Funded Projects FAIR Research IT

We will develop metasynth, open-source software for synthetic data generation based on a transparent generative metadata format, which allows researchers to openly publish a privacy-preserving synthetic version of their sensitive tabular data. Unlike existing synthetic data software, we limit the amount of statistical information in our models, to make it easy for researchers and data managers to openly publish an example of their data without privacy concerns. For data owners, this makes the data findable and accessible. For researchers, this makes it easier to start working with sensitive data and it makes their work more interoperable and reusable.

Metasynth is currently a pilot; funding from this programme will be crucial in creating a full-featured implementation for real-world use within and beyond Utrecht University.

This project will deliver a generic and FAIRified implementation of an open-source software package - CKAN - which makes research (meta)data discoverable. This exercise will enable findability, and consequently highlights areas for improvement in accessibility, interoperability and reusability of research (meta)data.

The 4CAT: Capture and Analysis Toolkit is a web-based toolkit that enables researchers and students to collect and process social media data. Hosting our own instance of this one stop shop application facilitates UU researchers in collecting social media data via APIs provided by various platforms via standardized routes. The toolkit also provides large amounts of processing scripts for analysing and visualizing collected data through established methods.

FAIR principles have been around for a while and mostly being applied to research outputs. To provide high-quality support to the research community in data management, privacy compliance, open science, and writing grant proposals, the information about the research projects, outputs, Data Management Plans (DMP) and privacy scans must be FAIR as well. DMP is a document that explains how the involved parties are going to make the research data and output of FAIR and in compliance with the open science practices. A privacy scan demonstrates the project's data protection compliance in general, including any eventual data sharing of personal data for scientific research purposes. Both documents mentioned need to be updated and tracked during the funded project. Currently, each research support serving entity e.g., RDM support, ITS, and Research Support Office (RSO), has its own platform to track the support level of the projects to ensure they have provided high-quality support to their clients in a timely manner. We aim to design and create a tracking indication system that interfaces existing data sources and enriches them with additional tracking information and capabilities. The system will help us and other research support services to monitor the progress of running research projects and assign tasks to stakeholders to meet project requirements and deadlines.

Researchers from “the long tail of science” struggle to publish their data in an Interoperable manner. This is partly due to the perceived, non-Interoperable nature of data obtained in unique (non-standard) experiments typical for the long tail of science, and partly to a lack of international (meta)data standards and tools. This project will develop best practices and tools to aid researchers to:

1. identify Interoperable data within their datasets, and
2. publish these for international dissemination.

As a case study, we will use data generated by researchers in 92 geophysical laboratories in Europe, which are part of the EPOS Multi-Scale Laboratories (MSL) community. Experience and tool sharing will aid the broader long tail of science within and beyond Utrecht University.

We aim to transform regular live microscope analysis of biological specimens to an online, interactive environment where imaged specimens and analysis overlays are available for training, documentation and future reanalysis.

Cognitive Mapping (CM) is a research method used to study the beliefs held by citizens and elites. It elicits beliefs from text or via surveys or focus groups. CM analysis focusses on causal and normative relationships between concepts which are graphically represented in a semantic network. Applying the technique is complex and requires technical or coding skills. With this project we aim to create a user-friendly CM software specifically also for social scientists with limited technical and programming skills and thereby to lower the threshold for other researchers to use CM in their research, and to this in a transparent and reproducible manner.

Environmental health studies increasingly use GeoAI models. However, these models are often inaccessible and hard to reuse by the scientific community because too many projects fail to adhere to the FAIR principles. We will break down the barriers that are preventing cross-disciplinary usage of GeoAI by developing the necessary building blocks for UU and other researchers to integrate with Huggingface, a leading online and open source platform that enables collaborative building, training and deployment of machine/deep learning models within containers that can be used as Virtual Research Environments (VREs), and heavily promotes accessibility, interoperability and reusability through its frontend and API.

We will create a free and open source software (FOSS) Python version of the simulation model IMAGE-land. This is a central part of the IMAGE integrated assessment model (IAM) model, used to make projections of future environmental change and effects of possible response strategies to support international policy processes. IMAGE-land is currently programmed in (outdated) Fortran code (~2000 lines of code), closed source, and owned by het Planbureau voor de Leefomgeving (PBL). This jeopardizes replication and re-use. The planned FOSS is part of and contains building blocks.

The History Database of the Global Environment (HYDE) provides global land use data for the past 12,000 years at different time intervals with 5’arcmin spatial resolution. HYDE is regarded as the most up-to-date, comprehensive, and widely used long-term historical land use reconstruction model available. Presently, these data are not easily accessible. This funding will allow us to build a FAIR workflow with existing computing facilities, to provide public spatial data repositories for data storage and interactive web-based maps and graphs. The aim is to provide these data to the public for reuse in a structured, replicable manner.

We have previously built an inscriptional database and an interactive map. Our purpose for this application to the FAIR Research IT innovation fund is to integrate these into an interactive platform documenting Jewish historical migration into Europe during the Roman and Medieval periods, and to make this data available for further research as linked open data in RDF format.

The project will build upon the SUMMERFAIR project funded by ZonMW project. The aim of the SUMMERFAIR project is making existing data sets interoperable and reusable by mapping them to a common vocabulary (ontology). The SUMMERFAIR project has produced an ontology and prototypes of data mapping and analyses tools. The project ends in January 2023. To enable researchers within and outside UU and UMCU further development and testing is required "at the working floor".

Aim of the SUMMERFAIR spring edition is to turn the ontology and other tools into a more transparant and robust approach, that can be reused by researchers.