Training and Education in Data Literacy
- Dr. Lena Steinmann, Universität Bremen, Data Science Center
- Dr. Holger Israel, PTB Braunschweig
In accordance with the Data Literacy Charter , the consortium NFDI4Phys views data literacy – the ability to collect, manage, evaluate, and apply data in a critical manner – as a core competence of the 21st century. Hence, our goal is to promote data literacy within the NFDI4Phys community and beyond to foster a sustainable use of data following the FAIR principles and an open-data culture. In order to achieve that, we will develop a comprehensive training and education program for different status groups (from student to professor) covering both interdisciplinary as well as domain-specific skills. We will develop different learning formats including:
- Hands-on learning in workshops and labs,
- Module-based learning providing maximum flexibility,
- Project-based learning dealing with complex real-world topics.
The German Physical Society (Deutsche Physikalische Gesellschaft e. V.; DPG), which is the largest physics society in the world, will be a key partner and act as a multiplier in promoting data literacy throughout the physics community. We plan to host data literacy summer schools for early-career researchers through the DPG, allowing us to reach a large audience. Moreover, in order to raise awareness for data literary across all status including senior scientists and professor, we will introduce data literacy as regular topic at DPG conferences. Additionally, we plan to establish a cross-physics panel on data literacy in collaboration with the other physics-related consortia (PUNCH4NFDI , FAIRmat , DAPHNE4NFDI), with the DPG playing an important role in connecting the different consortia. Moreover, we also have the “Young DPG” (jDPG) , a network of students and early-career scientists in the field of physics, as partner, who will organize a PhD symposium on data literacy to raise awareness within their peer-group. At student level, we intend to establish a blue print for a general studies module on data literacy that can easily be implemented at the different NFDI4Phys locations and modified according to local needs.
The interdisciplinary offers addressing skills such as critical thinking, problem solving, computational thinking, data ethics, data citation, data sharing will be developed in close collaboration with other NFDI consortia as part of our engagement in cross-cutting topics. Together with other NFDI consortia, we will collaborate with the in Bremen developed cross-disciplinary training for doctoral candidates in research data management and data science “Data Train”. We will actively contribute to the development of Data Train’s interdisciplinary curriculum and teaching modules. The courses and workshops offered through Data Train can be used as best practices and also implemented at other NFDI4Phys locations. Moreover, the Data Science Center at the University of Bremen will be an important partner in developing and offering courses and workshops for PhDs and Postdocs to advance their data science skills such as programming, model building, data visualization, or advanced analytics (machine learning).
- Prof. Dr. Thorsten Fehr
- Prof. Dr. Uwe Engel
Task Area Community Interaction Surveys is concerned with conducting remote surveys to identify white spots of RDM in our commmunities and advise other task areas where to recruit new use cases. In addition, and in collaboration with Task Area Metadata & Ontologies, we will query the structure of an Ontology of Physics via automated patching of large datasets covering domain semantic order. We will combine the set of tools available via Survey Methodology and contextual binary decision-making experiments. A modern Ontology of Physics will be constructed as a probability network of nodes and links. The most probable sub-graph will be identified as the current Ontology of Physics. We will do this in a two step process. First we allow for a free textual response. Probabilities of nodes and links will be determined by Entity Typing [Reference] or another appropriate tools of semantic textual analysis based on machine learning algorithms. Second, in order to ensure a proper ontology, we provide a pre-selected terminology gathered in the first step which is allowed to be arranged according to majority opinion within our domains.
Domains & Interfaces
- Domain Leaders
The Task Area Community Interaction Domains & Interfaces establishes contact between domains within the consortium and via our interfaces to other consortia. Our spokesperson and all domain leaders together with interested colleagues from other consortia identify mutual topics of interest for discussion and possible formal agreement. We are fostering our guiding principle of a dual infrastructure consisting of technology and human scientific discourse alike.
Central to our goal, minimal heterogeneous RDM of physical laboratories, is the dissemination of results obtained and services rendered by our task areas into the various domain specific communities. While the Task Area Community Interaction Surveys is concerned with finding and addressing specific needs within our domains and concentrates on knowledge spread out over the communities, Domains & Interfaces distributes the results of our work back into them.
In addition to RDM, we are focusing mainly on the role of information in different disciplines of Transdisciplinar Physics which typically differ in complexity of their field. Key to measuring complexity is the amount and especially the kind of information [Floridi 2011, Jost 2020, Ellis 2020] different disciplines gather about their objects of study. In Statistical Physics, we find that the classical Shannon entropy measures the probability distribution of states. In cell biology, we realize that each protein carries certain functional information. In Computer Science, we are interested about the algorithmic transformation of an input to an output. Cognitive Science asserts how we perceive our environment and control behavior accordingly. Psychology is concerned with the assessment of our perceptions and ensuing actions. Finally, Philosophy forms assessments and principles into knowledge and is concerned with a theory of the mind.
All these statements make sense in one way or another, but can we somehow come up with a more concise way of formulation? Indeed, Luciano Floridi provides a precise and thorough classification of information in his central book on The Philosophy of Information [Floridi 2011]. Especially, he performs a sequential construction to develop the attributes which data need to have in order to count as knowledge. Semantic information is neccessarily well-formed, meaningful and truthful. Well-informed data becomes meaningful by action based-semantics of an autonomous-agent solving the symbol grounding problem by herself interacting with the environment. Knowledge ensues than by being informed by relevant accounted data.
We will engage in semantic mapping of various ontologies as used by neighboring disciplines and provide semantic analysis where appropriate and needed. Semantic work is performed in close collaboration and guidance by the working group on semantics established in the FDO forum.