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This dataset contains records of an experimental research project on energetic expenditure during walking and carrying burdens in a sample of humans, of both sexes, aged from 23 to 50 years old. The dataset also contains some anthropometrical and body composition data of the subjects. Metabolic rates were also recorded during the different tests, performed at the BioEnergy and Motion Lab (LabBioEM) of National Research Center on Human Evolution (CENIEH, Spain) in 2015. Main outcomes and findings of the experimental project were published in several works (Prado-Nóvoa et al., 2019; Vidal-Cordasco et al., 2017; Zorrilla-Revilla et al., 2017). The list of references and the description of recorded variables are included in the data and document files.
Data Types:
  • Tabular Data
  • Dataset
  • Document
There is a detailed Readme.pdf in the files for the informations about the dataset. The main purpose is providing a dataset for the vibration behavior of a robot manipulator system under the control input of model-associative vibration control (MAVC) prodecure. Velocity profile is shown as [∗,𝑡𝑐𝑜𝑛,𝑡𝑑𝑒𝑐,𝑡𝑚] in study. In the case studies for both simulations and experiments, the parameters are varied as follows; 𝑡𝑐𝑜𝑛 can be valued as 0, 𝑡1ℎ or 2𝑡1ℎ, 𝑡𝑑𝑒𝑐 can be valued as 𝑡1ℎ,2𝑡1ℎ,3𝑡1ℎ,4𝑡1ℎ or 5𝑡1ℎ and 𝑡𝑚 can be valued as 1 or 1.5 seconds for corresponded 90 or 135 angular displacements. Thus thirty different velocity profiles are produced with aim to performed on system. Cases are invastigated with and without performing the MAVC procedure. Than the robot manipulator is examined for both unloaded and loaded cases, therefore total one hundred twenty cases are occured. More details can be found in related study.
Data Types:
  • Image
  • Tabular Data
  • Dataset
  • Document
Butterfly larvae were sampled in field margins at maize anthesis. Larvae were sampled at peak maize anthesis, in maize field margins in Lleida in 2013; two separate anthesis periods were contemplated according to planting date of the maize crops. The first maize crop planted around 15-March to 15-April flowers in July (anthesis I), the second maize crop planted around 15-May to 15-June following harvest of winter cereal, flowers in August (anthesis II). Ten and twelve sites were sampled at each anthesis period, selecting 100 m of two different field margins. All potential larval host plants of butterflies were sampled. The preferred sampling methodology was visual inspection, but in some cases beating sampling was applied. Plant dimensions, sampling method and sampling time were recorded. All Lepidoptera larvae were collected, reared at the laboratory to adult, and identified to species if possible following literature. The excel files show the mean number of butterfly and moth larvae recorded at each sampling site (per 100 m margin). Other information given includes the total plant area sampled and the time spent searching the larval host plants per site.
Data Types:
  • Tabular Data
  • Dataset
The data supporting all analyses presented in the results of each experiment for manuscript "Differences in time-based task characteristics help to explain the age-prospective memory paradox" (Accepted for publication in Cognition on 31st March, 2020) are provided here as supplementary online material. For each experiment, we provide each participant’s demographic data; proportion correct score for event, time-of-day, and time-interval cued tasks for each setting (i.e., Virtual Week and MEMO); and contextual data (MEMO). The contextual data for Experiment 1 includes reported location and activity; for Experiment 2 and 3: location, activity, and retrospective memory test (recognition of quiz type). Note: odd quiz numbers (e.g., “quiz 1.1”; day 1 quiz 1] are always time-of-day quizzes; and even quiz numbers (e.g., “quiz 1.2”; day 1 quiz 2) are always time-interval quizzes.
Data Types:
  • Software/Code
  • Dataset
Primary and raw data from the manuscript "CACNA1S haploinsufficiency confers resistance to New World arenavirus infection"
Data Types:
  • Other
  • Tabular Data
  • Dataset
  • Document
The Kälin and Kochenov Quality of Nationality Index (QNI) ranks the objective quality of nationalities worldwide. It explores three internal factors (economic strength, human development, and peace and stability) and four external factors (diversity and weight of travel freedom and diversity and weight of settlement freedom) which are used to measure the value of virtually all nationalities worldwide. Peace and stability counts for 10% of aggregate value, all other six factors count for 15% each. The QNI has been created by Dr. Christian H. Kälin, Chairman of Henley & Partners, and Prof. Dimitry Kochenov, Professor of European Constitutional Law and Citizenship at the University of Groningen. This dataset is the basis of the Kälin and Kochenov Quality of Nationality Index, edited by Dimitry Kochenov and Justin Lindeboom (Hart Publishing 2019). Measurement and sources: 1) Economic Strength of the country conferring the nationality is measured by GDP, excluding NRR, with power purchasing parity (PPP). GDP with PPP and NRR have been collected from the World Bank. All figures are normalized to a 0-15% scale. 2) Peace and Stability of the country conferring the nationality is measured by reference to the Global Peace Index. All figures are normalized to a 0-10% scale. 3) Human Development of the country conferring the nationality is measured by reference to the UN Human Development Index. All figures are normalized to a 0-15% scale. 4) Diversity of Settlement Freedom refers to the number of foreign countries in which a nationality's holders can freely settle (including the right to work there) without having to obtain a visa or with visa-on-arrival. All figures are normalized to a 0-15% scale. Data is gathered through extensive research with the assistance of regional experts. 5) Weight of Settlement Freedom measures the qualitative value of the foreign countries in which a nationality's holder is allowed to settle freely. Each settlement destination is valued by reference to its Economic Strength and Human Development. The aggregate value of all settlement destinations determines a nationality's weight of settlement freedom. All figures are normalized to a 0-15% scale. 6) Diversity of Travel Freedom measures the number of destinations to which a nationality's holder can travel to visa-free or with visa-on-arrival. All figures are normalized to a 0-15% scale. This data is provided by the International Air Transport Association (IATA). 7) Weight of Travel Freedom measures the qualitative value of visa-free and visa-on-arrival travel destinations, and also relies on data provided by IATA. Each travel destination is valued by reference to its Economic strength and Human Development. The aggregate value of all travel destinations determines a nationality's weight of travel freedom. All figures are normalized to a 0-15% scale. This dataset contains metadata collected for the purpose of the QNI from 2011 to 2018, as well as the resulting rankings.
Data Types:
  • Tabular Data
  • Dataset
Interaction area data in MOOCs
Data Types:
  • Tabular Data
  • Dataset
Identifying Factors Affecting E-customer Loyalty in Gamified Trusted Store Platforms: A Case Study Analysis in Iran
Data Types:
  • Software/Code
  • Dataset
  • Text
  • File Set
Non-marginal (average) AWARE CFs and WSI CFs: We provide a shapefile, CSV file and KML file of the average AWARE characterization factors (CFs) based on the marginal AWARE CFs from Boulay et al. (2018). We also provide it together with average WSI factors from Pfister and Bayer (2014), since based on the UNEP SETAC recommendation, AWARE should be used together with an alternative scarcity method to test sensitivities (Jolliet et al. 2018). The XLS version of the average AWARE CFs is available from the original publication: Pfister S, Scherer L, Buxmann K (2020) Water scarcity footprint of hydropower based on a seasonal approach - Global assessment with sensitivities of model assumptions tested on specific cases. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2020.138188 DATA structure: The CSV files lists CFs for each month (01 to 12) and each methods: AWARE_01 stands for original marginal AWARE CFs of January, AWARE_a_01 represents the newly calculated average AWARE CFs for January, WSI_01 are the marginal WSI CFs for January and WSI_AVG_01 the average WSI CFs for January. The CSV file can be linked to WaterGAP watersheds based on the "BAS34S_ID" . The WaterGAP shapefile is e.g. available at http://www.wulca-waterlca.org/aware.html. The Shapefile and KML file follows the same order but are already linked to the watershed shapefile.
Data Types:
  • Geospatial Data
  • Tabular Data
  • Dataset
  • File Set
Daily Disposable Contact Lens Study SPSS Data Set
Data Types:
  • Software/Code
  • Dataset
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