Using spin to understand the formation of LIGO's black holes

With the detection of four candidate binary black hole (BBH) mergers by the Advanced LIGO detectors thus far, it is becoming possible to constrain the properties of the BBH merger population in order to better understand the formation of these systems. Black hole (BH) spin orientations are one of the cleanest discriminators of formation history, with BHs in dynamically formed binaries in dense stellar environments expected to have spins distributed isotropically, in contrast to isolated populations where stellar evolution is expected to induce BH spins preferentially aligned with the orbital angular momentum. In this work we propose a simple, model-agnostic approach to characterizing the spin properties of LIGO's BBH population. Using measurements of the effective spin of the binaries, which is LIGO's best constrained spin parameter, we introduce a simple parameter to quantify the fraction of the population that is isotropically distributed, regardless of the spin magnitude distribution of the population. Once the orientation characteristics of the population have been determined, we show how measurements of effective spin can be used to directly constrain the underlying BH spin magnitude distribution. Although we find that the majority of the current effective spin measurements are too small to be informative, with LIGO's four BBH candidates we find a slight preference for an underlying population with aligned spins over one with isotropic spins (with an odds ratio of 1.1). We argue that it will be possible to distinguish symmetric and anti-symmetric populations at high confidence with tens of additional detections, although mixed populations may take significantly more detections to disentangle. We also derive preliminary spin magnitude distributions for LIGO's black holes, under the assumption of aligned or isotropic populations

The data use ontology to streamline responsible access to human biomedical datasets

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.G.K., M.A.F., H.P., J.D.S., and M.C. were funded by EMBL-EBI Core Funds and Wellcome Trust GA4GH award number 201535/Z/16/Z. T. T. Boughtwood was funded by NHMRC GNT111353, GNT200001, and the Australian MRFF. P.A. was funded by ELIXIR Luxembourg. S.D. and K.R. were funded by the Broad Institute. M.A.H. and M.B. received funding from NIH #5R24OD011883. M.L. and M.C. were funded by the CINECA project (H2020 No 825775). N.M. and L.Z. were funded by H3ABioNet, NIH grant number U24HG006941. S.O. and C.Y. received funding from the Japan Agency for Medical Research and Development (AMED) under grant numbers JP19kk020501 and JP18kk0205012. A.A.P. was funded by NHGRI AnVIL, award number U24HG010262. F.P. was supported, in part, by the European Union’s Horizon 2020 research and innovation program under the EJP RD COFUND-EJP #825575. M.A.S. and E.J.v.E. were funded by FAIR genomes (ZonMW #846003201) and EOSC-Life (H2020 #824087