Data management principles


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Data management principles

  1. 1. Data management principles
  2. 2. Contents • Introduction • Formats: from text to relational structures • Global scientific metadata systems • Data availability and access • Principles of data policies • Value of data
  3. 3. Introduction • Basic principles of why data-mangement (see last week) – Selfish reasons – Altruistic reasons – Moral obligation  costs of generation of data • Nice ideas, nice examples, ... – Work behind is sometimes less nice – For datamanagement there some rules/techniques/principles
  4. 4. 1. Data formats • Large heterogeneity in data formats • Data format = the physical or electronic shape in which data is stored • Piece of paper with hand written text = data format • However focuss here: – Electronic data formats – Commonly used data formats
  5. 5. 1. Data formats • Why use which format? – Historical reasons: • Old data mostly in text based list formats • Software and technology is accompagning certain formats • Example: xml is only being used after its invention – Other reasons: • Depending on data generator: – Machine generated data (mostly ascii format) • Worldwide agreed formats for certain types of data – Facilitate exchange of data packages
  6. 6. 1. Data formats • Exchange of data formats – Most formats are exchangeable into eachother – Mostly top down: • Relational structure  spreadsheet  txt-based
  7. 7. Data formats: different classifications • Physical types: – ASCII – BINARY • Format types : 15 often used data types
  8. 8. Dataformat – ascii format (1) • Ascii: American Standard Code for Information Interchange • ASCII data are encoded so that the human reader can see and understand the values, because they are displayed as normal integers and real numbers. This means that the actual digital file contains print and display information for the human-readable characters, not the actual values of the data. The benefit of using ASCII data is that the user can see, understand and edit the file contents directly; the downside of using ASCII is that the data files are much larger.
  9. 9. Dataformat – ascii format (2) • Combination of letters and numbers • Readable by any computer • No complex software required
  10. 10. Dataformat – Binary data • Binary data are numeric data whose values are expressed in bits and bytes, instead of the human-readable ascii code. • Number values can be stored in much smaller files:  be read more rapidly (by machines) • the method for large datafiles, especially gridded data. • To use binary data: not so easy  interpreting steps are required
  11. 11. Dataformat – Binary data • Contents and structure of binary files may vary: – Type of data stored: • Bit (0-1) – 1 bit • Byte (0-255) – 8 bits • Short integer (-32,768 32,767) – 16 bits • Interpreter – translator is required
  12. 12. Data formats – 15 common used types • Text – files  Ascii/Binary • Spreadsheets • Relational structures • Others – Images – Maps
  13. 13. 1 & 2 : Auxiliary Formats • Auxiliary Formats - Information about data files; these are not really "data" files, but are included here for completeness – 1 Header Formats - Information about the format, location or geo-referencing; usually very short – 2 Metadata Formats - see also metadata
  14. 14. 3. Document • Digital data in proprietary formats (or sometimes just simple ASCII) designed for visual inspection, but not for data processing • ASCII ,MS Word DOC , WordPerfect , HTML , PDF - Adobe Acrobat , PS/EPS - PostScript/Encapsulated PS , Desktop publisher programs - all proprietary ...
  15. 15. 3. Document • Advantages: Very polished appearance; powerful editors available; compatibility with other major document editing software. • Disadvantages: (hard to use in data mining) – ASCII text must be extracted for the sections of interest. – Embedded images must be converted to more easily used GIF, JPG or BMP formats. PDF and PS/EPS very tricky to convert to other formats.
  16. 16. 4. Gridded data • File formats: – ASCII : example - SURFER (*.GRD) - with "DSAA" header lines – Binary : Plain binary grids: byte, short integer, long integer, single-precision or double-precision; with or without ASCII Header Files (see earlier)
  17. 17. 4. Gridded data • Creation of the Grid: – The gridded data file is created from scattered data points in the real world, by a process called "gridding." – mathematical methods to create the grid – algorithms are available to examine data points
  18. 18. 4. Gridded data • Gridded data files commonly contain more than a single grid – Data mostly avaiable for different parameters – Using sequences of XYZ dimensions and parameter dimensions – There is no "correct" way to construct files of multiple data grids • It is extremely important to document the sequence in which the dimensions (XYZ location, time, parameters) are "read." • Vector Grids: To represent vectors (literally arrows showing the direction of flow) in ocean and meteorological datasets two methods have been devised: provide the U and V components of the vector, or provide the direction and magnitude of the arrow. Both of these methods have been adapted to grids, for vector results from gridded models for instance. The grids can be contained in separate files, or sequentially listed in the same file.
  19. 19. 4. Gridded data • Advantages: – Saves storage space – XYZ storage which requires 3 data per gridpoint. – Binary takes much less space than ASCII. – Reading the data is usually a very straightforward creation of a • DO LOOP routine (or nest of routines) that follows the order in which the data were stored • Disadvantages: – Binary data are not liked by those who want "to see" their data at all times.
  20. 20. 5. Hard copy • Older, hard copy datasets • necessary evil – (pre-60s) ocean data has never been digitized • These datasets range from technical reports to hand- written log sheets and lab sheets. – Reports usually contain enough information to be successfully digitized – Manuscript holdings often require tedious collation and cross-referencing in order to assemble all the needed parts. – Datasets with missing critical parts (e.g. station data) exist, as well as analysis and synthesis reports containing statistics, graphs and tables, but no data.
  21. 21. 5. Hard copy • Examples: – Lab sheets – Journal articles – Technical Reports – 80-character punch cards - Included here because many locations lack the facilities to read them – Hand-annotated charts/graphs – Specimen identification cards – Diaries – Ship logs
  22. 22. 5. Hard copy • Risk of data loss: – Rule in many data centres: No paper data should be mailed or shipped unless photocopied. – All ORIGINAL paper data should be gathered by the data manager immediately after the relevant cruise and grouped into named folios whose contents are indexed. • All paper data should be submitted to supervised digitization as soon as possible. – Example: heritage library • Metadata of hard copy data: should fully describe the folios – numbers of pages – Color of frontpage – Other identifying characteristics • Advantages: They still exist. • Disadvantages: – Cannot be used in modern digital analysis. – Digital capture is very labor intensive. – Access is a tricky political issue in some institutions. • Compatibilities: Published papers in good condition can be scanned and converted to ASCII text with many commercial packages. (OCR techniques) – Controll afterwards ….
  23. 23. 5. Hard copy • From hard copy to digital copy ... – Technique used depends on aim and type of data – Often just transformed in ‘document’ format – If to other formats – often man-driven • In many cases going back to hard copy only way to work (due to lack of metadata, file versions, ...)
  24. 24. 6. Simple Images – Graphics file without earth mapping information – Interpretation is purely man-based – Very variable – Many file formats: • TIFF, GIF, JPG, BMP … • RAW versus compressed – RAW: all image information is stored without compression – Compressed: JPG/GIF information is compressed by extrapolation, reducing colors  smaller files but loss of information
  25. 25. 6. Simple images • Some images have added artistic borders - – outside the geographic grid: that obscure the pixel-to- coordinates relationship • Advantages – Quick visualization of data that may have originally been extremely complex. Subjective analyses that do not require positional accuracy. – Disadvantages Quantification difficult; synthesis nearly impossible unless with pictures derived in exactly the same fashion Compatibilities Nearly all graphic picture formats are interchangeable with editor programs.
  26. 26. 7. Geo-referenced images • Graphics file, with ancillary mapping information, showing 1 or more parameters of the earth's system in a rectilinear grid, usually derived by processing and decimation of very high-density information from aerial or space sensors. – Coordinates of pixel correspond to XY geo- coordinate. – Color of pixel represents a parameter
  27. 27. 7. Geo-referenced images • TIF files can be made into Geo-Referenced Image files by the addition of internal geographic tags, which require exact knowledge of the image dimensions and its proper location on the earth's surface. • JPG, TIF and BMP can be made into Geo-Referenced Image formats by the addition of header "world files," which require exact knowledge of the image dimensions and its proper location on the earth's surface. A world file is a simple ASCII file with the following contents: – X-pixel size (delta X) – Rotation term for row (normally zero) – Rotation term for column (normally zero) – Y-pixel size (delta Y) – X-coordinate of center of upper left pixel – Y-coordinate of center of upper left pixel • World files for TIF have the extension TFW; • world files for JPG have the extension JPW; • world files for BMP have the extension BPW.
  28. 28. 7. Geo-referenced images
  29. 29. 8-9-10. Mapping data • Mapping - Mapping data consisting of digital representations of individual objects (points, lines, polygons, etc.) – 8 XY- Mapping line objects, in X (usually longitude) and Y (usually latitude) coordinates only – 9 List- Mapping objects (points, lines, symbols, text, etc.) without topology or descriptive attributes – 10 Geographic Information System (GIS) - Mapping objects (points, lines, polygons, etc.) on the earth incorporated into robust data assemblages that contain additional detailed information about the properties and topologies of the objects. [NOTE: Most GIS systems can also accommodate gridded, geo-referenced image, relational and spreadsheet formats.]
  30. 30. 8. XY data • Description: – simplest kind of geographic information: • lines specified by their ordered X and Y coordinates. • country boundaries: separated by several different markers • ASCII Export Format from GEBCO Database/Software (actually YX in column order) • Advantages: Simple to write, easy to read (when ASCII). • Disadvantages: Contain no topological relationships between objects, or attributes of the objects. • Text is rendered as drawing instructions, and cannot be retrieved as recognizable data.
  31. 31. 9. Mapping data - List • ordered list of "map primitives" to be drawn: – such as points, lines, circles, labels, etc. • These formats are extremely specific to certain software. • They could almost be called "plotter formats" because they do little more than draw pictures of geographically referenced information. • Small amounts of data can be included, however, coded into the appearance of such primitives as the circle (variable diameters), the vector arrow (variable lengths), and contour lines (colors). • Advantages; Usually easy to read/write. • Disadvantages exists in many variant subtypes; MS Word and WordPerfect differ markedly in the versions they accept.
  32. 32. 10. Geographic Information System (GIS) • Charting and mapping: tools for natural resource management. • Digital methods are becoming much more common in ocean data analysis. • Geographic Information System (GIS) data formats contain complex, multi-theme collections of spatial information that can be used to create maps and charts, and to perform analyses. • The data formats that can support these systems are not just sufficient to draw maps, but also contain necessary ancillary data about the features included (in space and time). • NOTE: GIS files can be vector-type or raster-type, and many GIS software systems can handle both. Conversion utilities exist that can convert these files in either direction, although the raster-to- vector conversion often requires intensive quality control by skilled operators.
  33. 33. 10. Geographic Information System (GIS) • Software: – Esri/Mapinfo/Surfer/... • Recently: also many online gis-tools – OBIS – Open Gis standards : Open Geospatial Consortium • an international industry consortium of 334 companies, government agencies and universities participating in a consensus process to develop publicly available geoprocessing specifications. • Open Geospatial Consortium (OGC) protocols include Web Map Service (WMS) and Web Feature Service (WFS).
  34. 34. 10. Geographic Information System (GIS) • Formats Within This Group ESRI Shapefiles (SHP) , VPF • Advantages: – Rapid creation of new maps and charts using the same databases. – No laborious hand-drawing methods. – Synthesis of different kinds of information, on an as-needed basis, from a common pool of datasets. – Instant changes in projection, scale, coverage area, etc. • Disadvantages: – GIS formats tend to be very complex, and populating them with the actual data of interest is laborious. • Compatibilities Most of the major software systems now recognize each other's formats. – Most have ASCII export routines for simple versions of the internal datafiles (e.g. DXF).
  35. 35. 11. Message data • Ocean and meteorological data compressed into official (usually WMO-sanctioned) formats for transmission over approved international channels, especially the WMO's Global Telecommunications System (GTS). These highly compacted formats usually require unpacking programs before they can be used for analysis purposes. [The Self-Describing Formats BUFR and GRIB are also often used for data and analysis messages within the GTS.] • Formats : DBCP-x, AAXX, BBXX, EEAA, EEBB, EECC, EEDD , IIAA, IIBB, IICC, IIDD , JJXX, JJYY, PPAA, PPBB, PPCC, PPDD , QQAA, QQBB, QQCC, QQDD , TTAA, TTBB, TTCC, TTDD , UUAA, UUBB, UUCC, UUDD , VVAA, VVCC , YYXX , ZZYY • As an example, the JJYY format encodes real-time bathythermograph data; it replaces an older format, JJXX, used until 1995.
  36. 36. 11. Message data • Advantages : – Cheap and quick to send over often crowded circuits; widely accepted among non-technical marine community. – when of poor quality, they create a "placeholder" for the higher quality data which should follow • Disadvantages – Only very coarse resolution and/or low precision is possible due to the message format limitations.
  37. 37. 11. Message data This element defines an observation report on temperature, salinity and currents at one particular location on the ocean surface, or in subsurface layers.
  38. 38. 12. Relational database • A suite of spreadsheet-like tables with explicit links between them in special linkage arrangements (usually contained in additional tables). • This collection of linked tables, known as a Relational Database (RD), divides up very large initial tables into much smaller tables and eliminates much duplication of information that would otherwise be required. • Relational Databases require the use of special software (in which they are created, manipulated, and analyzed) called Relational Database Management Systems (RDMS). • Formats: MS Access, Oracle, Sybase, dBase, SQL Server
  39. 39. 12. Relational database • Advantages: – Enormously flexible systems, capable of most typical statistical and graphical analyses of data. – Some have immediate Web compatibility for publishing databases directly on the Internet; ability to exchange data (via I/O operations or direct linking). • Disadvantages – Ocean data are seldom published in commercial RDMS formats, due to the machine- and software-specific requirements they would carry with them. – Users cannot immediately "look at" their data, although this only requires simple queries that can written in minutes. • More about these formats later
  40. 40. 13. Spreadsheets • Spreadsheet formats are simply row-and-column data tables. • Easily be imported into several proprietary spreadsheet software programs and many public domain programs. • Each row is called a "record." • The separate "fields" may be labeled by a single "label row" at the beginning of the spreadsheet • Formats: EXCEL , WK* • Advantages – Extremely easy to create, read, quality-control and manipulate in commercial spreadsheet programs. Each record (data line) is unique and complete. • Disadvantages – Can be quite large, compared to binary files of the same data.
  41. 41. 14. Self describing data formats • Data files that contain information about their own contents and structure. • Collections of other format types : – Together with metadata about the main data components. • The rules and syntax : – provided by (international) oversight groups • Examples: – HDF - widely used for satellite data archives – NetCDF - widely used for gridded data and satellite data – BUFR - meteorological format for observations – GRIB - meteorological format for gridded data • Advantages: – Metadata and data are "married" within a single structure – Software programs can find and browse desired data by working with the data files themselves rather than external indexes. – Wide use has given rise to a long list of community software and "read" libraries. • Disadvantages: – There is steep learning curve for all these formats, due to their complexity and comprehensiveness.
  42. 42. 15. Stratified data formats • A very common method to reduce the large size of Spreadsheet format data is to take the slowly changing fields, which take up a lot of room in each record and to place them in a totally separate "Cruise/Station" record that precedes all "Data" records to which it refers. • Naturally, this new type of record will have a different format from the other records. • This process can be taken further, so that "Cruise" records, "Station" records, and "Data" records all have different formats. – significance in the order of the records: because each "Data" record takes its full meaning from the closest preceding "Cruise" and "Station" records. • ICES Standard Profile • Advantages: – Smaller in size than spreadsheet. • Disadvantages : – Tricky to write software, due to multiple line formats. – Usually the lines are formatted, so it is difficult for the human eye to read the data values. – Use with spreadsheet software is very limited (editing, block sorting/cutting/pasting) due to the different line formats. – Import to relational databases with "off the shelf" routines is impossible.
  43. 43. 15. Stratified data formats Cruiseid A B C stationid x y Z W Sampleid l p K Sampleid2 l2 p2 K2 Sampleid3 l3 p2 K3 Stationid x2 y2 Z2 W2 ... ... ... ... ...
  44. 44. 16. Extra - XML • Currently widely used • Data exchange format • Extensible Markup Language (XML)
  45. 45. 16. Extra - XML • Text based – small file size • Ascii format • Similar to stratified  hierarchy • Formats defined by international organisations (see also stratified) • Metadata can be embeded in data • Data exchange format – through internet • Both for data delivery & data request • Used in GIS in recent versions of software • Web technology (e.g. Newsitems, search engines, ...)
  46. 46. Extra: Relational databases Introduction
  47. 47. Introduction • Most common used data format next to spreadsheets. • Spreadsheets relatively easily • Research projects mostly claim data to be stored in relational database. • Understanding a relational structure opens the access to many data
  48. 48. Relational databases - Data mining • Exploration of data • Prerequisite: data should be available in a minable format - database • Database = electronic document storing data – Non-relational: 1 bulk system with non-related items (eg. Msexcel files, text-documents, non- related-tables) – Relational: all items (tables) are linked to each other (see further)
  49. 49. Relational databases Why using a database • Relational database: – All your data is stored in 1 file • Easy to retrieve data • Easy to backup – Data and metadata stored together • Data ... • Metadata: data about the data (documentation) – Many data-files contain undocumented values: – Species A has an abundance of 17 ( meaning of value 17?)
  50. 50. Relational databases Why using a database • All data in a good relational designed database is only stored once: – Example: species list  typing errors • Nudora thorakista • Nudora thorrakista • Nudora thorakhista • Nudora thorakisa – 1 species  species richness calculation: 4 – Solution: 1 table with each species 1 record and use it as a reference
  51. 51. Why using a database • Data is much more rigid ... – More difficult to make errors – E.g. Sorting in excell
  52. 52. Relational databases Principle - Exercise • A practical example to understand ... – Make a list of 15 people you know – Make a list of all genders – Make a list of characters and indicate for each character whether nice or not – Make a list of countries • Start coupling all your lists • You made a relational database
  53. 53. Relational database - biology Species person Places Sample Country Density Equipment
  54. 54. Species person Places Sample Country Density Equipment Which person was present on samplings in sweden?
  55. 55. Species person Places Sample Country Density Equipment Which species sampled with a core occur in densities higher than 40
  56. 56. Variable Var_value Taxonomy Photo Literature ... ... ... ...
  57. 57. Relational databases Principles • Think before you start ... – Structure of a database is the key to a good dataset – Structure has to translate the whole concept • One look at the structure (relational scheme) should explain the database
  58. 58. Relational databases - components • Tables – Basic structures containing the data – Structure of table important – ID • Relations – Definition of how different tables are connected and form a sense-full unit • Queries – Extractions of data from database
  59. 59. Table designs ... • A table consists of a series of Columns ... • Each record as such: – Different fields – Design of table must be done before data is entered – Each field: name, data type – Each field can also by formatted  layout Record ColumnField
  60. 60. Table designs ... • Field types: – Numeric – integer/double – Text – Date/Time – Memo – Autonumber  ID – Yes/No
  61. 61. Excercise on field types: • 12 • 15 jan 1988 • hallo • 12,456 • 12:56 • Azdazdazd azdda zda azdd dad zd dadazdzd azdazddazdd azdazd azdazd dzdzdzzd ada zzd azdaz dda azd da az d z azdzadazd a zd a azd azd z dd da a z a z zd d ddaa zd • 09:89
  62. 62. Special field in a table: key • A key = a unique identifier for a record – Example: pasport number: • Number in a database which is unique and relates to all data about you – Each record in a table gets also a key – This key is used to link tables to each other – Example: • Nudora sp1 – id: 123776 • Nudora sp2 – id: 34688 – Advantage: species name changes: linked taxa remain linked
  63. 63. Linking tables through id’s • Storing numbers is most effecient way to store data: • Nudora sp1 is found in the north sea with a density of 32 • Species 123776 is found in station 2 (North sea) with a density of 32 • Record in table density becomes: 123776 | 2 | 32
  64. 64. Setting up relations between tables • Relations: links between tables • Connecting tables through certain fields in a rigid way to each other • Advantage: database becomes a strong unity • Types of relations: – 1 to many – Many to many ( = 2 times 1 to many)
  65. 65. Examples of relations • Table places: field country (numeric) • Table countries – list of countries, each country has unique id • Relation is made between: – Field country in places – Field id in country • One to many relation: 1 record in table country linked to multiple records in places • No deleting of countries possible Places Country
  66. 66. Examples of relations • Many to many • Id of sample • Id of species • Table density: unique combination of sample, species ... Species Sample Density
  67. 67. Queries • All data in database: – Next step: get it out again – Selections on 1 table: by using filters – Selections on multiple tables: using queries – Queries can be saved and reused – Queries can be the basis for new queries
  68. 68. Sorting on tables • Sorting
  69. 69. Filtering on tables
  70. 70. Making a simple selection Query • Create ... Query in design view • Switching between views:
  71. 71. Making a simple selection Query • Select the tables and/or queries needed
  72. 72. Making a simple selection Query • Select the fields needed for output/selection/sorting
  73. 73. Making a simple selection Query • Select the fields needed for output/selection/sorting
  74. 74. Making a simple selection Query • Select the fields needed for output/selection/sorting
  75. 75. Making a simple selection Query • Select the fields needed for output/selection/sorting
  76. 76. Making a simple selection Query • Set the criteria
  77. 77. Making a simple selection Query • Select the values to out put and add sorting options
  78. 78. Output the results • Go to datasheet view
  79. 79. Making a simple selection Query • Special options ...
  80. 80. Exporting data • From msaccess it is possible to export to different formats! • Tables, queries, ... • Exports can be used to do further data mining: – Through MSExcell  making graphs – To do statistical analysis
  81. 81. Exporting data
  82. 82. Step by step demonstration • Open a database • Different items in database • Open tables, sorting, filtering • Table design • Relationships • Queries
  83. 83. Query operators = equals > Larger than < Smaller than >= larger than or equals Between ... And ... Is null Like ... Not like ...
  84. 84. Query operators
  85. 85. Query operators and both true or at least 1 true < Smaller than >= larger than or equals Between ... And ... Is null Like ... Not like ... >"q*" and <"u*" VOORNAAM René, Robbie, Stefan, Stijn, Tim, Tristam ="r*" or "s*" VOORNAAM Robbie, Stefan, Stijn
  86. 86. Intermezzo ... Design a dataset • Research project: – You work with 3 persons on it – You will sample 4 times on 3 locations – You will measure 5 environmental characteristics – You will identify all species – You will count them – Extra: you will measure each specimen – Task: design on paper how your dataset will look like
  87. 87. GLOBAL Scientific Data and Metadata systems
  88. 88. Global Change Master Directory • NASA's Global Change Master Directory (GCMD): – is a comprehensive directory of descriptions of data sets of relevance to global change research. – includes descriptions of data sets covering : • climate change, agriculture, the atmosphere, biosphere, hydrosphere & oceans, geology, geography, and human dimensions of global change. – freely searchable – Only metadata records: • nature of the data (e.g., parameters measured, geographic location, time range) • where stored. • Adding data description simple: – A web-based registration form – free of charge
  89. 89. Metadata standards (1) • Used to avoid the arbitrary use of properties when describing a dataset • A document that presents a set of statements: – rules of usage for metadata elements = metadata specification = metadata standard. • Some examples of metadata specifications: – The common metadata standards for describing geospatial datasets are ISO 19115, DIF and FGDC. – Common Communications Format: Developed by UNESCO and others as "a common bibliographic exchange format that would be useful both to libraries and other information services." - Used in UNESCO's library software – CDI: Common Data Index - Used to describe oceanographic cruises. The hard-copy forms were formerly known as ROSCOPS • Global catalog online at the ICES site – DIF: Directory Interchange Format: Format used by the Global Change Master Directory and MEDI Used to describe earth science datasets
  90. 90. Metadata standards (1) • Dublin Core ISO 15836: An element set for describing a wide range of networked resources, focusing on bibliographic needs. – also been used for other metadata documentation purposes. Also known as NISO Standard Z39.85 • FGDC: Content Standard for Digital Geospatial Metadata (CSDGM) from the [US] Federal Geospatial Data Committee: – Used to describe geospatial data The FGDC metadata standard is lengthy (>200 fields) and compliance with the standard has proved to be difficult. • FGDC/BDP: FGDC with Biological Data Profile Extension: – A standard agreed upon at the International Meeting of Cataloguing Experts held in Copenhagen in 1969; it provides a standard order and content for the description of monographic material and facilitates the international exchange of bibliographic information by standardizing the elements to be used in the bibliographic description, assigning an order to these elements in the entry, – specifying a system of symbols to be used in punctuating these elements` • ISO 19115 ISO 19115:2003 Geographic Information – contains almost 300 elements. However only a small number of these form part of the core metadata and only a few of those comprising the core metadata are mandatory. – ISO 19115 allows the creations of extensions and profiles. A profile is a formalised extension requiring registration of the profile.
  91. 91. Metadata standards (1) • MARC 21 Machine Readable Code: Most widely used format for bibliographic records – Several variants exist, e.g. US MARC • RDF: Proposed by W3C for cataloguing web resources Uses a complex syntax that incorporates the topology of the resource objects (i.e. captures relationships)
  92. 92. Metadata standards (1) • W3C: – World wide Web consortium – develops interoperable technologies (specifications, guidelines, software, and tools) • System used: – Propose a specification – Period of evaluation – After common agreement – Setting of standard – Examples: XML/SVG/HTML/PNG
  93. 93. ROSCOP • ROSCOP (Report of Observations/Samples collected by Oceanographic Programmes) • Conceived by IOC in the late 1960s • low level inventory: for tracking oceanographic data collected on Research Vessels • revised in 1990: re-named as CSR (Cruise Summary Report) • Disciplines included: – physical, chemical, and biological oceanography, fisheries, marine contamination/pollution, and marine meteorology.
  94. 94. FGDC • Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC) • Metadata Profile for Shoreline Data, FGDC-STD-001.2-2001 • The Federal Geographic Data Committee: – coordinates development of the National Spatial Data Infrastructure (NSDI). – The NSDI encompasses policies, standards, and procedures for organizations to cooperatively produce and share geographic data • promotes the coordinated development, use, sharing, and dissemination of geospatial data on a national basis.  US • This nationwide data publishing effort is known as the National Spatial Data Infrastructure (NSDI).
  95. 95. Marine Metadata • A key part of any marine dataset: is the accompanying metadata. • Metadata describe : – content, quality, condition and other characteristics of a dataset. – mechanism to describe data in a consistent form • Some formats: – CSR/ROSCOP . The CSR System (also known as ROSCOP forms) is used to support a global inventory of data collected at sea and to provide ready access to scientists, program managers and data managers to timely information on data collected. – DIF/MEDI. The DIF format has been developed by NASA's Global Change Master Directory (GCMD), is used by the Marine Environmental Data Inventory (MEDI) metadata catalogue. This format has also been adopted by a number of international programs, including UNEP. • Structured descriptions of marine datasets are the oldest environmental metadata, going back to the early 1960's with the creation of the "Report of Observations/Samples Collected from Oceanographic Platform" (ROSCOP) paper forms. ROSCOPs have been replaced by CSRs (see below), while a number of other major systems have been created to describe larger (or more complex) data collections. Three major systems of great importance to contemporary oceanography are described here:
  96. 96. • Marine Environmental Data Inventory • MEDI is a directory system: – marine related datasets and data inventories – IOC’s International Oceanographic Data and Information Exchange (IODE) system. – MEDI contains metadata or "data about data". • The aim of MEDI is to address the questions: – What data do we have? – When and where was it collected? – Who holds that data? • MEDI is a reference point for locating marine and coastal datasets • MEDI is not limited to governmental datasets or restricted to freely distributed data. • Structure of MEDI: – based on the Global Change Master Directory (GCMD) developed by NASA – both systems use the DIF metadata format. • The MEDI authoring tool: encourage data collectors and scientists to produce metadata descriptions for their datasets.
  97. 97. Data catalogues • The ability to discover and access oceanographic data resources for use in visualisation, planning, and decision support is an important requirement to support research and planning. • Data catalog or gateway provides search and access to referenced data • Populated with metadata which describe the attributes and contents of datasets, databases, images, maps, documents and other catalogs and collections of resources that are available both on-line and off-line. • Types: – Met-Ocean Data Catalogs - Catalogs (indexes) of data and products from all Biological, Chemical, Geological and Physical fields indicated in Sciences of Oceanography. The term "Met-Ocean" is used to emphasize the integration of marine meteorology and all aspects of climate into this category. – Remote Sensing Data Catalogs - Catalogs of data and products from remote sensing (usually satellites). – Ancillary and Applied Data Catalogs - Catalogs of data and products from all other fields useful to oceanography.
  98. 98. Practical Example of metadata datasets • MARBEF – Marine biodiversity and ecosystem functioning network of excellence – European network of institutes – One of main aims during projects: • Inventorize all data generated through project • Inventorize also older data – Marbef website: access tot datasets metadata
  99. 99. Practical Example of metadata datasets • What data is available from Mediterranean • To what taxon level is data available • What kind of environmental variables have been measured • Who is the dataset responsable • Experimental data? • What datasets are about molluscs • Geographic ranges of a dataset
  100. 100. Data policies - history
  101. 101. General background • Key principle: – Open access to data – as much as possible – Science – versus commercial – What is the economic value of data • Important in decisions about free availability of data • Fish stock/catch data • Sand extraction data • Oil/Gaz exploitation data – Lot of data is hidden: extracted by private companies
  102. 102. General background • Ways to calculate economic value of certain data – Biodiversity data • Question: – Global distribution data of a shrimp • What price to pay for such data – Global distribution of whale species • What price to pay? – Global distribution of Gaz hydrate areas • What price to pay?
  103. 103. General background • Data originator – data owner ... ??? – Master thesis at Ghent University: • Who is owner of the data? • Can you clame intellectual property? • When is intellectual property made? – Counting data of species – Weighing species – Identyfying species – Describing species – Genetic codes of species
  104. 104. General background • Data ownership depends a lot on paying instance for projects! – Example of belgium – Politics are replected in property rights of data • University • Flanders – IWT • National Science Foundation • European Projects • Private companies
  105. 105. History of data availability • Some milestones: – Up to 17th century: • Salons – letters – 1665 : first scientific journal : Philosophical Transactions for the Royal Society of London – Journals  libraries – Journals  sent to PAYING abbonees – Went on for 300 years
  106. 106. History of data availability • 1950 s: – Founding of science citation index • Number of scitations: – Influences journal impact – Influences CV of scientist • Hierarchy in journals • Evaluation method of science • Hierarchy journals  commercially interesting • Journals were bought by commercial companies • Price of journals raised • Smaller journals into problems • 1991: first preprint service
  107. 107. History of data availability • 2003: World Summit on the Information Society (WSIS) – Role of ICT – Access to information and knowledge – html • Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities
  108. 108. Berlin declaration
  109. 109. Data policies (1) • What? – Importance of setting rules – Official document in project framework – Official document accompagning a dataset • Contents: – Who is owner of data – Who may use what parts of the dataset – How to get access to data – On what timeframe is revision needed – What may be done with the data
  110. 110. Data policies (2) • Examples: – If data used: data originator co-author of publication • Generation of lot of extra publication (benefit to share data) – Data can be shared partly on global information systems • Example GBIF • Full dataset: small part is shared with GBIF • Advertisement of data – Rules like: 2-3-5 years after publication data becomes free – Datamanagement: oblige to preserve all data in good state