Computer Science Data Research Articles

Learning designs that empower: navigating sandbox data science at the intersection of computing, big data and social media

PurposeThere is a need for precollege learning designs that empower youth to be epistemic agents in contexts that intersect burgeoning areas of computing, big data and social media. The purpose of this study is to explore how “sandbox” or open-inquiry data science with social media supports learning.Design/methodology/approachThis paper offers vignettes from an illustrative youth study case that highlights the pedagogical prospects and obstacles tied to designing for open-ended inquiry with computational data science to access or “scrape” Twitter/X. The youth case showcases how social media can be taken up productively and in ways that facilitate epistemological agency, an approach where individuals actively shape understanding and knowledge-creation processes, highlighting the potentially transformative impact this approach might have in empowering learners to engage productively.FindingsThe authors identify three key affordances for learning that emerged from the illustrative case: (1) flexible opportunities for content-specific domain mastery, (2) situated inquiry that embodies next-generation science practices and (3) embedded computational skill development. The authors discuss these findings in relation to contemporary education needs to broaden participation in data science and computing.Originality/valueTo address challenges in current data science education associated with supporting sustained and productive engagement in computing-based data science, the authors leverage a “sandbox” approach – an original pedagogical framework to support open inquiry with precollege groups. The authors demonstrate how “big data” drawn from social media with high school-aged youth supports learning designs and outcomes by emphasizing learner interests and authentic practice.

Information Science Students’ Background and Data Science Competencies: An Exploratory Study

Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both “classic” information science competencies as well as core data science competencies among their students. Since data science competencies are often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the LIS training programs. In order to address these concerns, we report on an exploratory study through which we examined the 2020 and 2021 student classes of Bar-Ilan University's LIS graduate training, focusing on the computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that contrary to many of the concerns raised, students from the humanities performed as well (and in some cases significantly better) on data science competencies compared to those from the social sciences and had better success in the training program as a whole. In addition, students’ undergraduate GPA acted as an adequate indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students’ training is very much aligned with the rest of their training program.

Integrating Computational Data Science in University Curriculum for the New Generation of Scientists

Integration of computational data science (CDS) into the university curriculum offers several advantages for students, faculty and the institution. This article discusses the benefits to students of introducing CDS into the university curriculum with a focus on developing skills in cheminformatics, data analysis, structure–activity relationships, modelling and simulation. Moreover, CDS can enable students to engage with complex chemical and toxicological data in new and dynamic ways, helping them to develop a more nuanced understanding of the potential hazards and risks associated with different chemicals and substances. On the other hand, it can foster greater collaboration between students and faculty and with external partners in industry and government. This can lead to the development of more effective and efficient toxicological testing methods and tools to screen chemicals for potential hazards and aid the development of environmentally friendly chemicals. Overall, the integration of CDS into the university curriculum will help prepare the next generation of scientists giving them a competitive edge to make considerable contributions to green chemistry, designing safer chemicals and non-animal testing methods. It will enable them to tackle modern challenges facing society including identifying safer and more sustainable chemicals and predicting the health and environmental impacts of novel chemical substances.

Detection of road traffic anomalies based on computational data science

The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long-term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

Phyloreferences: Tree-Native, Reproducible, and Machine-Interpretable Taxon Concepts

Evolutionary and organismal biology have become inundated with data. At the same rate, we are experiencing a surge in broader evolutionary and ecological syntheses for which tree-thinking is the staple for a variety of post-tree analyses. To fully take advantage of this wealth of data to discover and understand large-scale evolutionary and ecological patterns, computational data integration, i.e., the use of machines to link data at large scale, is crucial. The most common shared entity by which evolutionary and ecological data need to be linked is the taxon to which they belong. We propose a set of requirements that a system for defining such taxa should meet for computational data science: taxon definitions should maintain conceptual consistency, be reproducible via a known algorithm, be computationally automatable, and be applicable across the Tree of Life. We argue that Linnaean names, the most prevalent means of linking data to taxa, fail to meet these requirements due to fundamental theoretical and practical shortfalls. We argue that for the purposes of data-integration we should instead use phylogenetic definitions transformed into formal logic expressions. We call such expressions phyloreferences, and argue that, unlike Linnaean names, they meet all requirements for effective data-integration.

Neumora launches with $500 million

In one of the largest biotech debuts this year, Neumora Therapeutics has raised $400 million in series A financing from more than a dozen venture capital firms, plus $100 million from Amgen. The firm’s goal: precision small-molecule therapies for a multitude of brain diseases. The start-up was quietly founded in late 2019 by several people at Arch Venture Partners, neurobiologist Morgan Sheng of Broad Institute of MIT and Harvard, and others. The company has since grown to a team of 90 people based at sites just outside Boston and San Francisco. Neumora has acquired four smaller start-ups that will help it hit the ground running. It has eight drug programs, plus a research collaboration with Amgen on additional programs for brain diseases including amyotrophic lateral sclerosis and schizophrenia. CEO and cofounder Paul L. Berns says the company is based on advances in neuroscience disease biology and computational data science. Neumora

The Right Tools for the Job: The Case for Spatial Science Tool-Building

This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze non-planar urban spatial networks have traditionally been inaccessible, a theoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open‐source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability.

The right tools for the job: The case for spatial science tool‐building

AbstractThis article was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze non‐planar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This article reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open‐source software development. Tool‐building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The article concludes with paths forward, emphasizing open‐source software and reusable computational data science beyond mere reproducibility and replicability.

A computational science approach to understanding human conflict

We discuss how computational data science and agent-based modeling, are shedding new light on the age-old issue of human conflict. While social science approaches focus on individual cases, the recent proliferation of empirical data and complex systems thinking has opened up a computational approach based on identifying common statistical patterns and building generative but minimal agent-based models. We discuss a reconciliation for various disparate claims and results in the literature that stand in the way of a unified description and understanding of human wars and conflicts. We also discuss the unified interpretation of the origin of these power-law deviations in terms of dynamical processes. These findings show that a unified computational science framework can be used to understand and quantitatively describe collective human conflict.

Spatial information and the legibility of urban form: Big data in urban morphology

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, conceptualize proposed designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.

Spatial Information and the Legibility of Urban Form: Big Data in Urban Morphology

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, propose designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives.

Data Science and Political Economy: Application to Financial Regulatory Structure

The development of computational data science techniques in natural language processing and machine learning algorithms to analyze large and complex textual information opens new avenues for studying the interaction between economics and politics. We apply these techniques to analyze the design of financial regulatory structure in the United States since 1950. The analysis focuses on the delegation of discretionary authority to regulatory agencies in promulgating, implementing, and enforcing financial sector laws and overseeing compliance with them. Combining traditional studies with the new machine learning approaches enables us to go beyond the limitations of both methods and offer a more precise interpretation of the determinants of financial regulatory structure.

Retention in Computer Science Course

Throughout its history, careers in fields related to Computer Science (CS) have expanded. However, there have been periods, like the year 2000 following the ’dot-com bust’ when skilled people had to change employers, invoking negative publicity. The effect of this on enrollment in CS education was dramatic and lasted until 2007. According to Computing Research Association (CRA), there was an amazing increase in CS enrollment in 2008. Total enrollment per department by majors and pre-majors in U.S. CS programs is up 6.2% in 2008, and if only majors are considered, the increase is 8.1% [1]. CS student data are similar in Canadian schools [1]. There is a significant increase in number of enrollments in Computer Science education since 2008 [1]. The vital next step is to retain the enrolled students in Computer Science courses. Keeping that in mind, our research has combined the understanding of retention issues with some action strategies. This paper describes a first year CS course with this objective, initial finding and recommends some strategies to help students to successfully complete the course. In this research, we seek the clear indicators of withdrawal from or unsuccessful completion of a first year CS course. We suggest strategies to reduce the withdrawal rate and provide students with greater confidence in their ability to succeed. Our recommendations are for example, adapting the curriculum to the learners expectations i.e., making early assignments easier and less intimidating. Overall, the number of female students in computer science is low [2] and becomes diminished when they withdraw from the courses. Our suggestion is designating support for female students through the course.