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Objectives: This study aimed to map out African health journals using publicly-available information on major databases.Methods: The authors searched the African Journals Online Library (AJOL) and Scientific Journal Rankings (SJR) databases from their inception in 1998 and 1996 respectively to 17 October 2020, and identified African health journals. The authors extracted data on journal scope, PubMed indexation, open-access status, publishing fees, Journal Publishing Practices and Standards rating and bibliometrics. The data were compared with health journals from other regions using the Chi-square test and odds ratio.Results: AJOL had 173 health journals registered on its database. One hundred (57.8%) journals were actively publishing. Fifty-seven (32.9%) had a 1-star Journal Publishing Practices and Standards rating and 4 (2.3%) had 2-star ratings. 112 (64.7%) had no star rating. The journal scope spanned all aspects of health. Few health journals were PubMed (n = 20) or SJR (n = 22) indexed. On average, African journals had lower total publications (median [IQR]: 52.0 [29.0–74.8] vs. 140.0 [75.8–272.5]), total references (55.0 [19.5–74.8] vs. 160.0 [42.0–519.8]), and H-index (12.2 [5.0–14.0] vs. 39.1 [10.0–53.0]) (P=0.01) compared to other regions.Conclusion: African health journals face unique challenges that require targeted interventions.

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more. [ABSTRACT FROM AUTHOR]

Exposure elements in open-access disaster databases that are relevant to critical infrastructure and basic services in the Sendai Framework on Disaster Risk Reduction (SFDRR) were transformed into spatial data, to investigate the impact of flash flood hazards in Kuala Lumpur, Malaysia. In this era of big data and hyper-connectivity, the availability of open-access data on exposure elements across scales and systems is largely unknown. Information on exposure elements and hazard susceptibility provide important insights to enhance community resilience, to move away from merely managing disasters to managing the risk of disasters, in line with the SFDRR. The case study of Kuala Lumpur enabled an assessment of information availability in existing disaster databases and within the national system, to facilitate informed decision-making. Findings reveal that there are a total of 26 databases on the internet that provide information on disasters and related elements; of which 18 are global, three are regional and four provide information at the national scale. However, only ten databases are open access where the user is able to easily retrieve information while others provide a “view only option”. The coverage of exposure elements in disaster databases is very poor where only five databases carried such information; and it is not useful for local scale application. Thus, information was sought from multiple open data sources within the national system and transformed into spatial data, to develop an exposure element data inventory for the city. There are 509 exposure elements within Kuala Lumpur, covering 33 private and government hospitals and community clinics; 189 public and private schools and institutions higher education; 261 facilities that provide basic services; and 26 features that represent of social and economic aspects. The exposure elements, which is coherent with the SFDRR, benefits decision-making when overlain with existing flood hazard zones and susceptible areas. Moving forward, emerging hazards due to climate change will be evaluated to strengthen informed decision-making and build community resilience in the city. The empowerment of local level research has great potential to advance open sharing of information on disaster and climate risks in the region.

This mini-review paper analyses the achievements of the Ocean Biogeographic Information System (OBIS), as a distributed global data system and as a community of data contributors and users. We highlight some issues and challenges and identify ways OBIS is trying to address these with developing community standards, protocols and best practices, applying new innovative technologies, improving human capacity through training, and establishing beneficial partnerships. With the release of the second generation of OBIS (OBIS 2.0), we now have a more solid foundation to build improved data processing/integration workflows, new data synthesis routines that add value to OBIS data, and new types of products and applications for scientific and decision-making. The future of OBIS will be in working toward an open and inviting process of co-developing OBIS as a global networked open-source data system that will enable the community to organize, document, and contribute analytical codes that interface directly with OBIS, provide analyses, and share results. The main challenges will be in mobilizing and organizing the scientific community to publish richer and high quality data more rapidly in support of developing robust and timely indicators of status and change on Essential Ocean Variables and Essential Biodiversity Variables.

Open-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30 m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in data-sparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a ‘must-have’ for any flood model. A high-accuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation.