Periodic Reporting for period 1 - SciLake (Democratising and making sense out of heterogeneous scholarly content)
Reporting period: 2023-01-01 to 2024-06-30
SciLake is a 3-year project that aims to leverage Science Knowledge Graphs (SKGs) as the foundation to establish the concept of the Scientific Lake: a research ecosystem designed to facilitate the creation, integration, and querying of SKGs. This ecosystem includes tools capable of extracting knowledge from unstructured data, enhancing SKG interoperability, supporting knowledge transformation, unifying and simplifying SKGs querying, and accelerating graph processing and analysis.
SciLake sets the following objectives:
- Overcome the underlying heterogeneity of scholarly content and address domain-specific and cross-disciplinary information needs
- Democratize scholarly content facilitating the content acquisition and the creation, interlinking, and management of community-based SKGs and related services
- Facilitate the identification of research trends and of valuable research objects of different types considering various aspects of research impact
- Facilitate the assessment of the reproducibility and replicability/repeatability of research works.
- Customize, test, and demonstrate the developed services in real-world pilots
- Leverage & further enrich EOSC services landscape
The progress towards these objectives during the RP1 is elaborated in the Technical Report (Part B).
SciLake’s key results include:
- A customisable Scientific Lake service, built on SKG technologies, which includes a suite of components designed to streamline the process of scientific knowledge acquisition, management, and navigation
- An SKG Interoperability Framework, built upon and extending existing standards, to standardize the way SKG contents are exposed to the developers of added-value services
- A customisable Smart Impact-driven Knowledge Discovery service, which leverages the Scientific Lake service to significantly enhance the ability of researchers to navigate the vast landscape of scientific outputs in the domains of interest
- A customisable Smart Reproducibility Assistance service, which leverages the Scientific Lake service to enrich the contained SKGs with valuable information for the relationships between research outputs to produce badges and indicators which can highlight easy-to-reproduce or well-reproduced research outputs.
Finally, SciLake has initiated 4 pilots, encompassing a diverse array of scientific domains (Neuroscience, Cancer, Transport, and Energy), to showcase and assess the value of the SciLake services and their capability to address needs of diverse research communities.
SciLake sets the following objectives:
- Overcome the underlying heterogeneity of scholarly content and address domain-specific and cross-disciplinary information needs
- Democratize scholarly content facilitating the content acquisition and the creation, interlinking, and management of community-based SKGs and related services
- Facilitate the identification of research trends and of valuable research objects of different types considering various aspects of research impact
- Facilitate the assessment of the reproducibility and replicability/repeatability of research works.
- Customize, test, and demonstrate the developed services in real-world pilots
- Leverage & further enrich EOSC services landscape
The progress towards these objectives during the RP1 is elaborated in the Technical Report (Part B).
SciLake’s key results include:
- A customisable Scientific Lake service, built on SKG technologies, which includes a suite of components designed to streamline the process of scientific knowledge acquisition, management, and navigation
- An SKG Interoperability Framework, built upon and extending existing standards, to standardize the way SKG contents are exposed to the developers of added-value services
- A customisable Smart Impact-driven Knowledge Discovery service, which leverages the Scientific Lake service to significantly enhance the ability of researchers to navigate the vast landscape of scientific outputs in the domains of interest
- A customisable Smart Reproducibility Assistance service, which leverages the Scientific Lake service to enrich the contained SKGs with valuable information for the relationships between research outputs to produce badges and indicators which can highlight easy-to-reproduce or well-reproduced research outputs.
Finally, SciLake has initiated 4 pilots, encompassing a diverse array of scientific domains (Neuroscience, Cancer, Transport, and Energy), to showcase and assess the value of the SciLake services and their capability to address needs of diverse research communities.
During RP1, SciLake partners have conducted a broad range of activities which lay the groundwork for the rest of the project. The most important achievements were:
- The collection and initial analysis of the SciLake pilot needs and requirements regarding the SciLake ecosystem (partly reported in D1.1)
- The design and implementation of the initial version of the SciLake ecosystem architecture (reported in D1.2)
- The design and implementation of the initial versions of the Scientific Lake service (D2.1) the Smart Impact-driven Knowledge Discovery service (D3.1) and the Smart Reproducibility Assistance service (D4.1)
- The groundwork for the creation of the domain-specific SKGs that will be used by the SciLake pilots
- The engagement of partners in various dissemination activities
- The collection and initial analysis of the SciLake pilot needs and requirements regarding the SciLake ecosystem (partly reported in D1.1)
- The design and implementation of the initial version of the SciLake ecosystem architecture (reported in D1.2)
- The design and implementation of the initial versions of the Scientific Lake service (D2.1) the Smart Impact-driven Knowledge Discovery service (D3.1) and the Smart Reproducibility Assistance service (D4.1)
- The groundwork for the creation of the domain-specific SKGs that will be used by the SciLake pilots
- The engagement of partners in various dissemination activities
As a RIA project, SciLake naturally focuses on innovation, striving to advance the current state of the art. Its main innovations are expected to be centered around the key technologies used for the creation of the Scientific Lake, the Smart Impact-driven Knowledge Discovery, and the Smart Reproducibility Assistance service.
Regarding the Scientific Lake service, the main contribution is expected to be in the field of the efficient Knowledge Graph mining and querying techniques. Notably, the AvantGraph component already incorporates innovative approaches that offer performance on par with, or superior to, state-of-the-art systems, under specific conditions. By testing scenarios within the SciLake pilot projects, the AvantGraph team aims to address cases of particular interest and develop advanced solutions that push the boundaries of the current technologies.
Regarding the Smart Impact-Driven Knowledge Discovery service, the primary contribution is expected to lie in the development of improved impact indicators for research outputs. Currently, no adequate impact indicators exist for research datasets or software hence, we aim to provide meaningful solutions for that. Additionally, we investigate variations of indicators for papers, incorporating additional factors (e.g. citation intent, topics) to offer a more nuanced impact assessment. Finally, we are experimenting on meaningful approaches to analyze and visualize the aggregated impact from various perspectives (e.g. topic, community) providing useful insights to the research community.
Regarding the Smart Reproducibility Assistance service, the main contribution is expected to be in novel indicators and badging approaches for the reproducibility of research publications.
The project is expected to also have other types of important contributions. SciLake is contributing to the landscape of open research infrastructures, offering support, components, and relevant technologies. To maximize its impact regarding that, SciLake members are actively participating in relevant initiatives, such as the Barcelona Declaration on Open Research Information. Additionally, the project is expected to impact the development of interoperability standards for SKGs and SciLake members are heavily involved in the RDA working group creating the SKG Interoperability Framework (SKG-IF). Finally, SciLake is placed in the core of the discussion for reforming the research assessment landscape. The EC Report on Research Assessment identified that SciLake is expected to contribute with infrastructure and dedicated tools and services.
Regarding the Scientific Lake service, the main contribution is expected to be in the field of the efficient Knowledge Graph mining and querying techniques. Notably, the AvantGraph component already incorporates innovative approaches that offer performance on par with, or superior to, state-of-the-art systems, under specific conditions. By testing scenarios within the SciLake pilot projects, the AvantGraph team aims to address cases of particular interest and develop advanced solutions that push the boundaries of the current technologies.
Regarding the Smart Impact-Driven Knowledge Discovery service, the primary contribution is expected to lie in the development of improved impact indicators for research outputs. Currently, no adequate impact indicators exist for research datasets or software hence, we aim to provide meaningful solutions for that. Additionally, we investigate variations of indicators for papers, incorporating additional factors (e.g. citation intent, topics) to offer a more nuanced impact assessment. Finally, we are experimenting on meaningful approaches to analyze and visualize the aggregated impact from various perspectives (e.g. topic, community) providing useful insights to the research community.
Regarding the Smart Reproducibility Assistance service, the main contribution is expected to be in novel indicators and badging approaches for the reproducibility of research publications.
The project is expected to also have other types of important contributions. SciLake is contributing to the landscape of open research infrastructures, offering support, components, and relevant technologies. To maximize its impact regarding that, SciLake members are actively participating in relevant initiatives, such as the Barcelona Declaration on Open Research Information. Additionally, the project is expected to impact the development of interoperability standards for SKGs and SciLake members are heavily involved in the RDA working group creating the SKG Interoperability Framework (SKG-IF). Finally, SciLake is placed in the core of the discussion for reforming the research assessment landscape. The EC Report on Research Assessment identified that SciLake is expected to contribute with infrastructure and dedicated tools and services.