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    1961 French 10 Francs Richelieu Coin: Authentication, Cataloging, and Digital Documentation

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    This article examines the 1961 French 10 francs Richelieu coin from historical, numismatic, and data-management perspectives. It covers the 1960 monetary reform, Richelieu's historical context, core coin attributes, a metadata model for digital cataloging, SQL table design, image-capture workflows, material and authenticity screening, grading and valuation methods, bulk import automation, and long-term preservation. It also includes practical Python and SQL examples for building a traceable coin collection database.

    1. Start with the Record: Basic Facts About This Coin

    1.1 Basic Attributes

    The basic attributes of this coin are as follows:

    Attribute Details Notes
    Issuer France Circulating coin from the Fifth Republic period
    Face value 10 francs “New franc” introduced after the 1960 monetary reform
    Main design Bust of Armand-Jean du Plessis de Richelieu Known as Cardinal Richelieu; his family name derives from the family estate of Richelieu
    Date on coin 1961 Actual striking often spanned multiple years
    Category Ordinary circulation coin Mintage was usually large; collecting value is mainly determined by condition and variety

    Two reminders:

    1. Special categories such as precious-metal commemorative coins and proof trial coins have completely different valuation logic from ordinary circulation coins and should be treated separately.
    2. Different coin catalogs may occasionally disagree on composition, fineness, diameter, weight, and surviving population. When building a database, it is advisable to preserve fields for both the numerical value and the data source to facilitate later verification.

    1.2 Background One: The 1960 Monetary Reform

    Starting on January 1, 1960, France introduced a monetary reform: 100 old francs were converted into 1 new franc, and subsidiary coins were also denominated in centimes. Inscriptions on circulation coins were subsequently changed from the old-franc-era “FRANCS” to “NOUVEAUX FRANCS,” and in some years this was abbreviated as “N.F.” From 1963 onward, coins no longer specially marked “nouveau” or “nouvelle” and were simply called francs.

    This leads to the first practical point for this coin: rely only on the date shown on the coin, not on memory-based conversion. Even if a coin is nominally “10 francs,” the metal content, composition, and purchasing power associated with coins around 1949 differ greatly from those after 1961. If a coin database stores only face value without composition and fineness, cross-year comparisons can easily lead to incorrect conclusions.

    1.3 Background Two: Why Richelieu?

    A coin is a form of national narrative that can be held in the hand. The choice of person or scene depicted on a coin reflects how a country understands its own history. Since the late 19th century, France has repeatedly featured figures such as Joan of Arc, Henry IV, Victor Hugo, and Charles de Gaulle on circulation and commemorative coins.

    Richelieu was chosen because he is widely regarded as one of the key architects of the centralized French state and of realist foreign policy. In a public memory dominated by the Enlightenment, the Revolution, and the republican tradition, the selection of a 17th-century chief minister shows that coin design itself can become material for historical research.

    2. From Bishop to Chief Minister: Richelieu’s Historical Position

    2.1 Life Timeline

    Date Event
    September 9, 1585 Born in Paris; the family residence is usually recorded as the Hôtel de Losse
    December 1606 Appointed Bishop of Luçon by Henry IV; consecrated bishop on April 17, 1607
    May 14, 1610 Henry IV was assassinated; Queen Marie de’ Medici became regent for the young Louis XIII
    1614–1615 Attended the Estates General as a representative of the Poitou clergy and delivered the closing address
    1615–1616 Entered the household of Queen Anne of Austria as almoner and later became secretary of state
    1617 Dismissed after Marie de’ Medici lost power; retired to his diocese and pursued theoretical study
    1622 Elevated to cardinal, becoming known as Cardinal Richelieu
    April 1624 Resigned as Bishop of Luçon
    1624–1642 Long dominated French government affairs
    1630 Consolidated power through the political crisis known as the “Day of the Dupes”
    December 4, 1642 Died in Paris; Louis XIII died the following year

    2.2 Policies and Influence

    Domestically, Richelieu implemented measures that strengthened royal authority and central fiscal control:

    • Expanded and deployed the system of intendants;
    • Reduced the military strongholds of regional nobles and the Huguenots;
    • Promoted mercantilism and supported overseas trading companies.

    Abroad, during the Thirty Years’ War, he first opposed the Habsburgs indirectly through subsidies and alliances, then entered the war directly in 1635. Culturally, he promoted the French Academy, which received a royal charter in 1635, and supported the Sorbonne. The Palais-Cardinal he built later became the Palais Royal.

    2.3 Assessment and Boundaries of Use

    Later assessments of Richelieu can be grouped into three broad categories:

    • One view sees him as a major shaper of absolutism and emphasizes his role in preserving national unity and resisting foreign enemies;
    • Another emphasizes the coercive and costly nature of his centralizing methods, including harsh suppression of regional resistance;
    • A third comes from literary narrative, especially the classic image of Richelieu as a villain in Alexandre Dumas’s trilogy, which has amplified stereotypical public perceptions of him.

    Collectors and researchers need to distinguish these perspectives: fictional portrayals cannot serve as historical evidence, and historical assessment should not be reduced to “good person” or “bad person.” Using a historical figure on a coin is more a choice of national narrative; it should not be treated as a complete historical record, nor should it be used to “rehabilitate” the figure.

    3. Why Collecting Is Essentially a Data Problem

    3.1 Three Attributes of a Collection Item

    • Physical attributes: Material, weight, dimensions, edge treatment, and other physical facts can be measured with instruments.
    • Symbolic attributes: Figures, inscriptions, dates, mint marks, engraving style, and other image or text information require image capture and manual interpretation.
    • Condition attributes: Grade, authenticity, damage, cleaning traces, and other features change over time and with storage conditions.

    Traditional collecting often records “face value, year, grade, and source” on a single card. As the number of items grows, the limitations of this method quickly become apparent: searching relies on manual browsing, comparison relies on memory, images are scattered across photo albums or phones, and valuation evidence is difficult to verify.

    3.2 Four Typical Pain Points of Manual Recording

    1. Same coin, different names: The same coin may be recorded under different names in catalogs, auction records, and personal notes, leading to duplicate or missing entries;
    2. Images become disconnected from objects: A file name such as “IMG_2037.jpg” may become impossible to match to a specific coin a few months later;
    3. Subjective condition descriptions: A note that simply says “very good” cannot support horizontal comparison;
    4. No audit trail: If grade or valuation is changed without a record, losses cannot be reviewed later.

    3.3 Modeling Principles

    Each coin should be treated as an “information object” requiring a stable identifier, layered metadata, and traceable history. The following Python example can be run directly. It uses dataclasses, enums, and JSON files as a lightweight storage approach, suitable for individuals and small teams starting out; as data volume grows, it can later be migrated to a relational database.

    1. from dataclasses import dataclass, field
    2. from datetime import datetime
    3. from enum import Enum
    4. import hashlib
    5. import json
    6. from pathlib import Path
    7. from typing import Optional
    10. class Grade(Enum):
    11.     """Simplified grading scale based on the international Sheldon 70-point system."""
    12.     VF20 = "Very Fine 20"
    13.     VF25 = "Very Fine 25"
    14.     EF40 = "Extremely Fine 40"
    15.     EF45 = "Extremely Fine 45"
    16.     AU55 = "About Uncirculated 55"
    17.     AU58 = "About Uncirculated 58"
    18.     MS60 = "Mint State 60"
    19.     MS63 = "Mint State 63"
    20.     MS65 = "Mint State 65"
    21.     MS67 = "Mint State 67"
    22.     MS70 = "Mint State 70"
    25. @dataclass
    26. class CoinRecord:
    27.     # Identity: globally unique and immutable once assigned
    28.     inventory_no: str
    29.     # Physical attributes
    30.     country: str = "France"
    31.     face_value: int = 10
    32.     currency: str = "FRF"
    33.     year: int = 1961
    34.     subject: str = "Richelieu"
    35.     diameter_mm: Optional[float] = None
    36.     weight_g: Optional[float] = None
    37.     # Condition attributes
    38.     grade: Optional[Grade] = None
    39.     cleaned: bool = False          # Whether the coin has been cleaned
    40.     notes: list[str] = field(default_factory=list)
    41.     # Provenance and audit
    42.     source: str = ""
    43.     created_at: str = ""
    44.     updated_at: str = ""
    45.     version: int = 1
    47.     def update(self, **changes):
    48.         """Any field change automatically increments the version and records a timestamp."""
    49.         for key, value in changes.items():
    50.             if hasattr(self, key):
    51.                 setattr(self, key, value)
    52.         self.updated_at = datetime.now().isoformat(timespec="seconds")
    53.         self.version += 1
    56. def save(record: CoinRecord, folder: str = "coins") -> None:
    57.     path = Path(folder)
    58.     path.mkdir(exist_ok=True)
    59.     target = path / f"{record.inventory_no}.json"
    60.     payload = record.__dict__.copy()
    61.     if isinstance(payload.get("grade"), Grade):
    62.         payload["grade"] = payload["grade"].value
    63.     # Compute a digest after serialization to detect accidental tampering with archived files
    64.     text = json.dumps(payload, ensure_ascii=False, sort_keys=True)
    65.     payload["record_hash"] = hashlib.sha256(text.encode("utf-8")).hexdigest()[:16]
    66.     target.write_text(json.dumps(payload, ensure_ascii=False, indent=2),
    67.                       encoding="utf-8")
    70. coin = CoinRecord(
    71.     inventory_no="FR-10FR-1961-RICH-001",
    72.     year=1961,
    73.     grade=Grade.MS63,
    74.     source="Personal transfer, with original packaging",
    75. )
    76. coin.created_at = datetime.now().isoformat(timespec="seconds")
    77. coin.update(notes=["Obverse and reverse photographed in sets; edge captured separately"])
    78. save(coin)
    79. print(json.dumps(coin.__dict__, ensure_ascii=False, indent=2))

    4. The Core of Digital Cataloging: A Metadata Model

    4.1 Six-Layer Field Structure

    Layer Representative fields Purpose
    Identifier layer Unique inventory number, catalog number, source batch Global search and association
    Description layer Country, face value, currency, year, subject figure, variety Classification and retrieval
    Physical layer Material, diameter, thickness, weight, edge treatment Initial authentication and valuation basis
    Condition layer Grade, defect flags, cleaning record, storage location Condition management and risk assessment
    Asset layer Image file name, resolution, hash value, color profile Binding images to physical objects
    Provenance layer Source, acquisition time, amount, prior records, modification history Audit and dispute resolution

    4.2 Relational Table Structure Example

    1. -- Main coin collection table
    2. CREATE TABLE coin (
    3.     inventory_no TEXT PRIMARY KEY,        -- Unique inventory number
    4.     country      TEXT NOT NULL,
    5.     face_value   INTEGER NOT NULL,
    6.     currency     TEXT NOT NULL,
    7.     year         INTEGER NOT NULL,
    8.     subject      TEXT,
    9.     diameter_mm  REAL,
    10.     weight_g     REAL,
    11.     grade        TEXT,
    12.     cleaned      INTEGER DEFAULT 0,
    13.     location     TEXT,                    -- Physical storage location
    14.     source       TEXT,
    15.     acquired_at  TEXT,
    16.     version      INTEGER DEFAULT 1,
    17.     updated_at   TEXT
    18. );
    20. -- Image assets: one coin can have multiple images
    21. CREATE TABLE coin_image (
    22.     image_id     TEXT PRIMARY KEY,        -- Hash value of image content
    23.     inventory_no TEXT NOT NULL REFERENCES coin(inventory_no),
    24.     view         TEXT NOT NULL,           -- Obverse / reverse / edge / close-up
    25.     file_name    TEXT NOT NULL,
    26.     width        INTEGER,
    27.     height       INTEGER,
    28.     color_space  TEXT,                    -- e.g. sRGB
    29.     dpi          INTEGER,
    30.     sha256       TEXT NOT NULL,
    31.     taken_at     TEXT
    32. );
    34. -- Change audit table: append-only; no modification
    35. CREATE TABLE coin_audit (
    36.     audit_id     INTEGER PRIMARY KEY AUTOINCREMENT,
    37.     inventory_no TEXT NOT NULL,
    38.     field_name   TEXT NOT NULL,
    39.     old_value    TEXT,
    40.     new_value    TEXT,
    41.     changed_by   TEXT,
    42.     changed_at   TEXT
    43. );
    45. CREATE INDEX idx_coin_year ON coin(year);
    46. CREATE INDEX idx_coin_image ON coin_image(inventory_no);

    It should be emphasized that the table structure is only a starting point. What truly determines the long-term value of the database is the coding rules:

    • Unique inventory number: Follow a rule such as “country code, face value, year, subject, serial number,” for example “FR-10FR-1961-RICH-001.” Once assigned, it should not be changed, even if the coin is sold or moved.
    • Multi-value fields: Edge types such as plain, reeded, and lettered edges, variety differences, and mint-mark positions should not be crammed into a single text field. They should be split into separate tables; otherwise conditional searching becomes impossible.
    • Controlled fields: Grade, country, edge type, and similar fields should use enums or controlled vocabularies to avoid overlapping expressions such as “UNC,” “uncirculated,” and “MS60-62.”
    • Nullable fields: When a measured value is unknown, leave it blank. Do not fill in 0 or guess a value. A zero will be treated as real data, and guessed values will pollute the entire database.

    5. Image Capture: The Quality Baseline for Digitization

    Stage Recommended parameters Reason
    Camera and lens Interchangeable-lens body plus macro lens, or macro extension tubes A 10-franc coin is roughly 2 cm in diameter, requiring approximately 1:1 magnification
    Resolution Long edge of at least 3000 pixels; same magnification for obverse and reverse The coin is small and edge details are fine, so sufficient sampling is required
    Lighting Diffused light from both sides at about 45 degrees; low-angle grazing light for the edge Front lighting avoids highlight reflections; grazing light highlights reeded details
    Color management Include a gray card or color card in each shot; output a lossless format with an ICC profile Ensures color consistency across batches
    Number of images One set each for obverse, reverse, and edge; add close-ups of defect locations Defects are frequent points of dispute in grading and valuation
    File organization Keep raw files read-only; store derived thumbnails separately Prevents post-processing from damaging original evidence

    5.2 Shooting Workflow

    1. Clean the equipment and wear powder-free gloves or use tweezers; touch only the edge of the coin;
    2. Mount the camera on a copy stand and enable electronic shutter or delayed shooting to avoid vibration;
    3. Photograph the gray card first, then the coin, so that each set of images has a traceable white-balance reference;
    4. Capture the obverse, reverse, and edge sets, retaining the original files;
    5. Import the images into the database and register the hash values and capture time.

    5.3 Automatic Registration Script

    1. import hashlib
    2. import mimetypes
    3. from pathlib import Path
    5. RAW_DIR = Path("incoming")      # Directory of incoming raw images awaiting registration
    6. ARCHIVE_DIR = Path("archive/images")  # Archive directory
    9. def file_sha256(path: Path, chunk: int = 1 << 20) -> str:
    10.     h = hashlib.sha256()
    11.     with path.open("rb") as f:
    12.         for block in iter(lambda: f.read(chunk), b""):
    13.             h.update(block)
    14.     return h.hexdigest()
    17. def register(directory: Path = RAW_DIR) -> list[dict]:
    18.     registry = []
    19.     for file in sorted(directory.glob("*")):
    20.         if file.suffix.lower() not in {".tif", ".tiff", ".jpg", ".png"}:
    21.             continue
    22.         digest = file_sha256(file)
    23.         # Use the hash as the filename for natural deduplication: re-importing the same image yields the same path
    24.         target = ARCHIVE_DIR / f"{digest[:2]}/{digest}.tif"
    25.         target.parent.mkdir(parents=True, exist_ok=True)
    26.         if not target.exists():
    27.             file.replace(target)   # First registration: move to archive
    28.         registry.append({
    29.             "image_id": digest,
    30.             "file_name": target.name,
    31.             "mime": mimetypes.guess_type(file.name)[0],
    32.         })
    33.     return registry

    Two engineering practices in this script are worth borrowing:

    • Content addressing: Using the file hash as the unique identifier prevents duplicate records when the same image is imported repeatedly.
    • Read-only archiving: Once an original file is archived, it is no longer modified in place. Cropping, sharpening, and watermarking are performed only on copies, ensuring that the original evidence remains intact.

    6. Authentication and Material Screening: An Engineerable Approach

    6.1 Step One: Basic Measurements

    Use a vernier caliper with 0.01 mm precision and an electronic scale with 0.01 g precision to measure diameter, thickness, and weight. If reference data from a known genuine example of the same variety is available, direct cross-comparison can be performed. If not, a personal reference database must first be established. Drawing conclusions from a single measurement carries high risk.

    6.2 Step Two: Archimedes Density Method

    Using the principle of water displacement, measure the coin’s density and compare it with theoretical values. This can quickly rule out obvious material substitution:

    1. from statistics import mean
    3. # Common alloy reference densities (g/cm³); source: publicly available general material data
    4. REFERENCE_DENSITY = {
    5.     "cupronickel": 8.90,   # Cupronickel
    6.     "nickel": 8.90,
    7.     "silver": 10.49,
    8.     "steel": 7.85,
    9. }
    12. def measure_density(mass_g: float, volumes_ml: list[float]) -> dict:
    13.     """volumes_ml contains multiple displacement measurements in mL (1 mL = 1 cm³)."""
    14.     volume = mean(volumes_ml)
    15.     return {
    16.         "mass_g": round(mass_g, 3),
    17.         "volume_cm3": round(volume, 3),
    18.         "density": round(mass_g / volume, 3),
    19.     }
    22. def match_material(density: float, tolerance: float = 0.3) -> str:
    23.     for name, ref in REFERENCE_DENSITY.items():
    24.         if abs(density - ref) <= tolerance:
    25.             return name
    26.     return "undetermined"
    29. result = measure_density(6.50, [0.729, 0.731, 0.730])
    30. print(result, match_material(result["density"]))

    Key points for use:

    1. Take multiple measurements and use the average to reduce errors caused by air bubbles;
    2. Temperature, purity, and plating can all affect the result;
    3. A density match is a necessary condition, not a sufficient one;
    4. Professional authentication usually requires a combination of methods such as density measurement, X-ray fluorescence analysis, ultraviolet observation, and microscopic inspection.

    6.3 Step Three: Examine Image and Text Details

    Counterfeit and high-quality imitation coins often reveal themselves through the following details:

    • The font size or position of the date is offset;
    • The proportions of the portrait are distorted, such as stiff facial lines or blurred collar and crown structures;
    • Inscription strokes are uneven in thickness, and the curves of rounded letters appear unnatural;
    • The depth of edge reeding is uneven, or the pitch is irregular;
    • The coin field shows flow marks or casting pores;
    • The edge shows traces of grinding or adhesive repair.

    For ordinary circulation coins, the most common problem is not a finely made high-quality counterfeit, but rather re-engraved dates after wear. A low-grade coin may be re-engraved to imitate a high-grade year. Such “repaired coins” can be identified by using side lighting to examine the field texture and checking for abnormal grinding around the date.

    6.4 Build a Personal Reference Database

    It is advisable to retain a set of reference measurements for each known genuine variety, including weight, diameter, density, and edge photographs. These should be collected using the same equipment and workflow to avoid systematic bias caused by different devices.

    7. Grading, Valuation, and Survivorship: Structuring Subjective Judgment

    7.1 Key Points of Condition Grading

    The internationally used Sheldon 70-point scale divides coins from PO1, the poorest condition, to MS70, perfect uncirculated condition, into 70 grades. When building a personal database, it is possible to simplify this into five tiers first: XF, AU, MS60-62, MS63-64, and MS65 and above. Refinement can come later.

    Three points require attention when judging:

    1. High-point locations: Circulation wear usually appears first on high points of the portrait;
    2. Design differences: Different designs have different high-point locations, so the same wear-reading template cannot be applied universally;
    3. Original luster: The luster of cleaned or over-worn coins differs from original mint luster. Cleaning traces should be recorded separately in the “defects” field rather than merged into the grade.

    7.2 Valuation Method

    Valuation cannot rely on a single transaction record. A single sale price can be heavily affected by a seller’s urgency, grade disputes, and additional packaging. A three-step method is recommended:

    1. Collect comparable samples: Historical transaction records from the same country, year, variety, and similar grade, with at least 5 to 10 samples;
    2. Remove outliers: Remove samples whose prices deviate significantly from the median, and use the median rather than the mean as the reference baseline;
    3. Record timestamps: Metal prices, exchange rates, and market sentiment all affect prices. Valuation records must include time references to avoid explaining today’s market with prices from three years ago.
    1. from statistics import median
    3. def suggest_price(samples: list[float], cutoff: int = 5) -> dict:
    4.     """Provides an estimated value range based on comparable sales; makes no definitive claims."""
    5.     if len(samples) < cutoff:
    6.         return {"status": "Insufficient samples", "n": len(samples)}
    7.     ordered = sorted(samples)
    8.     med = median(ordered)
    9.     # Remove samples that deviate significantly from the median
    10.     filtered = [x for x in ordered if 0.5 * med <= x <= 2.0 * med]
    11.     return {
    12.         "n": len(filtered),
    13.         "median": round(med, 2),
    14.         "q1": round(filtered[len(filtered) // 4], 2),
    15.         "q3": round(filtered[(3 * len(filtered)) // 4], 2),
    16.         "note": "Reference range only; not a transaction guarantee",
    17.     }

    7.3 About “Survivorship”

    Mintage is not the same as survivorship. A circulation coin with a mintage of tens of millions may still be relatively scarce at MS65 and above. Conversely, a low-mintage proof coin may have a relatively high surviving population if storage conditions were good. Therefore, the grade distribution reflects actual scarcity better than mintage alone. This is also why grade and defects should be maintained as separate fields in a personal database.

    8. Bulk Import and an Automation Pipeline

    When a collection reaches a certain scale, such as several hundred coins or more, manual registration becomes a bottleneck. A typical automation pipeline can be divided into five stages:

    1. Capture: Use a fixed copy stand to photograph obverse, reverse, and edge sets with unified resolution and color configuration;
    2. Register: Automatically archive using a file-naming convention and content hash, as shown in the Chapter 5 script;
    3. Enter data: Fill in physical-layer fields with measurement data, and manually supplement information that is difficult to automate, such as variety and mint-mark location;
    4. Validate: Check required fields, value ranges, such as whether the diameter falls within a reasonable interval, and whether images correspond to records;
    5. Import: Write data into the main table and audit table, and generate a readable summary report.

    The validation stage can be implemented with a lightweight rule set:

    1. RULES = [
    2.     ("year_between", lambda r: 1900 <= r["year"] <= datetime.now().year),
    3.     ("diameter_positive", lambda r: r.get("diameter_mm", 0) > 0),
    4.     ("weight_positive", lambda r: r.get("weight_g", 0) > 0),
    5.     ("image_exists", lambda r: Path(r["image_path"]).exists()),
    6. ]
    9. def validate(record: dict) -> list[str]:
    10.     return [name for name, rule in RULES if not rule(record)]

    Operational recommendations:

    • Run validation after each bulk import before writing data to the formal database;
    • Data that fails validation should enter a “pending review” queue and not be written to the main table;
    • Perform a hash comparison across the entire database once a month to confirm that archived images have not been accidentally modified.

    9. Long-Term Preservation and Risk Management

    9.1 Physical Preservation

    • Use acid-free holders and inert materials; avoid prolonged contact with PVC, which can cause corrosion of the coin surface;
    • Recommended storage conditions are approximately 18–20°C and 40%–50% relative humidity; avoid drastic fluctuations in temperature and humidity;
    • Do not clean coins yourself. Cleaning damages original toning and mint luster, and this is an irreversible loss in grading and valuation;
    • Copper-based coins are sensitive to sulfides and chlorides. Long-term exposure can cause irreversible “bronze disease.”

    9.2 Digital Asset Protection

    • Follow the “3-2-1” backup principle: at least 3 copies, on 2 different types of media, with 1 copy stored off-site;
    • Conduct recovery drills regularly to confirm that backup files are genuinely usable;
    • Register hash values for database export files as well, to prevent silent corruption;
    • Retain audit logs for critical records, so that any modification can be traced to the person, time, and field changed.

    9.3 Data Lifecycle

    • When adding new fields, retain the semantics of old fields; do not reuse deprecated field names;
    • Once a catalog-numbering rule is established, it is not advisable to change it midway, because that would invalidate all historical associations;
    • Conduct an annual “health check” of the database: measure null-value rates, duplicate records, and records without images, and use the results to prioritize supplementary entry work.

    10. Common Issues Checklist

    Issue Cause Response
    Duplicate record for the same coin Missing unique identifier or image-hash deduplication Establish a unique-number rule and compare hashes before import
    Images do not match physical coins File names lack semantics and there is no registration workflow Use content-addressed archiving and write the inventory number during import
    Condition descriptions cannot be compared horizontally Only subjective terms such as “very good” are used Adopt the 70-point scale or a unified simplified grading system
    Valuation fluctuates excessively Too few samples and no timestamps Use multiple comparable sales, the median, and recorded time references
    Value decreases after cleaning Mistaken belief that “cleaner is better” Do not clean; record abnormal traces in the defects field
    Green powdery corrosion appears on copper coins Storage humidity is too high Control temperature and humidity; isolate the coin if necessary and consult a professional

    Conclusion

    A 1961 10-franc Richelieu coin has a modest face value and a large mintage, yet it carries three layers of information at once: the economic history of monetary reform, the political history of personal narrative, and the collector’s own practices of authentication and preservation.

    Placing it into a structured database is not about “engineering” the collection. It is about making every judgment verifiable and every modification traceable. As a collection grows, what ultimately determines collecting quality is often not “how many coins were acquired,” but “how many were recorded, and how accurately they were recorded.” From unique identifiers and image hashes to grading enumerations, density screening, and reference databases, the ultimate goal of this method is to ensure that each coin can be accurately identified, compared, and passed on.

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