Datasets within this collection

Filter Results
269 results
  • Dropen is a fully automatic drop image analysis software in order to study the wetting properties of solid surfaces. It also includes the manual selection and fitting approaches. This software is developed in MATLAB using GUIDE. Regarding the identification of contact points two approaches are provided in Dropen: 1. Automatic: determines the left and right contact points automatically using a precise image processing code; 2. Manual: let the user select the left and right contact points manually by pushing the “Manual selection” button. In the case of the wrong selection of contact points the software let the user try two more times without a need to push the “Manual selection” button again. Regarding the contact angle measurement, the software provides a fully- automatic convolution mask method and also fitting with the circle and polynomial equations. So, the contact angle measurement panel includes: 1. Mask method: the fully automatic contact angle measurement, 2. Fitting: includes: a. Circle b. Polynomial: user can change the polynomial order. Regarding our studies, the optimum polynomial order for superhydrophilic droplets is two and for the rest of the droplets is Three. So, it is recommended to do not to change the polynomial order. After choosing the contact point identification and contact angle measurement methods, push the RUN button to find the results in the Result panel. In addition, the magnified images of the left and right contact areas are presented there. The drop contour line and the contact points have been shown with blue and red markers on the image, respectively. It is possible to save the contact angle data, as well as the left and right contact area images by pushing the corresponding push buttons. This software is developed in SEFILAB at the Department of Materials science, University of Milano-Bicocca, and is available at the UNIMIB repository (http://dx.doi.org/10.17632/wzchzbm58p.3). This repository will be updated by the new modified versions of the software.
    Data Types:
    • Other
    • Software/Code
    • Image
    • Dataset
  • ODMR_pentacene_picene_single_crystal
    Data Types:
    • Dataset
  • Data Types:
    • Other
    • Dataset
  • Supplementary Data: pages 2-12: daily mean global radiation (column 3, W/m2) pages 13-23: daily mean relative humidity (column 3, %) pages 24-34: daily mean global temperature (column 3, °C)
    Data Types:
    • Dataset
    • Document
  • Related Article: Santanu Pathak, Parnika Das, Tilak Das, Guruprasad Mandal, Boby Joseph, Manjulata Sahu, S. D. Kaushik, Vasudeva Siruguri|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|1034|doi:10.1107/S2053229620013960
    Data Types:
    • Dataset
  • Related Article: Santanu Pathak, Parnika Das, Tilak Das, Guruprasad Mandal, Boby Joseph, Manjulata Sahu, S. D. Kaushik, Vasudeva Siruguri|2020|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|76|1034|doi:10.1107/S2053229620013960
    Data Types:
    • Dataset
  • Uncovering the underdrawings (UDs), the preliminary sketch made by the painter on the grounded preparatory support, is a keystone for understanding the painting's history including the original project of the artist, the pentimenti (an underlying image in a painting providing evidence of revision by the artist) or the possible presence of co-workers’ contributions. The application of infrared reflectography (IRR) has made the dream of discovering the UDs come true: since its introduction, there has been a growing interest in the technology, which therefore has evolved leading to advanced instruments. Most of the literature either report on the technological advances in IRR devices or present case studies, but a straightforward method to improve the visibility of the UDs has not been presented yet. Most of the data handling methods are devoted to a specific painting or they are not user-friendly enough to be applied by non-specialized users, hampering, thus, their widespread application in areas other than the scientific one, e.g., in the art history field. We developed a computer-assisted method, based on principal component analysis (PCA) and image processing, to enhance the visibility of UDs and to support the art-historians and curators’ work. Based on ImageJ/Fiji, one of the most widespread image analysis software, the algorithm is very easy to use and, in principle, can be applied to any multi- or hyper-spectral image data set. In the present paper, after describing the method, we accurately present the extraction of the UD for the panel “The Holy Family with St. Anne and the Young St. John” and for other four paintings by Luini and his workshop paying particular attention to the painting known as “The Child with the Lamb”.
    Data Types:
    • Collection
  • Supplemental material, sj-pdf-1-asp-10.1177_0003702820949928 for Behind the Scene of “The Holy Family with St. Anne and the Young St. John” by Bernardino Luini: A Computer-Assisted Method to Unveil the Underdrawings by Michele Caccia, Letizia Bonizzoni, Marco Martini, Raffaella Fontana, Valeria Villa and Anna Galli in Applied Spectroscopy
    Data Types:
    • Document
  • Related Article: Nicola Panza, Armando di Biase, Silvia Rizzato, Emma Gallo, Giorgio Tseberlidis, Alessandro Caselli|2020|Eur.J.Org.Chem.|2020|6635|doi:10.1002/ejoc.202001201
    Data Types:
    • Dataset
  • Related Article: Michele Fiore, Samuel Wheeler, Kevin Hurlbutt, Isaac Capone, Jack Fawdon, Riccardo Ruffo, Mauro Pasta|2020|Chem.Mater.|32|7653|doi:10.1021/acs.chemmater.0c01347
    Data Types:
    • Dataset
1