DEADLINE: 9. April 2013, 4pm
Open Distributed Science for Ageing Research
Track Record (1-2 pages)
Background
Ageing
The proposed crowdsourcing project
The objectives of the application
The proposed solution that utilises crowdsourcing
Reasoning/Inference
Applicable to other fields
The uniqueness and benefits of the proposed project
Strategic relevance to BBSRC
Description of the management of the project
The management structure of the project
A clear strategy for engaging and managing the „crowd‟
Recent work relevant to the proposal can be subdivided into four categories:
Social. Major initial users of the proposed framework were identified. These are researchers who are doing ageing research as well as scientists from other fields and non-academics with interest in ageing and healthy lifespan extension. Close participation with national and international communities/organization such as International Longevity Alliance (ILA) [http://longevityalliance.org/], Heales [[https://heales.org/'](https://heales.org/], Strategies of Engineered Neglegbile Sensecence (SENS) [http://www.sens.org/], and LongeCity [http://www.longecity.org], Health Extension community [http://www.healthextension.co/] - representatives of the crowd, are guaranted. Relationships with open software communities were established and a community for building up the proposed project is already present.
Technical. A proof of concept of an editable, changeable and programmable data management system was created [http://denigma.de/]. Denigma is a modular constructed crowdsourcing enabled Digital Decipher Machine which includes for the here planned purpose useful applications and data units. Further we are involved in research and development of an industrial semantic web application and development of complex semantic queries [http://www.exigenservices.com/why/value-oriented-architecture].
Organizational. An organization was founded to oversee the inception of Open Distributed Science [http://denigma.org]. This organization coordinates the development of the necessary information technological infrastructures [[https://github.com/denigma'](https://github.com/denigma].
Scientific. We established the biggest data repositories on the biology of ageing which includes HAGR, Human Ageing Genomics Resources [http://genomics.senescence.info/], Digital Ageing Atlas [http://ageing-map.org/] and Denigma’s Datasets, Expressions and Lifespan applications [http://denigma.de/lifespan/].
All these resources need to be taken to the next level.
The participants provides the following expertise.
Joao Pedro de Magalhães, University of Liverpool (UK).
Alex Freitas, University of Kent (UK).
Michel Dumontier, Carleton University (Canada). (KR, data integration, hypothesis testing)
Dmitry Borisoglebsky, Exigen Services Latvia (Latvia). Provides expertise on development of semantic web application and complex semantic scripts.
Paul Schofield, University of Cambridge (UK).
Georg Fuellen, Rostock University (Germany).
Dagmar Waltemath, Rostock University (Germany). Knowledge in standardisation efforts, integration of modeling and simulation data; data management (information retrieval, storage, version control).
Pat Langley, University of Auckland (New Zealand).
David Blokh, C.D. Technologies Ltd., Beer Sheva (Israel).
Ilia Stambler, Ben-Gurion University of the Negev, Beer Sheva (Israel).
Edouard Debonneuil, Heales (Europe) and International Longevity Alliance (Worldwide).
Aubrey de Grey, SENS Research Foundation (USA/UK).
Clemens Cap, Rostock University (Germany).
We have strong connections to active groups of interested people who have focus on advancing technologies in general and ageing research in particular. With that we have reasonably strong engineering, strong academic, and strong “crowd” background.
Aubrey de Grey is a world-known innovative expert on ageing research. Dmitry Borisoglebsky leads the development of advanced semantic scripts in a Semantic Web application for software development. Ilia Stambler and Edouard Debonneuil are leading members of ageing related communities (ILA and Heales). Pat Langely is a computer scientist who applies computational approaches to the biology of ageing. Aubrey de Grey and Pat Langley together are developing web-accessible models of ageing for the purposes of education. Paul Schofield is an expert biomedical informatics and mammalian ageing. Georg Fuellen is the leading bioinformatician expert and systems biologist on ageing. Dagmar Waltemath is famous for standardisation efforts in computational biology, especially for modeling, simulation and the integration with ontologies. Joao Pedro de Magalhães is the pioneer of the genomics of ageing and creator of the best ageing online resources. Alex Freitas is a computational scientists with competence in various data mining techniques and their application on biological phenomena such as ageing. David Blokh is an bioinformatician and mathematics who uses information technology to solve biomedical problems. Clemens Cap is a system architect and develops distributed and ubiquitous applications and services. Michel Dumontier is the world-leading expert on improving our ability to represent and reason about biomedical knowledge, from experimental data to general textbook knowledge, towards predictive system for personalized medicine. Michel Dumontier develops and applies semantic web applications.
Background
Ageing
Ageing kills over 150.000 persons every day. Age-associated diseases and disability represent a huge and growing morbidity in an ageing human population, reflected in an increasing clinical load and burden on national economies. The cost of caring for people with dementia alone is about £108 billion, a cost set to rise with the rising age of the European population, identifying this domain of biology as one of increasing socioeconomic concern as well as fundamental research interest. In the UK 10 million people are over 65 years old with projections suggesting 5½ million more elderly people in 20 years time.
Despite many theories and mechanisms being proposed, a systematic approach for ageing research is missing and no unifying concepts have yet emerged. Knowledge pertaining to the biology of ageing covers all levels of systems biology and is a true multiscale problem encompassing single molecules, the whole body and cognitive processes. The supporting data on ageing is disseminated between databases with different schemas, the published literature and informal reports, making the integration and utilization of this heterogeneous knowledge very challenging, not least because of the range of expertise domains it encompasses, from population genetics to neuroscience.
Ontologies provide a flexible data model to conceptualize complicated and heterogeneous knowledge in form of a controlled formal vocabulary, but ontologies are more: An ontology is an explicit, formal specification of a shared conceptualization, thus rendering this knowledge accessible to machine reasoning. Ontology engineering is normally accomplished by a small group of experts, but this does not scale with the ever-increasing data amount in such a complicated field like ageing research.
The primary research question of the here proposed project is whether a sophisticated knowledge base on ageing established by the synergistic combined work of machines and humans enables logical inference of ageing factors and potential anti-ageing interventions [http://denigma.de/]. Additionally we see a major infrastructural problem with ageing research. The current ageing research is (i) highly fragmented, (ii) has no unified data source, and (iii) the crowd participation is very restricted due tools and methods designed for academia. We see these issues as the major bottlenecks to crowdsourcing for ageing research and bioscience in general.
The proposed crowdsourcing project
The objectives of the application
This project aims to develop a valid inclusive framework for open distributed research on ageing. This is a multi-disciplinary project incorporating aspects, such as organisational structure, knowledge engineering, research project management, business process, data management, motivation, and education. The objectives are as follows.
Establish an organisation for open distributed science focused on ageing research.
Create a description framework for ageing related data and investigations.
Establish data interoperability, integration & exploration via advanced information technologies.
Develop semantic tools & methods to be used in open distributed science for ageing research.
Develop autonomic knowledge management system for coordination of education & research.
Validate the crowdsourcing approach against gold standard expert curated data sets.
Apply inference & reasoning on the knowledge base to derive new facts and testable hypotheses.
Investigate how other fields could benefit from the developed tools, methods and concepts.
The proposed solution that utilises crowdsourcing
The framework for the proposed project includes an organization, a knowledge base, a knowledge management system, evaluation and reasoning procedures. The frameworks core is a semantic web application. Establishment of an Open Distributed Science Organization
For bootstrapping novel ways on how to conduct science and coordinating individuals as well as initial tasks an organization specifically devoted for Open Distributed Science has been established. It is a non-profit organization made up by passionate volunteers and a strong user-community, guaranteeing support beyond the duration of the grant. The organization will include a scientific advisory board of experts in the fields of ageing, information technology and disciplines in which crowdsourcing has been successfully established. A primarily responsibility of the organization is the promotion and the hyping of the developed platform on social networks and alternative media (including the BBC) so that its mission can reach many individuals from diverse groupings and get sustainable viral. Description Framework of Ageing
Knowledge on ageing is present majorly in informal and semi-formal formats as well as inaccessible in the mind of specialists. For information to be usable, it must be stored and organized in ways that allow us to access, analyze and annotate it and to relate it to other Information; only then we can begin to understand what it means; only with the acquisition of meaning do we acquire knowledge [Attwood et al. 2009].
Experts on ageing, students and enthusiastic volunteers will jointly together with ontologists establish initial application-ontologies (Ontology Development). These includes an ontology on the ageing process itself, on healthy lifestyles and interventions, on ageing-related investigations and ageing-relevant data as well as research in general. For this a middle-out approach is chosen, i.e. focusing on the most important concepts and specializing as well as generalizing where necessary. The ontologies will take advantage of the existing ones and complement them with the domain of ageing science (Ontology Mapping and Merging)
Using these ontologies informal/semi-formal data will be converted into a formal knowledge base by Ontology Learning and Population.
Informal data is majorly in the form of texts. With the help of these ontologies natural language processing of the literature will be performed to automatically derive facts and statements as well as create a semantic graph.
Databases on individual datasets mapped by the ontology will be converted into standardised formats. The knowledge base will therefore provide a modular access to multiscale knowledge. Graph-like data will be converted into semantic graphs.
Users can explore these dynamically visualized graphs interactively via semantic searches and are able to formulate statements via Constraint English to add, alter or annotate facts.
Automatic inconsistency checks, moderation by experts, revision control and semantic trust system will guarantee data quality.
Participations are encouraged to include their health data and those of the relatives and see as well as explore it in relations with scientific facts. Users are allowed to directly associate their names with the data or to be anonymously via the usage of pseudonyms. The system in turn will provide healthy recommendations to improve the lifestyle of the users. Advanced information technology
In addition to Semantic Web technologies, algorithms and methodologies from the fields of Data Mining and Machine Learning as well as Information Theory will be extensively used. Semantic tools & methods
Real time tools and methods that enable functionalities like semantic videochat, graph visualization, data editing, and analyses as well as a merit earning system will provide the necessary infrastructural parts/features for efficient and well coordinated collaborative work of huge groups on the same knowledge base with a vast amount of heterogeneous data.
The user interface includes an intuitive and interactive dynamic graph representation (networks with nodes and edges) that allows to add/change entities and relations. Users can provide informal descriptions to nodes/edges which will be automatically converted into formal facts. Additionally users can query the graph directly with the use of Constraint English. Advanced users can type in the to executing statements directly to formulate complex queries. Videochats with other users and experts to discuss facts and tasks or teach each other concepts can be established instantly anywhere on the system. Autonomic knowledge management
The framework functions as an educational system that teaches users about the ageing process as well as formal thinking and semantic web technologies in an interactive and game-like fashion. Therefore, it will provide users with the necessary expert knowledge to add valuable contributions. The system itself is a Web Intelligence that learns from the users (by machine learning), evaluates their input, assigns users roles, tasks as well as trust and enables personalization. In such, “knowledge goes in and knowledge goes out”.
Automatic algorithms like those employed in natural language processing, database and dataset parser, ontology mapping and merging, etc. are error prone. Further the literature itself is full of contradictions and false discoveries which can be revealed in a knowledge base where the ink is never dry. The goal of the proposed system is to identify the truth via logical reasoning. If two scientists are in a dispute, they can simply argue: “Let us calculate!”
Simply tasks (duties) that can be done in parallel independently by many users are outsourced via the system. The tasks are majorly automatically generated (microtasks generator) and involve questions regarding the facts in the knowledge base. Experts and innovative users among the crowd are encouraged to formulate own microtasks as well as macrotasks (quests) which upon approval will be automatically outsourced too. Successful completion of a task credits users with a data credits. Data credits can be used by users to gain access to more functionalities and to level up in a profession (role-playing concept).
Tasks can be very simply to more complex and therefore give different amounts of data credits depending on the expected work load and required time for completion. Starting with the confirmation whether a certain statement is correct according to the derived information source, to questions about ontology alignments and improving linkage and better usage of more commonly used concepts and resources over to the identification of certain datasets and facts, up to real research questions. Solution to complex tasks that cannot be evaluated by the system easily alone need to be assessed by volunteering experts that will automatically messaged. Tasks can also involve the typical stages of the research process cycle, e.g. providing a literature review and ensuring its correct semi-automated conversion into a formal representation to the analysis and discussion of research results by for example describing the information content of figures and tables. Basically, microtask are for the identification and validation of knowledge. Simple tasks are for example regarding the correct definitions, siblings, superclasses/superproperties and subclasses/subproperties, and instances.
Each user will have her/his own knowledge base that is a subset of the systems knowledge base and contains the by the user in microtask and macrotasks covered entities and relations. By doing microtasks users are constantly undergoing qualification tests. Knowledge Validation
In addition to the constant evaluation by users the system automatically assess whether the input makes sense in light of the knowledge base by applying logic reasoning and automated integrity checks and its pseudo experience due to dynamic programming. Changes by an user itself leads to the generation of microtasks to ask other users for confirmation of the validity of this change. Changes by the users which are from the system qualified as trusted are applied immediately, while those of newcomers and poorly performing users require confirmation by other users before applied and transferring data credits. This will ensure that valid data is included while spammers are excluded. The knowledge base keeps revision control of its state so that invalid statements and changes can be traced back to user and time of change and reverted. The quality improvement between different states can be assessed by comparing different graphs.
It is expected that the user contributions will result into a significant improvement of it the automated knowledge acquisition procedures from informal, semi-formal and external formal resources. The improvement of precision and recall will be measured. We anticipate that the crowd will dramatically improve precision without losing recall. Further users may identify missing valid entities, properties and classes as well as mappings. The crowd contribution will be compared to the contribution by domain experts to assess their added value.
The improvements of the knowledge base by human contribution will in turn lead to enhancement of the automated knowledge acquisition procedures that are based on the ontologies. In such the system will learn from user participations and iteratively become better and better.
Reasoning/Inference
For making new discoveries on the basis of the assembled knowledge base, which will be the largest compilation of data related to health and ageing ever assembled, primarily Logic, Data Mining techniques, concepts of Information Theory will be utilized.
Participations are able to articulate ageing hypotheses and their claims with published articles and their associated data. The system will evaluate whether the hypotheses is supported by the knowledge base by applying logical reasoning and additional assess how likely a certain hypotheses will be to be true. Making novel valid discoveries will be acknowledged with data credits and a leveling up in the scientific profession.
Programmers and developers will get direct programmatic access to the giant graphs and modular access to individual semantically annotated datasets. Data mining ontologies will provide information on what type of methodology/decipher algorithm can be applied on it to infer new knowledge. Those participants that chose the Developer profession can gain data credits by building up new functionalities to the system and analysing data in large-scale.
With the help of the ontologies (especially those on processes and projects) we are not only able to infer knowledge about ageing, we may also be able to infer what should be done for this type of research problem to be experimentally proven.
The from the system suggested interventions (e.g. genetic manipulation or drugs) and the user community most well supported hypotheses can be investigated experimentally by us [http://pcwww.liv.ac.uk/~aging/; http://www.sens.org/] or other interested research groups.
Applicable to other fields
Ageing research is used here as a case study, the developed concepts, methodologies, tools as well as best practises will be broadly applicable to other biomedical disciplines and research questions besides the causes of ageing. We will assess how to extend the approach to other fields. Overall we will produce a case study to explore new ways to conduct science that can be applied to other fields and scientifically validate the crowd sourcing approach on a specific research question.
The uniqueness and benefits of the proposed project
The proposed project is unique as it develops a Web Intelligence based on the synergy of humans and machines that will yield novel knowledge on the mechanisms of ageing via logical reasoning and inference. Many related projects require supporting software like those we are developing under open source licenses. The UK will become the forefront.
Strategic relevance to BBSRC
This project benefit the following strategic priorities:
Ageing research: lifelong health and wellbeing. The project is using ageing research as the major case study of crowdsourcing for bioscience. We develop ontologies, annotate data, develop software and methods for crowd to utilize during ageing research, and validate it during a number of cases directly related to ageing research.
Systems approaches to the biosciences. Technology development for the biosciences. Data driven biology. We will develop an inclusive framework for data driven biology. This framework will include ontologies, interfaces for annotation of various data sources, and interfaces and tools for data retrieval. This framework will include tools and methods for critical areas stages of research projects (which we identified) starting from idea generation to development of publications. This framework will include educational resources for novice participants to learn about ageing and the framework.
New strategic approaches to industrial biotechnology. We will develop and test extensively. Our group is focused in developing of a community of non-professional researchers doing science. This community will utilise professionals from various disciplines, therefore such people will network, gain knowledge and ideas. Some of these ideas will be related to fundamental science, other -- to engineering science. Networking plus engineering science mean the foundation of new biotechnology Subject Matter Expertises. Evidence for need and potential longer term options for continuation of the project.
The developed framework should be maintained, new components should be developed. We will try to develop a sustainable business processes and organisation.
J.P. Magalhães, who is highly experienced in establishing online resources for ageing research, will coordinate the endeavour. A. Freitas will supervise the use of data mining methods to analyse integrated ageing-related datasets. Bioinformatician M. Dumontier will coordinate the collection, representation and retrieval of relevant biomedical data. IT professional D. Borisoglebsky will lead the enterprise and semantic web aspects. The ontologists P. Schofield and M. Dumontier will oversee the integration of existing biomedical ontologies and the developments of ontologies related to ageing research by experts and the crowd. Computational biologists G. Fuellen, D. Waltemath and P. Langley will supervise the generation of biomedical and biochemical models and simulations as well as their combination with ontologies. E. Debonneuil and A. de Grey will coordinate the collection and processing of professional and lay expertise, as distributed among members of ageing research and advocacy communities, such as Heales and ILA, LongeCity and SENS Research and Education Foundation. D. Blokh and I. Stambler will employ information theoretical methods to integrate diverse data related to ageing. C. Cap is an expert in digital social networks and will conduct research with regard to multi-faceted knowledge bases. All partners will be part of a project committee that will include other UK/EU scientists (to be invited). We will appoint a scientific advisory board with names to be appointed, which incorporates people from other domains where crowdsourcing has been successfully utilized. The project will be managed by an organization devoted specifically to this project.
Our strategy involves the following points:
Establish roles and business processes via trial projects
Learning from the best (utilise experience of open source foundations)
Utilise online communities such as ILA
The crowd is everyone in the world who wants to contribute. Initial users will be expected to be majorly experts on ageing, programmers interested in open source development and technological advances as well as other people with interests on health extension and information technology. The ratio of professional to non-professional is expected to change as the system matures with a trend of an increasingly proportion of non-professionals.
Underlying the platform will be a game with purpose. The overall aim of the game is to construct a “Digital Decipher Machine” and to decode and encode information in the web on the ageing process by supplying it with knowledge and functionalities. It will have a strong role-playing game concept. Users can chose one or more of the following professions: Scientist, Developer, or Artist. Participants can earn data credits by correctly completing microtasks (duties) and macrotasks (quests) which may involve expertise in biomedical science, information technologies and/or design. For this we introduced a merit system which includes the possibility to level up to a higher profession and the ability to promote one’s own published research. Users with higher levels are allowed to supervise other users, teach them an allocate them directly microtasks by investing data credits.
In all phases during the development and deployment of the system the crowd will be engaged via gamification, achievement systems, changing agendas and the continued additions of functionalities as well as regular contests (on knowledge, AI & media creation). References
Attwood TK, Kell DB, McDermott P, Marsh J, Pettifer SR, Thorne D (2009) Calling International Rescue: knowledge lost in literature and data landslide! The Biochemical journal 424: 317-33.
Everything except personal data that user do not like to share will be published as high quality open linked data.
Establish an organisation for open distributed science focused on ageing research.
Create a description framework for ageing related data and investigations.
Establish data interoperability, integration & exploration via advanced information technologies.
Develop semantic tools & methods to be used in open distributed science for ageing research.
Develop autonomic knowledge management system for coordination of education & research.
Validate the crowdsourcing approach against gold standard expert curated data sets.
Apply inference & reasoning on the knowledge base to derive new facts and testable hypotheses.
Investigate how other fields could benefit from the developed tools, methods and concepts.
This attachment type is to be used by applicants to provide a diagrammatic workplan for the
proposed project. It must not be used to extend the case for support. This document is mandatory for the CSBS call. It should be submitted as a standalone document (not embedded in the Track Record and Case for Support) as document type “Workplan”.
Preliminary work for establishing the framework were already done in the year 2013., which includes the creation of a first prototype, building up a community and founding an organization. The initial description framework will be expected to reach a significant detail in the beginning of the year 2014. In this year massive crowdsourcing will be initialized by advertising and promotion in social networks media and peer-articles. In the same year all advanced information technologies including semantic methodologies are expected to be fully functional integrated. In the years 2015, all validation tests should be passed. The proof of concept that inference about mechanistic insights on ageing and anti-ageing interventions can be effectively generated will be achieved by no later than 2016. Subsequently possibilities to extend the approach to other disciplines will be deeply explored in the year 2017.
CV of the Principal Investigator: up to 2 pages;
CVs for all Co-Investigators and named researchers: up to 2 pages per person
Letters of Support – please ensure that all letters of support are on headed paper and that they are signed and dated. Only directly relevant Letters of Support should be submitted.
