Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software,but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption.

According to a recent survey conducted by the journal Nature, a large percentage of scientists agrees we live in times of irreproducibility of research results [1]. They believe that much of what is published just cannot be trusted. While the results of the survey may be biased toward respondents with interest in the area of reproducibility, a concern is recognizable. Goodman et al. discriminate between different aspects of reproducibility and dissect the term into ‘material reproducibility’ (provision of sufficient information to enable repetition of the procedures), ‘results reproducibility’ (obtaining the same results from an independent study; formerly termed ‘replicability’) and ‘inferential reproducibility’ (drawing the same conclusions from separate studies) [2]. The validity of data is threatened by many issues, among others by poor utility of public information, poor protocols and design, lack of standard analytical, clinical practices and knowledge, conflict of interest and other biases, as well as publication strategy.

Supplementary Materials for the article, including datasets for the analyses, the analyses reports, R scripts to reproduce them and the article's figures data.

There is growing interest in enhancing research transparency and reproducibility in economics and other scientific fields. We survey existing work on these topics within economics, and discuss the evidence suggesting that publication bias, inability to replicate, and specification searching remain widespread in the discipline. We next discuss recent progress in this area, including through improved research design, study registration and pre-analysis plans, disclosure standards, and open sharing of data and materials, drawing on experiences in both economics and other social sciences. We discuss areas where consensus is emerging on new practices, as well as approaches that remain controversial, and speculate about the most effective ways to make economics research more credible in the future.

This work is a detailed companion reproducibility paper of the methods and experiments proposed in three previous works by Lastra-Díaz and García-Serrano, which introduce a set of reproducible experiments on word similarity based on HESML and ReproZip with the aim of exactly reproducing the experimental surveys in the aforementioned works. This work introduces a new representation model for taxonomies called PosetHERep, and a Java software library called Half-Edge Semantic Measures Library (HESML) based on it, which implements most ontology-based semantic similarity measures and Information Content (IC) models based on WordNet reported in the literature.

Unlike most other SAPA datasets available on Dataverse, these data are specifically tied to the reproducible manuscript entitled "The SAPA Personality Inventory: An empirically-derived, hierarchically-organized self-report personality assessment model." Most of these files are images that should be downloaded and organized in the same location as the source .Rnw file. A few files contain data that have already been processed (and could be independently re-created using code in the .Rnw file) - these are included to shorten the processing time needed to reproduce the original document. The raw data files for most of the analyses are stored in 3 separate locations, 1 for each of the 3 samples. These are: Exploratory sample - doi:10.7910/DVN/SD7SVE Replication sample - doi:10.7910/DVN/3LFNJZ Confirmatory sample - doi:10.7910/DVN/I8I3D3 . If you have any questions about reproducing the file, please first consult the instructions in the Preface of the PDF version. Note that the .Rnw version of the file includes many annotations that are not visible in the PDF version (https://sapa-project.org/research/SPI/SPIdevelopment.pdf) and which may also be useful. If you still have questions, feel free to email me directly. Note that it is unlikely that I will be able to help with technical issues that do not relate of R, Knitr, Sweave, and LaTeX.