dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSIONS AND FURTHER WORK In this paper we have presented the advantages of creating a new type of an information visualization chart that is able to describe the structure of CSV files. CSV files are not always easy to understand. Their structure may be very complex, with large number of columns and/or rows, non-regular number of columns in each row, different types of data in each cell and so on. With the growing OD trend, large amount of information is published over the Internet. Abundant variety and quantity of public data is now available for different usage scenarios. Many OD datasets are published in the CSV format. If the user cannot interpret efficiently OD CSV files, the potential of these datasets cannot be exploited and they may become useless. By providing better tools to the user that ease the understanding and exploitation of such data, we increase the potential use of these datasets.
Beyond the possibility the user has to analyse more efficiently the files, potential errors can be detected -and corrected. These factors make the use of OD CSV files more convenient and efficient. According to the literature, it seems that there are not many techniques to analyse tabular information. with the data of the public budget for two different months). This would help to detect inconsistencies between file generations. Another challenging task is the scalability problem: can the technique cope with very large files? How will the system work when processing several large datasets? Finally, but not less important, the technique should be as user-friendly as possible. The user should be able to understand easily the information showed in the chart and the interaction must be easy and fluid.
Herschel PACS and SPIRE The LMC has been observed with Herschel as part of the successor project of SAGE, HERITAGE (HERschel Inventory of The Agents of Galaxy's Evolution;  Meixner et al. 2010 Meixner et al. , 2013)). The Herschel PACS (Photodetector Array Camera and Spectrometer;  Poglitsch et al. 2010)  and SPIRE (Spectral and Photometric Imaging Receiver;  Griffin et al. 2010)  and data have been reduced by the HER-ITAGE consortium. The LMC was mapped at 100 and 160 µm with PACS (PSF FWHMs of ∼7. 7 and ∼12 respectively) and 250, 350 and 500 µm with SPIRE (PSF FWHMs of 18 , 25 and 36 respectively). The SPIRE 500 µm map possesses the lowest resolution (FWHM: 36 ) of the IR-to-submm dataset. Details of the Herschel data reduction can be found in  Meixner et al. (2013). We particularly refer the reader to the §3.
LABOCA observations and data reduction LABOCA is a submm bolometer array installed on the APEX (Atacama Pathfinder EXperiment) telescope in North Chile. Its undersampled field of view is 11. 4 and its PSF FWHM is 19. 5, thus a resolution equivalent to that of the SPIRE 250 µm instrument onboard Herschel. N11 was observed at 870 µm with LABOCA in  December 2008 and April, July and September 2009 (Program ID: O-081. F-9329A-2008 -PI: Hony). A raster of spiral patterns was used to obtain a regularly sampled map. Data are reduced with BoA (BOlometer Array Analysis Software) 1. Every scan is reduced individually. They are first calibrated using the observations of planets (Mars, Uranus, Venus, Jupiter and Neptune) as well as secondary calibrators (PMNJ0450-8100, PMNJ0210-5101, PMNJ0303-6211, PKS0537-441, PKS0506-61, CW-Leo, Carina, V883-ORI, N2071IR and VY-CMa).
Zenith opacities are obtained using a linear combination of the opacity determined via skydips and that computed from the precipitable water vapor 2. We also remove dead or noisy channels, subtract the correlated noise induced by the coupling of amplifier boxes and cables of the detectors. Stationary points and data taken at fast scanning velocity or above an acceleration threshold are removed from the time-ordered data stream. Our reduction procedure then includes steps of median noise removal, baseline correction (order 1) and despiking. The reduced scans are then combined into a final map in BoA. As noticed in  Galametz et al. (2013) , the steps of median noise removal or baseline correction are responsible for the over-subtraction of faint extended emission around the bright structures.
Preparation of the IR/submm data for the analysis We use the SAGE and HERITAGE MIPS, PACS and SPIRE maps of the LMC reprocessed by  Gordon et al. (2014)  in the following analysis. They include an additional step of foreground subtraction in order to remove the contamination by Milky Way (MW) cirrus dust. The morphology of the MW dust contamination has been predicted using the integrated velocity H i gas map along the LMC line of sight and using the  Desert et al. (1990)  model to convert the H i column density into expected contamination in the PACS and SPIRE bands. The image background is also estimated using a surface polynomial interpolation of the external regions of the LMC and subtracted from each image. All the maps are convolved to the resolution of SPIRE 500 µm (FWHM: 36 ) using the convolution kernels developed by Aniano et al.
We find that only 10% of the total dust mass (∼2. 7±0. 5 × 10 3 M ) resides in these individual clumps. Using the same dataset than this analysis and a single temperature blackbody modified by a broken power-law emissivity (BE-MBB),  Gordon et al. (2014)  produced a dust mass map of the whole LMC. They obtain a total dust mass that is a factor of 4-5 lower than values derived from standard dust models like the  Draine & Li (2007)  models. We convolve our Σ dust mass map to their 56 working resolution to position our dust estimates (for the same area) in that dust mass range. We find that the mass estimated for the whole N11 region in  Gordon et al. (2014)  is 2. 5 times lower than the dust mass we derive (on average 2. 4 times lower for Σ dust < 0. 2 M pc -2 and 2. 6 times lower for Σ dust > 0.
The gas reservoirs in N11 5. 1. 1 Atomic gas  Kim et al. (2003)  produced a high-resolution H i data cube of the LMC by combining their ATCA data with observations of the Parkes 64-m radio telescope from  Staveley-Smith et al. (2003). The final cube has a velocity resolution of 1. 649 km s -1 and a spatial resolution of 1. The H i intensity map of the N11 complex was obtained by integrating the data cube over the 190 < v hel < 386 km s -1 velocity range. This excludes the galactic contamination caused by the low galactic latitude of the LMC (b ∼ -34 • ). We transform this integrated H i intensity map to atoms cm -2 units assuming that the 21cm line is optically thin across the complex. We discuss potential consequences of this assumption in §5. 3. 3. We finally fit the distribution of pixels outside the LMC by a Gaussian and subtract the peak value (as a background estimate) from the data.
2010). The corresponding probability distribution of the GDR is shown on the right panel. The vertical red line indicates the Galactic total GDR value (i. e. 158;  Zubko et al. 2004 ). The inset shows the histogram of ISM elements not normalized. Regions for which we have CO coverage (thus molecular information) are overlaid in darkgrey. Figure  11. Dust surface density as a function of the total gas surface density. The molecular mass is estimated using an X CO factor of 4. 7 × 10 20 cm -2 (K km s -1 ) -1 on the left panel and a Galactic X CO factor of 2. 0 × 10 20 cm -2 (K km s -1 ) -1 on the right panel. On both plots, black points are ISM elements where MAGMA CO data is available and grey points where MAGMA CO data is not available. The larger squares overlaid over the individual ISM elements indicate the averaged Σ dust per bins of Σ gas (we choose regular bins in logarithmic scale).
H i dominates the local gas masses in many regions of the complex. Figure  11  shows the relation between the dust surface density and the atomic+molecular gas surface density (MAGMA X CO case on the left panel and Galactic X CO case on the right panel). Black points distinguish ISM elements where MAGMA CO data is available in the publicly released map. They represent about 45% of our ISM elements. Grey points indicate elements for which the CO information is not available. They are identical in both panels. Some of these 'grey' elements might have CO emission (but are simply not covered). This is probably the case for regions that possess a non-negligible dust surface density at the top of the "grey cloud" of points. Additional molecular gas would shift these elements and tighten the relation between Σ dust and Σ gas.
A PRINCIPAL COMPONENT ANALYSIS OF THE IR/SUBMM DATASET In this paper, we have analyzed the local characteristics of dust grains across the star forming region N11. However, the properties we derive are dependent on the assumptions made on the dust composition in the SED modeling technique we have selected. In this last section, the goal would be to explore the capacity of a data processing method based a Principal Component Analysis (PCA) to decompose the various dust populations contributing to the local SEDs in N11 with no a priori on the dust populations.
Method The Karthunen-Loève transform, more familiarly called Principal Component Analysis (hereafter PCA), is the orthogonal projection of a set of data into a new system of coordinates. This technique has been used on many astronomical objects and for multiple purposes: analysis of spectral cubes  (Mékarnia et al. 2004 ), reduction of dimensionality for SED libraries  (Han & Han 2014) , among many others. Our SED modeling of the N11 complex has enabled us to build a library of spectra across the complex. If we take into account pixels with a 1-σ detection in the Herschel bands, this leaves us with 21392 individual dust SEDs. Our goal is to apply a PC decomposition to this multispectral data cube. Using the fully modeled SEDs in lieu of the individual bands provides a much better constraint of the SED shape in each resolution element and will decrease the biases linked with noise in the data.
The first step of our analysis is to compute the covariance matrix of the multispectral data cube (we use the IDL function CORRELATE). Since we are mostly interested in the dust SED here, we keep the spectral coverage from 8 µm to 1000µm. Each SED in our database is sampled with 117 wavelength points between these two wavelengths. We use the logarithm of our local models to perform the PCA decomposition. From the covariance matrix, we can calculate the eigenvectors and eigenvalues (we use the IDL function EIGENQL, with the covariance option). This directly provides us with an ensemble of vectors that constitute the main building blocks of our local SEDs. The total number of eigenvectors derived in a PCA is equal to the number of spectral elements used for the analysis. We obtain 117 independent eigenvectors.
ACKNOWLEDGMENTS We would like to first thank the referee for his/her careful reading of this paper and useful suggestions. We would also like to thank  Karl Gordon  for providing us with the reprocessed Herschel maps and the  MegaSAGE consortium  for our motivating collaboration and meetings. We acknowledge financial support from the  NASA Herschel Science Center , JPL contracts #  1381522 , #  1381650 & # 1350371. Meixner acknowledges support from  NASA  grant,  NNX14AN06G , for this work. This publication is based on data acquired with the  Herschel Space Observatory. The Herschel/PACS instrument has been developed by  MPE (Germany) ;  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain).
Background: Bacteriorhodopsin (bR) is the simplest known light driven proton pump and has been heavily studied using structural methods: eighty four X-ray diffraction, six electron diffraction and three NMR structures of bR are deposited within the protein data bank. Twenty one X-ray structures report light induced structural changes and changes induced by mutation, changes in pH, thermal annealing or X-ray induced photoreduction have also been examined. Scope of review: We argue that light-induced structural changes that are replicated across several studies by independent research groups are those most likely to represent what is happening in reality. We present both internal distance matrix analyses that sort deposited bR structures into hierarchal trees, and difference Fourier analysis of deposited X-ray diffraction data.
Major conclusions: An internal distance matrix analysis separates most wild-type bR structures according to their different crystal forms, indicating how the protein's structure is influenced by crystallization conditions. A similar analysis clusters eleven studies of illuminated bR crystals as one branch of a hierarchal tree with reproducible movements of the extracellular portion of helix C towards helix G, and of the cytoplasmic portion of helix F away from helices A, B and G. All crystallographic data deposited for illuminated crystals show negative difference density on a water molecule (Wat402) that forms H-bonds to the retinal Schiff Base and two aspartate residues (Asp85, Asp212) in the bR resting state. Other recurring difference density features indicated reproducible side-chain, backbone and water molecule displacements.
A landmark contribution to the field was made 15 years later when the first atomic level model of bR  [15]  was also solved by electron diffraction. Since this time there has been an explosion of interest in the structural biology of bR, with eighty four X-ray diffraction, six electron diffraction and three NMR structures of bR now deposited in the protein data bank (Table  1 ). Many of these structures report conformational changes after treating crystals with light, pH changes, thermal annealing, X-ray photo-reduction, or after one or several residues were mutated. In most cases, the observed conformational changes were argued to be relevant to the mechanism of proton pumping by bR. Twelve years ago we summarized the structural results that emerged from the first intermediate trapping studies on 3D crystals of bR  [16] [17] [18] [19] [20]  and bR mutants studied by X-ray  [21]  and electron diffraction  [22].
Hierarchal clustering analysis of wild-type bR resting structures Table  1  summarizes all structures of bR deposited in the pdb. These structures are grouped according to the collaboration of scientists that deposited the structure, with each sub-group of structures being ordered according to the first structure deposited by each of these collaborations. When representing the data in this tabulated form it is striking that a tremendous amount of scientific effort has gone into understanding the structural biology of bR. One also observes a positive effect of increasingly stringent requirements from several journals that crystallographic observations be deposited before publication, cumulating in the protein data bank making the submission of experimental data mandatory from January 2008, since the practice of releasing experimental data had varied from group to group prior to this date.
This scoring algorithm proceeds by first selecting the protein domains of interest, and here we select all transmembrane α-helix domains (helices A to G) as defined by the header of pdb entry 1QHJ (TM A: 6-32; TM B: the difference internal distance matrix between any two pdb entries is calculated by subtracting one internal distance matrix from the other, Δ[C α ] ij = [C α ] i -[C α ] j for pdb entries i and j; and the scoring function S ij relating pdb entries i and j is calculated as the average of the absolute value of all elements within this difference internal distance matrix: S ij = Avg(|Δ[C α ] ij |). In cases for which one or more of the selected C α atoms are not present in either of the two pdb files, S ij is calculated for the subset of C α atoms present in both structures. From the set of S ij between all selected pdb entries, a hierarchal tree was drawn by sequentially clustering all pdb entries with their nearest neighbor (ie.
selecting i and j corresponding to the smallest value of S ij ) using single linkage clustering in MATLAB  [48]  and plotted using dendrogram. For pdb files containing both a dark and an illuminated state in the same file (1X0I, 1QKP, 1P8H, 1O0A, 1M0M, 1M0K, 1CWQ) only the dark state was considered in this part of the analysis. Fig. 2  shows the results of this sorting algorithm when applied to sort all the deposited pdb structures of wild-type bR. A very striking outcome of this analysis is that a clear majority of the X-ray structures of wild-type bR (26 of 29) are very similar, being grouped in the lower portion of the hierarchal tree with a threshold of the average internal difference matrix value S ij ≤ 0. 25 Å (Fig. 2 ) relative to its nearest neighbor. Moreover, these structures of wild-type bR largely self-sort according to their crystallization media and space group.
It is striking that one NMR structure (1R2N) clusters beside 1BRR within this group, but closer inspection of that work  [46]  reveals that this X-ray structure was used as a model to aid the interpretation of the NMR data and thus 1R2N cannot be regarded as a truly independent structure. Raising the bar to S ij ≤ 0. 25 Å incorporates the 1. 55 Å LCP structure of Luecke (1C3W), which is the most cited of any X-ray structure of bR; the LCP structure of Büldt (1CWQ); and the sub-branch of five bR structures solved from bicelle grown crystals mentioned above. At the other end of the tree, pdb entry 1L0M differs most from all other structures of bR, which may have been anticipated since this NMR structure was solved by piecing together sub-structures of helical fragments of bR solved by NMR spectroscopy and then combing these into a single structure using a number of reasonable inter-helical.
S ij (Å) Fig. 2. Hierarchal clustering analysis tree calculated from all deposited pdb entries describing the resting conformation of wild-type bR. This tree has been sorted according to the average of the difference internal distance matrix: S ij = Avg(|Δ[C α ] ij |) calculated as Δ[C α ] ij = [C α ] i -[C α ] j for pdb entries i and j where [C α ] is the internal distance matrix calculated on C α atoms. It is striking that this analysis sorts the sub-branches largely according to the choice of crystallization media. These results are described in Section 3. constraints  [47]. A trend is also observed that structures solved by electron diffraction have, with time, become closer to the X-ray diffraction structures since the three wild-type bR structures from the Henderson group  [15, 22, 49]  and the two electron diffraction structures from Fujiyoshi  [27, 50] , arrange chronologically with the most recent structures further down in the hierarchal tree.
Hierarchal tree of structural perturbations Given this wealth of data on how the bR structure is affected by light, mutation or other physical perturbations we ask: what C α movements have been reproducibly observed and which of these are uniquely lightinduced? To this end we adapt the internal distance matrix analysis presented above that identified similarities and differences between two pdb entries of wild-type bR, to now identify similarities and differences between two pairs of pdb entries (excited versus resting). This extension is rather straightforward and, in practice, the only change required is to the choice of scoring function. Again we begin by selecting all transmembrane α-helix domains (TM A: For each pdb file of interest (pdb excited ) i we identify the corresponding resting bR state (pdb resting ) i , which can usually be extracted from the original publication or the pdb entry.
The difference internal distance matrix is then calculated as: Δ[C α ] i = [C α excited ] i -[C α resting ] i. In this case the function S i (Δ[C α ] i ) = Avg(| [C α excited ] i -[C α resting ] i |) gives a measure, in units of Å, of the average change in internal distances when moving from the resting to the excited state. Although it contains some information (tabulated as S i in Fig. 3 ), it is not used to draw the hierarchal tree. Instead we calculate the Pearson correlation coefficient (PCC) which relates two difference internal distance matrices:  more of the selected C α atoms are not present in any one of the four relevant pdb files (two excited, two resting), PCC ij is calculated for the subset of C α atoms present in all four structures. From the complete set of PCC ij relating all relevant pdb entries, a hierarchal tree is drawn by sequentially clustering these pairs with their nearest neighbors (ie.
selecting i and j with PCC ij closest to 1) using single linkage clustering in MATLAB  [48]  and plotted using dendrogram. Why do we adopt PCC ij here whereas we used S ij to sort the wildtype bR hierarchal tree? The answer is that PCC ij maintains the information on the nature and direction of the C α movements. For example, should a movement of a portion of helix C towards helix G be observed in two pairs of pdb entries: (pdb excited -pdb resting ) i and (pdb excited -pdb resting ) j then PCC ij would yield a positive correlation. On the other hand should one pair show this movement of helices towards each other but another pair shows this movement away from each other, this would yield a negative PCC ij correlation. Similarly, movements of helix A relative to F are not directly correlated with movements of helix C relative to G when using PCC ij , whereas both would increase the value of S i (ΔC i ).
4A ). Thus, while the X-ray structures of the D85S, D85S/F219L and acid blue forms of bR are high quality and interesting crystallographic studies, they appear to have limited relevance to light driven structural changes during the bR photocycle. The next cluster is formed by two K-intermediate pdb entries (1QKO-1QKP gnd ; 1QKP exc -1QKP gnd ; where "exc" denominates a lightactivated state and "gnd" denominates the resting sate when a pdb entry contains both intermediate and resting structures  [16] ) and one M-intermediate study (1M0M exc -1M0M gnd  [65] ). What is striking about this cluster is that the average change in distance between C α atoms is perturbed only very slightly, S i (ΔC i ) ≤ 0. 01 Å for all three entries, and this is an order of magnitude lower than typically seen when comparing independent wild-type bR structures (Fig.
4D ). We note that pdb entries 1O0A, 1P8H and 1P8U all have a c-axis approximately 3 Å shorter than earlier bR structures from the same group (1M0K, 1M0L, 1M0M, Table  1 ) and a similar checkerboard pattern can be generated if one refines two bR structures against the same data but with the c-axis arbitrarily shortened in one case. These observations indicate that this apparent compression may stem from slight errors in determining the crystallographic c-axis. Proceeding down the tree, we reach a sub-branch consisting of two structures of the D96G/F171C/F219L triple mutant independently solved by electron diffraction (1FBK-1FBB  [22] ) and X-ray crystallography (4FPD-1KGB  [74] ). These pdb entries display some of the largest structural perturbations, having an average internal distance matrix change S(1FBK-1FBB) = 0.
Finally we arrive at the main branch of light-induced conformation changes in bR. Allowing a (somewhat arbitrary) threshold of PCC ij ≥ 0. 597 defines a set of eleven paired pdb entries, all of which concern studies of light-induced structural changes in 3D bR crystals illuminated at temperatures ranging from 150 K to 293 K, include structures of L, M and O states (Table  2 ). This set of studies incorporates structures from all major groups to contribute to the field: Luecke (1F4Z-1F50)  [20] ; Lanyi (2NTW-2NTU)  [69]  and (2I20-2I1X)  [68] ; Büldt (1CWQ exc -1CWQ gnd ); Glaeser (1KG8-1KGB)  [63] ; Kouyama (3VI0-3VHZ)  [73] , (1DZE-1IW6)  [71] , (1IW9-1IW6)  [71]  and (2ZZL-1IW6)  [72] ; and ourselves (1E0P-1QHJ)  [18]  and (1VJM-1QHJ)  [62]. Moreover a twelfth structure (1C8S-1C8R)  [17] , which did not model large cytoplasmic regions of helices E and F, also lies adjacent to this cluster.