Recent searches

No recent searches

Search options

Only available when logged in.
sigmoid.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
A social space for people researching, working with, or just interested in AI!

Administered by:

Server stats:

612
active users

sigmoid.social: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.4.1

#ResearchManagement

0 posts0 participants0 posts today
OpenAIRE

Struggling with duplicate entries, incorrect affiliations, or outdated metadata?

The #OpenOrgs platform helps research institutions maintain accurate, structured, and discoverable #metadata.

Key features include:

•Institutional login & easy metadata management
•Unique OpenOrgs ID for every institution
•Verification through ISNI & ROR databases

-Master these tools today! Enroll in our tutorial: openplato.eu/course/view.php?i

#ResearchManagement#OpenScience#OpenAIRE
ing.grid

New Dataset published at ing.grid! "Simplified Object Detection for Manufacturing: Introducing a Low-Resolution Dataset", by Jonas Maximilian Werheid, Shengjie He, Tobias Hamann, Anas Abdelrazeq, and Robert Schmitt inggrid.org/article/id/4133/ #ResearchManagement #EngineeringSciences #RDM

ing.gridSimplified Object Detection for Manufacturing: Introducing a Low-Resolution DatasetMachine learning (ML), particularly within the domain of computer vision (CV), has established solutions for automated quality classification using visual data in manufacturing processes. Object detection as a CV method for quality classification provides a distinct advantage in enabling the assessment of items within the manufacturing environment, regardless of their location in images. However, substantial challenges remain regarding labeled data availability in manufacturing contexts, training examples, data imbalance, and the complexity of incorporating these methods into real-world applications. Furthermore, real-world datasets often lack adherence to FAIR principles, which limits their accessibility and interoperability, especially for small- and medium-sized enterprises (SMEs) working to integrate object detection into their manufacturing processes. In this article, we present a low-resolution 640x640 dataset based on plastic bricks for object detection, featuring two quality labels to identify minor surface defects as an example of quality classification. We analyze the dataset using a YOLOv5 model on three different dataset sizes, while accounting for class imbalance, to demonstrate the accuracy of an object detection model in a simple manufacturing use case. The mean Average Precision mAP@0.5 for correctly identifying instances in our testing dataset ranges from 0.668 to 0.774, depending on dataset size and class imbalance. While our focus is on demonstrating object detection with low-resolution images and limited data availability, the generated data and trained model also adhere to FAIR principles.Therefore, these resources are made available with proper metadata to support their reuse and further investigation into object detection tasks for similar quality classification use cases in manufacturing.
ORCID Organization

🔗 Streamline Research, Save Time with ORCID
How can integrating ORCID reduce research management tasks? Find out in our upcoming webinar featuring Fundação para a Ciência e a Tecnologia (FCT). By integrating with ORCID, Portuguese researchers are saving time and minimizing manual updates.
📅 Date/Time: 20 Nov @ 16:00 UTC

🔗 Don't miss it! Register here: orcid-org.zoom.us/webinar/regi
#ResearchEfficiency #ORCID #ResearchManagement #AcademicInnovation #FutureOfResearch

ing.grid

New manuscript published at ing.grid! "A survey on the dissemination and usage of research data management and related tools in German engineering sciences", by Tobias Hamann, Amelie Metzmacher, Marcos Alexandre Galdino, Anas Abdelrazeq, and Robert Heinrich Schmitt inggrid.org/article/id/4073/ #RDM #ResearchManagement #EngineeringSciences

ing.gridA survey on the dissemination and usage of research data management and related tools in German engineering sciencesAs the amount of collected and analysed data increases, a need for data management arises to ensure its usability. This also applies in research. This challenge can be addressed by Research Data Management (RDM), which brings clear focus on the reusability of data. To understand the status quo of the application of research data management in engineering sciences in Germany, as well as possible challenges and improvement chances, a survey was conducted over the last quartal of 2020. Over 168 (n=168) researchers from the engineering sciences in Germany provided their view via a questionnaire that contains 216 question items. The results give intel on the interviewees knowledge and perceived relevance of research data management in their daily research activities. For instance, the application of research data management related tasks, data sharing with third parties, usage of different tools, and the involvement of different file formats were part of the survey. The survey closed with questions regarding RDM specifications, support structures, and questions on reasons that could prevent researchers from adapting sustainable RDM. This paper presents the results of the study, providing an overview over the current RDM in engineering and pointing out possible measures and strategies to foster it, namely the integration of guidance and education for research data management. Along the paper, we publish the collected data set to enable further analysis and reuse (e.g. for extended statistical analysis).
ORCID Organization

🔗 Streamline Research, Save Time with ORCID

How can integrating ORCID reduce research management tasks? Find out in our upcoming webinar featuring Fundação para a Ciência e a Tecnologia (FCT). By integrating with ORCID, Portuguese researchers are saving time and minimizing manual updates.
📅 Date/Time: 20 Nov @ 16:00 UTC
🔗 Don't miss it! Register here: orcid-org.zoom.us/webinar/regi

#ResearchEfficiency#ORCID#ResearchManagement
TrendingLive feeds
About