The European Landscape of Qualitative Social Research Archives: Methodological and Practical Issues

In this article I set about describing current practices in archiving and reusing qualitative data. I discuss where can you find archived sources of qualitative data, and discuss some of the debates surrounding methodological, ethical and theoretical considerations relating to re-using data. I then address more pragmatic issues involved acquiring, preserving, providing access to and supporting the use of the data. Where best do qualitative data collections sit?in traditional libraries or archives alongside historical documents or as part of more holistic digital collections of contemporary social science research resources? This question relates to accessibility, resource discovery and cataloging methods, data preparation and documentation and promotional and outreach efforts to encourage data use. The ESDS Qualidata unit at the UK Data Archive is used as case study for showcasing archival practices, and is situated within the broader European landscape of social science-oriented data archives. Infrastructure requirements for running an archive are discussed and a look forward future developments

Guide to Social Science Data Preparation and Archiving: Best Practice Throughout the Data Life Cycle

http://deepblue.lib.umich.edu/bitstream/2027.42/134032/1/dataprep.pdfDescription of dataprep.pdf : Boo

Applications of Research Data Management at GESIS Data Archive for the Social Sciences

The chapter "Applications of Research Data Management at GESIS Data Archive for the Social Sciences" explores ways in which an archive - i.e. an organization whose work has a strong focus on preservation and dissemination of digital data - can become involved in research data management (RDM). The Data Archive looks back on a long history of working with researchers to make their data re-usable and accessible since 1960. Today it provides support for Research Data Management across the entire data lifecycle by offering a wide range of tools and services tailored to the needs of different types of stakeholders. The chapter gives an overview of selected tools and services offered in the areas of metadata and data documentation, data preparation, data publication, and long-term preservation. To illustrate how support for research data management plays out in different settings, three case studies for typical scenarios are presented: 1) The European Values Survey (EVS), a large international longitudinal survey studying basic human values across Europe. 2) The German Longitudinal Election Study (GLES), a national survey program with a comprehensive approach to gain insights into the German federal elections. 3) A data center in the health sector which decided to make data originally collected to support policy-making available to research

Qualitative Research and Data Support: The Jan Brady of Social Sciences Data Services?

This chapter gives an overview of the context of qualitative data and the resulting support needs of qualitative researchers at various stages of the research data lifecycle. The current state of qualitative data support services in social sciences librarianship is then explored by reporting on (1) an analysis of social sciences data librarian job postings, (2) a survey of social sciences librarians, and (3) an examination of online research guides describing qualitative data support services presently offered by social sciences librarians. Finally, this chapter concludes with recommendations for how social sciences librarians might embark on the expansion of their qualitative data support services. Additional information about the book is available at https://databrarianship.wordpress.com

Provenance of "after the fact" harmonised community-based demographic and HIV surveillance data from ALPHA cohorts

Background: Data about data, metadata, for describing Health and Demographic Surveillance System (HDSS) data have often received insufficient attention. This thesis studied how to develop provenance metadata within the context of HDSS data harmonisation - the network for Analysing Longitudinal Population-based HIV/ AIDS data on Africa (ALPHA). Technologies from the data documentation community were customised, among them: A process model - Generic Longitudinal Business Process Model (GLBPM), two metadata standards - Data Documentation Initiative (DDI) and Standard for Data and Metadata eXchange (SDMX) and a data transformations description language - Structured Data Transform Language (SDTL). Methods: A framework with three complementary facets was used: Creating a recipe for annotating primary HDSS data using the GLBPM and DDI; Approaches for documenting data transformations. At a business level, prospective and retrospective documentation using GLBPM and DDI and retrospectively recovering the more granular details using SDMX and SDTL; Requirements analysis for a user-friendly provenance metadata browser. Results: A recipe for the annotation of HDSS data was created outlining considerations to guide HDSS on metadata entry, staff training and software costs. Regarding data transformations, at a business level, a specialised process model for the HDSS domain was created. It has algorithm steps for each data transformation sub-process and data inputs and outputs. At a lower level, the SDMX and SDTL captured about 80% (17/21) of the variable level transformations. The requirements elicitation study yielded requirements for a provenance metadata browser to guide developers. Conclusions: This is a first attempt ever at creating detailed metadata for this resource or any other similar resources in this field. HDSS can implement these recipes to document their data. This will increase transparency and facilitate reuse thus potentially bringing down costs of data management. It will arguably promote the longevity and wide and accurate use of these data

Raising Standards - Lowering Barriers: Documentation of, access to and preservation of research data at the London School of Hygiene & Tropical Medicine

Most research funders are now requiring that data collected in projects they have financed should in principle be made available to the wider scientific community. This report provides a basis for LSHTM to develop guidance, infrastructure and expertise to meet these expectations. Underlying the move towards data sharing and improved access is the creation of adequate documentation of datasets. We argue that there is an overwhelming scientific case to improve the standard of documentation of the data we collect. This will be of great benefit to the research teams that generate the data in the first place, as well as significantly lowering the costs of providing data to third parties. While the level of documentation that we should aspire to will vary from study to study, there is a minimal standard which we need to ensure. Beyond this, the advent of web-based documentation, in which it is possible to provide easy-to-use and powerful ways of displaying information down to the level of individual variables is now possible at relatively low cost. Aside from issues of documentation, there are important principles of access that include the need to protect confidentiality and to ensure that appropriate consent has been obtained from participants, the importance of establishing transparent procedures for access and making decisions on requests for data. This is a rapidly moving area, with new initiatives being launched on an almost monthly basis. The School is in a good position to be one of the leading institutions in the UK in the way we document and make accessible our research data. This will not only ensure we are compliant with the requirements of funders, but it will also mean that the quality of the science we do is improved

Re-using archived qualitative data – where, how, why?

“Qualitative data” are the central issue of this article. Qualitative data are a particular category of data within the social sciences, where data have been predominantly of a quantitative nature. Qualitative data could enrich social science research in many ways. The re-use of this particular type of data is however a new challenge for social science data archives. A new methodology has to be developed when dealing with these data, based on a combination of social science methodology and traditional archival descriptions. An additional question discussed in the article is what the best place should be for archiving and disseminating qualitative data: in research (social science) data archives or in the more traditional libraries and archives

Development of self-archiving tools to support archiving, analysis and re-use of qualitative data

The potential to share and re-use qualitative archived data has garnered much interest in recent years. This increased attention can be attributed mainly to advances in both data documentation standards and digital archiving technologies, which provide users with the ability to archive, share and disseminate qualitative research materials. However, there remain theoretical and epistemological barriers to and implications for the sharing and re-use of qualitative study data. One way to address these issues is by studying research practices (with practitioners’ active involvement), in combination with developing software tools that support digital archiving of qualitative studies. Semantic technologies, combined with metadata standards and documentation schemas have the potential to enhance qualitative data documentation, archiving and analysis. In fact, it has been established that data documentation is one of the key elements that enables data archiving. The use of appropriate standard documentation frameworks is crucial to data archives’ exposure and has a direct impact on the discoverability, search and retrieval of archived data. The technological aspect of this study has been the development of a self-archiving toolkit that makes use of such technologies. The purpose of this work was to allow users, with varying levels of research experience (e.g. from undergraduate student researchers up to more experienced senior researchers) to avail of the benefits offered by qualitative digital archiving. To complement the technological developments undertaken, the present study also explored the practices of different researchers: undergraduate student researchers, researchers involved in teaching research-oriented modules, as well as senior researchers. This exploration focused on the collection, organisation, analysis and presentation of qualitative data and how these relate to and can be supported by digital archiving to enable researchers to organise, disseminate, and visualise research collections

Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?

The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly to the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This article focuses on some of the key research findings of the eight nodes, organized into six topics: (1) improving census and survey data-quality and data collection methods; (2) using alternative sources of data; (3) protecting privacy and confidentiality by improving disclosure avoidance; (4) using spatial and spatio-temporal statistical modeling to improve estimates; (5) assessing data cost and data-quality tradeoffs; and (6) combining information from multiple sources. The article concludes with an evaluation of the ability of the FSS to apply the NCRN’s research outcomes, suggests some next steps, and discusses the implications of this research-network model for future federal government research initiatives