Open Data for Global Science

The global science system stands at a critical juncture. On the one hand, it is overwhelmed by a hidden avalanche of ephemeral bits that are central components of modern research and of the emerging ‘cyberinfrastructure’4 for e-Science.5 The rational management and exploitation of this cascade of digital assets offers boundless opportunities for research and applications. On the other hand, the ability to access and use this rising flood of data seems to lag behind, despite the rapidly growing capabilities of information and communication technologies (ICTs) to make much more effective use of those data. As long as the attention for data policies and data management by researchers, their organisations and their funders does not catch up with the rapidly changing research environment, the research policy and funding entities in many cases will perpetuate the systemic inefficiencies, and the resulting loss or underutilisation of valuable data resources derived from public investments. There is thus an urgent need for rationalised national strategies and more coherent international arrangements for sustainable access to public research data, both to data produced directly by government entities and to data generated in academic and not-for-profit institutions with public funding. In this chapter, we examine some of the implications of the ‘data driven’ research and possible ways to overcome existing barriers to accessibility of public research data. Our perspective is framed in the context of the predominantly publicly funded global science system. We begin by reviewing the growing role of digital data in research and outlining the roles of stakeholders in the research community in developing data access regimes. We then discuss the hidden costs of closed data systems, the benefits and limitations of openness as the default principle for data access, and the emerging open access models that are beginning to form digitally networked commons. We conclude by examining the rationale and requirements for developing overarching international principles from the top down, as well as flexible, common-use contractual templates from the bottom up, to establish data access regimes founded on a presumption of openness, with the goal of better capturing the benefits from the existing and future scientific data assets. The ‘Principles and Guidelines for Access to Research Data from Public Funding’ from the Organisation for Economic Cooperation and Development (OECD), reported on in another article by Pilat and Fukasaku,6 are the most important recent example of the high-level (inter)governmental approach. The common-use licenses promoted by the Science Commons are a leading example of flexible arrangements originating within the community. Finally, we should emphasise that we focus almost exclusively on the policy—the institutional, socioeconomic, and legal aspects of data access—rather than on the technical and management practicalities that are also important, but beyond the scope of this article

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Time to Switch to Second-line Antiretroviral Therapy in Children With Human Immunodeficiency Virus in Europe and Thailand.

Background: Data on durability of first-line antiretroviral therapy (ART) in children with human immunodeficiency virus (HIV) are limited. We assessed time to switch to second-line therapy in 16 European countries and Thailand. Methods: Children aged <18 years initiating combination ART (≥2 nucleoside reverse transcriptase inhibitors [NRTIs] plus nonnucleoside reverse transcriptase inhibitor [NNRTI] or boosted protease inhibitor [PI]) were included. Switch to second-line was defined as (i) change across drug class (PI to NNRTI or vice versa) or within PI class plus change of ≥1 NRTI; (ii) change from single to dual PI; or (iii) addition of a new drug class. Cumulative incidence of switch was calculated with death and loss to follow-up as competing risks. Results: Of 3668 children included, median age at ART initiation was 6.1 (interquartile range (IQR), 1.7-10.5) years. Initial regimens were 32% PI based, 34% nevirapine (NVP) based, and 33% efavirenz based. Median duration of follow-up was 5.4 (IQR, 2.9-8.3) years. Cumulative incidence of switch at 5 years was 21% (95% confidence interval, 20%-23%), with significant regional variations. Median time to switch was 30 (IQR, 16-58) months; two-thirds of switches were related to treatment failure. In multivariable analysis, older age, severe immunosuppression and higher viral load (VL) at ART start, and NVP-based initial regimens were associated with increased risk of switch. Conclusions: One in 5 children switched to a second-line regimen by 5 years of ART, with two-thirds failure related. Advanced HIV, older age, and NVP-based regimens were associated with increased risk of switch

Blood glutathione S-transferase-π as a time indicator of stroke onset

Ability to accurately determine time of stroke onset remains challenging. We hypothesized that an early biomarker characterized by a rapid increase in blood after stroke onset may help defining better the time window during which an acute stroke patient may be candidate for intravenous thrombolysis or other intravascular procedures

Clinical performances of the molecules for discriminating tPA treated (N = 12) vs. ineligible patients (N = 104).

*<p>The AUC of GST-π was significantly better than AUC of DJ-1 (p = 0.016). No significant difference was obtained between GST-π vs. NDKA (p = 0.11) and NDKA vs. DJ-1 (p = 0.14).</p><p>An AUC above 0.80 was estimated as significant (significance 0.05 and power 0.95, made with Power tests/Sample size item from pROC software).</p

Clinical performances of the 3 best molecules for discriminating early (blood sampling between 0 and 3 h after symptoms onset, N = 22) vs. late stroke patients (blood sampling strictly after 3 h, N = 81).

<p>NPV: negative predictive value/PPV: positive predictive value.</p>*<p>The AUC of GST-π was significantly better than AUC of NDKA and DJ-1 (p = 0.034 and 0.020 respectively). No significant difference was obtained between AUC of NDKA and of DJ-1 (p = 0.63).</p><p>An AUC above 0.74 was estimated as significant (significance 0.05 and power 0.95, made with Power tests/Sample size item from pROC software).</p

Blood concentration of the 5 best molecules in early and late stroke patients.

*<p>Significant concentration was set at 0.01 after Bonferroni correction (Mann-Whitney U tests, two-tailed tests).</p

General characteristics of the 2 cohorts.

<p>NA: not available.</p>▵<p>: All patients were collected within the first 3 h after symptom onset.</p

Detailed characteristics of the stroke patients (cohort 1).

†<p>: Fischer exact tests.</p>*<p>Among them, 3 patients presented initially an ischemia and a hemorrhage as a secondary event.</p>**<p>Among them, 7 patients presented initially an ischemia and a hemorrhage as a secondary event.</p

Time course of the blood concentrations of GST-π, NDKA, DJ-1, NT-proBNP and IL-6 after stroke onset.

<p>The grey dashed lines correspond to the median concentration of the molecules in the control patients. The grey lines connect the median of the box plots. 0–3 h: N = 22, 3–6 h: N = 15, 6–12 h: N = 8, 12–24 h: N = 45 and 24–36 h: N = 13. Note that there is no overlap between the different subgroup of times and that each patient has only one blood sampling within the 36 h after stroke onset.</p