dataesr/scientific-paragraphs-categorization huggingface dataset:
Because all these variables carry the same biological information, we only used RSA in the statistical analyses. Intrinsic disorder measures for all residues were obtained using the DisEMBL version 1. 4 program  (Linding et al. 2003) , after porting the original Python script to Python 3. 0. We considered a site to be in a disordered region if its hotloop index was greater than 0. 1204 and the site was not predicted by DSSP to be in a secondary structure motif. We considered sites labelled as disordered as not having any secondary structure, and we combined the results of DSSP and DisEMBL into a single discrete variable with states "no structure", "alpha helix", "3-10 helix", "pi helix", "strand", "beta bridge", "turn", "bend", and "disordered". In order to test whether the coevolving sites of a group are in proximity in the three-dimensional structure, we measured the distance between the two alpha carbon atoms of the residues.
In order to annotate sites with their secondary structure labels (helix and strand numbers), mmCIF files were used, and annotations parsed using scripts developed with the 'BioPython. PDB' package  (Hamelryck and Manderick 2003). We computed the N sub statistic (see above), considering whether sites are in the same motif or not. Sites in helices and strands were analyzed separately, and sampling was done in each case without further conditioning or by conditioning on evolutionary rates or RSA, with a 10% similarity threshold and with 1,000 replicates.
Statistical analyses. All statistical analyses were conducted with the R statistical software (R Core Team 2020) using the 'ggplot2'  (Wickham 2016) , 'cowplot'  (Wilke 2020)  and 'ggpubr'  (Kassambara 2020)  packages for results visualization. Methods overlap (Figure  3 ) was plotted using the 'UpSetR' package  (Gehlenborg 2019). To assess whether the number of sites detected as coevolving by two or more methods was significant, we defined the statistic S as the number of sites detected by at least two methods. We randomized the positions detected as coevolving independently for each method and computed S for the pseudo dataset. We repeated the procedure 10,000 times and computed a P-value as (x + 1) / 10001, where x is the number of cases where S in the randomized data sets is at least equal to the value of S on the nonrandomized data.
GLMM analyses were conducted in the R statistical software (R Core Team 2020). The lme4 package for R  (Bates et al. 2015)  failed to estimate parameters for our models. We further tried different estimation procedures: (1) penalized quasi-likelihood, using the 'glmmPQL' function from the 'MASS' package  (Venables and Ripley 2002) , and (2) Laplace approximation, (3) adaptive Gaussian quadrature, (4) sequential reduction with the 'glmm' function from the 'glmmsr' package  (Ogden 2019). All methods converged to the same model parameters, but P-values could only be obtained with the 'glmmPQL' method after increasing the default number of iterations. In one case (index 8), convergence was not reached after 100 iterations. We report the results from the Laplace method but used the 'glmmPQL' fitted models to compute variance inflation factors (VIF) using the 'vif' function from the 'car' package  (Fox and Weisberg 2019) , which is not compatible with the 'glmmsr' package.
The Dugo-Cota et al. (  2019 ) phylogeny includes 57 of the 66 Hispaniola species. Because fitting the IW model assumes complete sampling of the extant species of the focal island, we inserted the missing Hispaniola species by assigning them to random locations within the Hispaniola subclades that they have been hypothesized to belong to. Information on the nine missing species and the detailed rationale for including them in a given subclade are given in Supplementary Table  S1. There is no genetic data available on GenBank for these missing species because they have been recently described, are known from a single specimen, or are possibly extinct. We used a set of functions from the phytools R package to assign missing species to clades  (Revell, 2012). Four of the missing species were previously considered subspecies, and have recently been elevated to species, and we thus randomly inserted them at any height along the tip branch of the species they were previously assigned to.
We therefore did not include it in the analyses, as it is unlikely to modify the main findings. We extracted colonization and branching times for each of the five Hispaniola radiations from these data. Colonization times were assumed to be the stem ages of the Hispaniola clades, as the stem age marks the divergence from the mainland sister clade (which is assumed to be due to the colonization event). Information on each of the Hispaniola clades is given in Table  1  and the phylogeny is shown in Figure  2. As the downstream DAISIE analyses are computationally demanding, we wanted to use only one data set for subsequent analyses. To perform an informed selection of the tree, we fitted the CS and the IW model with no anagenesis to each of the 100 sets of trees. The results of the analysis on the 100 sets of trees are shown in Supplementary Table  S2.
The preferred model in all trees was CS. We thus used only tree set 52, which was the one with the highest likelihood for CS, for all subsequent analyses (hereafter "empirical data set"). This may seem to introduce a bias in favor of the CS model, but we note that all loglikelihood differences (and all parameter estimates) were very similar across the 100 sets of trees: the loglikelihood differences between CS and IW were between 5. 2 and 6. 6 with a median of 5. 9 and set 52 had a loglikelihood difference of 6. 4. All 100 data sets would have led to the same conclusions in our model comparison (see Results). All sets of trees and corresponding DAISIE colonization/branching time R objects are provided in the Supplementary Material. The simulation code functions to compute the likelihood under the two models, and a tutorial on how to run simulations and perform model-fitting are available in the R package DAISIE on CRAN and on Github (https://github.
Bootstraps The analyses fitting the CS model to each of 1000 CS and 1000 IW simulated data sets were all completed successfully. For the analyses fitting the IW model to the same data sets, some runs could not be completed within the limit we set (10 days). For the CS simulated data sets this was 0. 8% of the simulations and for the IW simulated data sets this was 1. 6%. These were all data sets with little information (only a single clade) where the estimation procedure went to very high values of the rate of cladogenesis and colonization. We used the loglikelihood that was obtained after 10 days which is thus an underestimate of the maximum IW loglikelihood, but the ML may not be much higher than this value after 10 days. However, even if we make the unlikely assumption that in all of these aberrant simulated data sets the IW model is a better fit, they are so rare that our qualitative conclusion that the CS model is a better fit does not change.
SUPPLEMENTARY MATERIAL Data available from the Dryad Digital Repository: http://dx. doi. org/10. 5061/dryad. t1g1jwt3n. FUNDING RSE and LV thank the Dutch Research Council (NWO) for financial support through VICI and VIDI grants, respectively. LV thanks the Deutsche Forschungsgemeinschaft (DFG) for an Eigene Stelle grant (VA 1102/1-1). BH was supported by the Laboratoires d'Excellences (LABEX) TULIP (ANR-10-LABX-41). CV thanks the Spanish Government for funding project CGL2016-75227-P within which the data were collected. RSE and BH thank the bilateral French-Dutch Van Gogh Program for funding work visits.
ACKNOWLEDGEMENTS We thank  Ally Phillimore  for early discussions that provided the original motivation for this study. We thank  Pedro Neves ,  Joshua Lambert ,  Ally Phillimore ,  Tanja Stadler , and two anonymous reviewers for helpful comments on the manuscript, and  Hanno Hildenbrandt  for a major improvement in the performance of the DAISIE package. We thank the  Center for Information Technology of the University of Groningen  for their support and for providing access to the Peregrine high performance computing cluster.
| qMRI map computation and pre-processing Quantitative MRI maps were computed from the raw MRI data using the hMRI toolbox  (Tabelow et al. , 2019)  and bespoke analysis scripts written in MATLAB (The MathWorks Inc. , Natick, MA, USA). Maps of the MRI parameter R2* were computed separately from the raw echo images with MTw, PDw and T1w contrast, from the regression of the log signal with the corresponding echo times  (Weiskopf, Callaghan, Josephs, Lutti, & Mohammadi, 2014). The value of the MDI described in  Castella et al. (2018)  was computed for each R2* map, as provided by the hMRI toolbox  (Tabelow et al. , 2019). Maps of Magnetization Transfer estimates (MRI parameter MTsat), were computed as described in  (Helms, Dathe, & Dechent, 2008; Helms, Dathe, Kallenberg, & Dechent, 2008; Lutti et al.
We computed measures of noise heteroscedasticity at the global level of a tissue type as well as at the individual voxel level. At the global level, our measure of heteroscedasticity was the coefficient of determination R 2 , that is, the fraction of the variance of image noise that follows a polynomial dependence on the MDI. To test for residual heteroscedasticity at the voxel level, we conducted Engle's ARCH tests of the serial dependence of the residuals in each voxel of the MRI data, with a maximum lag of 40 data points  (Engle, 1982). The fraction of voxels with significant heteroscedasticity was calculated after FDR-correction using the Benjamini-Hochberg procedure  (Glickman et al. , 2014).
| Specificity We assessed the specificity of the OLS and WLS methods by monitoring the rate of false positives in two types of image analysis frequently conducted in neuroscience studies. I. In a subset of data (N = 123; up to 10 images per age bin of 5 years when available), the participants' age was randomly scrambled between the images before conducting the analysis of age-associated differences in R2* described above. Any positive result would therefore be a false positive. II. In the subset of data with a narrow age range (56-58 y. o. ; N = 129), we conducted two-sample T tests for the comparison of two subgroups. Similarly, any positive results would therefore be a false positive. Analyses were repeated with N 1 = 2, 5, 10, 20 30 and 60 images in the first group. We repeated both types of specificity analyses 1,000 times, monitoring the rate of false positives across repetitions at the voxel and cluster levels (p < 0.
| DISCUSSION In this study, we introduce a method that accounts for the degradation of image quality due to motion in the analysis of MRI data. This method addresses an important limitation of existing approaches for quality control, which only enable the removal of the most degraded images from analysis. From an index of image quality, the proposed method computes weights that capture the noise level in each image, leading to increased sensitivity to brain change in an analysis. This method is based on restricted maximum likelihood, available in most image analysis software suites. The implementation used here, within the SPM software  (Friston, Stephan, Lund, Morcom, & Kiebel, 2005) , is commonly used to account for differential noise levels in, for example, group-comparison studies ('non-sphericity').
To facilitate uptake by interested users, we provide a customised version of the hMRI toolbox  (Tabelow et al. , 2019)  with a dedicated QUIQI module available from the GUI  (Lutti, Di Domenicantonio, Corbin, Phillips, & Callaghan, 2021). The powers of the MDI used to define the REML basis functions can be set freely from F I G U R E 5 Using motion regressors as design covariates does not restore heteroscedasticity. In OLS analyses, statistical F tests of the variance of the R2* maps associated with powers of the MDI show statistically significant results in 3. 2% and 5. 5% of voxels in grey and white matter, respectively (p < 0. 05, FWE-corrected) (a). In WLS analyses, significant results are found in 0. 1% of voxels only (p < 0. 05, FWE-corrected). Noise heteroscedasticity remains present for both tissue types in OLS analyses, despite the motion regressors, but is not present in WLS analyses (b) the user interface.
QUIQI allows the plotting and polynomial fitting of image noise versus the MDI ('QUIQI check'). On the model of this study, this allows users to assess heteroscedasticity levels in their analysis data, with or without QUIQI, and helps identify the optimal set of basis functions for the REML estimation. We emphasise that this feature runs independently from the analysis and that the polynomial fits are not used subsequently. Instead, the use of REML for the computation of the weights provides estimates of ELBO as measures of noise model efficiency (the analysis code attached to this manuscript includes a customised version of the 'spm_est_non_sphericity' SPM function that provides the estimate of the REML ELBO computed by SPM). Similarly, we do not recommend the computation of the weights directly from the global noise estimates as factors independent of motion degradation such as analysis model or data type (e.
ACKNOWLEDGMENTS This work was supported by the  Swiss National Science Foundation  (grant no  320030_184784 (AL) ) and the  Fondation ROGER DE SPOELBERCH. The  Wellcome Centre for Human Neuroimaging  is supported by core funding from the  Wellcome  [ 203147/Z/16/Z ]. CP is Senior Research Associate at the  FRS-F. N. R. S. , Belgium. BD  is supported by the  Swiss National Science Foundation  ( NCCR Synapsy , project grant numbers  32003B_135679 ,  32003B_159780 ,  324730_192755  and  CRSK-3_190185 ) and the  Fondation Leenaards. The MRI data were acquired on the MRI platform of the  Clinical Neuroscience Department, Lausanne University Hospital.
To respond to this challenge, the science and management communities must place higher priority on mobilising existing and emerging knowledge sources to inform current and future resource use and mitigation strategies. This approach requires a more complete picture of the current salmon ecology data and knowledge landscape, new mechanisms to enable data mobilisation and re-use, and new research to describe and parameterise the responses of wild populations to habitat changes. Here we present a unique interface for registering and linking data resources relevant to the Atlantic salmon life cycle that can address the data mobilisation aspect of these challenges. The Salmon Ecosystem Data Hub is a salmonspecific metadata catalogue, natively interoperable with many existing data portals, which creates a low resistance pathway to maximise visibility of data relevant to Atlantic salmon.
, 2016). This shift is supported by the reported upward trend of familiarity among researchers of principles such as Findable, Accessible, Interoperable and Reusable Data (FAIR,  Wilkinson et al. , 2016)  in the State of Open Data reports  (Digital Science et al. , 2021)  as well as the growth of online repositories, e. g. , Global Biodiversity Information Facility (GBIF), Knowledge Network for Biocomplexity (KNB), Long Term Ecological Research Network (LTERN), and Ocean Tracking Network (OTN). Alongside an increasing online presence, the use of persistent Digital Object Identifiers (DOI) has emerged as a method for citing datasets, creating an incentive to publish data that parallels established academic norms for scientific publications  (Data Citation Synthesis Group, 2014). Knowledge mobilisation is then of prime importance during knowledge exchange between researchers, resource managers, and policy makers so that science may be implemented as actions in the real world  (Geijzendorffer et al.
Current management interests span multiple organisations across multiple jurisdictions. The provision of advice on managing high seas mixed stock fisheries is coordinated by the North Atlantic Salmon Conservation Organization (NASCO). This is supported by stock advice from the International Council for the Exploration of the Seas (ICES) (ICES, 2021a), geared towards the operation of nationally regulated coastal or individually managed in-river salmon fisheries. While the current NASCO-ICES stock assessment process fulfils its required international functions, it also underpins the production of some nationally scaled conservation advice and associated strategies (e. g. , Conservation Regulations). Although the outputs of coordinated international stock assessments are freely available (ICES, 2021a), the underlying data are not, and the modelling outputs are not readily transferable for integration into regular management decision-making that is being undertaken by river managers at the scale of specific threatened salmon stocks.
Most practical local (e. g. , single river stock) Atlantic salmon management activity is restricted to local estuarine/riverine fishery management or other freshwater actions aimed to maximise annual production and safeguard recruitment (e. g. ,  Thorstad et al. , 2021). These activities largely rely upon the provision and use of locally available data (Fig. 1 ) and the integration of data from a limited number of audit points (such as counts of smolts leaving or adults returning to monitored river systems) to underpin modelling of abundance. Atlantic salmon management should benefit from the extensive research and monitoring investment over recent decades, with integration of rich and well-mobilised data resources. Instead, much of the applicable data resources that could provide valuable insights into the factors responsible for salmon population declines are widely dispersed across a diverse community, aggregated at varying biological and geographical scales, and present challenges for harmonisation and further collective synthesis (ICES, 2020a).
, 2019 (Crozier et al. , , 2021;; Hare et al. , 2016; Hyatt et al. , 2017; Scheuerell et al. , 2006 Scheuerell et al. , , 2021;; Walsh et al. , 2020). Focusing attention towards improving understanding of the processes driving the reduced marine survival of Atlantic salmon, data acquisition and data mobilisation play key roles in linking surveillance networks (e. g. , GBIF, GOOS, OTN) to research and the development of adaptive management strategies. Several wide-scale physical environmental data sets that are applicable for describing certain ocean conditions are easily accessed and well mobilised (e. g. , sea surface temperature, salinity, colour and currents, derived from remote sensing and delivered through platforms such as Copernicus and ERDDAP). However, challenges remain for accessing, re-purposing and reevaluating their relevance alongside other resources to assist salmon management in light of recent additions or new knowledge (ICES, 2020a).
The hierarchical nature of possible environmental drivers of salmon marine survival  (Olmos et al. , 2019; Vollset et al. , 2022)  focuses attention on the relevance and descriptive power of available data resources for use in directing future management challenges. Application of space and time data filters, guided by recent salmon migration studies  (Almodóvar et al. , 2020; Gilbey et al. , 2021; Olmos et al. , 2019; Rikardsen et al. , 2021; Strøm et al. , 2019)  may help better refine and describe the applicability of available marine datasets, and improve their use.
Harnessing data to assist future Atlantic salmon management One current advance that demonstrates the power of harnessing and synthesising data from multiple jurisdictions is in the development of a life cycle model for Atlantic salmon at the scale of the North Atlantic Ocean (ICES, 2021b). The dynamics of all stock units considered by ICES (Northern Europe, Southern Europe and North America) are jointly analysed within a singular unified model, developed in a Bayesian framework. It allows for analysing (hindcasting) historical time-series of data to infer changes in the marine survival and maturation schedule over the last five decades. Formulating the dynamics of all stock units in a single model using a harmonised demographic scheme provides a tool for modelling covariation in key life history transitions among different populations that may share part of their migration routes at sea and may be exploited by the same marine fisheries  (Olmos et al.
, 2019). Results were used to quantify the amount of temporal variation in those key life history traits that is accounted for by changes in sea surface temperature and primary productivity  (Olmos et al. , 2019). The life cycle modelling framework hence constitutes an important tool for future improvement of our understanding of the drivers and mechanisms that shape the dynamic of Atlantic salmon populations. The modelling framework is easily expandable and provides an opportunity to assimilate new sources of information when they become available (e. g. , better proxies for salmon growth and survival). This new framework comes with an online database and a suite of R programs, including an R Shiny application for web interface, offering a mechanism to harmonise data from multiple sources into a format used directly by the model.
Unrestricted access to data is a prerequisite for any collective hypothesis testing process that advances our understanding of the potential mechanisms that shape population dynamics and productivity, like those behind recent changes in marine growth  (Todd et al. , 2021; Vollset et al. , 2022)  and early maturation of salmon  (Tréhin et al. , 2021). This needs to be supported by improved data mobilisation efforts pooled across disciplines and spatio-temporal scales. In response we present the Salmon Ecosystem Data Hub (SalHub) as a mobilisation platform for Atlantic salmon-focused activities. This online tool offers the salmon research and management communities the opportunity to be jointly involved in efforts to mobilise data to progress our current understanding of factors affecting survival, and assist with implementing new adaptive management strategies.
It creates the basis for organising a rich and growing collection of datasets that will guide researchers to sources to enable collective hypothesis testing. In its current form SalHub is a metadata cataloguing tool for a designated community  (Baker et al. , 2015) , where already mobilised knowledge and data can be indexed and more opaque resources can begin to be transitioned towards FAIR without exposing users to the full complexities of metadata authoring. It is a place where stakeholders in Atlantic salmon conservation and management may begin to describe and index data resources into a context specific structure, derived from the salmon's life cycle. To promote user confidence initially SalHub is accessible only to registered users. Going forward, the aim is to establish this tool as a publicly accessible presence, promoting open data whilst still maintaining a layer of restriction for opt-out resources, e.
g. , embargoed information or data identified as potentially sensitive. Users agree to encourage resource sharing and academic collaboration through a Memorandum of Understanding. The Missing Salmon Alliance Likely Suspects Framework team has launched the application as a Minimum Viable Product as part of an Agile (http://agilemanifesto. org/) development plan. The application is ready to develop further functionality and as the user base grows it is anticipated that the research and management communities will provide guidance for subsequent development opportunities. This system is built upon a range of modern and open-source technologies (Fig. 2 ). The core data structures of the web application are a graph database (NEO4J), an extension of the mathematical graph  (Trudeau, 2013)  and more commonly referred to as a Labelled Property Graph (LPG).