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D ATA M ANAGEMENT FOR
E DUCATION R ESEARCH
2    G OALS FOR TODAY ’ S SESSION

     In this session you will:
            Understand opportunities and reasons for data
             management planning

            Learn about best practices for collecting and
             organizing research data

            Learn about best practices for protecting privacy
             and confidentiality in data

            Learn about resources and support available to
             researchers at UCLA

    GSE&IS                                                   3/28/2008
3
Data Life-cycle




                  http://www.data-archive.ac.uk/create-manage/life-cycle

                  GSE&IS                                                   3/28/2008
4    D ATA M ANAGEMENT P LANS


            What is a data management plan?
            Why do I need to write one?
            What tools are available to help me?




    GSE&IS                                     3/28/2008
5
                     W HAT IS A DATA
                  MANAGEMENT PLAN ?




    A data management plan is a document that
       describes what you will do with your data
     during your research and after you complete
                    your research




     GSE&IS                                                       3/28/2008
                              From Carly Strasser, Caliufornia Digital Library
6
                     W HAT IS A DATA
            MANAGEMENT PLAN , PART 2

       Elements of a data management plan
                What are your data?

                What formats will you be using?

                How will you describe this data?

                What intellectual property and privacy rights are
                 associated with this data?

                How will you share this data? If you don’t plan on sharing
                 it, why not?

                How much will your data management cost?
        GSE&IS                                                       3/28/2008
7
                      W HY CREATE A DATA
                      MANAGEMENT PLAN ?


        Fulfill requirements from funding
         agencies




        Fulfill requirements from journals
        Regardless of the requirements,
         good data management is an
         essential skill for researchers.
    GSE&IS                                    3/28/2008
8
             W HY ALL THESE NEW
                  REQUIREMENTS ?




    GSE&IS                   3/28/2008
9           TOOLS FOR CREATING A DMP


       DMP Tool - https://dmp.cdlib.org
       ICPSR -
        http://www.icpsr.umich.edu/icpsrweb/conte
        nt/datamanagement/dmp/elements.html
       The UCLA Social Science Data Archive (that’s
        us!) - http://dataarchives.ss.ucla.edu/


        GSE&IS                               3/28/2008
10
                                         P LANNING & ORGANIZING
                                                    DATA COLLECTION

                               What kind of data is being collected?
In planning your research
you should think about


                               What methods will you use to collect the data?
                               How would describe your data so that others can use
                                it without your help?
                               Where do you plan to store your data? If you plan to
these issues:




                                share your data with others, how do you plan to do
                                this?
                               Where can you get training?
                               Where can you get help?
                                GSE&IS                                      3/28/2008
11                 D IFFERENT   FILE TYPES           =
         DIFFERENT DATA MANAGEMENT

        Video          •Project overview
                        •Participants
        Audio
                        •Privacy
        Qualitative    •Intended use now and
        Quantitative         in the future
                        •Equipment and
                              Software
                        •Metadata
          GSE&IS                         3/28/2008
12           D ATA C OLLECTION –                VIDEO

•Create a Project overview
 *Participants and events, main point of the video, structure,
 participant/observer/interviewer relationship
 *Specific problems you hope the video will solve
 *Intended uses; whether or not publicly sharable
 *Consent of participants; Protection of
       privacy/confidentiality
•Choose pre- and post-production or analysis software:
 *open source vs proprietary standards
•Keep detailed metadata – keep lots of copies
            GSE&IS                                    3/28/2008
13                   M ETADATA –            VIDEO
•Type/Format                    •System req, for access
    *DVD, HD, mpeg, mov,              *Windows media
•Run time                             *QuickTime
    *hours, min, sec                  *RealPlayer
•Title                             •Download req.
                                      *size of file
•Producer/author
                                      *software needed
•Date(s)
                                   •Contact info
    *real time video was made
                                   •Persistent identifier
•Location(s)
    *place of production           •Other documentation
    *geographical areas               *annotations, docs
•Content                           •Video clips (if app.)
    *annotations                                            3/28/2008
Data collection – Audio
   14


•As with video, begin with a Project overview
•Equipment - portable audio recorder most useful (pre- and post-
     recording)
• Software for recording and editing
     *Computing specification needed and what is available to you
     *Open source vs proprietary software
• Plan how you will manage during and after project
     *Always keep original file and edited file copies; use highest
     quality possible
     *Web hosting/streaming , CD or DVD storage
     *Licensing and privacy issues
• Maintain your metadata from the very beginning
     *File naming conventions
     *File formats – MP3, WAV, AIFF or lossless FLAC (compressed or
     uncompressed)
                 GSE&IS                                     3/28/2008
15
                        Metadata – Audio
Key pieces of information needed:

• Structural                                • Technical schemas (most important for
    *Relationship to other audio files in   re- use and preservation)
     same project                               *AudioMD
    *Time period                                *Adobe's XMP (Extensible Metadata
    *Geographic and location details            Platform)
                                                *MPEG-7
• Descriptive
    *Title, Creator, Subject, Description of • Embedded
    project, content and Coverage                *Do not rely on this as a metadata
                                                 schema or preservation resource
• Administrative
    *Rights, licensing, who can use

                       GSE&IS                                                 3/28/2008
16           D ATA COLLECTION – QUALITATIVE

    Examples :
                                           Observation field notes/technical
   In-depth/unstructured interviews,
                                            fieldwork notes
    including video
                                           Case study notes
   Semi-structured interviews
                                           Minutes of meetings
   Structured interview
    questionnaires containing              Press clippings
    substantial open comments
                                           Court transcripts
   Focus groups

   Unstructured or semi-structured         File format:
    diaries                                 text , ascii, rtf, etc.
                     GSE&IS                                           3/28/2008
17       D ATA ANALYSIS –         QUALITATIVE

   Dedoose
    *Cross-platform app for analyzing text, video, and
    spreadsheet data (analyzing qualitative, quantitative,
    and mixed methods research).
   NVivo, ATLAS-tiand MAXQDA
    *Organizes projects into raw data, coding tree, coded
    data, and associated memos and notes to be saved
   NUD*IST – no longer really used, prefer Nvivo

                 GSE&IS                              3/28/2008
18              M ETADATA - Q UALITATIVE
   Interviews, transcriptions, oral histories, etc.
    *Unique identifier, a name or number, uniform layout, numbered
    pages
    *Note date, place, interviewer name and interviewee details
    *Use speaker tags, have line breaks between turn-takes
    *Use pseudonyms to anonymize personal identifying information
   Metadata schema QuDEx
    *Analysis software can output to this schema
    *Covers:
       *creator       *method
       *date/time *place
       *size,         *unique identifier
       *codes         *coding structure                      3/28/2008
19
             D ATA COLLECTION - QUANTITATIVE
    Surveys, numerically coded records, spreadsheets

    Variety of methods: f-2-f, phone, mail, web
     *Survey monkey, Qualtrix

    Documented with questionnaire, codebook
     *Question wording, universe, sampling, weighting, unit of analysis,
     geography, time period, coding format/structure,

    Consent of participants; Protection of privacy/confidentiality

    Choose pre- and post-production or analysis software:
     *open source vs proprietary standards

    Keep detailed metadata
                  GSE&IS                                              3/28/2008
20            M ETADATA - Q UANTITATIVE
    Hypothesis, data collection method
    Names of those involved in the project
    File structure: question text, variables, variable names/labels,
     value names/labels, values, frequencies, missing data, recodes,
     branching, interviewer instructions
    Disclosure analysis
    Storage formats, distribution formats , persistent identifier
    Source of data if not from survey
    Source of funding, if any
    Data Documentation Initiative (DDI)
                  GSE&IS                                             3/28/2008
21
                                D ATA P ROTECTION -
                                       CONFIDENTIALITY


             If your research involves human subjects, you will
              need to consider both legal and ethical
              obligations in managing and sharing your data.
             Confidentiality refers to the agreement between
              the researcher and the participant about how the
              participant's identifiable private information will
              be handled, managed, and disseminated.
             As a researcher, you need a clear view about how
              to protect the privacy of your research subjects.
     From: MANTRA: Research Data Management Training
           http://datalib.edina.ac.uk/mantra/dataprotection.html



     GSE&IS                                                        3/28/2008
22           C ONFIDENTIALITY, PART 2

        How to minimize risk of disclosure:
                 If possible, collect the necessary data without using
                  personally identifying information.

                 If personally identifying information is required, de-
                  identify your data upon collection or as soon as possible
                  thereafter.

                 Avoid transmitting unencrypted personal data
                  electronically.

                 Be careful with indirect identifiers.


         GSE&IS                                                     3/28/2008
23
            D ATA M ANAGEMENT R OLLOUT
                                S URVEY




     JISC Data Management Rollout Project Survey Results- 2012- http://damaro.oucs.ox.ac.uk/outputs.xml
            GSE&IS                                                                               3/28/2008
24            I NTELLECTUAL P ROPERTY

             Know your rights as a data producer
              and data consumer.
             Ownership of data
             Three legal mechanisms for sharing
              data:
              1.   Contracts
              2.   Licenses
              3.   Waivers
     GSE&IS                                     3/28/2008
25                                             R ESOURCES
   UKDA tools for creating and managing data:
    http://www.data-archive.ac.uk/media/2894/managingsharing.pdf
   MANTRA – online learning tool/tutorial
    http://datalib.edina.ac.uk/mantra/
   JISC Digital Media Advice
    http://www.jiscdigitalmedia.ac.uk/advice/
     http://www.jiscdigitalmedia.ac.uk/creating
   UCLA SSDA resources for data management
    http://www.sscnet.ucla.edu/issr/da/archive%20tutorial/preparingdata.ht
    ml
   ICPSR resources for documenting and preserving
    http://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/fra
    mework.html
                     GSE&IS                                        3/28/2008
   Qualidatahttp://www.esds.ac.uk/qualidata/about/introduction.asp
26       Q UESTIONS AND D ISCUSSION




     GSE&IS                     3/28/2008

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Data Management for Education Research

  • 1. D ATA M ANAGEMENT FOR E DUCATION R ESEARCH
  • 2. 2 G OALS FOR TODAY ’ S SESSION In this session you will:  Understand opportunities and reasons for data management planning  Learn about best practices for collecting and organizing research data  Learn about best practices for protecting privacy and confidentiality in data  Learn about resources and support available to researchers at UCLA GSE&IS 3/28/2008
  • 3. 3 Data Life-cycle http://www.data-archive.ac.uk/create-manage/life-cycle GSE&IS 3/28/2008
  • 4. 4 D ATA M ANAGEMENT P LANS  What is a data management plan?  Why do I need to write one?  What tools are available to help me? GSE&IS 3/28/2008
  • 5. 5 W HAT IS A DATA MANAGEMENT PLAN ? A data management plan is a document that describes what you will do with your data during your research and after you complete your research GSE&IS 3/28/2008 From Carly Strasser, Caliufornia Digital Library
  • 6. 6 W HAT IS A DATA MANAGEMENT PLAN , PART 2  Elements of a data management plan  What are your data?  What formats will you be using?  How will you describe this data?  What intellectual property and privacy rights are associated with this data?  How will you share this data? If you don’t plan on sharing it, why not?  How much will your data management cost? GSE&IS 3/28/2008
  • 7. 7 W HY CREATE A DATA MANAGEMENT PLAN ?  Fulfill requirements from funding agencies  Fulfill requirements from journals  Regardless of the requirements, good data management is an essential skill for researchers. GSE&IS 3/28/2008
  • 8. 8 W HY ALL THESE NEW REQUIREMENTS ? GSE&IS 3/28/2008
  • 9. 9 TOOLS FOR CREATING A DMP  DMP Tool - https://dmp.cdlib.org  ICPSR - http://www.icpsr.umich.edu/icpsrweb/conte nt/datamanagement/dmp/elements.html  The UCLA Social Science Data Archive (that’s us!) - http://dataarchives.ss.ucla.edu/ GSE&IS 3/28/2008
  • 10. 10 P LANNING & ORGANIZING DATA COLLECTION  What kind of data is being collected? In planning your research you should think about  What methods will you use to collect the data?  How would describe your data so that others can use it without your help?  Where do you plan to store your data? If you plan to these issues: share your data with others, how do you plan to do this?  Where can you get training?  Where can you get help? GSE&IS 3/28/2008
  • 11. 11 D IFFERENT FILE TYPES = DIFFERENT DATA MANAGEMENT  Video •Project overview •Participants  Audio •Privacy  Qualitative •Intended use now and  Quantitative in the future •Equipment and Software •Metadata GSE&IS 3/28/2008
  • 12. 12 D ATA C OLLECTION – VIDEO •Create a Project overview *Participants and events, main point of the video, structure, participant/observer/interviewer relationship *Specific problems you hope the video will solve *Intended uses; whether or not publicly sharable *Consent of participants; Protection of privacy/confidentiality •Choose pre- and post-production or analysis software: *open source vs proprietary standards •Keep detailed metadata – keep lots of copies GSE&IS 3/28/2008
  • 13. 13 M ETADATA – VIDEO •Type/Format •System req, for access *DVD, HD, mpeg, mov, *Windows media •Run time *QuickTime *hours, min, sec *RealPlayer •Title •Download req. *size of file •Producer/author *software needed •Date(s) •Contact info *real time video was made •Persistent identifier •Location(s) *place of production •Other documentation *geographical areas *annotations, docs •Content •Video clips (if app.) *annotations 3/28/2008
  • 14. Data collection – Audio 14 •As with video, begin with a Project overview •Equipment - portable audio recorder most useful (pre- and post- recording) • Software for recording and editing *Computing specification needed and what is available to you *Open source vs proprietary software • Plan how you will manage during and after project *Always keep original file and edited file copies; use highest quality possible *Web hosting/streaming , CD or DVD storage *Licensing and privacy issues • Maintain your metadata from the very beginning *File naming conventions *File formats – MP3, WAV, AIFF or lossless FLAC (compressed or uncompressed) GSE&IS 3/28/2008
  • 15. 15 Metadata – Audio Key pieces of information needed: • Structural • Technical schemas (most important for *Relationship to other audio files in re- use and preservation) same project *AudioMD *Time period *Adobe's XMP (Extensible Metadata *Geographic and location details Platform) *MPEG-7 • Descriptive *Title, Creator, Subject, Description of • Embedded project, content and Coverage *Do not rely on this as a metadata schema or preservation resource • Administrative *Rights, licensing, who can use GSE&IS 3/28/2008
  • 16. 16 D ATA COLLECTION – QUALITATIVE Examples :  Observation field notes/technical  In-depth/unstructured interviews, fieldwork notes including video  Case study notes  Semi-structured interviews  Minutes of meetings  Structured interview questionnaires containing  Press clippings substantial open comments  Court transcripts  Focus groups  Unstructured or semi-structured File format: diaries text , ascii, rtf, etc. GSE&IS 3/28/2008
  • 17. 17 D ATA ANALYSIS – QUALITATIVE  Dedoose *Cross-platform app for analyzing text, video, and spreadsheet data (analyzing qualitative, quantitative, and mixed methods research).  NVivo, ATLAS-tiand MAXQDA *Organizes projects into raw data, coding tree, coded data, and associated memos and notes to be saved  NUD*IST – no longer really used, prefer Nvivo GSE&IS 3/28/2008
  • 18. 18 M ETADATA - Q UALITATIVE  Interviews, transcriptions, oral histories, etc. *Unique identifier, a name or number, uniform layout, numbered pages *Note date, place, interviewer name and interviewee details *Use speaker tags, have line breaks between turn-takes *Use pseudonyms to anonymize personal identifying information  Metadata schema QuDEx *Analysis software can output to this schema *Covers: *creator *method *date/time *place *size, *unique identifier *codes *coding structure 3/28/2008
  • 19. 19 D ATA COLLECTION - QUANTITATIVE  Surveys, numerically coded records, spreadsheets  Variety of methods: f-2-f, phone, mail, web *Survey monkey, Qualtrix  Documented with questionnaire, codebook *Question wording, universe, sampling, weighting, unit of analysis, geography, time period, coding format/structure,  Consent of participants; Protection of privacy/confidentiality  Choose pre- and post-production or analysis software: *open source vs proprietary standards  Keep detailed metadata GSE&IS 3/28/2008
  • 20. 20 M ETADATA - Q UANTITATIVE  Hypothesis, data collection method  Names of those involved in the project  File structure: question text, variables, variable names/labels, value names/labels, values, frequencies, missing data, recodes, branching, interviewer instructions  Disclosure analysis  Storage formats, distribution formats , persistent identifier  Source of data if not from survey  Source of funding, if any  Data Documentation Initiative (DDI) GSE&IS 3/28/2008
  • 21. 21 D ATA P ROTECTION - CONFIDENTIALITY  If your research involves human subjects, you will need to consider both legal and ethical obligations in managing and sharing your data.  Confidentiality refers to the agreement between the researcher and the participant about how the participant's identifiable private information will be handled, managed, and disseminated.  As a researcher, you need a clear view about how to protect the privacy of your research subjects. From: MANTRA: Research Data Management Training http://datalib.edina.ac.uk/mantra/dataprotection.html GSE&IS 3/28/2008
  • 22. 22 C ONFIDENTIALITY, PART 2  How to minimize risk of disclosure:  If possible, collect the necessary data without using personally identifying information.  If personally identifying information is required, de- identify your data upon collection or as soon as possible thereafter.  Avoid transmitting unencrypted personal data electronically.  Be careful with indirect identifiers. GSE&IS 3/28/2008
  • 23. 23 D ATA M ANAGEMENT R OLLOUT S URVEY JISC Data Management Rollout Project Survey Results- 2012- http://damaro.oucs.ox.ac.uk/outputs.xml GSE&IS 3/28/2008
  • 24. 24 I NTELLECTUAL P ROPERTY  Know your rights as a data producer and data consumer.  Ownership of data  Three legal mechanisms for sharing data: 1. Contracts 2. Licenses 3. Waivers GSE&IS 3/28/2008
  • 25. 25 R ESOURCES  UKDA tools for creating and managing data: http://www.data-archive.ac.uk/media/2894/managingsharing.pdf  MANTRA – online learning tool/tutorial http://datalib.edina.ac.uk/mantra/  JISC Digital Media Advice http://www.jiscdigitalmedia.ac.uk/advice/ http://www.jiscdigitalmedia.ac.uk/creating  UCLA SSDA resources for data management http://www.sscnet.ucla.edu/issr/da/archive%20tutorial/preparingdata.ht ml  ICPSR resources for documenting and preserving http://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/fra mework.html GSE&IS 3/28/2008  Qualidatahttp://www.esds.ac.uk/qualidata/about/introduction.asp
  • 26. 26 Q UESTIONS AND D ISCUSSION GSE&IS 3/28/2008

Hinweis der Redaktion

  1. Introduce selves
  2. This is the intro slide – LibbieWelcome everyone and thank for attending. These days most of the research you conduct and the kind of materials you will gather for research will be in digital format. Whether you conduct surveys, conduct focus groups, videotape, analyze transcribed interviews, or create spreadsheets with data points from a variety of sources, most of the time you will be doing so in a digital environment. What we hope for today is that we can give you a brief introduction and perhaps provide you with ways to do research that you can use now and in your future careers.And as some of you already know, as researchers it will be important for you to be able to use your data whenever you want to, or share it with others, or publish it in some form. And many funding agencies urge or even require you to do this. Today we will talk about how you can best organize and manage your data, and we will let you know about the tools and support available to help you do itBut some kinds of data have identifiers that make it difficult to share or re-use. Today we will provide you with some information on the steps you can take to protect privacy and confidentiality. And finally we are here for you to ask us questions and, we hope, get answers that help you move forward with your work.
  3. I thought I would show you an interactive version of the data life cycle. You can use it when you plan your research and we will be going through these sections in our talk today. And then we will begin with Rebekah discussing data management plans.Go to demo at UKDA http://www.data-archive.ac.uk/create-manage/life-cycle
  4. One of the reasons that we are here today is to learn how to create a data management plan. By a show of hands, how many of you know how to create a data management plan? You guys are researchers. You collect data, you analyze it, you write about it, hopefully you publish the results of that analysis. Where does a data management plan fit into all that? In this part of the presentation we’re going to talk about what a data management plan is, why you need to know how to write one, and the tools that are available to help you do that.
  5. First what is a data management plan? “A document that describes what you will do with your data during your research and after you complete your research.” Basically, it is a document that describes the lifecycle of your data. An important thing to remember though is that a data management plan is a live document that is never finished. You should review your plan regularly throughout your project and make adjustments when necessary.
  6. So, what is in a data management plan? What do the granting agencies want to see?We’ll talk more about these individual elements throughout the presentation, but some of the questions you need to think about when writing a data management include (read slide)  These are important questions for a number of reasons and if you don’t consider them at the beginning of your project, it may be too late to go back and fix it later.
  7. -Funding agencies requirements- The most compelling reason is that several funding agencies, including the National Science Foundation, are now requiring data management plans to be included as a part of your research proposal. This is not a daunting requirement; it is a two-page explanation of the lifecycle of your data and we are here to help youJournal requirements- The second reason is that some journals are now requiring data along with submission of your publication, the most notable example being the journal Nature. -Essential skill for researchers- Good data management ensures the integrity and reproducibility of your research results. If someone accuses you three years after publication of falsifying your research, what are you going to show them to prove you did your research honestly and accurately? Good data management and preservation will allow you to show them exactly how you moved through your research, what steps you took, what tools you used, and what decisions you made. It allows for reproducibility which is the gold standard of science. Additionally, good data management protects you from data loss and enhances your data security. In a way, we should be grateful for these new requirements because it encourages us to do better, more transparent research and perhaps even saves you time and money in the long run.
  8. Data collection is usually the most expensive part of research. Funding agencies are hoping to maximize their investments by making data available for reanalysis and secondary use. Unfortunately, current science practices make it difficult, if not impossible to reuse data. Data gets lost, computers crash, researchers don’t document their data so that others can use their data or replicate findings. The goal of these data management plans, is to manage data so that it can be shared and ultimately reused for future research. When you share your data and other people can use it that means you get credit for your data through Data citation. New metrics being developed for impact factors. NSF Bio Sketch has recently changes in Include “Research Products” not just “Research Publications.” Studies have already shown in astronomy and physics that when data is released with a publication, the publication is cited more often. It can be trusted.
  9. All of these tools are free and available to UCLA graduate students. DMP Tool from the California Digital Library. Will give step by step instruction and guidance in writing a DMP. Shows examples of data management plans. Useful for very general information.ICPSR- trusted resource for data management and may be an option for a digital repository. Not only will they work with you in depositing your data, but their website a great resource for learning more about data management plans. The UCLA Social Science Data Archive. That’s us! Libbie and I are in Rolfe Hall and we are a free and personal resource for the UCLA community. Libbie can help direct you to the best formats, the best repositories, best practices. Feel free to use us when you need to put a DMP together. It could be the element of your proposal that helps you stand out. Next, Libbie is going to walk you through best practices for your data lifecycle and the best ways to manage your research data.
  10. So, as you probably know from other aspects of your life, the more you plan up front and the better organized you are, the easier things go. This is true in research as well.Notice that these questions are similar to those you should address in a data management plan. Remind: talk to the Archive from the very beginning of the project and when preparing the data management plan. Archive can advise on what steps to take and what resources there are for help, whether it is in finding software tools, organizing data, deciding how to manage the data during the project and after, and what you would need to do to be sure the data can be preserved for the long term.As we go through these, and as you think of your own projects, consider these questions:Could the files be useful as a long-term resource?Will the files need to be accessed at a later date?Do the files have any significant value (intellectual or financial)?If the files have little or no value at present, could this change in time?
  11. So now, let’s get specific: We can’t cover all the how-to’s today but we wanted to give you some specifics on managing data for some key file formats many of you will use, and links to resources. And remember that we are here to help you as you go along with your project.Each kind of data you produce has particular requirements for you to keep in mind when you are first collecting your data, when you or organizing your data during a project, and if you do this, you will have materials that will be much easier to preserve for the long term. So, we’ll cover features of video, audio, qualitative and quantitative files.
  12. So let’s talk about video … here are some key items to prepare at the beginning of your project about collecting your data and describing what you will do with it once you are finished. You will need these pieces of information when you prepare a DMP. There are video archives at some research universities and some social science archives also handle video materials. The best place I know of for managing education research videos, such as video of classroom settings, teachers, children’s activities, etc. is http://drdc.uchicago.edu/what/video-research.html Data Research and Development Center at University of Chicago. They have published a fantastic guide that is also on their site.
  13. You should plan to get in the habit of documenting all aspects of your data collection and organization. The term for this kind of information is “Metadata”. So one of the most important aspects of managing you video files is to ensure you have kept track of the details listed here – or metadata. You will need to describe these in your data management plan. You can use a spreadsheet to write down this kind of detail.If you can, use care in choosing equipment and record in formats that can be easily maintained for the long term. Any control you can assert over the process will be important. The JISC guides are a big help and provide lots of detail.http://www.jiscdigitalmedia.ac.uk/creatingMetadata schemas:Gateway to Educational Materials (GEMs) instructional topics hierarchy (www.thegateway.org/),Sharing: resource list of archvies at the Open Video Project http://www.open-video.org/Many times a researcher will choose to share data via a website they have designed. This is fine as long as the researcher can make a long term commitment to keep the website up. Some decide to store files on a departmental server. This approach will not suffice in a DMP. You have to be able to specify a specific preservation approach and a place where the files will be maintained over the long term.Youtube is not an archival long term preservation site – prefer Vimeo http://vimeo.com/Best practice is to make several copies and keep in more than one place.
  14. Now let’s turn to audio. This format for collecting audio materials, would most likely be recorded interviews, oral histories, recordings of focus groups, discussion, etc. As with video, you need a project overview and it should contain the following details, for each audio file that is part of a project. http://audacity.sourceforge.net free open source audio management tool - if just recording interviews can usually go with cheapest/simplest tools such as Adobe audition 3.0 In case they ask: The advantage of lossy methods over lossless methods is that in some cases a lossy method can produce a much smaller compressed file than any lossless method, while still meeting the requirements of the application. Lossy methods are most often used for compressing sound, images or videos.
  15. These are the kinds of information that you need to document for your recordings; this is so that an archive or a future user with whom you share the data, can understand what you did and how to use the material. ALA document on audio http://www.ala.org/alcts/sites/ala.org.alcts/files/content/resources/preserv/audio_metadata.pdfAs with video, the metadata you keep needs to be kept for each recording and you can do this in a spreadsheet or use text processing tool. Internet Archive, Library of Congress have archives of audio materials; may also be available at research institutions.
  16. Qualitative data is somewhat difficult to characterize and the standards for collecting an organizing vary depending on the project. Here are some examples, taken from the ICPSR and UKDA websites on the types of data considered “qualitative”. Two recognized archives for qualitative data are Qualidata in the UK and the Henry Murray Center in the US. Audio and even video files can be transcribed and analyzed using qualitative software tools.Qualidata http://www.esds.ac.uk/qualidata/about/introduction.aspHenry Murray Center http://www.murray.harvard.edu/So when you are preparing a data management plan, you can specify the archive into which you will deposit your data. Both Qualidata and the HMC are recognized by funding agencies as employing best practices for long term data management.
  17. I am mentioning these because you need to work with a tool that is robust enough to handle your data and also output in formats that are archivable. There are several techniques used to analyze – try to save out your data in non-software dependent format – all of these packages will output a migratable format for preservation including: raw data, coding tree, coded data, and associated memos and notes to be saved.We are interested to know if people would like to have workshops in how to use these tools.
  18. ESDS Qualidata is working to encourage the development of data documentation standards using XML. The Data Exchange Tools and Conversion Utilities (DExT) project proposed an XML schema, QuDEx, to represent annotated and complex multimedia data.This is just an example of the kinds of information you need to record, and much of this will be in your audio or video files already. Each transcript needs to be well described and documented separately from the transcript itself. Again, use consistent file naming conventions.
  19. So quantitative data is usually thought of as survey data, but it can also refer to spreadsheet or other numerically coded material, for example, administrative records. There are a number of ways that surveys are carried out and lots of people like to use free or nearly free tools for web surveys. One key consideration is that these free tools rarely let you output datasets in a format that you can use for preservation; this is often only available from the paid versions. If you think you will do this kind of work frequently it is worth investing in the paid version of these tools.In preparing your data management plan, you should consider the way you will be collecting the data.
  20. These are the recommended points to address when you are managing your survey data. Ask how many people use surveys as a data gathering method? Survey data or numerical statistical files need a huge amount of documentation if you plan to share. And in your data management plans you need to address each of these areas. As Rebekah said at the beginning of the session, ICPSR is the best place to go for help in developing a data management plan for survey data. (Bring up web site again if needed) http://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/elements.htmlhttp://www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/framework.html
  21. This will be my section on Data Protection including Confidentiality and Intellectual Property RightsDMPs are written prior to IRB review. This gives you a chance to think about how you will treat the confidentiality of your subjects, how you will obtain informed consent, how you will protect their identity after data collection and publication. Some repositories, such as the UCLA SSDA and ICPSR offer dark archives as well open access. Make sure that the repository that you choose supports restricted access if you need it. Some archives will put the identified data in a dark archive and make a de-identified version of the dataset available to other researchers. Think about whether or not this is an option for you. **keep in mind that each file format you use (audio, video, statistical file) and each kind of data collection (oral history, recorded interview) will have different challenges for you as you protect privacy and confidentiality.
  22. There are best practices for keeping identifiable data secure Note: Be careful about indirect identifiers. Some data repositories will allow you to keep a copy of identifiable data in a dark archive while making available a copy of your data stripped of identifiers.
  23. Intellectual Property rights are always murky, but even more so with data. The most important thing to remember is that facts are not covered by copyright and, in most discussions of intellectual property and data, most discussions of IP law as it relates to data, treat data as being synonomous with facts. However, if they are arranged and selected in certain ways they may be. It is difficult at times to know what data you can use, for what purposes, and how you should cite the data. Similarly, when you create a data set you want to make it clear to others how they can cite your data and what they can do with the dataset. If you are working for the University of California, they own your data. However, as a data collector you have rights to say how your data should be shared, used, and cited, assuming those rights weren’t already established in your grant. As previously mentioned, some granting agencies have data sharing requirements. There are three legal mechanisms for sharing your data: licenses, contracts, and waivers. Discuss terms of use. Creative commons.
  24. We have covered a huge amount of information in a very short time and we want you to know that we are here to help you as you proceed with you work. You can meet with us one-on-one and we encourage you to do this as you begin your work.On this page are the key resources we have used in our presentation and you can refer back to them at any time.Explain why each is included.And now we can take any questions you may have.
  25. Thanks, etc.