Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

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How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

Abstract The information need to be in accordance with precise and reliable quality criteria, especially in legal environments. In Brazil, publication grows daily, such as norms and changes in legislation. In this sense, it is necessary to know the information sources and monitor the changes, seeking to satisfy the needs of users of legal information. From this perspective, this study aimed to analyze the satisfaction of users of legal information in meeting their informational needs by the Librarian. This is an exploratory-descriptive research, which data collection was done from the application of a structured questionnaire to law professionals. It was found that the level of specificity of legal information is a decisive factor and of paramount importance in professional practice, and that the level of reliability of the sources of legal information, recognized by the great majority of respondents, is linked to the presentation of authorship and / or authority. As to the accuracy of the information, it has been shown that it is directly linked to the consistency of the informational data. Finally, in the recognition and appreciation of the work of the librarian, the respondents consider it to be extremely important for the success of the legal advisory services.

The JURIST website is a web-based service that provides access to a wide range of legal news, research services, and educational resources. The site aggregates legal news from around the world, with a focus on international law, human rights, and legal developments in various countries. The website also offers a bespoke data store, where users can purchase and access crawled data from various sources.

The JURIST website is curated by a team of law students and experts, who provide in-depth analysis, commentary, and interviews with experts in the field. The site features regular dispatches from around the world, covering legal news and developments from Asia, Africa, Europe, and the Americas. It also publishes features on a range of legal topics, including human rights, international law, and legal education. The website is a valuable resource for legal professionals, researchers, and students seeking to stay up-to-date on the latest legal developments and trends.

Project Overview Trends toward open science practices, along with advances in technology, have promoted increased data archiving in recent years, thus bringing new attention to the reuse of archived qualitative data. Qualitative data reuse can increase efficiency and reduce the burden on research subjects, since new studies can be conducted without collecting new data. Qualitative data reuse also supports larger-scale, longitudinal research by combining datasets to analyze more participants. At the same time, qualitative research data can increasingly be collected from online sources. Social scientists can access and analyze personal narratives and social interactions through social media such as blogs, vlogs, online forums, and posts and interactions from social networking sites like Facebook and Twitter. These big social data have been celebrated as an unprecedented source of data analytics, able to produce insights about human behavior on a massive scale. However, both types of research also present key epistemological, ethical, and legal issues. This study explores the issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership, with a focus on data curation strategies. The research suggests that connecting qualitative researchers, big social researchers, and curators can enhance responsible practices for qualitative data reuse and big social research. This study addressed the following research questions: RQ1: How is big social data curation similar to and different from qualitative data curation? RQ1a: How are epistemological, ethical, and legal issues different or similar for qualitative data reuse and big social research? RQ1b: How can data curation practices such as metadata and archiving support and resolve some of these epistemological and ethical issues? RQ2: What are the implications of these similarities and differences for big social data curation and qualitative data curation, and what can we learn from combining these two conversations? Data Description and Collection Overview The data in this study was collected using semi-structured interviews that centered around specific incidents of qualitative data archiving or reuse, big social research, or data curation. The participants for the interviews were therefore drawn from three categories: researchers who have used big social data, qualitative researchers who have published or reused qualitative data, and data curators who have worked with one or both types of data. Six key issues were identified in a literature review, and were then used to structure three interview guides for the semi-structured interviews. The six issues are context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Participants were limited to those working in the United States. Ten participants from each of the three target populations—big social researchers, qualitative researchers who had published or reused data, and data curators were interviewed. The interviews were conducted between March 11 and October 6, 2021. When scheduling the interviews, participants received an email asking them to identify a critical incident prior to the interview. The “incident” in critical incident interviewing technique is a specific example that focuses a participant’s answers to the interview questions. The participants were asked their permission to have the interviews recorded, which was completed using the built-in recording technology of Zoom videoconferencing software. The author also took notes during the interviews. Otter.ai speech-to-text software was used to create initial transcriptions of the interview recordings. A hired undergraduate student hand-edited the transcripts for accuracy. The transcripts were manually de-identified. The author analyzed the interview transcripts using a qualitative content analysis approach. This involved using a combination of inductive and deductive coding approaches. After reviewing the research questions, the author used NVivo software to identify chunks of text in the interview transcripts that represented key themes of the research. Because the interviews were structured around each of the six key issues that had been identified in the literature review, the author deductively created a parent code for each of the six key issues. These parent codes were context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. The author then used inductive coding to create sub-codes beneath each of the parent codes for these key issues. Selection and Organization of Shared Data The data files consist of 28 of the interview transcripts themselves – transcripts from Big Science Researchers (BSR), Data Curators (DC), and Qualitative Researchers (QR)...

This is a survey study of 43 judges from the British House of Lords, the Caribbean Court of Justice, the High Court of Australia, and the Supreme Courts of Ireland, India, Israel, South Africa, Canada, New Zealand, and the United States on the use of foreign law in constitutional rights cases. As the focus of attempts to both explain and justify the use of foreign law in constitutional discourse, the attitudes of apex judges are clearly at issue. The study aims to shed light on how common law judges view foreign law as a source of argument in constitutional rights matters, and how they "see" transnational sources. The data provide the basis for preliminary testing of globalist theory (associated with Anne-Marie Slaughter, Vicki Jackson and Chris McCrudden). More generally, they lend a practical insight to jurisprudential debates invoking the nature of judicial reasoning in appellate courts. We find that the conception of judges citing foreign law as a source of persuasive authority is of limited application. Citational opportunism and the aspiration to membership of an emerging international "guild" appear to be equally important strands in judicial attitudes towards foreign law. We argue that their presence is at odds with Ronald Dworkin's theory of legal objectivity, and revealed in a manner meeting his own methodological standard for attitudinal research.

Legal Case Reports in Australia

An Exploratory Study of Legal Citing Practices

By UCI [source]

About this dataset

This dataset contains an array of legal case reports from Australia's Federal Court of Australia (FCA). These cases originate from the years 2006-2009 and have been downloaded from AustLII, a free online legal database. It provides valuable insight into automatic summarization and citation analysis research.

Each document contains catchphrases, citation sentences, citation catchphrases, and classification schemes which allows us to further explore their relationships with one another. Catchphrases are found in each document as a reference tool for summarization experiments. Citation sentences describe the subsequent cases that refer to the present case while citation catchphrases offer snippets (where available) of both later cases that cite the present case as well past its cited by it counterparts. Citation classes are expressed within each document helping to further identify how these citations are treated within text or further research scenarios

The utility of this data set has been likened to PRICAI 2012 volume LNCS 7458 pages 40–52 presented by Filippo Galgani, Patrick Compton and Arapahoe Hoffmann demonstrating its potential for summarizing textual data on legal issues as well information related to knowledge aquisition for categorizaion purposes inLegal Case Reports datasets such as PKAW 2012 volume LNAI 7457 pages 118-132 also produced by Filippo Galgani and Arapahoe Hoffman or AI 2010 Advances in Artificial Intelligence vol 6464 Lecture Notes Computer Science Pages 445 – 454 produced by Filippo Galgagni alone This dataset is a great resource for anyone looking to dive deeper into studying Australian Legal Case Reports from 2006-2009 and their implications on automated summarization techniques time-based matching, extraction systems natural language processing and more!

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Research Ideas

• Analyze legal cases to assess the impact of court decisions and research popular legal trends;
• Conduct automated summarization of legal texts to determine their relevance from a citation perspective;
• Use the citation information in the dataset for categorizing and classifying legal case reports

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit UCI.

Data source: the whole question list of the "legal consultation" section of China law network and all the answers to the questions answered; Data collection time: December 2017 to February 2018. Data collection: Python script is written to automatically acquire text crawler and comply with the webpage robots protocol; Processing method: store MongoDB database and export; Data format: CSV and json; Data description: Question_1, question_2 contains around 1.2 million data volume, for China law web plate "legal advice" question list information, including consultants belong to, the content of counseling problems, consulting problem belongs to field, consulting; Answer_1, answer_2 contains around 2.1 million data, for the China law of "legal advice" plate has to solve network problems list all lawyers answer content, including lawyers answer content, lawyers answer, lawyers answer time for details. Instructions: mainly extracted the question text and sorted into corpus.

The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].

The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.

The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.

The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).

The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.
The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.

The available files are as follows:

• documents-texts.zip --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.
• documents-metadata.csv --> Contains a CSV file with the metadata for each document used as a source for the case studies.

This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].

[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.

[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.

[3] R. K. Yin, Case study research and applications. Sage, 2018.

Belgian Statutory Article Retrieval Dataset (BSARD)

Legal Q&A Dataset for Law Information Retrieval

By maastrichtlawtech (From Huggingface) [source]

About this dataset

In the train.csv file, you will find a vast array of legal questions along with the relevant statutory articles that provide answers or guidance on these questions. Each question is associated with specific statutory article IDs. Additionally, the categorical information such as categories, subcategories, and extra descriptions are provided to offer further context to the legal queries.

Similarly, the test.csv file presents a set of legal questions along with their corresponding statutory article IDs. However, in this case, not only do you have access to category and subcategory labels but also detailed extra descriptions that can assist in understanding the particular nuances or background information related to each question.

Lastly, for those interested in exploring synthetic data for law information retrieval tasks, the synthetic.csv file contains synthesized legal questions paired with corresponding statutory article IDs.

It is important to note that this dataset does not include specific dates associated with each entry. It is solely focused on providing an extensive collection of legal questions and their corresponding statutory articles to facilitate research and development in law information retrieval applications.

With its comprehensive coverage and carefully curated data entries encompassing various categories and subcategories within Belgium's legal framework, BSARD serves as a valuable resource for researchers working on natural language processing (NLP), machine learning algorithms designe

How to use the dataset

Introduction:

Dataset Overview: The BSARD dataset consists of three main files: train.csv, test.csv, and synthetic.csv. Each file contains legal questions along with additional information such as statutory article IDs, categories, subcategories, and extra descriptions.

File Descriptions:

• train.csv: This file contains legal questions from real-life scenarios that were used for training purposes.

• test.csv: The test.csv file includes unseen legal questions along with their corresponding statutory article IDs, categories, subcategories, and extra descriptions. It serves as a benchmark to evaluate model performance.

• synthetic.csv: Synthetic legal questions are present in this file that can help in diversifying the training data when necessary.

Understanding the Columns:

Each dataset file consists of several columns that play an essential role in conducting law information retrieval tasks:

• question: This column holds the actual legal question text.

• category: Represents the broad category to which each legal question belongs.

• subcategory: Indicates the specific subcategory under which each question falls.

• extra_description (optional): Provides further contextual or additional information related to specific legal questions.

Using the Dataset Effectively:

• Preprocessing:

  • Remove any unnecessary characters from the text.
  • Consider removing stop words or performing stemming/lemmatization if appropriate for your task.
  • Normalize case sensitivity based on your requirements.

• Training Phase (using train.csv):

  • Analyze statistical properties of categories/subcategories in order to understand their distributions accurately.
  • Employ suitable algorithms like classification models or natural language processing techniques based on the task's requirement.
  • Leverage additional information in the extra_description column to extract more valuable features.

• Evaluation Phase (using test.csv):

  • Develop a model using the training set and apply it to unseen legal questions from the test set.
  • Analyze performance metrics such as accuracy, precision, recall, or F1-score depending on your evaluation goals.

• Synthetic Data (synthetic.csv):

  • Utilize synthetic data to augment your training dataset and increase its diversity when necessary.

Conclusion:

Research Ideas

• Legal research: The dataset can be used for legal research purposes, where researchers can analyze the legal questions and statutory articles to gain insights into specific areas of law or identify common legal issues.
• Information retrieval system development: The dataset can be used to develop and train information retrieval systems specifically designed for retrieving relevant statutory articles based on legal questions....

Trusted Research Environments (TREs) enable analysis of sensitive data under strict security assertions that protect the data with technical organizational and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks & their slight technical variations. To shine light on these problems, we give an overview of existing, publicly described TREs and a bibliography linking to the system description. We further analyze their technical characteristics, especially in their commonalities & variations and provide insight on their data type characteristics and availability. Our literature study shows that 47 TREs worldwide provide access to sensitive data of which two-thirds provide data themselves, predominantly via secure remote access. Statistical offices make available a majority of available sensitive data records included in this study.

Methodology

We performed a literature study covering 47 TREs worldwide using scholarly databases (Scopus, Web of Science, IEEE Xplore, Science Direct), a computer science library (dblp.org), Google and grey literature focusing on retrieving the following source material:

• Peer-reviewed articles where available,
• TRE websites,
• TRE metadata catalogs.

The goal for this literature study is to discover existing TREs, analyze their characteristics and data availability to give an overview on available infrastructure for sensitive data research as many European initiatives have been emerging in recent months.

Technical details

This dataset consists of five comma-separated values (.csv) files describing our inventory:

• countries.csv: Table of countries with columns id (number), name (text) and code (text, in ISO 3166-A3 encoding, optional)
• tres.csv: Table of TREs with columns id (number), name (text), countryid (number, refering to column id of table countries), structureddata (bool, optional), datalevel (one of [1=de-identified, 2=pseudonomized, 3=anonymized], optional), outputcontrol (bool, optional), inceptionyear (date, optional), records (number, optional), datatype (one of [1=claims, 2=linked records]), optional), statistics_office (bool), size (number, optional), source (text, optional), comment (text, optional)
• access.csv: Table of access modes of TREs with columns id (number), suf (bool, optional), physical_visit (bool, optional), external_physical_visit (bool, optional), remote_visit (bool, optional)
• inclusion.csv: Table of included TREs into the literature study with columns id (number), included (bool), exclusion reason (one of [peer review, environment, duplicate], optional), comment (text, optional)
• major_fields.csv: Table of data categorization into the major research fields with columns id (number), life_sciences (bool, optional), physical_sciences (bool, optional), arts_and_humanities (bool, optional), social_sciences (bool, optional).

Additionally, a MariaDB (10.5 or higher) schema definition .sql file is needed, properly modelling the schema for databases:

• schema.sql: Schema definition file to create the tables and views used in the analysis.

The analysis was done through Jupyter Notebook which can be found in our source code repository: https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb

The purpose of the study was to explore how local law enforcement were responding to the crime of human trafficking after the passage of the Trafficking Victims Protection Act (TVPA) in 2000. The first phase of the study (Part 1, Law Enforcement Interview Quantitative Data) involved conducting telephone surveys with 121 federal, state, and local law enforcement officials in key cities across the country between August and November of 2005. Different versions of the telephone survey were created for the key categories of law enforcement targeted by this study (state/local investigators, police offices, victim witness coordinators, and federal agents). The telephone surveys were supplemented with interviews from law enforcement supervisors/managers, representatives from the Federal Bureau of Investigation's (FBI) Human Trafficking/Smuggling Office, the United States Attorney's Office, the Trafficking in Persons Office, and the Department of Justice's Civil Rights Division. Respondents were asked about their history of working human trafficking cases, knowledge of human trafficking, and familiarity with the TVPA. Other variables include the type of trafficking victims encountered, how human trafficking cases were identified, and the law enforcement agency's capability to address the issue of trafficking. The respondents were also asked about the challenges and barriers to investigating human trafficking cases and to providing services to the victims. In the second phase of the study (Part 2, Case File Review Qualitative Data) researchers collected comprehensive case information from sources such as case reports, sanitized court reports, legal newspapers, magazines, and newsletters, as well as law review articles. This case review examined nine prosecuted cases of human trafficking since the passage of the TVPA. The research team conducted an assessment of each case focusing on four core components: identifying the facts, defining the problem, identifying the rule to the facts (e.g., in light of the rule, how law enforcement approached the situation), and conclusion.

Title: California Lawyers: Yellow Pages Dataset⚖️

Overview:

Dive into the heart of California's legal landscape with our meticulously curated dataset, "California Lawyers: Yellow Pages Dataset⚖️." This collection offers a panoramic view of over 800 legal professionals and firms dotted across the Golden State, each entry a beacon of potential insight into the dynamics of legal services in this vibrant region. From bustling metropolises like Los Angeles and San Francisco to the serene landscapes of Napa Valley, our dataset is a gateway to understanding the fabric of California's legal community.

Data Science Application:

Given its focused scope, this dataset is perfectly poised for niche explorations within the realm of legal analytics. Whether you're a budding data scientist seeking to hone your skills in data cleaning and visualization or an academic looking to analyze the distribution of legal services across different Californian locales, this dataset provides a solid foundation. It's an ideal resource for small-scale predictive modeling, network analysis to explore relationships within the legal community, or even sentiment analysis on the review snippets to gauge client satisfaction across different legal domains.

Column Descriptors:

• Address: Pinpoints the physical location of the legal service, allowing for geographical analysis.
• InfoSnippet: Offers a brief description of the firm or professional, useful for text analysis and categorization.
• Name: The name of the law firm or lawyer, essential for identity and reference.
• Phone: Contact information, key for network analysis.
• Rating: Client-provided ratings, offering a quantitative measure of satisfaction.
• ReviewSnippet: Client reviews in brief, a rich text data source for sentiment analysis.
• URL: The Yellow Pages link to the full listing, connecting the dataset to a broader pool of information.
• Website: Direct websites of the firms or professionals, a doorway to in-depth qualitative research.

Ethically Mined Data:

We're committed to ethical data practices. This dataset was compiled with respect for privacy, consent, and data protection norms, ensuring that all information is publicly available, aggregated with care, and presented with the intention to inform and educate.

Acknowledgments:

Heartfelt thanks go to the platforms that made this endeavor possible. Kaggle, for providing a vibrant community and platform for data science enthusiasts to learn, share, and grow and also the Yellow pages for being platform of the data.

In some countries, banks are firms’ key source of financing. In others, firms look mainly to credit markets to meet their financial needs. Why should this be so? New research suggests that a country’s legal tradition strongly influences which financial system becomes dominant there.

overview: The corona jurisprudence of the Federal Constitutional Court (BVerfG-Corona) is a fully automated compilation of all decisions of the Federal Constitutional Court associated with the novel coronavirus (SARS-CoV-2). This data set is based on the corpus of the decisions of the Federal Constitutional Court (CE-BVerfG) and documents all decisions that contain the templates "Corona", "SARS-CoV" or "COVID" in full text. Please note the codebook of the CE-BVerfG! It contains important information on the correct use of the data set. It also helps in deciding which variant is best for you. I usually recommend the PDF collection for traditional research and the CSV files for quantitative research. The BVerfG-Corona does not contain a CSV variant of the decisions, please use the CE-BVerfG for this.

Update: This data set is updated approximately every 3 months during the corona pandemic. I always publish notifications about new and updated data sets promptly on Twitter at @FobbeSean. Key dataDeadline: January 8, 2021Scope of content: 56 decisions (version 2021-01-08)Formats: PDF and TXT Source code and compilation report. The entire creation process is fully automated and documented in detail from version 2021-01-08. With each compilation of the complete data set, a comprehensive compilation report is created in an attractively designed PDF format (similar to the codebook).The compilation report contains the complete source code, documents relevant calculation results, provides time stamps accurate to the second and is provided with a clickable table of contents. It is stored together with the source code. If you are interested in details of the creation process, please read this first. The complete source code is publicly available and permanently accessible in the scientific archive of CERN under this link: https://doi.org/10.5281/zenodo.4459416 Cryptographic signatures. The integrity and authenticity of the individual archives of the data record are ensured by a two-phase signature. In phase I, hash values ​​are calculated for each ZIP archive during compilation using two different methods (SHA2-256 and SHA3-512) and documented in a CSV file. In phase II this CSV file is signed with my personal secret GPG key. This procedure ensures that the compilation can be carried out by anyone, especially in the context of replications, but that there is still personal guarantee of results. The CSV file with the hash checksums created during the compilation of the data set is provided with my personal GPG signature. The public key corresponding to this version is stored with both the data set and the source code. It has the following characteristics: Name: Sean Fobbe (fobbe-data@posteo.de)Fingerprint: FE6F B888 F0E5 656C 1D25 3B9A 50C4 1384 F44A 4E42 No copyright: public domainIn accordance with Section 5 (1) UrhG, there is no copyright in the decision-making texts and official guidelines, as they are official works. § 5 UrhG is to be applied analogously to official databases (BGH, decision of September 28, 2006 - I ZR 261/03, "Saxon tendering service"). All my own contributions (e.g. by compiling and adapting the metadata) and thus the entire data set are completely copyright-free in accordance with a CC0 1.0 Universal Public Domain License. DisclaimerThis data set is a private scientific initiative and has no connection with the Federal Constitutional Court or with the editors of the BVerfGE. Further Open Access Publications (Fobbe)You can find more open data here: https://zenodo.org/communities/sean-fobbe-data/You can find my code repository here: https://zenodo.org/communities/sean-fobbe-code/Full texts of regular publications are available here: https://zenodo.org/communities/sean-fobbe-publications/ ContactFound a mistake? Suggestions? Either report this in the issue tracker on GitHub or send me an email to fobbe-data@posteo.de

Objective: Examine peer-reviewed scientific articles that used internal industry documents in the chemical sector to reveal corporate influence. Summarize sources of internal documents used in prior scientific papers to identify ongoing corporate strategies within the chemical field. Compare the corporate strategies identified in the chemical sector with the ones identified already identified in the pharmaceutical sector (1). Propose a theoretical framework for categorizing and examining the different form of corporate capture at play. Design: Performed a scoping review to pinpoint scientific papers employing internal industry/corporate documents within the chemical sector. Methods: We conducted a systematic search using broad and case study-derived keywords, detailed in the appendix. This resulted in 351 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method (2) to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo's ghost management framework. Results: The final results included and analyzed 18 scientific papers (3–20). Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n=16), regulatory capture (n=15), professional capture (n=7), civil society capture (n=6), media capture (n=4), legal capture (n=4), technological capture (n=3), and market capture (n=2). Comparative Analysis The limited scientific literature meeting our criteria confirms early findings by Wieland et al (21), highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, shows a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong’s pharmaceutical corporate capture review, which identified 37 papers before 2022 (1), our chemical industry findings reveal a lower count, with only 18 papers identified. Comparing pharmaceutical and chemical scoping reviews, lower variations emerge across scientific (n=28 vs. n=16), professional (n=16 vs. n=7), and market captures (n=4 vs. n=2). The chemical industry shows higher instances of regulatory (n=6 vs. n=15), civil society (n=4 vs. n=6), media (n=3 vs. n=4), and technological captures (n=2 vs. n=3) compared to the pharmaceutical industry. Both industries employ conflicts of interest and legitimization strategies to deflect public policy inquiries and protect their interests. However, a notable distinction lies in their objectives. While the analysis of the pharmaceutical industry showed corporate focus on profit maximization through biased promotion of health products, the analysis of the chemical sector shows corporate emphasis on the institutionalization of ignorance, the evasion of liability, and the pre-emption of regulatory actions. Strengths Our scoping review shows how internal documents can reveal how the chemical industry strategically institutionalizes ignorance to manage business risks. It exposes intentional efforts by chemical corporations to promote ignorance and foster conflicts of interest, thereby legitimizing their business models and safeguarding corporate interests. We shared our research findings on the Dataverse/ Borealis platform (https://doi.org/10.5683/SP3/SQQJCA), making them accessible for future studies to apply the same analytical framework seamlessly. Limitations We excluded papers that did not meet our research criteria, prioritizing those that analyzed internal corporate documents for uncovering covert ghost management captures. Beyond scientific literature, various grey literature sources have conducted quality investigations on ghost management strategies in the chemical industry. Also, market concentration and other corporate captures can be investigated using publicly available resources. Despite searching scientific papers in various languages, no relevant publications were found outside of English. This presents an opportunity for future research to conduct a separate scoping review.

This data collection was undertaken to gather information on the extent of police officers' knowledge of search and seizure law, an issue with important consequences for law enforcement. A specially-produced videotape depicting line duty situations that uniformed police officers frequently encounter was viewed by 478 line uniformed police officers from 52 randomly-selected cities in which search and seizure laws were determined to be no more restrictive than applicable United States Supreme Court decisions. Testing of the police officers occurred in all regions as established by the Federal Bureau of Investigation, except for the Pacific region (California, Oregon, and Washington), since search and seizure laws in these states are, in some instances, more restrictive than United States Supreme Court decisions. No testing occurred in cities with populations under 10,000 because of budget limitations. Fourteen questions to which the officers responded were presented in the videotape. Each police officer also completed a questionnaire that included questions on demographics, training, and work experience, covering their age, sex, race, shift worked, years of police experience, education, training on search and seizure law, effectiveness of various types of training instructors and methods, how easily they could obtain advice about search and seizure questions they encountered, and court outcomes of search and seizure cases in which they were involved. Police department representatives completed a separate questionnaire providing department characteristics and information on search and seizure training and procedures, such as the number of sworn officers, existence of general training and the number of hours required, existence of in-service search and seizure training and the number of hours and testing required, existence of policies and procedures on search and seizure, and means of advice available to officers about search and seizure questions. These data comprise Part 1. For purposes of comparison and interpretation of the police officer test scores, question responses were also obtained from other sources. Part 2 contains responses from 36 judges from states with search and seizure laws no more restrictive than the United States Supreme Court decisions, as well as responses from a demographic and work-experience questionnaire inquiring about their age, law school attendance, general judicial experience, and judicial experience and education specific to search and seizure laws. All geographic regions except New England and the Pacific were represented by the judges. Part 3, Comparison Data, contains answers to the 14 test questions only, from 15 elected district attorneys, 6 assistant district attorneys, the district attorney in another city and 11 of his assistant district attorneys, a police attorney with expertise in search and seizure law, 24 police academy trainees with no previous police work experience who were tested before search and seizure law training, a second group of 17 police academy trainees -- some with police work experience but no search and seizure law training, 55 law enforcement officer trainees from a third academy tested immediately after search and seizure training, 7 technical college students with no previous education or training on search and seizure law, and 27 university criminal justice course students, also with no search and seizure law education or training.

We conducted a systematic search using broad and case study-derived keywords, detailed in the appendix. This resulted in 318 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo's ghost management framework. The final results included and analyzed 15 scientific papers (3–17). Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n=13), regulatory capture (n=13), professional capture (n=7), civil society capture (n=6), media capture (n=4), legal capture (n=4), technological capture (n=3), and market capture (n=2). The limited scientific literature meeting our criteria confirms early findings by Wieland et al (18), highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, unveils a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong's pharmaceutical corporate capture review, which identified 37 papers before 2022 (1), our chemical industry findings reveal a lower count, with only 15 papers identified. Comparing pharmaceutical and chemical scoping reviews, lower variations emerge across scientific (n=28 vs. n=13), professional (n=16 vs. n=7), and market captures (n=4 vs. n=2). The chemical industry shows higher instances of regulatory (n=6 vs. n=13), civil society (n=4 vs. n=6), media (n=3 vs. n=4), and technological captures (n=2 vs. n=3) compared to the pharmaceutical industry. Both industries employ conflict of interests and legitimization strategies to deflect public policy inquiries and protect their interests. However, a notable distinction lies in their objectives. While the analysis of the pharmaceutical industry focuses on profit maximization through biased promotion of health products, the analysis of the chemical sector emphasizes the institutionalization of ignorance, the evasion of liability, and the pre-emption of regulatory actions.

This dataset comprises curated question-answer pairs derived from key legal texts pertinent to Indian law, specifically the Indian Penal Code (IPC), Criminal Procedure Code (CRPC), and the Indian Constitution. The goal of this dataset is to facilitate the development and fine-tuning of language models and AI applications that assist legal professionals in India.

Dataset Details:

• Sources: The questions and answers in this dataset are extracted from the Indian Constitution, Indian Penal Code (IPC), and the Code of Criminal Procedure (CrPC), ensuring relevance and accuracy in legal contexts.
• Content: Each entry in the dataset contains a clear and concise question alongside its corresponding answer. The questions are designed to cover fundamental concepts, key provisions, and significant terms found within these legal documents.

Use Cases:

• Legal Research: A valuable tool for lawyers, legal researchers, and students seeking to understand legal terminology and principles as outlined in Indian law.
• Natural Language Processing (NLP): This dataset is ideal for training AI models for question-answering systems that require a strong understanding of Indian legal texts.
• Educational Resources: Useful for creating educational tools and materials for law students and legal practitioners.

Note on Use and Limitations:

• Misuse of Dataset: This dataset is intended for educational, research, and development purposes only. Users should exercise caution to ensure that any AI applications developed using this dataset do not misrepresent or distort legal information. The dataset should not be used for legal advice or to influence legal decisions without proper context and verification.

• Relevance and Context: While every effort has been made to ensure the accuracy and relevance of the question-answer pairs, some entries may be out of context or may not fully represent the legal concepts they aim to explain. Users are strongly encouraged to conduct thorough reviews of the entries, particularly when using them in formal applications or legal research.

• Data Preprocessing Recommended: Due to the nature of natural language, the QA pairs may include variations in phrasing, potential redundancies, or entries that may not align perfectly with the intended legal context. Therefore, it is highly recommended that users perform data preprocessing to cleanse, normalize, or filter out any irrelevant or out-of-context pairs before integrating the dataset into machine learning models or systems.

• Dynamic Nature of Law: The legal landscape is subject to change over time. As laws and interpretations evolve, some answers may become outdated or less applicable. Users should verify the current applicability of legal concepts and check sources for updates when necessary.

• Credits and Citations: If you use this dataset in your research or projects, appropriate credits should be provided. Users are also encouraged to share any improvements, corrections, or updates they make to the dataset for the benefit of the community.

The Public Law Outline (PLO), introduced in April 2008 changed what was required of local authorities seeking to protect children through court proceedings. It imposed a pre-proceedings process to be used in all cases where the threshold for legal intervention (Children Act 1989, s.31) was met but proceedings to protect children were not immediately required. The process involves the local authority sending the parents a letter setting out their concerns and inviting them to a formal meeting with the social worker. The letter entitles the parents to legal aid for advice and representation at a pre-proceedings meeting at which plans for the children will be discussed. The process is intended to avoid the unnecessary use of care proceedings by encouraging the parents to work co-operatively with children’s social care services to improve their parenting or, if this is not possible, to narrow the issues in dispute and ensure proceedings are better prepared. The aim of the research is to examine the operation of the pre-proceedings process to see whether and how it is achieving what was intended. Specifically, the research establishes: 1) The extent to which local authorities use processes before starting care proceedings; 2) The similarities and differences between cases where process is and is not used. 3) The practices social workers, local authority lawyers, parents and parents representatives adopt in pre-proceedings meetings; 4) The impact of the process on child protection cases; and 5) parents' perceptions of the pre-proceedings process and its impact on their relationship with the Children's Social Care Department. Data sources included: Cases schedules completed by researchers from 207 Local authority legal department case files and court bundles; 69 in-depth interviews with professionals (lawyers, social workers and social work managers); fieldworker notes of 36 observations of pre-proceedings meetings; and 25 in-depth interviews with parents.

This collection provides users with data about R&D expenditure and R&D personnel broken down by the following institutional sectors: business enterprise (BES); government (GOV); higher education (HES); private non-profit (PNP), total of all sectors.

The R&D expenditure is broken down by source of funds; sector of performance; type of costs; type of R&D; fields of research and development (FORD); https://circabc.europa.eu/ui/group/c1b49c83-24a7-4ff2-951c-621ac0a89fd8/library/b4b841e5-d200-41bc-8f23-d0b1e034f689?p=1&n=10&sort=modified_DESC">socio-economic objectives (NABS 2007) and by regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level). The business enterprise sector is further broken down by economic activity (https://showvoc.op.europa.eu/#/datasets/ESTAT_Statistical_Classification_of_Economic_Activities_in_the_European_Community_Rev._2/data">NACE Rev.2); size class; industry orientation.

R&D personnel data are broken down by professional position; sector of performance; educational attainment level; sex; field of research and development (https://www.oecd.org/innovation/frascati-manual-2015-9789264239012-en.htm">FORD); regions (https://showvoc.op.europa.eu/#/datasets/ESTAT_Nomenclature_of_Territorial_Units_for_Statistics/data">NUTS 2 level); for the business enterprise sector is further broken down in size class and economic activity (NACE Rev.2). Researchers are further broken down by age class and citizenship.

The periodicity of R&D data are every two years, except for the key R&D indicators (R&D expenditure, R&D personnel (in Full Time Equivalent - FTE) and Researchers (in FTE) by sectors of performance) which are transmitted annually by the EU Member States (from 2003 onwards based on a legal obligation). Some other breakdowns of the data may appear on an annual basis based on voluntary data provisions.

The data are collected through sample or census surveys, from administrative registers or through a combination of sources.

R&D data are available for following countries and country groups:

• All EU Member States; Candidate Countries; EFTA Countries; The Organisation for Economic Cooperation and Development (OECD) is data provider for the United States of America, Japan, South Korea and China.
• Country groups: EU Member States, Euro Area States.

R&D data are compiled in accordance to the guidelines laid down in OECD (2015), https://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm">Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities and the European business statistics methodological manual for R&D statistics – 2023 edition - Manuals and guidelines - Eurostat