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Research Data Management 
and Librarians 
Presentation at Elsevier Library Connect Seminar, 
6 October 2014, Johannesburg,...
Introduction 
Internationally research data is increasingly recognised as a vital 
resource whose value needs to be preser...
Research Data Management: A (Brave) Complex New World 
Messy Complex 
Small Data 
Various formats 
Various devices 
Variou...
What is meant by Research Data? 
Research data, unlike other types of information, is 
collected, observed, created or gen...
What is research data management? 
• “the process of controlling the information generated during a 
research project” 
• ...
Why Manage Research Data? 
By managing research data you will: 
• Meet funding body grant requirements, e.g. NSF, NIH; 
• ...
Designing Data Management Plans 
Creating 
Data 
A Data Management Plan is “a formal document that outlines what you will ...
Data Capture/Collection 
Creating 
Data 
The action or process of “gathering and measuring information on variables of 
in...
Data Storage and Backup 
Creating 
Data 
Processing 
Data 
Analysing 
Data 
Data storage is the process of “preservation o...
Metadata Creation 
Creating 
Data 
Processing 
Data 
Analysing 
Data 
Preserving 
Data 
• Metadata is searchable, standard...
Data Cleansing, Verification & 
Validation 
Processing 
Data 
Analysing 
Data 
• Data Cleansing 
“refers to identifying in...
Data anonymisation 
Processing 
Data 
Analysing 
Data anonymisation is “the process of de-identifying sensitive data, whil...
Data Interpretation & Analysis 
Analysing 
Data 
Data interpretation and analysis “is the process of assigning meaning” to...
Data Publishing 
Analysing 
Data 
Data publishing 
This is the process of making research data underpinning the findings p...
Examples of Data Repository Software
Registry of Research Data Repositories 
• re3data.org is a global registry of research data repositories that covers 
rese...
Data Journals 
• A list of Data Journals – available at 
http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList 
• Examp...
Data Visualisation 
Analysing 
Data 
Data Visualisation is the visual representation of data, and is used to enable 
peopl...
Data Archiving 
Preserving 
Data 
Data archiving can be described as the process of retention and 
storage of valuable dat...
Data Preservation 
Preserving 
Data 
Data preservation is ”the process of providing enough representation 
information, co...
Linking Data to research outputs 
Preserving 
Data 
This is the process of connecting the underlying data relating to a sp...
Data Sharing 
Giving 
Access to 
• Sharing data is the process of opening up access to research data and 
making it availa...
Data sharing Methods 
The method for sharing data will depend on a variety of factors, 
including size and complexity of t...
Data repurposing/reuse 
Re-using 
Data 
• This is the process where secondary data (data that have been captured and 
anal...
Data Citation 
Re-using 
Data 
Data citation is the process of referencing (attributing and acknowledging) 
reused data in...
Data Citation: DOI 
Re-using 
Data 
DOI = Digital Object Identifier 
To enable a unique and persistent identification of a...
Provenance of Data 
• history of a data file or data set 
• this includes information 
o on the person(s) responsible for ...
Management of Big Data 
Big data can be described in terms of its characteristics: 
• Relative characteristics: denotes th...
Absolute Characteristics of Big Data 
• Volume: The scale of data that systems must ingest, process and 
disseminate; 
• V...
Role of Librarian in Big Data 
• Create awareness among researchers about Big Data Initiatives 
internationally 
• Create ...
Examples of International Initiatives 
Center for International Earth Science 
Information Network, 
EARTH INSTITUTE, COLU...
Pilot Projects at University of Pretoria 
• The UP Library Services implemented two data management pilot projects in 2013...
Next Phase 
Long-term Preservation 
Archival Information Package (AIP) 
• Bagit format (Bag-it and tag-it) 
• Bagit “bag” ...
Various stakeholders in RDM 
Executive Management 
Deans & Dept Heads 
IT Services 
Research Office 
Library 
Principal 
I...
Funders Funders Requirements: USA 
(Dietrich et al. 2012) 
http://www.istl.org/12-summer/refereed1.html
Funders Funders’ Requirements: UK 
http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies 
UK Digi...
Conclusion 
This presentation showed that although the RDM environment looks 
daunting the Library Professional can play a...
References 
• Analyzing and interpreting data. Syracuse, NY: Office of Institutional Research and Assessment, Syracuse 
Un...
References 
• CHOUDHURY, S. 2014. Public Institution perspective (Research Library). Presented at the Digital Media 
Analy...
References 
• DCC. 2014. Overview of funders data policies. Edinburgh, UK: Digital Curation Centre. [Online] 
available at...
References 
• DMPonline tool. Edinburgh, UK: Digital Curation Centre, 2014. [Online] available at 
https://dmponline.dcc.a...
References 
• HUADONG, G. 2014. Scientific Big Data for knowledge discovery. Presentation on 8 June 2014 
at the CODATA Wo...
References 
• MARQUES, D. 2013. Research data driving new services. Elsevier Library Connect, 25 February 
2013. [Online] ...
References 
• Research Data Services, University of Wisconsin-Madison. Madison, WI: University of Wisconsin 
Madison, 201....
References 
• SIMPSON, J. n.d. Data Masking and Encryption Are Different. IRI Blog Articles. [Online] available at 
http:/...
References 
• VINOGRADOV, S AND PASTSYAK, A. 2012. Evaluation of data anonymization tools. In: 
DBKDA 2012 : The Fourth In...
Research Data Management and Librarians
Research Data Management and Librarians
Research Data Management and Librarians
Research Data Management and Librarians
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Research Data Management and Librarians

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This presentation was delivered at the Elsevier Library Connect Seminar on 6 October 2014 in Johannesburg, 7 October 2014 in Durban and 9 October 2014 in Cape Town and gives an overview of the potential role that librarians can play in research data management

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Research Data Management and Librarians

  1. 1. Research Data Management and Librarians Presentation at Elsevier Library Connect Seminar, 6 October 2014, Johannesburg, 7 October 2014, Durban and 9 October 2014, Cape Town By Johann van Wyk (University of Pretoria)
  2. 2. Introduction Internationally research data is increasingly recognised as a vital resource whose value needs to be preserved for future research. This places a huge responsibility on Higher Education Institutions to ensure that their research data is managed in such a manner that they are protected from substantial reputational, financial and legal risks in the future. Librarians have a unique skillset to help these institutions navigate this complex environment. This presentation will highlight a number of potential roles librarians could play.
  3. 3. Research Data Management: A (Brave) Complex New World Messy Complex Small Data Various formats Various devices Various Versions Sensitive Data
  4. 4. What is meant by Research Data? Research data, unlike other types of information, is collected, observed, created or generated, for purposes of analysis to produce original research results http://www.docs.is.ed.ac.uk/docs/data-library/EUDL_RDM_Handbook.pdf
  5. 5. What is research data management? • “the process of controlling the information generated during a research project” • “Managing data is an integral part of the research process. How data is managed depends on the types of data involved, how data is collected and stored, and how it is used - throughout the research lifecycle”. http://www.libraries.psu.edu/psul/pubcur/what_is_dm.html
  6. 6. Why Manage Research Data? By managing research data you will: • Meet funding body grant requirements, e.g. NSF, NIH; • Meet publisher requirements • Ensure research integrity and replication; • Ensure research data and records are accurate, complete, authentic and reliable; • Increase your research efficiency; • Save time and resources in the long run; • Enhance data security and minimise the risk of data loss; • Prevent duplication of effort by enabling others to use your data; • Comply with practices conducted in industry and commerce; and • Protect your institution from reputational, financial and legal risk.
  7. 7. Designing Data Management Plans Creating Data 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: • Data Management Planning Tool (DMPTool) https://dmptool.org/ (University of California Curation Center of the California Digital Library) • DMPonline tool https://dmponline.dcc.ac.uk/ (Digital Curation Centre, UK) Librarians can play an advisory role
  8. 8. Data Capture/Collection Creating Data 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, scenarios, etc. Librarians can play their traditional role of information searching, - training and - consultation
  9. 9. Data Storage and Backup Creating Data Processing Data Analysing Data Data storage is the process of “preservation of data files in a secure location which can be accessed readily” (Research Data Services, University of Wisconsin-Madison, 2014) Data Backup is the process of “preserving additional copies of your data in a separate physical location from data files in storage”. Librarians can advise researchers on File Naming Conventions
  10. 10. Metadata Creation Creating Data Processing Data Analysing Data Preserving Data • 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) • Examples: - Dublin Core Metadata Element Set; - ISO 19115: 2003(E) — Geographic Information Metadata; - PREMIS Librarians, especially cataloguers have the skill-set to assist with metadata creation and to advise
  11. 11. Data Cleansing, Verification & Validation Processing Data Analysing Data • 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. This can be done by persons “who are less familiar with the data”, for example Librarians. (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).
  12. 12. Data anonymisation Processing Data Analysing 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). Data
  13. 13. Data Interpretation & Analysis Analysing Data 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.).
  14. 14. Data Publishing Analysing Data Data publishing 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). Librarians can be involved in creating and managing a data repository, and can give training and advise
  15. 15. Examples of Data Repository Software
  16. 16. Registry of Research Data Repositories • re3data.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.
  17. 17. Data Journals • A list of Data Journals – available at http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList • Example of data journal at Elsevier: “Data in Brief”
  18. 18. Data Visualisation Analysing Data Data Visualisation is the visual representation of data, and is used to enable people to both understand and communicate information through graphical and schematic avenues (Friendly, 2009: 2; Schnell and Shetterley, 2013: 3) From Xiaoru Yuan’s presentation at CODATA Workshop on 12 June 2014
  19. 19. Data Archiving Preserving Data Data archiving can be described as the process of retention and storage of valuable data (this is data that will be essential for future reference) for long-term preservation, so that the data will be protected from risk (i.e. loss, or corruption), and will be accessible for future use (Rouse, 2010).
  20. 20. Data Preservation Preserving Data Data preservation is ”the process of providing enough representation information, context, metadata, fixity, etc. to the data so that anyone other than the original data creator can use and interpret the data” (Ruth Duerr, National Snow and Ice Data Center as cited by Choudhury, 2014) The Librarian can assist researchers in preparing data for long-term preservation, by advising on metadata standards
  21. 21. Linking Data to research outputs Preserving Data This is the process of connecting the underlying data relating to a specific research output, e.g. journal article, thesis, etc to the research output itself. This can be done by adding a digital object identifier (DOI) to the dataset and including this in the metadata of the research output, or by citing the dataset (Callaghan et al., 2013). The Librarian can assist researchers, through training and consultation on DOIs and data citation methods
  22. 22. Data Sharing Giving Access to • 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.). Data
  23. 23. Data sharing Methods The method for sharing data will depend on a variety of factors, including size and complexity of the dataset, sensitivity of the data collected, and anticipated number of requests for data sharing. Researchers could (1) Take responsibility for sharing data themselves, or (2) Use a data archive, or (3) Use a combination of these methods.
  24. 24. Data repurposing/reuse Re-using Data • This is the process where secondary data (data that have been captured and 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).
  25. 25. Data Citation Re-using Data 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 : • Publication Manual of the American Psychological Association (APA, 2009) • Oxford Manual of Style (OUP, 2012) • Data Citation Awareness Guide (ANDS, 2011) • Data Citation: What you Need to Know (ESRC, 2012) The Librarian can assist researchers, through training and consultation in data citation methods
  26. 26. Data Citation: DOI Re-using Data DOI = Digital Object Identifier To enable a unique and persistent identification of a digital object A DOI is a unique alphanumeric string assigned by a registration agency (the International DOI Foundation) to identify a digital object, e.g. a data set. Metadata about the object is stored together with the DOI name. This may include a location, such as a URL, where the object can be found. (Wikipedia) For example: http://dx.doi.org/10.1000/182 DOI Registry Registrant Specific Object The Librarian can assist researchers, through training and consultation on DOIs
  27. 27. Provenance of Data • history of a data file or data set • this includes information o on the person(s) responsible for the data set o context of the data set o revision history, including additions of new data and error corrections (Strasser et al., 2012: 7, 11)
  28. 28. Management of Big Data Big data can be described in terms of its characteristics: • Relative characteristics: denotes those datasets which cannot be acquired, managed or processed on common devices within an acceptable time; • Abolute chacteristics defines big data through Volume, Variety, Veracity and Velocity (Huadong, 2014) Big Data is part of a new science paradigm called Data Intensive Science, where Scientists are overwhelmed with data sets from many different sources, e.g. captured by instruments, generated by simulations, and generated by sensor networks
  29. 29. Absolute Characteristics of Big Data • Volume: The scale of data that systems must ingest, process and disseminate; • Variety: the complexity of the types of information handled (many sources and types of data both structured and unstructured) • Velocity: the pace at which data flows in and out from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices • Veracity: refers to the biases, noise and abnormality in data http://inside-bigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/
  30. 30. Role of Librarian in Big Data • Create awareness among researchers about Big Data Initiatives internationally • Create awareness among colleagues about the activities, workgroups and task groups of CODATA (Committee on Data for Science and Technology, of the International Council for Science) and Research Data Alliance • Become a member of a number of CODATA task groups
  31. 31. Examples of International Initiatives Center for International Earth Science Information Network, EARTH INSTITUTE, COLUMBIA UNIVERSITY Computer Network Information Center, CAS World Data Center for Microorganisms Institute of Remote Sensing and Digital Earth, CAS Dept of Earth Sciences Institute for environment and Human Security Thetherless World Constellation International Society for Digital Earth
  32. 32. Pilot Projects at University of Pretoria • The UP Library Services implemented two data management pilot projects in 2013-2014: • Institute for Cellular and Molecular Medicine (ICMM) and the Neuro-Physio-Group • An Open Source Document Management System was customised for this purpose • Why Alfresco? • Open Source • Captured provenance of data • Had a versioning function • Good metadata function • Easy to integrate with other software • Workflow function gave supervisor overview of progress of students • Sync function with dropbox and Google Drive • Drag and Drop function • File Sharing function • Mobile App
  33. 33. Next Phase Long-term Preservation Archival Information Package (AIP) • Bagit format (Bag-it and tag-it) • Bagit “bag” contains: • Bag declaration file, manifest file, data files, metadata file (XML) • METS wrapper • Dublin Core and MODS(Descriptive Metadata) • PREMIS (Preservation Metadata)
  34. 34. Various stakeholders in RDM Executive Management Deans & Dept Heads IT Services Research Office Library Principal Investigator/Researcher Funders Publishers External (disciplinary) data repositories (De Waard, Rotman and Lauruhn, 2014)
  35. 35. Funders Funders Requirements: USA (Dietrich et al. 2012) http://www.istl.org/12-summer/refereed1.html
  36. 36. Funders Funders’ Requirements: UK http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies UK Digital Curation Centre
  37. 37. Conclusion This presentation showed that although the RDM environment looks daunting the Library Professional can play an essential and much needed role in determining the success of Research Data Management initiatives at Higher Education Institutions. This vast, untamed and complex environment is waiting for someone to conquer it. Librarians have the necessary skillset to do that. May this motto also become our victory cry: “Veni, vidi, vici” – I came I saw I conquered
  38. 38. References • 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). • CALLAGHAN, S. et al. 2013. Connecting data repositories and publishers for data publication. Presentation delivered on 7 February 2013 at the OpenAIRE Interoperability workshop, University of Minho, held 7-8 February 2013. Braga, Portugal: University of Minho Gualtar Campus. [Online] available at http://openaccess.sdum.uminho.pt/wp-content/ uploads/2013/02/7_SarahCallaghan_OpenAIREworkshopUMinho.pdf (Accessed 19 September 2014). • CHARLES, K. 2012. Comparing enterprise data anonymization techniques. Newton, MA: TechTarget. [Online] available at http://searchsecurity.techtarget.com/tip/Comparing-enterprise-data-anonymization-techniques (Accessed 18 September 2014).
  39. 39. 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/wp-content/ uploads/2014/03/DC_DMASM_2014.pdf (Accessed 24 September 2014). • CORMODE, G. AND SRIVASTAVA, D. 2009. Anonymized data: generation, models, usage. Tutorial at SIGMOD, July 2009. [Online] available at http://dimacs.rutgers.edu/~graham/pubs/papers/anontut.pdf (Accessed 17 September 2014). • CORTI, L. et al. 2014. Managing and sharing research data: a guide to good practice. Los Angeles: SAGE. • Data Management Planning Tool (DMPTool). Oakland, CA: University of California Curation Center of the California Digital Library, 2014. [Online] available at https://dmptool.org/ (Accessed 24 September 2014). • Data sharing and implementation guide. Washington, DC: Institute of Education Sciences, U.S. Department of Education, n.d. [Online] available at http://ies.ed.gov/funding/datasharing_implementation.asp (Accessed 19 September 2014).
  40. 40. References • DCC. 2014. Overview of funders data policies. Edinburgh, UK: Digital Curation Centre. [Online] available at http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies (Accessed 24 September 2014). • DCC. 2014. What are metadata standards? Edinburgh, UK: Digital Curation Centre. [Online] available at http://www.dcc.ac.uk/resources/briefing-papers/standards-watch-papers/what-are-metadata-standards (Accessed 24 September 2014). • DE WAARD, A. AND ROTMAN, D. AND LAURUHN, M. 2014. Research data management at institutions: part 1: visions. Elsevier Library Connect, 6 February 2014. [Online] available at http://libraryconnect.elsevier.com/articles/2014-02/research-data-management-institutions-part-1- visions (Accessed 5 October 2014) • DIETRICH, D. et al. 2012. De-mystifying the data management requirements of research funders. Issues in Science and Technology Librarianship, Summer, 2012, No. 70. [Online] available at http://www.istl.org/12-summer/refereed1.html (Accessed 22 September 2012).
  41. 41. References • DMPonline tool. Edinburgh, UK: Digital Curation Centre, 2014. [Online] available at https://dmponline.dcc.ac.uk/ (Accessed 22 September 2014). • Edinburgh University Data Library Research Data Management Handbook, v.10, Aug, 2011. [Online] available at http://www.docs.is.ed.ac.uk/docs/data-library/ EUDL_RDM_Handbook.pdf (Accessed 25 September 2014). • FRIENDLY, M. 2009. Milestones in the history of thematic cartography, statistical graphics, and data visualization. [Sl.: s.n.] [Online] available at http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf (Accessed 19 September 2014). • HODSON, S. 2014. Global collaboration in data science: an introduction to CODATA. Presentation on 6 June 2014 at CODATA International Training Workshop in Big Data for Science for Researchers from Emerging and Developing Countries, Beijing, China, 4-20 June 2014.
  42. 42. References • HUADONG, G. 2014. Scientific Big Data for knowledge discovery. Presentation on 8 June 2014 at the CODATA Workshop on Big Data for International Scientific Programmes: Challenges and Opportunities, Beijing, China, 8-9 June 2014. • Library of Congress. 2014. PREMIS. Washington, DC: Library of Congress. [Online] available at http://www.loc.gov/standards/premis/ (Accessed 25 September 2014). • A list of data journals. Trac Integrated SCM and Project Management. [Online] available at http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList (Accessed 25 September 2014). • 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).
  43. 43. References • MARQUES, D. 2013. Research data driving new services. Elsevier Library Connect, 25 February 2013. [Online] available at http://libraryconnect.elsevier.com/articles/best-practices/2013- 02/research-data-driving-new-services (Accessed 5 October 2014) • NORMANDIEU, K. 2013. Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity. Inside Big Data. [Online] available at http://inside-bigdata.com/2013/09/12/beyond-volume-variety- velocity-issue-big-data-veracity/ (Accessed 25 September 2014). • The Oxford Dictionary. [sl.]: Oxford University Press, 2014. [Online] available at http://www.oxforddictionaries.com/us/ (Accessed 16 September 2014). • RAGHUNATHAN, B. 2013. The complete book of data anonymization: from planning to implementation. Broken Sound Parkway, NW: CRC Press, Taylor and Francis Group.
  44. 44. References • Research Data Services, University of Wisconsin-Madison. Madison, WI: University of Wisconsin Madison, 201. [Online] available at http://researchdata.wisc.edu/manage-your-data/data-backup-and-integrity/ (Accessed 24 September 2014). • Responsible conduct of research. DeKalb, 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/dctopic.html. (Accessed: 16 September 2014). • ROUSE, M. 2010. Data archiving. Techtarget. [Online] available at http://searchdatabackup.techtarget.com/definition/data-archiving (Accessed 19 August 2014) • SCHNELL, K. AND SHETTERLEY, N. 2013. Understanding data visualization. [Sl.]: Accenture. [Online] available at http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Tech-Labs-Data- Visualization-Full-Paper.pdf (Accessed 19 September 2014).
  45. 45. References • SIMPSON, J. n.d. Data Masking and Encryption Are Different. IRI Blog Articles. [Online] available at http://www.iri.com/blog/data-protection/data-masking-and-data-encryption-are-not-the-same-things/ (Accessed 18 September 2014). • STRASSER, C. et al. 2012. Primer on data management: what you always wanted to know. [Albuquerque,NM]: DataONE, [University of New Mexico], p1-11. [Online] available at http://www.dataone.org/sites/all/documents/DataONE_BP_Primer_020212.pdf (Accessed 28 August 2013) • 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/g8-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. 46. References • VINOGRADOV, S AND PASTSYAK, A. 2012. Evaluation of data anonymization tools. In: DBKDA 2012 : The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications, held 29 February-5 March, 2012, Reunion Island. Wilmington, DE: International Academy, Research, and Industry Association (IARIA). • What is data management? University Park, PA: Publishing and Curation Services, Penn State University Libraries, 2014. [Online] available at http://www.libraries.psu.edu/psul/pubcur/what_is_dm.html (Accessed 25 September 2014). • YUAN, X. 2014. Visualization and visual analytics. Presentation 0n 12 June 2014 at CODATA International Training Workshop in Big Data for Science for Researchers from Emerging and Developing Countries, Beijing, China, 4-20 June 2014.

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