tag:joss.theoj.org,2005:/papers/tagged/data%20fusionJournal of Open Source Software2022-10-09T08:06:59ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/37612022-10-09T08:06:59Z2022-10-10T00:01:28ZVolume Segmantics: A Python Package for Semantic Segmentation of Volumetric Data Using Pre-trained PyTorch Deep Learning
Modelsacceptedv0.2.62022-08-03 10:44:21 UTC782022-10-09 08:06:59 UTC720224691OliverN. f.KingDiamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK0000-0002-6152-7207DimitriosBellosRosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK0000-0002-8015-3191MarkBashamDiamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK, Rosalind Franklin Institute, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK0000-0002-8438-141510.21105/joss.04691https://doi.org/10.5281/zenodo.7143363Pythonhttps://joss.theoj.org/papers/10.21105/joss.04691.pdfsegmentation, deep learning, volumetric, images, pre-trainedtag:joss.theoj.org,2005:Paper/13222019-11-13T18:12:20Z2021-02-15T11:31:24ZEarthPy: A Python package that makes it easier to explore and plot raster and vector data using open source Python tools.accepted0.7.62019-11-06 22:31:37 UTC432019-11-13 18:12:20 UTC420191886LeahWasserEarth Lab, University of Colorado - Boulder0000-0002-8177-6550MaxwellB.JosephEarth Lab, University of Colorado - Boulder0000-0002-7745-9990JoeMcGlinchyEarth Lab, University of Colorado - Boulder0000-0003-2135-0168JennyPalominoEarth Lab, University of Colorado - Boulder0000-0003-4879-9299NathanKorinekEarth Lab, University of Colorado - Boulder0000-0003-0859-7246ChrisHoldgrafUniversity of California - Berkeley, Project Jupyter0000-0002-2391-0678TimHeadWild Tree Tech0000-0003-0931-369810.21105/joss.01886https://doi.org/10.5281/zenodo.3540957Pythonhttps://joss.theoj.org/papers/10.21105/joss.01886.pdfgis, raster data, vector data, remote sensingtag:joss.theoj.org,2005:Paper/10342019-07-02T10:33:47Z2021-02-15T11:32:05ZfitODBOD: An R Package to Model Binomial Outcome Data using Binomial Mixture and Alternate Binomial Distributions.acceptedv1.4.02019-06-13 08:51:25 UTC392019-07-02 10:33:47 UTC420191505AmalanMahendranDepartment of Statistics and Computer Science, Faculty of Science, University of Peradeniya.0000-0002-0643-9052PushpakanthieWijekoonDepartment of Statistics and Computer Science, Faculty of Science, University of Peradeniya.0000-0003-4242-101710.21105/joss.01505https://doi.org/10.5281/zenodo.3265356Rhttps://joss.theoj.org/papers/10.21105/joss.01505.pdffitODBOD, BOD, Over-dispersion, FBMD, ABDtag:joss.theoj.org,2005:Paper/8042019-02-10T11:19:07Z2021-02-15T11:32:41ZMultiblock PLS: Block dependent prediction modeling for Pythonacceptedv1.0.02019-01-06 20:42:11 UTC342019-02-10 11:19:07 UTC420191190AndreasBaumDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 324, DK-2800 Kgs. Lyngby, Denmark0000-0003-1552-0220LaurentVermueDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 324, DK-2800 Kgs. Lyngby, Denmark0000-0002-3403-762410.21105/joss.01190https://doi.org/10.5281/zenodo.2560303Pythonhttps://joss.theoj.org/papers/10.21105/joss.01190.pdfmultivariate statistics, data fusion, latent variables, exploratory analysis, data integration, MB-PLStag:joss.theoj.org,2005:Paper/6202018-10-11T14:10:58Z2021-02-15T11:33:00ZVerde: Processing and gridding spatial data using Green’s functionsacceptedv1.0.02018-09-14 05:19:45 UTC302018-10-11 14:10:58 UTC32018957LeonardoUiedaDepartment of Earth Sciences, SOEST, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA0000-0001-6123-951510.21105/joss.00957https://doi.org/10.5281/zenodo.1421979Pythonhttps://joss.theoj.org/papers/10.21105/joss.00957.pdfpython, geophysics, geospatialtag:joss.theoj.org,2005:Paper/4702018-04-21T17:34:55Z2021-02-15T11:33:27ZqualtRics: retrieve survey data using the Qualtrics APIacceptedv3.02018-04-20 13:13:59 UTC242018-04-21 17:34:55 UTC32018690JasperGinnEcole Polytechnique Federale de Lausanne (EPFL)0000-0002-5019-292310.21105/joss.00690https://doi.org/10.5281/zenodo.1165955Rhttps://joss.theoj.org/papers/10.21105/joss.00690.pdfsurvey data, API, Qualtrics