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1. SuperCLASS – III. Weak lensing from radio and optical observations in Data Release 1

   https://doi.org/10.1093/mnras/staa696  

   Harrison, Ian; Brown, Michael L; Tunbridge, Ben; Thomas, Daniel B; Hillier, Tom; Thomson, A P; Whittaker, Lee; Abdalla, Filipe B; Battye, Richard A; Bonaldi, Anna; et al (June 2020, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We describe the first results on weak gravitational lensing from the SuperCLASS survey: the first survey specifically designed to measure the weak lensing effect in radio-wavelength data, both alone and in cross-correlation with optical data. We analyse $$1.53 \, \mathrm{deg}^2$$ of optical data from the Subaru telescope and $$0.26 \, \mathrm{deg}^2$$ of radio data from the e-MERLIN and VLA telescopes (the DR1 data set). Using standard methodologies on the optical data only we make a significant (10σ) detection of the weak lensing signal (a shear power spectrum) due to the massive supercluster of galaxies in the targeted region. For the radio data we develop a new method to measure the shapes of galaxies from the interferometric data, and we construct a simulation pipeline to validate this method. We then apply this analysis to our radio observations, treating the e-MERLIN and VLA data independently. We achieve source densities of $$0.5 \,$$ arcmin−2 in the VLA data and $$0.06 \,$$ arcmin−2 in the e-MERLIN data, numbers which prove too small to allow a detection of a weak lensing signal in either the radio data alone or in cross-correlation with the optical data. Finally, we show preliminary results from a visibility-plane combination of the data from e-MERLIN and VLA which will be used for the forthcoming full SuperCLASS data release. This approach to data combination is expected to enhance both the number density of weak lensing sources available, and the fidelity with which their shapes can be measured. 

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2. A nearly century long (1920-2017) Siberian alder (Alnus viridis ssp. fruticosa) secondary growth record near Sagwon Bluffs Alaska, USA

   https://doi.org/10.18739/A27M0421K  

   Drew, Jackson; Bret-Harte, Marion; Buchwal, Agata; Heslop, Calvin (January 2023, NSF Arctic Data Center)

   This data set contains measures of growth ring widths of Siberian alder (Alnus viridis ssp. fruticosa) from along the Sagwon Bluffs, Alaska, USA that was used to address how alder respond to temperature overtime and how plant age impacts their climate sensitivity. There are six individual files. One, the growth ring width data from each cross-section or part taken from an individual Siberian alder. Two, a data set of the detrended chronology with climate data over the period of 1920-2017. Three, a data set of individual detrended secondary growth with temperature and age data over the period of 1977-2016. Four, a data set relating individual alder's first 30 years of growth to temperature and the time period it started growth in. Five, a data set of detrended growth from cross-sections taken from each Siberian alder, age class information, and temperature. Finally, a data set containing the number of ramets, nodule biomass per area, and nitrogen fixation rate. 

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3. Redefining Possible: Combining Phylogenomic and Supersparse Data in Frogs

   https://doi.org/10.1093/molbev/msad109  

   Portik, Daniel M; Streicher, Jeffrey W; Blackburn, David C; Moen, Daniel S; Hutter, Carl R; Wiens, John J (May 2023, Molecular Biology and Evolution)

   O'Connell, Mary (Ed.)

   Abstract The data available for reconstructing molecular phylogenies have become wildly disparate. Phylogenomic studies can generate data for thousands of genetic markers for dozens of species, but for hundreds of other taxa, data may be available from only a few genes. Can these two types of data be integrated to combine the advantages of both, addressing the relationships of hundreds of species with thousands of genes? Here, we show that this is possible, using data from frogs. We generated a phylogenomic data set for 138 ingroup species and 3,784 nuclear markers (ultraconserved elements [UCEs]), including new UCE data from 70 species. We also assembled a supermatrix data set, including data from 97% of frog genera (441 total), with 1–307 genes per taxon. We then produced a combined phylogenomic–supermatrix data set (a “gigamatrix”) containing 441 ingroup taxa and 4,091 markers but with 86% missing data overall. Likelihood analysis of the gigamatrix yielded a generally well-supported tree among families, largely consistent with trees from the phylogenomic data alone. All terminal taxa were placed in the expected families, even though 42.5% of these taxa each had >99.5% missing data and 70.2% had >90% missing data. Our results show that missing data need not be an impediment to successfully combining very large phylogenomic and supermatrix data sets, and they open the door to new studies that simultaneously maximize sampling of genes and taxa. 

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4. 2022 Annual Meeting

   https://doi.org/10.1785/0220220087  

   DUGDA, M.; KASSA, A. B.; POUCHARD, L.; DIRES, E.; MCDANIEL, L. (April 2022, Seismological Research Letters)

   Caplan-Auerbach, Jackie; Schmidt, D. (Ed.)

   Title: Efficient Access and Manipulation of Big Seismic Data from Disparate Sources Seismological data recordings have been growing exponentially in the last three decades. For instance, the data archive at the IRIS Data Management Center (DMC) grew from less than 10 Tebibytes in 1992 to greater than 750 Tebibytes today (in 2022). In addition to such big data archives, retrieving and merging data from various disparate seismic sources also creates big data which will enable obtaining higher-resolution seismic images and understanding phenomena such as earthquake cycles. Moreover, recent progress in geosciences with the application of AI/ML using big data has shown the potential of discovering patterns that were not previously recognized. However, aggregating large seismic datasets introduces its own challenges. Some of these challenges arise from the fact that many data centers have their own way of distributing data, and the format of the data and metadata are different in many cases. The objective of this investigation is the development of data access and manipulation tools for retrieval, merging, processing, and the management of big seismic data from disparate seismic data sources. We develop a free, open-source, direct data accessing, gathering, and processing software toolbox for disparate sources using Python. Aggregating data from different data centers will enable us to investigate the seismic structure beneath a region of interest at a higher resolution by merging the seismic databases. Such a merged dataset can be applied on studies around the boundaries between countries if those countries have different networks. One boundary region of geologic interest to exemplify the benefits of aggregated seismic datasets from different networks in two countries is the southern side of the Rio Grande Rift including the bordering areas between the US and Mexico. Previous more detailed seismic studies on Rio Grande were conducted mostly on the US side of the Rift Valley. 

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5. Foundational Tools for Coaching Data Storytelling

   https://doi.org/10.1145/3626253.3633432  

   Chen, Lujie Karen; Gong, Jiaqi; Yarnall, Louise (March 2024, ACM)

   Data storytelling is the skill to communicate data effectively and efficiently. Effective data storytelling goes beyond data visualization and focuses on explanation with clear rhetorical functions. It starts with a set of data insights collected from the data science workflow and involves iterative and interactive processes of filtering those insights into story slices, from which data stories can be created through ordering, organizing and narration. Data storytelling is an integral component of a well-rounded data science education, which complements foundational skills like quantitative reasoning and programming. Despite its significance, solid understanding of the theory and practice of developing data storytelling competency is lacking. Data storytelling is often perceived as a mythical process where quantitative information magically transforms into compelling narratives. Designing scalable coaching tools for data storytelling requires leveraging multidisciplinary expertise from learning science, computer science, data science, communication science, and human-centered design. In this workshop, we will share some initial findings and reflections from our interdisciplinary team searching for effective coaching methods and tools to support coaching data storytelling at scale. We will present results from literature reviews and expert interviews which will be packaged into a set of foundational tools such as mental model, cognitive processes and schema for story construction, assessment strategy, as well as preliminary ideas of tools to support data storytelling coaching. We hope to use this workshop to build a community of researchers and practitioners in coaching data storytelling in postsecondary formal and informal learning context. 

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