dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Records The dataset is available from the Infectious Diseases Data Observatory -IDDO at https://doi. org/10. 48688/ nx85-bv30  15  The ISARIC-COVID-19 dataset is a relational database consisting of 16 tables, each representing a domain of information set out in the CDISC SDTM data model. Unique identifiers link these with the suffix 'ID. ' For example, USUBJID refers to the subject's unique identifier, which is the primary key for assessing individual-level data; STUDYID contains the unique identifier for an individual hospital or network of hospitals. Each table defines and tracks different aspects of illness and treatment.
Data tables. The tables (i. e. , domains) currently included in the dataset are Demographics (DM), Disposition (DS), Environmental Risk (ER), Healthcare Encounters (HO), Inclusion/Exclusion Criteria (IE), Treatments and Interventions (IN), Laboratory Results (LB), Microbiology Specimen (MB), Reproductive System Findings (RP), Disease Response and Clinical Classification (RS), Clinical and Adverse Events (SA), Subject Visits (SV), Vital Signs (VS), COVID-19 Follow-Up questionnaire (CQ), Subject Characteristics (SC), and Pregnancy Outcomes (PO) (Supplementary Table  1 ); The majority of those tables are at a patient level, so it has a subject id (USUBJID) that that relates the information of a single patient distributed in the multiple tables. The Trial Summary (TS), Trial Inclusion Exclusion Criteria (TI), and Device Identifiers (DI) are study-level domains; thus, there is no individual patient-level data in those domains.
Instead, there is information about the uniqueness of each institution, for instance, the inclusion/exclusion criteria or the devices used at each hospital. Data collection times for each data type are presented in Fig. 3   [16] [17] [18]. As an example, we show in Fig. 4  a synthetic, representative subset of the available data for a female patient. The CDISC SDTM data model has several advantages. For example: (1) It can adapt to any number of events. Frequently recorded events such as vital signs, laboratory tests, and patient status scores are stored as a series of events. The order is recorded in the variables with the suffix 'DY, ' which describes the day of the observation relative to the patient's hospital admission date. For example, the variable 'VSDY' indicates the day when a particular vital sign was measured.
Events occurring within the same day can be further ordered using the variables with the suffix 'SEQ' , which captures the sequence of events independently of the day on which they occurred. (2) It captures whether or not a variable was collected for a given patient (this is critical to count denominators accurately in an aggregated collection of many different datasets). The model enables this by collecting the existence of a variable separately from the occurrence or completion of that variable. E. g. , if the CRF for a dataset includes data on fever, the model shows that this question was prespecified as FEVER_ PRESP = Yes; if the patient had a fever, it is captured as FEVER_OCCUR = Yes; if the patient was afebrile, it is registered as FEVER_OCCUR = No. Combining these two variables makes it possible to accurately quantify how many patients were evaluated for fever and how many had a fever.
This distinction is found in the ER, HE, IN, and SA tables. A full description of how SDTM is implemented for these data, Frequently Asked Questions, and other data tools are available within the IDDO suite of curation and data resources (https://www. iddo. org/tools-and-resources/data-tools) to assist analysts in understanding these nuances. The remaining tables contain study-level data (e. g. , Study Inclusion Exclusion Criteria and Device Identifiers); thus, there are no individual-level data in these domains. The dataset also contains a rich repository of free-text entries that capture more fine-grained information not included in the CRF solicited entries. Such information can be identified by applying simple search functions or Natural Language Processing (NLP) techniques to the **TERM variable.
Usage Notes The utility of the data collected is optimised by issuing regular open-access ISARIC COVID-19 Clinical Data Reports (https://isaric. org/research/covid-19-clinical-research-resources/evidence-reports/) and periodic updates to the ISARIC COVID-19 Dashboard (https://livedataoxford. shinyapps. io/ CovidClinicalDataDashboard/). Data are available for analysis through two mechanisms to maximize uptake: a collaborative mechanism for ISARIC partners who contribute data to the dataset and a data-sharing platform for external researchers. The sites that contribute to the data retain ownership and decision-making authority on their data at all times. It is essential to highlight that more countries are globally transitioning to digital-based healthcare systems. During the transitioning process, quality control measures are necessary to enhance the effectiveness of healthcare-related communication and data quality  19.
Data access. Staff from sites that contribute data to the dataset may access data for collaborative analysis via the ISARIC Partner Analysis scheme (https://isaric. org/research/isaric-partner-analysis-frequently-asked-questions/). Proposals for these analyses are governed and supported by ISARIC and executed with all data contributors' contributions, oversite, and accreditation  4, 10, 20. ISARIC provides statistical, clinical, and administrative support to promote analyses by partners who contribute the data, especially those based in low-resource settings. External researchers who have not contributed to the dataset are also welcome to submit a data access and analysis proposal via the IDDO platform (https://www. iddo. org/covid19). An independent Data Access Committee reviews these requests according to the Data Access Guidelines of the platform.
(https://www. iddo. org/covid19/data-sharing/accessing-data). Statistical analysis plans and outputs from both types of access can be viewed at: https://www. iddo. org/covid19/research/approved-uses-platform-data. Data management, curation, governance, and the data-sharing platform are free to use and supported by the ISARIC and IDDO data management teams. When shared through the governed data access mechanisms, the ISARIC COVID-19 database is provided as a collection of comma-separated value (CSV) files (i. e. , tables), along with scripts to help import the data into PostgreSQL and codes that enable the reuse of the data. Notably, where data transformations are made during the database construction process, care is taken not to modify raw study data. The teams performing analyses can develop analytic codes based on assumptions they deem appropriate.
Data use. The breadth of analyses published to date demonstrates the diversity of science that can be generated from these data. Examples include identification of unique COVID-19 symptomology at the extremities of age  21  ; to develop the ISARIC 4 C mortality score that outperformed existing scores and showed utility to directly inform clinical decision making  22  ; to identify temporal trends in inpatient journeys and inform resource needs in an evolving pandemic  10  , and to improve the diagnosis of acute kidney injury  23. Further analyses to develop natural language processing, understand neurological outcomes in COVID-19 and develop models that predict a range of outcomes. The use of such a large and diverse dataset is not without challenges. Robust interpretation of analytic outputs requires an understanding of the variation in recruitment practices between sites and during the course of the outbreak and the availability of treatments and facilities (e.
g. , ICUs and ventilators) across the range of resource settings. ISARIC's collaborative approach to research outputs addresses these challenges by involving all staff who contributed to the collection of data in the review of the analysis plans and manuscripts. When designing an analysis plan, researchers must also consider which data are and are not available from each site and account for high levels of missingness, particularly during regional peaks in COVID-19 transmission. The CDISC SDTM data model was selected for harmonisation of these data, specifically because it captures these aspects of data providence. Those using the dataset benefit from the richness of the model; however, they will need to master the challenges of its complexity. Tools to support understanding of the data model can be found at https://www.
Collaborative research. The ISARIC WHO characterization protocol has proven to be a successful strategy for generating standardized data from multiple sites that international researchers can access for analysis  18, 21, 22, [24] [25] [26] [27]. Having a pre-prepared protocol for clinical investigation of an emerging infectious disease established before the beginning of the COVID-19 pandemic allowed us to gather patient data very early in the pandemic. As a result, contributors benefited from clinical data captured in other regions before they experienced cases and improved confidence in a larger dataset. By implementing systems to harmonize global data, ISARIC and IDDO have made international collaboration more efficient 1. The evolution of these systems, including integrating epidemiological and genomic data to address new types of research questions, is in progress.
Finally, ISARIC's data governance model allows members and non-members to propose research questions that could be answered using this dataset, which has helped advance science and empowers scientists worldwide  4, 10, 20. This open and collaborative approach maximizes the scientific utility and public health impact of global data. With a focus on ensuring the representation of patient data and researchers from lower-resourced settings, the ISARIC network has accelerated understanding of COVID-19, advanced preparedness for future pandemics, and raised the bar on global collaboration for health.
Gordon, AC. ; speaker, advisory board or consultancy fees from Amgen, Astra Zeneca, Bayer, Bioserenity, Boehringer Ingelheim, Bristol-Myers Squibb, MSD, Novartis, Novo Nordisk, Vifor (all unrelated to the present work). Kalleberg, K. T. Is a founder and shareholder of the company Age Labs, which develops epigenetic tests, including one for COVID-19 severity. Kimmoun, A. declares personal fees (payment for lectures) from Baxter, Aguettant, Aspen. Kumar, D. declares grants and personal fees from Roche, GSK, and Merck, and personal fees from Pfizer and Sanofi. Kutsogiannis, D. J. declares personal fees for a lecture from Tabuk Pharmaceuticals and the Saudi Critical Care Society. Kutsyna, G. declares the study consulting fee for clinical trial ClinicalTrials. gov Identifier: NCT04762628. Laffey, J.
coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care-Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care-Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Innovative Medicines Initiative Joint Undertaking under Grant Agreement No.
115523 COMBACTE, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies, in-kind contribution; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health "Fondi Ricerca corrente-L1P6" to IRCCS Ospedale Sacro Cuore-Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland; Artificial Intelligence for Pandemics (A14PAN) at University of Queensland; the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424).
Acknowledgements The investigators acknowledge: This work is part of the Grand Challenges ICODA pilot initiative, delivered by  Health Data Research UK  and funded by the  Bill & Melinda Gates Foundation  and the  Minderoo Foundation. The philanthropic support of the donors to the  University of Oxford 's COVID-19  Research Response Fund; UK Foreign, Commonwealth and Development Office  and  Wellcome  [ 215091/Z/18/Z  and  220757/Z/20/Z ]; the  Bill & Melinda Gates Foundation  [OPP1209135]; the  National Institute for Health Research (NIHR ; award  CO-CIN-01 ); the  Medical Research Council  (MRC; grant  MC_PC_19059 ); the  NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool  in partnership with  Public Health England (PHE) (award 200907);  NIHR HPRU in Respiratory Infections at Imperial College London  with  PHE  (award 200927);  Liverpool Experimental Cancer Medicine Centre  (grant  C18616/A25153 );  NIHR Biomedical Research Centre at Imperial College London  ( award IS-BRC-1215-20013 );  NIHR Clinical Research Network  (infrastructure support);  CIHR Coronavirus Rapid Research Funding Opportunity   OV2170359  and was.
Code availability Processing codes for the ISARIC COVID-19 database are openly available online, and contributions from the research community to share these codes are encouraged. For this reason, a public code repository has been created along with this manuscript to develop and share code collectively: https://github. com/ ISARICDataPlatform/ISARICBasics. git. The content of this repository is under continuous development. Still, it has been seeded with code to generate patient-level datasets suitable for statistics and machine learning research, such as patient demographic, comorbid conditions at the time of admission, application of treatments, and severity scores, among others. It is possible for the research community to directly submit updates, improvements, and additions to the repository via GitHub.
For eruptions > M 7, global studies  (Decker, 1990; Simkin, 1993; Mason et al. , 2004; Deligne et al. , 2010; Brown et al. , 2014)  suggest a minimum global average recurrence rate of ∼ 1-2 times per 1 kyr  (Newhall et al. , 2018). Once record incompleteness is corrected for  (Kiyosugi et al. , 2015; Rougier et al. , 2016) , the value approaches 2 events per 1 kyr. At individual volcanoes, > M 7 recurrence rates range from 1 per 10 kyr to 1 per 100 kyr  (Newhall et al. , 2018). For eruptions > M 8, Rougier et al. (  2018 ) estimated a return period of 17 ka (95 % confidence interval 5. 5-48 ka) from analysis of the LaMEVE database  (Crosweller et al. , 2012). More recently,  Aspinall et al. (2021) , implementing a comprehensive stochastic uncertainty analysis, estimated the probability in the next 100 years of an eruption from the Aso volcano on the same scale as Aso-4.
Multi-omics profiling of a yeast panel The BYxRM Saccharomyces cerevisiae segregant panel used in this study results from a cross between a laboratory strain (BY4716) isogenic to the reference strain S288C, and a derivative of a wild isolate from a vineyard (RM11-1a). This cross was previously used to study the genetic contribution to molecular traits including RNA and protein levels and to test novel systems genetics approaches  (Brem et al, 2002; Foss et al, 2007; Albert et al, 2014). We grew the two parental haploid yeast strains and 110 of their recombinant offspring under tightly controlled conditions with multiple replicates for some of the strains  (Fig 1, Dataset EV3). The transcriptomes of 150 cultures were sequenced at high coverage (38-186x) allowing for the quantification of 5,429 transcripts in all samples (Dataset EV1).
The RNA-seq data were also used to infer the genotypes of the strains (Cl ement-Ziza et al, 2014)  (Appendix Fig S1, Dataset EV2) , which increased the number of unique markers compared with arraybased genotyping (3,593 unique markers compared with 2,957 markers from  Gillet et al, 2012). While the resolution of the QTL mapping is mainly limited by the number of segregants (i. e. , recombinations), this procedure allows for more accurate genotyping. In samples obtained from the same yeast cultures, we used SWATH-MS  (Gillet et al, 2012; Selevsek et al, 2015)  to measure the abundance of 1,862 proteins with < 1. 8% missing values across all samples  (Fig 2A, Dataset EV1). This represents a fourfold increase in the number of consistently quantified proteins compared with previous studies in the same cross  (Foss et al, 2007 (Foss et al, , 2011;; Picotti et al, 2013; Albert et al, 2014).
We also quantified the phosphorylation state of the proteins by SWATH-MS. After stringent filtering, we obtained the abundance of 2,116 phosphopeptides from 988 proteins with < 2. 4% missing values across all samples (Dataset EV1). These peptides correspond to a total of 3,716 phosphorylated residues, including serines (2,832, 76. 2%), threonines (825, 22. 2%), and tyrosines (59, 1. 6%). Only peptides that were not polymorphic between the parental strains were considered here. We observed that in most cases, protein abundance was positively correlated with the corresponding transcript level (average r = 0. 23), and the abundance of most phosphopeptides was positively correlated with the corresponding protein (average r = 0. 29)  (Appendix Fig S2). However, each layer can also be affected independently of the genetic effects on the other layers.
2E-16, Wilcoxon ranksum test). The fact that 525 (28%) pt traits and 165 (18%) phRes traits had heritability greater than 50% indicates that at least part of the residual variation is genetically determined and not just technical and/or biological noise  (Foss et al, 2011). Furthermore, while transcripts and protein abundance tend to reach greater heritability on average, there are many genes with greater heritability on the phospho-layer compared with their transcript and protein abundance  (Appendix Fig S3). This observation is suggesting direct genetic effects on phosphopeptides that are not (solely) mediated via protein abundance changes. We utilized a Random Forest-based mapping strategy to identify QTLs  (Michaelson et al, 2010) , resulting in the detection of 5,776 eQTLs, 2,078 pQTLs, and 1,327 ptQTLs at a false discovery rate (FDR) below 10% (Dataset EV5-S1, Fig EV1A  -E ).
The same fraction of transcripts and proteins had at least one QTL (77% at FDR<10% in both cases; Fig  2C ). This large proportion of proteins with at least one pQTL underlines the high quality of the proteomics data  (Foss et al, 2011). We also detected 1,595 phQTLs affecting 1,266 phosphopeptides (60%) and 466 phResQTLs affecting 389 phosphoresiduals (44%) (Dataset EV5-S1). The physiological importance (growth effects) of 76 of the phosphopeptides that we quantified had previously been analyzed using phospho-deficient mutants  (Vi eitez et al, 2022). Based on that data, we estimated that at least 39 of these phosphopeptides were functionally relevant. Others might still have an effect in growth conditions that were not studied by Vi eitez et al. Functional phosphopeptides were similarly likely to be affected by a phQTL (20 out of 39) compared with nonfunctional phosphopeptides (23 out of 37).
The first group contained genes for which the effects of an eQTL on the transcript and protein levels of its target gene were similar ("similar"). The second group contained genes for which the effects of an eQTL were repressed or even entirely buffered on the protein level ("buffered"). The third group contained genes for which proteins showed enhanced responses compared with their corresponding transcript ("enhanced"). As a fourth group, we added genes that were affected on their protein level by ptQTLs, without a significant effect on the corresponding transcript level ("protein only"). Gene Ontology (GO) enrichment revealed that genes with eQTLs in the "similar" group were enriched for genes related to protein import into the mitochondrial matrix (Dataset EV5-S4). Genes with "buffered" eQTL effects were strongly enriched for terms related to cytoplasmic translation, including the large subunit of the ribosome, and proteins localizing to the nucleolus (Dataset EV5-S5).
Although buffering of ribosomal proteins has been observed before  (Foss et al, 2011) , it could not be excluded that this was due to technical issues in protein quantification. However, this is unlikely to be the case here: first, because ribosomal proteins are relatively highly expressed and hence easily quantifiable by mass spectrometry; and second, because these proteins were affected by other pQTLs at a similar rate as the rest of the proteome (73% of proteins with buffered eQTLs had at least one pQTL). Genes affected by the group of "enhanced" eQTLs were strongly enriched for mitochondrial ribosomes and other terms related to mitochondrial translation (Dataset EV5-S6). Like buffered proteins, proteins affected by "protein-only" effects were also enriched for functions related to cytoplasmic translation (Dataset EV5-S7).
Phospho-traits are strongly associated with physiological traits In order to better understand the mechanisms by which genetic variation affects physiological traits, we integrated our molecular QTL data with two datasets that encompass chemical resistance traits  (Perlstein et al, 2007)  and morphological traits  (Nogami et al, 2007)  for subsets of the same yeast cross used in our study. In the former, growth was measured in the presence of 92 chemical compounds at multiple dosages each, resulting in a total of 307 trait measurements for 95 strains. The second dataset comprises 501 morphological parameters from high-throughput imaging for 55 strains. We found that QTLs affecting resistance and morphology were more likely to overlap with p-and phQTLs than with eQTLs (Dataset EV5-S8). Further, protein and phosphopeptide abundance was, on average, more strongly correlated with the physiological traits compared with transcript abundance  (Fig EV2 and Appendix Fig S11).