Researching Civilian Employees of the Federal Government
This blog post is intended to get you started on researching civilians who worked for the Federal Government. Civilian employment also includes records for those who worked for the Civilian Conservation Corps (CCC) or Works Project Administration (WPA) employment.
Although this is one of the older publications, there is a section about civilian employees of the Federal Government beginning on page 30 of this document:
An important thing to know is that personnel records become archival 62 years after the person’s employment by the Federal Government has ended. This 62-year time is calculated on a rolling date. Before 62 years has elapsed, the records are non-archival.
Prior to 62 years after the end of a person’s employment as a civilian employee of the Federal Government, their Official Personnel Folders (OPF) can only be accessed by the employee or an authorized third-party requestor. During that time, only limited information may be released to the general public through a Freedom of Information Act (FOIA) request.
The National Personnel Records Center’s (NPRC) Federal Records Center Program maintains the Official Personnel Folders (OPF) of former Federal civilian employees whose employment ended after 1952.
Learn how to access archival Official Personnel Folders (more than 62 years after the civilian employment ended) from: https://www.archives.gov/st-louis/opf
Once you know the agency where the civilian employee worked, look for its current website. In some cases, the name of the agency or its organization within the government may have changed, so investigate the history of the organization. Look for information about projects in which your ancestor had been involved.
Good luck researching your civilian employees of the Federal Government, and let me know how you do!
The Postmaster Finder is a useful database if you are researching U.S. Postmasters or Post Offices. You can look up a Postmaster by city or search the database by Postmaster name. This database has entries from 1971 or in some cases, earlier. Another interesting part of this website is links to other useful resources for researching the postal service at the National Archives.
To find the Postmaster in a city, select the link Postmasters by City
I searched for New York, New York.
And was rewarded with 96 entries (on 5 pages of results), going back to 1775.
If you know the name of the Postmaster and not where their Post Office was, you find out where they served by selecting the link Where Served to search for Postmasters by name.
The link for County and the link for State will let you search for Post Offices in those locations. Be sure to read the notes to the right of the search fields because they give helpful hints about how to search and what may not be included.
Another feature allows searching for locations by a range of ZIP Codes. To search by ZIP Code use the first 3 digits of the from ZIP Code and to ZIP Code. In the example below, I searched using the first three digits of a ZIP Code where I had lived in both the From and To fields.
“Sources of Historical Information on Post Offices, Postal Employees, Mail Routes, and Mail Contractors” can be found at: https://about.usps.com/publications/pub119.pdf
Thanks for such a warm reception at the Western New York Genealogical Society this past weekend. It was a pleasure to be talking about “Using AI for Genealogy” as part of their year-long fiftieth anniversary celebration, conducted over Zoom. At least fifteen states and two countries were represented in the audience.
The lecture was for people who have not already used AI tools but wanted to learn about them and how to start, AND for those who were already using the tools to share ideas about how to be more effective and expand their use.
It took over a day to obtain the ChatGPT data export that I mentioned during the lecture, but it did arrive later in the afternoon. As a reminder, this data export of all your chats can be requested by clicking on the profile icon on the lower left -> Settings -> Data Controls tab -> Export data. The link allows you to download a zipped file, and when you open it, use an HTML file to access your chats.
I wanted to share some of the great feedback from the audience:
“Fantastic ‘Gen AI 101’ and how to apply it to research!!!”
“Thank you so much! Very clear. Makes me want to go out and try it.!”
“Fantastic program!”
“This was perfectly demonstrated. Thank you!”
“Wow! So much information. Thank you so much.”
“I learned so much. No longer afraid to try it. Thank you.”
” Hope I can find the time to watch this over and over and over!”
You can embark on a captivating exploration at the crossroads of genealogy and artificial intelligence with our lecture on “Using AI in Genealogy,” conducted over Zoom. Presented by a seasoned genealogist who holds a Ph.D. in Computer Science & Engineering and is the author of “Crash Course on ChatGPT and Genealogy ,” this session promises practical ways to get started using text-to-text artificial intelligence, prompt engineering and other AI tools, with some technological background. AI tools into your genealogical research, along with some technological background about generative AI.
The reasonable pricing ensures accessibility for your group, and participants will receive a thoughtfully curated 5+ page handout. Additionally, we’ve included some optional ‘Homework, but not to turn in’ for those who are ready to delve deeper into the subject. Contact us now to secure an engaging, informative, and educational Zoom lecture for your group.
The reasonable pricing ensures accessibility for your group, and participants will receive a thoughtfully curated 5+ page handout. Additionally, we’ve included some optional ‘Homework, but not to turn in’ for those who are ready to delve deeper into the subject. Contact us now to secure an engaging, informative, and educational Zoom lecture for your group.
Despite what you might have heard, there is progress being made on anything an AI can help with, including handwriting-to-text. In this blog post, we will cover just a few of the AI tools available for transcribing images of handwritten documents into text. The conversion can be done using digital images created by scanning or photographing handwritten documents.
Transcribing documents (or important parts of documents) is a thing that I always recommend. Reading a document is passive. The motion of writing or typing a document forces us to engage different parts of our brain with its content.
Even if a tool pulls the text out of an image, there is still work to be done in checking the accuracy and formatting the text.
While this can be done with a pencil and piece of paper, I always write the transcription into a word processing document. A word processing document is easier to share and extract the key pieces of data. Be sure to store the original image and the transcription together on your computer.
NOTE: Always consider any privacy concerns before uploading documents to a website. While the website may not store the image, it may be used to train the AI model. Anything uploaded to a website usually travels through several stops on its way through the internet to the website and back.
NOTE: The results from these experiments are certainly influenced by the quality of material that is input. This means that your results may vary.
I am not affiliated with any of the products mentioned in this review.
Always check usage rights for what is generated by a tool.
As the “Unofficial Historian for the 51st Pioneer Infantry Regiment,” we are always on the lookout for materials that add to the understanding of the Regiment’s service in World War I. We located some letters and decided to try out some handwriting-to-text AI tools.
The beginning of one of the letters was:
OCR2Edit
OCR2Edit has tools to extract text from scans, images and includes more features. Since the tools are focused on text, and there was no explicit tool for converting handwriting into text, I had low expectations that this would be the right tool for the task.
At the time of writing this blog post, 3 tasks per hour could be done for free.
I selected the Image to text tool and followed the directions to start the process.
When the tool was done, I could download the text file with the transcription.
The transcription of the letterhead was good, but the handwritten part was not helpful.
Aspose
Aspose OCR app is an online tool is for turning handwritten notes to text.
The interface on this webpage is slightly awkward.
The first page of a letter uploaded and the “Recognize” button clicked. Then it is time to wait. It took a while to process the request, but there was an option to bookmark the page and return to it.
There are buttons for several of their other Optical Character Recognition (OCR) apps that might be more useful.
The format for download was selectable from a drop-down menu.
There was also an option to apply Automated Text correction.
The results are downloaded into a file named “results” which is less useful than a file that has the original filename in it.
There is a button for Options on the Home Page, where you can select: Enhance Contrast, Deskew Image and Upscale Resolution.
All of the options were selected in an attempt to get better results, but there was no improvement.
Pen TO Print
Pen TO Print was the best tool in this set of experiments.
Only the first 10 pages are free, so check out the pricing if you need to do more.
Select Handwriting to Text Converter. Then Add Files, by dragging and dropping the file or clicking the plus to open a dialog box to navigate to and select a file (or files). Then select Convert.
The text can be Download as Text or Word document, or copied to the Clipboard. The filename of a downloaded files is the original filename with “Pen2Print-Export” added to it. This feature helps keep track of the transcribed files on your computer.
This was by far the best of the tools that were tried. The output needs some minor corrections, and formatting. Both of these tasks will engage the brain, and make us think about the content.
When autumn comes, we think of going back to school. Genealogists are always learning, and webinars are a great way to do that. Presentations give us information, introduce us to new techniques or provide a new way of looking at our research. These resources in the blog post offer great classes and more.
Before I was a professor, I was a flight test engineer. My love of testing systems goes back to my early days working in a lab during college. My particular gift was always find a way to “break” hardware or software through use. My desire to investigate the use of ChatGPT in genealogy has definitely coincided with my enjoyment of testing. In this blog post, I take a look at what ChatGPT knows about GEDCOMs, how it builds one and how it can create a narrative when given an individual’s data formatted in a GEDCOM.
The technical jargon in this paragraph is available for those who want a slightly deeper understanding. In computer science, data can be grouped together in meaningful representation of things that live in the real world. A data structure is a way to group fields in a specific order for a program to input data, manipulate it, and output it. The way that genealogical data is formatted and shared is the GEnealogical Data COMmunication (GEDCOM) standard.
GEDCOM (Genealogical Data Communication) is a file format used to exchange genealogical data between different genealogy software programs. It is a standard format for saving family tree data, and it allows users to transfer their family tree data from one program to another.
GEDCOM files are saved with the extension “.ged” and are made up of text-based data that includes information about individuals, families, and events such as births, marriages, and deaths. The data is organized in a hierarchical format, with each record containing information about a single individual or event.
GEDCOM files can be used to create family trees, research family history, and share information with other genealogists. They are widely used by genealogy software programs and online genealogy databases. For example, you can export a GEDCOM from your family tree program or download a GEDCOM from Ancestry.com.
NOTE: DO NOT ENTER PRIVATE OR SENSITIVE DATA INTO ChatGPT. Your data is used for training, and is reviewed by OpenAI to verify that content complies with their policies and safety requirements. They may be used for training purposes.
I asked ChatGPT what it knew about GEDCOMs with prompts: What is a GEDCOM file? What is the GEDCOM standard? What are the fields in the GEDCOM standard?
ChatGPT answered reasonably well, except that it confidently stated the latest version of GEDCOM being used was 5.5.1. This is understandable because ChatGPT’s training ended in 2021. (As of the writing of this blog post, the current version is 7.0. For more information see the FamilySearch wiki entry for GEDCOM.)
Knowing that ChatGPT was using GEDCOM 5.5.1 was not a problem for these experiments.
Creating a GEDCOM
I would not choose to build a GEDCOM in this manner, but I could see how entering a narrative about ancestors into the prompt and let ChatGPT build the relationships from written language could be helpful. Beginning a family tree or adding a separate branch could be done by ChatGPT, then imported into a family tree program.
Investigating how effective ChatGPT was at creating a simple GEDCOM, I asked it to:
Create a GEDCOM file for James Charles McMahon, born 10 Oct 1920, father Joseph Francis McMahon, mother was Ella Small.
ChatGPT extracted the information from my request and filled in the fields. I only asked for a simple GEDCOM file, and had been very specific in what details to include. ChatGPT did fine with this request. You can see the button to copy the code so that I could store it in a file with a .ged extension that would be usable by a family tree program that conformed to the GEDCOM specification. In fact, it even warned me:
By the way, the clipboard next to the response lets a user copy the whole response so that a user can paste the response into the document of their choice. When clicked, the clipboard turns into a checkmark momentarily, then returns to being a clipboard. The thumbs up and thumbs down allow a user to provide additional feedback. If the feedback is thumbs down, another version of the reply is generated and a user has the opportunity to share whether the new one or previous response is better, or if they were the same. Giving feedback is always optional.
NOTE: This is a representation of an individual in a GEDCOM format and is not a file that can be directly imported into a family tree program. The header and footer information is not present, however, I could give ChatGPT that information and ask it to update the GEDCOM to include it.
I tried again with a new prompt that contained more details about the person’s life:
Create a GEDCOM file for James Charles McMahon, born 10 Oct 1920 in Brooklyn, Kings County, NY, father Joseph Francis McMahon, mother was Ella Small. James Charles McMahon died on 28 Nov 1987 in New York, New York, New York, US.
The response was filed the additional data correctly into the GEDCOM:
Using the GEDCOM as input to a family tree program
I asked for the file in a couple of different ways, but ChatGPT gave me only the section of the file for an individual. Rootsmagic had problems with importing this and creating a family tree, but after a little experimentation, I found that was because the was missing the header and trailer information. This was quickly remedied by editing the file.
It was interesting how the placeholder text for the birth and date information for the individual’s mother and father was inserted into the GEDCOM to be interpreted by the program. Of course, this could be fixed later in the conversation by asking for an updated GEDCOM with this information. As the chat went on, I also gave ChatGPT their marriage information and asked it to update the GEDCOM.
Creating a narrative from a GEDCOM
For my next experiment, I copied the second GEDCOM that ChatGPT had generated and fed it back into the prompt, asking:
Write a narrative for James Charles McMahon given his GEDCOM information:
0 @I1@ INDI
1 NAME James Charles /McMahon/
1 SEX M
[the rest of the file is not shown for brevity]
ChatGPT had learned details from our previous conversation, and inserted details about the individual learned from previous GEDCOMS. Starting the request in a new conversation brought its knowledge about the individual back to the nothing and the story included only the information from the prompt.
Of course, ChatGPT only uses what I told it. In reality, this individual was not an only child. Interestingly, after it writes that he grew up in a family of three, with himself and his parents, he was depicted as a beloved brother. This is due to large language models relying on their training to build the next part of their output.
Next, I checked if the format of the input mattered to ChatGPT, and made the GEDCOM data into one continuous stream, rather than distinct lines, in my prompt:
Write a narrative for James Charles McMahon given his GEDCOM information:
0 @I1@ INDI 1 NAME James Charles /McMahon/ 1 SEX M [the rest of the file is not shown for brevity]
ChatGPT did not need lines of the file to be formatted; it interpreted the data correctly then wrote a narrative. (This is also true when entering data from a table into the prompt.) Without information about the individual’s parents death, the model built the text that they survived him, and in the same sentence that they were deceased before his passing. ChatGPT can appear to loose its mind, so always proofread any output before using it.
Next, I carved out the lines for this individual from a GEDCOM that had been exported from a family tree program, complete with source citations embedded in the code. This text was used it as input to ChatGPT, and I asked it again to write a narrative from the GEDCOM. ChatGPT was successful in capturing the details it knew. It also created some generalizations like: “Throughout his life, James was a beloved member of his family and community.” It also added context without being prompted: “Though we don’t have much information about his specific experiences, we can imagine that he lived through many significant moments in history, including World War II and the civil rights movement.”
The tales that ChatGPT weaves from a user’s input can be a combination of technically accurate and fanciful. The facts that are input can be woven into a smoother and grammatically correct output. Any additional text that ChatGPT generates or additional contextual content it adds does need to be verified. ChatGPT is a generative language model that creates sentences without judgement, and those facts are presented as correct. (Always check the details that ChatGPT adds, as it may “hallucinate”!)
ChatGPT generates text with an optimistic tone. The tales do all seem to end on a positive note, reminding me of appending “and a good time was had by all” to a story.
As with any tool, how we used the output matters. ChatGPT has the flexibility to regenerate a response to our prompt, and we have the ability to edit the text as we see fit. This tool could be helpful to a genealogist trying to get started on that family history they have been planning to write. ChatGPT can help someone get around a writer’s block by providing a starting place. It can also proofread what you generate. All you have to do is ask.
It was instructive to see how the narrative text that was put into the prompt was translated into lines in the GEDCOM file. I enjoyed peaking under the hood of the implementation that is at the heart of family tree programs.
Let me know how you do, and send along any questions.
ChatGPT May 3 Version was used for these experiments. Expect ChatGPT to change over time as the technology matures.
Please check out other posts about ChatGPT and Artificial Intelligence:
