The Agricultural Research Service of the US Department of Agriculture (USDA) in collaboration with other government agencies has a program to track changes in the sodium content of commercially processed and restaurant foods. This monitoring program includes these activities: Tracking sodium levels of ~125 popular foods, called "Sentinel Foods," by periodically sampling them at stores and restaurants around the country, followed by laboratory analyses. Tracking levels of "related" nutrients that could change when manufacturers reformulate their foods to reduce sodium; these related nutrients are potassium, total and saturated fat, total dietary fiber, and total sugar. Sharing the results of these monitoring activities to the public periodically in the Sodium Monitoring Dataset and USDA National Nutrient Database for Standard Reference and once every two years in the Food and Nutrient Database for Dietary Studies. The Sodium Monitoring Dataset is downloadable in Excel spreadsheet format. Resources in this dataset:Resource Title: Data Dictionary. File Name: SodiumMonitoringDataset_datadictionary.csvResource Description: Defines variables, descriptions, data types, character length, etc. for each of the spreadsheets in this Excel data file: Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority-2 Foods - Monitoring.Resource Title: Sodium Monitoring Dataset (MS Excel download). File Name: SodiumMonitoringDatasetUpdatedJuly2616.xlsxResource Description: Microsoft Excel : Sentinel Foods - Baseline; Priority-2 Foods - Baseline; Sentinel Foods - Monitoring; Priority Foods - Monitoring.

TimeSpec4LULC is archived in 30 different ZIP files owning the name of the 29 LULC classes (one class is divided into two files since it is too large). Within each ZIP file, there exists a set of seven CSV files, each one corresponding to one of the seven spectral bands. The naming of each file follows this structure: IdOfTheClass_NameOfTheClass_ModisBand.csv For example, for band 1 of the Barren Lands class, the filename is: 01_BarrenLands_MCD09A1b01.csv Inside each CSV file, rows represent the collected pixels for that class. The first 11 columns contain the following metadata: - “IdOfTheClass”: Id of the class. - “NameOfTheClass”: Name of the class. - “IdOfTheLevel0”: Id of the FAO-L0 (i.e., countries). - “IdOfTheLevel1”: Id of the FAO-L1 (i.e., departments, states, or provinces depending on the country). - “IdOfThePixel”: Id of the pixel. - “PurityOfThePixel”: Spatial and inter-annual consensus for this class across multiple land-cover products, i.e., Purity of the pixel. - “DataAvailability”: percentage of non-missing data per band throughout the time series. - “Index_GHM”: average of Global Human Modification index (gHM). - “Lat”: Latitude of the pixel center. - “Lon”: Longitude of the pixel center. - “.geo”: (Longitude, Latitude) of the pixel center with more precision. And, the last 223 columns contain the 223 monthly observations of the time series for one spectral band from 2002-07 to 2021-01. Along with the dataset, an Excel file named 'Countries_Departments_FAO-GAUL' containing the FAO-L0 and the FAO-L1 Ids and names (following the FAO-GAUL standards) is provided.

SOURCES

It has 4 central portion, which shows Bangla text. One contains Only Bangla text(12179-unsuspicious, 7822-suspicious). Another one contains Bangla and English mixed(12725-unsuspicious, 7219-suspicious). Another one contains politically suspicious content(167-unsuspicious, 132-suspicious). Lastly, another contains the @name mentioned comment(6145-suspicious, 53855-unsuspicious). Finally, a CSV file contains all categorical Bangla Data. It contains 1,00,100+ data.

COLLECTION METHODOLOGY

Suspicious tweets- https://www.kaggle.com/datasets/syedabbasraza/suspicious-tweets Suspicious Tweets - https://www.kaggle.com/datasets/munkialbright/suspicious-tweets Suspicious Communication on Social Platforms - https://www.kaggle.com/datasets/syedabbasraza/suspicious-communication-on-social-platforms

Others are collected manually from Facebook comments. After collecting the Bangla comments, check dataset comment was understandable or not. Then step by step, each Excel file is converted into a datagram. then change the column name to the desired one('Detect' and 'Bangla Text'). I also drop some columns if needed. The files are saved in an Excel file because the CSV file can not contain Bangla text appropriately.

The 5 XLSX file are "suspicious_content(bangla)", "suspicious_content(bangla + english)", "suspicious_content(political)", "suspicious_content(including mention)" and "suspicious_content(all)". All the Excel files have only two columns, 'Detect' and 'Bangla Text'.

You will be able to see the dataset creation process in this link: https://www.kaggle.com/code/meherunnesashraboni/suspicious

suspicious

Bangla_Text

Detection

unsuspicious