Ethics and Epistemology in Big Data Research

Biomedical innovation and translation are increasingly emphasizing research using “big data.” The hope is that big data methods will both speed up research and make its results more applicable to “real-world” patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.     

Genetic research and consent: On the crossroads of human and data research

This paper explores the legal and ethical concept of human subject research in order to determine whether genetic research with already available biosamples and data falls within this concept. Although the ethical concept seems to have evolved to recognize research based on data as human research, from a supranational legal perspective this form of research is not considered human subject research. Thus human subject research regulations do not apply and therefore do not invoke the requirement of obtaining consent prior to using an individual’s biosample or genetic data in research. Furthermore, it remains ambiguous in both the legal and ethical realm whether the use of biosamples or genetic data without additional links to the individual would invoke the same safeguards as research involving additional or specific identifiers. Seeing that research based on already available biosamples and genetic data is not governed by rules concerning human subject research, the second part of the paper analyses whether any consent requirements apply for the further use of already available bio‐samples or genetic data in research. Whereas further use of biosamples is subject to considerably lax consent requirements under Article 22 of the Oviedo Convention, under the General Data Protection Regulation further use of genetic data might not be subject to a prior consent requirement at all, unless it is stipulated in national laws. When it comes to clinical trials, however, sponsors will have the possibility under Article 28(2) of Regulation 536/2014 to obtain open consent for further use of data in any kind of future research.     

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.     

Unlocking avian museum collections to enable and advance environmental change research

The rate and magnitude of contemporary changes in natural systems is unprecedented in the Earth's history. Studies of wild birds have been critically important in helping us understand and address these environmental changes. Avian collections provide a potentially unique perspective on change through time, but their role in environmental change research is limited by the availability of collections data. Here we describe how avian collections might be unlocked to enable environmental change research, and discuss the opportunities and constraints associated with this. We use the concept of the extended specimen to describe the types of data that could be unlocked from basic data for discoverability to enhanced data that might be directly applied to environmental change questions. We illustrate the type of environmental change research these data might support. We argue that data creation and access is currently limited by funding for digitization, a rather patchy understanding of the needs of the research community and less than adequate data-sharing by institutions and researchers. We develop a blueprint for addressing these issues which includes (1) improvements in sharing the data we are already creating and (2) building a better case for digitization at scale. As one of the largest avian collections in the world, the Natural History Museum, UK, is committed to unlocking our collections, but we will need input and support from the avian research community to do so.     

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.     

基于文献计量的生态系统观测研究网络长期观测数据应用研究

基于CNKI数据库,采用文献计量和知识图谱的方法,通过对应用生态系统观测研究网络长期定位观测数据的文献进行分析,探讨长期观测数据的应用领域、具体用途、用户特点及不同生态站数据的应用状况与研究主题,以期为提高生态系统观测研究网络长期观测数据的共享服务能力、充分发挥长期观测数据的价值提供参考。分析结果表明:生态系统观测研究网络长期观测数据受到越来越多学者的关注,其应用学科领域以林业、农业基础科学为主,同时不断扩展到其他学科中,呈多元化态势;数据主要在生态系统服务研究、模型模拟、人工林研究、水污染研究、生物多样性研究、小麦玉米研究、土壤水分研究等方面发挥作用;数据的主要用户群体为高等院校和科研院所,不同机构应用长期观测数据开展的研究各有侧重;各生态站的长期观测数据能够为揭示其所代表生态区和生态系统类型的生态系统结构与功能、能量流动与养分循环的变化规律,分析主要生态环境问题的现状、动态变化及驱动机制等方面提供重要支撑。最后,对生态系统观测研究网络长期观测数据应用的相关方面提出几点建议:(1)健全数据引用机制,制定相应的科学数据引用和著录标准;(2)发挥生态网络长期观测数据优势,开展专题数据产品的生产,充分开发生态网络长期观测数据的潜在价值;(3)加大和稳定生态站的经费投入,提高生态站的观测能力和水平,同时还要完善、优化生态站布局。     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.     

Integration of tools and resources for display and analysis of genomic data for protozoan parasites

Centralisation of tools for analysis of genomic data is paramount in ensuring that research is always carried out on the latest currently available data. As such, World Wide Web sites providing a range of online analyses and displays of data can play a crucial role in guaranteeing consistency of in silico work. In this respect, the protozoan parasite research community is served by several resources, either focussing on data and tools for one species or taking a broader view and providing tools for analysis of data from many species, thereby facilitating comparative studies. In this paper, we give a broad overview of the online resources available. We then focus on the GeneDB project, detailing the features and tools currently available through it. Finally, we discuss data curation and its importance in keeping genomic data 'relevant' to the research community.     

A prototype system for multilingual data discovery of International Long-Term Ecological Research (ILTER) Network data

Shared ecological data have the potential to revolutionize ecological research just as shared genetic sequence data have done for biological research. However, for ecological data to be useful, it must first be discoverable. A broad-scale research topic may require that a researcher be able to locate suitable data from a variety of global, regional and national data providers, which often use different local languages to describe their data. Thus, one of the challenges of international sharing of long-term data is facilitation of multilingual searches. Such searches are hindered by lack of equivalent terms across languages and by uneven application of keywords in ecological metadata. To test whether a thesaurus-based approach to multilingual data searching might be effective, we implemented a prototype web-services-based system for searching International Long-Term Ecological Research Network data repositories. The system builds on the use of a multilingual thesaurus to make searches more complete than would be obtained through search term-translation alone. The resulting system, when coupled to commodity online translation systems, demonstrates the possibility of achieving multilingual searches for ecological data.     

Adjusting the focus: A public health ethics approach to data research

This paper contends that a research ethics approach to the regulation of health data research is unhelpful in the era of population‐level research and big data because it results in a primary focus on consent (meta‐, broad, dynamic and/or specific consent). Two recent guidelines – the 2016 WMA Declaration of Taipei on ethical considerations regarding health databases and biobanks and the revised CIOMS International ethical guidelines for health‐related research involving humans – both focus on the growing reliance on health data for research. But as research ethics documents, they remain (to varying degrees) focused on consent and individual control of data use. Many current and future uses of health data make individual consent impractical, if not impossible. Many of the risks of secondary data use apply to communities and stakeholders rather than individual data subjects. Shifting from a research ethics perspective to a public health lens brings a different set of issues into view: how are the benefits and burdens of data use distributed, how can data research empower communities, who has legitimate decision‐making capacity? I propose that a public health ethics framework – based on public benefit, proportionality, equity, trust and accountability – provides more appropriate tools for assessing the ethical uses of health data. The main advantage of a public health approach for data research is that it is more likely to foster debate about power, justice and equity and to highlight the complexity of deciding when data use is in the public interest.     

Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data

Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org.     

城市生态系统研究中遥感技术的应用

本文提出并论证了城市生态系统研究与遥感的总体方案及主要研究方向和研究方法。可以认为,用遥感技术作为获取数据的手段,用动态大系统理论分析数据,并借助于专家系统和地理信息系统处理和管理数据,可以解决城市生态系统研究中全方位信息的收集和巨大信息量的处理两大难题;通过引入中间变量把非空间直接可测变量表述为空间直接可测量的函数,从而在专家系统和地理信息系统的支持下扩大遥感技术的应用领域。     

Owner controlled data exchange in nutrigenomic collaborations: the NuGO information network

New ‘omics’ technologies are changing nutritional sciences research. They enable to tackle increasingly complex questions but also increase the need for collaboration between research groups. An important challenge for successful collaboration is the management and structured exchange of information that accompanies data-intense technologies. NuGO, the European Nutrigenomics Organization, the major collaborating network in molecular nutritional sciences, is supporting the application of modern information technologies in this area. We have developed and implemented a concept for data management and computing infrastructure that supports collaboration between nutrigenomics researchers. The system fills the gap between “private” storing with occasional file sharing by email and the use of centralized databases. It provides flexible tools to share data, also during experiments, while preserving ownership. The NuGO Information Network is a decentral, distributed system for data exchange based on standard web technology. Secure access to data, maintained by the individual researcher, is enabled by web services based on the the BioMoby framework. A central directory provides information about available web services. The flexibility of the infrastructure allows a wide variety of services for data processing and integration by combining several web services, including public services. Therefore, this integrated information system is suited for other research collaborations.     

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of 'big data' research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European 'Network of Excellence' (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.     

DNA数据存储的科研概况国际对比与分析

全球数据量快速增长,成为数字经济发展的核心引擎,但传统数据存储介质受到功耗、体积、成本等限制,难以满足不断增长的数据存储需求。以脱氧核糖核酸(deoxyribonucleic acid, DNA)分子作为存储介质的新型存储方式引起了国内外高度重视,世界主要国家均对其研究进行了顶层规划,部署了一系列重要科研计划。但是,DNA数据存储作为一个新兴交叉研究领域,其发展的“源”与“流”仍存在需要深入分析的问题。针对该问题,从信息、半导体与合成生物学交叉融合的角度深入挖掘DNA数据存储发展的源头,对近年来国际上主要国家与地区在DNA数据存储领域的发展规划进行分析归纳,梳理国内外的科研项目规划布局,尤其是美国“半导体合成生物学联盟”推动的基础研究项目、美国国防部高级研究计划局(Defense Advanced Research Projects Agency, DARPA)与美国情报高级研究计划局(Intelligence Advanced Research Projects Activity, IARPA)推动的面向应用的集中攻关项目、欧盟的地平线2020计划以及我国的重点研发计划等。通过比较可...     

Virtual Reality: Beyond Visualization

Virtual reality (VR) has recently become an affordable technology. A wide range of options are available to access this unique visualization medium, from simple cardboard inserts for smartphones to truly advanced headsets tracked by external sensors. While it is now possible for any research team to gain access to VR, we can still question what it brings to scientific research. Visualization and the ability to navigate complex three-dimensional data are undoubtedly a gateway to many scientific applications; however, we are convinced that data treatment and numerical simulations, especially those mixing interactions with data, human cognition, and automated algorithms will be the future of VR in scientific research. Moreover, VR might soon merit the same level of attention to imaging data as machine learning currently has. In this short perspective, we discuss approaches that employ VR in scientific research based on some concrete examples.     

The scientific status of metazoan cladistics: why current research practice must change

Jenner, R. A. (2004). The scientific status of metazoan cladistics: why current research practice must change. —Zoologica Scripta, 33, 293–310. Metazoan phylogenetics is bustling with activity. The use of comprehensive morphological data sets in recent phylogenetic analyses of the Metazoa indicates that morphological evidence continues to play a key role in the reconstruction of metazoan deep history. In this paper I review the scientific status of morphological metazoan cladistics from the perspective of cladistic research cycles. Each research cycle consists of three main steps: (1) the compilation of a data matrix (2) the simultaneous evaluation of all possible cladograms in a character congruence test, and (3) the assessment of the relationship between evidence and hypothesis after finding the optimal tree. I identify a striking discrepancy between the sophistication of the analysis of given data sets (Step 2), and their compilation and the interpretation of the results (Steps 1 and 3). The latter two steps deserve far greater attention than is current practice. Uncritical and nonexplicit character selection, character coding, and character scoring seriously compromise Step 1. Careful comparative morphological study prior to data matrix construction is necessary to remedy this problem in future cladistic analyses. Step 2 is the locus of most recent advances in metazoan cladistics through the increasing availability of computing power, and the development of increasingly efficient phylogenetic software that allows analysis of large data sets. Failure to identify problems and errors generated in Step 1 of the research cycle is testament to the general failure of Step 3. Consequently, recent progress in metazoan cladistics is primarily analytical, while the only empirical anchor of the discipline receives surprisingly little attention. Not surprisingly, the first generation of modern metazoan phylogeneticists used computers principally as a relatively quick and easy means to generate abundant phylogenies from morphological data. The next phase should build on this foundation by critically testing these alternative hypotheses by a thorough qualitative reassessment and elaboration of morphological data matrices, and a more critical approach to data selection. A rigorous research program for metazoan cladistics can only be established when the cladistic research cycle is properly completed, and when subsequent research cycles are effectively linked to previous efforts.