cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.23671329
title: Barriers faced by the ward based community health workers when screening diabetes and hypertension in Fetakgomo-Tubatse sub-district, Limpopo Province

description: This data underpin the dissertation titled 'Experiences of ward based community health care workers regarding the utilisation of patients screening tools in Fetakgomotubatse sub-district, Limpopo province'. Ward based community health workers work in the community using different screening tools to check whether the patients are taking medications as well as to motivate patients to receive care. After screening patients are referred to hospitals or primary health care institutions.The data was collected inorder to explore the ward based community health workers experiences when they screen patients in the households,their coping experiences, positive experiences and challenging experiences. The aim of ward based community health workers screening patients in the community households is for early detection of the non communicable diseases and referring the patients before complications of the disease.
DOI: 10.25403/upresearchdata.23671329.v1
title: Barriers faced by the ward based community health workers when screening diabetes and hypertension in Fetakgomo-Tubatse sub-district, Limpopo Province

description: This data underpin the dissertation titled 'Experiences of ward based community health care workers regarding the utilisation of patients screening tools in Fetakgomotubatse sub-district, Limpopo province'. Ward based community health workers work in the community using different screening tools to check whether the patients are taking medications as well as to motivate patients to receive care. After screening patients are referred to hospitals or primary health care institutions.The data was collected inorder to explore the ward based community health workers experiences when they screen patients in the households,their coping experiences, positive experiences and challenging experiences. The aim of ward based community health workers screening patients in the community households is for early detection of the non communicable diseases and referring the patients before complications of the disease.
DOI: 10.34740/kaggle/dsv/6307178
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.23909814
title: Simulated hydrological data for the Three-River Headwater Region under SSP585

description: This dataset includes all important hydrological components such as streamflow, baseflow, surface flow, soil moisture content, terrestrial water storage. The dataset is simulated by HEC-HMS using SSP5 scenarios of NorESM global climate model. It is available in the DSS format and can be open in HEC-DSSvalue software.<br>
DOI: 10.5517/ccdc.csd.cc20mxm6
title: CCDC 1866188: Experimental Crystal Structure Determination

description: Related Article: Yik Yie Ng, Lisa Jiaying Tan, Shue Mei Ng, Yoke Tin Chai, Rakesh Ganguly, Yonghua Du, Edwin K. L. Yeow, and Han Sen Soo|2018|ACS Catalysis|8|11277|doi:10.1021/acscatal.8b02502
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.23670/ecnms.2023.2.12.1
title: Сравнение стратегий развития экономики страны

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.6.1
title: Основные показатели малого и среднего предпринимательства в США, Великобритании, Японии, Германии, ЕС и России, 2022 гг

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.7.1
title: Эволюция и ретроспектива современных определений и концептуальных основ шеринг-экономики

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.1
title: Основные показатели промышленности Иркутской губернии 1912-1935 гг.

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.6.2
title: Динамика количества малых предприятий в США, 2014–2022 гг

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.12.2
title: Производство основных видов импортозамещающих пищевых продуктов в Российской Федерации в 2017–2023 гг.

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.2
title: Динамика транспортного грузооборота на территории Иркутской области

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.6.3
title: Динамика количества занятой рабочей силы в малом бизнесе и её доли от совокупного количества, 2014–2022 гг

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.3
title: Число колхозов Иркутской области 1933-1939 гг.

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.4
title: Медицинские кадры Иркутской области 1934-1939 гг.

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.5
title: Развитие системы школьного образования Иркутской губернии/области

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.1.3
title: Данные об инвестициях в основной капитал по Нижегородской области

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.6
title: Динамика количества учебных заведений системы специального образования и учащихся в них в 1927, 1931 и 1940 годах

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.7
title: Выпуск специалистов из высших и средних учебных заведений Иркутской области 1937-1939 годы

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.8
title: Прирост кадров и компенсация выбытия по возрасту по отраслям

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.1.6
title: Сальдо внешней торговли России

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.8.9
title: Соотношение потребности в кадрах с фактическим выпуском

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.1.7
title: Сальдо внешней торговли Нижегородской области

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.1.8
title: Основные статьи экспорта Нижегородской области в страны СНГ

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.23670/ecnms.2023.2.1.9
title: Основные статьи экспорта Нижегородской области в страны дальнего зарубежья

description: Cifra. Экономика, Выпуск 2 (2) 2023
DOI: 10.26050/wdcc/ar6.c6daipicl
title: IPCC DDC: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.IPSL.IPSL-CM6A-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IPSL-CM6A-LR climate model, released in 2017, includes the following components: atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3. The model was run by the Institut Pierre Simon Laplace, Paris 75252, France (IPSL) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6daipiclsg
title: IPCC DDC: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP ssp245-GHG

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.IPSL.IPSL-CM6A-LR.ssp245-GHG' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IPSL-CM6A-LR climate model, released in 2017, includes the following components: atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3. The model was run by the Institut Pierre Simon Laplace, Paris 75252, France (IPSL) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6daipiclsn
title: IPCC DDC: IPSL IPSL-CM6A-LR model output prepared for CMIP6 DAMIP ssp245-nat

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.IPSL.IPSL-CM6A-LR.ssp245-nat' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IPSL-CM6A-LR climate model, released in 2017, includes the following components: atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3. The model was run by the Institut Pierre Simon Laplace, Paris 75252, France (IPSL) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.
DOI: 10.25345/c59c6sb3t
title: MassIVE MSV000092659 - The Daphne genus MS/MS dataset-test-0815
title: A small test for subsequent study of the daphne genus MS/MS data analysis
DOI: 10.21227/ehrj-z518
title: WUCT dataset

description: A 3D WUCT system using a single ultrasound transducer is designed and automated. The dataset consist of the WUCT reconstruction results dataset used to train U-Net based semantic segmentation model.  Also, dataset i) to study the effect of increase in the number of virtual transducer on reconstruction quality and, ii) effect of variation in the applied pulse width on the reconstruction are provided. The U-Net based semantic segmentation model is trained and used to evaluate dice coefficient corresponding to the phantom’s actual profile and reconstructed profile. The model’s segmentation accuracy is 95.12% and IOU score is 0.7785 on the validation dataset. The accuracy of the generated result is found to be upto 94% similar to the actual profile.
DOI: 10.5281/zenodo.5588282
title: MiraBest Batched Dataset

description: If using this dataset, please cite Porter & Scaife (2023), which provides a full description of its construction and use. The MiraBest Batched Dataset is a labelled dataset of Fanaroff-Riley (FR) galaxies drawn from Miraghaei and Best 2017. At present, the dataset consists of FRI, FRII and hybrid FR sources, labelled as either confidently or uncertainly classified, and with any nonstandard morphology noted. The MiraBest Batched Dataset is comprised of a total of 1256 images, with their class denoted by a three-digit number. First digit: FRI (1), FRII (2), hybrid source (3). Second digit: confidently classified (0), uncertaintly classified (1). Third digit: standard morphology (0), double-double (1), wide-angle tail (2), head-tail (4). Images were obtained from the VLA FIRST sky survey via SkyView virtual telescope. All images have dimensions of 150 x 150 pixels, with one pixel equivalent to an angular size of 1.8". The dataset is split into 7 training batches and a test batch, each composed of 157 images. The number of objects of each class in each batch is roughly consistent between batches, and the test batch contains at least one example of every class. This second version of MiraBest uses the same image generation methods as the previous, but now provides images from both VLA FIRST and NVSS, maintaining dimensions of 150 x 150 pixels for both; NVSS images hence have one pixel equivalent to an angular size of 15". These may be accessed from MiraBest_F and MiraBest_N, respectively. The sources included in each batch were kept consistent between the two datasets, to allow for direct comparison of performance; however, they are not identical to the batches of original version of MiraBest, although the overall batch class composition remains consistent.
DOI: 10.5281/zenodo.4288836
title: MiraBest Batched Dataset

description: If using this dataset, please cite Porter & Scaife (2023), which provides a full description of its construction and use. The MiraBest Batched Dataset is a labelled dataset of Fanaroff-Riley (FR) galaxies drawn from Miraghaei and Best 2017. At present, the dataset consists of FRI, FRII and hybrid FR sources, labelled as either confidently or uncertainly classified, and with any nonstandard morphology noted. The MiraBest Batched Dataset is comprised of a total of 1256 images, with their class denoted by a three-digit number. First digit: FRI (1), FRII (2), hybrid source (3). Second digit: confidently classified (0), uncertaintly classified (1). Third digit: standard morphology (0), double-double (1), wide-angle tail (2), head-tail (4). Images were obtained from the VLA FIRST sky survey via SkyView virtual telescope. All images have dimensions of 150 x 150 pixels, with one pixel equivalent to an angular size of 1.8". The dataset is split into 7 training batches and a test batch, each composed of 157 images. The number of objects of each class in each batch is roughly consistent between batches, and the test batch contains at least one example of every class. This second version of MiraBest uses the same image generation methods as the previous, but now provides images from both VLA FIRST and NVSS, maintaining dimensions of 150 x 150 pixels for both; NVSS images hence have one pixel equivalent to an angular size of 15". These may be accessed from MiraBest_F and MiraBest_N, respectively. The sources included in each batch were kept consistent between the two datasets, to allow for direct comparison of performance; however, they are not identical to the batches of original version of MiraBest, although the overall batch class composition remains consistent.
DOI: 10.6084/m9.figshare.23918646
title: Generated Naturalized streamflow data for all tributaries in the Transboundary Amudarya River Basin for 1931-2020.

description: Naturalized streamflow data simulated by HEC-HMS for the period of 1980--2020. The data is available in the form of DSS can be open in HEC-DSSvalue
DOI: 10.5281/zenodo.7868155
title: Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS

description: Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS". It includes the output of SCINPAS, available here: https://github.com/zavolanlab/SCINPAS, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.
DOI: 10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0
title: CORINE Land Cover 2018 (vector), Europe, 6-yearly - version 2020_20u1, May 2020

description: Corine Land Cover 2018 (CLC2018) is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. CLC service has a long-time heritage (formerly known as "CORINE Land Cover Programme"), coordinated by the European Environment Agency (EEA). It provides consistent and thematically detailed information on land cover and land cover changes across Europe. CLC datasets are based on the classification of satellite images produced by the national teams of the participating countries - the EEA members and cooperating countries (EEA39). National CLC inventories are then further integrated into a seamless land cover map of Europe. The resulting European database relies on standard methodology and nomenclature with following base parameters: 44 classes in the hierarchical 3-level CLC nomenclature; minimum mapping unit (MMU) for status layers is 25 hectares; minimum width of linear elements is 100 metres. Change layers have higher resolution, i.e. minimum mapping unit (MMU) is 5 hectares for Land Cover Changes (LCC), and the minimum width of linear elements is 100 metres. The CLC service delivers important data sets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as e.g. protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, monitoring urban land take, assessing developments in agriculture or dealing with water resources directives. CLC belongs to the Pan-European component of the Copernicus Land Monitoring Service (https://land.copernicus.eu/), part of the European Copernicus Programme coordinated by the European Environment Agency, providing environmental information from a combination of air- and space-based observation systems and in-situ monitoring. Additional information about CLC product description including mapping guides can be found at https://land.copernicus.eu/user-corner/technical-library/. CLC class descriptions can be found at https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/.
DOI: 10.5281/zenodo.7868154
title: Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS

description: Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS". It includes the output of SCINPAS, available here: https://github.com/zavolanlab/SCINPAS, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.
DOI: 10.5281/zenodo.8249820
title: Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS

description: Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS". It includes the output of SCINPAS, available here: https://github.com/zavolanlab/SCINPAS, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.
DOI: 10.5281/zenodo.8248775
title: Submissions DCASE 2023 Task4a

description: Predictions and technical reports of the systems submitted to DCASE 2023 Task4a including file name mapping and ground truth for public youtube evaluation set. Challenge results can be found on results page and additional post-processing independent evaluations can be found in [1]. [1] J. Ebbers, R. Haeb-Umbach, and R. Serizel, "Post-Processing Independent Evaluation of Sound Event Detection Systems", Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2023, arXiv: https://arxiv.org/abs/2306.15440
DOI: 10.5281/zenodo.8248774
title: Submissions DCASE 2023 Task4a

description: Predictions and technical reports of the systems submitted to DCASE 2023 Task4a including file name mapping and ground truth for public youtube evaluation set. Challenge results can be found on results page and additional post-processing independent evaluations can be found in [1]. [1] J. Ebbers, R. Haeb-Umbach, and R. Serizel, "Post-Processing Independent Evaluation of Sound Event Detection Systems", Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop, 2023, arXiv: https://arxiv.org/abs/2306.15440
DOI: 10.6084/m9.figshare.23957916.v1
title: Scoping Review Data

description: Data for Scoping Review