Government commits to public repository of consultant details “in principle”

BMJ ◽  
2021 ◽  
pp. n3115
Author(s):  
Elisabeth Mahase
Keyword(s):  
Public Repository

scholarly journals Efficient automated localization of ECoG electrodes in CT images via shape analysis

2021 ◽  
Vol 16 (4) ◽  
pp. 543-554
Author(s):  
Jessica Centracchio ◽  
Antonio Sarno ◽  
Daniele Esposito ◽  
Emilio Andreozzi ◽  
Luigi Pavone ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Focal Epilepsy ◽  
Support Vector ◽  
Epileptogenic Zone ◽  
Platinum Electrodes ◽  
Public Repository ◽  
Automated Method ◽  
Depth Electrodes ◽  
Ethical Approval ◽  
Cortical Regions

Abstract Purpose People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition. Methods The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes. Results The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes. Conclusions The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020.

scholarly journals WALDO! A Massive Public Repository of Global ELF/VLF Radio Data

2020 ◽  
Author(s):  
Morris Cohen ◽  
Mark Golkowski ◽  
Umran Inan ◽  
John DeSilva
Keyword(s):  
Public Repository ◽  
Radio Data

BioModels Database: a public repository for sharing models of biological processes

2013 ◽  
pp. 1-5
Author(s):  
Vijayalakshmi Chelliah ◽  
Nick Juty ◽  
Camille Laibe ◽  
Henning Hermjakob ◽  
Nicolas Le Novère
Keyword(s):  
Biological Processes ◽  
Public Repository ◽  
Biomodels Database

scholarly journals Automatic instance segmentation of mitochondria in electron microscopy data

2021 ◽  
Author(s):  
Luke Nightingale ◽  
Joost de Folter ◽  
Helen Spiers ◽  
Amy Strange ◽  
Lucy M Collinson ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Post Processing ◽  
Human Cortex ◽  
Electron Microscopy Data ◽  
Processing Procedure ◽  
Microscopy Data ◽  
Instance Segmentation

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

scholarly journals GO-PUB: Open-Select-Submit

2019 ◽  
Author(s):  
Dasapta Erwin Irawan ◽  
Andri Putra Kesmawan ◽  
Mochammad Tanzil Multazam ◽  
Eric Kunto Aribowo
Keyword(s):  
Open Source ◽  
Mobile Devices ◽  
Spatial Database ◽  
Scholarly Journal ◽  
Academic Publishing ◽  
Public Repository ◽  
Journal Publishing

An online ride-hailing app is a must-have app on your mobile devices, because it's features have been extended to meet almost modern urban needs. What if we could adopt the same features and functionalities for the academic publishing ecosystem. We proudly introduce the conceptual of GO-PUB. GO-PUB is an online app that provides a spatial database of scholarly journal publishers and to connect it with potential authors. Potential authors could find the perfect journal near their locations, complete with supporting pieces of information about the journal publishing system. The concept of GO-PUB is open source and cross platforms, hosted in public repository to make sure everyone could share their knowledge and contribution to the project.

scholarly journals Screen recordings as a tool to document computer assisted datacollection procedures

2019 ◽  
Author(s):  
Tobias Heycke ◽  
Lisa Spitzer
Keyword(s):  
Data Collection ◽  
False Positive ◽  
Computer Assisted ◽  
Public Repository ◽  
Specific Detail ◽  
Experimental Procedure ◽  
Psychological Science ◽  
Screen Capture ◽  
Original Procedure ◽  
Set Up

Recently in psychological science and many related fields, a surprisingly large amount of experiments could not be replicated by independent researchers. A non-replication could indicate that a previous finding might have been a false positive statistical result and the effect does not exist. However, it could also mean that a specific detail of the experimental procedure is essential for the effect to emerge, which might not have been included in the replication attempt. Therefore any replication attempt that does not replicate the original effect is most informative when the original procedure is closely adhered to. One proposed solution to facilitate the empirical reproducibility of the experimental procedures in psychology is to upload the experimental script and materials to a public repository. However, we believe that merely providing the materials of an experimental procedure is not sufficient, as many software solutions are not freely available, software solutions might change, and it is time consuming to set up the procedure. We argue that there is a simple solution to these problems when an experiment is conducted using computers: recording an example procedure with a screen capture software and providing the video in an online repository. We therefore provide a brief tutorial on screen recordings using an open source screen recording software. With this information, individual researchers should be able to record their experimental procedures and we hope to facilitate the use of screen recordings in computer assisted data collection procedures.

scholarly journals ChIPprimersDB: a public repository of verified qPCR primers for chromatin immunoprecipitation (ChIP)

2018 ◽  
Vol 47 (D1) ◽  
pp. D46-D49 ◽  
Author(s):  
Stefan Kurtenbach ◽  
Rohit Reddy ◽  
J William Harbour
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Public Repository

miND pipeline AWS EC2 installation and setup v2

2022 ◽  
Author(s):  
Andreas B Diendorfer ◽  
Kseniya.Khamina not provided ◽  
marianne.pultar not provided
Keyword(s):  
Data Analysis ◽  
Public Repository ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Ngs Data Analysis ◽  
Ngs Data ◽  
Data Analysis Pipeline

miND is a NGS data analysis pipeline for smallRNA sequencing data. In this protocol, the pipeline is setup and run on an AWS EC2 instance with example data from a public repository. Please see the publication paper on F1000 for more details on the pipeline and how to use it.

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