Maojo et al Table 1 Nanoinformatics challenges proposed by three recent initiatives Nanoinformatics 2020 Roadmap (2010)79 The ACTION-Grid White Paper (2010)80 Nanomaterial data gap workshops for discussing how to facilitate the exchange of data and information about engineered nanomaterials: their physicochemical properties, behavior in the environment, biological activity, etc Development of a meta-ontology for nanoinformatics from existing ontologies and structured databases, dealing with issues such as interoperability and federation and demonstrating its applicability across multiple domains Minimum information recommendations: minimal information required for nanomaterial characterization, quality assurance processes for data collection and curation, ranking system for quality and completeness of data/datasets A meta-crawler for searching nano-related information on the web, serving as an interface to relevant ontologies (browse and search), resources (semantic search), materials (structural search), literature (links back to relevant terminologies), and news Simulation challenge: development of simulation and modeling tools for nanotechnology, targeting specific materials and using standard nanostructures that are well characterized to compare and validate calculation tools Creation of a nanoinformatics infrastructure to collect, curate, annotate, organize, and archive the available data Design of extended web nano portals, linking groups and information around the world to facilitate data sharing Development of repositories/databases of use cases, resources, nanotoxicity data and, clinical trial experiments or on nano facilitating the reuse of data – such as ArrayExpress for genomic data Incorporation of regulatory aspects: standards, issues related to open data and source tools, quality control Translational nanoinformatics: linking basic research from the lab to the bedside and nano-information to the Electronic Health Record Dovepress A Research Strategy for Environmental, Health, and Safety Aspects of Engineered Nanomaterials (2012)81 Identify minimum characterization principles to develop standardized descriptors (that is, metadata) for engineered nanomaterials (ENMs) Establish uniform metadata to describe ENM manufacturing and distribution processes and to correlate lot-to-lot variability of ENM properties Develop ontologies and data formats to allow relevant data on gene and protein expression to be correlated with ENM toxicity mechanisms and, in particular, to develop an ontology “crawler” to aid in mapping relationships among ontologies Develop strategies for federating nanotechnology databases to allow seamless data exposure and data sharing while protecting intellectual property rights Develop new mechanisms for digital archiving and annotating and updating of methods, data, tools, and models to spur rapid and efficient formation of new targeted national and international scientific collaborations raises some very special challenges. Biopolymers are often discrete structures or sequences, whereas nanomaterials typically exhibit a dispersion of sizes, compositions, and surface coatings. Such dispersions are difficult to define and reduce to the precise codes typically needed to classify and describe objects and their interactions in informatics modeling. Second, given the wide array of nanomaterial types and their as yet largely unknown interactions within different tissues and organismal physiologies, analytic measurements to make direct comparisons will be difficult to “anchor” to the appropriate experimental and clinical contexts. Informatics approaches will need to synthesize the information from multiple techniques to describe different nanomaterials. Such advances could help to overcome the many gaps in data and knowledge that describe nanomaterials in the literature, since most materials are as yet incompletely characterized. It is worth noting the concurrence among the three dif- ferent reports summarized in Table 1. This perspective is a significant acknowledgement that informatics technologies must become more central and more integrated with other scientific technologies and practices.82 This change may owe 3872 submit your manuscript | www.dovepress.com Dovepress a great deal to the successful implementation of informatics in facilitating progress in genomics, proteomics, and metabolomics. The eventual goal of developing “intelligent” feedback processes – seen as a human–machine interaction that can incorporate learning from past experiments – among inquiry, design, testing, and refinement of bioparticles, can only be achieved with the intermediation of novel nanoin- formatics techniques that include: modeling, a wide range of relevant simulations, and the sophisticated analysis and interpretation of massive experimental datasets. This objec- tive goes beyond what is currently achieved with simple data mining, even in network science environments.24,81,83 In the next section, we comment on various areas that we consider the most challenging for nanoinformatics research. Nanoinformatics areas Over the last few years, the growing nanoinformatics commu- nity has begun to focus on an increasing number of research topics that could accelerate developments in nanomedicine. A nonexhaustive list of significant examples follows. International Journal of Nanomedicine 2012:7