Big data: the potential role of research data management and research data registries

Universities generate and hold increasingly vast quantities of research data – both in the form of large, well-structured datasets but more often in the form of a long tail of small, distributed datasets which collectively amount to ‘Big Data’ and offer significant potential for reuse. However, unlike big data, these collections of small data are often less well curated and are usually very difficult to find thereby reducing their potential reuse value. The Digital Curation Centre (DCC) works to support UK universities to better manage and expose their research data so that its full value may be realised. With a focus on tapping into this long tail of small data, this presentation will cover two main DCC, services: DMPonline which helps researchers to identify potentially valuable research data and to plan for its longer-term retention and reuse; and the UK pilot research data registry and discovery service (RDRDS) which will help to ensure that research data produced in UK HEIs can be found, understood, and reused. Initially we will introduce participants to the role of data management planning to open up dialogue between researchers and library services to ensure potentially valuable research data are managed appropriately and made available for reuse where feasible. DMPs provide institutions with valuable insights into the scale of their data holdings, highlight any ethical and legal requirements that need to be met, and enable planning for dissemination and reuse. We will also introduce the DCC’s DMPonline, a tool to help researchers write DMPs, which can be customised by institutions and integrated with other systems to simplify and enhance the management and reuse of data. In the second part of the presentation we will focus on making selected research data more visible for reuse and explore the potential value of local and national research data registries. In particular we will highlight the Jisc-funded RDRDS pilot to establish a UK national service that aggregates metadata relating to data collections held in research institutions and subject data centres. The session will conclude by exploring some of the opportunities we may collaboratively explore in facilitating the management, aggregation and reuse of research data

Open educational practices in Australia: a first-phase national audit of higher education

For fifteen years, Australian Higher Education has engaged with the openness agenda primarily through the lens of open-access research. Open educational practice (OEP), by contrast, has not been explicitly supported by federal government initiatives, funding, or policy. This has led to an environment that is disconnected, with isolated examples of good practice that have not been transferred beyond local contexts. This paper represents first-phase research in identifying the current state of OEP in Australian Higher Education. A structured desktop audit of all Australian universities was conducted, based on a range of indicators and criteria established by a review of the literature. The audit collected evidence of engagement with OEP using publicly accessible information via institutional websites. The criteria investigated were strategies and policies, open educational resources (OER), infrastructure tools/platforms, professional development and support, collaboration/partnerships, and funding. Initial findings suggest that the experience of OEP across the sector is diverse, but the underlying infrastructure to support the creation, (re)use, and dissemination of resources is present. Many Australian universities have experimented with, and continue to refine, massive open online course (MOOC) offerings, and there is increasing evidence that institutions now employ specialist positions to support OEP, and MOOCs. Professional development and staff initiatives require further work to build staff capacity sector-wide. This paper provides a contemporary view of sector-wide OEP engagement in Australia—a macro-view that is not well-represented in open research to date. It identifies core areas of capacity that could be further leveraged by a national OEP initiative or by national policy on OEP.</p

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

Report of the Stanford Linked Data Workshop

The Stanford University Libraries and Academic Information Resources (SULAIR) with the Council on Library and Information Resources (CLIR) conducted at week-long workshop on the prospects for a large scale, multi-national, multi-institutional prototype of a Linked Data environment for discovery of and navigation among the rapidly, chaotically expanding array of academic information resources. As preparation for the workshop, CLIR sponsored a survey by Jerry Persons, Chief Information Architect emeritus of SULAIR that was published originally for workshop participants as background to the workshop and is now publicly available. The original intention of the workshop was to devise a plan for such a prototype. However, such was the diversity of knowledge, experience, and views of the potential of Linked Data approaches that the workshop participants turned to two more fundamental goals: building common understanding and enthusiasm on the one hand and identifying opportunities and challenges to be confronted in the preparation of the intended prototype and its operation on the other. In pursuit of those objectives, the workshop participants produced:1. a value statement addressing the question of why a Linked Data approach is worth prototyping;2. a manifesto for Linked Libraries (and Museums and Archives and …);3. an outline of the phases in a life cycle of Linked Data approaches;4. a prioritized list of known issues in generating, harvesting &amp; using Linked Data;5. a workflow with notes for converting library bibliographic records and other academic metadata to URIs;6. examples of potential “killer apps” using Linked Data: and7. a list of next steps and potential projects.This report includes a summary of the workshop agenda, a chart showing the use of Linked Data in cultural heritage venues, and short biographies and statements from each of the participants

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

Sharing, and reusing quality information of individual digital datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.The development and baseline of the community FAIR-DQI guidelines document would not have been possible without the voluntary and dedicated effort of the domain experts of the International FAIR-DQI Community Guidelines Working Group. We would like to thank all members of the working group for their interest, participation, and contribution.Peer Reviewed"Article signat per 11 autors/es: Ge Peng , Carlo Lacagnina, Robert R. Downs, Anette Ganske, Hampapuram K. Ramapriyan, Ivana Ivánová, Lesley Wyborn, Dave Jones, Lucy Bastin, Chung-lin Shie, David F. Moroni"Postprint (published version

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity

Negative Statements Considered Useful

Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities