Challenges

All challenge pages are listed below; every challenge has sections titled "Background", "Solutions", and "Resources". Each of these challenges has a museum librarian listed as the owner, and lists a primary goal of either: expanding public access, improving AMNH research, or enhancing education.

The top of every challenge page has a listing of all the Hackathon Projects created to solve it.

The Background describes the problem(s) that the challenge is attempting to solve and overall goals.

Each challenge proposes several Solutions which a team of 5 or more people could successfully build a working prototype of during the hackathon.

Lastly, the Resources section contains links to relevant datasets, documentation, formats, or downloads of sample files specifically for the solutions to that challenge. Sample files are often included in this GitHub repository (<> Code tab above), under the "challenges" directory, in a sub-directory named for the challenge.

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• Untold Stories of Historic Personages: Tell the Story of a Prominent Woman Scientist, Explorer, or Researcher from the Museum (As many solutions as needed) {Chris Johnson}

• Map The Collections: Georeference Collections Specimens to Visualize Expeditions and Ecosystems in Space and Time. (n solutions) {Chris Johnson}

• Jellyfish Inspector: Use Computer Vision and Machine Learning to Reclassify Cnidarians. (2n solutions) {Fani Rodriguez}

• Virtual Fossil Fragmenter: Use Computer Vision and 3D Modeling to Identify Fossil Fragments of Shells, Bivalves, Trilobites. (n solutions) {Melanie Hopkins}

• The Eye of Maria: Build Simulations to Model the Effect of a Hurricane on Objects Drifting in the Ocean. (n solutions) {David Lindo}

• Iron Out the Kinks: Create Tools to Automate Rendering 3D Models of Marine Microbes from 2D Cross-sections. (2n solutions) {Aaron Heiss}

• Find The Plastic Flow: Map, Animate, and Quantify Sources and Travel Times of Plastic Found in the Gyres of The Great Pacific Garbage Patch. (n solutions) {David Lindo}

• Trilobite Vision: Use computer vision to count the segments of or identify other characteristics of trilobites. (n solutions) {Melanie Hopkins}

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Selected Challenges

6. The Great Pacific Garbage Patch: Map, animate, and quantify geographical sources and travel time scales of plastic found in the GPGP. We have data of plastic locations and ocean model data of ocean currents to backtrack from sinks to sources. (n solutions) {David Lindo} ?? ---CONSERVATION--- PlasticAdrift.org https://github.com/adriftICL and COPEPOD at NOAA

7. Trilobite Vision: Use computer vision to count the segments or identify other characteristics of trilobites. We have the AMNH trilobite database (where do these images come from? Do we have open data rights to them?). Ideally we want to detect large spines and link them to a particular identified segment. (Can the library API produce more trilobite images? From digital publications?) The goal is just to collect the data - then look at segmentation patterns through time, geo, et cetera. Possibly create an API for trilobite data? (n solutions) {Melanie Hopkins} ??

8. Jellyfish Inspector: Machine learning to help with classifying cnidarians. Reclassify images from literature. Possibly use crowdsourcing to help add "objectivity" Library API - Get list of journals for specific nematocyst / cnidarian related articles. Other online data sources to get additional images, Identify other publications that have nematocyst articles that can be collected: 1) Must identify the capsule within the image; 2) Must orient the capsule to the same direction.

9. ?

10. ?

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Candidates Being Reviewed

• [Geometric morphometrics]: A popular and powerful tool for describing shapes, such as bone or shell morphology, is through the analysis of configurations of point coordinates that represent the shape. Tools for semi-automating the collection of landmarks are being developed. However, bones and shells often have ornamentation on them that does not contribute to the description of the overall shape, but does add noise to the dataset. Maybe there is a way to smooth out bumpy or ridged surfaces of 3d surface reconstructions of bones and shells so that automated collection of points on those surfaces produce more accurate descriptions of the overall shape. Could be something that works in conjunction with MeshLab (This could be something that works in conjunction with the DinoJerks 2.0 challenge). (n solutions) {Melanie Hopkins} ??

• [DinoJerks 2.0]: Is it possible to build upon the work of the DinoJerks solution to make it easier or more efficient to select areas on scans? This is akin to the Brain Builder challenge of recognizing the shape of a fossil embedded in rock. (n solutions) {Melanie Hopkins} ??

• [Hotspots of the Ocean Exhibit]: Map the pathways and hotspots of visitors of the new ocean exhibit. The map could show a heat map with retention times at each hotspot. Could potentially use the bluetooth beacon system. Connecting to amnh-guest, tweeting, or instagramming might also be considered as a proxy. (n solutions) {David Lindo} ??

• [Painting the Ocean]: Design a software/tool that identifies the path of a particle or, even better, the path of an oil patch in the ocean using images. We could use images or videos from my lab (latest twitters to #ESClindo), satellite images from the BP oil spill publicly available though GOMRI, or data from a real dye release experiment in Florida (I would have to contact a colleague down in Miami for the latter).
 (n solutions) {David Lindo} ?? ---CONSERVATION---

• [Visualizing invisible ocean eddies]: Generate 4D visualizations of the tracks of the centroids of eddies. For example, images similar to the ones attached. We have ocean currents data from the MIT general circulation model that we generated for one of my projects and an algorithm to detect the center of rotating bodies of water. I have high research interest on that one because I have two projects recently funded by NOAA on eddies. One on eddies around the Hawaiian Archipelago and another one in the Gulf of Mexico. I also have a proposal pending on eddies around Cuba. As a background, hurricanes in the atmosphere are easy to identify and track, but the ID process is not that trivial with ocean eddies. These features are very important for the health of the ocean ecosystems because they trap nutrients, prey, and pollutants. They are also important for climate because they feed and trap phytoplankton and CO2. (n solutions) {David Lindo} ??

• [Mining for biological traits]: Let's say I want to collect a bunch of data about the absence or presence of certain characters across a wide range of trilobites. I would then want to query a bunch of literature, looking for keywords and then associate those with species names. The keywords might show up in the description for the species, or they might show up elsewhere in a description of the genus that the species belongs to (if all species in that genus shared the feature and was thus a notable or defining character of the genus). The algorithm would then have to both locate the terms and associate them properly with different taxon names. The reference libraries would be a glossary of terms and a taxonomy (the hierarchical structure would be known). {Melanie Hopkins}