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@INPROCEEDINGS{More:912226,
      author       = {More, Heather and Hofmann, Volker and Grün, Sonja and
                      Sandfeld, Stefan and Denker, Michael},
      title        = {{W}orkflows for metadata management in neuroscience},
      reportid     = {FZJ-2022-05426},
      year         = {2022},
      abstract     = {Tracking and documenting how data is generated, processed,
                      and analyzed is crucial for scientists to replicate and
                      reuse experimental results. However, the sheer quantity of
                      the resulting metadata generated by today’s complex
                      investigations can be difficult for researchers to record,
                      manage, and use effectively. The Helmholtz Metadata
                      Collaboration (HMC; https://helmholtz-metadaten.de) supports
                      scientists of the Helmholtz Association in creating and
                      handling metadata, to help make research results more
                      sustainable. Within HMC, interactions with scientific
                      disciplines are organized into six hubs. The Hub Information
                      focuses on metadata topics in materials science, physics,
                      computing, plant science, and neuroscience. We develop
                      software and semantic architectures to help manage and use
                      metadata, assess how FAIR (Findable, Accessible,
                      Interoperable, and Reusable; [1]) research outputs are, and
                      conduct education events for scientists. While we often work
                      on specific use cases, our goal is always to produce
                      solutions that can be generalized to other
                      disciplines.During a typical research workflow, it is
                      crucial that scientists are able to effectively (1) store
                      and link data and metadata, then use this metadata to (2)
                      inform analyses. To manage data and metadata in
                      neuroscience, we use several existing and freely available
                      software tools, and are developing additional tools to fill
                      in the gaps. We link these tools together using a workflow
                      developed with the open-source programming language Python.
                      Here, we summarize our current approaches and future
                      directions.To (1) store and link data and metadata, we
                      currently prefer odML documents for metadata and NIX files
                      for combining data and metadata. odML files store metadata
                      in an xml-based format containing key-value pairs in a
                      hierarchical structure – they support links between
                      metadata items within a document, and are designed for
                      storing metadata without data [2,3]. NIX files store raw and
                      preprocessed data and metadata together in a single file,
                      using a generic data structure and the odML model for
                      metadata – they support links between data and metadata
                      objects within a file [4]. Because a single NIX file
                      contains all the information about an experiment, it is easy
                      to store and to share individual data records with
                      collaborators. The NIX data model standardizes access to
                      data and metadata from different sources. For example, NIX
                      files can map the neuroscience-specific Neo data
                      representation [5], ensuring that data and metadata
                      annotations are consistently accessible.We are finding,
                      however, that many neuroscientists need to accurately
                      describe experimental setups where relationships between
                      elements are more nuanced than odML files can describe. To
                      address this problem, we are developing specific ontologies
                      and terminologies for aspects of experimental setups in
                      brain electrophysiology. Ontologies are an alternative
                      approach to represent and link metadata, and include precise
                      descriptions of defined elements and their relationships.
                      Consistent descriptions will help standardize metadata from
                      different experiments, facilitating data reuse.After data
                      and metadata have been stored, researchers face challenges
                      in using metadata to (2) inform analyses because they lack
                      tools to efficiently navigate the enormous amounts of
                      metadata generated by research projects. To solve this
                      problem, we are developing software to combine metadata from
                      multiple experiments into a database which users can
                      interactively explore by selecting different criteria. The
                      software condenses metadata to show only information chosen
                      by the user, in both a data table and a graphical overview.
                      This gives users an overview of their experiments to inform
                      further analyses.Making metadata descriptive and accessible
                      ensures that others can reproduce and build upon research
                      discoveries. We envision that our tools will help facilitate
                      open and reproducible science by making metadata easier to
                      collect and use.References:[1] Wilkinson, M.D., et al.,
                      2016. The FAIR Guiding Principles for scientific data
                      management and stewardship. Scientific Data 3, 160018.
                      https://doi.org/10.1038/sdata.2016.18[2] Grewe, J.,
                      Wachtler, T., Benda, J., 2011. A bottom-up approach to data
                      annotation in neurophysiology. Frontiers in Neuroinformatics
                      5, 16. https://doi.org/10.3389/fninf.2011.00016[3] Sprenger,
                      J., Zehl, L., Pick, J., Sonntag, M., Grewe, J., Wachtler,
                      T., Grün, S., Denker, M., 2019. odMLtables: A user-friendly
                      approach for managing metadata of neurophysiological
                      experiments. Front. Neuroinform. 13, 62.
                      https://doi.org/10.3389/fninf.2019.00062[4] Stoewer A,
                      Kellner CJ, Benda J, Wachtler T and Grewe J (2014). File
                      format and library for neuroscience data and metadata.
                      Front. Neuroinform. Conference Abstract: Neuroinformatics
                      2014. doi:10.3389/conf.fninf.2014.18.00027[5] Garcia S,
                      Guarino D, Jaillet F, Jennings T, Pröpper R, Rautenberg
                      PL, Rodgers CC, Sobolev A, Wachtler T, Yger P and Davison AP
                      (2014). Neo: an object model for handling electrophysiology
                      data in multiple formats. Frontiers in Neuroinformatics, 8,
                      10. http://doi.org/10.3389/fninf.2014.00010},
      month         = {Oct},
      date          = {2022-10-13},
      organization  = {Data Stewardship goes Germany,
                       Braunschweig (Germany), 13 Oct 2022 -
                       14 Oct 2022},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10 / IAS-9},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HMC - Helmholz Metadata Collaboration
                      $((DE-HGF)HMC_20200306)$ / Algorithms of Adaptive Behavior
                      and their Neuronal Implementation in Health and Disease
                      (iBehave-20220812)},
      pid          = {G:(DE-HGF)POF4-5235 / $G:(DE-HGF)HMC_20200306$ /
                      G:(DE-Juel-1)iBehave-20220812},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/912226},
}