Hunt Institute for Botanical Documentation
A Research Division of Carnegie Mellon University

Hunt Institute Archives Text Discovery Platform

Search a large and growing portion of our online collections, including handwritten documents.
PROTOTYPE

This prototype uses state-of-the-art artificial intelligence, including a vision-language model (VLM) capable of reading handwritten documents as well as typed and printed text, to create searchable transcriptions of digitized materials in the Hunt Institute Archives. This makes it possible to search the textual contents of individual pages, including material that may not be described in the archival catalog.

Use Keyword search for specific words, names, dates, scientific names, or phrases. Try Semantic search (experimental) to describe a topic, question, or kind of material when you do not know the exact wording used in the documents.

About the AI-generated transcriptions

The transcriptions are generated automatically from page images and may contain errors, especially with difficult handwriting, unusual names, multiple languages, image-quality problems, or complex layouts. They are intended primarily as a discovery aid rather than authoritative transcriptions.

Each result provides the generated transcription and links to the original digitized material and associated archival description so that readings can be checked against the source. The transcription workflow uses AI models run locally by the Hunt Institute.

About Keyword and Semantic search

Keyword search is the default and matches the wording in the transcriptions. Results contain all your terms. Use quotes for an exact phrase. Substring matching is supported, so aceae can find plant-family names ending in -aceae.

Semantic search (experimental) is useful when you know what kind of material you are looking for but do not know the words used in the documents. It ranks transcribed passages by similarity of meaning, so relevant results may not contain the exact words in your query.

Semantic queries can be broad research topics, descriptions of activities or relationships, or natural-language questions. For example:

Semantic search is not a chatbot: a question is used as a search query, and the system returns archival passages that appear conceptually related to it rather than generating an answer or summary. Short descriptions and ordinary research questions generally work better than lists of disconnected keywords. Quotation marks have no special meaning in Semantic mode. Cross-language matching may work in some cases, but it should not be treated as translation.

Keyword and Semantic search are complementary. Keyword search lets you require particular wording; Semantic search can surface differently worded passages about the same subject. Depending on the research question, trying both can reveal different useful material.

Open a result: use the prominent page-and-transcription link to see the metadata, PDF, and full transcription. Keyword-search terms are highlighted in the transcription.

Archives Collections Database (ArchivesSpace): the Collection, Item/Folder, and Digital Object links open the corresponding archival records. Collection-level dates describe the collection as a whole, not necessarily the specific item or page.

If a PDF does not load: on the detail page, use the Digital Object link, click “Go to file” in ArchivesSpace, and navigate to the page number shown here.

Current limitations
  • Automated transcriptions can contain missing or incorrect text or unintended repetition. Difficult handwriting, image quality, unusual layouts, and multiple languages can reduce accuracy. Always consult the original page image when an exact reading matters.
  • Semantic search remains experimental. Its rankings are an additional discovery aid, not a complete or definitive set of relevant results, and highly ranked pages can sometimes be only broadly related.
  • This is an active prototype. Search coverage, transcriptions, functionality, and the interface may continue to change as additional archival material is processed and the system is improved.

← Back to results

Page 16 · DO #4677 · 231_Rogers_Bx2FF38_r

Collection
David James Rogers (1918–2007) papers
Item/Folder
Scientific Reviews, 1968
Digital Object
DO #4677, page 16
Collection-level dates
1948–1977
Open PDF at page 16 ↗

Page transcription

-3-

of the second objective, namely, to place floristic-taxonomic research on
a modern, computerized, information-system basis.

In a sense, taxonomists, plant or animal, have always been the keepers
of the biological information system. In the plant sciences the floras and
systematic monographs are timed-honored, if non-automated, storage and re-
trieval systems which through the years the plant taxonomist has produced
and revised, as both the means and the ends of his own research, for the
benefit of all who need to know about the kinds of plants in the world.
The hierarchical classification of the organisms, with its binomial nomen-
clature providing unique addresses for parceling and storing information,
has afforded the ideal means of structuring the data file.

Today the taxonomist no longer is able to cope with his historic re-
sponsibilities. Published knowledge has accumulated to the point where it
no longer can be synthesized rapidly and concisely by conventional methods
and media--not even to the extent of keeping the classificatory system
itself up to date, to say nothing of handling new information within this
system. Likewise, botanical collections (herbaria) have grown to the size
where they no longer are comprehensible to the individual scientist, and
thus a great wealth of information often goes untapped for want of suffi-
cient manhours to search and retrieve. Even the seemingly simple task of
supplying the correct name or an accurate description for a native or cul-
tivated species of plant may require hours or even days of valuable pro-
fessional attention, owing to unstandardized terminology, outdated nomen-
clature, or dispersed and unwieldy sources of information. Yet in this
day of molecular biology and ecosystem ecology, when it often is a matter
of national interest to be able to correlate many precise, authoritative
facts about our plant resources in the shortest possible time, biologists,
public leaders, and laymen are depending on the taxonomist more than ever,
though perhaps unwittingly, even while his power to deliver is diminishing
ever faster.

The time is overdue for plant taxonomists to design and implement a
modern information system, using the latest methods of electronic data
processing and communication, so that they can regain their historic control
of the biological data bank. The core of this system must be a dy-
namic, multi-access, taxonomically-structured electronic data bank, designed
for rapid and flexible input, processing, and output. Centralized or
decentralized, it must be accessible to all potential users by means of
the most advanced communications network that is economically feasible.
We propose, therefore, that Flora North America be developed as such a
dynamic, computerized data bank and that it not be developed merely as a
"one-shot," static publication in which, relatively speaking, the state
of knowledge would be rigidly embalmed. Many books and other forms of
output should be possible as more or less automatic by-products of the
data bank, and instant update, revision, and cumulation would be cardinal
attributes of the information system. This is not to deny the initial
and continuing goal of producing a flora of conventional book form, per-
haps in 4-6 volumes, at some stage. For the foreseeable future this seems