Restructure (#4)

- move balanced metadata to root (b1c89652e6ab224c3a199677904c18cb1fd75311)
- restructure larger CSVs (750962a1e9334a38756d3331eb73fb8360e9b419)
- remove extra larger files (dc389c2a4fe2b9babd40772878c6f612862d7933)
- remove older files (090125eab9bda9702b3da49f5580092e55cdd53a)
- update dataset description (5c8b275f97b842068b9d098b4e46afee4b520ce9)
- emphasize species classification task (ae4e6aae2568e4905fdc397b814cadbb08d2d5ac)

README.md

@@ -1,257 +1,127 @@
 ---
-license: cdla-permissive-1.0
 language:
 - en
-pretty_name: LILA BC Camera Trap Data
+- la
+pretty_name: IDLE-OO Camera Traps
 tags:
 - biology
 - image
+- imageomics
 - animals
 - CV
+- balanced
 - camera traps
+- mammals
+- birds
+- reptiles
+- amphibians
+- lions
+- rodents
+- frogs
+- toads
+- island
+- desert
+- ohio
 size_categories:
-- 1M<n<10M
+- 1K<n<10K
+task_categories:
+- image-classification
+- zero-shot-classification
 ---
 
-<!--
+# Dataset Card for IDLE-OO Camera Traps
 
-NOTE: Add more tags (your particular animal, type of model and use-case, etc.).
-
-As with your GitHub Project repo, it is important to choose an appropriate license for your dataset. The default license is pddl (public domain dedication or CC0, see [Dryad's explanation of why to use CC0](https://blog.datadryad.org/2023/05/30/good-data-practices-removing-barriers-to-data-reuse-with-cc0-licensing/)). Alongside the appropriate stakeholders (eg., your PI, co-authors), select a license that is [Open Source Initiative](https://opensource.org/licenses) (OSI) compliant.
-For more information on how to choose a license and why it matters, see [Choose A License](https://choosealicense.com) and [A Quick Guide to Software Licensing for the Scientist-Programmer](https://doi.org/10.1371/journal.pcbi.1002598) by A. Morin, et al.
-See the [Imageomics policy for licensing](https://docs.google.com/document/d/1SlITG-r7kdJB6C8f4FCJ9Z7o7ccwldZoSRJKjhRAWVA/edit#heading=h.c1sxg0wsiqru) for more information.
-
-See more options for the above information by clicking "edit dataset card" on your repo.
-
-Fill in as much information as you can at each location that says "More information needed".
--->
-
-<!--
-Image with caption:
-|![Figure #](https://huggingface.co/imageomics/<data-repo>/resolve/main/<filename>)|
-|:--|
-|**Figure #.** [Image of <>](https://huggingface.co/imageomics/<data-repo>/raw/main/<filename>) <caption description>.|
--->
-
-<!--
-Notes on styling:
-
-To render LaTex in your README, wrap the code in `\\(` and `\\)`. Example: \\(\frac{1}{2}\\)
-
-Escape underscores ("_") with a "\". Example: image\_RGB
--->
-
-# Dataset Card for LILA BC Camera Trap Data
-
-<!-- Provide a quick summary of what the data is or can be used for. --> 
-
-## Dataset Description
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Homepage:** 
-- **Repository:** [related project repo]
-- **Paper:** 
-- **Leaderboard:** 
-- **Point of Contact:** 
-
-### Dataset Summary
-
-<!-- Provide a longer summary of what this data is. -->
-
-<!--This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).-->
-
-This dataset contains the LILA BC full camera trap information with notebook ([`lilabc_CT.ipynb`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/main/notebooks/lilabc_CT.ipynb)) exploring available data. The last run of this (in [commit 010ecf0](https://huggingface.co/datasets/imageomics/lila-bc-camera/commit/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e)) uses and produces the lila CSVs found [here](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data).
-More details on this are below in [Data Instances](#data-instances).
-
-Looks at potential test sets constructed from 7 different LILA datasets (uses [data/potential-test-sets/lila_image_urls_and_labels.csv](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-test-sets/lila_image_urls_and_labels.csv) (sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455) to separate them out):
-We're specifically interested in the following datasets identified in the [spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link) as labeled at the image-level.
-  - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/)
-  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
-  - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
-  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
-  - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps)
-  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
-  - [ENA24-detection](https://lila.science/datasets/ena24detection)
-
-There are 2,867,312 images in this subset (once humans and non-creatures are removed).
-
-[NOAA Puget Sound Nearshore Fish 2017-2018](https://lila.science/datasets/noaa-puget-sound-nearshore-fish) could be interesting for the combined categories, though it is _very_ general (has only three labels: `fish`, `crab`, `fish_and_crab`). It also isn't included in the CSV, so not explored further.
-
-More details on this provided in [Test Data Instances](#test-data-instances), below.
-
-**Repo file description at [commit 87e2e4d](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/87e2e4d46cf1e8daadd74b7738856a1e30754de3) when we were considering it for BioCLIP v1 testing:**
-
-Images have been deduplicated and reduced down to species designation, with the main CSV filtered to just those with species labels and only one animal per image. This was done by pulling the first instance of an animal so that there are not repeat images of the same animal from essentially the same time.
-
-The deduplicated collection ([lila_image_urls_and_labels_species.csv](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila_image_urls_and_labels_species.csv)) has 6,365,985 images (compared to the full dataset of 16,833,848 at time of download). Its [associated taxonomy mapping release](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila-taxonomy-mapping_release.csv).
-
-See the [LILA BC HF Dataset](https://huggingface.co/datasets/society-ethics/lila_camera_traps) for more inforamtion and updated data.
+IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) with a total of 2,586 images for species classification. Each of the 5 benchmarks is **balanced** to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species.
 
+### Dataset Description
 
+- **Curated by:** Elizabeth Campolongo, Jianyang Gu, and Net Zhang
+- **Homepage:** https://imageomics.github.io/bioclip-2/
+- **Paper:** TBA
 
 ### Supported Tasks and Leaderboards
-[More Information Needed]
+Image classification, particularly for species classification in camera trap images.
 
 ### Languages
-[More Information Needed]
+English, Latin
 
 ## Dataset Structure
 
 ```
 /dataset/
-    data/
-        lila-taxonomy-mapping_release.csv
-        lila_image_urls_and_labels.csv
-        lila_image_urls_and_labels_species.csv    # Outdated
-        lila_image_urls_and_labels_wHumans.csv
-        potential-test-sets/
-            lila-taxonomy-mapping_release.csv
-            lila_image_urls_and_labels.csv
-                filtered/
-                    ENA24-imbalanced.csv
-                    ENA24-balanced.csv
-                    ENA24-balanced-small.csv
-                    desert-lion-upper-lower-bound.csv
-                    desert-lion-upper-bound.csv
-                    desert-lion-balanced.csv
-                    island-lower-bound_common.csv
-                    island-lower-bound_family.csv
-                    island-imbalanced_family.csv
-                    island-balanced.csv
-                    island-imbalanced_common.csv
-                    ohio-small-animals-upper-lower-bound.csv
-                    ohio-small-animals-upper-bound.csv
-                    ohio-small-animals-balanced.csv
-                    orinoquia-upper-lower-bound.csv
-                    orinoquia-upper-bound.csv
-                    orinoquia-balanced.csv
+    desert-lion-balanced.csv
+    ENA24-balanced.csv
+    island-balanced.csv
+    ohio-small-animals-balanced.csv
+    orinoquia-balanced.csv
+    images/
+        desert-lion/
+            <image 1>
+            <image 2>
+            ...
+            <image 352>
+        ENA24/
+            <image 1>
+            <image 2>
+            ...
+            <image 1120>
+        island/
+            <image 1>
+            <image 2>
+            ...
+            <image 310>
+        ohio-small-animals/
+            <image 1>
+            <image 2>
+            ...
+            <image 468>
+        orinoquia/
+            <image 1>
+            <image 2>
+            ...
+            <image 336>
     notebooks/
         lilabc_CT.ipynb
         lilabc_CT.py
         lilabc_test-<dataset_name>.ipynb
-        lilabc_test-EDA.py
         lilabc_test-filter.ipynb
         lilabc_test-filter.py
+    potential-test-sets/
+        lila-taxonomy-mapping_release.csv
+        lila_image_urls_and_labels.csv
+        <dataset_name>_image_urls_and_labels.csv
 ```
 
-**Notes:**
- - `dataset_name` is one of `desert-lion`, `ENA24`, `island`, `ohio-small-animal`, or `orinoquia`. Each collection of `<dataset_name>-<size_indicator>` CSVs are created in their corresponding `lilabc_test-<dataset_name>` notebook.
- - All the "balanced" datasets and `ENA24-balanced-small.csv` have 12 images per species (or family, in the case of the island-balanced CSV). `ENA24-balanced.csv` has 56 images per species.
- - `upper-bound` are max 10K images per species, with no minimum (this often means the smallest classification class has just 1 image).
- - `upper-lower-bound` CSVs are max 10K images per species and minimum of 10.
- - ENA24 has a minimum of 56 images per species and a maximum of 893, so `ENA24-imbalanced.csv` is just all images containing a single species.
- - The island camera traps were mostly only labeled to family level, so there are common name and family versions. The `imbalanced` sets are just all images with common name or family designation, respectively. The `lower-bound` are only those with at least ten images per class (by common name and family), and `balanced` is just 12 images per family.
-
-
 ### Data Instances
 
-The [`data/lila_image_urls_and_labels.csv`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data/lila_image_urls_and_labels.csv) has all images with non-taxa (identified by `scientific_name`, `common_name`, and `kingdom` are null) or `human` original labels filtered out and has 10,104,328 images.
-7,521,712 have full 7-rank taxa, with 891 unique 7-tuple strings (908 unique including subranks), with 890 unique scientific names -- this count is from before humans were removed (there are 257,159 images with humans listed and they do have full 7-rank taxa).
-Final version at this stage has 9,849,119 images, 907 unique scientific names.
-
-**annotation_level**
-```
-sequence    4156306
-image       2892394
-unknown     2886844
-```
+**potential-test-sets/lila_image_urls_and_labels.csv:** Reduced down to the datasets of interest listed below (from [potential-test-sets/lila_image_urls_and_labels.csv](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-test-sets/lila_image_urls_and_labels.csv) (sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455)); all those with `original_label` "empty" or null `scientific_name` (these had non-taxa labels) were removed. 
+Additionally, we added a `multi_species` column (boolean to indicate multiple species are present in the image--it gets listed once for each species in the image) and a count of how many different species are in each of those images (`num_species` column).
+This was then subdivided into CSVs for each of the target datasets (`potential-test-sets/<dataset_name>_image_urls_and_labels.csv`) in `notebooks/lilabc_test-filter.ipynb`. Each dataset was evaluated and sampled in its associated notebook (`notebooks/lilabc_test-<dataset_name>.ipynb`).
 
-**non-taxa labels:**
-```
-original_label
-problem                   288579
-blurred                   184620
-ignore                    177546
-vehicle                    26445
-unknown                    26170
-snow on lens               17552
-foggy lens                 15832
-vegetation obstruction      6994
-malfunction                 5640
-unclassifiable              3484
-motorcycle                  3423
-misdirected                 2832
-other                       2474
-unidentifiable              1472
-foggy weather               1380
-lens obscured                866
-sun                          835
-end                          616
-fire                         578
-misfire                      400
-eye_shine                    328
-start                        321
-tilted                        56
-unidentified                  39
-```
-
-**Datasets with the non-taxa labels:**
-```
-dataset_name
-SWG Camera Traps                    650745
-Idaho Camera Traps                   66339
-NACTI                                26015
-WCS Camera Traps                     18320
-Wellington Camera Traps               3484
-Orinoquia Camera Traps                1280
-Island Conservation Camera Traps      1269
-Snapshot Serengeti                     568
-ENA24                                  293
-Channel Islands Camera Traps           159
-Snapshot Mountain Zebra                  7
-Snapshot Camdeboo                        3
-```
+There are 184 unique scientific names in this subset (180 by full 7-rank) of those labeled at the image-level (as indicated by the CSV). This was then subdivided into CSVs for each of the target datasets (`<dataset_name>-balanced.csv`).
+These were initially identified as image-level labeled datasets and those that are a meaningful measure of our biodiversity-focused model (e.g., includes rare species--those less-commonly seen, targeting areas with greater biodiversity). The balanced datasets for each are described below.
 
-### Test Data Instances
-
-**data/potential-test-sets/lila_image_urls_and_labels.csv:** Reduced down to the datasets of interest listed below; all those with `original_label` "empty" or null `scientific_name` (these had non-taxa labels) were removed. 
-Additionally, added a `multi_species` column (boolean to indicate multiple species are present in the image--it gets listed once for each species in the image) and a count of how many different species are in each of those images (`num_species` column).
-
-There are 367 unique scientific names in this subset (355 by full 7-rank), 184 unique among just those labeled at the image-level (180 by full 7-rank) (as indicated by the CSV). 
-This was then subdivided into CSVs for each of the target datasets (`data/potential-test-sets/<dataset_name>_image_urls_and_labels.csv`).
-These were initially identified from our [master spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?gid=0#gid=0), identifying image-level labeled datasets and those that are a meaningful measure of our biodiversity-focused model (e.g., includes rare species--those less-commonly seen, targeting areas with greater biodiversity).
-
-  - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) -- actually labeled by sequence, so not a good choice for testing
-  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
   - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
-  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
-  - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps) -- actually labeled by sequence, so not a good choice for testing
-  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
+      - 352 images: 32 species, with 11 images per species.
   - [ENA24-detection](https://lila.science/datasets/ena24detection)
+      - 1120 images: 20 species, with 56 images per species.
+  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
+      - 310 images: 16 species, with 10 images per species; 33 common names, 10 images per common name for all but 4 ("rooster", "petrel", "petrel chick", and "domestic chiecken"). This dataset was mostly just labeled to the family level.
+  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/):
+      - 468 images: 39 species, with 12 images per species.
+  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
+      - 336 images: 28 species, with 12 images per species.
 
-Multi-species counts (full):
-```
-num_species
-1.0     2753832
-2.0      114825
-3.0       13995
-4.0        1704
-5.0         230
-14.0         42
-```
-For Image-level labels:
-```
-num_species
-1.0    305821
-2.0      1154
-3.0         3
-```
-Looks like we'll have about 306K images across the 5 datasets that have image-level labels.
-
-
+**Notes:**
+ - `notebooks/lilabc_CT.ipynb` contains earlier analyses to understand the data provided by LILA BC (see commit [fe34008](https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps/commit/fe34008cba2ef33856291dd2d74cac21f6942cfc)).
+ - Not all notebooks will run under the current dataset organization (check the relative path, filenames have not changed).
 
 ### Data Fields
-[More Information Needed]
-<!--
-Describe the types of the data files or the columns in a CSV with metadata.
--->
-
-Each of the `<dataset_name>_<type>` CSVs has the following columns.
 
- - `dataset_name`: name of the LILA BC dataset
- - `url_gcp`, `url_aws`, `url_azure` are URLs to potentially access the image, we recommend `url_aws`.
- - `image_id`: unique identifier for the image.
+Each of the `<dataset_name>-balanced` CSVs has the following columns.
+ - `dataset_name`: full name of the LILA BC dataset, for reference in docs (this README) use the shorthand from the CSV names.
+ - `url_gcp`, `url_aws`, `url_azure` are URLs to potentially access the image, we used `url_aws` or  `url_gcp`.
+ - `image_id`: unique identifier for the image (provided by source).
  - `sequence_id`: ID of the sequence to which the image belongs.
  - `location_id`: ID of the location at which the camera was placed.
  - `frame_num`: generally 0, 1, or 2, indicates order of image within a sequence.
@@ -260,120 +130,139 @@ Each of the `<dataset_name>_<type>` CSVs has the following columns.
  - `common_name`: vernacular name of the animal in the image. For the island CSV, this is generally for the family, but it's a mix.
  - `kingdom`: kingdom of the animal in the image.
  - `phylum`: phylum of the animal in the image.
- - `class`: class of the animal in the image.
+ - `cls`: class of the animal in the image.
  - `order`: order of the animal in the image.
  - `family`: family of the animal in the image.
  - `genus`: genus of the animal in the image. About half null in the island CSVs.
  - `species`: species of the animal in the image. Mostly null in the island CSVs.
- - `num_sp_images`: number of images of that species in the dataset.
- For the island CSVs, instead of `num_sp_images` there are `num_fam_images` and `num_cn_images` representing the number of images for the family or common name, respectively.
+ - `filepath`: path to the image from the `images/` directory (`<dataset-name>/<image filename>`).
 
-Additionally, the `ohio-small-animals` CSVs have a `filename` column defined as `OH_sm_animals_<filename in url_aws>`.
+**Notes:**
 
+ - For all but the Ohio small animals dataset CSV, the images are named based on a `uuid` determined at the time of download. They were originally downloaded using the [distributed-downloader package](https://github.com/Imageomics/distributed-downloader), so they also have the following two columns:
+    - `hashsum_original`: MD5 hash of the original jpg image downloaded based on the CSV provided by LILA BC.
+    - `hashsum_resized`: MD5 hash of the resized image (based on setting to resize if over 720 pixels in any dimension).
+ - The `ohio-small-animals` CSV have a `filename` column defined as `OH_sm_animals_<filename in url_aws>` and a `md5` column containing the MD5 hash of the image as downloaded from the AWS bucket.
+ - The `island-balanced` CSV has an additional `num_cn_images` column indicating the number of images with that animal's common name.
 
 ### Data Splits
-[More Information Needed]
-<!--
-Give your train-test splits for benchmarking
--->
+
+These datasets were curated to create a small collection of camera trap image test sets. 
 
 ## Dataset Creation
 
 ### Curation Rationale
-[More Information Needed]
+As stated above, the goal of these datasets is to provide a collection of species classification test sets for camera trap images. Species classification within camera trap images is a real-world downstream use-case, on which a biological foundation model should be tested. These datasets were selected from those available on [LILA BC](https://lila.science/datasets) since they are labeled at the image-level, and would thus not include frames labeled as containing an animal when it is simply the animal's habitat. The [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/) were of particular interest for their stated purpose of assisting in the prevention of endangered island species' extinction and the varied ecosystems represented.
 
 ### Source Data
 
-#### Initial Data Collection and Normalization
-[More Information Needed]
-
-#### Who are the source language producers?
-[More Information Needed]
+The images and their labels come from the following 5 LILA BC datasets. The labels are provided at the image level (not sequence level). Please see the source links for more information on the individual datasets.
 
-### Annotations
+  - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
+  - [ENA24-detection](https://lila.science/datasets/ena24detection)
+  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
+  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
+  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
 
-#### Annotation process
-[More Information Needed]
 
-#### Who are the annotators?
-[More Information Needed]
+### Annotations
+Annotations provided by the source data providers ([aligned by LILA BC](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/)) are used for this test set.
 
 ### Personal and Sensitive Information
-[More Information Needed]
-
-<!-- For instance, if your data includes people or endangered species. -->
+These images come from an existing, public biodiversity data repository, which publishes them without associated GPS locations for the species in the images and they ensure the removal of all humans (who would otherwise have been labeled as such), so the there are no concerns.
 
 ## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
 
-### Discussion of Biases
-[More Information Needed]
+This collection of small balanced datasets was designed for testing the classification ability of [BioCLIP 2](https://github.com/Imageomics/bioclip-2) to classify species in camera trap images, a practical use-case and one on which it was not extensively trained.
 
-### Other Known Limitations
-[More Information Needed]
+### Bias, Risks, and Limitations
+The available species in these datasets is not a representative sample of species around the world, though they do cover a portion of species of interest to those collecting images using camera traps.
 
-<!-- For instance, if your data exhibits a long-tailed distribution (and why). -->
 
-## Additional Information
+## Licensing Information
 
-### Dataset Curators
-Elizabeth Campolongo
+This compilation is licensed under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/), same as the images and metadata which belong to their original sources (see citation directions below).
 
-### Licensing Information
 
-This compilation is licensed under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/), same as the images and metadata which belong to their original sources (see citation directions below).
+## Citation
 
-### Citation Information
+Please cite both this compilation and its constituent data sources:
 
-For test sets (provided citations on their LILA BC pages are included):
+```
+@dataset{idle-oo-camera-traps,
+  title = {{IDLE}-{OO} {C}amera {T}raps}, 
+  author = {Elizabeth G Campolongo and Jianyang Gu and Net Zhang},
+  year = {2025},
+  url = {https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps},
+  doi = {},
+  publisher = {Hugging Face}
+}
+```
+
+Please be sure to also cite the original data sources (provided citations on their LILA BC pages are included):
 
   - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
     - Balasubramaniam S. [Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference](https://etd.ohiolink.edu/acprod/odb_etd/etd/r/1501/10?clear=10&p10_accession_num=osu1721417695430687). Master’s thesis, The Ohio State University. 2024.
+    - Bibtex:
+      ```
+      @mastersthesis{balasubramaniam2024-oh-small,
+      author = {Balasubramaniam, S.},
+      title = {Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference},
+      school = {The Ohio State University},
+      year = {2024},
+      url = {http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687}
+      }
+      ```
   - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
+    - No citation provided by source, bibtex:
+      ```
+      @misc{lion-ct,
+      author = {Desert Lion Conservation},
+      title = {Desert Lion Conservation Camera Traps},
+      howpublished = {https://lila.science/datasets/desert-lion-conservation-camera-traps/},
+      month = {July},
+      year = {2024},
+      }
+      ```
   - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
     - Vélez J, McShea W, Shamon H, Castiblanco‐Camacho PJ, Tabak MA, Chalmers C, Fergus P, Fieberg J. [An evaluation of platforms for processing camera‐trap data using artificial intelligence](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14044). Methods in Ecology and Evolution. 2023 Feb;14(2):459-77.
+    - Bibtex:
+      ```
+      @article{velez2022choosing-orinoquia,
+        title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence},
+        author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John},
+        journal={arXiv preprint arXiv:2202.02283},
+        year={2022}
+      }
+      ```
   - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
+    - No citation provided by source, bibtex:
+      ```
+      @misc{island-ct,
+      author = {Island Conservation},
+      title = {Island Conservation Camera Traps},
+      howpublished = {https://lila.science/datasets/island-conservation-camera-traps/},
+      }
+      ```
   - [ENA24-detection](https://lila.science/datasets/ena24detection)
     - Yousif H, Kays R, Zhihai H. Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild. IEEE Transactions on Circuits and Systems for Video Technology, 2019. ([bibtex](http://lila.science/wp-content/uploads/2019/12/hayder2019_bibtex.txt))
+    - Bibtex:
+      ```
+      @article{yousif2019dynamic-ENA24,
+      title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild},
+      author={Yousif, Hayder and Kays, Roland and He, Zhihai},
+      journal={IEEE Transactions on Circuits and Systems for Video Technology},
+      year={2019},
+      publisher={IEEE}
+      }
+      ```
 
-[More Information Needed]
-<!--
-If you want to include BibTex, replace "<>"s with your info 
--for an associated paper:
-```
-@article{<ref_code>,
-  title    = {<title>},
-  author   = {<author1 and author2>},
-  journal  = {<journal_name>},
-  year     =  <year>,
-  url      = {<DOI_URL>},
-  doi      = {<DOI>}
-}
-```
--just for data:
-```
-@misc{<ref_code>,
-  author = {<author1 and author2>},
-  title = {<title>},
-  year = {<year>},
-  url = {https://huggingface.co/datasets/imageomics/<dataset_name>},
-  doi = {<doi once generated>},
-  publisher = {Hugging Face}
-}
-```
--->
-
-<!---
-If the data is modified from another source, add the following. 
-
-Please be sure to also cite the original data source:
-<citation>
--->
+## Acknowledgements
 
+This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). 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 National Science Foundation.
 
-### Contributions
+Additionally, we would like to acknowledge and thank [Labeled Information Library of Alexandria: Biology and Conservation (LILA BC)](https://lila.science) for providing a coordinated collection of camera trap images for research use.
 
-The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). 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 National Science Foundation.
+## Dataset Card Authors 
 
-<!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. -->
+Elizabeth G. Campolongo

data/lila_image_urls_and_labels.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1cb6c5e264db95cbeb52634763a5581ebe705461f22316c9deadf8c6ad20c84d
-size 7452744161

data/lila_image_urls_and_labels_species.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a5c4d35b108476ce87b9c9b91440279345dc35fbb2d57c43ee07da69b1ee8533
-size 2863476159

data/lila_image_urls_and_labels_wHumans.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:dfd1599bec3da3c252810b7bef4a3162a439677cbdd49735b1788f39adf1af62
-size 7631553713

data/potential-test-sets/filtered/desert-lion-upper-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:49584e3f9cd7f9e5ac0458f366779ac2222917a1f86ad671d361018483f2a114
-size 60820880

data/potential-test-sets/filtered/desert-lion-upper-lower-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:be643e7b4eaaf1c46c5d1c55cd62d77184e55b8edab5a4f2e3c7d80782ca273f
-size 56028990

data/potential-test-sets/filtered/island-imbalanced_common.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c9f8a03083b8579603ca4bfaaff89b86fb5012ca28531dc27a006ea427e7e1f0
-size 41982648

data/potential-test-sets/filtered/island-imbalanced_family.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a0fb2dfd80d2ef28630ecb276e9f2fb7a207c8cea009a1f4940545c749c0ff17
-size 37897112

data/potential-test-sets/filtered/island-lower-bound_common.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9f94c73b999364cd152907272918e634de714d640b1cb69bb5f24f97eb357617
-size 37903096

data/potential-test-sets/filtered/island-lower-bound_family.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d85c13874b9df3c86369115edf5c4fce3db36684bd47532eda16f84cb9e68a3d
-size 37894148

data/potential-test-sets/filtered/ohio-small-animals-upper-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:84648e040cae2f0aafbf9ce0489a0233301fb2d935ff78089e4779d1628f19e6
-size 74160481

data/potential-test-sets/filtered/ohio-small-animals-upper-bound_checksums.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:72456c743b741aeffd035a294bcb1f406670436a5d2215b43067799737e6cdc0
-size 12737032

data/potential-test-sets/filtered/ohio-small-animals-upper-lower-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7667248d1589e8dca60907590aef8680eae4072dc24e35ba20abe50bae027f54
-size 74136444

data/potential-test-sets/filtered/orinoquia-upper-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4436251d1f180d5cc93d3b02c437da256c3231771d2f1f6f42d0688e5eb4bf35
-size 35138377

data/potential-test-sets/filtered/orinoquia-upper-lower-bound.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:063f0c2f6805690cadfb28d90e50fffaade7b2cc950a30c036ea47a7ec98f730
-size 29981911