Highly Cited Papers & Hot Papers: ESI Explained

Essential Science Indicators (ESI), part of the Web of Science Core Collection, provides two distinct metrics for identifying influential research: Highly Cited Papers and Hot Papers. A Highly Cited Paper is a publication that has accumulated enough citations over the past ten years to place it in the top 1% of publications in its field for the same publication year. In contrast, a Hot Paper is a much more recent publication - from the past two years - that has demonstrated an exceptional burst of influence by receiving enough citations in the most recent two-month period to rank in the top 0.1% of publications in its field and added to the database in the same period. 25 Feb 2026 [1 min read]

Read More

Below is a comparison table for the two metrics.

	Highly Cited Papers	Hot Papers
Data Source	Publications: Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) Citations: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (AHCI)
Document Types	Regular scientific articles and review articles
Subject Classification	22 ESI research fields
Publication Coverage	Past 10 years	Past 2 years
Citation Threshold	Top 1% based on total citations over the 10-year period	Top 0.1% based on citations over the most recent 2-month period
Normalization	ESI research field and publication year	ESI research field and period added to Web of Science

The thresholds for Highly Cited and Hot Papers vary by field and publication age cohort. For example, in the 5th bi-monthly 2025 ESI data, Highly Cited Papers require Agricultural Sciences 2015 papers to have ≥199 citations (vs. ≥7 for 2025 papers) and Biology & Biochemistry papers to have ≥297 (2015) vs. ≥8 (2025); Hot Papers need Agricultural Sciences papers from Nov-Dec 2023 to gain ≥12 citations, while Biology & Biochemistry papers from Sep-Oct 2025 need ≥5. Note that Highly Cited thresholds ≤2 citations disqualify papers due to insufficient impact evidence.

More information can be viewed at the Library Guide on Highly Cited Papers and Hot Papers.

7 Common Research Data Management Mistakes

Research data management (RDM) is an essential component of responsible, reproducible, and impactful science. With organizations like NSFC, MOST, NIH, and Horizon Europe enforcing strict or promoting data sharing policies and the emphasis on FAIR (Findable, Accessible, Interoperable, Reusable) data growing stronger, poor RDM practices can derail careers, waste resources, and undermine trust in research. Despite its importance, many researchers, from PhD students to senior principal investigators, still fall into avoidable traps. In this blog, we will highlight seven of the most common research data management mistakes, drawn from real-world lab experiences, funder reports, and community discussions, along with practical steps to fix them. 18 Feb 2026 [3 min read]

Read More

The Seven Common Research Data Management Mistakes

 

1. No Data Management Plan (or Treating It as a Box-Ticking Exercise)

   The Mistake: Researchers often write a Data Management Plan (DMP) only when required by a grant application, then ignore it for the rest of the project. Some may copy and paste generic text that doesn't reflect their actual workflow.

    

   Why It Hurts: Without a living plan, data organization falls apart, compliance fails during audits, and reuse becomes impossible.

    

   How to Avoid It:

   • Treat the DMP as a living document—update it at major milestones.
   • Use tools like DMPTool or institution-specific templates that align with funder requirements.
   • Involve your whole team early: discuss roles, formats, storage, and sharing upfront.
    
2. Poor File Naming and Folder Organization

   The Mistake: Files named data_final_v3_corrected.xlsx, analysis_new.R, or scattered across desktops, USB drives, and email attachments.

    

   Why It Hurts: You (or your future self) waste hours hunting for files. Collaboration becomes chaotic, and reproducibility suffers.

    

   How to Avoid It:

   • Adopt a consistent naming convention: e.g., YYYYMMDD_Project_SampleType_Version_Description.ext (like 20260212_MouseCohort1_RNAseq_v1_raw.fastq).
   • Use a logical folder structure: Project → Phase (Raw / Processed / Analysis) → Date or Sample.
   • Document the convention in a README file at the project root.
    
3. Neglecting Backups and Ignoring the 3-2-1 Rule

   The Mistake: Relying solely on a laptop hard drive or institutional network drive without proper backups. "It won't happen to me" until a ransomware attack, spilled coffee, or hardware failure strikes.

    

   Why It Hurts: Data loss is devastating—years of work gone, projects stalled, publications retracted.

    

   How to Avoid It:

   • Follow the 3-2-1 backup rule: 3 copies of data, on 2 different types of media, with 1 copy off-site (or in the cloud).
   • Use automated tools: institutional storage + external drive + cloud (e.g., OneDrive, Google Drive, or research-focused options like Wasabi or AWS S3 with versioning).
   • Test restores periodically—a backup you can't restore is useless.
    
4. Skipping or Doing Poor Metadata and Documentation

   The Mistake: Assuming the data "speaks for itself" or adding metadata only at publication time (if at all).

    

   Why It Hurts: Data without context is unusable. Others can't find, understand, or reuse it—violating FAIR principles (especially Findable and Reusable).

    

   How to Avoid It:

   • Document early and often: use README files, codebooks, or metadata templates (e.g., DataCite schema).
   • Capture who, what, when, where, why, and how for every dataset.
   • Adopt domain standards (e.g., MIAME for microarray, MIBBI for biology) and tools like CEDAR or LabArchives for structured metadata.
    
5. No Version Control for Data and Analysis

   The Mistake: Overwriting files, using "final_v4_reallyfinal.docx" or no tracking of changes in scripts and processed data.

    

   Why It Hurts: Impossible to reconstruct exactly what analysis produced which result—a reproducibility nightmare.

    

   How to Avoid It:

   • Use Git + Git LFS or DVC for datasets and code.
   • For non-coders: tools like Synapse, or Dataverse support versioning natively.
   • Log every major change in a changelog or notebook (Jupyter/ Quarto/ R Markdown).
    
6. Ignoring Security, Privacy, and Ethical Requirements

   The Mistake: Storing sensitive human data on unsecured drives, sharing via Dropbox links, or failing to anonymize properly.

    

   Why It Hurts: Breaches lead to legal issues (PDPO, GDPR, HIPAA), loss of trust, funding bans, or harm to participants.

    

   How to Avoid It:

   • Classify data early (public /internal /sensitive/ restricted).
   • Use encryption, access controls, and secure platforms (secure institutional clouds).
   • Follow ethical guidelines: obtain IRB/ethics approval, document consent for sharing, and use de-identification tools.
   • Plan for controlled access repositories when full open sharing isn't possible.
    
7. Leaving Data Management Until the End (or Never)

   The Mistake: Waiting until manuscript submission, thesis defense, or grant closeout to organize, document, and deposit data.

    

   Why It Hurts: Hasty cleanup leads to errors, lost details, and missed opportunities for reuse. Many datasets never get shared.

    

   How to Avoid It:

   • Build RDM into the project workflow from day one.
   • Schedule regular "data sprints" (e.g., monthly cleanups).
   • Choose repositories early (Zenodo, Figshare, Dataverse, domain-specific ones) and deposit incrementally.
   • Reward good practices: credit data curation in evaluations and CVs.
    

Final Thoughts

Good research data management isn't about perfection—it's about intentional, sustainable habits that protect your work and amplify its impact. In 2026, with AI tools increasingly consuming and generating data, FAIR-compliant, well-managed datasets are becoming even more valuable (and expected).

 

Start small: pick one or two mistakes from this list that resonate most with your current workflow, implement a fix this week, and build from there. Your future self—and the broader research community—will thank you.

 

Further Reading:

• GO FAIR website for FAIR guidance
• NIH Data Management & Sharing Policy
• Tools roundup: OSF.io, DMPTool, re3data.org

Sage Policy Profiles: A Free Tool to Amplify Your Research Impact

Sage Policy Profiles is a new, free tool designed to help researchers track and visualize citations of policy documents related to their publications. Powered by Overton, the largest searchable index of policy documents worldwide, this tool enables researchers to locate where their work is referenced in policy discussions and easily visualize and share their findings. 11 Feb 2026 [2 min read]

Read More

This tool is specifically tailored for individual researchers wishing to highlight their policy impact. Users can access their own policy profiles and generate shareable links. However, unlike Overton, Sage Policy Profiles does not support searching for the profiles of other academics or exploring profiles by institutions, topics, or countries. 

What Does Sage Policy Profiles Offer? 

Sage Policy Profiles provides valuable insights into how researchers' work influences policy, along with tools to effectively communicate that impact. The personalized dashboard allows users to see precisely where their work is cited in policy documents, facilitating the export of policy mentions, visualization of data in various formats, and easy sharing of results with their networks. 

Key Features of the Interface: 

Number of Policy Citations 

Citations by Location

Citations Over Time

Additional features of the tool include: 

Second-order Citations: Identify instances where policies citing your work continue to influence further discussions and decisions. 

Personalized Alerts: Receive notifications for new citations or mentions in policy documents. 

PowerPoint Compatibility: Create shareable links for personalized dashboards that integrate seamlessly with PowerPoint. 

How to Set Up the Sage Policy Profile？ 

• Go to Sage Policy Profiles. 

• Sign up for an account. 

 

• Enter your ORCID ID (if available) or your full name as it appears on your publications. 

 

• If any of your publications are missing, you can search for them manually by entering their DOI. 

 

For more information, please refer to the website Social Science Space and the article "SAGEPolicyProfiles: A Treasure-Trove for Discovering Policy Impact." 

What Happens When the Library Receives an APC Waiver Request?

From a researcher’s point of view, submitting an Article Processing Charge (APC) waiver request may seem like a simple administrative step—submit the request and wait for a response. Behind the scenes, however, each request goes through a careful review process designed to ensure that APC waiver quotas are used fairly, consistently, and in accordance with publisher agreements. 4 Feb 2026 [2 min read]

Read More

When the Library receives an APC waiver request, the first step is to review the article in its full context. This typically involves checking the journal title, the corresponding author’s institutional affiliation, the article’s acceptance date, and the remaining APC waiver quotas available—both at the publisher level and for the applying author. While these details may appear straightforward, they form the foundation for determining whether an article is eligible under an APC waiver agreement (also referred to as a Transformative Agreement, Read‑and‑Publish Agreement, or Open Access Agreement).

The eligibility assessment is often the most time‑consuming part of the process. Library staff need to verify whether the corresponding author meets the affiliation requirements set out in the agreement, whether the author has sufficient quota remaining, and whether the article’s acceptance date falls within the agreement’s effective period. Even if an article is published during the agreement period, an earlier acceptance date may place it outside the agreement’s scope.

In practice, a single missing or unmatched detail can pause the review. The library staff may need to consult publisher platforms, or contact the author directly for clarification. These follow‑ups are not obstacles; they are essential checks that help prevent errors, unexpected charges, or disputes with publishers at a later stage.

Once all required information has been confirmed, the Library proceeds with a decision. If the article meets the agreement’s requirements, the Library approves the request and notifies the publisher, enabling the article to be published as open access without the author paying the APC directly. If the article is found to be ineligible, the Library will explain the reason clearly and, where possible, suggest alternative options, such as Green Open Access through repository deposit, e.g. CityUHK Scholars.

Authors sometimes wonder why this process cannot be fully automated or completed instantly. The reason is that APC waiver approvals involve contractual obligations and shared institutional quotas. Libraries are responsible for applying approvals consistently across all researchers and ensuring that quotas are managed responsibly and not exhausted prematurely. Each approval must also withstand later auditing and reporting, both internally and with publishers.

Researchers can help the process run more smoothly by providing complete and accurate information when submitting a request. Ensuring that CityUHK affiliation details and emails are correctly stated in the manuscript, noting the acceptance date, and responding promptly to clarification emails can significantly reduce turnaround time. Contacting the Library early, especially when working with tight publishing deadlines, also makes it easier to identify suitable open access options in advance.

Ultimately, the APC waiver approval workflow is a collaborative process. While it may involve several checks behind the scenes, the shared goal is to support open access publishing in a way that is sustainable, equitable, and compliant with publisher and funder requirements. Understanding what happens at the Library end can help set expectations and foster smoother cooperation between authors, publishers, and library support teams.

For any OA enquiries, feel free to contact the Library’s Open Access Services at lbopen@cityu.edu.hk.