Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies

• Fabio Le Piane,
• Mario Vozza,
• Matteo Baldoni and
• Francesco Mercuri

Beilstein J. Nanotechnol. 2024, 15, 1498–1521, doi:10.3762/bjnano.15.119

• from virtual systems, digital twins, computational modelling, HPC, edge computing, and Internet of Things, can, in principle, be directly integrated within data-centric frameworks. As we will see later on, however, the issues related to data integration are also relevant in this case. The analysis and

Mechanism of silica–lysozyme composite formation unravelled by in situ fast SAXS

• Tomasz M. Stawski,
• Daniela B. van den Heuvel,
• Rogier Besselink,
• Dominique J. Tobler and
• Liane G. Benning

Beilstein J. Nanotechnol. 2019, 10, 182–197, doi:10.3762/bjnano.10.17

• , normalizations and correction for transmission, instrumental background subtraction and data integration of the collected 2D data to 1D. Further data processing and water background subtraction, model fitting, validation and analysis, were performed through a custom-made script developed in GNU Octave [64][65

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

• Richard L. Marchese Robinson,
• Mark T. D. Cronin,
• Andrea-Nicole Richarz and
• Robert Rallo

Beilstein J. Nanotechnol. 2015, 6, 1978–1999, doi:10.3762/bjnano.6.202

• in distinct datasets are related. Data integration within searchable databases supports computational modelling via enabling data from multiple sources to be combined, in principle, for more robust, generalisable analysis and via facilitating the identification of data which are relevant to the needs

The Nanomaterial Data Curation Initiative: A collaborative approach to assessing, evaluating, and advancing the state of the field

• Christine Ogilvie Hendren,
• Christina M. Powers,
• Mark D. Hoover and
• Stacey L. Harper

Beilstein J. Nanotechnol. 2015, 6, 1752–1762, doi:10.3762/bjnano.6.179

• within the nanomaterial community, such as reducing collateral negative impacts and decreasing the time from development to market for this new class of technologies. Keywords: curation; data integration; interoperability; nanoinformatics; nanomaterials; Introduction The topic of Big Data, and its

• important to chemistry, materials science and toxicology fields as a whole. However, drawing on existing experience with standards development, data handling and data integration to address viable solutions for complex data integration within the scope of nanomaterial data may serve as a specific case that

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

• support of data integration has recently been described [22]. eNanoMapper supports ontology re-use, for example it re-uses the content of the NPO and BAO, through automated modular import of content subsets into an integrated whole. However, the scope of the ontology goes beyond any of the individually

Experiences in supporting the structured collection of cancer nanotechnology data using caNanoLab

• Stephanie A. Morris,
• Sharon Gaheen,
• Michal Lijowski,
• Mervi Heiskanen and
• Juli Klemm

Beilstein J. Nanotechnol. 2015, 6, 1580–1593, doi:10.3762/bjnano.6.161

• be found on the caNanoLab FAQ webpage (https://wiki.nci.nih.gov/x/UKml), accessible through the caNanoLab homepage under the “How To” box. Assistance is also provided by the in-house curator. Data integration and sharing To optimize the design and utility of nanomaterials in biomedicine, researchers

Using natural language processing techniques to inform research on nanotechnology

• Nastassja A. Lewinski and
• Bridget T. McInnes

Beilstein J. Nanotechnol. 2015, 6, 1439–1449, doi:10.3762/bjnano.6.149

• developing and implementing effective mechanisms for collecting, validating, storing, sharing, analyzing, modeling, and applying that information” [4]. Applications of nanoinformatics include data integration and exchange (e.g., caNanoLab, GoodNanoGuide), nanoparticle characterization (e.g., caNanoLab

• to promote the standardization of ontologies and common controlled vocabularies for data integration [17][18]. Recently, the eNanoMapper project has developed an ontology that merges and extends existing ontologies, including the NPO [19]. Ontologies in other languages, such as Japanese and Russian

• four parts: 1) nanomaterial environmental impact (NEI) modeling framework – similar to Framework for Risk Analysis of Multi-Media Environmental Systems (FRAMES), 2) data integration, 3) data management and access, and 4) model building. This web-based tool is supported by the company’s previously