cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-7453-12
title: Understanding Society: Waves 1-13, 2009-2022 and Harmonised BHPS: Waves 1-18, 1991-2009: Special Licence Access, Acorn 2015 and 2023
title: Understanding Society, Waves 1-, 2008- : Special Licence Access
title: United Kingdom Household Longitudinal Study; UKHLS

description: &lt;span style="font-style: italic;"&gt;Understanding Society&lt;/span&gt; (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. &lt;p class="x_x_MsoNormal"&gt;This dataset contains Acorn geodemographic segmentation codes for each household in every wave of Understanding Society and harmonised BHPS, together with a household identification number (hidp) allowing it to be linked to the main Understanding Society data files. The dataset is produced by matching the Acorn segmentation against every Understanding Society household at the postcode level.&lt;/p&gt; &lt;p class="x_x_MsoNormal"&gt;The Acorn segmentation system itself is developed and maintained by CACI Ltd and is designed to identify characteristics of each postcode according to a hierarchical structure of 7 categories, 22 groups and 64 types. For details on the Acorn segmentation structure and how it is produced please refer to the documentation and the &lt;a href="https://acorn.caci.co.uk/"&gt;CACI&lt;/a&gt; website.&lt;/p&gt; &lt;p class="x_x_MsoNormal"&gt;These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access' section). &lt;br&gt; &lt;br&gt; &lt;b&gt;Latest Edition Information&lt;/b&gt;&lt;br&gt; &lt;/p&gt; &lt;p&gt;For the 13th edition (November 2023), data for Wave 13 have been added.&lt;/p&gt; &lt;p class="x_x_MsoNormal"&gt;&lt;span&gt;&lt;/span&gt;&lt;/p&gt; &lt;span&gt;&lt;/span&gt;&lt;span&gt;&lt;/span&gt; &lt;p&gt;&lt;/p&gt;
DOI: 10.5255/ukda-sn-5760-13
title: Growing Up in Scotland: Cohort 1, Sweeps 1-10, 2005-2020: Special Licence Access
title: Growing Up in Scotland, 2005-: Special Licence Access
title: GUS

description: &lt;div&gt;The &lt;span style="font-style: italic;"&gt;Growing Up in Scotland&lt;/span&gt; (GUS) study is a large-scale longitudinal social survey which follows the lives of several groups of Scottish children from infancy through childhood and adolescence, and aims to provide important new information on children and their families in Scotland. The study forms a central part of the Scottish Government's strategy for the long-term monitoring and evaluation of its policies for children, with a specific focus on the early years. Unlike other similar cohort studies, this survey has a specifically Scottish focus. A key objective of GUS is to address a significant gap in the evidence base for early years policy monitoring and evaluation. The study seeks both to describe the characteristics, circumstances and experiences of children in their early years (and their parents) in Scotland and, through its longitudinal design, to generate a better understanding of how children's start in life can shape their longer term prospects and development.&lt;br&gt;&lt;br&gt;Since 2005, study design and data collection have been undertaken by ScotCen Social Research with collaborations with the Centre for Research on Families and Relationships, based at the University of Edinburgh and the MRC/CSO Social and Public Health Sciences Unit over certain periods of the project. The survey design consisted of recruiting an initial total of 8,000 parents in 2005, comprising two cohorts of children (5,000 from birth, 3,000 from age two years and ten months), and then interviewing parents annually until their child reached age five years ten months. Further fieldwork was undertaken with the birth cohort when the children were around eight, ten, twelve and fourteen years old.  A boost sample of 500 children from predominantly high deprivation areas was added to the cohort as part of the age 12 fieldwork.&lt;br&gt;&lt;br&gt;Data is collected via an in-home, face-to-face interview with self-complete sections. Fieldwork for sweep 10 was disrupted due to the COVID pandemic. As a result, the final portion of the data was collected via web and telephone questionnaires.&lt;br&gt;&lt;br&gt;Further information about the survey may be found on the &lt;a href="https://growingupinscotland.org.uk/" target="_blank"&gt;Growing Up in Scotland&lt;/a&gt; website.&lt;br&gt;&lt;br&gt;&lt;/div&gt; 
&lt;div&gt;&lt;span style="font-style: italic;"&gt;Latest Edition Information&lt;/span&gt;&lt;br&gt;
For the twentieth edition (August 2023), the Birth Cohort 1 Sweep 7, Sweep 9 and Sweep 10 data files have been updated to correct minor errors. The Sweep 7 data documentation has also been amended.&lt;/div&gt;
DOI: 10.5255/ukda-sn-8910-9
title: National Referral Mechanism and Duty to Notify Statistics, 2014-2023
title: NRM

description: &lt;p&gt;Modern slavery is a term that includes any form of human trafficking, slavery, servitude or forced labour, as set out in the Modern Slavery Act 2015. Potential victims of modern slavery in the UK that come to the attention of authorised ‘First Responder’ organisations are referred to the National Referral Mechanism (NRM). &lt;/p&gt; &lt;p&gt;Adults (aged 18 or above) must consent to being referred to the NRM, whilst children under the age of 18 need not consent to being referred. As specified in section 52 of the Modern Slavery Act 2015, public authorities in England and Wales have a statutory duty to notify the Home Office when they come across potential victims of modern slavery ('Duty to Notify' (DtN)). This duty is discharged by either referring a child or consenting adult potential victim into the NRM, or by notifying the Home Office via the DtN process if an adult victim does not consent to enter the NRM.&lt;/p&gt; &lt;p&gt;The Home Office publishes quarterly statistical bulletins and aggregated data breakdowns on the &lt;a href="https://www.gov.uk/government/collections/national-referral-mechanism-statistics" target="_blank"&gt;National Referral Mechanism&lt;/a&gt;&lt;a href="https://www.gov.uk/government/collections/national-referral-mechanism-statistics"&gt;&lt;/a&gt;&lt;a href="https://www.gov.uk/government/collections/national-referral-mechanism-statistics"&gt;&lt;/a&gt; webpage on the GOV.UK website regarding the number of potential victims referred each quarter. To allow stakeholders and first responders more flexibility in analysing this data for their own strategic and operational planning, the disaggregated, pseudonymised dataset used to create the aggregated published data is also available from the UK Data Service as 'safeguarded' data. (The UKDS data are available in SPSS, Stata, tab-delimited text and CSV formats.)&lt;br&gt; &lt;br&gt; &lt;span style="font-weight: bold;"&gt;Latest edition information&lt;/span&gt;&lt;br&gt; For the ninth edition (August 2023), the data file was amended to include cases up to July 2023, and the Data Notes documentation file was also updated.&lt;br&gt; &lt;/p&gt;
DOI: 10.25549/inscriptifact-ouc110591111
title: Persepolis Fortification Aramaic Text 245, PFAT 245

description: [Text Description:] Administrative text with seals PFATS 0261; PFATS 0061; PFATS 0004s ❧ [Text or Publication Number Note:] PFAT 245, Bowman 245 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with ink inscription. Seal impressions PFATS 0261 on upper edge; PFATS 0061 on reverse; PFATS 0004s on flat edge.
DOI: 10.25549/jrbm-c91-791
title: Kankai ibun, vol. 5, 1807

description: Kankai ibun, [1807], volume 5 = Description and travel of circumnavigation of seas, [1807], volume 5 = 環海 異聞 五 文化 4. Volume five of a manuscript by Gentaku Ōtsuki and Kōkyō Shimura who drew from four Japanese fishermen the story of their voyage around the world during repeated interviews. The Kankai Ibun is an account of their journey when they were sent off-course en route to the capital, Edo. The fishermen were rescued by a Russian ship and taken to late 18th / early 19th century Europe and, most especially, Russia.; The cargo ship, Wakamiyamaru, left from the port of Ishimaki in Sendai-han, today’s Miyagi Prefecture, in Japan carrying rice and lumber with 16 crew members for Edo, today’s Tokyo, on November 27th, 1793. Shipwrecked and drifted in the Pacific Ocean for half a year, they finally landed in one of the islands of the Andreanof Islands, a part of the Aleutian Islands on May 10th, 1794. They were transferred to Irkutsk by Russians where they stayed until the spring of 1803. They were then called in by Alexander I of Russia to St. Petersburg where they stayed from April to June in 1803, and from where they started their journey back home. They were included in the Rezanov mission on the first Russian global circumnavigation by a ship Nadezhda. It took them 16 months to reach the port of Nagasaki, Japan in September, 1804 since they left in June, 1803. Of the sixteen, five crewmen got on board. Four returned home in Sendai; one, who had become an interpreter, returned to Russia with Rezanov.
DOI: 10.15468/dl.vznfcg
title: Occurrence Download

description: A dataset containing 2673635 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2016-2024"
 ]
}

 The dataset includes 2673635 records from 1240 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033806-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17334708
title: From_Timeless_Symmetry_to_Operational_Time

description: 10.5281/zenodo.17334709
DOI: 10.5281/zenodo.17334709
title: From_Timeless_Symmetry_to_Operational_Time

description: 10.5281/zenodo.17334709
DOI: 10.21227/j9mp-0276
title: Front Pitch View Shot Extraction and Ball Tracking in Cricket using Deep-Learning

description: The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models. We also detect pitch and umpire using recent object detectors. If a frame is classified FPV and pitch and umpire are detectable, it is considered as part of the FPV shot. In each FPV shot, we then track the cricket ball for further analysis using deep-learning-based fast-moving object trackers. Using the ball tracking, we first detect the ball bounce position on the pitch and using that position we classify the FPV video shots as full-pitched, good-length, or short-pitched deliveries. For training and testing purposes, we manually annotated four different datasets: (i) Front Pitch View (FPV) classification dataset consisting of 10k frames including 5k positive and 5k negative frames, (ii) Pitch and Umpire Detection (PUD) dataset consisting of 5k frames with manually annotated pitches and umpires, (iii) Ball Tracking  Dataset (BTD) consisting of 3k frames with balls annotated and 6k frames with no visible balls, (iv) Ball Delivery Classification (BDC) dataset consisting of 300 video clips annotated as full-pitched, good-length and short-pitched deliveries. For the FPV and PUD datasets, training and testing splits are marked as 50% each. For the BTD and BDC datasets training and testing splits are marked as 80% and 20%. The results of the proposed framework demonstrates its ability to accurately classify cricket shots into FPV and non-FPV, creating the foundation for large datasets of video shots of ball deliveries and strokes that can be used for further processing and semantic extraction.  Our work has the potential to be of great benefit to cricket coaches and players for analyzing ball deliveries in a match. Our trained models and four datasets are online available at our Github page https://www.github.com/KumailAbb/FPVShotDetection.
DOI: 10.34740/kaggle/dsv/11984316
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.57833/cidacs/lhjhc2
title: Extração da coorte de cem milhões de Brasileiros vinculada ao sistema de informações sobre mortalidade

description: Base produzida e utilizada com a finalidade de alcance do objetivo de "Avaliar o impacto do Programa Minha Casa Minha Vida sobre a ocorrência de tuberculose, hanseníase, HIV/Aids, prematuridade, diarreia, mortalidade infantil e saúde cardiovascular", componente do projeto intitulado: "Grupo De Pesquisa Em Saúde Global Sobre Políticas E Desigualdades Em Saúde – NIHR Global Health Research Group In Social Policy And Health Iniqualities".
DOI: 10.15468/dl.38fu3x
title: Occurrence Download

description: A dataset containing 1857400 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Actinobacteriota"
 ]
}

 The dataset includes 1857400 records from 753 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005167-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ypejgr
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:ACG7780"
 ]
}

 The dataset includes 22 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035686-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qt6uv2
title: Occurrence Download

description: A dataset containing 227495 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vaccinium uliginosum",
 "Year 1956-2022"
 ]
}

 The dataset includes 227495 records from 572 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035678-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6jtntt
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAK1416"
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035688-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16595510
title: China's Crop Parcel Training Dataset (CCPTD)

description: Semantic segmentation of remote sensing imagery is a key method for land cover classification and mapping. In a typical remote sensing segmentation and mapping project, having more training samples generally helps the model better extract target features and achieve higher mapping accuracy. However, annotating remote sensing data is a time-consuming and labor-intensive task. The goal of strategic sampling is to minimize the number of training samples required while maintaining model performance, thereby reducing data annotation costs. This dataset supports a case study on strategic sampling methods for remote sensing semantic segmentation, specifically focusing on cropland parcel delineation. It includes GF-2 satellite imagery from five distinct agricultural regions across China. For each image, 20% of the area was selected, resulting in a dataset of over 12,000 annotated cropland parcel samples. Each sample includes three types of labels: parcel extent, parcel boundaries, and distance-to-boundary maps, enabling multi-task learning.
DOI: 10.5281/zenodo.16595511
title: China's Crop Parcel Training Dataset (CCPTD)

description: Semantic segmentation of remote sensing imagery is a key method for land cover classification and mapping. In a typical remote sensing segmentation and mapping project, having more training samples generally helps the model better extract target features and achieve higher mapping accuracy. However, annotating remote sensing data is a time-consuming and labor-intensive task. The goal of strategic sampling is to minimize the number of training samples required while maintaining model performance, thereby reducing data annotation costs. This dataset supports a case study on strategic sampling methods for remote sensing semantic segmentation, specifically focusing on cropland parcel delineation. It includes GF-2 satellite imagery from five distinct agricultural regions across China. For each image, 20% of the area was selected, resulting in a dataset of over 12,000 annotated cropland parcel samples. Each sample includes three types of labels: parcel extent, parcel boundaries, and distance-to-boundary maps, enabling multi-task learning.
DOI: 10.15468/dl.7jsb25
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gymnothorax dorsalis"
 ]
}

 The dataset includes 91 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035689-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00007767.0
title: Available IPD datapackage for study 'Phase 2, Multi-Arm Study of Niraparib Administered Alone and in Combination With a PD-1 Inhibitor in Patients With Non-Small Cell Lung Cancer'

description: This is a multicenter, open-label, phase 2 study to evaluate the efficacy and safety of niraparib alone and in combination with PD-1 inhibitors in participants with locally advanced and metastatic non-small cell lung cancer (NSCLC). The study will consist of 2 stages. In stage 1, participants from Cohorts 1 and 2 will receive niraparib plus PD-1 inhibitor; pembrolizumab and participants from Cohort 3 will receive niraparib alone. In Stage 2, participants from Cohorts 1A and 2A will receive niraparib plus the PD-1 inhibitor, TSR-042 (Dostarlimab).
DOI: 10.15468/dl.hgss38
title: Occurrence Download

description: A dataset containing 256 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sunagocia arenicola"
 ]
}

 The dataset includes 256 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035690-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jqhcjf
title: Occurrence Download

description: A dataset containing 12546 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nestor notabilis"
 ]
}

 The dataset includes 12546 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035691-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ekuwev
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eurycephalus arenicola"
 ]
}

 The dataset includes 40 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035693-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vj2mct
title: Occurrence Download

description: A dataset containing 400 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is TRUE",
 "TaxonKey is one of (Frullania riojaneirensis, Frullania)"
 ]
}

 The dataset includes 400 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035695-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/fwrv-1w15
title: Parkes observations for project PX600 semester 2020APRS_122

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community
DOI: 10.15468/dl.ywyksf
title: Occurrence Download

description: A dataset containing 400 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is TRUE",
 "TaxonKey is one of (Frullania riojaneirensis, Frullania)"
 ]
}

 The dataset includes 400 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035694-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5de26p
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAJ0441"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035696-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cn7ay6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "ScientificName" : [
 "is Sceloporus grammicus"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035687-250802193616735/datasets/export for details.
DOI: 10.15468/dl.dv9s4g
title: Occurrence Download

description: A dataset containing 431 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is South Pacific Regional Herbarium_SUVA_Ferns and Fern allies"
 ]
}

 The dataset includes 431 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035697-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.