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zeekayandHanzo Dev dced1dcfb9 Publish under the org, not a person

Bylines, paper authorship, correspondence addresses and the founder blurb under
each post named an individual. They name the organisation that published the
work instead: the brand on posts and pages, the research byline on papers, and
research@ in place of a personal address. Both are what these repositories
already used elsewhere — nothing new was coined.

Co-authors are untouched. Where a paper named someone else alongside, they stay
named; only the one byline changed. Bibliography entries, running heads,
PDF metadata and affiliations move with it, so a citation still resolves to
whoever the cited paper names.

Sample and fixture data uses the placeholder name these tests already use
elsewhere, so no assertion changed meaning.

Co-authored-by: Hanzo Dev <[email protected]>

2026-09-01 08:55:40 -07:00

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 \section{Conclusion}
 \label{sec:conclusion}
 We have presented ZooCoord, a decentralized research coordination protocol that addresses the reproducibility crisis through cryptographic experiment commitments, federated execution graphs with end-to-end provenance, and a token-incentivized verification marketplace.
 Deployment across 14 research groups processing 847 experiments demonstrates 90.7\% successful reproduction, 67\% reduction in time-to-reproduction, and 340\% increase in data sharing, along with early detection of 23 potential reproducibility failures.
 The protocol is specified as ZIP-008 and released as open-source infrastructure on the Zoo Network.
 Future work will extend ZooCoord to support wet-lab experiment tracking through IoT-integrated laboratory instrumentation, develop domain-specific execution graph templates for common conservation research methodologies, and formalize the game-theoretic security properties of the verification market under stronger adversarial models.
 % ============================================================
 % References
 % ============================================================
 \begin{thebibliography}{30}
 \bibitem[Afgan et~al.(2018)]{afgan2018galaxy}
 Afgan, E., Baker, D., Batut, B., et~al. (2018).
 \newblock The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.
 \newblock {\em Nucleic Acids Research}, 46(W1):W537--W544.
 \bibitem[Bartling(2020)]{bartling2020blockchain}
 Bartling, S. (2020).
 \newblock Blockchain for open science and knowledge creation.
 \newblock {\em Frontiers in Blockchain}, 2:16.
 \bibitem[Begley and Ellis(2012)]{begley2012drug}
 Begley, C.~G. and Ellis, L.~M. (2012).
 \newblock Drug development: Raise standards for preclinical cancer research.
 \newblock {\em Nature}, 483(7391):531--533.
 \bibitem[Chambers(2013)]{chambers2013registered}
 Chambers, C.~D. (2013).
 \newblock Registered Reports: A new publishing initiative at Cortex.
 \newblock {\em Cortex}, 49(3):609--610.
 \bibitem[Di~Tommaso et~al.(2017)]{ditommaso2017nextflow}
 Di~Tommaso, P., Chatzou, M., Floden, E.~W., et~al. (2017).
 \newblock Nextflow enables reproducible computational workflows.
 \newblock {\em Nature Biotechnology}, 35(4):316--319.
 \bibitem[Freedman et~al.(2015)]{freedman2015economics}
 Freedman, L.~P., Cockburn, I.~M., and Simcoe, T.~S. (2015).
 \newblock The economics of reproducibility in preclinical research.
 \newblock {\em PLOS Biology}, 13(6):e1002165.
 \bibitem[Gr\"{u}ning et~al.(2018)]{gruning2018practical}
 Gr\"{u}ning, B., Chilton, J., K\"{o}ster, J., et~al. (2018).
 \newblock Practical computational reproducibility in the life sciences.
 \newblock {\em Cell Systems}, 6(6):631--635.
 \bibitem[Hamburg(2019)]{hamburg2019desci}
 Hamburg, S. (2019).
 \newblock Decentralized science: Toward a new paradigm for scientific publishing and funding.
 \newblock {\em Preprint, arXiv:1907.13422}.
 \bibitem[Hasan and Salah(2019)]{hasan2019blockchain}
 Hasan, H.~R. and Salah, K. (2019).
 \newblock Combating deepfake videos using blockchain and smart contracts.
 \newblock {\em IEEE Access}, 7:41596--41606.
 \bibitem[Hutson(2018)]{hutson2018artificial}
 Hutson, M. (2018).
 \newblock Artificial intelligence faces reproducibility crisis.
 \newblock {\em Science}, 359(6377):725--726.
 \bibitem[Koepsell(2020)]{koepsell2020blockchain}
 Koepsell, D. (2020).
 \newblock Blockchain and the future of scientific publishing.
 \newblock {\em Science and Engineering Ethics}, 26:1491--1495.
 \bibitem[McPhillips et~al.(2015)]{mcphillips2015yesworkflow}
 McPhillips, T., Song, T., Kolisnik, T., et~al. (2015).
 \newblock YesWorkflow: A user-oriented, language-independent tool for recovering workflow information from scripts.
 \newblock {\em International Journal of Digital Curation}, 10(1):298--313.
 \bibitem[Miao et~al.(2018)]{miao2018provdb}
 Miao, H., Li, A., Davis, L.~S., and Deshpande, A. (2018).
 \newblock ProvDB: Provenance-enabled lifecycle management of collaborative data analysis workflows.
 \newblock {\em IEEE Data Engineering Bulletin}, 41(4):26--38.
 \bibitem[Moreau et~al.(2013)]{moreau2013prov}
 Moreau, L., Missier, P., Belhajjame, K., et~al. (2013).
 \newblock PROV-DM: The PROV data model.
 \newblock {\em W3C Recommendation}.
 \bibitem[M\"{o}lder et~al.(2021)]{molder2021sustainable}
 M\"{o}lder, F., Jablonski, K.~P., Letcher, B., et~al. (2021).
 \newblock Sustainable data analysis with Snakemake.
 \newblock {\em F1000Research}, 10:33.
 \bibitem[Nosek et~al.(2018)]{nosek2018preregistration}
 Nosek, B.~A., Ebersole, C.~R., DeHaven, A.~C., and Mellor, D.~T. (2018).
 \newblock The preregistration revolution.
 \newblock {\em Proceedings of the National Academy of Sciences}, 115(11):2600--2606.
 \bibitem[Open Science Collaboration(2015)]{openscience2015estimating}
 Open Science Collaboration (2015).
 \newblock Estimating the reproducibility of psychological science.
 \newblock {\em Science}, 349(6251):aac4716.
 \bibitem[Smaldino and McElreath(2016)]{smaldino2016natural}
 Smaldino, P.~E. and McElreath, R. (2016).
 \newblock The natural selection of bad science.
 \newblock {\em Royal Society Open Science}, 3(9):160384.
 \bibitem[Stodden et~al.(2018)]{stodden2018empirical}
 Stodden, V., Seiler, J., and Ma, Z. (2018).
 \newblock An empirical analysis of journal policy effectiveness for computational reproducibility.
 \newblock {\em Proceedings of the National Academy of Sciences}, 115(11):2584--2589.
 \bibitem[Tenorio-Forn\'{e}s et~al.(2019)]{tenorio2019open}
 Tenorio-Forn\'{e}s, A., Jacynycz, V., Llop-Vila, D., et~al. (2019).
 \newblock Towards a decentralized process for scientific publication and peer review using blockchain and IPFS.
 \newblock {\em Proceedings of HICSS}, pages 4635--4644.
 \bibitem[Wilkinson et~al.(2016)]{wilkinson2016fair}
 Wilkinson, M.~D., Dumontier, M., Aalbersberg, I.~J., et~al. (2016).
 \newblock The FAIR Guiding Principles for scientific data management and stewardship.
 \newblock {\em Scientific Data}, 3:160018.
 \bibitem[Wright(2021)]{wright2021desci}
 Wright, A. (2021).
 \newblock Decentralized science: The rise of DeSci DAOs.
 \newblock {\em Nature Biotechnology}, 39:1319--1320.
 \bibitem[Zoo Labs Foundation Research(2025a)]{zoo2025dso}
 Zoo Labs Foundation Research (2025a).
 \newblock Decentralized Semantic Optimization: Collaborative knowledge curation on distributed networks.
 \newblock {\em Zoo Improvement Proposal ZIP-001}.
 \bibitem[Zoo Labs Foundation Research(2025b)]{zoo2025poai}
 Zoo Labs Foundation Research (2025b).
 \newblock Proof of AI: Consensus mechanisms for verifiable machine learning.
 \newblock {\em Zoo Improvement Proposal ZIP-002}.
 \bibitem[Zoo Labs Foundation Research(2025c)]{zoo2025network}
 Zoo Labs Foundation Research (2025c).
 \newblock Zoo Network Architecture: A decentralized infrastructure for open science.
 \newblock {\em Zoo Technical Report ZTR-001}.
 \end{thebibliography}

143 lines 6.7 KiB TeX Raw Permalink Blame History

143 lines

6.7 KiB

TeX

Raw Permalink Blame History