Research

The diagrams below were prepared by Jenna Hartel , based upon the work of Robert A. Stebbins. These diagrams may be reproduced      without permission, but please acknowledge their source as:...

OCLC publishes findings from the Building a National Finding Aid Network project, including archival user research and data analysis

A perspective on encoded archival standards and archival discovery supported by research.

Libraries, as most organizations and businesses, are heavily affected by the COVID-19 pandemic. Not only do libraries offer information and resources on many topics from multiple viewpoints, but also serve …

Assessment projects can be intimidating and overwhelming. I have these same thoughts each time I begin a new project. I wonder if my data collection tools will be effective in …

Join other members of the OCLC Research Library Partnership for an informal, small group discussion about the development of bibliometric and research impact (BRI) services.

In this webinar, librarians from three research universities describe the services, collaborations, and strategies they are employing to develop research impact services at their institutions.

Join the OCLC Research Library Partnership for a skills-building workshop

The Declaration on Research Assessment recognizes the need to improve the ways in which the outputs of scholarly research are evaluated.

2021 Competency Model for Bibliometric Work Please note: the above linked PDF is the text-readable PDF for screen readers with proper headings. For the inclusion of the visual gradient tables, plea…

In this webinar, librarians from three research universities describe the services, collaborations, and strategies they are employing to develop research impact services at their institutions.

Research libraries are establishing BRI services to support researchers & administrators alike. Learn about BRI efforts at three US research universities.

Ideas, Insight & Inspiration

global library cooperative

DuckDuckGo. Privacy, Simplified.

:koko: Compiler-enforced homogeneous JSON objects for Purescript - Risto-Stevcev/purescript-homogeneous-objects

Tuple data type and utility functions

This page documents the utility functions and data structures that support the FFI layer by providing JSON parsing, JSON construction, error handling, and helper operations. These utilities bridge the

Kernels & AI inference engine for mobile devices

Real-time object detection demands high throughput and low latency, necessitating the use of hardware accelerators. NPU is specialized hardware designed to accelerate the calculation of deep learning models, providing better energy efficiency and parallel processing performance than existing CPUs or GPUs. In particular, it plays an important role in reducing latency and improving processing speed in applications that require real-time processing. In this paper, we construct a real-time object detection system based on YOLOv3, utilizing Neubla’s Antara NPU, and propose two approaches for performance optimization. First, we ensure the continuity of NPU inference by allowing the CPU to process data in advance through double buffering. Second, in a multi-NPU environment, we distribute tasks among NPUs through queue-based processing and analyze the performance limits using Amdahl’s law. Experimental results demonstrate that compared to a CPU-only environment, applying the NPU in single buffering improved throughput by 2.13 times, double buffering by 3.35 times, and in a multi-NPU environment by 4.81 times. Latency decreased by 1.6 times in single and double buffering, and by 1.18 times in the multi-NPU environment. The accuracy remained consistent, with 31.4 mAP on the CPU and 31.8 mAP on the NPU.