Name Methodology

Name Sources

Namedary's baby name database is built from documented name records collected from multiple sources, with a minimum requirement of at least one recorded usage for every name included in the database. Our goal is to provide a reliable collection of names supported by recorded data, together with information about their usage, popularity, origins, meanings, and other characteristics.

Our statistical data currently includes official birth name records from the United States, the United Kingdom, Canada, and Australia. Each dataset is obtained directly from the responsible national or regional authority and preserves the original spellings and recorded usage. Namedary also maintains additional documented name data collected from other sources over time. Regardless of source, a name must have at least one recorded usage to be included in the Namedary database.

Official Data Sources

Official birth statistics are the primary source for recorded name usage and popularity data. Because each country publishes data independently, coverage varies by jurisdiction and release schedule. Namedary imports new releases after they become publicly available and maintains the corresponding source and update information.

Additional Name Data

In addition to official birth statistics, Namedary maintains name records collected and documented from other sources since the website first became publicly available on December 12, 2023. These records expand the database beyond the countries and statistical datasets currently covered by our official sources.

Additional name records are researched, reviewed, and maintained by Namedary. They are not user-submitted and are not generated solely for the purpose of expanding the database. Where reliable information is available, additional details such as origin, meaning, pronunciation, and historical context may be associated with a name.

Inclusion Criteria

A name is included in the Namedary database only when at least one recorded usage is available in the underlying source data. This minimum threshold applies across all sources and helps maintain a focused database of documented names rather than including names solely because they are theoretically possible or have been listed without evidence of usage.

Names with only a small number of recorded occurrences remain eligible for inclusion. These names represent documented but extremely rare usage and may be identified as extremely rare where appropriate. The presence of a name in Namedary does not imply that it is currently common or widely used.

Annual Updates

Official statistical sources are updated according to their own publication schedules. Namedary imports new releases after they become publicly available and records the most recent update date for each official source.

Additional name records are maintained separately and may be updated as new source data is researched, reviewed, or verified. When new records provide additional evidence of usage, the corresponding name data and statistics may also be updated.

Name Database Construction

Namedary currently indexes names that can be represented using the English alphabet.

Official birth datasets contain yearly records rather than a predefined list of names. Namedary builds its name database by extracting every distinct given name published across all available years and jurisdictions listed in the Name Sources section.

Building the Name Database

Each published dataset is scanned to identify unique given names. Once a name appears in an official source, it becomes eligible for inclusion in the Namedary database. Existing names are not duplicated when they reappear in later years or additional regions.

Name Standardization

Before storage, each extracted name undergoes a lightweight standardization process. Leading and trailing whitespace is removed, names containing unsupported characters are excluded, and capitalization is normalized to a consistent title-case format. These steps improve consistency while preserving the original spelling of the name itself.

Ongoing Expansion

As new official datasets are released, Namedary checks for names that have not previously appeared in the database. Newly documented names are added, while existing entries continue to accumulate additional statistical data through the methodologies described in later sections.

Names Directory and Name Collections

Namedary organizes names in two complementary ways: the Names Directory and the gender-based Name Collections. They serve different purposes, although both lead to the same individual name information.

The Names Directory at /names is designed for looking up individual names. From there, each name leads to its own detailed page at /names/{nameslug} (eg: /names/liam, /names/sophia...), where Namedary analyzes the name's characteristics, including its gender classification, origin, meaning, popularity, syllables, style, themes, and other available information. The directory includes the complete name database, including names with insufficient or unavailable gender data.

The Name Collections (All Baby Names, Boy Names, Girl Names, Unisex Names) at /{gender}-names are designed for discovering names rather than looking up one specific name. These collections group names by gender category and provide dedicated lists based on characteristics such as origin, meaning, aesthetic, starting letter, ending letter, popularity, syllables, style, theme, and length.

The gender and other characteristics used in these collections are based on the classification and data available for each individual name on its detailed page. As a result, the collections provide a structured way to discover names that share particular characteristics, while the Names Directory provides the detailed analysis used to understand each name individually.

The All Baby Names collection includes the complete set of names without applying a gender filter. Unlike the general Names Directory, however, it is organized specifically for name discovery and can be explored through the same characteristic-based lists as the other gender collections.

Gender Classification

Namedary determines a name's primary gender classification using aggregated official birth records from all supported data sources. Rather than relying on individual countries or specific time periods, the classification is based on the combined number of recorded male and female births across all available years.

This approach provides a stable long-term view of how a name has historically been used. Country-specific gender usage remains available as supplemental information but does not determine the primary classification shown throughout the website.

Classification Method

For each name, Namedary sums all recorded male and female births from every official dataset. The percentage of male usage is then calculated from the combined totals.

Male Usage (%) = (Male Births ÷ Total Births) × 100

Classification Thresholds

Unknown Gender: Fewer than three recorded births, or no gender information is available. A name may still be assigned a gender classification on its individual detail page even when its recorded usage is limited to one or two births. However, gender-specific lists include only names with sufficient recorded usage to meet the required gender threshold for the corresponding Boy, Girl, or Unisex category. Names that do not meet these thresholds are shown only in Baby/all-gender lists.

Namedary applies a conservative 75% threshold before assigning a name to the Boy or Girl categories. Names with substantial recorded usage by both sexes remain classified as Unisex, reducing ambiguous classifications caused by relatively balanced usage patterns.

Names with fewer than three recorded births are classified as Unknown because the available evidence is insufficient for reliable gender classification. This threshold also aligns with the disclosure policies of many official birth datasets, which often suppress or omit extremely low-frequency records.

Annual Recalculation

Gender classifications are generated entirely through an automated process without manual adjustments. Whenever new official birth data becomes available, the aggregated birth counts and gender percentages are recalculated. If the updated distribution crosses a classification threshold, the name's classification is updated accordingly.

Name Origin & Etymology Methodology

Namedary combines large-scale linguistic datasets with a multi-stage normalization and classification process to estimate the most probable historical origin of a name. Rather than relying on a single source or a direct string match, the system analyzes documented etymological relationships, resolves language variants, removes structural noise, and applies a consistent weighting model to classify each name.

Data Sources and Processing Scale

Our origin classification system is built on a large collection of documented etymological records. Before any origin is assigned, every record passes through multiple validation and normalization stages to remove duplicates, normalize language labels, and exclude entries without meaningful linguistic value.

• Total analyzed etymological records: 4,222,811 documented etymological records processed from the Wiktionary Etymology Database.
• Total mapped names: 50,516 popular names successfully matched to standardized language classifications.

Origin Classification Model

Every origin assigned by Namedary is derived from the documented etymological evidence collected during the Name Meaning Methodology. Rather than assigning an origin directly from an individual etymology record, the system evaluates the complete body of linguistic evidence supporting each name before determining its primary historical origin.

Many names have passed through several languages over centuries. For example, a modern English name may have been transmitted through Old French before ultimately deriving from an earlier Germanic, Hebrew, Greek, or other historical source. Simply selecting the most recent transmission language can produce misleading origin labels.

To improve consistency, Namedary applies an internal weighting model that analyzes the documented etymological relationships together with standardized language classifications developed during the editorial process. The objective is to identify the most probable primary historical origin supported by the available evidence, rather than stopping at a common intermediate transmission language.

Language Normalization

Historical sources often refer to the same language using different names or historical variants. Our normalization process consolidates these references into standardized language groups wherever appropriate. For example, language variants such as Biblical Hebrew or Late Latin are normalized to their corresponding master language classifications while preserving the underlying etymological evidence.

The system also removes duplicate relationships, filters non-lexical metadata, and standardizes inconsistent language identifiers before the final origin classification is produced.

How Etymology Records Are Displayed

The etymology section on each name page summarizes documented linguistic relationships associated with that name. Because the underlying database stores individual etymological relationships rather than a complete chronological chain for every entry, these records are grouped by language for readability instead of being presented as a reconstructed historical timeline.

Consequently, the display order should not always be interpreted as the exact historical sequence of language transmission. The summarized records are intended to provide supporting linguistic evidence alongside the classified primary origin.

Methodology Principles

• Use documented etymological relationships as the primary source of evidence.
• Normalize language variants into consistent master classifications.
• Remove duplicate and non-linguistic records before analysis.
• Apply a deterministic weighting model to improve origin consistency across all names.
• Present supporting etymological records grouped by language without implying a complete historical timeline where evidence is incomplete.

Our methodology is designed to provide a transparent, reproducible, and consistent approach to large-scale name origin classification while clearly distinguishing between the classified primary origin and the supporting etymological evidence.

Understanding Etymological Relationship Types

The etymology records displayed throughout Namedary describe documented linguistic relationships between names and historical word forms. Each relationship type represents a different kind of connection recorded in the underlying etymological sources. Depending on the available evidence, a name may contain one or several of the following relationship categories.

• Inheritance and language transmission (Inherited from, Borrowed from, Derived from) describe how a name or word entered another language through continuous linguistic evolution or historical borrowing.
• Scholarly and specialized borrowings (Learned Borrowing, Semi-learned Borrowing, Orthographic Borrowing, Unadapted Borrowing) indicate that a word was introduced through literature, religion, education, or direct adoption rather than everyday spoken usage.
• Word formation and morphology (Root, Prefix, Suffix, Affix, Compound of, Blend of, Back-formation) explain how a name was constructed from smaller linguistic elements or earlier lexical forms.
• Historical linguistic relationships (Cognate of, Doublet with, Etymologically Related to) identify names or words that share a common historical ancestor or broader linguistic relationship, even when no direct parent-child evolution is documented.
• Translation and semantic influence (Calque of, Semantic Loan of, Phono-semantic Matching) describe cases where meanings, sounds, or expressions were transferred between languages rather than inherited directly.
• Commemorative naming (Named after) indicates that a name originated in honor of a historical figure, geographical location, mythological character, deity, or another notable entity.
• Shortened forms (Clipping, Abbreviation, Initialism) represent names or words created by shortening an earlier form while preserving all or part of its original identity.

These relationship types are intended to explain the linguistic evidence associated with a name. Since the underlying etymological database records individual historical relationships rather than complete evolutionary chains for every entry, the records are grouped by language for readability and should not always be interpreted as a strict chronological timeline.

Name Meaning Methodology

Namedary's name meanings are developed through a combination of editorial research, community contributions, established linguistic references, and AI-assisted editorial drafting. Our objective is to provide concise, historically grounded explanations that reflect the most widely accepted understanding of a name's origin, meaning, and cultural background whenever reliable evidence is available.

Editorial Research

The majority of name meaning entries are written or expanded by the Namedary editorial team. These descriptions are based on established etymological research, historical naming traditions, religious sources, linguistic studies, and other reputable reference materials. Rather than listing isolated facts, our editors combine these sources into clear summaries that explain both a name's meaning and the historical context behind it.

Community Contributions & Editorial Review

Namedary incorporates community insights to complement official statistics with regional interpretations, cultural usage, and historical context. To maintain data integrity, all user input undergoes a structured review process:

• Submitting Feedback & Revisions: Readers can initiate a contribution by selecting Meaning Revisions on any name profile. This allows users to request corrections or submit updated meanings directly through the revision interface.

• Transparent Version Tracking: Every name entry features a dedicated Meaning History log. This public record tracks previous definitions and modifications over time, ensuring full transparency in how an entry evolves.

• Back-End Moderation: User contributions do not publish immediately. Every submission is routed to an admin moderation panel for editorial review, where entries are verified for factual accuracy, edited for clarity, and formally approved before appearing on the site.

Linguistic References

More than 45,000 name meaning entries incorporate lexical or etymological information derived from Wiktionary. These references are primarily used to document historical word origins, root elements, and established semantic interpretations. Wiktionary serves as one of several linguistic references rather than the sole source for any individual description.

AI-Assisted Editorial Drafting

For rare, uncommon, or poorly documented names, Namedary may use AI-assisted editorial drafting to help produce an initial description based on the available linguistic and historical evidence. AI is used as a writing assistant rather than an authoritative source. These entries follow the same editorial methodology as other name meanings and may be revised as additional evidence or scholarly research becomes available.

Our Editorial Principles

We aim to distinguish between established etymologies, traditional interpretations, and disputed theories whenever possible. When multiple explanations exist, we prioritize the interpretation most widely supported by reputable linguistic or historical sources and avoid presenting uncertain theories as established fact.

Meaning Classification Methodology

Namedary groups names by shared semantic concepts to help users discover names with similar meanings. Meaning categories are intended for browsing and comparison and do not replace the complete meaning, etymology, or historical analysis available on each individual name page.

Classification Source

Meaning classification is based on the standardized Core Meaning Keywords, which is generated from the complete name meaning according to the Name Meaning Methodology. Each summary consists of one to five words that represent the name's primary semantic concepts.

Grouping Process

Rather than grouping names by their full meaning descriptions, Namedary classifies names using the concepts contained in the Meaning Summary. An AI-assisted semantic classification process identifies the key concepts represented by the summary and assigns the name to the corresponding Meaning categories.

During indexing, common English function words—including articles, conjunctions, prepositions, pronouns, and auxiliary verbs—are excluded because they do not contribute meaningful semantic concepts. The remaining words become Meaning categories. For example, the summary "Beautiful gift of God" is classified under the Meaning categories Beautiful, Gift, and God.

Index Normalization

To improve indexing consistency, grammatical variations are normalized using English stemming. Related forms such as love, loved, loving, and loves are recognized as belonging to the same word family during indexing and search. This normalization improves discovery without affecting the displayed Meaning category.

Meaning Pages

Each Meaning page lists names that have been classified under the same semantic concept. These pages are designed to help users explore names sharing a common meaning. For complete explanations, historical context, linguistic development, and etymological analysis, readers should refer to the individual name page.

Name Characteristics & Pronunciation Methodology

Consensus Arbitration & Priority-Tiered Pipeline

To ensure absolute data integrity for syllable counts and phonetic configurations across our directory of 260,000+ records, we utilize a custom cross-source arbitration protocol. Instead of relying blindly on a single dictionary, our system extracts data from multiple standard linguistic repositories and resolves discrepancies using a hybrid Consensus Voting and Hierarchical Priority model.

Data Ingestion & Arbitration Logic

When processing a name's phonological profile, the backend evaluates all available data points through three distinct scenarios to extract a unified profile (syllable count and phonetic slug):

• Single-Source Isolation: If only one linguistic repository contains records for a specific name, that profile is directly ingested to maximize database coverage.
• Multi-Source Consensus: When multiple repositories contain data and unanimously agree on the structural syllable count, the phonetic data is extracted from the highest-priority source present in that agreement.
• Hierarchical Divergence Resolution: In cases of phonetic or syllabic disagreement (e.g., source anomalies handling pluralized or possessive variants), the engine terminates arbitration and enforces a strict authority hierarchy to determine the winning dataset: CMU > OpenDict > Namedary.

Our Core Phonetic Ingestion Tiers

1. Authority Tier: CMU Pronouncing Dictionary CMU

Our highest-priority pronunciation resource is the CMU Pronouncing Dictionary (Carnegie Mellon University), a widely used pronunciation lexicon in computational linguistics. It provides the foundational ARPAbet phoneme transcriptions and lexical stress markers used throughout our pronunciation pipeline.

2. Crowdsourced Tier: OpenDictData Project Opendict

As a secondary pronunciation resource, we use the OpenDictData project. Its word-to-IPA mappings extend our coverage beyond the entries available in CMUdict, particularly for international, contemporary, and less common name variants.

3. Neural G2P Tier: Namedary Pronunciation Model Namedary

For names that are not available in our reference pronunciation dictionaries, Namedary uses a custom-trained Grapheme-to-Phoneme (G2P) neural model to estimate their pronunciation. The model generates ARPAbet phoneme sequences and supports our downstream syllable and stress analysis.

The model is built on the T5ForConditionalGeneration architecture with a custom-trained T5Tokenizer. It was trained for approximately 126,000 steps on curated pronunciation data derived from the CMU Pronouncing Dictionary, allowing the model to learn the spelling-to-pronunciation patterns represented in CMU's ARPAbet transcriptions. Training converged to a validation loss of approximately 0.02–0.03.

Traceable Source Attribution

Every name's pronunciation is assigned to its highest-priority available source through our pronunciation pipeline. Name pages explicitly identify whether the pronunciation comes from CMUdict, OpenDictData, or the Namedary Neural G2P Model.

How We Determine Syllable Count

The number of syllables for each first name, middle name, and surname is determined algorithmically from the IPA pronunciation selected by the pronunciation pipeline above. The system analyzes the IPA phoneme sequence and identifies syllabic vowel nuclei to estimate the total syllable count. This means syllable information is derived from the selected pronunciation rather than from the spelling of the name.

How We Determine Written Length

Written length is determined by counting the letters in the normalized written form of each name. This measurement is based solely on spelling and is independent of pronunciation and syllable count.

How We Determine Vowels and Starting & Ending Sounds

Vowel information, starting sound, and ending sound are derived from the selected IPA pronunciation. The system analyzes the IPA phoneme sequence to identify the vowel sounds present in the pronunciation, as well as the initial and final phonetic sounds. These characteristics therefore describe how a name is pronounced rather than how it is spelled.

How We Determine Starting & Ending Letters

Starting and ending letters are determined directly from the normalized written form of the name. The starting-letter attribute identifies the first letter, while the ending-letter attribute identifies the final letter. These characteristics are based solely on spelling and do not depend on the name's pronunciation.

Name Aesthetic Classification Methodology

Namedary's Name Aesthetic analysis is designed to describe the overall stylistic impression of a name using multiple observable characteristics rather than subjective opinion. Each classification is supported by a combination of phonetic analysis, semantic characteristics, and established cultural associations, allowing different types of evidence to contribute to the final aesthetic.

Name Ranking Methodology

Namedary's popularity rankings are generated from the official birth-record datasets described in Name Sources. Each ranking represents the popularity of names during a specific calendar year using the latest complete data available for each supported region.

Ranking Eligibility

Only names with at least one recorded birth during the selected year are eligible for ranking. Names with no recorded births in that year are excluded from the ranking for that country or worldwide dataset.

Country Rankings

Each country's rankings are calculated independently using its own official birth records. Names are ordered by the number of babies given that name during the selected year, with the highest birth count receiving Rank #1.

If two names have the same number of recorded births, the tie is resolved using their long-term historical popularity across all available years, giving higher priority to the name with stronger historical usage.

Because every country has different population sizes, reporting practices, and naming preferences, country rankings should be interpreted independently rather than compared directly with one another.

Gender-Specific Rankings

Boy, Girl, and Unisex rankings are generated separately using the gender classification described in Gender Classification.

• Boy rankings use only recorded male births.
• Girl rankings use only recorded female births.
• Unisex rankings combine both male and female births for names classified as unisex.

Worldwide Rankings

Worldwide Rankings measure how popular a name is across multiple countries rather than within a single country. The rankings combine official datasets from the United States, United Kingdom, Canada, and Australia into a single global popularity index.

Since these countries have substantially different population sizes, raw birth totals are not combined directly. Instead, each country's birth counts are converted into standardized birth frequencies based on births per million records.

The normalized popularity scores from all supported countries are then combined to produce a Worldwide Popularity Score for each name. Names that perform consistently well across multiple countries generally achieve higher Worldwide Popularity Scores than names that are highly popular in only one region.

After all Worldwide Popularity Scores have been calculated, names are sorted from highest to lowest score. The resulting position becomes the Worldwide Rank.

A name does not need to appear in every supported country to receive a Worldwide Rank. However, names with no recorded births in any supported country during the selected year are excluded.

Annual Updates

Country rankings are updated after complete official data becomes available for the selected year. For countries whose national statistics are compiled from multiple states or provinces, rankings are published only after sufficient regional data has been released.

Worldwide Rankings are updated using the latest year for which sufficiently complete data is available across all supported countries. As official releases occur at different times, the latest available ranking year may vary between individual countries and the Worldwide dataset.

Important Notes

• Lower rank numbers indicate greater popularity.
• Rank #1 represents the most popular name for that ranking and year.
• Worldwide Rank is based on normalized popularity rather than raw birth totals.
• Rank #1 Worldwide does not necessarily mean Rank #1 in every individual country.
• Country rankings should not be compared directly because each country has different demographics and naming patterns.

Name Trending Methodology

Namedary identifies trending names by combining two signals: improvement in ranking and a minimum level of birth volume. This approach is designed to distinguish meaningful upward movement from large ranking fluctuations among names with very few births.

1. Ranking Improvement

For a name that is already ranked in both the previous year and the current year, Namedary measures how much its ranking has improved relative to its previous-year rank.

The ranking improvement is calculated using:

Rank Improvement (%) = ((Previous Rank - Current Rank) / Previous Rank) × 100

A lower numerical rank represents a better position. Therefore, a movement from rank 100 to rank 80 represents a positive improvement:

((100 - 80) / 100) × 100 = 20%

To qualify as a trending name, the ranking improvement must be greater than 10%.

For example, a change from rank 100 to rank 95 represents a 5% improvement and does not meet the trend requirement. A change from rank 100 to rank 85 represents a 15% improvement and passes the ranking requirement.

2. Minimum Birth Volume

Ranking movement alone is not sufficient because names with very small birth volumes can move substantially in the rankings from relatively small changes in the number of births.

To reduce this effect, Namedary calculates the average birth volume across all names in the same region and uses 10% of that average as the minimum birth-volume threshold.

The regional mean is calculated as:

Mean Birth Volume = Total Birth Volume / Total Number of Names

The minimum volume required for a trend is then:

Minimum Trend Volume = Mean Birth Volume × 10%

For example, if the mean birth volume for a region is 5,017 births per name, the minimum volume for a name to qualify is:

5,017 × 10% = 501.7 births

Because birth volume is recorded as a whole number, a name must therefore have more than 501.7 births, effectively requiring at least 502 births, to satisfy the minimum-volume condition.

3. Existing Names That Are Already Ranked

A name that was ranked in both years is classified as trending only when both conditions are satisfied:

• Its rank improves by more than 10% compared with the previous year.
• Its current-year birth volume is greater than 10% of the regional mean.

For example, suppose a name moves from rank 200 to rank 170 and has 700 births in the current year, while the regional mean is 5,017.

Its ranking improvement is:

((200 - 170) / 200) × 100 = 15%

Its minimum-volume threshold is:

5,017 × 10% = 501.7

The name satisfies both requirements and is therefore classified as Trending.

4. Newly Ranked Names

A name that was not ranked in the previous year cannot have a conventional percentage rank improvement because there is no previous rank to use in the formula.

These names are evaluated separately. A name can qualify as a New Trend when it moves from unranked in the previous year to a ranked position in the current year, provided that its current-year birth volume exceeds 10% of the regional mean.

For example, a name moving from unranked to rank 1,500 would still qualify as a New Trend if its current-year birth volume is above the minimum threshold. The exact rank of 1,500 is not used to calculate a percentage change because there is no previous numerical rank.

5. Why Both Conditions Are Required

The two conditions measure different aspects of a name's momentum. Rank improvement identifies whether the name is moving upward significantly, while birth volume ensures that the movement occurs at a meaningful level of name usage.

A name with a large rank improvement but extremely few births may simply be experiencing a small-data fluctuation. Conversely, a name with substantial birth volume but little ranking improvement is not necessarily showing a strong upward trend.

Namedary therefore requires both signals before assigning a trending label. The result is a trend measure focused on names that are both gaining ranking position and showing sufficient current-year usage.

Trend Formula Summary

For an already-ranked name:

Trending = Rank Improvement > 10% AND Current Birth Volume > (Regional Mean × 10%)

For a newly ranked name:

New Trend = Previous Year Unranked AND Current Year Ranked AND Current Birth Volume > (Regional Mean × 10%)

The mean and the resulting minimum birth-volume threshold are calculated separately for each dataset, allowing the same methodology to be applied consistently across Worldwide, United States, United Kingdom, Canada, and Australia rankings.

Ranking Dataset Reference

The following table summarizes the official datasets currently used to calculate both Most Popular Rankings and Trending Rankings. It includes the latest supported data year, the number of birth records analyzed, and the total number of ranked names available for each region.

How Rankings and Trends Are Displayed on Namedary

Namedary provides transparent and detailed name popularity data across summary cards and dedicated detail sections, all compiled from official government birth records.

1. From Name Cards

Each name card summarizes key position data and popularity indicators:

• Latest Ranking (#XX): Displays the name's rank position for the latest available dataset using the format #XX (e.g., #1, #5, #13).
• Global & Regional Badges: The primary badge at the top-right shows the Worldwide position (represented by a globe icon), while the bottom section displays specific country ranks next to their respective flags (United States, United Kingdom, Canada, Australia).
• Trend Indicators: Visual icons display popularity momentum and trending status:
  • Top 10 Trending: Marked with a flame icon, these names show the strongest recent growth in usage.
  • Top 30 Trending: Shown with an upward arrow enclosed in a target ring, these names are rapidly climbing in popularity.
  • Top 100 Trending: Indicated with a simple upward arrow, these names are showing notable upward movement.

2. From Name Popularity Section

On specific name detail pages (for example, Liam's Name Popularity), rankings are expanded into visual mini-charts and regional callouts:

• Mini Trend Visualizations: Each region features a preview chart where green vertical bars represent the percentage of births and a violet line shows historical rank changes over time.
• Regional Breakdown & Data Years: Displays regional rank positions along with the specific dataset year (e.g., #1 Boy's name (2025)) and trending highlights.

3. Interactive Popup & Historical Overview

Clicking into any active ranking element provides full access to detailed historical trends:

• Click-to-Explore Rule: Every rank button on a name card and every chart card in the popularity section—unless disabled—indicates the availability of historical ranking records.
• Full Interactive Chart: Clicking any active button or chart opens a popup modal with tabs for Worldwide, US, UK, CA, and AU datasets. This view displays full multi-decade ranking curves, birth count breakdowns, and year-by-year ranking tables.

Name Popularity Methodology

Namedary evaluates name popularity using a time-decayed statistical model based on historical birth records from the United States, United Kingdom, Canada, and Australia. The methodology measures how frequently a name appears within the available birth data while giving greater weight to more recent years.

Popularity Data Sources

Popularity is calculated independently for four regional datasets (Data Origin): United States, United Kingdom, Canada, Australia.

Each name may contain a yearly chart for one or more regions. A chart records yearly birth counts by gender, for example:

{ "2024": { "male": 13702, "female": 17 }, "2025": { "male": 13360, "female": 59 } }

The calculation uses the available yearly records for each name rather than relying only on lifetime totals.

Reference Year

The calculation uses the year immediately preceding Namedary's configured worldwide current year (Ranking Dataset Reference):

$referenceYear = Lasted Official Worldwide Yearly Data - 1;

This prevents the popularity calculation from treating a potentially incomplete current-year dataset as a complete year of birth statistics.

Time-Decay Weighting

Historical birth counts are weighted according to their age. The model uses a fixed 0.90 annual decay factor.

The reference year receives a weight of 1.00. Each preceding year receives 90% of the weight of the following year:

• Reference year: 1.000000
• 1 year earlier: 0.900000
• 2 years earlier: 0.810000
• 3 years earlier: 0.729000
• 4 years earlier: 0.656100

For each name, the weighted historical volume is calculated by multiplying each year's total births by its time-decay weight and summing the results. This means recent popularity has a stronger influence than older historical popularity, while long-term historical presence is still retained.

Regional Popularity Normalization

Because the four countries have different birth volumes, raw birth counts cannot be compared directly. Each region therefore receives its own normalized popularity score.

For a given region, the model first calculates the total time-decayed birth volume represented by the available charts in that region. A name's regional score is then calculated as:

Regional Score = (Time-Decayed Births of the Name / Time-Decayed Regional Births) × 1,000,000

The result represents the name's observed frequency per one million births within that regional dataset.

Regional Percentile

Each region has its own score distribution. Only names with an existing chart and positive time-decayed birth volume participate in that region's percentile distribution.

Percentiles are calculated using a midrank method. Names with identical scores receive the same percentile, ensuring that ties cannot be divided into different popularity levels simply because of database order.

A missing chart is not interpreted as zero popularity. It means that there is no available regional evidence for that name.

Regional Popularity Levels

Regional popularity is classified using fixed percentile boundaries:

These boundaries are thresholds rather than fixed quotas. The methodology therefore does not attempt to force an exact percentage of names into each level. The resulting distribution depends on the actual statistical distribution of name popularity.

Worldwide Popularity

Worldwide popularity combines the four regional percentile results rather than combining raw birth counts.

Each available regional percentile receives equal weight, regardless of the size of that country's birth population. This prevents a larger market from dominating the worldwide classification solely because it contains more births.

The worldwide composite is calculated as:

Worldwide Score = Average of the available regional percentiles

For example, if a name has percentiles of 95 in the United States, 91 in the United Kingdom, 88 in Canada, and 84 in Australia, its worldwide score is the average of those four values.

Missing Regional Data

If a name has no chart for a region, that region contributes no evidence to the worldwide composite and is excluded from the average.

If a chart exists but its weighted birth volume is zero, the name receives a regional percentile of 0 and that zero is included in the worldwide calculation. This distinguishes an observed zero from a complete absence of data.

Worldwide Popularity Levels

The worldwide composite score is evaluated using the same popularity boundaries as the regional scores:

Because worldwide popularity requires a strong combined performance across the available regional datasets, Popular is intentionally selective. The resulting number of names in each level is determined by the actual distribution of worldwide composite scores rather than by predetermined percentages.

How to Interpret Popularity

Popularity describes a name's relative statistical strength within Namedary's available birth-data universe. A higher level indicates that the name has a stronger observed presence compared with other names in the same regional or worldwide calculation.

The classification is therefore not simply a count of historical births. It combines recency, normalized frequency, regional comparison, and cross-country consistency into a single popularity classification.

Name Style Methodology

Namedary's Name Styles classify names according to measurable patterns in their historical and recent usage. The classification is based on the shape, persistence, intensity, and timing of a name's recorded birth history rather than on subjective impressions about whether a name "feels" modern, classic, vintage, or unique.

A Style is an analytical classification, not a statement of historical fact or an intrinsic quality of a name. A name may qualify for more than one Style during the analysis, but Namedary assigns a single primary Style only when one statistical pattern is sufficiently more strongly supported than the alternatives. Names that do not show a distinctive enough pattern are intentionally left without a Style.

Data and Observation Period

Style calculations use Namedary's Worldwide historical birth-name time series. Each name is evaluated year by year using the birth volume recorded for the applicable gender.

For the current methodology, 2025 is the latest complete observation year. Data from 2026 is excluded from Style calculations because the year is not yet complete, even where partial 2026 records are available.

The historical observation window begins in 1910 for boy and girl names and 1983 for unisex names. These starting points define the available timeline against which persistence and lifetime usage are measured.

For each name, the system calculates several time-series measures, including:

• Total lifetime volume: the sum of recorded births across the applicable historical timeline.
• Active-year ratio: the proportion of available years in which the name had at least one recorded birth.
• Lifetime average: total lifetime volume divided by the full observation timeline, including years in which the name had no recorded births.
• Peak year and peak volume: the year and volume at which the name reached its highest recorded annual usage.
• Recent average: average annual volume during the recent period from 2021 through 2025.
• Recent share: the proportion of total lifetime volume represented by the recent period.
• Recent-to-peak ratio: recent average volume divided by the name's historical peak volume.

For unisex names, the yearly volume used by the classifier combines the gender series stored for the name before calculating these measures.

How Style Eligibility Is Determined

Each Style is evaluated independently. The classifier does not start with a fixed ranking such as "Unique before Vintage" or "Modern before Classic." Instead, every Style produces a candidate only when its own statistical conditions are met.

This separation is important because a name can legitimately exhibit more than one recognizable pattern. For example, a name can have a long historical presence while also experiencing a recent resurgence. In such cases, both Style candidates are retained for comparison rather than one being discarded simply because another rule was evaluated first.

Dynamic Thresholds for Statistical Rarity

Two Styles use thresholds derived from the distribution of the names themselves rather than fixed worldwide birth-count cutoffs.

These thresholds are calculated separately for each gender group using names with usable positive historical volume:

• 15th percentile of total lifetime volume — used for Hidden Gem.
• 15th percentile of active-year ratio — used for Hidden Gem.
• 55th percentile of active-year ratio — used for Unique.
• 40th percentile of lifetime average volume — used for Unique.

Percentiles allow the classification to adapt to the actual distribution of Namedary's name database rather than assuming that the same absolute number of births represents rarity for every gender or historical population.

The Eight Name Styles

Timeless

Timeless identifies names with exceptionally persistent and substantial usage across generations. The name must combine all three characteristics:

• appears in at least 85% of its available historical years;
• averages at least 100 births per year across the full timeline; and
• still averages at least 50 births per year during 2021–2025.

The three measures are evaluated together so that a name cannot qualify merely because it was historically common. A Timeless name must demonstrate both long-term continuity and meaningful present-day usage.

Classic

Classic identifies names with substantial historical continuity that remain meaningfully used today without requiring a specific revival pattern.

A name qualifies when it:

• appears in at least 35% of available historical years;
• reached its historical peak no later than 2018; and
• either averages at least 10 births per year in 2021–2025 or retains at least 1.5% of its historical peak volume in the recent period.

The earlier the historical peak, the stronger the evidence for an established long-term character. Classic therefore represents durable historical presence rather than simply high current popularity.

Retro

Retro identifies names strongly associated with a past mid-to-late twentieth-century era that have subsequently fallen to very low levels without showing a meaningful recent rebound.

The historical peak must fall between 1940 and 1995, with a minimum peak volume of 25 births. The recent pattern must also satisfy all of the following:

• recent average is below 3% of the historical peak;
• recent average is below 50 births per year; and
• 2023–2025 usage is no more than 20% higher than the 2020–2022 average.

This combination distinguishes a genuinely historical, diminished usage pattern from a name that has begun to return.

Vintage

Vintage identifies names with an older historical peak, a substantial subsequent decline, and a measurable modern revival.

The historical peak must occur before 2010. The period after the historical peak and before the recent comparison period is searched for the historical trough. The name must then satisfy all three conditions:

• the historical trough is no more than 50% of the old peak;
• the 2023–2025 average is at least 1.25 times the 2020–2022 average; and
• the recent average has recovered to at least 1.5 times the historical trough.

This is deliberately different from simply detecting annual growth. A Vintage classification requires an older peak, a substantial decline, and a sustained recovery pattern.

Modern

Modern identifies names whose historical usage is strongly concentrated in the recent era.

A name qualifies when:

• its historical peak occurs in 2020 or later; and
• more than 60% of its lifetime recorded volume occurs during the recent period of 2021–2025.

This means Modern is based on the concentration and timing of usage, not simply on whether a name is currently popular.

Rising Star

Rising Star identifies names undergoing a strong recent acceleration from an established prior baseline.

The system compares the average annual volume in 2023–2025 with the average in 2020–2022. A name qualifies when:

• a non-zero prior baseline exists;
• recent usage is at least 2 times the 2020–2022 average;
• the historical peak occurs in 2023 or later; and
• the recent average is at least 10 births per year.

A zero historical baseline is not treated as artificial percentage growth. This prevents a change such as 0 to 10 from being interpreted in the same way as a genuine doubling from an established level.

Unique

Unique identifies names that have maintained a relatively persistent historical presence while remaining low-volume overall.

Eligibility is calculated separately for each gender using two dynamic thresholds:

• the name's active-year ratio must be at or above the 55th percentile of its gender group; and
• its lifetime average must be at or below the 40th percentile of its gender group.

The lifetime average is calculated over the entire available timeline, not only the years in which the name appeared. This distinction is important: a name that appears sporadically should not become "Unique" merely because its births are divided only by its active years.

Unique therefore represents a particular statistical pattern: historically persistent, but never heavily used overall.

Hidden Gem

Hidden Gem identifies names that have both very limited historical volume and sparse historical appearance.

A name must fall at or below both gender-specific 15th-percentile thresholds:

• the 15th percentile of total lifetime volume; and
• the 15th percentile of active-year ratio.

This distinguishes Hidden Gem from Unique. A Unique name tends to remain visible across a large portion of its historical timeline despite low usage. A Hidden Gem has both low accumulated volume and limited historical exposure.

Resolving Multiple Possible Styles

A name can satisfy multiple Style definitions. Namedary therefore does not assign a Style merely because a particular rule appears earlier in the code.

Instead, every eligible candidate receives an evidence strength based on how strongly the name exceeds the criteria that define that Style.

Each individual criterion is normalized to a common evidence scale:

• 0.50 represents a criterion that has only just met its threshold;
• values closer to 1.00 indicate substantially stronger evidence beyond the threshold.

When a Style requires multiple criteria, their normalized evidence is combined using a geometric mean. This prevents one exceptionally strong measurement from hiding another criterion that is only marginally satisfied.

When more than one Style candidate remains, the candidate with the stronger overall statistical evidence is selected only when its evidence strength exceeds the next-best candidate by at least 0.10. When no candidate has a sufficiently clear advantage, Namedary deliberately assigns no Style rather than forcing an arbitrary result.

This approach means the classification is evidence-driven rather than based on a universal priority such as "Classic always outranks Vintage." The Style shown for a name is determined by the statistical pattern that is most clearly supported for that specific name.

Why Some Names Have No Style

Not every name is expected to receive a Style. A historical chart can be valid without exhibiting a sufficiently distinctive pattern for any of the Style definitions, and several Style candidates can sometimes be too close to distinguish reliably.

Namedary therefore treats no Style as a valid analytical outcome. This avoids turning a descriptive classification system into a forced label assigned to every name in the database.

Style Explanations on Individual Name Pages

When a name receives a Style, Namedary generates a name-specific explanation from the same statistical evidence used for the classification. The explanation identifies the relevant measurements, such as historical persistence, lifetime average volume, recent concentration, historical peak, decline, or recovery.

When multiple Style candidates were eligible, the explanation also describes the competing pattern and why the selected Style has stronger evidence for that particular name. The explanation is therefore derived from the classification data rather than from a subjective editorial description.

Methodology Principles

Namedary's Style system is designed around four principles:

• Measurable: classifications are based on explicit historical usage metrics and defined thresholds.
• Comparative: rarity-oriented thresholds are derived from the distribution of names within the relevant gender group.
• Evidence-based: overlapping Style candidates are compared using the strength of their underlying statistical signatures rather than a fixed editorial priority order.
• Conservative: when the evidence does not clearly support a distinctive Style, no Style is assigned.

As the underlying historical name database is updated, the dynamic percentile thresholds may change. A Style classification should therefore be understood as a statistical description of the name's recorded usage pattern under the current Namedary methodology and observation period, rather than as a permanent characteristic of the name.

Biblical Names Methodology

Biblical nomenclature represents the historical foundation of Western naming traditions. To bypass subjective modern compilations or denominational biases, our platform utilizes an advanced computational linguistics pipeline. We extract, analyze, and map names directly from the original textual layers of the Old and New Testaments using raw, untranslated scripts.

1. Core Linguistic Source Datasets & Word Alignments

Our data pipeline ingests high-fidelity, syntactically analyzed linguistic corpora to trace names back to their earliest documented etymological layers. The primary engine processes the following specialized resources:

• Macula Hebrew & Macula Greek: Robust, grammatically tagged source datasets representing the original biblical manuscripts in ancient Hebrew, Aramaic, and Greek.
• UBS Alignment Framework: Official text-critical alignments and translation guidelines provided by the United Bible Societies (UBS).
• Eflomal-Generated Alignments: Advanced statistical word-alignment methodologies executed via the eflomal software pipeline, allowing our system to accurately anchor ancient inflected lemmas to modern, readable nomenclature variants.

2. Data-Mining Scale and Quantifiable Metrics

Rather than relying on isolated dictionary definitions, Namedary processes the entire canonical script as a vast textual network. The scope of our text-mining operations includes the following metrics:

• 36,899 Textual Citations: Our pipeline parses and audits 36,899 distinct verses and cross-references across the scriptures to verify every single occurrence of a name entity.
• 2,507 Distinct Name Variants: Initial extraction isolates 2,507 unique raw, alternative, and morphologically inflected names embedded within the textual lineage.
• 1,311 Validated Human Names: Through strict algorithmic filtering, our system eliminates obscure geographic identifiers or strictly non-human labels, arriving at exactly 1,311 authenticated, real-world human names currently actively utilized in modern nomenclature.
• Dynamic Verse Rendering: Every single processed citation, passage, and contextual verse is preserved in our database and displayed dynamically on individual name profile pages to show exactly where and how the name appears in historical text.

3. Algorithmic Matching Logic & Canonical Scope

A name within Namedary's core database of over 260,000+ records is awarded the Biblical designation if, and only if, it satisfies our multi-tiered string containment logic:

• Exact and Alternative Matching: The name must perfectly match the primary biblical name or a documented alternative variant/spelling variation recorded in one or more scriptural passages.
• Comprehensive Canonical Scope: Names are cross-referenced across the entire biblical canon. The system maps book names to their standard 3-letter academic identifiers, dividing them into two foundational historical eras:

By implementing this programmatic database architecture, Namedary delivers a transparent, verifiable, and deeply academic resource for exploring ancient names backed by hard textual data.

Greek Mythology Names Methodology

This classification identifies given names that also appear in Greek mythology. Rather than determining the linguistic origin of a name, it documents verifiable name matches between modern personal names and characters recorded in ancient Greek mythological traditions. Each association is based on structured reference data and is presented as an additional cultural connection.

Data Source

The dataset is built from a curated reference database containing 1,718 Greek mythological figures. Each record includes the character's primary name, Greek spelling (when available), Roman equivalent, mythological category, gender, immortality status, and a concise descriptive summary.

Matching Process

Every mythological name is normalized before comparison with the Namedary database of personal names. The matching pipeline applies consistent formatting rules, including the removal of parenthetical qualifiers, leading articles (such as "The"), and alternative writing formats. Compound names such as Argia/Argea or Deioneus or Deion are separated into individual searchable forms while preserving the original source record.

Exact matches are prioritized whenever the mythological figure has a single canonical name. Alternative spellings or multiple recorded names are retained as additional matches so users can explore related mythological references without affecting the primary classification.

Classification Groups

To improve consistency and browsing, matched figures are grouped into broad mythological categories based on their traditional roles.

• Gods & Goddesses — Olympians, Titans, and other major deities.
• Nature & Element Deities — Deities associated with the sea, sky, agriculture, nature, health, and the underworld.
• Legendary Mortals — Heroes, kings, queens, amazons, seers, and other notable human figures.
• Creatures & Monsters — Mythical creatures, giants, and other legendary beings.

Coverage

Using this methodology, the current dataset identifies 320 personal names with at least one Greek mythology association, including 115 boy names, 118 girl names, and 11 unisex names. Some names are associated with multiple mythological figures and therefore may display more than one matching record.

Interpretation

A Greek mythology association indicates that a personal name is shared with one or more mythological figures. It should not be interpreted as evidence that the modern given name necessarily originated from Greek mythology, nor that every bearer of the name is historically connected to that tradition. These associations are presented as documented cultural and historical name correspondences for reference and exploration.

Royal Names Methodology

Namedary identifies royal names using documented historical genealogy records rather than predefined or manually curated name lists. A name is included when it appears as part of the recorded personal name of a documented member of a European royal or aristocratic family.

Source Data

The royal names dataset is derived from the Royal Constellations genealogy project, which traces approximately 1,000 years of ancestral relationships among European royal and noble families. The project documents thousands of historical individuals connected through dynastic marriages and shared ancestry, illustrating how many of today's European royal houses are historically related.

Classification Method

During processing, each person's recorded name is normalized by removing titles, Roman numerals, ordinal suffixes (such as "2nd" or "3rd"), parenthetical house names, placeholder records, and other non-name elements. The remaining name components are then matched against the Namedary database. If a component matches an existing given name, it is recorded as having documented royal or aristocratic usage.

This methodology considers all valid components of a recorded personal name rather than only the first given name. As a result, a name may qualify if it has been used as a first name, middle name, or another documented part of a royal or aristocratic person's name.

What Royal Names Mean on Namedary

A royal name on Namedary indicates that the name has documented historical usage by at least one member of a European royal or aristocratic family included in the source genealogy. It does not necessarily mean the name originated within royalty, was exclusive to royal families, or was widely used throughout royal history.

Dataset Coverage

The current collection was generated from approximately 2,800 documented historical individuals in the source genealogy. After matching and validation against the Namedary database, the dataset currently includes 878 royal names, consisting of 362 boy names, 366 girl names, and 19 unisex names. These figures may change as either the genealogy dataset or the Namedary database continues to expand.

Celestial Names Methodology

This methodology outlines the algorithmic framework used to cross-reference anthropological name data against verified astronomical registries. By establishing strict computational rules, the platform ensures that all celestial classifications are anchored in empirical astronomical data rather than subjective or arbitrary associations.

The core relational database integrates official records synchronized with the International Astronomical Union’s (IAU) Working Group on Star Names (WGSN) and verified exoplanetary archives current to 2026. The system processes a structured master catalog comprising five distinct celestial datasets:

• 165 Confirmed Exoplanets: Extrasolar planets assigned officially sanctioned proper names.
• 10 Planets: Major planetary bodies recognized within our solar system.
• 178 Satellites: Natural moons and satellites officially recorded orbiting planetary bodies.
• 571 Named Stars: The definitive directory of stars assigned unique proper names by the IAU.
• 89 Standard Constellations: The modern boundaries and designations mapping the celestial sphere.

To establish a verifiable link between nomenclature and astronomical entities, the data ingestion pipeline executes a two-tiered taxonomic validation process:

• Direct Correspondence (Exact Matching): The algorithm automatically validates records where the input name matches the proper noun of an official celestial body with absolute orthographic identity.
• Prefix-Based Structural Affinity (Partial Matching): For celestial root words that meet a required baseline character length, the system evaluates derived names. A candidate name must initiate with the exact spelling of the celestial object, and its structural variance is tightly bound by an edit-distance metric. This constraint ensures that any linguistic extension or suffix retains the definitive core identity and historical lineage of the source astronomical entity.

Natural Name Associations Methodology

The Natural Name Associations on this website are generated through a structured data pipeline that combines internationally recognized biological databases with our personal name database. Instead of relying on manually curated lists, every association is created by matching given names against scientific and common biological names using consistent validation and classification rules.

Currently, the system evaluates more than 260,000+ given names against millions of botanical and zoological records while applying multiple filtering stages designed to minimize false matches and maintain taxonomic consistency.

Classification Principles

Throughout this website, categories such as Flower-Inspired, Tree-Inspired, and Bird-Inspired represent a classification framework rather than a statement about the historical origin or etymology of every given name.

A name is included in one of these categories when it has a verifiable association with accepted scientific names or widely recognized common names belonging to selected biological families included in our classification system.

This approach allows the same given name to belong to multiple natural categories when supported by independent biological evidence.

Natural Name Data Sources

World Flora Online (WFO)

Botanical associations are primarily derived from World Flora Online (WFO), one of the world's most comprehensive taxonomic references for plants with 1,701,823+ botanical records.

• Only accepted taxonomic names are processed.
• Only genus and species ranks are included.
• Botanical families are resolved from the official taxonomic hierarchy.
• Scientific names are normalized before matching.
• Exact genus matches receive higher priority than species-level matches.

Global Biodiversity Information Facility (GBIF)

Common-name associations are generated from 1,500,816+ vernacular name records cross-referenced with 7,746,725+ GBIF taxonomic records, published through the Global Biodiversity Information Facility (GBIF).

• Common names are tokenized into individual words.
• Punctuation and formatting are normalized.
• Duplicate words are removed.
• Words representing colors, geography, habitats, anatomy, descriptive adjectives, biological descriptors, and common stop words are excluded.
• The remaining candidate words are matched against the personal name database.

Family-Based Classification

Taxonomic families are the foundation of the classification system. Rather than assigning categories on a species-by-species basis, every association is evaluated according to its biological family, providing a consistent taxonomic framework across the entire dataset.

Only selected families that are appropriate for each category are included. This prevents unrelated plants, agricultural crops, grasses, geographical references, or ambiguous biological records from being classified as Flower-, Tree-, or Bird-Inspired names.

Flower-Inspired Names

Flower-Inspired Names are identified from accepted botanical names and common plant names belonging to curated botanical families traditionally associated with flowers and ornamental flowering plants.

Included botanical families: Rosaceae · Liliaceae · Orchidaceae · Asteraceae · Oleaceae · Iridaceae · Ranunculaceae · Ericaceae · Violaceae · Magnoliaceae · Malvaceae · Hydrangeaceae · Nymphaeaceae · Primulaceae · Boraginaceae · Papaveraceae · Theaceae · Lamiaceae · Caryophyllaceae · Rubiaceae · Begoniaceae · Geraniaceae · Onagraceae · Balsaminaceae · Campanulaceae · Verbenaceae · Asparagaceae · Asphodelaceae · Cannaceae · Colchicaceae · Gentianaceae · Gesneriaceae · Polemoniaceae · Proteaceae · Saxifragaceae · Tropaeolaceae · Escalloniaceae · Linaceae · Plantaginaceae · Plumbaginaceae · Resedaceae

Tree-Inspired Names

Tree-Inspired Names are identified from accepted botanical names and common plant names belonging to curated families composed predominantly of woody trees and closely related arboreal plants.

Included botanical families: Fagaceae · Salicaceae · Oleaceae · Betulaceae · Pinaceae · Cupressaceae · Lauraceae · Magnoliaceae · Sapindaceae · Myrtaceae · Arecaceae · Fabaceae · Juglandaceae · Ebenaceae · Moraceae · Anacardiaceae · Ulmaceae

Bird-Inspired Names

Bird-Inspired Names are identified from common bird names belonging to selected avian families included in the classification framework.

Included avian families: Muscicapidae · Troglodytidae · Alaudidae · Turdidae · Fringillidae · Sturnidae · Hirundinidae · Columbidae · Anatidae · Falconidae · Accipitridae · Pandionidae · Corvidae · Strigidae · Scolopacidae · Apodidae · Ardeidae · Alcedinidae · Psittacidae · Phoenicopteridae