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NASA NTRS2012

Aquarius iPhone Application

The Office of the CIO at JPL has developed an iPhone application for the Aquarius/SAC-D mission. The application includes specific information about the science and purpose of the Aquarius satellite and also features daily mission news updates pulled from sources at Goddard Space Flight Center as well as Twitter. The application includes a media and data tab section. The media section displays images from the observatory, viewing construction up to the launch and also includes various videos and recorded diaries from the Aquarius Project Manager. The data tab highlights many of the factors that affect the Earth s ocean and the water cycle. The application leverages the iPhone s accelerometer to move the Aquarius Satellite over the Earth, revealing these factors. Lastly, this application features a countdown timer to the satellite s launch, which is currently counting the days since launch. This application was highly successful in promoting the Aquarius Mission and educating the public about how ocean salinity is paramount to understanding the Earth.

Estes, Joseph C., Jr.↗

NASA NTRS2012

Coastal Changes in Temperature and Salinity Observed during Hurricane Isaac Recorded and Downloaded by NASA DRIFTERs Moored in Heron Bay and at Half Moon Island, Louisiana

Major changes in salinity (approx.14 ppt.) and temperature (approx.40C) were continuously registered by two prototype NASA DRIFTERs, surface moored floaters, that NASA's Applied Science and Technology Project Office (ASTPO) has developed. The DRIFTER floating sensor module is equipped with an Arduino open-source electronics prototyping platform and programming language (http://www.arduino.cc), a GPS (Global Positioning System) module with antenna, a cell phone SIM (Subscriber Identity Module) card and a cellular antenna which is used to transmit data, and a probe to measure temperature and conductivity (from which salinity can be derived). The DRIFTER is powered by a solar cell panel and all the electronic components are mounted and sealed in [ waterproof encasement. Position and measurement data are transmitted via short message service (SMS) messaging to a Twitter site (DRIFTER 002@NASADRIFTER_002 and DRIFTER 004@NASADRIFTER_004), which provides a live feed. These data are the imported into a Google spreadsheet where conductivity is converted to salinity, and graphed in real-time. The spreadsheet data will be imported into a webpage maintained by ASTPO, where it will be displayed available for dO\\1lload.

Kalcic, Maria↗

NASA NTRS2013

Space Place Prime

Space Place Prime is public engagement and education software for use on iPad. It targets a multi-generational audience with news, images, videos, and educational articles from the Space Place Web site and other NASA sources. New content is downloaded daily (or whenever the user accesses the app) via the wireless connection. In addition to the Space Place Web site, several NASA RSS feeds are tapped to provide new content. Content is retained for the previous several days, or some number of editions of each feed. All content is controlled on the server side, so features about the latest news, or changes to any content, can be made without updating the app in the Apple Store. It gathers many popular NASA features into one app. The interface is a boundless, slidable- in-any-direction grid of images, unique for each feature, and iconized as image, video, or article. A tap opens the feature. An alternate list mode presents menus of images, videos, and articles separately. Favorites can be tagged for permanent archive. Face - book, Twitter, and e-mail connections make any feature shareable.

Fitzpatrick, Austin J.↗

NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗

NASA NTRS2017

Global Precipitation Products at NASA GES DISC for Supporting Agriculture Research and Applications

This presentation describes precipitation products available at the NASA GES DISC that support agricultural research. XXXX Key environmental variables for agriculture: precipitation, temperature, water (soil moisture), solar radiation, NDVI, etc. Rainfed agriculture - major farming practices that rely on rainfall for water. Rainfed agriculture: >95% of farmed land (sub- Saharan Africa); 90% (Latin America); 75% (Near East and North Africa); 65% (East Asia); 60% (South Asia). Precipitation is very important for rainfed agriculture. Droughts can cause severe damage. Precipitation information can be used to monitor the growing season. The Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA data centers, located in Greenbelt, Maryland, USA. The GES DISC is a major data archive center for global precipitation, water & energy cycles, atmospheric composition, and climate variability Global and regional precipitation datasets (satellite-based and data assimilation Data services (subsetting, format conversion, online visualization, etc.) User services are available FAQs, How to (recipes), Glossary, etc. Social media (Twitter, YouTube, User forum) Help desk (phone, email, online feedback) Training materials (ARSET => Applied Remote Sensing Training) Liu,

precipitation↗

NASA NTRS2018

It's Time for Color Vision Deficiency Friendly Color Maps in the Radar Community

Color Vision Deficiency (CVD) is a decreased ability to discern between particular colors. 8% of men and 0.4% of women have some form of CVD. An informal poll of AGU and AMS twitter followers yielded 10% of 70 respondents self identifying as having CVD. When presenting data on a two-dimensional plane it is common to use colors to represent values, the mapping between values and colors is known as a colormap. Colormap choice is personal and is influenced by: (1) Ability to highlight scientifically interesting data. (2) Institutional choices (supervisor insists on a certain colormap). (3) Domain dominance of a particular colormap (common in the radar community). Colormap choice should be influenced by: (1) Ability to highlight scientifically interesting data. (2) Perceptual uniformity (thus not creating artificial structure). (3) Approachability by those with visual impairments (CVD). (4) Ideally, but not always achievable, reproducibility in greyscale.

Colormap↗

NASA NTRS2018

Lyman-Alpha Comes of Age

The Lyman-alpha (Lyα) line is the strongest recombination line of hydrogen, which constitutes the dominant share of normal matter in the Universe. Fifty-one years ago, it was predicted that Lyα emission should appear prominently in the spectra of young primordial galaxies. For the next 30 years, this prediction was tested by many surveys, but none found the predicted galaxies at the predicted luminosities. The blame for these non-detections was squarely cast on the resonant nature of the Lyα line, which requires Lyα photons to scatter off many hydrogen atoms before escaping from a galaxy. This can hugely increase the path that they traverse, and consequently also the probability that they will be absorbed by dust. This remained the dominant explanation of the missing high-redshift lines for decades, despite some dissenting opinions about the effect of dust. The spectra of nearby galaxies from the International Ultraviolet Explorer are inconclusive about what allows galaxies to be Lyα emitters. The ‘missing’ lines were finally detected 20 years ago, at flux levels much below the original predictions - a difference that we understand to be predominantly due to the hierarchical growth of galaxies, such that star-forming objects at high redshift are much less massive than anticipated in the original prediction. The discovery and study of Lyα galaxies has blossomed into an invaluable method for understanding the youngest stages of galaxy formation, the circumgalactic medium, and ultimately the epoch of reionization. The high equivalent widths of Lyα galaxies from the earliest high-redshift surveys show that they are low-mass galaxies dominated by young stellar populations. The Sakura CLAW workshop (which used the Twitter hashtag #SakuraCLAW) gathered more than 110 participants (Fig. 1) who showcased progress that pushed the frontiers of Lyα galaxy science in sample size, sensitivity, redshifts and physical understanding..

Malhotra, Sangeeta↗

DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗

DOE OSTI2022

Huntsman

SAND2021-15137 O The Huntsman library includes two main components: WebComms, which enhances Apache's HTTP Client by providing commonly needed capabilities in this domain to simplify web access as well as TwitterComms, which provides enhancements to the Twitter4J library for Twitter access. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Ridgway, Elliott↗

DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one’s peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the “pattern” by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗