In 2023, there were around 14,500 7-Eleven outlets in Thailand, indicating an increase from the previous year. 7-Eleven has the highest number of convenience stores in Thailand, operated by CP All Plc. The convenience store market in Thailand In contrast to the early 2000s, when hyper- and supermarkets were the leading channels to buy groceries and personal care products, the last ten years saw a sharp increase in convenience stores. In 2023, convenience stores had a higher market share of fast-moving consumer goods (FMCG) than supermarkets. The leading four brands in this segment are 7-Eleven, Lotus Go Fresh, and Mini Big C. The need for easy and quick solutions for today’s fast-paced consumer needs has generated innovations like fresh coffee stations or cooking stations inside the stores. Furthermore, there is the possibility to buy mobile phone SIM cards and data packages. Thailand’s convenience store market leader Opened its first store in 1988, 7-Eleven was the first convenience store chain in Thailand. It has since become the most popular convenience store chain in the country. Food and beverages still account for around 75 percent of total sales. Since the COVID-19 outbreak, the company shifted its focus to expanding online business using applications such as 24 Shopping and 7-Eleven TH. All in all, 7-Eleven has over 40 million registered users on different platforms.
In fiscal year 2023, more than 84 thousand Seven-Eleven stores were in operation worldwide, representing an eight-year-high. Seven-Eleven is a convenience store chain owned by Seven & i Holdings Co. The company operated a share of 7-Eleven stores directly through consolidated subsidiaries, whereas the majority was run by franchisees.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.
The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)
***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}
***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt
***Licenses or restrictions***
CC-BY
Overview: 423: Area between the average lowest and highest sea water level at low tide and high tide. Generallynon-vegetated expanses of mud, sand or rock lying between high and low water marks. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication. Completeness: The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product. Consistency: The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations. Positional accuracy: The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it. Temporal accuracy: The dataset contains predictions and uncertainty layers for each year between 2000 and 2019. Thematic accuracy: The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.
Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
License information was derived automatically
In fiscal year 2023, Seven-Eleven Japan operated more than 21.5 thousand stores within the domestic market, representing an increase from about 19.4 thousand stores in fiscal 2016. Seven-Eleven is a convenience store chain owned by Seven & I Holdings Co.
The 4 km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5 sea surface temperature (SST) dataset is a reanalysis of historical AVHRR data that have been improved using extensive calibration, validation and other information to yield a consistent research quality time series for global climate studies. This SST time series represents the longest continual global ocean physical measurement from space. Development of the Pathfinder dataset is sponsored by the NOAA National Oceanographic Data Center (NODC) in collaboration with the University of Miami Rosensteil School of Marine and Atmospheric Science (RSMAS) while distribution is a collaborative effort between the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the NODC. From a historical perspective, the Pathfinder program was originally initiated in the 1990s as a joint NOAA/NASA research activity for reprocessing of satellite based data sets including SST. The AVHRR is a space-borne scanning sensor on the National Oceanic and Atmospheric Administration (NOAA) family of Polar Orbiting Environmental Satellites (POES) having an operational legacy that traces back to the Television Infrared Observation Satellite-N (TIROS-N) launched in 1978. AVHRR instruments measure the radiance of the Earth in 5 (or 6) relatively wide spectral bands. The first two are centered around the red (0.6 micrometer) and near-infrared (0.9 micrometer) regions, the third one is located around 3.5 micrometer, and the last two sample the emitted thermal radiation, around 11 and 12 micrometers, respectively. The legacy 5 band instrument is known as AVHRR/2 while the more recent version, the AVHRR/3 (first carried on the NOAA-15 platform), acquires data in a 6th channel located at 1.6 micrometer. Typically the 11 and 12 micron channels are used to derive SST sometimes in combination with the 3.5 micron channel. For the Pathfinder SST algorithm only the 11 and 12 micron channels are used. The NOAA platforms are sun synchronous generally viewing the same earth location twice a day (latitude dependent) due to the relatively large AVHRR swath of approximately 2400 km. The highest ground resolution that can be obtained from the current AVHRR instruments is 1.1 km at nadir. This particular dataset is produced from Global Area Coverage (GAC) data that are derived from an on-board sample averaging of the full resolution global AVHRR data. Four out of every five samples along the scan line are used to compute on average value and the data from only every third scan line are processed, yielding an effective 4 km resolution at nadir. The collection of NOAA satellite platforms used in the AVHRR Pathfinder SST time series includes NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-17, and NOAA-18. These platforms contain afternoon orbits having a daytime ascending node of between 13:30 and 14:30 local time (at time of launch) with the exception of NOAA-17 that has a daytime descending node of approximately 10:00 local time. SST AVHRR Pathfinder includes separate daytime and nighttime daily, 5 day, 8 day, monthly and yearly datasets. This particular dataset represent daytime monthly averaged observations.
The Berkeley Multimodal Human Action Database (MHAD) contains 11 actions performed by 7 male and 5 female subjects in the range 23-30 years of age except for one elderly subject. All the subjects performed 5 repetitions of each action, yielding about 660 action sequences which correspond to about 82 minutes of total recording time. In addition, we have recorded a T-pose for each subject which can be used for the skeleton extraction; and the background data (with and without the chair used in some of the activities). Figure 1 shows the snapshots from all the actions taken by the front-facing camera and the corresponding point clouds extracted from the Kinect data. The specified set of actions comprises of the following: (1) actions with movement in both upper and lower extremities, e.g., jumping in place, jumping jacks, throwing, etc., (2) actions with high dynamics in upper extremities, e.g., waving hands, clapping hands, etc. and (3) actions with high dynamics in lower extremities, e.g., sit down, stand up. Prior to each recording, the subjects were given instructions on what action to perform; however no specific details were given on how the action should be executed (i.e., performance style or speed). The subjects have thus incorporated different styles in performing some of the actions (e.g., punching, throwing). Figure 2 shows a snapshot of the throwing action from the reference camera of each camera cluster and from the two Kinect cameras. The figure demonstrates the amount of information that can be obtained from multi-view and depth observations as compared to a single viewpoint.
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The Actions are: 1- Jumping in place 2- Jumping jacks 3- Bending 4- Punching 5- Waving(two hands) 6- Waving(one hand) 7- Clapping Hands 9- Throwing a ball 10- Sit Down 11- Stand Up 12- T-Pose
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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This dataset forms part of an ongoing single-site Randomized Clinical Trial (RCT) involving adult burn patients admitted to the Intensive Care Unit (ICU). The key details of the study include:
For more detailed information about the data, please refer to the associated article titled "Article not submitted"
Last Update: 23/10/2023 Author: Jose Gabriel Cordoba Silva
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
BackgroundBangladesh is one of the most densely populated countries in the world, with more than one-third of its people living in cities, and its air quality is among the worst in the world. The present study aimed to measure knowledge, attitudes and practice (KAP) towards air pollution and health effects among the general population living in the large cities in Bangladesh.MethodsA cross-sectional e-survey was conducted between May and July 2022 among eight divisions in Bangladesh. A convenience sampling technique was utilized to recruit a total of 1,603 participants (55.58% males; mean age: 23.84 ± 5.93 years). A semi-structured questionnaire including informed consent, socio-demographic information, as well as questions regarding knowledge (11-item), attitudes (7-item) and practice (11-item) towards air pollution, was used to conduct the survey. All analyses (descriptive statistics and regression analyses) were performed using STATA (Version 15.0) and SPSS (Version 26.0).ResultsThe mean scores of the knowledge, attitudes, and practice were 8.51 ± 2.01 (out of 11), 19.24 ± 1.56 (out of 21), and 12.65 ±5.93 (out of 22), respectively. The higher scores of knowledge, attitudes, and practice were significantly associated with several socio-demographic factors, including educational qualification, family type, residential division, cooking fuel type, etc.ConclusionsThe present study found a fair level of knowledge and attitudes towards air pollution; however, the level of practice is not particularly noteworthy. The finding suggests the need to create more awareness among the general population to increase healthy practice to reduce the health effects of air pollution.
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In 2023, there were around 14,500 7-Eleven outlets in Thailand, indicating an increase from the previous year. 7-Eleven has the highest number of convenience stores in Thailand, operated by CP All Plc. The convenience store market in Thailand In contrast to the early 2000s, when hyper- and supermarkets were the leading channels to buy groceries and personal care products, the last ten years saw a sharp increase in convenience stores. In 2023, convenience stores had a higher market share of fast-moving consumer goods (FMCG) than supermarkets. The leading four brands in this segment are 7-Eleven, Lotus Go Fresh, and Mini Big C. The need for easy and quick solutions for today’s fast-paced consumer needs has generated innovations like fresh coffee stations or cooking stations inside the stores. Furthermore, there is the possibility to buy mobile phone SIM cards and data packages. Thailand’s convenience store market leader Opened its first store in 1988, 7-Eleven was the first convenience store chain in Thailand. It has since become the most popular convenience store chain in the country. Food and beverages still account for around 75 percent of total sales. Since the COVID-19 outbreak, the company shifted its focus to expanding online business using applications such as 24 Shopping and 7-Eleven TH. All in all, 7-Eleven has over 40 million registered users on different platforms.