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GIS DATASETS: Research_area.zip: the vector map data of the study area; LC08_L1TP_122034_20180417_20180501_01_T1_B3.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B4.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B5.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B8.TIF:the original image data of four bands; Program DATASETS: py.zip:U-net network file(U-Net.py), evaluation procedure(Analysis.py), cutting program(cut_png.py)
Data Types:
  • Image
  • Dataset
  • Text
  • File Set
This data is survival information of normal and rib fractured calves delivered from Holstein dams in Japan. The data includes ID of calves, farmers, symptoms of calves, breeds, birth date, treated date, traded date, died date, survival days, status, and group.
Data Types:
  • Tabular Data
  • Dataset
Overview: This study uses a set of criteria to examine cold air outbreaks (CAOs) across the globe from 1979 – 2018 and to determine how CAOs have changed over the last 40 years. We found CAOs occur most frequently in the Northern Hemisphere, with as many as 8 CAO days per year in North America and Eurasia. CAOs were found to have decreased in size, intensity, frequency, and duration across much of the globe, with the largest decreases in Alaska, Canada, and the North Atlantic, while an increase in CAOs was observed in Eastern Europe, Central Eurasia, and the Southern Ocean. Early and late winter CAOs have also become much less frequent in most regions. Data Used: Two-meter temperature (T2m) data was acquired from the NCEP/NCAR (NNR) climate reanalysis dataset (National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) and the recently released ERA5 reanalysis data set from the European Center for Medium-Range Weather Forecasts (ECMWF). ERA5 T2m was acquired at a 1 degree spatial resolution on an hourly timescale and converted to daily mean T2m while NNR daily mean T2m was acquired at a T62 gaussian grid (192 longitude and 94 latitude) spatial resolution from 1979 - 2018. CAO Methods: Three criteria for a CAO were designed to capture the most extreme CAOs while being flexible enough to capture the entire evolution of the event. 1.) Magnitude: The magnitude criterion requires the daily mean temperature to be at or below the 2.5th percentile threshold of deseasonalized 2-meter temperature (T2m). The daily mean T2m must also be at or below 20 degrees Celsius with a departure from the climatological mean of at least -2 degrees Celsius. 2.) Spatial Extent: The daily spatial extent, which is a summation of all contiguous grid points that meet the magnitude criteria, must be at least 1,000,000 km2. 3.) Duration: The duration criterion requires the magnitude criterion for the entire CAO be met for at least five consecutive days and begins on the first day in which the spatial extent criterion is met and ends on the last day the spatial extent criterion is met. How to use and interpret data: There are 3 files: 1.) and excel file of all CAOs for both the NNR and ERA5 (separate tabs). Because the ERA5 data is the primary data set used in this study it has two additional columns of data, one for the region of the CAO and one for the hemisphere of the CAO. 2.) A .mat file (MATLAB) of all the ERA5 CAO data. The column headers are as follows: [1. daily data for each CAO event, 2. onset date, 3. duration, 4. Mean z-score 5. mean z-score per gridpoint, 6. total duration per gridpoint 7. daily z-score per gridpoint 8. temperature anomaly each day, 9. Region 10. hemisphere] 3.) A similar .mat file, but for the NNR CAOs. Differences: columns 4 and 5 and 11 in the NNR file are not in the ERA5 file (shift headers). These were used in calculations but omitted from ERA5 file for size restraints.
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
  • File Set
In 2018, ambient air pollution caused 4.2 million deaths worldwide. This work comparatively evaluated the concentrations of 16 atmospheric pollutants / gaseous contaminants from 5 Brazilian urban centers (São Paulo, Salvador, Rio de Janeiro, Belo Horizonte e Londrina), with intense vehicular flow and different characteristics, such as location type (coastal or non-coastal), demographic density and weather conditions, use of passive sampling. Six simultaneous passive sampling campaigns were performed using the AnalyseAr kit for consecutive periods of 7 and 14 days.
Data Types:
  • Dataset
  • Document
The procreative statistical framework of musical note structures produces a crucial role in multimedia music classification and reconstruction strategies. Another most significant thing for harmonious music composition is the rhythmic structures that provide musical performance in a harmonic form. This paper has illustrated computational music theory and allied factors to regulate what human beings can acquire, remember, and reconstruct music for sustaining intangible cultural heritage. The music strings or symbols are also imperative factors that assist the musicians as performance guidelines. To afford a syntactic outline of musical note arrangements, a stochastic model along with probabilistic context-free music grammar has been illustrated in this paper. The state transition analysis has also been incorporated in terms of transition table and diagram to demonstrate which state can move to the other one within a finite automaton depending on the behaviors of the current state and associated transition rule. Petri net has been used for modeling and simulating the projected complex music composition framework to analyze system performances. The Petri net simulation-based reachability and system efficiency have been evaluated for analyzing the effectiveness of the proposed event-driven architecture. For incorporating real data into the projected framework, the music composition and reconstruction tool has also been demonstrated. The system performance evaluation metric has shown that around 92% efficiency level has been achieved by analyzing the projected music composition model.
Data Types:
  • Tabular Data
  • Dataset
  • Document
  • Audio
List of articles exported from Web of Science for bibliometric analysis
Data Types:
  • Tabular Data
  • Dataset
Supplementary Table 1. Demographic Characteristics of 1039 Study Participants Supplementary Table 2. Common Cognitive Biases Affecting Adherence
Data Types:
  • Dataset
  • Document
This data set describes the medicinal plant species used in the treatment of snakebites in Acholi, Teso and Karamoja sub regions of Uganda. It includes the how traditional medicinal practitioners (TMP) prepare and administer the medicinal plants, the parts used, type of snake envenomation treated and the frequency of citation. The data set also includes the biodata of the TMP.
Data Types:
  • Tabular Data
  • Dataset
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].
Data Types:
  • Tabular Data
  • Dataset
Data for kidney function and biochemical analysis in normal, cisplatin treated and HEA treated rats
Data Types:
  • Tabular Data
  • Dataset
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