The Social and Moral Responsibilities of Scientists and Enigneers
Social and moral responsibility of scientists and engineers
SPACE SCIENCE↗
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Starting Salaries of Engineers and Scientists
Starting salaries of engineers and scientists - patterns of demand affecting changes in salary since World War II
SCIENTIST↗
The work of Russian scientists on the founding of a theory of interplanetary flight
Interplanetary flight theories developed by Soviet scientists
U.S.S.R.↗
Climate Change: On Scientists and Advocacy
Last year, I asked a crowd of a few hundred geoscientists from around the world what positions related to climate science and policy they would be comfortable publicly advocating. I presented a list of recommendations that included increased research funding, greater resources for education, and specific emission reduction technologies. In almost every case, a majority of the audience felt comfortable arguing for them. The only clear exceptions were related to geo-engineering research and nuclear power. I had queried the researchers because the relationship between science and advocacy is marked by many assumptions and little clarity. This despite the fact that the basic question of how scientists can be responsible advocates on issues related to their expertise has been discussed for decades most notably in the case of climate change by the late Stephen Schneider.
climate change↗
Quark Matter 2017: Young Scientist Support
Quark Matter conference series are amongst the major scientific events for the Relativistic Heavy Ion community. With over 30 year long history, the meetings are held about every 1½ years to showcase the progress made in theoretical and experimental studies of nuclear matter under extreme conditions. The 26th International Conference on Ultra-relativistic Nucleus-Nucleus Collisions (Quark Matter 2017) was held at the Hyatt Regency Hotel in downtown Chicago from Sunday, February 5th through Saturday, February 11th, 2017. The conference featured about 180 plenary and parallel presentations of the most significant recent results in the field, a poster session for additional presentations, and an evening public lecture. Following the tradition of previous Quark Matter meetings, the first day of the conference was dedicated entirely to a special program for young scientists (graduate students and postdoctoral researchers). This grant will provided financial support for 235 young physicists facilitating their attendance of the conference.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners
Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. Here, we advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.
54 ENVIRONMENTAL SCIENCES↗
Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity
Evaluating a climate model’s fidelity (ability to simulate observed climate) is a critical step in establishing confidence in the model’s suitability for future climate projections, and in tuning climate model parameters. Model developers use their judgement in determining which trade-offs between different aspects of model fidelity are acceptable. However, little is known about the degree of consensus in these evaluations, and whether experts use the same criteria when different scientific objectives are defined. Here, we report on results from a broad community survey studying expert assessments of the relative importance of different output variables when evaluating a global atmospheric model’s mean climate. We find that experts adjust their ratings of variable importance in response to the scientific objective, for instance, scientists rate surface wind stress as significantly more important for Southern Ocean climate than for the water cycle in the Asian watershed. There is greater consensus on the importance of certain variables (e.g., shortwave cloud forcing) than others (e.g., aerosol optical depth). We find few differences in expert consensus between respondents with greater or less climate modeling experience, and no statistically significant differences between the responses of climate model developers and users. In conclusion, the concise variable lists and community ratings reported here provide baseline descriptive data on current expert understanding of certain aspects of model evaluation, and can serve as a starting point for further investigation, as well as developing more sophisticated evaluation and scoring criteria with respect to specific scientific objectives.
54 ENVIRONMENTAL SCIENCES↗
Apollo Next Generation Sample Analysis (ANGSA): an Apollo Participating Scientist Program to Prepare the Lunar Sample Community for Artemis
As a first step in preparing for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA). ANGSA was designed to function as a low-cost sample return mission and involved the curation and analysis of samples previously returned by the Apollo 17 mission that remained unopened or stored under unique conditions for 50 years. These samples include the lower portion of a double drive tube previously sealed on the lunar surface, the upper portion of that drive tube that had remained unopened, and a variety of Apollo 17 samples that had remained stored at -27 °C for approximately 50 years. ANGSA constitutes the first preliminary examination phase of a lunar “sample return mission” in over 50 years. It also mimics that same phase of an Artemis surface exploration mission, its design included placing samples within the context of local and regional geology through new orbital observations collected since Apollo and additional new “boots-on-the-ground” observations, data synthesis, and interpretations provided by Apollo 17 astronaut Harrison Schmitt. ANGSA used new curation techniques to prepare, document, and allocate these new lunar samples, developed new tools to open and extract gases from their containers, and applied new analytical instrumentation previously unavailable during the Apollo Program to reveal new information about these samples. Most of the 90 scientists, engineers, and curators involved in this mission were not alive during the Apollo Program, and it had been 30 years since the last Apollo core sample was processed in the Apollo curation facility at NASA JSC. There are many firsts associated with ANGSA that have direct relevance to Artemis. ANGSA is the first to open a core sample previously sealed on the surface of the Moon, the first to extract and analyze lunar gases collected in situ, the first to examine a core that penetrated a lunar landslide deposit, and the first to process pristine Apollo samples in a glovebox at -20 °C. All the ANGSA activities have helped to prepare the Artemis generation for what is to come. The timing of this program, the composition of the team, and the preservation of unopened Apollo samples facilitated this generational handoff from Apollo to Artemis that sets up Artemis and the lunar sample science community for additional successes.
79 ASTRONOMY AND ASTROPHYSICS↗
Tutorial: Lessons Learned for Behavior Analysts from Data Scientists
Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.
42 ENGINEERING↗
Advanced data science toolkit for non-data scientists – A user guide
Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.
36 MATERIALS SCIENCE↗
First Simultaneous Observation of STEVE and SAR Arc Combining Data From Citizen Scientists, 630.0 nm All‐Sky Images, and Satellites
Abstract On September 28, 2017 citizen scientist observations at Alberta, Canada (51°N, 113° W) detected aurora and a thin east‐west purplish arc, known as strong thermal emission velocity enhancement (STEVE) that lasted less than 20 min. All‐sky imagers at subauroral latitudes measured stable auroral red (SAR) arcs during the entire night. The imager at Bridger, MT (45.3°N, 108.9°W) also measured a STEVE. The overlapping geometry allowed to determine that the height of STEVE was 225–275 km. STEVE is brighter in the 630.0 nm images in the West and almost merges with the SAR arc in the East. A DMSP satellite pass in the southern hemisphere was at the conjugate location of the Bridger imager during the STEVE observation. When mapped into the northern hemisphere intense subauroral ion drift and subauroral polarization streams were detected associated with the two optical signatures measured in 630.0 nm.
Martinis, C.↗
New science in plain sight: Citizen scientists lead to the discovery of optical structure in the upper atmosphere
A glowing ribbon of purple light running east-west in the night sky has recently been observed by citizen scientists. This narrow, subauroral, visible structure, distinct from the traditional auroral oval, was largely undocumented in the scientific literature and little was known about its formation. Amateur photo sequences showed colors distinctly different from common types of aurora and occasionally indicated magnetic field–aligned substructures. Observations from the Swarm satellite as it crossed the arc have revealed an unusual level of electron temperature enhancement and density depletion, along with a strong westward ion flow, indicating that a pronounced subauroral ion drift (SAID) is associated with this structure. These early results suggest the arc is an optical manifestation of SAID, presenting new opportunities for investigation of the dynamic SAID signatures from the ground. On the basis of the measured ion properties and original citizen science name, we propose to identify this arc as a Strong Thermal Emission Velocity Enhancement (STEVE).
79 ASTRONOMY AND ASTROPHYSICS↗
Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists
Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.
Computer Science↗
Attracting and Retaining Top Scientists and Engineers at U.S. National Laboratories and Universities: Listening to the Next Generation
Society depends upon scientists and engineers to help address critical challenges in national security, efficient and sustainable energy systems, environmental stewardship and other complex areas of global importance. In the US, national laboratories and universities have traditionally served as research and development engines and sources of critical expertise in service to the nation
99 GENERAL AND MISCELLANEOUS↗
COMBIgor (A data assistant for combinatorial scientists in Igor Pro) [SWR-19-16]
Combinatorial materials science experiments involve synthesis and spatially-resolved characterization of thin film sample libraries. As a result, the modern combinatorial materials science laboratory produces large, diverse, and complex data sets, requiring automation of analysis and visualization techniques. COMBIgor is a software package aimed to assist a combinatorial material scientists with large volumes of experimental data. This package, written in Igor Pro, offers a systematic approach to the loading, processing, and visualizing combinatorial data sets. COMBIgor is designed to be expanded on a laboratory, group, or individual basis to adopt additional loading, processing and visualization capabilities. By utilizing COMBIgor software package the burden of data management in combinatorial materials science experiments can be significantly reduced.
Bauers, Sage↗
ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists
Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.
Sangkeun, MattLee↗
DOE Network 2025: Network Research Problems and Challenges for DOE Scientists (Workshop Report)
The growing investments in large science instruments and supercomputers by the US Department of Energy (DOE) hold enormous promise for accelerating the scientific discovery process. They facilitate unprecedented collaborations of geographically dispersed teams of scientists that use these resources. These collaborations critically depend on the production, sharing, moving, and management of, as well as interactive access to, large, complex data sets at sites dispersed across the country and around the globe. In particular, they call for significant enhancements in network capacities to sustain large data volumes and, equally important, the capabilities to collaboratively access the data across computing, storage, and instrument facilities by science users and automated scripts and systems. Improvements in network backbone capacities of several orders of magnitude are essential to meet these challenges, in particular, to support exascale initiatives. Yet, raw network speed represents only a part of the solution. Indeed, the speed must be matched by network and transport layer protocols and higher layer tools that scale in ways that aggregate, compose, and integrate the disparate subsystems into a complete science ecosystem. Just as important, agile monitoring and management services need to be developed to operate the network at peak performance levels. Finally, these solutions must be made an integral part of the production facilities by using sound approaches to develop, deploy, diagnose, operate, and maintain them over the science infrastructure.
97 MATHEMATICS AND COMPUTING↗