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There are four main parts to generating Linked Open Data for SEA projects.
Our processes heavily rely on Gnu Make which requires a unix based operating system.
If you're running Windows 10 then we recommend installing Ubuntu. Files on Windows can be accessed from within Ubuntu via the path /mnt/c
macOS:
Applications/Utilities)xcode-select --installmacOS is Unix based so it's similar to Linux. It doesn't, however, come with a package manager. There are two popular options, Homebrew and MacPorts. Both are great and will do what you need but what you use will come down to personal preference. Personally I chose Homebrew due to its ease of use.
To install Homebrew, run the command
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Ubuntu:
sudo apt-get update to make sure you have the most recent package version and dependency informationsudo apt-get install build-essential patch ruby-dev zlib1g-dev liblzma-dev gitTo build the data you will need to have:
To get these files you may request them from the repository manager. (See top of this page.)
macOS:
macOS comes with Ruby preinstalled so you can skip this step – unless you want to install the latest stable version, in which case you can run the following:
brew install ruby
Ubuntu:
sudo apt-get install rubyThe Ruby scripts for generating RDF/TTL files rely on a package called linkeddata which in turn depends on an HTML, XML, SAX, and Reader parser called Nokogiri. There are also some other dependencies you'll need. The commands for installing these are the same for macOS and Ubuntu.
sudo gem install nokogiri
sudo gem install linkeddata
sudo gem install prawn
sudo gem install prawn-table
sudo gem install levenshtein
sudo gem install opencage-geocoderTo make a local clone of the repository, run the following command in your terminal:
git clone https://github.com/SolidarityEconomyAssociation/open-data.gitIn order to deploy the files to the web server you will need SSH access to the server. This will require you to generate an SSH Key and have someone with server access add the public key to ~/.ssh/authorised_keys for each account you need access to.
If you already have access to the server, you can grant new machines access.
To generate SSH keys, follow these steps:
In macOS, enter the following command in the Terminal window.
ssh-keygen -t rsaIn Ubuntu, enter the following command.
ssh-keygenThis starts the key generation process. When you execute this command, the ssh-keygen utility prompts you to indicate where to store the key.
Press the ENTER key to accept the default location. The ssh-keygen utility prompts you for a passphrase.
Type in a passphrase. You can also hit the ENTER key to accept the default (no passphrase). However, this is not recommended. Warning! You will need to enter the passphrase a second time to continue.
After you confirm the passphrase, the system generates the key pair.
Your identification has been saved in /Users/myname/.ssh/id_rsa.
Your public key has been saved in /Users/myname/.ssh/id_rsa.pub.
The key fingerprint is:
ae:89:72:0b:85:da:5a:f4:7c:1f:c2:43:fd:c6:44:38 myname@mymac.local
The key's randomart image is:
+--[ RSA 2048]----+
| |
| . |
| E . |
| . . o |
| o . . S . |
| + + o . + |
|. + o = o + |
| o...o * o |
|. oo.o . |
+-----------------+
Your private key is saved to the id_rsa file in the .ssh directory and is used to verify the public key you use belongs to the same account.⚠️ Never share your private key with anyone!
Your public key is saved to the id_rsa.pub;file and is the key you upload to your Triton Compute Service account.
On macOS, you can save this key to the clipboard by running this:
pbcopy < ~/.ssh/id_rsa.pubOn Ubuntu, you will need to cat the key and copy it manually:
cat ~/.ssh/id_rsa.pubTo be able to log into the remote server via ssh from a new machine, the machine needs to be whitelisted. This is done by adding the public key from the machine to a file called authorized_keys that's found in the .ssh folder in the home folder of the user account that you want access to (/home/user/.ssh or ~/.ssh if you're currently logged in to that account).
Assuming you still have the public key copied to your clipboard, run the following on the remote server:
nano ~/.ssh/authorized_keysAnd paste the public key on a new line at the end of the file. You can add a space and an identifier to the end of the key if you like (anything after the space is ignored and can be used as a note).
To be able to use the scripts in this document you'll need to do this on the accounts joe and admin.
On the machine you're running the scripts from, add the following to a file called config in your local .ssh folder.
nano ~/.ssh/configHost sea-0-joe
Hostname 51.15.116.30
Port 22
IdentityFile ~/.ssh/id_rsa
HostbasedAuthentication yes
PubkeyAuthentication yes
PasswordAuthentication no
User joe
Host sea-0-admin
Hostname 51.15.116.30
Port 22
IdentityFile ~/.ssh/id_rsa
HostbasedAuthentication yes
PubkeyAuthentication yes
PasswordAuthentication no
User adminA username and password will also be required to access Virtuoso Conductor (the triplestore). I've put this on the wiki.
This process involves taking a source CSV (the data about initiatives that's collected by the mapping project coordinators) and converting it into a CSV that adheres to a standard format (read a description of what's happening)
To start the process, navigate into the folder for the project you're working on:
cd [project_name]/[project_version]/Place your original data in the folder name 'original-data' and follow the naming convention of the files in there. You'll now need to make a change to the csv.mk file before running it to make sure it knows to use the new source data.
Open the file in a text editor and look for the line (the start and end may be slightly different to this)
ORIGINAL_CSV := $(SRC_CSV_DIR)2019-09-17-original.csvChange the text after $(SRC_CSV_DIR) to match the filename of your new source data. Save and close the file.
Now run the csv.mk Makefile
make -f csv.mk edition=experimentalThe -f flag tells make you want it to run a named Makefile, in this case csv.mk. The editions=experimental variable provides us with a way to work in multiple environments depending on our needs. We might, for instance, want to try something new out without overwriting existing data. The options for each edition are stored in editions/[name].mk.
Once run, standard.csv will be placed in the generated-data/[edition]/ folder and initiatives.csv and report.csv will be placed in generated-data/[edition]/csv/. initiatives.csv is used as an intermediate when generating standard.csv. report.csv will contain any notes generated in the process – these will appear in the final column alongside the initiative the note relates to.
In the case of an error, you will need to manually remove these files to run the Makefile again:
rm -rf generated-data/[edition]/csv
rm generated-data/[edition]/standard.csv
rm generated-data/[edition]/www/doc/*.*The following step is to generate the RDF, TTL, HTML representations of the data. From the same location, run the following command:
make -f ../../tools/se_open_data/makefiles/generate.mk edition=experimentalThe generated files will have been placed in the generated-data/[edition]/www folder. Like the CSV files, you may occasionally need to manually remove this folder to run the Makefile again.
(Requires server access)
In order for the triplestore to be populated, the files need to be deployed to the webserver. To do this, run the command
make -f ../../tools/se_open_data/makefiles/deploy.mk edition=experimental --dry-runSince running the script will delete live files, the dry-run flag allows you to make sure you're deploying to the correct location first. If you're happy, run the same script again without the --dry-run flag.
make -f ../../tools/se_open_data/makefiles/deploy.mk edition=experimental
When deploying a large number of files, the process which synchronises the local directory with the one on the remote server can hang, perhaps due to printing out large amounts of log information. If this is observed you may need to manually run the rsync command and disable the -v option. See issue.
One the files are live, the triplestore needs to be populated. To do this, run the following command and look out for the message at the end - this is a multi-step process.
make -f ../../tools/se_open_data/makefiles/triplestore.mk edition=experimentalOnce this is complete you should see the following message at the bottom of the output:
**** IMPORTANT! **** **** The final step is to load the data into Virtuoso with graph named https://w3id.solidarityeconomy.coop/sea-lod/[graph]/:
**** Execute the following command, providing the password for the Virtuoso dba user:
**** ssh sea-0-admin 'isql-vt localhost dba <password> /home/admin/Virtuoso/BulkLoading/Data/[some_numbers]/loaddata.sql'Before you run the last command, you need to open up the Graph view in Virtuoso Conductor (requires login) and remove the existing graph (beware - this is irreversible so make sure you remove the right one. The one you're looking for is listed in the message above after the text "**** The final step is to load the data into Virtuoso with graph named").
Once you've deleted the graph, come back to your command line, copy and paste the line starting ssh sea-0-admin from the message. Now replace ,password. with the virtuoso password and run the command.
ssh sea-0-admin 'isql-vt localhost dba <password> /home/admin/Virtuoso/BulkLoading/Data/[some_numbers]/loaddata.sql'1 You can probably ignore this Conductor logged error message.
2 Don't use browser refresh to see if a graph has been deleted or uploaded to the triple store. It can be fatal. See here.
When this has finished, check that it has been added to the list of graphs in Virtuoso. Very occasionally it doesn't appear in the list and will need to be added again. In this situation, just run the last command in your terminal again.
This standard format is defined in the module SeOpenData::CSV::Standard::V1. At the time of writing, this contains the following:
id: "Identifier",
name: "Name",
description: "Description",
organisational_structure: "Organisational Structure",
primary_activity: "Primary Activity",
activities: "Activities",
street_address: "Street Address",
locality: "Locality",
region: "Region",
postcode: "Postcode",
country_name: "Country Name",
homepage: "Website",
phone: "Phone",
email: "Email",
twitter: "Twitter",
facebook: "Facebook",
companies_house_number: "Companies House Number",
latitude: "Latitude",
longitude: "Longitude",
geocontainer: "Geo Container",
geocontainer_lat: "Geo Container Latitude",
geocontainer_lon: "Geo Container Longitude"The text to the left of the colon is the key or symbol that's used to reference the values internally – we'll get back to this. The text to the right of the colon is the name that will be used for each field in the standard CSV that's generated. For example, the CSV headers will appear like this, with each of the initiative's Identifiers appearing on the Identifier column, names in the Name column and so on:
| Identifier | Name | Description | ... | Geo Container Latitude | Geo Container Longitude |Within each project there is a script called converter.rb. These can be found in each of the `data/[project_name]/[project_version]/ folders.
converter.rb takes the values from the input CSV and pipes them into the output CSV. The data can either be passed straight through to the output or it can be processed before passing it on.
To pass the data through just assign the header name to the symbol from the standard. For instance, if the files that we want to use as the Identifier is in a field called ID in the source data then InputHeaders should contain a key of id (the symbol for Identifier in the output) with a value of ID (the header of the field containing the Identifier in the source). E.g.:
InputHeaders = {
id: "ID",
name: "Name",
description: "Description"
}When the script is run, it will run through each row in the source CSV and place each item in the ID column in the Identifier column of the output, the Name fields in the Name output column and the Description fields in the Description column.
If we need to change any of the source data or process it in some other way – checking for validity for instance - then we can define a method with the same name as the symbol we want to populate. The output of the method will then be passed to the output. For instance, if we want to combine several fields from the input into one in the output we can use the following:
InputHeaders = {
id: "ID",
name: "Name",
description: "Description"
# ... Other headers
address1: "Address1",
address2: "Address2",
address3: "Address3"
}
def street_address
[
!address1.empty? ? address1 : nil,
!address2.empty? ? address2 : nil,
!address3.empty? ? address3 : nil
].compact.join(OutputStandard::SubFieldSeparator)
endThis method returns a string made up of the three fields address1, address2 and address3 (if they're populated). Each of these fields have been added to the InputHeader map so they can be referenced in the method.