The Dimensions bulk export is designed for users who need to do analysis over significant portions of the Dimensions data. This document is intended as a quick overview of how you can use this and what the data looks like.
While Dimensions provides an API for easily accessing specific documents or groups of documents with various filters, some tasks require using a significant portion of the total data. There are two key difficulties:
The Dimensions bulk export is aimed at tackling these two issues. It is not intended as a replacement for the API, but as a complement. Many tasks will be best served by the API, and we would highly encourage users to start with the API rather than rebuilding what we can offer. However, if you are doing analysis over a significant portion of the data (network analysis is a common use case), then the bulk export should make things easier.
The data is provided as folders on AWS S3. The data is put together as JSONL files, where each line is a single JSON document representing a publication, grant, patent document, etc.. Each file will contain up to 10000 documents (typically 10000 but sometimes smaller). These files should not be relied on to contain any logical grouping (e.g. by date or publisher), the batch sizes are chosen to make downloading/processing simpler and specific sizes should not be relied upon. More specific information on the regularity and the folder structure can be found in the dedicated document on the different document types.
The following table shows the regularity and the amount of data to be expected as of November 2020. Updates are normally delivered daily. If a particular consecutive daily update is missing, it means there are no updates available for that day.
|Delivery||Amount of records||Size||regularity|
|Publications||baseset||113 M||> 3 TB||2-4 times a year|
|update||8 - 80 K||About 20 - 800 MB||daily|
|Patents||baseset||135 M||About 360 GB||2-4 times a year|
|updates||50K - 1M||About 4 - 30 GB||weekly|
|Grants||always full set||5.6 M||About 14 GB||10-12 times a year|
|Clinical Trials||baseset||650 K||About 2.5 GB||2-4 times a year|
|updates||1 - 5K||About 5 - 35 MB||daily|
|Data sets||baseset||10 M||About 22 GB||1-2 times a year|
|updates||2 - 200 K||5 MB - 2 GB||daily|
|(Technical) Reports||baseset||About 1 M||About 15 GB||1-2 times a year|
|updates||1 - 10 K||5 - 25 MB||daily|
|Policy Documents||baseset||715 K||~550 MB||1-3 times a year|
|updates||~ 1 - 10 K||10 MB||weekly|
For accessing the Dimensions data, a specific folder in Amazon S3 is provided where the data can be accessed in a read only format. The credentials will be passed along using secure communication.
Dimensions provides AWS credentials to allow access for a variety of tools, e.g.:
Configuring your tool with credentials:
S3 Bucket Path: ai.dimensions.data/sourcename Access Key ID: XXXX Secret Access Key: XXXX
Make sure that you put in the whole path, including the source you want to access. You will find the specific path in the documentation of each source further down below in the “S3 bucket path” chapters. After setting up credentials in the chosen tool, downloading the file is a simple drag and drop operation.
With Amazon’s command line tools to download a single file:
aws s3 cp s3://ai.dimensions.data/sourcename/YYYYMMDD target
To download significant amounts or to pull all updates, the sync command is very helpful (it only downloads new or not successfully downloaded files):
aws s3 sync s3://ai.dimensions.data/sourcename/YYYYMMDD target
Note that for both of these, the target can be another location on S3.
If you store your data there, it can be much faster to copy things directly like this rather than via your own machines. This is especially true within the same region (US-EAST-1) if you set your threads high.
Data will be provided as an initial, large set of files called a baseset. The initial folder will look something like this:
Grants will be provided as a single folder each release. The other releases will receive regular updates during a release.
For new data, a new folder will be created with new and updated documents on a regular basis. These documents will be complete, that is if anything changes then the whole document will appear again (so no complex diffing is required):
The folders will be orderable lexicographically, and there is no correspondence between the filenames in one update to another.
0000000099_0000000099/records_0000001.jsonl does not contain the updates to
0000000001_0000000098/records_0000001.jsonl it is simply the first batch of updated files.
While these may seem like restrictions, the goal is to provide you with a simple process for getting up to date quickly rather than provide complex guarantees for random access.
Download each folder in order (lexicographically) If an item has an ID you have seen before, the previous document with that ID should be replaced.
When items are deleted (for example, when they are discovered to be a duplicate and merged) they will appear with their ID and an obsolete status (details in the schemas).
Over time, significant updates will happen across the data. To prevent the situation of having to process years of daily updates and seeing the same documents repeatedly, a new baseset will be created. The current and previous baseset will always be updated, to smooth the switchover. This allows time to prepare for any format changes as well.
Each provided group of baseset & updates will be placed in a folder with a date, and so you can pick the latest date for the latest data.
There are several structures in the data that are shared between different content types. The current latest formats for these are
People for publication authors, grant investigators, etc.
Funding for links to grants and funders.
Categories for classification into FOR, RCDC, etc.
The categories contain IDs that can be looked up in the categories lookup releases in the following location
Over time, we update the machine learning models or training data behind the classifications. When there are significant changes, we update the version of the model.
The version used for a classification is provided in the categories list under the key
To support migration from one model to another, new models will be added in preview before being released to production. Once a new model is released, the previous version will be deprecated.
Currently, all production models are version 1.
The S3 bucket path to access publications:
This format version is backwards compatible with versions 5 and 6, no fields have been removed.
There are several new fields to be aware of:
concepts_scoresprovides relevance scores for each concept identified.
open_access_categories_v2is in preview, with an updated definition of open access. When this is released into production we will notify users that they can now rely on it.
resulting_publication_doiallows linking from preprints to their final published version.
mesh_termshas been added, which includes all subheadings. The headings on their own remain under
The S3 bucket path to access patents:
The latest release in s3://ai.dimensions.data/patents/20210309/ contains documents that may be
obsolete in format version 3 (see JSON schema here).
active can be either updates or new documents, while
obsolete indicates that the previous version of this documentshould be deleted.
The S3 bucket path:
With the release in s3://ai.dimensions.data/grants/20201020/ we started provding documents that comply with this format: see JSON schema here.
The S3 bucket path:
With the release in s3://ai.dimensions.data/clinicaltrials/20210615/ we started provding documents that comply with this format: see JSON schema here.
The S3 bucket path:
With the release in s3://ai.dimensions.data/datasets/20210929/ we started providing the data as compressed gzip files. The records comply with this format: see JSON schema here.
The S3 bucket path to access reports:
Documents provided validate against this JSON schema.
The S3 bucket path to access policy documents:
The latest release in s3://ai.dimensions.data/policydocuments/20210428 contains documents in this format: see JSON schema here.