sensLAB: TESTING MOTION AND PRESENCE SENSORS FOR SMART LIGHTING

For further efforts in saving energy in lighting installations, intelligent sensors are indispensable. Despite the fact such sensors are available precise and reproducible measurements of the coverage areas are missing. Therefore, METAS decided in 2020 to install the world's first manufacturer-independent fully automated measurement system to test passive infrared motion and presence sensors according to the recently published standard IEC 63180:2020. The test facility allows reproducible measurements of radial and tangential movements by moving different scaled and heated dummies on linear tracks. Furthermore, the presence detection can be measured with an automated robot arm. The test results are provided in a digital test report and the data is available in a machine-readable format to be further processed in designing and planning softwares.

Coronavirus Resistance Database (CoV-RDB): SARS-CoV-2 susceptibility to monoclonal antibodies, convalescent plasma, and plasma from vaccinated persons

As novel SARS-CoV-2 variants with different patterns of spike mutations have emerged, the susceptibility of these variants to neutralization by antibodies has been rapidly assessed. However, neutralization data are generated using different approaches and are scattered across different publications making it difficult for these data to be located and synthesized. The Stanford Coronavirus Resistance Database (CoV-RDB; https://covdb.stanford.edu ) is designed to house comprehensively curated published data on the neutralizing susceptibility of SARS-CoV-2 variants and spike mutations to monoclonal antibodies (mAbs), convalescent plasma (CP), and vaccinee plasma (VP). As of October 2021, CoV-RDB contains 186 publications including 64 (34%) containing 7,328 neutralizing mAb susceptibility results, 96 (52%) containing 11,390 neutralizing CP susceptibility results, and 125 (68%) containing 20,872 neutralizing VP results. The database also records which spike mutations are selected during in vitro passage of SARS-CoV-2 in the presence of mAbs and which emerge in persons receiving mAbs as treatment. The CoV-RDB interface interactively displays neutralizing susceptibility data at different levels of granularity by filtering and/or aggregating query results according to one or more experimental conditions. The CoV-RDB website provides a companion sequence analysis program that outputs information about mutations present in a submitted sequence and that also assists users in determining the appropriate mutation-detection thresholds for identifying non-consensus amino acids. The most recent data underlying the CoV-RDB can be downloaded in its entirety from a Github repository in a documented machine-readable format.

OSD2F: An Open-Source Data Donation Framework

The digital traces that people leave through their use of various online platforms provide tremendous opportunities for studying human behavior. However, the collection of these data is hampered by legal, ethical and technical challenges. We present a framework and tool for collecting these data through a data donation platform where consenting participants can securely submit their digital traces. This approach leverages recent developments in data rights that have given people more control over their own data, such as legislation that now mandates companies to make digital trace data available on request in a machine-readable format. By transparently requesting access to specific parts of this data for clearly communicated academic purposes, the data ownership and privacy of participants is respected and researchers are less dependent on commercial organizations that store this data in proprietary archives. In this paper we outline the general design principles, the current state of the tool, and future development goals.

CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations

Different causative therapeutics for CF patients have been developed. There are still no mutation-specific therapeutics for some patients, especially those with rare CFTR mutations. For this purpose, high-throughput screens have been performed which result in various candidate compounds, with mostly unclear modes of action. In order to elucidate the mechanism of action for promising candidate substances and to be able to predict possible synergistic effects of substance combinations, we used a systems biology approach to create a model of the CFTR maturation pathway in cells in a standardized, human- and machine-readable format. It is composed of a core map, manually curated from small-scale experiments in human cells, and a coarse map including interactors identified in large-scale efforts. The manually curated core map includes 170 different molecular entities and 156 reactions from 221 publications. The coarse map encompasses 1384 unique proteins from four publications. The overlap between the two data sources amounts to 46 proteins. The CFTR Lifecycle Map can be used to support the identification of potential targets inside the cell and elucidate the mode of action for candidate substances. It thereby provides a backbone to structure available data as well as a tool to develop hypotheses regarding novel therapeutics.

A protocol for a Delphi-style workshop to design a minimum standard for data reporting in systematic reviews

Evidence syntheses are considerable undertakings requiring substantial efforts to complete. Most data generated during a review are typically never made publicly available: a small fraction is typically provided alongside a review (typically not machine-readable). Full, publicly available review data in a standardised, machine-readable format, could radically increase the impact, transparency, efficiency, rigour, reusability, and legacy of evidence syntheses. Using a Delphi-style, stakeholder-driven approach, we plan to develop minimum standards and standardised formats for data from systematic reviews (and related systematic maps). We will collate suggestions from a broad group of stakeholders, using several rounds of anonymous voting and review to improve group consensus on necessary data and formatting. We will host an online workshop with a smaller group of stakeholders to refine and finalise a short-list of recommended minimum standards for the necessary data, formatting and file types to allow machine readability of the data associated with evidence syntheses.

Building schematic of Vienna in the late 1920s

Urban archives provide rich information on historical data. To a large extent, these data are not available in machine-readable format and therefore not linkable with other datasets. The “Häuser-Kataster der Bundeshauptstadt Wien” is a building schematic for the city of Vienna for the end of the 1920s. While this schematic was used as a knowledge base for real estate and finance business about 100 years ago, it has been used in the 2000s to manually map the historic building periods by property. We use the analog version and produced a machine-readable version to assign the historic addresses, building periods and number of floors to a building stock model down the road. The dataset has been complemented with codes of cadastral communities from the late 2010s to enable geotagging of the historic building data. To avoid unnecessary duplication of efforts by others and to share the dataset with urban historians and the public, we provide the dataset under creative common license.

Linked Data and Metadata

Data is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.