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  • Discovering History: An Analysis of Secondary Literature Cited in the American Historical Review, 2010-2015

    Work
    Creator: Pearce, Alexa L.
    Description: This dataset accompanies a study that seeks to contribute to a clearer understanding of the discovery ecosystem in academic research libraries. Using historical literature as a case study, extensive citation analysis is employed to both reveal characteristics of secondary historical literature as well as to test a broad disciplinary discovery environment that includes six specific search platforms. By enhancing our understanding of where and how specific types of resources are –or are not—discoverable, as the case may be, this study can provide evidence to better inform the appropriate role and placement of various search platforms in a user’s process. This citation analysis drew upon all secondary literature that was cited in the American Historical Review (AHR) during a six-year period, from 2010 through 2015. The AHR is the official publication of the American Historical Association (AHA) and, as stated on its website, has served as “the journal of record for the historical profession in the United States since 1895.” Additionally, the AHR represents all subfields of history in its research articles and reviews of new scholarship. For this study, the author gathered citations from research articles only, excluding reviews. For the purposes of testing the library discovery environment, the author aimed to include citations that a researcher would be likely to identify by using library research tools, as opposed to archival finding aids. Recognizing that some tools included in this study, such as JSTOR and Historical Abstracts, do not index archival sources, the author decided to focus on published and secondary materials. All citations to archival sources, government information, and other unpublished manuscript materials were excluded. Additionally, citations to newspaper and general or popular press articles published prior to 1900 were excluded. Citations to entire periodicals, as opposed to articles, were also excluded. Books from all date ranges were included. Citations to non-scholarly newspaper and magazine articles published after 1900 were included. Citations to published primary sources were also included in the population of citations, as one may reasonably expect to locate them in a research library setting. The resulting population comprised 22,572 citations. After separating out duplicate citations, the total number was 19,937. Using a random number generator, the de-duplicated list of citations was re-ordered in order to select a random sample of 400, which affords a confidence level of 95% and a confidence interval of 5. The first step in analysis was to characterize each citation according to format, publication date, and language. Secondly, the author searched for all citations in the sample in the 6 different search platforms listed above. The primary question for each database included in the study was how comprehensively it represented the population of AHR citations, as represented by the random sample selected for this study. In order for a given citation to count as present in a particular database, it had to be represented in the format in which it was cited. For example, if a search for a cited book turned up only a dissertation, with the same author and very similar title, the analysis found that the citation was not present. For book chapters cited with authors and titles, it was not necessary for chapters to have their own records in order to be counted as present but it was necessary for them to be discernible among search results as chapters, such as in a table of contents listing. In order to expedite the search process, the author searched Historical Abstracts and America: History and Life simultaneously on the EBSCO platform. For all of the platforms except Google Scholar, the author performed advanced searches, entering both title and author information for each citation. All searching took place between February and May of 2017. The results presented here reflect the content available to search in each platform at the time of investigation.
  • Understanding Ecosystem Services Adoption by Natural Resource Managers and Research Ecologists: Survey Data

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    Creator: Evans, Mary Anne, Low, Bobbi S, Engel, Daniel D, and Schaeffer, Jeff
    Description: This dataset was compiled as an attempt to understand how natural resource managers and research ecologists in the Great Lakes region integrate the ecosystem services (ES) paradigm into their work. The following text is the adapted abstract from a thesis associated with this data. Ecosystem services, or the benefits people obtain from ecosystems, have gained much momentum in natural resource management in recent decades as a relatively comprehensive approach to provide quantitative tools for improving decision-making and policy design. However, to date we know little about whether and how natural resource practitioners, from natural resource managers to research ecologists (hereafter managers and ecologist respectively), have adopted the ES paradigm into their respective work. Here, we addressed this knowledge gap by asking managers and ecologists about whether and how they have adopted the ES paradigm into their respective work. First, we surveyed federal, state, provincial and tribal managers in the Great Lakes region about their perception and use of ES as well as the relevance of specific services to their work. Although results indicate that fewer than 31% of the managers said they currently consider economic values of ES, 79% of managers said they would use economic information on ES if they had access to it. Additionally, managers reported that ES-related information was generally inadequate for their resource management needs. We also assessed managers by dividing them into identifiable groups (e.g. managers working in different types of government agencies or administrative levels) to evaluate differential ES integration. Overall, results suggest a desire among managers to transition from considering ES concepts in their management practices to quantifying economic metrics, indicating a need for practical and accessible valuation techniques. Due to a sample of opportunity at the USGS Great Lakes Science Center (GLSC), we also evaluated GLSC research ecologists’ integration of the ES paradigm because they play an important role by contributing requisite ecological knowledge for ES models. Managers and ecologists almost unanimously agreed that it was appropriate to consider ES in resource management and also showed convergence on the high priority ES. However, ecologists appeared to overestimate the adequacy of ES-related information they provide as managers reported the information was inadequate for their needs. This divergence may reflect an underrepresentation of ecological economists in this system who can aid in translating ecological models into estimates of human well-being. As a note, both CSV files in this dataset have two tabs: 1) the raw data, and 2) an index describing each column. The dataset for the research ecologists has had some data removed as it could be considered personally identifiable information due to the small sample size in that population. The surveys associated with both datasets have also been included in PDF format.
  • Marroquíes Bajos Bioarchaeological Project

    Work
    Creator: Beck, Jess
    Description: These data include skeletal and dental inventories, assessments of skeletal and dental pathology, and the age and sex of individuals buried at Necropolis 1, Necropolis 2, and Necropolis 4 at the Copper Age site of Marroquíes Bajos. They are shared here in accordance with the NSF Data Management Plan associated with Doctoral Dissertation Improvement Grant BCS-1440017.
  • Characteristics of Informal Caregivers who Provide Transportation Assistance to Older Adults

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    Creator: Eby, David W and Molnar, Lisa J
    Description: Data can contained in an Excel spreadsheet formatted such that each row is a separate participant and each column is a separate question. This file is called: EbyEtAl-TransportCaregiver. A data dictionary that gives the text for each question and the response categories mappings are contained in another Excel Spreadsheet. This file is called: EbyEtAl-TransportCaregiverDictionary. The text of the survey, the development of weights, and response rate calculations can be found in the Deep Blue report discussed previously.
  • Subjective Effect Reports of Food

    Work
    Creator: Schulte, Erica M
    Description: The data set supports a study investigating which foods may be most implicated in addictive-like eating by examining how nutritionally diverse foods relate to loss of control consumption and various subjective effect reports. Participants (n = 501) self-reported how likely they were to experience a loss of control over their consumption of 30 nutritionally diverse foods and rated each food on five subjective effect report questions that assess the abuse liability of substances (liking, pleasure, craving, averseness, intensity). Hierarchical cluster analytic techniques were used to examine how foods grouped together based on each question. Highly processed foods, with added fats and/or refined carbohydrates, clustered together and were associated with greater loss of control, liking, pleasure, and craving. The clusters yielded from the subjective effect reports assessing liking, pleasure, and craving were most similar to clusters formed based on loss of control over consumption, whereas the clusters yielded from averseness and intensity did not meaningfully differentiate food items. The associated study applies methodology used to assess the abuse liability of substances to understand whether foods may vary in their potential to be associated with addictive-like consumption. Highly processed foods (e.g., pizza, chocolate) appear to be most related to an indicator of addictive-like eating (loss of control) and several subjective effect reports (liking, pleasure, craving). Thus, these foods may be particularly reinforcing and capable of triggering an addictive-like response in some individuals. Future research is warranted to understand whether highly processed foods are related to these indicators of abuse liability at a similar magnitude as addictive substances. The data set is presented in both .sav format for use with SPSS software and in csv format.
  • Neighborhood effects : Information and Education Environment

    Work
    Creator: Data Driven Detroit, Reddy, Shruthi, Veinot, Tiffany C, Goodspeed, Robert, Okullo, Dolorence, Clarke, Phillipa J., and Gomez-Lopez, Iris N.
    Description: The information and education environment refers to: 1) the presence of information infrastructures such as broadband Internet access and public libraries in a location; 2) a person’s proximity to information infrastructures and sources; 3) the distribution of information infrastructures, sources and in a specific location; and 4) exposure to specific messages (information content) within a specific location. Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.
  • Neighborhood Effects: Food Environment

    Work
    Creator: Data Driven Detroit, Gomez-Lopez, Iris N., Goodspeed, Robert, Okullo, Dolorence, Veinot, Tiffany C., and Yan, Xiang (Jacob)
    Description: The food environment is: 1) The physical presence of food that affects a person’s diet; 2) A person’s proximity to food store locations; 3) The distribution of food stores, food service, and any physical entity by which food may be obtained; or 4) A connected system that allows access to food. (Source: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm) Data included here concern: 1) Food access; and 2) Liquor access. Spatial Coverage for most data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area, Michigan, USA. See exception for grocery store data below.
  • Data Supplement: Self-Confirming Price-Prediction Strategies for Simultaneous One-Shot Auctions

    Work
    Creator: Wellman, Michael P.
    Description: For each game: - file in JSON format with raw payoff data - text file with game-theoretic analysis results
  • Neighborhood Effects : Community Characteristics and Health in Metropolitan Detroit

    Creator: Yan, Xiang (Jacob), Veinot, Tiffany C, Data Driven Detroit, Clarke, Phillipa J., Goodspeed, Robert, Gomez-Lopez, Iris N., and Okullo, Dolorence
    Description: This collection was produced as part of the project, “A ‘Big Data’ Approach to Understanding Neighborhood Effects in Chronic Illness Disparities.” The Investigators for the project are Tiffany Veinot, Veronica Berrocal, Phillipa Clarke, Robert Goodspeed, Daniel Romero, and VG Vinod Vydiswaran from the University of Michigan. The study took place from 2015-2016, with funding from the University of Michigan’s Social Sciences Annual Institute, MCubed, and the Sloan and Moore Foundations. Contact: Tiffany Veinot, MLS, PhD Office: 3443 North Quad Phone: 734/615-8281 Email: tveinot@umich.edu
  • Neighborhood Effects Active Living Resources

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    Creator: Data Driven Detroit, Reference USA, City of Detroit, Veinot, Tiffany C., and ESRI
    Description: Active living resources include spaces and organizations that facilitate physical activity, including 1) park land, 2) recreation areas (including parks, golf courses, amusement parks, beaches and other recreational landmarks); and 3) recreation centers (including gyms, dancing instruction, martial arts instruction, bowling centers, yoga instruction, sports clubs, fitness programs, golf course, pilates instruction, personal trainers, swimming pools, skating rinks, etc.) Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.