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Scientific conferences and meetings have an important role in research, but they also suffer from a number of disadvantages: in particular, they can have a massive carbon footprint, they are time-consuming, and the high costs involved in attending can exclude many potential participants. The COVID-19 pandemic has led to the cancellation of many conferences, forcing the scientific community to explore online alternatives. Here, we report on our experiences of organizing an online neuroscience conference, neuromatch, that attracted some 3000 participants and featured two days of talks, debates, panel discussions, and one-on-one meetings facilitated by a matching algorithm. By offering most of the benefits of traditional conferences, several clear advantages, and with fewer of the downsides, we feel that online conferences have the potential to replace many legacy conferences.
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The unsolvable problem or the unheard answer?: a dataset of 24,669 open-source software conference talks
Talks at practitioner-focused open-source software conferences are a valuable source of information for software engineering researchers. They provide a pulse of the community and are valuable source material for grey literature analysis. We curated a dataset of 24,669 talks from 87 open-source conferences between 2010 and 2021. We stored all relevant metadata from these conferences and provide scripts to collect the transcripts. We believe this data is useful for answering many kinds of questions, such as: What are the important/highly discussed topics within practitioner communities? How do practitioners interact? And how do they present themselves to the public? We demonstrate the usefulness of this data by reporting our findings from two small studies: a topic model analysis providing an overview of open-source community dynamics since 2011 and a qualitative analysis of a smaller community-oriented sample within our dataset to gain a better understanding of why contributors leave open-source software.
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- Award ID(s):
- 2150217
- PAR ID:
- 10392544
- Date Published:
- Journal Name:
- MSR '22: Proceedings of the 19th International Conference on Mining Software Repositories
- Page Range / eLocation ID:
- 348 to 352
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT Cochlear hair cell stereocilia bundles are key organelles required for normal hearing. Often, deafness mutations cause aberrant stereocilia heights or morphology that are visually apparent but challenging to quantify. Actin-based structures, stereocilia are easily and most often labeled with phalloidin then imaged with 3D confocal microscopy. Unfortunately, phalloidin non-specifically labels all the actin in the tissue and cells and therefore results in a challenging segmentation task wherein the stereocilia phalloidin signal must be separated from the rest of the tissue. This can require many hours of manual human effort for each 3D confocal image stack. Currently, there are no existing software pipelines that provide an end-to-end automated solution for 3D stereocilia bundle instance segmentation. Here we introduce VASCilia, a Napari plugin designed to automatically generate 3D instance segmentation and analysis of 3D confocal images of cochlear hair cell stereocilia bundles stained with phalloidin. This plugin combines user-friendly manual controls with advanced deep learning-based features to streamline analyses. With VASCilia, users can begin their analysis by loading image stacks. The software automatically preprocesses these samples and displays them in Napari. At this stage, users can select their desired range of z-slices, adjust their orientation, and initiate 3D instance segmentation. After segmentation, users can remove any undesired regions and obtain measurements including volume, centroids, and surface area. VASCilia introduces unique features that measures bundle heights, determines their orientation with respect to planar polarity axis, and quantifies the fluorescence intensity within each bundle. The plugin is also equipped with trained deep learning models that differentiate between inner hair cells and outer hair cells and predicts their tonotopic position within the cochlea spiral. Additionally, the plugin includes a training section that allows other laboratories to fine-tune our model with their own data, provides responsive mechanisms for manual corrections through event-handlers that check user actions, and allows users to share their analyses by uploading a pickle file containing all intermediate results. We believe this software will become a valuable resource for the cochlea research community, which has traditionally lacked specialized deep learning-based tools for obtaining high-throughput image quantitation. Furthermore, we plan to release our code along with a manually annotated dataset that includes approximately 55 3D stacks featuring instance segmentation. This dataset comprises a total of 1,870 instances of hair cells, distributed between 410 inner hair cells and 1,460 outer hair cells, all annotated in 3D. As the first open-source dataset of its kind, we aim to establish a foundational resource for constructing a comprehensive atlas of cochlea hair cell images. Together, this open-source tool will greatly accelerate the analysis of stereocilia bundles and demonstrates the power of deep learning-based algorithms for challenging segmentation tasks in biological imaging research. Ultimately, this initiative will support the development of foundational models adaptable to various species, markers, and imaging scales to advance and accelerate research within the cochlea research community.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3524842.3528488
