Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.

Tests on organic photovoltaics (OPV) mini modules, fabricated through a R2R process, in air and without hazardous solvents have been conducted in order to compare their outdoor performance, in Belo Horizonte, Brazil, and Bangor, North Wales, and assess the impact of the latitude and climate of the installation on the power generation and modules' lifetime. The test showed different profiles of degradation for each region and formulation, with a surprisingly faster degradation in Bangor. One of the possible sources of the increased degradation is the greater levels of condensation observed in Bangor. To verify the impact of condensation on the module stability, indoor tests have been conducted to relate the dew point depression to module degradation times. The results show that condensation is a significant stress factor in OPV and should be considered more prominently in reliability studies.

The application of data analytical approaches to understand long-term stability trends of organic photovoltaics (OPVs) is presented. Nearly 1900 OPV data points have been catalogued, and multivariate analysis has been applied in order to identify patterns, produce models that quantitatively compare different internal and external stress factors, and subsequently enable predictions of OPV stability to be achieved. Analysis of the weights associated with the acquired predictive model shows that for light stability (ISOS-L) testing, the most significant factor for increasing the time taken to reach 80% of the initial performance (T $_{80}$ ) is the substrate and top electrode selection, and the best light stability is achieved with a small molecule active layer. The weights for damp-heat (ISOS-D) testing shows that the type of encapsulation is the primary factor affecting the degradation to T $_{80}$ . The use of data analytics and potentially machine learning can provide researchers in this area new insights into degradation patterns and emerging trends.