Linked Earth Wiki - User contributions [en]

Using LiPD files

2019-05-16T18:45:41Z

Michaelerb: /* LiPD Utilities in Python 3 */

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

The LiPD utilities are compatible with Windows. However, most of the more advanced data analysis tools are only available for Mac or Linux platforms. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install an Ubuntu virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC directly from the [https://www.python.org/ Python website]. '''Make sure to select the Python 3 version'''.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

Another option is to use the [https://www.continuum.io/downloads Anaconda] Python release.

==LiPD Utilities==

{{See also|LiPD Utilities}}

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

If you run into any errors, they may indicate other things you need to install first. Next, start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
data_single = lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
data_all = lipd.readLipd('/path/to/data/') # Load all LiPD files in a directory. Or...
data_selected = lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

The data will will be stored in a dictionary, from which you can access all of the data.

Some files contain multiple time series. To rearrange the data to see all of the time series data, use the extractTs() function:

<syntaxhighlight lang="python">
data_all_ts = lipd.extractTs(data_all)
</syntaxhighlight>

An array of dictionaries is now contained in the variable "data_all_ts". (If you're unfamiliar with python, read a primer to get a better understanding.) From here, the data can be sorted with the filterTs() function, according to metadata fields. For example:

<syntaxhighlight lang="python">
data_NH = lipd.filterTs(data_all_ts,'geo_meanLat > 0')
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check what fields are in your data set.

After using the extractTs command above, you can access data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all_ts[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. To get started with some more commands, read the quickstart guide on Github: [https://github.com/nickmckay/LiPD-utilities/blob/master/Examples/Welcome%20LiPD%20-%20Quickstart.ipynb Quickstart Guide].

If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

{{See also|Pyleoclim}}

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

Please note that some of the dependencies used by Pyleoclim are unavailable to Windows users.

==CSV files==

[[File:Example of Unzip LiPD file.png | thumb | right |400px | Example of an unzipped LiPD files. The data is contained in the csv files while the metadata is stored in the JSON-LD file]]

If you need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

[[File:BestPractices Download csv file.png | thumb | right | 400 px | Downloading a csv file]]

You can also access the .csv file on the wiki and download them.

M. P. Erb

2019-05-15T18:28:33Z

Michaelerb: Set PropertyValue: Email (L) = michael.erb@nau.edu

[[Category:Person (L)]]
{{#set:
Current Affiliation=Northern Arizona University|
Current Position=Postdoctoral Scholar|
Email (L)=michael.erb@nau.edu|
Expertise=Climate_Dynamics|
Expertise=Climate_Modeling|
Github ID=michaelerb|
Google Scholar ID=Michael P. Erb|
Has User ID=Michaelerb|
Highest Degree=Ph.D.|
Name (L)=Michael Erb|
OrcidNumber (L)=orcid.org/0000-0002-1187-952X|
Picture URL=Http://www.michaelerb.org/uploads/5/1/9/5/51950695/5461846.jpg?459|
Subscribes To=Category:Uncertainty_Working_Group|
Twitter ID=@MichaelPErb|
University=Rutgers, The State University of New Jersey|
Website=http://www.michaelerb.org/}}

M. P. Erb

2019-05-15T18:28:24Z

Michaelerb: Set PropertyValue: Current Affiliation = Northern Arizona University

[[Category:Person (L)]]
{{#set:
Current Affiliation=Northern Arizona University|
Current Position=Postdoctoral Scholar|
Email (L)=erbm@usc.edu|
Expertise=Climate_Dynamics|
Expertise=Climate_Modeling|
Github ID=michaelerb|
Google Scholar ID=Michael P. Erb|
Has User ID=Michaelerb|
Highest Degree=Ph.D.|
Name (L)=Michael Erb|
OrcidNumber (L)=orcid.org/0000-0002-1187-952X|
Picture URL=Http://www.michaelerb.org/uploads/5/1/9/5/51950695/5461846.jpg?459|
Subscribes To=Category:Uncertainty_Working_Group|
Twitter ID=@MichaelPErb|
University=Rutgers, The State University of New Jersey|
Website=http://www.michaelerb.org/}}

Using LiPD files

2018-08-14T16:50:05Z

Michaelerb: /* LiPD Utilities in Python 3 */ Updated the commands so that they work again.

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

The LiPD utilities are compatible with Windows. However, most of the more advanced data analysis tools are only available for Mac or Linux platforms. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install an Ubuntu virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC directly from the [https://www.python.org/ Python website]. '''Make sure to select the Python 3 version'''.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

Another option is to use the [https://www.continuum.io/downloads Anaconda] Python release.

==LiPD Utilities==

{{See also|LiPD Utilities}}

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

If you run into any errors, they may indicate other things you need to install first. Next, start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
data_single = lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
data_all = lipd.readLipd('/path/to/data/') # Load all LiPD files in a directory. Or...
data_selected = lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

The data will will be stored in a dictionary, from which you can access all of the data.

Some files contain multiple time series. To rearrange the data to see all of the time series data, use the extractTs() function:

<syntaxhighlight lang="python">
data_all_ts = lipd.extractTs(data_all)
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) From here, the data can be sorted with the filterTs() function, according to metadata fields. For example:

<syntaxhighlight lang="python">
data_NH = lipd.filterTs(data_all_ts,'geo_meanLat > 0')
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check what fields are in your data set.

After using the extractTs command above, you can access data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all_ts[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. To get started with some more commands, read the quickstart guide on Github: [https://github.com/nickmckay/LiPD-utilities/blob/master/Examples/Welcome%20LiPD%20-%20Quickstart.ipynb Quickstart Guide].

If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

{{See also|Pyleoclim}}

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

Please note that some of the dependencies used by Pyleoclim are unavailable to Windows users.

==CSV files==

[[File:Example of Unzip LiPD file.png | thumb | right |400px | Example of an unzipped LiPD files. The data is contained in the csv files while the metadata is stored in the JSON-LD file]]

If you need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

[[File:BestPractices Download csv file.png | thumb | right | 400 px | Downloading a csv file]]

You can also access the .csv file on the wiki and download them.

Using LiPD files

2018-07-03T20:20:18Z

Michaelerb:

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

The LiPD utilities are compatible with Windows. However, most of the more advanced data analysis tools are only available for Mac or Linux platforms. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install an Ubuntu virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC directly from the [https://www.python.org/ Python website]. '''Make sure to select the Python 3 version'''.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

Another option is to use the [https://www.continuum.io/downloads Anaconda] Python release.

==LiPD Utilities==

{{See also|LiPD Utilities}}

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

If you run into any errors, they may indicate other things you need to install first. Next, start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. To get started with some more commands, read the quickstart guide on Github: [https://github.com/nickmckay/LiPD-utilities/blob/master/Examples/Welcome%20LiPD%20-%20Quickstart.ipynb Quickstart Guide].

If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

{{See also|Pyleoclim}}

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

Please note that some of the dependencies used by Pyleoclim are unavailable to Windows users.

==CSV files==

[[File:Example of Unzip LiPD file.png | thumb | right |400px | Example of an unzipped LiPD files. The data is contained in the csv files while the metadata is stored in the JSON-LD file]]

If you need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

[[File:BestPractices Download csv file.png | thumb | right | 400 px | Downloading a csv file]]

You can also access the .csv file on the wiki and download them.

Main Page

2018-01-05T16:23:49Z

Michaelerb:

[[File:EarthLinked_Banner_blue_NoShadow.jpg|center|440px|alt=LinkedEarthLogo]]

__NOCACHE__

<div style = "text-align:center;">Welcome to the LinkedEarth Wiki!</div>

The LinkedEarth wiki is an [https://earthcube.org EarthCube]-funded platform that:
#enables the curation of publicly-accessible database by paleoclimate experts, and
#fosters the development of standards, so paleoclimate data are easier to analyze, share, and re-use. 

==Getting started with LinkedEarth==

<gallery mode = "packed-overlay" widths=150px heights=100px>
File:JoinUsIcon MainPage.png | [http://linked.earth/aboutus/membership/ Join LinkedEarth]
File:GettingStartedIcon MainPage.png | [[Get_Started_with_the_LinkedEarth_wiki | StartUp Guide]]
File:Browse The Wiki.png | [[Special:random | Browse existing pages]]
File:BrowseIcon.png| [[MD982181.Khider.2014 | Browse a curated dataset]]
File:BrowseIcon.png | [[MD982176.Stott.2004 | Browse a curated dataset with Models]]
</gallery>

Several tutorials are also available on our [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel]!

==Community Activities==

Join a [[:Category:Working Group | working group]] (WG) and participate in the discussion about [[Paleoclimate_Data_Standards | paleoclimate data standards]].

'''New''': Each working group has polls waiting for community input. ''You must be logged in to vote.''

In the Linked Earth context, a WG is a self-organized coalition of knowledgeable experts who elaborate and discuss the components of a [[Paleoclimate_Data_Standards | data standard]] for their topic(s) of interest. WGs may be created by any member of the LinkedEarth community. Currently, the LinkedEarth community is organized around {{PAGESINCATEGORY:Working Group}} working groups.

'''To join a WG, do so on your LinkedEarth wiki profile page'''.

<gallery mode = "packed-overlay" widths=150px heights=150px>
File:Open_data_standards_trim.png | [[Paleoclimate Data Standards]]
File:WorkingGroups MainPage Icons.png | [[:Category:Working Group | Join a Working Group]]
File:Ontology_wordcloud.png | [[LinkedEarth Ontology]]
</gallery>

== Contribute Datasets ==

We recommend using the [[Linked Paleo Data | LiPD]] format to get your data on the wiki. The Python [[LiPD Utilities]] allow to create a LiPD from an Excel template, wich can be downloaded [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx here]. For instructions on filling out this template, see the page "[[Creating a LiPD file]]".

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[Creating a LiPD file]]
File:ExcelLogo.png | [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx LiPD Template]
File:AnnotateDatasetImage.png | [[Dataset Tutorial | Upload a LiPD dataset]]
</gallery>

== Working with LiPD Datasets ==

The [[LiPD Utilities]] (available in R, Python, and Matlab) allow to read/write LiPD files, extract and collapse timeseries objects, and filter datasets according to your search criteria. Data analysis packages are currently available in R ([[GeoChronR]]) and Python ([[Pyleoclim]]). A large body of [https://github.com/CommonClimate/PAGES2k_phase2 Matlab code exists already] to analyze the [[PAGES2k]] dataset. If you are unfamiliar with using LiPD files, a good place to start is [[Using LiPD files]].

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[LiPD Utilities]]
File:RLogo.jpeg | [[GeoChronR]]
File:PythonLogo.jpg | [[Pyleoclim]]
</gallery>

==Current State of the Database==

The LinkedEarth Wiki currently contains '''{{PAGESINCATEGORY:Dataset (L)}} datasets''', mostly from the [[PAGES2k]] compilation. We are in the process of adding more Holocene records, as well as >100 deep-sea cores from the LR04 benthic stack compilation. Please contact us if you want to lead a '''data drive''' to flesh it out some more.

=== Dataset Location ===



{{#ask: [[Category:Dataset_(L)]]
| ?CollectedFrom_(L).Coordinates
| ?Name_(L)
| ?ArchiveType
| markercluster=on
| showtitle=off
| zoom=1
| searchmarkers=all
| maxzoom=14
| minzoom=1
| limit=1000
| height=510
| template=LiPDLocation
| format=leaflet
}}

=== Recently Contributed Datasets ===

{{#ask: [[Category:Dataset (L)]]
| ?CollectedFrom (L).CoordinatesFor=LiPD Dataset
| ?ArchiveType
| ?CollectedFrom (L).Coordinates
| mainlabel=-
| limit=10
| format=broadtable
| sort=Modification date
| order = descending
}}

=== Search the Database by Archive===



<gallery mode = "packed-overlay" widths=200px heights=200px>
File:Coral_Archive_200px.jpg | [[:Category:Coral | Coral]]
File:Documents_Archive_200px.jpg | [[:Category:Document | Document]]
File:GlacierIce_Archive_200px.jpg | [[:Category:GlacierIce | Glacier Ice]]
File:Lake_Archive_200px.jpg | [[:Category:LakeSediment | Lake Sediment]]
File:MarineSediment_Archive_200px.jpg | [[:Category:MarineSediment | Marine Sediment]]
File:Mollusk_Archive_200px.jpg | [[:Category:MolluskShells | Mollusk Shells]]
File:Peat_Archive_200px.jpg | [[:Category:Peat | Peat]]
File:Sclerosponge_Archive_200px.jpg | [[:Category:Sclerosponge | Sclerosponge]]
File:Speleothem_Archive_200px.jpg | [[:Category:Speleothem | Speleothem]]
File:Wood_Archive_200px.jpg | [[:Category:Wood | Wood]]
</gallery>

== More about LinkedEarth ==

Website: [http://linked.earth LinkedEarth Website]

Programmatic access to the wiki information: [http://wiki.linked.earth/Dataset_discovery http://wiki.linked.earth/Dataset_discovery]

Social Media: [https://www.facebook.com/LinkedEarth-1018909201501056/?fref=ts Facebook] | [https://twitter.com/Linked_Earth Twitter] | [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel] | [https://github.com/LinkedEarth GitHub]

== Linked Earth Totals ==
''Last changed On: 12 Dec 2017'' Linked Earth contains [http://wiki.linked.earth/Category:Compilation_(L) 6 Compilations], [http://wiki.linked.earth/Category:Dataset_(L) 700 Datasets], [http://wiki.linked.earth/Category:Observation_(L) 4044 Observation] and [http://wiki.linked.earth/Category:Publication_(L) 921 Publications] from [http://wiki.linked.earth/Category:Person_(L) 785 Authors]. The contents of the wiki have been contributed by [http://wiki.linked.earth/Special:ListUsers 163 Scientists] from [http://wiki.linked.earth/Property:University 26 Institutions], organized in [http://wiki.linked.earth/Category:Working_Group 14 Working Groups].

Main Page

2018-01-05T16:22:22Z

Michaelerb:

[[File:EarthLinked_Banner_blue_NoShadow.jpg|center|440px|alt=LinkedEarthLogo]]

__NOCACHE__

<div style = "text-align:center;">Welcome to the LinkedEarth Wiki!</div>

The LinkedEarth wiki is an [https://earthcube.org EarthCube]-funded platform that:
#enables the curation of publicly-accessible database by paleoclimate experts, and
#fosters the development of standards, so paleoclimate data are easier to analyze, share, and re-use. 

==Getting started with LinkedEarth==

<gallery mode = "packed-overlay" widths=150px heights=100px>
File:JoinUsIcon MainPage.png | [http://linked.earth/aboutus/membership/ Join LinkedEarth]
File:GettingStartedIcon MainPage.png | [[Get_Started_with_the_LinkedEarth_wiki | StartUp Guide]]
File:Browse The Wiki.png | [[Special:random | Browse existing pages]]
File:BrowseIcon.png| [[MD982181.Khider.2014 | Browse a curated dataset]]
File:BrowseIcon.png | [[MD982176.Stott.2004 | Browse a curated dataset with Models]]
</gallery>

Several tutorials are also available on our [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel]!

==Community Activities==

Join a [[:Category:Working Group | working group]] (WG) and participate in the discussion about [[Paleoclimate_Data_Standards | paleoclimate data standards]].

'''New''': Each working group has polls waiting for community input. ''You must be logged in to vote.''

In the Linked Earth context, a WG is a self-organized coalition of knowledgeable experts who elaborate and discuss the components of a [[Paleoclimate_Data_Standards | data standard]] for their topic(s) of interest. WGs may be created by any member of the LinkedEarth community. Currently, the LinkedEarth community is organized around {{PAGESINCATEGORY:Working Group}} working groups.

'''To join a WG, do so on your LinkedEarth wiki profile page'''.

<gallery mode = "packed-overlay" widths=150px heights=150px>
File:Open_data_standards_trim.png | [[Paleoclimate Data Standards]]
File:WorkingGroups MainPage Icons.png | [[:Category:Working Group | Join a Working Group]]
File:Ontology_wordcloud.png | [[LinkedEarth Ontology]]
</gallery>

== Contribute Datasets ==

We recommend using the [[Linked Paleo Data | LiPD]] format to get your data on the wiki. The Python [[LiPD Utilities]] allow to create a LiPD from an Excel template, wich can be downloaded [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx here]. For instructions on filling out this template, see the page "[[Creating a LiPD file]]".

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[Creating a LiPD file]]
File:ExcelLogo.png | [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx LiPD Template]
File:AnnotateDatasetImage.png | [[Dataset Tutorial | Upload a LiPD dataset]]
</gallery>

== Working with LiPD Datasets ==

The [[LiPD Utilities]] (available in R, Python, and Matlab) allow to read/write LiPD files, extract and collapse timeseries objects, and filter datasets according to your search criteria. Data analysis packages are currently available in R ([[GeoChronR]]) and Python ([[Pyleoclim]]). A large body of [https://github.com/CommonClimate/PAGES2k_phase2 Matlab code exists already] to analyze the [[PAGES2k]] dataset. A good place to start is [[Using LiPD files]].

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[LiPD Utilities]]
File:RLogo.jpeg | [[GeoChronR]]
File:PythonLogo.jpg | [[Pyleoclim]]
</gallery>

==Current State of the Database==

The LinkedEarth Wiki currently contains '''{{PAGESINCATEGORY:Dataset (L)}} datasets''', mostly from the [[PAGES2k]] compilation. We are in the process of adding more Holocene records, as well as >100 deep-sea cores from the LR04 benthic stack compilation. Please contact us if you want to lead a '''data drive''' to flesh it out some more.

=== Dataset Location ===



{{#ask: [[Category:Dataset_(L)]]
| ?CollectedFrom_(L).Coordinates
| ?Name_(L)
| ?ArchiveType
| markercluster=on
| showtitle=off
| zoom=1
| searchmarkers=all
| maxzoom=14
| minzoom=1
| limit=1000
| height=510
| template=LiPDLocation
| format=leaflet
}}

=== Recently Contributed Datasets ===

{{#ask: [[Category:Dataset (L)]]
| ?CollectedFrom (L).CoordinatesFor=LiPD Dataset
| ?ArchiveType
| ?CollectedFrom (L).Coordinates
| mainlabel=-
| limit=10
| format=broadtable
| sort=Modification date
| order = descending
}}

=== Search the Database by Archive===



<gallery mode = "packed-overlay" widths=200px heights=200px>
File:Coral_Archive_200px.jpg | [[:Category:Coral | Coral]]
File:Documents_Archive_200px.jpg | [[:Category:Document | Document]]
File:GlacierIce_Archive_200px.jpg | [[:Category:GlacierIce | Glacier Ice]]
File:Lake_Archive_200px.jpg | [[:Category:LakeSediment | Lake Sediment]]
File:MarineSediment_Archive_200px.jpg | [[:Category:MarineSediment | Marine Sediment]]
File:Mollusk_Archive_200px.jpg | [[:Category:MolluskShells | Mollusk Shells]]
File:Peat_Archive_200px.jpg | [[:Category:Peat | Peat]]
File:Sclerosponge_Archive_200px.jpg | [[:Category:Sclerosponge | Sclerosponge]]
File:Speleothem_Archive_200px.jpg | [[:Category:Speleothem | Speleothem]]
File:Wood_Archive_200px.jpg | [[:Category:Wood | Wood]]
</gallery>

== More about LinkedEarth ==

Website: [http://linked.earth LinkedEarth Website]

Programmatic access to the wiki information: [http://wiki.linked.earth/Dataset_discovery http://wiki.linked.earth/Dataset_discovery]

Social Media: [https://www.facebook.com/LinkedEarth-1018909201501056/?fref=ts Facebook] | [https://twitter.com/Linked_Earth Twitter] | [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel] | [https://github.com/LinkedEarth GitHub]

== Linked Earth Totals ==
''Last changed On: 12 Dec 2017'' Linked Earth contains [http://wiki.linked.earth/Category:Compilation_(L) 6 Compilations], [http://wiki.linked.earth/Category:Dataset_(L) 700 Datasets], [http://wiki.linked.earth/Category:Observation_(L) 4044 Observation] and [http://wiki.linked.earth/Category:Publication_(L) 921 Publications] from [http://wiki.linked.earth/Category:Person_(L) 785 Authors]. The contents of the wiki have been contributed by [http://wiki.linked.earth/Special:ListUsers 163 Scientists] from [http://wiki.linked.earth/Property:University 26 Institutions], organized in [http://wiki.linked.earth/Category:Working_Group 14 Working Groups].

Main Page

2018-01-05T16:21:33Z

Michaelerb: I moved the link to "Using LiPD files" so that it is more obvious. I think that people would skip over the "see also" section, so I moved it into the text.

[[File:EarthLinked_Banner_blue_NoShadow.jpg|center|440px|alt=LinkedEarthLogo]]

__NOCACHE__

<div style = "text-align:center;">Welcome to the LinkedEarth Wiki!</div>

The LinkedEarth wiki is an [https://earthcube.org EarthCube]-funded platform that:
#enables the curation of publicly-accessible database by paleoclimate experts, and
#fosters the development of standards, so paleoclimate data are easier to analyze, share, and re-use. 

==Getting started with LinkedEarth==

<gallery mode = "packed-overlay" widths=150px heights=100px>
File:JoinUsIcon MainPage.png | [http://linked.earth/aboutus/membership/ Join LinkedEarth]
File:GettingStartedIcon MainPage.png | [[Get_Started_with_the_LinkedEarth_wiki | StartUp Guide]]
File:Browse The Wiki.png | [[Special:random | Browse existing pages]]
File:BrowseIcon.png| [[MD982181.Khider.2014 | Browse a curated dataset]]
File:BrowseIcon.png | [[MD982176.Stott.2004 | Browse a curated dataset with Models]]
</gallery>

Several tutorials are also available on our [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel]!

==Community Activities==

Join a [[:Category:Working Group | working group]] (WG) and participate in the discussion about [[Paleoclimate_Data_Standards | paleoclimate data standards]].

'''New''': Each working group has polls waiting for community input. ''You must be logged in to vote.''

In the Linked Earth context, a WG is a self-organized coalition of knowledgeable experts who elaborate and discuss the components of a [[Paleoclimate_Data_Standards | data standard]] for their topic(s) of interest. WGs may be created by any member of the LinkedEarth community. Currently, the LinkedEarth community is organized around {{PAGESINCATEGORY:Working Group}} working groups.

'''To join a WG, do so on your LinkedEarth wiki profile page'''.

<gallery mode = "packed-overlay" widths=150px heights=150px>
File:Open_data_standards_trim.png | [[Paleoclimate Data Standards]]
File:WorkingGroups MainPage Icons.png | [[:Category:Working Group | Join a Working Group]]
File:Ontology_wordcloud.png | [[LinkedEarth Ontology]]
</gallery>

== Contribute Datasets ==

We recommend using the [[Linked Paleo Data | LiPD]] format to get your data on the wiki. The Python [[LiPD Utilities]] allow to create a LiPD from an Excel template, wich can be downloaded [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx here]. For instructions on filling out this template, see the page "[[Creating a LiPD file]]".

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[Creating a LiPD file]]
File:ExcelLogo.png | [http://wiki.linked.earth/File:LiPDv1.2_template.xlsx LiPD Template]
File:AnnotateDatasetImage.png | [[Dataset Tutorial | Upload a LiPD dataset]]
</gallery>

== Working with LiPD Datasets ==

The [[LiPD Utilities]] (available in R, Python, and Matlab) allow to read/write LiPD files, extract and collapse timeseries objects, and filter datasets according to your search criteria. Data analysis packages are currently available in R ([[GeoChronR]]) and Python ([[Pyleoclim]]). A large body of [https://github.com/CommonClimate/PAGES2k_phase2 Matlab code exists already] to analyze the [[PAGES2k]] dataset. A good place to start is {{Using LiPD files}}.

<gallery mode = "packed-overlay" widths=100px heights=100px>
File:LiPD Logo.jpg | [[LiPD Utilities]]
File:RLogo.jpeg | [[GeoChronR]]
File:PythonLogo.jpg | [[Pyleoclim]]
</gallery>

==Current State of the Database==

The LinkedEarth Wiki currently contains '''{{PAGESINCATEGORY:Dataset (L)}} datasets''', mostly from the [[PAGES2k]] compilation. We are in the process of adding more Holocene records, as well as >100 deep-sea cores from the LR04 benthic stack compilation. Please contact us if you want to lead a '''data drive''' to flesh it out some more.

=== Dataset Location ===



{{#ask: [[Category:Dataset_(L)]]
| ?CollectedFrom_(L).Coordinates
| ?Name_(L)
| ?ArchiveType
| markercluster=on
| showtitle=off
| zoom=1
| searchmarkers=all
| maxzoom=14
| minzoom=1
| limit=1000
| height=510
| template=LiPDLocation
| format=leaflet
}}

=== Recently Contributed Datasets ===

{{#ask: [[Category:Dataset (L)]]
| ?CollectedFrom (L).CoordinatesFor=LiPD Dataset
| ?ArchiveType
| ?CollectedFrom (L).Coordinates
| mainlabel=-
| limit=10
| format=broadtable
| sort=Modification date
| order = descending
}}

=== Search the Database by Archive===



<gallery mode = "packed-overlay" widths=200px heights=200px>
File:Coral_Archive_200px.jpg | [[:Category:Coral | Coral]]
File:Documents_Archive_200px.jpg | [[:Category:Document | Document]]
File:GlacierIce_Archive_200px.jpg | [[:Category:GlacierIce | Glacier Ice]]
File:Lake_Archive_200px.jpg | [[:Category:LakeSediment | Lake Sediment]]
File:MarineSediment_Archive_200px.jpg | [[:Category:MarineSediment | Marine Sediment]]
File:Mollusk_Archive_200px.jpg | [[:Category:MolluskShells | Mollusk Shells]]
File:Peat_Archive_200px.jpg | [[:Category:Peat | Peat]]
File:Sclerosponge_Archive_200px.jpg | [[:Category:Sclerosponge | Sclerosponge]]
File:Speleothem_Archive_200px.jpg | [[:Category:Speleothem | Speleothem]]
File:Wood_Archive_200px.jpg | [[:Category:Wood | Wood]]
</gallery>

== More about LinkedEarth ==

Website: [http://linked.earth LinkedEarth Website]

Programmatic access to the wiki information: [http://wiki.linked.earth/Dataset_discovery http://wiki.linked.earth/Dataset_discovery]

Social Media: [https://www.facebook.com/LinkedEarth-1018909201501056/?fref=ts Facebook] | [https://twitter.com/Linked_Earth Twitter] | [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel] | [https://github.com/LinkedEarth GitHub]

== Linked Earth Totals ==
''Last changed On: 12 Dec 2017'' Linked Earth contains [http://wiki.linked.earth/Category:Compilation_(L) 6 Compilations], [http://wiki.linked.earth/Category:Dataset_(L) 700 Datasets], [http://wiki.linked.earth/Category:Observation_(L) 4044 Observation] and [http://wiki.linked.earth/Category:Publication_(L) 921 Publications] from [http://wiki.linked.earth/Category:Person_(L) 785 Authors]. The contents of the wiki have been contributed by [http://wiki.linked.earth/Special:ListUsers 163 Scientists] from [http://wiki.linked.earth/Property:University 26 Institutions], organized in [http://wiki.linked.earth/Category:Working_Group 14 Working Groups].

Quick Guide to Editing Wiki Pages

2017-06-29T17:35:20Z

Michaelerb: /* Editing existing wiki pages */ I added a little bit of introductory text to make this page more friendly for people getting started. Seeing so many different commands right away might be daunting, when in fact editing a wiki page is very easy.

Once you are [[Get Started with the LinkedEarth wiki | registered]], you can start editing existing pages, creating new ones, and move/delete obsolete pages. The pages you are allowed to edit depends on your [[User Privileges | editorial level]].

This guide provides basic instructions on how to use the MediaWiki format. If you require more help, use this [https://www.mediawiki.org/wiki/Help:Contents Help] or [mailto:linkedearth@gmail.com contact us].

The quickest way to figure out how to do something is to find and example of what you want to do from another page, or on Wikipedia, and copy the source code from there.

=Editing existing wiki pages=

Once logged in to your account, you can edit most of the wiki pages by clicking on "edit" at the top of the page (figure 1).

[[File:Location_of_edit_button.png|thumb|none|700px|Figure 1: Location of the "Edit" Tab]]

To edit text, you can get started right away. Once you click that "edit" button, just find the section you want to edit and type normally to add useful information or expand on a topic. If you want to add a new section, format something, or add a poll or an image, use the remainder of this page as a reference. You don't need to know everything right away--Just jump in. You can always refer back to this page later on.

==Quick Markup Guide==

{| class="wikitable"
! style="width: 50%" | What it looks like
! style="width: 50%" | What you type
|-
|
''Italic Text''
|<pre>
''Italic Text'' </pre>
|-
|
'''Bold Text'''
|<pre>
'''Bold Text'''
</pre>
|-
|
'''''Bold and italic'''''
|<pre>
'''''Bold Text'''''
</pre>
|-
|
<strike> strike text </strike>
| <pre>
<strike> strike text </strike>
</pre>
|}

== Section Headings and Lists==

{| class="wikitable"
! style="width: 33%" | Description
! style="width: 33%" | What it looks like
! style="width: 34%" | What you type
|-
|
Headings

* Skip Level 1, it is page name level.
* An article with 4 or more headings automatically creates a Table of Contents.

|
==Level 2==
=== Level 3===
====Level 4 ====
===== Level 5 =====
====== Level 6 ======
|<pre>
==Level 2==
=== Level 3===
====Level 4 ====
===== Level 5 =====
====== Level 6 ======
</pre>
|-
|
Bullet List
|
* Level 1
** Level 2
*** Level 3
|<pre>
* Level 1
** Level 2
*** Level 3
</pre>
|-
|Numbered List
|
# Level 1
## Level 2
### Level 3
|<pre>
# Level 1
## Level 2
### Level 3
</pre>
|-
| Comment
|

Comments are visible only in the edit zone
|<pre>

Comments are visible only in the edit zone
</pre>
|}

== Links ==

=== Internal Links ===

{| class="wikitable"
! style="width: 33%" | Description
! style="width: 33%" | What it looks like
! style="width: 34%" | What you type
|-
|
Internal Link
|
[[Main Page]]
|<pre>
[[Main Page]] </pre>
|-
| Piped Link
Use different text for the link
|
[[Main Page|Go to the Main Page]]
|<pre>
[[Main Page|Go to the Main Page]]
</pre>
|-
|Word-ending links, following so called "link trail rules"
|
[[Radioisotopes in Corals]]
| <pre>
[[Radioisotopes in Corals]] </pre>
|-
| Internal Link to the current page's talk page
|
[[{{TALKPAGENAME}}|Discussion]]
| <pre>
[[{{TALKPAGENAME}}|Discussion]] </pre>
|-
| Internal link to an image or a file of other types
| [[media:PSM.jpg]]
| <pre>
[[media:PSM.jpg]]</pre>
|-
| Internal link to the user's user page
| [[Special:MyPage]]
|<pre>
[[Special:MyPage]]</pre>
|-
| Internal link to a category page
| [[:Category:Working_Group]]
|<pre>
[[:Category:Working_Group]]</pre>
|-
| Internal link to a property page
| [[:Property:HasUnits_(L)]]
|<pre>
[[:Property:HasUnits_(L)]]</pre>
|}

=== External Link===

{| class="wikitable"
! style="width: 33%" | Description
! style="width: 33%" | What it looks like
! style="width: 34%" | What you type
|-
|External link
| https://www.wikipedia.org
| <pre> https://www.wikipedia.org </pre>
|-
|External link with different label
| [https://www.wikipedia.org Wikipedia]
| <pre> [https://www.wikipedia.org Wikipedia]</pre>
|-
| Numbered external link
| [https://www.wikipedia.org]
| <pre> [https://www.wikipedia.org] </pre>
|-
|Mailto link
| [mailto:linkedearth@gmail.com email us]
| <pre> [mailto:linkedearth@gmail.com email us] </pre>
|}

==Images==

To insert an image into an article ('''For a LiPD file, use this [[Special:WTLiPD | page]]'''), click on the "Upload file" button in the lefthand menu (Figure 2).

[[File:TutorialFig23.png|thumb|none|700px|Figure 2: uploading an image or document to the Linked Earth wiki.]]

First select the document you want to upload, then just hit the "Upload file" button at the bottom of the page.

'''Be Specific in the name file''': If you wish to upload a picture of your archive, do NOT name the file 'MyArchive.jpg'. Read the [[Best Practices]] guide to learn how to properly name your file.

This file can then be referenced using an internal link described [[#Internal Links | above]].

== Tables and Equations ==

Tables and equations are more complicated. For a more thorough explanation, see Wikipedia's pages on [https://en.wikipedia.org/wiki/Help:Displaying_a_formula formulas] and [https://en.wikipedia.org/wiki/Help:Table tables].

===Tables===

The following code

<pre>
{| class="wikitable"
|-
|+Table 1.
|-
! Header text 1 || Header text 2 || Header text 3
|-
| Example 1 || Example 2 || Example 3
|-
| Example A || Example B || Example C
|-
| Example ! || Example @ || Example #
|}
</pre>

results in:

{| class="wikitable"
|-
|+Table 1.
|-
! Header text 1 || Header text 2 || Header text 3
|-
| Example 1 || Example 2 || Example 3
|-
| Example A || Example B || Example C
|-
| Example ! || Example @ || Example #
|}

===Equations===

Equations use the LaTex markup language and are bounded by <nowiki><math></nowiki> and <nowiki></math></nowiki> tags. For instance, <nowiki><math> \delta^{18}O_{SMOW} = 1.03091 (\delta^{18}O_{PDB}) +30.91 </math></nowiki> results in:

<math> \delta^{18}O_{SMOW} = 1.03091 (\delta^{18}O_{PDB}) +30.91 </math>

==References==

All references consist of a citation and an actual reference. The citation is a tag that contains a shorthand form of the reference, and is followed, on first use in an article, by the completed reference.

The LinkedEarth wiki uses APA style for citation

To create a reference:
* First citation in the article:
<pre>Khider et al. <ref name = Khider_2014> Khider, D., Jackson, C. S., & Stott, L. D. (2014). Assessing millennial-scale variability during the Holocene: A perspective from the western tropical Pacific. Paleoceanography, 29(3), 143-159. doi:10.1002/2013pa002534 </ref></pre>

becomes Khider et al. <ref name = Khider_2014> Khider, D., Jackson, C. S., & Stott, L. D. (2014). Assessing millennial-scale variability during the Holocene: A perspective from the western tropical Pacific. Paleoceanography, 29(3), 143-159. doi:10.1002/2013pa002534 </ref>

*Subsequent citations
<pre> Khider et al.<ref name = Khider_2014 /></pre>

will automatically create a footnote marker that will link to the original reference: Khider et al. <ref name = Khider_2014 />

== Earth Science specific items ==

Nuclides are readily expressed using the SimpleNuclide2 extension. See [[NuclideExamples]].

== Adding a poll ==

The LinkedEarth wiki has [https://www.mediawiki.org/wiki/Extension:AJAXPoll polling capabilities]. To create a poll use the following code:

<pre>
<poll>
Question
Choice1
Choice2
Choice3
Choice4
</poll>
</pre>

For instance,

<pre>
<poll>
Are you a LinkedEarth member?
Yes
No
</poll>
</pre>

returns:

<poll>
Are you a LinkedEarth member?
Yes
No
</poll>

<div class="isa_error">

Use different questions/answer for polls on the wiki. Otherwise, the system would automatically vote for all of the polls containing the same question. So be very specific if you need to be!
However, if you think a poll can apply to multiple pages, you can copy and paste the same poll on multiple pages. For instance, if you create a poll on a common nomenclature for expressing d18O on the Speleothem working group, the same poll can be posted on the Marine Sediment, MARPA, Ice Core working group pages.
</div>

=== Editing an existing poll ===

<div class="isa_error">

Editing an exiting poll will '''reset the votes'''. If you need to edit a poll, make sure you keep the results of the previous poll.
</div>

For instance,
<pre>
<poll>
Are you a recent LinkedEarth member?
Yes
No
</poll>
This poll was edited by ~~~~ and had 2 No and 1 Yes answer before editing.
</pre>

returns this on the wiki:

<poll>
Are you a recent LinkedEarth member?
Yes
No
</poll>
This poll was edited by [[User:Khider|Deborah Khider]] ([[User talk:Khider|talk]]) 13:53, 18 October 2016 (PDT) and had 2 No and 1 Yes answer before editing.

== Signing your name ==

If you wish to sign your name on a page when making an edit, such as in the example above, use: <pre> ~~~~ </pre> [[User:Khider|Deborah Khider]] ([[User talk:Khider|talk]]) 13:55, 18 October 2016 (PDT)

=Creating a new wiki page=

To create a new page, type the link into your browser: [http://wiki.linked.earth/New_Page http://wiki.linked.earth/New_Page] and replace "New Page" above by the name that you want for the page.

Then, either select a category for this page (Figure 2):

[[File:TutorialFig16.png|thumb|none|300px|Figure 2: Selecting a category for a new page]]

or, just click on the "Create" link to create a page without any category (Figure 3)

[[File:TutorialFig17.png|thumb|none|200px|Figure 3: create page button, located on the bottom right of the page]]

Alternatively, you can search for the page you are looking for (remember that the wiki is case sensitive). If the page does not exist, you will be prompted to create it. You should '''always''' try to look for the term you want to define before creating a new page to avoid duplicate pages of similar concepts.

=Deleting an existing wiki page=

Go to the page you want to delete and select "Delete" from the drop down menu as shown in Figure 4.

[[File:TutorialFig18.png|thumb|none|300px|Figure 4: Wiki page option menu]]

Click on the "Delete" link, and delete the page. You will be prompted to give a reason for the deletion.

[[File:TutorialFig19.png|thumb|none|500px|Figure 5: Deleting menu]]

= Renaming wiki pages =

Wiki pages may need to be renamed because of typos in the page name or community agreement to rename a term in the LinkedEarth ontology. In order to rename an existing page without losing any of its contents, you should click on the "Move" button under the "More" menu on the top of the page (Figure 6)

[[File:TutorialFig21.png|thumb|none|500px|Figure 6: Moving an existing wiki page]]

You will be prompted to enter the new name for the page and a reason for the change.

[[File:TutorialFig22.png|thumb|none|700px|Figure 7: Moving a wiki page form.]]

=References=

Using LiPD files

2017-06-27T17:00:35Z

Michaelerb: /* LiPD Utilities in Python 3 */

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

The LiPD utilities are compatible with Windows. However, most of the more advanced data analysis tools are only available for Mac or Linux platforms. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install an Ubuntu virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC directly from the [https://www.python.org/ Python website]. '''Make sure to select the Python 3 version'''.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

Another option is to use the [https://www.continuum.io/downloads Anaconda] Python release.

==LiPD Utilities==

{{See also|LiPD Utilities}}

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. To get started with some more commands, read the quickstart guide on Github: [https://github.com/nickmckay/LiPD-utilities/blob/master/Examples/Welcome%20LiPD%20-%20Quickstart.ipynb Quickstart Guide].

If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

{{See also|Pyleoclim}}

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

Please note that some of the dependencies used by Pyleoclim are unavailable to Windows users.

==CSV files==

[[File:Example of Unzip LiPD file.png | thumb | right |400px | Example of an unzipped LiPD files. The data is contained in the csv files while the metadata is stored in the JSON-LD file]]

If you need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

[[File:BestPractices Download csv file.png | thumb | right | 400 px | Downloading a csv file]]

You can also access the .csv file on the wiki and download them.

Using LiPD files

2017-06-27T16:57:37Z

Michaelerb: /* A Note for Windows Users */

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

The LiPD utilities are compatible with Windows. However, most of the more advanced data analysis tools are only available for Mac or Linux platforms. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install an Ubuntu virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC directly from the [https://www.python.org/ Python website]. '''Make sure to select the Python 3 version'''.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

Another option is to use the [https://www.continuum.io/downloads Anaconda] Python release.

==LiPD Utilities==

{{See also|LiPD Utilities}}

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

{{See also|Pyleoclim}}

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

Please note that some of the dependencies used by Pyleoclim are unavailable to Windows users.

==CSV files==

[[File:Example of Unzip LiPD file.png | thumb | right |400px | Example of an unzipped LiPD files. The data is contained in the csv files while the metadata is stored in the JSON-LD file]]

If you need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

[[File:BestPractices Download csv file.png | thumb | right | 400 px | Downloading a csv file]]

You can also access the .csv file on the wiki and download them.

Get Started with the LinkedEarth wiki

2017-06-26T23:54:52Z

Michaelerb: /* Where to start */

Getting started on the LinkedEarth wiki can seem a daunting task if one has never tried to edit pages on Wikipedia or other wiki platforms. The LinkedEarth team put together this guide to help you get started with the wiki from uploading a new dataset to property annotation and contributing your expert knowledge. The goal is to make this process as seamless as possible. If you have any suggestions on how to improve the LinkedEarth wiki, please [mailto:linkedearth@gmail.com contact us].

A [https://www.youtube.com/watch?v=EQZEemFb6g0 video tutorial] is available on our [https://www.youtube.com/channel/UCo7yzNTM__4g5H-xyWV5KbA YouTube channel].

= Before you start =

== Getting a Linked Earth wiki account ==

To contribute to the LinkedEarth wiki, you need to be a member of the LinkedEarth Community. To get an account, use [http://linked.earth/?wpgform_qv=1002 the membership form].

==Logging in the Wiki ==

To log in, just click on the link at top right of the wiki and insert your user/password as shown on Figure 1.

[[File:TutorialFig1.png|thumb|none|500px|Figure1: Log in form]]

== Create your personal page ==

The Linked Earth wiki allows to create different types of pages. Personal pages are a special type of wiki pages, which can be updated to reflect your contributions, working group membership, your LinkedEarth wiki privileges, etc... To edit your personal page, click on your name at the top of any wiki page.

[[File:TutorialFig12.png|thumb|none|400px|Figure 2: location of the personal page for a user]]

You will be re-directed to your profile page. To edit any field, click on the box on the right and enter your text. Personal pages are only accessible by members of the LinkedEarth community who must be logged in to access them.

[[File:ProfileEditField.png|thumb|none|600px|Figure 3: editing personal information on your wiki page]]

To add a profile picture, first [[Special:Upload | upload]] the picture you'd like to use onto the wiki. Then click on the placeholder and use the full wiki link when prompted.

To join a [[:Category:Working_Group| working group]], click on the Working Group field and a list of available working groups will automatically be displayed. Click on the Working Group you'd like to join.

[[File:ProfileWG.png|thumb|none|600px|Figure 4: joining a working group]]

=Why we need you=

Thanks to the effort of the PAGES2k group, we already have over 600 records ready to be uploaded on the LinkedEarth wiki. However, there are hundreds more of legacy data and new datasets being generated. It would be impossible for a team of three geoscientists to contribute these datasets. This is why we need your help! The more datasets are contributed, the more useful the platform becomes.

Which benefits will you get from using LinkedEarth? All the datasets hosted here are in a standard format, ready to be used in scientific workflows. The workflows can be as simple as creating a map or plotting datasets or using more advanced statistics and spectral analyses. The LinkedEarth team is developing some of these workflows for the community, and will promote social coding so that everyone can share best practices. A standard format also ensures that workflows will work with new datasets being uploaded onto the system.

The LinkedEarth wiki is also a platform where scientific knowledge can be contributed. It turns that paleoclimatology is an enigma, even to fellow climate scientists. The wiki is a way to collaboratively fill those knowledge gaps. While we have written some wiki pages to get started, there are still many more that need to be written, updated, or improved!

=Where to start=

We have put together several tutorial to get you started with the wiki:

* [[User Privileges]]: Learn more about the various user roles on the wiki and their associated privileges.
* [[Quick Guide to Editing Wiki Pages]]: The markup language for all mediawiki sites, including the LinkedEarth wiki, is called "wikitext". It is a bit different from HTML, but the principle is similar.
* [[Dataset Tutorial]]: Learn how to upload a dataset onto the platform and edit the various properties.
* [[Discussion Page Tutorial]]: The platform has a channel that support discussion of the various terms used on the platform. The page uses a thread to keep replies to a particular topic organized. The extension automatically keeps track of the user name as well as date and time of the response.
* [[Best Practices]]: Set of rules when editing a wiki page. This is meant to keep the discussions organized and avoid the multiplication of properties that relate to the same concept.
* [[Using LiPD files]]: If you've downloaded some LiPD files and want to do analysis with them, start here.

=Searching=

There are several ways you can search the wiki:

*You can use the search box in the upper right-hand corned. You can search for the name of a page, or any term that appears in a page.
*Within any article, you can use CTRL (or CMD on a Mac) + F and search for a term on that page.

Using LiPD files

2017-06-26T23:51:12Z

Michaelerb: /* CSV files */

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

A Linux environment is recommended for data analysis. Mac or Linux users should already have access to Linux, so they can skip the rest of this section. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC: [https://www.python.org/ Python]. Make sure to select the Python 3 version.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built functions which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended. Also, re-zipping an unzipped LiPD file will not turn it into a valid LiPD file.

Using LiPD files

2017-06-26T23:49:06Z

Michaelerb: /* Pyleoclim */

Using LiPD files

2017-06-26T23:47:22Z

Michaelerb: A brief introduction to pyleoclim.

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

A Linux environment is recommended for data analysis. Mac or Linux users should already have access to Linux, so they can skip the rest of this section. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC: [https://www.python.org/ Python]. Make sure to select the Python 3 version.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built analyses which can save time and effort. Install pyleoclim with:

pip install pyleoclim

To get started with pyleoclim, see the [[Pyleoclim]] wiki page. In particular, read the quickstart guide: [https://github.com/LinkedEarth/Pyleoclim_util/blob/master/Example/PyleoclimQuickstart.ipynb Quickstart Guide].

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-26T23:09:49Z

Michaelerb: /* Pyleoclim */

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

A Linux environment is recommended for data analysis. Mac or Linux users should already have access to Linux, so they can skip the rest of this section. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC. A recommended python distribution is [https://www.continuum.io/downloads Anaconda]. Make sure to select the Python 3 version.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

[[Pyleoclim]] is another primary way of interacting with LiPD files. While the LiPD Utilities offer a more manual approach to analysis, Pyleoclim has a variety of pre-built analyses which can save time and effort.

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-26T22:59:03Z

Michaelerb: Added some additional information for Windows users.

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==A Note for Windows Users==

A Linux environment is recommended for data analysis. Mac or Linux users should already have access to Linux, so they can skip the rest of this section. If you're using Windows and would like to start using Linux, here are two options:
# Ask your university for an account on their Linux machine. Then use an ssh program like [http://www.putty.org/ PuTTY] to connect from your PC. An X-windows program like [https://sourceforge.net/projects/xming/ Xming] is also needed to produce graphical windows.
# If option one isn't a possibility, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.

If you insist on working within Windows, you can install Python on your PC. A recommended python distribution is [https://www.continuum.io/downloads Anaconda]. Make sure to select the Python 3 version.

After installing python, open a Windows command prompt. Despite cosmetic similarities, a Windows command prompt is different from a Linux terminal and generally uses different commands. To launch Python from here, type "python". To install python packages in Windows, exit python and type:

python -m pip install package_name

where "package_name" is the name of a python package, such as LiPD. However, not all packages may be available for Windows, so Linux is still recommended.

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-24T06:22:26Z

Michaelerb: Minor edit.

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

<blockquote>'''''A note for Windows users:''' While programs like Python exist for Windows, you'll have more flexibility if you work in a Linux environment. To start working with Linux, you could ask your university for an account on their Linux machine, then use a program like ssh to connect. Alternately, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.''</blockquote>

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-24T06:21:32Z

Michaelerb: /* LiPD Utilities in Python 3 */

Note: This page is a work in progress (moreso than most wiki pages).

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

<blockquote>'''''A note for Windows users:''' While programs like Python exist for Windows, you'll have more flexibility if you work in a Linux environment. To start working with Linux, you could ask your university for an account on their Linux machine, then use a program like ssh to connect. Alternately, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.''</blockquote>

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.
</syntaxhighlight>

Different data sets may not have all of the same fields, so use the ".keys()" command to check. Now, let's make a simple figure:

<syntaxhighlight lang="python">
# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-24T06:19:18Z

Michaelerb: /* LiPD Utilities */

Note: This page is a work in progress (moreso than most wiki pages).

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

<blockquote>'''''A note for Windows users:''' While programs like Python exist for Windows, you'll have more flexibility if you work in a Linux environment. To start working with Linux, you could ask your university for an account on their Linux machine, then use a program like ssh to connect. Alternately, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.''</blockquote>

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

After reading a LiPD file, use the extractTs() function to extract the data and metadata within the file:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data. Different data sets may not have all of the same fields, so use the ".keys()" command to check. Let's save some data to different variables and make a simple figure:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.

# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-24T06:17:52Z

Michaelerb: Added an example of how to use the LiPD Utilities.

Note: This page is a work in progress (moreso than most wiki pages).

After downloading a LiPD file, there are a number of ways to use it. The recommended ways are to use the LiPD utilities or pyleoclim.

__TOC__

==LiPD Utilities==

The [[LiPD Utilities]] are a primary way to interact with LiPD files. The utilities are available on [https://github.com/nickmckay/LiPD-utilities GitHub] in Matlab, R, and Python language. All three languages support reading and writing a LiPD file, extracting and collapsing time series, and filtering time series.

===LiPD Utilities in Python 3===

<blockquote>'''''A note for Windows users:''' While programs like Python exist for Windows, you'll have more flexibility if you work in a Linux environment. To start working with Linux, you could ask your university for an account on their Linux machine, then use a program like ssh to connect. Alternately, you could install a Linux virtual machine (e.g. [https://www.virtualbox.org/ VirtualBox]) on your PC.''</blockquote>

To use LiPD Utilies in Python, first make sure you have Python 3 installed. If you don't, one option is [https://www.continuum.io/downloads Anaconda].

Next install LiPD Utilities:

pip install LiPD

Start python with the command "python". Then import lipd:

<syntaxhighlight lang="python">
import lipd
</syntaxhighlight>

There are many functions in the LiPD utilities, but here are just a few. First, read a LiPD file:

<syntaxhighlight lang="python">
lipd.readLipd('/path/to/data/file.lpd') # Load a specific LiPD file. Or...
lipd.readLipd() # Load a LiPD file through a GUI.
</syntaxhighlight>

To extract the data and metadata in a LiPD file, use the extractTs() function:

<syntaxhighlight lang="python">
data_all = lipd.extractTs()
</syntaxhighlight>

A list of dictionaries is now contained in the variable "data_all". (If you're unfamiliar with python, read a primer to get a better understanding.) Each dictionary contains data and metadata fields. From here, it's easy to start using the data. Different data sets may not have all of the same fields, so use the ".keys()" command to check. Let's save some data to different variables and make a simple figure:

<syntaxhighlight lang="python">
data = data_all[0] # Save the first time series object to a new variable. Replace "0" with a different
# number to select a different time series object if there are more than one.
print(data.keys()) # Print the names of all data and metadata fields.
print(data['dataSetName']) # Print the contents of one field: the name of the data set.
year = data['year'] # Save the time values to a new variable.
values = data['paleoData_values'] # Save the data values to a new variable.

# Make a simple figure with matplotlib.
import matplotlib.pyplot as plt
plt.plot(year,values)
plt.title("Name: "+data['dataSetName']+", archive: "+data['archiveType'])
plt.xlabel(data['yearUnits'])
plt.ylabel(data['paleoData_variableName'])
plt.show()
</syntaxhighlight>

From here, explore a LiPD file some more on your own. There are other commands in the LiPD utilities, but the ones above are enough to access the data on a basic level. If you'd like to use pre-built functions to explore the paleo data, see the Pyleoclim section farther down this page.

==Pyleoclim==

==CSV files==

If you’re in a jam and need a plaintext version of the data, all LiPD files contain .csv files of the raw data. Simply unzip your LiPD file to find a .csv file. However, a central goal of LiPD is to put paleoclimate data into a standardized format which common analysis scripts can be built for, so using .csv files more than necessary is not recommended.

Using LiPD files

2017-06-24T00:56:10Z

Michaelerb: Added some descriptions.

Using LiPD files

2017-06-23T23:46:06Z

Michaelerb: Created page.

After downloading a LiPD file, there are a number of ways to use it. The recommended way is the use the LiPD utilities.

DSDP610.Lisiecki.2005

2017-06-12T17:58:15Z

Michaelerb: Added PropertyValue: PartOfCompilation (L) = LR04 benthic stack

[[Category:Dataset_(L)]]{{#set:
ArchiveType=marine sediment|
CollectedFrom (L)=DSDP610.Lisiecki.2005.Location|
Contributor (L)=L.E._Lisiecki|
Contributor (L)=M.E._Raymo|
DatasetVersion (L)=0.0.0|
IncludesPaleoData (L)=DSDP610.Lisiecki.2005.PaleoData1|
LiPDVersion=1.2|
Name (L)=DSDP610.Lisiecki.2005|
PartOfCompilation (L)=LR04_benthic_stack|
PublishedIn (L)=Publication.10.1029/2004PA001071|
PublishedIn (L)=Publication.DSDP610.Lisiecki.2005}}

DSDP610.Lisiecki.2005

2017-06-12T17:57:51Z

Michaelerb: Added PropertyValue: DatasetVersion (L) = 0.0.0

[[Category:Dataset_(L)]]{{#set:
ArchiveType=marine sediment|
CollectedFrom (L)=DSDP610.Lisiecki.2005.Location|
Contributor (L)=L.E._Lisiecki|
Contributor (L)=M.E._Raymo|
DatasetVersion (L)=0.0.0|
IncludesPaleoData (L)=DSDP610.Lisiecki.2005.PaleoData1|
LiPDVersion=1.2|
Name (L)=DSDP610.Lisiecki.2005|
PublishedIn (L)=Publication.10.1029/2004PA001071|
PublishedIn (L)=Publication.DSDP610.Lisiecki.2005}}

Publication.DSDP610.Lisiecki.2005

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: New page Publication.DSDP610.Lisiecki.2005 with Category: Publication_(L)

[[Category:Publication_(L)]]

Publication.DSDP610.Lisiecki.2005

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: Edit Page

[[Category:Publication_(L)]]{{#set:|
Author (L)=M.E._Raymo|
Author (L)=D._Hodell|
Author (L)=E._Jansen|
Journal (L)=Paleoceanography|
Pages (L)=645-672|
PubDataUrl=Manually Entered|
PublicationYear (L)=1992|
Title (L)=Response of deep ocean circulation to initiation of Northern Hemisphere glaciation (3-2Ma)|
Volume (L)=7}}

D. Hodell

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: New page D. Hodell with Category: Person_(L)

[[Category:Person_(L)]]

D. Hodell

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: Edit Page

[[Category:Person_(L)]]{{#set:|
Name (L)=D. Hodell}}

E. Jansen

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: New page E. Jansen with Category: Person_(L)

[[Category:Person_(L)]]

E. Jansen

2017-06-12T17:56:08Z

Michaelerb: WTLiPD: Edit Page

[[Category:Person_(L)]]{{#set:|
Name (L)=E. Jansen}}

PYTB9HSRS6Y.benthic d18o.Resolution

2017-06-12T17:56:07Z

Michaelerb: WTLiPD: New page PYTB9HSRS6Y.benthic d18o.Resolution with Category: Resolution_(L)

[[Category:Resolution_(L)]]

PYTB9HSRS6Y.benthic d18o.Resolution

2017-06-12T17:56:07Z

Michaelerb: WTLiPD: Edit Page

[[Category:Resolution_(L)]]{{#set:|
HasMaxValue (L)=37.7241|
HasMeanValue (L)=4.6114179012346|
HasMedianValue (L)=3.5174499999998|
HasMinValue (L)=0.66750000000002}}

PYTXAW2JBRM.depth.Resolution

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: New page PYTXAW2JBRM.depth.Resolution with Category: Resolution_(L)

[[Category:Resolution_(L)]]

PYTXAW2JBRM.depth.Resolution

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: Edit Page

[[Category:Resolution_(L)]]{{#set:|
HasMaxValue (L)=37.7241|
HasMeanValue (L)=4.6114179012346|
HasMedianValue (L)=3.5174499999998|
HasMinValue (L)=0.66750000000002}}

PYTFC9GMHD1.lr04 age

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: New page PYTFC9GMHD1.lr04 age with Category: InferredVariable_(L)

[[Category:InferredVariable_(L)]]

PYTFC9GMHD1.lr04 age

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: Edit Page

[[Category:InferredVariable_(L)]]{{#set:|
FoundInTable (L)=DSDP610.Lisiecki.2005.paleo1measurement1|
HasColumnNumber (L)=2|
HasMaxValue (L)=3585.7955|
HasMeanValue (L)=2738.1235338462|
HasMedianValue (L)=2656.934|
HasMinValue (L)=2091.6961|
HasUnits (L)=kyr BP|
HasVariableID (L)=PYTFC9GMHD1|
InferredVariableType (L)=Age|
Name (L)=lr04 age|
TakenAtDepth (L)=PYTXAW2JBRM.depth}}

PYTB9HSRS6Y.benthic d18o

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: New page PYTB9HSRS6Y.benthic d18o with Category: MeasuredVariable_(L)

[[Category:MeasuredVariable_(L)]]

PYTB9HSRS6Y.benthic d18o

2017-06-12T17:56:06Z

Michaelerb: WTLiPD: Edit Page

[[Category:MeasuredVariable_(L)]]{{#set:|
FoundInTable (L)=DSDP610.Lisiecki.2005.paleo1measurement1|
HasColumnNumber (L)=3|
HasMaxValue (L)=4.36|
HasMeanValue (L)=3.3708|
HasMedianValue (L)=3.34|
HasMinValue (L)=2.27|
HasProxySystem (L)=ProxySystem.MarineSediment.D18ODefaultSensor.D18O|
HasResolution (L)=PYTB9HSRS6Y.benthic_d18o.Resolution|
HasUnits (L)=per mil|
HasVariableID (L)=PYTB9HSRS6Y|
MeasuredOn (L)=DSDP610|
Name (L)=benthic d18o|
ProxyObservationType (L)=D18O|
TakenAtDepth (L)=PYTXAW2JBRM.depth}}

DSDP610

2017-06-12T17:56:05Z

Michaelerb: WTLiPD: New page DSDP610 with Category: PhysicalSample_(L)

[[Category:PhysicalSample_(L)]]
[[Category:MarineSediment]]

DSDP610

2017-06-12T17:56:05Z

Michaelerb: WTLiPD: Edit Page

[[Category:PhysicalSample_(L)]]
[[Category:MarineSediment]]{{#set:|
Name (L)=DSDP610}}

PYTXAW2JBRM.depth

2017-06-12T17:56:05Z

Michaelerb: WTLiPD: New page PYTXAW2JBRM.depth with Category: MeasuredVariable_(L)

[[Category:MeasuredVariable_(L)]]

PYTXAW2JBRM.depth

2017-06-12T17:56:05Z

Michaelerb: WTLiPD: Edit Page

[[Category:MeasuredVariable_(L)]]{{#set:|
FoundInTable (L)=DSDP610.Lisiecki.2005.paleo1measurement1|
HasColumnNumber (L)=1|
HasMaxValue (L)=178.64999|
HasMeanValue (L)=148.90919966154|
HasMedianValue (L)=148.50999|
HasMinValue (L)=120.76|
HasProxySystem (L)=ProxySystem.MarineSediment.DefaultSensor|
HasResolution (L)=PYTXAW2JBRM.depth.Resolution|
HasUnits (L)=m|
HasVariableID (L)=PYTXAW2JBRM|
MeasuredOn (L)=DSDP610|
Name (L)=depth}}

DSDP610.Lisiecki.2005.PaleoData1

2017-06-12T17:56:04Z

Michaelerb: WTLiPD: New page DSDP610.Lisiecki.2005.PaleoData1 with Category: PaleoData_(L)

[[Category:PaleoData_(L)]]

DSDP610.Lisiecki.2005.PaleoData1

2017-06-12T17:56:04Z

Michaelerb: WTLiPD: Edit Page

[[Category:PaleoData_(L)]]{{#set:|
FoundInMeasurementTable (L)=DSDP610.Lisiecki.2005.paleo1measurement1}}

DSDP610.Lisiecki.2005.paleo1measurement1

2017-06-12T17:56:04Z

Michaelerb: WTLiPD: New page DSDP610.Lisiecki.2005.paleo1measurement1 with Category: DataTable_(L)

[[Category:DataTable_(L)]]

File:DSDP610.Lisiecki.2005.paleo1measurement1.csv

2017-06-12T17:56:04Z

Michaelerb: WTLiPD: Initial upload

File:DSDP610.Lisiecki.2005.paleo1measurement1.csv

2017-06-12T17:56:04Z

Michaelerb: Protected "File:DSDP610.Lisiecki.2005.paleo1measurement1.csv": Dataset restrictions ([Edit=Allow only administrators] (indefinite) [Move=Allow only administrators] (indefinite) [Upload=Allow only administrators] (indefinite))

DSDP610.Lisiecki.2005.paleo1measurement1

2017-06-12T17:56:04Z

Michaelerb: WTLiPD: Edit Page

[[Category:DataTable_(L)]]{{#set:|
HasFileName (L)=File:DSDP610.Lisiecki.2005.paleo1measurement1.csv|
IncludesVariable (L)=PYTXAW2JBRM.depth|
IncludesVariable (L)=PYTFC9GMHD1.lr04_age|
IncludesVariable (L)=PYTB9HSRS6Y.benthic_d18o|
MissingValue=nan|
PaleoDataTableName=paleo1measurement1}}

DSDP610.Lisiecki.2005.Geometry

2017-06-12T17:56:03Z

Michaelerb: WTLiPD: New page DSDP610.Lisiecki.2005.Geometry with Category: Geo:Geometry_(L)

[[Category:Geo:Geometry_(L)]]