Introduction to the Research Data Center PsychData
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1 Introduction to the Research Data Center PsychData INA DEHNHARD ERICH WEICHSELGARTNER Leibniz- Institute for Psychology Information (ZPID) Trier, Germany
2 DATA SHARING DATA MANAGEMENT RESEARCH DATA CENTER PSYCHDATA
3 DATA SHARING Started with the continuing developments of technology & growing interdependence among scientimic disciplines Research data = valuable product of scientimic process! Funding agencies and journals require or encourage data managment practices! Organizations like DataCite support the citation of datasets and data discovery
4 Data Sharing: Benefits ScientiMic inquiry Impact and visibility of research Reanalysis, metaanalysis & replications New research & improvement of research methods No expensive duplication of data sets Requirement of funding agencies or journals Resources for training & education
5 Data Sharing: Obstacles Legal and ethical barriers Time and effort for data management Little or no credits Competition between researchers Detection of weaknesses in the statistical analyses
6 Data Sharing Research data center, data archive, Journal (supplement material or data paper) Institutional repository Project (or personal) website Informally data sharing between researchers
7 Data Sharing Data Management Aus: Dr. D. Shotton (2009). ADMIRAL.
8 DATA MANAGEMENT Data management covers all aspects of handling, organising, documenting and enhancing research data, and enabling their sustainability and sharing.(uk Data Archive, 2014; archive.ac.uk/create- manage/planning- for- sharing) Good data management practices are essential in research, to make sure that research data are of high quality, are well organised, documented, preserved and accessible and their validity controlled at all times. This results in efpicient and excelling research. Well managed data are easily shared and can thus be used for new research or to duplicate and validate existing research. (UK Data Service, 2014, data.aspx)
9 Reasons for data management Good scientimic practice Data long- term availability & interpretability Data quality Requirements (Funding agencies, Journals ) Data Sharing
10 Data long-term availability The underlying data researchers analyze to come to their published conclusions becomes less and less accessible to researchers over the years. (Vines et al, 2014) (D) Predicted probability that the data were extant (either shared or exist but unwilling to share ) given that we received a useful response.
11 Requirements NIH, USA (2003) All National Institutes of Health funded research (> $500K) must have a plan to address the sharing and archiving of data. Wellcome Trust, UK (2010) All our funded researchers should maximise access to their research data with as few restrictions as possible. European Commission (2013) Pilot on Open Research Data (Horizon 2020): Enhance data access and culture of sharing. Data management plan (DMP) mandatory.
12 Data management Some important aspects: Data management plans Sensitive data: anonymisation & informed consent Data documentation Data long- term availability
13 Data management plans Advantages Calculate costs & resources in advance Embedding data managment aspects early in the research process avoids workload More time & personal resources at the beginning of a project (Requirement of funding agencies (NIH (USA), Wellcome Trust (UK), ))
14 Data management plans Content: Kind of data and metadata generated Data quality and sustainability Roles and responsibilities Ethical and legal issues Plans for data sharing/ data deposit
15 Data management plans Tools and Checklists: DMPonline tool of the Digital Curation Centre DMPTool California Digital Library Checklists in different data management manuals
16 Sensitive data & anonymisation Levels of anonymisation: Absolutely anonymised data De- facto anonymised data Formally or not anonymised data
17 Sensitive data & data sharing Absolutely anonymised data IdentiPication of persons is impossible! public use Piles De- facto anonymised data IdentiPication only possible with an excessive amount of time, expenses and manpower! scientipic use Piles Formally or not anonymised data
18 Informed consent Informed consent: Provides sufmicient information on purpose of the research, all aspects of participation and data use Freely given Active communication! Do not prohibit data sharing using restrictive language on consent forms!
19 Informed consent Consent for data sharing: Information, how data will be preserved and used in the long- term Information about protection of conmidentiality, e.g. anonymisation Information, how and when data will be shared
20 Informed Consent UK Data Archive Managing and Sharing Data : SAMPLE CONSENT FORM FOR INTERVIEWS
21 Data documentation Metadata: DeMinition Study- level vs data- level metadata Metadata in the social sciences: Examples
22 Metadata: Definition Data or information, that describes (research) data in a structured form. (vgl. Jensen et al, 2011) Data about data Resource discovery (machine understandable) Standardised
23 Metadata Study- level metadata: Author, title, funding, data collection method Data- level metadata: Variable description = codebook (Variable name, - label, value labels, missing values )
24 Metadata: Standards Dublin Core International standard for the description of digital as well as physical objects 15 core elements (Dublin Core Element Set) Additional terms extending or remining this set Deutsche Übersetzung: nb.info/ x/34
25 Metadata: Standards Data Documentation Initiative (DDI) International standard for the description of social, behavioral and economic data Expressed in Xml Since release of DDI3.0: Research data lifecycle
26 Data long-term availability
27 Data long-term availability Store & back- up master copies Use non- proprietary or open standard formats (ASCII, xml, csv, ) Migrate data at regular intervals Back- ups, different forms of storage (CD, DVD, hard drive, magnetic media,..) Controlling access (password, )
28 Literature Data Management Manuals ICPSR: Guide to Social Science Data Preparation and Archiving UK Data Archive: Managing and Sharing Data ZPID: Datenmanagement und Data Sharing in der Psychologie
29 RESEARCH DATA CENTER PSYCHDATA PsychData Research data center for Psychology Data sharing platform for psychological quantitative data Developed 2002 by the Leibniz- Institute for Psychology Information (ZPID) at Trier, Germany Funded by the German Research Foundation (DFG)( ) Accredited by the German Data Forum
30 PsychData PsychData Research data center for Psychology Data documentation Data long- term preservation Data sharing
31 PsychData metadata PsychData documentation: Compatible with the international metadata standards D.C. and DDI 2.0 Domain specimic (psychological data) Study- level metadata Variable- level metadata
32 PsychData: Study-level metadata
33 PsychData: Variable-level metadata Variable name Variable label Ques.on/ Instruc.on text Range of data codes Range of missing data codes Value labels Missing data labels
34 Data deposit with PsychData Selection criteria:! peer- reviewed publication! quantitative, digital data PsychData deposit contract Submit research data, study description and all materials necessary for documentation
35 Data deposit with PsychData Data depositor Research data Documen- tation Study descrip- tion Data depositor Final revision Standardized documentation Data validation PsychData Long- term preservation Data dissemination PsychData
36 Data deposit with PsychData Advantages: Share data with the scientimic community Long- term availability Legal issues are addressed (respondents conmidentiality; Authorship) Visibility
37 PsychData: Data dissemination Data user Select a study PsychData ScientiMic Use Files Data use contract Free of charge Data user
38 Psychdata: Visibility Data citation via doi Study description published on the web Included in metadata search engines (PubPsych, da ra, DataCite, ) Linking with PsychAuthors, PubPsych
39 Visibility
40 Visibility
41 Data management with PsychData DataWiz: An automated assistant for the management of psychological research data Expert system which provides the knowledge base and procedural support for data sharing Project start: Octobre 2015 Funded by German Research Foundation Prototype: MyPsychData (
42 Thanks for your attention! Member of the German Data Forum Using DOIs to cite and link to research data Partly funded by the German Research Foundation
43 Literature 1 Deutsche Forschungsgemeinschaft. (2013). Vorschläge zur Sicherung guter wissenschaftlicher Praxis: Empfehlungen der Kommission Selbstkontrolle in der Wissenschaft (ergänzte Auflage). Weinheim, Deutschland: Wiley-VCH. Digital Curation Centre (n.d.) DMPonline. Zugriff am Verfügbar unter ICPSR. (2012). Guide to social science data preparation and archiving. Best practice through the data life cycle (5. Aufl.). Ann Arbor, MI: Inter-University Consortium for Political and Social Research. Zugriff am Verfügbar unter Jensen, U., Katsanidou, A. & Zenk-Möltgen, W. (2011). Metadaten und Standards. In S. Büttner, H.-C. Hobohm & L. Müller (Hrsg.), Handbuch Forschungsdatenmanagement (S ). Bad Honnef: Bock + Herchen. NIH. (2003). NIH Data Sharing Policy and Implementation Guidance. Bethesda, MD: National Institutes of Health. Zugriff am Verfügbar unter Shotton, D. (2009). The ADMIRAL Project. Zugriff am Verfügbar unter:
44 Literature 2 University of California Curation Center of the California Digital Library (n.d.) DMPTool. Zugriff am Verfügbar unter van den Eynden, V., Corti, L., Woollard, M., Bishop, L. & Horton, L. (2011). Managing and sharing data. Best practice for researchers (3. Aufl.). Colchester: UK Data Archive. Zugriff am Verfügbar unter Vines, T. H., Albert, A., Andrew, R. L., De barre, F., Bock, D.G., Franklin, M. T., Gilbert, K. J., Moore, J. S., Renaut, S., & Rennison, D. J. (2014). The Availability of Research Data Declines Rapidly with Article Age. Current Biology, 24,
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