ilastik: interactive machine learning for (bio)image analysis

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance

bioimage-io/spec-bioimage-io: v0.4.9post1

Changes requests: set User-Agent to "ci" in a CI environment (#523) @FynnBe fixed Author & Maintainer schema(require -> required) (#511) @mese79 update and set update_format docstring (#505) @FynnBe :rocket: Features add get_resolved_source_path (#508) @FynnBe add close method to tqdm dummy (#502) @FynnBe add bioimageio_description to email field (#501) @FynnBe :beetle: Fixes fix sha value of weights.onnx (#510) @FynnB

bioimage-io/spec-bioimage-io: v0.4.9post5

<h2>Changes</h2> <ul> <li>improve cached file name for zenodo api changes (#536) @FynnBe</li> </ul&gt

constantinpape/z5: 2.0.17

<h2>What's Changed</h2> <ul> <li>const static → static const by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/214</li> <li>Add Python 3.11 to CI builds by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/212</li> <li>A couple cosmetic fixes by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/215</li> <li>Update the dev environments by @constantinpape in https://github.com/constantinpape/z5/pull/219</li> <li>Fix typos found by codespell by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/220</li> <li>Update GitHub Actions by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/221</li> <li>In Python > 3.3, mock is part of unittest by @DimitriPapadopoulos in https://github.com/constantinpape/z5/pull/222</li> <li>Drop boost requirement by @sameeul in https://github.com/constantinpape/z5/pull/224</li> <li>Drop boost reference from docs and recipe by @sameeul in https://github.com/constantinpape/z5/pull/225</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/constantinpape/z5/compare/2.0.16...2.0.17</p&gt

An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin