# An Analysis of the Supply of Open Government Data
## 3 Material and Methods  
### Data Collection and Preprocessing  
A common task in the economics and data science fields is the collection of datasets from different sources in order to discover knowledge, patterns, and trends. This activity represents some challenges, such as the diversity of the data structure, formats, and time consistency, among others. In this research, we adopted the OSMEN workflow methodology (shown in Figure 4), which is the acronym of obtain, scrub, explore, model, interpret, proposed by [56], to deal with these challenges. This methodology was proposed in the data science research community in order to systematically collect data and provide research transparency and result reproducibility. Following this workflow to solve our research question, the first step was to select and gather the data from several sources, such as (1) the Global Open Data Index (GODI) from the Open Knowledge Foundation, (2) the global wage report from the International Labor Organization, (3) real interest rates from the World Bank, (4) the democracy index from the Economist Intelligence Unit, (5) the education index from the United Nations, (6) age data from the United Nations, (7) Internet use from the International Telecommunication Union, (8) the corruption perception index (CPI) from Transparency International, (9) the global competitiveness report (GCR) from the World Economic Forum, and (10) the gross domestic product (GDP) per capita and purchasing power parity (PPP) from  
Figure 4: Illustration of the data science pipeline developed for our research. the World Bank. In this research, we defined the GODI indicator as our dependent variable (also called the response or target variable), and the other information extracted from these datasets became our independent variables (also called label variables). Once the phase of data acquisition was completed, the next tasks were data integration and cleaning. The former involved analyzing and merging heterogeneous datasets. For example, some information was published in a long format and other datasets as wide formats containing different periods of time. The latter is associated with keeping consistency among datasets, such as homologating the names of countries because some datasets have different name labels (e.g., in some datasets, the country name was denoted as the United States of America or Venezuela, RB, and in other datasets, the country names appeared as the United States or Venezuela, respectively). Another aspect related to the cleaning process was to identify elements such as missing values, outliers, or other noise elements that could affect the quality of our model [57]. The next step was related to feature engineering, particularly feature creation, extraction, and selection. Some of our collected variables were categorical data. Therefore, we needed to create new variables in order to perform our models. This process is known as one-hot encoding in machine learning or dummy variables in econometrics. Then, we performed feature extraction, implementing principal component analysis (PCA), which is the process of dimensionality reduction from a large number of attributes without losing meaningful information by removing redundant data. This reduction process helped to identify features that could be more conducive to our analysis [58]. After performing PCA, our analysis indicated that there was multicollinearity in our independent variables. The topic of multicollinearity is an ongoing research area in feature engineering due to its implications when using diverse datasets [59; 60; 61]. For this reason, we include in the next section a variable selection process and a robustness check, based on an economic domain knowledge approach [62] and justified on the theoretical background introduced in the literature [63]. In order to complete a full sample with the desired variables for our empirical analysis, our final cross-sectional dataset was constituted by 18 variables and 49 observations during the year 2016.