The perceived value of the academic library: a systematic review - search history

Supplemental material – literature searc

The perceived value of the academic library: a systematic review – Data extraction form and risk of bias assessment

Supplemental material - Data extraction form and risk of bias assessmen

The perceived value of the academic library: a systematic review - excluded studies

Supplemental material – excluded studie

Library stories: a systematic review of narrative aspects within and around libraries

Background. Libraries are increasingly trying to communicate the library’s contributions and telling the library stories. Stories can be a component of impact assessment and thus add nuance to an assessment. Evaluations of libraries can include collecting and presenting stories of change, which can serve as evidence in impact assessments. The narrative field allows for many different approaches to a narrative perspective in the study of libraries, but the existing literature provides little overview of these studies

Dataset for SmartGridComm2018 paper

Dataset for SmartGridComm2018 pape

SBL for Multiple Parameterized Dictionaries

SBL for Multiple Parameterized Dictionaries This repository contains the python-code used in [1]. Included are (i) the code for the proposed sparse Bayesian learning (SBL)-based algorithm, (ii) the code for the newtonized orthogonal matching pursuit (NOMP) algorithm [2] used for comparison, (iii) additional files such as for the generalized subpattern assignment (OSPA) metric [3]. The repositor contains three demo examples (example_.py). The script example_crossing.py reproduces Figure 2 from [1]. Repository structure: |- pySBL/pySBL.py Code for the proposed algorithm |- pyOMP/pyOMP.py Implementation of the NOMP method for comparison |- dictionary_functions.py Implementation of the (parameterized) dictionary functions |- gopsa.py Implementation of the OSPA used to evaluate the results. |- example_radar.py Demo example with a single radar comparing SBL and OMP |- example_multiradar.py Demo Example applying SBL to multiple radars \ example_crossing.py Demo example of two crossing targets (Fig. 2) The code was tested using python 3.13 and numpy 2.2.3. The full spec of the miniconda environment is found in python_env.txt References [1] Moederl J., Westerkam, A. M., Venus, A. and Leitinger, E., "A Block-Sparse Bayesian Learning Algorithm with Dictionary Parameter Estimation for Multi-Sensor Data Fusion", submitted to the IEEE 28th International Conference on Information Fusion, Rio de Janeiro, Brazil, Jul 7-11, 2025. [2] B. Mamandipoor, D. Ramasamy, and U. Madhow, "Newtonized orthogonal matching pursuit: Frequency estimation over the continuum," IEEE Trans. Signal Process., vol. 64, no. 19, pp. 5066-5081, Oct. 2016. [3] A. S. Rahmathullah, A. F. Garcia-Fernandez, and L. Svensson,"Generalized optimal sub-pattern assignment metric," in 20th Int. Conf. Inf. Fusion, Xi'an, China, Jul. 10-13, 2017

Performance of conceptual framework elements for the retrieval of qualitative health literature: data

Data for each of the included studie

GHOST-IoT-data-set

The GHOST-IoT-data-set is a public data-set containing IoT network traffic collected with the deployment of the GHOST's capturing module in a real life smart-home installation, with multiple network interfaces and devices. During the capturing, abnormal functioning of devices has been intentionally triggered, to construct a data-set that enables the development of mechanisms that can conduct behavioral analysis against the activity in the smart-home

Datasæt for losscalculation paper

Indeholder tidssekvenser for estimeret effekttab i lavspændingsnet og dertilhørende konfidens interval for estimater

Approximate Bayesian Computation method for calibrating the Propagation Graph model using Summaries

This code is for learning the parameters of the polarimetric propagation graph model from summaries called temporal moments using approximate Bayesian computation