【英語論文の書き方】第82回　「研究データと関連文書の管理（パート1）：研究内容を文書で厳密に記録することがなぜ大切なのか」について論文翻訳・英文校正　研究者専門の翻訳会社　ワールド翻訳サービス

【英語論文の書き方】第82回　「研究データと関連文書の管理（パート1）：研究内容を文書で厳密に記録することがなぜ大切なのか」について

2021年6月18日 17時11分

第81回では「データ以外のもの（パート2）」を取り上げました。

第82回（今回）のテーマは
「研究データと関連文書の管理（パート1）：研究内容を文書で厳密に記録することがなぜ大切なのか」についてです。

研究者は、データを長期的に残し、
未来の研究者がそのデータを利用できるように
保管することがますます求められています。

Kathy R. Berensonは、この課題について
完璧で素晴らしい概要を2018年に出版しています*が、
それは心理学分野の研究にフォーカスしたものとなっています。
（*Berenson, K.R. 2018, Managing Your Research Data and Documentation. American Psychological Association. 105 p. including index）

今回の4部作の記事では、彼女が書いた内容を踏まえて、
もっと実地調査や実験室での研究にフォーカスした概要をお伝えします。

どのような研究を行うにしても、入力データの品質、
その記録、分析の仕方を管理する方法を見つけることが大切です。

ぜひご参考になさってください。

Managing your study data and the supporting documentation. Part 1: Why it’s important to rigorously document your study and archive the data

By Geoffrey Hart

Increasingly, researchers are being asked to archive their data so that it remains available in the long term and can be used by future researchers. Kathy R. Berenson (2018) provides a thorough and excellent overview of this subject, but she focuses on psychology research. In this series of four articles, I’ll build upon what she’s written to provide a summary that focuses more on field and laboratory research.

Note: Berenson, K.R. 2018. Managing Your Research Data and Documentation. American Psychological Association. 105 p. including index (https://www.apa.org/pubs/books/4313048)

In part 1 of this series of four articles, I’ll discuss why it’s important to document your research and archive the data. In part 2, I’ll describe the project files you should create as part of your documentation. In part 3, I’ll discuss data validation and some important notes about any software you develop to support your analysis. In part 4, I’ll describe how to set up replication files—collections of data and methods that you can give to a colleague who wants to use your data or methods in their own research.

Whatever type of research you perform, it’s important to find ways to control the quality of your input data and standardize how you recorded and analyzed the data. This is particularly important for long-term research projects, in which you will need to apply a consistent, reliable, effective, and efficient method for many years so that the resulting multi-year dataset lets you combine data from multiple years to support deeper and more powerful analyses.

An important way to accomplish this goal is to go beyond archiving your data so that it will be available to future researchers: you should also document your data, methods, and analytical procedures, as this makes it easier for you to review what you’ve done and makes it easier for future researchers to understand what you’ve done so they can benefit from and build on your results. Replicability is an essential part of science, particularly in fields of research whose outcome will directly affect human lives. You’ve probably heard of the “replication crisis” (https://en.wikipedia.org/wiki/Replication_crisis), in which researchers try to replicate a study and obtain different or even contradictory results.

In studies of biological systems, including humans, so many factors vary between studies that the associated experimental conditions also change, and “identical” results are rarely possible. To mitigate this problem, it’s necessary to document your research sufficiently well that future researchers could repeat it for the same study system, under similar conditions, and obtain identical results. (This is essential in fields where your data will be useful to support meta-analyses of large datasets from multiple studies.) The better your documentation, the more likely it is that that replication will be successful because you have eliminated a major source of replication failure: the use of different or inconsistent methods. If future researchers fail to replicate your research, they can propose a plausible explanation that doesn’t relate to inconsistent methods.

Your documentation should be so complete and detailed that another researcher could control their experimental conditions well enough that if they repeated your study, they would be likely to obtain similar results. Alternatively, if you gave them your dataset, they should be able to perform the same calculations and statistical analyses that you performed and obtain the same results.

This careful approach to documenting your research also lets you compare your results more effectively with previously published results. For example, if you’ve studied a specific ecosystem and want to claim that another researcher’s results support your conclusions, you must find studies of an ecosystem that is sufficiently similar to your study system to allow a fair comparison. The greater the difference in species, climatic conditions, anthropogenic impacts, and other factors, the better the support. The more similar the methodology, the better the support. Any differences provide a plausible explanation for differences in the results. Alternatively, you must be able to recognize when an ecosystem is sufficiently different from your study system that the differences can explain any contrast between the results of the two studies. Such comparisons are easiest if the research has been documented sufficiently well that you truly understand what the other researchers did and how it differs from what you did. This also increases the likelihood that other researchers who perform meta-analyses can determine whether to include your results in their analysis.

These suggestions are reasonable, but the biggest problem you’ll encounter lies in how to manage your data and the documentation that explains it. (Most researchers receive no formal training in managing and documenting their data, and have to invent their own methods. Berenson’s 2018 book provides a detailed explanation. Here, I’m providing an overview based on her work.) Determining how to manage your data and documentation is difficult, in part because modern research generates large quantities of complex data that you must manage. Fortunately, there are logical ways to proceed that make this task easier and more effective. One of the key strategies is to develop objective, clear methods for organizing, processing, and analyzing your data—and to combine this with such a good explanation of what you’ve done that another researcher could exactly repeat what you did. I’ll discuss these issues in the remaining three parts of this series of articles.

One final thing to keep in mind before we begin exploring how to manage your data and methods: It’s increasingly rare for research to have no ethical considerations, particularly when the research results have implications for human health and safety or for the world that supports our lives. These ethical considerations include the need to manage your data in such a way that it can be shared. In particular, research that received government funding should be freely available to all the taxpayers who supported that research, including other researchers. This issue is so important that the American Psychological Association now asks its members to archive their data so that it remains available for a minimum of 5 years. A better target might be 10 years, particularly for important and innovative research or research that was difficult and expensive to perform. Other ethical considerations include how to safeguard the privacy of individuals (e.g., by anonymizing their data), how to protect rare or endangered species (e.g., by not providing detailed location instructions that would let unethical collectors steal those organisms), and how to avoid harm to future researchers (e.g., by explicitly documenting potentially dangerous procedures and how to avoid those problems).

In Part 2 of this article, I’ll discuss how to set up a structure that will help you manage your data and the associated documentation.

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【英語論文の書き方】第82回 「研究データと関連文書の管理（パート1）：研究内容を文書で厳密に記録することがなぜ大切なのか」について 論文翻訳・英文校正 研究者専門の翻訳会社 ワールド翻訳サービス