13  Guides

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13.1 Data collection issues

Recognising potential data collection issues at the experimental design stage can be helpful in ensuring that there are sufficient and accurate data at the analysis stage. Some situations that can arise are:

  • missing data,
  • change in instrument to measure response,
  • loss of data integrity (i.e. issues related to record, storage and access of data), and/or
  • non-compliance of experimental protocol.

In planning an experiment, it’s not prudent that the most statistical optimal design is produced. It is better to produce a design that technician can follow without great difficulty than produce a complex design that’s likely to result in a mistake in carrying it out.

13.2 Ethics

While the aim of experiments may be to advance our knowledge or understanding, this doesn’t mean all experiments should be conducted. The Belmont report by the National Commission for the Proptection of Human Subjects of Biomedical and Behavioral Research (1978) outlines the ethical principles and guidelines for the protection of human subjects of research. A similar guideline would be available for animal subjects.

Besides the protection of the subjects, the experiment should not go ahead if there is little possibility of extracting meaningful analysis. These include cases where the sample size is small so no conclusive evidence can be drawn from the experiment, or where the collected data does not answer the aim of the experiment.

13.3 Protocol

Some domains require experimental protocol to be pre-registered.

Some specific reporting protocols for certain fields, e.g. 

  • Consolidated Standards of Reporting Trials (CONSORT) for reporting randomised trial
  • Good Clinical Practice (GCP) is an international ethical and scientific quality standard for the design, conduct, performance, monitoring, auditing, recording, analyses and reporting of clinical trials.
  • The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement offers guidelines for reporting observational studies. It provides a checklist of key items to include in research publications to enhance the quality of reporting and reproducibility.
  • MIAME (Minimum Information About a Microarray Experiment) guidelines provide recommendations for reporting microarray experiments. They focus on the information needed to interpret and reproduce microarray data, ensuring that data quality is maintained.
  • MIATA Guidelines: The MIATA (Minimum Information About T Cell Assays) guidelines provide a framework for reporting T cell assays in immunology research. It aims to ensure that critical information about the experimental setup and results is documented for reproducibility and interpretation.
  • ARRIVE Guidelines: The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines are intended to improve the reporting of animal research studies. It emphasizes the complete and transparent reporting of essential experimental details to enhance reproducibility and reduce bias.
  • Minimum Information Standards (MIS): Minimum Information Standards are developed for specific types of experiments or data types. Examples include MIAPE (Mass Spectrometry-based Proteomics Experiments), MIAPAR (Minimum Information About a Plant Aerobiology Experiment), and MIAME (Minimum Information About a Microarray Experiment).
  • GSC Standards: The Genomic Standards Consortium (GSC) has developed various standards for data and metadata reporting in genomics research. Examples include MIGS/MIMS/MIMARKS for microbial genomes and MIxS for environmental genomic studies.
  • NIST Guidelines: The National Institute of Standards and Technology (NIST) has developed various guidelines for data quality, security, and interoperability, particularly in fields like cybersecurity and IT.
  • Earth Science Data Standards: In Earth science and environmental research, organizations like NASA and NOAA have established data standards and protocols for data collection, sharing, and archiving.