Legal and IP data is the collection of data and metadata about these related subjects. Legal data includes information about cases, judges, jurisdictions, and so on. IP data includes all information about creative works.
This data is, at its core, legal and publicly available. Creative works are, if not submitted as patents to governmental bodies, then subject to legal copyright protections.
Legal data comes from public, though (until recently) sometimes difficult to access case data from law books. Laws and cases are increasingly being recorded digitally instead of physically, and many projects engage in digitizing case texts and making them accessible to anyone with an internet connection.
Additional legal data may include interviews with lawyers and judges.
Common attributes of legal data range from metadata like case name, docket number, court and decision date, decision, and jurisdiction. The text of the cases themselves is the actual data. And while clerks and volunteers work to digitize this data, the volume of it makes accuracy a problem for public-access legal databases such as that of the Caselaw Project’s.
Legal data attributes also overlaps with other industry-specific data. A lawyer specializing in banking law requires banking and financial industry data, for example.
Finally, IP data attributes contain trademark, media type, owner name, and jurisdiction.
This data is primarily aggregated into databases for reference purposes. However, developers have made digitization and search functions incredibly easy, with translation, image-search, speech-to-text, and other features.
That is not all, though: with big data comes artificial intelligence programs. For legal data, AI programs can analyze judges’ decisions and predict their rulings, suggesting approaches arguments that lawyers may find successful.
Law firms may also use legal data as competitor and market analysis. For example, comparing their rates and compensation packages to other firms in their field and area.
Meanwhile, advances in artificial intelligence signal a revolution in IP law. AI has begun to create: to paint, to write, to compose music. In response, questions about how to assign intellectual property rights have arisen all over the world.
Humans have already reviewed most of this data countless times before making it available online, so there is little quality control needed for the data itself. Problems may arise in the digitization and collection of the data in larger databases, mentioned above regarding the public-access Caselaw Project. However, mistakes in public databases are easily remedied.
Data collected and used by vendors that provide additional analysis and services is more difficult to test for quality. In this case, consider the vendor’s reputation and request a sample dataset to check for completeness, consistency, and relevancy. Timeliness or frequency of update is less important for this category, depending on your field, as new cases may take years to complete and be published.
“Our viewership has increased by over 240% since 2007 when we first started putting art objects online. The Walters’ website has received almost one million unique visits this year with the works of art site contributing 24% of that viewership,” said Manager of Web and Social Media Dylan Kinnett. “We hope that this percentage will continue to increase as new users share images on social networks, tag objects and curate their own exhibitions.”
Google Dataset Search provides quality, continuously-updating data of all kinds for both researchers, data analysts, journalists, and the general public. They aim to enable the free and open discovery of all kinds of data and metadata in the world.
The platform also offers a Dataset Developer Page to help people add structured data to their datasets or to resolve any other problems.
Premonition Litigation Database standardizes civil court documents from across the US. Users can search for data by judge, keyword, etc.
PatentSight Data & Analysis tracks patent applications and news, provides risk assessments, and performs disruptive technologies analyses