@article{Zaragozi2021c,

title = {Development of a Common Framework for Analysing Public Transport Smart Card Data},

author = {Benito Zaragozí and Sergio Trilles-Oliver and Aaron Gutiérrez and Daniel Miravet},

doi = {https://doi.org/10.3390/en14196083},

issn = {1996-1073},

year  = {2021},

date = {2021-09-24},

journal = {Energies},

volume = {14},

number = {19},

pages = {6083},

abstract = {The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data.},

keywords = {Open science, public transport, smart data card},

pubstate = {published},

tppubtype = {article}

}

@article{Zaragozí2020,

title = {Leveraging container technologies in a GIScience project: A Perspective from Open Reproducible Research},

author = {Benito M. Zaragozí and Sergio Trilles-Oliver and José T. Navarro-Carrión},

doi = {https://doi.org/10.3390/ijgi9030138 },

year  = {2020},

date = {2020-04-16},

journal = {ISPRS International Journal of Geo-Information},

volume = {9},

pages = {138},

abstract = {Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project},

keywords = {Open science, Reproducible research},

pubstate = {published},

tppubtype = {article}

}

@article{Granell2018,

title = {Reproducible research and GIScience: an evaluation using AGILE conference papers},

author = {Daniel Nüst and Carlos Granell-Canut and Barbara Hofer and Markus Konkol and Frank O Ostermann and Rusne Sileryte and Valentina Cerutti},

url = {https://doi.org/10.7717/peerj.5072},

doi = {10.7717/peerj.5072},

issn = {2167-8359},

year  = {2018},

date = {2018-01-01},

journal = {PeerJ},

volume = {6},

pages = {e5072},

keywords = {AGILE, Data science, GIScience, Open access, Open science, Reproducible research},

pubstate = {published},

tppubtype = {article}

}