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Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.
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Scientific Visualization: Enriching Vocabulary via the Human Hand
As scientific data grow larger and more complex, an equally rich visual vocabulary is needed to fully articulate its insights. We present a series of images that are made possible by a recent technical development “Artifact-Based Rendering,” a component of our broader effort to create a methodology for scientific visualization that draws on principles of art and design.
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- Award ID(s):
- 1704604
- PAR ID:
- 10163259
- Date Published:
- Journal Name:
- Proceedings of the IEEE VIS Arts Program (VISAP) 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract: 100 words Jurors are increasingly exposed to scientific information in the courtroom. To determine whether providing jurors with gist information would assist in their ability to make well-informed decisions, the present experiment utilized a Fuzzy Trace Theory-inspired intervention and tested it against traditional legal safeguards (i.e., judge instructions) by varying the scientific quality of the evidence. The results indicate that jurors who viewed high quality evidence rated the scientific evidence significantly higher than those who viewed low quality evidence, but were unable to moderate the credibility of the expert witness and apply damages appropriately resulting in poor calibration. Summary: <1000 words Jurors and juries are increasingly exposed to scientific information in the courtroom and it remains unclear when they will base their decisions on a reasonable understanding of the relevant scientific information. Without such knowledge, the ability of jurors and juries to make well-informed decisions may be at risk, increasing chances of unjust outcomes (e.g., false convictions in criminal cases). Therefore, there is a critical need to understand conditions that affect jurors’ and juries’ sensitivity to the qualities of scientific information and to identify safeguards that can assist with scientific calibration in the courtroom. The current project addresses these issues with an ecologically valid experimental paradigm, making it possible to assess causal effects of evidence quality and safeguards as well as the role of a host of individual difference variables that may affect perceptions of testimony by scientific experts as well as liability in a civil case. Our main goal was to develop a simple, theoretically grounded tool to enable triers of fact (individual jurors) with a range of scientific reasoning abilities to appropriately weigh scientific evidence in court. We did so by testing a Fuzzy Trace Theory-inspired intervention in court, and testing it against traditional legal safeguards. Appropriate use of scientific evidence reflects good calibration – which we define as being influenced more by strong scientific information than by weak scientific information. Inappropriate use reflects poor calibration – defined as relative insensitivity to the strength of scientific information. Fuzzy Trace Theory (Reyna & Brainerd, 1995) predicts that techniques for improving calibration can come from presentation of easy-to-interpret, bottom-line “gist” of the information. Our central hypothesis was that laypeople’s appropriate use of scientific information would be moderated both by external situational conditions (e.g., quality of the scientific information itself, a decision aid designed to convey clearly the “gist” of the information) and individual differences among people (e.g., scientific reasoning skills, cognitive reflection tendencies, numeracy, need for cognition, attitudes toward and trust in science). Identifying factors that promote jurors’ appropriate understanding of and reliance on scientific information will contribute to general theories of reasoning based on scientific evidence, while also providing an evidence-based framework for improving the courts’ use of scientific information. All hypotheses were preregistered on the Open Science Framework. Method Participants completed six questionnaires (counterbalanced): Need for Cognition Scale (NCS; 18 items), Cognitive Reflection Test (CRT; 7 items), Abbreviated Numeracy Scale (ABS; 6 items), Scientific Reasoning Scale (SRS; 11 items), Trust in Science (TIS; 29 items), and Attitudes towards Science (ATS; 7 items). Participants then viewed a video depicting a civil trial in which the defendant sought damages from the plaintiff for injuries caused by a fall. The defendant (bar patron) alleged that the plaintiff (bartender) pushed him, causing him to fall and hit his head on the hard floor. Participants were informed at the outset that the defendant was liable; therefore, their task was to determine if the plaintiff should be compensated. Participants were randomly assigned to 1 of 6 experimental conditions: 2 (quality of scientific evidence: high vs. low) x 3 (safeguard to improve calibration: gist information, no-gist information [control], jury instructions). An expert witness (neuroscientist) hired by the court testified regarding the scientific strength of fMRI data (high [90 to 10 signal-to-noise ratio] vs. low [50 to 50 signal-to-noise ratio]) and gist or no-gist information both verbally (i.e., fairly high/about average) and visually (i.e., a graph). After viewing the video, participants were asked if they would like to award damages. If they indicated yes, they were asked to enter a dollar amount. Participants then completed the Positive and Negative Affect Schedule-Modified Short Form (PANAS-MSF; 16 items), expert Witness Credibility Scale (WCS; 20 items), Witness Credibility and Influence on damages for each witness, manipulation check questions, Understanding Scientific Testimony (UST; 10 items), and 3 additional measures were collected, but are beyond the scope of the current investigation. Finally, participants completed demographic questions, including questions about their scientific background and experience. The study was completed via Qualtrics, with participation from students (online vs. in-lab), MTurkers, and non-student community members. After removing those who failed attention check questions, 469 participants remained (243 men, 224 women, 2 did not specify gender) from a variety of racial and ethnic backgrounds (70.2% White, non-Hispanic). Results and Discussion There were three primary outcomes: quality of the scientific evidence, expert credibility (WCS), and damages. During initial analyses, each dependent variable was submitted to a separate 3 Gist Safeguard (safeguard, no safeguard, judge instructions) x 2 Scientific Quality (high, low) Analysis of Variance (ANOVA). Consistent with hypotheses, there was a significant main effect of scientific quality on strength of evidence, F(1, 463)=5.099, p=.024; participants who viewed the high quality evidence rated the scientific evidence significantly higher (M= 7.44) than those who viewed the low quality evidence (M=7.06). There were no significant main effects or interactions for witness credibility, indicating that the expert that provided scientific testimony was seen as equally credible regardless of scientific quality or gist safeguard. Finally, for damages, consistent with hypotheses, there was a marginally significant interaction between Gist Safeguard and Scientific Quality, F(2, 273)=2.916, p=.056. However, post hoc t-tests revealed significantly higher damages were awarded for low (M=11.50) versus high (M=10.51) scientific quality evidence F(1, 273)=3.955, p=.048 in the no gist with judge instructions safeguard condition, which was contrary to hypotheses. The data suggest that the judge instructions alone are reversing the pattern, though nonsignificant, those who received the no gist without judge instructions safeguard awarded higher damages in the high (M=11.34) versus low (M=10.84) scientific quality evidence conditions F(1, 273)=1.059, p=.30. 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