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1. How to Hackathon: Socio-technical Tradeoffs in Brief, Intensive Collocation

   https://doi.org/10.1145/2818048.2819946  

   Trainer, Erik H.; Kalyanasundaram, Arun; Chaihirunkarn, Chalalai; Herbsleb, James D. (January 2016, Computer Supported Cooperative Work)

   Hackathons are events where people who are not normally collocated converge for a few days to write code together. Hackathons, it seems, are everywhere. We know that long- term collocation helps advance technical work and facilitate enduring interpersonal relationships, but can similar benefits come from brief, hackathon-style collocation? How do participants spend their time preparing, working face-to- face, and following through these brief encounters? Do the activities participants select suggest a tradeoff between the social and technical benefits of collocation? We present results from a multiple-case study that suggest the way that hackathon-style collocation advances technical work varies across technical domain, community structure, and expertise of participants. Building social ties, in contrast, seems relatively constant across hackathons. Results from different hackathon team formation strategies suggest a tradeoff between advancing technical work and building social ties. Our findings have implications for technology support that needs to be in place for hackathons and for understanding the role of brief interludes of collocation in loosely-coupled, geographically distributed work. 

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2. NCBI’s Virus Discovery Hackathon: Engaging Research Communities to Identify Cloud Infrastructure Requirements

   https://doi.org/10.3390/genes10090714  

   Connor, Ryan; Brister, Rodney; Buchmann, Jan; Deboutte, Ward; Edwards, Rob; Martí-Carreras, Joan; Tisza, Mike; Zalunin, Vadim; Andrade-Martínez, Juan; Cantu, Adrian; et al (September 2019, Genes)

   A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon. 

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3. AI4EIC Hackathon: PID with the ePIC dRICH

   https://doi.org/10.1051/epjconf/202429508004  

   Fanelli, Cristiano; Giroux, James; McSpadden, Diana; Rajput, Kishansingh; Suresh, Karthik; Cisbani, Evaristo; Deconinck, Wouter; Walter, Eric; Bressan, Andrea; Diefenthaler, Markus; et al (January 2024, EPJ Web of Conferences)

   De_Vita, R; Espinal, X; Laycock, P; Shadura, O (Ed.)

   The inaugural AI4EIC Hackathon unfolded as a high-point satellite event during the second AI4EIC Workshop at William & Mary. The workshop itself boasted over two hundred participants in a hybrid format and delved into the myriad applications of Artificial Intelligence and Machine Learning (AI/ML) for the Electron-Ion Collider (EIC). This workshop aimed to catalyze advancements in AI/ML with applications ranging from advancements in accelerator and detector technologies—highlighted by the ongoing work on the ePIC detector and potential development of a second detector for the EIC—to data analytics, reconstruction, and particle identification, as well as the synergies between theoretical and experimental research. Complementing the technical agenda was an enriched educational outreach program that featured tutorials from leading AI/ML experts representing academia, national laboratories, and industry. The hackathon, held on the final day, showcased international participation with ten teams from around the globe. Each team, comprising up to four members, focused on the dual-radiator Ring Imaging Cherenkov (dRICH) detector, an integral part of the particle identification (PID) system in ePIC. The data for the hackathon were generated using the ePIC software suite. While the hackathon presented questions of increasing complexity, its challenges were designed with deliberate simplifications to serve as a preliminary step toward the integration of machine learning and deep learning techniques in PID with the dRICH detector. This article encapsulates the key findings and insights gained from this unique experience. 

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4. Conference report: 2018 materials and data science hackathon (MATDAT18)

   https://doi.org/10.1039/c9me90018g  

   Ferguson, Andrew L.; Mueller, Tim; Rajasekaran, Sanguthevar; Reich, Brian J. (January 2019, Molecular Systems Design & Engineering)

   The National Science Foundation (NSF) 2018 Materials and Data Science Hackathon (MATDAT18) took place at the Residence Inn Alexandria Old Town/Duke Street, Alexandria, VA over the period May 30–June 1, 2018. This three-day collaborative “hackathon” or “datathon” brought together teams of materials scientists and data scientists to collaboratively engage materials science problems using data science tools. The materials scientists brought a diversity of problems ranging from inorganic material bandgap prediction to acceleration of ab initio molecular dynamics to quantification of aneurysm risk from blood hydrodynamics. The data scientists contributed tools and expertise in areas such as deep learning, Gaussian process regression, and sequential learning with which to engage these problems. Participants lived and worked together, collaboratively “hacked” for several hours per day, delivered introductory, midpoint, and final presentations and were exposed to presentations and informal interactions with NSF personnel. Social events were organized to facilitate interactions between teams. The primary outcomes of the event were to seed new collaborations between materials and data scientists and generate preliminary results. A separate competitive process enabled participants to apply for exploratory funding to continue work commenced at the hackathon. Anonymously surveyed participants reported a high level of satisfaction with the event, with 100% of respondents indicating that their team will continue to work together into the future and 91% reporting intent to submit a white paper for exploratory funding. 

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5. Conference report: 2018 materials and data science hackathon (MATDAT18)

   A.L. Fergusaon, T. Mueller (January 2019, Molecular systems design & engineering)

   The National Science Foundation (NSF) 2018 Materials and Data Science Hackathon (MATDAT18) took place at the Residence Inn Alexandria Old Town/Duke Street, Alexandria, VA over the period May 30–June 1, 2018. This three-day collaborative “hackathon” or “datathon” brought together teams of materials scientists and data scientists to collaboratively engage materials science problems using data science tools. The materials scientists brought a diversity of problems ranging from inorganic material bandgap prediction to acceleration of ab initio molecular dynamics to quantification of aneurysm risk from blood hydrodynamics. The data scientists contributed tools and expertise in areas such as deep learning, Gaussian process regression, and sequential learning with which to engage these problems. Participants lived and worked together, collaboratively “hacked” for several hours per day, delivered introductory, midpoint, and final presentations and were exposed to presentations and informal interactions with NSF personnel. Social events were organized to facilitate interactions between teams. The primary outcomes of the event were to seed new collaborations between materials and data scientists and generate preliminary results. A separate competitive process enabled participants to apply for exploratory funding to continue work commenced at the hackathon. Anonymously surveyed participants reported a high level of satisfaction with the event, with 100% of respondents indicating that their team will continue to work together into the future and 91% reporting intent to submit a white paper for exploratory funding. 

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