Beyond Repositories: Problem-solving-oriented #or2017

Beyond Repositories: From Resource-oriented towards Problem-solving-oriented by Dr Xiaolin Zhang, National Science Library, Chinese Academy of Sciences

With the ubiquitous deployment of digital ecosystems, developing repositories to meet next generation needs and functions become an imperative and increasingly active efforts. However, a paradigmatic shift may be needed to prepare repositories to go outside the resource-orientation box, as JISC report “The future of data-driven decision making” puts it, “[I]t is not sufficient simply to focus on exposing, collecting, storing, and sharing data in the raw. It is what you do with it (and when) that counts”.

The presentation first discusses the emerging digital ecosystems in research, learning, publishing, smart campus/cities, knowledge analytics, etc. where traditional content/repositories are just a small part of stories.

Then an exploration is made about making repositories embedded into, integrated with, and proactively contributing to user problem-solving workflows in digital ecosystems such as scholar hub, research informatics, open science, learning analytics, research management, and other situations.

Further effort is attempted to understand (admittedly preliminarily) strategies for repositories to be transformed into part of problem-solving-oriented services, including, but not limited to, 1) enhancing the interoperability to be re-usable to third part “users”, 2) developing repositories into smart content with application contexts, and 3) developing smart contextualization capabilities to better serve multiple, varied, and dynamically integrating problem-solving processes.

[I’ve previously blogged a keynote by Dr Zhang at THETA 2015.] He has a new perspective since moving jobs two years ago.

104 research institutes, 55,000 researchers. Various repositories eg NSFC Repository for Basic Research, CALIS IR portal of 40+ universities. Research data sharing platform, and Chinese Academy of Science distributed research data management and integrative service platform.

  1. Changes in the digital ecosystems
    • Steady progress of repositories but numbers don’t tell the story – better to look at how users use it. Most still collection based and local applications are the main service. What if we move away from repository-based approach. Imagine new scenarios out in society. What do they need?
    • All media and content can be data (including processes, relations, IoT devices, tweets). Can be smart – and semantic publishing will be the new normal. Knowlege as a Service.
    • Transformation from subscription to open access. Born digital = born linkable.
    • eScience is the knowledge system – opening up data-intensive scientific discovery. Not just about access, it’s a different way of doing science
    • Open Science again more than open access, but open evaluation, open process, open collaboration. (Displays open science taxonomy). Even social science now incorporating computational methods.
    • eLearning creating a new knowledge ecosystem. Things changing quickly. In the classroom everyone (200 students) uploading content and system going down even though made plans for it only 2 years ago. Flipped classrooms where students do work before the class in digitally collaborative environments, multimedia-rich laboratories so students can interact with each other. Requires intelligent campus and services. eStudent Center where student’s whole learning life is together to be analysed; university center can look at trends etc
    • Knowledge analytics – converging data science, computer science, information science. Open source tools for data visualisation and analysis. Data analytics can become new infrastructure
    • Moving into the Machine Learning Age? 7.5 million university graduates every year in China
  2. Explorations to re-orient repositories
    • Towards working labs: Elsevier Knowledge Platform; WDCM
    • From resources to problem solving, eg digital healthcare needing knowledge from literature but also from wearables and other devices; eg intelligent cities with data, linking, analysis, to answer questions.
  3. Challenges in re-developing repositories
    • Re-purpose and reposition repositories? but outside the scholarly communication environment? Eg using big data in smart cities – scholarly knowledge plays a huge role here. Eg learning analytics where we combine data on students (grades, interactions on Moodle).
    • Cycle: environmental scanning -> idea/design/testing -> R&D -> data management -> Data analysis -> dissemination -> preservation/reuse -> evaluation -> environmental scanning
    • Interoperability cf W3C recommendations
    • Identify/select/developed/integrate value-added services (not all work together, but some aren’t meant to). How to turn content into computable data? how to develop rich and smart media resources? eg How to turn powerpoints into actionable data?
    • Working on automatic translation, domain interaction dynamics, scientometrics tools, social network metrics, automatic thesaurus/k-graph development. Hard for students to select a topic when there’s open-source tools already out there about it! Calculations and results become objects to be reused.
    • Representing knowledge with knowledge graphs. which can enable intelligent applications. Text analytics, RDF data management. eg SpringerNature SciGraph – turning all papers into semantic network of knowledge.
    • Too many vocabularies! Some used by many people, some very common (eg Schema.org ) and general – but also very specific ones eg neuron ontology; Internet of Things developing their own. Ontology mapping tools? Cross-language linking of knowledge graphs and smart data eg Chinese/English Wikipedia pages.
    • What about when live machines join the integration and we put our data into real-life processes? Geospatial/temporal/event/methods/workflows-identifiable.

Are these real life scenarios really relevant to our repositories? If not we’ve got a problem! Is what we’re doing now getting us into these scenarios? Are we talking/collaborating with people in these scenarios? They’re not necessarily going to approach us! Time for us to think and act before it’s too late.

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