https www flickr comphotosinfocux8450 RDM Research Data Management

https: //www. flickr. com/photos/infocux/8450 RDM Research Data Management Presented by Johann van Wyk University of Pretoria and Carnegie Corporation of New York Capstone Conference, held 25 March 2019 at the Kievits Kroon County Estate and Conference Centre, Pretoria, South Africa

Overview • What is meant by Research Data? • What is Research Data Management? • Why Manage Research Data? • Research Data Lifecycle • An Overview of Research Data Management Activities • Various Stakeholders • Issues that Institutions need to address • Example: University of Pretoria • Other Helpful Tools

Introduction Over the last number of years the management of research data has been highlighted in articles, books, conference papers, workshops, web sites and fora. BUT: What is meant with the concept “research data”? What is meant with the concept “research data management” (RDM)? 3

What is meant by ‘Research Data’? Definition “Research data, unlike other types of information, is collected, observed, created or generated, for purposes of analysis to produce original research results” (Edinburgh University Data Library Research Data Management Handbook, 2011) http: //www. docs. is. ed. ac. uk/docs/data-library/EUDL_RDM_Handbook. pdf 4

Types of Research Data • • 5 Numeric data Visual data (still, moving and animation) Audio data Textual data

Examples of Other types of data (related to the research process) • Referencing data (e. g. list of literature consulted to generate the data, and list of secondary datasets created by others that were consulted) Funding data (contains information about sources of funding that was used to conduct the research) Collaboration data (comprise personal information and details about co-researchers on a specific research project) Administrative data (e. g. protocol development info, info on protocol defense, registrations for clinical trials, ethical clearance forms, permission forms from respondents etc. ) • • • NB! NOTE THIS IS NOT RESEARCH DATA! 6

What is Research Data Management? “Research Data Management concerns the organisation of data, from its entry to the research cycle through to the dissemination and archiving of valuable results” “Research Data Management is part of the research process and aims to make the research process as efficient as possible, and meet expectations and requirements of the University, research funders, and legislation” University of Leicester, http: //www 2. le. ac. uk/services/research-data/rdm/what-is-rdm 7

RDM: a brave new world Messy Various formats Complex y r o t i s o p e R Open a t Data ion Da t a v r Various devices e Various Versions s e Copyright License Pr a t a Data D Polic DOI y a t a Small Data D Data Journals Big d e Data k n i L A Sensitive Data Dat nonym Visualisati on a Fo isati on rma ts

Why manage research data? • Research Data is viewed as a scholarly product just like journal articles, theses and books, and should therefore be managed effectively • Data (especially digital data) is fragile and easily lost. Proper research data management practices ensure that your data is not inadvertently changed, manipulated or lost, but secure • RDM ensures research data and records are accurate (without errors), complete, authentic and reliable. This then increases the quality of analyses • Well-managed and accessible data allows others to validate, verify and replicate findings • RDM protect researchers and institutions from reputational, financial and legal risk, when others question research results and outcomes 9

Why manage research data? • RDM facilitates the sharing of research data and enable the usage of your data by others and in the process prevent others from re-inventing the wheel. This can also lead to valuable discoveries by others • RDM enhances your reputation as researcher, and increase your citations when other researchers use your data and cite your data and other research outputs • RDM provides opportunities for collaboration with other researchers within your discipline, or even with other disciplines • RDM enables researchers to meet funding body grant requirements, e. g. NSF, NIH, and NRF • RDM enables researchers to meet publisher requirements 10

Research Data Lifecycle • • • Data Repurposing/Re-use • Data Citation Designing Data Management Plans Data Capture Data Storage Metadata creation Creating Data Re-using Data • • • Processing Data Giving Access to Data • Data Sharing • Data Publishing • Link Data to Outputs Analysing Data Preserving Data • • Data Archiving Data Preservation Metadata Creation Link data to outputs • • Data Storage Metadata Creation Data Cleansing Data Verification Data Validation Data Anonymisation Data Interpretation & Analyis Data Visualisation Based on UK Data Archive Lifecycle

An overview of Various Activities in the RDM Lifecycle 12

Designing Data Management Plans A Data Management Plan is “a formal document that outlines what you will do with your data during and after you complete your research” (The University of Virginia Library, 2014). Data Management Planning Tools: § DMPTool https: //dmptool. org/ (University of California Curation Center of the California Digital Library) § DMPonline tool https: //dmponline. dcc. ac. uk § SA DMP Tool https: //secure. dirisa. ac. za/SADMPTool/ 13

Data Capture/Collection The action or process of “gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes” (Responsible conduct of research, n. d. ; The Oxford Dictionary, 2014). Examples of data collection methods: Observations, textual or visual analysis, interviews, focus group interviews, surveys, tracking, experiments, case studies, literature reviews, questionnaires, data from sensors, model outputs, etc. If you want to use data sets that other researchers have published (secondary data), you can find it at: Or Data Citation Index(Web of Science) 14

Data Storage & Backup Data storage is the process of “preservation of data files in a secure location which can be accessed readily”. Data Backup is the process of “preserving additional copies of your data in a separate physical location from data files in storage”. (Research Data Services, University of Wisconsin-Madison, 2014) It is important to save and organize your files and the folders correctly, so that you can find and identify the file easily. (For example do not name your file just Test 1 or Test 2) Specific file naming conventions exist to help your in naming a file correctly for future reference: • See File Naming Rules, University of Edinburgh http: //www. ed. ac. uk/records-management/staffguidance/electronic-records/naming-conventions 15

Metadata Creation Each dataset has to be accompanied by Metadata is searchable, standardised and structured “information that describes a dataset” and explains “the aim, origin, time references, geographic location, creating author, access conditions and terms of use of a data set” (Corti et al. , 2014: 38; USGS Data Management Website, 2014) There are various types of metadata schemas, e. g. Dublin Core, MODS, PREMIS, ISO 19115: 2003(E), but most data systems use Dublin Core. Some disciplines have their own metadata schemas 16

Data Interpretation and Analysis Data interpretation and analysis “is the process of assigning meaning” to the gathered information and ascertaining “the conclusions, significance, and implications of the findings” (Analyzing and Interpreting Data, n. d. ). 17

Data Visualisation is the visual representation of data, and is used to enable people to both understand communicate information through graphical and schematic avenues (Friendly, 2009: 2; Schnell and Shetterley, 2013: 3) From Xiaoru Yuan’s presentation, CODATA Workshop on 12 June 2014 18

Data Cleansing, Verification & Validation • Data Cleansing “refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc. parts of the data and then replacing, modifying, or deleting this dirty data’ (Wikipedia) • Data Verification “the process of evaluating the completeness, correctness, and compliance of a dataset with required procedures to ensure that the data is what it purports to be. (Martin and Ballard, 2010: 8 -9; US EPA, 2002: 7) • Data validation process “to determine if data quality goals have been achieved and the reasons for any deviations. Validation checks that the data makes sense”. (Martin and Ballard, 2010: 8; US EPA 2002: 15). 19 https: //powerofus. force. com/articles/Resource/ Focus-on-Your-Data

Data anonymisation • Data anonymisation is “the process of de-identifying sensitive data, while preserving its format and data type” • (Raghunathan, 2013: 4). Anonymisation Techniques - Examples: Generalisation, Suppression, Permutation, Pertubation, Substitution, Shuffling, Number and Date Variance, Nulling-out (Charles, 2012; Cormode and Srivastava, 2009; Raghunathan , 2013: 172 -182; Simpson, n. d. ; Vinogradov and Pastsyak, 2012: 163). 20

Data Sharing • Sharing data is the process of opening up access to research data and making it available to other researchers (Corti et al. , 2014: 2). • Data sharing provides “opportunities for other researchers to review, confirm or challenge research findings” (Data sharing and implementation guide, n. d. ). 21

Data Publishing (Sharing) This is the process of making research data underpinning the findings published in peer-reviewed articles, available for readers and reviewers in an appropriate repository, or “as supplementary materials to a journal publication” (Corti et al 2014: 197; Marques, 2013). Data Journals A more recent development has been the appearance of data journals. These journals publish data papers that describe a dataset, and also give an indication in which repository the dataset is available (Corti et al. 2014: 7 -8). A List of Data Journals can be found at http: //proj. badc. rl. ac. uk/preparde/blog/Data. Journals. List Issues that needs to be addressed in data publishing are: IP rights, copyright, plagiarism, ethical issues around the data, the appropriate repository to publish in, data citation/referencing, licensing of the data and embargoing of data 22

Registry of Research Data Repositories • re 3 data. org is a global registry of research data repositories that covers research data repositories from different academic disciplines. • It presents repositories for the permanent storage and access of data sets to researchers, funding bodies, publishers and scholarly institutions. • It can be used a tool for the easy identification of appropriate data repositories to store research data 23

Data repurposing/re-use • This is the process where secondary data (data that have been captured analysed by other researchers) can be re-analysed, reworked or -used for new analyses, and compared with contemporary data (Corti et al. , 2014: 169) • This process “also enables research where the required data may be expensive, difficult or impossible to collect”, e. g. large scale surveys, or historic data (Corti et al. , 2014: 169). 24

Data Citation (referencing) Data citation is the process of referencing (attributing and acknowledging) reused data in a similar fashion as traditional sources of information (Corti et al. 2014: 197). Helpful Sources : • • Data Citation Awareness Guide (ANDS, 2017) How to Cite Data Sets and Link to Publications (DCC, 2015) • • Quick Guide to Data Citation (IASSIST, 2012) Citing and referencing Data Files (Monash University, 2019) 25

Various Stakeholders in RDM Executive Management Funders Library Deans & Dept Heads External (disciplinary) data repositories Publishers Principal Investigator/ Researcher IT Services Research Office (De Waard, Rotman and Lauruhn, 2014)

Issues that Institutions needs to address • • 27 Compiling a Research Data Management Policy Choosing an appropriate software platform that can service as an institutional Research Data Repository (e. g. Figshare, Islandora, Dataverse, etc. ) Connect to /install a Data Management Planning Tool system Integrate the Research Data Management process with the ethical processes at the institution Make provision for systems on which active data can be stored/backed up (e. g. electronic lab note books, staging repositories such as HUBzero, Alfresco, etc. ) Make provision for an archival system on which data can be preserved for long term Training of researchers in the various RDM activities and processes

Example: University of Pretoria • • 28 Conducted two surveys on RDM Practices at UP in 2009 and in 2013 Appointed a Research Data Manager in 2013 Implemented 5 pilot projects (2013 -2014) to gain an understanding of researchers’ RDM needs Developed a new RDM policy to replace outdated policy of 2007 – new policy adopted by UP Executive in September 2017 Investigation of Data Repository Systems 2016 -2018 Implementation of Research Data Management Readiness Training Toolkit Implementation of new Research Data Repository solution for UP (Figshare) - currently

Investigation of Research Data Repository software solutions – University of Pretoria Product evaluation criteria • Consulted with various UP stakeholders to obtain their input. Consulted with external stakeholders at the NEDICC workshop held at the CSIR; • Consulted with peer Universities; and • Utilised various selection criteria from other institutions e. g. Leeds University, Texas Digital Library and the RDA RPRD IG Matrix (http: //tinyurl. com/RPRD-matrix) selection criteria as a basis and adapted it according to UP specific requirements. Product Short Listing • Product scan of products being used internationally; and • Most commonly used products at universities similar to UP (size and research activity).

Investigation of Research Data Repository software solutions - University of Pretoria Product Evaluation • UP’s formal Request For Information (RFI) process was followed. • Product evaluation criteria list was compiled and send to short listed vendors together with standard RFI documentation. • The requested information was received from the vendors and prepared for scoring. • Products were scored and evaluated. • Evaluation went through two rounds: In the second round more focus was placed on Open Source products

Investigation of Research Data Repository software solutions – University of Pretoria Evaluation Criteria § Functional / Business criteria: Deposit and Upload; Re-Usability; Identity and Access Management; Reporting; Discovery; Preservation § Non Functional: Repository Architecture; Data Management; Data Governance § Technical aspects: Back-end Management; Integration; Infrastructure § Vendor specific: Support, Training, Usage of Product § Performance requirements § Integration requirements § Costs

UP Figshare Repository

Training RDM FUNDAMENTALS Session 1 – What is RDM and RDM at UP & Jargon Busting Session 2 – UP RDM Policy Overview RDM Readiness Training Toolkit RDM ESSENTIALS Session 1 – Data Management Plans Session 2 – Data Citation & Licensing DATA INTENSIVE Session 1 – Data Analysis Session 2 – Data Creation Session 3 – Data Visualisation Session 3 – Metadata Standards for Data Session 4 – Data Cleaning Session 4 – Data Repositories Session 5 – Data Dissemination Session 5 – Data Curation & Preservation Session 6 – More sessions to be identified All Staff Librarians & Researchers Session 6 – Data Enrichment Session 7 – Data Interpretation Specialists in Library & Researchers

How the Training is delivered at UP In-house Workshops • Each theme is covered twice a week • Attendees: Library Staff, staff from University Research Office, and UP researchers • Workshops are repeated on request Narrated Power. Points of these workshops • Available on our Digital Scholarship Website (https: //makerspace. up. ac. za/)

Next steps § Investigate tools that can support Active Data (the Research -in-Process data), e. g. my. Tardis, Hubzero, Alfresco § Investigate long term preservation tools § Finalise storage solution (e. g. African Research Cloud) § Implementation of repository solution (Currently being implemented) § Communication Plan/Marketing of repository solution (in process) § Training of researchers / Library staff (in process)

Helpful tools • • Guide: How to Cite Data Sets and Link to Publications (DCC) http: //www. dcc. ac. uk/resources/how-guides/cite-datasets Guide: How to License Research Data (DCC) http: //www. dcc. ac. uk/resources/how-guides/license-research-data Finding an appropriate repository to upload my final data for publication Go to http: //www. re 3 data. org/ (re 3 data is a global registry of research data repositories). UP Libguide on RDM - http: //up-za. beta. libguides. com/c. php? g=356288 (Provides information on various aspects on RDM, as well as training events on RDM at UP) 36

Helpful Tools (2) Tracking the Impact of my Research Data • Impactstory – a tool to track impact (bibliometrics) http: //impactstory. org • Data Citation Index (Web of Science) - Counts formal and informal citations of datasets by papers - http: //apps. webofknowledge. com/ Free Online Courses on Research Data Management – MANTRA – Free Online Research Data Management Training Course http: //datalib. edina. ac. uk/mantra/ – Essentials 4 Data Support Course (Netherlands)- free online introductory course for those people who (want to) support researchers in storing, managing, archiving and sharing their research data http: //datasupport. researchdata. nl/en 37

Thank You! 38

References • • 39 Analyzing and interpreting data. Syracuse, NY: Office of Institutional Research and Assessment, Syracuse University, n. d. [Online] available at https: //oira. syr. edu/assessment/assesspp/Analyze. htm (Accessed 18 September 2014). BALL, A. 2014. How to License Research Data. (DCC How-to Guides). Edinburgh: Digital Curation Centre. Available online: http: //www. dcc. ac. uk/resources/how-guides (Accessed 20 August 2015). BALL, A. AND DUKE, M. 2015. How to Cite Datasets and Link to Publications. (DCC How-to Guides). Edinburgh: Digital Curation Centre. [Online] available at http: //www. dcc. ac. uk/resources/howguides (Accessed 20 August 2015) BALL, A. & DUKE, M. 2015. How to track the impact of Research Data with metrics. (DCC How-to Guides). Edinburgh: Digital Curation Centre. [Online] available at http: //www. dcc. ac. uk/resources/howguides (Accessed 20 August 2015)

References • BALL, A. & Duke, M. 2015. How to cite datasets and link to publications. (DCC How-to Guides). Edinburgh: Digital Curation Centre. [Online] available at http: //www. dcc. ac. uk/resources/howguides (Accessed 6 March 2019) CALLAGHAN, S. , MURPHY, F. , TEDDS, J. , ALLAN, R. , KUNZE, J. , LAWRENCE, R. , MAYERNIK, M. S. , WHYTE, A. & PREPARDE PROJECT TEAM. 2013. Connecting data repositories and publishers for data publication. Presentation delivered on 7 February 2013 at the OPENAIRE Interoperability Workshop, 7 -8 February, University of Minho Gualtar Campus, Braga, Portugal. [Online] available at http: //openaccess. sdum. uminho. pt/wpcontent/uploads/2013/02/7_Sarah. Callaghan_Open. AIREworkshop. UMin ho. pdf (Accessed 24 March 2019). CHARLES, K. 2012. Comparing enterprise data anonymization techniques. Newton, MA: Tech. Target. [Online] available at http: //searchsecurity. techtarget. com/tip/Comparing-enterprise-dataanonymization-techniques (Accessed 24 March 2019). • • 40

References • CHOUDHURY, S. 2014. Public Institution perspective (Research Library). Presented at the Digital Media Analysis, Search and Management (DMASM), 2014. [Online] available at http: //dataconservancy. org/wpcontent/uploads/2014/03/DC_DMASM_2014. pdf (Accessed 24 September 2014). CORMODE, G. & SRIVASTAVA, D. 2009. Anonymized data: generation, models, usage. Tutorial presented at the 2009 ACM SIGMOD International Conference on Management of Data, 2 July, Providence, Rhode Island, New York. [Online] available at http: //dimacs. rutgers. edu/~graham/pubs/papers/anontut. pdf CORTI, L. et al. 2014. Managing and sharing research data: a guide to good practice. Los Angeles: SAGE. Data Citation Awareness guide. Australian National Data Service, 2017. [Online] available at https: //www. ands. org. au/guides/data-citationawareness (Accessed 4 March 2019). • • • 42

References • Data Management Planning Tool (DMPTool). Oakland, CA: University of California, California Digital Library, 2015. [Online] available at https: //dmptool. org/ (Accessed 20 August 2015). • DMPOnline tool, 2015. Edinburgh, UK: Digital Curation Centre. [Online] available at https: //dmponline. dcc. ac. uk (Accessed 20 August 2015). Edinburgh University Data Library Research Data Management Handbook, 2011. [Online] available at http: //www. docs. is. ed. ac. uk/docs/datalibrary/EUDL_RDM_Handbook. pdf (Accessed 22 March 2019). Essentials 4 Data Support Course. [sl. : sn. ] [Online] available at http: //datasupport. researchdata. nl/en (Accessed 20 August 2015). • • • 42 FRIENDLY, M. 2009. Milestones in the history of thematic cartography, statistical graphics, and data visualization. [Sl. : s. n. ] [Online] available at http: //math. yorku. ca/SCS/Gallery/milestone. pdf (Accessed 19 September 2014).

References • • • 43 Impactstory. [n. d. ] [sl. : s. n. ]. [Online] available at https: //impactstory. org/ (Accessed 20 August 2015). MANTRA: Free Online Research Data Management Training Course. Edinburgh, UK: University of Edinburgh. [Online] available at http: //datalib. edina. ac. uk/mantra/ (Accessed 20 August 2015) MARQUES, D. 2013. Research data driving new services. Elsevier Library Connect, 25 February 2013. [Online] available at https: //libraryconnect. elsevier. com/articles/research-data-driving-newservices (Accessed 24 March 2019) MARTIN, E. AND BALLARD, G. 2010. Data management best practices and standards for Biodiversity data applicable to Bird Monitoring Data. U. S. North American Bird Conservation Initiative Monitoring Subcommittee. [Online] available at http: //www. nabci-us. org/ (Accessed 24 September 2014). MONASH UNIVERSITY. 2019. Citing and referencing: data files. [Melbourne, Australia]: Monash University Library. [Online] available at https: //guides. lib. monash. edu/citing-referencing/data-files (Accessed 4 March 2019).

References • • • 44 RAGHUNATHAN, B. 2013. The complete book of data anonymization: from planning to implementation. Broken Sound Parkway, NW: CRC Press, Taylor and Francis Group. re 3 data. org: registry of research data repositories, n. d. [sl. : s. n. ]. [Online] available at http: //www. re 3 data. org/ (Accessed 20 August 2015). Research Data Services, University of Wisconsin-Madison, WI: University of Wisconsin Madison, 201. [Online] available at http: //researchdata. wisc. edu/manage-your-data/data-backup-andintegrity/ (Accessed 24 September 2014). Responsible conduct of research. De. Kalb, Illinois: Northern Illinois University Faculty Development and Instructional Design Center, n. d. [Online] available at: http: //ori. dhhs. gov/education/products/n_illinois_u/datamanagement/dc topic. html. (Accessed: 16 September 2014). SCHNELL, K. & SHETTERLY, N. 2013. Understanding data visualization. [Sl. ]: Accenture. [Online] available at https: //web. archive. org/web/20150421122002/http: //www. accenture. co m/Site. Collection. Documents/PDF/Accenture-Tech-Labs-Data. Visualization-Full-Paper. pdf Accessed 24 March 2019).

References • • 45 SIMPSON, J. n. d. Data masking and encryption are different. IRI Blog Articles, web log post. [Online] available at http: //www. iri. com/blog/data-protection/data-masking-and-dataencryption-are-not-the-sam-things Special Interest Group on Data Citation. 2012. Quick guide to data citation. (Sl. : IASSIST). [Online] available at https: //www. icpsr. umich. edu/files/ICPSR/enewsletters/iassist. html (Accessed 6 March 2019). Standard naming conventions for electronic records: the rules. Edinburgh: University of Edinburgh, 2015. [Online] available at http: //www. recordsmanagement. ed. ac. uk/Info. Staff/RMstaff/RMprojects /PP/File. Name. Rules/Rules. htm (Accessed 20 August 2015). Statement on Open Access to Research Publications from the National Research Foundation (NRF)-Funded Research. Pretoria: National Research Foundation, 2015. [Online] available at http: //www. nrf. ac. za/media-room/news/statement-open-accessresearch-publications-national-research-foundation-nrf-funded (Accessed 20 August 2015).

References • UK DATA ARCHIVE. 2019. Research data lifecycle. Colchester, UK: University of Essex; Manchester, UK: University of Manchester; London, UK: JISC. [Online] available at https: //www. ukdataservice. ac. uk/managedata/lifecycle (Accessed 24 March 2019). • UNITED STATES ENVIRONMENTAL PROTECTION AGENCY (US EPA). 2002. Guidance on Environmental Data Verification and Data Validation: EPA QA/G-8. Washington, DC: Environmental Protection Agency. [Online] available at http: //www. epa. gov/QUALITY/qs-docs/g 8 -final. pdf (Accessed 24 September 2014). USGS Data Management. [Online] available at http: //www. usgs. gov/datamanagement/describe/metadata. php (Accessed 19 August 2014). • 46

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