The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment. The nanosafety data is predominantly recorded in Excel spreadsheet files. Although the spreadsheets are quite convenient for the experimentalists, they also pose great challenges for the consequent processing into databases due to variability of the templates used, specific details provided by each laboratory and the need for proper metadata documentation and formatting. In this paper, we present a workflow to facilitate the conversion of spreadsheets into a FAIR (Findable, Accessible, Interoperable, and Reusable) database, with the pivotal aid of the NMDataParser tool, developed to streamline the mapping of the original file layout into the eNanoMapper semantic data model. The NMDataParser is an open source Java library and application, making use of a JSON configuration to define the mapping. We describe the JSON configuration syntax and the approaches applied for parsing different spreadsheet layouts used by the nanosafety community. Examples of using the NMDataParser tool in nanoinformatics workflows are given. Challenging cases are discussed and appropriate solutions are proposed.

Raman spectra from the same sample might differ between instruments and over time depending on the spectrometer, optical path, or sample environment, among others. There is a need to harmonize and standardize characterization by Raman spectroscopy, enabling end users to share and reuse digital spectroscopic data through FAIR databases. In this context, we present an open‐source, MIT‐licensed, Python package called ramanchada2 that collects existing and novel state‐of‐the‐art algorithms, allowing the users to generate, read, visualize, and process Raman spectra, with special emphasis on instrument calibration. A number of input formats are supported, including those from DFT simulations. This package also offers a tool for the generation of synthetic spectra based on user specifications for data augmentation and algorithm benchmarking. NeXus is introduced as an input and output format, enabling the possibility to package into a single file data and metadata, including data processing information, from multiple and even from different types of experiments, for example, XRD, Raman, and biological assays, using a harmonized structure and terminology. To facilitate Raman spectra analysis by end users, we developed “Oranchada” add‐on for data‐mining open‐source software Orange, a user‐friendly wrapper of all ramanchada2 functionalities with predefined harmonization workflows, as well as an online application for calibration.

Ensuring high-quality service from one end user to another, over multiple interconnected networks, is central to the aims and approach of GEANT2, the pan-European research and education network. In order to provide users with reliable and consistent performance monitoring data from multiple networks, GEANT2's performance measurement and monitoring joint research activity, in co-operation with Internet2 and ESnet, is developing a comprehensive network monitoring facility - perfSONAR. Within the framework of this activity, we have developed an open source, publicly accessible, easy to use, and yet powerful standalone graphical user interface client, capable of querying all available perfSONAR services and displaying the returned data in a concise manner. This paper gives a short overview of the existing state-of-the-art solutions, introduces the fundamental concepts of the perfSONAR infrastructure, and describes the design, implementation, and use cases of PerfsonarUI, while highlighting its main advantages. Possible future enhancements of PerfsonarUI are also outlined