Maryland Shared Open Access Repository
MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.
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Exploring Access to College and Career Resources in High/Low-Poverty Schools: An analysis of resource distribution in Maryland Public High Schools
(2025-05-21) Davis, Alicia J.; Wyatt-Nichol, Heather; Seabrook, Renita; Holcomb-McCoy, Cheryl; The University of Baltimore. College of Public Affairs; The University of Baltimore. Doctor of Public Administration
The present research examines inequities in college and career resources between High-poverty and Low-poverty schools in the Maryland State Public School system. This study utilizes data from Maryland State Department of Education Division and Assessment for the academic school year 2022-2023, which is considered public record. The units of analyses include 248 Maryland State Public High Schools, located in 23 Counties throughout Maryland. In SY2022-SY2023, there was a student enrollment of 409,729. This study examined data from 179 traditional high schools, 49 charter schools, and 20 vocational-technical high schools. Stratified sampling was used to examine 25 High-poverty and 61 Low-poverty schools identified by MSDE. Two analyses were conducted, an ANOVA and simple linear regression to examine disparities in resources distributed between High-poverty and Low-poverty schools. Results of analyses showed High-poverty schools received significantly less resources required for college and career preparation.
A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration
(Wiley, 2014-06-05) Lobo, Daniel; Feldman, Erica B.; Shah, Michelle; Malone, Taylor J.; Levin, Michael
Amphibians and molting arthropods have the remarkable capacity to regenerate amputated limbs, as described by an extensive literature of experimental cuts, amputations, grafts, and molecular techniques. Despite a rich history of experimental effort, no comprehensive mechanistic model exists that can account for the pattern regulation observed in these experiments. While bioinformatics algorithms have revolutionized the study of signaling pathways, no such tools have heretofore been available to assist scientists in formulating testable models of large-scale morphogenesis that match published data in the limb regeneration field. Major barriers to preventing an algorithmic approach are the lack of formal descriptions for experimental regenerative information and a repository to centralize storage and mining of functional data on limb regeneration. Establishing a new bioinformatics of shape would significantly accelerate the discovery of key insights into the mechanisms that implement complex regeneration. Here, we describe a novel mathematical ontology for limb regeneration to unambiguously encode phenotype, manipulation, and experiment data. Based on this formalism, we present the first centralized formal database of published limb regeneration experiments together with a user-friendly expert system tool to facilitate its access and mining. These resources are freely available for the community and will assist both human biologists and artificial intelligence systems to discover testable, mechanistic models of limb regeneration.
Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search
(Springer Nature, 2014-06-10) Budnikova, Marianna; Habig, Jeffrey W.; Lobo, Daniel; Cornia, Nicolas; Levin, Michael; Andersen, Tim
The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism.
Limbform: a functional ontology-based database of limb regeneration experiments
(Oxford University Press, 2014-12-15) Lobo, Daniel; Feldman, Erica B.; Shah, Michelle; Malone, Taylor J.; Levin, Michael
Summary: The ability of certain organisms to completely regenerate lost limbs is a fascinating process, far from solved. Despite the extraordinary published efforts during the past centuries of scientists performing amputations, transplantations and molecular experiments, no mechanistic model exists yet that can completely explain patterning during the limb regeneration process. The lack of a centralized repository to enable the efficient mining of this huge dataset is hindering the discovery of comprehensive models of limb regeneration. Here, we introduce Limbform (Limb formalization), a centralized database of published limb regeneration experiments. In contrast to natural language or text-based ontologies, Limbform is based on a functional ontology using mathematical graphs to represent unambiguously limb phenotypes and manipulation procedures. The centralized database currently contains >800 published limb regeneration experiments comprising many model organisms, including salamanders, frogs, insects, crustaceans and arachnids. The database represents an extraordinary resource for mining the existing knowledge of functional data in this field; furthermore, its mathematical nature based on a functional ontology will pave the way for artificial intelligence tools applied to the discovery of the sought-after comprehensive limb regeneration models. Availability and implementaion: The Limbform database is freely available at http://limbform.daniel-lobo.com . Contact: michael.levin@tufts.edu
Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration
(PLOS, 2015-06-04) Lobo, Daniel; Levin, Michael
Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures, and reveal what external signals will trigger desired changes of large-scale pattern. Despite recent advances in bioinformatics, extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation. The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex, self-regulating pattern to emerge. To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning, it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes. For example, planarian regeneration has been studied for over a century, but despite increasing insight into the pathways that control its stem cells, no constructive, mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations. We present a method to infer the molecular products, topology, and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic, surgical, and pharmacological experiments. We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together, our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration. This method provides an automated, highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form.