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By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]
Importantly, data harvested into RN tools can be repurposed, especially if available as Linked Open Data (RDF triples). These RN tools enhance research support activities by providing data for customized, web pages, CV/biosketch generation and data tables for grant proposals.
An alternative data set can be compiled from various sources such as financial transactions, sensors, mobile devices, satellites, public records, and the internet. [3] [8] [9] [10] Alternative data can be compared with data that is traditionally used by investment companies such as investor presentations, SEC filings, and press releases. [11]
Computer-assisted (or aided) qualitative data analysis software (CAQDAS) offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, [1] grounded theory methodology, etc.
Research suggests that the inclusion of alternative data in credit files could bring many of these individuals into the credit fold. [4] That is, non-financial positive payment information, such as rents or utility payments, may give credit agencies enough information to rate previously unscorable individuals known as the unbanked.
The specific use cases and characteristics is an active research field in bibliometrics, providing much needed data to measure the impact of altmetrics itself. Public Library of Science has an Altmetrics Collection [ 65 ] and both the Information Standards Quarterly and the Aslib Journal of Information Management recently published special ...
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [ 4 ] Data science is "a concept to unify statistics , data analysis , informatics , and their related methods " to "understand and analyze actual phenomena " with data . [ 5 ]
Sensitivity analysis is essentially the exploration of the multidimensional input space, which grows exponentially in size with the number of inputs. Therefore, screening methods can be useful for dimension reduction. Another way to tackle the curse of dimensionality is to use sampling based on low discrepancy sequences. [9]
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