Research resources: curating the new eagle-i discovery system

Development of biocuration processes and guidelines for new data types or projects is a challenging task. Each project finds its way toward defining annotation standards and ensuring data consistency with varying degrees of planning and different tools to support and/or report on consistency. Further, this process may be data type specific even within the context of a single project. This article describes our experiences with eagle-i, a 2-year pilot project to develop a federated network of data repositories in which unpublished, unshared or otherwise ‘invisible’ scientific resources could be inventoried and made accessible to the scientific community. During the course of eagle-i development, the main challenges we experienced related to the difficulty of collecting and curating data while the system and the data model were simultaneously built, and a deficiency and diversity of data management strategies in the laboratories from which the source data was obtained. We discuss our approach to biocuration and the importance of improving information management strategies to the research process, specifically with regard to the inventorying and usage of research resources. Finally, we highlight the commonalities and differences between eagle-i and similar efforts with the hope that our lessons learned will assist other biocuration endeavors

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases

Contexts and Contributions: Building the Distributed Library

This report updates and expands on A Survey of Digital Library Aggregation Services, originally commissioned by the DLF as an internal report in summer 2003, and released to the public later that year. It highlights major developments affecting the ecosystem of scholarly communications and digital libraries since the last survey and provides an analysis of OAI implementation demographics, based on a comparative review of repository registries and cross-archive search services. Secondly, it reviews the state-of-practice for a cohort of digital library aggregation services, grouping them in the context of the problem space to which they most closely adhere. Based in part on responses collected in fall 2005 from an online survey distributed to the original core services, the report investigates the purpose, function and challenges of next-generation aggregation services. On a case-by-case basis, the advances in each service are of interest in isolation from each other, but the report also attempts to situate these services in a larger context and to understand how they fit into a multi-dimensional and interdependent ecosystem supporting the worldwide community of scholars. Finally, the report summarizes the contributions of these services thus far and identifies obstacles requiring further attention to realize the goal of an open, distributed digital library system

Cognitive Decay And Memory Recall During Long Duration Spaceflight

This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

Promoting access to public research data for scientific, economic, and social development

It is now commonplace to say that information and communications technologies are rapidly transforming the world of research. We are only beginning to recognize, however, that management of the scientific enterprise must adapt if we, as a society, are to take full advantage of the knowledge and understanding generated by researchers. One of the most important areas of information and communication technology (ICT)-driven change is the emergence of escience, briefly described as universal desktop access, via the Internet, to distributed resources, global collaboration, and the intellectual, analytical, and investigative output of the world’s scientific community.The vision of e-science is being realised in relation to the outputs of science, particularly journal articles and other forms of scholarly publication. This realisation extends less to research data, the raw material at the heart of the scientific process and the object of significant annual public investments.Ensuring research data are easily accessible, so that they can be used as often and as widely as possible, is a matter of sound stewardship of public resources. Moreover, as research becomes increasingly global, there is a growing need to systematically address data access and sharing issues beyond national jurisdictions. The goals of this report and its recommendations are to ensure that both researchers and the public receive optimum returns on the public investments in research, and to build on the value chain of investments in research and research data. To some extent, research data are shared today, often quite extensively within established networks, using both the latest technology and innovative management techniques. The Follow Up Group drew on the experiences of several of these networks to examine the roles and responsibilities of governments as they relate to data produced from publicly funded research. The objective was to seek good practices that can be used by national governments, international bodies, and scientists in other areas of research. In doing so, the Group developed an analytical framework for determining where further improvements can be made in the national and international organization, management, and regulation of research data.The findings and recommendations presented here are based on the central principle that publicly funded research data should be openly available to the maximum extent possible. Availability should be subject only to national security restrictions; protection of confidentiality and privacy; intellectual property rights; and time-limited exclusive use by principal investigators. Publicly funded research data are a public good, produced in the public interest. As such they should remain in the public realm. This does not preclude the subsequent commercialization of research results in patents and copyrights, or of the data themselves in databases, but it does mean that a copy of the data must be maintained and made openly accessible. Implicitly or explicitly, this principle is recognized by many of the world’s leading scientific institutions, organizations, andagencies. Expanding the adoption of this principle to national and international stages will enable researchers, empower citizens and convey tremendous scientific, economic, and social benefits. Evidence from the case studies and from other investigation undertaken for this report suggest that successful research data access and sharing arrangements, or regimes, share a number of key attributes and operating principles. These bring effective organization and management to the distribution and exchange of data. The key attributes include: openness; transparency of access and active dissemination; the assignment and assumption of formal responsibilities; interoperability; quality control; operational efficiency and flexibility; respect for private intellectual property and other ethical and legal matters; accountability; and professionalism. Whether they are discipline-specific or issue oriented, national or international, the regimes that adhere to these operating principles reap the greatest returns from the use of research data. There are five broad groups of issues that stand out in any examination of research data access and sharing regimes. The Follow Up Group used these as an analytical framework for examining the case studies that informed this report, and in doing so, came to several broad conclusions: • Technological issues: Broad access to research data, and their optimum exploitation, requires appropriately designed technological infrastructure, broad international agreement on interoperability, and effective data quality controls; • Institutional and managerial issues: While the core open access principle applies to all science communities, the diversity of the scientific enterprise suggests that a variety of institutional models and tailored data management approaches are most effective in meeting the needs of researchers; • Financial and budgetary issues: Scientific data infrastructure requires continued, and dedicated, budgetary planning and appropriate financial support. The use of research data cannot be maximized if access, management, and preservation costs are an add-on or after-thought in research projects; • Legal and policy issues: National laws and international agreements directly affect data access and sharing practices, despite the fact that they are often adopted without due consideration of the impact on the sharing of publicly funded research data; • Cultural and behavioural issues: Appropriate reward structures are a necessary component for promoting data access and sharing practices. These apply to both those who produce and those who manage research data.The case studies and other research conducted for this report suggest that concrete, beneficial actions can be taken by the different actors involved in making possible access to, and sharing of, publicly funded research data. This includes the OECD as an international organization with credibility and stature in the science policy area. The Follow Up Group recommends that the OECD consider the following: • Put the issues of data access and sharing on the agenda of the next Ministerial meeting; • In conjunction with relevant member country research organizations, o Conduct or coordinate a study to survey national laws and policies that affect data access and sharing practices; o Conduct or coordinate a study to compile model licensing agreements and templates for access to and sharing of publicly funded data; • With the rapid advances in scientific communications made possible by recent developments in ICTs, there are many aspects of research data access and sharing that have not been addressed sufficiently by this report, would benefit from further study, and will need further clarification. Accordingly, further possible actions areas include: o Governments from OECD expand their policy frameworks of research data access and sharing to include data produced from a mixture of public and private funds; o OECD consider examinations of research data access and sharing to include issues of interacting with developing countries; and o OECD promote further research, including a comprehensive economic analysis of existing data access regimes, at both the national and research project or program levels.National governments have a crucial role to play in promoting and supporting data accessibility since they provide the necessary resources, establish overall polices for data management, regulate matters such as the protection of confidentiality and privacy, and determine restrictions based on national security. Most importantly, national governments are responsible for major research support and funding organizations, and it is here that many of the managerial aspects ofdata sharing need to be addressed. Drawing on good practices worldwide, the Follow Up Group suggests that national governments should consider the following: • Adopt and effectively implement the principle that data produced from publicly funded research should be openly vailable to the maximum extent possible; • Encourage their research funding agencies and major data producing departments to work together to find ways to enhance access to statistical data, such as census materials and surveys; • Adopt free access or marginal cost pricing policies for the dissemination of researchuseful data produced by government departments and agencies; • Analyze, assess, and monitor policies, programs, and management practices related to data access and sharing polices within their national research and research funding organizations. The widespread national, international and cross-disciplinary sharing of research data is no longer a technological impossibility. Technology itself, however, will not fulfill the promise of escience.Information and communication technologies provide the physical infrastructure. It is up to national governments, international agencies, research institutions, and scientists themselves to ensure that the institutional, financial and economic, legal, and cultural and behavioural aspects of data sharing are taken into account