Is the process of working with data to find useful information, draw conclusions, and support decision-making. It helps businesses, researchers and other professionals to gain insights and make data-driven decisions.
To perform it, some steps need to be followed:
Identify the question or problem you want to answer or solve with data.
Collect the relevant data from various sources.
Analyse the data using appropriate tools and techniques, such as statistics, data mining, data visualization, etc.
Interpret the results of the analysis and communicate the findings and recommendations to the stakeholders or audience.
Data analyses is crucial to our field of work because it enables us to:
Evaluate toxicity and bioavailability of contaminants in soil samples, using various methods and techniques, such as chemical analysis, bioassays, biomarkers, and bioindicators.
Compare the results of different toxicity tests, using standardized protocols and guidelines, such as those developed by the US EPA, the OECD, and the EU.
Identify the most sensitive and relevant species, endpoints, and exposure scenarios for assessing the ecological risk of contaminants in soil.
Interpret the ecological significance and implications of the observed effects, using statistical tools and models, such as dose-response curves, effect thresholds, and extrapolation methods.
Communicate the findings and recommendations to the stakeholders and decision-makers, using clear and concise reports, graphs, and tables.
Designing and conducting more efficient and reliable toxicity tests, using appropriate experimental designs, quality control measures, and data validation procedures.
Developing and validating new or alternative toxicity tests, using novel or emerging approaches, such as molecular, cellular, or genetic methods.
Integrating and synthesizing data from multiple sources and levels of biological organization, using advanced tools and techniques, such as meta-analysis, weight-of-evidence, and systems biology.
Statistical tools
R is a powerful and versatile software for statistical analysis and data visualization. It can be used to:
Perform basic and advanced statistical tests, such as t-tests, ANOVA, regression, clustering, etc.
Create and manipulate data structures, such as vectors, matrices, data frames, lists, etc.
Plot and explore data using various types of graphs.
Write and execute custom functions and scripts to automate and extend the functionality of R.
Advantages and disadvantages
R is free and open source, which means anyone can download, use, modify, and share it without any cost or restriction.
R is cross-platform, which means it can run on different operating systems, such as Windows, Mac, Linux, etc.
R is flexible and expressive, which means it can handle complex and diverse data and problems and allow users to express their ideas and logic concisely.
R is interactive and dynamic; it can execute commands and display results in real time and allow users to modify and update their code and output easily.
R is community-driven and collaborative, having a large and active user base that contributes to its development and improvement, and provides support and guidance to other users through various channels.
Has a steep learning curve.
Has a high memory requirement, which means it can be slow and inefficient when dealing with large and complex data sets and may cause errors or crashes due to insufficient memory allocation.
