Neuroscience Information Framework

Neuroscience Information Framework

@neuinfo.org

162 followers18 following2.3K posts

Join NIDDK-CR for a demo on NIDDK-CR Data Challenge Management Platform — a secure, cloud-based solution for managing scientific competitions that power innovation in diabetes research. 📅 Saturday, June 21 ⏰ 2-2:30 PM 📍NIDDK Data Science Corner at Exhibit Hall. #NIH #NIDDK

📢 Explore further the importance of tidy data! Check out this insightful blog by @memartone on #OpenData Commons for Traumatic Brain Injury (ODC-TBI). Don't miss it! 📚🔍 Read here:#DataManagement #TidyData #DataStandards #TBI odc-tbi.org/about/odc_tbi_…

Have you ever wondered whether a preprint has been published already? A new tool, PrePrintMatch, was created by Peter Eckmann and has been used to visualize global inequities in publishing. ow.ly/rwpf50NcuZz ow.ly/ICEV50NcuZB

ow.lyPreprintMatch: A tool for preprint to publication detection shows global inequities in scientific publicationPreprints, versions of scientific manuscripts that precede peer review, are growing in popularity. They offer an opportunity to democratize and accelerate research, as they have no publication costs or a lengthy peer review process. Preprints are often later published in peer-reviewed venues, but these publications and the original preprints are frequently not linked in any way. To this end, we developed a tool, PreprintMatch, to find matches between preprints and their corresponding published papers, if they exist. This tool outperforms existing techniques to match preprints and papers, both on matching performance and speed. PreprintMatch was applied to search for matches between preprints (from bioRxiv and medRxiv), and PubMed. The preliminary nature of preprints offers a unique perspective into scientific projects at a relatively early stage, and with better matching between preprint and paper, we explored questions related to research inequity. We found that preprints from low income countries are published as peer-reviewed papers at a lower rate than high income countries (39.6% and 61.1%, respectively), and our data is consistent with previous work that cite a lack of resources, lack of stability, and policy choices to explain this discrepancy. Preprints from low income countries were also found to be published quicker (178 vs 203 days) and with less title, abstract, and author similarity to the published version compared to high income countries. Low income countries add more authors from the preprint to the published version than high income countries (0.42 authors vs 0.32, respectively), a practice that is significantly more frequent in China compared to similar countries. Finally, we find that some publishers publish work with authors from lower income countries more frequently than others.

Come listen to Maryann Martone (@neuinfo @INCForg @sparc_science @odc_sci ) talk about ‘Open Neuroscience and the Meaning of FAIR’ (#SfN22 #SfN2022 David Kopf Neurotics lecture) Monday, November 14, 10:30–11:30 a.m @SfNTweets

Thanks to NSF for funding FAIR open science infrastructure. It looks like Neuroscience will be teaming up with Geoscience to improve how we all communicate about core facilities and instruments using persistent unique identifiers. #RRID #OpenScience arl.org/news/arl-appla…

arl.orgARL Applauds NSF Open Science Investment — Association of Research LibrariesThe Association of Research Libraries (ARL) commends the ongoing commitment of the US National Science Foundation (NSF) to open science. NSF today announced awards for 10 new projects focused on...

Interested in doing some open source development for a great cause this summer? Apply to SPARC's Google Summer of Code 2022 project! Work with SPARC DRC mentors and also receive a stipend from Google! Applications are being accepted until 4/19. sparc.science/news-and-event…

sparc.scienceGoogle Summer of Code (GSoC) SPARC project: Applications Due on April 19Applications are being accepted to work with SPARC Data and Resource Scientists as part of Google's Summer of Code program. Contribute tangibly to the creation of new SPARC Portal functionality.

"Is the Scientific Literature Better Today Than It Was 10 Years Ago? A Journey from RRIDs to the Rigor and Transparency Index". The lecture will be moderated by Prof. Ulrich Dirnagl (BIH QUEST Center for Responsible Research). Friday, 25 March 2022 bihealth.org/en/notices/bih…

bihealth.orgBIH Lecture "Is the scientific literature better today than it was 10 years ago?" by Anita Bandrowski - News - BIH at CharitéA Journey from RRIDs to the Rigor and Transparency Index.

Congratulations to the @INCF Active Segmentation for ImageJ group for publishing on "The Active Segmentation Platform for Microscopic Image Classification and SegmentationThis is a nice output from a long running and engaged group. mdpi.com/2076-3425/11/1…

mdpi.comThe Active Segmentation Platform for Microscopic Image Classification and SegmentationImage segmentation still represents an active area of research since no universal solution can be identified. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. The article presents an automated image segmentation and classification platform, called Active Segmentation, which is based on ImageJ. The platform integrates expert domain knowledge, providing partial ground truth, with geometrical feature extraction based on multi-scale signal processing combined with machine learning. The approach in image segmentation is exemplified on the ISBI 2012 image segmentation challenge data set. As a second application we demonstrate whole image classification functionality based on the same principles. The approach is exemplified using the HeLa and HEp-2 data sets. Obtained results indicate that feature space enrichment properly balanced with feature selection functionality can achieve performance comparable to deep learning approaches. In summary, differential geometry can substantially improve the outcome of machine learning since it can enrich the underlying feature space with new geometrical invariant objects.