On November 15, 2021, President Biden signed the Bipartisan Infrastructure Law (BIL), which invests more than $13 billion directly in Tribal communities across the country and makes Tribal communities eligible for billions more. For further explanation of the law please visit https://www.congress.gov/bill/117th-congress/house-bill/3684/text. These resources go to many Federal agencies to expand access to clean drinking water for Native communities, ensure every Native American has access to high-speed internet, tackle the climate crisis, advance environmental justice, and invest in Tribal communities that have too often been left behind. On August 16, 2022, President Biden signed the Inflation Reduction Act into law, marking the most significant action Congress has taken on clean energy and climate change in the nation’s history. With the stroke of his pen, the President redefined American leadership in confronting the existential threat of the climate crisis and set forth a new era of American innovation and ingenuity to lower consumer costs and drive the global clean energy economy forward. More information on this can be found here: https://www.whitehouse.gov/cleanenergy/inflation-reduction-act-guidebook/. This dataset illustrates the locations of Bureau of Indian Affairs projects funded by the Bipartisan Infrastructure Law and Inflation Reduction Act in Fiscal Year 2022, 2023, and 2024. The points illustrated in this dataset are the locations of Bureau of Indian Affairs projects funded by the Bipartisan Infrastructure Law and Inflation Reduction Act in Fiscal Year 2022 and 2023. The locations for the points in this layer were provided by the persons involved in the following groups: Division of Water and Power, DWP, Ecosystem Restoration, Irrigation, Power, Water Sanitation, Dam Safety, Branch of Geospatial Support, Bureau of Indian Affairs, BIA.GIS point feature class was created by Bureau of Indian Affairs - Branch Of Geospatial Support (BOGS), Division of Water and Power (DWP), Ecosystem Restoration, Irrigation, Bureau of Indian Affairs (BIA), Tribal Leaders Directory: https://www.bia.gov/service/tribal-leaders-directory/tld-csvexcel-dataset, The Department of the Interior | Strategic Hazard Identification and Risk Assessment Project: https://www.doi.gov/emergency/shira#main-content

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.

Brazilian? You can read a Portuguese version of this article here.

Context

Last year, while I was attending a data science course in Germany, my country was impeaching its president. My colleagues asked me to explain what was happening in Brazil and the possible political outcomes in South America. Although I was able to give a general context and tell multiple arguments in favor and against the impeachment, deep inside, my answer was "I really don't know".

Understanding what happens in Politics is something that takes a lot of effort and research. When I decided I had to use my tech skills to make myself a better citizen, I dived into government data and started Operation Serenata de Amor.

After reporting hundreds of politicians for small acts of corruption and learning how to encourage the population to engage in the democratic processes, my studies drove me to understand the legislative activity.

Brazilians elect 594 citizens to be their representatives in the National Congress. How can we be sure that they are not defending their own interests or those who paid for their campaigns? My way, as a data scientist, is to ask the data.

Content

The National Congress of Brazil is composed of a Lower (Chamber of Deputies) and an Upper House (Federal Senate). In the first version of this dataset, you are going to find data only from the Chamber of Deputies. With 513 representatives, 86% of the congresspeople, I hope you have enough data to explore for some time.

Would be impossible for me, a citizen without government ties, to collect this data without the help of public servants. I processed 9,717 fixed-width files and 73 XML's made officially available by the Chamber of Deputies and created 5 CSV's containing the same information. Multiple fields of the same file telling the same thing (e.g. body_id, body_name and body_abbreviation) were removed.

Data on session attendance, votes, and propositions since past century were collected and scripted in a reproducible manner. The data collection and pre-processing scripts are available in a GitHub repository, under an open source license.

Everything was collected from the Chamber of Deputies website at December 27, 2017, containing the whole legislative activity of the year. Presence and votes date from 1999, propositions go as far as 1946.

When in question about the legislative process and how the sessions work in real world, the Internal Regulation of the Chamber of Deputies is the best Portuguese documentation for research. It's free!

Acknowledgements

Since the data was collected from a government website and the Brazilian law states that access to this information is free to any citizen, I am placing my own work published here in Public Domain.

I'd like to thank the hundreds of people financially supporting the work of Operation Serenata de Amor and those responsible for passing the Information Access bill in 2011.

Inspiration

The legislative activity should tell the history while it's happening. How much has the Congress changed over the past decades? Do the congresspeople maintain the same political views or they vary on a weekly basis? Do people vote together with their state or party peers? How often? Can you model an algorithm to tell us the real parties inside Brazilian Congress?

Context:

On a typical day in the United States, police officers make more than 50,000 traffic stops. The Stanford Open Policing Project team is gathering, analyzing, and releasing records from millions of traffic stops by law enforcement agencies across the country. Their goal is to help researchers, journalists, and policymakers investigate and improve interactions between police and the public.

If you'd like to see data regarding other states, please go to https://www.kaggle.com/stanford-open-policing.

Content:

This dataset includes stop data from AZ, CO, CT, IA, MA, MD, MI and MO. Please see the data readme for the full details of the available fields.

Acknowledgements:

This dataset was kindly made available by the Stanford Open Policing Project. If you use it for a research publication, please cite their working paper: E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, V. Ramachandran, C. Phillips, S. Goel. (2017) “A large-scale analysis of racial disparities in police stops across the United States”.

Inspiration:

• How predictable are the stop rates? Are there times and places that reliably generate stops?
• Concerns have been raised about jurisdictions using civil forfeiture as a funding mechanism rather than to properly fight drug trafficking. Can you identify any jurisdictions that may be exhibiting this behavior?

This dataset contains information on New York City bills from 1998 through 2024. The New York City Council is the City’s legislative body, responsible for proposing, debating, and voting on legislation that affects all aspects of city governance. The legislative process for Introductions in NYC follows these steps: Bill Introduced – A bill is introduced by a Council Member and assigned a number. Committee Hearings – The bill is referred to a committee for review, where public hearings may be held for discussion, public comment, and stakeholder engagement. Committee Vote – The committee votes on whether to advance the bill to the full Council. Full Council Vote – If approved by the committee, the bill is voted on by the entire Council. A majority vote is required for passage. Mayoral Action – Once passed by the Council, the bill is sent to the Mayor, who may sign it into law, veto it, or allow it to become law without a signature. If vetoed, the Council can override the veto with a two-thirds majority vote. Enactment – Once signed or passed via override, the bill is assigned a local law number and becomes part of the city’s legal code. Bills may be enacted immediately, or upon a date defined in the bill. This dataset provides information on all introductions, regardless of whether they were enacted.

Analysis of ‘Local Law 14 Health Data - HS School’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/424ab1f5-0ebe-4872-ab9d-ca05e38cbdc0 on 26 January 2022.

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

"""Local Law 14 (2016) requires that the NYCDOE provide citywide Health Education data, dis aggregated by community school district, city council district, and each individual school. Data reported in this report is from the 2015-16 school year. "" This report provides information about the number and percent of students receiving one semester of health education as defined in Local Law 14 as reported through the 2015-2016 STARS database. It is important to note that schools self-report their scheduling information in STARS.

This report consists of 10 tabs:

1. Health Education Standards
2. HS - School
3. HS-District
4. JS-City Council District
5. MS-School
6. MS-District
7. Ms-City Council District
8. Efficacy
9. Compliance

10. LGBTQ Inclusivity

11. Health Education Standards

This tab provides information on the New York State Health Education Requirements and Standards. These requirements can be found in NYS Education Commissioner’s Regulation Subchapter G Part 135.

1. HS - School

This tab includes school level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - District

This tab includes district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. HS - City Council District

This tab includes city council district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data.

1. Ms - School

This tab includes school level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - District

This tab includes district level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course.

1. MS - City Council District

This tab includes Cit

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

Dataset Overview

This dataset contains global COVID-19 case and death data by country, collected directly from the official World Health Organization (WHO) COVID-19 Dashboard. It provides a comprehensive view of the pandemic’s impact worldwide, covering the period up to 2025. The dataset is intended for researchers, analysts, and anyone interested in understanding the progression and global effects of COVID-19 through reliable, up-to-date information.

Source Information

• Website: WHO COVID-19 Dashboard
• Organization: World Health Organization (WHO)
• Data Coverage: Global (by country/territory)
• Time Period: Up to 2025

The World Health Organization is the United Nations agency responsible for international public health. The WHO COVID-19 Dashboard is a trusted source that aggregates official reports from countries and territories around the world, providing daily updates on cases, deaths, and other key metrics related to COVID-19.

Dataset Contents

• Country/Region: The name of the country or territory.
• Date: Reporting date.
• New Cases: Number of new confirmed COVID-19 cases.
• Cumulative Cases: Total confirmed COVID-19 cases to date.
• New Deaths: Number of new confirmed deaths due to COVID-19.
• Cumulative Deaths: Total deaths reported to date.
• Additional fields may include population, rates per 100,000, and more (see data files for details).

How to Use

This dataset can be used for: - Tracking the spread and trends of COVID-19 globally and by country - Modeling and forecasting pandemic progression - Comparative analysis of the pandemic’s impact across countries and regions - Visualization and reporting

Data Reliability

The data is sourced from the WHO, widely regarded as the most authoritative source for global health statistics. However, reporting practices and data completeness may vary by country and may be subject to revision as new information becomes available.

Acknowledgements

Special thanks to the WHO for making this data publicly available and to all those working to collect, verify, and report COVID-19 statistics.

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

URL: https://sparse.tamu.edu/LAW

Laboratory for Web Algorithmics (LAW), Università degli Studi di Milano http://law.di.unimi.it/index.php

When using matrices in the LAW/ group in the collection, please follow the citation instructions at http://law.di.unimi.it/datasets.php

If you publish results based on these graphs, please acknowledge the usage of WebGraph and LLP by quoting the following papers:

@inproceedings{BoVWFI, author ="Paolo Boldi and Sebastiano Vigna", title = "The {W}eb{G}raph Framework {I}: {C}ompression Techniques", year = 2004, booktitle="Proc. of the Thirteenth International World Wide Web Conference (WWW 2004)", address="Manhattan, USA", pages="595--601", publisher="ACM Press" }

@inproceedings{BRSLLP, author = "Paolo Boldi and Marco Rosa and Massimo Santini and Sebastiano Vigna", title = "Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks", booktitle="Proceedings of the 20th international conference on World Wide Web", year = 2011, publisher="ACM Press" }

Collaborative Open Legal Data (COLD) - French Law

COLD French Law is a dataset containing over 800 000 french law articles, filtered and extracted from France's LEGI dataset and formatted as a single CSV file. This dataset focuses on articles (codes, lois, décrets, arrêtés ...) identified as currently applicable french law. A large portion of this dataset comes with machine-generated english translations, provided by Casetext, Part of Thomson Reuters using OpenAI's GPT-4. This… See the full description on the dataset page: https://huggingface.co/datasets/harvard-lil/cold-french-law.

"""Local Law 14 (2016) requires that the NYCDOE provide citywide Health Education data, dis aggregated by community school district, city council district, and each individual school. Data reported in this report is from the 2015-16 school year. "" This report provides information about the number and percent of students receiving one semester of health education as defined in Local Law 14 as reported through the 2015-2016 STARS database. It is important to note that schools self-report their scheduling information in STARS. This report consists of 10 tabs: 1. Health Education Standards 2. HS - School 3. HS-District 4. JS-City Council District 5. MS-School 6. MS-District 7. Ms-City Council District 8. Efficacy 9. Compliance 10. LGBTQ Inclusivity Health Education Standards This tab provides information on the New York State Health Education Requirements and Standards. These requirements can be found in NYS Education Commissioner’s Regulation Subchapter G Part 135. HS - School This tab includes school level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data. HS - District This tab includes district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data. HS - City Council District This tab includes city council district level data on the number of students that received a semester (one credit) of health instruction, as well as the number of June and August graduates meeting the HS health requirements for the 2015-2016 school year. Note that students are not required to receive health instruction at any particular grade level in high school, only prior to graduating. Additionally, values less than 100% do not necessarily imply that students graduated without meeting credit requirements. In very rare cases, these values may indicate missing or incomplete historical transcript data. Ms - School This tab includes school level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course. MS - District This tab includes district level data on the number of 6-8 graders that received a semester (one half-unit) of health instruction, as well as the number of 8th graders meeting the middle school health requirements for the 2015-2016 school year. Note that this regulation does not require students to receive health instruction at any particular grade level in middle school, only prior to completing 8th grade. However, a student may advance to the next grade without completing the course. MS - City Council District This tab includes Cit

Note:- Only publicly available Legal data can be worked upon.

Unlock a world of legal information with APISCRAPY's user-friendly services – Federal Court Data, State Court Record Data, legal data scraping, and PACER Data. We've made it simple for anyone, from legal professionals to researchers and businesses, to access over 100 million publicly available legal records.

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Legal Data Scraping Made Easy: We've simplified the process of gathering legal data, making it accessible for everyone.

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Over 100 Million Records: APISCRAPY provides access to a vast database of over 100 million publicly available legal records, offering unparalleled insights.

Whether you're a legal professional, researcher, or business looking for easy access to legal information, APISCRAPY's services cater to your needs. Choose us for straightforward and comprehensive legal data services, where simplicity and accessibility meet for your convenience.

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Dataset Card for "thailaw"

  English

Thai Law Dataset (Act of Parliament)

Data source from Office of the Council of State, Thailand. https://www.krisdika.go.th/ This part of PyThaiNLP Project. License Dataset is public domain.

Download https://github.com/PyThaiNLP/thai-law/releases This hub based on Thailaw v0.2.

  Thai

คลังข้อมูลกฎหมายไทย (พระราชบัญญัติ)

ข้อมูลเก็บรวบรวมมาจากเว็บไซต์สำนักงานคณะกรรมการกฤษฎีกา https://www.krisdika.go.th/… See the full description on the dataset page: https://huggingface.co/datasets/pythainlp/thailaw.

This dataset provides statistics on EUR-Lex website from two views: type of content and number of legal acts available. It is updated on a daily basis.

1) The statistics on the content of EUR-Lex (from 1990 to 2018) show

a) how many legal texts in a given language and document format were made available in EUR-Lex in a particular month and year. They include:

• corrigenda and amending acts
• legal acts, both those that are in force and those that are no longer in force
• proposals, whether awaiting adoption or already adopted.

Since the eight parliamentary term, parliamentary questions are no longer included.

b) bibliographical notices by sector (e.g. case-law, treaties).

2) The statistics on legal acts (from 1990 to 2018) provide yearly and monthly figures on the number of adopted acts (also by author and by type) as well as those repealed and expired in a given month.

Analysis of ‘2019-2020 Local Law 14 Health Education Report - Final’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/937886fe-ba9e-4784-9f91-a471c732de55 on 26 January 2022.

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

Local Law 14 enacted in 2016 requires the Department of Education of the New York City School District to submit to the Council an annual report concerning health education for the prior school year. This report provides information about the provision of health education instruction, including the number and percentage of students in grades six through eight who have received 54 hours of health education and those who have completed at least one semester of health education, the total number and percentage of students in grades six through twelve who have completed the required number of lessons in HIV/AIDS education (5 lessons for grade six and 6 lessons for grades seven through twelve), as reported through the STARS database for the 2019-20 school year. It is important to note that schools self-report their scheduling information in STARS. Furthermore, with the shift to remote learning in March 2020, NYSED waived instructional seat time requirements for all academic subjects for the remainder of the 2019-20 school year. As a result, health education scheduling data for students in grades six through eight is reported as of the mid-year of the 2019-20 school year.

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

The United States Code ("Code") contains the general and permanent laws of the United States, arranged into 54 broad titles according to subject matter. The organization of the Code was originally established by Congress in 1926 with the enactment of the act of June 30, 1926, chapter 712. Since then, 27 of the titles, referred to as positive law titles, have been restated and enacted into law by Congress as titles of the Code. The remaining titles, referred to as non-positive law titles, are made up of sections from many acts of Congress that were either included in the original Code or subsequently added by the editors of the Code, i.e., the Office of the Law Revision Counsel, and its predecessors in the House of Representatives. Positive law titles are identified by an asterisk on the Search & Browse page. For an explanation of the meaning of positive law, see the Positive Law Codification page.

Each title of the Code is subdivided into a combination of smaller units such as subtitles, chapters, subchapters, parts, subparts, and sections, not necessarily in that order. Sections are often subdivided into a combination of smaller units such as subsections, paragraphs, subparagraphs, clauses, subclauses, and items. In the case of a positive law title, the units are determined by Congress in the laws that enact and later amend the title. In the case of a non-positive law title, the organization of the title since 1926 has been determined by the editors of the Code and has generally followed the organization of the underlying acts 1 as much as possible. For example, chapter 7 of title 42 sets out the titles, parts, and sections of the Social Security Act as corresponding subchapters, parts, and sections of the chapter.

In addition to the sections themselves, the Code includes statutory provisions set out as statutory notes, the Constitution, several sets of Federal court rules, and certain Presidential documents, such as Executive orders, determinations, notices, and proclamations, that implement or relate to statutory provisions in the Code. The Code does not include treaties, agency regulations, State or District of Columbia laws, or most acts that are temporary or special, such as those that appropriate money for specific years or that apply to only a limited number of people or a specific place. For an explanation of the process of determining which new acts are included in the Code, see the About Classification page.

The Code also contains editorially created source credits, notes, and tables that provide information about the source of Code sections, their arrangement, the references they contain, and their history.

The law contained in the Code is the product of over 200 years of legislating. Drafting styles have changed over the years, and the resulting differences in laws are reflected in the Code. Similarly, Code editorial styles and policies have evolved over the 80-plus years since the Code was first adopted. As a result, not all acts have been handled in a consistent manner in the Code over time. This guide explains the editorial styles and policies currently used to produce the Code, but the reader should be aware that some things may have been done differently in the past. However, despite the evolution of style over the years, the accuracy of the information presented in the Code has always been, and will always remain, a top priority.

Content

This dataset is a snapshot of the XML version of the United States Code. It is not a suitable for any form of legal work and is intended for research purposes only.

The data are stored in a large json dictionary, indexed by the title of the code.

Acknowledgements

This dataset was released by the United States Government Publishing Office. You can find the original dataset here.

Overview

The Corpus of Decisions: International Court of Justice (CD-ICJ) collects and presents for the first time in human- and machine-readable form all published decisions of the International Court of Justice (ICJ). Among these are judgments, advisory opinions and orders, as well as their respective appended minority opinions (declarations, separate opinions and dissenting opinions). The International Court of Justice has kindly made available these documents on its website.

The International Court of Justice (ICJ) is the primary judicial organ of the United Nations and one of the most consequential courts in international law. Called the ‘World Court’ by many, it is the only international court with general thematic jurisdiction. While critics occasionally note the lack of compulsory jurisdiction and sharply limited access to the Court, its opinions continue to have an outsize influence on the modern interpretation, codification and wider development of international law. Every international legal textbook covers the workings and decisions of the Court in extenso and participation in international moot courts such as the Philip C. Jessup Moot Court without regular reference to and citation of the International Court of Justice’s decisions is unthinkable.

This data set is designed to be complementary to and fully compatible with the Corpus of Decisions: Permanent Court of International Justice (CD-PCIJ), which is also available open access.

A peer-reviewed academic paper describing the construction and relevance of the data set entitled 'Introducing Twin Corpora of Decisions for the International Court of Justice (ICJ) and the Permanent Court of International Justice (PCIJ)' was published open access by the Journal of Empirical Legal Studies (JELS) in April 2022.

If you use the data set for academic work, please cite both the JELS paper and the precise version of the data set you used for your analysis.

Updates

The CD-ICJ will be updated two times per year, ideally every six months. In case of serious errors an update will be provided at the earliest opportunity and a highlighted advisory issued on the Zenodo page of the current version. Minor errors will be documented in the GitHub issue tracker and fixed with the next scheduled release.

The CD-ICJ is versioned according to the day the data was acquired from the website of the Court, in the ISO format YYYY-MM-DD. Its initial release version is 2021-11-23.

Notifications regarding new and updated data sets will be published on my academic website at www.seanfobbe.com or via Twitter at @FobbeSean.

Recommended Variants

Please refer to the Codebook regarding the relative merits of each variant. All variants are available in either English or French. Unless you have very specific needs you should only use the variants denoted 'BEST' for serious work.

Features

• Fully compatible with the Corpus of Decisions: Permanent Court of International Justice (CD-PCIJ)
• 27 variables
• Public Domain (CC-Zero 1.0)
• Open and platform independent file formats (PDF, TXT, CSV)
• Extensive Codebook
• Compilation Report explains construction and validation of the data set in detail
• Large number of diagrams for all purposes (see the 'ANALYSIS' archive)
• Diagrams are available as PDF (for printing) and PNG (for web display), tables are available as CSV for easy readability by humans and machines
• Secure cryptographic signatures
• Publication of full source code (Open Source)

Key Metrics

Version: 2021-11-23

Temporal Coverage: 31 July 1947 – 12 October 2021

Documents: 2169 (English) / 2160 (French)

Tokens: 15,108,060 (English) / 15,463,747 (French)

File Formats: PDF, TXT, CSV

Source Code and Compilation Report

With every compilation of the full data set an extensive Compilation Report is created in a professionally layouted PDF format (comparable to the Codebook). The Compilation Report includes the Source Code, comments and explanations of design decisions, relevant computational results, exact timestamps and a table of contents with clickable internal hyperlinks to each section. The Compilation Report and Source Code are published under the same DOI: https://doi.org/10.5281/zenodo.3977177

For details of the construction and validation of the data set please refer to the Compilation Report.

Disclaimer

This data set has been created by Mr Seán Fobbe using documents available on the website of the International Court of Justice (https://www.icj-cij.org). It is a personal academic initiative and is not associated with or endorsed by the International Court of Justice or the United Nations.

The Court accepts no responsibility or liability arising out of my use, or that of third parties, of the documents and information produced, used or published on the Zenodo website. Neither the Court nor its staff members nor its contractors may be held responsible or liable for the consequences, financial or otherwise, resulting from the use of these documents and information.

Academic Publications (Fobbe)

Website — www.seanfobbe.com

Open Data — zenodo.org/communities/sean-fobbe-data

Code Repository — zenodo.org/communities/sean-fobbe-code

Regular Publications — zenodo.org/communities/sean-fobbe-publications

Contact

Did you discover any errors? Do you have suggestions on how to improve the data set? You can either post these to the Issue Tracker on GitHub or write me an e-mail at fobbe-data@posteo.de

This file set is the basis of a project in which Stephanie Pywell from The Open University Law School created and evaluated some online teaching materials – Fundamentals of Law (FoLs) – to fill a gap in the knowledge of graduate entrants to the Bachelor of Laws (LLB) programme. These students are granted exemption from the Level 1 law modules, from which they would normally acquire the basic knowledge of legal principles and methods that is essential to success in higher-level study. The materials consisted of 12 sessions of learning, each covering one key topic from a Level 1 law module.The dataset includes a Word document that consists of the text of a five-question, multiple-choice Moodle poll, together with the coding for each response option.The rest of the dataset consists of spreadsheets and outputs from SPSS and Excel showing the analyses that were conducted on the cleaned and anonymised data to ascertain students' use of, and views on, the teaching materials, and to explore any statistical association between students' studying of the materials and their academic success on Level 2 law modules, W202 and W203.Students were asked to complete the Moodle poll at the end of every session of study, of which there were 1,013. Only one answer from each of the 240 respondents was retained for Questions 3, 4 and 5, to avoid skewing the data. Some data are presented as percentages of the number of sessions studied; some are presented as percentages of the number of respondents, and some are presented as percentage of the number of respondents who meet specific criteria.Student identifiers, which have been removed to ensure anonymity, are as follows: Open University Computer User code (OUCU) and Personal Identifier (PI). These were used to collate the output from the Moodle poll with students' Level 2 module results.

Abstract

In this supplementary material, I provide a comprehensive dataset of German legislation collected and analyzed for the purposes of my dissertation project. It includes the new measures of legislative significance developed in the dissertation, as well as several accompanying (independent or identification) variables based on the characteristics of individual legislative processes and administrative information. This supplementary material includes the data, an extensive codebook that outlines each individual variable, and the required R code to replicate the analyses performed in the dissertation and to reproduce and extend the data. The code is provided in an interoperable way that enables easy execution. Several preliminary datasets are made available along with the final dataset. See the attached description document for more details and a precise instruction.

Details

The final dataset “leg_sig.csv” covers all legislative processes considered in the German Bundestag from 1991 to 2021 – 6,602 submitted bills and 3,206 enacted laws. It is a special feature of this dataset that it connects bills to finalized laws and thus covers the complete legislative procedure. For each legislative process, extensive information was collected and transformed into 142 variables (154 variables in the reproduced version). Most of the data were collected from official sources of the German Bundestag, i.e., the DIP repository (Deutscher Bundestag / Bundesrat, n.d.) and the Bundestag’s official data handbook (Deutscher Bundestag, 2024). Data from the German Federal Statistical Office (Statistisches Bundesamt [Destatis], 2024a, 2024b, 2024c, 2024d) and the Federal Election Supervisor (Der Bundeswahlleiter, 2022) were added subsequently. Enacted laws were obtained from the private webpage OffeneGesetze.de (Wehrmeyer et al., n.d.).

Target group

This data and supplementary material may be of interest to: legislative studies scholars, students of German lawmaking and legislation, replication research testing lawmaking arguments with significant (instead of all) legislation, research on the “significance” of political action, public policy scholars aiming to quantify the content of laws, legal scholars aiming to measure the juridification, expansion or detail of law.

Related publication

Garwe, Christoph (2025). Conceptualizing and Measuring Legislative Significance – an Application to the German Bundestag. [Dissertation, Leibniz Universität Hannover]. Institutionelles Repositorium der Leibniz Universität Hannover. https://doi.org/10.15488/18305

This dataset contains immunization status of kindergarten students in California in schools. Explanation of the different immunizations is in the attached data dictionary. The California Health and Safety Code Section 120325-75 requires students to provide proof of immunization for school and child care entry. Additionally, California Health and Safety Code Section 120375 and California Code of Regulation Section 6075 require all schools and child care facilities to assess and report annually the immunization status of their enrollees.

The annual kindergarten assessment is conducted each fall to monitor compliance with the California School Immunization law. Results from this assessment are used to measure immunization coverage among students entering kindergarten. Not all schools reported. This data set presents results from the kindergarten assessment and immunization coverage in kindergarten schools by county. To review individual school coverage and exemption rates in a separate lookup format, go to the School Lookup page at the Immunization Branch's Shots for School website: http://www.shotsforschool.org/lookup/

To see the PDF reports by year go to:https://www.shotsforschool.org/k-12/reporting-data/

See the attached file 'Notes on Methods' for data suppression in the '2016-17 ' data and after.

For earlier years of data: https://www.shotsforschool.org/k-12/reporting-data/