Ancestris, Version 11 Notes

Ancestris 11, an update from the previously reviewed version 10, is much more robust now. As a major improvement, the development team put up comprehensive documentation on their site.

After having gotten used to the interface, which is customizable, things have improved. Navigating the three editor panes is easy, just pick the one you like and go with it.

The output options are still bare-bones web page output. I’d like to see something like a register-style report than have whatever I’ve chosen to “print out” be put up on the internet. It’s still easier to have Lifelines do the report for me. I wrote a modified version of one that came with Lifelines to mimic the New England Historical Genealogical Society’s format.

Ancestris is a free genealogy program written in Java. It runs on Windows, Macintosh, and Linux systems.


GenealogicNG news

GenealogicNG is coming along. Not without nits and other bits, however; there always are ….

The first nit is that I’ve not found a good gedcom parser written in Rust. The one I did find barfs json and is built for that sort of data structure, rather than gedcom. There is a more recent, somewhat, fork of it, but the fork is still similar to the original in reading and writing data.

The second nit is that the json parsers need to read in to data structures similar to the source they come from. In this case it’s the GenealogicNG database, so I’d have to code another implementation for the structures I’ve already created.

Either I do the json parser route or continue to figure out the rest of the gedcom parser’s inner workings.

The first bit is that I’ve gotten some data out of the parser and into the glNG.db file. It’s only three pieces, and it’s taken me a couple of weeks to figure out how to do that. Now that I’ve got some of the basic structure of the parser figured out, things should go faster.

The second bit is that I’ve discovered a few typos in the SQL that the database runs. They’ve been fixed.

As an interesting aside, I’ve discovered a product which can help me figure out how the parser works. It’s an AI thingy called Safurai. Safurai is currently in beta, so it’s probably not the best, but I used it to rewrite a few pieces of my code, and they look, and work, better.

An Update on GenealogicNG

I’ve just completed a very basic, rudimentary implementation of the database in Rust. Rust is an open-source programming language and will be used for future development of GenealogicNG. Right now, it’s only the CRUD (create, read, update, and delete) functions that are usable. There is no interface, fancy, or otherwise. 🙂

The code is available at GitHub (

The background materials are also at GitHub (

Also, here are a few posts from the past on this site:



What could be better? Books? Genealogy? Both!

I’ve been watching Just Genealogy on YouTube and it is fascinating. Craig Scott, a prominent genealogist hosts short, highly informative clips on genealogy and books.

Mr. Scott is proprietor of Heritage Books, a long-time fixture in the history and genealogy business. He has branched out to YouTube to present new books in his stock and discuss them with viewers. He also accepts viewer questions to address such issues as “I’ve looked at all the records, now what?”

His regular clips on the genealogy standards put out by the Board for Certification of Genealogists are also interesting. He analyzes and comments on the basis for each standard in the revised second edition of the guide, here.

This is an engaging series that I’ll keep an eye on.

Thanks, Mr. Scott!


Genealogy as a Form of Data Analysis

This paper is loosely based on the Wikipedia article entitled “Data Analysis” and the book Mastering Genealogical Proof.

Genealogists use raw data to accumulate and analyze patterns and trends toward establishing a Genealogical Proof. Evidence in the genealogical community is generally understood as pieces of data that are arranged through collection, sifting, and arranging. Evidence, positive or negative, is acquired through examining and modeling data using generally accepted processes. One such process is to use the computer application Evidentia. Other processes enabling the development of evidence are those used in the legal and forensics professions (e.g., DNA analysis).

Each point of data genealogists use is inspected, cleansed, transformed, and modelled. Most serious genealogists use the Genealogical Proof Standard. While this standard is more qualitative than quantitative, the results are the same, actionable information used to formulate decisions.

The Genealogical Proof Standard follows, simply: Formulating a research question, gathering data sources, considering the information in those sources, formulating evidence from that information, and finally constructing a proof statement. The process is generally iterative since there is no such thing as a final statement of proof in genealogy.

While traditional data analysis is generally thought to be quantitative, there is much similarity to the genealogical research process. The steps in data analysis are analogous to the process used by genealogy professionals. Data analysis begins with a research question, followed by compiling source information, and finally, generating actionable conclusions.

Research Question

Sometimes thought of as a hypothesis, the research question is the beginning of both genealogical research and data analysis. Genealogists formulate a question by asking something such as “Who was Joan Jones’ mother?” Data analysts ask, “How is product A better than product B?” The answers come in basically the same way for both.

Data Collection, Processing, and Cleaning

To answer the research question, both genealogists and data analysts collect, process and classify data relevant to the issue. Almost all data is seen as relevant to analysts, but genealogists often go further, collecting source material relevant not only to the issue, but also surrounding the issue. Data analysts, on the other hand, are more focused on the question itself, locating only data relevant to products A and B.

The difference between traditional and genealogical data analysis is that genealogists have much more fuzzy information to deal with. Items like local and regional history books may include data about their question. Such items are generally not relevant to a data analyst focused on a product research project, unless it involves cultural appropriation, i.e., the Korean car makers’ KIA Tucson vehicle. 😊

Exploratory Analysis

Genealogists often explore different sets of data to glean information and evidence relevant to their questions. Similarly, a traditional data analyst will do the same, focusing more on specific items than general items.

Modelling and Algorithms

There are no “real” algorithms for genealogists to apply to their data findings. There is, however, a Genealogical Data Model, which was constructed to help genealogists apply their data to real-world projects. The Genealogical Data Model was originally constructed to be a basis for software, but since it was completed, no software has used the GDM (except for The Master Genealogist, which used large parts of it).

Data Products and Communications

Genealogists use a proof model to present data and their formulation of the evidence they’ve compiled. A traditional data analyst uses a tool such as business intelligence software to present their findings. The only real difference between the two is that they present findings in a different way.