Martin Øvsthus

• Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine; Csucs, Gergely & Puchades, Maja A. [Show all 10 contributors for this article] (2025). DeMBA: a developmental atlas for navigating the mouse brain in space and time. Nature Communications. 16. doi: 10.1038/s41467-025-63177-9. Full text in Research Archive Show summary

  Studies of the adult mouse brain have benefited from three-dimensional (3D) atlases providing standardised frameworks for data analysis and integration. However, few 3D atlases exist for the developing mouse brain, and none cover more than a few ages. This fails to represent the dynamic nature of development and is insufficient for researchers whose data fall between the ages of existent atlases. Existing atlases also neglect the spatial correspondence between ages, making comparison across ages difficult. We present the Developmental Mouse Brain Atlas (DeMBA), a 4D atlas encompassing every postnatal day from 4 to 56. DeMBA was created by using existing brain templates for six developmental time points and interpolating intermediate ages. DeMBA is incorporated in software for spatial registration and analysis, enabling brain-wide quantification of features of interest. Furthermore, we provide the software package CCF translator, allowing coordinates or image volumes to be transformed between ages for comparison. These resources provide age-specific spatial frameworks incorporated in accessible analytic pipelines for developmental mouse brain data.

• Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine; Csucs, Gergely & Puchades, Maja [Show all 9 contributors for this article] (2024). DeMBA: a developmental atlas for navigating the mouse brain in space and time. bioRxiv. ISSN 2692-8205. doi: 10.1101/2024.06.14.598876. Full text in Research Archive Show summary

  Studies of the adult mouse brain have benefited from three-dimensional (3D) atlases providing standardised frameworks for data analysis and integration. However, few 3D atlases exist for the developing mouse brain, and none cover more than a few ages. This fails to represent the dynamic nature of development and is insufficient for researchers whose data fall between the ages of existent atlases. Existing atlases also neglect the spatial correspondence between ages, making comparison across ages difficult. We present the Developmental Mouse Brain Atlas (DeMBA), a 4D atlas encompassing every postnatal day from 4 to 56. DeMBA was created by using existing brain templates for six developmental time points and interpolating intermediate ages. DeMBA is incorporated in software for spatial registration and analysis, enabling brain-wide quantification of features of interest. Furthermore, we provide the software package CCF translator, allowing coordinates or image volumes to be transformed between ages for comparison. These resources provide age-specific spatial frameworks incorporated in accessible analytic pipelines for developmental mouse brain data.

• Blixhavn, Camilla Hagen; Reiten, Ingrid; Kleven, Heidi; Øvsthus, Martin; Yates, Sharon Christine & Schlegel, Ulrike [Show all 11 contributors for this article] (2024). The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases. Frontiers in Neuroinformatics. 18. doi: 10.3389/fninf.2024.1284107. Full text in Research Archive Show summary

  Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

• Øvsthus, Martin; Swieten, Maaike Margot Helène van; Puchades, Maja; Tocco, Chiara; Studer, Michèle & Bjaalie, Jan G. [Show all 7 contributors for this article] (2024). Spatially integrated cortico-subcortical tracing data for analyses of rodent brain topographical organization. Scientific Data. 11. doi: 10.1038/s41597-024-04060-y. Full text in Research Archive Show summary

  The cerebral cortex extends axonal projections to several subcortical brain regions, including the striatum, thalamus, superior colliculus, and pontine nuclei. Experimental tract-tracing studies have shown that these subcortical projections are topographically organized, reflecting the spatial organization of sensory surfaces and body parts. Several public collections of mouse- and rat- brain tract-tracing data are available, with the Allen mouse brain connectivity atlas being most prominent. There, a large body of image data can be inspected, but it is difficult to combine data from different experiments and compare spatial distribution patterns. To enable co-visualization and comparison of topographical organization in mouse brain cortico-subcortical projections across experiments, we represent axonal labelling data as point data in a common 3D brain atlas space. We here present a collection of point-cloud data representing spatial distribution of corticostriatal, corticothalamic, corticotectal, and cortico

• Kleven, Heidi; Reiten, Ingrid; Blixhavn, Camilla Hagen; Schlegel, Ulrike; Øvsthus, Martin & Papp, Eszter Agnes [Show all 10 contributors for this article] (2023). A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting. Frontiers in Neuroinformatics. 17. doi: 10.3389/fninf.2023.1154080. Full text in Research Archive Show summary

  Brain atlases are widely used in neuroscience as resources for conducting experimental studies, and for integrating, analyzing, and reporting data from animal models. A variety of atlases are available, and it may be challenging to find the optimal atlas for a given purpose and to perform efficient atlas-based data analyses. Comparing findings reported using different atlases is also not trivial, and represents a barrier to reproducible science. With this perspective article, we provide a guide to how mouse and rat brain atlases can be used for analyzing and reporting data in accordance with the FAIR principles that advocate for data to be findable, accessible, interoperable, and re-usable. We first introduce how atlases can be interpreted and used for navigating to brain locations, before discussing how they can be used for different analytic purposes, including spatial registration and data visualization. We provide guidance on how neuroscientists can compare data mapped to different atlases and ensure transparent reporting of findings. Finally, we summarize key considerations when choosing an atlas and give an outlook on the relevance of increased uptake of atlas-based tools and workflows for FAIR data sharing.

• Tocco, Chiara; Øvsthus, Martin; Bjaalie, Jan G.; Leergaard, Trygve B. & Studer, Michèle (2022). The topography of corticopontine projections is controlled by postmitotic expression of the area-mapping gene Nr2f1. Development. ISSN 0950-1991. 149(5). doi: 10.1242/dev.200026. Full text in Research Archive Show summary

  ABSTRACT Axonal projections from layer V neurons of distinct neocortical areas are topographically organized into discrete clusters within the pontine nuclei during the establishment of voluntary movements. However, the molecular determinants controlling corticopontine connectivity are insufficiently understood. Here, we show that an intrinsic cortical genetic program driven by Nr2f1 graded expression is directly implicated in the organization of corticopontine topographic mapping. Transgenic mice lacking cortical expression of Nr2f1 and exhibiting areal organization defects were used as model systems to investigate the arrangement of corticopontine projections. By combining three-dimensional digital brain atlas tools, Cre-dependent mouse lines and axonal tracing, we show that Nr2f1 expression in postmitotic neurons spatially and temporally controls somatosensory topographic projections, whereas expression in progenitor cells influences the ratio between corticopontine and corticospinal fibres passing the pontine nuclei. We conclude that cortical gradients of area-patterning genes are directly implicated in the establishment of a topographic somatotopic mapping from the cortex onto pontine nuclei.

• Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi & Yates, Sharon Christine [Show all 9 contributors for this article] (2018). Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies. Frontiers in Neuroanatomy. 12. doi: 10.3389/fnana.2018.00082. Full text in Research Archive Show summary

  In experimental neuroscientific research, anatomical location is a key attribute of experimental observations and critical for interpretation of results, replication of findings, and comparison of data across studies. With steadily rising numbers of publications reporting basic experimental results, there is an increasing need for integration and synthesis of data. Since comparison of data relies on consistently defined anatomical locations, it is a major concern that practices and precision in the reporting of location of observations from different types of experimental studies seem to vary considerably. To elucidate and possibly meet this challenge, we have evaluated and compared current practices for interpreting and documenting the anatomical location of measurements acquired from murine brains with different experimental methods. Our observations show substantial differences in approach, interpretation and reproducibility of anatomical locations among reports of different categories of experimental research, and strongly indicate that ambiguous reports of anatomical location can be attributed to missing descriptions. Based on these findings, we suggest a set of minimum requirements for documentation of anatomical location in experimental murine brain research. We furthermore demonstrate how these requirements have been applied in the EU Human Brain Project to optimize workflows for integration of heterogeneous data in common reference atlases. We propose broad adoption of some straightforward steps for improving the precision of location metadata and thereby facilitating interpretation, reuse and integration of data.

• Bjerke, Ingvild Elise; Øvsthus, Martin; Papp, Eszter Agnes; Yates, Sharon Christine; Silvestri, L & Fiorilli, J [Show all 11 contributors for this article] (2018). Data integration through brain atlasing: Human Brain Project tools and strategies. European psychiatry. ISSN 0924-9338. 50, p. 70–76. doi: 10.1016/j.eurpsy.2018.02.004. Full text in Research Archive Show summary

  The Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.

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• Puchades, Maja; Bjerke, Ingvild Elise; Carey, Harry Alexander Chandler; Kleven, Heidi; Øvsthus, Martin & Yates, Sharon Christine [Show all 10 contributors for this article] (2025). QUINT workflow now supports whole brain analysis using the Developmental Mouse Brain Atlas (DeMBA). Full text in Research Archive Show summary

  INTRODUCTION: Three-dimensional, open access digital brain atlases are important resources in neuroscience research, opening new possibilities for automated analyses in atlas defined regions of interest. The EBRAINS QUINT workflow [1,2] has been developed to support standardized atlas-based analysis of sectioned tissue without the need for coding ability. It features integrated brain atlases for the adult rat and mouse brain and is increasingly used in the neuroscience community. However, a recurrent request from many QUINT users interested in understanding brain development, has been to include brain atlases representing different developmental ages, to so achieve better spatial registration for brains from young individuals. METHODS: To meet this need, we have now integrated the novel Developmental Mouse Brain Atlas (DeMBA), a four-dimensional atlas covering every postnatal day from 4 to 56 [3]. This atlas features segmentations from the widely adopted Allen Mouse Brain CCF and the Kim developmental atlases, that have been transformed and interpolated to the same ages, ensuring comparable region definitions across ages [4]. RESULTS AND DISCUSSION: The DeMBA atlas was implemented in the registration software QuickNII and VisuAlign and shared as specific packages together with the atlas itself on EBRAINS [4]. Those versions require usage of the specific Filebuilder utility shared within the packages. Next, the DeMBA atlas was implemented in a new Nutil Quantifier version (v1.1) and thereby the complete QUINT analysis can now be performed on young mouse brains. All the software tools and the DeMBA atlas are shared through the EBRAINS Knowledge graph [5]. We here present the updated QUINT workflow and give examples of preliminary developmental data analysis. References: 1.Yates, S. et al. (2019) QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain. Front. Neuroinform. 13, 1–14. https://doi.org/10.3389/fninf.2019.00075 2. Puchades MA, Yates SC, Csucs G, Carey H, Balkir A, Leergaard TB and Bjaalie JG (2025) Software and pipelines for registration and analyses of rodent brain image data in reference atlas space. Front. Neuroinform. 19:1629388. https://doi.org/10.3389/fninf.2025.1629388 3.Carey, H., Ovsthus, M., Kleven, H., Yates, S. C., Csucs, G., Puchades, M. A., Bjaalie, J. G., Leergaard, T. B., & Bjerke, I. E. (2024). Developmental mouse brain atlas (DeMBA) with continuous coverage of postnatal day 4 to 56 (v1) [Data set]. EBRAINS. https://doi.org/10.25493/V3AH-HK7 4.Carey, H., Kleven, H., Øvsthus, M. et al. DeMBA: a developmental atlas for navigating the mouse brain in space and time. Nat Commun 16, 8108 (2025). https://doi.org/10.1038/s41467-025-63177-9 5.https://search.kg.ebrains.eu Acknowledgeme

• Kleven, Heidi; Øvsthus, Martin; Carey, Harry Alexander Chandler; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. [Show all 7 contributors for this article] (2024). Three-dimensional reference atlases for integration and analysis of data from the postnatal mouse brain. Full text in Research Archive
• Reiten, Ingrid; Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Aasebø, Ida Elisabeth Jørgensen & Yates, Sharon Christine [Show all 12 contributors for this article] (2020). Data sharing through the online EBRAINS platform: a new service for brain research. Full text in Research Archive Show summary

  Enhancing the reproducibility and transparency of research is an emerging theme across scientific disciplines, driven by new technological advances, and captured by the Open Science concept. The heterogeneity of research data, which often hinders direct comparisons of findings, adds a layer of complexity to this effort. To address these challenges in neuroscience, the Human Brain Project has developed a new research infrastructure, EBRAINS, providing tools and services to the neuroscientific community. The EBRAINS data curation service offers comprehensive stewardship for sharing experimental and computational data. New workflows and standards for neuroscience data and metadata management have been developed to make the research results discoverable, comparable across modalities, and possible to reanalyse and reuse in new combinations. Here we present our workflows and curation services tailored for sharing heterogeneous neuroscience data. We demonstrate the integration of such data in the infrastructure, and highlight some practicalities for researchers who want to share their neuroscience data through EBRAINS.

• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Kleven, Heidi; Zehl, Lyuba; Bjerke, Ingvild Elise & Schmid, Oliver [Show all 17 contributors for this article] (2019). Neuroinformatics platform for making neuroscience data Findable, Accessible, Interoperable, and Reuseable. Full text in Research Archive
• Andersson, Krister Andreas; Blixhavn, Camilla Hagen; Zehl, Lyuba; Zarfarnia, Sara; Köhnen, Stefan & Hilverling, Anna [Show all 19 contributors for this article] (2019). Resources for making neuroscience data FAIR. The Human Brain Project invites researchers to share, find, and use data via the new EBRAINS infrastructure. Full text in Research Archive
• Schlegel, Ulrike; Blixhavn, Camilla Hagen; Andersson, Krister Andreas; Yates, Sharon Christine; Øvsthus, Martin & Bjerke, Ingvild Elise [Show all 11 contributors for this article] (2019). Integrating and analysing heterogeneous rodent neuroscience data using three-dimensional brain reference atlases. Full text in Research Archive Show summary

  Achieving advances in the field of neuroscience with its rapidly growing number of published data requires integration across many scales and levels of investigation. Such integration is challenging due to the heterogeneous nature of the data, and the difficulty of comparing data from different studies. Key aspects include lack of standards for presentation of data and experimental parameters (metadata), and variable practices for assigning and reporting anatomical location in the brain. The EU Human Brain Project (HBP) is addressing these challenges by establishing an infrastructure of neuroinformatic tools and data curation services through which disparate neuroscience data can be shared, used and analysed. Three-dimensional (3D) open access brain reference atlases provide anatomical context for all the shared data, easing comparison and interpretation of findings. We here present HBP workflows for assigning metadata describing anatomical locations to different types of neuroscience data, and workflows for extracting, quantifying and co-visualizing morphological features from multiple datasets in 3D anatomical brain atlas viewers. We highlight the added value of mapping data to a common atlas framework in example studies, and demonstrate new analytic opportunities enabled by combining image analysis tools with information from a 3D brain reference atlas. The HBP now invites the community to use the new research infrastructure established.

• Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister Andreas; Bjaalie, Jan G. & Leergaard, Trygve B. (2018). Best practices for determining and documenting neuroanatomical locations in the rodent brain. Full text in Research Archive
• Blixhavn, Camilla Hagen; Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Kleven, Heidi & Puchades, Maja [Show all 8 contributors for this article] (2018). Data integration through digital brain atlasing: Making diverse neuroscience data discoverable using Human Brain Project infrastructure. Full text in Research Archive
• Øvsthus, Martin; Bjerke, Ingvild Elise; Yates, Sharon Christine; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2018). Mapping topographical organization in mouse brain subcortical axonal projections using Human Brain Project infrastructure. Full text in Research Archive
• Andersson, Krister; Øvsthus, Martin; Bjerke, Ingvild Elise; Puchades, Maja; Telefont, Martin & Jeff, Muller [Show all 9 contributors for this article] (2017). Data integration through digital brain atlasing: Human Brain Project infrastructure. Full text in Research Archive Show summary

  The Human Brain Project is building an ICT-based scientific research infrastructure that will permit researchers to advance our knowledge in the fields of neuroscience through data exploration, analytics and simulation at multiple levels of brain organization. Experimental neuroscience is connected to the infrastructure through systems for organizing and managing heterogeneous research data. These data systems are initially tested by data producing laboratories in the HBP, and will ultimately be opened for the community. HBP data curation services support users in elevating the level of data consistency and in the migration of data to the open domain. The starting point for research projects that will use HBP resources is the HBP Collaboratory, a rich collaborative workspace which is open to the community. The Collaboratory provides guidance and access to resources, including storage for data and a workbench for entering and organizing metadata. As a central element, the Collaboratory provides high-quality reference atlases of the rodent and human brain, together with appropriate tools and workflows that allow users to register data to the atlases for their study, and to perform initial analysis of data. It also links to important external data repositories and services. Here we present an overview of currently available reference atlases, tools and workflows. We exemplify the use of these resources in a range of neuroscience projects, ranging from brain-wide mapping of molecular level information to identification of precise location of electrophysiology recording sites. With coordinates corresponding to reference atlas space, harvested through the workflow, valuable metadata for future search and analysis of data are captured. Furthermore, with data aligned to reference atlases, analysis of the spatial distribution of events, labeled elements, and regions of interest in image material is strongly supported. Following registration to reference atlas, subsequent image processing and analysis steps delivers lists of extracted features corresponding to atlas structures, enabling quantitative regional analysis. We exemplify analytical workflows producing automated quantification and spatial analysis of labeling in series of histological section images from whole rodent brain. These and other atlas related workflows will be made available as HBP software services.

• Bjerke, Ingvild Elise; Andersson, Krister; Øvsthus, Martin; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve Brauns (2017). Determining and documenting the anatomical location of experimental neuroscience data: Best practice recommendations. Full text in Research Archive Show summary

  Anatomical location is a key parameter for interpretation and comparison of neuroscientific data. Location is typically determined by looking up diagrams in anatomical reference atlases, communicated using anatomical terms, and shown in representative images. But the documentation provided varies considerably among scientific publications. Essential information about nomenclature and reference atlases, or criteria used to define boundaries of structures is often missing. This lack of accuracy limits the opportunities for comparing and integrating data from different publications, and could lead to failure in replicating scientific experiments. To clarify and address this challenge, we have investigated current practice for assigning and documenting anatomical location for different categories of experimental neuroscience data reported in > 120 articles investigating the rodent brain. Our findings show that the specificity and accuracy of anatomical documentation in most cases can be considerably improved with relatively simple procedures. We here suggest some general and method-specific recommendations for such improvements, and discuss how these steps may contribute to increase the accuracy of anatomical descriptions and data interpretation. We demonstrate how new three-dimensional rodent brain reference atlases, and associated software tools for spatial registration of brain image data to a common anatomical space, offer new opportunities for efficient integration and comparison of neuroscience data.

• Puchades, Maja; Csúcs, Gergely; Checinska, Martyna; Øvsthus, Martin; Bjerke, Ingvild Elise & Andersson, Krister [Show all 8 contributors for this article] (2017). Neuroinformatics tool and workflow for anchoring of serial histological images in rodent brain 3D space. Full text in Research Archive Show summary

  Reference atlases of the brain are important tools for assigning location to data captured in neuroscience experiments. Spatial alignment of sectional images to reference atlases is, however, challenging to perform for several reasons. Manual approaches applied to large series of sectional images are time consuming and, moreover, histological sections are often cut at angles deviating from the principal anatomical planes presented in conventional reference atlases. Novel 3D reference atlases and accompanying tools provide new opportunities for rapid and accurate spatial registration and integration of data in common atlas space. We here present QuickNII which is new tools for use with the Waxholm Space atlas for the rat brain and the Allen Mouse brain atlas for the mouse brain, and a workflow that allows users to 1) interactively generate customized atlas images (slices of the 3D reference atlas) corresponding to the plane of sectioning of any experimental image series, 2) superimpose atlas images onto experimental images using affine transformations to match key anatomical landmarks, 3) propagate the transformations across a series of images, 4) assign spatial reference atlas coordinates to the experimental images, and 5) allow viewing and analysis of the experimental data integrated in the reference atlas. We exemplify the workflow and use of our methods with a range of experimental data from neuroanatomical and neurophysiological investigations.

• Puchades, Maja; Øvsthus, Martin; Bjerke, Ingvild Elise; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns [Show all 7 contributors for this article] (2017). Data integration through digital brain atlasing: semiautomatic spatial registration of serial histological images to rodent brain 3D reference atlases. Full text in Research Archive Show summary

  Reference atlases of the brain are important tools for assigning location to data captured in neuroscience experiments. Spatial alignment of sectional images to reference atlases is, however, challenging to perform for several reasons. Manual approaches applied to large series of sectional images are time consuming and, moreover, histological sections are often cut at angles deviating from the principal anatomical planes presented in conventional reference atlases. Novel 3D reference atlases and accompanying tools provide new opportunities for rapid and accurate spatial registration and integration of data in common atlas space. We here present new tools for use with the Waxholm Space atlas for the rat brain and the Allen Mouse brain atlas, and workflow that allows users to 1) interactively generate customized atlas images (slices of the 3D reference atlas) corresponding to the plane of sectioning of any experimental image series, 2) superimpose atlas images onto experimental images using affine transformations to match key anatomical landmarks, 3) propagate the transformations across a series of images, 4) assign spatial reference atlas coordinates to the experimental images, and 5) allow viewing and analysis of the experimental data integrated in the reference atlas. We exemplify the work flow and use of our methods with a range of experimental data from neuroanatomical and neurophysiological investigations.

• Bjerke, Ingvild Elise; Andersson, Krister Andreas; Øvsthus, Martin; Puchades, Maja; Bjaalie, Jan G. & Leergaard, Trygve B. (2017). Navigating the rodent brain: Best practice recommendations for determining and documenting spatial location for neuroscience data. Full text in Research Archive
• Leprince, Y; Coello, Sebastian Christopher; Csúcs, Gergely; Chervakov, P; Darine, Dmitri Aleksandrovitsj & Øvsthus, Martin [Show all 12 contributors for this article] (2017). Interactive tools for registering of 2D and 3D images to reference atlases. Full text in Research Archive
• Andersson, Krister Andreas; Bell, Simon; Bjerke, Ingvild Elise; Øvsthus, Martin; Blixhavn, Camilla Hagen & Kleven, Heidi [Show all 18 contributors for this article] (2017). Submitting data and metadata through the HBP Data Workbench: Concepts, data flows, policies, and best practices. Full text in Research Archive
• Timonidis, Nestor; Bakker, Rembrandt; Øvsthus, Martin; Garcia-Amado, M; Porrero, C & Leergaard, Trygve B. [Show all 8 contributors for this article] (2017). Towards predicting the full mesoconnectome based on data fusion. Full text in Research Archive
• Puchades, Maja; Bjerke, Ingvild Elise; Øvsthus, Martin; Andersson, Krister; Csúcs, Gergely & Leergaard, Trygve Brauns [Show all 7 contributors for this article] (2016). Spatial registration of serial histological image data to reference brain atlases. Full text in Research Archive

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