dataesr/scientific-paragraphs-categorization huggingface dataset:
D. Quality of articles and a "natural selection" IPOL articles must be impeccable: every proposed algorithm needs to be reproduced online on any given image chosen or uploaded by readers. Each algorithm must be described in detail by the authors to check its conformity to the corresponding source code. The online execution of each algorithm must take no more than 30 seconds to spare the patience of readers (this often requires parallelization). Finally the results of the algorithm need to be useful, interesting, and competitive. The vast majority of the algorithms published in Image Processing and Computer Vision journals are far from meeting the IPOL's publication criteria. Most failed IPOL projects aborted when the authors realized that the algorithm described was incomplete, did not give all the results described on paper, was not feasible in a reasonable run time, or only worked on a certain type of data but not on any image.
For example, many algorithms for stereovision presuppose a pair of frontoparallel cameras. This is certainly a convenient simplification, but reduces the algorithm to a proof of concept, unsuited for any practical application on an actual stereo pair. Other authors with a potentially suitable algorithm were put off by the work of cleaning up and editing of their code to achieve a publishable code. It quickly became apparent that the IPOL journal would not reject a significant proportion of submissions, since most non-viable projects fail even before they get submitted.
The last sections address classic or new acquisition methods (Computational Photography, Infrared. ) The advantages of a reproducible algorithm should be obvious, but let us illustrate it. In stereovision, a reference benchmark for disparity map computation is known as the Middlebury stereo evaluation (http://vision. middlebury. edu/stereo/). More than 150 methods are put in competition on four stereo pairs, very few of them providing a complete algorithm or a source code. Authors just upload their images to the website, results are compared to the ground truth and they get ranked in the public database. During the implementation of two classic methods using adaptive neighborhoods  [12] ,  [14] , it was discovered that a post-processing must be applied to the main algorithm presented in  [12]  in order to reproduce similar results (there is no mention of a post-processing in the original article  [18] ).
F. Criticism to IPOL Authors and publishers have criticized IPOL for: i. the excessive effort required to arrive at a reproducible article (rigor, acceleration, running on any data) and to describe the code and deliver perfectly readable code; ii. the excessive length of the peer review (code reviewers are slow and very exigent); iii. the large number of objects to be published with the article: PDF file, source code, viable demo written in Python; iv. the journal being four years old did not yet receive an official impact factor. IPOL has already more than 500 citations on Google scholar for an average existence of two years for its articles  (2011, 2012, 2013, 2014). Articles published in 2011 and 2012 have been cited more than 20 times on average, with the top one reaching 395 citations.
v. to some authors it is frustrating to work so hard to elucidate algorithms designed by others. Even when those algorithms are inventive and original, they are generally incomplete and require considerable research effort. Nevertheless, the symbolic benefit of this work goes back in good part to the originators. vi. the restricted number of authorized libraries to support source code (like libjpeg, libtiff, fftw, etc. ) can be a criticism in particular from authors who are already invested in a library of a specific domain like computer vision with OpenCV [20], computational geometry with CGal  [21]  or discrete geometry with DGtal  [22]. Moreover, such library contributions are in general reviewed before integration in their main framework. Initially, the IPOL contribution relying on such a library was possible only with the inclusion of the library source code in the contribution code archive (as done for several articles of the special issue on DGCI 2011 conference  [23] ,  [24] ).
However, this restriction is going to be removed with the already allowed use of Matlab based demonstration and the use (under study) of OpenCV. The other libraries might be accepted in the future if needed. G. Authors and publishers praised IPOL for: vii. the immediate impact of their publications, reflected in the archive of experiments showing strong interest and use of the demos for technical issues far beyond the interests of the image processing community; viii. authors would like to link the experiments in the archive to their application domains and reveal what type of data remains a challenge; ix. the promise that this impact will continue due to the very existence of the demo; x. the fact that authors publishing these articles gain a tangible industrial and academic credibility, which greatly facilitates obtaining research funding.
PRODUCTION Conceptually, the main IPOL innovation is that the publication of algorithms forces researchers to write a very compact, fully reproducible pseudo-code and enables a universal disclosure of algorithms. Even the best written code is not easily readable and reusable, just because of its necessary prolixity: no understatement or ellipsis is possible when programming. A pseudo-code can be shorter by one or two orders of magnitude, while remaining unambiguous for a mathematician programmer. In sum, the mathematical definition of functions is often much shorter than their program counterpart. While the computer program is perishable, the pseudo-code is not. Programming languages, program libraries, and operating systems change. This actually causes recurrent maintenance problems. But the pseudo-code remains.
High-resolution structural information was obtained from lysozyme microcrystals (20 mm in the largest dimension) using raster-scanning serial protein crystallography on micro-and nano-focused beamlines at the ESRF. Data were collected at room temperature (RT) from crystals sandwiched between two silicon nitride wafers, thereby preventing their drying, while limiting background scattering and sample consumption. In order to identify crystal hits, new multi-processing and GUI-driven Python-based pre-analysis software was developed, named NanoPeakCell, that was able to read data from a variety of crystallographic image formats. Further data processing was carried out using CrystFEL, and the resultant structures were refined to 1. 7 A ˚resolution. The data demonstrate the feasibility of RT raster-scanning serial micro-and nanoprotein crystallography at synchrotrons and validate it as an alternative approach for the collection of high-resolution structural data from micro-sized crystals.
Introduction The advent of X-ray free-electron lasers (XFELs) has driven the emergence of serial protein crystallography  (Chapman et al. , 2011). In this novel approach, data are collected at room temperature (RT) from tens to hundreds of thousands of 'still' crystals, all of which are in different, more or less random, orientations. In contrast, the vast majority of the nearly 94 000 X-ray structures deposited in the PDB were obtained from data collected at cryogenic temperature (generally 100 K) from a single crystal rotated in an X-ray beam. Compared with RT, such low temperatures reduce by $100-fold the radiation damage that accumulates in crystals during data collection and degrades the structural information  (Nave & Garman, 2005). The price to pay is, however, that of reduced insight into protein conformational heterogeneity and dynamics  (Motlagh et al.
These SFX developments have been accompanied by active software development in order to contend both with the thinness of the three-dimensional reciprocal slice collected from still crystals and with the colossal amount of data produced during experiments. In SFX, samples are often presented to the X-ray beam by means of gas dynamic virtual nozzle (GDVN)-type injectors. GDVNs can be used to compress an aqueous crystal stream, thereby generating a liquid jet  (DePonte et al. , 2009). They can also be used to push a crystal-containing lipid cubic phase (LCP) across the X-ray beam  (Weierstall et al. , 2014). Liquid GDVN injectors require monumental amounts of sample (hundreds of milligrams), and thus are only suited for targets whose production, purification and crystallization are straightforward and streamlined.
A system based on acoustic droplet ejection has also been described  (Soares et al. , 2011). Common to all these approaches is that crystals are literally drowned in solvent, leading to high and fluctuating background scattering that complicates data processing. Alternate sample-handling methods are therefore amongst the most critical developments required for the further emergence of SFX. Recently, a method for the collection of data from crystalline samples dried onto fixed targets has been presented  (Hunter et al. , 2014) , as well as a goniometer-based approach to SFX  (Cohen et al. , 2014). The worldwide availability of SR sources raises the possibility that some of the experiments currently performed at XFELs could be implemented at synchrotrons. As already mentioned, the excitement elicited by XFELs has led scientific programmers to challenge themselves with the handling and processing of millions of diffractions patterns, making it now possible to process serial crystallographic data regardless of the X-ray sources they are collected at.
To date, at least two major software suites exist: CrystFEL  (White et al. , 2012)  and cctbx. xfel (Sauter et al. , 2013). Subtleties in the processing approaches of the two software suites have recently been discussed  (Sawaya et al. , 2014). In early 2014, a pioneering study reported serial synchrotron crystallography (SSX) experiments on Trypanosoma brucei cathepsin B crystals (CatB) mounted in a nylon loop at 110 K  (Gati et al. , 2014). Data were collected using a combination of raster scanning and oscillation, whereby evenly spaced (5 mm) volumes of the nylon-loop content were illuminated by a 4 Â 5 mm beam while oscillating the loop by 90 over the complete scan. Hence, a three-dimensional wedge, rather than a thin slice of the reciprocal lattice, was collected at each exposure. More recently, another consortium reported serial synchrotron crystallography data collected at RT from lysozyme crystals flowed across a 6 Â 9 mm X-ray beam inside a capillary  (Stellato et al.
Among the remaining issues are, however, the amount of sample consumed and the overall success rate. A more economical approach is thus still required for proteins whose production and crystallization cannot reach a manufacturing scale. In the present study, we report an alternative serial synchrotron crystallography approach allowing the collection of data at RT and in a pure raster-scanning mode; that is, without oscillating the crystals. For this proof of concept, we used tetragonal microcrystals of chicken hen egg-white lysozyme so as to allow a direct comparison of our data with those recently produced by SFX  (Boutet et al. , 2012)  and SSX  (Stellato et al. , 2014). To determine the minimal beam size to perform such experiments at SR facilities, data were collected using either a micro-focused or a nano-focused beam.
Samples were presented to the X-ray beam in a silicon nitride (Si 3 N 4 ) sandwich, thence avoiding the drying of samples while limiting background scattering and sample consumption. Identification of crystal hits, background subtraction and peak searching were performed using NanoPeakCell, a new multi-processing and GUI-driven Python-based pre-analysis software. Further processing of the data was carried out with CrystFEL, and structures collected using the micro-and nano-focused beams were refined to 1. 7 A ˚resolution. We propose the use of RT raster-scanning serial protein crystallography using SR microand nano-focused beams as an alternative approach for the collection of high-resolution structural data from micrometresized crystals. The proposed method is thrifty and virtually handling-free, and so can be used on fragile and scarce microcrystalline samples.
1a ). The washer was glued onto a small glass piece, itself glued onto a brass pin that was introduced into an adjustable magnetic crystal mount. The usable Si 3 N 4 surface was 2. 5 Â 2. 5 mm. For presentation to the X-ray beam, the mounts were either placed onto an xyz translation table (micro-focused beam; ESRF ID13-EH2) or onto a miniature hexapod (nano-focused beam; ESRF ID13-EH3). Figure  2  The new NanoPeakCell pre-analysis software for serial crystallography. (a) Workflow of the actions performed by NanoPeakCell. (b) Overview of the graphical user interface (GUI) of NanoPeakCell. On the left, the user can define important experimental parameters, as well as parameter values used during the hit-finding and peak-finding procedures (red inset). On the bottom left, the user can select the correction(s) to be performed and the output format(s) for saving hits (red inset).
Data sorting and processing Regardless of whether the data are collected at an XFEL or at an SR source, a major challenge in serial crystallography is hit finding. Also, background scattering must be subtracted to permit an accurate measurement of the diffracted intensities  (Boutet et al. , 2012). Data correction must, however, be performed cautiously. Indeed, in a given volume crystals and solvent are mutually exclusive, so the amount of background to be subtracted needs to be scaled on a per-pattern basis. Specific to the use of a Frelon CCD camera, raw images must  also be corrected for distortion and a flat-field correction applied (metrology correction). Together, these requirements prompted us to undertake the coding of a multi-processing, GUI-driven Python-based hit finder and corrector, which we named NanoPeakCell.
The flowchart of actions performed by NanoPeakCell is depicted in Fig. 2 (a) and includes conversion of images (the supported input formats include EDF, SMV, MCCD, CBF, TIFF, HDF5, SACLA-HDF5 and LCLS-XTC) into a suitable format for data processing with CrystFEL (HDF5 format) and/or cctbx. xfel ('pickle' format), as well as the generation of the parameter files and scripts needed to run CrystFEL in a user-friendly fashion (Fig. 2b ). Of note, NanoPeakCell not only selects hits (i. e frames with actual diffraction spots) on the basis of a single pixel being higher than a given threshold, but also checks whether a sufficient (user-provided) number of such pixels are present to allow further indexing. To allow determination of the effective resolution limit of the serial data set, NanoPeakCell additionally performs a maximum projection of selected hits on the fly (see Supplementary Fig.
S1 ). Finally, NanoPeakCell performs Bragg-peak finding using a local maximum algorithm and outputs the peak list in a supplementary data set of the HDF5 file, if the user selects for this output format, or in a text file otherwise. This can allow the rapid elimination of both blank and low-resolution images. Similar indexing rates were obtained when using the peak-finding function implemented in CrystFEL and that implemented in NanoPeakCell. Using the latter, however, avoids spending time optimizing the peakdetection parameters in CrystFEL. Accordingly, in this work, Bragg-peak finding was performed using NanoPeakCell and peak locations were fed into CrystFEL (using the --peaks=hdf5 option in the indexamajig module) for indexing  (Kirian et al. , 2011; White et al. , 2012 White et al. , , 2013)).
Structure determination and analysis Data were converted to mtz format and phased by molecular replacement using Phaser  (McCoy et al. , 2007)  with PDB entry 1ljn  (Harata & Kenai, 2002)  as the starting model. To verify whether the structural information contained in our data sets could have allowed automated model building, we attempted fitting of the full HEWL amino-acid sequence into various molecular-replacement maps using phenix. autobuild with default settings  (Terwilliger et al. , 2008). The maps were generated from molecular-replacement trials with six different lysozyme models. The first three models consisted of the mainchain and side-chain atoms from residues 1-127 (full protein), 1-65 (N-terminal half) or 66-127 (C-terminal half). The other three were composed of the main-chain atoms of residues 1-127, 1-65 or 66-127.
In the resulting 2mF o À DF c map from both data sets, quasi-perfect electron density is observed around the omitted residues (Figs. 4c  and 4d ). We attempted to compare our data sets (conservative micro and conservative nano) with a variety of previously published HEWL data sets, including (i) two low-dose data sets collected using conventional oscillation methods either at 100 K (PDB entry 1vds; S. Aibara, A. Suzuki, A. Kidera, K. Shibata, T. Yamane, L. J. DeLucas & M. Hirose, unpublished work; data collected on a rotating anode) or RT (PDB entry 3zek; in situ data collection on a synchrotron beamline;  Pinker et al. , 2013) , (ii) an SFX data set collected in vacuum and at RT using 40 fs XFEL pulses (PDB entry 4et8;  Boutet et al. , 2012)  and (iii) an SSX data set collected at RT by flowing a crystal slurry across a synchrotron X-ray beam (PDB entry 4o34;  Stellato et al.
, 2014). In the following, these data sets (PDB entries 1vds, 3zek, 4et8 and 4o34) are referred to as 'low-dose 100 K', 'low-dose 295 K', 'SFX' and 'Flow-SSX', respectively. Data sets were scaled using SCALEIT from CCP4  (Winn et al. , 2011; Supplementary Figs. S3 and S4). The unweighted R iso values between the various data sets are high, suggesting nonisomorphism, whereas the compilation of unit-cell parameters rather points towards all data sets (and the resulting structures) being isomorphous. A possible hypothesis to explain this apparent lack of isomorphism is that it emerges from errors in intensity distributions introduced by the Monte Carlo integration. Accordingly, and as observed previously by Schlichting and coworkers, the weighted R factors are drastically lower  (Boutet et al.
Quality of the structural information All data sets were phased by molecular replacement (MR) using the structure of HEWL in the tetragonal space group (PDB entry 1ljn) as the starting model. The resulting experimental maps were of excellent quality, as shown in Fig. 4  for the progressive micro and progressive nano (1. 7 A ˚resolution) data sets. To further evaluate the quality of the structural information contained in our data sets, MR phasing was attempted using incomplete starting models. All MR trials succeeded, even when as few as 20% of the total atoms of HEWL were used. The resulting experimental maps were of sufficient quality to allow the automated building of $98% of the main-chain atoms and 90-98% of the side-chain atoms. Supplementary Table  S1  recapitulates these results for the progressive nano data set.
Discussion We have reported a new approach for the collection of highresolution structural data from macromolecular crystals at RT using raster-scanning serial data collection. Two data sets were collected from tetragonal HEWL crystals on the ID13 beamline at the ESRF, using either a micro-focused (ID13-EH2) or a nano-focused (ID13-EH3) beam. Crystals were presented to the X-ray beam in an Si 3 N 4 sandwich at RT (Fig. 1 ). During the whole experiment, including controls, 1 mg of crystalline sample was consumed. In order to detect hits and correct for both distortion of the detector and background scattering (Fig. 3 ), we developed the Python-based software NanoPeakCell (Fig. 2 ) that performs these tasks in a timely (multi-processing) and user-friendly (graphical user interface) fashion. NanoPeakCell uses the FabIO library  (Knudsen et al.
Acknowledgements Financial support by the  ANR , the  CEA , the  CNRS  and the  UJF  is acknowledged. NC is supported by a grant from the  France Alzheimer Foundation. We thank  M. Weik ,  T. A. White ,  R. G. Sierra ,  C. Riekel ,  J. Rodriguez ,  E. Girard ,  D. Cascio ,  M. R. Sawaya  and  D. S. Eisenberg  for helpful and stimulating discussions, as well as for continuing support. We are indebted to  L. Lardie  `re and  L. Peyrin  for the threedimensional rendering of our sample preparation procedure shown in Fig. 1(f).
98) 3 8 0. 98 (0. 94) 3 5 0. 99 (0. 66) 3 4 0. 99 (0. 85) 4 2 0. 99 (0. 50) 4 2 R free 0. 264 0. 245 0. 261 0. 255 0. 239 0. 264 R work 0. 214 0. 230 0. 203 0. 226 0. 204 0. 223 R. m. s. deviations from ideal values} Bonds (A ˚) 0. 006 0. 006 0. 005 0. 006 0. 006 0. 005 Angles ( ) 0. 872 0. 901 0. 929 0. 864 0. 935 0. 878 Average B value (A ˚2) 39. 9 62. 3 44. 0 40. 8 44. 20 45. 20 Clashscore † † 6. 31 4. 23 3. 11 5. 34 4. 23 5. 17 Ramachandran plot † † Most favoured 98. 5 98. 5 97. 0 97. 7 98. 5 97. 8 Allowed 1. 5 1. 5 3. 0 2. 3 1. 5 2. 2 Disallowed 0 0 0 0 0 0 Rotamer outliers † † 0 2. 7 2. 7 0 1. 8 3. 5 PDB entry 4wl7 4wl6 n/a 4wg1 4wg7 n/a.
We report the development of the ReproGenomics Viewer (RGV), a multi-and cross-species working environment for the visualization, mining and comparison of published omics data sets for the reproductive science community. The system currently embeds 15 published data sets related to gametogenesis from nine model organisms. Data sets have been curated and conveniently organized into broad categories including biological topics, technologies, species and publications. RGV's modular design for both organisms and genomic tools enables users to upload and compare their data with that from the data sets embedded in the system in a cross-species manner.
INTRODUCTION Sexual reproduction in eukaryotes involves a wide spectrum of biological processes by which species give rise to new individuals and thus perpetuate. These include the formation of haploid gametes after meiosis, a specific type of cell division that takes place only in the germ line. In the male, the differentiation of germ cells into highly specialized spermatozoa is a complex and tightly regulated process called spermatogenesis. This developmental process involves the sequential and coordinated expression of thousands of genes, many of them testis-specific. Spermatogenesis has thus been widely explored by several microarraybased expression studies over the last two decades  (1, 2)  and several databases devoted to spermatogenesis and gametogenesis  (3) (4) (5)  or to reproduction in general  (6) (7) (8)  have been developed to organize and provide access to this massive quantity of data.
More recently, ultra-high-throughput next-generation sequencing (NGS) projects have imposed new challenges on the life science research community: the complex tasks of processing, hosting and interpreting these data  (9). The repositories or databases referred to above, however, cannot cope with several intrinsic features of NGS data. For instance, although microarrays provide an average measurement of gene or transcript expression that can be easily displayed, NGS offers quantification at a single-base resolution, a feature that could only be observed by specific visualization tools that can take into account both genome coordinates of sequenced nucleotides and coverage information along every genomic locus. Additionally, microarraybased expression databases are typically organized around annotated entities, i.