Explore the vast range of titles available.

Since the IMF launched the data standards initiatives a decade ago, 145 of its 184 member countries have participated. This 80 percent participation rate reaffirms the importance countries place on data transparency in the globalized economy, which the initiatives promote. The wide participation can be attributed to the consultative process that has allowed for the development of a coherent program that takes account of countries' capabilities, delineates clear responsibilities between the IMF and participating countries, and establishes effective monitoring procedures to ensure the credibility of the standards for policymakers, capital markets, and the general public. The approach has also provided checks and balances and fostered accountability. The initiatives may provide insights for the promotion of similar international standards.

The Data Dissemination Initiative was launched in the mid-1990s as part of a broader internationally-agreed-upon initiative to strengthen transparency and promote good governance practices by establishing standards and codes. Ten years later, the initiative is viewed as an integral part of the international financial architecture, and is considered to have improved the functioning of international financial markets and contributed to global financial stability. This volume reviews certain aspects of the development of and experience with the initiative over the past decade, and concludes by reflecting on potential challenges ahead and possible enhancements.

The effects of the IMF's data standards initiatives on sovereign borrowing costs in private capital markets are investigated for 26 emerging market and developing countries. Stable and significant panel econometric estimates indicate that subscription to the Special Data Dissemination Standard (SDDS) reduces launch spreads by an average of 20 percent while participation in the General Data Dissemination System (GDDS) reduces spreads for those countries with access to capital markets by an average of 8 percent. These estimates correspond to discounts of some 50 and 20 basis points, respectively. Evidence of similar discounts is also found when launch yields are analyzed.

\"Big Data\" is on the covers ofScience, Nature, theEconomist, andWiredmagazines, on the front pages of theWall Street Journaland theNew York Times.But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

GNPS is an open-access community-curated analysis platform for sharing natural product mass spectrometry data that enables continuous, automatic reanalysis of deposited 'living' data sets. The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu ), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Big data and the mechanisms by which it is produced and disseminated introduce important changes in the ways information is generated and made relevant for organizations. Big data often represents miscellaneous records of the whereabouts of large and shifting online crowds. It is frequently agnostic, in the sense of being produced for generic purposes or purposes different from those sought by big data crunching. It is based on varying formats and modes of communication (e.g., texts, image and sound), raising severe problems of semiotic translation and meaning compatibility. Crucially, the usefulness of big data rests on their steady updatability, a condition that reduces the time span within which this data is useful or relevant. Jointly, these attributes challenge established rules of strategy making as these are manifested in the canons of procuring structured information of lasting value that addresses specific and long-term organizational objectives. The developments underlying big data thus seem to carry important implications for strategy making, and the data and information practices with which strategy has been associated. We conclude by placing the understanding of these changes within the wider social and institutional context of longstanding data practices and the significance they carry for management and organizations.

Purpose Functional status and chronic health status are important baseline characteristics of critically ill patients. The assessment of frailty on admission to the intensive care unit (ICU) may provide objective, prognostic information on baseline health. To determine the impact of frailty on the outcome of critically ill patients, we performed a systematic review and meta-analysis comparing clinical outcomes in frail and non-frail patients admitted to ICU. Methods We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PubMed, CINAHL, and Clinicaltrials.gov. All study designs with the exception of narrative reviews, case reports, and editorials were included. Included studies assessed frailty in patients greater than 18 years of age admitted to an ICU and compared outcomes between fit and frail patients. Two reviewers independently applied eligibility criteria, assessed quality, and extracted data. The primary outcomes were hospital and long-term mortality. We also determined the prevalence of frailty, the impact on other patient-centered outcomes such as discharge disposition, and health service utilization such as length of stay. Results Ten observational studies enrolling a total of 3030 patients (927 frail and 2103 fit patients) were included. The overall quality of studies was moderate. Frailty was associated with higher hospital mortality [relative risk (RR) 1.71; 95% CI 1.43, 2.05; p  < 0.00001; I 2  = 32%] and long-term mortality (RR 1.53; 95% CI 1.40, 1.68; p  < 0.00001; I 2  = 0%). The pooled prevalence of frailty was 30% (95% CI 29–32%). Frail patients were less likely to be discharged home than fit patients (RR 0.59; 95% CI 0.49, 0.71; p  < 0.00001; I 2  = 12%). Conclusions Frailty is common in patients admitted to ICU and is associated with worsened outcomes. Identification of this previously unrecognized and vulnerable ICU population should act as the impetus for investigating and implementing appropriate care plans for critically ill frail patients. Registration: PROSPERO (ID: CRD42016053910).

This Resource describes the Image Data Resource (IDR), a prototype online system for biological image data that links experimental and analytic data across multiple data sets and promotes image data sharing and reanalysis. Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR). IDR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open-source platform for publishing imaging data. Thus IDR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.

Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.