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1. An Exploratory Interface for Dataset Repositories Using Cell-Centric Indexing

   https://doi.org/10.1109/BigData50022.2020.9378057  

   Johnson, Drake ; Register, Keith ; Davison, Brian D. ; Heflin, Jeff ( December 2020 , IEEE International Conference on Big Data (IEEE BigData 2020))

   null (Ed.)

   Large collections of datasets are being published on the Web at an increasing rate. This poses a problem to researchers and data journalists who must sift through these large quantities of data to find datasets that meet their needs. Our solution to this problem is cell-centric indexing, a novel approach which considers the individual cell of a dataset to be the fundamental unit of search, indexing the corresponding metadata to each individual cell. This facilitates a new style of user interface that allows users to explore the collection via histograms that show the distributions of various terms organized by how they are used in the dataset. 

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2. Rapid and cost-effective generation of single specimen multilocus barcoding data from whole arthropod communities by multiple levels of multiplexing

   https://doi.org/10.1038/s41598-019-54927-z  

   de Kerdrel, Guillemette A. ; Andersen, Jeremy C. ; Kennedy, Susan R. ; Gillespie, Rosemary ; Krehenwinkel, Henrik ( January 2020 , Scientific Reports)

   In light of the current biodiversity crisis, molecular barcoding has developed into an irreplaceable tool. Barcoding has been considerably simplified by developments in high throughput sequencing technology, but still can be prohibitively expensive and laborious when community samples of thousands of specimens need to be processed. Here, we outline an Illumina amplicon sequencing approach to generate multilocus data from large collections of arthropods. We reduce cost and effort up to 50-fold, by combining multiplex PCRs and DNA extractions from pools of presorted and morphotyped specimens and using two levels of sample indexing. We test our protocol by generating a comprehensive, community wide dataset of barcode sequences for several thousand Hawaiian arthropods from 14 orders, which were collected across the archipelago using various trapping methods. We explore patterns of diversity across the Archipelago and compare the utility of different arthropod trapping methods for biodiversity explorations on Hawaii, highlighting undergrowth beating as highly efficient method. Moreover, we show the effects of barcode marker, taxonomy and relative biomass of the targeted specimens and sequencing coverage on taxon recovery. Our protocol enables rapid and inexpensive explorations of diversity patterns and the generation of multilocus barcode reference libraries across whole ecosystems.

    

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3. POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events

   Vallurupalli, Sai ; Ghosh, Sayontan ; Erk, Katrin ; Balasubramanian, Niranjan ; Ferraro, Francis ( December 2022 , Conference on Empirical Methods in Natural Language Processing (EMNLP))

   Knowledge about outcomes is critical for com- plex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowdworkers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant’s influence over the event culmination. 

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4. Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events

   Vallurupalli, Sai ; Ghosh, Sayontan ; Erk, Katrin ; Balasubramian, Niranjan ; Ferraro, Francis ( December 2022 , Conference on Empirical Methods in Natural Language Processing (EMNLP))

   Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowdworkers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant’s influence over the event culmination. 

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5. Semantics-Aware Dataset Discovery from Data Lakes with Contextualized Column-Based Representation Learning

   https://doi.org/10.14778/3587136.3587146  

   Fan, Grace ; Wang, Jin ; Li, Yuliang ; Zhang, Dan ; Miller, Renée J. ( March 2023 , Proceedings of the VLDB Endowment)

   Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy. We utilize the cosine similarity between column embedding vectors as the column unionability score and propose a filter-and-verification framework that allows exploring a variety of design choices to compute the unionability score between two tables accordingly. Empirical results on real table benchmarks show that Starmie outperforms the best-known solutions in the effectiveness of table union search by 6.8 in MAP and recall. Moreover, Starmie is the first to employ the HNSW (Hierarchical Navigable Small World) index to accelerate query processing of table union search which provides a 3,000X performance gain over the linear scan baseline and a 400X performance gain over an LSH index (the state-of-the-art solution for data lake indexing). 

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