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Raw data is a relative term (see data), because even once raw data have been "cleaned" and processed by one team of researchers, another team may consider these processed data to be "raw data" for another stage of research. Raw data can be inputted to a computer program or used in manual procedures such as analyzing statistics from a survey.
Data are analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected.
Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
Wikipedia preprocessor (wikiprep.pl) is a Perl script that preprocesses raw XML dumps and builds link tables, category hierarchies, collects anchor text for each article etc. Wikipedia SQL dump parser is a .NET library to read MySQL dumps without the need to use MySQL database
Most raw formats implement lossless data compression to reduce the size of the files without affecting image quality. But some others use lossy data compression where quantization and filtering is performed on the image data. [38] [39] Sony's lossy 11+7 bit delta compression of raw data causes posterization under certain conditions. [41]
A data lake is a system or repository of data stored in its natural/raw format, [1] usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., [2] and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine ...
The acceptance of these transaction records into any new system could be very important for any verification of such imported data. [citation needed] In research, gaining access to source data may be cumbersome. Particularly where sensitive personal data is involved, security and redaction (obscuring information) may be an issue. [5]
The data within JHTDB can be accessed using "virtual sensors" with various access modes ranging from direct web-browser queries, access through Matlab, Python, Fortran and C programs executing on clients' platforms, to cut out services to download raw data. The data have been used in over 150 scientific publications.