Abstract
Archives have well-established description standards, namely the ISAD(G) and ISAAR(CPF) with a hierarchical structure adapted to the nature of archival assets. However, as archives connect to a growing diversity of data, they aim to make their representations more apt to the so-called linked data cloud. The corresponding move from hierarchical, ISAD-conforming descriptions to graph counterparts requires state-of-the-art technologies, data models and vocabularies. Our approach addresses this problem from two perspectives. The first concerns the data model and description vocabularies, as we adopt and build upon the CIDOC-CRM standard. The second is the choice of technologies to support a knowledge graph, including a graph database and an Object Graph Mapping library. The case study is the Portuguese National Archives, Torre do Tombo, and the overall goal is to build a CIDOC-CRM-compliant system for document description and retrieval, to be used by professionals and the public. The early stages described here include the design of the core data model for archival records represented as the ArchOnto ontology and its embodiment in the ArchGraph knowledge graph. The goal of a semantic archival information system will be pursued in the migration of existing records to the richer representation and the development of applications supported on the graph.

Abstract
Recent initiatives in data management recognize that involving the researchers is one of the more problematic issues and that taking into account the practices of each domain can ease this process. We describe here an experiment in the adoption of data description by researchers in the biomedical domain. We started with a generic lightweight ontology based on the Minimum Information for Biological and Biomedical Investigations (MIBBI) standard and presented it to researchers from the Institute of Innovation and Investigation in Health (I3S) in Porto. This resulted in seven interviews and four data description sessions using a RDM platform. The feedback from researchers shows that this intentionally restricted ontology favours an easy entry point into RDM but does not prevent them from identifying the limitations of the model and pinpointing their specific domain requirements. To complete the experiment, we collected the extra descriptors suggested by the researchers and compared them to the full MIBBI. Part of these new descriptors can be obtained from the standard, reinforcing the importance of common metadata models for broad domains such as biomedical research. © 2019, Springer Nature Switzerland AG.

Abstract
A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (similar to 100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (similar to 1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May-15 June 2017 in Vale Cobrao in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigao with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a similar to 4 km x 4 km swath horizontally and similar to 10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space-time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Abstract
Novel data collection tools, methods and new techniques in biotechnology can facilitate improved health strategies that are customised to each individual. One key challenge to achieve this is to take advantage of the massive volumes of personal anonymous data, relating each profile to health and disease, while accounting for high diversity in individuals, populations and environments. These data must be analysed in unison to achieve statistical power, but presently cohort data repositories are scattered, hard to search and integrate, and data protection and governance rules discourage central pooling.

Abstract
ProcessPAIR is a novel method and tool for automating the performance analysis in software development. Based on performance models structured by process experts and calibrated from the performance data of many developers, it automatically identifies and ranks potential performance problems and root causes of individual developers. However, the current calibration method is not fully automatic, because, in the case of performance indicators that affect other indicators in a conflicting way, the process expert has to manually calibrate the optimal value in a way that balances those impacts. In this paper we propose a novel method to automate this step, taking advantage of training data sets. We demonstrate the feasibility of the method with an example related with the Code Review Rate indicator, with conflicting impacts on Productivity and Quality.

Abstract