Share your research data

Digital Readiness and E- Business Success: Evidence from Emerging Markets

This dataset is the original data of the paper “Predicting the spatial distribution of invasive species richness using the combination of machine learning and geostatistical algorithms” .The dataset includes forest survey data, eco-climatic data, and topographic and geomorphological data. Among them, the forest survey data comes from the US Forest Inventory and Analysis (FIA) program, which collects information on the occurrence and distribution of invasive plants in all public and private forests in the United States. The ecological and climatic data includes 31 climatic variables, extracted from the WorldClim Global Climate Data (Version 2.1, https://www.worldclim.org/data/worldclim21.html). The topographic and geomorphological data includes three variables: elevation, soil carbon content, and aridity index. Among these, the elevation data comes from WorldClim Global Climate Data, soil carbon content data is extracted from the International Soil Reference and Information Centre (ISRIC-World Soil Information, https://www.isric.org/), and the aridity index is extracted from the Global Aridity Index (http://www.cgiar-csi.org/data). The definitions of each variable are as follows: prov_ID: Eco-region code; LAT/LON: Decimal latitude/longitude; Seasonability: SD of mean annual temperature; Alt: Altitude (m); PLT_TPA/Tpha: Trees per acre/hectare; RelDen: Successional development proportion (0–1); prpfor: Forested plot proportion; plt_drybio_adj/ha: Native tree biomass (English tons/acre/hectare); native_spp: Native tree species richness; PD_all: Phylogenetic diversity of tree species; PSV_all/var: Phylogenetic variability and variance; PSR_all/var: Phylogenetic richness and variance; PSE_all/PSC_all: Phylogenetic evenness/clustering; InvSpRichness: Invasive species richness; soilcarbon: 0–20 cm soil carbon content; aridity: Precipitation/evapotranspiration ratio; BIO1–BIO19: Standard climatic metrics (e.g., temperature, precipitation); vaprmin/max/range/avg: Water vapor pressure metrics (kPa); sradmin/max/range/avg: Solar radiation metrics (KJ/m²/day); windmin/max/range/avg: Wind speed metrics (m/s).

Since 2015, Indonesia has experienced a demographic bonus. The demographic bonus is a condition in which the productive age population (15-64 years) has a larger percentage compared to the non-productive age population (>64 years). In this range, Generation Z is the dominant generation. Uniquely, Generation Z is a generation that is very different from the previous generation because it was introduced to technology earlier. This has positive and negative impacts. The negative impacts include becoming individualistic and selfish, having difficulty focusing, and not liking normative things. These things need to be fixed to maximize the peak period of the demographic bonus. However, conventional learning is not an effective way. Therefore, the author chose to design an escape room-themed board game where the benefits of the escape room itself are directly proportional to the goals to be achieved from the existing problems. The author used a qualitative-descriptive method with a design thinking approach in this design. Based on the results of the distributed questionnaire, it was found that Generation Z realized they had shortcomings that needed fixing but did not understand how to fix them. So designing this board game is the right choice. Previous research has also stated that board games are a good learning option to increase students' interest in learning something. It is hoped that the design of this board game can help Generation Z's self-development in a fun way so that they can develop themselves and maximize Generation Z's performance during the demographic bonus period in Indonesia.

The dataset includes the original phenotypic data and the original blot images used in this study.

European Commission's role in State aid

The dataset in paper "Effects of Module Number and Connector Type on the Dynamic Response of Serial Multi-Module Floating Structures".

These codes and output reproduce the results of the paper available in its preliminary version at SSRN: https://ssrn.com/abstract=4890131 . Gilli, Mario and Sorrentino, Andrea, Characterization of the Set of Equilibria in Max-Min Group Contests Continuous Efforts and a Private Good Prize (July 09, 2024). University of Milan Bicocca Department of Economics, Management and Statistics (DEMS) Working Paper No. 541, Available at SSRN: https://ssrn.com/abstract=4890131 or http://dx.doi.org/10.2139/ssrn.4890131

Este dataset possui os dados históricos referente a projetos de desenvolvimento de software para avaliação do tempo (Z) em razão de quantidade de subtarefas (X) e esforço (Y), além da estimativa de tempo (Z) em razão da quantidade de subtarefas (X) de uma demanda.

data responden survey 1