BUSCADOR RECOLECTA

QUAM–AFM Lite is the scaled-down version of QUAM-AFM, the largest dataset of simulated Atomic Force Microscopy (AFM) images. This reduced version was generated from a selection of 1,755 molecules that span the most relevant bonding structures and chemical species in organic chemistry. Similar to the extended version, QUAM-AFM Lite contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 421,200 images with a resolution of 256x256 pixels.

The operational parameters include six different values for the cantilever oscillation amplitude (0.40, 0.60, 0.80, 1.00, 1.20, 1.40Å), 4 values of the elastic constant describing the tilting of the CO tip (0.40, 0.60, 0.80 and 1.00 N/m). The first parameter is freely chosen in the experiments in order to enhance different features of the image, while the last one reflects differences in the attachment of the CO molecule to the metal tip that are routinely observed and has been characterized in the experiments.

The data provided for each molecule includes, besides a set of AFM images, the ball–and–stick depiction, the IUPAC name, the chemical formula, the atomic coordinates, and the map of atom heights. In order to simplify the use of the collection as a source of information, we have developed a Graphical User Interface (GUI) that allows the search for structures by CID number, IUPAC name or chemical formula.

This dataset arises as a product of the research carried out in collaboration between Quasar Science Resources S.L. (https://quasarsr.com) and the Scanning Probe Microscopy Theory & Nanomechanics Research Group (SPMTH) (http://www.uam.es/spmth) at the Universidad Autónoma de Madrid (UAM), funded by the Comunidad de Madrid under the Industrial Doctorate Programme 2017 (project reference IND2017/IND-7793).

The main goal of this dataset is to provide a simplified version of QUAM-AFM that allows to analyse the distribution of information and/or the graphical interface without the need for a full download. The extended version, QUAM-AFM, supports the development of deep learning methods for molecular identification through AFM imaging. Once this project has concluded, this dataset is made freely accessible in order to facilitate and to promote research in a range of fields including Atomic Force Microscopy, on-surface synthesis and deep learning applications.

The reference dataset RP_LAC_2019_S2 was obtained from S2 images over a set of 56 mosaics (sampling units) sampled following a custom design for the Sentinel mosaic grid system and to represent both major fire regimes and regimes with lower burned area of Latin America and Caribbean in the different ecoregions. For each mosaic, S2 time series were defined based on a set of conditions to minimize cloudiness and ensure series length and minimum time lag between image pairs. The S2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters representing burned areas between the two dates that were combined into a synthetic burned area reference dataset. This dataset represents for each unit burned and unburned polygons and masked areas. The RP_LAC_2019_S2 dataset is part of the Burned Area Reference Database (BARD), a database that compiles multitemporal global and regional burned area reference datasets for Earth Observation burned area products validation.

Description of the project: This dataset has been developed for the validation of the S2BA (Sentinel-2 Burned Area) product in Latin America and Caribbean for the year 2019. S2BA is an automatic global burned area mapping algorithm (S2BA) based on Sentinel-2 Level-2A imagery in combination with Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectrometer (MODIS) active fire data. The algorithm and product was developed by the University of the Basque Country UPV/EHU under the "Proyecto Estratégico Análisis y explotación de información geoespacial (GeoInf) PES20/54” Estratégico Análisis y explotación de información geoespacial (GeoInf) PES20/54”

QUAM–AFM is the largest dataset of simulated Atomic Force Microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256x256 pixels. The 3D stacks are especially appropriate to tackle the goal of chemical identification within AFM experiments by using deep learning techniques.

The operational parameters include six different values for the cantilever oscillation amplitude (0.40, 0.60, 0.80, 1.00, 1.20, 1.40 Å), 4 values of the elastic constant describing the tilting of the CO tip (0.40, 0.60, 0.80 and 1.00 N/m). The first parameter is freely chosen in the experiments in order to enhance different features of the image, while the last one reflects differences in the attachment of the CO molecule to the metal tip that are routinely observed and has been characterized in the experiments.

The data provided for each molecule includes, besides a set of AFM images, the ball–and–stick depiction, the IUPAC name, the chemical formula, the atomic coordinates, and the map of atom heights. In order to simplify the use of the collection as a source of information, we have developed a Graphical User Interface (GUI) that allows the search for structures by CID number, IUPAC name or chemical formula.

This dataset arises as a product of the research carried out in collaboration between Quasar Science Resources S.L. (https://quasarsr.com) and the Scanning Probe Microscopy Theory & Nanomechanics Research Group (SPMTH) (http://www.uam.es/spmth) at the Universidad Autónoma de Madrid (UAM), funded by the Comunidad de Madrid under the Industrial Doctorate Programme 2017 (project reference IND2017/IND-7793).

The main goal of this dataset is to support the development of deep learning methods for molecular identification through AFM imaging. Once this project has concluded, this dataset is made freely accessible in order to facilitate and to promote research in a range of fields including Atomic Force Microscopy, on-surface synthesis and deep learning applications.