Update README.md

README.md

@@ -59,11 +59,7 @@ pretty_name: Labeled Information Library of Alexandria (LILA)
 
 ### Dataset Summary
 
-LILA BC is a repository for data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for biology and conservation.
-
-Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.
-
-LILA BC is intended to host data from a variety of modalities, but emphasis is placed on labeled images; it currently has over ten million labeled images.
+LILA BC is a repository for data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for biology and conservation. LILA BC is intended to host data from a variety of modalities, but emphasis is placed on labeled images; it currently has over ten million labeled images.
 
 See below for information about each specific dataset that LILA contains:
 
@@ -216,63 +212,169 @@ The original data set included a “human” class label; for privacy reasons, w
 
 <details>
 <summary> Channel Islands Camera Traps </summary>
+This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations.
+
+If you use these data in a publication or report, please use the following citation:
+
+The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset.
+
+For questions about this data set, contact [Nathaniel Rindlaub]([email protected]) at The Nature Conservancy.
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata.
 
 </details>
 
 <details>
 <summary> Idaho Camera Traps </summary>
+This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level.
+
+The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs).
 
+Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication. 
 </details>
 
 <details>
 <summary> Snapshot Serengeti </summary>
+This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/), the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem.
+
+Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images.
+
+The images and species-level labels are described in more detail in the associated manuscript:
+
+```bibtex
+@misc{dryad_5pt92,
+  title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna},
+  author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C},
+  year = {2015},
+  journal = {Scientific Data},
+  URL = {https://doi.org/10.5061/dryad.5pt92},
+  doi = {doi:10.5061/dryad.5pt92},
+  publisher = {Dryad Digital Repository}
+}
+```
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
 
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 <details>
 <summary> Snapshot Karoo </summary>
+This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species.
+
+Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv). 
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
 
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> Snapshot Kgalagadi </summary>
+This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale.
+
+Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv).
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
 
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> Snapshot Enonkishu </summary>
+This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies.
+
+Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv).
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
 
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> Snapshot Camdeboo </summary>
+This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species.
+
+Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv).
 
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> Snapshot Mountain Zebra </summary>
+This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year.
 
+Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv).
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> Snapshot Kruger </summary>
+This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation.
 
+Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv).
+
+For questions about this data set, contact [Sarah Huebner]([email protected]) at the University of Minnesota.
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 
 <details>
 <summary> SWG Camera Traps </summary>
+This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images.
+
+This data set is provided by the Saola Working Group; providers include:
+
+- IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG)
+- Asian Arks
+- Wildlife Conservation Society (Lao)
+- WWF Lao
+- Integrated Conservation of Biodiversity and Forests project, Lao (ICBF)
+- Center for Environment and Rural Development, Vinh University, Vietnam
+
+If you use these data in a publication or report, please use the following citation:
+
+SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset.
+
+For questions about this data set, contact [email protected].
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 
 </details>
 
 <details>
 <summary> Orinoquia Camera Traps </summary>
+This data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform.
 
+This data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty.
+
+The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI).
+
+If you use these data in a publication or report, please use the following citation:
+```bibtext
+@article{velez2022choosing,
+  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}
+}
+```
+For questions about this data set, contact [Juliana Velez Gomez]([email protected]).
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 ### Supported Tasks and Leaderboards
@@ -287,7 +389,8 @@ The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-s
 
 ### Data Instances
 
-Some datasets (e.g. ENA24) have bounding boxes, in which case their data instances look like the following:
+
+Some datasets (e.g. ENA24) have bounding boxes, in which case annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format.
 
 ```
   {'id': '1',
@@ -378,45 +481,44 @@ This dataset does not have a predefined train/test split.
 
 ### Curation Rationale
 
-The datasets that constitute LILA 
+The datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them.
 
 ### Source Data
 
 #### Initial Data Collection and Normalization
 
-[More Information Needed]
+N/A
 
 #### Who are the source language producers?
 
-[More Information Needed]
-
+N/A
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
+Each dataset has been annotated by the members of the project/organization that provided it.
 
 #### Who are the annotators?
 
-[More Information Needed]
+The annotations have been provided by domain experts in fields such as biology and ecology.
 
 ### Personal and Sensitive Information
 
-[More Information Needed]
+Some of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, the [LILA maintainers]([email protected]; in some cases it will be possible to release those images under an alternative license.
 
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
 
-[More Information Needed]
+Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with.
 
 ### Discussion of Biases
 
-[More Information Needed]
+These datasets do not represent global diversity, but are examples of local ecosystems and animals.
 
 ### Other Known Limitations
 
-[More Information Needed]
+N/A
 
 ## Additional Information
 
@@ -426,11 +528,11 @@ LILA BC is maintained by a working group that includes representatives from Ecol
 
 ### Licensing Information
 
-[More Information Needed]
+Many, but not all, LILA data sets were released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Check the details of the specific dataset you are using in its section above.
 
 ### Citation Information
 
-[More Information Needed]
+Citations for each dataset  (if they exist) are provided in its section above.
 
 ### Contributions