This dataset is used to investigate the effects of the global pandemic on China's outward foreign direct investment (OFDI) in the ASEAN region. The dataset using annual data from 2005-2020 to examine the impact of the global health crisis on China's OFDIs. The data for the year 2020 is a brand new dataset and based on authors' owned computations. The data is collected through the International Monetary Fund's the World Economic Outlook, The International Labour Statistics ILOSTAT, and The World Bank's World Economic Indicators.
Contributors:Elissa Cosgrove, Raheleh Sadeghi, Florencia Schlamp, Heather Holl, Mohammad Moradi Shaharbabak, Seyed Reza Miraei Ashtiani, Mats Troedsson, Monika Stefaniuk-Szmukier , Anil Prabhu, Stefania Bucca , Monika Bugno-Poniewierska, Barbara Wallner , Joel Malek, Douglas Antczak, Donald Miller, Andrew Clark, Samantha Brooks
The Arabian horse, one of the world’s oldest breeds of any domesticated animal, is characterized by natural beauty, graceful movement, athletic endurance, and, as a result of its development in the arid Middle East, the ability to thrive in a hot, dry environment. Here we studied 378 Arabian horses from 12 countries using equine single nucleotide polymorphism (SNP) arrays and whole-genome re-sequencing to examine hypotheses about genomic diversity, population structure, and the relationship of the Arabian to other horse breeds. We identified a high degree of genetic variation and complex ancestry in Arabian horses from the Middle East region. Also, contrary to popular belief, we could detect no significant genomic contribution of the Arabian breed to the Thoroughbred racehorse, including Y chromosome ancestry. However, we found strong evidence for recent interbreeding of Thoroughbreds with Arabians used for flat-racing competitions. Genetic signatures suggestive of selective sweeps across the Arabian breed contain candidate genes for combating oxidative damage during exercise, and within the “Straight Egyptian” subgroup, for facial morphology. Overall, our data support an origin of the Arabian horse in the Middle East, no evidence for reduced global genetic diversity across the breed, and unique genetic adaptations for both physiology and conformation.
This deposition includes sample annotation and Plink format filesets for genotyping data generated for the associated publication.
Full methods can be found in the publication, in summary: Genotype calls from each genotyping array batch and the whole genome sequences were combined sequentially using PLINK v. 1.90 . First, Affymetrix and GeneSeek calls were merged using PLINK with filters set to 90% SNP genotyping rate and 1% minor allele frequency. A subset of multi-origin ancestry Arabians (from mixed origins) were used to test all SNPs for Hardy Weinberg Equilibrium. Autosomal variants with P-values < 0.005 (corrected for multiple testing) were removed from the genotype files. SNPs derived from whole genome sequencing were then merged to generate the final set of variant calls for downstream analysis, removing any variant with lower than 80% genotyping rate, 1% minor allele frequency, or flagged as multi-allelic. Finally, samples with genotyping rate <95% were removed. After applying these filters, the data set included 343,367 SNPs.
The drying rate of the solar dryer was operationalized as farmer perception that the solar dryer quickly drives out moisture from fresh okra. Key elements in this construct included: 1) preference of the solar dryer to other drying methods; 2) perception that the solar dryer is fast; and 3) the belief that the solar encourages farmers to use it. For the perceived quality of the solar-dried product, the focus is on whether the solar drying method gives a good quality product that is attractive to consumers. According, the study operationalizes this construct as; 1) farmer evaluative attitude towards quality of the solar-dried product, and 2) farmer affection towards the solar-dried product. Based on the assumption that both drying rate and perceived quality of solar-dried might be important factors for the farmer to accept to use solar dryers, this study hypothesized follows:
H1: Drying rate of the solar dryer positively influences farmer acceptance of the solar dryer
H2: Perceived quality of solar-dried product positively influences farmer acceptance of the solar dryer
This study further presupposes that the marketability of the solar-dried product is the immediate precursor of farmer acceptance of the solar dryer. It is assumed that the farmer is likely to evaluate whether a solar-dried product is attractive to buyers prior to the acceptance of using the solar dryer. As derived from marketing attitudes literature, marketability of solar-dried products in this study relates to 1) evaluative attitude of ease of marketing; and 2) attractiveness to farmer and buyer of solar-dried product (Hegde et al., 2015; Kessy et al., 2018; Zhang et al., 2017). Thus, it is hypothesized as follows:
H3: Drying rate of the solar dryer positively influences perceived marketability of the solar-dried product
H4: Perceived quality of the solar-dried product positively influences perceived marketability of the solar-dried product
H5: Perceived marketability of the solar-dried product positively influences farmer acceptance of the solar dryer
We further test for the mediation relationships in this study as shown below:
H6: Perceived marketability of solar-dried product mediates drying rate in predicting farmer acceptance of the solar dryer.
H7: Perceived marketability of solar-dried product mediates quality of the solar-dried product in predicting farmer acceptance of solar dryer
Primary data was collected from okra farmers who had participated in field-level experimentation and training on solar dryers using a pre-tested and semi-structured questionnaire during the period of October 2018 - November 2018. This questionnaire had open-ended questions that capture metric data on socio-economic variability including production data measured in kg, for instance, quantities harvested, dried and sold. Others information captured were on socio-demographics such as age in years, education, family size, land, and gender. farmer acceptance of solar dryers data was collected
Contributors:Calvin Tsay, Adrian Caspari, Richard Pattison, Ted Johansson, Alexander Mitsos, Michael Baldea
Computational model of a single-product air separation unit (ASU) implemented in Modelica and gPROMS. This benchmark process can be used to test strategies for process control, production scheduling, and flexible operation. Several sample demand response operation scenarios are provided.
This repository is a copy of the Github repository (v1.0) which is publicly available at https://github.com/UCEEB/Distributed-building-identification. It contains a Matlab source code which implements the Distributed building grey-box model identification algorithm. The contents include a demo example of EnergyPlus building model identification.
Data used for ecosystem services (ESS) assessment in the region of Galicia (NW Spain) and the data resulting from this analysis, which in turn were used for the spatial planning of the regional green infrastructure. These data include the maps of the environmental variables used for the ES assessment, including the land use/cover data source used as proxy of ecosystems, as well as the maps of ESS provision potential resulting from the assessment and the maps obtained by the integration of groups of ESS and used for spatial planning of different zones of the green infrastructure. The methods used for ESS assessment and for spatial planning of the green infrastructure are described in “Green infrastructure spatial planning considering ecosystem services assessment and trade-off analysis. Application at landscape scale in Galicia region (NW Spain)”. In addition, the collection of R scripts that process the data of environmental variables and implement the methods for ES assessment are delivered. The maps are provided in geotif format so that they can be used in a Geographic Information System (GIS) for carrying out spatial analysis and integrating them with other spatial data or variables. In this way, these data can be combined with other spatial data, e.g. on urban planning, ecologic variables, future land use, etc., for decision-making on spatial planning or land management. The scripts can be used to replicate the ESS assessment methods or as a basis for developing new or improved methods.