Social and Behavioral Sciences Commons^™

Linking Old Librarianship To New: Aligning 5-Steps Of The Innovator's Dna In Creating Thematic Discovery Systems For The Everglades, L. Bryan Cooper, Margarita Perez Martinez

Works of the FIU Libraries

This poster presentation from the May 2015 Florida Library Association Conference, along with the Everglades Explorer discovery portal at http://ee.fiu.edu, demonstrates how traditional bibliographic and curatorial principles can be applied to: 1) selection, cross-walking and aggregation of metadata linking end-users to wide-spread digital resources from multiple silos; 2) harvesting of select PDFs, HTML and media for web archiving and access; 3) selection of CMS domains, sub-domains and folders for targeted searching using an API.

Choosing content for this discovery portal is comparable to past scholarly practice of creating and publishing subject bibliographies, except metadata and data are housed in …

Icils 2013: Australian Teacher Data [Spss], Australian Council For Educational Research (Acer)

ICT - Digital Literacy

This dataset (SPSS zipped) is a data source for the report: International Computer and Information Literacy Study 2013: Australian students’ readiness for study, work and life in the digital age.

The International Computer and Information Literacy study (ICILS) methodology is complex. Prior to undertaking any analyses, it is recommended data users become familiar with all the information (for example, plausible values and replicate weights) required to understand and analyse the data files.

Information about the Australian ICILS 2013 data files is available here.

The ICILS 2013 User Guide, to accompany ICILS data, is available for download here.

The …

Icils 2013: Australian Teacher Data [Sas], Australian Council For Educational Research (Acer)

ICT - Digital Literacy

This dataset (SAS zipped) is a data source for the report: International Computer and Information Literacy Study 2013: Australian students’ readiness for study, work and life in the digital age.

The International Computer and Information Literacy study (ICILS) methodology is complex. Prior to undertaking any analyses, it is recommended data users become familiar with all the information (for example, plausible values and replicate weights) required to understand and analyse the data files.

Information about the Australian ICILS 2013 data files is available here.

The ICILS 2013 User Guide, to accompany ICILS data, is available for download here. …

Icils 2013: Australian School Data [Spss], Australian Council For Educational Research (Acer)

ICT - Digital Literacy

This dataset (SPSS zipped) is a data source for the report: International Computer and Information Literacy Study 2013: Australian students’ readiness for study, work and life in the digital age.

The International Computer and Information Literacy study (ICILS) methodology is complex. Prior to undertaking any analyses, it is recommended data users become familiar with all the information (for example, plausible values and replicate weights) required to understand and analyse the data files.

Information about the Australian ICILS 2013 data files is available here.

The ICILS 2013 User Guide, to accompany ICILS data, is available for download here. …

Icils 2013: Australian School Data [Sas], Australian Council For Educational Research (Acer)

ICT - Digital Literacy

This dataset (SAS zipped) is a data source for the report: International Computer and Information Literacy Study 2013: Australian students’ readiness for study, work and life in the digital age.

The International Computer and Information Literacy Study (ICILS) methodology is complex. Prior to undertaking any analyses, it is recommended data users become familiar with all the information (for example, plausible values and replicate weights) required to understand and analyse the data files.

Information about the Australian ICILS 2013 data files is available here.

The ICILS 2013 User Guide, to accompany ICILS data, is available for download here. …

Icils 2013: Australian Student Data [Sas], Australian Council For Educational Research (Acer)

ICT - Digital Literacy

This dataset (SAS zipped) is a data source for the report: International Computer and Information Literacy Study 2013: Australian students’ readiness for study, work and life in the digital age.

The International Computer and Information Literacy study (ICILS) methodology is complex. Prior to undertaking any analyses, it is recommended data users become familiar with all the information (for example, plausible values and replicate weights) required to understand and analyse the data files.

Information about the Australian ICILS 2013 data files is available here.

The ICILS 2013 User Guide, to accompany ICILS data, is available for download here. …

Effective Practices For Interagency Data Sharing: Insights From Collaborative Research In A Regional Intervention, Pauline M. Mcguirk, Phillip O'Neill, Kathleen Mee

Faculty of Social Sciences - Papers (Archive)

Data sharing adds considerable value to interagency programs that seek to tackle complex social problems. Yet data sharing is not easily enacted either technically or as a governance practice, especially considering the multiple forms of risk involved. This article presents insights from a successful data sharing project in a major region in east coast Australia involving a federally funded research partnership between two universities and a number of human services agencies. The Spatial Data Analysis Project sought to establish a community of practice for devising data sharing protocols and embedding data sharing into agency practices. Close dialogue between the project …

Propensity Score Weighting For Addressing Under-Reporting In Mortality Surveillance: A Proof-Of-Concept Study Using The Nationally Representative Mortality Data In China, Kang Guo, Peng Yin, Lijun Wang, Yibing Ji, Qingfeng Li, David Bishai, Shiwei Liu, Yunning Liu, Thomas Astell-Burt, Xiaoqi Feng, Jinling You, Jiangmei Liu, Maigeng Zhou

Faculty of Social Sciences - Papers (Archive)

Background: National mortality data are obtained routinely by the Disease Surveillance Points system (DSPs) in China and under-reporting is a big challenge in mortality surveillance. Methods: We carried out an under-reporting field survey in all 161 DSP sites to collect death cases during 2009 - 2011, using a multi-stage stratified sampling. To identify under-reporting, death data were matched between field survey system and the routine online surveillance system by an automatic computer checking followed by a thorough manual verification. We used a propensity score (PS) weighting method based on a logistic regression to calculate the under-reporting rate in different groups …