校閲者・コーディネーターの月替わりのコラム(2024)

Coexistence of Artificial intelligence (AI) with academia: a perspective（2024.01）

2024.02.02 Editor W. M.

As well as reaching into the future, AI is helping nations rediscover their past. From the cursive kuzushiji texts of Japan to the artistic Fraktur script of Germany, there is a wealth of information and knowledge that to date has been accessible to only a few scholars. AI is now increasing accessibility by recognizing language characters in millions of documents and transcribing it into modern language that can reach a much broader audience. A specific case is kuzushiji, a cursive form of Japanese that is understood by less than 1 in 10,000 of the native Japanese population. A combination of machine and deep learning has empowered computers to first recognize the ancient kuzushiji characters and then convert them into contemporary Japanese. Such work would take hundreds of years for human scholars to complete. The utility of AI in this and similar cases is clear, and few would argue that AI was harmful in this context. However, the automated transcription of texts can pose challenges in the academic sphere. For example, what happens if the majority of original texts portray only one perspective of the society in which they were originally written? Would this lead to a biased perception of that society? Also, who retains ownership of the transcribed data, and how will the transcribed data be used? Academics have a responsibility to ensure that community leaders are fully aware of the scope and aims of their studies, as well as the final destination of the processed data.

Natural sciences (Biology)（2024.03）

2024.04.19 Editor W. M.

The main goal of research in academic Computer Science is to design and optimize computers and the programs that they run. Studies in this field have real-world benefits including improved cyber security, better healthcare diagnostics and management, and more accurate predictive tools for forecasting financial markets and even the weather! What is the impact of AI in Computer Science? Before addressing this point, we need to take a step back and realize that AI itself is actually a subdiscipline of Computer Science. It can be thought of as one of the tools that Computer Science researchers use to improve the scope, power and efficiency of computing.

So how can AI be used to actually improve research in the field from which it was born? Although this might sound like a paradox, there are some clear examples of where this has been successful. For example, AI can help to improve software code by making it more efficient, or by detecting and fixing bugs. It can also strengthen cybersecurity by looking for vulnerabilities in software that may be susceptible to cyberattacks, and providing protection when an attack is launched. In the examples above, AI is trained on large datasets to recognize issues with pre-existing code. Increasingly, however, students and professionals are turning to AI to actually write code for them from scratch! Here, AI can be given human instructions written or spoken in regular language and convert them into code. Currently, this still requires a degree of human intervention to check the code is actually doing what the user intended. However, improvements in this use of AI could eventually make basic programming accessible to non-experts, and can free up the time of more experienced computer scientists so that they can perform more creative and strategic roles.

2024.05.24 Editor W. M.

Artificial intelligence (AI) is rapidly transforming the world of medicine, holding immense potential to revolutionize everything from disease diagnosis and treatment planning to drug discovery and scientific breakthroughs. By analyzing vast amounts of medical data and identifying complex patterns, AI is becoming a powerful tool for healthcare professionals, offering them crucial insights and aiding them in providing better patient outcomes.

AI holds great promise for addressing some key healthcare disparities. For example, many developing countries cannot afford state of the art DNA or RNA sequencing platforms that are required to match cancer patients to optimal treatments. However, images of cancer biopsies can be obtained by staining samples with low-cost reagents. Using many thousands of these images, the hope is that AI algorithms can be trained to identify the features of samples that are associated with specific DNA mutations. Clinicians in developing countries can then match these DNA mutations to specific cancer drugs to customize therapy for individual patients. At the recent American Association for Cancer Research annual meeting in San Diego, AI was prominently featured in many sessions. Translational researchers told us how they are using AI to integrate multiple types of ‘omic’ and clinical data to identify biomarkers that can predict which patients will respond to different drugs. Clinician-scientists explained that AI algorithms can be trained to make diagnoses, or predict treatment outcomes, from ‘looking’ at MRI scans. Companies showcased their generative AI platforms that are capable of generating novel compounds that may one day be advanced into clinical trials. Unlike the temporary darkness during the solar eclipse at the conference, the future looks very bright for AI applications in medicine!

2024.06.19 Editor W. M.

In the previous articles, we have taken a look at the role of AI in specific academic disciplines. This final article provides a ‘helicopter view’ of how AI can be used to support processes that are common to research institutes across all disciplines.

Streamlined Recruitment: AI can automate tasks like screening resumes and scheduling interviews for research positions. It can also analyze researcher performance data and publications to identify potential candidates for new projects. Conversely, researchers who are on the job market can make use of some relatively simple AI tools to find prospective employers that match their career goals and personal skillsets.

Grant Writing Assistance and Progress Tracking: AI can analyze successful grant proposals to identify key elements and writing styles. Researchers can leverage this to generate outlines, suggest relevant literature, and even draft initial sections. Additionally, AI-powered dashboards can track progress on active grants, highlighting milestones and upcoming deadlines, ensuring timely completion and compliance.

Demand Forecasting and Inventory Management: AI can also help the purchasing staff at academic institutes in their analysis of past ordering patterns and research project timelines to predict future material needs. This allows for proactive ordering, reducing delays and preventing stockouts. AI can also optimize inventory levels, minimizing storage costs and ensuring researchers have the materials they need when they need them. Additionally, AI can identify frequently reordered items and suggest bulk purchases for cost savings.

Despite these clear benefits, it is important to acknowledge the limitations of AI prior to deploying it in key support areas.

Creativity and Originality: AI struggles with tasks requiring genuine creativity and out-of-the-box thinking. Grant proposals often hinge on novel research ideas and innovative methodologies, which AI can't replace. Human expertise remains crucial for crafting compelling narratives and justifying the significance of the proposed research.

Ethical Considerations: Bias in AI algorithms can lead to unfair hiring practices or skewed grant recommendations. Researchers need to be aware of these limitations and carefully vet AI tools to ensure they are unbiased and ethically sound. Also, when used to assist grant writing, the grant applicant(s) must ensure that they take steps to check and modify AI-generated text and tailor it to their own research prior to submission. Otherwise they run the risk of being labelled plagiarists. Ultimately, AI should be seen as a valuable tool to empower human researchers, not replace them. By leveraging the strengths of AI for administrative tasks and initial guidance, researchers can free up time and mental space to focus on the core aspects of research: conceptualizing groundbreaking ideas and conducting rigorous investigations.

2024.07.11 コーディネーター

今年の1月から半年間、続きました校閲者W.Mの連載コラム、実は途中から日本語訳をAIが担当しておりました。

皆様は気付きましたか？

W.Mもコラム内で申しております通り、AIだけの力では未だ及ばぬところも多々あります。AIの翻訳も素晴らしいものでしたが、では、人間の翻訳はいかがなものか？ぜひ、弊社の翻訳者A.Nによる最終章の翻訳をご覧ください。

今後もご依頼時にAIx人力翻訳とするか、人力のみの翻訳とするか、ご検討時のサンプルとしていただけますと幸いでございます。

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学術プロセスの合理化におけるAIの活用

以前の記事では、特定の学術分野においてAIが果たす役割についてみてきました。最終回となる今回は、あらゆる分野の研究機関に共通する学術プロセスのサポートに対してAIがどのように活用できるか、その可能性について概説したいと思います。

採用活動の合理化：AIの活用により、研究職ポジションの人材を募集する際の履歴書の選別や面接の日程調整といった作業を自動化することができますし、また研究者の業績データや論文を分析することで、新規プロジェクトの候補者選定も可能です。立場を変えると、就職活動中の研究者は、比較的簡便なAIツールを活用することで、自身のキャリア目標や研究者自身のスキルセット（スキル、能力、経験）に合致する雇用候補を見つけることができます。

研究費申請書の作成支援と進捗管理： AIは過去に研究費獲得に成功した申請書を分析し、重要なポイントや書き方を抽出することができます。研究者はこれらを活用して、アウトラインの作成、関連した文献の提案、第一段階の草案作成まで行うことができます。さらに、AIを搭載したデジタルダッシュボードは、進行中の研究課題の進捗状況を追跡し、マイルストーンや以降の期限を強調して表示することで、適時遂行と期限遵守を確実にします。

需要予測と在庫管理：過去の発注パターンや研究プロジェクトのスケジュールをAIで分析することにより、研究機関の購入担当者が将来の物品需要を予測する際に役立ちます。これにより先手を打った発注が可能になり、発注の遅れを減らし、在庫切れを防ぐことができます。また、AIは在庫量を最適化し、保管費用を最小限に抑え、研究者が必要な時に必要な材料を確保することができます。さらに、AIは頻繁に再注文される品目を特定して大量購入を提案することで、コスト削減も可能になります。

このようにAIには明確な利点がありますが、重要なサポート分野に対してはAIを導入する前にその限界を認識しなければなりません。

創造性と独創性： AIにとって、真の創造性や既成概念にとらわれない思考を必要とする仕事は苦手と言えます。多くの場合、研究費申請では斬新な研究アイデアや革新的な方法論が鍵となりますが、この点はAIでは代替できないものでしょう。説得力のあるストーリーを作り上げ、申請研究の意義を納得させるためには、人間の専門知識が不可欠であることに変わりはありません。

倫理的配慮： 万が一、AIのアルゴリズムに偏りがあると、不公平な雇用慣行や偏った研究費審査につながる可能性があります。こういったAIがもつ限界を研究者は認識した上で、AIツールが公正中立、かつ倫理にかなっていることを慎重に吟味する必要があります。また、研究費申請者が申請の補助に使用する場合は、提出前にAIが生成した文章を確認、修正し、自身の研究に合うように調整する手順を確実に踏まなければなりません。そうでなければ、盗作者というレッテルを貼られる危険性があります。

結局のところ、AIは人間の研究者に取って代わるものではなく、あくまでも研究者に役立つ貴重なツールと見なされるべきです。管理業務や初期指導にAIの長所を活用することで、研究者は時間と精神的余裕を確保し、研究の核心部分である画期的なアイデアの着想や緻密な調査に集中して取り組むことができるのです。

Predatory Journals: An Escalating Threat to Academic Credibility（2024.08）

2024.09.05 Editor T. J.

1. Verify the Journal's Evaluation and Credibility

• DOAJ (Directory of Open Access Journals): Lists reliable open access journals.
• JCR (Journal Citation Reports): Provides impact factors and other metrics to evaluate the quality of journals.

2. Thoroughly Examine the Journal's Website

• Contact Information: Check if there is a clear address and contact information. Be cautious if there are vague contact details or editorial staff use free or personal email domains.
• Editorial Board: Check the names and affiliations of the editorial board members and reviewers. If in doubt, contact these scholars directly to verify their involvement.
• Publication Time and Acceptance Rate: Be wary of journals that promise unusually short publication times or high acceptance rates.

3. Investigate the Journal's Reputation

• Beall’s List: Provides a list of predatory journals. Although not comprehensive, it is a useful reference.
• Researcher Networks and Forums: Verify the journal’s reputation with colleagues and within your field’s community. Experiences and opinions from other researchers can be valuable.

4. Check the Publishing Costs and Fees

• Upfront Fees: Be cautious of journals that demand high upfront fees or have unclear pricing structures. Reputable journals typically provide clear information about costs.

5. Assess the Quality of the Papers

• Published Papers: Read a few papers published in the journal to evaluate the quality of content and rigor of reviews. A high presence of low-quality papers is a red flag.

6. Verify Indexed Databases

• Databases like Scopus and PubMed: Check if the journal is indexed in major academic databases. Journals listed in these databases are generally more reliable.

7. Consult with Professional Institutions if in Doubt

• University or Research Institution Libraries: Seek advice on the journal’s credibility.
• Committee on Publication Ethics (COPE): Provides guidelines on publication ethics and can offer advice.

2024.10.03 Editor T. J.

Mastering Manuscripts: Essential Tips for Writing Your First Scientific Paper

Writing your first scientific manuscript can be a daunting task, but understanding the essential do’s and don’ts can significantly improve the quality of your paper and increase its chances of being accepted for publication. This brief guide offers practical advice on what you should and shouldn’t do in each main section of your manuscript.

(1) Hook your readers with a captivating introduction

Do: Write a clear and concise introduction that provides necessary background, explains the rationale for your study, and states your research question or hypothesis. Aim for three to four short paragraphs that engage the reader and set the stage for your research.

Don’t: Overwhelm the reader with excessive background information. Keep your introduction focused and directly relevant to your research question. Avoid restating study findings in the final paragraph; this is repetitive and should be reserved for the abstract.

(2) Fill your methods with plenty of detail

Do: Provide a thorough description of your methods so that other researchers can replicate your study. Include details on materials, procedures, data collection, and analysis. Referring to your lab notebook can be a useful starting point.

Don’t: Omit crucial methodological details: Incomplete or vague descriptions can undermine the reliability and reproducibility of your results. Uniquely, you should not use a personal voice in this section – instead, use a passive, impersonal voice to maintain objectivity.

Example:

• “Proteins were analyzed by Western blotting” – Yes!
• “We analyzed protein expression by Western blotting” – No!

(3) Showcase your results with clarity and precision

Do: Organize your results logically, using tables, figures, and graphs to summarize key data. Use an active voice to engage readers with your discoveries.

Don’t: Include raw data or superfluous details. Focus on the results that are most pertinent to your research question and conclusions. Additional data can be placed in supplementary materials if necessary.

To be continued…

2024.11.25 Editor T. J.

(4) Unpack the significance of your findings

Do: Interpret your results in the context of existing research. Discuss their significance, implications for the field, and any limitations of your study to demonstrate how analytical you can be.

Don’t: Overstate your conclusions or ignore the limitations of your study. Instead, you must be honest and balanced to maintain credibility and demonstrate your objectivity.

(5)Wrap it up with a strong conclusion

Do: Reinforce the key points of your research and offer new insights, such as potential applications or future research directions.

Don’t: Introduce new information or arguments in the conclusion. Keep the conclusion focused on summarizing and reflecting on your results.

(6)Take time to perfect and polish your submission package

Once your draft is complete, ensure you:

• Seek constructive feedback: Ask colleagues or mentors to review your manuscript. They might spot errors or offer different perspectives that enhance your paper.
• Proofread like a perfectionist: Carefully check for grammatical errors, typos, and inconsistencies. A well-edited manuscript facilitates smoother peer review and publication.
• Follow journal guidelines to the letter: Adhere to the formatting and style guidelines of your target journal, including those for figures and supplementary files. Attention to detail is crucial.
• Ensure clarity and flow: Use clear, concise language, avoid jargon, and ensure that your manuscript is well-organized with logical headings and flow.
• Stay objective: Avoid exaggerative terms like "revolutionary" or "groundbreaking" and use precise, evidence-based language to maintain objectivity and credibility in scientific writing. You can do so by referring to statistical significance wherever possible.

Summary

Writing a scientific manuscript is both complex and rewarding. By following these key do’s and don’ts, you can enhance the quality of your paper and improve its chances of publication. Remember, persistence and attention to detail are essential in scientific writing and with practice, you'll refine your skills and gain confidence.