Crowd-funded micro-grants for genomics and "big data": an actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy

Current trends in and future potential of crowdfunding to finance R&D of treatments for neglected tropical diseases

A serious lack of funding exists for the research and development (R&D) of therapeutics, diagnostics, and preventive measures for neglected tropical diseases (NTDs). Hence, crowdfunding to finance R&D for NTDs has high importance, because it is a new and alternate source of capital. This study explores current trends of crowdfunding for R&D for NTDs. Our study showed that, although the number of crowdfunding campaigns for NTDs has been increasing since 2010, crowdfunding overall has not reached its full potential. Several factors contributing positively to the success of crowdfunding campaigns were identified. These and the promotion of the crowdfunding ecosystem could aid the unlocking of its potential as a complementary financing source to conventional funding practices of R&D for NTDs.

Citizen science to further precision medicine: from vision to implementation

The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can improve both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication. This perspective, based on a workshop held at the 2018 AMIA Annual Symposium, identifies current barriers and needed changes to facilitate broad adoption of a citizen science-based approach in healthcare.

Evolutionary genomics can improve prediction of species' responses to climate change

Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and the transformation of entire ecosystems. Many species are challenged by the pace of GCC because they might not be able to respond fast enough to changing biotic and abiotic conditions. Species can respond either by shifting their range, or by persisting in their local habitat. If populations persist, they can tolerate climatic changes through phenotypic plasticity, or genetically adapt to changing conditions depending on their genetic variability and census population size to allow for de novo mutations. Otherwise, populations will experience demographic collapses and species may go extinct. Current approaches to predicting species responses to GCC begin to combine ecological and evolutionary information for species distribution modelling. Including an evolutionary dimension will substantially improve species distribution projections which have not accounted for key processes such as dispersal, adaptive genetic change, demography, or species interactions. However, eco-evolutionary models require new data and methods for the estimation of a species' adaptive potential, which have so far only been available for a small number of model species. To represent global biodiversity, we need to devise large-scale data collection strategies to define the ecology and evolutionary potential of a broad range of species, especially of keystone species of ecosystems. We also need standardized and replicable modelling approaches that integrate these new data to account for eco-evolutionary processes when predicting the impact of GCC on species' survival. Here, we discuss different genomic approaches that can be used to investigate and predict species responses to GCC. This can serve as guidance for researchers looking for the appropriate experimental setup for their particular system. We furthermore highlight future directions for moving forward in the field and allocating available resources more effectively, to implement mitigation measures before species go extinct and ecosystems lose important functions.

The grant is dead, long live the data - migration as a pragmatic exit strategy for research data preservation

Open research, data sharing and data re-use have become a priority for publicly- and charity-funded research. Efficient data management naturally requires computational resources that assist in data description, preservation and discovery. While it is possible to fund development of data management systems, currently it is more difficult to sustain data resources beyond the original grants. That puts the safety of the data at risk and undermines the very purpose of data gathering.

PlaSMo stands for ‘Plant Systems-biology Modelling’ and the PlaSMo model repository was envisioned by the plant systems biology community in 2005 with the initial funding lasting until 2010. We addressed the sustainability of the PlaSMo repository and assured preservation of these data by implementing an exit strategy. For our exit strategy we migrated data to an alternative, public repository with secured funding. We describe details of our decision process and aspects of the implementation. Our experience may serve as an example for other projects in a similar situation.

We share our reflections on the sustainability of biological data management and the future outcomes of its funding. We expect it to be a useful input for funding bodies.

Participation in global value chain and green technology progress: evidence from big data of Chinese enterprises

This study examined the stimulative effects of Chinese enterprises' participation in the global value chain (GVC) on the progress of their green technologies. Using difference-in-difference panel models with big data of Chinese enterprises, we measured influencing factors such as enterprise participation degree, enterprise scale, corporate ownership, and research and development (R&D) investment. The results revealed that participation in the GVC can considerably improve the green technology levels in all enterprises, except state-owned ones. However, the older an enterprise, the higher the sluggishness is likely to be in its R&D activities; this is particularly true for state-owned enterprises. The findings provide insights into the strategy of actively addressing Chinese enterprises' predicament of being restricted to the lower end of the GVC.

What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness

The global bioeconomy is generating new paradigm-shifting practices of knowledge co-production, such as collective innovation; large-scale, data-driven global consortia science (Big Science); and consortia ethics (Big Ethics). These bioeconomic and sociotechnical practices can be forces for progressive social change, but they can also raise predicaments at the interface of law, human rights, and bioethics. In this article, we examine one such double-edged practice: the growing, multivariate exploitation of Big Data in the health sector, particularly by the private sector. Commercial exploitation of health data for knowledge-based products is a key aspect of the bioeconomy and is also a topic of concern among publics around the world. It is exacerbated in the current age of globally interconnected consortia science and consortia ethics, which is characterized by accumulating epistemic proximity, diminished academic independence, "extreme centrism", and conflicted/competing interests among innovation actors. Extreme centrism is of particular importance as a new ideology emerging from consortia science and consortia ethics; this relates to invariably taking a middle-of-the-road populist stance, even in the event of human rights breaches, so as to sustain the populist support needed for consortia building and collective innovation. What role do law, human rights, and bioethics-separate and together-have to play in addressing these predicaments and opportunities in early 21st century science and society? One answer we propose is an intertwined ethico-legal normative construct, namely trustworthiness. By considering trustworthiness as a central pillar at the intersection of law, human rights, and bioethics, we enable others to trust us, which in turns allows different actors (both nonprofit and for-profit) to operate more justly in consortia science and ethics, as well as to access and responsibly use health data for public benefit.

From big data analysis to personalized medicine for all: challenges and opportunities

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

Toward a Literature-Driven Definition of Big Data in Healthcare

Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

Ready to put metadata on the post-2015 development agenda? Linking data publications to responsible innovation and science diplomacy

Metadata refer to descriptions about data or as some put it, "data about data." Metadata capture what happens on the backstage of science, on the trajectory from study conception, design, funding, implementation, and analysis to reporting. Definitions of metadata vary, but they can include the context information surrounding the practice of science, or data generated as one uses a technology, including transactional information about the user. As the pursuit of knowledge broadens in the 21(st) century from traditional "science of whats" (data) to include "science of hows" (metadata), we analyze the ways in which metadata serve as a catalyst for responsible and open innovation, and by extension, science diplomacy. In 2015, the United Nations Millennium Development Goals (MDGs) will formally come to an end. Therefore, we propose that metadata, as an ingredient of responsible innovation, can help achieve the Sustainable Development Goals (SDGs) on the post-2015 agenda. Such responsible innovation, as a collective learning process, has become a key component, for example, of the European Union's 80 billion Euro Horizon 2020 R&D Program from 2014-2020. Looking ahead, OMICS: A Journal of Integrative Biology, is launching an initiative for a multi-omics metadata checklist that is flexible yet comprehensive, and will enable more complete utilization of single and multi-omics data sets through data harmonization and greater visibility and accessibility. The generation of metadata that shed light on how omics research is carried out, by whom and under what circumstances, will create an "intervention space" for integration of science with its socio-technical context. This will go a long way to addressing responsible innovation for a fairer and more transparent society. If we believe in science, then such reflexive qualities and commitments attained by availability of omics metadata are preconditions for a robust and socially attuned science, which can then remain broadly respected, independent, and responsibly innovative. "In Sierra Leone, we have not too much electricity. The lights will come on once in a week, and the rest of the month, dark[ness]. So I made my own battery to power light in people's houses." Kelvin Doe (Global Minimum, 2012) MIT Visiting Young Innovator Cambridge, USA, and Sierra Leone "An important function of the (Global) R&D Observatory will be to provide support and training to build capacity in the collection and analysis of R&D flows, and how to link them to the product pipeline." World Health Organization (2013) Draft Working Paper on a Global Health R&D Observatory.

Glaucomics: a call for systems diagnostics for 21(st) century ophthalmology and personalized visual health

This article analyzes and theorizes the current knowledge silos at the intersection of omics science, ophthalmology, personalized medicine, and global visual health. Visual disorders represent one of the largest health care expenditures in the United States, costing $139 billion per year. In middle-income and industrialized countries, glaucoma is a World Health Organization priority category eye disease, known for difficulties in its early diagnosis, chronic progressive nature, and large person-to-person differences in drug efficacy and safety. A complex disease, glaucoma is best conceptualized as a syndrome displaying an ostensibly common clinical end-point, but with vastly heterogeneous molecular underpinnings and host-environment interactions. About 12% of all global blindness is attributable to glaucoma. Glaucomics is a term that we coin here so as to introduce omics science and systems diagnostics to ophthalmology, a field that can benefit enormously from personalized medicine, and which has sadly lagged behind in systems diagnostics compared to fields such as oncology. We define glaucomics as the integrated use of multi-omics and systems science approaches towards rational discovery, development, and tandem applications of diagnostics and therapeutics, for glaucoma specifically, and for personalized visual health, more broadly. We propose that glaucoma is one of the neglected lowest hanging fruits and actionable targets for omics and systems diagnostics in 21(st) century ophthalmology for the salient reasons we describe here. Additionally, we offer an analysis on two of the most pertinent neglected tropical diseases (NTDs), trachoma and river blindness, which continue to plague visual health in developing countries. We conclude with a call for research on omics applications in glaucoma and personalized visual health.

The emergence of commercial genomics: analysis of the rise of a biotechnology subsector during the Human Genome Project, 1990 to 2004

BACKGROUND

Development of the commercial genomics sector within the biotechnology industry relied heavily on the scientific commons, public funding, and technology transfer between academic and industrial research. This study tracks financial and intellectual property data on genomics firms from 1990 through 2004, thus following these firms as they emerged in the era of the Human Genome Project and through the 2000 to 2001 market bubble.

METHODS

A database was created based on an early survey of genomics firms, which was expanded using three web-based biotechnology services, scientific journals, and biotechnology trade and technical publications. Financial data for publicly traded firms was collected through the use of four databases specializing in firm financials. Patent searches were conducted using firm names in the US Patent and Trademark Office website search engine and the DNA Patent Database.

RESULTS

A biotechnology subsector of genomics firms emerged in parallel to the publicly funded Human Genome Project. Trends among top firms show that hiring, capital improvement, and research and development expenditures continued to grow after a 2000 to 2001 bubble. The majority of firms are small businesses with great diversity in type of research and development, products, and services provided. Over half the public firms holding patents have the majority of their intellectual property portfolio in DNA-based patents.

CONCLUSIONS

These data allow estimates of investment, research and development expenditures, and jobs that paralleled the rise of genomics as a sector within biotechnology between 1990 and 2004.