dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we found in previous studies cited in this manuscript (that concerns the reconstruction of climate modes, but not of climate fields), several issues in the classical methodological approaches. Our objective here is to assist paleoclimate experts in making the best out of their proxy databases with valid and robust statistical assessments. More specifically, using a new metric that we discuss below, we show how to evaluate different reconstructions of the same climate index but with different methodological choices (regression method, proxy network, length of the period on which the regression model is built). The wide range covered by the scores shows that the selection of these inputs is an important step to obtain a reconstruction as robust as possible. Furthermore, to make the production of such reconstructions more straightforward and facilitate its use to potential users, we have developed a code that simply requires a few parameters as input and that provides a set of different alternative reconstructions of a given climate index for a given proxy record database.
In addition, the code provides an ensemble of scores that evaluate the different reconstructions, each produced with different methodological choices. Thus the user of CliMoRec (see " Response to the short comment from Astrid Kerkweg: 'Executive Editor comment on gmd-2018-21 ' ") can finally pick the one that has the best scores. This is why we do not submit this paper to Climate of The Past, as we would like to make climate signal reconstructions more transparent and easily accessible and verified by the community. Furthermore, we believe that CliMoRec could be improved in the future by including further refinements in follow up versions which constitute an additional reason for which we prefer to submit this paper to GMD. Last but not least, we believe that providing sufficient level of details concerning the mathematical rationale behind our methods is very useful, an information that is hidden in the appendix in journals like Climate of the Past, which are more focused on the scientific results.
However, in the new version of the code, by using the Nash -Sutcliffe Coefficient of Efficiency, we have found significantly better results for the Random Forest and the Elastic-net methods than for the PLS and the PCR methods (this is illustrated in the Fig. R1  that will replace Fig. 7  of the previous manuscript), which shows that adding these methods even in a low-dimension study such as in ours can be more efficient than using classical forms of linear regression. Additionally the code we provide allows to choose the network of proxy records that is used for the reconstruction. As the number of available paleoclimate data is constantly growing (even if it does not reach hundreds of thousands yet), we claim that regression methods adapted to high-dimensional problems such as Random Forests will sooner or later, become particularly useful for climate index reconstructions.
, 2014). 3. Data assimilation approaches, for instance the Last Millennium Reanalysis framework  (Hakim et al. , 2016; Singh et al. , 2018). All of these methods have code that is publicly archived, often in open-source languages like R. Restricting themselves to antiquated regression methods forces the authors play a dubious game of optimization on the various training and verification sets, to offset the disadvantage of restricting the network to a gap-less training set. This is suboptimal on methodological and computational grounds. (2) In this study, we focus on climate variability modes, which is only a part of the global climate. We applied dedicated methods aiming at improving the reconstruction of these modes. Our techniques can certainly be further improved, but as it stands, we believe that they add new potentialities to the regression approaches currently at use.
This paper is actually clarifying and adding methodological clue and gives an accessible tool to help paleoclimatologists to build more robust climate index reconstructions. Although our approach and the approaches mentioned by the reviewer aim at reconstructing past climate, the question and focus of the paper is not to show if one is better than the other, but to try to further develop one of them. Concerning data assimilation methods, we certainly agree that these are very useful methods, but we do not believe that these methods, difficult to implement and thus not accessible to all paleoclimatologists, necessarily discard other more simple statistical models. We believe that science can benefit from a variety of approaches, all together contributing to identify robust results. (3) Therefore, we acknowledge the existence of the three methods depicted by the reviewer, and discuss them shortly in our manuscript, but we do not think there are decisive arguments showing that our approach is necessarily weaker, although this is not the scope of this paper to prove it at this stage.
An uncertainty band 2*s. e. is calculated for each of the 50 individual reconstructions and the envelope of this 2*s. e. uncertainty bands is our estimate of the total uncertainty range of the final reconstruction (as a sum of the regression uncertainty plus the parameter uncertainty). (3) We added regression uncertainties in a table and on the figures where the reconstructions are shown (Fig. 11  of the last version of the manuscript). Also, the code we deliver provide standard errors for each member of a given final reconstruction. 1. 4 Statistical Models are Models too (1) I feel compelled to point out that this is a journal about models, so it would be desirable to discuss the advantages of the methodological choices on modeling grounds: each of them models the data and uncertainties in various ways, and it would seem natural for such modeling assumptions and choices to be discussed here (more so than say, Climate of the Past, where the current manuscript would be a better fit in present form).
a b s t r a c t This data article makes available the informed computation of the whole Protein Data Bank (PDB) to investigate diffraction anisotropy on a large scale and to perform statistics. This data has been investigated in detail in "X-ray diffraction reveals the intrinsic difference in the physical properties of membrane and soluble proteins"  [1]. Diffraction anisotropy is traditionally associated with absence of contacts in-between macromolecules within the crystals in a given direction of space. There are however many case that do not follow this empirical rule. To investigate and sort out this discrepancy, we computed diffraction anisotropy for every entry of the PDB, and put them in context of relevant metrics to compare X-ray diffraction in reciprocal space to the crystal packing in real space.
These metrics were either extracted from PDB files when available (resolution, space groups, cell parameters, solvent content), or calculated using standard procedures (anisotropy, crystal contacts, presence of ligands). More specifically, we separated entries to compare soluble vs membrane proteins, and further separated the later in subcategories according to their insertion Contents lists available at ScienceDirect journal homepage: www. elsevier. com/locate/dib in the membrane, function, or type of crystallization (Type I vs Type II crystal packing). This informed database is being made available to investigators in the raw and curated formats that can be re-used for further downstream studies. This dataset is useful to test ideas and to ascertain hypothesis based on statistical analysis.
Data In these data, X-ray diffraction anisotropy is calculated for each entry of the Protein Data Bank and put into perspective with relevant structural information, such as solvent content, resolution, crystal contacts, space group, presence of ligands, etc. , to investigate correlations. The aim of these data is to investigate differences between soluble and membrane proteins, so these two types of proteins were identified and separated. Membrane proteins were further separated into subclasses according to their insertion in the membrane, fold, or function to link differences with biology.
Data mining and computation As of February 24th, 2016, a local copy of the RCSB Protein Data Bank (PDB) was made including all the deposited structures in PDB formatted coordinate files as well as all the crystallographic structure factors in mmCIF format. To this date, out of 115,888 available structures, 103,530 were solved by X-ray crystallography and 92,995 related structure factors files were accessible. For further processing, these last were converted from mmCIF to CCP4 MTZ format with the sf-convert software version 1. 204 (developed at RCSB and downloadable at http://deposit. pdb. org/software). By this mean, 92,930 structure factors files were successfully converted, while 65 were not due to various file format issues. We then developed an automated Linux script in Bash programming language that sequentially performed the following tasks for each of the PDB/MTZ couple of files obtained previously: -Several data were directly extracted from the PDB file header: the most recent deposition/revision year (PDB 'REVDAT' record), the resolution in angstrom ('REMARK 2 RESOLUTION' record), the space group and the unit cell parameters ('CRYST1' record), the data collection temperature ('REMARK 200 TEMPERATURE' record), all compounds/ligands sorted by their 3-letters hetID codes ('HETNAM' records), as well as the list of terms relevant to the entry ('KEYWDS' record).
-In addition to these keywords and for each membrane proteins entries, we extracted the name of the protein from the 'Membrane proteins of known 3D structure' database (http://blanco. biomol. uci. edu/mpstruc/) leaded by S. H. White (University of California, Irvine). -When available, the solvent content (in percent) was also retrieved ('REMARK 280 SOLVENT CONTENT' record). If not, it was calculated using the program matthews_coef from the CCP4 software suite version 7. 0  [3]. -When applicable, the percentages of four classes of protein secondary structure elements (helices, strands, turns and coils) were calculated using the program mkdssp version 2. 2. 7 included in CCP4. -A crystal contacts ratio value was determined by dividing the number of crystal contacts in the unitcell (computed using ncont from CCP4 with a maximum distance cutoff of 4.
0 Å) by the total number of atoms, including heteroatoms and solvent (i. e. all 'ATOM' and 'HETATM' PDB records). -We employed the 'UCLA Diffraction Anisotropy Server'  [4]  script that we modified to take advantage of the last available revisions of CCP4. Thus, for each PDB entry, the anisotropic delta-B value was computed with Phaser  [5]  both using amplitude and intensity data, when available. Furthermore, the resolution limits at which F/σ(F) drops below 3. 0 was determined using the program Truncate from CCP4, this for each of the 3 principle axes of the anisotropic ellipsoid. A 'delta_res' value was then deducted by subtracting the lowest resolution limit to the highest one. In addition, the Wilson B-factor was computed with Phaser using amplitude and intensity data, when available.
Curation For reasons described in  [6]  and the behavior of anisotropy over the years (Fig S6D in ref  [6] ), we decided to only retain structures obtained after 2005 in order to compare entries of similar difficulty levels and susceptible to have comparable anisotropic behavior. Also, in order to compare reasonably well-behaved structures, only data diffracting to less than or equal to 5 Å resolution were kept, and anisotropic delta-B values on amplitudes above 150 Å 2 were rejected. In addition, all crystal contacts ratio over 1 were removed from the analysis. Thus, our final dataset consisted of 76,458 entries with 74,928 and 1411 calculated anisotropic delta-B values on amplitudes (soluble and membrane proteins, respectively); and 23,125 and 487 values on intensities.
Subsets extraction From this curated database, 13 subsets were then extracted based on distinct structural or biological criteria. These last derived from the classification provided by the 'Membrane proteins of known 3D structure' database (http://blanco. biomol. uci. edu/mpstruc/). These subsets are: soluble proteins; membrane proteins; membrane proteins structures solved in detergents, lipidic cubic phase (extracted as described by M. Caffrey  [7] ) or bicelles; α-helical or β-barrel transmembrane proteins; monotopic membrane proteins; membrane ATPase, electron-transfer, channel, receptor and transporter proteins. Finally, two other subsets (embedded membrane proteins and proteins with extramembranous domains) were extracted based on visual inspection of their spatial arrangements information, visualized using the 'Orientations of Proteins in Membranes' (OPM) database  [8].
Code availability The present database is generated using an automated Linux Bash script we developed (tested on CentOS 7. x). This last is available with no restrictions upon request to the corresponding author. It can be executed on any Linux distribution as long as the CCP4 software suite version 7. 0 (or superior) is installed. Moreover, a local copy of the PDB is also required: this includes all the deposited structures in PDB formatted coordinate files as well as all the crystallographic structure factors in mmCIF format to be converted in MTZ format. An additional Linux Bash script performing these file mirroring and conversion steps is available upon demand as well.
Model The numerical model used in this work combines the rotational evolution model of  Gallet & Bouvier (2013 , 2015)  and the modified orbital evolution model of  Bolmont et al. (2012 , see Bolmont & Mathis 2016). The dissipation of the dynamical tide inside the star is treated in the convective envelope as in  Gallet et al. (2017a , who followed Ogilvie 2013 and Mathis 2015b)  and the stellar structure is from the stellar evolution code STAREVOL (see  Amard et al. 2016, and references therein). We developed this code that combines these two numerical approaches so as to study, in a more realistic way through the addition of the decoupling between the radiative core and the convective envelope, the impact of the star-planet interaction on the surface rotation rate of low-mass stars. This code is specifically designed for stars between 0.
Stellar evolution This work is based on a grid of non-rotating stellar models we computed with the code STAREVOL (see e. g. Amard et al. 2016)  for a range of initial masses between 0. 5 and 1. 0 M at solar metallicity (Z = 0. 0134;  Asplund et al. 2009 ). The references for the basic input microphysics (equation of state, nuclear reactions, and opacities) can be found in  Amard et al. (2016)  and in  Lagarde et al. (2012). The initial helium abundance and mixing length parameter are calibrated without atomic diffusion to reproduce a non-rotating Sun with respect to the solar mixture of  Asplund et al. (2009)  with a 10 -5 precision for the luminosity and the radius at the age of the Sun. The corresponding mixing length parameter and initial helium abundance are α MLT = 1. 6267 and Y = 0. 2689.
7-0. 8 M ). Can tidal star-planet interactions reproduce these observations? If it is the case, then what could be the insights about the initial distribution of massive close-in planets around low-mass PMS stars, such as the initial location and the mass of the planets so as to reproduce the observations in the Pleiades? Our model predicts that this rotational kink should also be present for slightly less massive stars, namely 0. 7-0. 6 M , corresponding to V -K s ≈ 3. 3. However, this behaviour is not observed in the data from  Stauffer et al. (2016). It could not be explained by a shift in the mass estimation between the STAREVOL code used in this work and the  Baraffe et al. (2015b)  code used in  Stauffer et al. (2016)  since both models are quite similar and isochrones from these two models produce the same theoretical stellar masses (Louis Amard's PhD thesis and private communication).
Acknowledgements. We thank the anonymous referee and the editor for their fruitful comments and suggestions. We thank our colleague  Philippe Delorme  for the very useful discussions of some aspects of this paper. F. G acknowledges financial support from the  CNES  fellowship. The authors acknowledge financial support from the  Swiss National Science Foundation (FNS)  and from the  French Programme National de Physique Stellaire PNPS of CNRS/INSU. F. G. and C. C. acknowledge financial support from the  Swiss National Science Foundation  and  294   SEFRI  for project C. 140049 under  COST Action TD 1308 Origins  (PI C. C. ). This work results from the collaboration of the  COST Action TD 1308. S. M. and E. B. acknowledge funding by the  European Research Council  through  ERC  grant  SPIRE 647383  and support from the  CNES-PLATO  grant at  CEA Saclay.
Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the first beamline in the world to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014, the beamline has processed over 42 000 samples. Improvements have been made to the speed of the sample-handling robotics and error management within the software routines. The workflows initially put into place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterization; this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal.
, 2016). At the time of writing, the beamline has processed more than 42 000 samples representing a wide range of projects, from those that require extensive screening to find the best diffracting crystal  (Na et al. , 2017; Sorigueet al. , 2017; Naschberger et al. 2017)  to smallmolecule fragment screening  (Cheeseman et al. , 2017; Hiruma et al. , 2017)  and experimental phasing at high and low resolutions  (Kharde et al. , 2015; Muir et al. , 2016; Sabbadin et al. , 2018). The beamline is able to deal with a wide range of samples by combining parameters provided by the user with information gathered during processing. The workflows that were initially put into place have performed very well, but many enhancements remained possible. Here, we describe how the hardware and algorithms have been improved to increase the amount and the quality of data collected on MASSIF-1.
Hardware improvements One of the most time-consuming, and important, steps in the X-ray centring process is the initial mesh scan that locates and characterizes the crystal. When first implemented on MASSIF-1, a rotation of ! was included in the scan in order to avoid still images that can cause problems in processing. This implementation required that triggering of the acquisition of images was instigated by the ! axis, meaning that each line of the mesh was treated as a separate data collection. The preparation required between data collections led to additional time being taken for the scan. We have now implemented a scan that includes no rotation of the ! axis and only requires movements of the high-precision Y/Z stage beneath the RoboDiff  (Nurizzo et al. , 2016). This allows the triggering of acquisition to be made by the motor position, allowing the whole mesh to be launched as a single data collection  (Aishima et al.
Whatever the reason, it can lead to a data set being lost. We therefore introduced a check in the characterization step that ensures that the four images have a diffraction signal. If one image has a signal that is below 10% of the top signal, a recovery routine is launched. This involves three short line scans (50 mm above and below the current centred position) being launched over the currently centred position. In most cases it corrects the error. In 2017, 13 776 samples were processed on MASSIF-1 and centring recovery was launched 221 times. This represents a centring-error rate of 1. 6%, which should be reduced further by being able to detect and recover incorrectly centred samples. 2. 3. 2. Low-resolution data collection. Calculating an optimized strategy for low-resolution (>4 A ˚) data collection can be problematic, but is very important as useful information can be extracted from carefully collected data at these resolutions.
, 2010)  and that has been shown to be beneficial in many cases  (Polsinelli et al. , 2017). Radiation damage can often make it difficult to collect complete data, or data with sufficient anomalous signal, from a single crystal or a single position within a crystal. A new experiment type is now available on MASSIF-1 that will automatically collect multiple partial data sets from positions within a homogenous volume of a crystal. This can lead to improved data quality, increased resolution and higher anomalous peaks. This is the first fully automated helical data-collection protocol that also accounts for the heterogeneity of crystal diffraction quality. Multiple-position/crystal experiments are selected either by specifying a number of positions in the diffraction plan for the sample or by requesting MXPressP or MXPressP_SAD (for a pseudohelical strategy or a pseudohelical with SAD strategy, respectively) in the 'experiment type' field for the required samples in ISPyB  (Delageniere et al.
The number of allowed detected volumes is then specified by a comment in ISPyB. If multiple crystals have been selected, each point is then centred as usual and a complete data set is collected according to user input requirements. If MXPressP is selected, the top volume is centred and four characterization images are collected. A strategy is then calculated for a complete data set and the data are collected; for MXPressP_SAD the strategy is calculated for structure solution by SAD  (Svensson et al. , 2015). As usual, in the case of indexing failure a default data collection of 180 is collected (240 for triclinic and 360 for SAD data collection). Once completed, a strategy is then calculated to collect a complete data set from the N positions determined in the mesh scan that are within 70% of the value of position 2.
The strategy, as usual, accounts for the volume of the positions, beam diameter etc. Again, in the case of a failure in indexing default data collections are performed at each position using a full dose and a rotation range determined by 180 /N (240 /N for triclinic and 360 /N for SAD). Each partial data set has a 5 overlap with the next to assist with scaling. We are, for the moment, remaining cautious with pseudohelical data collection by collecting a full single-position complete data set from the best volume. The reason for this is twofold: (i) we have observed that crystal heterogeneity can often lead to a number of the partial data sets being of varying quality despite the stringent quality threshold that we have implemented and (ii) we are eager to compile a large amount of data on how and when helical data collection is superior to single-position data collection.
Acknowledgements We thank  Tony Warne  ( MRC-LMB, Cambridge, England ) for the gift of 1 -adrenergic GPCR crystals;  Josan Marquez  and  Guillaume Hoffman  ( EMBL, Grenoble, France ) for providing CrystalDirect supports with multiple crystals; and the  ESRF Automation Task Force (ATF)  and the  ESRF-EMBL Joint Structural Biology Group (JSBG)  for supporting the developments. We are also grateful to  Gordon Leonard  ( ESRF ) for comments on the manuscript.