dataesr/scientific-paragraphs-categorization huggingface dataset:
Most IRDiRC members-currently nearly 50 strong-have since incorporated its policies in their funding calls and some have chosen to exceed the requirements laid out, for instance in relation to data sharing. The IRDiRC Policies and Guidelines are the first, detailed agreement of major public and private funding organizations worldwide to govern rare disease research, and may serve as a template for other areas of international research collaboration. While it is too early to assess their full impact on research productivity and patient benefit, the IRDiRC Policies and Guidelines have already contributed significantly to improving transparency and collaboration in rare disease research.
Data sharing and standards To achieve the IRDiRC goals of contributing towards the development of 200 new therapies and means to diagnose most rare diseases by 2020, many types of resources and research data will be generated and shared; this will facilitate discovery of genes and treatments while ensuring efficient resource utilization. Ultimately, it is critical to the overall success of IRDiRC that datasets obtained from one project will be directly comparable to datasets obtained from another, even if generated using a different approach or technology  [8, 9]. This principle has been further developed into the "International Charter of principles for sharing bio-specimens and data"  [10] , which is endorsed by IRDiRC as one of the "IRDiRC Recognized Resources"  [11]. Other resources and infrastructures for rare diseases research, including DECIPHER, RD-Connect, and Pheno-meCentral, have embraced these principles and provide mechanisms for data sharing.
Guideline IRDiRC will promote active exchanges, events and activities between stakeholders, including patient organizations. A summary of IRDiRC Policies and Guidelines on generalized principles, data sharing and standards, ontologies, diagnostics, biomarkers, patient registries, biobanks, natural history, therapeutics, models, publication and intellectual property, and communication on IRDiRC. approaches for discovery is required to solve these unsolved conditions, and IRDiRC aims to gather key researchers and review strategies to address this challenge  [21]. International efforts to establish guidelines for the clinical reporting of genomic sequencing in a clinical setting, including the approach to report incidental findings, will expedite the delivery of high-throughput and cost-effective testing to the rare diseases patient community as a whole, e.
Patient registries Patient registries are organized databases where patient information, including demographic, medical, and family history, is collected, stored, and available for retrieval via standardized and secure methods. Patient registries are increasingly recognized as crucial tools for rare disease research  [26] , but there remains a clear need for their standardization, coordination, and further development. High-quality and standardized clinical data are critical for patient registries which requires the systematic use of ontologies such as the HPO (see above.
Natural history Performing natural history studies will facilitate the identification of disease characteristics that can be used when planning and conducting clinical investigations for rare disease therapies. Moreover, this knowledge will also serve to define a trial's target population, develop biomarkers for disease progression and therapeutic response, determine appropriate surrogate and relevant clinical endpoints, and decide on the study duration. As rare diseases are highly diverse in nature, there is no one set of data elements that can be recommended for data collection in all natural history rare disease studies; rather the disease characteristics should reflect the prominent features of the rare disease  [29].
Publication and intellectual property Rare disease research results should be rapidly shared and made highly visible to the scientific, health care, patient, pharmaceutical, and medical device communities. Their utility must be clearly demonstrated and potential users must have the opportunity to receive training in the techniques and tools developed. This includes negative results, which can be as important for the rare disease field as are new scientific breakthroughs (in relation to data sharing policies and guidelines). Intellectual Property (IP) is an important factor for the public and the private sector, in particular to cover the significant cost of developing new therapies. Issues related to IP rights need to be assessed and handled in accordance with fundamental ethical rules and principles.
g. , health system policy implementing HPO, ORPHA nomenclature, and data sharing policies. IRDiRC is investigating additional ways to promulgate the policies and guidelines and assess their impact, and will update them as required. More broadly from a genomics policy perspective, IRDiRC partners with the Global Alliance for Genomics and Health (GA4GH) to collaboratively develop policy and guidelines around consent and data sharing  [40] , and frameworks for ethical and secure data sharing  [41]. Similarly, IRDiRC is linked with the Global Genomic Medicine Collaborative (G2MC), an initiative to capture and disseminate best practices for genomic medicine in bioinformatics, education, evidence, pharmacogenomics, and policy across the global genomic medicine community. Other partnerships and initiatives specifically targeting rare diseases that are linked with IRDiRC include, but are not limited to: RD-Connect; TREAT-NMD; CARE for RARE Canada; Human Variome Project; RARE-Bestpractices; RD-Action; European Reference Networks (ERNs); the USA Office of Rare Diseases Research with its Rare Diseases Clinical Research Network (RDCRN); and the Undiagnosed Diseases Network International (UDNI).
Conclusions IRDiRC is focused on accelerating progress in the field of rare disease research through international cooperation and collaboration, with the ultimate goals of enabling the means to provide a diagnosis for all rare diseases patients, and to contribute to the development of new therapies for rare diseases. In order to increase the joint impact of rare diseases investment by funding agencies, industry, researchers, regulators, and rare diseases patient advocates, harmonization of efforts that address common roadblocks is needed. To assist in this task, IRDiRC developed a set of Policies and Guidelines, which are the principles that IRDiRC members agree to adhere to, focused on data sharing and standards, ontologies, diagnostics, biomarkers, patient registries, biobanks, natural history, therapeutics, models, publication and intellectual property, and communications about the Consortium.
Acknowledgements The " IRDiRC Policies and Guidelines " have been developed and validated by members of the  IRDiRC Consortium Assembly. Members at the time of writing include  Karen Aiach ,  Sultan Turki Al-Sedairy ,  Christopher Austin ,  Katherine Beaverson ,  Cindy Bell ,  Andrea Chiesi ,  Pedro Cortegoso Fernandez ,  Hugh Dawkins ,  Catherine Dargemont ,  Ruxandra Draghia-Akli ,  Christopher Gibson ,  Adam Hartman ,  Carlo Incerti ,  Sangeeta Jethwa ,  Daria Julkowska ,  Stephen Katz ,  Oleg Kvlividze ,  Denis Lacombe ,  Paul Lasko ,  Ning Li ,  Jean-Louis Mandel ,  Robert Mashal ,  Lucia Monaco ,  Brett Monia ,  Béatrice de Montleau ,  Katherine Needleman ,  Marie-Christine Ouillade ,  Willem Ouwehand ,  Melissa Parisi ,  Hyun-Young Park ,  David Pearce ,  Gualtiero Ricciardi ,  Peter Saltonstall ,  Ralph Schuster ,  Makoto Suematsu ,  Sharon Terry ,  David Thomson ,  Edward Trimble ,  Santa Tumminia ,  Sonja van Weely ,  Heikki Vilen ,  Lu Wang ,  Qing K Wang ,  Ashley Winslow ,  Ellen Welch ,  James Wu ,  Yoshihiro Yoneda.
Strengths and limitations of this study ► This individual participant data (IPD) meta-analysis brings together the largest dataset yet assembled to study the association of interarm blood pressure differences with mortality and morbidity. ► IPD permits a consistent approach to all of the available data that cannot be achieved with studylevel meta-analyses. It maximises statistical power to allow a full exploration of the prognostic association between interarm differences and timeto-event outcomes. ► Patient and public involvement is embedded at every step of this study. ► Inclusion of cohorts from around the world will maximise the ability to generalise our findings. ► We will have sufficient data to explore subgroup and sensitivity analyses addressing questions that cannot be answered at individual study level or through aggregated meta-analyses.
Introduction Individual cohort studies in various populations and study-level meta-analyses have shown interarm differences (IAD) in blood pressure to be associated with increased cardiovascular and all-cause mortality. However, key questions remain, such as follows: (1) What is the additional contribution of IAD to prognostic risk estimation for cardiovascular and all-cause mortality? (2) What is the minimum cut-off value for IAD that defines elevated risk? (3) Is there a prognostic value of IAD and do different methods of IAD measurement impact on the prognostic value of IAD? We aim to address these questions by conducting an individual patient data (IPD) meta-analysis. Methods and analysis This study will identify prospective cohort studies that measured blood pressure in both arms during recruitment, and invite authors to contribute IPD datasets to this collaboration.
All patient data received will be combined into a single dataset. Using one -stage meta-analysis, we will undertake multivariable timeto-event regression modelling, with the aim of developing a new prognostic model for cardiovascular risk estimation that includes IAD. We will explore variations in risk contribution of IAD across predefined population subgroups (eg, hypertensives, diabetics), establish the lower limit of IAD that is associated with additional cardiovascular risk and assess the impact of different methods of IAD measurement on risk prediction. Ethics and dissemination This study will not include any patient identifiable data. Included datasets will already have ethical approval and consent from their sponsors. Findings will be presented to international conferences and published in peer reviewed journals, and we have a comprehensive dissemination strategy in place with integrated patient and public involvement.
methods and analysIs aims and objectives This IPD meta-analysis has the following aims. 1. To undertake an updated systematic review with comprehensive literature searches to identify any hitherto unknown potentially eligible relevant IAD datasets and invite their chief investigators to join the collaboration. Searches will run and be updated to January 2017. 2. To standardise and combine IPD from prospective observational cohorts that measured BP in both arms during patient recruitment, and which Open Access collected morbidity and mortality outcomes in adult populations. 3. To develop overarching (across studies) multivariable time-to-event models for all-cause mortality, CVD mortality, non-fatal CVD events, risk for fatal and/ or non-fatal cardiovascular events, and for all-cause mortality, to establish any additional contribution of systolic IAD to risk prediction.
search strategy for identification of studies Relevant electronic databases (Medline, Old Medline, Medline in Process, Embase and CINAHL) will be searched for all articles published since their respective start dates that potentially meet the inclusion criteria (see online appendix: Medline search strategy). The search strategy will be verified through scoping searches checking for inclusion of all potentially relevant cohorts already known to the authors. Searches will be augmented by contacts with authors active in the field, and by dissemination of our interest through established cardiovascular intervention collaborations. Regular update searches will be run during the project.
eligibility criteria for studies Studies will be eligible for inclusion in the IPD dataset if they meet the following criteria. ► Design: prospective cohort studies. ► Population: adults aged 18 or over, with or without existing CVD, with a record of BP in both arms at recruitment. BP must have been measured in both arms during the same assessment but datasets based on either simultaneous or sequential methods of measurement will be included. ► Setting: community or primary care cohorts or (to account for varied healthcare systems) based in hospital clinic settings relevant to a wider population, for example, unselected diabetes or hypertension cohorts. Selected secondary or tertiary care cohorts such as those with renal disease or attending vascular disease clinics will be excluded. ► Sample size: no minimum sample size will be defined.
► Publication language and date: no restriction. ► Outcome: CVD mortality or all-cause mortality, fatal and non-fatal CVD events, or measures of cognitive decline. assessment Study selections will be undertaken by two authors independently. Disagreements will be resolved by discussion when possible, and failing that adjudication from the remaining reviewing authors will be sought. The quality of included cohorts will also be assessed using the Quality Improvement in Prognostic Studies (QUIPS) tool. Participant-level outcome data ► We will seek the number and timings of the following events: all-cause deaths, cardiovascular deaths, nonfatal cardiovascular events, any outcome assessments of cognition.
data transfer and cleaning Data will be transferred by the study teams in line with University of Exeter Standard Operating Procedures covering electronic transfer. 57 n successful transfer, the data will be imported into Microsoft SQL server as a table or table set per study for cleaning. We are requesting that data are anonymised before transfer; should this not happen, anonymisation will occur before the backup run (midnight) on the day of import to prevent identifiable information entering the backup sets and requiring further action. The table/ table sets for each study will remain separate throughout the cleaning process, maintaining data integrity prior to combining the study-level datasets, and will be versioned so a history of the cleaning operations can be reviewed as needed, in co-ordination with the statistician.
Descriptive analysis and data checking Study-level data on study and patient characteristics of included IPD studies will be summarised in detail, and compared with data published in the original study publication, if available. Comparison with study and patient characteristics (where available) of cohort studies that do not contribute data to the IPD analysis will be made to descriptively assess study inclusion bias. IAD will be calculated as the absolute difference between systolic BP in the right and left arms. If multiple values for BP are provided, the mean of the observations will be used. For each study, the mean IAD and proportions of patients with an IAD equal to or exceeding the three commonly reported cut-off values (5, 10 and 15 mm Hg) will be reported. We will note the number of studies that include the necessary variables for calculation of Framingham, 55 QRisk2  56  and other scores as emerge from the literature searches.
Individual patient data meta-analysis The outcomes to be investigated are (1) time to CVD mortality; (2) time to all-cause mortality; and (3) time to CVD morbidity. All analyses will be performed for each of these three outcome variables. We will undertake both one-stage and two-stage meta-analyses as follows; however, the focus will be on the one-stage meta-analysis. Initial two-stage models will be performed using combined data from all datasets, for all outcomes at 3, 5 and 10-year follow-up cut-off points, using study-level outcomes adjustment for baseline systolic BP, age and gender. Such models will be used for comparison with the results of equivalent one-stage models, and to estimate the I-squared heterogeneity statistic. 58 59 or the one-stage models, we will non-randomly select a subset of the datasets available to act as a validation dataset; the selected datasets will include participants of both genders and a range of ages.
60 To account for likely statistical heterogeneity of the datasets (investigated by two-stage models), we anticipate using random effects time to event analysis methods. We will aim to use random effects methods with a flexible parametric survival model. 61 62 Alternative parametric models will be considered if the dataset is amenable to such models. An initial series of models will investigate the association between IAD and outcome; IAD will be included as a random effect (allowed to vary within study) in all models, and all models will include individual study as a fixed effect. A series of models will investigate whether other patient-level covariates are also significant predictors of outcome, with inclusion of IAD within the model. Each individual covariate will be investigated by inclusion as a fixed effect in each model with only IAD and study.
Missing data The percentage of individual participant missing data will be reported for each study and participant-level covariate. Baseline covariates will be included in the modelling if the covariate has at least 50% data present in at least three studies. 66 Imputation of participant-level missing baseline data across the whole dataset will be performed; the imputation model will take account of individual study. The effect of inclusion of imputed data will be checked using a fixed effect model including only the imputed covariate (as well as IAD). Analyses including imputed data will be considered as sensitivity analyses only. The number of studies with study-level missing baseline data (ie, no data for a specified variable was collected within the study) will be reported for each variable.
Incorporation of IAD into a prognostic model based on the Framingham score We will aim to incorporate IAD (if found to be a significant prognostic factor for mortality/morbidity) into the Framingham scoring system for predicting cardiovascular risk. This model will be developed using the overall dataset including all studies that include the Framingham covariates. Calibration slopes comparing the observed and expected survival probabilities at different time points will be calculated, and combined in a two-stage meta-analysis for each approach. The pooled estimates from these models will be used to compare the calibration for each model.
Publication and inclusion bias We will undertake two-stage analyses to compare aggregate level data from contributing and non-contributing datasets to test for selection bias. We will qualitatively assess inclusion bias by comparing study-level data on key variables (eg, age, gender and BP) for cohorts that meet our inclusion criteria but for which consent to share data is not obtained, with our IPD dataset. We will also check whether our collection of studies may be affected by publication bias using a funnel plot and Egger's test for each of the outcome variables to confirm asymmetry. 70 71 ality assessment We will assess individual study quality using the QUIPS tool. 54 Based on the variation in quality observed, we will consider restriction of the models to higher quality studies, using criteria to be determined.
ethIcs and dIssemInatIon This is a secondary analysis of patient anonymised data. All data will have been obtained from studies where patients will have already given their consent and approval to participate. We will seek written permission for use of Open Access individual patient data from each individual study lead investigator. We will therefore not seek no further ethical approval. The study will be reported in accordance with the Preferred Reporting Items for a Systematic Reviewand Meta-analysis of Individual Participant Data (PRIS-MA-IPD) statement. 72 Findings will be presented at international conferences and published as open access articles in high-impact journals. Through targeted briefings, we will seek to inform authoritative national, European and global developers of clinical guidelines including the British Hypertension Society, UK National Institute for Health and Care Excellence and NHS commissioners and providers locally.
Effective ecosystem risk assessment relies on a conceptual understanding of ecosystem dynamics and the synthesis of multiple lines of evidence. Risk assessment protocols and ecosystem models integrate limited observational data with threat scenarios, making them valuable tools for monitoring ecosystem status and diagnosing key mechanisms of decline to be addressed by management. We applied the IUCN Red List of Ecosystems criteria to quantify the risk of collapse of the Meso-American Reef, a unique ecosystem containing the second longest barrier reef in the world. We collated a wide array of empirical data (field and remotely sensed), and used a stochastic ecosystem model to backcast past ecosystem dynamics, as well as forecast future ecosystem dynamics under 11 scenarios of threat. The ecosystem is at high risk from mass bleaching in the coming decades, with compounding effects of ocean acidification, hurricanes, pollution and fishing.
As in many coral reefs around the world, threats are predicted to increase in the future  [11] , so there is an urgent need to understand interactions among threats and evaluate potential levers for management  [14]. We collated and analysed a wide array of empirical data (field and remotely sensed), and used a stochastic ecosystem model to assess the MAR with the RLE criteria  [2, 4]. The RLE lists ecosystems in eight categories of risk largely mirrored on the IUCN Red List of Threatened Species, including three threatened categories (Vulnerable, Endangered, and Critically Endangered) defined by quantitative criteria. Two criteria assess spatial symptoms of ecosystem collapse: declines in spatial distribution (criterion A) and small distribution size (criterion B). Two criteria assess functional symptoms, namely environmental degradation (criterion C) and biotic disruption (criterion D).
This enabled us to produce stable model trajectories over long time frames, and to investigate the effect of initial values on model behaviour. The model was parameterized with data from historical studies (1970s) and contemporary data from sites in 'very good' condition according to the Reef Health Index (electronic supplementary material, appendix S1)  [16]. There was little difference in community composition among parameterizations, so we used sites in very good condition to instantiate the historical reconstruction. Second, we assessed the ability of the model to recreate known ecosystem dynamics based on disturbances occurring over the period 1966-2015 (historical reconstruction; electronic supplementary material, appendix S1). We validated the ecosystem model by collating empirical data (116 survey observations over the 50-year period) on coral cover, herbivorous fish biomass, and piscivorous fish biomass (electronic supplementary material, appendix S2).
We used the most likely scenarios to derive risk categories and plausible bounds under criteria A2a, D2a and E  [2]. We instantiated the model with empirical data on benthic cover and consumer biomass collected in 2013 (electronic supplementary material, appendix S2). For each scenario, we conducted 500 Monte Carlo runs of 100 years each with 5 years burn-in, parallelized with NIMROD  [26]. To estimate risk, it is necessary to define the endpoint of ecosystem decline (i. e. the point at which an ecosystem is considered collapsed)  [4]. Within the RLE, 'an ecosystem is Collapsed when it is virtually certain that its defining biotic or abiotic features are lost from all occurrences, and the characteristic native biota are no longer sustained'  [4]. Coral cover is a commonly used indicator of coral reef state  [17]  and is the most readily available indicator in the MAR  [13].