A. Usecase/Applications possible with the data:

1. Keep yourself updated- You can fetch and store daily updates of legal cases from multiple courts of your choice, allowing you to be informed about ongoing and pending cases.

2. Keep a check on your clients- You can make searches about your clients by using their names or case numbers to see if their legal cases are open across multiple courts. You can also build your client base as you go along.

3. Systematize your services- Fetch, store, and organize data of various legal cases from multiple sources of your choice to systematically optimize your services by searching for repeated clients or cases. You can do so by a. Searching for your client in multiple databases b. Grouping similar pending legal cases c. Putting forth your service for cases that lack attorneys

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

The 2014 Survey of State Attorneys General (SAG) collected information on jurisdiction, sources and circumstances of case referrals, and the participation of attorneys general offices in federal or state white-collar crime task forces in 2014. White-collar crime was defined by the Bureau of Justice Statistics (BJS) as: "any violation of law committed through non-violent means, involving lies, omissions, deceit, misrepresentation, or violation of a position of trust, by an individual or organization for personal or organizational benefit." SAG sought to analyze how attorneys general offices as an organization in all 50 states, the District of Columbia, and U.S. territories respond to white-collar offenses in their jurisdiction. BJS asked respondents to focus on the following criminal and civil offenses: bank fraud, consumer fraud, insurance fraud, medical fraud, securities fraud, tax fraud, environmental offenses, false claims and statements, illegal payments to governmental officials (giving or receiving), unfair trade practices, and workplace-related offenses (e.g., unsafe working conditions). Variables included whether or not offices handled criminal or civil cases in the above categories, estimated number of cases in each category, and what types of criminal or civil sanctions were imposed on white-collar offenders. Researchers also assessed collaboration with partners outside of state attorneys offices, whether cases were referred for federal or local prosecution, and what circumstances lead to referring cases to state regulatory agencies. The extent to which state attorneys offices maintain white-collar crime data was also recorded.

UniCourt provides legal data on law firms that’s been normalized by our AI and enriched with other public data sets to connect real-world law firms to their attorneys and clients, judges they’ve faced and types of litigation they’ve handled across practice areas and state and federal (PACER) courts.

AI Normalized Law Firms

• UniCourt’s AI locates and gathers variations of law firm names and spelling errors contained in court data and combines them with bar data, business data, and judge data to connect real-world law firms to their litigation. • Avoid bad data caused by frequent law firm name changes due to firm mergers, named partners leaving, and firms dissolving, leading to lost business and bad analytics. • UniCourt’s unique normalized IDs for law firms let you quickly search for and download all of the litigation involving the specific firms you’re interested in. • Uncover the associations and relationships between law firms, their lawyers, their clients, judges, and their top practice areas across different jurisdictions.

Using APIs to Dig Deeper

• See a full list of all of the businesses and individuals a law firm has represented as clients in litigation. • Easily vet the bench strength of law firms by looking at the volume and specific types of cases their lawyers have handled. • Drill down into a law firm’s experience to confirm which judges they’ve appeared before in court. • Identify which law firms and lawyers a particular firm has faced as opposing counsel, and the judgments they obtained.

Bulk Access to Law Firm Data

• UniCourt’s Law Firm Data API provides you with structured, cleaned, and organized legal data that you can easily connect to your case management systems, CRM, and other internal applications. • Get bulk access to law firm Secretary of State registration data and the names, emails, phone numbers, and physical addresses for all of a firm’s lawyers. • Use our APIs to create tailored legal marketing campaigns for law firms and their attorneys with the exact practice area expertise and the right geographic coverage you want to target. • Power your case research, business intelligence, and analytics with bulk access to litigation data for all the court cases a firm has handled and set up automated data feeds to find new cases they’re involved in.

This data depository contains all experimental materials, data, and code for Spamann, Lawyers' Role-Induced Bias ... All experimental materials (i.e., exercise and survey instrument) are in the pdf file Spamann_experimentalmaterials_all.pdf. The dataset Newman.dta (Stata 14.2) contains the data collected. The Stata do-file Spamann_role_bias_code.do generates the three figures and other reported statistical information reported in the version of the paper originally posted to SSRN in May 2019. Spamann_role_bias_code_revised.do generates the four figures and other reported statistical information reported in the revision submitted to JLS in March 2020 and ultimately accepted by the journal. Both do-files use Newman.dta. Newman.dta is the result of merging 6 csv files generated by Qualtrics in each of the six semesters from students' survey responses. These 6 csv files, and the do-file rawdata_merge_clean.do to merge them, are also included.

Comprehensive dataset of 23,802 Divorce lawyers in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

1. General description This dataset contains the national directory of lawyers drawn up by the National Council of Bars and Law Societies pursuant to the provisions of the second paragraph of Article 21-1 of Law No 71-1130 of 31 December 1971 in force. The National Council of Bars and Law Societies (‘CNB’) is the non-profit institution responsible for representing the profession of lawyer, in particular before the public authorities. As part of its public service mission relating to the organisation of the regulated profession of lawyer, it was entrusted with the task of compiling and making accessible the list of lawyers registered on the roll of a bar association. The national directory lists all lawyers registered in a bar association and practising as lawyers in France. The national directory thus gathers data from all 164 French bars. It is drawn up and updated on the basis of the information that all 164 bars transmit under their responsibility to the NBC. It is a photograph at a moment T of the profession, whose composition is by essence moving. Every day applications for registration are submitted in the bars (inputs) but also requests for omission or cessation of activity (outputs). All applications are examined by the bar associations and a decision is taken by the relevant Bar Council, which, in fine, feeds into the CNB’s national directory. This dataset is released under an Etalab 2.0 open license. ### 2. Description of file format The supported character set is ANSI extended ASCII. The file, in CSV (comma separated value) format, is named directory-avocats-yyyymmdd.CSV, where yyyymmdd is the date of export of the file. The file consists of records, the record separator is the carriage return (CR+LF). Records are made up of fields, the field separator is the semicolon (;). The first line of the file contains the global export information of the reference directory. ### 3. Update frequency The content of the national directory shall be updated on a monthly basis. ### 4. Description of the data model The data model consists of the following: Descriptif Nom du Barreau Nom de l'avocat Prénom de l'avocat Raison sociale de la structure d'exercice SIREN de la structure d'exercice Adresse ligne 1 de la structure d'exercice Adresse ligne 2 de la structure d'exercice Code de la structure d'exercice Ville de la structure d'exercice Spécialité 1 de l'avocat Spécialité 2 de l'avocat Spécialité 3 de l'avocat Date de prise de serment de l'avocat Langue(s) parlée(s) par l'avocat CHAINE (127) > Note: The names of the bars in this list are merely informative. Any name of bar association may be required to change following a deliberation by the Council of the Bar Association (e.g. the Bar of Bourg-en-Bresse becomes, following the deliberation of its Council of the Order on 06/10/2009, the ‘Bar of Ain’. Similarly, following the decision of its Council of the Order of 01/10/2009, the Bar of Rodez becomes the ‘Bar of Aveyron’). >

A dataset of keywords that are relevant to lawyers, including their definitions, synonyms, antonyms, search volume and costs.

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

This dataset was created through an anonymous survey of solicitors in England and Wales, conducted between 12 November 2019 and 13 January 2020. Respondents answered a series of questions regarding their use of AI technology, as well as their training for and attitudes to the use of technology in their work. After discarding partial responses, the dataset comprises a total of 353 valid responses.

The proposed research will explore the potential and limitations of using artificial intelligence (AI) in support of legal services. AI's capabilities have made enormous recent leaps; many expect it to transform how the economy operates. In particular, activities relying on human knowledge to create value, insulated until now from mechanisation, are facing dramatic change. Amongst these are professional services, such as law.

Like other professions, legal services contribute to the economy both through revenues of service providers and through benefits provided to clients. For large business clients, who can choose which legal regime will govern their affairs, UK legal services are an export good. For small businesses and citizens, working within the domestic legal system, UK legal services affect costs directly. Yet unlike other professions, the legal system has a dual role in society. Beyond the law's role in governing economic order, the legal system is more fundamentally a structure for social order. It sets out rules agreed on by society, and also the limits of politicians' ability to enact these rules.

Consequently, the stakes for AI's implementation in UK legal services are high. If mishandled, it could threaten both economic success and governance more generally. Yet if executed effectively, it is an opportunity to improve legal services not only for export but also for citizens and domestic small businesses. Our research seeks to identify how constraints on the implementation of AI in legal services can be relaxed to unlock its potential for good.

One major challenge is the need for 'complementary' adjustments. Adopting a disruptive new technology like AI requires changes in skills, training, and working practices, without which the productivity gains will be muted. We will investigate training and educational needs for lawyers' engagement with technology and programmers' engagement with law. With private sector partners, we will develop education and training packages that respond to these needs for delivery by both universities and private-sector firms. We will investigate emerging business models deploying AI in law, and identify best practice in governance and strategy. Finally, we will compare skills training and technology transfer in the UK with countries such as the US, Hong Kong and Singapore, and ask what UK policymakers can learn from these competitors. To the extent that these issues are also faced by other high-value professional services, these parts of our results will also have relevance for them.

However, the dual role of the legal system poses unique challenges that justify a research package focusing primarily on this sector. There are constitutional limits to how far law's operation can be adjusted for economic reasons: we term this second constraint 'legitimacy'. We will map how automation in dispute resolution might trigger constitutional legal challenges, how these challenges relate to types of dispute resolution technology and types of claim, and use the resulting matrix to identify opportunities for maximum benefit from automation in dispute resolution.

A third constraint is the limits of technological possibility. AI systems rely on machine learning, which reaches answers by identifying patterns in very large amounts of data. Its limitations are the size of the datasets needed, and its inability to provide an explanation for how the answer was reached. This poses particular difficulties for law, where many applications require or benefit from reasons being given. We will explore the possibility for frontier AI technologies to deliver legal reasoning.

The research will involve a mix of disciplinary inputs, reflecting the multi-faceted nature of the problem: Law, Computer Science, Economics, Education, Management and Political Economy. Working closely with private-sector partners will ensure our research benefits from insights into, and testing against, real requirements.

The American Bar Association’s annual Survey on Lawyer Discipline (SOLD) reports complaints and charges regarding lawyer misconduct, the caseload per disciplinary attorney, and each state’s budget for attorney discipline. We develop five measures of attorney discipline which are: 1. COMPLAINTS – the percent of attorneys in the state who receive complaints from the public 2. CHARGED – the percent of attorneys that are charged with some form of misconduct during the year 3. CHARGED/ COMPLAINTS – the percent of the attorneys receiving COMPLAINTS that are eventually CHARGED with malpractice 4. BUDGET (in dollars) – the state’s annual budget for implementing attorney discipline relative to the number of attorneys 5. CASELOAD – the number of AD cases per state disciplinary attorney per year

In our study entitled 'Attorney Discipline, the Quality of Legal Systems and Economic Growth within the United States' we examine the relation between attorney discipline and state economic growth. Here, we include the panel dataset of the five attorney discipline measures used in that study 'Attorney Discipline Data 2000-2017'.

We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.

CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. This dataset presents a fundamental task to lawyers and is both legally meaningful and difficult from an NLP perspective (F1 of 0.4 with a BiLSTM baseline). The citing context from the judicial decision serves as the prompt for the question. The answer choices are holding statements derived from citations following text in a legal decision. There are five answer choices for each citing text. The correct answer is the holding statement that corresponds to the citing text. The four incorrect answers are other holding statements.

To read more about the dataset, please see our paper or our blogpost.

The data included here is the information in the NYS Attorney Registration Database that is deemed public information pursuant to 22 NYCRR 118.

About this Dataset

Recently verified (1/5/18) list of attorneys in the greater SF Bay Area. We guaranty our list and will replace any emails that bounce from this list. We offer our lists for hundreds, often thousands less than other email list providers. If you are thinking about renting an email list, we offer our lists for sale at nearly the same price as most list rental companies. Why rent when you can buy it for the same price?

Category

Attorney email Lists

Keywords

attorneys,lawyers,california

Row Count

8592

Price

$399.00

A. SUMMARY Please note that the "Data Last Updated" date on this page denotes the most recent DataSF update and does not reflect the most recent update to this dataset. To confirm the completeness of this dataset please contact the District Attorney's office at districtattorney@sfgov.org. This data set includes all criminal cases prosecuted by the District Attorney’s Office that have reached a final resolution, or disposition. A case is resolved when a final determination has been made in court (i.e., an acquittal, conviction, dismissal, or a judge finds a defendant has successfully completed diversion). A case may also be resolved through other means. For example, a case may be re-indicted, a defendant may be released to another agency's custody, etc. Because of the case tracking systems used in the San Francisco Superior Court, these cases are assigned new court numbers and thus show up as distinct cases in the data available to the District Attorney’s Office. Lastly, sometimes a defendant will have multiple criminal cases related to the same incident or similar types of crimes. The defendant may plead guilty to one case, and in exchange, the other cases will be dropped. The San Francisco Superior Court codes these cases as "1385 PC - Guilty Plea to Other Charge". More information about this dataset can be found under the “Case Resolutions” section on the Data Dashboards page Disclaimer: The San Francisco District Attorney's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed. B. HOW THE DATASET IS CREATED When a case prosecuted by the District Attorney’s Office reaches a final resolution, or disposition, relevant data is manually entered into the District Attorney Office's case management system. Data reports are pulled from this system on a semi-regular basis, cleaned, anonymized, and added to Open Data. C. UPDATE PROCESS We strive to update this dataset at the beginning of every week. However, the creation of this dataset requires a manual pull from the Office's case management system and is dependent on staff availability. D. HOW TO USE THIS DATASET Please review the “Case Resolutions” section on the Data Dashboards page for more information about this dataset. E. Related DATASETS District Attorney Actions Taken on Arrests Presented District Attorney Cases Prosecuted

Analysis of ‘State's Attorney Felony Cases - Initiation Results’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/06c90343-938d-4119-b016-d0d211de71b8 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Initiation results data presented here reflects all of the arrests that came through the door of the State's Attorneys Office (SAO). An initiation is how an arrest turns into a “case” in the courts. Most cases are initiated through a process known as felony review, in which SAO attorneys make a decision whether or not to prosecute. Cases may also be indicted by a grand jury or, in narcotics cases, filed directly by law enforcement (labeled "BOND SET (Narcotics)" in this data). Included in this data set are the defendant counts by initiation and year. This data includes felony cases handled by the Criminal, Narcotics, and Special Prosecution Bureaus. It does not include information about cases processed through the Juvenile Justice and Civil Actions Bureaus.

--- Original source retains full ownership of the source dataset ---

Registered patent practitioners are individuals who have passed the USPTO's registration exam and met the qualifications to represent patent applicants before the USPTO. Trademark practitioners are attorneys who are active members in good standing of the bar of the highest court of any State. NOTE: Because Trademark attorneys need not apply for registration to practice trademark law before the USPTO, the USPTO does not maintain a roster of trademark attorneys. The names of attorneys who specialize in trademark law may be found online, or by contacting a local bar association. Most state bar websites offer a searchable online registry of their attorneys, and many bar associations operate attorney referral service programs.

The Initiation results data presented here reflects all of the arrests that came through the door of the State's Attorneys Office (SAO). Included in this data set are the defendant counts by gender and initiation, their associated offense type, and year.

An initiation is how an arrest turns into a “case” in the courts. Most cases are initiated through a process known as felony review, in which SAO attorneys make a decision whether or not to prosecute. Cases may also be indicted by a grand jury or, in narcotics cases, filed directly by law enforcement (labeled "BOND SET (Narcotics)" in this data). Included in this data set are the defendant counts by initiation and year. This data includes felony cases handled by the Criminal, Narcotics, and Special Prosecution Bureaus. It does not include information about cases processed through the Juvenile Justice and Civil Actions Bureaus.

About this Dataset

Recently verified (1/5/18) list of attorneys in the State of Louisiana including the New Orleans metro area. We guaranty our list and will replace any emails that bounce from this list. Whether you are marketing to Louisiana attorneys, looking for a new job, or performing market research our list provides the mailing address, phone, and email address you need to run any kind of campaign. Please send us any questions you have about our list and we will respond shortly. We offer our lists for hundreds, often thousands less than other email list providers. If you are thinking about renting an email list, we offer our lists for sale at nearly the same price as most list rental companies. Why rent when you can buy it for the same price?

Category

Attorney email Lists

Keywords

attorneys,lawyers,louisiana,email

Row Count

7319

Price

$299.99

The KHOJ (Know Your High Court Judges) dataset includes data on more than 1700 judges appointed between 1993 (after the creation of the collegium) and 2021. The dataset captures information across 43 variables including the personal, educational and professional backgrounds of India’s High Court judges. It opens pathways for researchers who are looking to probe deeper or wider into the composition of the High Courts and those who want to undertake jurimetrics studies which explore the linkage between judicial behaviour and the background of judges. The core philosophy behind building such a dataset is the realization that people of the country should have more information about judges whose decisions have a real impact on such people's lives. This dataset is the result of a joint effort over 15 months involving more than 30 students and 10 professionals who volunteered their time and efforts in preparing this dataset. This was a collaboration between NLUO’s Centre for Public Policy, Law and Good Governance, Agami and CivicDataLab. It started with the Summer of Data 2021 programme where students from across the country became the original data creators using official and publicly accessible data sources. {"references": ["https://www.epw.in/journal/2022/42/law-and-society/glass-ceiling-high-courts.html", "https://www.indiaspend.com/data-gaps/few-women-many-lawyers-what-a-new-dataset-on-high-court-judges-reveals-835627"]} More info here - https://justicehub.in/initiatives/khoj-india