cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc23xhbw
title: CCDC 1963798: Experimental Crystal Structure Determination

description: Related Article: Nuno Reis Conceição, Oksana V. Nesterova, Cyril Rajnák, Roman Boča, Armando J. L. Pombeiro, M. Fátima C. Guedes da Silva, Dmytro S. Nesterov|2020|Dalton Trans.|49|13970|doi:10.1039/D0DT02652B
DOI: 10.5517/ccdc.csd.cc23xh8t
title: CCDC 1963796: Experimental Crystal Structure Determination

description: Related Article: Nuno Reis Conceição, Oksana V. Nesterova, Cyril Rajnák, Roman Boča, Armando J. L. Pombeiro, M. Fátima C. Guedes da Silva, Dmytro S. Nesterov|2020|Dalton Trans.|49|13970|doi:10.1039/D0DT02652B
DOI: 10.5517/ccdc.csd.cc25bkx0
title: CCDC 2006162: Experimental Crystal Structure Determination

description: Related Article: Nuno Reis Conceição, Oksana V. Nesterova, Cyril Rajnák, Roman Boča, Armando J. L. Pombeiro, M. Fátima C. Guedes da Silva, Dmytro S. Nesterov|2020|Dalton Trans.|49|13970|doi:10.1039/D0DT02652B
DOI: 10.5517/ccdc.csd.cc25hpft
title: CCDC 2011076: Experimental Crystal Structure Determination

description: Related Article: Tanmay Rom, Avijit Kumar Paul|2020|Dalton Trans.|49|13618|doi:10.1039/D0DT02796K
DOI: 10.5517/ccdc.csd.cc24v2vy
title: CCDC 1991280: Experimental Crystal Structure Determination

description: Related Article: Laura Rodríguez-Cid, E. Carolina Sañudo, Ana M. López-Periago, Arántzazu González-Campo, Núria Aliaga-Alcalde, Concepción Domingo|2020|Cryst.Growth Des.|20|6555|doi:10.1021/acs.cgd.0c00742
DOI: 10.5517/ccdc.csd.cc24v2y1
title: CCDC 1991283: Experimental Crystal Structure Determination

description: Related Article: Laura Rodríguez-Cid, E. Carolina Sañudo, Ana M. López-Periago, Arántzazu González-Campo, Núria Aliaga-Alcalde, Concepción Domingo|2020|Cryst.Growth Des.|20|6555|doi:10.1021/acs.cgd.0c00742
DOI: 10.25345/c57f4p
title: MassIVE MSV000086405 - IKAROS/IKZF1 cooperates with GFI1 to control gene expression in T-cell Acute Lymphoblastic Leukemia
title: To identify potential GFI1 cooperators in T-ALL, we applied the BioID (Biotin IDentification) proximity-dependent labeling method to screen for interacting proteins. We generated doxycycline-inducible, GFI1-BirA*-expressing CCRF-CEM cells. In parallel, cells transduced with empty vector or BirA* only were generated as controls. Cells were treated with doxycycline and biotin, lysates collected, and biotinylated proteins isolated with streptavidin sepharose beads. To identify the complete cohort of GFI1-interacting proteins, the streptavidin-enriched proteins were subjected to proteomic LC-MS/MS. Three replicates were used for each condition (empty vector, BirA* and GFI1-BirA*).
DOI: 10.21227/a8ph-tr51
title: IMDb Movie Reviews Dataset

description: This dataset contains nearly 1 Million unique movie reviews from 1150 different IMDb movies spread across 17 IMDb genres - Action, Adventure, Animation, Biography, Comedy, Crime, Drama, Fantasy, History, Horror, Music, Mystery, Romance, Sci-Fi, Sport, Thriller and War. The dataset also contains movie metadata such as date of release of the movie, run length, IMDb rating, movie rating (PG-13, R, etc), number of IMDb raters, and number of reviews per movie.
DOI: 10.5281/zenodo.4242928
title: Land Use and Land Cover Change 2000-2016 in Mozambique

description: This repository includes land use and land cover maps of Mozambique for 2000, 2005, 2010 and 2016 years. The methodology is based on remote sensing methodology and include satellite image collection and compositing (annual cloud-free and shadow free Landsat images for 2000, 2005, 2010 and 2016), delineation of a large collection of training plots based on National Land Cover Classification system level 1, supervised classification using a machine learning algorithm (Random Forest) and post-processing steps. The LULCC map for 2016 show area statistics of 45.0% (35.8 Mha) of dry forest, 37.0% (29.3 Mha) of grassland and fallow, 13.7% (10.8 Mha) of cropland 2.0% (1.6 Mha) of wetlands, 1.3% (1 Mha) of other categories (rocks, sands, or bare soils), 0.3% (271,000 ha) of Mangroves, and 0.1% (673.1 ha) of urban areas. The deforestation over the 2000-2016 period is estimated to have been 207,272 ha per year. The methodology and statistics are presented in the report included in this repository. Theses maps are outputs from the study "An Analysis of Land Use Changes and Land Degradation in Mozambique" conducted by Nitidae and CIRAD in the LAUREL project.
DOI: 10.5281/zenodo.4242927
title: Land Use and Land Cover Change 2000-2016 in Mozambique

description: This repository includes land use and land cover maps of Mozambique for 2000, 2005, 2010 and 2016 years. The methodology is based on remote sensing methodology and include satellite image collection and compositing (annual cloud-free and shadow free Landsat images for 2000, 2005, 2010 and 2016), delineation of a large collection of training plots based on National Land Cover Classification system level 1, supervised classification using a machine learning algorithm (Random Forest) and post-processing steps. The LULCC map for 2016 show area statistics of 45.0% (35.8 Mha) of dry forest, 37.0% (29.3 Mha) of grassland and fallow, 13.7% (10.8 Mha) of cropland 2.0% (1.6 Mha) of wetlands, 1.3% (1 Mha) of other categories (rocks, sands, or bare soils), 0.3% (271,000 ha) of Mangroves, and 0.1% (673.1 ha) of urban areas. The deforestation over the 2000-2016 period is estimated to have been 207,272 ha per year. The methodology and statistics are presented in the report included in this repository. Theses maps are outputs from the study "An Analysis of Land Use Changes and Land Degradation in Mozambique" conducted by Nitidae and CIRAD in the LAUREL project.
DOI: 10.6084/m9.figshare.13146443.v2
title: Leaf Features-Oak decline-dataSet. Forest pathology

description: The data is related to some Persian oak leaf features influenced by Charcoal oak disease.
DOI: 10.5519/qd.gig7efco
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (164 records)
DOI: 10.5281/zenodo.4121958
title: Land Use and Land Cover Map of Mount Namuli and surroundings in Mozambique

description: This repository includes the land use and land cover map of the Gurué region and the Mount Namuli in Zambézia province in Mozambique, using remote sensing and Sentinel 2 images. Gurué is located in the northwest of the Zambezia province and 150 km from the Malawi border. The Mount Namuli Massif, located in the north of the city, covers an area of about 200 km² at an altitude above 1200 m (Timberlake et al., 2009). The highest point of the massif, Mount Namuli, reaches 2,419 m. It is the second highest peak in Mozambique after Mount Binga (2,436 m) located in the Chimanimani National Reserve (Manica Province). The region surrounding Mount Namuli is inhabited by local communities who rely on it heavily for ecosystem services. Although, the area’s biodiversity is greatly threatened by conversion of forests and grasslands by these communities for subsistence and local market agriculture. There is minimal local government involvement in the area for conservation activities or social services, and thus there has been no effective management of natural resources. Mount Namuli is relatively small in extent but incredibly diverse and a part of the unique mountain island chain of inselbergs in northern Mozambique. Mount Namuli’s slopes are covered by a mosaic of forests, grasslands, and agricultural land. Rates of habitat loss, particularly across high conservation value areas above 1,200 meters, are increasing, driven primarily by the introduction of crops, such as the Irish potato, which exhaust the soils. The high rates of forest conversion underway on the mountain’s upper slopes must be halted immediately and long-term plans for natural resource management must be implemented if Mount Namuli’s remaining biodiversity is to be retained. The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a machine learning algorithm (Random Forest) and finally, calculation of land occupation statistics. The methodology and statistics are presented in the report included in the repository.
DOI: 10.5281/zenodo.4116104
title: Land Use and Land Cover Map of Mount Namuli and surroundings in Mozambique

description: This repository includes the land use and land cover map of the Gurué region and the Mount Namuli in Zambézia province in Mozambique, using remote sensing and Sentinel 2 images. Gurué is located in the northwest of the Zambezia province and 150 km from the Malawi border. The Mount Namuli Massif, located in the north of the city, covers an area of about 200 km² at an altitude above 1200 m (Timberlake et al., 2009). The highest point of the massif, Mount Namuli, reaches 2,419 m. It is the second highest peak in Mozambique after Mount Binga (2,436 m) located in the Chimanimani National Reserve (Manica Province). The region surrounding Mount Namuli is inhabited by local communities who rely on it heavily for ecosystem services. Although, the area’s biodiversity is greatly threatened by conversion of forests and grasslands by these communities for subsistence and local market agriculture. There is minimal local government involvement in the area for conservation activities or social services, and thus there has been no effective management of natural resources. Mount Namuli is relatively small in extent but incredibly diverse and a part of the unique mountain island chain of inselbergs in northern Mozambique. Mount Namuli’s slopes are covered by a mosaic of forests, grasslands, and agricultural land. Rates of habitat loss, particularly across high conservation value areas above 1,200 meters, are increasing, driven primarily by the introduction of crops, such as the Irish potato, which exhaust the soils. The high rates of forest conversion underway on the mountain’s upper slopes must be halted immediately and long-term plans for natural resource management must be implemented if Mount Namuli’s remaining biodiversity is to be retained. The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a machine learning algorithm (Random Forest) and finally, calculation of land occupation statistics. The methodology and statistics are presented in the report included in the repository.
DOI: 10.6084/m9.figshare.13146443.v3
title: Leaf Features-Oak decline-dataSet. Forest pathology

description: The data is related to some Persian oak leaf features influenced by Charcoal oak disease.
DOI: 10.6084/m9.figshare.13146443
title: Leaf Features-Oak decline-dataSet. Forest pathology

description: The data is related to some Persian oak leaf features influenced by Charcoal oak disease.
DOI: 10.6084/m9.figshare.13146443.v4
title: Leaf Features-Oak decline-dataSet. Forest pathology

description: The data is related to some Persian oak leaf features influenced by Charcoal oak disease.
DOI: 10.5281/zenodo.3712323
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to February 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.3783736
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to April 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.3873484
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to May 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.3926718
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to June 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.3970574
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to July 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.4013501
title: TheyBuyForYou Knowledge Graph data dump

description: Data dump of the Knowledge Graph (KG) for the TheyBuyForYou project in RDF format containing procurement data and company data (for matching suppliers) for the period January 2019 to August 2020. In addition to the KG data dump in RDF, we have also made available the source data input for the KG, i.e., procurement release data from OpenOpps and company data (for matching suppliers) from OpenCorporates in JSON format.
DOI: 10.5281/zenodo.4126332
title: Land Use and Land Cover Map of Ribaue Mountains (Mount Ribaue and Mount M'paluwe) in Mozambique

description: This repository includes the land use and land cover map of the Ribaue Mountains and surroundings (Ribaue district, Nampula province, Mozambique), using remote sensing. The Ribaue massif is a series of granite inselbergs in northern Mozambique near the town of Ribaue in Nampula Province. The main area of the massif is made up of the Serra de Ribaue to the west and the Serra de M'paluwe to the east. The inselbergs rise from a relatively flat landscape at ca 500-600 m altitude up to 1675 m on Monte M'paluwe. They form part of a belt of granite rock outcrops, inselbergs and mountains, running NE-SW across Nampula and Zambezia provinces and including Mt Inago (1804 m) and Mt Namuli (2419 m) to the southwest of the Ribaue massif. This belt is considered as a center of endemism. Overall the site supports 15 nationally endemic plant taxa (plants that only occur in Mozambique), 11 near-endemics (plants that are restricted to Mozambique and neighbouring countries) and 10 taxa that are threatened with extinction on the Global IUCN Red List. Steeply sloping granite rock outcrops, mid-altitude moist forest and miombo woodland are the dominant habitat types at the Ribaue massif. The site also includes smaller areas of gallery forest, marsh, seasonal stream gullies, seepage on granite rock, and shaded granite cliffs. The methodology used in this study is based on a classical approach of remote sensing: satellite image collection (cloud-free and shadow free Sentinel 2, 10 m resolution, two season), identification of land use typology (based on field campains), delineation of training plots, supervised classification of land use using a machine learning algorithm (Random Forest) and finally, calculation of land occupation statistics. The methodology and statistics are presented in the report included in the repository.
DOI: 10.5281/zenodo.4117511
title: Scientific and vernacular names of miombo woody species identified in the Gilé National Park and surroundings in Mozambique

description: The Gilé National Park (GNAP) is located in the northeastern part of Zambezia province in Mozambique. Species names were recorded from local field guide in Lomwe language during floristic inventories. Inventories were carried out in mature Miombo forest and Miombo regeneration areas during the dry seasons in 2015, 2016 and 2019, inside the GNAP (core area and buffer zone) and in the surroundings of the GNAP. Correspondences in scientific name were then carried out using determinations with field identification guides and with the checklist of the vernacular names of vascular plants in Mozambique (de Koning, 1993).
DOI: 10.6084/m9.figshare.13184906
title: Quantifying the Accessibility to HealthcareFacilities in the Wake of COVID-19 withMulti-Criteria Decision Analysis (MCDA)

description: The data and files support the analysis of accessibility to nearest healthcare facilities across 44 locations in Los Angeles city using an MCDA [1] approach. In this dataset, the raster files are layers used to compute a cost layer using a linear sum approach. The backlink raster and cost distance are used to generate least cost paths to the healthcare facilites which are then combinied with the social vulnerability index dataset from the Center of Disease Control [2] to compute an accessiblity index.
DOI: 10.6084/m9.figshare.13184906.v1
title: Quantifying the Accessibility to HealthcareFacilities in the Wake of COVID-19 withMulti-Criteria Decision Analysis (MCDA)

description: The data and files support the analysis of accessibility to nearest healthcare facilities across 44 locations in Los Angeles city using an MCDA [1] approach. In this dataset, the raster files are layers used to compute a cost layer using a linear sum approach. The backlink raster and cost distance are used to generate least cost paths to the healthcare facilites which are then combinied with the social vulnerability index dataset from the Center of Disease Control [2] to compute an accessiblity index.
DOI: 10.25934/00004187.1
title: IPD datapackage for study 'Validation of Daily Telephone Self-Assessment in the Study of Antidepressant Treatment Outcome' and research request: 'Tolerability of duloxetine in the elderly and in adults: a systematic review and individual patient data meta-analysis of randomized controlled trials versus placebo.'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.21227/8h9a-fk22
title: Model 3

description: test 3
DOI: 10.5281/zenodo.4242968
title: Temperature response of water-saturated compact Longmenshan limestone and porous Rajasthan sandstone to changes in confining pressure

description: Three data sets are stored in this data submission. The first data set includes the temperature response of water-saturated compact Longmenshan limestone (L27) and porous Rajasthan sandstone (RJS) to changes in confining pressure under drained/undrained conditions. The second data set includes the thermal properties of rock-forming minerals and estimations of thermal characteristic time/distance. The third data set includes the internal temperature evolution of the water-saturated sample within 1 s after instantaneous loading in models M-01, M-02 and M03, in which the thermal properties of the solid grains are set to be that of gypsum, average values of the main rock-minerals and <em>α</em>-quartz, respectively.
description: This data set will be used in the following manuscript: Xiaoqiu Yang, Weiren Lin, Osamu Tadai, Xin Zeng, Junfeng Zhao, Hehua Xu. Theoretical and experimental analyses of temperature responses of water-saturated rocks to changes in confining pressure. Ready to submit to Journal of Geophysical Research: Solid Earth.
DOI: 10.21227/3thd-q132
title: model 3.5

description: test
DOI: 10.21227/8g1g-6527
title: model 3.6

description: test
DOI: 10.5281/zenodo.4242988
title: EMUE-D2-3-TSPConcentration

description: Conformity assessment of mass concentration of total suspended particulate matter in air The example shows how to calculate risks of false decisions in the conformity assessment of test results, according to the framework of JCGM 106:2008, in the case in which a normal distribution is not a valid assumption for modelling prior information on the measurand. As a case study, test results of mass concentration of Total Suspended Particulate Matter (TSPM) in ambient air, collected in the proximity of three stone quarries located in Israel, are considered; for each quarry, a log-normal distribution is chosen as the prior distribution. [Activity A1.2.3]. Files contained in the dataset are: - A123_Report.pdf: Report “Conformity assessment of mass concentration of total suspended particulate matter in air” - A123_Report.tex: LaTeX file to be compiled in order to produce A123_Report.pdf - CompendiumA123.bib: bibliography file - Q1data.txt: dataset of test results collected in proximity of quarry 1 - Q2data.txt: dataset of test results collected in proximity of quarry 2 - Q3data.txt: dataset of test results collected in proximity of quarry 3 - A123_Global_risk_TSPM.r: calculation of the global consumer's and producer’s risks relevant to the three datasets - A123_Specific_risk_TSPM.r: calculation of the specific consumer's and producer’s risks relevant to the three datasets - Quarry_distr.jpeg: histograms of data in Q1data.txt, Q2data and Q3data.txt - Global_risks.jpeg: plot of consumer’s and producer’s global risks - Specific_risks.jpeg: plot of consumer’s and producer’s specific risks
description: The example relies on the following publications: - Ilya Kuselman, Shamai Shpitzer, Francesca Pennecchi and Cathy Burns. Investigating out-of-specification test results of mass concentration of total suspended particulates in air based on metrological concepts—a case study, Air Qual Atmos Health, 5, 269–276, 2012 (https://doi.org/10.1007/s11869-010-0103-6) - Ilya Kuselman, Francesca Pennecchi, Cathy Burns, Aleš Fajgelj and Paolo de Zorzi. IUPAC/CITAC Guide: Investigating out-of-specification test results of chemical composition based on metrological concepts (IUPAC Technical Report). Pure and Applied Chemistry, 84(9), 2012 (https://doi.org/10.1351/PAC-REP-11-10-04)