Search results for " dataset"

The Global Soil Mycobiome consortium dataset for boosting fungal diversity research

2021

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at https://doi.org/10.1007/s13225-021-00493-7. Fungi are highly important biotic components of terrestrial ecosystems, but we still have a very limited understanding about their diversity and distribution. This data article releases a global soil fungal dataset of the Global Soil Mycobiome consortium (GSMc) to boost further research in fungal diversity, biogeography and macroecology. The dataset comprises 722,682 fu…

WiseNET : an indoor multi-camera multi-space dataset with contextual information and annotations for people detection and tracking

2019

Nowadays, camera networks are part of our every-day life environments, consequently, they represent a massive source of information for monitoring human activities and to propose new services to the building users. To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 sin…

WATER DISTRIBUTION NETWORKS REHABILITATION BASED ON A SMALL INFORMATION DATASET

2012

A georeferenced dataset of Italian occurrence records of the phylum Rotifera

2023

We report a dataset of known and published occurrence records of Italian taxa from species (and subspecies) to family rank of the phylum Rotifera; we considered only Bdelloidea, Monogononta, and Seisonacea, and did not include Acanthocephala. The dataset in-cludes 15,525 records (12,015 of which with georeferenced coordinates) of 584 valid species and subspecies names and other taxa at family level, gathered from 332 published papers. The published literature spans the period from 1838 to 2022, with the lowest number of papers published during the first half of the twentieth century, followed by an increasing number of papers, from 20 to more than 60 in each decade. The Italian regions with…

Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis

2012

AbstractThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of pre…

Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach

2017

Visual Saliency aims to detect the most important regions of an image from a perceptual point of view. More in detail, the goal of Visual Saliency is to build a Saliency Map revealing the salient subset of a given image by analyzing bottom-up and top-down factors of Visual Attention. In this paper we proposed a new method for Saliency detection based on colour and scale analysis, extending our previous work based on SIFT spatial density inspection. We conducted several experiments to study the relationships between saliency methods and the object attention processes and we collected experimental data by tracking the eye movements of thirty viewers in the first three seconds of observation o…

Methods and Techniques for Multi-source Data Analysis and Fusion

This work has been inspired by the recent trend in remote sensing and environmental data acquisition. Remote sensing techniques allow us to measure information about an object without touching it. In the last decades remote sensing via satellites has been used in various applications such as Earth observation, weather and storm predictive analysis, atmospheric monitoring, climate change, human-environment interactions. Sensors on airborne and satellite platforms have been recording signals from space for many years, giving rise to a huge amount of data. Some data are processed on-board but others are treated and post-processed in ground stations. Signal and image processing are widely appli…

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

2023

Funder: Amazon Web Services (AWS) Imagine Grant

A methodology for optimisation of solar dish-Stirling systems size, based on the local frequency distribution of direct normal irradiance

2021

Abstract In geographical areas where direct solar irradiation levels are relatively high, concentrated solar energy systems are one of the most promising green energy technologies. Dish-Stirling systems are those that achieve the highest levels of solar-to-electric conversion efficiency, and yet they are still among the least common commercially available technologies. This paper focuses on a strategy aimed at promoting greater diffusion of dish-Stirling systems, which involves optimizing the size of the collector aperture area based on the hourly frequency distributions of beam irradiance and defining a new incentive scheme with a feed-in tariff that is variable with the installed costs of…

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

2022

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction …