Block, William C.

Kramer, Stefan

German

BieColl - Bielefeld eCollections

Opportunities and challenges across the research data lifecycle, and related activities at Cornell University1William C. Block & Stefan KramerCornell Institute for Social and Economic Research (CISER) Bielefeld Colloquium on Knowledge Infrastructure, 2010-10-15What is CISER?2More at: http://ciser.cornell.edu/Where is Cornell University?3Source: http://www.cornell.edu/maps/state.cfm4Some potential problems with own data (that’s not (well) managed) for researchersWhere is it?How safe is it, where(ever) it is?(How) can/may I give it to someone else?Where is the graduate assistant who organized, analyzed, … the data now?Hmm, what format were those files in again?Can my computer and software still read/open/use it?5Sharing & preserving data: why (would researchers want to)? Collaboration with fellow researchers on current projects Future use/access by others (public/limited, open/restricted) and self Making research findings replicable, help avoid duplication Requirements from funding agencies, journal publishers, own institution May help in tenure/promotion process Making research data citable6Working with faculty to deposit data In local/institutional AND domain/subject repositories …e.g. eCommons@Cornell AND ICPSR Domain/subject repositories are not infallible, so institutional repositories provide a “backup” tooData managementMetadataIdeaSearch & DiscoveryiCollectionll iAnalysis & Processingl ii Publication li iArchivingi iResearch study is conceived and planned, methodologies selected, funding sources exploredExisting data sources are sought and explored – also happens for basic research needsResearch instruments are designed; data are collected through surveys, interviews, etc. – and from existing data sourcesCollected data are merged, cleaned, analyzed, subsetted, coded, harmonized, linked, etc.Final datasets are made publicly accessible – e.g. via researcher’s and/or department’s and/or journal publisher’s web siteFinal datasets are deposited for long-term preservation –e.g., into institutional or domain repositoryIdeally begins early in data lifecycle to assure long-term preservation and access of data.  One activity is metadata preparation and its exposure to external search toolsLifecycle of social science research dataBy search tools utilizing metadata from data stores, new research databecomes available for finding and exploring by researchers8 Includes activities through the data lifecycle to assure that data remain or become understandable, usable, accessible, and findable – by the researchers compiling and analyzing the data themselves, and others for re-use or verification – such as:– Establishing naming and labeling conventions for variables, files, directory structures– Documenting newly recoded and computed variables– Determining appropriate file formats for analysis & processing (current research project use) and long-term preservation– Migrating files to different formats to preserve their usability with available software– Creating and maintaining metadata (about the data) Better to start at earlier stages of data lifecycle than try to “retrofit”later!Data managementCollectionll tiAnalysis & Processingl i   r iPublication li ti  Archivingr i iData managementCollectionll iAnalysis & Processingl ii Publication li iArchivingi i“Archives that preserve and disseminate social and behavioral data perform a critical service to the scholarly community and to society at large, ensuring that these culturally significant materials are accessible in perpetuity. The success of the archiving endeavor, however, ultimately depends on researchers’ willingness to deposit their data and documentation for others to use.”ICPSR Guide to Social Science Data Preparation and Archiving: 4th Edition, p. 3http://www.icpsr.umich.edu/files/ICPSR/access/dataprep.pdfi ii iResearcher buy-in is essential for data archivingIdeally, the archiving endeavor achieves researcher buy-in in alllifecycle stages involving data management activities – not just at the final point of archival deposit. 10Researchers and metadata creation/maintenance Researchers will tend to describe their data only as much as necessary for their own use, for current project But: no one knows their data better than they do Needed: easy-to-use tools, and outreach to researchers, for sustainable metadata production – some actions may be performed by researchers, others by their institution’s data service providersCollectionll tiAnalysis & Processingl i   r i Publication li ti  Archivingr i i11Possible improvement in the search & discovery stage of research data lifecycle from the university library endSource: http://www.loc.gov/marc/bibliographic/bd008c.htmlhttp://yufind.library.yale.edu/yufind/12Desirable search or browse functions for numeric data in social sciencesNot (easily) offered by most data catalogs, but often needed by data searchers, in addition to topic … such as:Time span (example: 1970 - present)Time frequency (example: annually)Geographic extent (example: all of United States)Geographic granularity (example: county level)Methodology, sample (example: survey of adults aged 18-24)http://ssrs.yale.edu/statcat/13Data Documentation Initiative (DDI) DDI 3 designed to support the social science data lifecycle with metadata Powerful – but also complex!  Used by national statistical agencies, data archives, etc. Tools for using DDI being developed –choosing the right ones for specific institutional needs is key Has the elements to capture information targeted in social science data searchesSource: http://www.ddialliance.org/ 14Searching for texts (or images, or videos) differs from common search needs for social science research dataTypical search for books or journal articles targets author, title, subject, publ. date or issue (depending on topical or known-item searching)Example of alibrary catalogNot geared towards dataChallenges of finding data 1: institutional catalogs maycontain pointers to data, but are focused on other types of content15Challenges of finding data 2: there are many data-focused archive catalogs … but often as “information silos”Different search inputs, different search outputs, no easy way to search all at once, and not in “data-targeting”ways16Exposing and indexing the holdings of data archives and publications in standardized metadata formats could enable web-scale discovery through new cross-collection search engine functions built to exploit that metadataMetadataSearch for dataabout: ___From (year): ___To (year): ___In (geography):___at the level of: ___Collected via: ___etc., etc.: ___BetterSearch & Discoveryi17Linking of research data with papers, articles, dissertations, etc. Data is one “raw material”behind published research Bidirectional links between research results and research data would enhance discovery of both –finding publications could help find data and vice versa Challenge: creating and maintaining these linksFrom ICPSR’s Bibliography of Data-Related Literature (http://www.icpsr.umich.edu/icpsrweb/ICPSR/citations/)18Making research data available for web-based analysis Most repository platforms make content, incl. datasets, available for downloading But for many audiences, such as introductory methodology classes or “the public,” analysis of downloaded data is asking too much (lacking software or skills) Possible solution: web-based analysis, exploration, visualization of locallycreated data, e.g. through Berkeley SDA or Google Fusion TablesOutput generated via http://sda.berkeley.edu/quicktables/quickconfig.do?gss08 Making research data available for web-based visualization Could universities load locally created social science research data into a system like … Gapminder?19Existing infrastructure at Cornell, aligned with the data lifecycle Planning and collection– Survey Research Institute fee-for-service survey unit at Cornell study design, data collection Increasing partnership with CISER Cornell National Social Survey20Existing infrastructure at Cornell, aligned with the data lifecycle Planning and collection (cont.)– Colectica Designer Commercial product survey design  study documentation data collection statistical command file generation built on DDI 321Existing infrastructure at Cornell, aligned with the data lifecycle Planning and collection (cont.)– Research Data Management Services Group New development/collaboration Potentially very significant Return to in a minute22Existing infrastructure at Cornell, aligned with the data lifecycle Analysis and Collaboration– CISER Research Computing Restricted Data Environment– CRADC– Census RDC Help Desk/Workshops Close collaboration with Cornell Statistical Consulting Unit23Existing infrastructure at Cornell, aligned with the data lifecycle Analysis and Collaboration– DataStaR Data staging repository Collaboration, data sharing during analysis stage Publishes data and metadata elsewhere Project of Mann Library at Cornell24Existing infrastructure at Cornell, aligned with the data lifecycle Archiving and Discovery– CISER Data Archive– eCommons@Cornell CU’s institutional repository DSpace, text documents, limited capacity for data– ICPSR25Relationship building Research Data Management Services Group (RDMSG)– Grows out of pending NSF requirement regarding data management plans– Partners include: CISER, CUL, DISCOVER Research Services Group, Astronomy, CAC– “Meeting Funders’ Data Policies:  Blueprint for a RDMSG”26Relationship building Cornell Population Program– “Young” Population Program (formed 2008)– Looking for “Signature” Data product– Cornell National Equivalencies File (CNEF)27Relationship building Cornell National Social Survey (CNSS)– annual Cornell National Social Survey is conducted by the Survey Research Institute (SRI). It polls adults aged 18 and over on a wide range of current public policy and socioeconomic topics– CISER creates integrated data, web extraction system– Value increases over time– Connection to Cornell Researchers28Relationship building Relationships with Individual Faculty/Researchers– Mildred Warner– Concerned about at-risk data– Connections to data sources/urgent timing29CISER Staffing Initiatives Building on the CISER Data Archive (in existence since 1981): CISER Research Data Management Librarian (Stefan Kramer) CISER Research Associate– Good research/data skills– Available for hire to Cornell research projects Cornell Programming Staff– Front end/backend programming30Thank you for your time & attention!The  EndWilliam C. Blockblock@cornell.eduStefan Kramerstefan.kramer@cornell.edu

Opportunities and challenges across the research data lifecycle, and related activities at Cornell University

https://biecoll.ub.uni-bielefeld.de/index.php/kwi/article/download/637/883

Opportunities and challenges across the research data lifecycle, and related activities at Cornell University

Abstract

Similar works

Full text

Available Versions

BieColl - Bielefeld eCollections