  Citation: Reyes-Vera, E.; Botero-Valencia, J.S.; Arango-Bustamante, K.; Zuluaga, A.; Naranjo, T.W. Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method. Data 2022, 7, 56. https:// doi.org/10.3390/data7050056 Academic Editor: Li-Yueh Hsu Received: 16 February 2022 Accepted: 15 April 2022 Published: 29 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). data Data Descriptor Microscopic Imaging and Labeling Dataset for the Detection of Pneumocystis jirovecii Using Methenamine Silver Staining Method Erick Reyes-Vera 1,2, * ,† , Juan S. Botero-Valencia 3,† , Karen Arango-Bustamante 2 , Alejandra Zuluaga 2 and Tonny W. Naranjo 2,4 1 Department of Electronics and Telecommunications, Instituto Tecnológico Metropolitano ITM, Medellin 050034, Colombia 2 Medical and Experimental Mycology Group, Corporación para Investigaciones Biológicas, Medellin 050034, Colombia; karango@cib.org.co (K.A.-B.); azuluaga@cib.org.co (A.Z.); tonny.naranjo@upb.edu.co (T.W.N.) 3 Department of Mechatronics and Electromechanics, Instituto Tecnológico Metropolitano ITM, Medellin 050034, Colombia; juanbotero@itm.edu.co 4 School of Health Sciences, Universidad Pontificia Bolivariana, Medellin 050031, Colombia * Correspondence: erickreyes@itm.edu.co † These authors contributed equally to this work. Abstract: Pneumocystis jirovecii pneumonia is one of the diseases that most affects immunocompro- mised patients today, and under certain circumstances, it can be fatal. On the other hand, more and more automatic tools based on artificial intelligence are required every day to help diagnose diseases and thus optimize the resources of the healthcare system. It is therefore important to develop techniques and mechanisms that enable early diagnosis. One of the most widely used techniques in diagnostic laboratories for the detection of its etiological agent, Pneumocystis jirovecii, is optical microscopy. Therefore, an image dataset of 29 different patients is presented in this work, which can be used to detect whether a patient is positive or negative for this fungi. These images were taken in at least four random positions on the specimen holder. The dataset consists of a total of 137 RGB images. Likewise, it contains realistic, annotated, and high-quality microscope images. In addition, we provide image segmentation and labeling that can also be used in numerous studies based on artificial intelligence implementation. The labeling was also validated by an expert, allowing it to be used as a reference in the training of automatic algorithms with supervised learning methods and thus to develop diagnostic assistance systems. Therefore, the dataset will open new opportunities for researchers working in image segmentation, detection, and classification problems related to Pneumocystis jirovecii pneumonia diagnosis. Dataset: https://doi.org/10.17605/OSF.IO/WQME8. Dataset License: CC-By Attribution 4.0 International. Keywords: Pneumocystis jirovecii pneumonia; Grocott’s methenamine silver; microscopy; diagnosis; labeling; digital image processing; non-destructive tests 1. Summary Pneumonia is a common respiratory infection that primarily affects the alveoli and the distal bronchial tree of the lungs. It is an infection caused by viruses, bacteria, fungi, or other germs that causes inflammation of one or both lungs and can be treated if detected early [1–3]. In the case of pneumonia caused by fungus, the principal etiological agent is an opportunistic fungal pathogen called Pneumocystis jirovecii. Pneumocystis jirovecii pneumonia (PCP) is an opportunistic fungal infection that is potentially fatal, especially Data 2022, 7, 56. https://doi.org/10.3390/data7050056 https://www.mdpi.com/journal/data