Background Onset of dementia before the age of 65 years is usually referred to as young onset dementia (YOD). Out of the total prevalence of dementia, it is estimated that 6‐9% are accounted for by YOD, but data to build robust estimates on is very limited in this specific dementia subgroup. Valid epidemiological data on the prevalence of people with YOD is needed to adequately provide dedicated services and care. This systematic review aimed to collate data from published literature and estimate the prevalence of YOD. Method A comprehensive literature search in PubMed, Embase, CINAHL and PsychINFO was conducted to identify population‐based studies on the prevalence of dementia in a population aged under 65. Articles published between 1990 and 30 November 2018 were screened independently by two authors in two phases: 1) screening titles and abstracts, 2) screening full texts, according to the PRISMA‐guidelines. Data were extracted and all articles were assessed on quality and risk of bias. Study authors were contacted when data was missing or to obtain full texts. Random‐effect meta‐analyses were pooled estimates and assessed sources of between‐study differences. The study is registered with PROSPERO, number CRD42019119288. Result The systematic search yielded 10,370 articles. After screening and cross‐referencing 88 articles were included in the review. Eligible articles were pooled together in meta‐analyses. Subgroups were made amongst others for type of dementia, age ranges and sex. The overall pooled estimate for young onset dementia over all age ranges and all types of dementia was 0.20% (95%CI = 0.15‐0.26). The prevalence for females was 0.25% (95%CI = 0.16‐0.35), whereas the prevalence for males was 0.16% (95%CI = 0.10‐0.23). Prevalence ranged from 0.01% (95% CI= 0.00‐0.01) in the age group 30‐34, to 0.98% (95% CI = 0.72‐1.28) in the age group 60‐64. Heterogeneity across studies was high, articles differed in many methodological aspects. Meta‐regression and subgroup analyses into sources of between‐study differences in prevalence estimates are currently analyzed and will be presented. Conclusion Articles eligible for meta‐analysis showed an overall prevalence of 0.21%, with females having a slightly higher prevalence compared to males, and an increased prevalence with increasing age.

Do researchers share their quantitative data and are the quantitative results that are published in political science journals replicable? We attempt to answer these questions by analyzing all articles published in the 2015 issues of three political behaviorist journals (i.e., Electoral Studies, Party Politics, and Journal of Elections, Public Opinion & Parties)—all of which did not have a binding data-sharing and replication policy as of 2015. We found that authors are still reluctant to share their data; only slightly more than half of the authors in these journals do so. For those who share their data, we mainly confirmed the initial results reported in the respective articles in roughly 70% of the times. Only roughly 5% of the articles yielded significantly different results from those reported in the publication. However, we also found that roughly 25% of the articles organized the data and/or code so poorly that replication was impossible.

52Background: Glycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of 53

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Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec