Strategies for Reusing Archival Assessment Data

This poster showcases how Univeristy of Maryland Libraries' Special Collections and University Archives analyzed and re-purposed collection assessment data gathered from 2013 to use as the basis for a new, data-driven workflow for establishing processing priorities, addressing a backlog of 'hidden' collections, and overhauling the processing guidelines across several curatorial units. The poster demonstrates how commonly collected assessment data can be leveraged to revamp outdated processes, and highlights the impact (and limitations) of assessment within special collections

Archival Systems and Off-Site Storage

Although libraries have been storing materials off-site for decades, archives have only recently begun to send collections off-site. This has major implications for the systems and workflows we use to manage and retrieve materials. At the University of Maryland (UMD) Libraries, we use a combination of systems to make materials accessible at our off-site storage facility. For example, we use Aeon to manage researcher accounts and requests. We are currently split between two management systems as we upgrade from a homegrown Microsoft Access database to ArchivesSpace, which will also be our discoverability system for the public. Additionally, we have print materials that are discoverable via UMD's online library catalog. These systems would ideally integrate in order for patrons and staff alike to have a seamless experience when requesting and managing off-site collections. Our situation is not unique. During this roundtable, participants will discuss the systems they use to manage their archival and special collections materials, as well as the systems-related challenges they face as they move collections off-site. Participants will discuss and brainstorm possible solutions and workarounds for integration and enhanced access

Supporting Solidarity: Appraising and Collecting Online Content Surrounding the Women's Marches in Maryland

Report and presentation from the MARAC conference in Buffalo, NY on October 28, 2017. S23, "Documenting Social Protest: Lessons Learned from the Women's March." This project took place in the context of an entire course on archival appraisal at the University of Maryland and had powerful implications for archival outreach as activism as well as the tools needed to carry out collection development for born-digital materials. We used Archive-It to crawl social media pages and decided to focus on local solidarity marches in Maryland as the national Women's March was already well-documented. As students, we learned that activism and outreach are integral to the archival profession; we have to be able to explain why we as archivists want to document social protest

ArchivesSpace Tech Demo: Manage Your Content

ArchivesSpace is an open-source collection management system for archives and museums that has both public and staff user interfaces. It allows organizations to manage and provide web access to their holdings. This technology demonstration provides insight on the advantages and disadvantages of adopting this open-source software in a repository

AbSC Statement of Values

A statement of values developed in support of the Abolition in Special Collections working group

Implications and Concerns Regarding the Mammogram Debate

Screening procedures that detect breast cancer in its early stages are an important element of preventative health care for all women. When official guidelines and recommendations for screening are modified, their changes impact health care at both the population and individual patient levels. Recently, the United States Preventive Service Task Force (USPSTF) has developed new recommendations regarding when to start mammogram screening for breast cancer in women of average risk for the development of breast cancer. This article discusses the rationale behind the updated USPSTF recommendations and also presents the current American Cancer Society (ACS) guidelines

Understanding the Biogeochemical Drivers of Dissolved Organic Carbon Dynamics: A Multiscalar Approach

Variability in DOC export from forested headwater catchments has been attributed to an array of different hydrologic, biotic and geochemical drivers. A major challenge in understanding DOC dynamics has been relating long-term regional trends and patterns to specific processes at the catchment scale. We address this challenge by integrating methodologies that analyze trends and processes across different spatial scales. On the regional scale, we evaluate long-term monotonic trends in flow-adjusted DOC concentrations of headwater streams from the eastern United States. The direction of the trends is compared with catchment attributes compiled in a comprehensive and publicly available dataset, “Catchment Attributes and Meteorology for Large-Sample Studies” (CAMELS). At the catchment scale, we tested specific process-based hypotheses on the role of changes in rain composition (ionic strength and pH variations) on aggregate stability in riparian vs hillslope derived soils. This is accomplished through leaching experiments performed on top-soil cores from two forested headwater catchments (the Susquehanna Shale Hills Critical Zone Observatory in Pennsylvania and the Sleepers River Research Watershed in Vermont). The results of the trend analysis display that there is clear regional heterogeneity in the dynamics of DOC in headwater catchments. However, while there was no clear evidence of geographic continuity, there is evidence of continuity in catchment attributes. Our results suggest that long-term increased DOC mobilization is in part a product of catchments with low landscape connectivity becoming more frequently connected as a function of increasing frequency in high precipitation storm events. Results of the leaching experiments indicate that DOC mobilization and aggregate stability as a function of rain composition are spatially specific on both the catchment and regional scale. Soils from the Sleepers River Watershed in Vermont consistently leached more DOC with aggregates destabilizing under lower ionic strength treatments. In the Susquehanna Shale Hills Critical Zone Observatory, DOC mobility and aggregate stability did not appear to respond the differently to the treatments, but did show variability based on catchment position

Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning Analysis

BackgroundSpeech data for medical research can be collected noninvasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important, as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits the potential scalability and cost savings associated with language-based screening. ObjectiveTo better understand the use of automatic transcription for classification of neurodegenerative disease, namely, Alzheimer disease (AD), mild cognitive impairment (MCI), or subjective memory complaints (SMC) versus healthy controls, we compared automatically generated transcripts against transcripts that went through manual correction. MethodsWe recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-to-moderate AD, (n=44, 30%), MCI (n=20, 13%), SMC (n=8, 5%), as well as healthy controls (n=77, 52%) living in the community. Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, logistic regression, Gaussian naive Bayes, and random forests were used. ResultsThe transcription software showed higher confidence scores (P.05) for speech from healthy controls compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. However, manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models. ConclusionsWe found that automatically transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI, or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. However, manual addition of pauses is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data

Streams as mirrors: reading subsurface water chemistry from stream chemistry

The shallow and deep hypothesis suggests that stream concentration-discharge (CQ) relationships are shaped by distinct source waters from different depths. Under this hypothesis, baseflows are typically dominated by groundwater and mostly reflect groundwater chemistry, whereas high flows are typically dominated by shallow soil water and mostly reflect soil water chemistry. Aspects of this hypothesis draw on applications like end member mixing analyses and hydrograph separation, yet direct data support for the hypothesis remains scarce. This work tests the shallow and deep hypothesis using co-located measurements of soil water, groundwater, and streamwater chemistry at two intensively monitored sites, the W-9 catchment at Sleepers River (Vermont, United States) and the Hafren catchment at Plynlimon (Wales). At both sites, depth profiles of subsurface water chemistry and stream CQ relationships for the 10 solutes analyzed are broadly consistent with the hypothesis. Solutes that are more abundant at depth (e.g., calcium) exhibit dilution patterns (concentration decreases with increasing discharge). Conversely, solutes enriched in shallow soils (e.g., nitrate) generally exhibit flushing patterns (concentration increases with increasing discharge). The hypothesis may hold broadly true for catchments that share such biogeochemical stratifications in the subsurface. Soil water and groundwater chemistries were estimated from high- and low-flow stream chemistries with average relative errors ranging from 24 to 82%. This indicates that streams mirror subsurface waters: stream chemistry can be used to infer scarcely measured subsurface water chemistry, especially where there are distinct shallow and deep end members