Research Data Management Training Manual

Transcription

1 Research Data Management Training Manual i

2 The World Agroforestry Centre, an autonomous, non-profit research organization, aims to bring about a rural transformation in the developing world by encouraging and enabling smallholders to increase their use of trees in agricultural landscapes. This will help to improve food security, nutrition, income and health; provide shelter and energy; and lead to greater environmental sustainability. We are one of the 15 centres of the Consultative Group on International Agricultural Research (CGIAR). Headquartered in Nairobi, Kenya, we operate six regional offices located in Brazil, Cameroon, India, Indonesia, Kenya and Malawi, and conduct research in 18 other countries around the developing world. We receive funding from over 50 different investors. Our current top 10 investors are Canada, the European Union, Finland, Ireland, the Netherlands, Norway, Denmark, the United Kingdom, the United States of America and the World Bank. World Agroforestry Centre, Nairobi, Kenya, Publisher: World Agroforestry Centre Compilation: Leroy Mwanzia Design and Layout: Martha Mwenda World Agroforestry Centre United Nations Avenue, Gigiri P. O. Box Nairobi, Kenya. Phone + (254) Fax + (254) Via USA phone (1-650) Via USA fax (1-650) [email protected] Website: ii Data Management Training

3 Contents 1 ABOUT THE COURSE INTRODUCTION OBJECTIVES LEARNING OUTCOMES INTRODUCTION TO DATA MANAGEMENT CONCEPTS NEED FOR DATA MANAGEMENT DATA MANAGEMENT PLANNING NEED FOR DATA MANAGEMENT PLAN CREATING A DATA MANAGEMENT PLAN LOCAL DATA MANAGEMENT LOCAL FOLDER MANAGEMENT VERSIONING FILE FORMATS DATA STORAGE DATA BACKUP DATA ENTRY PEN DATABASE CASE OPENING THE DATABASE FORMS WHEN TO USE FORMS AND TABLES ENTERING DATA VALIDATION NAVIGATING RECORDS DATA TYPES MODIFYING DATA EDITING RECORDS DELETING RECORDS DATA DOCUMENTATION AND METADATA INTRODUCTION PROJECT LEVEL DOCUMENTATION DATA LEVEL METADATA DATA SHARING AND ARCHIVING WHY SHARE RESEARCH DATA HOW TO SHARE YOUR DATA ADVANTAGES OF DATA ARCHIVING OPEN DATA OWNERSHIP AND INTELLECTUAL PROPERTY INTELLECTUAL PROPERTY AND DATA MANAGEMENT INTRODUCTION TO DATAVERSE INTRODUCTION TO DATAVERSE DATAVERSE FEATURES USING DATAVERSE ACCESSING DATAVERSE CREATING A NEW STUDY ADDING META DATA ADDING FILES PERMISSIONS SUBMITTING THE STUDY FOR REVIEW DEACCESSION OF A STUDY REFERENCES

4 CGIAR Research Program 6 - Forests, Trees and Agroforestry: Livelihoods, Landscapes and Governance A third of our planet is covered with forests and about 1 billion people depend on forest resources for their everyday lives. Forests are a nutritional bounty and provide essential services to mainstream agriculture. Without them, future food supplies will be compromised. Forests play a vital role in slowing the pace of climate change through carbon storage and in helping countries adapt to severe weather events. Yet, in the time it takes to read this webpage, an area of forest roughly equal to 100 football fields (45 hectares) will have been cleared to make way for agriculture, mining, pastures and other nonforest uses, or will have been degraded by unsustainable and illegal logging and other poor land-use practices. The CGIAR Research Program - Forests, Trees and Agroforestry: Livelihoods, Landscapes and Governance responds to a call for an urgent, strong and sustained effort focused on forest management and governance, given the crucial role of forests in confronting some of the most important challenges of our time: climate change, poverty, and food security. The Center for International Forestry Research leads the program in partnership with Bioversity International, the International Center for Tropical Agriculture and the World Agroforestry Centre. The centers collaborate with leading national research institutes and other organizations. They partner with knowledge-sharing experts to maximize outreach and share research results with policy and practitioner partners, who can use and share this knowledge on the ground in the developing world. The program is made up of five research components: Smallholder production systems and markets, with a focus on boosting the productivity and sustainability of forestry and agroforestry, increasing incomes in forested areas, and improving policies and institutions that affect land rights for the rural poor; Management and conservation of forest and tree resources, which involves research into threats to important tree species, conserving high-value tree species, improving silviculture practices, and developing ways to resolve conflicts over resource rights; Landscape management of forested areas for environmental services, biodiversity conservation and livelihoods, which explores the drivers and consequences of forest transition in which deforested and degraded lands are restored for environmental goods and services; 2 Data Management Training

5 Climate change adaptation and mitigation, which considers how forests, trees and agroforestry can play a role in climate change mitigation and also how they can help people adapt to climate change; and Impacts of trade and investment on forests and people, which seeks to understand the effects of forest-related trade and investment and to improve efforts to mitigate the negative and enhance the positive impacts. Impact driven and innovative, the program s eventual impact will enhance the management and use of forests, agroforestry and tree genetic resources across the landscape, from farms to forests. The initiative will target 46% of global forest cover, 1.3 billion hectares of closed forests and 500 million hectares of open and fragmented forests. 3

6 1 About the Course 1.1 Introduction This document is a description of an introductory Data Management Training Course, organised by the World Agroforestry Centre. Data Management is a key component of every project that is carried out under the CGIAR Research Program 6 Forests, Trees and Agroforestry (CRP6). All research data generated from the projects should be of high quality and managed efficiently. Research data, like all research output should therefore be delivered to the people who need them - scientists, practitioners, donors, development agencies, policymakers, media and NGOs- while adhering to these standards. The main aim of this training is to encourage research scientists and research projects to allocate resources to data management and data sharing as a tool for scientific knowledge sharing. 1.2 Objectives The objectives of the data management training are to: a) Provide CRP6 partners with the necessary understanding and capability to handle data in this project as well as in future projects. b) Promote the use of dataverse as an easy and economic application for in-house data management and archiving. c) Assist the partners in developing a Research Data Management Plan for each project 1.3 Learning outcomes On completion of the training course the participants should be able to. a) Recognise the importance of good practice in managing research data in general and apply it within their own work context. b) Apply knowledge gained to be able to draw up a data management plan and maintain it throughout the project life. c) Be able to organise and document their data efficiently during the course of a project. d) Be aware of the options available for securely storing and backing up data. e) Use Dataverse to manage and archive their research data. 4 Data Management Training

7 2 Introduction to Data Management Think Ahead Quiz: What is Data? True or False: In research projects is only the information and observations made as part of scientific inquiry considered research data? Answer: False. Data also includes documents, procedures, scripts and all data elements that comprise research observation, findings or outcomes including primary materials and analysed data. Therefore questionnaires, methodologies, scripts and models are also considered research data. 2.1 Concepts Terms commonly used in data management are usually defined differently depending on the context and field of study. For this training we approach data management in the context of doing research projects. Data are raw facts and statistics collected together for reference or analysis. Research data is data that is collected, observed, or created, for purposes of analysis to produce original research results. Research data would include all data that comprise research observations, findings or outcomes, including primary materials and analysed data. Research data can be classified into 4 types of data [1]. 1. Observational: data captured in real-time, usually irreplaceable, examples: Sensor data, telemetry, survey data, sample data, neuroimages. 2. Experimental: data from lab equipment, often reproducible, but can be expensive, examples: gene sequences, chromatograms and toroid magnetic field data. 3. Simulation: data generated from test models where model and metadata (inputs) are more important than output data, examples: climate models, economic models. 4. Derived or compiled: data that is reproducible (but very expensive), examples: text and data mining, compiled database, 3D models, data gathered from public documents Research data may include all of the following: Text or Word documents, spreadsheets Models, algorithms, scripts Laboratory notebooks, field notebooks, diaries Questionnaires, transcripts, codebooks Audiotapes, videotape, photographs, films Test responses Slides, artefacts, specimens, samples Data files Database contents including video, audio, text, images 5

8 Contents of an application such as input, output, log files for analysis software, simulation software, schemas Methodologies and workflows Standard operating procedures and protocols Research Data Life Cycle There is a need to manage data throughout the research data life cycle shown in Figure 1 below. Figure 1: Research Data Cycle (UK Data Archive. 2012) Different data management activities are carried during the stages of the life cycle. These activities are listed below. Creating data Designing the research Plan data management (formats, storage etc.) Plan consent for sharing Collect data (experiment, observe, measure, simulate) Capture and create metadata 6 Data Management Training

9 Processing data Enter data, digitize, transcribe, translate Check, validate, clean data Anonymise data where necessary Describe data Manage and store data Analysing data Interpret data Derive data Produce research outputs Author publications Prepare data for preservation Preserving data Migrate data to best format Migrate data to suitable medium Back-up and store data Create metadata and documentation Archive data Giving access to data Distribute data Share data Control access Establish copyright Promote data Re-using data Follow-up research New research Undertake research reviews Scrutinize findings Teach and learn Data Management is the processes involved in creating, obtaining, transforming, sharing, protecting, documenting and preserving data. [3] Data management therefore includes all activities associated with data other than the direct use of the data itself. It may include things like; 7

10 data entry data cleaning data organisation data backups data archiving data sharing or publishing data security and confidentiality 2.2 Need for Data Management Preserving research data Proper management, archiving and sharing of data ensures that your research data will be available to you and other researchers for a long time. Preserving the evidence of your writings ensure you preserve your unique contribution to research. It also ensure you research will not fall prey to information entropy. Figure 2: Information Entropy (Michener et al. 1997) 8 Data Management Training

11 Meeting funding Agency or Partnership agreement requirement Many funding agencies and partnership agreements require researchers to share data usually by depositing it in an archive. Some funding agencies are increasingly requiring data management plans as part of the project proposal. Figure 3: Proposal Requirement Figure 4: Program Participant Agreement 9

12 Increasing your research efficiency and saves time Documenting your data throughout its life cycle saves time because it ensures that in the future you and others will be able to understand and use your data. Also careful planning of data management at the start of the project ensures that time is not wasted when preparing data for analysis e.g. statistics and modelling. Planning for your data management needs ahead of time will also save you time and resources in the long run. Ensure data quality Planned data management ensures that research data and records are accurate, complete, authentic and reliable. This is done by putting quality checks or procedures at every stage of the data life cycle. Facilitating science through interoperable discovery and access Making your data available to other researchers through searchable repositories ensures that other scientists can build on your work and therefore preventing duplication of effort. It reinforces open scientific enquiry and can lead to new unanticipated discoveries. Increases visibility of your research Sharing you data also promotes your work and demonstrates continued use to the data and relevance of the research. It can also provide direct credit to the researcher as a research output in its own right. Exercise: Data Life Cycle In this activity you will be considering the data life cycle of a research project. Think of your past or current research project and try and identify the data management activities or procedures you think should be or should have been carried out at each stage of the data life cycle. See an example of a past project below. 10 Data Management Training

13 Figure 5: Example project life cycle ( 11

14 3 Data Management Planning Think Ahead Quiz: Should data management planning be thought of a simple administrative task that is done at the beginning of the study, with little or no intention to implement planned data management measures? Answer: At the moment in the research cycle, the cost of implementing late data management sharing measures can be prohibitively high. Implementing data management measures during the planning and development stages of research will avoid later panic and frustration. Many aspects of data management can be embedded in everyday aspects of research co-ordination and management and in research procedures. A data management plan is a formal document you develop at the start of your research project which outlines all aspects of your data i.e. what you will do with your data during and after your research project. Data management planning helps you ensure your research data is accurate, complete, reliable, and secure both during and after you complete your research. Your data management plan may describe: What research data you will be creating or collecting. Who will be responsible for each aspect of the management plan you are developing. What policies (funding, institutional, and legal) will apply to your data. How will data be organised (folder structures, file naming conventions, file versioning). How will data be documented during the collection and analysis phase of your research. What data management practices (backups, storage, access control, archiving) you will be using to store and secure your data. What facilities and equipment will be required (hard-disk space, backup server, repository). Who will have ownership and access rights to your data. How will the data be preserved and made available in the long term once your research is completed. [4] 3.1 Need for data management plan A goal without a plan is just a wish. Antoine de Saint-Exupery( ) To realize all the benefits of data management we need to plan for it. However some funding bodies may require a data management plan. 12 Data Management Training

15 3.2 Creating a data management plan Data management plan checklist What type of data will be produced? Will it be reproducible? What would happen if it got lost or became unusable later? How much data will it be, and at what growth rate? How often will it change? Who will use it now, and later? Who controls it (Principal investigator, student, lab, Institution, funder)? How long should it be retained? e.g. 3-5 years, years, permanently Are there tools or software needed to create/process/visualize the data? Any special privacy or security requirements? e.g., personal data, high-security data Any sharing requirements? e.g., funder data sharing policy Any other funder requirements? e.g., data management plan in proposal Is there good project and data documentation? What directory and file naming convention will be used? What project and data identifiers will be assigned? What file formats? Are they long-lived? Storage and backup strategy? When will I publish it and where? Is there ontology or other community standard for data sharing/integration? Who in the research group will be responsible for data management? Data Management Plan Components A simple data management plan may include the following components. [4] A. Context Basic project information: Name of the project Aim & purpose of the project Funding body/bodies Duration Partner institutions 13

16 Data collection: What kind of data will be created or captured? (Data description, including anticipated volume, type, content to be created). How will the data be collected? Have you surveyed existing data, in your own institution and from third parties? What existing datasets could you use or build upon? Related policies: Funding body requirements. Institutional or research group guidelines. Responsibilities: Your responsibilities as a researcher. Staff/organisational (PIs, supervisors, colleagues, Project Manager, School etc.) roles and responsibilities for implementing this plan. Adherence When will adherence to this data management plan be checked or demonstrated? Who will do this? How and when will this data management plan be reviewed? B. Organising data & file formats File structure Folder structure File and folder naming conventions Versioning File formats File transformation C. Documentation and metadata Some examples of data documentation: Laboratory notebooks & experimental protocols Questionnaires, codebooks, data dictionaries Software syntax and output files Information about equipment settings & instrument calibration Database schema Methodology reports Provenance information about sources of derived data 14 Data Management Training

17 Metadata What contextual details are needed to make the data you capture or collect meaningful? How will you create or capture these metadata? What form will the metadata take? To what extent will metadata creation be automated? Which metadata standards will you use? D. Storage and security Storage Where (physically) will you store the data? On what media will you store the data? Whose responsibility is the storage of the data? How will you transmit the data, if required? Back-up How will you back up the data How regularly will backups be made? Whose responsibility will this be? Security How will you manage access arrangements and data security? How will you enforce permissions, restrictions and embargoes? Other security issues E. Data protection, rights and access Ethical and privacy issues Are there ethical and privacy issues? If so, how will these be resolved? Confidentiality. Is the data personal data in terms of the Data Protection Act 1998 (the DPA)? What have you done to comply with your obligations under the DPA? IPR (Intellectual Property Rights) Is the dataset covered by copyright or the Database Right? If so, who owns the copyright and other intellectual property? How will the dataset be licensed if rights exist? 15

18 F. Preservation, sharing and licensing What is the long-term strategy for maintaining, curating and archiving the data? On what basis will data be selected for preservation? How long will (or should) data be kept beyond the life of the project? How will you dispose of/transfer sensitive data? Which archive/repository/central database/ data centre have you identified as a place to deposit data? Appraisal and retention timeframes (ideally with definite figures) What transformations will be necessary to prepare data for preservation / data sharing? What related (representation) information will be deposited? Data Management Plan tools and template Normally you will not have to create a data management plan from scratch. Most funding agencies or institutions that require a data management plan will provide tools and/or templates to assist you in creating a data management plans. The Digital Curation Centre (DCC) provides an online tool, DMP Online ( to enable you to build and edit DMPs with a view to the requirements stipulated by the major UK funders. They also have a generic template in the tool that can be used for any project. The tool itself requires registration. However, the DMP template and the DMP Checklist are both available without registration ( The DCC data management plan template is provided as a hand-out in this training. Exercise: Use the DCC data management plan template to help point out relevant data management topics you should consider when planning this project. 16 Data Management Training

19 4 Local Data Management Think Ahead Quiz: What is in a name? Does how you name your research files matter in data management? Answer: Clear, unique and descriptive file names that describe the contents of the file are important for effective data management. This is especially true when you are dealing with multiple people working on the project. 4.1 Local Folder Management A well organised folder structure and clear, descriptive and unique file and folder names makes it easier to find and keep track of data files. Good file management practises are required to enable you to identify, locate and use your research data files efficiently and effectively. [4] File and Folder names should be constructed for easy management by various data systems. The following are some general guidelines for folder and file names: Names should contain only numbers, letters, dashes, and underscores - no spaces or special characters. Lower-case names are less software and platform dependent and are preferred. If you use mixed case file names (for readability), make sure that you do not have two filenames which differ only by case. When choosing a name, check for any system limitations on the use of special characters and file name length. For practical reasons of legibility and usability, file names should not be more than 64 characters in length, usually you can construct a meaningful name with less than 25 characters File Naming Good file names can provide useful cues to the content and status of a file; they can uniquely identify a file and can help in classifying files. File names may contain information such as project acronyms, study title, location, investigator name or initials, year(s) of study, data type, version number, date and file type. Do not use generic data file names that may conflict when moved from one location to another. Ensure filenames are independent of location and if you work on more than one computer ensure that your files are synchronised. Consider how scalable your data file naming policy needs to be e.g. if you want to include the project number, don t limit your project number to two digits, or you can only have ninety nine projects. 17

20 Best practice for naming files is to: create unique, meaningful but brief names The file name should reflect the contents i.e. Identify the activity or project in the file name use file names to classify broad types of files avoid using spaces and special characters avoid very long file names Examples of Bad File Names Mydata.xls, 2001_data.csv, best version.txt Figure 6: Story Told by File Names (Federation of Earth Science Information Partners. 2012) 18 Data Management Training

21 Better File Name LandscapeMosaic _TZ_2000_ HHIncome.xls LandscapeMosaic Project Name TZ Site Name Year HHIncome What was measured xls File type Benefits of consistent file naming The benefits of consistent and unique data file labelling are: Data files are distinguishable from each other within their containing folder Data file naming prevents confusion when multiple people are working on shared files or when files from different investigators are combined in a directory or FTPsite. Data files are easier to locate and browse Data files can be retrieved not only by the creator but by other users Data files can be sorted in logical sequence Data files are not accidentally overwritten or deleted This enables precise search and discovery of particular files. Different versions of data files can be identified If data files are moved to other storage platform their names will retain useful context Folder Structure Think carefully on how best to organise your folder structure so as to make files easy to locate, this is especially true when working in a collaborative environment. Below we have outlined some general guidelines on folder structure. When organizing files, directory top-level folder should include the project title, unique identifier, and date (year). The substructure should have a clear, documented naming convention; for example, each run of an experiment, each version of a dataset, and/or each person in the group. [1] 19

22 Figure 7: Good folder structure (UK Data Archive. 2012) 20 Data Management Training

23 4.2 Versioning It is important to ensure that different copies or versions of files held in different formats or locations, and information that are cross-referenced between files are all subject to version control. It can be difficult to locate a correct version or to know how versions differ after some time has elapsed. A version control strategy depends on whether files are used by single or multiple users, in one or multiple locations and whether or not versions across users or locations need to be synchronised or not. It is also important to keep track of master versions of files, for example the latest iteration, especially where data files are shared between people or locations, e.g. on both a PC and a laptop. Checks and procedures may also need to be put in place to make sure that if the information in one file is altered, the related information in other files is also updated. Best practice is to: decide how many versions of a file to keep, which versions to keep, for how long and how to organise versions identify milestone versions of files to keep uniquely identify files using a systematic naming convention record version and status of a file, e.g. draft, interim, final, internal record what changes are made to a file when a new version is created record relationships between items where needed, e.g. relationship between code and the data file it is run against; between data file and related documentation or metadata; or between multiple files track the location of files if they are stored in a variety of locations regularly synchronise files in different locations, e.g. using MS SyncToy software maintain single master files in a suitable file format to avoid version control problems associated with multiple working versions of files being developed in parallel identify a single location for the storage of milestone and master versions of files Turn on versioning or tracking in collaborative documents or storage utilities such as Wikis, GoogleDocs etc Consider using version control software e.g. Subversion, TortoiseSVN Examples of file versions date recorded in the file name or embedded within the file HealthProj_Kisumu_ version numbering in the file name (v1, v2, v3 or 00.01, 01.00) BGHSurveyProcedures_00_04 version description in the file name or embedded within the file (draft, final) FoodInterview_1_draft FoodInterview_1_final [8] 21

24 Some structured examples of maintaining version control [document name] [version number] [status: draft/final]: Smith_interview_July2010_V1_DRAFT Lipid-analysis-rate-V2_definitive2001_01_28_ILB_CS3_V6_AB_edited [4] Figure 8: Example Version Control Data table. Source - UK Data Archive 4.3 File Formats A file format describes the way information is organised or encoded in a computer file. A program or application must be able to recognise the file format in order to access data within the file. All digital information is designed to be interpreted by computer programs to make it understandable and is - by nature - software dependent. All digital data are thus endangered by the obsolescence of the hardware and software environment on which access to data depends. Despite the backward compatibility of many software packages to import data created in previous software versions and the interoperability between competing popular software programs, the safest option to guarantee long-term data access and usable data is to convert data to standard formats that most software are capable of interpreting, and that are suitable for data interchange and transformation. [7] Formats that are most likely accessible in the future are: Non-proprietary Open, documented standard Common usage by research community Standard representation (ASCII, Unicode) Unencrypted Uncompressed 22 Data Management Training

25 Examples of these formats are OpenDocument Format (ODF), ASCII, tab-delimited format, commaseparated values, XML - as opposed to proprietary ones. Some proprietary formats, such as MS Rich Text Format, MS Excel, SPSS, are widely used and likely to be accessible for a reasonable, but not unlimited, time. Examples of preferred formats Documents PDF/A or Open Document Format text (.pdf, odt), not MS Word Tabular data Delimited ASCII text e.g. CSV (.csv,.txt,.tab) Open Document Spreadsheet (.ods), not MS Excel Images Tiff (.tif), not JPEG Digital Audio Free Lossless Audio Codec (FLAC -.flac), not MP3 Digital Video MPEG-4(.mp4), not Quicktime File format Conversion Data may need to be converted from the original format to a preferred data preservation format in preparation for long-term storage. When data are converted from one format to another - through export or by using data translation software - certain changes may occur to the data. It is important for you to understand what is at risk for the type of data you are working with. Potential risks for loss or corruption on conversion or migration to new media: For data held in statistical packages, spreadsheets or databases, some data or internal metadata such as missing value definitions, decimal numbers, formulae or variable labels may be lost during conversion to another format, or data may be truncated For textual data, editing such as highlighting, bold text or headers/footers may be lost. For other numeric files: special characters (such as quotation marks), end of line returns, last characters in rows (due to row size limitations), last rows (due to row number limitations) For database files: as numeric files, but also relations between items in a table and between tables. For Image files: loss of layers, colour fidelity, resolution, sound quality, etc. For Multimedia: as image files, but attention to frame rates, codecs and wrappers is needed. 23

26 4.4 Data Storage Through the course of your research you must ensure that all your research data, regardless of format, is stored securely, backed up and maintained regularly. You should estimate the volume of data required for your project at an early stage, probably, while drawing up your data management plan. It is also a good idea to consider including costs for data storage in funding proposal. Data storage is crucial to a research project for the following reasons: Properly storing data is a way to safeguard your research investment. Data may need to be accessed in the future to explain or augment subsequent research. Other researchers might wish to evaluate or use the results of your research. Stored data can establish precedence in the event that similar research is published. Storing data can protect research subjects and researchers in the event of legal allegations. Type and Amount of Data Key considerations for data storage are: Thorough documentation to allow data to be appropriately used in the future Storage format that is easily adaptable to evolving computer hardware and software Rapid access to the data Fast read/write rates Low cost Ability to archive the data Removability A backup system, such as storing data on CDs [6] Digital Data Storage Media Networked Drives Networked drives are managed by staff centrally or within your School, College or organization. It is highly recommended that you store your research data on regularly backed up networked drives such as: Fileservers managed by your research group or school. Fileservers managed by Information Services. This way you will ensure that your data will be: Stored in a single place and backed up regularly. Available to you as and when required. Stored securely minimising the risk of loss, theft or unauthorised use. External Storage Devices External storage devices such as hard drives, USB flash drives, Compact Discs (CDs) and Digital Video Discs (DVDs), can be an attractive option for storing your data due to their low cost and portability. However, they are not recommended for the long term storage of your data, particularly, your master copies as: 24 Data Management Training

27 Their longevity is not guaranteed, especially if they are not stored correctly, for example, CDs and hard drives degrade; tapes shrink in the long term. They can be easily damaged, misplaced or lost. Errors with writing to CDs and DVDs are common. They may not be big enough for all the research data, so multiple disks or drives may be needed. They pose a security risk. Data should be regularly migrated to new media. Personal Computers and Laptops Personal computers (PCs) and laptops are convenient for storing your data temporarily. However, they should not be used for storing master copies of your data. Local drives may fail or PCs and laptops may be lost or stolen leading to an inevitable loss of your data. [4] 4.5 Data Backup Making back-ups of files is an essential element of data management. Regular back-ups protect against accidental or malicious data loss and can be used to restore originals if there is loss of data. Accidental or malicious loss of data can be due to: hardware faults or failure software or media faults virus infection or malicious hacking power failure human errors by changing or deleting files Choosing a precise back-up procedure to adopt depends on local circumstances, the perceived value of the data and the levels of risk considered appropriate for the circumstances. For many researchers, carrying out an informal risk analysis provides an indication of back-up needs. Should you back up particular data files or back up the entire system? What will you need to restore in the event of data loss? If your institution can restore your system then you may wish to take responsibility only for your data files. If it cannot, you may wish to take full responsibility for your own system back-ups. Where applicable this should include portable computers or devices, non-network computers and home-based computers. Where data contain personal information, care should be taken to only create the minimal number of copies needed, e.g. a master file and one back-up copy. Does your institution have a back-up policy? Most institutions have a back-up policy for data that are held on an institutions network space. You should check with your university about any strategies and policies in place. If you are not happy with the robustness of the solution you should maintain an independent back-up of critical files. How often should you back up? To reduce risk as far as possible, back-ups should be made after every change to data or at regular intervals. You can use an automated back-up process to back up frequently used and critical data files. Microsoft SyncToy is an easy-to-use method of synchronising files in different locations. 25

28 Which media should I use? The choice of media on which to store back-up files depends on the quantity of files, type of data, and the preferred method of backing up. Examples include recordable CD/DVD, networked hard drive, removable hard drive or magnetic tape. If you are backing up many small data files on a daily basis, copying them to a recordable CD probably suffices but if you are making back-ups of very large quantities of data from a networked hard drive, a removable hard drive or even magnetic tape is probably more convenient. Where should I store my back-ups? Depending on the form of back-up and the risks associated with data loss, it is most convenient to keep back-up files on a networked hard drive. For critical data, which are not available elsewhere, we would recommend that you adopt offline storage on recordable CD/DVD, removable hard drive or magnetic tape. Physical media can be safely stored in another location. Most manufacturers provide recommendations for the best storage conditions of physical media. Validation of back-up copies It is important that you verify and validate back-up files regularly by fully restoring them to another location and comparing them with the original. Back-up copies can be checked for completeness and integrity, for example by checking the MD5 checksum value, file size and date. [8] Exercise: Folder Management For this exercise consider your research project and all the data files collected and documentation produced by the project. Layout what would be the most effective folder structure for your project. Please see a simple example of a project structure below. Figure 9: Example Validation of backup copies. 26 Data Management Training

29 5 Data Entry PEN Database Case Think ahead quiz: Data that are collected as part of a scientific research project ultimately prove or disprove the PI s hypotheses and justify a body of research to the public at large. Which statement is true about data collection in scientific research? Ensuring validity of the data is the key to successful research. Ensuring reliability of the data is the key to successful research. Ensuring reliability and validity are equally important. Data collection is actually not a key part of scientific research, since many researchers use previously collected data. Answer: Ensuring reliability and validity are equally important. Ensuring reliability and validity of the data are equally important during data collection. When data collection is carried out according to these 2 rules, researchers will be able to accurately assess, replicate, and disseminate their results. Read on to learn more. [6] The databases are all designed to look like the physical questionnaire. The first page on the physical questionnaire will also be the first in the database. Data entry personnel only will be working with two modules of the database: Forms and Tables. Other modules i.e. macros and queries pertain to added functionality of the database such as carrying over values of a variable from one page to the next and it is recommended that these should not be modified by persons other than the database designers. 5.1 Opening the database Opening the database to begin data entry can be done in two ways: a) If operating Windows 2000 and above or XP go to the start menu, click on Programs and locate Microsoft Access then click on it. Figure 10: Example Opening the Database. 27

30 Once you have Microsoft Access open, go to the File menu and click Open. Locate the folder under which the database is stored, click on the database that you want to open so that it is highlighted in blue then click Open at the bottom right of the Open dialogue box. Figure 11: Example Opening the Database. This will open the Main Microsoft Access window which by default opens to the forms that are in the database. Double click on the form that you want to open. When beginning data entry for the first time, this should be the first form at the top since the forms are named according to what section of the questionnaire they come from. Figure 12: Example Opening the Database. b) Navigate the folders on your computer and locate the Microsoft Access database that you want to open. 28 Data Management Training

31 Figure 13: Example Opening the Database. Double clicking on the icon for the database will take you to the Main Microsoft Access window which by default opens to the forms that are in the database. Double click on the form that you want to open. 5.2 Forms There are two types of forms: Main forms and sub forms. A main form is the main level on which data is being collected e.g. a village or a household. A sub form is a form within a form e.g. within a household there are multiple forest products collected. Main forms have one-to-one relationship i.e. for one household there is one village code, district code etc while sub forms have a one-to-many relationship i.e. for one household there are multiple forest products collected, multiple fish types collected etc. 5.3 When to use Forms and Tables It is possible to have a database that has only tables and no forms but because the forms look like the questionnaire, it eases data entry. All the data that is entered into a form is stored in a table of the same name e.g. data entered into the form qtrhhd_b_fup will be stored in the table named qtrhhd_b_fup. When entering data from or making corrections in a single questionnaire, use the form. When making modifications for multiple records use the tables e.g. if district A had the code 10 and this code was later on changed to 11, this change would have to be made for all the villages that fall under district A. It would be tedious to make the change form by form. 29

32 5.4 Entering Data Upon opening the form into which data is to be entered, begin data entry. To move to the next question on the questionnaire (also known as a field) press the Tab key. To go back to the previous field press the combination Shift + Tab. As data is entered, the number of records in the database can be seen at the bottom of the form. Figure 14: Example Entering data in forms and tables. To go to the next page, click on the button labeled Next Page. Doing so filters the data so that the values in the header section of the form are carried over to the next page automatically. Note: At the bottom of the form there are the words 1 of 1 (Filtered). This does not mean that there is only one record in the database. To see how many records there are in total, close the form then re-open it by double clicking on it. Tip: If the value for a new record is the same as the previously entered record, rather than re typing the value the combination control + apostrophe ( ) can be used to copy the value. To go to the previous page, close the current page and open the form for the previous page by double clicking on it. 30 Data Management Training

33 Locate the desired record by searching for it using the header information. For instance to search for the household whose code is 26, place the cursor in the household code field then press the combination control + F. Type in 26 next to the words Find What: and press Enter. To find the next occurrence of 26, click on the button labeled Find Next. Figure 15: Example Entering data in forms and tables. 5.5 Validation One advantage of using Access forms for data entry is the ability to validate the data as it is entered. This allows you to ensure only data in a certain range or of a certain type is entered in a particular field. This goes to increasing the accuracy of the entered data and reduces the data cleaning process at a later stage. 31

34 Figure 16: Example Validating data in Access. 5.6 Navigating records To go through the records one by one use the buttons with a single arrow which are located at the bottom of the form. The one facing left will take you one record down and the one facing right will take you one record up. To go to the first record in the database, click on the left facing arrow with a line in front of it ( <) and to go to the last record use the right facing arrow with a line after it (> ). The button with an arrow followed by a star (>*) will bring up a new record. Note: When using these buttons in a form that has a sub form in it, to navigate in the sub form place the cursor in the white box that has the record number in it at the bottom of the sub form. To navigate in the main form use the same type of white box that is at the very bottom of the page. Tip: When the database has been closed and then re opened, to begin entering a new questionnaire, go to the first page and click new record button. 32 Data Management Training

35 5.7 Data types In Access, each variable is a field (or what you would normally call a column in Excel). Each field has a field name, data type, and a description. Select table design view to see these characteristics Figure 17-19: Examples of Data types. Field name: is the name of the variable. e.g. houscode. We have tried to make these as informative as possible while keeping them short. Many of the PEN partners work with Stata which has a limit on the length so we have designed this database with the Stata s limits on the length of a variable name which is

36 Data type: There are a number of data types however, because this particular database will be used in many time and currency zones, we have opted to use only text and numbers for all fields. Text has only been allowed for names (household member, administrative regions etc). All other fields will be numbers and you will need to enter the codes from the codelist. We return to data types below. Description: This is a descriptive phrase that says something about the field i.e. the question being answered Field Properties Both text and numbers have properties. For text the most important property is the field size which ranges from 0 to 255 characters. In all cases we have used the default length of 50 characters however this can be increased. More interesting however are the properties of numbers. The most important properties are: Field Size Decimal Places Validation Rule Figure 20: Example of field properties. 34 Data Management Training

37 5.7.2 Field Size This defines the precision with which numbers will be stored. Numbers can be stored as bytes, integers, long integers, single, double and decimal. Byte: Values between 0 and 255 will be stored as bytes. example Field name Field Description Field Values hhc_sex sex of member 0 Male 1 Female The field hhc_sex is the sex of the household member and takes on two values 0 and 1. We do not allow for negative values because we assume everyone will fall in one of those classes. Furthermore, such a field does not have decimals so we hold it as a byte. Integer (and Long Integer) Fields with larger numbers in the range 32,768 through +32,767 will be stored as integers (larger non decimal fields can be stored as long integers). Long integer stores numbers from 2,147,483,648 to 2,147,483,647. example Field name Field Description Field Values hhc_educ Education of member 0 30 or -9 or Single and Double Fields with negative or positive values as well as decimal points will be stored as single or double. The former takes up to 7 decimal places where as the latter goes up to 15. A more detailed discussion on these properties can be found in any material that discusses data precision. Decimals For fields held as single and double, you can set the number of decimal places. 35

38 5.7.3 Validation rules Recall that each field has validation rules. These are rules are useful to data entry errors. Figure 17: An example of validation rules. We have used the code book to anticipate what data will be entered and set this range in the validation rules. Validation rules instruct Access what values can be entered for any given field. Other examples of validation rules: Is Null Or Between 1 And 15 Is Null Or Between 200 And 400 Or -9 If you try to enter a value that violates the validation rules, access will reject the value and warn you. We have also tried to put some informative text in the validation text which is the message you get when access rejects a value you try to enter. 5.8 Modifying Data Validation rules The most common modification you will likely make is to change the validation rules. Typically you may want to increase the upper limit or add a new negative value. This is quite straight forward, simply select the field you are interested in and scroll down to the validation rule to make changes. Important! At the end of the project, PEN would like to compile a comprehensive dataset which means we will append multiple databases to make one final database. Access requires that fields being appended are identical. You therefore need to keep a record of all changes you make so that we can make them in all other databases. We cannot emphasize this enough! Other changes may be to include decimal places in fields we have defined as non decimal place. To do this change the field size and then add the appropriate number of decimal places. Again we request that you make a record of this change. Please keep a table like the one below: Table Name Field Name Property changed Was Has been changed to qtr_b_fup fup_pdt Validation rule Is Null Or Between 1 And 9 Or Between 21 And 36 Or Between 51 And 59 Or Between 71 And 74 qtr_b_fup fup_unpx Decimal places Auto 2 Is Null Or Between 1 And 10 Or Between 21 And 36 Or Between 51 And 59 Or Between 71 And Data Management Training

39 Note: Some validation rules (e.g tenure types) are really long and may be troublesome to modify in the validation rule window. The best way to change such validation rules is to use the expression builder. 1. Click on the small button next at the end of the validation rule 2. The expression builder will pop up and then you can make the modifications Figure 18 and 19: Example of validation rules and expression builders. 37

40 5.9 Editing records You may occasionally make mistakes whilst entering data and need to correct them. Editing records in access is not so different from editing records in Excel. Place the cursor where you want to make the change and type. And as in Excel, you can undo this change Deleting records You may want to delete a record that has been entered. To do this: 1. Select the records you want to delete by left clicking in the grey column at the extreme left and dragging downwards. 2. Press delete Figure 17: How to delete records in Access. 38 Data Management Training

41 You will get a warning that you are about to delete a number of rows, confirm the deletion by clicking Yes or cancel by clicking No. Exercise: Data Entry Tools Review all the data entry tools you use for this project and ask the instructor any questions or clarifications you require on data entry. 39

42 6 Data Documentation and Metadata 6.1 Introduction A crucial part of making data user-friendly, shareable and with long-lasting usability is to ensure they can be understood and interpreted by any user. This requires clear data description, annotation, contextual information and documentation. Data documentation explains how data were created or digitised, what data mean, what their content and structure are, and any manipulations that may have taken place. It ensures that data can be understood during research projects, that researchers continue to understand data in the longer term and that re-users of data are able to interpret the data. Good documentation is also vital for successful data preservation. [8] It is critical to begin to document your data at the very beginning of your research project, even before data collection begins; doing so will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project. Data documentation can be viewed in two different levels Project or Study Level Data Level We will look at the two levels in more detail ahead. Metadata are a subset of core data documentation, which provides standardised structured information that lets you find, understand and use the data. It could for example include explaining of the purpose, origin, time references, geographic location, creator, access conditions and terms of use of a data collection. 6.2 Project level documentation Project level documentation provides the overall specifications and instructions of what the project was meant to do and why, how it went about meeting its goals, where the research was done and when it was done. It should include an overview of the research context and design, data collection methods, data preparation and results or findings. The project level documentation enables the user to understand how to make best use of the data for their purposes. Good project-level data documentation includes the information on: the context of data collection: project history, aims, objectives and hypotheses data collection methods: data collection protocols, sampling design, instruments used, hardware and software used, data scale and resolution, temporal coverage and geographic coverage, and digitisation or transcription methods structure of data files, number of cases, records, variables and relationships between files data sources used and provenance of materials, e.g. for transcribed or derived data data validation, checking, proofing, cleaning and other quality assurance procedures carried out, such as checking for equipment and transcription errors, calibration procedures, data capture resolution and repetitions, or editing, proofing or quality control of materials 40 Data Management Training

43 modifications made to data over time since their original creation and identification of different versions of datasets for time series or longitudinal surveys, changes made to methodology, variable content, question text, variable labelling, measurements or sampling information on data confidentiality, access and use conditions, where applicable [8] Data documentation would include Country reports, technical reports, working papers, questionnaires, interview instructions and research methods. Figure 18: Example of Project Level Documentation Archived 6.3 Data Level Data level documentation describes the files and tables that make the dataset and also each variable that makes up a file or table of a dataset. Data documentation can be embedded in data, such as variable and code descriptions in databases or headers in an interview transcript. Alternatively, information about data items can be recorded in a structured document. Documenting data at the data level includes: names, labels and descriptions for variables, records and their values explanation of codes and classification schemes used codes of, and reasons for, missing values derived data created after collection, with code, algorithm or command file used to create them weighting and grossing variables created and how they should be used data list describing cases, individuals or items studied, for example for logging qualitative interviews [8] 41

44 6.3.1 Labelling and Coding All structured, tabular data should have cases or records and variables adequately documented with: Names, labels and descriptions for all variables, fields, records and their values Variable labels should: o o o be brief with a maximum of 80 characters indicate the unit of measurement, where applicable reference the question number of a survey or questionnaire, where applicable e.g. variable q11hexw with label Q11: hours spent taking physical exercise in a typical week - the label gives the unit of measurement and a reference to the question number (Q11b) Code labels e.g. variable p1sex = sex of respondent with codes 1=female, 2=male, -8=don t know, -9=not answered Coding or classification schemes used, ideally with a bibliographic reference e.g. Standard Occupational Classification a list of codes to classify respondents jobs; ISO 3166 alpha-2 country codes - an international standard of 2-letter country codes Codes of, and reasons for, missing data - blanks, system-missing or 0 values are best avoided e.g. 99=not recorded, 98=not provided (no answer), 97=not applicable, 96=not known, 95=error [8] Embedding data documentation Data-level descriptions can be embedded within a data file itself. Many data analysis software packages have facilities for data annotation and description, as variable attributes (labels, codes, data type, missing values), data type definitions, table relationships, etc. Statistical e.g. SPSS Variable descriptions and attributes (codes, data type, missing values) of each variable in the data file can be documented in Variable View or via syntax, whereby embedded data documentation is then contained in the SPSS command file Databases e.g. MS Access Variable descriptions and attributes can be documented in Design View and relationships between tables and files can be created Spreadsheets e.g. MS Excel An additional worksheet within the data file can contain data-related documentation. 42 Data Management Training

45 Figure 19: Embedded Data in SPPS file (UK Data Archive. 2012) Figure 20: Embedded Data in Access Table (UK Data Archive. 2012) 43

46 6.4 Metadata Metadata is often defined as data about data or information about information. In the digital world, metadata is usually structured textual information that describes something about the creation, content, or context of an individual file or collection of many digital files. Content relates to what the object contains or is about and is intrinsic to an information object. Context indicates the who, what, why, where, and how aspects associated with the object s creation and is extrinsic to an information object. Metadata lets you and others find, understand and use data; therefore the metadata accompanying your data should be written for a user 20 years into the future. What does that person need to know about how to use your data properly? Prepare the metadata for a user who is unfamiliar with your project, methods, or observations. A small amount of time invested in documenting your data will save money in the future. Data producers and users cannot afford to be without documented data. The initial expense of documenting data clearly outweighs the potential costs of duplicated or redundant data generation. Metadata for online data catalogues or discovery portals are often structured to international standards or schemes such as Dublin Core, ISO for geographic information, Data Documentation Initiative (DDI), Metadata Encoding and Transmission Standard (METS) and General International Standard Archival Description (ISAD(G)). The most common standard is the DDI is an international XML-based descriptive metadata standard for social science data used by most social science data archives in the world Creating metadata for your dataset When creating metadata you should answer the following questions about your data. What does the data set describe? Why was the data set created? Who produced the data set and Who prepared the metadata? How was each parameter measured? What assumptions were used to create the data set? When and how frequently were the data collected? Where were the data collected and with what spatial resolution? (include coordinate reference system) How reliable are the data? what is the uncertainty, measurement accuracy?; what problems remain in the data set? What is the use and distribution policy of the data set? How can someone get a copy of the data set? 44 Data Management Training

47 An example of the kind of metadata fields to include when submitting datasets would include: dataset title name, institution and contact details of data owner/researcher abstract keywords/subject categories, indexed using in-house thesaurus funding source and award number temporal coverage (data collection start and end dates) geographic coverage (country, region, longitude/latitude) data availability/access conditions copyright holder data parameters: type, format, sample size/units publications access conditions DOI 45

48 7 Data Sharing and Archiving 7.1 Why share research data Research data are valuable resource, usually requiring much time and money to be produced. Many data have a significant value beyond usage for the original research. Sharing research data: encourages scientific enquiry and debate promotes innovation and potential new data uses leads to new collaborations between data users and data creators maximises transparency and accountability enables scrutiny of research findings encourages the improvement and validation of research methods reduces the cost of duplicating data collection increases the impact and visibility of research promotes the research that created the data and its outcomes can provide a direct credit to the researcher as a research output in its own right provides important resources for education and training [8] However other reasons also exist on why to share research data. Research funders: Many research funders are insisting that publicly funded research data should as far as possible be openly made available to the scientific community. Data sharing policies tend to allow researchers exclusive data use for a reasonable time period to publish the results of the data. Journals: Journals increasingly require data that form the basis for publications to be shared or deposited within an accessible database or repository. For example Nature journals have a policy that requires authors to make data and materials available to readers, as a condition of publication, preferably via public repositories [10] 7.2 How to share your data There are various ways to share research data, including: depositing them with a specialist data centre, data archive (e.g. UK data archive or IQSS dataverse) or data bank submitting them to a journal to support a publication depositing them in an institutional repository, Dataverse allows institutions to create such repositories for free. making them available online via a project or institutional website making them available informally between researchers on a peer-to-peer basis 46 Data Management Training

49 Each of these ways of sharing data has advantages and disadvantages: data centres may not be able to accept all data submitted to them; institutional repositories may not be able to afford long-term maintenance of data or support for more complex research data; and websites are often ephemeral with little sustainability. [8] 7.3 Advantages of Data Archiving The advantages of depositing data with a specialist data centre or data archive include: assurance that data meet set quality standards long-term preservation of data in standardised accessible data formats, converting formats when needed due to software upgrades or changes safe-keeping of data in a secure environment with the ability to control access where required regular data back-ups online resource discovery of data through data catalogues access to data in popular formats licensing arrangements to acknowledge data rights standardised citation mechanism to acknowledge data ownership promotion of data to many users monitoring of the secondary usage of data management of access to data and user queries on behalf of the data owner 7.4 Open Data Open data is data that can be freely used, reused and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike. Open data carries the following important characteristics Availability and Access: the data must be available as a whole as and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form. Reuse and Redistribution: the data must be provided under terms that permit reuse and redistribution including the intermixing with other datasets. Universal Participation: everyone must be able to use, reuse and redistribute - there should be no discrimination against fields of endeavour or against persons or groups. For example, non-commercial restrictions that would prevent commercial use, or restrictions of use for certain purposes (e.g. only in education), are not allowed. Any kind of data can be open including scientific research data, data from governments and books. 47

50 Any kind of data can be open including scientific research data, data from governments and books. Exercise Write down five reasons why researchers may be reluctant to share data. Please see possible reasons and solutions below courtesy of the UK data archive [16]. REASONS NOT TO SHARE DATA REPLIES OR ARGUMENTS IN FAVOUR OF SHARING REASONS NOT TO SHARE DATA 1 My data is not of interest or use to anyone else. 2 I want to publish my work before anyone else sees my data. 3 I have not got the time or money to prepare data for sharing 4 If I ask my respondents for consent to share their data then they will not agree to participate in the study. REPLIES OR ARGUMENTS IN FAVOUR OF SHARING It is! Researchers want to access data from all kinds of studies, methodologies and disciplines. It is very difficult to predict which data may be important for future research. Who would have thought that amateur gardener s diaries would one day provide essential data for climate change research? Your data may also be essential for teaching purposes. Sharing is not just about archiving your data but about sharing them amongst colleagues. Data sharing will not stand in the way of you first using your data for your publications. Most research funders allow you some period of sole use, but also want timely sharing. Also remember that you have already been working with your data for some time so you undoubtedly know the data better than anyone coming to use them afresh. If you are still concerned you can embargo your data for a specific period of time. It is important to plan data management early in the research data lifecycle. Data management ideally becomes an integral part of your research practice, reduces time and financial costs and greatly enhancing the quality of the data for your use too. Don t assume that participants will not participate because data sharing is discussed. Talk to them they may be less reluctant than you might think, or less concerned over data sharing! Make it clear that it is entirely their decision, whereby they can decide whether their data can be shared, independent of them participating in the research. Explain clearly what data sharing means, and why it may be important. But they are still free to consent or not. You can always explain what data archiving means in practice for their data. If you have not asked permission to share data during the research, then you can always return to gain retrospective permission from participants. 48 Data Management Training

51 5 I am doing highly sensitive research. I cannot possibly make my data available for others to see. 6 I am doing quantitative research and the combination of my variables discloses my participant s identity. 7 I have collected audiovisual data and I cannot anonymise them, therefore I cannot share these data. 8 I have made promises to destroy my data once the project finishes. 9 My data have been gathered under complete assurances of confidentiality. 10 My data collection and resulting transcripts are in a foreign language. 11 It is impossible to anonymise my transcripts as too much useful information is lost. 12 My data collection contains data which I have purchased and it cannot be made public. 13 Other researchers would not understand my data at all - or may use them for the wrong purpose. The first thing is to ask respondents and see if you can get consent for sharing in the first instance. Anonymisation procedures can help to protect identifying information. If these first two strategies are not appropriate then consider controlling access to the data or embargoing for a period of time. Also data that is held in the UK Data Archive is not publically available. Only registered researchers can gain access to the data. Quantitative data can be anonymised through processes of aggregation, top coding, removal of variables, or controlled access to certain variables (i.e. postcodes). Visual data can be anonymised through blurring faces or distorting voices, but this can be time consuming and costly to carry out. It can mean losing much of the value of the data. It is better to ask for consent to share data from participants in an unanonymised form, and/or control access to the data. Why were such promises made? Always avoid making unnecessary promises to destroy data. There is usually no legal or ethical need to do so, except in the case of personal data. But that certainly would not apply to research data in general. Also consider where you have received this advice from? You may need to negotiate with research ethics committee or ethics boards about this agreement. Again why was such an assurance made? It is best to avoid unnecessary promises. Anonymisation procedures can be implemented to protect identities, but confidentially can never be completely guaranteed. You can also consider controlling access to the data. This should not be a problem. The UK Data Archive can accept foreign language transcripts although translations into English are preferred. Get in touch with us at the UK Data Archive. We may be able to help and it might not be as difficult as it looks. Also, access controls on the data may be a better solution than anonymisation if too much useful information would be lost. It is important to know who holds the copyright to the data you are using and to obtain the relevant permissions. You need to be aware of the licence conditions of the data you are using and what you can and cannot do with the data. Producing good documentation and providing contextual information for your research project should enable other researchers to correctly use and understand your data. 14 There is IPR in the data. This should not be a problem if you seek copyright permission from the owner of the intellectual property rights. This is best done early on in the research project, but could be sought retrospectively. 49

52 8 Ownership and Intellectual Property Intellectual Property Rights (IPR) (e.g. copyright, patents, etc) is a societal innovation to manage relationships among competing groups by defining a role for creators, enforceable by statute and contract. Working with any type of data often involves navigating a complex field of obligations, restrictions and rights. Especially in regards to transmitting and sharing data as well as keeping data secure, it is important to understand what obligations you as a researcher have to both protect your subjects rights. It is also important to understand who has rights to the data you use or produce and what obligations those rights require of you. It is therefore imperative that the ownership of the research data is clarified prior to the commencement of a project. Future storage and reuse are directly affected by the intellectual property rights of research data. Data ownership should be documented via a Research Data Management Plan. Data ownership is affected by: the commercial potential of the research data; whether the research data is acquired through organisational collaborations; project funding agreements; researcher status (Intellectual property regulations apply to all staff of research institutions with sometimes a notable differences between research staff and research students); Whether institution-owned or third-party data has been utilised during the conduct of research. [11] Failure to clarify rights at the start of the research process can lead to unexpected limitations to: your research, its dissemination, future related research projects, and associated credit or profit. It can also cause you legal trouble 50 Data Management Training

53 8.1 Intellectual Property and Data Management Good intellectual property (IP) practice starts in the planning stages of a research project. In general, researchers should be aware of the context of their work since professional and scientific ethics of access and IP vary. Are there national or international policy expectations with regard to data access and management? Are there local or indigenous communities or other groups that have an interest in the research program? A project should start discussions about data sharing, credit and attribution, privacy and cultural sensitivities, and access issues at the project s inception. To increase the potential for inclusivity, we recommend the following practical steps for researchers: Data collection and management practices should be designed to account for IP concerns, including proper attribution and credit. Database fields can be added to help track attribution and credit, and to flag records and fields that may not be appropriate for public access. Researchers should be aware of laws and norms that may regulate documentation developed in their projects. These may need to be incorporated into data management practices. Research should consider working toward collaborative relationships with different stakeholders where appropriate. Data management practices discussed above may also need to accommodate documentation of concepts of ownership and data [12] 51

54 9 Introduction to Dataverse 9.1 Introduction to Dataverse The Dataverse Network is an application to publish, share, reference, extract and analyse research data. It facilitates making data available to others, and enables replication of work. Researchers and data authors, publishers and distributors and even affiliated institutions get credit for their work. A Dataverse Network hosts multiple Dataverses. Each Dataverse contains studies or collections of studies. Each study contains cataloguing information that describes the data plus the actual data files and complementary files. Figure 21: Dataverse Network 52 Data Management Training

55 9.1.1 Types of Dataverses For organisations Used by archives, libraries, journals, schools Enable contributors to upload data Organize studies by collections Search across a universe of data Control access and terms of use Collaborate with other catalogs and partners: OAI-PMH, LOCKSS, Z39.50, DDI For Scholars Brand it like your own website. Upload any type of data. Establish a persistent data citation Facilitate data discovery Provide live analysis Receive permanent storage space Figure 22: Types of Dataverses 53

56 9.1.2 Hosting There are two approaches: You can download and install the Dataverse Network Application and effectively become a host; or You can create a Dataverse on Institute for Quantitative Social Science (IQSS) Dataverse Network at Harvard University. This Network is open to all researchers, publishers and data distributors. The first option gives you more control but includes added responsibility & cost since you require an I.T. specialist to setup and maintain the dataverse. 9.2 Dataverse features Dataverse Branding You can make your dataverse archive have the same look and feel as your organisations or personal website. This helps you maintain a consistent look between your website and your archive even if the archive is not hosted in your organisation. Figure 23: Dataverse Branding 54 Data Management Training

57 9.2.2 Data Citation Dataverse allows to cite research digital data from published printed work. You also get a citation even if your data is not based on published work. The data citation is automatically generated when study is created. Data Citation format: Author, Date, Title, Persistent Identifier Universal Numerical Fingerprint (UNF) Distributor or other optional fields [ ] Persistent Identifiers offer permanent and reliable links to digital objects. Uses the handle system. e.g. hdl:1902.1/15673 Universal Numerical Fingerprint is a cryptographical number applied on tabular quantitative data (uploaded either as STATA, SPSS, CSV and TAB with a special metadata file for the last two). It is used to uniquely identify and verify data e.g. 5:G22I+TtPQPAyFcRT6SrUfA== Citation Example Frank Place; Patti Kristjanson; Steve Staal; Russ Kruska; Tineke dewolff; Robert Zomer; E C Njuguna, 2005, Replication data for: Development pathways in medium-high potential Kenya: a meso-level analysis of agricultural patterns and determinants., UNF:5:G22I+TtPQPAyFcRT6SrUfA== World Agroforestry Centre [Distributor] V1 [Version] Research credit Dataverse enables you to give credit to everyone involved in the generation of the research data. The authors of the data get credit, the organisation they work for gets credit, the distributor of the data gets credit and you can also give recognition to the funder of the research project. Figure 24: Dataverse gives credit 55

58 9.2.4 Designed for Research Data Data-format aware Input formats: CSV, TAB, SPSS, STATA, GraphML Export: reformat, subset, analyze Preservation-reformatting Semantic fingerprints Research data workflows Researcher can deposit the data directly Multiple workflows: closed, review-and-release, wiki Versioned a copy of each release of data maintained. Find distributed resources Can provide a portal to distributed resources (OAI-PMH harvesting client) Data can also include meta data for harvesting Flexible licensing Access control for research groups Layered usage terms Data request workflow Flexible Data uptake Archive any file type 1 file max size = 2GB No size limit to study Figure 25: Dataverse - all data types accepted 56 Data Management Training

59 9.2.5 Data reformatting, preservation recoding and subsetting SPSS, STATA, CSV and TAB files (CSV and TAB with metadata files) are converted to tabular data files These files are reformatted so that they can be downloaded in a variety of formats This reformatting ensures there s no lock in to any statistical vendor. Data Formats may also change in future but the files uploaded to dataverse are in open formats. This is data preservation. You can also view online summary information for the variables The variable names and labels were all taken from the files that were uploaded Dataverse allows you to recode the variables of the uploaded files and download the recoded variables. Dataverse also allows you to download only a subset of the variables. Figure 26 - Summary information Data Discovery Dataverse provides a good data discovery framework. A dataverse provides the capability to browse and search studies within that dataverse or across the entire Dataverse Network. All metadata fields describing the study and the data are indexed allowing advanced fieldbased searches. 57

60 9.2.7 Data Security through permissions When a study is released the default is for public access You can choose to restrict the entire study to named users You can also choose to restrict individual files within a study even if the study itself has public access A study cannot be seen by others until it is released A released study can be de-accessed at any time to remove it from public access it will still remain in your Dataverse unless you delete it Figure 27: Dataverse permissions summary 58 Data Management Training

61 10 Using Dataverse Think Ahead Quiz: Data Citation True or False: No scientific citation format exists for datasets. Answer: False, various scientific citations do exist for data including one that is automatically created for you by the Dataverse application. We will use the ICRAF dataverse as an example of how to use dataverse, however during the exercises we will have session that will allow users to create their own dataverse Accessing dataverse Click on the following link and login into the Dataverse network. You need to be logged into the ICRAF Dataverse to enable you to add and edit studies. Figure 28: Log into dataverse. 59

62 Figure 29 - Enter your username and password. Once logged in you will be redirected to the ICRAF Dataverse homepage as shown below. Figure 30: ICRAF Dataverse homepage 60 Data Management Training

63 10.2 Creating a new study. To create a new study, you should go to the options menu on the top right- hand corner as highlighted on figure 4. This leads you to the study options page. If you are a contributor, you will see the study options only and if you are an administrator, you will see the Dataverse options displayed. Figure 31: Options tab. Figure 32: Create new study. 61

64 You will have to agree to the ICRAF Dataverse terms of use before your study is created as shown in figure 6 below. Figure 33: Terms of use. The create study page shown in the figure below is displayed. Figure 34: Create a study page. 62 Data Management Training

65 10.3 Adding Meta data. When creating a study, you will first be asked to enter some information about the study. You are encouraged to enter as much metadata as possible, but only the study title is required to get started. You can edit or add more metadata at any time. ICRAF recommends that you at least add the following metadata fields Title of (data) study Related publication (if any) Name(s) of people who were the primary collectors / contributors of the data and their affiliations A short abstract of what the study is about (if a related publication exists you may omit this) Keywords Country (ies) where the data was collected You can click on the blue + sign to see more fields under a particular section e.g. to add more authors. By default Dataverse only shows the required and recommended fields, to see all the fields select Show All Fields at the top of the page as seen in figure 8. Figure 35: cataloguing fields options. 63

66 Fill in the metadata required and click the save button. Figure 36: save button. The study version notes window will be shown in figure 9 will be displayed before saving. You can add any notes on a particular version of the study that you are saving. The study version notes option will be availed to you every time you edit metadata or add content. Figure 37: study version notes window. 64 Data Management Training

67 10.4 Adding files. Once you have created a study, select add files from the option box. Figure 38: add files Browse to the file and select Open. It will automatically appear in the upload files table as shown in figure below. To simplify display, especially with large studies, categorize your files (for instance, Questionnaire or Data File ). Each study has its own categories, so when you upload a file, simply type the name of the category you d like to create in the left-hand box, then select save. If you have previously created a category, select it from the drop down menu, then select the file or files you d like to place in that category. You can always edit categories and edit or delete files later by selecting the appropriate action from the top left hand corner of the study page as shown in the figure below. You may also add new files and edit cataloguing information by clicking the relevant options from the same place. Figure 39: choose a file type. 65

68 Figure 40: File types. Figure 41: Description and save file. 66 Data Management Training

69 10.5 Permissions. This applies for the curators and the administrators of a dataverse. If you wish to restrict access to your study s data files. You can configure this by selecting permissions from the options on the top left corner of the study page. This leads you to the Manage Study Permissions page. You are able to restrict or permit access to all files uploaded to the study, or selected files only. You can restrict all users, or permit selected users to access the study or files. You may also allow users to request access to the study, so that they would have to send an requesting for access. The study owner would then decide whether to grant access to the files requested by the user. Figure 42 - Permissions tab. Figure 43: File permission settings. 67

70 10.6 Submitting the study for review. The study has to be released to become operational otherwise it remains as a draft copy which is only available to you as the creator of the study. To release the study, Click on submit for review link. You will receive an to confirm this. An administrator will then set the appropriate permission and make sure everything is okay before releasing it. You will once again receive an to confirm that the study has been released. Figure 44 Submit for review. Figure 45 Confirmation - Review. 68 Data Management Training

71 Figure 46 - Confirmation - Study has been released Deaccession of a study You can at any time remove any released study from being accessed by anybody. This can be done by selecting the Deaccession option from the options box on the top left hand corner of the study. This makes the study totally inaccessible to all users and the public. Please note that the study is not permanently deleted and you and the Dataverse administrator can access it by selecting to view deaccessioned studies. To permanently delete a study please contact the Research Methods group. Figure 47 Deaccession of the study. Exercise: Login into the test dataverse created for this training and create a study with relevant metadata. Upload some data files to your study and share your data with other scientists. 69

72 11 REFERENCES 1. MIT Libraries (n.d.) Manage Your Data: Data Management: Subject Guides: MIT Libraries. [online] Available at: [Accessed: 24 May 2012]. 2. Australian National Data Service (2010) Data management planning. [online] Available at: ands.org.au/guides/data-management-planning-awareness.html [Accessed: 24 May 2012]. 3. Cambridge University Library (2010) Incremental Project - Explanation of Terms. [online] Available at: [Accessed: 24 May 2012]. 4. EDINA and Data Library, University of Edinburgh (2012) Research Data MANTRA [online course]. [online] Available at: [Accessed: 25 May 2012]. 5. U.S. Geological Survey (2012) Metadata in plain language. [online] Available at: usgs.gov/tools/metadata/tools/doc/ctc/ [Accessed: 26 May 2012]. 6. The Office of Research Integrity (2012) Guidelines for Responsible Data Management in Scientific Research. [online] Available at: [Accessed: 26 May 2012]. 7. Federation of Earth Science Information Partners (2012) Data Management Course Outline. [online] Available at: [Accessed: 6 Jun 2012] 8. UK Data Archive (2012) Create & Manage Data. [online] Available at: ac.uk/create-manage [Accessed: 6 Jun 2012]. 9. Michener, W. K., J. W. Brunt, J. J. Helly, T. B. Kirchner, and S. G. Stafford Nongeospatial metadata for the ecological sciences. Ecological Applications 7: Nature Publishing Group (2009) Nature journals policy on availability of materials and data. [online] Available at: [Accessed: 08 Jun 2012]. 11. The University of Western Australia (2012) Intellectual Property - Research Data Management Toolkit - Guides at University of Western Australia. [online] Available at: content.php?pid=319161&sid= [Accessed: 08 Jun 2012] 12. Opencontext.org (2012) About Open Context: User Privacy. [online] Available at: opencontext.org/about/intellectual-property [Accessed: 08 Jun 2012]. 13. Muliro, J, Mwanzia L. K. World Agroforestry Centre (ICRAF), Nairobi (Kenya). Research Methods Group 2012 Creating studies and uploading data on dataverse. Nairobi, Kenya World Agroforestry Centre (ICRAF), Nairobi (Kenya), 14p. Open access 14. Betty Abang, Ronnie Babigumira User s guide for PEN s Microsoft Access Databases. Center for International Forestry Research, Indonesia. 15. The Dataverse Network Project (2012) Dataverse Study and Data Administration. [online] Available at: [Accessed: 08 June 2012]. 16. Louise Corti, Veerle Van den Eynden, Libby Bishop & Bethany Morgan-Brett. (2011). Managing and Sharing Data - Training Resources. UK Data Archive, University of Essex ( 70 Data Management Training

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