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160 results for “legal”
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
<p>This page includes legal datasets used in the paper GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis.</p> <p>The Guided Lexrank algorithm is applied to dataset <a href="../api/records/13696090/draft/files/special_appeal.csv/content" target="_blank" rel="noopener noreferrer">special_appeal.csv</a> to summarize the texts of legal documents. The obtained summary and the texts of the topics contained in dataset <a href="../api/records/13696090/draft/files/themes.csv/content" target="_blank" rel="noopener noreferrer">themes.csv</a> are submitted to the BM25 algorithm for similarity assessment. From a list of topics, the GLARE method produces a ranking with suggested topics for a given document.</p>
Mapa Mental: Protección del trabajador desde la normatividad legal vigente en SST
<p>Mapa mental donde se visualiza tres normas en materia de Seguridad y Salud en el Trabajo que le aportan a la Protección del trabajador, y estas son: Ley 1562 de 2012, Decreto 1072 de 2015 y Resolución 0312 de 2019.</p>
Data for "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"
<p>Combined data reposatory for the output of the project "Meat and dairy substitutes – better for health and the environment? Impacts on nutrition and sustainability; consumer perspectives; ethical and legal considerations"</p> <p><em>Kombiniertes Datenarchiv für die Ergebnisse des Projekts "Fleisch- und Milchersatzprodukte – besser für Gesundheit und Umwelt? Auswirkungen auf Ernährung und Nachhaltigkeit, die Sicht der Konsumentinnen und Konsumenten sowie ethische und rechtliche Überlegungen"</em></p> <p>The project was funded by the Foundation for Technology Assessment (TA-Swiss) "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>".</p> <p><em>Das Projekt wurde von der Stiftung für Technologiefolgen-Abschätzung (TA-Swiss) finanziert "<a href="https://ror.org/02shtak05">ror.org/02shtak05</a>". </em></p>
Spanish Workers' Statute Legal Relations and RDF
<p>These datasets result from extracting legal events and relationships from the Spanish Workers' Statutes and structuring the extracted data in an RDF graph. After a set of experiments conducted with GPT-3.5 using scarce annotated data from previous works <a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6432">[1]</a>, a 5-shot learning approach was applied to the full text of the Spanish Workers’ Statute. Approximately 1500 relations were extracted in a JSON format, structured into a dataset, and represented in an RDF graph.</p>
Legal Aspects of Research Data
<p>A video introduction to the legal aspects of research data management. </p> <p>Find out more about research data and open data training from: <a href="https://discipline-workshops.com/">https://discipline-workshops.com/</a></p>
Business Models in Energy Communities: an analysis through legal lenses
<p>This research explores energy communities (EC) and their business models’ attributes. We develop a conceptual framework, which combines and extends the social, economic, environmental, and technological dimensions of value generation to include the legal dimension. The latter has been considered only implicitly in previous studies on this sector. Applying this framework to forty business cases of energy communities allows to identify six business model (BM) archetypes representative of ECs. This study can encourage and support new ventures in this sector to model their strategy and comply with the requirements.</p>
Know Your Research Rights: The Legal Perspective on Copyright and Open Science
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to two copyright lawyers, Malcolm Bain and Lucrezia Berto, about what the legal framework of research creation and sharing is. Who owns your research? What needs to be considered before you make it open? </p> <p><strong>Episode Links:</strong></p> <p><a href="https://acrosslegal.com/en/">Across Legal</a></p> <p><a href="https://www.linkedin.com/in/malcolm-bain-9216415/?originalSubdomain=es">Malcolm Bain</a></p> <p><a href="https://www.linkedin.com/in/lucrezia-berto/?originalSubdomain=es">Lucrezia Berto</a></p>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</p>
Ground Truth Dataset with mappings of companies to OpenCorporates legal entities
<p>Ground Truth Dataset with mappings of companies to OpenCorporates legal entities. Available data per company: company name, country of headquarters, state of headquarters (in case of US companies) and address of headquarters.</p>
Global Legal Cannabis Market 2024 to 2033
<p><a href="https://www.custommarketinsights.com/report/legal-cannabis-market/" target="_blank" rel="noopener">Legal Cannabis Market Size</a>, Trends and Insights By Strain (THC, CBD), By Species (Cannabis Indica, Cannabis Sativa, Cannabis Hybrid), By Source (Marijuana, Hemp), By End-User (Pharmaceutical Companies, Food and Beverage Companies, Personal Care Products, Research and Development Centers), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>Reports Description</strong></p> <p>As per the current market research conducted by the CMI Team, the global <a href="https://www.custommarketinsights.com/report/legal-cannabis-market/" target="_blank" rel="noopener"><strong>Legal Cannabis Market</strong></a> is expected to record a CAGR of <strong>23.5%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>32.4 Billion</strong>. By 2033, the valuation is anticipated to reach USD <strong>216.5 Billion</strong><strong>.</strong></p> <p>The legal cannabis market seems promising with huge growth expected in the coming years. Reasons for this expansion are due to the increasing legalization of cannabis for both medical and recreational use across various regions, particularly in North America and Europe.</p> <p>More countries will legalize the drug, increasing the market to a wide consumer base and, in turn, increasing demand for various cannabis products. Innovations in edibles, beverages, as well as wellness-oriented topical product formulations are increasing customer interest in the field, further establishing a deep inroad of the same on consumers.</p> <p>This shows shifting perspectives by consumers of being involved in cannabis, towards becoming more holistic wellness opportunities further pushing the market footprint even more.</p> <p>Consumer acceptance combined with a growth in more scientific development, will increase quality along with product safety factors also meaning customers will increasingly end up placing their faith as well in the said product. Advanced cultivation and extraction techniques are where the companies are investing better quality products are then produced to meet very stringent regulatory standards.</p> <p>Strategic partnerships and mergers will continue to shape the market landscape, enabling companies to expand their offerings and strengthen their market positions. The legal cannabis market is growing enormously with changing social views, increasing regulation, and a newly formed product space constantly shifting to consumer needs and demand.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59056" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59056</a></p>
SIRIUS Project - comparative dataset on socio-cultural, political and legal indicators
<p>This is an ad hoc dataset on socio-economic, cultural, political and legal indicators on migration covering all SIRIUS countries (Czech Republic, Denmark, Finland, Greece, Italy, Switzerland, United Kingdom), created by retrieving and systematizing a number of indicators available in the most relevant databases.</p> <p>The dataset has been created as part of the work package 2 of the SIRIUS Project "Skills and Integration of Migrants, Refugees and Asylum Applicants in European Labour markets". The work package, titled “Legal barriers and enablers”, examined the legal and institutional framework of migration and asylum, integrated with critical insights on the cultural and socio-economic environment of the SIRIUS countries. </p> <p><strong>Acknowledgments and disclaimers</strong><br> This research was conducted under the Horizon 2020 project ‘SIRIUS’ (770515).<br> The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein.</p>
Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production
<p>This database contains selected variables associated with the rural economic sector of the Brazilian Legal Amazon distributed at municipal level by technological trajectories (TT) – techno-productive trajectories and their technological variants (TTP) -, as defined and theoretically justified by Costa (2021, p. 217-219).</p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in a given territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 441 and 442 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These <em>patterns</em> are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work.</p> <p>The territory of the Brazilian Legal Amazon encompasses 772 municipalities: all from eight states (Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins) and part of the State of Maranhão (west of the 44ºW meridian).</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 1995, 2006 and 2017 Agricultural Censuses. The credit data for 2017 are from the Central Bank of Brazil.</p> <p>The dataset is organized as: Zen1995_LegalAmazon_Inicial.csv; Zen2006_LegalAmazon_Inicial.csv and Zen2017_LegalAmazon_Inicial.csv. In each table the column names are self-explanatory.</p> <p> </p> <p>Reference:</p> <ul> <li>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2). <p> </p> <p> </p> <p> </p> </li> </ul>
Figure 4. CBR Implementation-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The development of the case based reasoning module is in done in java net-beans. Proper<br> verification and validation of this module was done by the legal experts. The cases related to<br> Transfer of property act were collected from different legal databases and compiled. The necessary<br> keywords were framed, which were used in searching for the related cases. The following Fig 2.0<br> gives the screen shot of the CBR module develoed in Java Net beans.</p>
Fig 3 : VisiRule Implementation Module-1-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The code of this flowchart is developed by the VisiRule in FLEX/ Prolog. As the source<br> code very huge it has not be incorporated in the paper.</p>
Figure 1 - VisiRule architecture-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>In the development of RBR we used the intelligent flowcharting approach. VisiRule is a tool<br> for creating decision support software purely by drawing flowcharts. The end result is Flex or<br> Prolog code which is automatically generated, compiled and ready to run, but which can also be<br> copied and used in a separate program. Not only can VisiRule be used by people with minimal<br> programming skills. VisiRule also enhances productivity by considerably reducing the time it takes<br> to produce a decision support system. VisiRule is an intelligent flowcharting tool in two senses.<br> Firstly, it is used to create knowledge-based systems and, secondly, it intelligently guides the<br> construction process by constraining what you can and can't do on the basis of the semantic content<br> of the emerging program. VisiRule provides the automatic construction of menu dialogues from<br> questions. These are populated by items inferred from expression boxes throughout the flowchart<br> tree which have a path to the question.</p>
Fig. 3 in Identification of Muscidae (Diptera) of medico-legal importance by means of wing measurements
Fig. 3 Wing of male of Musca domestica. The numbered points indicate the landmarks used for wing measurements
Fig. 2 in Identification of Muscidae (Diptera) of medico-legal importance by means of wing measurements
Fig. 2 Males of selected muscid species representing genera used in this study. a Neomyia cornicina (Fabricius). b Phaonia pallida (Fabricius). c Polietes lardarius (Fabricius). d Stomoxys calcitrans Linnaeus. e Thricops simplex (Wiedemann)
Fig. 1 in Identification of Muscidae (Diptera) of medico-legal importance by means of wing measurements
Fig. 1 Males of selected muscid species representing genera used in this study. a Azelia nebulosa Robineau-Desvoidy. b Eudasyphora cyanicolor (Zetterstedt). c Graphomya maculata (Scopoli). d Helina impuncta (Fallén). e Muscina levida (Harris). f Mydaea urbana (Meigen). g Hydrotaea dentipes (Fabricius). h Musca domestica Linnaeus
FIGURE 5 in Reproductive biology of seven fish species of commercial interest at the Ramsar site in the Baixada Maranhense, Legal Amazon, Brazil
FIGURE 5 | Relative frequency of gonadal maturation stages during the rainy and dry seasons. (A) Cichla monoculus; (B) Hassar affinis; (C) Hoplias malabaricus; (D) Plagioscion squamosissimus; (E) Prochilodus lacustris; (F) Pygocentrus nattereri; and G. Schizodon dissimilis, caught in Baixada Maranhense Protection Area, between January 2012 and December 2016. Immature (IP); Developing phase (DP); Spawning capable (SP); Regression (RP); Regeneration (RGP), F (Female) and M (Males).
FIGURE 2 in Reproductive biology of seven fish species of commercial interest at the Ramsar site in the Baixada Maranhense, Legal Amazon, Brazil
FIGURE 2 | Distribution of the relative frequency by length classes, gonadal maturation stages, and sexes: (A) Cichla monoculus; (B) Hassar affinis; (C) Hoplias malabaricus; (D) Plagioscion squamosissimus; (E) Prochilodus lacustris; (F) Pygocentrus nattereri; and (G) Schizodon dissimilis, caught in Baixada Maranhense Protection Area, between January 2012 and December 2016. Immature (IP); Developing phase (DP); Spawning capable (SP); Regression (RP); Regeneration (RGP), F (Female) and M (Males).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.