Instance maps as an organising concept for complex experimental workflows as demonstrated for (nano)material safety research

• Benjamin Punz,
• Maja Brajnik,
• Joh Dokler,
• Jaleesia D. Amos,
• Litty Johnson,
• Katie Reilly,
• Anastasios G. Papadiamantis,
• Amaia Green Etxabe,
• Lee Walker,
• Diego S. T. Martinez,
• Steffi Friedrichs,
• Klaus M. Weltring,
• Nazende Günday-Türeli,
• Claus Svendsen,
• Christine Ogilvie Hendren,
• Mark R. Wiesner,
• Martin Himly,
• Iseult Lynch and
• Thomas E. Exner

Beilstein J. Nanotechnol. 2025, 16, 57–77, doi:10.3762/bjnano.16.7

• biological compartments as a visual representation to guide the data curation process [28], that is, to highlight where changes to the nanomaterial may have occurred and, thus, where additional characterisation information would be needed. Instances were designed to capture the necessary metadata needed to

• complete understanding of nanomaterial behaviour. The NIKC curation team was tasked with translating these experimental studies into nanomaterial instances and identifying important metadata associated with each instance. This was done by categorising experimental data into one of five categories, namely

• time when material, medium, and properties are being described together. A study could have as many instances as needed to describe each of the potential material transformations. For quality assurance and quality control (QA/QC) purposes, the curation team needed a way to compare defined instances and

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

• Shan He,
• Julen Segura Abarrategi,
• Harbil Bediaga,
• Sonia Arrasate and
• Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

• connections regarding methodology and used databases to our previous publications. For each PTML model development, data download/compilation, data curation, and so on were carried out separately by researchers. First, the database of antineurodegenerative drugs (ADs) was downloaded from ChEMBL by Alonso and

• included another set of discrete variables used to codify the nature/quality of data. These variables are cd4, the type of target, cd5, the type of assay, cd6, the data curation, cd7, the confidence score, and cd8, the target mapping. Specifically, the target types are n(cd4) = 6 (single protein, organism

• , tissue, non-molecular target, and ADMET), and the assay types are n(cd5) = 3 (binding, functional, and ADMET). In addition, data curation has n(cd6) = 3 different values (auto-curation, expert, and intermediate), the confidence scores are n(cd7) = 4 (9: direct single protein target assigned, 1: target

Spindle-like MIL101(Fe) decorated with Bi₂O₃ nanoparticles for enhanced degradation of chlortetracycline under visible-light irradiation

• Chen-chen Hao,
• Fang-yan Chen,
• Kun Bian,
• Yu-bin Tang and
• Wei-long Shi

Beilstein J. Nanotechnol. 2022, 13, 1038–1050, doi:10.3762/bjnano.13.91

• . Fang-yan Chen: conceptualization, project administration, supervision, resources, writing - review & editing. Kun Bian: investigation, data curation, writing - review & editing. Yu-bin Tang: supervision, formal analysis, methodology. Wei-long Shi: funding acquisition, conceptualization, project

Determination of elastic moduli of elastic–plastic microspherical materials using nanoindentation simulation without mechanical polishing

• Hongzhou Li and
• Jialian Chen

Beilstein J. Nanotechnol. 2021, 12, 213–221, doi:10.3762/bjnano.12.17

• Natural Science Foundation of China (51103173). Author Contributions Chen Jialian: Investigation. Li Hongzhou: Conceptualization, Methodology, Software, Validation, Formal analysis, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration

Nanoinformatics for environmental health and biomedicine

• Rong Liu and
• Yoram Cohen

Beilstein J. Nanotechnol. 2015, 6, 2449–2451, doi:10.3762/bjnano.6.253

• properties, toxicity behavior, processing information, production levels, environmental releases, and more. Accordingly advanced informatics techniques are urgently required for the collection and curation, management (e.g., achieving and sharing), analysis and modeling of the large amount of data involved

• implementing effective mechanisms for collecting, validating, storing, sharing, analyzing, modeling, and applying that information.” [1]. At present, nanoinformatics focuses primarily on: nano-data management and database development, nano-data curation, assessment of the value of information in nano-data

• nanomaterials. In one contribution, the latest version of caNanoLab is described along with a critical discussion of the challenges associated with database development for nanomaterials, as well as the needs for nano-data curation and sharing by the biomedical research community [4]. The latest development of

Nanocuration workflows: Establishing best practices for identifying, inputting, and sharing data to inform decisions on nanomaterials

• Christina M. Powers,
• Karmann A. Mills,
• Stephanie A. Morris,
• Fred Klaessig,
• Sharon Gaheen,
• Nastassja Lewinski and
• Christine Ogilvie Hendren

Beilstein J. Nanotechnol. 2015, 6, 1860–1871, doi:10.3762/bjnano.6.189

• nanoscience to compare and integrate information across diverse fields of study through informatics (i.e., nanoinformatics). This paper is one in a series of articles on the data curation process in nanoinformatics (nanocuration). Other articles in this series discuss key aspects of nanocuration (temporal

• nanocuration is nascent, with few individuals formally trained in data curation or utilizing available nanocuration resources (e.g., ISA-TAB-Nano). Additional emphasis on the potential benefits of cultivating nanomaterial data via nanocuration processes (e.g., capability to analyze data from across research

• groups) and providing nanocuration resources (e.g., training) will likely prove crucial for the wider application of nanocuration workflows in the scientific community. Keywords: curation; informatics; nanoinformatics; nanomaterials; workflows; Introduction A tremendous growth in resources and tools to

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

• School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, OR, USA 10.3762/bjnano.6.179 Abstract The Nanomaterial Data Curation Initiative (NDCI), a project of the National Cancer Informatics Program Nanotechnology Working Group (NCIP NanoWG), explores the critical

• aspect of data curation within the development of informatics approaches to understanding nanomaterial behavior. Data repositories and tools for integrating and interrogating complex nanomaterial datasets are gaining widespread interest, with multiple projects now appearing in the US and the EU. Even in

• these early stages of development, a single common aspect shared across all nanoinformatics resources is that data must be curated into them. Through exploration of sub-topics related to all activities necessary to enable, execute, and improve the curation process, the NDCI will provide a substantive

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

• corona, and modelling approaches were extracted from these data for statistical analysis. Data quality considerations While there is a common agreement on the importance of data curation, there is no well established common understanding of how it should be performed. Approaches range from simple data

• is necessary. This approach is also in line with the intention “not to exclude automatically the unreliable data from further considerations” [32] and that “there is unlikely to be a single out-of-the-box solution that can be applied to the problem of data curation. Instead, an approach that

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

• nanotechnology data curation and sharing in the biomedical research community. We will also describe the latest version of caNanoLab, caNanoLab 2.0, which includes enhancements and new features to improve usability such as personalized views of data and enhanced search and navigation. Keywords: caNanoLab

• WG established a subgroup focused on developing guidelines for data curation, and is in the process of writing a series of consensus papers on curation workflows, data completeness and quality, curator responsibilities, metadata, and integration between datasets and databases, as an overview of

• current curation practices and recommendations (Nanomaterial Data Curation Initiative, https://nciphub.org/groups/nanotechnologydatacurationinterestgroup) [20][21]. In line with the goals of this subgroup, the journal Nature Nanotechnology recently published an editorial to announce their plans to