Transportation - Data Papers
A recent report [1] has made it clear, how reluctant we are as researchers to share and document our data, often the results of years of work and of substantial expenditures. It is not necessary to stress how much work gets lost, when data sets disappear unarchived, how much work gets duplicated for lack of data access. Transportation wants to fill one of the gaps in the process of publishing data sets. Its recently added ‘Data paper’ category allows researchers to report on substantial or innovative data collection exercises. These papers will allow the colleagues to get credit for this difficult work, but also support them in the further reporting of their respective studies, as the data questions have been answered already and do not need to be rehearsed again and again.
These are reviewed research papers focussing on the data collection process and its content. A paper will be eligible, if it
- reports a substantially innovative or large policy relevant data set
- discusses the contents and the design, theory or modelling considerations behind it, including an a priori assessment and ex-ante measurement of the response burden [2]
- discusses the pre-tests and any resulting changes
- describes the response process fully (sample frame, response behaviour, protocol and changes during the process, incentives, AAPOR response rates)
- provides evidence on the representativeness (if relevant) of the sample obtained
- provides basic substantial results and how they compare to relevant benchmark surveys
- provide examples of the survey forms or CATI scripts in the supporting materials
- explains how, where and when the data will be available for third parties. There is no requirement for the data to be open access, but papers on such data sources will be preferred.
We hope to enrich the field with deeper insights into the data collection process and with freely available data sources enabling further analysis and comparison across time and space.
- CWTS and Elsevier (2017) Open data: The researcher perspective, CWTS, Universiteit Leiden, Leiden.
- See for example: Axhausen, K.W., B. Schmid and C. Weis (2015) Predicting response rates updated, Arbeitsberichte Verkehrs- und Raumplanung, 1063, IVT, ETH Zürich, Zürich.