Datasets:

imageomics
/

Hawaii-beetles

@@ -168,15 +168,12 @@ Our data does not contain any personal or sensitive information.
 This dataset comprises pinned beetle specimens collected from the [NEON PUUM site](https://www.neonscience.org/field-sites/puum) 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](https://data.neonscience.org/data-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.
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-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".
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 ## 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](https://www.neonscience.org/field-sites/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). -->
 ### Recommendations

 This dataset comprises pinned beetle specimens collected from the [NEON PUUM site](https://www.neonscience.org/field-sites/puum) 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](https://data.neonscience.org/data-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](https://www.neonscience.org/field-sites/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