Search

The present Excel workbook template collects in several sub-table the relevant dimensions to be scored in order to deliver a scorecard for any scientific project in Pharmaceutical environment. The template has been used during IMI-FAIRplus project to collect information and score-prioritize project datasetsto allow a more rational rank for further FAIRification activity downstream.

The present Excel workbook template collects in several sub-table the relevant dimensions to be scored in order to deliver a scorecard for any scientific project in Pharmaceutical environment. The template has been used during IMI-FAIRplus project to collect information and score-prioritize project datasetsto allow a more rational rank for further FAIRification activity downstream.

In a paper written by by Vanhoucke et al. (2016), an overview of artificial and empirical project databases has been given for integrated project management and control. These databases are collections of the most well-known and widespread data instances available in literature for the construction of a baseline schedule, the analysis of schedule risk or the use for project control. The current paper serves as a follow-up study to further elaborate on the use of these data instances, and to give researchers an incentive to use these datasets for their research on the development and validation of new algorithms for project scheduling. Therefore, unlike the general focus of the previous paper on baseline scheduling, schedule risk analysis and project control, the focus on the current paper is restricted to resource-constrained project scheduling. The intention of this follow-up overview is fourfold. First and foremost, a procedure is proposed to facilitate the reporting of best known solutions for the well-known single- and multi-mode resource-constrained project scheduling problem to minimize the project makespan. Secondly, the paper reports our best known solutions we obtained so far, and reflects on the network and resource parameters that increase the project complexity. In doing so, areas to focus on for future research are detected, and an attempt to define hard problem instances is given. Thirdly, a new dataset is presented for the resource-constrained project scheduling problem that is much more diverse in both the network topology and resource scarceness and will enable the future researcher to develop algorithms to solve a wider range of project problems. Finally, the paper also adds some links to tutorials and other relevant information to stimulate researchers to download the data and update best known solutions once available. We acknowledge the financial support provided by the “Bijzonder Onderzoeksfonds” (BOF) and by the National Bank of Belgium (NBB) for the project with contract number BOF12GOA021 and by the Fundação para a Ciência e Tecnologia with contract number FRH/ BSAB/127795/2016. info:eu-repo/semantics/publishedVersion

project SGS-2016-018 Data and Software Engineering for Advanced Applications Project Management (PM) and Software Process Improvement (SPI) are complex activities demanding decisions which are not clear-cut even when using a defined pro- cess based on best practices proven as beneficial to product quality and project suc- cess. This is due to specific context surrounding each software development project and the fact that much of the guidance is in textual form not suitable for automatic processing. An deep know-how and extensive manual analysis of the project data and progress is therefore needed to support the correct PM and SPI decisions. This analysis is time and resource consuming, and prone to overlooking important data and reaching incorrect conclusions potentially derailing the project even further. The goal of this work is to represent the project data using a unified metamodel allowing cross-examination of different projects, detection of defined errors in PM (anti-patterns) as well as points of divergence from the defined software development processes. The expected benefit is streamlining the anti-pattern detection activities, leaving more time to handle their occurrences properly, while minimizing their ef- fects on product quality and project success. To reach the goal we analyzed software development methodologies, Application Lifecycle Management (ALM) tools com- monly used in PM, techniques of software process modeling and current research in methods and frameworks to aid PM and SPI efforts. We also review well-known PM anti-patterns from literature and present a method for their operationalization. We then propose an approach to analyze project data from ALM tools and detect anti- pattern occurrence in it. The approach is partially validated through implementation of an experimental tool.

One of the most significant problems faced by project managers is to effectively distribute the project resources and effort among the various project activities. Most importantly, project success depends on how well, or how balanced, the work effort is distributed among the project phases. This paper aims to obtain useful information regarding the correlation of the composition of effort attributed in phases for around 1,500 software projects of the ISBSG R11 database based on a promising statistical method called Compositional Data Analysis (CoDA). The motivation for applying this analysis is the observation that certain types of project data (effort distributions and attributes) do not relate in a direct way but present a spurious correlation. Effort distribution is compared to the project life-cycle activities, organization type, language type, function points and other prime project attributes. The findings are beneficial for building a basis for software cost estimation and improving future empirical software studies. 2012 IEEE.