Moss Storm (puppypaul5)
A current and fully-referenced dataset of resources and technologies for rice provision system is presented in this paper. These data served as model input data for the first multi-objective spatio-temporal optimisation of Philippine rice value chains. Data on available farmland area and their characteristics, such as paddy rice yield, rice farming costs and GHG emissions, are reported. As scenarios were developed for optimal rice value chains of integrated food and non-food production, estimates on the spatio-temporal demands on food, energy, fuels and chemical are presented. Data on sale prices and GHG emission factors of the raw materials and products are also compiled. Processing and transporting technologies involved in the modelling have their economic and operating parameters presented in this paper. This dataset has been collated through academic journals, technical papers and government agencies; all of which have been properly referenced. These data are valuable to various stakeholders of the rice industry across the globe aiming to understand rice value chains optimisation studies and to conduct further scenario development under different conditions and assumptions.Recently, the use of the citizen-sensors (people generating and sharing real data by social media) for detecting and disseminating emergency events in real-time have shown a considerable increase because people at the place of the event, as well as elsewhere, can quickly post relevant information on this type of alerts. Here, we present an emergency events dataset called UrbangEnCy. The dataset contains over 25500 texts in Spanish posted on Twitter from January 19th to August 19th, 2020, with emergencies and non-emergencies related content in Ecuador. We obtained, cleaned and, filtered these tweets and, then we selected the location and temporal data as well as tweet content. Besides, the data set includes annotations regarding the type of tweet (emergency / non-emergency) as well as additional nomenclature used to describe emergencies in the Center for immediate response service to emergencies (ECU 911) of Ecuador and international emergency services agencies (ESAs). UrbangEnCy dataset facilitates evaluating data science performance, machine learning, and natural language processing algorithms used with supervised and unsupervised problems re- related to text mining and pattern recognition. The dataset is freely and publicly available at https//doi.org/10.17632/4x37zz82k8.The data presented in this article are supplementary data related to the research article entitled "The Copenhagen Tool A research tool for evaluation of BLS educational interventions" (Jensen et al., 2019). We present the following supplementary materials and data 1) a standardized scenario used to introduce the test for gathering data on internal structure and additional response process; 2) test sheets used for rating test participant via video recordings; 3) interview-guide for collecting additional response process data; 4) items deemed relevant but not essential for laypersons, first responders and health personnel in the modified Delphi consensus process; 5) inter-rater reliability values for raters using the essential items of the tool to evaluate test participants via video recordings; 6) main themes from coding interviews with raters; 7) comparison of rater results and manikin software output.We have performed synchrotron computed tomography on two different fiber-reinforced composites while they were being continuously in-situ loaded in 0° tension. One material is a glass/epoxy laminate and the other is a carbon/epoxy laminate. The voxel size is 1.1 µm, which allows clear recognition of the glass fibers, but not distinct individual carbon fibers. For each material, four loading steps are selected with approximately 0, 40, 73, and 95% of the failure load, and the 3D images of the four volumes from each material are overlaid
