dataesr/scientific-paragraphs-categorization huggingface dataset:
Golden dataset construction In order to perform golden dataset curation, we focused on articles where pathways were explicitly described (i. e. no missing steps, and available intermediate compounds). We retrieved compound structures in PubChem  (Kim et al. 2019 ) and EC numbers from Brenda  (Jeske et al. 2019 ) based on enzyme name as given in the article. We selected pathways of strictly more than 1 step. We then verified for each step that we had chemical rules available in our RetroRules  (Duigou et al. 2019 ) database to encode the described transformations. This ensures a fair comparison between tools using our publicly available reaction rules, in order to evaluate separately retrosynthesis tools and the underlying chemical rules. The detailed list with references is available as Supplementary Table  4 , and the pathways as Supplementary Data 1.
Hardware We ran our tests on 2 calculation clusters and 1 personal computer. Tests involving using the NoSQL Database and media supplementation were run on a desktop computer with the following characteristics: CPU is Intel(R) Xeon(R) W-2145 CPU @ 3. 7 GHz and 32G of RAM. Tests for the golden dataset evaluation were performed on the Migale (https://migale. inra. fr) cluster (2. 0 to 2. 4 GHz CPUs). Allocated resources per job were set to 1 vCPU and 20 GB. Tests for the LASER database evaluation were run on the IFB cluster (http://taskforcenncr. gitlab. cluster. france-bioinformatique. fr/doc/cluster-desc/) (2. 3 GHz CPUs). Allocated resources per job were set to 1 and 40 GB. Cluster tests were run using snakemake 5. 4. 0  (Köster and Rahmann 2012).
Acknowledgements We are grateful to the  INRA   MIGALE bioinformatics platform  (https://migale. inra. fr)for providing computational resources. We would like to thank  French Institute of Bioinformatics (IFB ,  ANR-11-INBS-0013 , https://www. france-bioinformatique. fr) for providing storage and computing resources on its Core Cluster. M. K is supported by  DGA  ( French Ministry of Defense ) and  Ecole Polytechnique. J. -L. F. acknowledges support from  BBSRC/ EPSRC  (grant number  BB/M017702/1 ) and from the  ANR  (grant numbers  ANR-15-CE21-0008  and  ANR-17-CE07-0046 ). We thank  Pablo Carbonell  for providing the toxicity data.
The problem does not appear to be settled, but the availability of published data on prefrontal grey and white matter volume has allowed researchers to contrast their different methodological approaches using the same data. Here we present a phylogenetic comparative analysis of primate neuroanatomy based on a sample of magnetic resonance imaging (MRI) data from 65 specimens coming from 34 different primate species. We acquired and made openly available high-resolution MRI data for 33 specimens from 31 different species. This is part of an ongoing effort to digitise the Vertebrate Brain Collection of the National Natural History Museum of Paris. The remaining 33 specimens come from different openly accessible sources. All the data has been indexed in the collaborative neuroimaging website BrainBox (http://brainbox.
pasteur. fr, Heuer et al. 2016) , to facilitate access and foster community-driven data analysis projects. This dataset can be used to perform detailed analyses of neocortical anatomy, beyond volumetric measurements. We looked at several global measurements of neocortical folding, including estimations of global gyrification, total folding length, average fold wavelength and average fold depth. After considering various alternative evolutionary models, our results indicate that the BM model provided the best fit to the data, suggesting that random change may be a main force in primate neuroanatomical evolution. Based on the BM model, we provide estimations of the ancestral values for the different phenotypes under study, as well as estimations of the evolutionary rates of phenotypic change.
Data Sources Magnetic resonance imaging (MRI) data was obtained for 66 individuals across 34 different primate species. Thirty one brains from 29 species were obtained from the Vertebrate Brain Collection of the National Museum of Natural History (MNHN) of Paris (see information on Data Acquisition below). Eleven MRI datasets were downloaded from our Brain Catalogue website (https://braincatalogue. org): one crab-eating macaque, one gorilla, and 9 chimpanzees donated by the National Chimpanzee Brain Resource (NCBR, kindly provided by Chet Sherwood and William D. Hopkins, http://www. chimpanzeebrain. org). The bonobo, gibbon and a second gorilla were downloaded from NCBR, from within the data provided by James Rilling and Thomas Insel  (Rilling and Insel, 1999). Three further macaque datasets, one rhesus macaque and two crab-eating macaques, were kindly provided by the Pruszynski Lab and downloaded from Zenodo (Arbuckle et al 2018).
8 additional macaque datasets, 4 rhesus and 4 crab-eating macaques, were downloaded from the IoN site of PrimeDE  (Milham et al 2018). Finally, the surfaces from 10 human brains were selected and downloaded from the New York site of the ABIDE 1 dataset, through the ABIDE preprocessed project (http://preprocessed-connectomes-project. org/abide, Craddock et al 2013). These subjects were unaffected controls, 20 to 30 years old, and had a good quality surface reconstruction upon expert visual examination. A list of the included species can be found in Table  1. Scripts to automatically download these datasets are available in the accompanying GitHub repository: https://github. com/neuroanatomy/34primates.
Data Acquisition The 31 brains from the Vertebrate Brain Collection of the MNHN were scanned at the Center for Neuroimaging Research (CENIR) of the Institut du Cerveau et de la Moëlle Épinière (ICM, Paris, France). High resolution MRI images were acquired using either a 3T Siemens Tim Trio system, a 3T Siemens Prisma, or an 11. 7T Bruker Biospec. Each dataset was acquired with a 3D gradient-echo sequence (FLASH). Parameters (Field of View, Matrix size, TR, TE) were adjusted so as to obtain the highest resolution possible with our scanner (from 100 to 450 µm isotropic). TR and TE were always chosen as minimum. Flip angle was fixed to 20° at 3T and 10° at 11. 7T. The number of averages was chosen to maintain a scanning time below 12 hours.
Data Preprocessing The MRI data from the MNHN was converted to Nifti 1 format (Cox et al 2004) using FLS 5. 0. 10 (Jenkinson et al 2012, https://fsl. fmrib. ox. ac. uk) and dcm2niix (Chris Rorden, version v1. 0. 20170724, https://www. nitrc. org/projects/mricrogl/). We used our web tool Reorient (https://github. com/neuroanatomy/reorient) to rotate the brains so that the sagittal plane was always straight, the superior/inferior directions were respected (we cannot verify whether the left/right orientations are correct, we only assume they are, and we visually check that no flips were introduced by our analysis pipeline), and the axis of the corpus callosum is horizontal. We also used Reorient to crop the brains. All this MRI data was uploaded to Zenodo (https://zenodo. org), and the links are provided in the accompanying GitHub repository.
The chimpanzees, the bonobo, the gibbon and the gorilla from the NCBR were converted to Nifti 1 format, and we used the DenoiseImage tool included in ANTs  (Avants et al 2009)  to improve the signal-to-noise ratio. The data was then reoriented and cropped using Reorient, contrast inhomogeneities were corrected using N4BiasFieldCorrection from ANTS  (Tustison et al 2010) , and finally the intensity range was manually limited to prevent regions with high intensity from affecting the global contrast (usually the optic nerves). The chimpanzee data was processed using Freesurfer (Dale et al 1999, Fischl et al 2001) using the script recon-all-chimps. sh in the accompanying GitHub repository.
Data quality control Our data comprise post mortem as well as in vivo MRI scans and vary in tissue conservation, resolution and signal-to-noise ratio. Some of the MRIs include only the extracted brain, others include the brain and the skull, and finally a few others include the entire body of the animal. We generated images of one coronal, axial and sagittal slice using Nilearn  (Abraham et al 2014)  to perform a first visual quality control. A thorough visual quality control was later performed during the manual segmentation stage. Quantitative indications of data quality were obtained by measuring signal-to-noise ratios: We computed the ratio of the signal of interest divided by the standard deviation of the background (region without signal). The background region of interest was automatically detected by identifying the first peak in the MRI's histogram.
Manual segmentation and surface reconstruction All the MRI data, except for the chimpanzees and the humans, were segmented using BrainBox (Heuer et al 2016, http://brainbox. pasteur. fr), a Web application for the visualisation, annotation and real-time collaborative segmentation of MRI data. Offline, we used StereotaxicRAMON, Thresholdmann, Segmentator and ITK-SNAP to generate a first mask of the cerebrum. StereotaxicRAMON (https://github. com/neuroanatomy/StereotaxicRAMON) provides manual editing tools, a series of topology-preserving mathematical morphology operators, as well as a real-time 3D visualisation of the manual segmentations. Thresholdmann (https://github. com/neuroanatomy/thresholdmann) enables the generation of binary segmentation masks by using a threshold that can be adjusted locally: the value of the threshold at intermediate points is then interpolated using radial basis functions.
Segmentator (Gulban et al 2018a, b, https://github. com/ofgulban/segmentator) enables the generation of binary segmentation masks through the interactive manipulation of a 2-D histogram where the x-axis represents grey level and the y-axis represents the magnitude of the gradient at each point of the image. Finally, ITK-SNAP (Yushkevich et al 2006, http://www. itksnap. org) is a general tool for manual medical image segmentation. The semi-automatically obtained masks were uploaded to BrainBox, where we created a project centralising all the data. The BrainBox project can be accessed here: http://brainbox. pasteur. fr/project/BrainCataloguePrimates. The main manual segmentation tasks performed in BrainBox involved removal of the cerebellum, brainstem and optic nerves; delineation of sulci missed by the automatic segmentation; and reconstruction of damaged tissue parts (see Figure  1  for examples).
After manual segmentation was finished and reviewed by at least one more person, we implemented a script in Python 3. 6 to download all the data using BrainBox's RESTful API (the script is included in the accompanying GitHub repository). The manually segmented masks were then transformed into triangular meshes using the CBS tools (Bazin et al 2014, https://www. nitrc. org/projects/cbs-tools). The workflow included the following steps: mask binarisation, transformation of the mask into a probability function, and extraction of an isosurface using the connectivity-consistent Marching Cubes algorithm  (Han et al 2003). All meshes were then processed using the following steps: soft Laplacian smoothing to remove the shape of voxels, decimation down to 3 vertices per mm 2 (https://github. com/cnr-istivclab/vcglib/tree/master/apps/tridecimator), removal of isolated vertices, and non-shrinking Taubin smoothing  (Taubin 1995) , implemented in our tool Mesh Geometry (https://github.
The average wavelength (W) of folding in the cerebrum can be estimated as the ratio between the surface area of the convex hull (S h ) and half the total folding length (L):  If we further approximate, as  Prothero and Sundsten (1984) , the profile of a fold to be like a square function (folds going straight up and down), we have that the total surface of the cerebrum mesh should be:. In the case of the hypothetical unfolded version of the mesh, the surface should be (because D=0). We then have that. We used Mesh Geometry to split the left and right hemispheres, and the command line qhull (Barber et al 1996, http://www. qhull. org) to compute their convex hulls. The scripts necessary to compute all these measurements are available in the accompanying GitHub repository.
Statistics and phylogenetic comparative analyses We downloaded phylogenetic tree data for our 34 primate species from the 10k trees website (Arnold et al 2010, https://10ktrees. nunn-lab. org/Primates/downloadTrees. php). This website provides a Bayesian inference of primate phylogeny based on 17 genes. We obtained the consensus tree as well as a sample of 100 trees in proportion to their posterior probabilities. We used R 3. 5. 0 (R Core Team 2018, http://www. R-project. org) for our statistical analysis. Measurements of surface area, volume, folding length and folding number varied over several orders of magnitude and were log-transformed before analysis. Phylogenetic independent contrasts (PICs, Felsenstein 1985) were computed using the packages ape  (Paradis 2012 ) and phytools  (Revell 2011) , with multiple observations per species.
We fitted different evolutionary models (Brownian Motion, Ornstein-Uhlenbeck with a single alpha, with one alpha per phenotype, with a full multivariate matrix of alpha values, and the Early Burst model) using the package Rphylopars  (Goolsby et al. 2016) , which allows the analysis of multivariate phenotypes with multiple observations per species. The Brownian Motion (BM) model supposes that phenotypes diffuse randomly through the branches of the phylogenetic tree with intensity controlled by the parameter σ (σ 2 is the variance of the Brownian process). Under the BM model, phenotypes of species that have diverged recently should then be more similar than those of species that have diverged earlier. The Ornstein-Uhlenbeck (OU) model supposes that phenotypic variation is not only random, but is also attracted to an evolutionarily advantageous value, with a strength controlled by the parametre alpha (when alpha=0, the OU model is equivalent to the BM model).
Data collected We obtained cerebrum surface reconstructions for 65 individuals from 34 different primate species (we excluded only 1 specimen from the original 66 datasets collected, a red howler monkey, due to extensive tissue damage. The MRI is nevertheless available in the BrainBox project as well as in Supplemental Table  S1 ). Table  1  displays the complete list of species included, the number of individuals per species, and information on provenance. Figure  3  shows dorsal views of our reconstructions conserving a homogeneous scale (only one individual per species). The amount of neocortical folding was strongly related to cerebral volume: small Strepsirrhini primates had a basically unfolded neocortex, as well as several of the Platyrrhini primates (New World monkeys) in our sample.
Discussion The study of the evolution of the primate brain should allow us to better understand the origin of our own cognition. It should also provide information on the sources of normal and pathological variability of human neuroanatomya major challenge for neuroscience today (Zilles and Amunts 2013). In a similar way as the analyses of genomes for multiple species allow us to detect highly conserved or rapidly evolving regions, an analysis of neuroanatomical evolution and conservation should allow us to detect the traces of evolution in different brain systems and regions. It should also allow us to evaluate the degree of phenotypic conservation across species, providing a framework to better understand natural variability, and to distinguish it from pathological variability. It is difficult, however, to access and to analyse comparative  Here, we provide open access to a collection of 66 brain MRI datasets from 34 different primate species.
These MRIs can be directly visualised and annotated in BrainBox using just a Web browser. In addition to indexing some of the data already online, we have scanned and made available 31 primate brain MRIs from 29 different species (23 species not previously available online), most of them with an isotropic resolution of 300 microns. We used Zenodo (https://zenodo. org) for storing the data. By using Zenodo each dataset is assigned a persistent identifier (digital object identifier, DOI), which facilitates data citation, tracking authorship and provenance. Other researchers willing to share their MRI data could similarly store it in Zenodo and index the URL in BrainBox. This would enable the decentralised creation of a community-curated collection of primate MRI data (the complete process of uploading the data and indexing it in BrainBox should not take more than 15 minutes).
Using BrainBox, we were able to manually segment our MRI data, and to create topologically correct 3D surface reconstructions. The scripts necessary to programmatically download all our data and reproduce our statistical analyses are available on GitHub (https://github. com/neuroanatomy/34primates). Our aim is to make the data easily accessible to facilitate collaborative projects, reproducibility, and to encourage neuroscientists and citizen scientists to participate in advancing our understanding of primate brain diversity and evolution. We have used this collection to study the variation and evolution of neocortical folding in primates. Here, we used surface-based maps of mean curvature to measure total folding length. Folding length varied from less than 4 cm in the grey-mouse lemur to up to 16 m in humans.
primate brain data, by contrast to genetics where large open access databases such as GenBank (Benson et al 2017) provide information on thousands of different species. The series of reports by Stephan and Frahm (Stephan et al 1981, 1991, Frahm et al 1982) on regional brain volumes have been an important reference for the field, and their data tables have been used by many comparative brain analyses through the years. Primate brain MRI data is on the contrary only available for a few selected species. Two important resources are the PRIMate Data Exchange Initiative (PRIME-DE, Milham et al 2018, http://fcon_1000. projects. nitrc. org/indi/indiPRIME. html) and the National Chimpanzee Brain Resource (http://www. chimpanzeebrain. org). PRIME-DE shares open MRI data mostly for rhesus and crab-eating macaques, and NCBR shares open MRI data for several chimpanzee brains plus 9 other primate species (squirrel monkey, capuchin monkey,.
Acknowledgements We thank  Marion Fouquet  and  Nicolas Traut  for their help with the quality control and selection of the human data from ABIDE. We thank  Helen D'Arcueil ,  Alex de Crespigny ,  Emmanuel Gilissen  and  Chet Sherwood  for allowing us to make accessible their MRI data in the Brain Catalogue. We thank  Spencer Arbuckle  and  Andrew Pruszynski  for sharing their macaque data with us and making it openly available. Data acquisition was funded by the  MNHN programme e-Museum. The development of BrainBox was supported by the  Wellcome Trust  through the  Open Science Prize. KH was supported by the  Max Planck International Research Network on Aging  and the  Max Planck Institute for Human Cognitive and Brain Sciences. OFG was supported by  NWO VIDI  grant  864-13-012.
We present constraints on extensions of the minimal cosmological models dominated by dark matter and dark energy, ΛCDM and wCDM, by using a combined analysis of galaxy clustering and weak gravitational lensing from the first-year data of the Dark Energy Survey (DES Y1) in combination with external data. We consider four extensions of the minimal dark energy-dominated scenarios: 1) nonzero curvature Ω k , 2) number of relativistic species N eff different from the standard value of 3. 046, 3) time-varying equation-of-state of dark energy described by the parameters w0 and wa (alternatively quoted by the values at the pivot redshift, wp, and wa), and 4) modified gravity described by the parameters µ0 and Σ0 that modify the metric potentials. We also consider external information from Planck cosmic microwave background measurements; baryon acoustic oscillation measurements from SDSS, 6dF, and BOSS; redshift-space distortion measurements from BOSS; and type Ia supernova information from the Pantheon compilation of datasets.
We use CMB, CMB Lensing, BAO, RSD, and Supernova Ia measurements in various combinations. Our final set of external data, described in more detail below, is similar to that used in Y1KP; the main differences are that we add RSD measurements from BOSS, and that we update the JLA supernova dataset used in Y1KP to the more recent Pantheon results. We treat the likelihoods of individual external datasets as independent, simply summing their log-likelihoods. We now describe the individual external datasets that we add to DES data in our combined analysis.
(  8 ),  (9) , and  (10). Because we take this empirically driven approach, we include certain data elements in which the quasistatic approximation would not be expected to hold, most importantly the near-horizon scales for the ISW effect. Although not rigorously theoretically justified, a similar approach with respect to inclusion of the ISW effect at large scales was taken in, for example,  [96]. Practically, this choice has the benefit of providing an important constraint on τ from external CMB data, which is useful in breaking degeneracies. We use CosmoSIS with a version of MGCamb 8  [123, 124]  8 https://aliojjati. github. io/MGCAMB/mgcamb. html modified to include the Σ, µ parametrization to compute the linear matter power spectrum and the CMB angular power spectra. For some sets of (Σ 0 , µ 0 ) MGCamb returns an error; we estimated this region of parameter space can be avoided by imposing an additional hard prior µ 0 < 1 + 2Σ 0.
We do this to speed up the evaluation of nonflat models, as in our implementa- 2. Impact of assumptions and approximations adopted in our analysis, demonstrated on synthetic data (that is, noiseless DES data centered on the theoretical expectation, along with actual external data). Each column shows one of the cosmological parameters describing ΛCDM extensions; the dotted vertical line is the true input value of that parameter in the DES data vector (which does not necessarily coincide with the parameter values preferred by the external data). The vertical shaded bands show the marginalized 68% CL constraints in the baseline model for the DES-only synthetic data (blue) and DES+external. The horizontal error bars show the inferred constraint for each individual addition to the synthetic data vector which are listed in rows; they match the shaded bands for the baseline case.
The autocorrelation length estimates how long a chain needs to be in order for new "steps" to be uncorrelated with previous ones. We then split chains into several uncorrelated segments and verify that marginalized parameter constraints do not change significantly when these segments are compared with each other. The typical number of samples of the posterior in these chains is between two and three million. We tion of CAMB, particularly when evaluating CMB lensing, sampling over many chains at full precision is impractical. We checked that this approximation has a minor effect on the shape of the posterior. 10 https://bitbucket. org/joezuntz/cosmosis/ 11 The recommended methods for convergence testing (as well as the documentation for emcee) can be found in https://emcee. readthedocs. io/ have also verified in select cases that this procedure leads to excellent agreement with the 1D marginalized parameter posteriors achieved by Multinest, so both samplers are used interchangeably in what follows.