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This is a case study of the packaging in the Educational Toy Industry with Eye tracking. There are two data sources: the first [observations.csv] is the data obtained in Eye tracking with Gazepoint Analysis and consists of 350 records and 16 columns (variables): Media ID: 0 for EDUCA o 1 for DISET; Media Name: EDUCA o DISET; Media Duration (sec - U = UserControlled): duration of the visualization; AOI ID: id of the area of interest; AOI Name: name of the area of interest AOI 0 : NUMBER OF QUESTIONS AND TOPICS, AOI 1: DRAW OF TOPICS (for EDUCA) / DESCRIPTIVE MESSAGE (for DISET),  AOI 2: BRAND,  AOI 3: RECOMMENDED AGE,  AOI 4: PRODUCT FAMILY,  AOI 5: PRODUCT NAME,  AOI 6: GAME PICTURE; AOI Start: Starting time of analysis of the area of interest (0 if it is a static image, but it can be different from 0 for a video where it is interested to monitor something that comes in the instant 10 sec., for example); AOI Duration (sec - U = UserControlled): Active duration of the area of interest (matches the total duration, but in a video we can monitor something just a few seconds); User ID, User Name, User Gender, User Age, Time to 1st View (sec): Time (in seconds) until you see each area of interest for the first time (If the value is -1 means that the area has not been visualized); Time Viewed (sec): Time (in seconds) that the area of interest has been visualized; Time Viewed (%): Time (in percentage) that the area of interest has been visualized (relative to the overall time dedicated); Fixations (#): Number of fixations or times that user user has looked at that area of interest, being able to look at several parts of it, counting each part as a fixation; Revisits (#): Number of revisits made to the area of interest, coming from another part of the image/video. The second data source [users.csv] refers to the users who participated in the study and consists of 25 records and 8 columns that show their social situation (sex, personal situation, number of children, age, etc.). These tables were cross-referenced in order to explain the data obtained by Eye Tracking, together with the characteristics of each individual. After eliminating the redundant, empty and repeated variables, and after assigning a suitable format to the remainder, a dataset of 13 columns and 350 rows was obtained [AOI_Statistics_for_each_user.csv].
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  • Tabular Data
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Data for kidney function and biochemical analysis in normal, cisplatin treated and HEA treated rats
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  • Tabular Data
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Data showed we obtained clinical isolates of Staphylococcus aureus including Methicillin-resistant S. aureus (MRSA) and Methicillin-sensitive S. aureus (MSSA) strains and evaluated the antibacterial efficacy of prodigiosin alone or in combination with different metal ions on clinical isolates of MRSA and MSSA strains. The results showed the metal ions Cu(II), Al(III), and Zn(II) can inhibit the cell growth of MRSA and MSSA at concentrations between 0.1 mM and 1.5 mM. and we found that also Zn(II) and Al(III) in combination with PG resulted in a significant synergistic antibacterial effect against MRSA and MSSA.
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Tha dataset contains ballistocardiography signals together with ECG. We studied changes in the signals related with breath holding. The BCG sensor dataset is obtained from twenty tested individuals. The schedule of measuring was in two types (V1 and V2) described in description. Every breath-holding was done for the time of the approximatelly 30 seconds. For measurement a force plate in the form of bed was used. The force plate had four tensometers embedded in its four legs. Each tensometer could measure the force in three orthogonal directions with a precision of up to 0.1 N. Therefore, 12 force signals were obtained. The ECG signal was measured simultaneously with the force measurement. All the signals were registered using a 24-bit AD converter with a sampling rate of 1 kHz. Measurement took place in laboratory at Faculty of Science, University of Hradec Králové, Czech republic.
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in-situ observation data at the Mt. Waliguan and Shangri-La
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Research data used in our article "Amino acid δ13C and δ15N patterns from sediment trap time series and deep-sea corals: implications for biogeochemical and ecological reconstructions in paleoarchives" Table 1. Bulk and amino acid δ13C records in deep-sea sediment traps (S2) and coral skeletons (A2 and A11) in Monterey Bay, California. Table 2. Bulk and amino acid δ15N records in deep-sea sediment traps (S2) and coral skeletons (A2 and A11) in Monterey Bay, California. Table EA1. δ13C and δ15N values (‰) of individual amino acids in in-house reference standard (homogenized cyanobacteria) analyzed along with field samples. Table EA3. Estimated vs. measured δ13C and δ15N values (‰) of export production in Monterey Bay, California.
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Data includes ESG scores and some financial indicators of companies from different countries and industries.
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This dataset comprises different databases related to the Twitter posts around coronavirus. The general dataset consists of 8,982,694 Twitter posts (tweets). All the data collected was searched using the keyword “Coronavirus”. The 8.98M were gathered from January 21 to February 12, 2020, i.e. 23 days.
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  • Tabular Data
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The data includes six updated chronological frames of sediment cores (ECMZ, MZ02, MD06-3040, EC2005, MD06-3042, and MZ01) using Clam (version 2.2) program via R software, heavy minerals in core ECMZ, and Holocene variations in mass accumulation rate (MAR) of mud on the East China Sea shelf in the western Pacific.
Data Types:
  • Tabular Data
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From a sample of 391 Vietnamese respondents aged from 15 to 47 years, the present study found that geographical regions and behaviors in using social media have a positive impact on the risk perception of COVID-19 epidemic in Vietnam.
Data Types:
  • Tabular Data
  • Dataset
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