Updated [More info needed] sections

Updated following sections:
- Annotation process
- Who are the annotators?
- Considerations for Using the Data
- Bias, Risks, and Limitations
- Recommendations

README.md

@@ -32,7 +32,7 @@ description: "Collection of individual ground-beetle (Coleoptera: Carabidae) spe
 
 # Dataset Card for Hawaii Beetles
 
-Collection of ground beetle specimen images; specimens collected by the [U.S. National Ecological Observatory Network (NEON)](https://www.neonscience.org/) at the [Pu'u Maka'ala Natural Area Reserve](https://www.neonscience.org/field-sites/puum) on the Island of Hawai'i (the Big Island). This collection includes both group images (by-tray) and the individual segmented individuals.
+Collection of ground beetle specimen images; specimens collected by the [U.S. National Ecological Observatory Network (NEON)](https://www.neonscience.org/) at the [Pu'u Maka'ala Natural Area Reserve (PUUM)](https://www.neonscience.org/field-sites/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
 
@@ -49,7 +49,7 @@ Collection of ground beetle specimen images; specimens collected by the [U.S. Na
 ## 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](https://www.neonscience.org/) at the [Pu'u Maka'ala Natural Area Reserve (PUUM)](https://www.neonscience.org/field-sites/puum) on the Island of Hawai'i (the Big Island). 
+All specimens were collected by [NEON](https://www.neonscience.org/) at the [(PUUM)](https://www.neonscience.org/field-sites/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.
 
 
@@ -143,11 +143,27 @@ This dataset is a collection of images taken of the ground beetle collection at
 After imaging all the specimens, the data curation team segmented the individuals and measured the elytra length and width for each.
 
 #### Annotation process
-[More Information Needed]
-<!-- This section describes the annotation process such as annotation tools used, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+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.
+
+<!-- Trait annotations were generated using the TORAS (Trait Observation and Recording Annotation System) tool for precise measurements on high-resolution images of pinned specimens. Annotators manually marked coordinate pairs defining the endpoints of key morphological features: 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`). Euclidean distances were calculated in pixels (`px_scalebar`, `px_elytra_max_length`, `px_basal_pronotum_width`, `px_elytra_max_width`) and calibrated to centimeters (`cm_scalebar` = 1.0 constant; others converted using the scalebar calibration factor). Annotations focused on dorsal views to ensure visibility of taxonomically relevant features, with quality control to verify focus, lighting, and label legibility. Digital measurements were validated against manual caliper-based measurements, achieving sub-millimeter precision with transparent error quantification. A subset of 64 specimens was annotated independently by three annotators to assess inter-annotator agreement, and the results demonstrated high consistency (average RMSE ≈ 0.024 cm, R² ≈ 0.94). For validating TORAS as a tool, TORAS-based digital measurements were validated against manual caliper measurements on physical specimens, achieving sub-millimeter precision (RMSE ≈ 0.015 cm; R² > 0.97).-->
 
 #### Who are the annotators?
-[More Information Needed]
+
+  - 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 Climate 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.
+
+<!-- 
+Annotations were performed by a team of researchers and students participating in the Experiential Introduction to AI and Ecology Course organized by the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Climate Center. Key contributors included S M Rayeed, Mridul Khurana, Alyson East, and Elizabeth G. Campolongo. other co-authors are Samuel Stevens, Iuliia Zarubiieva, Jiaman (Lisa) Wu, Isadora E. Fluck, Scott C. Lowe, Evan D Donso -- with oversight from advisors Graham W. Taylor and Sydne Record. The fieldwork and imaging occurred at the PUUM site in Hawaii from January 15-29, 2025.
+-->
+
 <!-- This section describes the people or systems who created the annotations. -->
 
 
@@ -156,19 +172,38 @@ After imaging all the specimens, the data curation team segmented the individual
 Our data does not contain any personal or sensitive Information.
 
 ## Considerations for Using the Data
-[More Information Needed]
+
+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 APIs 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.
+
 <!--
 Things to consider while working with the dataset. For instance, maybe there are hybrids and they are labeled in the `hybrid_stat` column, so to get a subset without hybrids, subset to all instances in the metadata file such that `hybrid_stat` is _not_ "hybrid".
 -->
 
-### Bias, Risks, and Limitations
-[More Information Needed]
-<!-- This section is meant to convey both technical and sociotechnical limitations. Could also address misuse, malicious use, and uses that the dataset will not work well for.-->
+## 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.
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. Could also address misuse, malicious use, and uses that the dataset will not work well for. For instance, if your data exhibits a long-tailed distribution (and why). -->
 
-<!-- For instance, if your data exhibits a long-tailed distribution (and why). -->
 
 ### Recommendations
-[More Information Needed]
+
+  - Mitigating Geographic Bias: To address the limited geographic scope of the Hawai‘i dataset, combine it collections from other NEON terrestrial sites across multiple domains. 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, augment the dataset with external image and trait repositories (e.g., GBIF, iDigBio, or other museum collections). This will expand coverage across genera and species, facilitating more balanced training datasets and more robust cross-species generalization in machine learning models.
+  - 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.
+  - 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. 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.
+
+<!--
+  - Combine with multi-domain NEON data (vial specimens, other sites) for continental analyses
+  - Augment with external image/trait datasets to balance long-tailed distribution
+  - For AI applications, augment with additional images to address the small sample size and ensure models account for regional morphological variation.
+  - Validate any automated trait extractions against the provided manual measurements, and report error rates transparently.
+  - When linking to NEON environmental data, use identifiers like `plotID` and `collectDate` for accurate integration, enabling studies on trait-environment interactions.
+  - Avoid using for continental-scale inferences without supplementary data, and consider ethical applications in biodiversity monitoring and conservation to align with the dataset's ecological focus.
+-->
+
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->