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Experimental brain research in animal models generates large amounts of disparate data of different modality, format, and spatial scale (Sejnowski et al., 2014). To manage and exploit the growing resource of neuroscience data it is now widely recognized that the data must be shared in accordance with the FAIR principles (Wilkinson et al., 2016), ensuring that data are findable, accessible, interoperable and reusable for future analyses (see e.g., Abrams et al., 2022). This trend has resulted in a growing volume of neuroscience data being made accessible through various data repositories and infrastructures (Ferguson et al., 2014; Jorgenson et al., 2015; Ascoli et al., 2017; Amunts et al., 2019). While free-text searches based on structured metadata are typically implemented in such databases (Clarkson, 2016), possibilities for more sophisticated queries, visualizations, and analysis depend on a harmonization across data files with different formats, scales, and organization (Zaslavsky et al., 2014; Abrams et al., 2022).

Anatomical information is widely used to provide a common context for harmonizing and comparing neuroscience data (Martone et al., 2004; Bassett and Sporns, 2017). The availability of open-access 3D rodent brain reference atlases (Oh et al., 2014; Papp et al., 2014; Wang et al., 2020; Kleven et al., 2023a) has opened up new opportunities for combining and analyzing data that have been aligned to a common spatial framework (Leergaard and Bjaalie, 2022). This allows researchers to integrate and analyze data from different sources within a common anatomical context more easily. For example, spatial registration procedures allow image data to be directly compared and analyzed based on atlas coordinates or annotated brain structures (Puchades et al., 2019; Tappan et al., 2019; Tyson and Margrie, 2022; Kleven et al., 2023b), e.g., through use of computational analyses of features of interest in atlas-defined regions of interest (Kim et al., 2017; Bjerke et al., 2018b, 2023; Yates et al., 2019; Kleven et al., 2023a,b). For other data types, such as locations of electrode tracts, 3D reconstructed neurons, or other features of interest, procedures and tools have been developed to represent the data as coordinate-based points of interest allowing validation or visualization of locations (Bjerke et al., 2018b; Fiorilli et al., 2023).

Atlases, tools, and resources for building, viewing, and using collections of spatially registered data have also proven to be fundamental for digital research infrastructures, such as the Allen Brain Map data portal¹ and to some extent also the EBRAINS Research Infrastructure.² But while the Allen institute provides collections of systematically generated homogenous and standardized image data spatially integrated in a 3D atlas, EBRAINS allows the research community to share a wide variety of data. These data may be related to anatomical locations using anatomical terms, reference to stereotaxic coordinates, or spatial registration to atlases. Thus, the location documentation provided with published data is as disparate as the data themselves—ranging from coordinate-based documentation defining the position of data in an atlas, to anatomical terms, illustrations, and unstructured descriptions (Bjerke et al., 2018a). The specification of such location metadata varies considerably, and a common standard for storing them is lacking in neuroscience. This poses a challenge to effectively utilize the metadata for spatial queries, co-visualization, and other analytic purposes. To achieve the ambitions of the community to accumulate and re-use neuroscience research data in agreement with the FAIR principles, it is necessary to represent metadata describing anatomical locations (spatial metadata) in a standardized and machine-readable format.

To address this challenge, we developed the Locare workflow (from locãre, latin: to place) for representing disparate neuroscience data in a simplified and standardized manner. The workflow was developed based on a large collection of diverse experimental data from mouse and rat brains shared via the EBRAINS Knowledge Graph.³ The available location documentation, specifying data location through points of interest, images, or semantic descriptions determines the starting point of the workflow, which through different workflow routes outputs geometric objects. We here present Locare as a generic workflow for specifying interoperable spatial metadata for neuroscience data, and exemplify how it can be used to specify anatomical locations for different data types as geometric objects in atlas space using a JSON format. The LocareJSON schemas allow representation of data in a simplified and standardized format that can enable spatial search, co-visualization, and analyses of otherwise disparate neuroscience data. The Locare workflow provides a solution for defining heterogeneous neuroscience data as atlas-defined geometric objects in a machine-readable format, which in turn can be utilized to represent data as interoperable objects in a 3D anatomical atlas and develop spatial query functionalities. The workflow is here presented in context of the EBRAINS Research Infrastructure but is generally applicable to any infrastructure of databases holding neuroscience data.

The Locare workflow builds on several years of experience with assisting researchers to share and present their experimental research data through the EBRAINS Research Infrastructure. As part of this effort, we investigated how to integrate and represent rat and mouse data sets in three-dimensional (3D) brain atlases. The workflow was established using 186 mouse brain data sets and 94 rat brain data sets available from the EBRAINS Knowledge Graph by 11 May 2023. An overview of all data set titles and type of location documentation is provided in Supplementary Table 1. The data sets included data files in various formats, structured metadata, and a data descriptor including summary, materials and methods, usage notes and explanation of data records. Several data sets were also associated with journal publications containing additional images and/or textual information about the anatomical location of the data. In some cases, we were in contact with data providers (custodians of the data shared through EBRAINS) directly and received additional information. These 280 data sets were contributed by 480 different researchers and acquired using 25 different experimental methods. The anatomical locations of observations or measurements in these data sets were documented using images (n = 116), semantic descriptions only (n = 123), or by specification of coordinates for points of interest (POIs; n = 41).

We here present the Locare workflow and a collection of JSON schemas (LocareJSON) for representing the location of data as geometric objects in 3D atlases. First, we outline the generic steps of the workflow, followed by a description of three different routes for use of the workflow based on the type of location documentation available. Second, we describe the LocareJSON schemas for storing the geometric objects. Lastly, we demonstrate the workflow through nine use-cases representing five different experimental approaches and all the geometrical object types defined by the LocareJSON schemas. Figure 2 gives an overview of the input (location documentation) and output (geometric object representation) for each use-case and visualizes their outputs in their respective 3D target atlases. A summary of details for each use-case is found in Supplementary Table 2.

The Locare workflow specifies different ways in which highly variable documentation describing the anatomical location of neuroscience data can be used to create representations of the data as geometric objects in a reference atlas space. The collection of LocareJSON schemas exemplify how such objects can be structured in a machine-readable way. The workflow was established and validated using 280 rat and mouse brain data sets generated using highly different methodologies (Supplementary Table 1). These data sets, shared on the EBRAINS Knowledge Graph between 2018 and 2023, allowed us to categorize the location documentation into three main categories. The geometric object data created for the nine examples used to demonstrate the Locare workflow (Figure 2) are shared as derived data sets on EBRAINS with links to their source data sets (Supplementary Table 2). In our use-cases, coordinates were specified using tools provided via the EBRAINS Research Infrastructure, but numerous other tools for generating 3-D geometric objects and coordinates (see Tyson et al., 2022; Fuglstad et al., 2023) may also be suitable as a starting point to create Locare JSON files. Below, we consider the potential impact, advantages, and limitations of the Locare workflow, including the geometric representations it delivers, and discuss possibilities for utilizing such geometric representations for visualization and spatial queries.

The FAIR guiding principle for data management and stewardship emphasize machine-readability and use of persistent identifiers to optimize reuse of scientific data (Wilkinson et al., 2016). Web-based open data infrastructures, structured metadata, and copyright licenses make data findable, accessible, and re-usable, while use of standardized file formats ensure interoperability of data files with different tools and among similar types of data (Pagano et al., 2013). In the context of the FAIR principles, the Locare workflow allows creation of machine-readable files representing the anatomical location and relevance of different data that otherwise would be difficult to find, access, and compare. By defining geometric objects using atlas-based coordinates, the data representations are spatially integrated and interoperable, in the sense that they can be co-visualized using viewer tools and utilized in various computational processes, including spatial search.

Our use-cases (Figure 2; Supplementary Table 2) show that the usefulness of location documentation depends more on the amount and level of detail of the documentation provided, than the method used to obtain the data. This highlights the need for good reporting practices. It is well established that the amount and consistency of metadata provided with research data varies considerably (see Bjerke et al., 2018a, 2020a), which in turn also contributes to the known problems with low replicability and reproducibility of studies (Goodman et al., 2016; Stupple et al., 2019). The different routes through the Locare workflow accommodates the variability of location documentation typically provided with experimental data sets, thus guiding researchers to define the most specific geometric representations possible with the documentation available for their data sets. In this way, data generated using the same methodology may be represented by different geometric objects when the available metadata differ. The location of a neuronal reconstruction can be defined as a singular point in an atlas (Figure 2G), or only as a mesh representing an entire anatomical subregion when less specific location documentation was provided (Figure 2C). Similarly, a series of histological images registered to an atlas may also be represented in different ways; as polygons representing the locations of sections in atlas space (use-case B), or as a population of points representing specific cellular features extracted from section images (Figure 2F). Improved routines for recording and sharing location documentation for neuroscience data will enable more precise spatial representation of data (Bjerke et al., 2018a; Tyson and Margrie, 2022; Kleven et al., 2023a).

The most detailed and accurate spatial representations of data are achieved by spatial registration of images showing anatomical features. A range of image registration tools are available (Puchades et al., 2019; Tappan et al., 2019; Carey et al., 2023; for review, see Tyson and Margrie, 2022), tailored for different types of 2D or 3D image data, and compatible with different brain atlases. Both manual and automated methods exist for different applications. Scripts are available for converting the output from the spatial registration tool QuickNII to LocareJSON polygon schema (see Figures 2A,B). Similar scripts can readily be adapted to different tools. Once images are spatially registered to an atlas, they can be used to specify points or volumes of interest, such as labeled objects (Figures 2F,G), electrode recording sites (Figure 2C), or tracer injection sites (Figure 2A).

The location of POIs, derived from text descriptions or extracted from atlas-registered images, can result in any geometric object representation. When coordinates for POIs have been extracted, an important consideration is therefore which geometric object would best represent it. There might be several alternatives, as, e.g., in the case of electrode tracks. A point can be used to represent the end or the entry point of the electrode (although the end point is usually most relevant as this is where recordings are made), and a line string may represent both the end and entry point, which would be appropriate when there are recording sites along the track (see Figure 2I, where a linear electrode array with 16 recording sites along the electrode was used). If the radius of the object (e.g., the electrode) is known, points and line strings may alternatively be replaced by spheres and cylinders, by introducing the radius of the object. Determining a radius should be the preferred practice as it benefits both visualization and spatial query purposes. In many cases, however, information about the radius is missing. Whether a best approximation is the better choice must be evaluated on a case-by-case basis.

The Locare workflow defines how the location of disparate neuroscience data can be represented as geometric objects in an atlas space. The workflow was developed using rat and mouse data sets with associated atlases, tools, and resources shared via the EBRAINS Research Infrastructure. The concept of data integration through geometric representations is generic and system independent, and the Locare workflow is therefore in principle applicable for other species for which an open access 3D brain atlas is available, such as, e.g., the zebrafish larvae (Kunst et al., 2019), macaque (Balan et al., 2024), or human brain (Amunts et al., 2020).

With the Locare workflow, we propose a streamlined approach to specify, organize, and store information about anatomical positions in the brain, yielding machine-readable files suitable for search engines, viewers, and other tools. The focus is to represent the location of data in a simplified and standardized format, rather than aiming to integrate the actual data files. We believe this will ensure the relevance of the workflow even when facing new methods, tools, and file formats. Standardized representation of data as geometric objects in 3D coordinate space can be utilized in spatial queries of neuroscience databases. Spatial queries will likely make it easier for researchers to find and reuse relevant data compared to free-text searches, and possibly open for more analytic approaches for re-use of shared data (Cao et al., 2023).

We envision that the Locare workflow can guide researchers describing anatomical locations in their data, and provide a starting point for defining new standards for current and future platforms, thus making neuroscience data more findable, accessible, interoperable and reusable, in accordance with the principles set forward by Wilkinson et al. (2016). Future work will include extension of the concept and workflow to human and non-human primate data and implementation into software for querying and accessing the location and distribution of neuroscience data through atlases.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

CB: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – original draft. IR: Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing – review & editing. HK: Data curation, Investigation, Methodology, Validation, Writing – review & editing. MØ: Data curation, Investigation, Methodology, Validation, Writing – review & editing. SY: Investigation, Methodology, Validation, Writing – review & editing. US: Data curation, Investigation, Methodology, Validation, Writing – review & editing. MP: Data curation, Investigation, Methodology, Validation, Writing – review & editing. OS: Data curation, Investigation, Methodology, Project administration, Software, Writing – review & editing. JB: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing – review & editing. IB: Conceptualization, Data curation, Methodology, Supervision, Validation, Visualization, Writing – review & editing. TL: Project administration, Resources, Supervision, Visualization, Writing – review & editing, Conceptualization, Funding acquisition, Methodology.