dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical modelling Predictors of total carbonate excretion. Before modelling carbonate excretion rates, we used bivariate correlations to identify potential multicollinearity among all explanatory variables, including two covariates related to our methodologies (total sampling period and titration method). The titration method was strongly correlated with all environmental variables as one protocol was applied to all samples from Palau and the other to all samples from Australia and The Bahamas. Titration method was therefore initially excluded from our models. Conversely, salinity was relatively strongly correlated with RIL (r = -0. 60) in our dataset. Therefore, these variables were alternatively included in our models (i. e. , the same models were fitted twice including either RIL or salinity as a covariate).
All models were fitted by weighting the response variable based on whether fish were kept individually or in groups. Although some fish were kept in relatively large groups (up to 13 individuals), most were kept individually (61% of tanks). Therefore, to avoid overweighting observations from groups larger than two individuals, we gave a weight of two to all observations derived from fish kept in groups of two or more individuals. We built linear models by first including body mass which was the known major predictor. We then added either RIL or salinity, which in our exploratory data analysis showed the strongest correlation with the response after accounting for body mass. Lastly we included temperature, AR, and total sampling period, either alone or in combination. This procedure resulted in 18 linear models which were then refitted including taxonomic family as a group-level effect.
Acknowledgements We thank the staff at the  Palau International Coral Reef Centre (PICRC) ,  Cape Eleuthera Institute, Heron Island  and  Moreton Bay Research Stations  for support during fieldwork,  S. Flotow  for his help with SEM-EDX analysis, and  P. Lewin ,  S. Bröhl , and  E. Fossile  for their help during data collection in Palau. This work was primarily funded through the  2017-2018 Belmont Forum and BiodivERsA REEF-FUTURES project under the BiodivScen ERA-Net COFUND program  (awarded to DM) through the  French National Research Agency  ( ANR-18-EBI4-0005 ) (DM and VP) and the  DFG  ( BE6700/1-1 ) (SB). Data collection in The Bahamas and Australia and sample analysis was funded by the  UK Natural Environment Research Council (NERC)  grants  NE/K003143/1  (CTP and RWW),  NE/ G010617/1  (CTP and RWW), and  NE/H010041/1  (RWW), and a  Biotechnology and Biological Sciences Research Council (BBSRC)  grant  BB/J00913X/1  (RWW).
Data availability The data generated and/or analysed in this study have been deposited in the Zenodo repository (https://doi. org/10. 5281/zenodo. 7530092)  92. The intestinal length data used in this study are freely available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 5172790)  89. Data underlying all figures in the main text and Supplementary Information are available in the Zenodo repository (https://doi. org/10. 5281/ zenodo. 7530455)  93.
Compared to the ECM for only y t used in Cummins et al. [17] , Beenstock et al. [41] , the vector ECM has the advantage that it treats both the x t and y t on equal footing  [24]. Confidence intervals can easily be calculated for the scaling factor by performing OLS fits of the restricted VECM, e. g. , using the cajorls function in the urca package in R  [42].
Results from CMIP5 Climate Model Simulations The three different estimation methods have been applied to historical Global Mean Surface Temperature (GMST) simulations from 1860-2004 for 16 climate models that participated in CMIP5 (see Table  1 ). Figure  1  shows time series of the GMST anomalies of the first simulation of each model together with those from the HadCRUT3 gridded dataset observations. It can be clearly seen that all the series are non-stationary and trending upwards-using the augmented Dickey-Fuller  [43, 44]  test for unit root, all the series are found to be integrated of order 1, i. e. , year-to-year differences are stationary (  [23]  Ch. 4). The model simulated series tend to follow the observed series but there are notable deviations (e. g. , HadGEM2-CC is warmer than the observed series right up until 1940).
Data Availability Statement: The original data were freely obtained from: Historical observed and CMIP5 simulations of GMST: https://climexp. knmi. nl (accessed on 26 July 2023); Historical CO 2 estimates: https://esgf-node. llnl. gov/search/input4mips/ (accessed on 26 July 2023). The R code and data are freely available from https://github. com/stormrisk/cointegration (accessed on 26 July 2023). Code for the methods is briefly described in Appendix B.
, 2016 )-which we term the supralateral occipital sulci (ventral: slocs-v; dorsal: slocs-d) and posterior angular sulci  (ventral: pAngs-d; dorsal: pAngs-d). We then utilized morphological (depth and surface area), architectural (gray matter thickness and myelination), and functional (resting-state functional connectivity) data available in each participant to assess whether the most common of these structures (slocs-v) was dissociable from surrounding sulci. Finally, we assessed whether the updated view of the LPC/LPOJ sulcal landscape provided cognitive insights using a model-based, data-driven approach  (Voorhies et al. , 2021)  relating sulcal morphology to behavior on tasks known to activate regions within this cortical expanse (for example, reasoning and spatial orientation  (Gur et al.
, 2015)  (Methods), the slocs-v is likely located outside of visual field maps extending into this cortical expanse (Fig. 5a ). Nevertheless, when also projecting the map of the mean R 2 metric from the HCP retinotopy dataset from participants shared by Benson and colleagues  (Benson et al. , 2018)  (Methods), the slocs-v is in a cortical expanse that explains a significant amount of variance (left hemisphere: R 2 mean = 19. 29, R 2 max = 41. 73; right hemisphere: R 2 mean = 21. 17, R 2 max = 44. 23; Fig. 5b ). In terms of anatomical connectivity, as the slocs-v co-localizes with cytoarchitectonically defined PGp (Fig. 4 ) and previous studies have examined the anatomical connectivity of the probabilistically defined PGp, we can glean insight regarding the anatomical connectivity of slocs-v from these previous studies  (Caspers et al.
morphology to behavior, we leveraged behavioral data related to spatial orientation (Variable Short Penn Line Orientation Test), relational reasoning (Penn Progressive Matrices Test), and processing speed (Pattern Completion Processing Speed Test; Supplementary Methods for task details). We selected these tasks as previous functional neuroimaging studies have shown the crucial role of LPC/LPOJ in relational reasoning and spatial orientation  (Gur et al. , 2000; Karnath, 1997; Vendetti and Bunge, 2014; Wendelken, 2014) , while our previous work relating sulcal morphology to cognition uses processing speed performance as a control behavioral task  (Voorhies et al. , 2021; Willbrand et al. , 2022b).
Additionally, and as described by Glasser and colleagues  (Glasser et al. , 2013) , the T1-w/T2-w images were bias-corrected for distortion effects using field maps. We then extracted the average T1-w/T2-w ratio values across each vertex for each sulcus using custom Python code  (Miller et al. , 2021). The architectural features of all LPC/LPOJ sulci are documented in Supplementary Fig. 7. To assess whether these four metrics differed between the slocs-v and surrounding sulci (cSTS3 and lTOS), we ran a repeated measure analysis of variance (rm-ANOVA) with the within-participant effects of sulcus (slocs-v, cSTS3, and lTOS), metric (surface area, depth, cortical thickness, and myelination), and hemisphere (left and right). Rm-ANOVAs (including sphericity correction) were implemented with the aov_ez function from the afex R package.
Effect sizes for the ANOVAs are reported with the partial eta-squared metric (η2). Post-hoc analyses were computed with the emmeans function from the emmeans R package (p-values corrected with Tukey's method). We also repeated these analyses for the three cSTS components  (Petrides, 2019; Segal and Petrides, 2012)  and the two intermediate parietal sulcal components (ips: aipsJ and pips  (Petrides, 2019; Zlatkina and Petrides, 2014) ; detailed in the Supplementary Results and Supplementary Fig. 8 ) as these components, to our knowledge, have not been quantitatively compared in previous work.
Functional analyses To determine if the slocs-v is functionally distinct from surrounding sulci, we generated functional connectivity profiles using recently developed analyses  (Miller et al. , 2021; Willbrand et al. , 2023a Willbrand et al. , , 2022a)). First, we used resting-state network parcellations for each individual participant from Kong and colleagues  (Kong et al. , 2019) , who generated individual network definitions by applying a hierarchical Bayesian network algorithm to produce maps for each of the 17 networks in individual HCP participants. Importantly, this parcellation was conducted blind to both cortical folding and our sulcal definitions. Next, we resampled the network profiles for each participant onto the fsaverage cortical surface, and then to each native surface using CBIG tools (https://github.
We used cross-validation to optimize the shrinking parameter for the LASSO regression. Conventionally, we selected the model parameters that minimized the cross-validated mean squared error (MSE cv )  (Heinze et al. , 2018). The optimization was performed with the GridSearchCV function sklearn in Python, which allowed us to determine the model parameters minimizing the MSE cv. To evaluate the performance of any model selected by the LASSO regression, as in prior work  (Voorhies et al. , 2021; Willbrand et al. , 2023b; Yao et al. , 2022) , we measured the model performance for the relevant behavioral task using nested model comparison. With leave-one-out cross-validation (LooCV), we compared the LASSO-selected model with the predictors to a model with all left hemisphere sulci as predictors.
Acknowledgments This research was supported by  NSF   CAREER Award   2042251  (PI Weiner). Neuroimaging and behavioral data were provided by the HCP,  WU-Minn Consortium  (PIs:  David Van Essen  and  Kamil Ugurbil ;  NIH  Grant  1U54-MH-091657 ) funded by the  16 NIH Institutes and Centers  that support the  NIH Blueprint for Neuroscience Research , and the  McDonnell Center for Systems Neuroscience at Washington University. We thank  Jacob Miller ,  Benjamin Parker , and  Willa Voorhies  for helping develop the analysis pipelines implemented in this project. We also thank the HCP researchers for participant recruitment and data collection and sharing, as well as the participants who participated in the study.
Participants Data for the young adult human cohort analyzed in the present study were from the Human Connectome Project (HCP) database (https://www. humanconnectome. org/study/hcp-young-adult/overview). Here, we used 72 participants (50% female, 22-36 years old, and 90% right-handed; there was no effect of handedness on our behavioral tasks; Supplementary materials) that were also analyzed in several prior studies  (Hathaway et al. , 2023; Miller et al. , 2021 Miller et al. , , 2020;; Willbrand et al. , 2023b Willbrand et al. , , 2023c Willbrand et al. , , 2022a)). HCP consortium data were previously acquired using protocols approved by the.
Neuroimaging data acquisition Anatomical T1-weighted (T1-w) MRI scans (0. 8 mm voxel resolution) were obtained in native space from the HCP database. Reconstructions of the cortical surfaces of each participant were generated using FreeSurfer (v6. 0. 0), a software package used for processing and analyzing human brain MRI images (surfer. nmr. mgh. harvard. edu)  (Dale et al. , 1999; Fischl et al. , 1999). All subsequent sulcal labeling and extraction of anatomical metrics were calculated from these reconstructions generated through the HCP's version of the FreeSurfer pipeline  (Glasser et al. , 2013).
Probability maps Sulcal probability maps were generated to show the vertices with the highest alignment across participants for a given sulcus. To create these maps, the label file for each sulcus was transformed from the individual to the fsaverage surface with the FreeSurfer mri_label2label command (https://surfer. nmr. mgh. harvard. edu/fswiki/mri_label2label). Once each label was transformed into this common template space, we calculated the proportion of participants for which each vertex was labeled as the given sulcus with custom Python code  (Miller et al. , 2021; Voorhies et al. , 2021). For vertices with overlap between sulci, we employed a "winner-take-all" approach such that the sulcus with the highest overlap across participants was assigned to that vertex. Alongside the thresholded maps, we also provide constrained maps [maximum probability maps (MPMs)] at 20% participant overlap to increase interpretability (20% MPMs shown in Supplementary Fig.
This resource is aimed at non-experts, and early career researchers who may not have extensive coding or analytical knowledge. Users are introduced and guided through pre-processing options and data formatting steps which result in a peak table data frame. This peak table forms the basis of the next steps in the workflow, multivariate analysis and putative metabolite ID to give a list of potential compounds that are differentially expressed between groups of samples which can inform the hypothesis for downstream targeted analyses. Alongside some command-line interface, GUI software has also been utilised in the workflow, which can be simpler to learn and easier to operate for new and non-expert users of metabolomics data analysis software  [12]. Notably, all software approaches discussed here are free, as the authors believe it is important that the discussed pipelines are accessible.
Overview and Workflow Diagram This tutorial guides the user through the untargeted metabolomics workflow that has been developed with some explanation of what each stage achieves. Further details are available in step-by-step guides on the associated website (https://untargetedmetabolomics-workflow. netlify. app/ accessed on (27 January 2023)), which includes links to relevant open-source tools, and our own interoperable code where appropriate. This tutorial covers the steps required to process LC-ESI-MS data, however detailed instructions for processing MALDI-ToF-MS and DI-ESI-MS using similar open-source tools are also available on the associated website. An index of openly-available datasets is provided at https://untargeted-metabolomicsworkflow. netlify. app/00_overview/06_demo-data/ (accessed on 9 March 2023).
Converting Data to Open Format Using Proteowizard Converting proprietary data files (which contain a large amount of data and metadata about the run in separate files) to a more manageable format, such as. mzML (the standard open-data format for mass spectrometry  [17] ) is essential. We have developed this workflow using. RAW files, which are specific to Waters software and are not compatible with many open-source tools. To convert. RAW to. mzML, Proteowizard software  [18]  is used. Proteowizard is capable of converting many other proprietary file formats and guidance is available through their extensive documentation at https://proteowizard. sourceforge. io/ doc_users. html accessed on (20 February 2023). Proteowizard comprises two applications: SeeMS and MSConvert. SeeMS is useful for viewing chromatograms and spectra without access to proprietary software like MassLynx.
Preprocessing Data Untargeted metabolomics datasets can be several GB in size! To get from compressed. mzML files to a tractable peak table that can be interrogated with multivariate statistics, it is necessary to "tidy" the data. A peak table is a data-frame consisting of aligned spectra with concentration or intensity values against a set of features-mass to charge ratio (m/z) or m/z with retention time (RT). The file size will be dependent on sample number but will be smaller than the. mzML files. Different downstream tools for multivariate statistics will require the peak table in slightly different formats, so the code included in this guide will help with formatting for some common uses (e. g. , MetaboAnalyst one factor and two factor peak tables) as well as helping format treatment information as metadata so that peak tables can be interrogated.
• The above criteria are very important when processing data as they can have a big impact on data quality however the parameters may vary with different datasets and different analysis methods. The importance of these factors have been discussed previously by  [19]. By the end of this stage, data will be processed into a single table containing all the m/z and intensity values required for down-stream analysis. This stage relies on the use of open-source software (XCMS online  [20]  for LC-ESI-MS and MassUp  [21]  for MALDI-ToF-MS and DI-ESI-MS) to process the data. These provide user interfaces for well-documented R packages (XCMS  [22]  and MALDIquant  [23]  respectively) and provide the advantage of coping well with large datasets and, in the case of XCMS online, being run remotely. For detailed instructions on pre-processing, consult stage 04 of our online guide (https://untargeted-metabolomics-workflow.
To limit false positives it is important to consider the native separation in the data (i. e. , through an unsupervised ordination, like PCA) to provide a robust biological justification for comparing two particular classes. The analyses exemplified here are by no means the only option, and it is highly recommended that tools such as MetaboAnalyst  [24]  are employed by the researcher to explore all analytical avenues available. In the online guide, demonstration is given on how to perform these analyses using a free online platform and how to run some alternative code in R. MetaboAnalyst is an online platform on which untargeted metabolomics data can be loaded, normalised, analysed and visualised. However, there is a strong emphasis on detailed statistics that may be more appropriate for targeted analyses, so the user must have a clear understanding of their objectives in choosing amongst the options.
MetaboAnalyst is interoperable with R and the underlying code can be accessed using the button at the top left of the "Results" page. The advantage of running the code is that the user can integrate it with other analyses (and formatting for figures). Examples of figures produced with this approach can be found in Figure  3. In contrast, the advantage of the MetaboAnalyst GUI is that it guides the user through the process and has some useful sense-checks and vignettes available. MetaboAnalyst is interoperable with R and the underlying code can be accessed using the button at the top left of the "Results" page. The advantage of running the code is that the user can integrate it with other analyses (and formatting for figures). Examples of figures produced with this approach can be found in Figure  3.
In contrast, the advantage of the MetaboAnalyst GUI is that it guides the user through the process and has some useful sense-checks and vignettes available. Details can be found via the excellent tutorials and documentation provided by MetaboAnalyst  [25]. It is also possible to analyse the same peak tables using SIMCA (Umetrics) or other proprietary softwares. However, it is much harder (and more costly) to use these remotely, and it is harder to document any analysis for sharing with other researchers. Other software worth considering includes MSDial, MetaboKit and MeV  [26] [27] [28].
What Are My Metabolites? It is very important to consider that this stage of the metabolomic process is not automated and can be incredibly time-consuming and challenging to do, so it is advisable that the preceding analysis has been adequately assessed for its effectiveness before committing time at this stage. Details can be found via the excellent tutorials and documentation provided by MetaboAnalyst  [25]. It is also possible to analyse the same peak tables using SIMCA (Umetrics) or other proprietary softwares. However, it is much harder (and more costly) to use these remotely, and it is harder to document any analysis for sharing with other researchers. Other software worth considering includes MSDial, MetaboKit and MeV  [26] [27] [28].
What Are My Metabolites? It is very important to consider that this stage of the metabolomic process is not automated and can be incredibly time-consuming and challenging to do, so it is advisable that the preceding analysis has been adequately assessed for its effectiveness before committing time at this stage. Annotating metabolomic features is challenging-there are some automated annotations included with e. g. , XCMS that rely on the CAMERA package  [29]  amongst others. However, these often struggle with unusual experimental structures and/or large datasets, or "unusual" (i. e. , non-human) metabolites. Thus, reducing the number of metabolomic features to those that are causing a significant (in terms of reliability and magnitude) difference between two classes of samples is advisable.
To ascertain the identity of these features, comparing the m/z (or m/z at specific RT) values highlighted by multivariate analysis with databases of reference m/z and with experimental data from the literature (usually available in a publication or in repositories like MetaboLights  [30]  and Metlin  [31] ) is key. Stage 07 of the online guide provides guidance on using a range of databases to help annotate "metabolites of interest" (https://untargeted-metabolomics-workflow. netlify. app/07_putative-metabolite-id/ accessed on (27 January 2023)). These include:.
• METLIN to search by m/z; • KEGG PATHWAY and KEGG COMPOUND  [32]  to corroborate likelihood of detecting certain compounds in the study organism/sample and to gain insight on biological function; • Data repositories such as MetaboLights; • Details of how to find other relevant databases (MassBank, PubChem, MetaCyc, Metabolomics Workbench  [33] [34] [35] [36] ); • Reporting Metabolomics Standards Initiative (MSI) identification levels (see also  [37] ).
Sharing Metabolomics Data Metabolomics data from even a small study can be very large. It can also be very complex. But there are ways of sharing it with the wider scientific community (and indeed the public) without too much trouble. It is insufficient to only prepare a data availability statement or simply share graphs or peak tables. Metabolomics data can be analysed in lots of different ways, so it is important to comply with the FAIR principles  [38] : • Findable • Accessible • Interoperable • Reusable Institution-based data repositories are an option, but they often require extra levels of support to submit large datasets and there is no guarantee that access to other researchers is feasible. More useful is a field-specific repository where data will be made available together with other relevant data sets.
Furthermore, these repositories provide guidance on appropriate data formatting, allowing it to be compatible with other published data to form part of potential future meta-analyses. Some journals will have specific guidelines on which repository to use  [39]. Time should be set aside from the outset of any project for submitting data to a repository. It is not optional! MetaboLights is a data repository specific to metabolomics studies  [30]. Data from NMR, GC-MS, LC-MS, and MALDI amongst others, may be submitted. The repository is maintained and curated by the European Bioinformatics Institute (EMBL-EBI) meaning that the data it holds is well-formatted and integrated with several other standardised databases and ontologies (ways of describing methods, data and metadata). This "future-proofs" the data stored, making it not only open-access but also more findable and reusable, as well as facilitating integration with other -omics data, if required.