Metadata Federation of PICES Member Countries

(Document pdf contains 193 pages) Executive Summary (pdf, < 0.1 Mb) 1. Introduction (pdf, 0.2 Mb) 1.1 Data sharing, international boundaries and large marine ecosystems 2. Objectives (pdf, 0.3 Mb) 3. Background (pdf, < 0.1 Mb) 3.1 North Pacific Ecosystem Metadatabase 3.2 First federation effort: NPEM and the Korea Oceanographic Data Center 3.2 Continuing effort: Adding Japan’s Marine Information Research Center 4. Metadata Standards (pdf, < 0.1 Mb) 4.1 Directory Interchange Format 4.2 Ecological Metadata Language 4.3 Dublin Core 4.3.1. Elements of DC 4.4 Federal Geographic Data Committee 4.5 The ISO 19115 Metadata Standard 4.6 Metadata stylesheets 4.7 Crosswalks 4.8 Tools for creating metadata 5. Communication Protocols (pdf, < 0.1 Mb) 5.1 Z39.50 5.1.1. What does Z39.50 do? 5.1.2. Isite 6. Clearinghouses (pdf, < 0.1 Mb) 7. Methodology (pdf, 0.2 Mb) 7.1 FGDC metadata 7.1.1. Main sections 7.1.2. Supporting sections 7.1.3. Metadata validation 7.2 Getting a copy of Isite 7.3 NSDI Clearinghouse 8. Server Configuration and Technical Issues (pdf, 0.4 Mb) 8.1 Hardware recommendations 8.2 Operating system – Red Hat Linux Fedora 8.3 Web services – Apache HTTP Server version 2.2.3 8.4 Create and validate FGDC-compliant Metadata in XML format 8.5 Obtaining, installing and configuring Isite for UNIX/Linux 8.5.1. Download the appropriate Isite software 8.5.2. Untar the file 8.5.3. Name your database 8.5.4. The zserver.ini file 8.5.5. The sapi.ini file 8.5.6. Indexing metadata 8.5.7. Start the Clearinghouse Server process 8.5.8. Testing the zserver installation 8.6 Registering with NSDI Clearinghouse 8.7 Security issues 9. Search Tutorial and Examples (pdf, 1 Mb) 9.1 Legacy NSDI Clearinghouse search interface 9.2 New GeoNetwork search interface 10. Challenges (pdf, < 0.1 Mb) 11. Emerging Standards (pdf, < 0.1 Mb) 12. Future Activity (pdf, < 0.1 Mb) 13. Acknowledgments (pdf, < 0.1 Mb) 14. References (pdf, < 0.1 Mb) 15. Acronyms (pdf, < 0.1 Mb) 16. Appendices 16.1. KODC-NPEM meeting agendas and minutes (pdf, < 0.1 Mb) 16.1.1. Seattle meeting agenda, August 22–23, 2005 16.1.2. Seattle meeting minutes, August 22–23, 2005 16.1.3. Busan meeting agenda, October 10–11, 2005 16.1.4. Busan meeting minutes, October 10–11, 2005 16.2. MIRC-NPEM meeting agendas and minutes (pdf, < 0.1 Mb) 16.2.1. Seattle Meeting agenda, August 14-15, 2006 16.2.2. Seattle meeting minutes, August 14–15, 2006 16.2.3. Tokyo meeting agenda, October 19–20, 2006 16.2.4. Tokyo, meeting minutes, October 19–20, 2006 16.3. XML stylesheet conversion crosswalks (pdf, < 0.1 Mb) 16.3.1. FGDCI to DIF stylesheet converter 16.3.2. DIF to FGDCI stylesheet converter 16.3.3. String-modified stylesheet 16.4. FGDC Metadata Standard (pdf, 0.1 Mb) 16.4.1. Overall structure 16.4.2. Section 1: Identification information 16.4.3. Section 2: Data quality information 16.4.4. Section 3: Spatial data organization information 16.4.5. Section 4: Spatial reference information 16.4.6. Section 5: Entity and attribute information 16.4.7. Section 6: Distribution information 16.4.8. Section 7: Metadata reference information 16.4.9. Sections 8, 9 and 10: Citation information, time period information, and contact information 16.5. Images of the Isite server directory structure and the files contained in each subdirectory after Isite installation (pdf, 0.2 Mb) 16.6 Listing of NPEM’s Isite configuration files (pdf, < 0.1 Mb) 16.6.1. zserver.ini 16.6.2. sapi.ini 16.7 Java program to extract records from the NPEM metadatabase and write one XML file for each record (pdf, < 0.1 Mb) 16.8 Java program to execute the metadata extraction program (pdf, < 0.1 Mb) A1 Addendum 1: Instructions for Isite for Windows (pdf, 0.6 Mb) A2 Addendum 2: Instructions for Isite for Windows ADHOST (pdf, 0.3 Mb)

User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)

User’s Guide to For2R: A Module of Fortran 95 Output Routines Compatible with the R Statistics Language

For2R is a collection of Fortran routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 For2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the For2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer For2R to others in the hope that they will find it useful. (PDF contains 31 pages)