dataesr/scientific-paragraphs-categorization huggingface dataset:
The IGM accretion onto more mas- The dark crosses in the background are data by M12, the yellow points our linear fits to the metallicity profiles of massive, quiescent galaxies (off the SFMS) and the orange points our linear fits to the metallicity profiles of massive, star-forming galaxies (on the SFMS). sive galaxies with higher total surface density is higher in the beginning but shuts down faster than for less massive and less dense galaxies (their Fig. 3 ). With the decrease in gas supply, once more metallicity converges towards an equilibrium value, as can be seen in the centres of our high µ galaxies. The comparison to two chemo-dynamical models suggests that our observational finding of steeper metallicity gradients in galaxies with lower µ can be explained. Our recovered relation can either be interpreted as the impact of (i) the halo spin parameter on the inside-out growth of exponential disks or (ii) smaller radial velocities in galaxies of earlier type.
Conclusion In this work, we present new optical longslit spectra for 27 low-mass galaxies from the xGASS  (Catinella et al. 2018 ) and xCOLD GASS surveys  (Saintonge et al. 2017). By combining the new data with data from xGASS and xCOLD GASS, we investigated the relation between gas-phase oxygen abundance, gas content, and star formation. In particular, we focused on metallicity gradients and the local metallicity at different galactocentric radii and their correlation to global galaxy properties. Our findings can be summarised as follows: -While there is a number of global galaxy properties that correlate with the metallicity gradient, various statistical analyses suggest that only the stellar mass surface density µ drives metallicity gradients. Other correlations come about as log µ correlates with these global galaxy properties.
2015; Belfiore et al. 2017b; Sanchez et al. 2018; Sánchez et al. 2016a,b)  and SAMI  (Croom et al. 2012; Poetrodjojo et al. 2018) , we find that our results agree within the errors for high-mass galaxies. In the lower stellar mass regime we observe relatively steep gradients. These discrepancies can not be explained by sample selection but potentially by small sample statistics. We expect further discussions in the literature over trends with metallicity gradients for galaxies at stellar masses, M ≤ 10 10 M , or with low stellar mass surface densities (small to no bulges). Furthermore, it is vital that metallicity gradients are measured from metallicities at similar radial regions. Once data points inwards of 0. 5 r eff,r are included in the gradient measurement, which was not done by the MaNGA team, our results start to differ significantly.
Acknowledgements. We would like to thank the referee for a constructive report that helped improving this paper. KL would like to thank  Virginia Kilborn  and  Gabrielle Pezzulli  for valuable discussions during the making of this paper. LC is the recipient of an  Australian Research Council   Future Fellowship  ( FT180100066 ) funded by the  Australian Government. Parts of this research were supported by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. Besides software packages already mentioned in the main body of this paper, this work has also made use of Python 6 and the Python packages: astropy (Astropy Collaboration et al. 2013), NumPy 7 , matplotlib 8 (Hunter 2007), pandas (Reback et al.
2020;McKinney 2010) and seaborn 9. Furthermore TOPCAT has been used (Taylor 2005). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France (Ochsenbein et al. 2000); the SIMBAD database, operated at  CDS, Strasbourg, France  (Wenger et al. 2000); TOPCAT (Taylor 2005); the "Aladin sky atlas" developed at  CDS, Strasbourg Observatory, France  (Bonnarel et al. 2000;Boch & Fernique 2014);  NASA's Astrophysics Data System Bibliographic Services. Funding for the SDSS and SDSS-II has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , the  U. S. Department of Energy , the  National Aeronautics and Space Administration , the  Japanese Monbukagakusho , the  Max Planck Society , and the  Higher Education Funding Council for England.
Motivation: The investigation of the structure of biological systems at the molecular level gives insight about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results: Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that 1) abstracts the underlying protein sequence, structure or fold, 2) allows the use of shape retrieval methods to screen large database of protein structures to identify surficial homologs and possible interacting partners, 3) opens an extension of the protein structure-function paradigm towards a protein structure-surface(s)-function paradigm.
e. the projection(s) of the protein topography in the 2D space  [20] , 4) shape retrieval methods based on the moments of 3D Zernike polynomials that best fit the molecular surface shape  [21] , 5) shape retrieval methods based on geometric learning  [22, 7]. The evaluation of the performance of shape retrieval methods in the literature is performed classically during the SHREC community benchmark  [23]  where joint efforts between the structural bioinformatics and the computer vision communities have been performed to develop benchmarking datasets on protein shapes  [24, 25, 26, 27, 28]. In the present work, we evaluate the performance of four different shape retrieval methods (3D-Surfer  [21] , PANORAMA  [20] , ShapeDNA  [18]  and VFH  [19] ) on the complete cross-comparison of the SHREC 2019 protein shapes benchmarking dataset (5298 shapes)  [27].
PANORAMA, ShapeDNA and VFH have shown top performances on non-protein shapes benchmarks  [29, 30, 31, 32]. As a reference, we include different protein structure comparison methods (CE  [33] , DeepAlign  [34] , TM-Align  [35]  and lDDT  [36] ). We also illustrate the performance of these shape retrieval methods on calmodulin, a protein displaying large conformational changes. Finally, we highlight the ability of these methods to identify distant surficial homologs. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that abstracts the underlying protein sequence, structure or fold and allows the use of shape retrieval methods to screen large database of protein structures to identify surficial homologs and possible interacting partners.
Datasets Set A has been designed for the evaluation of the performance of shape retrieval methods on protein shapes for the community benchmark SHREC 2019  [27]. The dataset comprises 5298 experimental conformations of protein domains extracted from 211 PDB entries resolved by NMR. It is available at http://shrec2019. drugdesign. fr. Set A classification relies on the Structural Classification of Proteinsextended (SCOPe) database  [37, 2]. The lowest hierarchical level -called Domain hierarchical level-links the SCOPe database to the Protein Data Bank (PDB)  [38]. The following inclusion procedure was applied on all SCOPe entries. A PDB structure was included if 1) its conformers display the same number of atoms, 2) it belongs to the α, α + β or α/β structural classes of the SCOPe database, 3) at least four orthologous protein structures exist and satisfy the previous inclusion rules.
A total of 211 PDB entries satisfying all these criteria were selected and assigned to 17 classes (following the SCOPe Protein hierarchical level). Apart from the Protein hierarchical level, the dataset contains sub-classes along two hierarchical sublevels. The Species hierarchical level contains 54 classes corresponding to the different species. The Domain hierarchical level is composed of 241 classes corresponding to the initial SCOPe classification. For each structure of the dataset, the solvent excluded surface (SES)  [39]  was computed using EDTSurf  [40]  with default parameters. EDTSurf outputs triangular meshes stored as. ply file, converted to. off and. pcd formats, required by the different shape comparison methods. Set B consists in 16 protein structures that were studied in  [8].
The following protein couples in set B display high surface shape similarity and low sequence identity defined by their PDB ID: 1jzn (chain A) -1g1q (chain A), 1bar (chain A) -1rro, 1ryp (chain B) -1gwz, 1a31 -1cy0, 1tbp -1t7p, 1b3t -1adv, 2nwl -2bbh and 2b2i -2cfp.
Shape retrieval methods In 3D-Surfer, the protein global surface information is represented with 3D Zernike Descriptors (3DZD), mathematical moment-based invariants of 3D functions  [8]. The molecular surface of the protein is triangulated using MSROLL  [41]  and mapped onto a 3D grid from which 3DZD descriptors are calculated for each protein. The similarity between two given protein surfaces is quantified by the Euclidean distance between their two respective descriptors. 3D-Surfer is only available on-line  [21]  and takes a PDB file as an input. In PANORAMA  [20] , the panoramic views, i. e. molecular surface maps, are acquired through cylindrical projections of the protein surface. The feature extraction relies on the use of two 2D transforms. Once the descriptor is extracted for each protein surface of the dataset, the Manhattan and the Canberra distances are used to quantify the overall similarity between the two protein surfaces.
Therefore, MMLigner generates zero to several possible structural alignments for a pair of input structures. We present here the coverage of the best structural alignment; it is expressed as the fraction of residues being aligned (the smallest protein is taken as the reference). KPax  [50]  is a flexible backbone structural alignment program design to circumvent the limitations of the rigid 3D superposition algorithms. KPax starts by detecting short, local sets of 7 residues, then uses dynamic programming to generate an optimal, global alignment using Gaussian functions to score the structural alignments (rigid structural alignment). The residue pairs structurally aligned are then assigned to a segment, and the remaining residues are considered for a new alignment step until no further residue sets of 7 residues can be aligned.
Runtime The runtimes are presented in Table  2 , with a distinction between the runtimes for the descriptors calculation and the runtimes for the descriptors comparison. Results for 3D-Surfer are not reported since it is a full webservice. In total runtime, we observe that VFH is the fastest, followed by PANORAMA, and ShapeDNA, respectively. The structure comparison methods mean computation times for CE, DeepAlign, and TM-Align are 2s, 0. 2s, and 0. 1s, respectively. Table  2. Runtimes (in seconds) to compute the descriptor for the largest and the smallest SES of the dataset and to compute the distance between two SES using the evaluated shape retrieval methods. For information, EDTSurf takes 10. 9 s and 1. 85 s to compute the largest and smallest SES, respectively.
Proteins displaying large conformational changes: the calmodulin case Proteins are dynamical objects that may undergo large structural, conformational changes. In such cases, usual rigid-body superpositionbased comparison methods display some well-known weaknesses: only Cα (or backbone) atoms are taken into consideration; they are sensitive to the changes of orientation between the domains of multi-domains proteins; models bearing unrealistic structural features (steric clash, for instance) are not penalized  [52, 53]. In set A, a few classes displayed conformational variability; we focus here on the specific example of the Xenopus laevis calmodulin, whose structure undergoes a large and ample re-arrangement of its domains. Xenopus laevis calmodulin is composed of two domains linked by a threeresidue coil that allows an ample motion of one domain with respect to the other (Supplementary Table  S1 ), resulting in very different conformations (pdb entry 1dmo, chain A, 30 conformers).
Protein shape retrieval The performance of the shape retrieval methods was evaluated on the hierarchical dataset of protein shapes (set A) that is exclusively composed of NMR structures. We decided to use NMR structures because they represent the biological dynamics of proteins with different models based on constraints derived from experimental data  [55, 56]. The inclusion of these different models allowed to evaluate the influence of the conformational variability of each protein in our dataset on the performance of the evaluated methods in retrieval. It is to note that the variability of side chain conformations displayed a low influence on the overall performance of the evaluated methods in retrieval (Supplementary Figure  S2 ). This evaluation highlighted the high performance of 3D-Surfer, the only shape retrieval method evaluated in the present work originally designed to compare protein shapes.
On the contrary, the high performance of 3Dsurfer can be explained by the fact that Zernike moments are based on spherical harmonics. Spherical harmonics represent a basis function on the surface of a sphere. This descriptor is particularly adapted to globular proteins that mainly compose the datasets used in this study. PANORAMA describes the cylindrical projection by a discrete transform which is a sum of functions defined on a circle. These functions are well-fitted to describe the globular shape of the proteins composing the datasets projected on a cylinder as well.
Method At the Species hierarchical level, orthologous proteins are separated in different classes. Most shape retrieval methods classified orthologous proteins within the same class, resulting in a loss of performance in retrieval compared to the Protein hierarchical level. Similarly, at higher recall values, the best structure-based methods still outperform the shape-based methods (Figure  1  and Table  1 ). These results point at potential ways for future improvements such as taking additional molecular surface features (electrostatics, hydrophobicity…) into consideration  [7]. Concerning the computational costs associated with the shape retrieval methods, it is important to note that in application cases such as large databases screening, the speed of calculation of the distance between the methods descriptors is a key factor contrarily to the cost associated to the calculation of the descriptor that can be performed only once per object and stored for future use.
These results shed lights on the versatility of shape retrieval methods. While structure-based methods require either rigid-body superposition (CE, TM-Align…) or a high similarity between the objects to be compared (lDDT, CAD-score…), shape-based methods, due to the abstract protein molecular surface representation, may be better suited to the blind classification of large datasets including highly heterogeneous protein structures  [11, 5, 4] , such as a screening of the Protein Data Bank  [38]. Further work dedicated to this specific task would be beneficial to the community.
Similarly, these methods may be beneficial to the protein structure classification where reference databases still require manual intervention from human experts. With this in mind, shape-based methods may enrich the pool of methods available for protein comparison able to retrieve proteins with similar shape but different topologies  [11, 59]. This could also be useful for identifying possible interacting partners  [7]  since molecular shape plays a crucial role in binding  [6, 3, 60]. Shape retrieval methods could then be used for creating a structural classification of proteins based on their surfaces  [4, 61] , rather than evolutionary distances or fold categories as in SCOP  [47]  or CATH  [48]  opening the possibility to extend the protein structure-function paradigm towards a protein structure-surface(s)-function paradigm.
Conclusion In this work, we evaluated the performance of four shape retrieval methods from the computer vision field (3D-Surfer, PANORAMA, Shape-DNA, and VFH) on a protein shapes dataset. On this dataset, 3D-Surfer and PANORAMA outperformed Shape-DNA and VFH. On a selected example displaying large conformational changes (calmodulin), all shape retrieval methods displayed a reasonable performance in recognizing their different conformations within the dataset. Different structure comparison methods were used as a reference in this study (CE, DeepAlign and TM-Align). TM-Align slightly outperformed shape retrieval methods in the retrieval task, but failed in tracking the large conformational changes of the calmodulin. For the calmodulin case, the superposition-free structure comparison method lDDT outperformed all the other evaluated methods in identifying the different conformers of calmodulin.
We also identified 6 pairs of distant surficial homologs that could be used for future studies on protein surficial similarity search. Finally, shape retrieval methods were associated with larger computational costs compared to classical structural alignment methods but this additional cost is still compatible with the treatment of large structural datasets. Geometric learning methods could be beneficial here since their computational cost seems to be lower. This work confirms the interest of protein molecular shape as a higherlevel description of the protein structure that 1) abstracts the underlying protein sequence, structure or fold, 2) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners, 3) opens an extension of the protein structure-function paradigm towards a protein structure-surface(s)function paradigm.
While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al. , 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL and SPM) across the remaining steps of the pipeline.
We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e. g. choice of first-level signal model had the most impact for the ds000001 dataset, while first-level noise model was more influential for ds000109 dataset). We also observe areas of the analysis workflow where changing the software package causes minimal di↵erences in the final results, finding that the group-level results were largely una↵ected by which software package is used to model the low-frequency fMRI drifts.
At around the same time the first in a series of Many Labs studies was published, where numerous analysis teams have tried to replicate results from the psychology literature across a diverse range of samples. Of the 51 studies re-evaluated in the first three Many Labs projects, roughly 60% yielded significant e↵ects  (Klein et al. , 2014 , 2018 , Ebersole et al. , 2016). The field of functional magnetic resonance imaging (fMRI) for human brain mapping has not come away unharmed from the replication crisis. On the contrary, the large degree of flexibility in neuroimaging analysis workflows has been pinpointed as an aspect of the field that can hinder reproducibility  (Ioannidis, 2005). The crux of the problem is that two di↵erent analysis pipelines applied to the same dataset are unlikely to give the same result.
Therefore, as an increasing number of analytical tools and techniques have become available to researchers, this has also increased the potential to yield distorted findings with inflated levels of false activations. When combined with selective reporting practices -where only methods that return a favourable outcome are likely to end up being published -the consequences of this can be severe, leading to fMRI e↵ect sizes that are misrepresented and often overstated in the neuroimaging literature  (Simmons et al. , 2011, Szucs and Ioannidis, 2017). In one of the most comprehensive studies in this area, a single publicly available fMRI dataset was analyzed using over 6,000 unique simulated workflows, constructed by enumerating all possible pipeline combinations from an array of commonly implemented analysis procedures  (Carp, 2012).
Across the tens of thousands of thresholded results maps generated by these workflows, a substantial degree of variability was observed in both the sizes and locations of significant activation. In a more recent study, 70 independent research teams were tasked with testing 9 hypotheses on the same fMRI dataset, with no constraints placed on how each team approached their analysis  (Botvinik-Nezer et al. , 2020). Consequently, no two teams chose the same analysis workflow, and once again the plurality of methodological approaches manifested as variability in the final scientific outcomes, this time with considerable disagreement between the 70 teams' hypothesis test results. Overall, these investigations have forewarned practitioners not to fall victim to a version of insanity where we apply di↵erent workflows over and over again and expect the same results.
In  Bowring, Maumet, and Nichols (2019)  (BMN), we discovered that it is not just the procedures comprising the analysis pipeline that can induce variation across fMRI results, but also the choice of software package through which the analysis is conducted. We reanalyzed three datasets connected to three published task-fMRI studies within the three most widely-used neuroimaging software packages -AFNI, FSL, and SPM -reproducing the original publication's analysis workflows in each package as closely as possible so that the di↵erence in software was the only changing variable. We then applied a range of similarity metrics to quantify the di↵erences between each software's final group-level results. While qualitatively certain patterns of signal were observed across all three packages' statistical results maps, our quantitative comparisons displayed marked di↵erences in the size, magnitude and topology of activated brain regions, and we ultimately concluded that weak e↵ects may not generalize across software.
Now we revisit that work, seeking to understand where in the analysis pipeline the greatest variation between analysis software is induced. We substantially extend the analyses carried out for BMN, running the same three datasets through a series of 'hybrid' pipelines that employ a common processing strategy across parts of the workflow (e. g. by implementing a common fMRIPrep preprocessing strategy) and then interchange pipeline elements between software for the remaining stages of the analysis. By comparing all sets of our analysis results,we isolate the key stages of the workflow where the three packages diverge. Ultimately, we find that the variation between the packages' results is largely attributable to sizable processing di↵erences at a handful of key analysis stages, but that these sources of variability can be heterogeneous across datasets.