present size of pE-DB is comparable with the initial size of PDB (then Brookhaven Data Bank), which started with seven structures in 1971  (20). Considering the importance of structural disorder, there can be no doubt that it will rapidly grow in size. To this end, we encourage researchers to submit their ensembles and the corresponding primary experimental data. We will also consider including additional types of data, such as fluorescence resonance energy transfer (FRET) data, which might rapidly gain importance in determining dynamic structures  (50). The database already holds unfolded ensemble(s) of globular proteins  (29) , which may lead to a better understanding of protein folding, and also address the question as to whether IDPs are fundamentally different from denatured states of folded proteins [cf.
the term 'natively denatured proteins' for IDPs  (51) ]. Furthermore, with the development of methods that are able to probe molecular motions on the timescale of ps to ns or beyond, the deposition of structural ensembles might be of direct relevance for structured proteins that populate multiple conformational substates in the course of fulfilling their biological functions, as in allostery or enzyme catalysis, for example  (15). In conjunction with these goals, the database will help establish the quality, reliability and descriptive power of structural ensembles. Current ensembles are often criticized but never critically evaluated, and the ready availability of supporting data in pE-DB will now enable development of standard methods for analysis and quality control. Three types of analyses can be anticipated.
It is straightforward to analyze the structural features of ensembles, such as distribution of secondary structure or hydrodynamic parameters. More demanding will be to establish whether ensembles are realistic in terms of the distribution of conformational energies and agreement with the primary restraint data. Last but not least, there is a fundamental need to understand the connection between structural ensembles and protein function. Often, arguments about the function of an IDP are elaborated on the basis of knowledge of the targetbound, folded state, with total neglect of the dynamics and structural distribution of the unbound state. To stimulate further development of the field, we also encourage users to recalculate ensembles, deposit them in the database and assess the quality of different versions.
These efforts will all contribute to development and acceptance of standardized protocols for quality control, for eventual incorporation into the pE-DB data deposition pipeline. In the medium-or long-term, we even anticipate that a competition analogous to the Critical Assessment of Structure Prediction (52) could be implemented for de novo calculation of structural ensembles of IDPs. The real transition in the life of the database will come when demands from the community for data deposition as a requirement of publication will be raised; in the digital world, it certainly will not take 18 years as in the case of the PDB  (20). Either way, if we accomplish all these goals, this novel structural resource will help to extend the structure-function paradigm to include the disordered state of proteins (4) and will aid the development of therapeutics for debilitating diseases such as cancer and neurodegeneration  (21, 22).
Measurements of volcanic SO 2 fluxes Remote measurements of magmatic volatiles have focused a lot on SO 2 because it is arguably the most readily measurable by absorption spectroscopy, due to its low background concentration in the atmosphere (∼ 1 ppbv in clean air;  Breeding et al. , 1973) , and the strong and distinctive structures in its absorption spectrum both at ultraviolet (UV) and thermal infrared (TIR) wavelengths (see the HITRAN database;  Rothman et al. , 2009). Measurements of volcanic SO 2 fluxes are widely carried out from the surface since the mid-seventies using the Correlation Spectrometer (COSPEC) instrument  (Stoiber et al. , 1983; Stix et al. , 2008). More recently, consolidated measurements of total emission fluxes of SO 2 have also been made possible for a network of active degassing volcanoes (Network for Observations of Volcanic and Atmospheric Change -NOVAC;  Galle et al.
The field of view of OMI corresponds to a 2600 km wide spatial swath. In this way complete global coverage is achieved within one day. Depending on the position across the track, the size of an OMI pixel varies from 13 km ×24 km at nadir to 13 km ×128 km for the extreme viewing angles at the edges of the swath. It should be stressed that due to a blockage affecting the nadir viewing port of the sensor, the radiance data of OMI are altered at all wavelengths for some particular viewing directions of OMI. This row anomaly (changing over time) can affect the quality of the products and hence reduce the spatial coverage of the data (see http://www. knmi. nl/ omi/research/product/rowanomaly-background. php). In this study, the OMI row anomaly pixels have been filtered by using the quality flag (QualityFlag PBL, Bit 11) in the OMSO2 L2 files.
Smoothing of the resulting flux series may be necessary to make these spikes disappear. A second approach is to fit an analytic function to the mass time series; the time-dependent flux is then straightforwardly obtained by applying Eq. (  5 ) to the fitted curve. In the following, the function chosen to fit the data has the form M = K 4 + (K 1 • t -K 4 ) • exp(-K 2 • t K 3 ), (7) where K 1,2,3,4 are the fit parameters. This form was chosen to fit the basic skewed shape of the time series mass curve. Hereafter, we will refer to the "skewed shape" method to designate this flux calculation technique. Note that fitting a function to a naturally variable dataset is not necessarily easy, and may lead to systematic errors especially for eruptions having a long duration and/or a complex behavior.
3 ). The analysis was made for a sequence of four consecutive SO 2 images right after the start of the eruption; each time, the traverses were defined for a well-chosen portion of the plume (close to the volcano). Following the traverse method formulation (Eq. 4), we calculate the SO 2 fluxes using the MERRA wind fields (available at http://disc. sci. gsfc. nasa. gov/giovanni/overview/) extracted at the location of the volcano and the time of observations. It allows us to reconstruct a high-resolution chronology of the SO 2 flux during the eruption  (Merucci et al. , 2011). This is shown in Fig. 4 , where the results from four consecutive IASI snapshots (4 June p. m. to 6 June a. m. ) are displayed. A striking feature is the fairly good overlap of the flux data for two successive overpasses, in spite of the constant wind field used in the calculation (for each IASI image).
6 ); its altitude was between 4 and 8 km (based on wind direction data from ECMWF). We investigated the 2011-2012 Nyamulagira eruption for the first month (when the signal was the strongest) using the UV sensors (OMI and GOME-2) as they are the most suitable to monitor this event owing to their good sensitivity down to the surface. A strong signal of SO 2 could also be detected with IASI especially at the beginning of the eruption, but the data are more difficult to use because of increasing errors on the columns with decreasing plume altitude (the plume of SO 2 was low in the atmosphere). Since the plume altitude is not known exactly, we simply took the available middle tropospheric altitude (center of mass heights of 6 and 7. 5 km for GOME-2 and OMI, respectively) for the SO 2 vertical column calculation.
Results From inspection of the SO 2 column distributions, it appears that the GOME-2 and IASI data are quite similar, but the sequence of SO 2 images also shows differences that can be attributed (at least partly) to plumes of SO 2 at different altitudes. In principle, the inversion scheme exploits the instrumental complementarities to resolve the injection profiles in the altitude range extending from the surface to the lower stratosphere. At the beginning of the eruption, SO 2 is massively injected at high altitudes and the IASI retrieval is found to be optimal, owing to its large column applicability and 12 h revisiting time (as opposed to 24 h for GOME-2). Shortly after the first injection episode, SO 2 is more evenly distributed throughout the atmosphere, conditions for which GOME-2 provides information on the total atmospheric SO 2 column, including the contribution from the lower troposphere (not probed as efficiently by IASI).
4. 3. 3. Nevertheless, the overall good agreement between the observed and modeled SO 2 columns for the other plumes suggests that it is neither   10a ), results of the skewedshape method applied to GOME-2 data and used as a priori in the inversion (blue dotted line; z: 16 km), results of the delta-M method applied to IASI data (magenta and red lines for half-daily and daily mass estimates; z: 16 km) and results of the traverse method applied to MODIS (cyan squares). Lower panel: same as upper panel (but with a focus on the first four days of eruption) with flux results of the traverse method for three IASI images at the beginning of the eruption. a serious nor a general problem. From Fig. 10 , one can also see that not all plume maxima are well captured. This points to limitations in the approach used (in terms of spatial and time resolution) to capture the complexity and details of the emissions  (Eckhardt et al.
On 17 June 2011 substantially lower fluxes (80-250 kT day -1 ) are observed and SO 2 plumes are mostly below 6 km. This second phase of the eruption is mostly effusive (see Sect. 1. 1). It should be noted that the retrieved emissions in the lower troposphere for 13-14 June 2011 have higher uncertainties. This is essentially because the GOME-2 instrument was operating in its narrow swath mode on 14 June 2011 and no useful data was available on that day. 4. The delta-M method (Sect. 3. 3) was applied to IASI columns (assuming a plume height of 16 km). One can see that the results (displayed in Fig. 11 , magenta and red lines) capture nicely the large SO 2 flux at the start of the eruption and then agree well with the fluxes for the layers in the 6-18 km altitude range. After 20 June, the IASI signal becomes small and the delta-M method returns negative values, because of errors in the IASI retrievals and assumed SO 2 e-folding time.
For the first two days of eruptions (13-14 June 2011), it was possible to apply the traverse method (Sect. 3. 2) to portions of plumes measured by IASI. The results are shown in the lower panel in Fig. 11 , which is a zoom on the first four days of the eruption. The results agree fairly well with the flux estimates in the 6-18 km range (where IASI has a good sensitivity to SO 2 ). Moreover, the results from the traverse method enable us to consolidate the flux results at the very beginning of the eruption (early plume). 5. The SO 2 fluxes have also been estimated by applying the traverse method (Sect. 3. 2) to MODIS measurements. The pressure, temperature and humidity profiles, needed for the SO 2 retrieval computation (see Sect. 2. 4), derive from ECMWF data considering a 3 • × 3 • grid around Nabro.
If the observed discrepancies are due to the action of the plume, it is reasonable to assume that several hours after the start of the eruption (12 June 2011 around 20:30 UTC) the physical and chemical processes that occurred in-situ have stopped, so that the plume can be considered as passive again. As a test, we have run a FLEXPART simulation starting on 14 June 2011 (i. e. , more than 30 h after the start of the eruption) using the IASI SO 2 observations as emission cells, the source strengths being modulated by the corresponding measured SO 2 masses. Only the data with latitudes above 20 • N have been kept and the simulations assumed a uniform SO 2 profile between 15 and 18 km  (Vernier et al. , 2013; Fromm et al. , 2013). The results are shown in Fig. 12  (right plot) on 16 June 2011 at the time of IASI measurements (morning overpass).
One can see that, compared to the modeled columns using the a posteriori emissions, the simulated columns are much closer to the IASI observations. The fact that the pattern extending from West Mongolia to central China could be reasonably well reproduced indicates that the wind data are compatible with the real dispersion of the plume. This suggests that the differences between the observations and the original FLEXPART simulations are due -at least partly -to the action of the plume released in the first hours of the eruption. More work is needed to study the transport of the Nabro SO 2 plume and to ascertain the importance of the Asian monsoon circulation in the uplift of this plume  (Bourassa et al. , 2012).
Intercomparison of GOME-2 and IASI As shown in Sect. 4. 3. 2, the inversion modeling method applied to the eruption of Nabro provides good results. However, the technique implicitly assumes that the SO 2 columns from GOME-2 and IASI are compatible with each other, in the sense that differences in the column amount are only due to deviations from the actual plume height (Fig. 1 ). The aim of this section is to investigate whether this assumption is fulfilled. In a first step, we used the information on the SO 2 plume heights provided by the a posteriori FLEXPART simulations. For this purpose, we calculate, at each time step and for each grid point, the height of the SO 2 center of mass using the optimized (non-zero) SO 2 concentration profiles. The retrieved SO 2 heights are then used to investigate the consistency of the SO 2 column datasets from GOME-2 and IASI.
More quantitatively, the modeled and observed plume heights are found to be in good agreement, with differences in the 1-2 km range. Although it does not constitute a proper validation, the comparison of Fig. 13  tends to consolidate the inverted emissions and related plume height estimations. Next we compare the GOME-2 and IASI SO 2 data by accounting for the effect of altitude. This was achieved by interpolating the averaging kernels (estimated for each GOME-2 and IASI measurement; Fig. 1 ) at the corresponding FLEX-PART SO 2 plume heights. The calculation supplies conversion factors that are then applied to the respective vertical columns from both sensors. For the conditions where no information on the altitude was available, we took the plume height value from the closest grid point to the corresponding measurement.
The results are displayed in Fig. 14  (lower panel). Again, the GOME-2 and IASI SO 2 masses data are in excellent agreement. These results strengthen the inversion modeling approach presented in Sects. 4. 3. 1 and 4. 3. 2 and also confirm that the largest errors on the UV and TIR SO 2 columns is the error made by assuming a wrong SO 2 altitude. One can argue that for individual scenes the discrepancies between GOME-2 and IASI can be much higher than what has been presented here, because of saturation issues. Indeed, the GOME-2 DOAS retrieval tends to underestimate the SO 2 amount for columns larger than 50-100 DU, whereas the IASI algorithm is designed to cope with nonlinear effects due to a large SO 2 amount  (Clarisse et al. , 2012). However, from the inspection of the SO 2 columns (measured by IASI), we believe this translates to small differences for the time series shown in Fig.
, 2008). The methods have been applied to data from UV-visible (GOME-2 and OMI) and thermal infrared (IASI and MODIS) sounders. The three eruptions discussed in this work are very different: Puyehue-Cordón Caulle belongs to the explosive regime, whilst that of Nyamulagira belongs to the effusive regime. We have seen that the third event, that of Nabro (Eritrea), is intriguing as it fundamentally was an effusive one which released -during its first phases -an exceptional gas jet reaching tropopause levels. Although each method has advantages and weaknesses, overall, the flux results from the different methods are consistent and often complementary. We have shown that the satellite measurements are able to provide SO 2 fluxes with a sampling of a day or less (∼ 2 h in the best case), and with a total error below 50 %.
A summary of the techniques and its main features is given in Table  1. The analysis also enables us to compare properly the SO 2 data from the different sensors used and, in general, the time series of SO 2 flux/mass agree fairly well. More specifically, we have studied the complementarity of the (collocated) GOME-2 and IASI data. The SO 2 mass time series from GOME-2 and IASI are in excellent agreement when comparison is restricted to the altitude range where both instruments are sensitive to SO 2 : as this call for the plume altitude to be the key-factor of the satellite SO 2 data accuracy; further studies on space-borne SO 2 fluxes are expected to benefit from the advent of sensitive algorithms retrieving simultaneously the plume height and the vertical column of SO 2  (Yang et al. , 2010; Nowlan et al.
AUDIO DESIGN FOR CAMEO Synthesizing prior work on audio signals, we experiment with feedback techniques to support the social process of communication. Specifically, we incorporate them in the design of three CAMEO features -Speaker Identification, Audio Presence, and Entry/Exit announcements  [15]. We focus on the functionality and design of assistive audio cues for each of these features. Adding audio cues on to an already overloaded communication channel with multiple speakers provided ample opportunity to be distracting and inconsider-ate. We had to try many approaches and various kinds of audio feedback cues to find reasonable candidates to formally evaluate. We chose to work with Apple's GarageBand 1  software. We pre-recorded two ten-minute teleconference calls between five people and used these as the base tracks in Garageband.
Apparatus and Sounds. The apparatus used was identical to the first two studies. Mumble 3  was used to host the conference call. All the participants were requested to download the Mumble client and follow the instructions that were provided. A mac mini was used to run the python script that generated the prompts. The three entry and exit prompts that were used are speech-based, iconic, and metaphoric (Table  4 ). The prompts are dynamically created using Apple's textto-speech engine, and pre-recorded audio of a door opening and closing. The Python script was also set up to use the Mumble server's Ice remote procedure call interface to arbitrarily disconnect people every thirty seconds.
The SNOOPY code Equations (  1 )-(  4 ) are solved using SNOOPY, a three-dimensional (3D) incompressible spectral code. SNOOPY is based on the FFTW 3 library to compute 3D Fourier transforms using domain decomposition. It uses a low-storage, third-order Runge-Kutta (RK3) scheme to compute all the terms except for the linear dissipation terms, which are integrated implicitly. All non-linearities are solved using a pseudo-spectral algorithm, avoiding aliasing errors with the 2/3 rule  (Canuto et al. 1988). Shearing-sheet boundary conditions are implemented by solving the equations in a sheared frame comoving with the mean Keplerian flow. This algorithm is similar to the FARGO scheme  (Masset 2000) , although it can be extended to arbitrary order in space and time. This procedure implies a periodic remap in Fourier space, which is performed every t remap = |2A|L y /L x , where L x and L y are, respectively, the radial and azimuthal extents of the shearing box  (Umurhan & Regev 2004).
SNOOPY is now a welltested code, having been used for both hydrodynamical and MHD problems such as resistive MRI, dynamo and subcritical transitions to turbulence. We have implemented a new module in SNOOPY to account for the Hall effect. This scheme integrates the Hall term using the same RK3 and pseudo-spectral algorithms employed elsewhere in the code. To guarantee stability, the integration time step must be smaller than all of the physical time-scales of the system. When the Hall effect dominates the dynamics, the shortest physical time-scale is given by the whistler-wave time-scale at the grid, τ w ≡ ω -1 H ( x/π H ) 2. This constraint is quite demanding, as it implies that very short time steps are needed to ensure stability ( t < √ 3 τ w ; see Appendix B). The numerical results presented in Section 3.
This procedure gives two peaks in the spectra, which correspond to the eigenfrequencies of the right-and left-handed waves. The agreement between the analytical and numerical solutions is very good all the way down to the grid scale (the Nyquist frequency k N H = 3. 2π), a benefit of SNOOPY's spectral decomposition. Note that these tests were carried out with ν = η = 0, demonstrating that the code can stably propagate whistler waves without the need for explicit dissipation. In Appendix B, we prove analytically why this is true for the RK3 integration scheme used in SNOOPY. For our second test, we simulated the Hall-shear instability (HSI) described by  Kunz (2008, hereafter K08). The basic ingredients of this instability are a background shear, which generates a streamwise magnetic-field component from a transverse one, and the Hall effect, which induces a circular polarization that (for certain wavevectors) conservatively reorients stream-wise magnetic fields into the transverse direction.
We add to the magnetic field a constant poloidal component whose magnitude is determined by a choice of initial β. These runs have been integrated up to t = 630, corresponding to 100 orbits in a Keplerian shearing box; our fiducial run was integrated to t = 2000. Finally, we introduce an averaging procedure • , which denotes a volume and time average. The time average is performed using data only from t = 100 (or, in some cases, t = 300) to the end of the run, in order to avoid transient effects from the (arbitrary) initial conditions and the initial growth of channel modes. We close this section by providing a translation between our dimensionless parameters and those employed by SS02. Those authors used X = 2 -1 H and Re M = η , and focused mainly on initial values of X = 1, 2 and 4. For their fiducial initial β = 3200, this implies H /H ∼ a few × 10 -2.