Course for Doctoral Students RESEARCH DATA MANAGEMENT AND OPEN DATA 25th July 2015, Social Science Data Arhives, Faculty of Social Sciences, University of Ljubljana ECPR Summer School 2015 PREPARING DATA AND DOCUMENTATION FOR DIGITAL CURRATION Irena Vipavc Brvar, Social Science Data Archives Content •Which things should I save and how • Documentation • Data • Metadata (standards) •What tools are there SHARING MY RESEARCH Data should be user-friendly, shareable and with long- lasting usability. -> ensure they can be understood and interpreted by any user This requires clear data description, annotation, contextual information and documentation. Documentation Data documentation might include: • a survey questionnaire • an interview schedule • records of interviewees and their demographic characteristics in a qualitative study • variable labels in a table • published articles that provides background information • description of the methodology used to collect the data Source: UK Data Service What should be captured? Any useful documentation such as: • final report, published reports, user guide, working paper, publications, lab books Information on dataset structure • inventory of data files • relationships between those files • records, cases... Variable-level documentation • labels, codes, classifications • missing values • derivations and aggregations Source: UK Data Service What should be captured? Contextual information about project and data • background, project history, aims, objectives, hypotheses • publications based on data collection Data collection methodology and processes • data collection process and sampling • instruments used - questionnaires, showcards, interview schedules • temporal/geographic coverage • data validation - cleaning, error checking • compilation of derived variables • weighting: factors and variables, weighting process • secondary data sources used Data confidentiality, access and use conditions • anonymisation carried out • consent conditions/procedures • access or use conditions of data Source: UK Data Service Data - level documentation Certain types of data file may contain important information which should be preserved: • variable/value labels; document metadata; table relationships and queries in relational databases; GIS data layers/tables Some examples: • SPSS: variable attributes documented in Variable View (label, code, data type, missing values) • MS Access: relationships between tables • ArcGIS: shapefiles (layers) and tables in geodatabase; metadata created in ArcCatalog • MS Excel: document properties, worksheet labels (where multiple) Source: UK Data Service Data - level documentation: variable names All structured, tabular data should have cases/records and variables adequately documented with names, labels and descriptions. Variable names might include: • question number system related to questions in a survey/questionnaire e.g. Q1a, Q1b, Q2, Q3a • numerical order system e.g. V1, V2, V3 • meaningful abbreviations or combinations of abbreviations referring to meaning of the variable e.g. oz%=percentage ozone, GOR=Government Office Region, moocc=mother occupation, faocc=father occupation • for interoperability across platforms - variable names should be max 8 characters and without spaces Source: UK Data Service Data - level documentation: variable labels Similar principles for variable labels: • be brief, max. 80 characters • include unit of measurement where applicable • reference the question number of a survey or questionnaire 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) • Codes of, and reasons for, missing data avoid blanks, system - missing or '0' values e.g. '99=not recorded', '98=not provided (no answer)', '97=not applicable', '96=not known', '95=error' • Coding or classification schemes used, with a bibliographic ref e.g. Standard Occupational Classification 2000 - a list of codes to classify respondents' jobs; ISO 3166 alpha-2 country codes - an international standard of 2 - letter country codes Source: UK Data Service Data - level documentation: transcripts Qualitative data/text documents: • interview transcript speech demarcation (speaker tags) • document header with brief details of interview date, place, interviewer name, interviewee details, context Source: UK Data Service METADATA Metadata – data about data Describe your survey using standard International standards/schemes • Data Documentation Initiative (DDI) • ISO19115 • Dublin Core • Metadata Encoding and Transmission Standard (METS) • Preservation Metadata Maintenance Activity (PREMIS) BASIC STRUCTURE OF DDI 2.* - Section 1.0 ‐ Document Description consists of bibliographic information that c an be considered as the header whose elements uniquely describe the full contents of the compliant DDI file. - Section 2.0 ‐ Study Description consists of information about the data collection. This section includes information about who collected and who distributes the data, about the scope and coverage, sampling (if relevant), data collection methods and processing, citation requirements, etc. Controlled Vocabulary Multilingual XML Semantic and technical interoperability BASIC STRUCTURE OF DDI 2.* • Section 3.0 ‐ Data Files Description provides information about the Data file(s). • Section 4.0 ‐ Variable Description provides a detailed description o f variables, including (when relevant) t he variable type, variable and value labels, literal questions, computation or imputation methods, instructions to interviewers, universe, descriptive statistics, etc. • Section 5.0 ‐ Other Study Related Materials allows for the inclusion of other materials related to the study such as questionnaires, user manuals, computer programs, interviewer manuals, maps, coding information, etc. Colectica for Excel Nesstar Publisher Nesstar Publisher – a sophisticated authoring environment that can publish data from a variety of sources (including SPSS, SAS, Excel etc.). The tool includes a specialised metadata editor, data and metadata validation routines and metadata templates that provide standardisation and control. Easy editing/creation and export of DDI documented datasets with XML experience needed. Tools to compute/recode/label new, or existing, variables to be added to a dataset before publishing. Tools to validate metadata and variables. The ability to import and export data to the most common statistical formats, including delimited files. The ability to include automatically generated frequency and summary statistics for each variable. Multilingual - Arabic, Chinese, English, French, Portuguese, Russian and Spanish and more.