imageomics/Hawaii-beetles

Dataset Card for Hawaii Beetles

Collection of ground beetle specimen images; specimens collected by the U.S. National Ecological Observatory Network (NEON) at the Pu'u Maka'ala Natural Area Reserve (PUUM) on the Island of Hawai'i (the Big Island). This collection includes both group images (by-tray) and the individual segmented individuals.

Dataset Details

Dataset Description

This dataset comprises 1,614 high-resolution PNG images of individual ground-beetle specimens (Coleoptera: Carabidae) representing 14 distinct species. The group images from which the individuals were segmented are also included. All specimens were collected by NEON at the (PUUM) site on the Island of Hawai'i (the Big Island). Each image is accompanied by trait annotations and measurements, providing valuable data for morphological and ecological analyses of these ground-beetle species from the Hawaii NEON site.

Key Uses

• species-level classification or retrieval
• object detection / instance segmentation on natural-history collections
• automated extraction of morphological traits (elytra length)
• ecological modeling via linkage to NEON environmental streams

Dataset Structure

group_images/
    IMG_<id>.png
    ...
individual_specimens/
    IMG_<id>_specimen_<number>_<taxonID>_<individualID>.png
    ...
images_metadata.csv
trait_annotations.csv

README.md

Data Fields

images_metadata.csv: Individual specimen identification information with collection location and date information from NEON.

• individualImageFilePath: Path to the individually cropped beetle image, e.g., individual_specimens/IMG_<id>_specimen_<number>_<taxonID>_<individualID>.png, where <id> matches the group image and <number> indicates the beetle's position within that group image. Ex: individual_specimens/IMG_0093_specimen_1_MECKON_NEON.BET.D20.000001.png.
• groupImageFilePath: Path to grouped beetle images. e.g., group_images/IMG_<id>.png where IMG_<id> is the image name assigned by the camera roll. Ex: group_images/IMG_0093.png.
• individualID: Unique identification number assigned by NEON to each individual beetle. This begins with NEON.BET.D20 to show that the unique ID corresponds to a beetle from the National Ecological Observatory Network's Domain 20, followed by six digits. Ex: NEON.BET.D20.000001.
• taxonID: All pinned beetles in this dataset have been identified to genus and species. Here a six-letter code is given specifying the first three letters of the genus followed by the first three letters of the specific epithet. Ex: MECKON.
• ScientificName: Binomial scientific name of the specimen (<Genus specific epithet>, ex: Mecyclothorax konanus).
• plotID: A numeric code that corresponds to the NEON plot in which the individual beetle was collected (numbers, up to 18).
• trapID: The cardinal direction (E - east, S - south, W - west) indicating which side of the plot the beetle was collected from.
• plotTrapID: A three letter code that corresponds to the NEON plot from which the individual beetle was collected along with direction (ex: 006W for plotID 6 and trapID W).
• collectDate: The date NEON staff collected the beetle specimen from the pitfall trap. It follows the YYYYMMDD format.
• ownerInstitutionCode: NEON owner code (NEON).
• catalogNumber: NEON catalog number (ex: DP1.10022.001).

trait_annotations.csv: Individual specimen annotation/measurement information. See Figure 1 in Annotations for a visual representation of the annotation and measurement process.

• groupImageFilePath: File path to grouped beetle specimen images. Format: group_images/IMG_<id>.png, where IMG_<id> corresponds to the unique image identifier assigned by the camera system.
• BeetlePosition: Ordinal position of the individual beetle specimen within the group image (dorsal view). Specimens are numbered sequentially from top to bottom: topmost specimen = 1, subsequent specimens = 2, 3, 4, etc.
• individualID: Unique identifier assigned by NEON to each individual beetle specimen. This begins with NEON.BET.D20 to show that the unique ID corresponds to a beetle from the National Ecological Observatory Network's Domain 20, followed by six digits. Ex: NEON.BET.D20.000001. This allows for linking to the images_metadata.csv and additional metadata provided through the NEON data portal.
• coords_scalebar: X and Y coordinate pairs defining the endpoints of the 1 cm reference scalebar, positioned in the upper or upper-left portion of each image. Ex: "[[5713.91, 3045.68, 5701.21, 2265.92]]".
• coords_elytra_max_length: X and Y coordinate pairs defining the endpoints of the maximum elytral length measurement. Measured from the midpoint of the elytro-pronotal suture (junction between pronotum and elytra) to the midpoint of the elytral apex (posterior terminus of the elytra). Ex: "[[3865.5, 1245.87, 3881.25, 1045.81]]".
• coords_basal_pronotum_width: X and Y coordinate pairs defining the endpoints of the basal pronotal width measurement at the elytro-pronotal junction. Ex: "[[3922.92, 1046.2, 3872.53, 1035.06]]".
• coords_elytra_max_width: X and Y coordinate pairs defining the endpoints of the maximum elytral width measurement. Represents the greatest transverse distance across both elytra, measured orthogonal to the elytral length axis. Ex: "[[3960.08, 1145.79, 3814.38, 1123.85]]".
• px_scalebar: Euclidean distance between coordinate endpoints of the reference scalebar (coords_scalebar) expressed in pixels¹.
• px_elytra_max_length: Euclidean distance between coordinate endpoints of the maximum elytral length (coords_elytra_max_length) expressed in pixels¹.
• px_basal_pronotum_width: Euclidean distance between coordinate endpoints of the basal pronotal width (coords_basal_pronotum_width) expressed in pixels¹.
• px_elytra_max_width: Euclidean distance between coordinate endpoints of the maximum elytral width (coords_elytra_max_width) expressed in pixels¹.
• cm_scalebar: Calibrated length of the reference scalebar in centimeters. Constant value of 1.0 cm as this represents the standard reference scale used for all measurements.
• cm_elytra_max_length: Calibrated maximum elytral length in centimeters², calculated by converting pixel measurements using the scalebar calibration factor.
• cm_basal_pronotum_width: Calibrated basal pronotal width in centimeters² at the elytro-pronotal suture, calculated by converting pixel measurements using the scalebar calibration factor.
• cm_elytra_max_width: Calibrated maximum elytral width in centimeters², representing the greatest transverse dimension across the fused elytra, calculated by converting pixel measurements using the scalebar calibration factor.

1: The measurement is up to 14 decimal places.

2: The measurement is up to 3 decimal places. To get measurements with more numerical precision (i.e. additional decimal places), use this equation: cm_<measurement> = px_<measurement>/px_scalebar.

Dataset Creation

This dataset was compiled as part of the field component of the Experiential Introduction to AI and Ecology Course run by the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. This field work was done on the island of Hawai'i January 15-29, 2025.

Curation Rationale

Ground beetles (Coleoptera: Carabidae) serve as critical bioindicators for ecosystem health, providing valuable insights into biodiversity shifts driven by environmental changes. Understanding their distribution, morphological traits, and responses to environmental conditions is essential for ecological research and conservation efforts. While the National Ecological Observatory Network (NEON) maintains an extensive collection of carabid specimens, these primarily exist as physical collections, restricting widespread research access and large-scale analysis. Despite the ecological significance of invertebrates, global trait databases remain heavily biased toward vertebrates and plants, leaving a critical “invertebrate gap” that hinders comprehensive ecological analyses, particularly for hyper-diverse groups like carabids. Existing beetle datasets lack standardized, high-resolution trait measurements like those provided here, limiting trait-based ecological studies. Morphological traits, such as elytra length and width, are paramount because they directly link to ecological processes like dispersal, niche partitioning, and responses to environmental stressors, enabling predictive modeling of biodiversity under global change.

Source Data

The specimens come from the PUUM NEON site. For more information about general NEON data, please see their Ground beetles sampled from pitfall traps page.

Our team photographed the beetles in 2025, using Canon EOS DSLR (model 7D).

Data Collection and Processing

Beetles were collected by PUUM NEON field technicians from 2018 through 2024.

Specimens and identification are provided by NEON Ground beetles sampled from pitfall traps.

Who are the source data producers?

This dataset is a collection of images taken of the ground beetle collection at the PUUM NEON field site, collected by their technicians from 2018 through 2024. The associated labels and metadata are provided by the NEON team and were recorded based on the labels associated to each pinned specimen.

Annotations

After imaging all the specimens, the data curation team segmented the individuals and measured the elytra length and width for each.

Figure 1. Artificial depiction of trait measurement “recipe” for pinned specimens. Left: Fictional group image of pinned beetles with scalebar, middle: one individual specimen with three traits measured (elytral length in red, basal pronotum width in blue, and maximum elytral width in green), right: measurement of the centimeter scalebar. Images shown in this figure were generated using Microsoft CoPilot.

Annotation process

Trait annotations were produced using TORAS (Trait Observation and Recording Annotation System), a high-precision tool designed for detailed morphological measurements on high-resolution images of pinned beetle specimens. Annotators manually placed coordinate pairs marking the endpoints of key anatomical landmarks: the 1 cm reference scalebar (coords_scalebar), maximum elytral length (coords_elytra_max_length), basal pronotal width at the elytro-pronotal junction (coords_basal_pronotum_width), and maximum elytral width (coords_elytra_max_width). From these coordinates, Euclidean distances were computed in pixels (px_scalebar, px_elytra_max_length, px_basal_pronotum_width, px_elytra_max_width) and converted to centimeters using the scalebar calibration factor (cm_scalebar = 1.0 cm). Annotations were performed exclusively on dorsal-view images to maximize visibility of diagnostic morphological traits. Rigorous quality control ensured that each image met predefined standards for focus, illumination, and label legibility.

For validation, a subset of 64 specimens was measured physically with digital calipers by three independent annotators. These same specimens were then used for two complementary analyses:

1. Inter-annotator agreement, assessing consistency among the three caliper-based measurements (average RMSE ≈ 0.024 cm, R² ≈ 0.94); and
2. TORAS vs. calipers, comparing digital TORAS-derived measurements against the mean of the three manual caliper measurements, demonstrating sub-millimeter precision (RMSE ≈ 0.015 cm; R² > 0.97).

Together, these results confirm that TORAS measurements closely reproduce manual ground-truth measurements while maintaining high inter-annotator consistency, establishing the reliability and reproducibility of the annotation process for quantitative morphological trait extraction.

Who are the annotators?

• Annotations were conducted by a team of researchers and students from the Experiential Introduction to AI and Ecology Course, jointly organized by the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center.
• Primary contributors include S. M. Rayeed, Mridul Khurana, Alyson East, and Elizabeth G. Campolongo, with additional contributions from Samuel Stevens, Iuliia Zarubiieva, Jiaman (Lisa) Wu, and Scott C. Lowe. Evan D. Donso, a NEON field technician, assisted with specimen handling, data collection, and trait measurement using calipers.
• All annotation work was performed under the supervision of advisors Graham W. Taylor and Sydne Record. Fieldwork and imaging were carried out at the NEON PUUM site between January 15–29, 2025.

Personal and Sensitive Information

Our data does not contain any personal or sensitive information.

Considerations for Using the Data

This dataset comprises pinned beetle specimens collected from the NEON PUUM site between 2018 and 2024, representing 14 identified species within the Carabidae family. While taxonomically and geographically constrained, the dataset provides high-quality, standardized imagery and trait data suitable for AI, computer vision, and ecological modeling applications. Each specimen image is a high-resolution dorsal view, optimized for automated trait extraction, object detection, and segmentation. No ventral or lateral views are included. Trait measurements—such as elytral length and width—are fully calibrated using a 1 cm reference scalebar and have been validated to sub-millimeter precision, ensuring reliability for quantitative analyses. Specimens can be linked to NEON’s environmental and ecological data streams, including climate, vegetation, and co-located taxa (e.g., plants, mammals, and birds), via shared identifiers such as plotID, trapID, plotTrapID, and collectDate. For programmatic integration, users may access broader NEON metadata through the NEON API using individualID or sampleCode. All images adhere to FAIR data principles, supporting findability, accessibility, interoperability, and reusability across biodiversity and ecological research platforms. Overall, this dataset serves as a robust foundation for trait-based ecological modeling, species-level computer vision tasks, and integration with multi-domain NEON data, provided users account for its limited geographic and taxonomic scope.

Bias, Risks, and Limitations

The dataset exhibits several inherent biases and limitations that should be considered when interpreting results or developing models. Geographically, it is limited to a single tropical site (PUUM), which is not representative of the diverse environmental conditions found across the continental United States, such as deserts, temperate forests, or taiga ecosystems. Taxonomically, the dataset includes only 14 of more than 40,000 known carabid species, with a long-tailed distribution dominated by a few genera — primarily Mecyclothorax and Trechus — thus underrepresenting the broader diversity of the Carabidae family. Sampling bias arises from the exclusive use of pitfall traps, which preferentially capture ground-active and diurnal beetles while largely excluding arboreal or flying taxa. There is also limited coverage of intraspecific variation, as specimens do not span a wide range of geographic clines, life stages, or microhabitats. From a technical perspective, imaging artifacts such as minor glare or partial label obstruction may persist despite quality control procedures. The dataset’s scale — with 1,614 images — makes it relatively small for standalone large-scale machine learning applications without data augmentation. Finally, there is a risk of misuse, as AI models trained solely on this dataset may exhibit poor generalization when applied to other regions, species, or imaging conditions, underscoring the importance of cross-dataset validation and ecological context awareness.

Not all beetle specimens that are collected during a sampling event are pinned (and thus imaged and included in this dataset). If a species has a high abundance (n>10) at a given plot in a 2 week sampling bought, and all individuals of that species are identified by parataxonomists with a high degree of confidence, then that taxa may not included in this dataset for that plot-date combination.

Recommendations

• Mitigating Geographic Bias: To address the limited geographic scope of the Hawai‘i dataset, consider combining it with collections from other NEON terrestrial sites across multiple domains (e.g., 2018 NEON Ethanol-preserved Ground Beetles and Sentinel Beetles). This integration will enable continental-scale analyses of trait–environment relationships and improve ecological generalizability across biomes.
• Balancing Taxonomic Representation: To reduce the effects of the long-tailed species distribution, one can augment the dataset with external image and trait repositories (e.g., GBIF, iDigBio, or other museum collections). This has the potential to expand coverage across genera and species, facilitating more balanced training datasets and more robust cross-species generalization in machine learning models. When combining taxonomic data from multiple sources, be sure to align the taxonomic backbone used for labels to ensure full alignment. TaxonoPy was developed to accomplish this type of alignment (for TreeOfLife-200M).
• For AI and computer vision applications, researchers should augment the dataset with additional images to overcome the relatively small sample size and enhance model robustness. Expanding image diversity across species, sites, and lighting conditions will help models better capture regional morphological variation and reduce overfitting to the specific imaging setup used for the Hawai‘i specimens. As noted above, different sources may use different taxonomic backbones, this should be accounted for in any compilation (e.g., with TaxonoPy).
• When developing or testing automated measurement pipelines, users are strongly encouraged to validate all digital trait extractions against the provided manually verified measurements. Reporting quantitative error rates (e.g., RMSE, bias, R²) will ensure transparency and maintain the high standard of reproducibility established in the original validation study, which demonstrated sub-millimeter accuracy for elytral traits.
• For ecological analyses, it is essential to link specimen-level traits to NEON environmental data using identifiers such as plotID and collectDate. This enables spatially and temporally explicit studies on trait–environment relationships, including responses to climate gradients, habitat conditions, or ecological disturbances.
• Researchers should avoid drawing continental-scale ecological or evolutionary inferences based solely on this dataset, as it represents a single tropical site. Broader-scale interpretations require supplementary datasets that capture geographic and taxonomic variation. As noted above, be sure to align the taxonomic naming from disparate sources (e.g., with TaxonoPy). Moreover, users are encouraged to consider the ethical implications of AI deployment in biodiversity monitoring and conservation, ensuring that research derived from this dataset aligns with its intended purpose of advancing ecological understanding and supporting conservation outcomes.

Licensing Information

Images and associated metadata: Creative Commons Attribution 4.0.

Citation

If you use this dataset in your research, please cite the dataset, source data (specimen collection and metadata), and our paper. Please also include the NEON acknowledgements provided below.

Dataset:

@dataset{rayeed2025HawaiiBeetles,
  title  = {Hawaii Beetles},
  author = {S M Rayeed and Mridul Khurana and Alyson East and Samuel Stevens and Iuliia Zarubiieva and Jiaman (Lisa) Wu and Isadora E. Fluck and Scott C. Lowe and Elizabeth G.
Campolongo and Evan D. Donoso and Tanya Berger-Wolf and Hilmar Lapp and Charles V Stewart and Graham W. Taylor and Sydne Record},
  year   = {2025},
  url    = {https://huggingface.co/datasets/imageomics/Hawaii-beetles},
  note   = {Version 1.0, CC-BY-4.0},
  doi    = {}
}

Specimens (Source Data):

@misc{NEON-pinned-specimens,
  title     =  {{NEON} biorepository Carabid collection (pinned vouchers, ID: b33569cb-c4aa-4acd-83d6-d6d1e04c4c90)},
  author    =  {{Bernice Pauahi Bishop Museum}},
  publisher =  {National Ecological Observatory Network (NEON)},
  month     =  {jan},
  year      =  {2025},
  note      =  {Accessed on-site, at Domain 20, in January 2025},
  url       =  {https://biorepo.neonscience.org/portal/collections/misc/collprofiles.php?collid=97}
}

@misc{NEON-pinned-beetles-metadata,
  url = {https://data.neonscience.org/data-products/DP1.10022.001},
  author = {{National Ecological Observatory Network (NEON)}},
  keywords = {diversity, taxonomy, community composition, species composition, population, invertebrates, abundance, beetles, Carabidae, insects, DNA sequences, COI, DNA barcoding, ground beetles, pitfall traps, material samples, archived samples, bet, introduced species, invasive species, native species, biodiversity},
  language = {en},
  title = {Ground beetles sampled from pitfall traps (DP1.10022.001), provisional data},
  publisher = {National Ecological Observatory Network (NEON)},
  year = {2025},
  note = {Accessed January 2025}
}

Paper: Coming Soon!

Acknowledgements

This work was supported by both the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). The ABC Global Center is funded by the US National Science Foundation under Award No. 2330423 and Natural Sciences and Engineering Research Council of Canada under Award No. 585136. This dataset draws on research supported by the Social Sciences and Humanities Research Council.

S. Record and A. East were additionally supported by the US National Science Foundation's Award No. 242918 (EPSCOR Research Fellows: NSF: Advancing National Ecological Observatory Network-Enabled Science and Workforce Development at the University of Maine with Artificial Intelligence) and by Hatch project Award #MEO-022425 from the US Department of Agriculture’s National Institute of Food and Agriculture.

This material is based in part upon work supported by the U.S. National Ecological Observatory Network (NEON), a program sponsored by the U.S. National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material uses specimens and/or samples collected as part of the NEON Program.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US National Science Foundation, the US Department of Agriculture, the Natural Sciences and Engineering Research Council of Canada, or the Social Sciences and Humanities Research Council.