Research on ERIH PLUS approved SSH journals present in OpenCitations Meta database

Starting from the ERIH-PLUS index of Social Science and Humanities approved journals dataset

ERIHPLUSapprovedJournals.csv (downloaded 27/04/2023) we want to retrieve all the publications belonging to one of those journals, included in OpenCitations Meta database (https://opencitations.net/meta#:~:text=For%20each%20publication%2C%20the%20metadata,and%20PubMed%20Identifiers%20(PMIDs).)

In order to fulfill this task, we download the OpenCitations Meta data dump.

To process the files we run the file main.py : this script processes CSV files in parallel.

This is how it works:

with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
```This line creates a ProcessPoolExecutor with 4 worker processes. The with statement is used to ensure that the executor is properly closed once the processing is done. The number 4 represents the maximum number of worker processes that will be created to execute the tasks concurrently. You can adjust this number based on the resources available on your system.

results = executor.map(process_file_wrapper, [(f, erih_plus_df) for f in batch_files])

The executor.map() function is used to apply the process_file_wrapper function to a list of input arguments. In this case, the input arguments are tuples, each containing a file from the batch_files and the erih_plus_df DataFrame. The executor.map() function returns an iterator that yields the results of applying the process_file_wrapper function to each tuple in the list.

all_results.extend(results)

Processing the ERIH-PLUS Journals dataset and matching venues identifiers with OpenCitations Meta dump's venues.

For this step, the function process_meta_csv () is called. It takes in input as parameters

1. the chunk of the csv that is being processed

2. and the ERIH-PLUS dataset as a dataframe.

Processing DOAJ data dump and adding Open Access information to final df (output dataframe of step 1.1.)

For this step, the function process_doaj_file() is called.

This function takes in input

1. final df

2. the path for the DOAJ data dump

and returns a Dataframe merged_data structured as follows:

A	B	C	D	E
meta:br/060167	[4522-4592, 5687-3452]	503890	56	True
meta:br/060167	[2423-5251, 8763-2891]	876390	12	Unknown

This is how the function works:

Fetch Open Access information from DOAJ

Create a dictionary of Open Access ISSNs

• Using the fetched data from DOAJ, create a dictionary with ISSNs as keys and Open Access status (True/Unknown) as values. Merge Open Access information with the main dataframe

• Using the dictionary created before, create a new column in the main dataframe indicating the Open Access status for each journal. we can use the map() function in pandas to achieve this.

• Example code:

merged_data['Open Access'] =merged_data['OC_ISSN'].map(open_access_dict)

Fill missing Open Access information with 'Unknown'

• Some ISSNs may not have a corresponding entry in the Open Access dictionary. In this case, we cannot assume whether these journals are Open Access. Fill the missing values in the 'Open Access' column with 'Unknown'.

Our second and third research question are

1. What are the disciplines that have more publications?

2. What are countries providing the largest number of publications and journals?

These information are both present in the ERIH-PLUS dataset.

Disciplines:

As we can see every journal can have multiple disciplines:

In order to count the disciplines and understand which one has the highest number of publications we will need to disassemble them and map them individually with the venue.

Countries:

For the countries we will do the same.

We take into consideration that some journals have no specified country. To tackle this issue we can try to see if it is present in the DOAJ dataset.

Checking wether missing countries in erih-plus are present in doaj dataset, where Country of Publisher contains the same country information as ERIH-PLUS Country of Publication.

This is done by a python function that does the following:

1. Selects all ISSN in ERIH-PLUS that have no country

2. checks if the ISSN exists in DOAJ dataset

3. if yes, retrieves the country information in the Country of Publisher column

4. add that information to ERIH-PLUS filtered dataset.

A	B
Spain	...
Italy	identifier
Unknown	identifier

By merging mergeing the disciplines dataset and the countries dataset with merged_data , by means of the unique OMID, we add to the dataset the publication count for each venue.

The output datasets will look like this:

A	B	C
Cultural Studies	...	12
Art and Art History	identifier	12
Art and Art History	identifier	65
Cultural Studies	identifier
Human Geography and Urban Studies
Human Geography and Urban Studies

Disciplines_df

A	B	C
Spain	identifier	12
Italy	identifier	65

Countries_df

1. To find the countries that have the highest number of publication we will sum the values in Publication_count column for every country.

2. This information will be saved in a dictionary that has the Country name as key and the total count of publication for each country as value.

3. To find the disciplines with the highest number of publication we will follow the same process but it will need to be done with both of the disciplines classifications.

4. To answer to our research question "How many of the SSH journals are available in Open Access according to the data in DOAJ?"

We will simply count the rows that have a "yes" value in the Open Access column.

For each visualization, we use Python libraries like Matplotlib, Seaborn, Plotly to create the charts and maps.

Analyzing the coverage of publications in SSH journals

• Visualization: Bar chart showing the number of publications for each journal in the ERIH-PLUS list. The x-axis represents the journals, and the y-axis represents the number of publications. Identifying the disciplines with the most publications

• Visualization: Bar chart showing the number of publications for each discipline. The x-axis represents the disciplines, and the y-axis represents the number of publications. Identifying the countries providing the largest number of publications and journals

• Visualization 1: Choropleth map showing the number of publications by country. Each country is colored according to the number of publications, with darker colors representing higher publication counts.

• Visualization 2: Bar chart showing the number of journals for each country. The x-axis represents the countries, and the y-axis represents the number of journals. Fetching the list of Open Access journals from DOAJ

• Visualization: Pie chart showing the proportion of Open Access SSH journals (according to ERIH-PLUS) based on DOAJ data. The chart will have two segments: Open Access journals and Non-Open Access journals.