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41 results for “Legislative”
Word2Vec model - Czech legislation
<p>Word2Vec embedding model trained on Czech legislation (from April 2020) corpus using gensim implementation with the following parameters in addition to default settings:</p> <ul> <li>vector dimension = <span class="math-tex">\(400\)</span>,</li> <li>window size = <span class="math-tex">\(10\)</span>,</li> <li>word minimum count = <span class="math-tex">\(10\)</span>,</li> <li>sample = <span class="math-tex">\(10^{-5}\)</span>.</li> </ul>
Exported Definitions, References, Document Structure from EU Legislations
<p>A sample corpus of definitions, references, and document structures extracted from the EUR-LEX corpus of EU Legislations.</p>
The Biodata of Legislative Candidates for Indonesian General Election 2019
<p>The dataset of biodata of Legislative Candidates for General Election 2019 is crawled from the Indonesian General Election Committee. We remove privacy information, such as the birth of data and home address. We only store the year of birth and the city of home. The dataset is in CSV file.</p>
Semantic annotation of a part of the Italian Copyright Legislation
<p>The dataset is a structured JSONL file focusing on copyright law. Each entry contains key fields that annotate legal texts, mainly in Italian. These fields include:</p> <p>1. ID: A unique numerical identifier.</p> <p>2. Text: Contains the actual legal provisions.</p> <p>3. Chapter ID & Heading: Identifiers and titles for chapters, categorizing the legal text.</p> <p>4. Article and Paragraph ID: Further break down of the text into articles and paragraphs.</p> <p>5. Insertions: Highlights inserted text fragments in the legal text.</p> <p>6. References: Cites external references with URLs and descriptions.</p> <p>7. Entities: Labels sections of the text, identifying their beginning and ending offsets.</p> <p>8. Relations: Intended to describe relationships between entities, although this field is empty in the sample.</p> <p>9. Comments: A field for comments, also empty in the sample.</p> <p> </p>
Pond data: physical, chemical, and biological characteristics with scientific and United States of America state definitions from literature and legislative surveys
Ponds are often identified by their small size and shallow depths, but the lack of a universal definition hampers science and weakens legal protection. In order to determine a working definition of ‘pond’, we conducted a literature search for scientific definitions, a U.S. state survey for management definitions, and looked at pond ecosystem function using data from the literature search. Our dataset includes physical, chemical, and biological data for 1327 waterbodies ≤ 20 ha in surface area and ≤ 9 m in maximum or mean depth from our literature review. These data have a global distribution, we include a table of latitudes and longitudes, and span many years (1946-2019). We have also included a table of 54 pond definitions from the literature review and a table of U.S. state definitions of ponds, wetlands, and lakes resulting from our survey.
Brazilian Federal legislations and Health Professional Councils regulations about telemedicine, according to historical phases and public policy purposes from 1990 to 2018.
<p>The file contains one excel file with two datasheets, one with legislations from Brazilian Federal Government and other from Health Professional Councils, from 1990 to 2019. Each spreadsheet has the original database, the number ID of the normative, what institution the document is from, its publication date, the abstract (in Portuguese), its public URL, historical phases and the purpose of it.</p>
Code and Data for: Donor activity is associated with US legislators' attention to political issues
<p>Contains data, code, and annotations for:</p> <blockquote> <p>Goel P, Malkin N, Gaynor SW, Jojic N, Miler K, Resnik P (2023) Donor activity is associated with US legislators’ attention to political issues. PLoS ONE 18(9): e0291169. https://doi.org/10.1371/journal.pone.0291169</p> </blockquote>
Figure 1 in Developing biosecurity plans for non-native species in marine dependent areas: the role of legislation, risk management and stakeholder engagement
Figure 1. Five-stage approach for risk assessment management of NNS in Shetland, adapted from the ecosystem-based risk management framework (Cormier et al. 2013).
Australian Federal Legislation - Principal acts in force
<p>This dataset is a corpus with 884 documents in legal Australian English. These documents are part of the Australian Federal Legislation principal acts in force as of 29 December 2018.</p> <p>The documents are offered in two forms: (1) as pure txt documents (2) as RDF turlte documents following the ELI schema (European Legislation Identifier ontology).</p>
Evaluation of the shucking of certain species of scallops contaminated with domoic acid with a view to the production of edible parts meeting the safety requirements foreseen in the Union legislation - Summary statistics on occurrence and consumption data and exposure assessment results
<p>DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields).</p> <p>The link to the catalogues of controlled terminologies can be found under "Related identifiers”.</p> <p><strong>Annex_</strong> DomoicAcid</p> <p>Table of contents</p> <p><br> Table A1</p> <p>Description of FoodEx2 codes used to describe scallop species and their anatomical parts</p> <p>Table A2</p> <p>Data cleaning steps applied to occurrence data on domoic acid in scallops</p> <p>Table A3</p> <p>Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg)</p> <p>Table A4</p> <p>Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample)</p> <p>Table A5</p> <p>Descriptive statistics of body tissue weights (g) of scallops as submitted by data providers</p>
Questionnaire on gender and age-related peculiarities in informed consent to clinical trials – National legislation
<p>European experts from the six selected countries (Germany, Spain, Austria, France, Italy, and United Kingdom) included in research done within task 1.3 (Ethical and legal review of gender and age-related issues associated with the acquisition of informed consent) participated in a survey on gender and age-related peculiarities in informed consent to clinical trials within national legislations.</p> <p>Experts were selected for their high-level scientific expertise in the fields relevant to the objectives of task 1.3. A short questionnaire on “Gender and age-related peculiarities in informed consent to clinical trials within national legislations" has been prepared and circulated to contact experts. This questionnaire was meant to identify the legal review process and collect up-to-date data. It was structured in 10 queries, exclusively aimed at obtaining hard law and soft law information pertaining to the topics addressed in task 1.3.</p>
Data for ms. Dairy cattle welfare – the relative effect of legislation, industry standards and labelled niche production in five European countries
<p>Repository R 1: Scores on dimension values and weight on dimension from 38 international experts. </p> <p>Repository R2: Country Benchmark scores from 38 international experts.</p>
Replication package for 'Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe'
<p>Replication package for the paper "Medically assisted reproduction and non-normative family forms: legislation and public opinion in Europe", accepted for publication in <em>European Societies</em> (2024). </p> <p>This repository provides the R code to replicate the results. It utilizes data from the European Values Study (available at: https://europeanvaluesstudy.eu/) and an original database on the timing of MAR access legislation for single women and same-sex female couples in Europe. </p> <p> </p> <p> </p>
Dataset from: Design and analysis of tweet-based election models for the 2021 Mexican legislative election
<p>Processed data used for the analysis of the manuscript with title "Design and analysis of tweet-based election<br> models for the 2021 Mexican legislative election", by Vigna-Gómez et al.</p>
Going, going, gone: evidence for loss of an endemic species pair of threespine sticklebacks (Gasterosteus aculeatus) with implications for protection under species-at-risk legislation
<p>Genomic extinction occurs when the unique combination of genetic traits that characterize distinct phenotypes is eliminated by introgressive hybridization even if population size is greater than zero. Benthic and limnetic threespine sticklebacks (<em>Gasterosteus aculeatus</em>) constitute reproductively isolated undescribed biological species that have evolved independently in several lakes in southwestern British Columbia, Canada (known as "species pairs" in each lake). Here we investigated whether the two species that comprise the pair from Enos Lake, southeastern Vancouver Island, remain as two distinct gene pools. Multi-season samples (>1200 fish) obtained over two years from throughout the lake and assayed for variation in morphological traits characteristic of the two species (i.e., body depth, dorsal spine count, gill raker counts) and at 12 microsatellite DNA loci consistently indicated the existence of only a single group of sticklebacks. There was no consistent evidence of two groups in any morphological trait, and mean gill raker counts were consistently intermediate (20–21) to those of known benthics (~18) and limnetics (~24) which together comprised strikingly bimodal distributions in historical samples. Genetic analyses employing model-based clustering also consistently indicated the presence of only a single genetic group of sticklebacks. Compared to historical samples and to benthics and limnetics from other lakes, no Enos Lake fish could be identified confidently as a pure benthic or limnetic. Our results provide the strongest evidence yet that the Enos Lake sticklebacks now consist of a single morphological and genetic population of sticklebacks, that the unique combination of genetic and morphological traits that characterized benthic and limnetic sticklebacks no longer exist, and that their current status under Canada's <em>Species-at Risk Act</em> as Endangered should be re-evaluated.</p>
British Departmental Documents and Legislation 2000 -- 2020
<p>A dataset of sentences from British legislation ('leg') and the documents of 12 central departments ('cabinet_office', 'DCMS', 'DE', 'DEFRA', 'DHSC', 'DWP', 'FCO', 'home_office', 'MOD', 'MOH', 'MOJ', 'treasury') produced between 2000 and 2020. For each JSON object, each key corresponds to legislation or a department and each value is a tuple where the first element is a document title and the second element is a sentence from the document referred to by the title. </p> <p>Files titled by year (e.g. 2003.json) contain sentences from documents published during title year. merged.json contains sentences from all years of publication. </p> <p>The documents from which this dataset was constructed are available from https://www.gov.uk/search, https://webarchive.nationalarchives.gov.uk/search/ and https://www.legislation.gov.uk/. </p>
Partial collection of LynxDocuments of the Italian Copyright legislation
<p>The dataset is organized as LynxDocuments and is designed to annotate Italian copyright law. Compliant with the Lynx framework for legal document management, this dataset is particularly relevant for legal informatics and semantic web technologies. It employs various RDF vocabularies and ontologies to ensure structured and machine-readable annotations.</p> <p>Key Features:</p> <p>1. Lynx Compliant Structure: The dataset is tagged as `lkg:LynxAnnotation`, aligning it with the Lynx ontology. This makes it easily integrable into broader legal informatics platforms that use the Lynx standard.</p> <p>2. Content Scope: The dataset includes detailed legal provisions found within the `nif:anchorOf` and `nif:annotationUnit` fields. These provisions elaborate on the categories of creative works protected under Italian copyright law, such as literature, music, arts, and software.</p> <p>3. Legal Context: Specific references to existing legal frameworks, including the "Convenzione di Berna" and "legge 20 giugno 1978, n. 399," are provided, thereby adding context to the legal provisions.</p> <p>4. Types of Creative Works: The dataset goes beyond general categories to specify various forms of protectable works, including collective works and software.</p> <p>5. Semantic Annotations: Annotations are ontology-based, primarily using labels like `schema1:CreativeWork`. These semantic tags aid in automated reasoning and facilitate data integration across multiple legal documents.</p> <p>6. Metadata: The dataset includes metadata fields like `ns1:taConfidence` for confidence scores and `ns1:taAnnotatorsRef` for annotator references, adding an additional layer of credibility and accountability.</p>
Going, going, gone: evidence for loss of an endemic species pair of threespine sticklebacks (Gasterosteus aculeatus) with implications for protection under species-at-risk legislation
Open the record for dataset details and reuse information.
War of Words: The Competitive Dynamics of Legislative Processes
<p><strong>Update: A newer version of this dataset is available <a href="https://zenodo.org/record/4709248#.YXesJS8itqs">here</a>.</strong><strong> </strong>It comes with features extracted from the MEPs, the edits, and the dossiers, such as the <strong>nationality</strong> of MEPs, the <strong>type of law </strong>being edited, and the <strong>text of the edits</strong>. Check it out!</p> <p>This upload contains the dataset presented and used in the paper:</p> <blockquote> <p>Kristof, V., Grossglauser, M., Thiran, P., <a href="https://infoscience.epfl.ch/record/275473/"><em>War of Words: The Competitive Dynamics of Legislative Processes</em></a>, The Web Conference, April 20-24, 2020, Taipei, Taiwan</p> </blockquote> <p><strong>Read Section 2.2 of the paper to learn more about the European legislative process. </strong>The code to process and use the dataset can be found on <a href="https://github.com/indy-lab/war-of-words">GitHub</a>.</p> <p>The dataset is split into two legislature periods of the European Parliament, the 7th (<strong>war-of-words-ep7.txt</strong>) and the 8th (<strong>war-of-words-ep8.txt</strong>) legislature. Here is a snippet to load the dataset (for EP7 in this example) in Python:</p> <pre><code class="language-python">import json with open('path/to/war-of-words-ep7.txt') as f: dataset = [json.loads(l) for l in f.readlines()] </code></pre> <p>In the two text files, each line is a data point representing a <em>conflict between edits</em>. It is encoded as a JSON list of dictionaries, where each dictionary is an edit. Each edit has the following structure:</p> <pre><code class="language-json">{ 'edit_id': 163187, // Unique edit identifier. 'accepted': True, // Label. 'dossier_ref': 'ENVI-AD(2012)487738', // Reference to dossier (see below). 'authors': [ // List of authors. { 'id': 4550, // Unique MEP identifier (see below). 'name': 'Jill EVANS', // MEP name. 'rapporteur': False // Whether the MEP is rapporteur. }, ], }</code></pre> <p>You can assume that:</p> <ul> <li>Each data point has at least one edit.</li> <li>If there is only one edit, then it is <em>in conflict with the status quo </em>(see Section 4 of the paper).</li> <li>If there are two or more edits in conflict, then they are all in conflict against each other <em>and</em> they are in conflict with the status quo (see Section 4 of the paper).</li> <li>At most one edit is accepted in each data point.</li> <li>In each legislature, each edit has a unique identifier.</li> <li>There are no timestamps associated with edits (see Section 3 of the paper). </li> </ul> <p>The <strong>dossier_ref</strong> can be used to get more information on the dossier. It is formatted as <strong>COMM-TYPE(YEAR)PENUMBER </strong>(this follows the notation of file names used by the Parliament Secretariat), where</p> <ul> <li><strong>COMM</strong> is the <a href="https://www.europarl.europa.eu/committees/en/about/introduction">committee</a> identifier (4 capital letters)</li> <li><strong>TYPE</strong> is either <strong>AD</strong> (opinion) or <strong>A{7,8}</strong> (report for EP7 or EP8, see Section 2.2 of the paper)</li> <li><strong>YEAR</strong> is the year the dossier has been voted</li> <li><strong>PENUMBER</strong> is the "PE number", a document identifier used by the European Parliament</li> </ul> <p>You can browse the Parliament documents to find details about the dossier for <a href="https://www.europarl.europa.eu/committees/en/archives/7/document-search">EP7</a> and <a href="https://www.europarl.europa.eu/committees/en/archives/8/document-search">EP8</a> (the PE number field should be enough).</p> <p>The parliamentarians (MEPs, for Member of the European Parliament) have a unique identifier that you can use to get more details about them on the Parliament website: Go to <strong>https://www.europarl.europa.eu/meps/en/MEP_ID</strong>, where <strong>MEP_ID </strong>is the id of the MEP of interest.</p> <p>This dataset is vowed to become richer: I will add more features, as I am able to extract them.</p> <p> </p> <p><strong>Don't hesitate to <a href="mailto:victor.kristof@epfl.ch?subject=Question%20about%20the%20War%20of%20Words%20dataset">reach out to me</a> if you have any questions!</strong></p> <p> </p> <p>To cite this work:</p> <pre><code>@inproceedings{kristof2020war, author = {Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick}, title = {War of Words: The Competitive Dynamics of Legislative Processes}, year = {2020}, booktitle = {Proceedings of The Web Conference 2020}, pages = {2803–2809}, numpages = {7}, location = {Taipei, Taiwan}, series = {WWW '20} }</code></pre> <p> </p>
Data from: A prioritised list of invasive alien species to assist the effective implementation of EU legislation
1. Effective prevention and control of invasive species generally relies on a comprehensive, coherent and representative list of species that enables resources to be used optimally. European Union (EU) Regulation 1143/2014 on invasive alien species (IAS) aims to control or eradicate priority species, and to manage pathways to prevent the introduction and establishment of new IAS; it applies to species considered of Union concern and subject to formal risk assessment. So far, 49 species have been listed but the criteria for selecting species for risk assessment have not been disclosed and were probably unsystematic. 2. We developed a simple method to systematically rank invasive alien species according to their maximum potential threat to biodiversity in the EU. We identified 1323 species as potential candidates for listing, and evaluated them against their invasion stages and reported impacts, using information from databases and scientific literature. 3. 900 species fitted the criteria for listing according to IAS Regulation. We prioritised 207 species for urgent risk assessment, 59 by 2018 and 148 by 2020, based on their potential to permanently damage native species or ecosystems; another 336 species were identified for a second phase (by 2025), to prevent or reverse their profound impacts on biodiversity; and a further 357 species for assessment by 2030. 4. Policy implications. We propose a systematic, proactive approach to selecting and prioritising invasive alien species for risk assessment to assist European Union policy implementation. We assess an unprecedented number of species with potential to harm EU biodiversity using simple methodology that we developed, and recommend which species should be considered for risk assessment in a ranked order of priority along the timeline 2018-2030, based on their maximum reported impact and their invasion history in Europe.
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OpenNeuro
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