cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-8775-1
title: Scottish Household Survey, 2019
title: Scottish Household Survey, 1999-
title: SHS

description: &lt;p&gt;The &lt;em&gt;Scottish Household Survey&lt;/em&gt; (SHS) is a continuous survey based on a sample of the general population in private residences in Scotland. It is financed by the Scottish Government (previously the Scottish Executive). The survey started in 1999 and up to 2011 followed a fairly consistent survey design. From 2012 onwards, the survey was substantially redesigned to include elements of the &lt;em&gt;Scottish House Condition Survey&lt;/em&gt; (SHCS) (also available from the UK Data Service), including the physical survey. The SHS is run through a consortium led by Ipsos MORI. The survey is designed to provide reliable and up-to-date information on the composition, characteristics, attitudes and behaviour of private households and individuals, both nationally and at a sub-national level and to examine the physical condition of Scotland's homes. It covers a wide range of topics to allow links to be made between different policy areas.&lt;/p&gt;&lt;p&gt;Further information about the survey series, and links to publications, can be found on the Scottish Government's &lt;a title="Scottish Household Survey" href="https://www.gov.scot/collections/scottish-household-survey/" style="background-color: rgb(255, 255, 255);"&gt;Scottish Household Survey&lt;/a&gt; webpages.&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-weight: bold;"&gt;COVID-19 restrictions&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Due to COVID-19 restrictions, the SHS was conducted by telephone or via MS Teams in 2020 and 2021 (SNs 9186 and 9187). Face-to-face interviewing resumed for SHS 2022 (SN 9294) when restrictions had been lifted.&lt;/p&gt; 
&lt;p&gt;&lt;br&gt;&lt;/p&gt;
DOI: 10.25346/s6eecgix/8cy1oy
title: plane_8.jpg
DOI: 10.25346/s6eecgix/den2qn
title: plane_7.jpg
DOI: 10.25346/s6eecgix/rvfjzt
title: plane_6.jpg
DOI: 10.25346/s6eecgix/kyotbr
title: plane_5.jpg
DOI: 10.25346/s6eecgix/7tlkj4
title: plane_4.jpg
DOI: 10.25346/s6eecgix/xt7kvi
title: plane_3.jpg
DOI: 10.25346/s6eecgix/z2lgmg
title: plane_2.jpg
DOI: 10.25346/s6eecgix/x6ad3m
title: plane_1.jpg
DOI: 10.3886/e238476v1
title: CMS - Patient Characteristics of Laboratory-Confirmed COVID-19 Hospitalizations from the COVID-NET Surveillance System

description: The Coronavirus Disease 2019 (COVID-19) Hospitalization Surveillance Network (COVID-NET) a network that conducts active, population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations among children and adults. COVID-NET, along with the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET) and the Influenza Hospitalization Surveillance Network (FluSurv-NET), comprise the Respiratory Virus Hospitalization Surveillance Network (RESP-NET). The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. COVID-NET is CDC’s source for important data on rates of hospitalizations associated with COVID-19. Hospitalization rates show how many people in the surveillance area are hospitalized with COVID-19, compared to the total number of people residing in that area.
DOI: 10.25346/s6eecgix/anl3s8
title: plane_0.jpg
DOI: 10.25346/s6eecgix/kc3iw5
title: openlime.min.js
DOI: 10.25346/s6eecgix/uwfofi
title: info.json
DOI: 10.25346/s6eecgix/xhomz4
title: index.html
DOI: 10.25346/s6eecgix/tqb3wm
title: skin.svg
DOI: 10.25346/s6eecgix/apbpbx
title: skin.css
DOI: 10.25346/s6eecgix/octgwe
title: plane_8.jpg
DOI: 10.15468/dl.99jj6h
title: Occurrence Download

description: A dataset containing 781 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Squatina squatina)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 781 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031295-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/g1iijv
title: plane_7.jpg
DOI: 10.25346/s6eecgix/bqjj1l
title: plane_6.jpg
DOI: 10.25346/s6eecgix/8soknk
title: plane_5.jpg
DOI: 10.25346/s6eecgix/5lvkem
title: plane_4.jpg
DOI: 10.5255/ukda-sn-8776-1
title: Children and Young People's Patient Experience Survey, 2018
title: National Health Service Patient Survey Programme, 2003-
title: CYP18

description: &lt;p&gt;The National Patient Survey Programme is one of the largest patient survey programmes in the world. It provides an opportunity to monitor experiences of health and provides data to assist with registration of trusts and monitoring on-going compliance. Understanding what people think about the care and treatment they receive is crucial to improving the quality of care being delivered by healthcare organisations. One way of doing this is by asking people who have recently used the health service to tell the Care Quality Commission (CQC) about their experiences.&lt;br&gt; &lt;br&gt;The CQC will use the results from the surveys in the regulation, monitoring and inspection of NHS acute trusts (or, for community mental health service user surveys, providers of mental health services) in England. Data are used in CQC Insight, an intelligence tool which identifies potential changes in quality of care and then supports deciding on the right regulatory response. Survey data will also be used to support CQC inspections.&lt;br&gt; &lt;br&gt; Each survey has a different focus. These include patients' experiences in outpatient and accident and emergency departments in Acute Trusts, and the experiences of people using mental health services in the community.&lt;br&gt; &lt;br&gt; &lt;i&gt;History of the programme&lt;br&gt;&lt;/i&gt;&lt;br&gt; The National Patient Survey Programme began in 2002, and was then conducted by the Commission for Health Improvement (CHI), along with the Commission for Healthcare Audit and Inspection (CHAI). Administration of the programme was taken over by the Healthcare Commission in time for the 2004 series. On 1 April 2009, the CQC was formed, which replaced the Healthcare Commission.&lt;br&gt; &lt;br&gt; Further information about the National Patient Survey Programme may be found on the CQC &lt;a href="http://www.cqc.org.uk/content/surveys" title="Patient Survey Programme" ,="" target="_blank"&gt;Patient Survey Programme&lt;/a&gt; web pages.&lt;br&gt; &lt;br&gt; &lt;/p&gt; &lt;p class="x_x_x_x_x_MsoNormal"&gt;The &lt;span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;Children and Young People's Patient Experience Survey, 2018&lt;/span&gt; is the third national children's survey conducted by CQC. It represents the experiences of nearly 33,179 children and young people who received inpatient or day case care in 129 acute and specialist NHS trusts in 2018. Further information can be found in the CQC document &lt;a title="2018 Children and Young People's Patient Experience Survey Statistical Release" href="https://www.cqc.org.uk/sites/default/files/20191119_cyp18_statisticalrelease.pdf"&gt;2018 Children and Young People's Patient Experience Survey Statistical Release&lt;/a&gt;.&lt;br&gt;&lt;/p&gt;
DOI: 10.25346/s6eecgix/w1q3h2
title: plane_3.jpg
DOI: 10.5255/ukda-sn-8782-1
title: Employer Skills Survey, 2019: Special Licence Access
title: Employer Skills Surveys, 2003- : Special Licence Access
title: ESS 2019

description:   
&lt;p style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;The Employer Skills Survey 2019 (ESS 2019) gathered labour market intelligence (LMI) on employer skills needs and training activity among employers in England, Northern Ireland and Wales. It is the fifth in the biennial series of Employer Skills Surveys dating back to 2011 collecting LMI from multiple countries of the UK, although unlike the four earlier studies ESS 2019 does not include employers in Scotland.&lt;/p&gt;&lt;p style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;From 2010-2017, the Employer Skills Survey sat alongside the Employer Perspectives Survey (EPS) to produce insights that complemented each other, with the two surveys run in alternate years (EPS was last conducted UK-wide in 2016 - see SN 8338). The focus of the EPS was primarily outward-looking, covering provision of and engagement with the wider skills system, whereas the ESS had a more inward-looking focus assessing the current skills position and skill needs of employers. For ESS 2019, the two surveys were in effect merged. This involved adding many of the questions used in the EPS series to those of the ESS series. To avoid an excessively long questionnaire the merger of the survey involved more extensive modularisation of the questionnaire than used previously. This is discussed in the questionnaire chapter of the Technical Report.&lt;/p&gt;&lt;p style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;As in previous years, the 2019 ESS had two main elements:&lt;/p&gt;&lt;ul type="disc" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;&lt;li&gt;The core survey: covering such topics as recruitment, skills gaps, training and workforce development, upskilling needs, vocational qualifications, apprenticeships and traineeships.&lt;/li&gt;&lt;li&gt;The Investment in Training follow-up survey: covering the investment establishments make in training their staff.&lt;/li&gt;&lt;/ul&gt;&lt;p style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;The study consists of a) 81,013 telephone interviews with employers at the local establishment level; and b) 10,225 follow-up interviews with employers to capture data on Investment in Training. The study captures detailed and comprehensive information from employers on: their skills needs both from the external labour market and their existing staff; their activities to develop skills among existing employees; and their working practices.&lt;/p&gt;&lt;p style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;Further information, including reports and technical documents, can be found on the gov.uk &lt;a href="https://www.gov.uk/government/publications/employer-skills-survey-2019-uk-excluding-scotland-findings"&gt;Employer Skills Survey, 2019&lt;/a&gt; webpage.&lt;/p&gt;
&lt;p&gt;&lt;span&gt;&lt;/span&gt;&lt;span&gt;&lt;/span&gt;&lt;/p&gt;
&lt;p&gt;&lt;/p&gt;
DOI: 10.5255/ukda-sn-8783-1
title: Survey of Employment Tribunal Applications, 2018
title: Survey of Employment Tribunal Applications, 1992-
title: SETA 2018

description: &lt;p&gt;The &lt;span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"&gt;Survey of Employment Tribunal Applications&lt;/span&gt; (SETA) series aims to provide information on the characteristics of the parties in, and the key features of, employment tribunal cases.&lt;br&gt;The headline aims of the 2018 study were to:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;get information on the characteristics of employment tribunal claimants and employers;&lt;/li&gt;&lt;li&gt;assess the costs of going to tribunals for claimants and employers; and&lt;/li&gt;&lt;li&gt;monitor the performance of the employment tribunal claim process.&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;The first SETA was undertaken in 1987, with subsequent surveys undertaken in 1992, 1998, 2003, 2008 and 2013. The survey methodology used in 2018 was very similar to that used in 2013. &lt;br&gt;&lt;br&gt;A total of 2,663 interviews were carried out; 1,373 interviews with claimants and 1,290 with employers. The average interview length in the claimant survey was 30 minutes and in the employer survey 26 minutes.&lt;/p&gt;&lt;p&gt;Further information may be found on the gov.uk &lt;a title="Survey of Employment Tribunal Applications 2018" href="https://www.gov.uk/government/publications/survey-of-employment-tribunal-applications-2018"&gt;Survey of Employment Tribunal Applications 2018&lt;/a&gt; webpage.&lt;/p&gt;&lt;p&gt;This is the seventh survey in a series dating back to 1987. The 1998, 2003, 2008 and 2013 surveys (SNs 4894, 6714, 5022 and 7727 respectively) are available at the UK Data Service. &lt;/p&gt;
DOI: 10.15468/dl.r5cfaq
title: Occurrence Download

description: A dataset containing 171 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "OccurrenceStatus is Present",
 "TaxonKey is Chamaecrista scleroxylon"
 ]
}

 The dataset includes 171 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031296-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6eecgix/mqgmrj
title: plane_2.jpg
DOI: 10.5255/ukda-sn-8786-1
title: National Survey of Sexual Attitudes and Lifestyles, 2010-2012: Open Access Teaching Dataset
title: Natsal-3

description: &lt;p&gt;&lt;/p&gt; &lt;p&gt;The British &lt;a href="https://www.natsal.ac.uk/home.aspx"&gt;National Surveys of Sexual Attitudes and Lifestyles&lt;/a&gt; (Natsal) are some of the largest and most detailed studies of sexual attitudes and behaviour in the world.&lt;/p&gt;&lt;p class="x_x_MsoNormal"&gt; &lt;/p&gt;&lt;p&gt;This open access teaching dataset contains data from Natsal-3, which interviewed 15,162 adults aged 16-74 in 2010-2012. The data are taken from the original Natsal-3 study accessible via the UK Data Service, held under &lt;a title="SN 7799" href="https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=7799"&gt;SN 7799&lt;/a&gt;. To make the data accessible for teaching via an open data licence, a subset of variables and cases has been selected. Some demographic variables have also been recoded and a new continuous variable measuring attitudes towards sexual behaviours has been created. &lt;/p&gt; &lt;p class="x_x_MsoNormal"&gt;&lt;/p&gt;
DOI: 10.15468/dl.4tqh33
title: Occurrence Download

description: A dataset containing 318281 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 countrycode countrycode,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, countrycode, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, countrycode, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, countrycode, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, countrycode, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, countrycode, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, countrycode, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'PE'
 AND (occurrence."year" &gt;= 1000 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence.hascoordinate = TRUE)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year",
 occurrence.countrycode

 The dataset's 318281 records were derived from 1156 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031264-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.10302.20800
title: Prescription drugs 21-10-04
DOI: 10.13140/rg.2.2.35048.60163
title: Equisetum sylvaticum L., 1753
DOI: 10.6084/m9.figshare.28519496
title: Data for the Journal Paper Machine Learning-Based Context-Aware Lemmatization for Low-Resource Languages: A Case Study of Setswana

description: Efficient natural language processing (NLP) tools for Setswana are essential for improving human-machine interaction, yet the language remains underrepresented in computational linguistics due to its complex morphology and limited linguistic resources. This study introduces a context-aware machine-learning-based lemmatization model for Setswana, addressing challenges in word sense disambiguation and morphological analysis. Unlike previous rule-based lemmatizers, which process words in isolation, this model incorporates contextual information using Naïve Bayes (NB) and N-gram embeddings to improve lemma prediction accuracy. The proposed model was trained and evaluated using a manually annotated Setswana corpus, integrating part-of-speech (POS) tagging and named entity recognition (NER) as key linguistic features. Performance evaluation, based on accuracy (70.32%), precision (70%), recall (65%), and F1-score (66%), demonstrates the model’s effectiveness in resolving polysemous words, a challenge not addressed by existing Setswana lemmatization approaches. Comparative analysis with prior studies highlights that machine-learning models outperform rule-based approaches in capturing contextual dependencies, although dataset size and feature selection remain critical to performance improvement. This research marks a significant advancement in Setswana NLP, establishing a foundation for future hybrid models that integrate deep learning and rule-based techniques for enhanced accuracy. The study contributes to the development of computational tools for low-resource languages, paving the way for their inclusion in modern information retrieval, machine translation, and conversational AI systems.
DOI: 10.6084/m9.figshare.28519496.v3
title: Data for the Journal Paper Machine Learning-Based Context-Aware Lemmatization for Low-Resource Languages: A Case Study of Setswana

description: Efficient natural language processing (NLP) tools for Setswana are essential for improving human-machine interaction, yet the language remains underrepresented in computational linguistics due to its complex morphology and limited linguistic resources. This study introduces a context-aware machine-learning-based lemmatization model for Setswana, addressing challenges in word sense disambiguation and morphological analysis. Unlike previous rule-based lemmatizers, which process words in isolation, this model incorporates contextual information using Naïve Bayes (NB) and N-gram embeddings to improve lemma prediction accuracy. The proposed model was trained and evaluated using a manually annotated Setswana corpus, integrating part-of-speech (POS) tagging and named entity recognition (NER) as key linguistic features. Performance evaluation, based on accuracy (70.32%), precision (70%), recall (65%), and F1-score (66%), demonstrates the model’s effectiveness in resolving polysemous words, a challenge not addressed by existing Setswana lemmatization approaches. Comparative analysis with prior studies highlights that machine-learning models outperform rule-based approaches in capturing contextual dependencies, although dataset size and feature selection remain critical to performance improvement. This research marks a significant advancement in Setswana NLP, establishing a foundation for future hybrid models that integrate deep learning and rule-based techniques for enhanced accuracy. The study contributes to the development of computational tools for low-resource languages, paving the way for their inclusion in modern information retrieval, machine translation, and conversational AI systems.
DOI: 10.6084/m9.figshare.28519496.v2
title: Data for the Journal Paper Machine Learning-Based Context-Aware Lemmatization for Low-Resource Languages: A Case Study of Setswana

description: Efficient natural language processing (NLP) tools for Setswana are essential for improving human-machine interaction, yet the language remains underrepresented in computational linguistics due to its complex morphology and limited linguistic resources. This study introduces a context-aware machine-learning-based lemmatization model for Setswana, addressing challenges in word sense disambiguation and morphological analysis. Unlike previous rule-based lemmatizers, which process words in isolation, this model incorporates contextual information using Naïve Bayes (NB) and N-gram embeddings to improve lemma prediction accuracy. The proposed model was trained and evaluated using a manually annotated Setswana corpus, integrating part-of-speech (POS) tagging and named entity recognition (NER) as key linguistic features. Performance evaluation, based on accuracy (70.32%), precision (70%), recall (65%), and F1-score (66%), demonstrates the model’s effectiveness in resolving polysemous words, a challenge not addressed by existing Setswana lemmatization approaches. Comparative analysis with prior studies highlights that machine-learning models outperform rule-based approaches in capturing contextual dependencies, although dataset size and feature selection remain critical to performance improvement. This research marks a significant advancement in Setswana NLP, establishing a foundation for future hybrid models that integrate deep learning and rule-based techniques for enhanced accuracy. The study contributes to the development of computational tools for low-resource languages, paving the way for their inclusion in modern information retrieval, machine translation, and conversational AI systems.
DOI: 10.13140/rg.2.2.12923.64801
title: subcorpus de futebol” (2,7 milhões de palavras)
DOI: 10.15468/dl.p23k2m
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Fungi)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-127.5 75,-120 75,-120 82.5,-127.5 82.5,-127.5 75))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042311-251009101135966/datasets/export for details.
DOI: 10.13140/rg.2.2.20597.91364
title: Marsh Horsetail
DOI: 10.15468/dl.tf5yq6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Insecta)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((172.5 7.5,180 7.5,180 15,172.5 15,172.5 7.5))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042310-251009101135966/datasets/export for details.
DOI: 10.13140/rg.2.2.30192.38406
title: Turning Movement Survey Sheet (3-way junctions)
DOI: 10.15468/dl.2uxy3g
title: Occurrence Download

description: A dataset containing 325 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Drypetes natalensis (Harv.) Hutch."
 ]
}

 The dataset includes 325 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037751-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.