Cataloging the 3D web: the availability of educational 3D models on the internet
Bibliographic record
Abstract
Purpose – The number of 3D models available on the internet to both students and educators is rapidly expanding. Not only are the 3D model collections of popular websites like Thingiverse.com growing, organizations such as the Smithsonian Institution and NASA have also recently begun to build collections of 3D models and make these openly accessible online. Yet, even with increased interest in 3D printing and 3D scanning technologies, little is known about the overall structure of the 3D models available on the internet. The paper aims to discuss this issue. Design/methodology/approach – To initiate this project, a list was built of 33 of the most widely used 3D model websites on the internet. Freely downloadable models, as well as models available for purchase or as 3D printed objects were included in the list. Once the list of 33 websites was created, the data for each individual 3D model in the collections was manually assembled and recorded. The titles of the 3D models, keywords, subject headings, license information, and number of views and downloads were recorded, as this information was available. The data were gathered between January and May 2015, and compiled into a CSV database. To determine how online 3D model content relates to a variety of educational disciplines, relevant subject terms for a variety of educational disciples were extracted from the EBSCO database system. With this list of subject terms in hand, the keywords in the CSV database of model information were searched for each of the subject terms, with an automated process using a Perl script. Findings – There have been many teachers, professors, librarians and students who have purchased 3D printers with little or no 3D modelling skills. Without these skills the owners of these 3D printers are entirely reliant on the content created and freely shared by others to make use of their 3D printers. As the data collected for this research paper shows, the vast majority of open 3D model content available online pertains to the professions already well versed in 3D modelling and Computer Aided Design design, such as engineering and architecture. Originality/value – Despite that fact that librarians, teachers and other educators are increasingly using technologies that rely on open 3D model content as educational tools, no research has yet been done to assess the number of 3D models available online and what educational disciplines this content relates to. This paper attempts to fill this gap, providing an overview of the size of this content, the educational disciplines this content relates to and who has so far been responsible for developing this content. This information will be valuable to librarians and teachers currently working with technology such as 3D printers and virtual reality, as well as those considering investing in this technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.035 | 0.047 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.034 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".