Sophia Pieschnik

• Golla, Archana; Pieschnik, Sophia; Kobchenko, Maya; Reiten, Ingrid; Chatzopoulos, Serafeim & Rojas, César Parra [Show all 16 contributors for this article] (2026). Building an Integrated Scientific Knowledge Graph for Neuroscience: Enhancing Research Impact Analysis and Optimizing Data Curation. doi: 10.5281/ZENODO.18864403. Full text in Research Archive Show summary

  The Neuroscience pilot of the EU-funded SciLake project seeks to tackle the complexities of fragmented and disorganized open research data. This initiative has united partners with expertise in knowledge management with curators from EBRAINS (RRID: SCR_019260), an open research infrastructure focused on brain-related studies. The innovative Scientific Knowledge Graph (SKG) (https://bip.imsi.athenarc.gr/search/neuroscience-pilot) created through this pilot serves as a cohesive framework for data integration while enabling the use of value-added services provided by SciLake. The developed SKG combines curated datasets from EBRAINS with extensive neuroscience research products from the OpenAIRE neuroscience gateway (https://neuroscience.openaire.eu/). The SKG incorporates controlled terms from openMINDS (RRID: SCR_023173) as nodes and utilizes an entity recognition model to classify and map these terms in relevant texts, enriching the connections among research products. Additionally, the SciLake services deploy language models to extract mentions of research artifacts (RAs) and enhance the interlinking of research products. Fully integrated with impact-driven discovery and reproducibility tools and leveraging the analytical capabilities of SciLake, the SKG serves as a powerful resource for contextualizing curation efforts and exploring research trends, ultimately optimizing curation processes in the neuroscientific landscape.

• Pieschnik, Sophia; Benediktsdottir, Signy; Karadag, Naz; Papp, Eszter Agnes; Puchades, Maja & Leergaard, Trygve B. [Show all 7 contributors for this article] (2026). Digital data collections for discovery-driven neuroscience. doi: https:/fens2026.abstractserver.com/program/#%2Fdetails%2Fpresentations%2F4724. Full text in Research Archive Show summary

  The transition to open science has led to a growing volume of shared neuroscience data in different data repositories. While the amount and diversity of research data available is extensive, data reusability is often limited by a lack of appropriate metadata annotations according to the FAIR guiding principles (Wilkinson et al., 2016). The EBRAINS Research Infrastructure (RRID:SCR_019260) shares well-annotated datasets to facilitate data discovery and reuse across studies and modalities via the EBRAINS Knowledge Graph (KG; RRID:SCR_017612). The EBRAINS KG currently indexes 1100+ heterogeneous datasets, annotated with standardised metadata following the openMINDS metadata framework (RRID:SCR_023173). We present two examples for building data collections via the KG Search functionality using a combination of multi-term text queries and faceted filtering to exemplify the potential of data driven workflows. First, we explore the brain-wide distribution of calbindin- and parvalbumin-positive neurons in rodent brains, identifying a comprehensive data collection generated using the QUINT workflow (Yates et al., 2019). Related datasets are connected through metadata including species and techniques. Second, we collect BIDS-compliant macaque datasets, containing NIfTI files, enabling the combination of neuroimaging data from different laboratories. Filtering and query options for experimental methods, content types and data organisation standards enable the selection of data for viewing and analysis using EBRAINS tools, highlighting their interoperability. These examples demonstrate how discovery-driven neuroscience is facilitated by FAIR annotated data across a range of studies.

• Pieschnik, Sophia; Najafi, Peyman; Pernet, Cyril; Bjaalie, Jan G.; Leergaard, Trygve B. & Davison, Andrew [Show all 7 contributors for this article] (2025). bids2openminds: facilitating automated conversion between neuroscience data standards. Full text in Research Archive Show summary

  BIDS and openMINDS are expert- and community-driven standards that facilitate FAIR data management in the field of neuroscience. BIDS is a commonly adopted data model that standardizes the file organization and metadata annotations per dataset within a data repository, such as openNeuro.org or publicneuro.eu. The openMINDS metadata framework provides integrated metadata models for Linked Data, libraries of ontology-based metadata instances and supports rich search capabilities in graph database systems, such as the EBRAINS Knowledge Graph. To facilitate ingestion of metadata from BIDS datasets into such graph database systems, we developed the bids2openminds converter. bids2openminds is an open-source project, provided as a python package with a command line interface (https://github.com/openMetadataInitiative/bids2openminds). It builds on the functionalities of, pyBIDS and openMINDS_Python. However semantic mapping between BIDS and openMINDS terminologies had to be hard coded. Currently, the converter supports the BIDS modalities structural and functional Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), and Intracranial Electroencephalography (iEEG). We demonstrate the metadata conversion using bids2openminds on BIDS compliant, open access datasets. Converted to openMINDS and registered in the EBRAINS Knowledge Graph, data and metadata have an increased findability via the search user interface and advanced query options. Through the semantic integration data are linked to other datasets within and beyond the EBRAINS database, increasing their interoperability and reusability. The bids2openminds converter facilitates automatic registration of BIDS compliant datasets in openMINDS-based graph databases, fostering interoperability and alliances across different data management systems (e.g., publicneuro.eu and EBRAINS).

• Benediktsdottir, Signy; Golla, Archana; Blixhavn, Camilla Hagen; Hennestad, Eivind; Kleven, Heidi & Najafi, Peyman [Show all 16 contributors for this article] (2024). Towards FAIR neuroscience: an efficient workflow for sharing and integrating data. Full text in Research Archive
• Pieschnik, Sophia; Najafi, Peyman; Bjaalie, Jan G.; Leergaard, Trygve Brauns; Davison, Andrew P. & Zehl, Lyuba (2024). Automated conversion of BIDS datasets to Linked Data using openMINDS. Full text in Research Archive
• Zehl, Lyuba; Pieschnik, Sophia; Najafi, Peyman; Bjaalie, Jan G.; Leergaard, Trygve Brauns & Davison, Andrew P. (2024). bids2openminds: extracting linked data from brain imaging data structures. Full text in Research Archive
• Pieschnik, Sophia; Golla, Archana; Reiten, Ingrid; Schlegel, Ulrike; Hennestad, Eivind & Hübenette, Saira Jameela [Show all 20 contributors for this article] (2023). Pioneering open neuroscience - the role of the curation services. Full text in Research Archive

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