GEO 802, Data Information Literacy

GEO 802, Data Information Literacy
Data documentation through metadata This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Please cite this work as “Whitmire, Amanda L. (2014). Research Data Management Curriculum, Lecture 9: Data documentation through metadata. Oregon State University Libraries. Retrieved [date] from: Slides credited to ‘DataONE’ have the following citation: : “DataONE Education Module: Metadata. DataONE. Retrieved Jan. 5, From GEO 802, Data Information Literacy Winter Lecture 6 Gary Seitz, MA

Lesson 6 Outline Definition of metadata
Illustrate the value of metadata to data users FAIR data Let’s spend some time reviewing the syllabus and getting acquainted with what you can expect for the next 11 weeks. Image credit: Surveying by Luis Prado from The Noun Project Examples of metadata standards Luis Prado from The Noun Project

What is metadata? Metadata – Data about data
(Almost) The same product – two different qualities of metadata.

What is metadata? Metadata – Data about data

From field notes to datasets
Average temperature of observation for each species Species Average Temperature Temperature Standard Deviation Number of Observations Minimum Temperature Maximum Temperature Northern Red-legged Frog 4.4 --- 1 Tailed Frog 7.0 3.0 3 4 10 Arizona Toad 10.0 Strecker's Chorus Frog 10.5 2.0 11 9 16 Oregon Spotted Frog 11.0 15.5 2 22 New Jersey Chorus Frog 11.5 4.5 17 Wood Frog 12.5 5.5 897 28.8 Spring Peeper 13.2 5.6 569 -1 32 13.3 5.9 27 After returning from the field, scientists will transfer field notes into spreadsheets and other types of databases in preparation for their data analysis. Displayed here is a partial copy of a data set taken from the website “Frog Watch”. Notice there is no indication of Celsius or Fahrenheit in the “temperature” column. This is a simple example of how it is difficult to understand a dataset without all of the information. Slide credit: DataONE

Working with data When you provide data to someone else, what types of information would you want to include with the data? When you receive a dataset from an external source, what types of details do you want to know about the data? If you were to share your data, what type of information would be most useful to understand the data set? Alternatively, when receiving data from an external source, what information is needed to understand the data set? Metadata contains information about the dataset that allows it to be understood when shared amongst scientists. Slide credit: DataONE

Working with data Providing data: Receiving data:
Why were the data created? What limitations, if any, do the data have? What does the data mean? How should the data be cited if it is re-used in a new study? Receiving data: What are the data gaps? What processes were used for creating the data? Are there any fees associated with the data? In what scale were the data created? What do the values in the tables mean? What software do I need in order to read the data? What projection are the data in? Can I give these data to someone else? Metadata contains information about a data set, in a standardized format, such that it can be understood and re-used. Slide credit: DataONE

The value of metadata LTER metadata is EML BLS metadata is DDI
USFS metadata is ISO19115

Information entropy DATA DETAILS TIME Time of data development
Specific details about problems with individual items or specific dates are lost relatively rapidly General details about datasets are lost through time DATA DETAILS Retirement or career change makes access to “mental storage” difficult or unlikely This graph illustrates the phenomenon of “information entropy”, associated with research. At the time of the research project, a scientists memory is fresh. Details about the development of the dataset are easily recalled, and it is a good time to document information about the process. Over time, memory of the details begins to fade. A variety of circumstances can intervene, and eventually detailed knowledge about the dataset fades. Without a metadata record, this data might be unusable. A dataset it not considered complete without a metadata record to accompany it. Slide credit: DataONE Accident or technology change may make data unusable Loss of data developer leads to loss of remaining information TIME (From Michener et al 1997)

Information entropy Sound information management, including metadata development, can arrest the loss of dataset detail. DATA DETAILS Sound data management is best achieved with making metadata creation a part of the workflow. Not only can it keep the individual scientist organized, but the data has a much better chance of being re-used by future scientists. Slide credit: DataONE TIME

What is metadata? Contextual information for data is called metadata — literally “data about data” “Structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.” NISO, Understanding Metadata It can be used to describe (author, title, publication date …) physical items as well as digital items (documents, audio-visual files, images, datasets, etc.) Metadata can take many different forms, from free text (such as read-me files) to standardized, structured, machine-readable content For data to be useful, it will also need subject-specific metadata (reagent names, experimental conditions, population demographic…) More info:

What is metadata? Metadata is: Data ‘reporting’ WHO created the data?
WHAT is the content of the data? WHEN were the data created? WHERE is it geographically? HOW were the data developed? WHY were the data developed? Metadata is data about data. It describes the content, quality, condition, and other characteristics of a dataset. Metadata records answer questions such as: Why was the data set created? What processes were used to create the data set? What projection is the data in? When was the data last updated? Who created the data? What scale was used? What fields are in the table? What do the values in those fields mean? Who do I contact about getting more information about the data? How do I obtain a copy of the data? Do the data cost anything? Are there any limitations to the data? Metadata is a valuable tool. Metadata records preserve the usefulness of data over time by detailing methods for data collection and data set creation. Metadata greatly minimizes duplication of effort in the collection of expensive digital data and fosters the sharing of digital data resources. Slide notes, credit: DataONE Photo by Michelle Chang. All Rights Reserved

Metadata “The metadata accompanying your data should be written for a user 20 years into the future - what does that person need to know to use your data properly? Prepare the metadata for a user who is unfamiliar with your project, methods, or observations.” Slide credit: DataONE Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC)

Metadata in real life You use it all the time…
Metadata is all around us. . .from Mp3 players, to nutrition labels, to library card catalogues. For example, a card catalogue tell us more information than just the title of the book, they also tells the user: Who is the author? Who published the book? What subject area does the book fall in? And finally, where is it located in the library? Another example of metadata that we see in our daily lives is the nutrition and ingredient information on food labels. Nutrition labels answer questions such as: What ingredients were used? Who made the food? How many calories per serving? How many servings in the can? What percentage of daily vitamins are in each serving? Slide notes, credit: DataONE

Concerns about creating metadata
Solution workload required to capture accurate robust metadata incorporate metadata creation into data development process – distribute the effort time and resources to create, manage, and maintain metadata include in grant budget and schedule readability / usability of metadata use a standardized metadata format discipline specific information and ontologies ‘profile’ standard to require specific information and use specific values Metadata does require time and effort to create. The workload, however, is reduced when metadata creation is incorporated into the data development process and the effort is distributed among data contributors. Metadata creation and management should be treated as a standard data development procedure and resources for staff and time should be included in project and proposal work plans and budgets. The use of a standardized metadata format and the development of discipline specific ‘profiles’ of metadata can enable data users to quickly find needed information and address data developer concerns about metadata use and comprehension. Slide credit: DataONE

The value of metadata Metadata helps… Data creators Data users
Metadata is useful to Data Users, Data Developers, and Organizations. In this era of data sharing, collaboration, and need for information organization, metadata can serve multiple purposes. Slide credit: DataONE Organizations

What ist the value to data creators?
Metadata allows data creators to: Avoid data duplication Share reliable information Publicize efforts – promote the work of a scientist and his/her contributions to a field of study Metadata records will help avoid data duplication because researchers can determine if data already exists. Scientists are able to share reliable information about a dataset by creating metadata and passing it along with the dataset. Scientists wishing to reuse a dataset can be confident of its origins, data quality, and other valuable information about the data. Metadata also allow data creators to publicize the valuable data they have collected by making the metadata available on clearinghouses and other publically available venues. Metadata can be used in citation practices, thus increasing the visibility of the data. Slide credit: DataONE CC image by US Embassy Guyana on Flickr

What is the value to data users?
Metadata gives a user the ability to: Search, retrieve, and evaluate data set information from both inside and outside an organization Find data: Determine what data exists for a geographic location and/or topic Determine applicability: Decide if a data set meets a particular need Discover how to acquire the dataset you identified; process and use the dataset CC image by ASEE on Flickr Metadata allows the user to search for and access data from a variety of sources. A search for metadata can be constricted to a geographic boundary, thus showing the user what data has been collected in a particular region. Metadata records help users determine whether the data will be applicable for use in a particular study. Finally, metadata records are of value to data users, because they determine how a dataset can be acquired, and if there are any restrictions on how the data can be used. Slide credit: DataONE

What does a metadata record look like?
Ocean Currents and Biogeochemistry: Nearshore Water Profiles (Monthly CTD and Chemistry; SBC-LTER) web link New York City Community Health Survey, 2009 (ICPSR) Mountain hemlock tree-ring width chronologies from the western Oregon Cascade Mountains (USFS Research Data Archive) LTER metadata is EML BLS metadata is DDI USFS metadata is ISO19115

FAIR principles 2016 Published by FORCE 11 (= representatives from science, funding institutions, publishers, libraries, archives) Goal: Optimal processing of research data for both man and machine FAIR = Findable, Accessible, Interoperable, Reusable FAIR = 15 principles o.pdf or

Findability Persistente Identifiers (PID): e.g.. digital object identifier DOI Machine readable Metadaten Title, author Context, Quality and Characterization of the data How were the data generated? Which information is needed to interpretate the data? To Do: Deposit your data in a repository which assigns a PID to the dataset and allows you to provide detailed descriptive (and machine-readable) metadata. Make sure that your data are well documented and described. Create metadata while or shortly after the data are generated.

Accessibility Free access to data for any person with Internet access (no fees or other restrictions) constraints Data subject to data protection or personal rights data from international collaborations with countries that prohibit data sharing At least the metadata should be accessible. To Do: Consider which of your data can and will be shared and, if necessary, plan how they can be modified (e.g. anonymized) in order to be shareable. If your data cannot be made openly accessible, this has to be explained in the DMP. Metadata need to be provided in any case: Clearly define who can access the actual data and specify how.

Interoperability Data and metadata are compatible between two computer systems Open data formats (files can be opened with freely available software) Use of a standardized vocabulary When creating your metadata, keep in mind that a computer will read your metadata record. Therefore, it is important not to use tabs, indents, and special characters because they can be misunderstood by a computer. To Do: Provide machine readable data and metadata in an accessible language. Annotate data with resolvable vocabularies / ontologies / thesauri that are commonly used in your field. Properly cite associated data sets: Provide PID in the metadata and describe the scientific link between the cited data set and your data set.

Reusability - Metadata contains all the information necessary to understand the data. The categories of metadata are explained or are self-explanatory. - Data is reproducible and clearly understandable. - Information about licenses is included in the metadata. Whenever possible, the data is released for further use. It is important to note that the FAIR Data Principles do not require researchers to share all their data without any restrictions. Rather they advocate applying a standard procedure when sharing research data for reuse, so that humans and computer systems can easily find, interpret and use them under clearly defined conditions. The FAIR Data Principles are being adopted by a growing number of research funding organisations (e.g. Horizon 2020, NIH). To Do: Provide complete metadata for each file and address the following points: What does the dataset contain, how was it generated, how was it processed. (incl. lab conditions, parameter settings, name and version of software used, etc.) What are the scope and limitations of the data? Have the data been published before? Does it contain data from someone else? Consider which of your data can and will be labelled for reuse and apply an appropriate copyright license, such as Creative Commons CC 0, CC BY, etc.

Compare the metadata of two different datasets
Exercise: FAIR Data Compare the metadata of two different datasets

Choosing metadata standards
Image courtesy of Viv Hutchinson Slide credit: DataONE

Metadata standards Dublin Core (DC), Darwin Core (DwC), EML, DDI, NBII, FGDC/CSDGM, ISO 19139, ISO 19115, DIF, LDIF, e-GMS, AGLS, METS, MODS, PREMIS, OAI-PMH, MARC, CDWA, CIDOC/CRM, DACS, DIG35, GILS, GML, ISBD, LCSH, KML, MARCXML, MEI, MODS, MIX, OAIS, ANSI/NISO Z39.88, PB Core, PRISM, QDC, RDF, SGML, VSO, XML, XMP Joke: “Metadata schemes are like toothbrushes – everybody agrees that you should use one, but nobody wants to use someone else’s.”

What is a metadata standard?
A Standard provides a structure to describe data with: Common terms to allow consistency between records Common definitions for easier interpretation Common language for ease of communication Common structure to quickly locate information In search and retrieval, standards provide: Documentation structure in a reliable and predictable format for computer interpretation A uniform summary description of the dataset An established standard provides common terms, definitions and structure that allow for consistent communication. The use of standards also support search and retrieval in automated systems. Slide credit: DataONE CC image by ccarlstead on Flickr

Multiple standards exist
Darwin Core | biological diversity, taxonomy Dublin Core | general DDI (Data Documentation Initiative) | social & behavioral sci. DIF (Directory Interchange Format) | environmental sci. EML (Ecological Metadata Language) | ecology, biology ISO 19115| geographic data Browse by discipline:

Multiple metadata standards exist: examples
Dublin Core Element Set Emphasis on web resources, publications FGDC Content Standard for Digital Geospatial Metadata (CSDGM) Emphasis on geospatial data Biological Data Profile (BDP) of the CSDGM Profile to the CSDGM emphasis on biological data (and geospatial) ISO 19115/ Geographic information: Metadata Emphasis on geospatial data and services There are many standards available to document data. Each has a different focus, yet ask for similar information about the data set.

Ecological Metadata Language (EML) Focus on ecological data Darwin Core Emphasis on museum specimens Geography Markup Language (GML) Emphasis on geographic features (roads, highways, bridges) OGC® WaterML WaterML 2.0 is a standard information model for the representation of water observations data: There are many standards available to document data. Each has a different focus, yet ask for similar information about the data set.

Biology Earth Science General Research Data Physical Science Social Science & Humanities There are many standards available to document data. Each has a different focus, yet ask for similar information about the data set.

Browse through metadata standards
Exercise: Metadata Standards Browse through metadata standards

How to write quality metadata
Preparing to write metadata Tips for writing a quality metadata record CC image by fangblog on Flickr The topics in this module will illustrate steps for preparation and writing of quality metadata.

Steps to create quality metadata
Organize your information Did you write a project abstract to obtain funding for your proposal? Re-use it in your metadata! Did you use a lab notebook or other notes during the data development process that define measurements and other parameters? Do you have the contact information for colleagues you worked with? What about citations for other data sources you used in your project? Write your metadata using a metadata tool Review for accuracy and completeness Have someone else read your record Revise the record, based on comments from your reviewer Review once more before you publish Steps in creating quality metadata includes the following: Organize your information. Before you begin gather your resources, particularly anything you may have already written about the dataset for another purpose. For example, a grant proposal that has a well-written abstract and purpose for the research is a great resource. Write your metadata. Review the record for accuracy and completeness. Ask someone else read your record. Revise your information based on comments from your reviewer, then review it once more before you publish it.

Creating robust metadata is in your OWN best interest!
Summary Metadata is documentation of data A metadata record captures critical information about the content of a dataset Metadata allows data to be discovered, accessed, and re-used A metadata standard provides structure and consistency to data documentation Standards and tools vary – select according to defined criteria such as data type, organizational guidance, and available resources Metadata is of critical importance to data developers, data users, and organizations Metadata can be effectively used for: data distribution data management project management Metadata completes a dataset. Creating robust metadata is in your OWN best interest! Metadata is documentation of data A metadata record captures critical information about the content of a dataset Metadata allows data to be discovered, accessed, and re-used A metadata standard provides structure and consistency to data documentation Standards and tools vary – select according to defined criteria such as data type, organizational guidance, and available resources Metadata is of critical importance to data developers, data users, and organizations Metadata can be effectively used for: data distribution data management project management Metadata completes a dataset. Slide credit: DataONE

Additional Material

Metadata tools Morpho Metavist:
a metadata editor for the Federal Geographic Data Committee (FGDC) spatial metadata standard Steps in creating quality metadata includes the following: Organize your information. Before you begin gather your resources, particularly anything you may have already written about the dataset for another purpose. For example, a grant proposal that has a well-written abstract and purpose for the research is a great resource. Write your metadata. Review the record for accuracy and completeness. Ask someone else read your record. Revise your information based on comments from your reviewer, then review it once more before you publish it.

Tips for writing quality metadata
Do not use jargon Define technical terms and acronyms: CA, LA, GPS, GIS : what do these mean? Clearly state data limitations E.g., data set omissions, completeness of data Express considerations for appropriate re-use of the data Use “none” or “unknown” meaningfully None usually means that you knew about data and nothing existed (e.g., a “0” cubic feet per second discharge value) Unknown means that you don’t know whether that data existed or not (e.g., a null value) CC image by kruuscht on Flickr Think about the long-term effects of writing good metadata. Avoid using jargon and take the time to define all technical terms and acronyms. Clearly state data limitations – this may include any omissions to the data, or how complete the dataset is based on the data collection parameters. Define the use of none or unknown: None usually means that you knew about data but nothing existed. Unknown means you don’t know whether that data existed or not.

Titles, Titles, Titles… Titles are critical in helping readers find your data While individuals are searching for the most appropriate data sets, they are most likely going to use the title as the first criteria to determine if a dataset meets their needs. Treat the title as the opportunity to sell your dataset. A complete title includes: What, Where, When, Who, and Scale An informative title includes: topic, timeliness of the data, specific information about place and geography Titles are critical in helping researchers find data. While searching for appropriate datasets to include in their research, a researcher is most likely to use the title as the first criteria in determining if a dataset meets their needs. This enables you to treat the title as an opportunity to sell your dataset! A complete title includes the What, Where, When, Who, and Scale about the data. A more informative title will also include topic, timeliness of the data, specific information about place and geography.

A Clear Choice: Which title is better? Rivers OR Greater Yellowstone Rivers from 1:126,700 U.S. Forest Service Visitor Maps ( ) Greater Yellowstone (where) Rivers (what) from 1:126,700 (scale) U.S. Forest Service (who) Visitor Maps ( ) (when) CC image by dolfi on Flickr This example illustrates the importance of descriptive titles in metadata records. The title “Greater Yellowstone Rivers from 1:126,720 Forest Visitor Maps ( )”, gives enough detail for a reader to discern whether they might like more information about your data from your metadata record.

Be specific and quantify when you can! The goal of a metadata record is to give the user enough information to know if they can use the data without contacting the dataset owner. Vague: We checked our work and it looks complete. Specific: We checked our work using a random sample of 5 monitoring sites reviewed by 2 different people. We determined our work to be 95% complete based on these visual inspections. CC image by PNASH on Flickr One goal of a metadata record is to give a reader enough information about your data, that s/he could re-use it without contacting you, the dataset owner. “We checked our work and it looks complete” is too vague for a reader to assess quality control on the dataset, for example. More specific language gives the reader more information about how the data was collected and analyzed.

Select keywords wisely Use descriptive and clear writing Fully qualify geographic locations Use thesauri for keywords whenever possible Example: USGS Biocomplexity Thesaurus (over 9,500 terms) Select your keywords wisely. Think about the many ways someone might search for your data. Use descriptive and clear writing. Fully document geographic locations. Use thesauri whenever possible for keywords. Keywords are essential for locating records in clearinghouses quickly and efficiently. Use of standard thesauri, such as the USGS Biocomplexity Thesaurus, makes selecting keywords easier, and helps keep records consistent in content. CC image by Marco Arment on Flickr

Vocabularies for metadata description
In addition to selecting a metadata standard or schema, whenever possible you should also use a controlled vocabulary. A controlled vocabulary provides a consistent way to describe data - location, time, place name, subject. Controlled vocabularies significantly improve data discovery. It makes data more shareable with researchers in the same discipline because everyone is ‘talking the same language’ when searching for specific data e.g. plants, animals, medical conditions, places. Etc 1. Start by browsing Controlling your Language: a Directory of Metadata Vocabularies from JISC in the UK. Make sure you scroll down to 5. Conclusion - it’s worth a read. Select your keywords wisely. Think about the many ways someone might search for your data. Use descriptive and clear writing. Fully document geographic locations. Use thesauri whenever possible for keywords. Keywords are essential for locating records in clearinghouses quickly and efficiently. Use of standard thesauri, such as the USGS Biocomplexity Thesaurus, makes selecting keywords easier, and helps keep records consistent in content.

Remember: a computer will read your metadata Do not use symbols that could be misinterpreted: Examples: # % { } | / \ < > ~ Don’t use tabs, indents, or line feeds/carriage returns When copying and pasting from other sources, use a text editor (e.g., Notepad) to eliminate hidden characters When creating your metadata, keep in mind that a computer will read your metadata record. Therefore, it is important not to use tabs, indents, and special characters because they can be misunderstood by a computer. If you are copying and pasting content from other sources into your metadata record it is prudent to use a text editor as a middle step to prevent applications from adding in unnecessary characters in the background of your text.

Summary Review your final product:
Does the documentation present all the information needed to use or reuse the data? Remember: a well-written title and good keywords are critical in data discovery When reviewing your metadata record, if you did not know anything about this dataset would you be able to use it based on the metadata you created? Does the documentation adequately present all the information needed to use or reuse the data represented? Are any pieces of information missing (such as projection information, source citations, and process steps)? CC image by jugbo on Flickr

Summary Creating robust metadata is in your OWN best interest!
Metadata is documentation of data A metadata record captures critical information about the content of a dataset Metadata allows data to be discovered, accessed, and re-used A metadata standard provides structure and consistency to data documentation Standards and tools vary – select according to defined criteria such as data type, organizational guidance, and available resources Metadata is of critical importance to data developers, data users, and organizations Metadata can be effectively used for: data distribution data management project management Metadata completes a dataset. Creating robust metadata is in your OWN best interest! Metadata is documentation of data A metadata record captures critical information about the content of a dataset Metadata allows data to be discovered, accessed, and re-used A metadata standard provides structure and consistency to data documentation Standards and tools vary – select according to defined criteria such as data type, organizational guidance, and available resources Metadata is of critical importance to data developers, data users, and organizations Metadata can be effectively used for: data distribution data management project management Metadata completes a dataset.

Literature Disciplinary Metadata | Digital Curation Centre ( Kozlowski, W. (2014). Guidelines for basic “readme” style scientific metadata. Rudstam L. G., Luckey, F., & Koops, M. (2012). Water quality in offshore Lake Ontario during intensive sampling years 2003 and 2008: Results from the LOLA (Lake Ontario Lower Foodweb Assessment) Program. Metadata is documentation of data A metadata record captures critical information about the content of a dataset Metadata allows data to be discovered, accessed, and re-used A metadata standard provides structure and consistency to data documentation Standards and tools vary – select according to defined criteria such as data type, organizational guidance, and available resources Metadata is of critical importance to data developers, data users, and organizations Metadata can be effectively used for: data distribution data management project management Metadata completes a dataset.

Submitting metadata: docBUILDER
docBUILDER is a free, online tool for creating, updating, and submitting DIF metadata entries to the GCMD. Provides a check-list of required, highly recommended, and recommended fields to populate. Checks for valid and complete values in metadata fields. Allows for templates when creating entries with duplicate information. Users can register for an account at Users can access docBUILDER at Slide 4: Submitting Metadata docBUILDER is a free metadata authoring tool provided by the GCMD for creating, updating, and contributing DIF metadata entries. The tool is easy to use. It gives you a visual checklist of required, highly recommended, and recommended fields to populate. In the process of submitting your metadata, the tool will check for valid and complete values in metadata fields. For your convenience, the tool allows you to use templates when you want to create multiple entries that have duplicate information. On this slide, we have provided the links for you to register for an account on docBUILDER, and to access and use docBUILDER.

Using docBUILDER Submit Your Document Validate Your Document
to the GCMD Validate Your Document and/or Get Help Checked When Completed Slide 5: Using docBUILDER On this slide, we’ve provided a glimpse of the docBUILDER interface that highlights the features mentioned in the previous slide. The menu in blue at the top of the tool gives you many options. The File dropdown menu allows you to Save, Enter, or Add a metadata record. The Document dropdown menu allows you to Validate, Preview and Submit your metadata document. The Help dropdown menu allows you to access the docBUILDER and metadata authoring tutorials. You can also contact the GCMD staff if you have additional questions. In this view of the docBUILDER interface, you can see that when you create a metadata record by populating the fields, the tool automatically adds a check to the field as it is completed. When users are finished editing their metadata record they can click the Submit to GCMD button in the top right hand corner to send their metadata. After review, the record will be approved by the science coordinators, and loaded into the GCMD database. You can also use docBUILDER to generate templates when you want to submit multiple records that have repeatable information in separate required fields. The online tutorials give you step by step directions for creating and editing templates. Populate Metadata Fields

Using docBUILDER example of fileds and entering information
Science Keywords Data Center Slide 6: Using docBUILDER - Example of Fields and Entering information On this slide, we show you several key fields in the docBUILDER metadata authoring tool. On the left, you can see that the tool allows you to select the Earth Science keywords that describe the topics covered by your data set from a searchable and scrollable list. On the right, you can see how you would enter the name of the data center that will provide the data, and contact information for a person at the data center. As you fill out the field and click continue, the information you enter is automatically saved.

Browse through a data repository
Exercise Choose one of the 4 specialised data repositories below, or find another data repository of interest - particularly one in a discipline you are unfamiliar with and spend some time browsing around your chosen repository to get a feel for the data available. Earth System Grid GEOSS Datenportal Australian Data Archive USGS Water Data Think about how the data here differs from data you are familiar with. Consider for example, format, size and access method. Browse through a data repository Does not include, “any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This "recorded" material excludes physical objects (e.g., laboratory samples).” This narrow definition mostly takes a retrospective view of your dataset, in that it does not account for raw and intermediate data that may be critical to the research process but that don’t become part of the ’final’ dataset. Data types could be: Observational Experimental Simulated Derived Reference or canonical

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