The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives

The Archives Unleashed project aims to improve scholarly access to web archives through a multi-pronged strategy involving tool creation, process modeling, and community building -- all proceeding concurrently in mutually --reinforcing efforts. As we near the end of our initially-conceived three-year project, we report on our progress and share lessons learned along the way. The main contribution articulated in this paper is a process model that decomposes scholarly inquiries into four main activities: filter, extract, aggregate, and visualize. Based on the insight that these activities can be disaggregated across time, space, and tools, it is possible to generate "derivative products", using our Archives Unleashed Toolkit, that serve as useful starting points for scholarly inquiry. Scholars can download these products from the Archives Unleashed Cloud and manipulate them just like any other dataset, thus providing access to web archives without requiring any specialized knowledge. Over the past few years, our platform has processed over a thousand different collections from over two hundred users, totaling around 300 terabytes of web archives.This research was supported by the Andrew W. Mellon Foundation, the Social Sciences and Humanities Research Council of Canada, as well as Start Smart Labs, Compute Canada, the University of Waterloo, and York University. We’d like to thank Jeremy Wiebe, Ryan Deschamps, and Gursimran Singh for their contributions

SBcoyote: An Extensible Python-Based Reaction Editor and Viewer

SBcoyote is an open-source cross-platform biochemical reaction viewer and editor released under the liberal MIT license. It is written in Python and uses wxPython to implement the GUI and the drawing canvas. It supports the visualization and editing of compartments, species, and reactions. It includes many options to stylize each of these components. For instance, species can be in different colors and shapes. Other core features include the ability to create alias nodes, alignment of groups of nodes, network zooming, as well as an interactive bird-eye view of the network to allow easy navigation on large networks. A unique feature of the tool is the extensive Python plugin API, where third-party developers can include new functionality. To assist third-party plugin developers, we provide a variety of sample plugins, including, random network generation, a simple auto layout tool, export to Antimony, export SBML, import SBML, etc. Of particular interest are the export and import SBML plugins since these support the SBML level 3 layout and render standard, which is exchangeable with other software packages. Plugins are stored in a GitHub repository, and an included plugin manager can retrieve and install new plugins from the repository on demand. Plugins have version metadata associated with them to make it install plugin updates. Availability: https://github.com/sys-bio/SBcoyote

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behavior

<div><p>A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model’s sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled <i>in vivo</i>. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor ROR<i>γ</i>t, is sufficient to drive switching of Th17 cells towards an IFN-<i>γ</i>-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from <a href="http://www.york.ac.uk/ycil/software" target="_blank">http://www.york.ac.uk/ycil/software</a>.</p></div

Out of Africa and into the Sunshine State : tracking an exotic invader

Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Comparative Media Studies, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 26-33).This is the story of an invasive species and one man's quest to eradicate it. The Nile monitor lizard (Varanus niloticus), smaller cousin of the famed Komodo dragon, grows into six feet of carnivorous, ill-tempered muscle. The animal's size and aggression make it a poor candidate for the exotic pet trade, but the species nevertheless obtained popularity in the 1990s. Two decades later, the descendants of released Nile monitors are breeding in the coastal town of Cape Coral, Florida, where the lizards benefit from extensive drainage canals and a buffet of native wildlife-and they're spreading. Herpetologist Todd Campbell has devoted more than a decade of his research to these reptiles, attempting to understand how they got here, how their invasion is wreaking havoc on native ecosystems, and most of all, how to eliminate them for good. The challenges he's faced along the way echo the wider concerns of fighting invasive species, which represent one of the greatest threats to global biodiversity and ecosystems but are poorly studied and rarely prioritized. This thesis follows the trajectory of the Nile monitor from its native Africa to southern Florida, exploring what it is about this lizard's natural history, ecology, and allure to reptile enthusiasts that has made it a charismatic symbol of the perils of biological invasion.by Erin Maureen Weeks.S.M.in Science Writin

Natural enemies and biodiversity : the double-edged sword of trophic interactions

Natural enemies, that is, species that inflict harm on others while feeding on them, are fundamental drivers of biodiversity dynamics and represent a substantial portion of biodiversity as well. Along the life history of the Earth, natural enemies have been involved in probably some of the most productive mechanisms of biodiversity genesis; that is, adaptive radiation mediated by enemy-victim coevolutionary processes. At ecological timescales, natural enemies are a fundamental piece of food webs and can contribute to biodiversity preservation by promoting stability and coexistence at lower trophic levels through top-down regulation mechanisms. However, natural enemies often produce dramatic losses of biodiversity, especially when humans are involved

An approach to assess the quality of Jupyter projects published by GLAM institutions

GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as “GLAM Labs” and “Collections as Data” provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models. Author summary Prediction is challenging but recently developed Machine Learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the hardship of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions that modulate species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances by training models just with easily obtained abiotic variables and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.O.G. acknowledges support provided by the Ministerio de Ciencia, Innovacion y Universidades (RYC-2017-23666). O.G. and I.B. acknowledge financial support provided by the Secretaria de Estado de Investigacion, Desarrollo e Innovacion (CGL2017-92436-EXP, SIMPLEX and RTI2018-098888-A-I00, MeDiNaS). J.G. acknowledges financial support provided by the Ministerio de Ciencia, Innovacion y Universidades (PGC2018-093854-B-I00). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript