dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we organized the recommendations into step-by-step guidelines that follow the typical software development process to make them easier to implement. This led to the first minimal and actionable step-by-step guidelines for making biomedical research software FAIR such that all the requirements of the FAIR4RS principles are met, i. e. , the FAIR-BioRS guidelines. User support tool: FAIRshare. FAIRshare is inspired by the software called SODA for SPARC, which has been developed by our team to assist researchers funded by the NIH SPARC (Stimulating Peripheral Activity to Relieve Conditions) program in making their data FAIR according to the SPARC data standards  [71] [72] [73]. Specifically, FAIRshare combines intuitive user interfaces and automation to guide and assist the researchers through the suitable processes for making their research data and software FAIR and sharing it in an adequate repository.
FAIRshare is a cross-platform desktop software application, allowing researcher's data to remain on their computer or a storage medium of their choice until they are ready to share it on a suitable archival repository. It is built using Electron, GitHub's framework for building cross-platform desktop applications using web technologies (HTML, CSS, JavaScript, NodeJS). Vue. js is used as the frontend framework to build intuitive, interactive, and responsive user interfaces. The backend is built using Flask, a microweb framework written in Python. The use of Python for the backend of the application was motivated by the popularity of Python in the biomedical research field and the availability of relevant existing packages and APIs for curating research data and software. More details are available in the dedicated GitHub repository for FAIRshare (c.
Data availability The data associated with this manuscript consists of a file named 'data. xlsx' that contains details about the data collected and processed during the review of relevant resources. Since no FAIR guidelines were found for structuring review data, we structured the dataset according to the SPARC Data Structure (SDS), which provides a broad data and metadata structure to organize biomedical research data according to the FAIR principles  72. The SPARC data curation software SODA for SPARC  [71] [72] [73]  was used to organize the data and prepare the metadata files. The dataset is maintained in a GitHub repository called "Data" in the FAIR-BioRS GitHub organization and the latest version associated with this manuscript (v3. 0. 0) is also archived on Zenodo  74  under the permissible Creative Commons Attribution 4.
research data and software FAIR and sharing it on an adequate repository. FAIRshare is free to use and developed under the permissible open-source MIT license to encourage community contributions for updating existing workflows and including new ones. Details about the development of FAIRshare are provided in the Methods section. The current version of FAIRshare (v2. 1. 0) and its documentation (v5. 0. 0) can be accessed through their respective GitHub repositories and their Zenodo archive (c. f. Code Availability section).
The optical regime is crucial for determining the basic stellar parameters, such as effective temperature (T eff ), surface gravity (log g), and abundances  (Hillier 2020; Simón-Díaz 2020; Brands et al. 2022 ) Key information on wind clumping and mass-loss rates ( Ṁ) only become reliable when optical and near-infrared (NIR) observations are added. Knowledge of the NIR regime is also critical for observations with instrumentation on the James Webb Space Telescope (JWST) and the extremely large telescopes, which will predominately shift our focus to longer wavelengths. Despite the great potential of ULLYSES to transform our knowledge of massive stars, this Legacy dataset is not complete without observations in the optical and NIR regimes. Thus, the X-Shooting ULLYSES (XShootU) 3  project was conceived to obtain complementary high-quality spectra of the ULYSSES targets with X-shooter at the European Southern Observatory (ESO) Very Large Telescope (VLT;  Vernet et al.
The science requirements are described, as are initial results on data reduction and data analysis processes. We show how the legacy spectroscopic dataset of ULLYSES and XShootU can increase our knowledge of massive stars at low Z. The organisation of the XShootU collaboration is described in the appendix. Already published data (Table  B. 2, plotted in Fig. 3 ) may naively suggest that properties of LMC and SMC stars are known, but these pre-ULLYSES results have been derived for relatively small and heterogeneous datasets, and gaps are evident. To make matters worse, spectral analyses that derive stellar properties have also been heterogeneous, as different authors have not only used different tools, distances, and baseline abundances, but also different wavelength ranges. To make progress, not only do the spectroscopic datasets need to be uniform -as provided by ULLYSES and XShootUbut so does the spectral analysis approach.
3  (filled symbols). Estimated parameters for targets lacking contemporary analyses are also shown (open symbols). XShootU obtained a complementary dataset over the optical to NIR wavelength range for all ULLYSES targets that have not previously been observed with X-shooter. This resulted in a sample of 129 4  LMC stars and 103 SMC stars. In addition, three very low-Z stars were included in the sample. The ULLYSES and XShootU datasets are not taken simultaneously in time, although, apart from a few exceptions, the vast majority of ULLYSES sources are not known variables. In reality, most stars are variable to some level, so care still needs to be taken when interpreting the data, but we do not anticipate this to be a massive issue. Existing ESO Science Archive Facility data are part of both spectroscopic and time-dependent aspects of XShootU.
This slit-fed (11 slit length) spectrograph provides simultaneous coverage of the wavelength region between 300-2500 nm, divided into three arms; UVB (300 λ 500 nm), VIS (500 λ 1000 nm), and NIR (1000 λ 2500 nm). Xshooter's wide wavelength coverage made it the instrument of choice for the purpose of building an optical-NIR legacy dataset.
Signal-to noise ratio Some VLT instruments provide higher spectral resolution in certain wavelength regimes (e. g. UVES), but to build a A154, page 5 of 28 homogeneous database with a wide spectral coverage could only be achieved with X-shooter. In addition to the wide wavelength coverage, a high signal-to-noise ratio (S/N) in addition to sufficient spectral resolution are essential to determine the fundamental stellar parameters and the abundances for various temperature regimes populated by OB and WR stars. For the preparation of the X-shooter proposal, we estimated the required S/N, experimenting on a typical SMC mid-O dwarf/giant with cmfgen model spectra degraded to X-shooter's spectral resolution. We found that the determination of basic stellar parameters such as T eff and log g became prohibitive for quantitative interpretation if the S/N drops below 100 per resolution element.
Final perspectives The XShootU project is expected to provide many pieces of data, models, and new physics of massive stars in low-Z environments. It is important to stress that the overarching aim is to provide a high-quality homogeneous optical database that is complementary to ULLYSES. These legacy datasets are critical for a correct interpretation of unresolved high-Z observations with JWST/NIRSpec  (Curti et al. 2023; Carnall et al. 2023; De Barros et al. 2019). The next goal is to provide uniformly determined stellar and wind parameters from the combined UV and optical datasets. For this part of the project, it is not only critical to include the correct NLTE physics, but also to test the various spectral synthesis codes and analyses. A key science aim is to quantify "how" the massloss rate declines with decreasing metallicity.
This does not A154, page 11 of 28 necessarily simply imply a determination of a power-law exponent, as the slope may easily vary with stellar parameters or Z itself  (Vink et al. 2001; Kudritzki 2002; Sander & Vink 2020; Marcolino et al. 2022; Rickard et al. 2022). In order to obtain an empirical Ṁ = f (Z) relationship, we not only require mass-loss rates and clumping properties, but simultaneously also need to obtain the underlying stellar parameters (T eff , log g, L, and M) as these parameters enable mass-loss properties from a given object in one galaxy to be compared to those from an entirely different object located elsewhere. The XShootU dataset, coupled with surveys of H ii regions in the LMC and SMC such as SDSS LVM 6 (Local Volume Mapper), represents a significant opportunity to advance the stateof-the-art in our understanding of how stars shape their environment.
A. 3. Stellar Atmospheres and Benchmarking (WG 3) WG 3 was set up to foster discussions and cooperation between researchers performing the spectroscopic analysis of the XShootU datasets. Part of this work is described in the main part of the paper. Several sub-groups were organised by WG 3 members to address specific science questions and topics of interest. This ranges from the determination of the full photospheric and wind properties of the sample at large, to very specific issues such as studying the effects of rotational mixing on stars with different masses/ages through abundance studies. The three main codes that are employed for the spectroscopic analysis are the NLTE expanding wind codes CMFGEN, FASTWIND, and PoWR  (Hillier & Miller 1998; Puls et al. 2020; Gräfener et al. 2002) , and the plane parallel NLTE code TLUSTY for stars without strong winds.
1977; Spitzer 1978). Key stellar properties for the structure and dynamics of these regions are the flux in far-UV and extreme-UV photons and the stellar wind mass-loss rate and terminal velocity. The density distribution, chemistry, and equilibrium temperature of the photoionised gas also play important roles  (Ferland et al. 2017). Because the LMC and SMC have substantially lower metallicities than the Galaxy, their H ii regions are ideal test cases for the impacts of stellar ionising flux  (Voges et al. 2008; Oey & Kennicutt 1997)  and winds (e. g. Oey 1996; Oey & Smedley 1998)  in low-metallicity environments typical of high-redshift galaxies. The XShootU dataset in the Magellanic Clouds represents a significant opportunity to advance the state of the art in our understanding of how stars shape their environment.
A. 11. Auxiliary/New Data (WG 11) There are multiple ways in which auxiliary data can help increase the value of the ULLYSES/XShootU library, including different wavelength regimes (e. g. X-rays) and observing strategies. For example, the intermediate spectral resolving power of the X-shooter data only partially resolves spectral lines, especially for stars with low projected rotational velocities (v sin i), A154, page 19 of 28 and higher resolution spectra are needed for a detailed spectroscopic analysis. WG 11 will provide high-resolution optical spectroscopy of a selection of slow rotating ULLYSES targets. Targets were selected on the basis of showing narrow, resolved C iii 1176 Å lines in their UV spectra or having low v sin i reported in the literature. Between December 2021 and December 2022, we obtained high resolution (R ∼ 40 000) and high S/N (∼ 200 in the 4100 Å region) optical spectra with the 6.
5m Magellan Clay telescope (+MIKE spectrograph) for 48 ULLYSES targets. Basic reductions confirmed the high quality of the data, and final reductions are underway. The Magellan/MIKE data will be made available to the massive stars community. WG11 also plans to organise archival searches aimed to identify data that will enhance the ULLYSES/XShootU library: -High spectral resolution optical spectra of ULLYSES targets, for example obtained with the UVES, FLAMES, FEROS, and HARPS instruments. -UV spectra obtained with IUE, FUSE, and HST.
A. 14. Unusual Objects (WG14) Finally, the data offer many opportunities to study key objects. For example, combining the radial velocities from XShootU with the exquisite proper motions from Gaia DR3, we can reconstruct the space motions of ULLYSES targets, which can be used to identify runaway and walkaway stars. Moreover, Gaia DR3 light-curves can be used in conjunction with radial velocities of known eclipsing binaries to estimate fundamental stellar parameters, providing unique tests of stellar evolution. U B V Ref J H K s Ref (B - V) 0 E(B - V) Ref M V Binary? Ref MIKE? (mag) (mag) (mag) (mag) (mag) (mag) (mag) (mag) (mag) SMC targets 2dFS 163 O8 Ib(f) • • • 0. 10 19 -5. 03 • • • • • • • • • AzV 43, 2dFS 700 • • • 0. 09 19 -4. 02 • • • • • • • • • NGC330 ELS 4 = Rob B37 B2. 5 Ib U B V Ref J H K s Ref (B - V) 0 E(B - V) Ref M V Binary? Ref MIKE? (mag) (mag) (mag) (mag) (mag) (mag) (mag) (mag) (mag) AzV 177 O4 V((f)) 31 13.
Acknowledgements. We thank the  ESO  support staff for the help in the preparation of the observations, in the scheduling, and for carrying out the observations at Paranal. We also thank the  ESO  support staff, in particular  Carlo Felice Manara. We also thank the  ULLYSES teams at STScI , in particular,  Julia Roman-Duval  and  STScI  Director  Ken Sembach , for executing and enabling the  ULL-YSES Director 's  Discretionary program. Based on observations obtained with the  NASA/ESA Hubble Space Telescope , retrieved from the  Mikulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute (STScI). STScI  is operated by the  Association of Universities for Research in Astronomy, Inc. under  NASA  contract  NAS 5-26555. JSV and ERH gratefully acknowledge support from  STFC  via grant  ST/V000233/1.
772086 ). RI gratefully acknowledges support by the  National Science Foundation  under Grant No. AST-2009412. JMA acknowledges support from the  Spanish Government Ministerio de Ciencia e Innovación  through grant  PGC2018-095 049-B-C22. CJKL gratefully acknowledges support from the  International Max Planck Research School for Astronomy and Cosmic Physics at the University of Heidelberg  in the form of an  IMPRS PhD fellowship. NDK acknowledges support from the  National Solar Observatory , which is managed by the  Association of Universities for Research in Astronomy, Inc. and funded by the  National Science Foundation. MG and FN gratefully acknowledge funding by grants  PID2019-105552RB-C41  and  MDM-2017-0737   Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (INTA-CSIC)  by the  Spanish Ministry of Science and Innovation/ State Agency of Research   MCIN/AEI/10.
6. Stellar Libraries(WG 6)    Stellar spectral libraries are one of the main ingredients of stellar population synthesis models, which in turn are powerful tools in the study of fundamental properties of unresolved stellar systems. One of the major deficiencies of the empirical libraries available today is the coverage of hot and young stars at low Z (Hill et al. 2022). A variety of empirical stellar spectral libraries, collected with different primary goals, are publicly available (see Table 1 in Verro et al. 2021   for a recent summary). In terms of spectral resolving power, only the X-shooter Spectral Library (XSL) and ELODIE are comparable to the XShootU dataset. ULLYSES + XShootU is by far the most complete, highest-S/N, and highest resolution library of hot, massive stars with the broadest spectral coverage.
Deep learning Classification compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs.
controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance.
Introduction Posttraumatic stress disorder (PTSD) is a prevalent and debilitating disorder, with a world-wide prevalence rate of 3. 9 %  (Kessler et al. , 2017; Koenen et al. , 2017). Current clinical assessments of PTSD rely solely on reported subjective experiences, overlooking objective biomarkers, which may lead to many cases of PTSD being undetected or misdiagnosed  (Sumpter and McMillan, 2005). Recent advances in computational power and data-driven computational approaches, especially supervised machine learning, have been helpful in devising tools to objectively diagnose psychiatric disorders  (Liu et al. , 2015; van Loo et al. , 2012; Bzdok and Meyer-Lindenberg, 2018). These approaches improve diagnosis by mining neuroimaging datasets, generating clinically relevant inferences at the individual level  (Lama et al.
Third, most studies estimated classification performance via cross-validation (i. e. , all samples are used in building the prediction model), without testing classification performance using independent yet-to-be-seen test data. For example, a recent review in depression has shown that only 4 of 66 studies evaluated classification performance using a holdout dataset, with all four containing less than 200 samples  (Gao et al. , 2018). However, for machine learning models to be useful in real-world clinical settings, predictive models need large samples that enable the evaluation of model performance on an unseen holdout dataset or independent cohorts. In PTSD, only a handful of studies exist, with none exploring the reproducibility of findings using multimodal brain imaging across multiple sites.
, 2019). Several neuroimaging studies show promising results for autoencoders in the classification of Alzheimer's disease  (Suk et al. , 2015; Ju et al. , 2019) , attention deficit hyperactivity disorder (ADHD)  (Liu et al. , 2021) , autism spectrum disorder (ASD)  (Eslami et al. , 2019) , and schizophrenia  (Pinaya et al. , 2019; G Li et al. , 2020). Yet, the potential of autoencoders for multi-site classification of PTSD remains unknown. To address the gaps in knowledge, here we used machine learning approaches in large-scale multimodal datasets from a heterogenous sample that obtained through the Enhancing Neuro-Imaging Genetics through Meta-Analysis (ENIGMA) PTSD and Psychiatric Genetics Consortium-(PGC) consortium PTSD working groups. First, we assessed classification performance between PTSD and controls using traditional machine learning methods; 2) assess the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance; and 3) assess the generalizability and reproducibility of both models for X.
In this study, we built a DVAE for high dimensionality data reduction  (Han et al. , 2019). The latent variables were used as new features and input into traditional machine learning approaches for classification. Instead of developing a system capable only of classifying individuals into patients and controls, we sought to capture the key feature information in the latent space using the DVAE model. We first trained the model using controls, and subsequently applied the model to data from PTSD patients. Our intent was that the model would first learn the features representing healthy brain function and then retrieve the latent variables in PTSD patient data for capturing deviation of brain features from controls  (Pinaya et al. , 2019). Third, we assessed the generalizability and reproducibility of the classification model across heterogeneous datasets from multiple sites.
The generalizability of machine learning to classify neuroimaging data is of great concern. Tremendous variability across studies inhibits the creation of a clear body of reliable knowledge from distinct studies  (Cai et al. , 2020). The ENIGMA-PGC consortium combines multimodal imaging and clinical data from multiple sites, enabling the development of models based on large samples. This offers an unprecedented opportunity for testing the generalizability and reproducibility of classification models to unseen datasets with vastly different characteristics compared to the sample used for model building. We evaluated generalizability across sites by assessing the classification performance for each site, and then by using Leave-One-Site-Out Cross-Validation (LOSOCV) to test how well the model generalized to independent cohorts.
Participants Table  1  summarizes the descriptive information for each imaging modality. We analyzed brain MRI data from 7925 individuals (3477 structural-MRI; 2495 resting state-fMRI; and 1953 diffusion-MRI). Of these 7925 individuals, 498 individuals had all 3 modalities, 736 had 2 and 6691 only had 1 modality. Demographic information for each imaging modality are summarized in Supplemental Table  1~3. Inclusion and exclusion criteria for each cohort are summarized in Supplemental Table  4. Depending on the cohort, current PTSD was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders (DSM) IV or V criteria, using the following standard instruments: Clinician-Administered PTSD Scale-IV (CAPS-IV), CAPS-5 (DSM-V), Structured Clinical Interview (SCID-IV) (DSM-IV), Mini International Neuropsychiatric Interview (MINI) 6.
Image preprocessing The brain features included in machine learning analysis are presented in Fig. 1. All imaging data were acquired at the contributing sites and processed with standardized protocols established by the ENIGMA Consortium  (Nunes et al. , 2020; Renteria et al. , 2017). The specific set of imaging features used in this study are summarized in supplemental Table  5~7. S-MRI: T1-weighted images were processed using the FreeSurfer processing stream to create individual subject thickness maps (http:// surfer. nmr. mgh. harvard. edu/). The cortex of each hemisphere was parcellated into 34 cortical regions of interest (ROIs) using the Desikan-Killiany atlas  (Klein and Tourville, 2012). To match what we excluded for rs-fMRI data, 10 ROIs that are part of the motor or occipital lobes were removed from further analysis.
Rs-fMRI: Resting-state images were acquired at each site and preprocessed at a single location (Duke University). Preprocessing was implemented in ENIGMA HALFpipe workflow (https://github. com/ HALFpipe/HALFpipe) based on fMRIPrep. Briefly, processing steps for T1w image include skull stripping, tissue segmentation, and spatial normalization to MNI space. Processing steps for functional images include motion correction using FSL MCFLIRT, slice time correction using AFNI 3dTshift for slice-timing correction, susceptibility distortion correction, and co-registration to the reference T1-weighted image using FSL FLIRT, and spatial normalization and warping to the template space using the MNI_2009 template. Each voxel was smoothed using signal from neighboring voxels with AFNI 3dBlurInMask followed by weighting by an isotropic Gaussian kernel.