What This Does
Collect and analyze research evaluation metrics for individual researchers, research groups, or research institutions. Obtain quantitative indicators such as the number of papers, citations, and h-index, and support the visualization of research strength and the organization of performance data needed when applying for competitive funding.
Required Inputs
- Analysis Target: Individual name (researcher), research group name, or institution name
- Purpose of Analysis: Any of the following
- Organizing performance records for research grant applications
- Analysis of institutional research strength (for IR purposes)
- Investigation of potential collaborative research partners
- Research evaluation of departments and divisions
- Target Period(Optional): Period to be analyzed (default: most recent 5 years)
- Research Field(Optional): When performing relative evaluation by field
What It Produces
- Basic Metrics
- Number of papers (total, annual trend)
- Number of citations (total, annual trend)
- h-index / h5-index
- i10-index
- Detailed Analysis
- Top papers (highest number of citations)
- Co-authorship network (main co-authors)
- Distribution of publication journals (by impact factor range)
- International co-authorship rate
- OA (open access) rate
- Positioning in the Field Context(When a field is specified)
- Comparison with the field average
- Indicators equivalent to FWCI (Field-Weighted Citation Impact)
- Percentile within the field
- Data for Visualization
- Data table for annual trend graphs
- Data for co-authorship networks
Procedure
1. Identification of Researcher/Institution
To obtain accurate data, confirm the following identifiers:
- ORCID: https://orcid.org/ Search with
- Scopus Author ID: Unique ID in Scopus
- Web of Science Researcher ID
- researchmap ID: https://researchmap.jp/ (Japanese researchers)
- Google Scholar profile URL
To prevent confusion with researchers who have the same surname and given name, narrow down by affiliated institution and field.
2. Data Collection
Collect metrics from the following databases:
2a. Public Databases
- Google Scholar: h-index, i10-index, number of citations (if the profile is public)
- Semantic Scholar: Number of papers, number of citations, impact indicators
- OpenAlex: Open bibliographic database, API available
- researchmap: Performance information of Japanese researchers
2b. Institutionally Subscribed Databases (if available)
- Scopus / SciVal: Elsevier's bibliographic database
- Web of Science / InCites: Clarivate's bibliographic database
- Dimensions: Digital Science's database
2c. Supplementary Information Sources
- KAKEN(KAKEN database): https://kaken.nii.ac.jp/ — KAKEN grant adoption records
- J-GLOBAL: https://jglobal.jst.go.jp/ — Information on Japanese researchers and literature
- CiNii Research: https://cir.nii.ac.jp/ — Japanese academic information
3. Calculation of indicators
3a. Basic indicators
| Indicator | Definition | Notes |
|---|---|---|
| Number of papers | The number of peer-reviewed papers published during the target period | Specify whether preprints and conference abstracts are included |
| Citation count | The total number of times a paper has been cited by other papers | Specify whether self-citations are included or not |
| h-index | The largest h such that there are at least h papers cited at least h times | Note that values differ depending on the database |
| i10-index | The number of papers cited at least 10 times | A Google Scholar-specific indicator |
3b. Advanced indicators
- FWCI: The ratio of citation counts normalized by field, publication year, and document type (1.0 = world average)
- Top 10% paper rate: The proportion of papers that rank in the top 10% of their field by citation count
- International co-authorship rate: The proportion of papers co-authored with overseas institutions
- OA rate: The proportion of papers published as open access
4. Providing context
Since numbers alone are meaningless, provide the following context:
- Field practices: The levels of paper counts and citation counts differ greatly by field
- Example: Medical fields tend to have many co-authors and a high number of papers, while mathematics has few
- Career stage: The expected levels differ between early-career and senior researchers
- Differences between databases: Google Scholar has broad coverage, while Scopus/WoS are selective
- Self-citations: Note when the self-citation rate is extremely high
5. Report creation
Create the report with the following structure:
# 研究業績分析レポート
## 対象: [研究者名 / 機関名]
## 期間: [YYYY年 - YYYY年]
## データ取得日: [YYYY-MM-DD]
## データソース: [使用したデータベース]
### 基本指標サマリー
（表形式）
### 年次推移
（年別の論文数・被引用数）
### トップ論文
（被引用数上位5-10本）
### 分析コメント
（文脈を踏まえた解釈）
### 注意事項
（データの限界、解釈上の注意点）
Quality standards
- Specify the data sources and record the date of retrieval
- If values differ across multiple databases, list all of them and explain the reasons for the differences
- Do not present numerical values without field context (whether h-index = 20 is "high" depends on the field)
- Specify how self-citations are handled
- Note the limitations of the indicators (h-index favors senior researchers, paper count depends on the field, etc.)
- Be aware of differences in database coverage (coverage is broader in the order Google Scholar > Scopus > WoS)
- Encourage caution against using a single indicator for research evaluation
Available Tools
- WebSearch / WebFetch: Database searching and data retrieval
- Read / Write: Reading and writing reports
- Bash: Data aggregation and statistical calculation