This article offers an assessment of current data practices in the citizen science, community science, and crowdsourcing communities. We begin by reviewing current trends in scientific data relevant to citizen science before presenting the results of our qualitative research. Following a purposive sampling scheme designed to capture data management practices from a wide range of initiatives through a landscape sampling methodology (Bos et al. 2007), we sampled 36 projects from English-speaking countries. The authors used a semi-structured protocol to interview project proponents (either scientific leads or data managers) to better understand how projects are addressing key aspects of the data lifecycle, reporting results through descriptive statistics and other analyses. Findings suggest that citizen science projects are doing well in terms of data quality assessment and governance, but are sometimes lacking in providing open access to data outputs, documenting data, ensuring interoperability through data standards, or building robust and sustainable infrastructure. Based on this assessment, the paper presents a number of recommendations for the citizen science community related to data quality, data infrastructure, data governance, data documentation, and data access.

Citizen science is an important vehicle for democratizing science and promoting the goal of universal and equitable access to scientific data and information. Data generated by citizen science groups have become an increasingly important source for scientists, applied users and those pursuing the 2030 Agenda for Sustainable Development. Citizen science data are used extensively in studies of biodiversity and pollution; crowdsourced data are being used by UN operational agencies for humanitarian activities; and citizen scientists are providing data relevant to monitoring the sustainable development goals (SDGs). This article provides an International Science Council (ISC) perspective on citizen science data generating activities in support of the 2030 Agenda and on needed improvements to the citizen science community's data stewardship practices for the benefit of science and society by presenting results of research undertaken by an ISC-sponsored Task Group.

Fortran 90 provides a rich set of array intrinsic functions. They form a rich source of parallelism and play an increasingly important role in automatic support of data parallel programming. However, there is no such support if these intrinsic functions are applied to sparse data sets.We address this open gap by presenting an efficient library for parallel sparse computations with Fortran 90 array intrinsic operations.Our method provides both compression schemes and distribution schemes on distributed memory environments applicable to higherdimensional sparse arrays. Sparse programs can be expressed concisely using array expressions, and parallelized with the help of our library.Preliminary experimental results on an IBM SP2 workstation cluster show that our approach is promising in supporting efficient sparse matrix computations on both sequential and distributed memory environments.

One of the recent Web developments has focused on the opportunities it presents for social tagging through user participation and collaboration. As a result, social tagging has changed the traditional online communication process. The interpretation of tagging between humans and machines may create new problems if essential questions about how social tagging corresponds to online communications, what objects the tags refer to, who the interpreters are, and why they are engaged are not explored systematically. Since all reasoning is an interpretation of social tagging among humans, tags, and machines, it is a complex issue that calls for deep reflection. In this paper, we investigate the relevance of the potential problems raised by social tagging through the framework of C. S. Peirce's semiotics. We find that general phenomena of social tagging can be well classified by Peirce's ten classes of signs for reasoning. This suggests that regarding social tagging as a sign and systematically analyzing the interpretation are positively associated with the ten classes of signs. Peircean semiotics can be used to examine the dynamics and determinants of tagging; hence, the various uses of this categorization schema may have implications for the design and development of information systems and Web applications.

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