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In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users' interaction which can be used to perform a data‐driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. It incorporates multimodal data, combining information from various sources such as user profiles, photo metadata, and crowdsourced features. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self‐reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non‐professionals. As far as we know, the results presented in this work represent an important novelty for identifying expertise in the domain of photography, which researchers from various domains can utilise for related applications. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing ubiquity and accessibility of teledermatology applications, few studies have comprehensively surveyed their features and technical standards. Importantly, features implemented after the point of capture are often intended to augment image utilization, while technical standards affect interoperability with existing healthcare systems. We aim to comprehensively survey image utilization features and technical characteristics found within publicly discoverable digital skin imaging applications. Materials and Methods: Applications were identified and categorized as described in Part I. Included applications were then further assessed by three independent reviewers for post‐imaging content, tools, and functionality. Publicly available information was used to determine the presence or absence of relevant technology standards and/or data characteristics. Results: A total of 20 post‐image acquisition features were identified across three general categories: (1) metadata attachment, (2) functional tools (i.e., those that utilized images or in‐app content to perform a user‐directed function), and (3) image processing. Over 80% of all applications implemented metadata features, with nearly half having metadata features only. Individual feature occurred and feature richness varied significantly by primary audience (p < 0.0001) and function (p < 0.0001). On average, each application included under three features. Less than half of all applications requested consent for user‐uploaded photos and fewer than 10% provided clear data use and privacy policies. Conclusion: Post‐imaging functionality in skin imaging applications varies significantly by primary audience and intended function, though nearly all applications implemented metadata labeling. Technical standards are often not implemented or reported consistently. Gaps in the provision of clear consent, data privacy, and data use policies should be urgently addressed. [ABSTRACT FROM AUTHOR]

Introduction: In order to advance Open, transparent, and reproducible science and data-driven research, data generated in scientific studies need to be openly shared in a manner that makes them persistently Findable, Accessible, Interoperable, and Reusable (=FAIR) by both humans and machines as codified in the FAIR Principles [1]. Funders and publishers alike are aligning their data policies with these principles. For example, NASA's new Science Information Policy SPD-41 [2] lays out the specific requirements that the Science Mission Directorate expects for data to be approved as FAIR. Implementation of such policies requires infrastructure for FAIR-compliant data curation that allows mission scientists and researchers who generate post-mission data to readily comply with these data policies. In this presentation we will describe how the Astromaterials Data System (https://www.astromat.org) ensures FAIR astromaterials sample data. We will also connect Astromat's FAIR data services to the recommendations of the Planetary Data Ecosystem Independent Review Board (PDE-IRB) released in 2021 [3]. Astromaterials Data System (Astromat). The Astromaterials Data System is a NASA-funded data facility that develops and maintains online systems and services for publishing, disseminating, and archiving newly acquired data as well as for restoring and preserving historic data generated by the analysis of astromaterials samples. Astromat's systems, services, policies, procedures, and management have been designed to comply with the FAIR principles for data and with the TRUST principles for Transparent, Responsible, User Focused, Sustainable, and Technologically stable repositories [4]. Among the services that make Astromat curated data FAIR are: 1) documentation of data with rich metadata for discovery and reuse, specifically extensive documentation of data provenance (e.g., methods of data and sample acquisition and processing) and data quality; 2) review of submitted content by data curators, who engage with users to augment and enrich, if necessary, documentation, 3) registration of data with DOI to make the data citable; 5) inclusion of persistent identifiers in the metadata (e.g., DOI for cited publications, IGSN for cited samples, funding award numbers, and mission identifiers), so that datasets can be linked to other digital resources; 6) clear licenses for access and reuse of curated data; 7) machine-readable interfaces (API) to access metadata and data; 8) use of community best practices and data standards, including vocabularies. In order to further enhance reusability and impact of Astromaterials sample data, Astromat data curators synthesize historic and new data into a comprehensive, analysis-ready data store that allows users to explore and mine astromaterials data in ways that have not been possible before, using new technologies such as Machine Learning and Artificial Intelligence. Astromat is also integrating the content of MetBase (https://metbase.org), the world's largest compilation of meteorite data, to make its data collection as comprehensive as possible. Astromaterials Data Archiving Special Study. Additional demands for the management of astromaterials data have emerged driven by a new generation of sample-return missions and subsequent sample analysis that require an assessment of the current landscape of astromaterials data archiving capabilities and capacity. Astromat is conducting a Special Study for NASA, currently underway, that will result in a report with recommendations for a NASA Astromaterials Data Archive that provides a scalable and standards-compliant solution for archiving and disseminating astromaterials data as Open and FAIR data. We will report on outcomes of this Special Study. [ABSTRACT FROM AUTHOR]