The interviews included in this folder were conducted as part of a longitudinal study in writing development published in Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). Interviews were conducted upon students' entry into the study (files labelled "entry") and exit from the study (files labelled "exit"). To learn more about this study, please see the epublication https://doi.org/10.3998/mpub.10079890 and the website https://www.developingwritersbook.org/pages/about/about-the-study/.
The writing samples included in this folder were collected as part of a longitudinal study in writing development published in Developing Writers in Higher Education: A Longitudinal Study (University of Michigan Press, 2019). Writing samples were chosen and uploaded by students as part of the study and come from lower and upper level courses. To learn more about this study, please see the epublication https://doi.org/10.3998/mpub.10079890.
The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.
Manganese in the sedimentary record has been interpreted by many as a powerful redox proxy for paleoenvironments, and yet very little work has been done to ensure that the manganese-rich minerals in the rock record are actually recording primary signals. In the accompanying manuscript, we present an in-depth characterization of the manganese mineralogy from two correlated regions recording the Transvaal Supergroup in South Africa with markedly different alteration histories to investigate if there can be post-depositional emplacement of manganese-rich minerals. The data uploaded here are X-ray absorption spectra of (1) manganese standard minerals that were useful in our analyses and (2) minerals from an important well-characterized sample that may be useful as comparative standards in future studies.
This project aimed to discover and analyze the molecular mechanism of synthesis of two particular fucosylated oligosaccharide products in a mutant enzyme, Thermatoga maratima Alpha-L-Fucosidase D224G, whose wild type performs the opposite reaction (cleavage of fucosyl glycosidic bonds). Discovery of the mechanism was performed using an unbiased simulations method known as aimless shooting, whereas analysis of the mechanism in terms of the energy profile was performed using a separate method known as equilibrium path sampling. The data here concerns the latter method. and The contents of the atesa_master.zip are the ATESA GitHub project. A Python program for automating transition path sampling with aimless shooting using Amber. https://github.com/team-mayes/atesa