Over the past five years, different organisations have increasingly called for science to become more open and reproducible. They have endorsed a set of data-management principles known as the FAIR (Findable, Accessible, Interoperable, Reusable) principles. As such, there is a growing trend towards the open availability of research data, as researchers continue to enhance reproducibility by enabling sharing and opening of their findings and datasets. However, there is not yet a standardised way to openly enable access to datasets while keeping control of their final use, potentially obtaining benefits from their utilisation. This paper introduces Smardy, an EU-funded project which is deploying a traceable FAIR-compliant open innovation marketplace for data. Its innovative method for data exchange consists of the use of blockchain for controlling access rights to data, with data models able to grant access according to policies completely kept under the control of the data owner/producer. We also describe how Smardy employs dimensionality reduction techniques to automatically generate FAIR–compliant metadata, statistical fingerprinting to identify derivated datasets, and watermarking to help data owners trace the distribution of multiple copies of a dataset.

Open Science is widely regarded as a culture that is characterised by the transparency and broad accessibility of scholarly work, where researchers share openly artefacts almost immediately and with a very wide audience. The overarching aim of this paper is to document the systematic development of a European postgraduate programme on Open Science and Research Data Management developed by the TRAINRDM project. TRAINRDM is a 30-month European Union funded project, which aims to develop a training network around Open Science and Research Data Management. We have applied a comprehensive survey collecting 239 responses from researchers across Europe, representative of 2.58 million individuals i.e. the total number of researchers employed in the EU-27 region. We then mapped out existing skills and offerings at different TRAINRDM partner institutions to produce a fully-online postgraduate programme with micro-credentials, fully distributed delivery, and compliance to FAIR principles to address academic and industrial research needs. The main outputs of the project are a training programme for Early Career Researchers delivered in Summer 2022, and a the postgraduate programme (Master degree) to be fully validated under the European Qualifications Framework at Level 7 and delivered in 2023. The TRAINRDM curricula, teaching materials, data, and software are openly released under CC BY 4.0 and GPL licenses.

Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.

Crowd Monitoring is receiving much attention. An increasingly popular technique is to scan for mobile devices, notably smartphones. We take a look at scanning for such devices by recording WiFi packets. Although research on capturing crowd patterns using WiFi detections has been done, there are not many published results when it comes to tracking movements. This is not surprising when realizing that the data provided by WiFi scanners is susceptible to many seemingly erroneous and missed detections, caused by the use of randomized network addresses, overlap between scanners, high variance in WiFi detection ranges, among other sources. In this paper, we investigate various techniques for cleaning up sets of raw detections to sets that can subsequently be used for crowd analytics. To this end, we introduce two different quality metrics to measure the effects of applying the various techniques. We test our approach using a data set collected from 27 WiFi scanners spread across the downtown area of a Dutch city where at that time a 3-day multi-stage festival took place attended by some 130,000 people.