A Spatial Collaboration: Building a Multi-Institution Geospatial Data Discovery Portal

As academic education and research increasingly take advantage of geospatial data and methodologies, we see a corresponding exponential growth in the number of available geospatial resources in the form of GIS datasets and scanned historical maps. However, users can experience difficulty finding these resources due to the unconnected multitude of platforms and clearinghouses that host them. Additionally, the resources are not always well described with web semantic metadata that facilitates discovery. In response to this challenge, The Big Ten Academic Alliance Geospatial Data Project began in 2015 to provide discoverability, facilitate access, and connect scholars to geospatial resources. Our project leverages a multi-institutional collaboration and open source technologies to improve discovery for users of geospatial data and scanned maps. We outline collaborative workflows and strategies for a successful multi-institution collaboration

Toward Culturally Competent Archival (Re)Description of Marginalized Histories

Influenced by the radical archives movement, panelists discuss their (re)processing projects for which they wrote or rewrote descriptions in culturally competent approaches. Their case studies include materials regarding underrepresented peoples and historically oppressed groups who are marginalized from or maligned in the archival record. Targeted to processors, this session aims to teach participants to apply their cultural competencies in writing finding aids through an introduction to cultural competency framework, the case study examples, and a short audience-participation exercise

A Spatial Collaboration: Building a Multi-Institution Geospatial Data Discovery Portal

As academic education and research increasingly take advantage of geospatial data and methodologies, we see a corresponding exponential growth in the number of available geospatial resources in the form of GIS datasets and scanned historical maps. However, users can experience difficulty finding these resources due to the unconnected multitude of platforms and clearinghouses that host them. Additionally, the resources are not always well described with web semantic metadata that facilitates discovery. In response to this challenge, The Big Ten Academic Alliance Geospatial Data Project began in 2015 to provide discoverability, facilitate access, and connect scholars to geospatial resources. Our project leverages a multi-institutional collaboration and open source technologies to improve discovery for users of geospatial data and scanned maps. We outline collaborative workflows and strategies for a successful multi-institution collaboration

Evaluation of Corporate Sustainability

As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor’s hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis

Enhancing Search and Browse Using Automated Clustering of Subject Metadata

The Web puzzle of online information resources often hinders end-users from effective and efficient access to these resources. Clustering resources into appropriate subject-based groupings may help alleviate these difficulties, but will it work with heterogeneous material? The University of Michigan and the University of California Irvine joined forces to test automatically enhancing metadata records using the Topic Modeling algorithm on the varied OAIster corpus. We created labels for the resulting clusters of metadata records, matched the clusters to an in-house classification system, and developed a prototype that would showcase methods for search and retrieval using the enhanced records. Results indicated that while the algorithm was somewhat time-intensive to run and using a local classification scheme had its drawbacks, precise clustering of records was achieved and the prototype interface proved that faceted classification could be powerful in helping end-users find resources.http://deepblue.lib.umich.edu/bitstream/2027.42/58766/1/07hagedorn.pd