cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25375/uct.22242058
title: Dataset Health Committees and disability

description: <br> <strong>Community participation and the right to health for people with disability: Health Committees’ understanding of and role in ensuring health care for persons with disability.</strong> <strong>ABSTRACT</strong> <strong>INTRODUCTION</strong>: People with disabilities encounter major barriers that prevent them from realising their right to health in South Africa. Health committees are legislated structures for community participation in health at a local level. This study investigated how health committee members understand and practise their role in community participation and how this advances the right to health for persons with disability. <strong>METHODS</strong>: A qualitative study was conducted with three health committees in the Cape Town Metropole in the Western Cape province of South Africa purposively selected for the study. Three facility managers and eight health committee members took part in focus group discussions and semi-structured interviews, supplemented by participant observations of committee meetings. Additionally, semi-structured interviews were conducted with two disability activists. These methods were used to gain a rich understanding of health committees’ roles and practise in relation to persons with disability. Thematic content analysis was used to analyse the data. <strong>RESULTS</strong>: The main research findings were: (i) health committees did not prioritise disability on their respective agendas; (ii) persons with disability were not adequately represented on health committees; (iii) health committees exhibited poor understanding of disability barriers relating to health; (iv) lack of egalitarian values led to persons with disability not trusting the health committee, and distrust amongst health committee members; lastly (v) health committees augment health facility operations instead of fulfilling their governance and oversight function. These factors may have contributed to health committees not helping to advance the right to health for persons with disability. <strong>CONCLUSION</strong>: Health committees should include mandated representation of persons with disability, whilst addressing marginalisation directed toward them persons with disability on committees. Training of health committees, as well as networking with organisations of persons with disability, could help improve their limited understanding of disability. Health committees should consider addressing disability as a human rights issue, which critically involves community mobilisation, raising awareness around issues of disability and promoting agency amongst persons with disability to claim their rights. The dataset consists of focus groups, interviews and observations with three health committees as well as a document review. The data focus on understanding how health committees represent disability issues in health services. The dataset is available on reasonable request.
DOI: 10.25375/uct.22242058.v1
title: Dataset Health Committees and disability

description: <br> <strong>Community participation and the right to health for people with disability: Health Committees’ understanding of and role in ensuring health care for persons with disability.</strong> <strong>ABSTRACT</strong> <strong>INTRODUCTION</strong>: People with disabilities encounter major barriers that prevent them from realising their right to health in South Africa. Health committees are legislated structures for community participation in health at a local level. This study investigated how health committee members understand and practise their role in community participation and how this advances the right to health for persons with disability. <strong>METHODS</strong>: A qualitative study was conducted with three health committees in the Cape Town Metropole in the Western Cape province of South Africa purposively selected for the study. Three facility managers and eight health committee members took part in focus group discussions and semi-structured interviews, supplemented by participant observations of committee meetings. Additionally, semi-structured interviews were conducted with two disability activists. These methods were used to gain a rich understanding of health committees’ roles and practise in relation to persons with disability. Thematic content analysis was used to analyse the data. <strong>RESULTS</strong>: The main research findings were: (i) health committees did not prioritise disability on their respective agendas; (ii) persons with disability were not adequately represented on health committees; (iii) health committees exhibited poor understanding of disability barriers relating to health; (iv) lack of egalitarian values led to persons with disability not trusting the health committee, and distrust amongst health committee members; lastly (v) health committees augment health facility operations instead of fulfilling their governance and oversight function. These factors may have contributed to health committees not helping to advance the right to health for persons with disability. <strong>CONCLUSION</strong>: Health committees should include mandated representation of persons with disability, whilst addressing marginalisation directed toward them persons with disability on committees. Training of health committees, as well as networking with organisations of persons with disability, could help improve their limited understanding of disability. Health committees should consider addressing disability as a human rights issue, which critically involves community mobilisation, raising awareness around issues of disability and promoting agency amongst persons with disability to claim their rights. The dataset consists of focus groups, interviews and observations with three health committees as well as a document review. The data focus on understanding how health committees represent disability issues in health services. The dataset is available on reasonable request.
DOI: 10.21227/rkqj-zd61
title: Pathological images for gland segmentation

description: The self-collected 30000 pathological images for gland segmentation, including training images and annotations.
DOI: 10.34808/farj-4b08
title: Analysis of induction of DNA DSBs on telomeres in A549 cells

description: Data consist of an analysis of DNA DSBs on telomeres. A549 were treated with compounds (TXT4, MTX, DMSO) at IC90 concentration for the indicated time. Images were acquired with an LSM 800 inverted laser scanning confocal microscope (Carl Zeiss; Dresden, Germany) equipped with an Airyscan detector using a ×63 1.4 NA Plan Apochromat objective (Carl Zeiss). Colocaliasation of spots (pH2AX and TRF2) was performed using ZEN system 2.3 (Carl Zeiss; Dresden, Germany) and exported to .txt format.
DOI: 10.34808/69c8-7w23
title: Microscopy analysis of A549 and H460 cells' mitochondria after exposure to TXT2 and TXT4

description: The data set contains live-cell imaging of A549 and H460 cells' mitochondria after exposure to TXT2 and TXT4. Cells were exposed for 6 h to the tested compounds at their IC90 concentration or to DMSO. Then, mitochondrial labelling was performed by incubating the cells with a MitoTracker Green FM probe (Thermo Fisher Scientific) and Hoechst 33342 (Thermo Fisher Scientific). Images were acquired with an LSM 800 inverted laser scanning confocal microscope (Carl Zeiss, Jena, German), equipped with an Airyscan detector for high-resolution confocal scanning using a ×63 1.4 NA Plan Apochromat objective (Carl Zeiss). Cells were incubated in an incubation chamber at 37°C with 5% CO2.
DOI: 10.34808/j6gz-r329
title: The effect of anthraquinone derivatives on telomerase activity

description: The data set contains images of the PCR products obtained from the Telomerase Repeated Amplification Protocol (TRAP). The effect of anthraquinone derivatives (TXT1-TXT9) on telomerase activity was comparatively examined using the following reference compounds: BIBR1532, TMPyP4  and mitoxantrone (MTX).
DOI: 10.34808/w4a3-pm16
title: Western blot analysis of DDR- and apoptosis-related proteins of A549 and H460 cells after exposure to TXT2 and TXT4

description: The data set contains full-length blot images of DDR- and apoptosis-related proteins of A549 and H460 cells after exposure to TXT2 and TXT4.
DOI: 10.6084/m9.figshare.22256155
title: Rumen viral genome catalogs

description: The rumen viral genome catalog was constructed by sequencing virus-like particles in the rumens of 22 Japanese cattle. The completeness of viral genomes was assessed using CheckV. RVG1 includes viral genomes that were classified as “Complete,” “High-quality,” and “Medium-quality”.
DOI: 10.34808/w2r6-mh38
title: Confocal microscopy analysis of DNA DSB in telomerase negative cells after exposure to TXT2 and TXT4

description: The data sets contain confocal microscopic images showing the γ-H2AX with TRF2 after treatment of NHBE and U2OS cell lines with TXT2 and TXT4 in equitoxic concentrations. Images were acquired with an LSM 800 inverted laser scanning confocal microscope (Carl Zeiss; Dresden, Germany) equipped with an Airyscan detector using a ×63 1.4 NA Plan Apochromat objective (Carl Zeiss).  Nuclei were counterstained with DAPI.
DOI: 10.34808/zfrv-bs73
title: Flow cytometry analysis of DNA DSBs in telomerase positive and negative cell lines after treatment with TXT2 and TXT4

description: Flow cytometry analysis of DNA DSBs in telomerase positive (A549, H460) and negative (NHBE2594, U2OS) cell lines after treatment with equitoxic concentrations of TXT2 and TXT4. MTX and DMSO were used as positive and negative controls, respectively.
DNA DSBs were detected using anti-phospho-Histone H2AX, Alexa 488 conjugate, a marker of double-strand DNA breaks.
DOI: 10.34808/fts0-e890
title: Flow cytometry analysis of caspase 3/7 activation in A549, H460 and U2OS cells after exposure to TXT2 and TXT4

description: The data contains flow cytometry analysis of caspase 3/7 activation in A549, H460 and U2OS cells after exposure to equitoxic concentrations of TXT2 and TXT4. DMSO and MTX were used as positive and negative controls, respectively. Caspase 3/7 activity was detected using CellEvent™ Caspase-3/7 Green Flow Cytometry Assay Kit (#C10427, Thermo Fisher Scientific). Analysis was performed with a Guava EasyCyte 8 cell sorter (Merck Millipore)
DOI: 10.5281/zenodo.7765317
title: Auroral Data for RL-PMAFs

description: Auroral Data used in the paper(Automatic Recognition and Localization of Poleward Moving Auroral Forms (PMAFs) from all-sky Auroral Videos).<br> "test20051.rar", "test20061.rar" and "test20071.rar" are the test data. "train.rar" is the train data. "val1.rar" is the validation data.
DOI: 10.5281/zenodo.7765316
title: Auroral Data for RL-PMAFs

description: Auroral Data used in the paper(Automatic Recognition and Localization of Poleward Moving Auroral Forms (PMAFs) from all-sky Auroral Videos).<br> "test20051.rar", "test20061.rar" and "test20071.rar" are the test data. "train.rar" is the train data. "val1.rar" is the validation data.
DOI: 10.34808/c3kr-qs15
title: Study of the ability of TXT-compounds to promote telomeric G4 DNA thermal stabilization using FRET melting assay

description: The data set contains a DNA melting screen obtained from the FRET melting assay. Thermal stabilities of G-quadruplex structures were analysed in the presence and in absence of ligands. TMPyP4 and MTX were used as references. Measurements were carried out in triplicate using a LightCycler 480 thermocycler (Roche Life Science) with a filter setting of 465 nm/510 nm at 30–95°C with a rate of 0.01°C/s.
DOI: 10.34808/h1se-3y50
title: Lignocellulosic biomass pretreatment - FTIR data

description: Lignocellulosic pretreated biomass has been analysed using FRIT spectroscopy to demonstrate changes occuring in biomass structure
DOI: 10.34808/z6y8-tp72
title: RSM for pre-treatment optimisation

description: Box Behnken Design was prepared to define optimal contitions for biomass pretreatment
DOI: 10.5281/zenodo.7765721
title: Data set for "Tectonic evolution of the Tibetan Plateau during the late Cretaceous to early Eocene: Insights from geochemical records in the Fenghuoshan Group, Hoh Xil Basin"

description: The mineral compositions, major and trace element gechemical data for the sediements from Fenghuoshan Group, Hoh Xil Basin.
DOI: 10.5281/zenodo.7765720
title: Data set for "Tectonic evolution of the Tibetan Plateau during the late Cretaceous to early Eocene: Insights from geochemical records in the Fenghuoshan Group, Hoh Xil Basin"

description: The mineral compositions, major and trace element gechemical data for the sediements from Fenghuoshan Group, Hoh Xil Basin.
DOI: 10.5281/zenodo.7722816
title: Possible but Rare: Safe and Just Satisfaction of National Human Needs in Terms of Ecosystem Services - Supplemental Data

description: This supplemental datasheet provides the country-level data used for calculation in the manuscript "Possible but Rare: Safe and Just Satisfaction of National Human Needs in Terms of Ecosystem Services." For a summary of the results and insights, check the main text. Please check the supplemental information to follow the detailed calculations using this data.
DOI: 10.5281/zenodo.7722815
title: Possible but Rare: Safe and Just Satisfaction of National Human Needs in Terms of Ecosystem Services - Supplemental Data

description: This supplemental datasheet provides the country-level data used for calculation in the manuscript "Possible but Rare: Safe and Just Satisfaction of National Human Needs in Terms of Ecosystem Services." For a summary of the results and insights, check the main text. Please check the supplemental information to follow the detailed calculations using this data.
DOI: 10.6084/m9.figshare.22331227.v2
title: Whole-genome variants dataset of 209 local chickens

description: <em>Whole-genome variants dataset of 209 local chickens</em>
DOI: 10.5281/zenodo.7445918
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This dataset includes (i) an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS), and (ii) a knowledge graph containing machine-actionable metadata from 470 SWeML systems derived during a systematic mapping study.
DOI: 10.5281/zenodo.7445917
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This resource is part of our submission to ESWC 2023 resource track, which includes: Datasets:<br> - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components,<br> - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers,<br> - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2]. These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)<br> [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit
description: cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software].
DOI: 10.5281/zenodo.7472498
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This resource is part of our submission to ESWC 2023 resource track, which includes: (i) an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> (ii) a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> (iii) a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns,<br> (iv) a Knowledge Graph containing machine-actionable metadata from 476 SWeML systems derived during a Systematic Mapping Study (SMS) [1] based on the resource (i-iii), and<br> (v) a Java toolkit for transforming SMS result metadata into a KG. To find the latest SNAPSHOT-version of the resource, we refer interested reader to our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)
description: cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software].
DOI: 10.5281/zenodo.7480694
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This resource is part of our submission to ESWC 2023 resource track, which includes: Datasets:<br> - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components,<br> - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers,<br> - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2]. These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)<br> [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit
description: cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software].
DOI: 10.5281/zenodo.7525624
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This resource is part of our submission to ESWC 2023 resource track, which includes: Datasets:<br> - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components,<br> - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers,<br> - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2]. These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)<br> [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit
description: cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software].
DOI: 10.5281/zenodo.7765894
title: Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG)

description: This resource is part of our submission to ESWC 2023 resource track, which includes: Datasets:<br> - Folder "pattern" - a set of SWeMLS patterns represented based on OPMW and P-Plan ontology,<br> - Folder "shapes" - a set of SHACL constraints to check the conformance of SWeML Systems against SWeMLS patterns as well as a set of SHACL-AF rules to generate links between system components,<br> - File "swemls-ontology.ttl" - an ontology to represent Semantic Web resources and Machine Learning systems (SWeMLS),<br> - File "swemls-instances.ttl" - a set of triples representing the extracted metadata from 476 SWeML systems and papers,<br> - File "swemls-kg.ttl" - an integrated and validated KG containing all above files, including enrichment from SHACL-AF rules using "swemls-toolkit" [2]. These resources are produced based on the result of the Systematic Mapping Study (SMS) reported in [1]. The latest SNAPSHOT-version of the resource can be accessed through our resource landing page: https://w3id.org/semsys/sites/swemls-kg/ [1] Breit, A., Waltersdorfer, L., Ekaputra, J.F., Sabou, M., Ekelhart, A., Iana, A., Paulheim, H., Portisch, J., Revenko, A., Ten Teije, A., van Harmelen, F.: Combining Machine Learning and Semantic Web -A Systematic Mapping Study (under review). ACM CSUR (2022)<br> [2] Source code of swemls-toolkit is available at: https://github.com/semanticsystems/swemls-toolkit
description: cite as: Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart, Heiko Paulheim, Anna Breit, Artem Revenko, Laura Waltersdorfer, Kheir Eddine Farfar, and Sören Auer, (2022). Semantic Web resources and Machine Learning systems - Knowledge Graph (SWeMLS-KG) [Data set & Software].