Update README.md

README.md

@@ -69,37 +69,149 @@ See below for information about each specific dataset that LILA contains:
 
 <details>
 <summary> Caltech Camera Traps </summary>
-
+This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty.
+More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/).
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+For questions about this data set, contact [email protected].
+
+If you use this data set, please cite the associated manuscript:
+```bibtex
+@inproceedings{DBLP:conf/eccv/BeeryHP18,
+  author    = {Sara Beery and
+               Grant Van Horn and
+               Pietro Perona},
+  title     = {Recognition in Terra Incognita},
+  booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
+               Germany, September 8-14, 2018, Proceedings, Part {XVI}},
+  pages     = {472--489},
+  year      = {2018},
+  crossref  = {DBLP:conf/eccv/2018-16},
+  url       = {https://doi.org/10.1007/978-3-030-01270-0\_28},
+  doi       = {10.1007/978-3-030-01270-0\_28},
+  timestamp = {Mon, 08 Oct 2018 17:08:07 +0200},
+  biburl    = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
 </details>
 
 <details>
 <summary> ENA24 </summary>
+Overview
+
+This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”.
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+Please cite this manuscript if you use this data set:
+```bibtex
+@article{yousif2019dynamic,
+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}
+}
+```
+For questions about this data set, contact [Hayder Yousif]([email protected]).
 
 </details>
 
 <details>
 <summary> Missouri Camera Traps </summary>
-
+This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames. 
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+If you use this data set, please cite the associated manuscript:
+```bibtex
+@article{zhang2016animal,
+  title={Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification},
+  author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming},
+  journal={IEEE Transactions on Multimedia},
+  volume={18},
+  number={10},
+  pages={2079--2092},
+  year={2016},
+  publisher={IEEE}
+}
+```
+For questions about this data set, contact [Hayder Yousif]([email protected]) and [Zhi Zhang]([email protected]).
 </details>
 
 <details>
-<summary> NACTI </summary>
+<summary> North American Camera Trap Images (NACTI) </summary>
+This data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds).
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+Please cite this manuscript if you use this data set:
+```bibtex
+@article{tabak2019machine,
+  title={Machine learning to classify animal species in camera trap images: Applications in ecology},
+  author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others},
+  journal={Methods in Ecology and Evolution},
+  volume={10},
+  number={4},
+  pages={585--590},
+  year={2019},
+  publisher={Wiley Online Library}
+}
+```
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
+
+For questions about this data set, contact [email protected].
 
 </details>
 
 <details>
 <summary> WCS Camera Traps </summary>
+This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations.
+
+Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files.
+
+You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps).
 
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 </details>
 
 <details>
 <summary> Wellington Camera Traps </summary>
+This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images).
+
+If you use this data set, please cite the associated manuscript:
+```bibtex
+@article{anton2018monitoring,
+  title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science},
+  author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U},
+  journal={Journal of Urban Ecology},
+  volume={4},
+  number={1},
+  pages={juy002},
+  year={2018},
+  publisher={Oxford University Press}
+}
+```
+
+This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/).
 
+For questions about this data set, contact [Victor Anton]([email protected]).
 </details>
 
 <details>
 <summary> Island Conservation Camera Traps </summary>
+This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands.
+
+The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images.
+
+In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files.
+
+For questions about this data set, contact [David Will]([email protected]) at Island Conservation.
+
+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. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license.
 </details>
 
 <details>