Each work package in the consortial interface measure adresses one established interface of NFDI4Phys with another consortium.
Task DO10.1 PUNCH4NFDI
Within the TA EI new concepts for Q-RDM will be developed for quantum computing and Q-AI. Quantum computing is rapidly evolving in the fields of high energy and astroparticle physics which are involved in PUNCH4NFDI. While there the activities are concentrated on developing algorithms and methods for particular applications, e.g. experiments at LHC and astroparticle telescopes, in NFDI4Phys complementary activities are carried out. In particular, it will be investigated how quantum data can be stored, managed and curated and how an efficient quantum/classical data flow can be realized. This will become a very important component of PUNCH4NFDI once the amount of quantum data generated will substantially increase. Thus, both NFDI consortia are complementing each other in a very constructive and even necessary way. We will work on a roadmap for FAIR Q-RDM together. Start date Q1/2023, Due date Q4/2025.
- Deliverable DO10.1
- Roadmap for Q-RDM.
Task DO10.2 DAPHNE4NFDI
We interface with DAPHNE4NFDI concerning scattering at biological samples. There is a long history of collaboration of our domains Biological Physics and Dynamics of Soft Matter performing scattering at DESY Hamburg. We will continue this tradition within the
NFDI and harmonize our RDM standards. We plan to work on a roadmap for RDM in the physics of scattering together. Start date Q1/2023, Due date Q4/2025.
- Deliverable DO10.2
- Roadmap for RDM in Scattering Physics.
Task DO10.3 FAIRmat
We interface with FAIRmat in the area of biological molecules. We collaborate in RDM of these compounds and on specific strategic questions within the NFDI. Recently, we organized a satellite meeting of the EBSA 2021 on RDM together. We plan to work on a roadmap
for FAIR RDM in the physics of condensed matter together. Start date Q1/2023, Due date Q4/2025.
- Deliverable DO10.3
- Roadmap for RDM in the Physics of Condensed Matter.
Task DO10.4 MATwerk / NFDI4Phy
Materials science is at the crossroad between equilibrium material properties as well as short- and long-term process-driven non-equilibrium materials behavior. Novel Materials, structured on the nano-, micro- and mesoscopic scale, can be manufactured process-driven as non-equilibrium yet multi-stable compounds. Long term material stability and durability depends typically on features like granularity and defect structure. The physics of these phenomenon requires techniques beyond solid state physics or linear elasticity theory. This is the core expertise of material sciences. Furthermore, we can learn and derive effective material models from biological materials and systems. Moreover, an integrated socio-technological approach requires transdisciplinary thinking with the human in the loop as future products need to be planed and manufactured in a process where architects, engineers, material scientist, as well as psychologists and sociologists work together to adapt material properties and product specifications to human needs and processing boundary conditions. In additions, humans are also in the loop as researchers carrying out experiments and collecting data. A successful NFDI needs to take a human-centric approach on data modeling and data acquisition. With its transdisciplinary agenda NFDI4Phys provides an ideal platform for such an endeavor. Indeed, digital Architecture which engages with the evolution of the very design process as an in-silico computational generation of architecture that integrates social, biological and material intelligence alike. The rising understanding of ecology as computational complexity paired with climatic and population growth issues challenges the discipline. It requires comprehensive sets of disparate data. Digital Architecture aims at designing the transformation of regions, cities, buildings, and construction components as a partial data-driven cybernetic design process. The goal is to learn about the impact and causal relationships of intelligent materials, sensors, biology, AI, biodiversity, population growth, social structures, and climate phenomena on the design process.
As a concrete task, Liss C. Werner and collaborators are working with our Task Areas to harmonize ontologies within the triangle/ rectangle Biotech, Architecture, (and) Materials Science, and Social Sciences in order to facilitate FAIR transdisciplinary research within our FDO model framework.
- Deliverable DO10.4
- FAIR Architecture
Task DO10.5 NFDI-Neuro/MaRDI4NFDI
Neuroscience is a strongly interdisciplinary research field where scientists with widely varying backgrounds such as biology, chemistry, psychology, mathematics, physics, computer science, medicine and engineering are closely collaborating for understanding the brain. Traditionally, theoretical physics is one of the pillars in (computational) neuroscience since it provides theories, methods and concepts for analyzing and understanding heterogeneous systems comprising many interacting elements. Theoretical and computational neurosciences live from the exchange of data, tools and models between experimentalists and theoreticians. The cross-disciplinary interaction between different labs is facilitated by FDO models.
Ideally, model development and theory building requires a continuous, recurrent exchange between theory and experiment: First, data is recorded and leads to a simplified model which reproduces the empirical observations. Then, the model generates predictions which are tested in the experiment. Subsequently, the new data motivates changes to the model and extends its predictive power. So new experiments can be launched, starting the cycle again. Facilitating this mutual exchange between specific collaboration partners, but also across the scientific community bears the potential to achieve models and theories which have a wider impact, and prevents having a multitude of isolated solutions which are never extended or updated after an initial publication. Our modular hierarchical FDOs ensure a “recurrent” exchange by facilitating the “way back” to the experimentalist as models and algorithms are part of the very same FDO. Relations between data and models are naturally described as hierarchical (meta)data. Inherent to \textcolor{red}{FDOs}, %FDOS,
the “history” of the scientific process with its mutual exchange and iterative inclusion of complementary data and models from other labs is recorded.
Development of appropriate tools for modular hierarchical FDOs for neuroscience requires interfacing NFDI4Phys with NFDI4Neuro and MaRDI4NFDI. While NFDI4Neuro builds up expertise in establishing infrastructures for brain data, MaRDI4NFDI will develop techniques for representing models and algorithms. Our task will be to evaluate how these different concepts can be integrated for boosting a recurrent exchange between experimentalists and theoreticians, with particular focus on evolving theories of neural computation.
- Deliverable DO10.5
- Established Data Cycles
Task DO10.6 TEXT+
The Text+ consortium will preserve text- and language-based research data in the long term and enable their broad use in science. The Text+ infrastructure will initially concentrate on digital collections, lexical resources and editions. These are of high relevance not only for the humanities but also for Physics as they provide digital access to curated text-based data. Indeed, Physics is currently starting to explore quantitative concepts of semantic information (Kolchinsky2018)., see A. Kolchinsky and D.H. Wolpert on Semantic information, autonomous agency and non-equilibrium statistical physics.
Our task is to start a dialog between Physics and the Humanities within NFDI. The pragmatic goal of this interface is to parse free text documents for relevant information. We understand a semantic metric to give a weighted ordering of semantic content in an abstract sense, i.e., we do not necessarily consider just numerical metrics, but nevertheless measures in semantic space that allow to quantify the structure of the qualitative. Currently, this interface is defined by Task 7 of the Domain TP, where our consortium gets support with NLP, and our TA FL with parsing. Based on the concept of semantic content, we will attempt to apply the notion of semantic similarity in order to connect local patches of an ontology acquired by remote surveys of the relevant physics community performed by TA SU. In general, we are guided by Floridi’s philosophy of information (Floridi2011). Our Speakers will engage in and foster wider dialogue on the structure of information in physics (order), biology (function), and the humanities (semantics). Moreover, it is crucial to realize that semantic information is structured modular-hierarchically in turn reflecting variant constructs of ideas in, e.g., different schools of semiotics, psychology or philosophy. Especially, assigning semantic meaning has a long and extensive history from ancient and medieval hermeneutics to Heidegger, Gadamer, and Popper, as well as multi-modal semiotics, see DO9.5 task semiotics, and of course the line of language philosophers from Frege, Russel, Wittgenstein to Noam Chomsky. We note that the modular hierarchy found in inanimate and living matter extends into the mind and its organization, which is not surprising since the substrate, into which the (new) mechanisms (Craver2019) of our mind are implemented, are the bodily neural networks of our brain, see Task DO10.5.
A transdisciplinary dialogue with Text+ is particularly timely since the availability of large digital collections, one of the research foci of Text+, has stimulated novel methods in an increasing number of Humanities disciplines that strive to combine insights from logic-based and hermeneutic approaches to semantics with distributional approaches that were first introduced in areas such as data mining and artificial intelligence (Baroni 2014, Baroni et al. 2003) for more discussion.
- Deliverable DO10.6
- Dialog established
Task DO10.7 NFDI4Biodiversity
The consortium NFDI4Biodiversity (Glöckner, Diepenbroek) works towards the conservation of global biodiversity by providing an extensive service portfolio and a wealth of environmental data. Within this interface, NFDI4Phys will develop a federated Physarum Portal (Oettmeier, Marwan) based on FDO technology (Lochau, Luther). Physarum polycephalum is a model system of basal cognition (Lyon et. al. 2021)
like Hydra, C. elegans, and planaria. More generally, we strive to incorporate Myxomycetes, which constitute a considerable part of the known biomass (Rojas 2021) into the repository system built by NFDI4Biodiversity (Diepenbroek). Conceptually, we employ this as a pilot project to harmonize our FDO concept with the LD philosophy of NFDI4Biodiversity. This will be a crucial proof of concept that a genuinely modular hierarchical FDO can be incorporated into a LD respository and vice versa.
- Deliverable DO10.7
- Harmony of Linked DO andFDO.
Task DO10.8 NFDI4BIOIMAGE.
The consortium NFDI4BIOIMAGE focuses on all steps of the research data life cycle for microscopy, biophotonic technologies and bioimage analysis. Considered objectives for bioimaging data include, but are not limited to standardization, storage concepts, and solutions for integration of discipline-specific data types. This is of extraordinary relevance for several of our UC dealing with microscopy as an important research method and hence we aim to collaborate with NFDI4BIOIMAGE in this field.
Our UC considered in this task have in common that there is a need for canonical workflows for processing image data in research and they provide the opportunity for NFDI4BIOIMAGE to investigate domain-specific issues of reproducibility, interoperability and re-use of data in NFDI4Phys. Furthermore, these UC give rise to the need for bioimaging and medical imaging interoperability, which will be addressed in collaboration with NFDI4BIOIMAGE and NFDI4Health.
Concrete and representative solutions for the integration and harmonization of RDM, developed in a domain specific context by our users, and the method-oriented RDM developed in NFDI4BIOIMAGE will be designed together via our common UC Plasma01 which is represented in NFDI4BIOIMAGE by M. Becker (INP). Furthermore, we plan to start a regular exchange with NFDI4BIOIMAGE to benefit from their expertise and standardized RDM solutions for handling bioimaging data in physics. Here, NFDI4Phys particularly brings in its forming power with respect to FDO-based RDM solutions.
The following use cases plan to collaborate with NFDI4BIOIMAGE from the beginning:
- Standards for NMR Imaging Physics, Matthias Günther, FHI for Digital Medicine, U Bremen
- Magnetic resonance imaging is a versatile imaging modality, which offers an tremendous amount of flexibility in the way how to acquire data (so-called MR sequences and protocols). These description are highly vendor-dependent, hard to maintain and reuse and lack any grade of sustainability. In this task, the participants will utilize and further develop tools, which allow to describe MR-sequences and protocols in a vendor-independent form and to replay it on systems of all vendors. Each acquired dataset will have a reference to the methods used to acquire it. This shall provide portability, provenance and full sustainability. We will harmonize this approach across the fields spanned by NFDI4Phys and NFDI4BIOIMAGE.
- Image Analysis of Histological Sections, Josef Käs, U Leipzig
- We are conducting image analysis on histological sections of tumor explants. These images and according findings will be stored for analysis via machine learning algorithms to identify new patterns and parameters for diagnostics. New histological data will be continuously recorded after new surgeries and also an existing pool of histological data will be included. This enables us to analyze thousands of samples and compare them to clinical outcomes. Especially the shapes of cells and nuclei will be analyzed. Generally, patterns for the different stages of cancer developments will be analyzed. Image analysis starts with segmenting into cancerous tissue, connective tissue, and fatty tissue. Nucleus segments in the cancer cell clusters are extracted with the StarDist model. Using the nucleus segments as initialization points and the edges of the cancer clusters as constraints,a watershed algorithm is used to approximate cell outlines. We are conducting image analysis on histological sections of tumor explants.
These images and according findings will be stored for analysis via machine learning algorithms. New histological data will be continuously recorded after new surgeries and also an existing pool of histological data will be included. This enables us to analyze thousands of samples and compare them to clinical outcomes. Especially shapes of cells and nuclei will as well as patterns for the different stages of cancer developments will be analyzed. Image analysis starts with segmenting into cancerous tissue, connective tissue, and fatty tissue. Nucleus segments in the cancer cell clusters are extracted with the StarDist model serving as initialization points and the edges of the cancer clusters as constraints, a watershed algorithm is used to approximate cell outlines.
- We are conducting image analysis on histological sections of tumor explants. These images and according findings will be stored for analysis via machine learning algorithms to identify new patterns and parameters for diagnostics. New histological data will be continuously recorded after new surgeries and also an existing pool of histological data will be included. This enables us to analyze thousands of samples and compare them to clinical outcomes. Especially the shapes of cells and nuclei will be analyzed. Generally, patterns for the different stages of cancer developments will be analyzed. Image analysis starts with segmenting into cancerous tissue, connective tissue, and fatty tissue. Nucleus segments in the cancer cell clusters are extracted with the StarDist model. Using the nucleus segments as initialization points and the edges of the cancer clusters as constraints,a watershed algorithm is used to approximate cell outlines. We are conducting image analysis on histological sections of tumor explants.
- Cell Motility, Joachim Rädler, LMU
- will contribute data management solutions based on OMERO. Time series of cells migrating on standardized tracks are stored on a file server for a large number of cell lines, mutants and conditions. To this end an ontology for cell migration and cell cytoskeleton markers will be developed. AI image analysis tools will be provided to converted image data into trajectories.
- Deliverable DO10.8
- Outsourcing Image Analysis Service
Task DO10.9 NFDI4Health.
NFDI4Health concentrates on consolidation of research in the medical sector as well as on establishing a research data infrastructure for personal health data. NFDI4Phys is interested to adopt procedures to ensure data security from NFDI4Health.
For medically relevant data such as individual brain data there should be a local data server (intranet) solution that could be used by fellow scientists who would be invited accordingly. All non-individual data might be hosted in cloud systems (internet) and made freely available after signing a respective declaration of appropriate data handling.
The effort in vendor-independent description of MR imaging methodology, see above Task 10.8 NFDI4BIOIMAGE is also highly relevant for clinical imaging data. Since the field of clinical imaging has additional restriction, this task has to deal with higher measures in terms of quality assurance. Data conversion schemas will be considered for enriching existing data with the new documentation schema for MR imaging methodology.
- Deliverable DO10.9
- Congruent Agenda of Consortia
Task DO10.10 NFDIDataScience / NFDI4Phys
NFDIDataScience (NFDI4DS) supports all steps of complex and interdisciplinary research data lifecycles in Data Science and Artificial Intelligence. Their task area Infrastructure and Services focuses on data analysis and interpretation with an emphasis on infrastructure and processing pipelines, i.e., computational workflows, harmonising various FDOs common to data-driven research, i.e., data, publications and software. The main contribution to their community is to provide a modular, scalable and extensible service-based architecture with the following components:
- a repository especially designed to host FDOs enhancing the portal user experience, including data, computational workflows and containerised software (their Measure 3.2) and
- an FDO registry for DS and AI projects pointing to external and internal research resources and harmonizing their metadata (their Measure 3.3)
Both the FDO repository and registry rely on metadata allowing the connection of data and other digital objects to be connected with each other, either present in the NFDI4DS repository/registry or somewhere else. While some metadata, for instance describing datasets and software, will be common, some other will be particular to our Physics domain. We will take advantage of the advances done by the NFDI4DS wrt common metadata so that our FDO repository becomes compatible with the one developed by NFDI4DS. The domain-specific metadata will be analysed and defined in our TA MO (see Deliverable MO2.2). It should be noticed that the domain-specific metadata required for our FDO repository aims at enabling users to search and filter FAIR data and solutions relevant to their own research so it should support actions such as comparing, assessing and reproducing Physics computational workflows.
This is almost exactly what NFDI4Phys is providing for its Physics community via our TA FL and TA FR. Indeed, both our teams are rooted within the FDO forum with multiple personnel. We are looking forward to a fruitful collaboration via mutual participation in our consortia as this will strengthen the service agenda for our communities. We are happy to liaise to the DIN e.V.
- Deliverable DO10.10
- FDO Repositories established
Task DO10.11 NFDI4Ing
This interfacial task with NFDI4Ing attempts to bind together process descriptions in material, optical and systems engineering within a multi-modal FDO model of production chains. We will compare the concepts associated with a digital twin as followed by the NFDI Archaetype Golo of NFDI4ing when analyzing research field data of distributed system with the modular hierarchical FDO Type Model approach as adopted by NFDI4Phys. Our goal is to harmonize conceptual approaches in order to map a digital twin onto an FDO type model.
- G-1 “Conception of a digital twin for the organisation and processing of research field data
- G-2 “Creating a digital master of a technical system”
- G-3 “Recommendations for creating and monitoring digital shadows”
- G-4 “Conversion of the extended concept of the digital twin into a ready to use process”
- Deliverable DO10.11
- Semantic mapping of digital twins to FDO models
Task DO10.12 MeTHODS & NFDI4Phys for Transdisciplinarity
NFDI4Phys shares with Methods the transdisciplinary agenda and acknowledges the currently largely orthogonal servicing of disciplines by our two consortia. We consider it as especially important that transdisciplinarity reaches out over the disciplinary spectrum to cross link scientific nodes via multidisciplinary centers like TIB to ensure congruency. NFDI4Phys is looking forward fostering our common enthusiasm for FDOs.
Deep immersion into the other discipline is a crucial element of transdisciplinarity. Physics will only enter the semantic space successfully when we realize that words can be analytic when placed right. Likewise Physics needs to expand its classical area of application and add new tools. 50 years ago, Physics was mainly about interaction of matter via dimensional fields. During the last 40 years we have been considering interactions in dimensionless networks. The next step we need to take is to enter the semantic space. In order to do so, we need to quantify the qualitative with semantic metrics. Conceptually, this will be a hard journey for science. We have to listen to our colleagues in the humanity departments and in sociology to be able to identify the relevant phenomena and effective fields and variables in systems which they have started discussing decades, if not centuries ago. However, after having done so, we should be able with a transdisciplinary agenda to close the gap between natural sciences and humanities. This process can only work when run bidirectional emanating from both sides. We believe Methods to be the right partner to reach the wisdom of a universal scholar