215,322 research outputs found
Software search is not a science, even among scientists: A survey of how scientists and engineers find software
Improved software discovery is a prerequisite for greater software reuse: after all, if someone cannot find software for a particular task, they cannot reuse it. Understanding people’s approaches and preferences when they look for software could help improve facilities for software discovery. We surveyed people working in several scientific and engineering fields to better understand their approaches and selection criteria. We found that even among highly-trained people, the rudimentary approaches of relying on general Web searches, the opinions of colleagues, and the literature were still the most commonly used. However, those who were involved in software development differed from nondevelopers in their use of social help sites, software project repositories, software catalogs, and organization-specific mailing lists or forums. For example, software developers in our sample were more likely to search in community sites such as Stack Overflow even when seeking ready-to-run software rather than source code, and likewise, asking colleagues was significantly more important when looking for ready-to-run software. Our survey also provides insight into the criteria that matter most to people when they are searching for ready-to-run software. Finally, our survey also identifies some factors that can prevent people from finding software
Software search is not a science, even among scientists: A survey of how scientists and engineers find software
Improved software discovery is a prerequisite for greater software reuse: after all, if someone cannot find software for a particular task, they cannot reuse it. Understanding people’s approaches and preferences when they look for software could help improve facilities for software discovery. We surveyed people working in several scientific and engineering fields to better understand their approaches and selection criteria. We found that even among highly-trained people, the rudimentary approaches of relying on general Web searches, the opinions of colleagues, and the literature were still the most commonly used. However, those who were involved in software development differed from nondevelopers in their use of social help sites, software project repositories, software catalogs, and organization-specific mailing lists or forums. For example, software developers in our sample were more likely to search in community sites such as Stack Overflow even when seeking ready-to-run software rather than source code, and likewise, asking colleagues was significantly more important when looking for ready-to-run software. Our survey also provides insight into the criteria that matter most to people when they are searching for ready-to-run software. Finally, our survey also identifies some factors that can prevent people from finding software
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Collaborative yet independent: Information practices in the physical sciences
In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data.
This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research.
Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
The CHAIN-REDS Semantic Search Engine
e-Infrastructures, and in particular Data Repositories and Open Access Data Infrastructures, are essential platforms for e-Science and e-Research and are being built since several years both in Europe and the rest of the world to support diverse multi/inter-disciplinary Virtual Research Communities. So far, however, it is difficult for scientists to correlate papers to datasets used to produce them and to discover data and documents in an easy way. In this paper, the CHAINREDS project’s Knowledge Base and its Semantic Search Engine are presented, which attempt to address those drawbacks and contribute to the reproducibility of science
Origins of Modern Data Analysis Linked to the Beginnings and Early Development of Computer Science and Information Engineering
The history of data analysis that is addressed here is underpinned by two
themes, -- those of tabular data analysis, and the analysis of collected
heterogeneous data. "Exploratory data analysis" is taken as the heuristic
approach that begins with data and information and seeks underlying explanation
for what is observed or measured. I also cover some of the evolving context of
research and applications, including scholarly publishing, technology transfer
and the economic relationship of the university to society.Comment: 26 page
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